Labour automation and challenges in labour inclusion in Latin America: regionally adjusted risk estimates based on machine learning

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cepal.bibLevelDocumento Completo
cepal.callNumberLC/TS.2023/121
cepal.divisionEngSocial Development Division
cepal.divisionSpaDivisión de Desarrollo Social
cepal.docTypeSeries
cepal.jobNumberS2301200_en
cepal.projectProject "Stratification and social mobility in middle-income countries. Challenges facing an uncertain future"
cepal.projectProyecto Mecanismo Regional para el Desarrollo en Transición
cepal.regionalOfficeSantiago
cepal.sdg8
cepal.topicEngLABOUR INCLUSION
cepal.topicEngEMPLOYMENT
cepal.topicSpaINCLUSIÓN LABORAL
cepal.topicSpaEMPLEO
cepal.workareaEngSOCIAL DEVELOPMENT
cepal.workareaSpaDESARROLLO SOCIAL
dc.contributor.authorEspíndola, Ernesto
dc.contributor.authorSuárez, José Ignacio
dc.contributor.institutionUnión Europea
dc.coverage.spatialEngLATIN AMERICA
dc.coverage.spatialSpaAMERICA LATINA
dc.date.accessioned2024-03-25T18:51:32Z
dc.date.available2024-03-25T18:51:32Z
dc.date.issued2024-03-25
dc.description.abstractIn recent decades, rapid technological progress has generated a growing interest in the transformation of the world of work. This concern is based on the potential of emerging technologies to replace tasks and roles traditionally performed by human beings, either partially or entirely. It is, therefore, essential to examine and understand the social, economic, and ethical implications of this process and seek solutions to harness the benefits associated with the automation of production processes and mitigate possible negative impacts. This paper seeks to estimate job automation's probabilities and risks and analyse its potential impacts on labour inclusion in Latin America. To this end, this document implemented a machine learning-based methodology adapted to the specific characteristics of the region using data from PIAAC surveys and household surveys. In this way, the aim is to build a probability vector of job automation adapted to the region. This vector can be reused in any source of information that contains internationally comparable occupational codes, such as household surveys or employment surveys. The study provides novel estimates of labour automation based on Latin American data and analyses the phenomenon in different aspects of labour inclusion and social stratification. The results show that the risks of automation vary among different social groups, which points to the need to build adapted and efficient policies that address the diverse needs that this process imposes. To this end, the document addresses different policy areas to promote effective labour inclusion in an era of rapid advances in intelligent technologies, ensuring that all individuals can access decent employment and so that these inequalities can be addressed effectively.
dc.formatTexto
dc.format.extent65 pages.
dc.format.mimetypeapplication/pdf
dc.identifier.unSymbolLC/TS.2023/121
dc.identifier.urihttps://hdl.handle.net/11362/69088
dc.language.isoeng
dc.publisherECLAC
dc.relation.isPartOfSeriesSerie Políticas Sociales
dc.relation.isPartOfSeriesNo245
dc.relation.translationLanguagespa
dc.relation.translationRecordAutomatización del trabajo y desafíos para la inclusión laboral en América Latina: estimaciones de riesgo mediante aprendizaje automático ajustadas a la región
dc.relation.translationUrihttps://hdl.handle.net/11362/68574
dc.subject.unbisEngEMPLOYMENT
dc.subject.unbisEngLABOUR MARKET
dc.subject.unbisEngAUTOMATION
dc.subject.unbisEngLABOUR POLICY
dc.subject.unbisEngRIGHT TO WORK
dc.subject.unbisSpaEMPLEO
dc.subject.unbisSpaMERCADO DE TRABAJO
dc.subject.unbisSpaAUTOMATIZACION
dc.subject.unbisSpaPOLITICA LABORAL
dc.subject.unbisSpaDERECHO AL TRABAJO
dc.titleLabour automation and challenges in labour inclusion in Latin America: regionally adjusted risk estimates based on machine learning
dc.type.coarlibro
dc.usergrouppubweb
dspace.entity.typePublication
relation.isAuthorOfPublication03f35c4f-ccdf-4763-922b-2614e68ca0d4
relation.isAuthorOfPublicatione6b209b9-4432-453a-be42-dd875e05aa5e
relation.isAuthorOfPublication.latestForDiscovery03f35c4f-ccdf-4763-922b-2614e68ca0d4
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