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

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  • 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
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    References listed on IDEAS

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    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.
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    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

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