In particular, a team from Massachusetts-based Northeastern University 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 David Lazer, distinguished professor of political science and computer and information science.
Lazer and his collaborators harnessed the power of algorithms to analyse call record data from two undisclosed European countries.
Their first study focused on unemployment at the community level, where they examined the behavioural traces of a mass layoff at an auto-parts manufacturing plant in 2006.
The findings revealed that job loss had a "systematic dampening effect" on mobility and social behaviour.
For example, the researchers found that the total number of calls made by laid-off individuals dropped 51 percent following their layoff when compared with non-laid off residents while their number of outgoing calls decreased 54 percent.
What's more, the month-to-month churn of a laid-off person's social network -- that is, the fraction of contacts called in the previous month that were not called in the current month -- increased approximately 3.6 percentage points relative to control groups.
The findings, published in 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.