For whom the bell tolls: The firm-level effects of automation on wage and gender inequality
Domini, Giacomo; Grazzi, Marco; Moschella, Daniele; Treibich, Tania
(2022) Research Policy, volume 51, issue 7, pp. 1 - 30
(Article)
Abstract
This paper investigates the impact of investment in automation- and AI-related goods on within-firm wage inequality in the French economy during the 2002–2017 period. We document that most wage inequality in France is accounted for by differences among workers belonging to the same firm rather than by differences between sectors,
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firms, and occupations. Using an event-study approach on a sample of firms importing automation- and AI-related goods, we find that spike events related to the adoption of automation- and AI-related capital goods are not followed by an increase in within-firm wage inequality or in gender wage inequality. Instead, wages increase by 1% three years after the events at different percentiles of the distribution. Our findings are not linked to the rent-sharing behavior of firms obtaining productivity gains from automation and AI adoption. Instead, if wage gains do not differ across workers along the wage distribution, worker heterogeneity will still be present. Indeed, in agreement with the framework in Abowd et al. (1999b), most of the overall wage increase is due to the hiring of new employees. This adds to previous findings presenting a picture of a ‘labor friendly’ effect of the latest wave of new technologies within adopting firms.
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Keywords: AI, Automation, Gender pay gap, Wage inequality, Strategy and Management, Management Science and Operations Research, Management of Technology and Innovation, A Journal
ISSN: 0048-7333
Publisher: Elsevier
Note: Funding Information: This paper has benefited from the comments of three anonymous referees, Jim Bessen, Valeria Cirillo, Liuchun Deng, and the participants at several conferences: the Workshop on Technology and the Labor Market, IMT Lucca, Dec 2020; European Network on the Economics of the Firm, Jan 2021; Maastricht University, Feb 2021; Technology and Policy Research Initiative, Boston University, Feb 2021; SPRU March 2021; University of Turin, April 2021; PRIN 2017 Workshop, April 2021; SIEPI, University of Parma, June 2021; JRC LET seminars, Sep 2021; LISER-IAB conference Sep 2021; DRUID, Copenhagen, Oct 2021; CAED, Coimbra, Nov 2021; CONCORDi, Nov 2021. This work has been partly supported by the European Commission under H2020, GROWINPRO, Grant Agreement 822781. Daniele Moschella received financial support from the Italian Ministry of Education and Research under the PRIN 2017 Program, Italy (Project code 201799ZJSN). This work is also supported by a public grant overseen by the French National Research Agency (ANR), France as part of the ?Investissements d'avenir? program (reference: ANR-10-EQPX-17, Centre d'acc?s s?curis? aux donn?es, CASD). The usual disclaimer applies. Funding Information: This work has been partly supported by the European Commission under H2020, GROWINPRO , Grant Agreement 822781 . Daniele Moschella received financial support from the Italian Ministry of Education and Research under the PRIN 2017 Program, Italy (Project code 201799ZJSN). This work is also supported by a public grant overseen by the French National Research Agency (ANR), France as part of the ‘Investissements d’avenir’ program (reference: ANR-10-EQPX-17, Centre d’accès sécurisé aux données, CASD). The usual disclaimer applies. Publisher Copyright: © 2022 The Author(s)
(Peer reviewed)
