The use of Big Data as covariates in area level small area models

Abstract

The timely, accurate monitoring of social indicators, such as poverty or inequality, at a fine grained spatial and temporal scale is a challenging task for official statistics, albeit a crucial tool for understanding social phenomena and policy making. Big Data sensed from the digital breadcrumbs that humans leave behind in their daily activities, mediated by the Information Communication Technologies, provide accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential of providing us with a novel microscope for understanding social complexity. We propose a model based area level approach that uses Big Data as auxiliary variables to estimate poverty indicators for the Local Labour Systems of the Tuscany region. This model allows us to take into account the measurement error in the auxiliary variables