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Researchers use mobile phone data to predict employment shocks

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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.

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 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.

A schematic view of the rela­tion­ship between job loss and call dynamics. A schematic view of the rela­tion­ship between job loss and call dynamics. Researchers used the calling behavior of indi­vid­uals to infer job loss and mea­sure its effects. They then mea­sured the vari­ables and included them in pre­dic­tions of unem­ploy­ment at the macroscale, sig­nif­i­cantly improving fore­casts. Image from Journal of the Royal Society Interface.

 

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.

The find­ings revealed that job loss had a “sys­tem­atic damp­ening effect” on their mobility and social behavior. 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. In terms of mobility, they found that the number of unique mobile phone towers vis­ited by people who had lost their jobs decreased 17 per­cent rel­a­tive to a random sample.

These results sug­gest that a user’s social inter­ac­tions see sig­nif­i­cant decline and that their net­works become less stable fol­lowing job loss,” the authors wrote. “This loss of social con­nec­tions may amplify the neg­a­tive con­se­quence asso­ci­ated with job loss observed in other studies.”

The paper’s second study ana­lyzed the call detail records of thou­sands of sub­scribers in a dif­ferent Euro­pean country, one that had expe­ri­enced macro­eco­nomic dis­rup­tions during the period in which the data was available.

This time, the researchers looked for behav­ioral changes that may have been caused by layoffs—fewer out­going calls, for example, or an increase in churn—to deter­mine whether those changes could pre­dict gen­eral unem­ploy­ment statistics.

Indeed, they found that changes in mobility and social behavior pre­dicted unem­ploy­ment rates before the release of offi­cial reports and more accu­rately than tra­di­tional fore­casts. Specif­i­cally, the researchers noted that their novel methods allowed them to pre­dict present unem­ploy­ment rates two-​​to-​​eight weeks prior to the release of tra­di­tional esti­mates and fore­cast future employ­ment rates up to four months ahead of offi­cial reports.

While Lazer praised the rapidity, accu­racy, and cost-​​effectiveness of collecting—and sub­se­quently analyzing—passively gen­er­ated data from dig­ital devices, he cau­tioned against viewing his group’s methods as a sub­sti­tute for survey-​​based approaches to detecting and pre­dicting future unem­ploy­ment rates. “We con­sider mobile phone data a pow­erful yet com­ple­men­tary tool,” he explained. “Big Data approaches are fast and inex­pen­sive, but the norms gov­erning phone use are con­stantly changing, forcing us to con­stantly cal­i­brate how we use them in con­nec­tion with other methodologies.”

-By Jason Kornwitz

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