IDEAS home Printed from https://ideas.repec.org/p/cop/wpaper/g-326.html
   My bibliography  Save this paper

Evaluating the impact of automation in long-haul trucking using USAGE-Hwy

Author

Listed:
  • Catherine Taylor
  • Robert Waschik

Abstract

We evaluate the macroeconomic effects of the introduction of automation in the long-haul trucking sectors in the United States, along with the output and employment impacts in the long-haul trucking sector itself, using the purpose-built computable general equilibrium (CGE) USAGE-Hwy model.1 We simulate the automation of long-haul trucking in the US by assuming that the fleet of long-haul trucks is converted for automation technology over the period 2021-2050 following a 'fast', 'medium' or 'slow' adoption path. After accounting for the cost of converting the fleet for automation, the efficiency and safety improvements contribute to an increase in real GDP and welfare in the US in 2050 of between 0.35-0.40 per cent. Despite the fact that automation technology obviates the need for most long-haul truck drivers, hiring of long-haul truck drivers remains positive throughout the simulation period in all scenarios, except for a five-year period under the 'fast' adoption of automation. Over this five-year period, at most 10,000 long-haul truck drivers per year are laid off. Given an annual occupational turnover rate for truck drivers of 10.5 per cent, the annual turnover of short-haul truck drivers in 2018 was almost 138,000, implying that the issue of layoffs of long-haul truck drivers should not be a significant concern when considering the adoption of automation in long-haul trucking.

Suggested Citation

  • Catherine Taylor & Robert Waschik, 2022. "Evaluating the impact of automation in long-haul trucking using USAGE-Hwy," Centre of Policy Studies/IMPACT Centre Working Papers g-326, Victoria University, Centre of Policy Studies/IMPACT Centre.
    • Handle: RePEc:cop:wpaper:g-326
    as

    Download full text from publisher

    File URL: https://www.copsmodels.com/ftp/workpapr/g-326.pdf
    File Function: Initial version, 2022-04
    Download Restriction: no
    File URL: https://www.copsmodels.com/elecpapr/g-326.htm
    File Function: Local abstract: may link to additional material.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Vermeulen & Jan Kesselhut & Andreas Pyka & Pier Paolo Saviotti, 2018. "The Impact of Automation on Employment: Just the Usual Structural Change?," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    2. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
    3. Raj, Alok & Kumar, J. Ajith & Bansal, Prateek, 2020. "A multicriteria decision making approach to study barriers to the adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 122-137.
    4. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    5. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    6. Dixon, Peter B. & Rimmer, Maureen T. & Waschik, Robert, 2017. "Linking CGE and specialist models: Deriving the implications of highway policy using USAGE-Hwy," Economic Modelling, Elsevier, vol. 66(C), pages 1-18.
    7. James Bessen, 2019. "Automation and jobs: when technology boosts employment," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 589-626.
    8. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    9. Maury Gittleman & Kristen Monaco, 2020. "Truck-Driving Jobs: Are They Headed for Rapid Elimination?," ILR Review, Cornell University, ILR School, vol. 73(1), pages 3-24, January.
    10. Simpson, Jesse R. & Mishra, Sabyasachee & Talebian, Ahmadreza & Golias, Mihalis M., 2019. "An estimation of the future adoption rate of autonomous trucks by freight organizations," Research in Transportation Economics, Elsevier, vol. 76(C).
    11. Engholm, Albin & Kristoffersson, Ida & Pernestal, Anna, 2021. "Impacts of large-scale driverless truck adoption on the freight transport system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 227-254.
    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. Filippo Bertani & Marco Raberto & Andrea Teglio, 2020. "The productivity and unemployment effects of the digital transformation: an empirical and modelling assessment," Review of Evolutionary Political Economy, Springer, vol. 1(3), pages 329-355, November.
    2. Ryan Gunderson, 2019. "Work time reduction and economic democracy as climate change mitigation strategies: or why the climate needs a renewed labor movement," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(1), pages 35-44, March.
    3. Geiger, Niels & Prettner, Klaus & Schwarzer, Johannes A., 2018. "Automatisierung, Wachstum und Ungleichheit," Hohenheim Discussion Papers in Business, Economics and Social Sciences 13-2018, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    4. Maury Gittleman & Kristen Monaco, 2020. "Truck-Driving Jobs: Are They Headed for Rapid Elimination?," ILR Review, Cornell University, ILR School, vol. 73(1), pages 3-24, January.
    5. Georg Graetz, 2019. "Labor Demand in the Past, Present, and Future," European Economy - Discussion Papers 114, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    6. Van Roy, Vincent & Vértesy, Dániel & Vivarelli, Marco, 2018. "Technology and employment: Mass unemployment or job creation? Empirical evidence from European patenting firms," Research Policy, Elsevier, vol. 47(9), pages 1762-1776.
    7. Genz, Sabrina & Bellmann, Lutz & Matthes, Britta, 2018. "Do German Works Councils Counter or Foster the Implementation of Digital Technologies?," IZA Discussion Papers 11616, Institute of Labor Economics (IZA).
    8. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    9. Judith Clifton & Amy Glasmeier & Mia Gray, 2020. "When machines think for us: the consequences for work and place," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 3-23.
    10. Sun, Wenyuan & Zhang, Zhonghui & Chen, Yang & Luan, Fushu, 2023. "Heterogeneous effects of robots on employment in agriculture, industry, and services sectors," Technology in Society, Elsevier, vol. 75(C).
    11. Crowley, Frank & Doran, Justin, 2019. "Automation and Irish Towns: Who's Most at Risk?," SRERC Working Paper Series SRERCWP2019-1, University College Cork (UCC), Spatial and Regional Economic Research Centre (SRERC).
    12. Warning, Anja & Weber, Enzo, 2018. "Digitalisation, hiring and personnel policy: evidence from a representative business survey," IAB-Discussion Paper 201810, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    13. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    14. Ben Vermeulen & Jan Kesselhut & Andreas Pyka & Pier Paolo Saviotti, 2018. "The Impact of Automation on Employment: Just the Usual Structural Change?," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    15. Manav Raj & Robert Seamans, 2018. "Artificial Intelligence, Labor, Productivity, and the Need for Firm-Level Data," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 553-565, National Bureau of Economic Research, Inc.
    16. Naudé, Wim, 2019. "The Race against the Robots and the Fallacy of the Giant Cheesecake: Immediate and Imagined Impacts of Artificial Intelligence," IZA Discussion Papers 12218, Institute of Labor Economics (IZA).
    17. van Hoorn, Andre, 2018. "The Political Economy of Automation: Occupational Automatability and Preferences for Redistribution," MPRA Paper 86460, University Library of Munich, Germany.
    18. Cebreros Alfonso & Heffner-Rodríguez Aldo & Livas René & Puggioni Daniela, 2020. "Automation Technologies and Employment at Risk: The Case of Mexico," Working Papers 2020-04, Banco de México.
    19. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    20. Gregory, Terry & Salomons, Anna & Zierahn, Ulrich, 2016. "Racing With or Against the Machine? Evidence from Europe," VfS Annual Conference 2016 (Augsburg): Demographic Change 145843, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    autonomous vehicles; driverless trucks; computable general equilibrium;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

    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:cop:wpaper:g-326. 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: Mark Horridge (email available below). General contact details of provider: https://edirc.repec.org/data/cpmonau.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.