where i is the log of the unit price of a food product in LGA _j_ at wave _t_ and D i is the share of people who live in the LGA covered by 3G/4G for different lengths of time (e.g., one year or more; 2 years or more; 3 years or more). The key food products include the five most consumed products PP among the poor who live under the $1.90 poverty line: rice, palm oil, beef, white beans, and groundnut oil. i includes the same set of time-varying household control variables as included in Equation 1 but now aggregated to the LGA level. XX Another way in which mobile can drive improvements in individual and household welfare is by enabling access to financial services, especially mobile money. Studies have shown that mobile money reduces transaction costs for users and helps households to better manage their cash-flows more effectively, enabling them to smooth consumption, manage risk and build working capital. It can also provide for higher incomes through remittances.<sup>23</sup> However, the adoption of mobile money in Nigeria was limited during the 2010-2016 period. Evidence from the World Bank Findex survey shows that the proportion of adults using mobile money in Nigeria was 2% in 2014 and 5% in 2017.<sup>24</sup> Given such low levels of usage, it is very unlikely that any impact of mobile broadband coverage on consumption or poverty was driven by mobile money access.

In terms of the labor market and price mechanisms, we run each of our regressions on several subsamples to explore heterogeneous effects by: gender; initial level of consumption of each household; region; age; area (urban/rural) of residence; education and literacy status of the household head or respondent, and the presence of children under the age of three in any of the three waves.

# Pre-treatment trends

By focusing on mobile broadband coverage and not mobile broadband usage (which is a household choice), the estimation strategy follows an intention-to-treat (ITT) framework, similar to Beuermann et al (2012). However, mobile operators do not expand or upgrade their networks randomly. Instead, they are likely to first target areas that will generate higher expected returns, and one would expect households in these areas to have higher levels of consumption and lower incidences of poverty.

In order for the ITT framework to produce unbiased results, we therefore rely on the assumption that outcomes for the ‘treated’ group of households (those that receive coverage) would have evolved in the same way as the ‘control’ group of households (those that do not receive coverage) if they had not benefitted from 3G or 4G coverage. This is not possible to test, but it is more likely to hold if the two groups have similar pre-treatment trends. We therefore exploit the fact that we have three waves of survey data and apply a test based on the approach proposed in Angrist and Pischke (2008). Specifically, we estimate: (3) i 1D i 2 i i ii ii ii+1 ii ii ii YY = αα + ββ + ββ XX + γγ + εε In equation 3, we test whether changes in the outcome of interest between Wave 1 and Wave 2 are predicted by getting coverage between Wave 2 and Wave 3. If the coefficient is statistically significant for consumption, this would suggest that consumption ‘leads’ coverage and so one could not conclude that coverage has a causal impact on consumption. On the other hand, if the coefficient is not

> 23 For a detailed review of the literature on the economic impact of mobile money, see Aron (2018). 24 https://globalfindex.worldbank.org/

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