This study examines the effects of the robotic subsidy policy in China’s manufacturing sector. The demand-side subsidy policy aims at encouraging manufacturing firms to invest in robotics by lowering the cost of purchase. Our difference-in-difference analysis reveals distributional impacts of municipality-level robot subsidies on manufacturing firms of different scales. Although the subsidy brings a 14.2% increase in the application of robot patents, the facilitated access to robotics has not transformed into new firm entries. Strikingly, new firm entry decreases by 23.5% after the policy implementation. On the other hand, robot subsidies have increased the revenue, total asset, and employment of larger manufacturing firms by 9.8%, 6.9%, and 6.7%, respectively. To interpret the mechanism, we develop a simplified framework incorporating financial frictions into a task-based model. The model reveals that idiosyncratic borrowing costs lead to an inefficient equilibrium by generally depressing automation adoption and creating automation dispersion across firms. Such ex-ante distortion results in a uniform subsidy disproportionately benefiting firms with better capital access, thus creating a trade-off in terms of efficiency: while the subsidy can enhance overall automation, it simultaneously exacerbates automation dispersion. To quantify the efficiency implications, we embed this simplified model into a dynamic heterogeneous-agent framework, calibrated to the 2010 productivity distribution, financial frictions, and robot density in the industrial sector in China. Our dynamic model reveals that a 20% robot subsidy narrows the gap between mean and optimal automation level by 22% percentage points, while raises automation dispersion by 49%. This results in a 1.23% increase in aggregate output at the cost of a 2.40% decline in TFP. This dynamic model proposes a novel mechanism that automation exacerbates capital misallocation by enlarging asset accumulation dispersion between workers and entrepreneurs. Controlling for this dynamic feedback could enhance the subsidy-induced output gain by an additional 26%

Many studies suggest a positive and monotonic relationship between technological progress and wage income inequality since 1980s for industrialized economies. We examine this topic in the context of the Chinese economy where new technologies like automation, AI and digitalization have witnessed worldís most rapid growth in the last decade. Surprsingly, we Önd an inverted U-Shaped relationship - a "Digital Kuznets Curve", using a panel dataset constructed in line with the newly published "2021 Categorization of Core Industries of Digital Economy". We then set out a task-based growth model with heterogenous human capital and occupational choice, and show that this hump-shaped relationship can emerge either by introducing an erosion e§ect of digitalization on worker ability that increasingly counterveils the skill-biasing e§ect, or by directly adding a dynamic learning cost that captures the externality of digitalization. Our study contributes to the understanding of the nature of digitalization in re-shaping labor market structure.

The side effect of automation on the economy has been discussed frequently, but little is known regarding its political consequences. This paper examines the causal effect that automation induces political costs for the local government. By combining the national individual-level panel data of political trust with the prefecture-level robot exposure rate in China biennially during the period 2012– 2018, we find that the development of automation would incur lower political trust in the Chinese local government. Furthermore, the impact may result from the risk of unemployment, intensified pessimism about local government, higher downside risk, and declining group participation, providing a few channels for the automation process to affect citizens’political trust. This paper provides empirical evidence for the impact of automation and the source of political legitimacy, contributing to the literature about automation by emphasizing the crucial role of government in coping with the technological progress and making good use of endogenous creative destruction.

We examine the determinants of U.S. equity traders’ choice of electronic versus intermediated execution. While traders exhibit a strong overall preference for automation, when the market is less liquid at order submission time, traders seek market maker automated and human order-matching services more often. Traders overall tendency to choose intermediaries is highly correlated with their demand for liquidity. Market maker participation rates are higher for more active and larger size traders. Traders who choose intermediaries more often trade more stocks, execute orders quicker, price orders more aggressively, and disperse their trading over longer periods of time. Although U.S. stock intermediaries continue to lose market share, our results highlight the important niche role these firms can play in an increasingly automated, electronically-driven marketplace.