twelve countries.<sup>4</sup> They find that arrival of fast internet causes an overall increase in the probability of holding a job as well as an increase in the probability that an individual will hold a skilled job. The probability of holding an unskilled job does not decrease. Most important, an examination of the impact of faster internet arrival on workers by educational attainment rather than by job categories highlights that the estimated increase in the employment rate is of comparable magnitude for those with primary school, secondary school, and tertiary education in all samples studied. Three subsequent country case studies – on Nigeria, Senegal, and Tanzania – have also added to this rapidly growing evidence base by exploring the impact of mobile internet availability (3G or 4G coverage) – instead of fixed terrestrial broadband – on jobs and welfare. The studies take advantage of geospatial information on the roll-out of mobile internet towers over time combined with at least two rounds of household data over a six to seven-year period. The job estimates from Nigeria show that internet availability had positive impacts: labor force participation and wage employment increase by 3 and 1 percentage points respectively after three or more years of exposure in areas with internet availability relative to those with no coverage (Bahia et al., 2020). The job estimates for Tanzania are similarly significant, with richer jobs data allowing a more detailed exploration (Bahia et al., 2023). The job estimates for Senegal are also positive for some job categories: 3G availability is associated with 5 percent higher formal employment than non-covered areas, though availability does not have a significant impact on overall employment (Masaki et al, 2020).

The answers to the second question, namely drivers of adoption, generally emphasize two factors: first, the business environment, including policies affecting the degree of competition, and second, firm characteristics, and in particular firm capabilities. For example, using data for 25 industries in 25 European countries and a difference-in-difference methodology, Andrews, Nicoletti and Timilotis (2018) find that both market incentives (for example, administrative burdens on start-ups and barriers to entry in services, both measured from the OECD product market regulation index, and barriers to exit and reallocation, as measured by their employment protection Index) as well as firm capabilities (low managerial quality, lack of ICT skills and poor matching of workers to jobs) explain cross-industry-country variations in adoption of various DTs such as enterprise resource planning and customer relationship management software, and cloud computing.

A more recent categorization focusing on developing countries has emphasized that the main factors associated with the adoption of smartphones and computers by enterprises, as well as of more sophisticated DTs relying on these access technologies, are related to ability to pay and willingness to use, as well as other factors:<sup>5</sup> Ability to pay for DTs

- **Affordability of DTs** , namely the ability to pay for DTs, is linked to both their costs (relative to expected enhanced returns to enterprise revenues) and access to finance, including the

> 4 For a review of the recent literature on Africa, see “Impacts of digital technology use on jobs and poverty” in Begazo, Blimpo and Dutz (2023, forthcoming), and on developing countries more broadly, see Hjort and Tian (2021).

> 5 For a review of the theoretical and empirical literature on technology adoption and diffusion, see Comin, Cirera and Cruz (2022), chapter 6. For adoption drivers of DTs including with network effects (like digital platforms), see Cusolito (2021). The SME digital lag in OECD countries is attributed to similar factors though with different relative emphases, including insufficient capital and missing complementary assets (ability to pay), lack of information and awareness and skills gaps (willingness to use), see OECD (2021) and references therein.

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