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Municipality synthetic Gini index for Colombia: A machine learning approach

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  • John Michael, Riveros-Gavilanes

Abstract

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.

Suggested Citation

  • John Michael, Riveros-Gavilanes, 2025. "Municipality synthetic Gini index for Colombia: A machine learning approach," MPRA Paper 123561, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:123561
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    File URL: https://mpra.ub.uni-muenchen.de/123561/1/MPRA_paper_123561.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Gini; Machine learning; Random forest; estimation; synthetic; economics;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • H7 - Public Economics - - State and Local Government; Intergovernmental Relations
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • P19 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Other

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