From Polarization to Convergence: How Data Agglomeration Shapes Within-City Income Disparities via Technological Agglomeration and Diffusion

This paper develops a mathematical model grounded in traditional agglomeration theory to analyze the impact of data factor agglomeration on income disparity. Using panel data from 290 prefecture-level cities over the period 2013–2023 and employing the National Big Data Comprehensive Pilot Zone as a proxy for data factor agglomeration, we conduct empirical tests. The results show that: (1) Data factor agglomeration narrows within-city income disparities on average and generates significant income gains for cities within 300 km, while distant cities beyond 600 km experience widening disparities. However, due to the limited number of clusters at the province level, the statistical significance of this effect is sensitive to the clustering level. (2) Data factor agglomeration widens income disparities in the eastern region, while it helps narrow disparities in the central and western regions, in cities with smaller economic and population scales, and in areas with lower levels of digital information infrastructure. Meanwhile, at the 10th percentile, data factor agglomeration narrows income disparities, whereas at the 90th percentile, it exacerbates income disparities. (3) Mechanism analysis shows that data factor agglomeration significantly promotes both technological agglomeration and technological diffusion, with the former associated with widening and the latter with narrowing income disparities.