# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 3 Material and Method
### Model Specification  
The primary methodology applied in this research is a panel data approach with a two-way fixed effects model, which allows us to control for both time-invariant and city-specific factors. The model is selected based on the current research methodology [34, 35, 36, 37]. We used this model to examine the relationship between AI adoption and employment quality across prefecture-level cities in China. The model is based on the following specifications:  
\[\text{Emp\_{[it]}}=\alpha 0+\alpha 1\text{Csmd\_{[it]}}+\alpha 2\text{X\_{[it]}}+ \lambda\_{[i]}+\eta\_{[t]}+\mu\_{[it]}\]  
where:  
\[\text{Emp\_{[it]}}\]  
represents the employment quality in city i during year t.  
\[\text{Csmd\_{[it]}}\]  
is the level of AI adoption in city i during year t.  
\[\text{X\_{[it]}}\]  
represents the control variables (e.g., urbanization rate, fiscal expenditure, etc.).  
\[\lambda\_{[i]}\]  
and  
\[\eta\_{[t]}\]  
capture the city and year fixed effects, respectively.  
\[\mu\_{[it]}\]  
is the error term.  
This model is appropriate for analyzing the panel data because it accounts for unobserved heterogeneity and ensures robust inferences about the relationship between AI and employment quality.  
The use of a two-way fixed effects model is grounded in the need to account for the potential unobserved heterogeneity between cities (cross-sectional dependence) and over time (temporal dependence). Panel data analysis is ideal for this context as it allows for the examination of both the time-series and cross-sectional dimensions of the data simultaneously, offering a deeper understanding of how AI adoption impacts employment quality in various regions. While the econometric model employed is widely used in empirical economic research, its application in the context of AI adoption in China's regional labor markets is a novel contribution. The model's robustness and its ability to isolate the effect of AI on employment outcomes across various regions provide new knowledge that was previously underexplored in the literature.  
Figure 3: Trends in AI adoption and employment quality.  
The data include information on employment quality (our dependent variable), as well as AI adoption (measured by industrial robot density), control variables (urbanization rate, fiscal expenditure, trade openness, and financial development), and other socioeconomic factors. The data are sourced from credible national databases, such as the China Statistical Yearbook, CSMAR, and IFR reports. The key assumption behind using panel data is that both cross-sectional (city-level) and time-series (yearly) variations provide valuable insights into the regional effects of AI adoption over time. Additionally, we assume that AI adoption in one year influences employment quality in subsequent years, but it does not suffer from endogeneity biases, as we control for fixed effects.  
To ensure the robustness of our findings, we performed a sensitivity analysis by applying different model specifications, including alternative measures of AI adoption and control variables. We also conducted lagged-variable analyses to address potential reverse causality between AI adoption and employment quality. In addition, we tested for potential outliers using a winsorization technique to minimize the influence of extreme values on the regression results. The sensitivity of the results was assessed through these alternative specifications, ensuring that our conclusions remain stable and reliable across various assumptions.  
We have also included a schematic diagram in Figure 4 that illustrates the research methodology and the steps involved in the knowledge generation process. The diagram outlines the stages from data collection to the econometric analysis, including the use of panel data, the two-way fixed effects model, and the sensitivity analysis. This visualization aims to provide a clearer understanding of the methodological approach and how it contributes to generating new knowledge about the effects of AI on employment quality.