Having a hectic job is bad enough, but having a hectic job that you dislike is worse. Such situations cause some people to resign early in search of better opportunities.
For the employer, that’s an asset lost. When viewed from another perspective, that’s time and effort in selecting potential employees down the drain, forcing one to go through the selection process again to look for a replacement.
Researchers from the National Bureau of Economic Research, however, have a solution for a more efficient hiring process: Using an algorithm that's been demonstrated to make better recommendations than humans regarding who to hire.
The Algorithm’s Performance
Studying 15 companies and more than 300,000 hires in low-skill service-sector jobs, such as data entry and call center work, the NBER researchers compared how long the employees who had been selected by a human stayed against that of those who were hired based on the algorithmic recommendations on a job test result.
The test included questions on personality, technical skills, cognitive skills, and suitability for the job. The applicants’ answers were ran through the algorithm, which then gave out a recommendation: Green for high-potential candidates, yellow for moderate potential, and red for the lowest-rated.
It was shown that, on average, greens stayed at the job 12 days longer than yellows, who stayed 17 days longer than reds (this is in the context of the average duration of employees in these jobs, which is around three months.).
Despite these data and the recommendations of the algorithm, people still chose otherwise. This may be due to overconfidence or bias. Some recruiters argue that they make these exceptions to hire more productive people, yet the numbers still prove them wrong. Of course, this doesn't conclusively mean that robots will be selecting the workers of tomorrow, but with additional research, the future of HR (human resources) may look vastly different—and somewhat more robotic.
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