{"id":4721,"date":"2015-06-17T06:42:10","date_gmt":"2015-06-17T06:42:10","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=4721"},"modified":"2015-06-17T06:42:10","modified_gmt":"2015-06-17T06:42:10","slug":"can-phone-data-detect-real-time-unemployment","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/can-phone-data-detect-real-time-unemployment\/","title":{"rendered":"Can phone data detect real-time unemployment?"},"content":{"rendered":"

Usage patterns vary when people are not working.<\/strong><\/em><\/span><\/p>\n

\"Illustration:<\/a>
Illustration: Christine Daniloff\/MIT<\/figcaption><\/figure>\n

CAMBRIDGE, Mass.<\/strong> —\u00a0If you leave your job, chances are your pattern of cellphone use will also change. Without a commute or workspace, it stands to reason, most people will make a higher portion of their calls from home \u2014 and they might make fewer calls, too.<\/span><\/p>\n

Now a study co-authored by MIT researchers shows that mobile phone data can provide rapid insight into employment levels, precisely because people\u2019s communications patterns change when they are not working.<\/span><\/p>\n

Indeed, using a plant closing in Europe as the basis for their study, the researchers found that in the months following layoffs, the total number of calls made by laid-off individuals dropped by 51 percent compared with working residents, and by 41 percent compared with all phone users. The number of calls made by a newly unemployed worker to someone in the town where they had worked fell by 5 percentage points, and even the number of individual cellphone towers needed to transmit the calls of unemployed workers dropped by around 20 percent.<\/span><\/p>\n

\u201cIndividuals 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,\u201d says Jameson Toole, a PhD candidate in MIT\u2019s Engineering Systems Division, and a co-author of the new paper. \u201cPeople\u2019s social behavior 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.\u201d<\/span><\/p>\n

When the factory closes<\/strong><\/span><\/p>\n

The paper, just published in the\u00a0Journal of the Royal Society<\/em>\u00a0Interface<\/em>, builds a model of cellphone usage that lets the researchers correlate cellphone usage patterns with aggregate changes in employment. The researchers believe the phone data closely aligns with standard unemployment measures, and may allow analysts to make unemployment projections two to eight weeks faster than those made using traditional methods.<\/span><\/p>\n

\u201cUsing mobile phone data to project economic change would allow almost real-time tracking of the economy, and at very fine spatial granularities \u2026 both of which are impossible given current methods of collecting economic statistics,\u201d says David Lazer, a professor at Northeastern University and a co-author of the paper.<\/span><\/p>\n

In addition to Toole and Lazer, the co-authors of the paper are Marta Gonzalez, an associate professor of civil and environmental engineering at MIT; Yu-Ru Lin of the University of Pittsburgh; Erich Muehlegger of the University of California at Davis; and Daniel Shoag of the Harvard Kennedy School.<\/span><\/p>\n

The study\u2019s starting point was an automotive plant in Europe that closed in 2006, leaving about 1,100 workers unemployed in a town of roughly 15,000 people. Having the information about the layoffs allowed the researchers to build an algorithm that, by analyzing phone-use patterns, assigns a probability that someone has become unemployed. The phone data was anonymous at the individual level, ensuring a certain level of privacy.<\/span><\/p>\n

The researchers then extended that usage model to see how well it corresponded with larger-scale unemployment, using eight quarters of unemployment data in 52 provinces of a European country.<\/span><\/p>\n

\u201cWe were looking for a way to use this data to really understand economic behavior and critical economic indicators,\u201d Toole says.<\/span><\/p>\n

A complement, not a substitute<\/strong><\/span><\/p>\n

However, the researchers emphasize that they are not proposing the new method as a replacement for time-tested ways of measuring unemployment. Instead, they see it as an additional tool for analysts.\u00a0<\/span><\/p>\n

\u201cThese methods should not be viewed as substitutes for current methods of collecting data about the economy as much as very powerful complements,\u201d Lazer says.<\/span><\/p>\n

Toole says the study is conceptually similar to the MIT-based \u201cBillion Prices Project,\u201d which uses sales data to develop nearly real-time inflation estimates. In the same way, he says, this research might make a methodological impact on unemployment estimates.<\/span><\/p>\n

\u201cThe Billion Price Project was one of the things that inspired us,\u201d Toole says.<\/span><\/p>\n

For her part, Gonzalez says she intends to construct future studies about cellphone usage changes to see how urban mobility, or a lack thereof, hinders economic opportunities for workers and unemployed people.<\/span><\/p>\n

\u201cIn the future we want to see how the same data can be used to further measure commuting challenges by income group,\u201d she says, adding that the researchers\u2019 intellectual \u201cmission\u201d is \u201cdeveloping methods that convert data from communication technologies into meaningful information.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Usage patterns vary when people are not working. CAMBRIDGE, Mass. —\u00a0If you leave your job, chances are your pattern of cellphone use will also change. Without a commute or workspace, it stands to reason, most people will make a higher portion of their calls from home \u2014 and they might make fewer calls, too. Now […]<\/p>\n","protected":false},"author":6,"featured_media":4722,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,28],"tags":[],"class_list":["post-4721","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","category-techbiz"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1-300x200.jpg",300,200,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",600,400,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",600,400,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",540,360,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",95,63,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",639,426,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/06\/MIT-Employment-Phone-1-1.jpg",150,100,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Research<\/a> Tech<\/a>","tag_info":"Tech","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/4721","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=4721"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/4721\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/4722"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=4721"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=4721"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=4721"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}