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| Automation Technology, Structural Change, and the Evolution of Income Distribution in China |
| LU Guojun, CUI Xiaoyong, WANG Dihai
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School of Economics, Peking University;
School of Economics, Fudan University |
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Abstract In the last two decades, the wage premium between China's service and manufacturing sectors has increased; however, the labor share pattern appears to be U-shaped. Concurrently, the processes of automation and tertiarization have accelerated in China. Following the incorporation of more industrial robots into factories and as the Chinese economy transitions from the manufacturing sector to the service sector, this paper explores the driving factors behind these trends and how they will affect factor income distribution.
We extend the task-based framework to the context of the manufacturing and service sectors, and then quantitatively investigate the implications of automation and tertiarization for primary income distribution. The core of our model is that the degree of automation is endogenous, firms adopt automation technologies to maximize their profits, and tasks are allocated to capital or labor following these factors' comparative advantage. Moreover, the adoption of automation and capital density are heterogeneous across sectors.
We extend the task-based framework to incorporate the manufacturing and service sectors, with a view to examining the quantitative impact of automation and tertiarization on primary income distribution. At the heart of our model is the notion that the extent of automation is endogenous. That is, firms adopt automation technologies to maximize their profits. In addition, the comparative advantages of various factors determine the allocation of tasks to either capital or labor. Finally, the adoption of automation and capital density is heterogeneous across sectors.
We use the lens of this model to derive three main findings. First, we observe that in addition to structural transformation, the adoption of endogenous automation technologies serves as a significant mechanism for the evolution of factor income distribution in China. Second, we find that both automation technologies and structural change jointly explain the U-shaped labor share pattern and the increasing sectoral wage premium. Automation technologies dominate the decrease phase in the U-shaped labor share pattern, while structural change dominates the increase phase on the other side. Both automation technologies and structural change play a crucial role in explaining the continuous growth of wage premiums. Finally, we demonstrate that factor and skill endowments are the driving factors behind automation technologies and structural change, which have significant implications for the factor income distribution pattern. In particular, deepening capital reduces the share of labor income and expands the wage premium. An increase in service labor supply increases the labor share and decreases the wage premium. However, manufacturing labor shortages reduce both the labor share and the wage premium.
This paper makes three contributions to the literature. First, we build a tractable but general model to investigate automation and structural changes in China, in addition to their implications for factor income distribution. This model can be used to discuss the patterns of both labor share and wage premium using a uniform framework. Hence, we supplement theory on the evolution of China's factor income distribution. Moreover, we complement studies that identify the influential factors in income distribution based on microdata and empirical methods by providing a macro perspective. As mentioned above, our model effectively explains the changes in the aggregate labor income share and the sector wage premium. Second, our model matches the changes in factor endowment, industry structure, employment structure, wage premium, and so on over the last two decades, especially the nonmonotonic pattern of aggregate labor share, which bolsters our confidence that multiple factors must affect the factor income distribution. Our second contribution is to shed light on the driving factors and the intermediate channels behind the pattern of the factor income distribution. Finally, we simulate how factor income distribution varies with factor and skill endowments. We decompose the total effects into the automation and structural change effects. In this respect, our work has both theoretical and practical significance and illuminates future patterns in factor income distribution.
In the future, increasing the human capital investment in manufacturing labor, gradually narrowing the skill gap between manufacturing labor and service labor, and encouraging the transfer of manufacturing labor to service labor are possible ways to improve the income distribution pattern.
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Published: 02 May 2023
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