Based on the O*NET job database, this research performs a typological analysis of US transportation and logistics jobs using two variables—the originality requirement of workers and the decision-making freedom by work design. Based on cluster analysis, we find that Express Lane jobs command the highest average pay due to the greatest worker originality required, have the most decision-making freedom allowed and are least vulnerable to automation. In contrast, Gridlock jobs are paid least on average due to the lowest worker originality, have the least decision-making freedom, and are most vulnerable to automation. In the middle range fall Slow Lane and Bumpy Road jobs, due to less decision-making freedom and lower worker originality required, respectively. Policy and managerial implications concerning training and work redesign are discussed in the context of technological advancements and automation.

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