and Timmis (2023) find the adoption of cloud computing is linked to firm-level productivity gains. Using data from a survey of 179 publicly traded firms in the United States about their business practices, information systems, and the analysis of information and publicly available financial data, Brynjolfsson et al. (2011) find that, on average, a one-standard-deviation increase in datadriven decision-making is associated with a 4.6 percent productivity gain.

There are also specific case studies on the effects of ICT on productivity in the United States. Jin and McElheran (2017) show how the purchase of cloud computing services improves the productivity of young companies, particularly in IT-intensive and high-uncertainty manufacturing industries. Agrawal and Goldfarb (2008) show how shared computer networks increased academic productivity at middle-tier universities. In health care, Miller and Tucker (2014) and McCullough, Parente, and Town (2016) show that electronic medical records (EMRs) are found to improve patient outcomes, and Lee, McCullough, and Town (2013) show that EMRs increase hospital productivity.

Studies from developing economies also document the productivity effects of the first wave of digitalization. Covering firms in the manufacturing sector across 82 developing economies, Cusolito, Lederman, and Peña (2020) find evidence of a larger productivity premium from using email or a website than from improving managerial capability or from exporting.<sup>6</sup> Evidence from approximately 20 developing economies shows that differences in technology sophistication, ranging from manual to advanced digital processes, account for 31 percent of cross-establishment dispersion in productivity (Cirera, Comin, and Cruz 2024). For Chinese-listed manufacturing firms, Lo et al. (2023) show that digital technology boosts productivity by reducing operating costs, improving resource allocation, and enhancing innovation capability. Greater internet connectivity led to a large and significant increase in net firm entry in South Africa, notably in sectors that use ICT extensively (e.g., finance), and in the productivity of existing manufacturing firms in Ethiopia (Hjort and Poulsen 2019). Comin et al. (2022) find that adoption of more sophisticated ICT in business processes by firms in Brazil, Senegal, and Viet Nam prior to the COVID-19 pandemic was associated with higher productivity during the pandemic.

The literature has likewise produced evidence about the productivity effects of technologies associated with the second wave of digitalization.<sup>7</sup> The increasing use of industrial robots in Europe, for example, is found to be associated with productivity increases at the country level in the 1990s and 2000s (Autor and Solomons 2018; Graetz and Michaels 2018). Cathles, Nayyar, and Ruckert (2020) conclude that the partial or full implementation of Industry 4.0 technologies, including robots, 3D printing, and the Internet of Things (IoT) is positively related to firm-level labor productivity, including after accounting for firm size.

If anything, the adoption of “smart” robots in advanced economies may limit productivity gains in firms in developing economies if it reduces the latter’s exporting opportunities. Whether industrial automation in advanced economies is resulting in reshoring has been the subject of a growing body of research. Artuc, Bastos, and Rijkers (2023), for instance, show that a 10-percentage point

> 6 A productivity boost associated with digital technology adoption at the firm level has also been found in studies in Argentina, Brazil, Colombia, and Mexico (Brambilla and Tortarolo 2018; Dutz et al. 2017; Ospino 2018; Iacovone and Pereira-López 2018). 7 There are also theoretical papers on the impact of AI and automation on TFP growth. For instance, Jones (2021) constructs a semi-endogenous growth model to illustrate that automation and artificial intelligence can contribute to long-run growth prospects by boosting TFP growth.

9