People are more likely to land high-paying jobs through friends of friends, or ‘weak ties’, than through their close friends or family
“These platforms experiment all the time with algorithms,” says co-author Sinan Aral, a network scientist at the Sloan School of Management at the Massachusetts Institute of Technology in Cambridge. LinkedIn had experimented with different tie strength for its connection algorithm. Aral and his co-authors studied those data retrospectively and found that weaker ties led to more job ‘transmissions’, or more instances of people getting new positions than did the strong ties — but only to a point.
The authors also found that weak ties were more important for job mobility in highly digitized industries — measured by those industries’ demands for skills in information technology, robotization and remote work, among other metrics — than in less-digitized ones. This result could be of interest both to academic recruiters and to scientists seeking job opportunities.
However, our finding of an inverted U-shaped relationship between tie strength and job mobility challenges that assumption, says Aral. It might be that there is a trade-off between novelty and volume of information, he says. With the weakest ties, connected parties get a trickle, not a torrent. “I think the literature really needs to go back and look at that.
One question, Kuchler says, is whether people are getting information about new jobs through their networks — or whether the connections serve as recommendations that help employers to identify good recruits. Another is whether hiring on the basis of weak ties results in better recruits, or just more convenience for the recruiter — something that she says might entrench inequalities in the job market.
Brynjolfsson says that he expects to see many more studies like this in the future. “We are in the midst of a revolution in measurement due to the digitization of not only connections at work, but the digitization of much of business and the economy,” he says. “The research in our paper is an example of the kind of large-scale, causal inference that was impossible until recently, but will soon become widespread.”Rajkumar, K., Saint-Jacques, G., Bojinov, I., Brynjolfsson, E. & Aral, S.
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