{"id":9017,"date":"2025-12-15T13:52:41","date_gmt":"2025-12-15T16:52:41","guid":{"rendered":"https:\/\/aaep.org.ar\/?p=9017"},"modified":"2025-12-15T13:52:43","modified_gmt":"2025-12-15T16:52:43","slug":"prediction-and-learning-of-exporting-firms-a-study-of-colombia","status":"publish","type":"post","link":"https:\/\/aaep.org.ar\/?p=9017","title":{"rendered":"Prediction and Learning of Exporting Firms: A Study of Colombia"},"content":{"rendered":"<iframe loading=\"lazy\" class=\"wonderplugin-pdf-iframe\" src=\"https:\/\/aaep.org.ar\/wp-content\/plugins\/wonderplugin-pdf-embed\/pdfjslight\/web\/viewer.html?v=2&file=https:\/\/aaep.org.ar\/works\/works2025\/4816.pdf\" width=\"100%\" height=\"600px\" style=\"border:0;\"><\/iframe>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper applies machine learning techniques to predict which manufacturing firms in Colombia are likely to become exporters, using data from the Encuesta Anual Manufacturera (EAM) and Encuesta de Desarrollo e Innovaci\u00f3n Tecnol\u00f3gica (EDIT) for the period 2015\u20132019. The objective is to estimate each firm\u2019s \u201cdistance to export\u201d through a probability score learned from the characteristics of existing exporters. Among the different algorithms tested, Logit with LASSO regularization delivers the best predictive performance, correctly identifying nearly three out of four actual exporters. Building on these predictions, the study introduces an exporting score, a probability measure that ranks firms by their proximity to the export margin. This score captures heterogeneity among non-exporters, anticipates entry and exit dynamics, and highlights sectoral and geographic clusters of latent export potential. In addition, the analysis shows that a set of firm level characteristics consistently emerge as the most relevant predictors across models: importer status, firm size, and combined spillovers, complemented by operational variables such as value added, inventories, and quality certification. The findings offer valuable insights for export promotion policies, enabling more targeted support for firms likely to enter international markets.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[29],"tags":[33],"class_list":["post-9017","post","type-post","status-publish","format-standard","hentry","category-anales","tag-aaep-anales-2025"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prediction and Learning of Exporting Firms: A Study of Colombia - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction and Learning of Exporting Firms: A Study of Colombia - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"og:description\" content=\"This paper applies machine learning techniques to predict which manufacturing firms in Colombia are likely to become exporters, using data from the Encuesta Anual Manufacturera (EAM) and Encuesta de Desarrollo e Innovaci\u00f3n Tecnol\u00f3gica (EDIT) for the period 2015\u20132019. The objective is to estimate each firm\u2019s \u201cdistance to export\u201d through a probability score learned from the characteristics of existing exporters. Among the different algorithms tested, Logit with LASSO regularization delivers the best predictive performance, correctly identifying nearly three out of four actual exporters. Building on these predictions, the study introduces an exporting score, a probability measure that ranks firms by their proximity to the export margin. This score captures heterogeneity among non-exporters, anticipates entry and exit dynamics, and highlights sectoral and geographic clusters of latent export potential. In addition, the analysis shows that a set of firm level characteristics consistently emerge as the most relevant predictors across models: importer status, firm size, and combined spillovers, complemented by operational variables such as value added, inventories, and quality certification. 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