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Truck-Driving Jobs: Are They Headed for Rapid Elimination?

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  • Maury Gittleman
  • Kristen Monaco

Abstract

The authors analyze the potential effects of automation on the jobs of truck drivers and conclude that media accounts predicting the imminent loss of millions of truck-driving jobs are overstated. Their conclusion is based on three main factors. First, the count of truck drivers is often inflated due to a misunderstanding of the occupational classification system used in federal statistics. Second, truck drivers do more than drive, and these non-driving tasks will continue to be in demand. Third, the requirements of technology, combined with complex regulations over how trucks can operate in the United States, imply that certain segments of trucking will be easier to automate than others. Long-haul trucking (which constitutes a minority of jobs) will be much easier to automate than will short-haul trucking (or the last mile), in which the bulk of employment lies. Although technology will likely transform the status quo in the trucking industry, it does not necessarily imply the wholesale elimination of the demand for truck drivers, as conventional accounts suggest.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:ilrrev:v:73:y:2020:i:1:p:3-24
    DOI: 10.1177/0019793919858079
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    References listed on IDEAS

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    1. Bronwyn H. Hall, 2004. "Innovation and Diffusion," NBER Working Papers 10212, National Bureau of Economic Research, Inc.
    2. 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.
    3. Burks, Stephen V. & Monaco, Kristen, 2018. "Is the U.S. Labor Market for Truck Drivers Broken? An Empirical Analysis Using Nationally Representative Data," IZA Discussion Papers 11813, Institute of Labor Economics (IZA).
    4. Melanie Arntz & Terry Gregory & Ulrich Zierahn, 2016. "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis," OECD Social, Employment and Migration Working Papers 189, OECD Publishing.
    5. 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.
    6. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    7. 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.
    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.
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    Cited by:

    1. Schuster, Amy M. & Agrawal, Shubham & Britt, Noah & Sperry, Danielle & Van Fossen, Jenna A. & Wang, Sicheng & Mack, Elizabeth A. & Liberman, Jessica & Cotten, Shelia R., 2023. "Will automated vehicles solve the truck driver shortages? Perspectives from the trucking industry," Technology in Society, Elsevier, vol. 74(C).
    2. Burks, Stephen V. & Monaco, Kristen, 2018. "Is the U.S. Labor Market for Truck Drivers Broken? An Empirical Analysis Using Nationally Representative Data," IZA Discussion Papers 11813, Institute of Labor Economics (IZA).
    3. Burks, Stephen V. & Kildegaard, Arne & Miller, Jason W. & Monaco, Kristen, 2023. "When Is High Turnover Cheaper? A Simple Model of Cost Tradeoffs in a Long‐Distance Truckload Motor Carrier, with Empirical Evidence and Policy Implications," IZA Discussion Papers 16477, Institute of Labor Economics (IZA).
    4. Aniruddh Mohan & Parth Vaishnav, 2022. "Impact of automation on long haul trucking operator-hours in the United States," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    5. Rafael Novella & David Rosas-Shady & Alfredo Alvarado, 2023. "Are we nearly there yet? New technology adoption and labor demand in Peru," Science and Public Policy, Oxford University Press, vol. 50(4), pages 565-578.
    6. Daniel Silva & Liliana Cunha, 2022. "Aside from Deterministic Prophecies, What Is Missing in the Contemporary Debate on Automation and the Future of Work? The Case of Automated Vehicles," Social Sciences, MDPI, vol. 11(12), pages 1-29, December.
    7. 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.
    8. Nassios, J. & Waschik, Robert & Dixon, P.B. & Rimmer, M., 2020. "Evaluating the impact of automation in long-haul trucking in the United States using USAGE-Hwy," Conference papers 333205, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    9. Ron Yang, 2022. "(Don’t) Take Me Home: Home Preference and the Effect of Self-Driving Trucks on Interstate Trade," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    10. Sindi, Safaa & Woodman, Roger, 2021. "Implementing commercial autonomous road haulage in freight operations: An industry perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 235-253.

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