Tracking Employment Shocks Using Mobile Phone Data

TL;DR

This paper develops novel methods to detect economic shocks like mass layoffs using mobile phone data, identifying affected individuals and improving unemployment rate forecasts.

Contribution

It introduces new structural break and Bayesian classification models to analyze call detail records for economic shock detection and impact assessment.

Findings

Successfully detected a large manufacturing plant closure using structural break models.

Identified affected individuals through changes in calling behavior post-layoff.

Improved regional unemployment forecasts by aggregating micro-level behavioral changes.

Abstract

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 records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant's closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators.