Towards real-time prediction of unemployment and profession

Abstract

At a societal level unemployment is an important indicator of the performance of an economy and risks in financial markets. This study provides the first confirmation that individual employment status can be predicted from standard mobile phone network logs externally validated with household survey data. Individual welfare and households’ vulnerability to shocks are intimately connected to employment status and professions of household breadwinners. By deriving a broad set of novel mobile phone network indicators reflecting users’ financial, social and mobility patterns we show how machine learning models can be used to predict 18 categories of profession in a South-Asian developing country. The model predicts individual unemployment status with 70.4% accuracy. We further show how unemployment can be aggregated from individual level and mapped geographically at cell tower resolution, providing a promising approach to map labor market economic indicators, and the distribution of economic productivity and vulnerability between censuses, especially in heterogeneous urban areas. The method also provides a promising approach to support data collection on vulnerable populations, which are frequently under-represented in official surveys.