Intelligent manufacturing and enterprise labor income share
Bibliographic Data
| ID | 21689201 |
|---|---|
| Authors | Yuqi Chen (0000-0001-9769-1167, Business School, Sun Yat-sen University), Zixi Wang (0000-0002-1176-1177, Economics and Management School, Wuhan University), Xinyue Hu (0009-0004-7826-2518, Zhongnan University of Economics and Law), Qinglin Zhang (0000-0002-7182-2795, Southwestern University of Finance and Economics, corresponding author) |
| Year | 2026 |
| Pages | 1-41 |
| Publication date | 2026-07-07 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of the Asia Pacific Economy (JOURNAL) |
| Journal identifiers | ISSN: 1354-7860 • E-ISSN: 1469-9648 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13547860.2026.2694483 |
| OpenAlex | W7167642285 |
| Language | EN |
| References cited | 80 |
Intelligent manufacturing has profound implications for the distribution of economic gains. Using panel data from China’s A-share manufacturing firms from 2010 to 2020, this study exploits the IM Pilot Demonstration Project as a quasi-natural experiment to identify its causal effects. The findings demonstrate that IM significantly increases the labor income share, with stronger effects observed in eastern regions, large cities, and firms operating in favorable market environments or exhibiting higher levels of digitalization. The complementarity effect between intelligent technologies and labor is especially pronounced in labor-intensive and non–high-tech industries. Mechanism analysis shows three driving channels: expanded production scale, optimization of firms’ human capital, and improvements in corporate ESG performance. Additional analysis indicates that IM also promotes firms’ total factor productivity, albeit with a notable time lag. Overall, this study offers valuable insights for policymakers seeking to navigate the distributional consequences of technological change
Exploit · Income distribution · Manufacturing · Panel data · Digital Transformation in Industry · Economic and Technological Innovation · Firm Innovation and Growth
Directed Technical Change
Intelligent Manufacturing in the Context of Industry 4.0
Tasks, Automation, and the Rise in U.S. Wage Inequality
Human Resource Management and Labor Productivity
The Rapid Adoption of Data-Driven Decision-Making
The Global Decline of the Labor Share
Computing Inequality
The Decline of the U.S. Labor Share
Capital-skill Complementarity and Inequality
Labor- and Capital-Augmenting Technical Change
AI and the Economy
Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation
The Fall of the Labor Share and the Rise of Superstar Firms
The Race between Man and Machine
Growth and Unemployment
Concentrating on the Fall of the Labor Share
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
The central role of the propensity score in observational studies for causal effects
The Skill Content of Recent Technological Change
Difference-in-Differences with multiple time periods
Examining the influence mechanism of artificial intelligence development on labor income share through numerical simulations
Has green innovation been improved by intelligent manufacturing?—Evidence from listed Chinese manufacturing enterprises
Intelligent transformation of the manufacturing industry for Industry 4.0
Does digital technology increase the labor income share of enterprises
How industrial robots affect labor income share in task model
Revisiting Event-Study Designs
Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years
Computing Productivity
Capital-Skill Complementarity
Distributional National Accounts
Does enterprise digitalization increase labor income share? Evidence from Chinese A-share listed enterprises
Do corporate income tax cuts decrease labor share? Regression discontinuity evidence from China
Investment in Human Capital and Personal Income Distribution
Robots and Firms
Automation and New Tasks
| Citation velocity | historical |
|---|---|
| Highly cited | No |