In this context, this study aims to clarify the impact of AI development on workers' overall employment quality and its sub-dimensions, explore the underlying mechanisms, and examine heterogeneity across different groups. To address these issues, this study combines city-level AI patent data with individual-level data from the China Family Panel Studies (CFPS), constructing a comprehensive employment quality index at the worker level and an AI patent density indicator at the city level. A fixed-effects model is employed for baseline regression analysis. To address potential endogeneity concerns, this study further applies an instrumental variable approach, the Heckman two-step method, and an exogenous shock identification strategy. In addition, a series of robustness checks are conducted.
The main findings are as follows. First, AI development significantly improves workers' overall employment quality： a one-standard-deviation increase in city-level AI patent density raises employment quality by 2.21%. This result holds after accounting for endogeneity and conducting multiple robustness tests. Second, analysis of the sub-dimensions of employment quality shows that AI development significantly increases labor income, improves welfare security, enhances job stability, and reduces the risk of overwork, while its effect on job satisfaction is statistically insignificant. Third, mechanism analysis indicates that AI development improves employment quality mainly by promoting occupational upgrading, strengthening human capital accumulation, and improving job-skill matching. Fourth, heterogeneity analysis indicates that the employment quality enhancement effect of AI development varies across groups. The positive effects are more prominent among workers with lower employment quality, female workers, and those with stronger non-cognitive skills.
Based on these findings, this study proposes several policy implications： actively expanding new forms of human-machine collaboration to create more high-quality jobs; building a lifelong learning system to facilitate workers' skill upgrading; and integrating non-cognitive skill development into the education system to enhance workers' comparative advantages in human-machine collaboration. Together, these measures can help achieve broad-based improvement in employment quality in the AI era. In summary, through rigorous empirical analysis, this study provides new micro-level evidence and theoretical explanations for understanding the evolution of employment quality in the AI era. It deepens theoretical insights into the role of technology in empowering workers, and offers more targeted policy implications for guiding AI development toward the promotion of high-quality employment and the creation of a more equitable labor market.
The results indicate that AI development significantly increases migrants' long-term settlement intentions, this conclusion remains robust after various robustness checks and addressing endogeneity concerns. Mechanism analysis reveals that, at the micro level, AI development enhances migrants' long-term settlement intentions by raising their income levels and labor market participation, thereby improving economic returns and employment stability. At the city level, AI development fosters migrants' long-term settlement intentions by stimulating urban economic growth, optimizing public services provision, and enhancing urban amenities. Heterogeneity analysis further demonstrates that the positive effect of AI development is more pronounced among high-skilled and high-income migrants, as well as those engaged in non-routine cognitive tasks, whereas low-skilled, low- to middle-income migrants, and those performing routine, easily replaceable tasks benefit significantly lesser. Further analysis reveals that AI development also exerts a notable positive effect on population migration behavior.
This study contributes to the literature in three main ways. First, in terms of research content, it integrates AI development into the analytical framework of population migration by focusing settlement intentions, thereby deepening the understanding of the nexus between technological change and population dynamics. Second, regarding research design, unlike most existing studies that rely on industrial robot adoption as a proxy for AI, this paper extracts firms' business scope data from the National Enterprise Credit Information Publicity System. By leveraging Large Language Models (LLMs) for keyword filtering, it constructs a city-level indicator of AI enterprise density. This approach more accurately measures the practical application and industrialization of AI, overcoming the manufacturing bias of robot-based data. Third, from a research perspective, this paper moves beyond the conventional view of migrants as a homogeneous group. By focusing on skill structures, it reveals the heterogeneous settlement decisions under technological shocks, providing new empirical evidence for the evolution of demographic structures.
Theoretically, this study elucidates how AI influences migrants' settlement intentions through labor market channels and urban amenities, enriching the discourse on migration. Practically, it advocates for inclusive AI development policies and the establishment of universal, forward-looking lifelong learning and reskilling systems. Particular emphasis should be placed on supporting low- and middle-skilled and low-income groups, ensuring that the dividends of AI development are shared more broadly to promote the synergy between technological progress and high-quality population development.
Empirical findings show that greater AI focus significantly increases both the variety and number of AI job postings, confirming that it creates new demand. This effect depends on a company's internal labor conditions. Companies with a higher proportion of high-skilled labor see stronger job demand creation due to better technological fit. In contrast, companies with inefficient labor allocation use AI focus to correct this imbalance, leading to more employment demand. A key mechanism is that companies rely more on external hiring than on internal training to adapt to technological change. This suggests AI focus may crowd out investment in retraining current staff, thereby generating external demand. The job demand creation effect is stronger in smaller companies, in non-manufacturing industries, and in regions with high-level AI development. Furthermore, AI focus changes the structure of labor demand： it concentrates educational requirements on bachelor's degrees, raises experience requirements, and leads to a balanced increase in salary levels across average, minimum, and maximum wages. This indicates trends of both polarization and inclusive upgrading of job quality.
This study contributes to the literature in three ways. First, it introduces a front-end cognitive perspective by analyzing strategic attention before actual technology adoption, offering early signals of AI's labor market impact. Second, it develops and tests a three-part framework explaining how AI focus affects employment demand by correcting labor misallocation, by leveraging high-skilled labors for better adaptation, and by crowding out internal training investments. This advances beyond simple views of technology replacing jobs. Third, it combines text-based measures of company strategy with detailed, real-time recruitment data, providing strong micro-level evidence on AI-driven labor demand.
The results have important policy implications. First, governments can promote a technology-friendly environment and provide technical interpretation to raise corporate AI awareness, using its job creation potential to ease structural employment pressures. Second, because companies strongly prefer external hiring, governments should guide them to balance recruitment with internal training to avoid over-relying on external human capital. Companies with labor imbalances should optimize their labor structure, while high-skilled companies should use their adaptive advantage. Third, the shift toward bachelor's degrees, greater experience, and balanced wage growth calls for corresponding educational reforms. Governments should guide universities to update AI-related curricula and strengthen continuing education to help labors adapt to new job demands.
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