Reconstructing Subnational Labor Indicators in Colombia: An Integrated Machine and Deep Learning Approach

Jaime Vera-Jaramillo

Papers from arXiv.org

Abstract: 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.

Date: 2025-08, Revised 2025-08
New Economics Papers: this item is included in nep-cmp
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2508.12514 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2508.12514

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().