# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 5 Discussion  
### Key Findings  
Several critical findings of this study are revealed. Initially, the results indicate that in the absence of control variables, AI generally has a negative effect on employment quality. That may suggest that automation driven by AI possibly reduces certain jobs and alters income levels by replacing certain types of jobs. Nevertheless, when control variables such as urbanization, openness, fiscal expenditure, and transportation infrastructure are included, the negative impact of AI becomes significant but remains minimal in magnitude, which suggests that its practical effect is limited. These findings highlight that AI's influence on employment quality has a context-dependent nature. The control variables' significance (e.g., fiscal expenditure's mitigating role) demonstrates that AI's labor market impacts are inseparable from broader economic structures. For example, the negative coefficient for trade openness (\(-0.0090433\)) reveals that globalization amplifies AI-driven displacement in labor-intensive sectors, a pattern observed in India and Brazil. Conversely, financial development's positive coefficient (\(+0.0337525\)*******) suggests that credit access enables SMEs to adopt AI sustainably, creating high-skilled roles. These findings advance the literature by framing AI not as a standalone disruptor but as a force mediated by macroeconomic governance--a perspective absent in prior studies [31]. Our findings are directly applicable to developing countries, particularly those undergoing rapid technological transitions, such as China. While much of the existing literature has focused on developed economies like the U.S. and Germany, where AI adoption has generally been more mature and concentrated in high-tech industries, our study highlights the heterogeneous impacts of AI across different regions of a developing country. In China, for example, AI adoption has led to different employment outcomes depending on the region's level of industrialization and education infrastructure. In more developed eastern regions, AI tends to enhance employment quality by creating high-skilled jobs, while in underdeveloped central and western regions, AI adoption has displaced low-skilled jobs and led to skill polarization. This contrast underscores the need for region-specific policy interventions to ensure that AI-driven economic growth benefits all sectors of the labor market. If the findings were more applicable to developed countries, we would explain that the different stages of technological adoption, infrastructure, and labor market structures between developed and developing countries play a critical role. In developed countries, AI has generally been integrated into high-tech industries with a skilled labor force, leading to net job creation in some sectors but displacement in others. In contrast, in developing countries, AI adoption may exacerbate labor market inequalities due to the higher concentration of low-skilled, labor-intensive jobs in certain regions. Therefore, while the general patterns we observe in our study may have some resonance with developed countries, the specific impacts of AI on employment in developing economies, particularly in rural or underdeveloped regions, can be more complex and require tailored policy responses. The findings have broad applicability to other developing countries, particularly those that are actively pursuing technological upgrades and AI adoption. In these economies, regional disparities in industrial structure, infrastructure, and human capital closely mirror the situation in China. For instance, the rural-urban divide and the difference in regional economic development seen in China are similar to the challenges faced in many developing countries. As such, the lessons learned from China's experience with AI adoption can provide valuable insights for other developing economies to ensure that AI-driven growth leads to equitable employment outcomes.