were linked by matching the coverage footprint for each radio bearer with the almost exact locations of households based on GPS coordinates, which are much more precise than previous studies.<sup>15</sup>

# **3. Estimation strategy**

In order to estimate the impact of mobile broadband coverage on household outcomes, we estimate the following equation: the following equation: (1) i 1D i 2 i i ii ii ii ii ii ii where i is the outcome for household YY = αα + ββ + ββ XX + γγ _i_ in local government authority (LGA) + εε _j_ in year _t,_ and D i denotes the treatment of our interest – access to 3G or 4G coverage. This treatment is operationalized ii ii by a binary variable that takes a value of 1 if the household lives in an area covered by 3G or 4G YY networks (and 0 otherwise). As the impact of coverage may change or evolve over time (for example due to mechanisms taking time to translate into observed outcomes), we apply the following variations of the treatment dummy:<sup>16</sup>

- Takes a value of 1 if the location of a household is covered at the time of the survey (0 otherwise);

- Takes a value of 1 if the location of a household has been covered for over one year;

- Takes a value of 1 if the location of a household has been covered for over two years;

- Takes a value of 1 if the location of a household has been covered for over three years.

Equation 1 accounts for time-invariant household fixed effects and time-fixed effects, which are captured by and respectively.<sup>17</sup> It also includes i , a set of time-varying household control variables. These include: household size; access to electricity; whether the household dwelling is ii ii ii owned; and an index measuring dwelling characteristics.αα γγ XX<sup>18</sup> This is broadly in line with the controls used in the literature.<sup>19</sup> Further, we add access to 2G coverage to ensure that the analysis isolates the impact of upgrading coverage to 3G/4G and does not conflate it with the impact of getting 2G coverage, which can be used for voice, SMS and limited internet access (all of which could impact our outcome

> 15 More specifically, we rely on GPS coordinates of households with the maximum offset of 45 meters to assign to each household the initial date of coverage (if any) by any combination of radio bearer and signal strength. In contrast, other studies have relied on data integration using buffer sizes between 1 to 5 kms in urban areas, and 1 to 20 kms in rural areas (see for instance DHS -- Demographic and Health Surveys).

> 16 In addition to identifying time dynamics with dummy variables, we also measured internet access as the cumulative number of months since a given household was first covered by 3G/4G services (instead of a binary approach). The results of this analysis are presented in Table A 1 in Appendix 1.

> 17 We also carry out a sensitivity analysis and estimate the model in first-differences. The results are presented in Table A 2 and are roughly similar to our fixed effects estimates. Given that we account for serial correlation in the fixed effects regression by clustering standard errors by household, the latter is our preferred strategy because it maximizes the number of observations, as not all households are included across all three waves of the survey. Moreover, any bias due to noncontemporaneous correlation between the error term and the independent variables is lower with fixed effects.

> 18 The index is based on whether the household has the following: solid walls; solid roof; solid floors; access to toilet; access to water toilet, and; kerosene, 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.

> 19 See for example for Nsabimana and Funjika (2019) and Beuermann et al (2012).

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