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The Risk of Automation in Argentina

Author

Listed:
  • Leonardo Gasparini

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), IIE-FCE - Universidad Nacional de La Plata, CONICET)

  • Irene Brambilla

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), IIE-FCE - Universidad Nacional de La Plata, CONICET)

  • Andrés César

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), IIE-FCE - Universidad Nacional de La Plata)

  • Guillermo Falcone

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), IIE-FCE - Universidad Nacional de La Plata, CONICET)

  • Carlo Lombardo

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), IIE-FCE - Universidad Nacional de La Plata, CONICET)

Abstract

In this paper we characterize workers’ vulnerability to automation in the near future in Argentina as a function of the exposure to routinization of the tasks that they perform and the potential automation of their occupation. In order to do that we combine (i) indicators of potential automatability by occupation and (ii) worker’s information on occupation and other labor variables. We find that the ongoing process of automation is likely to significantly affect the structure of employment. In particular, unskilled and semi-skilled workers are likely to bear a disproportionate share of the adjustment costs. Automation will probably be a more dangerous threat for equality than for overall employment.

Suggested Citation

  • Leonardo Gasparini & Irene Brambilla & Andrés César & Guillermo Falcone & Carlo Lombardo, 2020. "The Risk of Automation in Argentina," CEDLAS, Working Papers 0260, CEDLAS, Universidad Nacional de La Plata.
    • Handle: RePEc:dls:wpaper:0260
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    References listed on IDEAS

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    1. Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
    2. Alexandre Leblanc, 2012. "On estimating distribution functions using Bernstein polynomials," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 919-943, October.
    3. Francisco H. G. Ferreira & Jérémie Gignoux, 2011. "The Measurement Of Inequality Of Opportunity: Theory And An Application To Latin America," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(4), pages 622-657, December.
    4. Daniele Checchi & Vito Peragine, 2010. "Inequality of opportunity in Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(4), pages 429-450, December.
    5. Marc Fleurbaey & Vito Peragine, 2013. "Ex Ante Versus Ex Post Equality of Opportunity," Economica, London School of Economics and Political Science, vol. 80(317), pages 118-130, January.
    6. Lefranc, Arnaud & Pistolesi, Nicolas & Trannoy, Alain, 2009. "Equality of opportunity and luck: Definitions and testable conditions, with an application to income in France," Journal of Public Economics, Elsevier, vol. 93(11-12), pages 1189-1207, December.
    7. repec:dau:papers:123456789/1552 is not listed on IDEAS
    8. Andreas Peichl & Nico Pestel & Hilmar Schneider, 2012. "Does Size Matter? The Impact Of Changes In Household Structure On Income Distribution In Germany," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 58(1), pages 118-141, March.
    9. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
    10. Olivier Bargain & Tim Callan & Karina Doorley & Claire Keane, 2017. "Changes in Income Distributions and the Role of Tax‐Benefit Policy During the Great Recession: An International Perspective," Fiscal Studies, Institute for Fiscal Studies, vol. 38, pages 559-585, December.
    11. Judith Niehues & Andreas Peichl, 2014. "Upper bounds of inequality of opportunity: theory and evidence for Germany and the US," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 43(1), pages 73-99, June.
    12. Nicola Fuchs-Schuendeln & Dirk Krueger & Mathias Sommer, 2010. "Inequality Trends for Germany in the Last Two Decades: A Tale of Two Countries," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(1), pages 103-132, January.
    13. Paolo Brunori & Paul Hufe & Daniel Gerszon Mahler, 2017. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," Working Papers - Economics wp2017_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    14. Christian Dustmann & Johannes Ludsteck & Uta Schönberg, 2009. "Revisiting the German Wage Structure," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(2), pages 843-881.
    15. Francisco H.G. Ferreira & Jérémie Gignoux, 2011. "The Measurement of Inequality of Inequality of Opportunity: Theory and an Application to Latin America," Post-Print halshs-00754503, HAL.
    16. Annabelle Krause & Ulf Rinne & Simone Schüller, 2015. "Kick It Like Özil? Decomposing the Native-Migrant Education Gap," International Migration Review, Wiley Blackwell, vol. 49(3), pages 757-789, September.
    17. Robin Jessen, 2019. "Why has Income Inequality in Germany Increased From 2002 to 2011? A Behavioral Microsimulation Decomposition," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(3), pages 540-560, September.
    18. Andreas Peichl & Martin Ungerer, 2017. "Equality Of Opportunity: East Vs. West Germany," Bulletin of Economic Research, Wiley Blackwell, vol. 69(4), pages 421-427, October.
    19. François Bourguignon & Francisco H. G. Ferreira & Marta Menéndez, 2007. "Inequality Of Opportunity In Brazil," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 53(4), pages 585-618, December.
    20. Martin Biewen & Andos Juhasz, 2012. "Understanding Rising Income Inequality in Germany, 1999/2000–2005/2006," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 58(4), pages 622-647, December.
    21. Paolo Li Donni & Juan Rodríguez & Pedro Rosa Dias, 2015. "Empirical definition of social types in the analysis of inequality of opportunity: a latent classes approach," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 44(3), pages 673-701, March.
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    Cited by:

    1. Pablo de la Vega & Natalia Porto & Manuela Cerimelo, 2024. "Going green: estimating the potential of green jobs in Argentina," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 58(1), pages 1-18, December.

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    More about this item

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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