Tracking employment shocks using mobile phone data
Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail record (CDR) data from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe structural break and Bayesian classification models to detect a mass layoff and the individuals affected by it by observing changes in calling behavior. For these affected individuals, we find measure significant declines in social behavior and mobility following job loss. We then apply these findings to the macro level and show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of unemployment rates.