深度智能化:后机器换人时代生产率增长的动力  

Deep Intelligence:Drivers of Productivity Growth in the Post Machine-Labor Substitution Era

作　　者：王永进[1,2] 王文斌[1] 陈菲 Wang Yongjin;Wang Wenbin;Chen Fei

机构地区：[1]南开大学经济学院 [2]南开大学经济行为与政策模拟实验室

出　　处：《世界经济》2024年第12期165-192,共28页The Journal of World Economy

基　　金：国家社会科学基金重大项目(22&ZD074);国家自然科学基金面上项目(72073073、72473074)的资助。

摘　　要：智能制造能否成为中国实现弯道超车的抓手?一种观点认为,由于生产任务的种类是有限的,因此,在完成机器对人的替代之后,生产率进步就会陷入停滞,从而无法为未来经济增长提供持久动力。本文采用海关数据识别了深度智能化与广度智能化,认为虽然广度智能化在所有生产任务实现机器对劳动的替代后会陷入停滞,但深度智能化可以不断进行更迭,从而形成持久的经济增长动力。研究发现:(1)深度与广度智能化均可提升企业的全要素生产率,但前者的提升作用更为明显。因此,即使机器代替劳动的过程完成之后,深度智能化依然会持续促进生产率增长;(2)受融资约束和高昂固定成本的影响,深度智能化会提高企业的融资成本,因此对企业的融资能力提出了更高的要求;(3)深度与广度智能化通过创新赋能、产能效率赋能与劳动效率赋能三种渠道作用于生产率。Can intelligent manufacturing become China’s lever for leapfrogging?One perspective argues that since the range of production tasks is finite,productivity growth may stagnate after machines fully replace human labor,failing to provide sustained economic growth in the future.Using customs data,this paper distinguishes between intelligence at the intensive margin(deep intelligence)and intelligence at the extensive margin(broad intelligence).It highlights that while broad intelligence may plateau once machines have replaced labor in all production tasks,deep intelligence can continue to evolve,offering sustaining momentum for economic growth.The key findings are as follows:(1)Both deep and broad intelligence enhance firms’total factor productivity(TFP),but the productivity gains from deep intelligence are significantly more pronounced.Therefore,even after the completion of labor substitution by machines,deep intelligence continues to drive productivity growth.(2)Deep intelligence increases firms’financing costs due to its high fixed cost and the financing constraint.(3)Both forms of intelligence impact productivity through three channels:innovation,capacity efficiency,and labor efficiency.

关 键 词：智能制造 深度智能化 广度智能化 全要素生产率 

分 类 号：F224[经济管理—国民经济] F49F425