How to counter ageist language in job adverts

Does the language used in job adverts show evidence of employer bias against older workers and, if so, how can it be countered?

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Is the language used in job adverts aiding and abetting age discrimination against older workers?

A recent study suggests this is the case and will be of interest to those trying to counter discrimination in the hiring process. There has been extensive development of tools to screen job adverts for sexist language and many employers now run their job adverts through tools like Textio to check for language bias against women. Some have noted significant increases in the number of women applying to their jobs as a result.

So would the same work when it comes to ageism? The study, Older Workers Need Not Apply? Ageist Language in Job Ads and Age Discrimination in Hiring, published in IZA Institute of Labour Economics claims to be the first to link age stereotypes to evidence on actual age discrimination in hiring. It used methods from computational linguistics and machine learning to directly identify ageist stereotypes that may underlie age discrimination in hiring, based on callbacks to older and younger job applicants who send in their cvs.

The researchers found evidence that language related to stereotypes of older workers (for instance, “must be a technological native”) can predict discrimination against older workers, particularly men, although a previous study based on previous reports found compelling evidence of age discrimination against older women. For men, the evidence points to age stereotypes about health, personality and skill and predicts age discrimination, and for women, the age stereotypes are more based on personality.

The researchers say the study helps increase understanding about which stereotypes underlie age discrimination and can point to policy responses for reducing age discrimination. For example, job training, job coaching or educational campaigns can focus on addressing the relevant negative stereotypes, or efforts could be focused on improving hiring practices, for instance, by increasing the information available to employers that reduces the attribution of stereotypes to older workers to whom they do not apply. It can also provide information to agencies that enforce age discrimination laws on job-ad language that may predict employer discrimination in hiring. Their approach may also allow researchers to analyse text data when phrasing is complex, varied and not always obvious (for instance, the numerous ways employers can describe “communication skills”).



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