Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey

By Nikolas J. Zolas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Catherine Buffington, N. Goldschlag, L. Foster, Emin M. Dinlersoz. 2020

130 citations recorded by the source corpus.

We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.

Subfields: Management Science and Operations Research, Statistics and Probability, Economics & Operations Research

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Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “Appropriately designed systems can yield considerable business value and improve ﬁrm performance (Rock, 2019; Babina et al., 2021; Zolas et al., 2021), with AI tools facilitating the discovery process (Cockburn et al., 2018; Cheng et al., 2022).”
Generative AI at Work (2023, unknown): “Acemoglu et al. (2022); Zolas et al. (2020); Calvino and Fontanelli (2023) examine economy-wide data from the US and OECD and show that the adoption of AI tools is concentrated among large, young firms with relatively high productivity.”
Organizational Frictions and Increasing Returns to Automation: Lessons from AT&T in the Twentieth Century (2021, unknown): “Moreover, as Zolas et al. (2020) point out, if automation is both driven by scale economies and propels firm growth, it can create winner-take-all dynamics that give rise to ‘superstar’ firms (e.g., Lashkari et al. 2019, Autor et al. 2020, Aghion et al. 2020).”
Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “…show that although the rate of adoption for many of these digital technologies is relatively low and skewed toward larger firms, it has a hierarchical pattern in which the most sophisticated technologies are most frequently accepted only after more basic applications (e.g., Zolas et al., 2021).”