|
on Central and South America |
| By: | Guillermo Cruces; Diego Fernández Meijide; Sebastian Galiani; Ramiro H. Gálvez; María Lombardi |
| Abstract: | Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizationalselectioncompresseseducationalheterogeneity, leavingunclearwhetherAI narrows productivity gaps across individuals with substantially different levels of formal education. Weaddressthisquestionusingarandomizedonlineexperimentconductedoutside firms, in which1, 174 adults aged 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 designtargets 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 AIaccess, higher-education participants outperform lower-education participants by0.548standarddeviations; withAIaccess, thisgapfallsto0.139standarddeviations, implying that generative AI closes 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 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. |
| Keywords: | Productivity, artificial intelligence, education, human capital, inequality |
| JEL: | J24 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:udt:wpgobi:wp_gob_2026_03 |
| By: | Chomali, Laura; Campaña, Juan Carlos; Molina, José Alberto |
| Abstract: | Gender inequality in unpaid domestic labor persists despite rising female labor market participation across the developing world. Foodwork, understood as cooking, grocery shopping, and dishwashing, links this inequality to household health, with documented connections to dietary quality in a region facing diet-related chronic disease. Telework offers a pathway toward redistribution of these responsibilities. Using time-use data from four Latin American countries and seemingly unrelated regressions, we show that what matters is not whether telework occurs, but who teleworks. Joint telework raises foodwork by up to 250% (Guatemala) and eating time by up to 32% (Chile). Female-exclusive telework produces the sharpest asymmetry: women's foodwork, eating time, and paid hours change by up to 78%, 35%, and 10.3 weekly hours (Chile). Male-exclusive telework yields a smaller reversal: men's foodwork up to 123% (Guatemala), eating time up to 36% (Chile). Policies should prioritize joint and male-inclusive arrangements to generate redistributive effects within couples. |
| Keywords: | Intra-household allocation, Latin America, time use, working from home |
| JEL: | D13 J16 J22 O54 |
| Date: | 2026–04–26 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128883 |
| By: | Pedro C. Sant'Anna; Sulin Sardoschau; Aiko Schmeisser |
| Abstract: | Empirical studies of racial wage disparities typically rely on self-reported race and treat racial categories as fixed. This paper shows that racial classification in the labor market is produced by social perception, and that modeling this perception process is essential for measuring wage gaps. We combine two large-scale administrative data sets to construct three racial identity measures for 330, 000 workers in Brazil between 2003 and 2015: employer classification, self-identification, and an algorithmic skin-tone measure extracted from photographs. In over 20 percent of cases, self-identified and employer-ascribed race do not match, and employers disagree in their classification of the same worker. To quantify how race is constructed, we estimate a "race function" describing how employers map phenotypic cues, self-identification, local context, education, and employment histories into racial categories, showing that productivity-relevant factors shape perceptions. Holding skin tone constant, university graduates are 10 percentage points more likely to be perceived as White. Education whitens even conditional on self-declared race and within firm-by-occupation cells. Measured wage disparities differ depending on whether race is self-reported, employer-ascribed, or skin-tone based, and accounting for racial perceptions substantially changes estimated wage gaps. We show that conventional approaches overstate the role of productivity differences in explaining racial wage gaps. |
| Keywords: | Race, identity, disparity, wage gap, Brazil |
| JEL: | J15 J50 J71 Z10 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26074 |