{"id":8885,"date":"2025-12-15T12:29:53","date_gmt":"2025-12-15T15:29:53","guid":{"rendered":"https:\/\/aaep.org.ar\/?p=8885"},"modified":"2025-12-15T12:29:54","modified_gmt":"2025-12-15T15:29:54","slug":"the-potential-distributive-impact-of-ai-driven-labor-changes-in-latin-america","status":"publish","type":"post","link":"https:\/\/aaep.org.ar\/?p=8885","title":{"rendered":"The Potential Distributive Impact of AI-driven Labor Changes in Latin America"},"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\/4.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 examines how artificial intelligence (AI) adoption may affect labor markets in Latin America, focusing on inequality and poverty. Using household survey data from 14 countries, we apply four AI occupational exposure indices\u2014the AIOE, C-AIOE, GBB, and GENOE\u2014to assess which groups face greater risk. We validate these indices by comparing task profiles with those in high-income countries and adjust them for regional labor market features such as informality, wage structures, and union coverage. Results reveal significant heterogeneity: women, younger workers, the more educated, and those in formal employment face higher exposure to AI-driven changes. However, indices that incorporate task complementarities show less steep differences across income and education levels. Simulations suggest that displacement effects may lead to only moderate increases in inequality and poverty in the absence of mitigating policies.<\/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-8885","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 v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Potential Distributive Impact of AI-driven Labor Changes in Latin America - 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=\"The Potential Distributive Impact of AI-driven Labor Changes in Latin America - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"og:description\" content=\"This paper examines how artificial intelligence (AI) adoption may affect labor markets in Latin America, focusing on inequality and poverty. Using household survey data from 14 countries, we apply four AI occupational exposure indices\u2014the AIOE, C-AIOE, GBB, and GENOE\u2014to assess which groups face greater risk. We validate these indices by comparing task profiles with those in high-income countries and adjust them for regional labor market features such as informality, wage structures, and union coverage. Results reveal significant heterogeneity: women, younger workers, the more educated, and those in formal employment face higher exposure to AI-driven changes. However, indices that incorporate task complementarities show less steep differences across income and education levels. Simulations suggest that displacement effects may lead to only moderate increases in inequality and poverty in the absence of mitigating policies.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/aaep.org.ar\/?p=8885\" \/>\n<meta property=\"og:site_name\" content=\"Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-15T15:29:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-15T15:29:54+00:00\" \/>\n<meta name=\"author\" content=\"aaepolitica\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@aaepoficial\" \/>\n<meta name=\"twitter:site\" content=\"@aaepoficial\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"aaepolitica\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=8885#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=8885\"},\"author\":{\"name\":\"aaepolitica\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/#\\\/schema\\\/person\\\/7150b747ba20875259c74b2cd177316c\"},\"headline\":\"The Potential Distributive Impact of AI-driven Labor Changes in Latin America\",\"datePublished\":\"2025-12-15T15:29:53+00:00\",\"dateModified\":\"2025-12-15T15:29:54+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=8885\"},\"wordCount\":26,\"publisher\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/#organization\"},\"keywords\":[\"AAEP Anales 2025\"],\"articleSection\":[\"Anales\"],\"inLanguage\":\"es\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=8885\",\"url\":\"https:\\\/\\\/aaep.org.ar\\\/?p=8885\",\"name\":\"The Potential Distributive Impact of AI-driven Labor Changes in Latin America - 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