Not All Technological Change Is Equal: Disentangling Labor Demand Effects of Automation and Parts Consolidation
Kate Whitefoot and
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Christophe Combemale: Carnegie Mellon University
Erica Fuchs: Carnegie Mellon University
Kate Whitefoot: Carnegie Mellon University
No 726, 2019 Meeting Papers from Society for Economic Dynamics
We separate and directly measure the labor-demand effects of two simultaneous forms of technological change - automation and parts consolidation. We collect detailed shop - floor data from four semiconductor firms with different levels of automation and parts consolidation. For each process step, we collect task data and measure operator skill requirements, including operations and control, near vision, and dexterity requirements using the O*NET survey instrument. We then use an engineering process model to separate the effects of the distinct technological changes on these process tasks and operator skill requirements. Within an occupation we show that aggregate measures of technological change can mask the opposing skill biases of multiple simultaneous technological changes. In our empirical context, automation polarizes skill demand as routine, codifiable tasks requiring low and medium skills are executed by machines instead of humans, while the remaining and newly created human tasks tend to require low and high skills. Parts consolidation converges skill demand as formerly divisible low and high skill tasks are transformed into a single indivisible task with medium skill requirements and higher cost of failure. We propose a new theory for the differential labor effects of technological changes on tasks, and hence jobs. Understanding these differential effects of technologies on labor outcomes is a critical first step toward analyzing the impact of emerging technological changes on labor demand, and eventually markets.
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