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The dynamics of automation adoption: Firm-level heterogeneity and aggregate employment effects

Laura Bisio, Angelo Cuzzola, Marco Grazzi, Daniele Moschella · European Economic Review · 20 Jan 2025

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Abstract

We investigate the impact of investment in automation-related goods on adopting and non-adopting firms in the Italian economy during 2011–2019. We integrate datasets on trade activities, firms’, and workers’ characteristics for the population of Italian importing firms and estimate the effects on adopters’ outcomes within a difference-in-differences design exploiting import lumpiness in product categories linked to automation technologies (including robots). We find a positive average adoption effect on the adopters’ employment: firms are, on average, around 3% larger in terms of employment after an automation spike. Crucially, the employment effect is heterogeneous across firms: a positive effect is predominant among small firms, which are around 5% larger five years after the spike; on the contrary, a negative displacement effect is predominant among medium and large firms, with an employment contraction at five years of around −4%. This result can shed light on one potential reason behind the mixed results found in previous studies, which often rely on samples with different size distributions. We complete the framework with a 5-digit sector-level analysis showing that adopting automation technologies has an overall weak negative effect on aggregate employment, and with an analysis of the competition effects of automation, showing that non-adopters suffer a loss in sales and employment.

About this publication

Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.

Placed by: Tinbergen Institute A list. How sources are ranked.

Published
2025 (21 months ago)
Citations
14 (OpenAlex)

Citations in context

median 10 this paper: 14 0 591

14 citations — 67th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.

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