Learn how to turn satellite images of nighttime lights into estimates of regional income, then measure how unequal regions are within each country. Step by step in Python, we test whether regional inequality first rises and then falls as countries develop.
Do gaps between rich and poor regions within a country first widen and then narrow as the country develops? Using simulated data for 56 countries that mirrors a published study, we measure regional inequality and trace how it rises and falls with income, from simple to more flexible methods. This R tutorial comes with an interactive app that runs in your web browser.
Are poorer countries catching up to richer ones? Using international income data, we test whether poorer countries grow faster and whether income gaps across the world are shrinking, and we see how the answer changed after 2000. This Stata tutorial comes with an interactive app that runs in your web browser.
Why have poor countries grown faster than rich ones since around 2000, after decades of falling behind? We reproduce a 2021 study by Kremer, Willis and You showing that the policies, institutions and schooling that predict growth have themselves become more alike across countries. This Stata tutorial comes with an interactive app that runs in your web browser.
As countries grow richer, do the gaps between their regions first widen and then narrow, or do they widen again at the top? This Python tutorial uses satellite images of night lights for about 180 countries and compares each country with itself over time, finding an N-shaped pattern. It comes with an interactive app that runs in your web browser.
Do regions with high or low human development cluster together on the map of South America, and did those clusters shift between 2013 and 2019? This Python tutorial maps 153 regions and uses spatial statistics to test whether neighbors are more alike than chance would suggest. It comes with an interactive app that runs in your web browser.
Do poorer districts in Indonesia catch up with richer ones at the same pace everywhere? This Python tutorial uses a map-based regression that lets each relationship change from place to place, each at its own geographic scale, across 514 districts. It comes with an interactive app that runs in your web browser.
A geocomputational notebook to monitor subnational human development using Python. Besides exploratory data analysis, the notebook introduces geospatial mapping, spatial dependence, and spatial inequality.
The book provides a succinct review of the recent club convergence literature, a comparative view of developed and developing countries, and a tutorial on how to implement the club convergence framework in Stata and R.