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Who Uses Advanced Technologies? Evidence from Manufacturing Firms from 38 Countries in 2025

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  • Wagner, Joachim

    (Leuphana University Lüneburg)

Abstract

The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with, among others, higher productivity, higher product quality, more exports and better chances to survive any crisis. Better firms tend to use advanced technologies. Information on firm level determinants of adoption of these technologies, therefore, is important to inform industrial policies. This paper uses firm level data for manufacturing enterprises from 38 countries collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and firm characteristics. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between use of advanced technologies, firm characteristics and any control variables, we find that firms which use advanced technologies tend to be larger and more innovation orientated, while firm age does not matter.

Suggested Citation

  • Wagner, Joachim, 2026. "Who Uses Advanced Technologies? Evidence from Manufacturing Firms from 38 Countries in 2025," IZA Discussion Papers 18499, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18499
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    References listed on IDEAS

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    1. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    2. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    3. Joachim Wagner, 2016. "Exports and Productivity: A Survey of the Evidence from Firm Level Data," World Scientific Book Chapters, in: Microeconometrics of International Trade, chapter 1, pages 3-41, World Scientific Publishing Co. Pte. Ltd..
    4. Deng Liuchun & Plümpe Verena & Stegmaier Jens, 2024. "Robot Adoption at German Plants," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 244(3), pages 201-235, June.
    5. Ferwerda, Jeremy & Hainmueller, Jens & Hazlett, Chad J., 2017. "Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i03).
    6. Janos Ferencz & Javier López González & Irene Oliván García, 2022. "Artificial Intelligence and international trade: Some preliminary implications," OECD Trade Policy Papers 260, OECD Publishing.
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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis

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