Abstract
This study assesses how changes in AI patent stock affect employment, wages, productivity, and labor cost shares. It compares pooled estimates with firm-specific effects for UniCredit and Zerynth in Italy, representing the banking services and industrial tech sectors, respectively. Using a firm-level panel dataset from 2005 to 2024, the study applies task-based and skill-biased technological change frameworks to analyze how exposure to Artificial Intelligence (AI), proxied by AI patent stock, affects labor-market dynamics in the Italian economy. Our empirical strategy used fixed-effects regressions. The results show a dual pattern of technological adjustment. In pooled models, AI-related innovation has a positive and statistically significant effect on labor-market outcomes. However, when firms are analyzed separately, the effects diverge. For UniCredit, AI patent stock reduces employment and labor cost shares while raising productivity growth, with wage effects remaining small or insignificant. On the other hand, for Zerynth, AI patent stock produces consistently negative and significant effects on employment, wages, and labor cost shares. Overall, the findings highlight strong task-level substitution but heterogeneous firm-level outcomes, underscoring the need for targeted reskilling and labor-augmenting innovation policies in Italy’s digital transition.References
Abrardi, L., Cambini, C., & Rondi, L. (2022). Artificial intelligence, firms and consumer behavior: A survey. Journal of Economic Surveys, 36(4), 969–991. https://doi.org/10.1111/joes.12455
Acemoglu, D., & Restrepo, P. (2018). Modeling automation (NBER Working Paper No. 24321). National Bureau of Economic Research. http://www.nber.org/papers/w24321
Acemoglu, D., & Restrepo, P. (2019). Artificial intelligence, automation, and work. In The economics of artificial intelligence (pp. 197–236). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0008
Acemoglu, D., & Restrepo, P. (2020). The wrong kind of AI? Artificial intelligence and the future of labour demand. Cambridge Journal of Regions, Economy and Society, 13(1), 25–35. https://doi.org/10.1093/cjres/rsz022
Aghion, P., Antonin, C., & Bunel, S. (2021). The power of creative destruction: Economic upheaval and the wealth of nations. Harvard University Press.
Autor, D., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. NBER Working Paper Series, 15150, 1689–1699.
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333.
Barbieri, L., Mussida, C., Piva, M., & Vivarelli, M. (2020). Testing the employment and skill impact of new technologies. In Handbook of labor, human resources and population economics (pp. 1–27). Springer. https://doi.org/10.1007/978-3-319-57365-6_1-1
Bottero, M., & Schiaffi, S. (2022). Firm heterogeneity and the impact of policies: Evidence from Italy (Occasional Paper No. 674). Bank of Italy.
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Calvino, F., & Fontanelli, L. (2026). Decoding AI: An early look at how French firms use AI. Eurasian Business Review, 51–93. https://doi.org/10.1007/s40821-026-00347-9
Cirillo, V., Evangelista, R., Guarascio, D., & Sostero, M. (2021). Digitalization, routineness and employment: An exploration on Italian task-based data. Research Policy, 50(7), Article 104079. https://doi.org/10.1016/j.respol.2020.104079
Cirillo, V., Fanti, L., Mina, A., & Ricci, A. (2023). New digital technologies and firm performance in the Italian economy. Industry and Innovation, 30(1), 159–188. https://doi.org/10.1080/13662716.2022.2055999
Colombo, E., Mercorio, F., & Mezzanzanica, M. (2019). AI meets labor market: Exploring the link between automation and skills. Information Economics and Policy, 47, 27–37. https://doi.org/10.1016/j.infoecopol.2019.05.003
Costa, G. G., Pugliese, R., & Venier, F. (2026). Exploring artificial intelligence adoption among Italian firms: The AI readiness level. International Journal of Business Information Systems. Advance online publication. https://doi.org/10.1504/IJBIS.2025.10076399
Dalla Zuanna, A., Dottori, D., Gentili, E., & Lattanzio, S. (2024). An assessment of occupational exposure to artificial intelligence in Italy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5246679
Damioli, G., Van Roy, V., Vértesy, D., & Vivarelli, M. (2024). Drivers of employment dynamics of AI innovators. Technological Forecasting and Social Change, 201, Article 123249. https://doi.org/10.1016/j.techfore.2024.123249
DiNardo, J., Fortin, N. M., & Lemieux, T. (1996). Labor market institutions and the distribution of wages, 1973–1992: A semiparametric approach. Econometrica, 64(5), 1001. https://doi.org/10.2307/2171954
Felten, E. W., Raj, M., & Seamans, R. (2018). A method to link advances in artificial intelligence to occupational abilities. AEA Papers and Proceedings, 108, 54–57. https://doi.org/10.1257/pandp.20181021
Felten, E., Raj, M., & Seamans, R. (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal, 42(12), 2195–2217. https://doi.org/10.1002/smj.3286
Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth (OECD Artificial Intelligence Papers No. 15). OECD Publishing.
Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica, 77(3), 953–973.
Firpo, S., Fortin, N. M., & Lemieux, T. (2018). Decomposing wage distributions using recentered influence function regressions. Econometrics, 6(2), Article 28.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
Giczy, A. V., Pairolero, N. A., & Toole, A. A. (2022). Identifying artificial intelligence (AI) invention: A novel AI patent dataset. Journal of Technology Transfer, 47. https://doi.org/10.1007/s10961-021-09900-2
INAPP. (2023). Rapporto annuale 2023: Lavoro e formazione. Istituto Nazionale per l’Analisi delle Politiche Pubbliche (INAPP).
ISTAT. (2025a). Imprese e ICT: Anno 2024. Istituto Nazionale di Statistica.
ISTAT. (2025b). Imprese e ICT: Anno 2025. Istituto Nazionale di Statistica.
Manestra, S., & Morelli, A. P. (2013). Questioni di economia e finanza (Occasional Papers No. 727). Banca d’Italia. https://www.bancaditalia.it/pubblicazioni/qef/2022-0727/QEF_727_22.pdf
Misch, F., Park, B., Pizzinelli, C., & Sher, G. (2025). Artificial intelligence and productivity in Europe (IMF Working Paper No. WP/25/67). International Monetary Fund.
Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2024). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys, 38(5), 1622–1655. https://doi.org/10.1111/joes.12601
Webb, M. W. (2020). Essays in the economics of artificial intelligence [Doctoral dissertation, Stanford University].
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2026 Ebrima K. Ceesay, Mamadou Salieu Jallow, Cosimo Magazzino, Alasana Gitteh, Baseedy Bojang