This Empirical Paper was written by Emmanuel Letouzé, Espen Beer Prydz, Emma Samman, and Emilio Zagheni. Funding for this paper was generously provided by the Agence Française de Développement (AFD).

Socio-economic and demographic data even on such basic indicators as poverty levels and population size are notoriously lacking in poor countries. Increasingly, the intersection of Big Data and Official Statistics has received significant attention, especially Big Data’s potential to complement or even replace official statistics. Considerable enthusiasm has been expressed around the potential of what has been termed ‘Big Data’ to partly fill some key data gaps – for instance, to ‘leapfrog’ statistical systems in developing countries – but relatively few empirical analyses have addressed this theme to date.

Drawing and expanding on a rapidly growing body of literature, our study examines this question in the specific case of Côte d’Ivoire, using cell-phone data—in technical terms, Call-Detail (or Data) Records—(CDRs)—from  Orange  in  conjunction  with  two  other  datasets,  the  WorldPop  dataset,  which  provides population  data  derived  from  satellite  imagery,  and  the  recently  released  2013  Demographic  and  Health Survey (DHS).  Our study has two main aims: to predict multidimensional poverty at the sous-prefecture and sub-national levels;  and  to  predict  the  population  of  the  11  sub-national  regions  of  Côte  d’Ivoire  and  its  255  sous-prefectures.