Mobile phone data can provide insights in to employment levels, as people’s communication patterns change when they are not working, an MIT study has revealed.
Data scientists used a plant closing in Europe for their study and found that months after the redundancies, the total number of calls made by those no longer employed dropped by 51 per cent compared with those still in employment and by 41 per cent compared with all phone users.
The number of calls made by individuals recently made redundant to someone in the city where they worked also fell by five per cent.
“Individuals who we believe to have been laid off display fewer phone calls incoming, contact fewer people each month and the people they are contacting are different, said Jameson Toole, a PhD candidate in MIT’s Engineering Systems Division and co-author.
“People’s social behaviour diminishes and that might be one of the ways layoffs have these negative consequences. It hurts the networks that might help people find the next job.”
The study penned a model of mobile phone usage that allows researchers to tie it to aggregate changes in employment. The phone data closely aligns with standard unemployment measures, according to the researchers, and could allow analysts to make unemployment projections two to eight weeks faster than those made using traditional methods.
“Using mobile phone data to project economic change would allow almost real-time tracking of the economy and at very fine spatial granularities… both of which are impossible given current methods of collecting economic statistics,” says David Lazer, a professor at Northeastern University and a co-author of the paper.
The researchers extended their model to see how well it corresponded with large-scale unemployment, using the available public data. “We are looking for a way to use this data to really understand economic behaviour and critical economic indicators,” Toole said.
However, the data scientists warned that it is still a long way until the new method can replace the traditional ways of measuring unemployment, but that it can be used to gauge behaviours better by analysts.
Toole said the study was conceptually similar to the MIT-based “Billion Prices Project,” which uses sales data to develop nearly real-time inflation estimates. In the same way, he said, this research might make a methodological impact on unemployment estimates.
“In the future we want to see how the same data can be used to further measure commuting challenges by income group,” said Marta Gonzalez, co-author of the paper and an associate professor of civil and environmental engineering at MIT.