channel for the previous results, but arguably not the only one as it does not explain the statistically significant results for consumption and poverty after one year of coverage.

In terms of the effects of mobile broadband coverage on food prices, we do not find any significant effect on the unit prices of the key food products. Across all the five most consumed food products, none of the estimated effects turns out to be significant (see Table 12).

# Results for different subsamples

Starting with the three consumption outcomes, according to the results in Tables 13-15, we note a lack of statistical significances on the consumption and poverty levels of female-headed households. This is not only an artefact of the lower sample size, as the point estimates are also much lower - in some cases a whole order of magnitude.<sup>27</sup> One possible reason for this is that in Nigeria, women can face significant barriers to using mobile internet relative to men, which might prevent them from reaping the benefits of 3G/4G coverage. For example, in 2017 the gender gap in mobile internet usage in Nigeria was 32% (meaning that women were 32% less likely than men to use mobile internet).<sup>28</sup> The percentage change in total and food consumption is larger for the initially poorer (i.e. with consumption levels below the median in wave one) households. The results are almost identical between the two groups for non-food consumption. For food consumption, the effects are around twice the size. This result concurs with those on poverty discussed above. We do not detect any important differences in the effects on consumption between the North and South. In general, the results are less precise for each region separately than for the pooled sample. This may be driven not only by a lower sample size, but also by less variability within each region. The age of the household head does not seem to play an important role either, with the exception of extreme poverty where households with younger heads of family observer a higher decline in poverty.

We find a striking difference between urban and rural households. We find consistently positive point estimates of large magnitude for rural households, with statistical significance at the 1 percent or 5 percent level for all treatments of one year or longer. The results for urban households are mostly statistically insignificant, even after adjusting for the sample size, and not very consistent. The exception is for the effect of more than three years of coverage on food consumption, which is surprisingly negative and significant at the 1 percent level. The results in the final columns indicate, first, that households in which the head has at least a primary education benefit from increased consumption (after one year of coverage), while households in which the head has less than a primary education may have benefitted after three years of coverage (similar to female headed households, we have a much smaller sample size).<sup>29</sup> No systematic effect can be found for literacy status. Interestingly, the effect on food consumption is concentrated on households with children under 3 years of age, while the effect on non-food consumption is concentrated on households with no such children.

> 27 One can counterfactually compute standard errors for different hypothetical sample sizes, assuming that all the other components remain the same. If the female headed households had the same sample size as the male headed ones (i.e. 10,772), even the largest point estimate for total consumption after at least three years of coverage (0.034) would only be significant at the 10 percent level. For non-food consumption (point estimate of 0.04) it would barely at the five-percent critical value. All other positive estimates would be statistically insignificant.

> 28 See GSMA (2018).

> 29 The number of observations decreases dramatically when conditioning on education due to a large number of missing values.

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