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A Massive LinkedIn Study Reveals Who Actually Helps You Get That Job

ItemDate=2022-09-21 03:48:49 Status=publish


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According to one of the most influential theories in social science you’re more likely to nab a new position through your “weak ties ” loose acquaintances with whom you have few mutual connections.But the theory dubbed “the strength of weak ties ” after the title of Granovetter’s study lacked causal evidence for decades.

Now a sweeping study that looked at more than 20 million people on the professional social networking site LinkedIn over a five-year period finally shows that forging weak ties does indeed help people get new jobs.The strength of weak ties “is really a cornerstone of social science ” says Dashun Wang a professor at the Kellogg School of Management at Northwestern University who was not involved in the new study.“That really shook people up because assumptions about how people find the best jobs in life doesn’t look to be true—it looks like actually strangers might be the best contacts for you ” says Brian Uzzi also a professor at the Kellogg School of Management who was not involved in the new study.

But correlation is not causation and in the nearly 50 years since Granovetter first set down his idea researchers had not proved that an applicant’s weak ties are the specific thing that causes them to nab that new job.“We don’t know whether weak ties are correlated with goodness [such as new jobs] because weak ties themselves are good or because people who make weak ties have some unobserved characteristics that also make them more productive have good ideas and get better jobs promotions and wages.” As Wang puts it “People use this theory and associated concepts to explain a wide range of phenomena but there has not been a causal test for whether weak ties are causally linked to job opportunities.weak ties that might actually be the source of your next job ” he adds.The role that LinkedIn’s People You May Know function plays in gaining a new job demonstrates “the tremendous leverage that algorithms have on employment and probably other factors

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