geographical controls (Columns 7 and 8 in Table 2). Notably, households covered by 3G exhibit an extreme poverty rate lower by about 10 percent relative to households without 3G coverage.<sup>17</sup> While 3G coverage is also correlated negatively with moderate poverty (based on the international poverty line of $3.2 per day ), its effect is not robust to the inclusion of the additional set of controls (Columns 9 and 10 in Table 2).

It is plausible that the relationship between 3G coverage and welfare is being confounded by other factors. For instance, 3G coverage is likely correlated with the fiber network and with 2G cellular services, which are also likely to impact welfare directly. The estimated effects of 3G as reported in table 2 may thus be picking up the effects of fixed broadband Internet and mobile phone access together. Failure to control for these potential confounders could lead an overstatement of the relationship between 3G and welfare. As a robustness check, the same models as presented in table 2 are run, but with controls for connectivity to the fiber-optic network (or within 1 kilometer of the nearest fiber-optic transmission node) and controls for 2G coverage.<sup>18</sup> The main findings do not change (see appendix A, Table A.3). Furthermore, the main results are also robust to specifications including self-reported ownership of cellphone or Internet access as additional controls (see appendix A, Table A.4).<sup>19</sup> One may control for various demographic and geographical factors, but miss other, unobservable confounders that bias the estimates. To alleviate the issue of endogeneity, an instrumental variable approach is applied. The results of the two-stage least squares regressions are reported in table 3. The first-stage regression shows that the instrument is strongly and negatively correlated with 3G coverage (see appendix A, Table A.1). This makes sense given that the likelihood that households in a given EA are covered by 3G is expected to decline if the neighboring areas are distant from the closest 3G coverage areas. The two-stage least squares estimates show results that are similar to the results of the ordinary least squares regressions; the effects of 3G on total consumption are positive and statistically significant, and these effects are particularly pronounced in the case of nonfood consumption. As expected, the effects of 3G on poverty are also negative and statistically significant (though at the 0.10 level) in the two-stage least squares regressions.

The analysis also examined the results on various subsamples (Table 4). First, the sample was split into rural and urban areas. 3G coverage is significantly and positively correlated with consumption and negatively associated with poverty only for urban households (Table 4, panel a). Interestingly, this correlation is particularly pronounced for food consumption among urban households while 3G coverage is significantly and positively correlated with non-food consumption for rural households. The sample is also separated according to whether household consumption is below or above the median consumption level (in each survey). This exercise reveals that while the effect of 3G coverage is positive and significant for non-food consumption for poorer households, these effects lose

> 17 The point estimates do not change substantially if no weights are applied. According to our unweighted regressions, 3G coverage is associated with a 18 percent increase in total consumption and a 7 percent decrease in extreme poverty rate (see Table A.3 and A.4 in the appendix). To put this in context, similar work for Nigeria—integrating household panel data and historical coverage maps—finds an 11.1 percent increase in total consumption and a 7.9 percentage point decline in extreme poverty after three years of coverage (Bahia et al. 2020). Using data from 14 rural and geographically isolated areas in the Philippines between 2016 and 2018, Blumenstock et al. (2020) also find that the introduction of a new phone tower led to an increase in household income of 17 percent, and increased household expenditures by 10 percent.

> 18 Data on 2G coverage are also provided in the Mobile Coverage Maps database and by the mobile operators.

> 19 The self-reported ownership of cellphone or Internet access is measured by a positive expenditure on cellphone or Internet subscriptions.

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