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Informalidad municipal en Colombia

Author

Listed:
  • Karina Acosta
  • Juliana Jaramillo-Echeverri
  • Daniel Lasso
  • Alejandro Sarasti-Sierra

Abstract

Se estima que más del 50 % de la población laboral en Colombia pertenece al sector informal, un fenómeno persistente durante las últimas tres décadas. A pesar de la amplia literatura sobre la informalidad laboral y sus determinantes a nivel nacional o en las principales áreas urbanas, las tasas de informalidad municipales permanecen inexploradas en el país, debido a la falta de disponibilidad y calidad de los datos. En general, la información necesaria para medir la informalidad subnacional, ya sea a través del tamaño de la empresa, la afliación al régimen contributivo o la existencia de un contrato escrito, es escasa o incompleta, lo que difculta una estimación directa. En este trabajo se propone un ejercicio de medición para avanzar en el estudio de la informalidad en Colombia, estimando la informalidad laboral municipal entre 2005 y 2021. Los resultados muestran que, aunque la informalidad es persistentemente alta, está fuertemente concentrada. Además, se observa que, aunque la informalidad cayó paulatinamente entre 2005 y 2016 en todos los municipios, aquellos con tasas de informalidad más altas experimentaron un retroceso en estas ganancias en 2021. **** ABSTRACT: It is estimated that more than 50 % of the labor force in Colombia belongs to the informal sector, a persistent phenomenon over the last three decades. Despite extensive literature on informality and its determinants at the national level or in the main urban areas, municiapl informality rates remain unexplored in the country due to the lack of availability and quality of data. In general, the information necessary to measure sub-national informality, whether through frm size, afliation to social security, or the existence of a written contract, is scarce or incomplete, making direct estimation difcult. This study proposes a measurement exercise to contribute to the study of informality in Colombia, estimating municipal informality between 2005 and 2021. The results show that, although informality is persistently high, it is strongly concentrated. Furthermore, it is observed that, although informality gradually declined between 2005 and 2016 in all municipalities, those with higher informality rates experienced a setback in these gains in 2021.

Suggested Citation

  • Karina Acosta & Juliana Jaramillo-Echeverri & Daniel Lasso & Alejandro Sarasti-Sierra, 2024. "Informalidad municipal en Colombia," Documentos de trabajo sobre Economía Regional y Urbana 327, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:region:327
    DOI: 10.32468/dtseru.327
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    References listed on IDEAS

    as
    1. Jhon James Mora & Juan Muro, 2017. "Dynamic Effects of the Minimum Wage on Informality in Colombia," LABOUR, CEIS, vol. 31(1), pages 59-72, March.
    2. Carmen Elisa Fl√≥rez, 2002. "THE FUNCTION OF THE URBAN INFORMAL SECTOR IN EMPLOYMENT: Evidence from Colombia 1984-2000," Documentos CEDE 3595, Universidad de los Andes, Facultad de Economía, CEDE.
    3. Jorge Andrés Tamayo, 2008. "La tasa natural de desempleo en Colombia y sus determinantes," Borradores de Economia 491, Banco de la Republica de Colombia.
    4. Esther López-Vizcaíno & María José Lombardía & Domingo Morales, 2015. "Small area estimation of labour force indicators under a multinomial model with correlated time and area effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 535-565, June.
    5. Luis E. Arango & Luz A. Flórez & Laura D. Guerrero & Alejandra Morales-Rojas, 2020. "Minimum wage effects on labour informality: heterogeneity across demographic groups in Colombia," Borradores de Economia 1104, Banco de la Republica de Colombia.
    6. Li, Huilin & Lahiri, P., 2010. "An adjusted maximum likelihood method for solving small area estimation problems," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 882-892, April.
    7. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    8. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    9. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    10. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    11. Luis E. Arango & Luz A. Flórez, 2021. "Regional Labour Informality in Colombia and a Proposal for a Differential Minimum Wage," Journal of Development Studies, Taylor & Francis Journals, vol. 57(6), pages 1016-1037, June.
    12. Bernal Raquel, 2009. "The Informal Labor Market in Colombia: identification and characterization," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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    JEL classification:

    • J46 - Labor and Demographic Economics - - Particular Labor Markets - - - Informal Labor Market
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
    • O54 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Latin America; Caribbean
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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