Forecasting socioeconomic trends with cell phone records

Abstract

National Statistical Institutes typically hire large numbers of enumerators to carry out periodic surveys regarding the socioeconomic status of a society. Such approach suffers from two drawbacks:(i) the survey process is expensive, especially for emerging countries that struggle with their budgets and (ii) the socioeconomic indicators are computed ex-post i.e., after socioeconomic changes have already happened. We propose the use of human behavioral patterns computed from calling records to predict future values of socioeconomic indicators. Our objective is to help institutions be able to forecast socioeconomic changes before they happen while reducing the number of surveys they need to compute. For that purpose, we explore a battery of different predictive approaches for time series and show that multivariate time-series models yield R-square values of up to 0.65 for certain socioeconomic indicators.