The Impact of Urbanization on Income Inequality in Indonesia - Sodik, Jamzani; Murdiyanto, Eko; Artaningtyas, Wahyu Dwi (RSF Conference Series: Business, Management and Social Sciences, 2025)
Urbanization is often assumed to increase inequality in the early stages of development, but the evidence for Indonesia is inconsistent due to interprovincial heterogeneity and spatial fragmentation. A major gap is the lack of studies that combine the dynamics of the cross-time panel with explicit spatial diagnostics. This study reassesses the impact of urbanization on income inequality in Indonesia by highlighting variations between regions and their spatial grouping patterns. We used 33 provincial balanced panel data from 2010–2023 (462 observations), Hausman test-based estimator selection, cross-dependency tests (Pesaran, Frees, Friedman), and descriptive spatial diagnostics: Moran’s I annual, Moran scatterplot, and thematic maps. Concise statistics (mean Gini 0.36; range 0.23–0.45) were examined to uncover hidden heterogeneities, while distance-based weight matrices were critically evaluated against the archipelago context. The main results show that urbanization has no significant effect nationally on inequality, indicating that national urbanization masks local variations. Cross-dependencies appear unevenly: Frees indicates a strong correlation across sub-clusters, while global sizes are weaker, reflecting fragmented economic integration. Moran’s I increased to a peak around 2018 and remained significant into 2023, signaling persistent spatial polarization. Moran scatterplots reveal High–High clusters (e.g. DKI–West Java–DIY), Low–Low (e.g., North Maluku–NTT–West Papua), as well as High–Low outliers (e.g. Aceh, Papua), which is in harmony with the symptoms of the resource curse. The contribution of this study is a panel–spatial integrated reading framework that revises the illusion of urbanization homogeneity, and guides regional-based policy recommendations: strengthening inter-island connectivity, medium-sized urban development, and cluster-sensitive intervention design.