Seminario de Negocios 2026
El seminario se propone como un espacio para presentar nuevas investigaciones y fortalecer el conocimiento mutuo entre los miembros del profesorado.
Contacto: negocios@utdt.edu
Tel.: 5169 7301
Martes 14 de abril
Guillermo Cruces | Universidad de San Andrés
"Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment".
Abstract
Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizational selection compresses educational heterogeneity, leaving unclear whether AI narrows productivity gaps across individuals with substantially different levels of formal education. We address this question using a randomized online experiment conducted outside firms, in which 1,174 adults ages 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our design targets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AI access, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI access, this gap falls to 0.139 standard deviations, implying that generative AI closes about three quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing cognitive constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance.
Autores: Guillermo Cruces, Diego Fernández Meijide, Sebastián Galiani, Ramiro H. Gálvez, María Lombardi.
Guillermo Cruces es doctor en Economía por la London School of Economics and Political Science y licenciado en Economía por la Universidad Torcuato Di Tella. Es profesor plenario del Departamento de Economía de la Universidad de San Andrés e investigador principal del CONICET.
Viernes 13 de marzo
Prashant Bharadwaj | University of California
“Statistical Discrimination and the Distribution of Wages”
Abstract
We characterize the conditions under which the wage distributions for two groups are consistent with a general model of statistical discrimination. We adapt this theoretical characterization to develop a novel empirical test, the rejection of which we interpret as evidence of taste-based discrimination. In doing so, we provide a theoretical foundation via which the wage structure effect in the decomposition of wage distributions can be interpreted as evidence of taste-based discrimination. We provide a proof of concept application using Census and NLSY-79 data, which suggests taste-based discrimination at work against Black male workers in several broad occupation categories.
Prashant Bharadwaj is a Professor in the Department of Economics at the University of California, San Diego. His research focuses on development and labor economics. He is Associate Editor at the Journal of Development Economics and the Journal of Health Economics, and Editor in Chief of Economic Development and Cultural Change. His work has been published in leading journals including the American Economic Review, the Journal of the European Economic Association, and the Journal of Development Economics, among others.
Martes 14 de abril
Guillermo Cruces | Universidad de San Andrés
"Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment".
Abstract
Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizational selection compresses educational heterogeneity, leaving unclear whether AI narrows productivity gaps across individuals with substantially different levels of formal education. We address this question using a randomized online experiment conducted outside firms, in which 1,174 adults ages 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our design targets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AI access, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI access, this gap falls to 0.139 standard deviations, implying that generative AI closes about three quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing cognitive constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance.
Autores: Guillermo Cruces, Diego Fernández Meijide, Sebastián Galiani, Ramiro H. Gálvez, María Lombardi.
Guillermo Cruces es doctor en Economía por la London School of Economics and Political Science y licenciado en Economía por la Universidad Torcuato Di Tella. Es profesor plenario del Departamento de Economía de la Universidad de San Andrés e investigador principal del CONICET.
Especialista en economía pública y laboral, análisis distributivo y evaluación de políticas públicas en América Latina, ha publicado en revistas como Journal of Political Economy, American Economic Review: Insights y Journal of Public Economics, además de en libros académicos. Fue investigador invitado en Harvard University y en la University of California, Berkeley; es profesor visitante de la University of Nottingham e investigador asociado de J-PAL, CEPR e IZA. También fue Subsecretario de Desarrollo del Ministerio de Hacienda de la República Argentina y consultor de organismos internacionales.
Viernes 13 de marzo
Prashant Bharadwaj | University of California
“Statistical Discrimination and the Distribution of Wages”
Abstract
We characterize the conditions under which the wage distributions for two groups are consistent with a general model of statistical discrimination. We adapt this theoretical characterization to develop a novel empirical test, the rejection of which we interpret as evidence of taste-based discrimination. In doing so, we provide a theoretical foundation via which the wage structure effect in the decomposition of wage distributions can be interpreted as evidence of taste-based discrimination. We provide a proof of concept application using Census and NLSY-79 data, which suggests taste-based discrimination at work against Black male workers in several broad occupation categories.
Prashant Bharadwaj is a Professor in the Department of Economics at the University of California, San Diego. His research focuses on development and labor economics. He is Associate Editor at the Journal of Development Economics and the Journal of Health Economics, and Editor in Chief of Economic Development and Cultural Change. His work has been published in leading journals including the American Economic Review, the Journal of the European Economic Association, and the Journal of Development Economics, among others.