Understanding Labor Market Demand in Real Time in Argentina and Uruguay

This 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 information 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 comprehensive, 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.