Author: Catherine Taylor and Robert Waschik
We evaluate the macroeconomic effects of the introduction of automation in the long-haul trucking sectors in the United States, along with the output and employment impacts in the long-haul trucking sector itself, using the purpose-built computable general equilibrium (CGE) USAGE-Hwy model.1 We simulate the automation of long-haul trucking in the US by assuming that the fleet of long-haul trucks is converted for automation technology over the period 2021-2050 following a 'fast', 'medium' or 'slow' adoption path. After accounting for the cost of converting the fleet for automation, the efficiency and safety improvements contribute to an increase in real GDP and welfare in the US in 2050 of between 0.35-0.40 per cent. Despite the fact that automation technology obviates the need for most long-haul truck drivers, hiring of long-haul truck drivers remains positive throughout the simulation period in all scenarios, except for a five-year period under the 'fast' adoption of automation. Over this five-year period, at most 10,000 long-haul truck drivers per year are laid off. Given an annual occupational turnover rate for truck drivers of 10.5 per cent, the annual turnover of short-haul truck drivers in 2018 was almost 138,000, implying that the issue of layoffs of long-haul truck drivers should not be a significant concern when considering the adoption of automation in long-haul trucking.
JEL classification: O18, O33, C68.
Keywords: autonomous vehicles, driverless trucks, computable general equilibrium
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