arXiv 17 Aug 2025 · Econometrics
arXiv:2508.12514 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a unified multi-stage framework to reconstruct consistent monthly and annual labor indicators for all 33 Colombian departments from 1993 to 2025. The approach integrates temporal disaggregation, time-series splicing and interpolation, statistical learning, and institutional covariates to estimate seven key variables: employment, unemployment, labor force participation (PEA), inactivity, working-age population (PET), total population, and informality rate, including in regions without direct survey coverage. The framework enforces labor accounting identities, scales results to demographic projections, and aligns all estimates with national benchmarks to ensure internal coherence. Validation against official departmental GEIH aggregates and city-level informality data for the 23 metropolitan areas yields in-sample Mean Absolute Percentage Errors (MAPEs) below 2.3% across indicators, confirming strong predictive performance. To our knowledge, this is the first dataset to provide spatially exhaustive and temporally consistent monthly labor measures for Colombia. By incorporating both quantitative and qualitative dimensions of employment, the panel enhances the empirical foundation for analysing long-term labor market dynamics, identifying regional disparities, and designing targeted policy interventions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Organización Internacional del Trabajo (OIT) & Comisión Económica pa… (2022) Guía para la construcción de indicadores laborales comparables en América Latina | 0.737 | 3 | 2 | 100% |
| 2 | Organisation for Economic Co-operation and Development (OECD (2021) Machine learning and labour economics in developing contexts: A review | 0.737 | 3 | 2 | 100% |
| 3 | Departamento Administrativo Nacional de Estadística (DANE (2025) Boletín Técnico: Mercado laboral - Mayo 2020 | 0.644 | 2 | 2 | 100% |
| 4 | García-Peña, D (2017) Convergencia regional de la informalidad en Colombia: Un enfoque de econometría espacial | 0.511 | 2 | 1 | 100% |
| 5 | González-Herrera, M (2018) Desagregación temporal de series laborales: Métodos de Denton y Chow-Lin bajo restricciones informativas | 0.511 | 2 | 1 | 100% |
| 6 | Orozco-Castañeda, M. A., Sánchez-Torres, J. A., & Díaz, C. A (2024) Forecasting unemployment using support vector regression and neural networks with economic indicators and Google Trends | 0.511 | 2 | 1 | 100% |
| 7 | Pérez-Rosero, J. E., Rodríguez, A. C., & Barón, S (2025) Explainable machine learning for labor market forecasting in Colombia: A Gaussian Process and UMAP approach | 0.511 | 2 | 1 | 100% |
| 8 | Sánchez, A., & Morales, R (2015) Modelación bayesiana del desempleo en Chile con cobertura incompleta | 0.511 | 2 | 1 | 100% |
| 9 | Vidal, M., Sierra-Suárez, G., & Cerón, L. A (2024) Indicadores regionales del mercado laboral mediante aprendizaje automático | 0.511 | 2 | 1 | 100% |
| 10 | van Dijk, M., de Lange, T., van Leeuwen, P., & Debie, P (2022) Occupations on the Map: Using a Super Learner Algorithm to Downscale Labor Statistics | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 27 scored citations.