# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 4 Empirical Results and Analysis  
### Baseline Regression  
Table 2 demonstrates that AI adoption (coefficient: \(-0.0000355\)***) exerts a statistically significant but modest negative impact on employment quality overall. This result aligns with H2, which posits that AI disproportionately displaces low-skilled workers, particularly in regions with weaker human capital infrastructure (e.g., central/western China). However, the small effect size (\(-0.0000355\)) underscores the nuanced nature of AI's impact (RQ1). While automation displaces low-skilled roles, its broader economic benefits (e.g., productivity gains) may offset these losses in regions with robust policy frameworks. For example, eastern cities like Shanghai exhibit minimal declines in employment quality due to proactive reskilling programs (consistent with H3). This duality--the destruction of low-skilled jobs versus the creation of high-skilled opportunities--mirrors global patterns observed in the U.S. and Germany but is uniquely mediated by China's regional policy heterogeneity. The following analysis systematically examines how the empirical findings align with the study's hypotheses and research questions, bridging statistical results to their theoretical and practical implications. By mapping regression coefficients (e.g., Table 2's AI adoption coefficient of \(-0.0000355\)***) to the proposed hypotheses (H1-H3), this section clarifies whether AI's employment effects validate or challenge the study's foundational assumptions. Simultaneously, it contextualizes the findings within the broader research questions (RQ1-RQ3), addressing how regional disparities, industrial structures, and policy interventions shape AI's labor market outcomes. This synthesis not only provides empirical validation of the theoretical framework but also translates numerical results into actionable insights for policymakers, scholars, and stakeholders navigating AI's dual role as a disruptor and enabler of sustainable employment:  
* H1 (High-skilled job creation): Partially supported. While AI adoption correlates with high-skilled employment growth in eastern regions (Table 4, Column 2: \(+0.000116\)***), the aggregate negative coefficient in Table 2 reflects the dominance of low-skilled displacement in underdeveloped areas. * H2 (Regional disparities): Strongly supported. The baseline model's negative coefficient confirms AI's adverse effects on low-skilled workers, exacerbated by infrastructure gaps (e.g., central/western regions lack training programs to mitigate displacement). * RQ3 (Policy interventions): The modest effect size implies that targeted policies (e.g., reskilling subsidies) could neutralize AI's negative impacts, as seen in the eastern cities' success (\(+0.0656932\)*** for urbanization in Table 4). The baseline regression (Table 3) reveals that AI adoption significantly reduces employment quality (coefficient: \(-0.0000355\)***), providing strong support for H2, which posits that AI disproportionately displaces low-skilled workers in regions with weaker human capital. However, the modest effect size (\(-0.0000355\)) underscores the nuanced nature of AI's impact (RQ1). While automation displaces low-skilled roles, productivity gains and high-skilled job creation in tech-intensive regions (e.g., Shanghai) partially offset these losses, aligning with H1. This duality mirrors findings in the U.S. and Germany but is uniquely mediated by China's regional policy heterogeneity, a novel contribution to the literature on developing economies. This study advances the discourse on AI and labor markets by demonstrating that AI's employment impacts are inherently non-linear and regionally contingent, challenging deterministic narratives of a universal \"job apocalypse\".