This paper studies the evolution of economic and social disparities across subnational regions in South America using simple spatial convergence models.

This book introduces a modern framework to study the cross-country convergence dynamics of labor productivity and its proximate sources: capital accumulation and aggregate efficiency.

Across ASEAN regions, almost 60 percent of the differences in GDP per capita can be predicted by a luminosity-based measure of GDP. Based on this measure, regional inequality within most countries has not significantly decreased, spatial dependence is increasing, and spatial clusters (hotspots and coldspots) cross multiple national boundaries.

The paper incorporates some recent developments from the unsupervised machine learning literature to re-evaluate the cross-country convergence hypothesis in a context beyond GDP. The application of a distribution-based clustering algorithm suggests the formation of three local convergence clubs.