Citation

Toole JL, Lin YR, Muehlegger E, Shoag D, González MC, Lazer D. J. R. Soc. Interface 2015; 12(107): ePub.

Affiliation

Harvard Kennedy School, Harvard University, Cambridge, MA 02144, USA Lazer Laboratory, Department of Political Science and College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA.

Copyright

(Copyright © 2015, Royal Society)

DOI

10.1098/rsif.2015.0185

PMID

26018965

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 behaviour following the plant's closure. For these affected individuals, we observe significant declines in social behaviour and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviours, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure microeconomic behaviour and improve estimates of critical economic indicators.

Language: en