Newly released research claims that mathematical algorithms combined with recruitment software select better candidates than when hiring managers act alone.
NBER claims that their findings indicate that organizations can improve worker quality by limiting managerial discretion. They believe this is true because their report shows that when faced with similar applicant pools, managers who exercise more discretion typically end up with worse hires.
The survey contrasted the length of tenure between new hires made on the recommendation of results from job testing and those who were hired because of the hiring manager’s desires.
The testing contained questions about technical skills, personality traits, cultural fit and cognitive abilities. The applicant’s answers were run through an algorithm, which then made recommendations for high-potential, moderate potential, lowest potential candidates.
The results of the study reported that those found to be high-potential candidates from the testing stayed on the job for 12 days longer than the moderate candidates. Candidates tagged as low potential stayed 17 days fewer than moderate candidates.
Despite the findings of the testing, the study reported that hiring managers sometimes ignored the high-potential candidates as indicated by the testing and hired other candidates – leading to poor outcomes.
For example the study found that when recruiters hired a moderate candidate instead of a high-potential candidate, who was then hired at a later date to fill another open position, the high-potential candidate stayed at the job about 8 percent longer.
“That’s still a big deal, on average, when you’re hiring tens of thousands of people,” said NBER researcher Mitchell Hoffman, an assistant professor of strategic management to Bloomberg.