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 records (CDRs) from mobile phone...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Royal Society
2015
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| Online Access: | http://hdl.handle.net/1721.1/97149 https://orcid.org/0000-0002-8482-0318 |
| _version_ | 1826189903167225856 |
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| author | Toole, Jameson Lawrence Lin, Yu-Ru Muehlegger, Erich Shoag, Daniel Gonzalez, Marta C. Lazer, David |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Toole, Jameson Lawrence Lin, Yu-Ru Muehlegger, Erich Shoag, Daniel Gonzalez, Marta C. Lazer, David |
| author_sort | Toole, Jameson Lawrence |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:28:44Z |
| format | Article |
| id | mit-1721.1/97149 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:28:44Z |
| publishDate | 2015 |
| publisher | Royal Society |
| record_format | dspace |
| spelling | mit-1721.1/971492022-09-30T09:21:27Z Tracking employment shocks using mobile phone data Toole, Jameson Lawrence Lin, Yu-Ru Muehlegger, Erich Shoag, Daniel Gonzalez, Marta C. Lazer, David Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Engineering Systems Division Toole, Jameson Lawrence Toole, Jameson Lawrence Gonzalez, Marta C. 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. National Science Foundation (U.S.). Graduate Research Fellowship 2015-06-02T15:17:42Z 2015-06-02T15:17:42Z 2015-05 2015-03 Article http://purl.org/eprint/type/JournalArticle 1742-5689 1742-5662 http://hdl.handle.net/1721.1/97149 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. https://orcid.org/0000-0002-8482-0318 en_US http://dx.doi.org/10.1098/rsif.2015.0185 Journal of The Royal Society Interface Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Royal Society Toole |
| spellingShingle | Toole, Jameson Lawrence Lin, Yu-Ru Muehlegger, Erich Shoag, Daniel Gonzalez, Marta C. Lazer, David Tracking employment shocks using mobile phone data |
| title | Tracking employment shocks using mobile phone data |
| title_full | Tracking employment shocks using mobile phone data |
| title_fullStr | Tracking employment shocks using mobile phone data |
| title_full_unstemmed | Tracking employment shocks using mobile phone data |
| title_short | Tracking employment shocks using mobile phone data |
| title_sort | tracking employment shocks using mobile phone data |
| url | http://hdl.handle.net/1721.1/97149 https://orcid.org/0000-0002-8482-0318 |
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