世界银行-实时了解阿根廷和乌拉圭的劳动力市场需求（英）

Policy Research Working Paper11086Understanding Labor Market Demand in Real Time in Argentina and UruguayEvelyn VezzaGonzalo ZuninoLuis LaguingeHarry MorozIgnacio ApellaMarla SpivackSocial Protection and Labor Global DepartmentMarch 2025 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the Research Support TeamAbstractThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 11086This paper explores how job vacancy data can enhance labor market information systems (LMISs) in Argentina and Uruguay where, as in many countries, data on in-demand skills is lacking. By analyzing job postings collected over four years in Argentina and Uruguay, this study assesses the potential of vacancy data to fill labor market data gaps. The findings reveal that vacancy data capture labor market dynamics across time and geography, showing a strong correlation with traditional labor market indicators such as employment and unemployment. However, the data are biased towards higher-skilled occupations. Despite these limitations, the large volume of postings allows for robust inferences and provides valuable insights into skills demand. The study presents three key applications of the data: 1) using postings as a leading indicator of labor market health; 2) identifying in-demand skills; and 3) mapping similarities between occupations to improve the informa-tion available to job counselors to provide advice about job transitions. Finally, the paper contributes methodologically by developing both a manually created skills taxonomy and an experimental machine learning approach to classifying skills. The machine learning method, while less comprehen-sive, highlights in-demand skills and can complement the manual approach by keeping it up to date with minimal input. Overall, the paper demonstrates the potential of job vacancy data to improve LMISs and inform labor market policies in Argentina and Uruguay with immediate practical applications for labor market analysis, skills development, and workforce training.This paper is a product of the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy R