the market shares of other major providers, such as Expresso (22 percent) and Tigo (24 percent) (BuddeComm. 2020). Orange is also the first provider of 4G technologies in Senegal (Oduor 2016).

The data newly collected from providers show a much wider coverage of 3G technologies in Senegal than otherwise suggested by the Mobile Coverage Maps database. For instance, the median distance between the centroids of EAs in the 2017–18 ELEPS and the closest coverage areas of 3G technologies was 1.8 kilometers based on the Mobile Coverage Maps database (see map 1, panel b). Yet, the distance was only 0.4 kilometers based on the data provided by Orange-Sonatel for the same year (see map 1, panel c). There is thus a substantial discrepancy in mobile coverage data across the various sources of information. The analysis therefore combines the data from all the various sources of information to ensure that the 3G coverage maps are as complete as possible.

The analysis also uses the GPS coordinates of the centroids of EAs to match the locations of the households in the survey data and the fiber-optic nodes and mobile coverage maps. It would have been ideal if the exact locations of households were merged with spatial data on digital technologies. Nonetheless, the GPS coordinates of the centroids of EAs are the best source of geographical referencing data available for identifying the location of households. The same approach is adopted by Hjort and Poulsen (2019), who demonstrate that the distance between the terrestrial backbone infrastructure and sampling cluster locations still provides a reasonable measure of the accessibility to the Internet.

# **3. Estimation strategy**

To estimate the impact of fixed and mobile broadband Internet on household outcomes, the following equation is calculated: i 1D i i i , (1) where i is the outcome of interest, that is, the consumption or poverty status of household _i_ in region _r_ at year _t_ . In addition to total consumption, food and nonfood consumption are also separately ii considered. These consumption variables are all log-transformed because of the large skewness and yy kurtosis values. The poverty status of households is determined by the international extreme poverty line of $1.90 per day or low-middle income international poverty line of $3.20 per day.<sup>7</sup> To account for any potential bias arising from the sampling, we apply probability weights for our main results.<sup>8</sup> The use of sampling weights renders our estimates nationally representative. D i denotes access to fixed and mobile broadband Internet proxied by connectivity to the fiberoptic network and 3G coverage, respectively. Connectivity to fixed broadband Internet is ii−1 operationalized through a binary variable that takes a value of 1 if the household lives in an EA the centroid location of which is less than 1 kilometer from the closest functional fiber-optic node for fixed broadband Internet.<sup>9</sup> For mobile broadband Internet, the treatment is coded 1 if a given

> 7 <u>https://blogs.worldbank.org/developmenttalk/richer-array-international-poverty-lines.</u>

> 8 It is worth noting that our main analytical conclusions remain consistent regardless of whether sampling weights are applied in our regression. See the appendix for details.

> 9 The findings do not change if the distance threshold of 1 kilometer is shifted to other distances (see appendix A).

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