Macroeconomic Impacts of Automated Driving Systems in Long-Haul Trucking

Waschik, Robert; Friedman, Daniel; Taylor, Catherine; Boatner, Jasmine · 2021 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

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Summary

This report analyzes the potential macroeconomic impacts of adopting SAE Level 4 and Level 5 automated driving systems (ADS) in the U.S. long-haul trucking industry. Motivated by the central role of trucking in the economy and the uncertainty surrounding automation timelines, the study aims to quantify both direct productivity gains and indirect ripple effects across other sectors. It specifically addresses concerns regarding job displacement, evaluating whether automation would lead to mass layoffs or if natural occupational turnover could absorb workforce changes. The researchers utilized USAGE-Hwy, a dynamic computable general equilibrium (CGE) model of the U.S. economy tailored to detail transportation industries. The model simulated three adoption scenarios—slow, medium, and fast—over a 30-year period, assuming a fleet turnover rate of nine years and a maximum automation ceiling of 81.4 percent. The analysis incorporated shocks reflecting labor cost savings, fuel and capital cost reductions, and safety improvements, balanced against higher upfront technology costs. Unlike input-output models, the CGE approach accounts for economy-wide constraints and crowding-out effects, providing a balanced assessment of supply and demand adjustments. The results indicate that ADS adoption yields significant welfare-enhancing productivity improvements. Under the slow scenario, welfare increases by $35 per person annually, rising to $69 per person under the fast scenario. Annual earnings for all U.S. workers increase by $203 to $267 due to economy-wide productivity gains. Total U.S. employment is projected to rise by 26,400 to 35,100 jobs per year, despite a decline in long-haul truck driver positions. Crucially, the study finds that natural occupational turnover can offset driver job losses in the slow and medium scenarios, avoiding forced layoffs. Only the fast adoption scenario results in short-lived layoffs, peaking at 11,000 drivers in a single year, representing just 1.7 percent of the workforce. Additionally, GDP is expected to increase by at least 0.3 percent by year 30. The significance of this research lies in its challenge to narratives predicting widespread job loss from automation. By demonstrating that economic benefits can be realized without mass displacement, the report suggests that policy focus should remain on supporting displaced workers through retraining rather than preventing adoption. It also highlights the value of using CGE models to capture complex inter-industry spillovers, offering a more nuanced understanding of how transportation productivity shocks influence broader economic indicators like wages, employment, and GDP.

Key finding

Automation of long-haul trucking increases U.S. GDP by at least 0.3 percent by year 30 and raises annual worker earnings by $203 to $267, with employment losses for truck drivers offset by natural turnover in slow and medium adoption scenarios.

Methodology

modeling

Provenance

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• driverless ads