How Do Workers Perceive the Risks from Automation and the Opportunities to Retrain? Evidence from a Survey of Truck Drivers

Daniel W. Shoag (), Michael Strain and Stan Veuger
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Daniel W. Shoag: Case Western Reserve University

No 14249, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: How do truck drivers perceive the risk they face from automation and their opportunities to retrain for employment in a different occupation? Autonomous vehicle (AV) technology has made rapid progress in recent years, so these questions are likely salient to truckers. Based on surveys of the new RAND American Truck Driver Panel, we find that those drivers who are most concerned about automation are, counterintuitively, also most likely to say they intend to re-invest in driving by seeking additional endorsements or purchasing their own truck. This zero-sum "arms race" for remaining positions is socially inefficient, and it may be driven by incorrect information about outside options. Specifically, the effect disappears among those drivers who are most familiar with the generally low costs of community college. We show that this is consistent with a simple model in which idiosyncratic noise in the perceived cost of retraining can lead to inefficient outcomes. This mechanism suggests that effective information provision can have large positive externalities and welfare consequences. However, a calibration of labor market prospects suggests that information provision about the true costs of retraining may not be adequate to induce occupational switching if truckers believe wages for survivors will continue to grow. This points to another important role for perceptions about the future, and for a policy of information interventions.

Keywords: automation; autonomous vehicles; retraining (search for similar items in EconPapers)
JEL-codes: J23 J24 J62 J68 O33 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2021-04
New Economics Papers: this item is included in nep-cwa and nep-lma
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