Northeastern University computational social scientist David Lazer and his interdisciplinary research team have demonstrated that mobile phone data can be used to quickly and accurately detect, track, and predict changes in the economy at multiple levels. The findings, published
Northeastern University computational social scientist David Lazer and his interdisciplinary research team have demonstrated that mobile phone data can be used to quickly and accurately detect, track, and predict changes in the economy at multiple levels.
The findings, published Wednesday in the journal of the Journal of the Royal Society Interface, highlight the potential of mobile phone data to improve forecasts of critical economic indicators—information that is extremely valuable to policymakers in the public and private sectors.
In particular, the team found that call detail records can be used to predict unemployment rates up to four months before the release of official reports and more accurately than using historical data alone.
'Our findings are of great practical importance, potentially facilitating the identification of macroeconomic statistics faster and with much finer spatial granularity than traditional methods of tracking the economy,' said Lazer, a distinguished professor of political science and computer and information science.
'We are hopefully just beginning to learn what this data can tell us, and the promise of more accurate, less expensive, and higher-resolution measures of critical economic indicators is very exciting,' added lead author Jameson Toole, a doctoral student at the Massachusetts Institute of Technology. 'We hope that our results can be used to help policymakers react more rapidly to future economic downturns, giving them a more accurate picture of the state of the economy.'
In the paper, Lazer, Toole, and their collaborators—a quartet of experts in economics, engineering, public policy, and information science from MIT, Harvard University, the University of Pittsburgh, and the University of California, Davis—harnessed the power of algorithms to analyze call record data from two undisclosed European countries. Their first study focused on unemployment at the community level, where they examined the behavioral traces of a mass layoff at an auto-parts manufacturing plant in 2006.
Using call record data spanning a 15-month period between 2006 and 2007, they designed a so-called structural break model to identify mobile phone users who had been laid off. Then they tracked the mobility and social interactions of the affected workers, looking at several quantities related to their social behavior, including total calls, number of incoming calls, number of outgoing calls, and calls made to individuals physically located at the plant.
