household lives in an EA in which the centroid is covered by 3G technologies.<sup>10</sup> The treatment variables are lagged one year because it is most likely that there is some temporal lag between the arrival of digital infrastructure (e.g., fiber-optic nodes or 3G) and the manifestation (if any) of the welfare effect.<sup>11</sup> Because the first round of the 2011 survey does not reveal information on 3G mobile coverage, the welfare effect of 3G coverage can be tested only using the 2017–18 ELEPS. The analysis of the 4G network is not considered extensively in the main analysis of this paper because Senegal had not yet deployed a full 4G network in 2018, the year the latest household survey was collected. Indeed, 4G was only introduced in Senegal in 2016, and 4G coverage remains limited to a few urban centers of the country.<sup>12</sup> The set of control variables i includes household size, the marital status and sex of household heads, access to electricity, literacy of household heads, housing conditions (as measured in a ii composite index of dwelling characteristicsXX<sup>13</sup> ), and a group of EA-level variables, including nighttime lights, road density, and elevation above sea level.<sup>14</sup> In addition, and are region fixed effects and year fixed effects. Controlling for other types of infrastructure, such as road connectivity or electricity, ii ii is important given that, in most African countries, “a part of the backbone network runs parallel to θθ ττ other infrastructure such as roads or electricity cables” (Hjort and Poulsen 2019, 1053). Descriptive statistics on key outcome variables, treatment, and other covariates and controls are reported in table 1. The main object of interest is coefficient 1, which captures the effect of access to digital infrastructure on welfare and poverty. To account for the existence of heteroskedasticity and serial correlation, standard errors are clustered according to the relevant EAs. ββ To identify 1, the analysis assumes no endogeneity or E[ i |D i , i ] = 0. This assumption is violated if there are any unobservable, omitted variables that confound the relationship ii ii−1 ii ii ii between the outcome of interest and the treatment. It is plausible, for instance, that places covered by ββ εε XX , θθ , ττ 3G also tend to have local business environments that are more friendly to firm entry and job creation, which could, in turn, result in improvements in welfare. In this case, it would be incorrect to attribute improvements in welfare to 3G coverage alone.

> 10 The Mobile Coverage Maps database 3G coverage maps are provided as polygon shapefiles, while some of the 3G coverage maps shared by operators are available only as point shapefiles. In the case of the point shapefiles, households were coded as covered if they were in EAs in which the centroids were less than 1 kilometer from the closest coverage points.

11 As a robustness check, the contemporaneous effects of distribution terminals on consumption and poverty are also tested. The conclusions do not change whether the treatment is lagged or not (see appendix A, table A.2).

> 12 See Pop et al. (2018) for more detail . Although 4G is beyond the scope of this paper, 4G coverage maps received from Orange-Sonatel (for December 2017 and December 2018) are used to test the impact of 4G technologies on consumption and poverty. The results are reported in appendix A, Table A.7. The finding is that 4G coverage is positively correlated with consumption and negatively with poverty, but this relationship is not robust to the inclusion of demographic and geographical controls.

> 13 This wealth index is based on whether the household has the following: durable walls; durable roof; durable floors; access to toilet; access to water toilet; and electric or gas cooking fuel. Based on household responses to these questions, the index was calculated using a weighted average, with weights derived from principal component analysis. The index is used because including the different components as separate variables introduces multicollinearity in the regression. 14 Data on nighttime light, road density, and elevation are derived from NASA VIIRS (Visible Infrared Radiometer Suite), Meijer et al. (2018), and SRTM 90m DEM Digital Elevation Database, respectively. Positive expenditure on electricity bills is taken as a measure of access to electricity in both rounds of the survey because there is no other direct measure of household electricity access.

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