[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123141-en":3,"doc-seo-123141-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123141,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Municipality Synthetic Gini Index for Colombia - A Machine Learning Approach","This paper presents two synthetic estimations of the Gini coefficient at the municipality level for Colombia over 2000–2020. The approach uses multiple machine learning models to identify the best imputation specification, resulting in two Random Forest variants: one dominated by fixed effects and another dominated by time-varying factors. Synthetic Gini outputs are examined for both models, and public access links are generated. The fixed-effects model is described as relatively rigid, while the varying-factor model offers greater temporal variability, making it preferable for researchers.","Munich Personal RePEc Archive  \nMunicipality synthetic Gini index for Colombia: A machine learning approach  \nJohn Michael, Riveros-Gavilanes  \nVeeduria Estudios y Evaluacion de la Gestion Publica Colombiana, Corporacion Centro de Interés Público y Justicia-CIPJUS  \n1 February 2025  \nOnline at [https://mpra. ub. uni-muenchen. de/123561/](https://mpra. ub. uni-muenchen. de/123561/)  \n[MPRA Paper No. 123561](MPRA Paper No. 123561) , [posted 07 Feb 2025 11:35 UTC](posted 07 Feb 2025 11:35 UTC)  \nVeeduria Estudios y Evaluación de la Gestión Pública Colombiana-EEGPCCorporacion Centro de Interés Público y Justicia-CIPJUS  \nWorking Paper No. 001, January 2025, pp. 1-30  \n© RJ Project [https://sites.google.com/view/riveros-empirical-rj-project/home](https://sites.google.com/view/riveros-empirical-rj-project/home)  \nMunicipality Synthetic Gini Index for Colombia: A Machine Learning Approach  \nÍndice Sintetico Municipal de Gini para Colombia: Un enfoque de  \nMachine Learning  \nJohn Michael Riveros-Gavilanes 1  \nAbstract  \nThis 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.  \nKeywords: Gini, Machine learning, Random forest, estimation, synthetic, economics  \nAbstract  \nEste documento presenta dos estimaciones sinteticas del coeficiente de Gini anivel municipal en Colombia entre los años 2000-2020 . La metodología utilizavarios modelos de machine learning para seleccionar el mejor modelo para la imputación de datos. Esto deriva en dos modelos de Random Forest, el cual, el primero es characterizado por ser Dominante en Efectos Fijos, mientras elsegundo tiene un conjunto de variables Dominantes en Factores Variantes. Conestas estimaciones, el Índice Sintetico de Gini para los modelos es revisado, y links publicos son generados para su acceso. El modelo Dominante de Efectos Fijos es ”rigido” en contraste con el modelo de Dominante en Factores Variantes. Se recomienda a los investigadores usar el Índice Sintético con factores variantes por que contienen mayor variabilidad a través del tiempo.  \nPalabras Clave: Gini, Machine learning, Random forest, estimación, sintetico, economía  \n1. M. Sc. Economics. Ludwig-Maximilians-Universität-LMU München, Deutschland [E-mail:](E-mail: {riveros}@ms-researchhub.com)[ {riveros}@ms-researchhub.com](E-mail: {riveros}@ms-researchhub.com)  \nContents  \n1 Introduction 3  \n2 General process 5  \n2. 1 Data transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.2 Machine learning estimation ........................... 6  \n2.3 Interpretation of variables within the random forest ............. 7  \n2.4 Estimation with time-varying factors ...................... 12  \n2.5 Machine learning estimation with varying factors ............... 13  \n2.6 Geographical analysis .............................. 16  \n2.7 Descriptive Statistics ............................... 17  \n2.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  \n3 Introducción en Español 21  \n4 Proceso general 23  \n4. 1 Transformaciones de datos . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  \n4.2 Estimación mediante aprendizaje automático ................. 24  \n4.3 Interpretación de las variables dentro del bosque aleator","cbCail1DoaNyXPzo","https://ap.wps.com/l/cbCail1DoaNyXPzo","pdf",2246203,1,43,"English","en",105,"# Introduction\n# General process\n## Data transformations\n## Machine learning estimation\n## Interpretation of variables within the random forest\n## Estimation with time-varying factors\n## Machine learning estimation with varying factors\n## Geographical analysis\n## Descriptive Statistics\n## Conclusions\n# Introducción en Español\n# Proceso general\n## Transformaciones de datos\n## Estimación mediante aprendizaje automático\n## Interpretación de las variables dentro del bosque aleatorio (Random Forest)\n## Estimación con factores variables en el tiempo\n## Estimación de aprendizaje automático con factores variables\n## Análisis Geográfico\n## Estadísticas Descriptivas\n## Conclusiones","[{\"question\":\"What does the paper estimate for Colombia and for which period?\",\"answer\":\"It estimates the Gini coefficient synthetically at the municipality level for Colombia for the years 2000–2020.\"},{\"question\":\"How is the imputation model selected in the study?\",\"answer\":\"The methodology evaluates several machine learning models and selects the best-performing specification for imputing the Gini data.\"},{\"question\":\"What is the difference between the two Random Forest model variants?\",\"answer\":\"One model is characterized by dominant fixed effects, while the other is dominated by socioeconomically time-varying factors.\"}]","Municipality Synthetic Gini Index for Colombia - A Machine Learning Approach | PDF",1785814847,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"municipality-synthetic-gini-index-for-colombia-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/municipality-synthetic-gini-index-for-colombia-a-machine-learning-approach/123141/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the paper estimate for Colombia and for which period?","Question",{"text":75,"@type":76},"It estimates the Gini coefficient synthetically at the municipality level for Colombia for the years 2000–2020.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the imputation model selected in the study?",{"text":80,"@type":76},"The methodology evaluates several machine learning models and selects the best-performing specification for imputing the Gini data.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the difference between the two Random Forest model variants?",{"text":84,"@type":76},"One model is characterized by dominant fixed effects, while the other is dominated by socioeconomically time-varying factors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]