From CMU and Aspen Institute
This research project grew out of a desire to explore the impacts of emerging technologies on work. Specifically, the Carnegie Mellon University H. John Heinz III College (CMU) research team and the Aspen Institute Future of Work Initiative partnered together to explore potential impacts to long-haul truckers and policy responses to automation in the trucking industry.
The project began with a literature review to better understand possible workforce and industry impacts, as well as potential policy responses to the issue of automation in long-haul trucking. Our CMU research team was most interested in exploring likely workforce impacts on truckers’ wages, displacement, changes in job duties/skills, and any changes to demographics. Our team was also interested in understanding impacts of automation on the trucking industry.
After completing the introductory literature review, our team concluded that there was a lack of comprehensive publicly available policy analysis regarding the impacts of automation on the trucking workforce and industry. As a result, our team decided to implement the Delphi method, a systematic way to analyze expert opinion and make predictions, to gather primary data from stakeholders in the trucking industry, as well as policy and academic worlds.
As part of the Delphi method, our team administered a non-representative three-round survey to uncover several themes on workforce, industry, and potential policy responses. Our team received 40 responses from the first survey round, 30 from the second, and 26 from the third.
Survey participants were allowed to select multiple professional areas of expertise, and the most commonly selected areas across all three surveys were legislative policy and academia. Survey participants had the most consensus when predicting impacts to the long-haul trucking industry, with over 90% of survey two participants (28 out of 30) being optimistic about the impact of automation on the trucking industry. Expected improvements to the industry were a key driver of this optimism as the majority of participants believed automation would increase efficiency and reduce costs within the industry.
There was much more variation amongst survey participants’ answers when asked to predict potential workforce impacts from automation. There was a nearly even split when survey two participants were asked about their overall attitude towards the impact of autonomous technology on the long-haul trucking workforce, with 46.7% (14 out of 30 respondents) optimistic and 36.7% (11) pessimistic. Participants identified existing workforce trends such as the aging workforce, the current driver shortage, the adoption rate of autonomous technology, and increasing demand in the industry, as factors that would likely impact workforce issues.
With regards to potential policy responses, a majority of survey participants identified a limited role of government to handle the disruption of autonomous technology. The majority of participants believed government will enact performance and safety standards with respect to autonomous technology. But, when asked what government should do, the majority felt that government should not be primarily responsible for supporting displaced long-haul truckers as a result of automation. Many of these participants who did not believe government was responsible clarified that government intervention was not necessary beyond existing retraining and welfare options.
Results from our survey research suggest that there will be time for regulators and the larger government to prepare for the disruption of automation in the long-haul trucking industry. For the next 15 years, our survey participants largely agreed that the job of a truck driver will remain relatively the same. Autonomous technology, however, does promise to change future truck driving jobs. Given the uncertainty around the timing of potential impacts, we conclude that regulators and policymakers should focus in the near-term on better understanding how autonomous technology will be used in the long-haul trucking industry. Having a better sense of how the technology will be used will allow for more informed policy development on performance and safety standards, as well as workforce issues.