  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. At a societal level unemployment is an important
indicator of the performance of an economy. 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 percent 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.
