IDEAS home Printed from https://ideas.repec.org/p/bdr/region/327.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/dtseru.327
    Download Restriction: no

    File URL: https://libkey.io/10.32468/dtseru.327?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    8. Jorge Anfdr�s Tamayo, 2008. "La tasa natural de desempleo en Colombia y sus determinantes," Borradores de Economia 4545, Banco de la Republica.
    9. Bernal Raquel, 2009. "The Informal Labor Market in Colombia: identification and characterization," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrés Álvarez & Juan Camilo Chaparro & Carolina Gonz�lez & Santiago Levy & Dar�o Maldonado & Marcela Mel�ndez & Natalia Ram�rez & Marta Juanita Villaveces, 2022. "Reporte ejecutivo de la Misión de Empleo de Colombia," Documentos de trabajo 20156, Escuela de Gobierno - Universidad de los Andes.
    2. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    3. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    4. 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.
    5. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    6. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    7. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    8. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    9. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    10. Karim Zkik & Anass Sebbar & Oumaima Fadi & Sachin Kamble & Amine Belhadi, 2024. "Securing blockchain-based crowdfunding platforms: an integrated graph neural networks and machine learning approach," Electronic Commerce Research, Springer, vol. 24(1), pages 497-533, March.
    11. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    12. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    13. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    14. Jonas Botz & Diego Valderrama & Jannis Guski & Holger Fröhlich, 2024. "A dynamic ensemble model for short-term forecasting in pandemic situations," PLOS Global Public Health, Public Library of Science, vol. 4(8), pages 1-18, August.
    15. Daniel Boller & Michael Lechner & Gabriel Okasa, 2025. "The effect of sport in online dating: evidence from causal machine learning," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    16. Zhenchao Zhang & Weixin Luan & Chuang Tian & Min Su, 2025. "Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China," Land, MDPI, vol. 14(2), pages 1-20, January.
    17. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    18. Lyudmyla Kirichenko & Tamara Radivilova & Vitalii Bulakh, 2018. "Machine Learning in Classification Time Series with Fractal Properties," Data, MDPI, vol. 4(1), pages 1-13, December.
    19. John Ariza & Floro Alexander Retajac, 2021. "Composición y evolución de la informalidad laboral en Colombia durante el período 2009-2019," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 40(72), pages 115-148.
    20. Cini, Federico & Ferrari, Annalisa, 2025. "Towards the estimation of ESG ratings: A machine learning approach using balance sheet ratios," Research in International Business and Finance, Elsevier, vol. 73(PB).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdr:region:327. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.