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2025, 11, v.40 9-25
中国经济增长新动能探源:基于偏向型技术进步视角
基金项目(Foundation): 国家社会科学基金重大项目“中国城镇化阶段性特征统计测度及驱动效应评估研究”(20&ZD133)
邮箱(Email):
DOI: 10.20207/j.cnki.1007-3116.20250910.001
摘要:

为探求经济增长的新动能,从全要素生产率中将偏向型技术进步的增强效应剥离出来,需构建符合中国国情的要素增强型技术进步生产函数模型。依据2003—2022年281个地级及以上城市的面板数据进行测算,将城市总产出增长率分解为技术进步效应、要素投入效应、技术偏向效应和要素配置效应,为经济增长动能分解提供一个新框架。研究发现:(1)技术进步的驱动要素发生结构性转变。劳动增强型技术进步指数呈持续增长态势,而资本增强型技术进步2013年前边际贡献显著衰减,全要素生产率增长源实现资本驱动向劳动驱动的转换。(2)技术进步方向呈现出梯度差异,南方城市、东部城市、大型城市的劳动偏向型技术进步强度逐步弱化,欠发达地区的城市近十年有更明显的劳动偏向型技术进步。(3)经济增长动能分解显示,资本技术效应呈全域收敛趋势,而劳动技术效应呈现区域分异特征。研究揭示出中国经济增长新动能的来源、演进趋势及区域差异,为理解区域经济转型的要素重构逻辑提供新的经验证据。政府应制定差异化政策,建立技术资本要素适配体系,实施资本配置效率导向型政策,构建人力资本梯度补偿机制,破解要素配置的空间粘性。

Abstract:

China's “extensive” economic growth mode driven by investment is gradually shifting towards a high-quality economic development mode driven by innovation.Therefore, clarifying the new impetus of economic growth and achieving the transformation towards an “intensive” economic growth mode driven by efficiency have become an urgent and important task.Against the background of China's non-steady economic growth, it is important to explore the growth of total factor productivity(TFP) and its sources, as well as to analyze the new impetus of China's economic growth from the perspective of biased technological progress.In order to explore the new impetus of economic growth and separate the enhancing effect of biased technological progress from total factor productivity.It is necessary to construct a factor-augmented technological progress production function that aligns with China's national conditions.Based on panel data of 281 prefecture-level and above cities from 2003 to 2022,the total output growth rate of cities is decomposed into technological progress effect, factor input effect, technology bias effect, and factor allocation effect, providing a new framework for decomposing economic growth momentum.This study shows that there has been a structural shift in the driving factors of technological progress.The index of labor-augmenting technological progress shows a continuous growth trend, while the marginal contribution of capital-augmenting technological progress significantly declined before 2013.During the reporting period, the overall labor income share showed a U-shaped characteristic.The growth rate of total factor productivity was mainly contributed by capital technological progress before 2010,then it was jointly contributed by capital technological progress and labor technological progress.The source of total factor productivity growth achieved a paradigm shift from capital driven to labor driven.Regional heterogeneity analysis shows that there are gradient differences in the direction of technological progress.The intensity of labor biased technological progress in southern cities, eastern cities, and large cities has gradually weakened.Cities in less developed areas have seen more labor-oriented technological progress in the past decade.The decomposition of economic growth momentum shows that the capital technology effect exhibits a global convergence trend, while the labor technology effect exhibits regional differentiation characteristics.Before 2013,the capital investment effect of underdeveloped cities was generally higher than that of developed regions, but it showed the opposite trend after 2013.The overall trend of labor input effect in cities in different regions is same, but cities in developed regions have higher labor input effects than those in underdeveloped ones.This study reveals the sources, evolutionary trends, and regional differences of new driving forces for China's economic growth, providing new empirical evidence for understanding the logic of factor reconstruction in China's regional economic transformation.The government should formulate differentiated policies, establish a technology capital factor adaptation system, implement capital allocation efficiency oriented policies, and construct a human capital gradient compensation mechanism to break the spatial stickiness of factor allocation.

参考文献

[1] 李小克,李小平.中国全要素生产率演变的测度和多重效应分解:偏向性技术进步视角[J].经济研究,2022,57(4):191-208.

[2] 章上峰,许冰.时变弹性生产函数与全要素生产率[J].经济学(季刊),2009,8(2):551-568.

[3] 雷钦礼.通用技术进步框架下全要素生产率核算方法研究[J].统计研究,2022,39(7):31-42.

[4] 王晶晶,焦勇,江三良.中国八大综合经济区技术进步方向的区域差异与动态演进:1978—2017[J].数量经济技术经济研究,2021,38(4):3-21.

[5] SOLOW R M.Technical change and the aggregate production function[J].Review of economics and statistics,1957,39(3):312-320.

[6] ACEMOGLU D.Directed technical change[J].The review of economic studies,2002,69(4):781-809.

[7] KLUMP R,MCADAM P,WILLMAN A.Factor substitution and factor-augmenting technical progress in the United States:a normalized supply-side system approach[J].Review of economics and statistics,2007,89(1):183-192.

[8] LEóN-LEDESMA M A,MCADAM P,WILLMAN A.Identifying the elasticity of substitution with biased technical change[J].American economic review,2010,100(4):1330-1357.

[9] 戴天仕,徐现祥.中国的技术进步方向[J].世界经济,2010,33(11):54-70.

[10] 雷钦礼.偏向性技术进步的测算与分析[J].统计研究,2013,30(4):83-91.

[11] 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,2013,30(8):20-34.

[12] 袁礼,欧阳峣.发展中大国提升全要素生产率的关键[J].中国工业经济,2018(6):43-61.

[13] 邱语,张卫国.中国要素错配的时空演变研究[J].统计与信息论坛,2024,39(7):12-28.

[14] ANTONELLI C.Technological congruence and the economic complexity of technological change[J].Structural change and economic dynamics,2016,38:15-24.

[15] 封永刚,蒋雨彤,彭珏.中国经济增长动力分解:有偏技术进步与要素投入增长[J].数量经济技术经济研究,2017,34(9):39-56.

[16] FEDER C.A measure of total factor productivity with biased technological change[J].Economics of innovation and new technology,2018,27(3):243-253.

[17] ANTONELLI C,FEDER C.A long-term comparative analysis of the direction and congruence of technological change[J].Socio-economic review,2021,19(2):583-605.

[18] ZULETA H.Variable factor shares,measurement and growth accounting[J].Economics letters,2012,114(1):91-93.

[19] 李小平,李小克.偏向性技术进步与中国工业全要素生产率增长[J].经济研究,2018,53(10):82-96.

[20] 余东华,张鑫宇,孙婷.资本深化、有偏技术进步与全要素生产率增长[J].世界经济,2019,42(8):50-71.

[21] 郝枫.超越对数函数要素替代弹性公式修正与估计方法比较[J].数量经济技术经济研究,2015,32(4):88-105.

[22] FARE R E A.Biased technical change and the malmquist productivity index[J].Scandinavian journal of economics,1997,99(1):119-127.

[23] 杨翔,李小平,钟春平.中国工业偏向性技术进步的演变趋势及影响因素研究[J].数量经济技术经济研究,2019,36(4):101-119.

[24] 李静,楠玉.人力资本匹配与技能偏向技术进步[J].经济体制改革,2018(3):105-110.

[25] 黄先海,徐圣.中国劳动收入比重下降成因分析——基于劳动节约型技术进步的视角[J].经济研究,2009,44(7):34-44.

[26] ACEMOGLU D.Why do new technologies complement skills?Directed technical change and wage inequality[J].The quarterly journal of economics,1998,113(4):1055-1089.

[27] ACEMOGLU D.Equilibrium bias of technology[J].Econometrica,2007,75(5):1371-1409.

[28] ACEMOGLU D,AGHION P,BURSZTYN L,et al.The environment and directed technical change[J].American economic review,2012,102(1):131-166.

[29] BASU S,WEIL D N.Appropriate technology and growth[J].The quarterly journal of economics,1998,113(4):1025-1054.

[30] 张军,陈诗一,JEFFERSON G H.结构改革与中国工业增长[J].经济研究,2009,44(7):4-20.

[31] 黄燕萍,刘榆,吴一群,等.中国地区经济增长差异:基于分级教育的效应[J].经济研究,2013,48(4):94-105.

[32] 焦高乐,严明义.技术进步的来源、方向与工业节能减排[J].统计与信息论坛,2017,32(4):81-86.

[33] 涂正革,陈立.技术进步的方向与经济高质量发展——基于全要素生产率和产业结构升级的视角[J].中国地质大学学报(社会科学版),2019,19(3):119-135.

[34] 任韬,宋子琨.技能偏向型技术进步与中国制造业全要素生产率的提升[J].统计与信息论坛,2023,38(6):19-33.

[35] 樊纲,王小鲁,马光荣.中国市场化进程对经济增长的贡献[J].经济研究,2011,46(9):4-16.

[36] 王宁,史晋川.中国要素价格扭曲程度的测度[J].数量经济技术经济研究,2015,32(9):149-161.

[37] 章上峰,许冰.中国经济非稳态增长典型事实及解析[J].数量经济技术经济研究,2015,32(3):94-110.

[38] 陈瑾瑜.全要素生产率与技术进步间的差别及测算——几何微分法的应用[J].数量经济技术经济研究,2012,29(6):48-60.

[39] 袁鹏,朱进金.要素市场扭曲、技术进步偏向与劳动份额变化[J].经济评论,2019(2):73-87.

[40] 李影,方远平,毕斗斗.中国城市技术创新的空间差距及知识产权示范城市建设影响因素[J].统计与信息论坛,2025,40(3):117-128.

(1)以城区劳动力数量为分类依据,将2022年城区劳动力人数大于100万的地级及以上城市定义为大城市,反之则为小城市。

基本信息:

DOI:10.20207/j.cnki.1007-3116.20250910.001

中图分类号:F124.3

引用信息:

[1]程开明,滕蔓洲,刘书成.中国经济增长新动能探源:基于偏向型技术进步视角[J].统计与信息论坛,2025,40(11):9-25.DOI:10.20207/j.cnki.1007-3116.20250910.001.

基金信息:

国家社会科学基金重大项目“中国城镇化阶段性特征统计测度及驱动效应评估研究”(20&ZD133)

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