Simulations of the evolution of the economy with the addition of policy shocks (i.e., productivity shocks to the trucking sector that are expected from automation) were compared to the baseline scenario of a business-as-usual evolution of the economy to reflect the expected impacts of automation in long-haul trucking. The analysis incorporated estimates of expected capital cost savings, fuel cost savings, and safety improvements in addition to labor cost savings resulting from the removal of human drivers in the trucking industry. These estimated cost savings were balanced against higher upfront costs for purchasing the technology.

Simulation results showed that SAE Level 4 (High Driving Automation) and Level 5 (Full Driving Automation) automation of the long-haul trucking industry would do the following:

• Produce average welfare increases ranging from $35 per person per year (slow scenario) to $69 per person per year (fast scenario).
• Raise annual earnings for all U.S. workers by $203 per worker per year (slow scenario) or $267 per worker per year (fast scenario).
• Increase total U.S. employment by 26,400 to 35,100 jobs per year on average during the analysis period, despite decreases in employment for long-haul truck drivers. 
• Demonstrate that these positive economic impacts would not be accompanied by forced-lay-offs under the slow and medium adoption scenario. Only under the fast adoption scenario are there short-lived, small magnitude lay-offs with just 1.7 percent of the long-haul driver workforce during a five-year period.
• Increase GDP by at least 0.3 percent by 2050 (year 30 of the analysis period).