Getting the job: it’s not just who you know, but how you know them

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Your weak social ties, not your closest associates, might be your best source for information on job prospects.Credit: Getty

When it comes to landing a job, there’s a saying: “It’s not what you know, but who you know.”

In fact, science shows that how you know them matters, too. Researchers in the United States have proved a long-standing hypothesis in social science: that people are more likely to land high-paying jobs through friends of friends, or ‘weak ties’, than through their close friends or family.

In a paper published in Science last month, US scientists describe how they were able to establish a causal relationship between weak ties and job offers by scouring data from social-media network LinkedIn1. The team analysed data from LinkedIn’s ‘People You May Know’ algorithm, which suggests new connections to users.

“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. Very weak ties, in which new connections had only a small number of mutual contacts, did not bring employment dividends.

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. Although Aral and his co-authors did not study researchers specifically, he thinks that most science workplaces fall into the highly digitized category. “Science is information work in a lot of ways — there’s a lot of data analysis [and] sharing,” he says.

So how do weak connections foster these successful link-ups? It’s all about accessing novelty. “The theory behind the strength of weak ties was that people who you don’t know as well have different kinds of knowledge than you and your close friends,” says co-author Erik Brynjolfsson, director of the Digital Economy Lab at the Stanford Institute for Human-Centered Artificial Intelligence in California.

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.”

The study makes a strong case for the importance of weak ties on people’s careers, says Theresa Kuchler, an economist and finance specialist at New York University Stern School of Business in New York City. It opens up important questions about the underlying mechanisms of the ‘strength of weak ties’ hypothesis, she adds. Such work could help recruiters to understand and address factors that stifle diversity in careers, such as glass ceilings, in which certain groups of people are subtly barred from advancement in a profession.

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. “We may also want to learn about how ties are formed and, consequently, how people or groups of people who currently lack the necessary connections can build them,” Kuchler says.

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.”

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