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Biased Technological Change and Employment Reallocation

Author

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  • Zsofia Barany

    (Département d'économie)

  • Christian Siegel

    (University of Kent)

Abstract

To study the drivers of the employment reallocation across sectors and occupations between 1960 and 2010 in the US we propose a model where technology evolves at the sector-occupation cell level. This framework allows us to quantify the bias of technology across sectors and across occupations. We implement a novel method to extract changes in sector-occupation cell productivities from the data. Using a factor model we find that occupation and sector factors jointly explain 74-87 percent of cell productivity changes, with the occupation component being by far the most important. While in our general equilibrium model both factors imply similar reallocations of labor across sectors and occupations, quantitatively the bias in technological change across occupations is much more important than the bias across sectors.
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  • Zsofia Barany & Christian Siegel, 2018. "Biased Technological Change and Employment Reallocation," Sciences Po publications info:hdl:2441/7vl4otetog8, Sciences Po.
  • Handle: RePEc:spo:wpmain:info:hdl:2441/7vl4otetog8c6aaomlbfjid6fp
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    References listed on IDEAS

    as
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    Cited by:

    1. Cnossen, Femke & Piracha, Matloob & Tchuente, Guy, 2021. "Learning the Right Skill: The Returns to Social, Technical and Basic Skills for Middle-Educated Graduates," GLO Discussion Paper Series 979, Global Labor Organization (GLO).
    2. Michael J Böhm & Terry Gregory & Pamela Qendrai & Christian Siegel, 2021. "Demographic change and regional labour markets," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(1), pages 113-131.
    3. Rob Davies & Dirk van Seventer, 2020. "Labour market polarization in South Africa: A decomposition analysis," WIDER Working Paper Series wp-2020-17, World Institute for Development Economic Research (UNU-WIDER).
    4. Yang Shen, 2024. "Future jobs: analyzing the impact of artificial intelligence on employment and its mechanisms," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-33, April.
    5. Werner Pena & Christian Siegel, 2023. "Routine-biased technical change, structure of employment, and cross-country income differences," Studies in Economics 2301, School of Economics, University of Kent.
    6. Daron Acemoglu & Pascual Restrepo, 2022. "Tasks, Automation, and the Rise in U.S. Wage Inequality," Econometrica, Econometric Society, vol. 90(5), pages 1973-2016, September.
    7. Antonio Martins-Neto & Nanditha Mathew & Pierre Mohnen & Tania Treibich, 2021. "Is There Job Polarization in Developing Economies? A Review and Outlook," CESifo Working Paper Series 9444, CESifo.
    8. Náplava Radek, 2019. "Changing structure of Employment in Europe: Polarization Issue," Review of Economic Perspectives, Sciendo, vol. 19(4), pages 307-318, December.

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    More about this item

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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