It costs nothing to post job openings on a company website, which are then scooped up by Indeed and other online companies and pushed out to potential job seekers around the world. Employers may simply be fishing for candidates. Because these phantom jobs make the labor market look tighter than it really is, they are a problem for economic policy makers as well as for frustrated job seekers. Companies should take ads down when jobs are filled.
Trimming recruiters, who have expertise in hiring, and handing the process over to hiring managers is a prime example of being penny-wise and pound-foolish. This is based on the notion that something may be wrong with anyone who wants to leave his or her current job. The number one factor that would encourage the former to move is more money. For active candidates the top factor is better work and career opportunities.
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More active than passive job seekers report that they are passionate about their work, engaged in improving their skills, and reasonably satisfied with their current jobs. They seem interested in moving because they are ambitious, not because they want higher pay. I know of no evidence that passive candidates become better employees, let alone that the process is cost-effective. If you focus on passive candidates, think carefully about what that actually gets you.
Better yet, check your data to find out. It seems like a cheap way to go, but does it produce better hires? Many employers think so. A downside to referrals, of course, is that they can lead to a homogeneous workforce, because the people we know tend to be like us. This matters greatly for organizations interested in diversity, since recruiting is the only avenue allowed under U. The Supreme Court has ruled that demographic criteria cannot be used even to break ties among candidates. Tata is an exception: It has long done what I advocate. For college recruiting, for example, it calculates which schools send it employees who perform the best, stay the longest, and are paid the lowest starting wage.
Contrary to the popular belief that the U. Unfortunately, the main effort to improve hiring—virtually always aimed at making it faster and cheaper—has been to shovel more applicants into the funnel. Employers do that primarily through marketing, trying to get out the word that they are great places to work. Organizations are much more interested in external talent than in their own employees to fill vacancies.
Here are the top channels for quality hires. Much better to go in the other direction: Create a smaller but better-qualified applicant pool to improve the yield.
Every application also exposes a company to legal risk, because the company has obligations to candidates not to discriminate, for example just as it does to employees. More than a generation ago the psychologist John Wanous proposed giving applicants a realistic preview of what the job is like. That still makes sense as a way to head off those who would end up being unhappy in the job. Marriott has done the same, even for low-level employees.
During the dot-com boom Texas Instruments cleverly introduced a preemployment test that allowed applicants to see their scores before they applied. How to determine which candidates to hire—what predicts who will be a good employee—has been rigorously studied at least since World War I. The personnel psychologists who investigated this have learned much about predicting good hires that contemporary organizations have since forgotten, such as that neither college grades nor unstructured sequential interviews hopping from office to office are a good predictor, whereas past performance is.
There is remarkably little consensus even among experts. There is general agreement, however, that testing to see whether individuals have standard skills is about the best we can do. Can the candidate speak French? Can she do simple programming tasks? And so forth. But just doing the tests is not enough. The economists Mitchell Hoffman, Lisa B. Kahn, and Danielle Li found that even when companies conduct such tests, hiring managers often ignore them—and when they do, they get worse hires.
The psychologist Nathan Kuncel and colleagues discovered that even when hiring managers use objective criteria and tests, applying their own weights and judgment to those criteria leads them to pick worse candidates than if they had used a standard formula. What are they doing instead? Seventy-four percent do drug tests, including for marijuana use; even employers in states where recreational use is now legal still seem to do so.
Into the testing void has come a new group of entrepreneurs who either are data scientists or have them in tow. They bring a fresh approach to the hiring process—but often with little understanding of how hiring actually works. John Sumser, of HRExaminer, an online newsletter that focuses on HR technology, estimates that on average, companies get five to seven pitches every day —almost all of them about hiring—from vendors using data science to address HR issues.
These vendors have all sorts of cool-sounding assessments, such as computer games that can be scored to predict who will be a good hire. That aside, these assessments have spawned a counterwave of vendors who help candidates learn how to score well on them.
Lloyds Bank, for example, developed a virtual-reality-based assessment of candidate potential, and JobTestPrep offers to teach potential candidates how to do well on it. Especially for IT and technical jobs, cheating on skills tests and even video interviews where colleagues off camera give help is such a concern that eTeki and other specialized vendors help employers figure out who is cheating in real time.
The amount of time employers spend on interviews has almost doubled since , according to research from Glassdoor.
How much of that increase represents delays in setting up those interviews is impossible to tell, but it provides at least a partial explanation for why it takes longer to fill jobs now. Just winging it and asking whatever comes to mind is next to useless. More important, interviews are where biases most easily show up, because interviewers do usually decide on the fly what to ask of whom and how to interpret the answer.
For example, does the fact that an applicant belonged to a fraternity reflect experience working with others or elitism or bad attitudes toward women? Should it be completely irrelevant? Letting someone with no experience or training make such calls is a recipe for bad hires and, of course, discriminatory behavior. Think hard about whether your interviewing protocols make any sense and resist the urge to bring even more managers into the interview process. Culture fit is another area into which new vendors are swarming. Typically they collect data from current employees, create a machine learning model to predict the attributes of the best ones, and then use that model to hire candidates with the same attributes.
As with many other things in this new industry, that sounds good until you think about it; then it becomes replete with problems.https://triculamtanmo.tk
Your Approach to Hiring Is All Wrong
Given the best performers of the past, the algorithm will almost certainly include white and male as key variables. Machine learning models do have the potential to find important but previously unconsidered relationships. Machine learning, in contrast, can come up with highly predictive factors. Research by Evolv, a workforce analytics pioneer now part of Cornerstone OnDemand , found that expected commuting distance for the candidate predicted turnover very well.
And even that question has problems.
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The advice on selection is straightforward: Test for skills. Ask assessments vendors to show evidence that they can actually predict who the good employees will be. Do fewer, more-consistent interviews. You must have a way to measure which employees are the best ones. Why is that not getting through to companies? Surely this is a prime example of making the perfect the enemy of the good. Some aspects of performance are not difficult to measure: Do employees quit? Are they absent? Virtually all employers conduct performance appraisals.
Would you hire him again? We have removed the company names after learning that the specifics of their subcontracting practices had not been verified. Peter Cappelli is the George W. He is the author of several books, including Will College Pay Off? The newest development in hiring, which is both promising and worrying, is the rise of data science—driven algorithms to find and assess job candidates.
By my count, more than vendors are creating and selling these tools to companies. Unfortunately, data science—which is still in its infancy when it comes to recruiting and hiring—is not yet the panacea employers hope for. Vendors of these new tools promise they will help reduce the role that social bias plays in hiring. And the algorithms can indeed help identify good job candidates who would previously have been screened out for lack of a certain education or social pedigree.
But these tools may also identify and promote the use of predictive variables that are or should be troubling. Because most data scientists seem to know so little about the context of employment, their tools are often worse than nothing. But a failure to check for any real difference between high-performing and low-performing employees on these attributes limits their usefulness. Furthermore, scooping up data from social media or the websites people have visited also raises important questions about privacy.
Furthermore, is it fair that something you posted as an undergrad can end up driving your hiring algorithm a generation later?
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Another problem with machine learning approaches is that few employers collect the large volumes of data—number of hires, performance appraisals, and so on—that the algorithms require to make accurate predictions. Yet another issue is that all analytic approaches to picking candidates are backward looking, in the sense that they are based on outcomes that have already happened.