In order to test the validity of the CDR analysis, its findings were compared to World Bank poverty estimates, which are based on Guatemala’s National Living Conditions Survey (ENCOVI) for 2006 and 2011 and the 2002 Population and Housing Census. The ENCOVI is a traditional household survey, and its sample size does not provide reliable estimates at the municipal level. However, small-area estimation techniques<sup>7</sup> which combine the ENCOVI data with the census data allow for poverty to be estimated at the municipal level. For the purposes of the study these estimated rates were treated as “ground truth data.” In machine-learning analysis, ground truth data are obtained by direct observation, rather than by modeling or inference. In this context, however, the term refers to the poverty rates determined by standard statistical estimation methods, which provide the only existing measure of ground truth. Nonetheless, it should be borne in mind that all poverty-rate estimation methodologies are predicated on assumptions, and in this area no ground truth data can offer a perfect representation of reality.

Supervised machine-learning models, as described here, require a “training dataset,” which comprises benchmark data that represent the ground truth. Since CDRs relate directly to people, they can be considered unit-record data. Then it is the detail of the ground-truth data that largely determines the resolution of the model. In the case of Guatemala, ground-truth data are aggregated This is often the only available option when the ground truth data are based on estimations using household survey data, in which no mobile phone numbers are collected during the survey. In this case, individual-level features are extracted from the CDRs, then combined to form statistical aggregates at the chosen geographic level (e.g. mean, median, maximum or quantiles by region). These aggregates are used to build an area-level predictive model calculated for each area (e.g. poverty headcount). A relatively small sample size (for example, Guatemala’s 338 _municipios_ ) means that internal validation, such as cross-validation or hidden test data, is difficult, and some external validation may be required.

Poverty rates were calculated for each _municipio_ . Aggregate rural, urban and overall poverty rates were available for 2006, but only rural poverty rates were available for 2011.<sup>8</sup> The study used aggregated and encrypted CDR data for August 2013, which overlaps with the ENCOVI survey period. In 2013 Guatemala had 140 cellular accounts for every 100 people.<sup>9</sup> The model tested two types of prediction: (i) same-survey prediction, such as predicting the 2006 urban poverty rates based on a model of the relationship between the 2006 ENCOVI data and the 2013 CDRs; and (ii) different-survey prediction, such as predicting the 2011 rural poverty rates based on a model of the relationship between the 2006 ENCOVI data and the 2013 CDRs. Same-survey

> 7 See Elbers, Lanjouw and Lanjouw (2003).

> 8 These data come from a 2011 census of rural areas designed to gather information for social-protection programs. No national census was conducted that year.

> 9 World Development Indicators, 2016. This reflects multiple accounts per person. While this does not indicate that every person has a cell phone, it suggests that cell phone use is relatively high.

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