How Autonomous Trucks Could Help California’s Workers and Economy

Four takeaways from a recent study

California’s legislature is considering a bill (AB 316) that would require a human safety operator to be physically present within autonomous trucks while in operation.

While some may find the notion of a driverless truck unsettling, others have expressed concerns about the potential impact on jobs. But, what will be the actual impact of autonomous trucking in California?

In April, 2022, the Silicon Valley Leadership Group (SVLG) Foundation released a report that studied the impacts of adopting AV technology in California’s long-haul trucking industry, including impacts to labor markets and employment.

The SVLG report uses data from Bureau of Labor Statistics’ Occupational Employment Statistics, Census’ Vehicle Inventory and Use Survey, and a McKinsey Global Institute occupational analysis. The author conducted simulations to determine how the adoption of AV technology in California’s long-haul trucking industry will affect the economy.

The author, Robert Waschik of Victoria University, Melbourne, considered three adoption speed scenarios — fast, medium, and slow. Chart 2.1, below, shows the adoption rates over time across these three scenarios:

Under the fast scenario, all trucking businesses would adopt AV technology within 10–15 years of the report’s publication. After that point in time, Waschik estimated that 90% of new truck investment will be for trucks with AV technology. But Waschik also concluded that the fast adoption scenario was unrealistic based on technology available at the time of publication.

Using the various adoption scenarios Waschik modeled how the adoption of autonomous technology affects worker productivity, capital productivity, GDP, employment, and consumer welfare. Here are four key takeaways from the analysis:

1. Drivers would become more productive due to AV technology adoption.

The same amount of labor can now produce more due to technological change.

As long-haul trucks are updated with autonomous technology, each worker will become more productive, due to the ability of workers to focus on other tasks instead of tasks now completed by the autonomous tech. A driver who spent most of their time driving, now has time for other work since the truck is driving itself. Now, that same driver is more productive since they are able to ‘produce’ more — complete more work — as a result of the AV tech.

The report describes this as ‘labor saving technical change’, which occurs when technological innovations make each laborer more productive. In terms of autonomous trucking, this means that with the same number of drivers (labor), companies can now make additional trips (output).

Chart 2.2a, shown above, shows how labor savings for long-haul trucking change under the three autonomous technology adoption scenarios described above- fast, medium, and slow:

• As time progresses (shown on the horizontal axis), less labor is needed to produce the same amount of output, so the curves have a negative slope.
• The rate of change varies across adoption scenarios — the faster the automated technology is adopted, the faster labor productivity increases.
• As workers become more productive they are able to produce more output per worker.
• These labor savings occur most-rapidly in the fast adoption scenario, however, eventually all adoption scenarios reach similar levels of labor savings relative to baseline.

2. Trucks would become more productive due to AV technology adoption.

The same number of trucks can now produce more output than before due to capital improvements.

As companies adopt AV technology for long-haul trucks, capital productivity — think of this as the return on investing in buying a truck — also increases. After adoption, a given truck is more productive than prior to adoption since the automated technology allows the truck to operate longer hours than a human driver.

Chart 2.5a, shown above, depicts capital savings relative to baseline under each adoption scenario. As more trucking companies adopt AV technology, they need fewer trucks to produce the same amount of output, because capital is now more productive than before.

These potential capital productivity gains are especially important for policy considerations involving human drivers. The capital productivity gains from using autonomous technology without human drivers are likely to disappear if legislation requires a human safety operator to be present in the vehicle at all times.

3. Employment and wages would increase

As adoption of AV trucking increases, the same amount of labor in the economy can now be combined with more capital. This process causes labor productivity to increase due to workers having more access to capital.

To understand this mechanism, let’s consider an example — suppose a reporter is writing a story using a typewriter. If that reporter were given a computer with word processing software, the reporter could write faster and produce more stories than when using the typewriter. In this example, the amount of labor does not change, but the additional capital investment allows the worker to increase production.

As more companies invest in automation technology for trucks, production increases, driving up the demand for labor. When labor demand increases on aggregate, wages increase throughout the economy as a result of companies competing for workers. Chart 3.1a shows wage and employment outcomes for the economy under each adoption scenario.

Note that the chart above shows a slight increase in aggregate employment in all scenarios. This will occur as long as investment in each scenario is higher than baseline investment. From Waschik:

4. New hiring of long-haul truckers would likely increase

Recall that Waschik states that while the fast adoption path is modeled, it is unlikely to be realistic based on where technology was at the time of publication in 2022. The chart shown below indicates the expected number of new long-haul truckers hired annually under each scenario.

Note that both medium and slow scenarios show new hiring of long-haul truckers to be positive throughout the entire time period shown. This indicates that the model does not predict layoffs of long-haul truckers under the medium and slow scenarios.

Waschik considers turnover in similar jobs for which long-haul truck drivers are qualified, such as short-haul trucking. The data suggest that the annual turnover in short-haul truck drivers is great enough that even if layoffs occur in long-haul trucking, drivers will be able to find employment in the short haul trucking market.

Furthermore, data from Indeed.com indicate that short-haul trucking jobs (referred to as regional or local trucking) pay similarly to long-haul trucking jobs (referred to as on-the-road or OTR trucking), with the added benefit of allowing drivers to be home much more often than long-haul trucking jobs.

As California’s legislature considers AB 316, they should keep the findings from this report in mind. Fully autonomous long-haul trucking will likely benefit California’s economy, create jobs, and not result in the job loss that some fear.

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