IDEAS home Printed from https://ideas.repec.org/p/fem/femwpa/2022.33.html

Routinization, Within-Occupation Task Changes and Long-Run Employment Dynamics

Author

Listed:
  • Davide Consoli

    (INGENIO CSIC-UPV)

  • Giovanni Marin

    (Department of Economics, Society and Politics, University of Urbino Carlo Bo and SEEDS)

  • Francesco Rentocchini

    (European Commission, Joint Research Centre and Department of Economics, Management and Quantitative Methods, University of Milan)

  • Francesco Vona

    (University of Milan, Fondazione Eni Enrico Mattei and OFCE, Sciences Po Abstract: The present study adds to the literature on routinization and employment by capturing within-occupation task changes over the period 1980-2010. The main contributions are the measurement of such changes and the combination of two data sources on occupational task content for the United States: the Dictionary of Occupational Titles and the Occupational Information Network. We show that within-occupation reorientation away from routine tasks: i) accounts for 1/3 of the decline in routine-task use; ii) accelerates in the 1990s, decelerates in the 2000s but with significant convergence across occupations; iii) allows workers to escape the employment and wage decline, conditional on the initial level of routine-task intensity. The latter finding suggests that task reorientation is a key channel through which labour markets adapt to various forms of labour-saving technological change.)

Abstract

The present study adds to the literature on routinization and employment by capturing within occupation task changes over the period 1980-2010. The main contributions are the measurement of such changes and the combination of two data sources on occupational task content for the United States: the Dictionary of Occupational Titles and the Occupational Information Network. We show that within-occupation reorientation away from routine tasks: i) accounts for 1/3 of the decline in routine-task use; ii) accelerates in the 1990s, decelerates in the 2000s but with significant convergence across occupations; iii) allows workers to escape the employment and wage decline, conditional on the initial level of routine-task intensity. The latter finding suggests that task reorientation is a key channel through which labour markets adapt to various forms of labour-saving technological change.

Suggested Citation

  • Davide Consoli & Giovanni Marin & Francesco Rentocchini & Francesco Vona, 2022. "Routinization, Within-Occupation Task Changes and Long-Run Employment Dynamics," Working Papers 2022.33, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2022.33
    as

    Download full text from publisher

    File URL: https://feem-media.s3.eu-central-1.amazonaws.com/wp-content/uploads/NDL2022-033.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. Engberg, Erik & Koch, Michael & Lodefalk, Magnus & Schroeder, Sarah, 2025. "Artificial intelligence, tasks, skills, and wages: Worker-level evidence from Germany," Research Policy, Elsevier, vol. 54(8).
    3. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2025. "What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment," Research Policy, Elsevier, vol. 54(1).
    4. Marin, Giovanni & Vona, Francesco, 2023. "Finance and the reallocation of scientific, engineering and mathematical talent," Research Policy, Elsevier, vol. 52(5).
    5. Marco Capasso & Michael Spjelkavik Mark, 2019. "Visualizing the Evolving Fit of Education and Economy: The Case of ICT Education in Norway," LEM Papers Series 2019/40, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Wang, Linhui & Zhou, Huilin & Wan, Guanghua, 2025. "The impact of robots on unemployment duration: Evidence from the Chinese General Social Survey," China Economic Review, Elsevier, vol. 89(C).
    7. Florent Bordot & Andre Lorentz, 2021. "Automation and labor market polarization in an evolutionary model with heterogeneous workers," LEM Papers Series 2021/32, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    8. Emilio Colombo & Fabio Mercorio & Mario Mezzanzanica & Antonio Serino, 2024. "Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis2401, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    9. Marco Capasso & Michael Spjelkavik Mark, 2021. "The Evolving Economic Employment of ICT Education: The Case of Norway," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    10. Delaporte, Isaure & Peña, Werner, 2025. "The dynamics of disappearing routine jobs in Chile: An analysis of the link between deroutinisation and informality," World Development, Elsevier, vol. 189(C).
    11. Mauro Caselli & Edwin Fourrier-Nicolai & Andrea Fracasso & Sergio Scicchitano, 2024. "Digital Technologies and Firms’ Employment and Training," CESifo Working Paper Series 11056, CESifo.
    12. Christina Gathmann & Felix Grimm & Erwin Winkler, 2024. "AI, Task Changes in Jobs, and Worker Reallocation," CESifo Working Paper Series 11585, CESifo.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • 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

    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:fem:femwpa:2022.33. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Alberto Prina Cerai The email address of this maintainer does not seem to be valid anymore. Please ask Alberto Prina Cerai to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.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.