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What does Recency Bias mean in the Hiring World

What does Recency Bias mean in the Hiring World

Published on May 1st, 2023

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Recency bias is a phenomenon in the hiring world where employers are more likely to prefer recently-acquired skills or experiences over those that were acquired further in the past. Despite the fact that past experience may be just as valuable, researchers have found that employers tend to show a preference for candidates who possess recent experience.

Studies have shown this bias is particularly evident when it comes to studies and qualifications. For example, one study from reed.co.uk suggested employers are three times more likely to interview someone with an A Level certificate achieved within the last five years compared with applicants whose certificates date back 10 years or longer. Similarly, many job descriptions request graduates who obtained their degree within two or three years of applying for the role, regardless of the relevance to the job.

This is known as recency bias, and it can be a major obstacle for jobseekers who have taken time away from the workforce or who have older qualifications. This type of hiring discrimination has been linked to ageism, whereby applicants that are considered “too old” for a role are overlooked in favor of younger candidates. Recency bias also serves as an additional challenge for people returning to work after maternity leave or extended travel abroad, as well as those with gaps on their resumes due to health issues or long-term caregiving.

Recency bias may not always be intentional, but employers should ensure they are aware of its potential effects and take steps to avoid it when assessing candidate qualifications. A growing body of research suggests that employers should look beyond the most recent credentials or experiences when evaluating a candidate’s suitability for a role, as doing so could help to tap into the wealth of knowledge and experience offered by more experienced candidates.

In addition, employers can also take steps to ensure their hiring practices are unbiased by implementing measures such as blind recruitment processes. This involves removing personal details from applications before they reach recruiters, ensuring their decision-making is based solely on qualifications and skills rather than external factors such as age, ethnicity or gender. Making this kind of change can go a long way in helping to avoid potential recency bias in the hiring process.  

By taking these steps and understanding what recency bias is and how it can affect the hiring process, organisations can ensure they make unbiased decisions that result in a workforce with diverse experience and knowledge. 

This will enable them to make the best use of their talent pool and create an environment where everyone has an equal opportunity to succeed.

Statistics from a recent recruiter survey conducted by The Ladders showed that more than 75% of recruiters said they have seen recency bias when reviewing applications for positions. Additionally, nearly 80% of those surveyed agreed that it was an issue which needed to be addressed in order for them to have access to the most qualified candidates for any given job role.

The area where the effects of recent hiring can be seen most clearly is in recruiting. Thanks to the advancement of AI and ChatGPT, we now have tools like EasySource that can assist in removing recency bias. EasySource is a tool designed for recruiters that makes it easier for candidates to reach out to them using LinkedIn. It is priced for smaller organizations and is also free for a limited number of credits. With the use of pre-made templates, EasySource may help you customize your workflow. It also utilizes advanced filters to discover highly relevant prospects, including those with US work permit. You may also make incredibly customized LinkedIn in mails, Gmails, and invitations using ChatGPT and AI.


Authors

author

Thomas M. A.

A literature-lover by design and qualification, Thomas loves exploring different aspects of software and writing about the same.

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