The Effects of Artificial Intelligence and Robotics on Employment and Wages in Korean Manufacturing Firms

초록

This article analyzes the effects of two key automation technologies – artificial intelligence (AI) and robotics – on employment and wages in Korean manufacturing firms since the late 2010s. Drawing on firm-level data from the Survey of Business Activities and individual wage data from the Local Labor Force Survey, the analysis explores firm-and worker-level impacts. These technologies are predominantly adopted in large firms in the electronics and automotive sectors. Robotics has been widely adopted not only to enhance productivity, but also for other critical purposes such as cost reduction, safety improvement union, and avoidance, whereas AI – though still in its early stages of adoption – is increasingly explored for a broader set of objectives, including automation. The results reveal contrasting effects: AI adoption is associated with job creation and wage growth, whereas robotics tends to reduce both employment and wages – an outcome that diverges from the findings of existing firm-level studies. These negative effects appear to stem from Korea’s institutional context, where productivity gains remain a central driver of automation – particularly robotics – yet labor cost reduction perhaps plays an even more important role in shaping its adoption, as well as from diminishing marginal returns in industries with long-standing automation. Interestingly, these wage effects persist even when US-based automation exposure measures are applied, indicating some generalizable features. Nonetheless, the findings underscore that its economic impact depends not only on automation’s inherent characteristics, but also on the motivations and strategies behind its adoption. Among Korean manufacturing firms, capital-biased automation driven by robotics has contributed mainly to labor displacement without generating substantial productivity gains, in a way reminiscent of Acemoglu and Restrepo’s (2018) notion of “so-so automation.”. © 2025, Weizenbaum Institute. All rights reserved.

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