Municipality synthetic Gini index for Colombia: A machine learning approach

This paper presents two synthetic estimations of the Gini coefficient at a municipality level for Colombia in the years 2000-2020. The methodology relies on several machine learning models to select the best model for imputation of the data. This derives in two Random Forest models were the first is characterized by containing Dominant Fixed Effects, while the second contains a set of Dominant Varying Factors. Upon these estimations, the Synthetic Gini Coefficients for both models are inspected, and public links are generated to access them. The Dominant Fixed Effects models is rather ”stiff” in contrast to the Varying Factor model. Hence, for researchers it is recommended to use the Synthetic Gini Coefficient with Varying Factors because it contains greater variability across time than the Dominant Fixed Effects models.