# **1 Introduction**

Technology is the key driver of productivity differences across countries (Comin and Hobijn, 2010; Comin and Mestieri, 2018a; Easterly and Levine, 2001; Hall and Jones, 1999). Some estimates suggest that cross-country differences in the timing of adoption of new technologies account for at least a quarter of per capita income disparities (Comin and Hobijn, 2010). Despite the importance of technology in explaining productivity and income differences and the fact that technology is a buzz word for many policymakers, good measures of the use of technology at the firm level are scarce and confined to a few technologies and sectors. This data gap is especially large in developing countries, which makes it challenging to examine whether firms in developing countries use better technologies that already exist and why. Therefore, a deeper understanding of the meaning of new technologies, their use, and the obstacles to adoption requires appropriate tools and methodologies to measure their diffusion.

This paper describes the results of the “Firm-level Adoption of Technology” (FAT) survey implemented in Senegal.<sup>1</sup> It identifies some of the key obstacles for the adoption of new technologies faced by those firms. The paper describes with large granularity the level of technology use across firms and sectors, the relationship between technology use and performance, and provides some cross-country benchmark. The FAT survey developed by Cirera, Comin, and Cruz 2020 is an innovative tool to measure the use of technology at a granular level. The survey implemented in Senegal includes a nationally representative random sample of about 1,800 firms with 5 or more employees, both formal and informal, in agriculture, industry and services from the latest establishment census for Senegal.<sup>2</sup> In the paper, we analyze the adoption and use of technologies in Senegal through three key angles: (1) standard measures of technology related to general purpose technologies; (2) use of technologies applied to general business support functions; and (3) use of sectorspecific technologies. The standard firm-level measures of technologies refer to “traditional” measures of general-purpose technology (GPT) adoption, which enable firms to apply more technologies in non-specific tasks. It includes the access and usage of GPTs such as electricity, phone, computers, internet, cloud computer and digital platforms. We define general business support functions (GBF) as those tasks necessary in any firm, regardless of the sectors they are in, such as business administration, production planning, sales or payments methods. The sector specific business functions (SSBF) are those applied for business functions that are industry specific (e.g. land preparation in agriculture industries, or input testing in the

> 1This paper is part of a series of studies analyzing country-specific data from the FAT survey. Cirera et al. (2021b) and Cirera et al. (2021a) describes the results for Vietnam and the state of Cear ́a, in Brazil. Cirera et al. (2020) provides a cross-country analysis and provides further details about the FAT survey.

> 2See section Appendix C for more details about sampling and data in Senegal.

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