Tracking employment shocks using mobile phone data
Name
jltoole_unemployment_interface_all.pdf
Description
Main Article
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1.57 MB
Format
Adobe PDF
Checksum (MD5)
f797a86665d598240044cc56e247b909
Author(s)
Toole, Jameson Lawrence
Lin, Yu-Ru
Muehlegger, Erich
Shoag, Daniel
Gonzalez, Marta C.
Lazer, David
Date Issued
May 2015
Journal
Journal of The Royal Society Interface
Publisher
Royal Society
Citation
Toole, J. L., Y.-R. Lin, E. Muehlegger, D. Shoag, M. C. Gonzalez, and D. Lazer. “Tracking Employment Shocks Using Mobile Phone Data.” Journal of The Royal Society Interface 12, no. 107 (April 29, 2015): 20150185–20150185.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Engineering Systems Division
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1098/rsif.2015.0185
