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Tracking employment shocks using mobile phone data

Toole, JL and Lin, YR and Muehlegger, E and Shoag, D and González, MC and Lazer, D (2015) Tracking employment shocks using mobile phone data. Journal of the Royal Society Interface, 12 (107). ISSN 1742-5689

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Toole, JL
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Muehlegger, E
Shoag, D
González, MC
Lazer, D
Date: 6 June 2015
Date Type: Publication
Journal or Publication Title: Journal of the Royal Society Interface
Volume: 12
Number: 107
DOI or Unique Handle: 10.1098/rsif.2015.0185
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1742-5689
Date Deposited: 30 Jun 2015 15:24
Last Modified: 31 Mar 2021 08:55


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