A comprehensive, beginner-friendly Python replication of Lessmann and Seidel (2017) — turning satellite nighttime lights into predicted regional GDP, building five population-weighted inequality indices from scratch, exploring the cross-country dynamics of regional inequality, and estimating the regional Kuznets curve, its determinants, and a Conley spatial-HAC robustness check with PyFixest.
A beginner-friendly R replication of Lessmann (2014) on the spatial Kuznets curve — building the weighted coefficient of variation from simulated regional data, then estimating the inverted-U with cross-section OLS, two-way fixed effects in fixest, and the Robinson and Baltagi–Li semiparametric estimators.
Replicating the N-shaped Kuznets curve with panel data fixed effects in Python using PyFixest, from pooled OLS through two-way FE, turning point analysis, and determinants of regional inequality across 180 countries
This paper studies the evolution of economic and social disparities across subnational regions in South America using simple spatial convergence models.
Using a novel dataset, we analysis the spatio-temporal dynamics of income per capita across 34 provinces and 514 districts in Indonesia over the 2010–2017 period.
By applying a non-linear dynamic factor model, this article tests the club convergence hypothesis using a novel dataset of income at the district level. The results show significant five convergence clubs.
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.