For Work / Against Work
Debates on the centrality of work

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

by Gittleman, Maury; Monaco, Kristen (2020)

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.

Key Passage

Fears that new technology will lead to massive unemployment are not new, though many researchers contend that this time the outcome will differ because of the potentially powerful effects of computers, robots, and otherdigital technologies. While we have not tried to address the question of what will happen to employment in the economy as a whole, we have examined an important occupation, and one in which employment levels many predict will be hit hard in the not-too-distant future by technologies that were difficult to foresee as recently as a decade or two ago. Our case study of truck drivers suggests, however, that, at least for now, any loss of jobs as a result of automation will be more limited, especially compared with journalistic accounts but also as anticipated by a number of experts. (p.22)

Keywords

Machine Learning, Artificial Intelligence, Truck Driving, Worker Replacement

Themes

Self-Driving Cars, Robots, AI in Service Industries, Automation

Links to Reference

Citation

Share


How to contribute.