age discrimination research paper

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• Published: 11 February 2021

Age discrimination in the workplace hurts us all

• Joo Yeoun Suh   ORCID: orcid.org/0000-0003-1692-5959 1  

Nature Aging volume  1 ,  page 147 ( 2021 ) Cite this article

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When older workers are discriminated against, everyone is affected. Age discrimination negatively impacts not only individual workers but also their families and the broader economy, argues Joo Yeoun Suh.

As COVID-19 spreads throughout the United States and the rest of the world, the resulting disruptions to the economy mean that it is highly likely the incidence of age discrimination will increase. This may include employers laying off older staff members or not considering older candidates when rehiring. This short-term thinking ignores long-term consequences that will affect people of all ages.

Age discrimination takes an enormous toll on individual workers and their families, but it also has a substantial impact on the economy. According to the AARP’s recent report ‘ The Economic Impact of Age Discrimination ’, bias against older workers cost the US economy an estimated US$850 billion in gross domestic product (GDP), 8.6 million jobs and US$545 billion in lost wages and salaries in 2018 alone.

Meanwhile, previous experience points to an impending surge in age discrimination issues stemming from the current economic downturn. During the 2007–2009 Great Recession, age discrimination complaints related to hiring and firing increased by 3.4% and 1.4% , respectively, in concert with each percentage point increase in monthly unemployment rates. Unfortunately, the contraction the economy is undergoing as a result of the COVID-19 pandemic is even worse than that of the Great Recession.

Many older people believe that their age is a disadvantage when looking for a job. Evidence suggests that older job applicants get fewer callbacks than their younger counterparts with comparable resumes, contributing to extended periods of unemployment for many 50-plus jobseekers. This is especially true for women and minoritized racial groups, as incidents of age discrimination in the workplace often intersect with gender and racial discrimination. The reality is that those most likely to be affected by age discrimination are those least able to afford it. Lower-income workers may have fewer options to switch jobs, and historically disadvantaged racial and ethnic groups are more likely than others to feel trapped in their present role.

For months now, we have been seeing the impact of the pandemic on employment, with record-breaking numbers of unemployment claims filed in April and May of 2020 in the US. It is highly likely that age discrimination will persist after the pandemic if employers do not take steps to address it. To counteract these trends, federal and state anti-age discrimination laws must be vigorously enforced. Beyond that, we need to make changes in the way workplaces operate — changes that will help in the near term but will signal a permanent shift as well. Companies should implement robust practices that promote age-diverse work environments, and their workers of all ages should be provided the apprenticeship opportunities they need to thrive in the workplace as they age.

Access to job-protected paid sick leave or paid family leave will help older workers stay employed during the current health and economic crisis. Some states have temporarily broadened access to paid sick leave in response to the virus, and several major companies have taken action to provide their employees with paid sick leave to allow those who feel ill to stay home. Paid family leave is also important if a family member tests positive for COVID-19, potentially creating a need for quarantine and family caregiving.

Employers should consider how to make workplaces truly embrace age diversity and inclusion. They would be wise to do so, even from a business perspective. There are strong economic benefits for businesses to make such changes, including that an age-diverse workforce gives companies more insight into age-diverse marketplaces. AARP’s initiative, ‘ Living, Learning and Earning Longer ’, a collaboration with the Organization for Economic Co-operation and Development (OECD) and the World Economic Forum (WEF), advances the business case for age diversity and highlights promising practices from around the world. Efforts to cultivate multigenerational workforces, which span technology training and sharing career experiences and skills, have clear value for employers and employees alike.

If we are to benefit from the value that older workers bring to the workforce, businesses will need to make a serious commitment to concepts like the multigenerational workforce. Global executives are beginning to recognize that multigenerational workforces are key to business growth and success. However, more than half of global companies still do not include age in their company’s diversity and inclusion policy. Clearly more needs to be done to align systems to better respond to the demographics at large. Efforts to do so are necessary to create inclusive workplaces that take active steps to enable employees to realize their full and unique potential.

Company-sponsored programs that promote age diversity and inclusion in tangible ways would have encouraged 60% of those aged 50-plus who retired because of age discrimination to remain in the workforce longer . There is a compelling case for increasing age inclusion in the workforce beyond meeting legal mandates such as the Age Discrimination in Employment Act of 1967. People are living longer and either want or need to continue working. Providing these people with access to incomes ultimately creates a population with the resources to continue consuming and generating impact on the economy. But perceptions that prevent the hiring and advancement of older workers need to shift in order for these benefits to be captured.

This is an astoundingly difficult time for employees and employers alike. As we fight against COVID-19, we must not lose sight of older workers. With the skills and knowledge they’ve acquired over a lifetime, they can make enormous contributions to the work of pushing national and global economies toward recovery.

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Suh, J.Y. Age discrimination in the workplace hurts us all. Nat Aging 1 , 147 (2021). https://doi.org/10.1038/s43587-020-00023-1

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Introduction

Author contributions, conflicts of interest, acknowledgments, ageism in hiring: a systematic review and meta-analysis of age discrimination.

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Lucija Batinovic , Marlon Howe , Samantha Sinclair , Rickard Carlsson; Ageism in Hiring: A Systematic Review and Meta-analysis of Age Discrimination. Collabra: Psychology 3 January 2023; 9 (1): 82194. doi: https://doi.org/10.1525/collabra.82194

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We aimed to identify effect sizes of age discrimination in recruitment based on evidence from correspondence studies and scenario experiments conducted between 2010 and 2019. To differentiate our results, we separated outcomes (i.e., call-back rates and hiring/invitation to interview likelihood) by age groups (40-49, 50-59, 60-65, 66+) and assessed age discrimination by comparing older applicants to a control group (29-35 year-olds). We conducted searches in PsycInfo, Web of Science, ERIC, BASE, and Google Scholar, along with backward reference searching. Study bias was assessed with a tool developed for this review, and publication bias by calculating R-index, p-curve, and funnel plots. We calculated odds ratios for callback rates, pooled the results using a random-effects meta-analysis and calculated 95% confidence intervals. We included 13 studies from 11 articles in our review, and conducted meta-analyses on the eight studies that we were able to extract data from. The majority of studies were correspondence studies ( k =10) and came largely from European countries ( k =9), with the rest being from the U.S. ( k =3) and Australia ( k =1). Seven studies had a between-participants design, and the remaining six studies had a within-participants design. We conducted six random-effects meta-analyses, one for each age category and type of study design and found an average effect of age discrimination against all age groups in both study designs, with varying effect sizes (ranging from OR = 0.38, CI [0.25, 0.59] to OR = 0.89, CI [0.81, 0.97]). There was moderate to high risk of bias on certain factors, e.g., age randomization, problems with application heterogeneity. Generally, there’s an effect of age discrimination and it tends to increase with age. This has important implications regarding the future of the world’s workforce, given the increase in the older workforce and later retirement.

Age discrimination in hiring can have severe negative consequences for the individual worker (e.g., unemployment, forced early retirement) as well as for organizations and society at large (e.g., failing to meet demands on available workforce), and is also illegal in many countries. Older workers now make up the fastest growing segment of the workforce in most developed countries, and the numerous challenges they face has prompted some to refer to this group as “the new unemployables” (Wanberg et al., 2016) . Hence, recognizing and combating discrimination against older applicants is becoming an increasingly pressing issue. An important step in this endeavor is to grasp the magnitude and the onset of age discrimination in hiring (World Health Organization, 2021) , which is our aim with this systematic review.

An Aging Workforce

The workforce is increasingly getting older, for several reasons. Globally, the second half of the twentieth century was marked by a sharp rise in life expectancy, from 46.5 years in the early 1950s, to 65.4 years by the end of the 1990s (UNDP, 1999) . The increasing number of retirees and a decreasing size of the active workforce contributing to social security systems, lead to cutbacks in the provision of pensions internationally (Galasso & Profeta, 2004) . Aging populations and the associated financial demands on social security systems call for new political and economic directives. In an attempt to prevent the collapse of these systems, many countries aim at incentivizing the extension of work life past the retirement age (Neumark et al., 2017) . Many people also prefer to keep working until an older age (Wöhrmann et al., 2016) .

The Difficulties Facing Older Workers

Research suggests that older workers face difficulties on the labor market, and that these obstacles begin at an earlier age than might be expected. For starters, earnings tend to increase during the early career years until workers are middle-aged. At this point, they peak and then begin to decline distinctly (Carlsson & Eriksson, 2019) , which is explained by a decrease in the number of hours worked (Rupert & Zanella, 2015) . Indeed, unemployment duration tends to increase with age in most Western countries (OECD, 2018; Rupert & Zanella, 2015) . Seniors often transition to part-time retirement, bridge jobs at the end of their careers, or return to work after a period of retirement (Johnson et al., 2014; Maestas, 2010) , leaving them at risk of becoming unemployed. It seems that anti-discrimination legislation notwithstanding, older individuals face lower chances of attaining and holding employment compared to younger, equally competent ones, and these hardships are often observed for workers around the age of 50 and onwards (Wanberg et al., 2016) .

There could be several reasons for older workers’ lower chances on the labor market. Part of the explanation might be that a larger share of the older workforce have outdated job skills (Fossum et al., 1986) , are less familiar with modern job search methods (Gibson et al., 1993) , or less likely to move to a new location (Theodossiou & Zangelidis, 2009) . Nevertheless, there is reason to believe that recruiters often have stereotypes about older workers. In a survey with a representative sample of Swedish employers, Carlsson and Eriksson (2019) found widespread beliefs that workers’ flexibility/adaptability, ambition, and ability to learn new tasks start to decline as early as around the age of 40. Studies conducted in other cultures are consistent with this picture (Henkens, 2005; Posthuma & Campion, 2009; Taylor & Walker, 1998) , and further suggest that managers are sometimes concerned that older workers are less productive, or in poorer physical shape. Age stereotypes seem to be especially prevalent in some industries, such as finance, retailing, and information technology (Posthuma & Campion, 2009) .

Defining and Measuring Age Discrimination

Ageism occurs when people are categorized according to their age in ways that lead to injustice, harm, or disadvantage. The present review deals with direct discrimination in the form of disparate treatment. This refers to cases where employers apply different standards to individuals based on their group membership (Doyle, 2007; Gatewood & Field, 2001) . In the case of ageism against older people, it translates into treating older applicants unfairly. In contrast, disparate impact (or indirect discrimination) refers to systems that indirectly result in unequal outcomes for older and younger workers, and falls outside the scope of the present research.

Direct discrimination has been estimated or measured in a number of ways. However, in studies based on administrative or survey data, age effects may be confounded with effects of other potential worker characteristics that employers perceive but the researcher does not. If such characteristics correlate with age, omitted variable bias threatens the study’s validity (Bertrand & Duflo, 2017) . Additionally, surveys asking employers whether they treat applicants differently depending on age face shortcomings due to social desirability issues and possible lack of insight about discriminatory behaviors. Only experimental designs where age is randomly assigned to job applicants allow for a high degree of control and inferences of causality. In this systematic review, we thus focus only on studies with experimental designs, including field- as well as laboratory experiments. However, a requirement is that participants are real recruiters, rather than for example college students who lack personnel selection experience.

We divide experimental discrimination studies into two types. First, there is the scenario-based experiment, also called vignette study or factorial survey. Compared to surveys, vignette studies offer a broader range of situational or contextual factors (Hyman & Steiner, 1996) , and allow for causal inferences. Second, there are field experiments. These can be further divided into correspondence studies and audit studies. In correspondence studies, researchers construct applications and randomly assign older and younger age to the fictive applicants. They send the applications to a large number of real job openings, and the outcome variable is the response from employers in the form of callbacks (invitations to job interviews or further consideration in the selection process). Correspondence studies and vignette studies thus have in common that they have experimental designs, use fictitious applications, and focus on the first stage of selection in which the participants (in this case, recruiters) screen applicants based on their resumes. The key difference between the two is that in correspondence studies, the participants (i.e., employers) are unaware that their behavior is being monitored and they are thus unable to conceal discriminatory behaviors. Furthermore, the behavior is observed in a real-life high stakes setting compared to an artificial setting in vignette studies.

Audit studies are another type of field study that is similar to correspondence tests, but instead of callbacks, they measure actual job offers by using actors who pose as real job applicants attending employment interviews. The goal is to use applicants who differ on the category of interest (in this case, age) but are maximally equal to one another in all other aspects (Gaddis, 2018) . However, audit studies often fail to make applicants appear identical with respect to all other aspects (Neumark, 2012) . Additionally, they often suffer from low statistical power due to the high cost and effort involved in collecting data, as well as potential demand effects when auditors are not naive to the purpose of the study. Because of these limitations, we do not include audit studies in this review.

Previous Reviews

While there are original studies researching labor market discrimination based on various discrimination grounds, systematic reviews have been conducted mostly to aggregate data on ethnic discrimination (Lippens et al., 2023; Zschirnt & Ruedin, 2016) . Some reviews have provided overviews of correspondence experiments focusing on the most common discrimination grounds (e.g., ethnicity, gender, religion, sexual orientation, age; Bertrand & Duflo, 2017 ), with the most comprehensive review being Baert’s (2018) and their newly updated meta-analysis (Lippens et al., 2023) , which encompassed nearly all studies on hiring discrimination across all discrimination grounds, tested by correspondence experiments. They conducted a meta-analysis on 19 correspondence studies examining age discrimination in hiring, of which 17 were examining age discrimination towards older applicants, although there was not a clear distinction of which ages belonged to control and which to the discriminated group between studies. Contrary, our study represents the first preregistered systematic review and meta-analysis that specifically targets hiring discrimination based on applicants’ age, with predefined criteria of what constitutes older age and control age, which allows us a clearer interpretation of effects we find. By incorporating preregistration, transparent methods for extraction and calculation of effect sizes, and a focused scope, our research offers a unique and valuable contribution to the existing literature.

The overall objective of this systematic review is to investigate the size of age discrimination in hiring. Our review question is: How large is the effect of recruiters’ age discrimination against older (compared to younger) applicants in selection, according to correspondence testing and scenario experiments conducted between 2010 and 2019? In our preregistered protocol, we used the term recruitment instead of selection, but we subsequently realized that our preregistered inclusion criteria were in fact more narrow and that selection is the term that more accurately aligns with these. Additional modifications to the review question was further clarification of the population (recruiters) and of the comparator (younger applicants) to align with the preregistered inclusion criteria. As a secondary review question, we preregistered that we would aim at exploring the moderating effects of age groups, job type, and culture.

This systematic review was conducted according to Methodological Expectations of Cochrane Intervention Reviews (MECIR) (Higgins et al., 2022) and reported in accordance with the official PRISMA guidelines for reporting systematic reviews (Page et al., 2021) . The criteria were defined in the protocol, which was preregistered on OSF using the PROSPERO protocol template ( https://osf.io/cyft2 ). As far as possible, we retell the criteria verbatim from the protocol; however, in order to meet the reporting format, we had to make some changes to both wording (e.g., future to past tense) and structure (moving paragraphs). Below, we explain all deviations from the preregistered protocol.

Eligibility Criteria

The participants had to be active recruiters who have received applications sent out by researchers in correspondence studies (field experiments) or active recruiters who rated fictive job applicants (scenario experiments) between 2010 and December 31, 2019. Scenario experiments with only undergraduate students or non-recruiting staff as participants were excluded.

Intervention

Age had to be systematically and randomly assigned to the application (e.g., 30 vs. 50). We coded different levels of manipulation of age, based on the applicants’ age group: 40 - 49, 50 - 59, 60 - 65 (retiring age) or above 65. Whenever age groups spanned several ages, we averaged these ages and linked them to the corresponding age group of the age average.

Studies had to include the application of a person between 30 and 35 years of age, who did not signal any other type of minority group membership that is protected by anti-discrimination law (e.g., sexual orientation minority). The reason we planned to exclude applicants younger than 30 is because of the possibility of discrimination against young people, whereas the cut-off on 35 was intended to leave some range to the 40 year category in which studies found age discrimination to increase significantly. However, it turned out that several studies in the literature had cut-offs very close to ours, and we found it unreasonable to exclude those studies for that reason. We hence deviated from our preregistration, by including studies which had control groups aged 29 ( Neumark et al., 2016, 2019 ; Challe et al., 2016 (Study 1)), as well as one study whose comparators were applicants aged 35 and 36 ( Challe et al., 2016 (Study 3)), where we included both ages. Considering that we changed the inclusion criteria for the comparators in the content coding stage, we went back to the screening phase to check if any studies with 29-year-old comparators had been excluded during this stage. We found no additional studies with this comparator.

Age discrimination was operationalized as the callback rates in correspondence testing. For scenario experiments (or similar), age discrimination was operationalised as recruiters’ assessments (i.e., ratings, decisions, and judgements) about applicants’ employability, job suitability, desirability, hiring priority, recommendation of selection, or likelihood of being invited to an interview. We included studies which manipulate age and explicitly state the job types in which the applications are sent and studies which contain more than one age manipulation group. We excluded studies that rely on eye-tracking measurements or any other kinds of non-verbal behaviors of the participants (i.e., observations, reading time, biopsychological parameters, etc.); interviews, as well as studies which do not focus on the selection stage (more specifically, the stage in which the recruiters choose the potential interview and job offer candidates among the applicants based on their resumes). We further excluded studies that used ratings or statements that are general in nature (i.e., “I would prefer not to hire older applicants’’) and thus measure attitudes instead of discrimination. Highly artificial tasks (e.g., a speeded comparison test, implicit tests, priming studies) which do not resemble real-life screening procedures, were also excluded.

Study Types

We included studies that were correspondence studies or scenario experiments of fictitious applications focusing on the first stage of selection, in which the participants decide which applicants get through to the next stage based on the applicants’ resumes (i.e., CVs and or personal letter). Correspondence studies were included if they involved sending out fictitious resumes that differ based on age being randomly assigned to each resume to job posts for different work positions and investigate age discrimination in terms of call-back rates for different age groups. Scenario experiments were included if they involved presenting participants (active recruiters) with fictitious resumes with age randomly assigned to each resume.

We excluded studies if they were reported in books or book chapters, or published in any language other than English, but we had not stated this in our protocol. We found it unlikely that this type of research would be published as book chapters without there also being a stand-alone journal article, and we did not have the resources to go through multiple languages.

Information Source

We conducted searches in the following databases: Web of Science, PsycInfo, ERIC and BASE. As a parallel instrument, we used Google Scholar and backward reference search for literature. We also manually searched reference lists of relevant reviews. The language of studies was restricted to English. The search included studies conducted between 2010 and December 31, 2019. This 2010 cut-off allows us to focus on relatively recent studies which reflect today’s working culture and ensure that the majority of studies were conducted when anti-discrimination laws had been established. As the age discrimination laws implementation varies across countries (Lahey, 2010) , we decided to take 2010 as the cut-off year to ascertain that at least most of the European countries and the US have established laws. The 2019 cut-off excludes any study conducted during the pandemic, which might have considerably changed the labor market and would be a better focus of another paper.

Search Strategy

Generally, searches from all databases were downloaded and imported into the Zotero reference management software where they were saved and filtered. This way the search history was saved exactly as it was at the time of search. For further details on our search strategy, such as our search terms and search settings, see OSF ( https://osf.io/cvn48/ ). Keywords reported in the protocol have been used and combined to create searches.

In addition to planned search strategies, we also conducted manual search of reference lists of full-text included studies, and extracted eligible articles from the reference lists of two reviews: Baert (2018) and Bertrand & Duflo (2017) . The searches were last updated on April 6th, 2021 in ERIC, April 5th, 2021 in PsycInfo, April 11th, 2021 in BASE, April 8th, 2021 in Web of Science Core Collection, and April 9th, 2021 in Google Scholar.

We deviated in using the search strings when conducting the searches in databases, particularly searching through ERIC and PsycInfo. We included thesaurus terms of the key concepts (i.e., “Age discrimination”, “Personnel selection”, and “Recruitment” for ERIC and “Recruitment”, “Employment Discrimination”, “Age Discrimination”, “Ageism”, “Aged (Attitudes Toward)”, and “Aging (Attitudes Toward)” for PsycInfo) along with the keywords “labour market” and “age bias” which were concluded to improve search precision.

Following the MECIR manual, the search was updated on April 24, 2023, as more than six months had passed since the original searches were performed. We refined our search strategies to generate more sensitive search strings by removing the search strings for the job type category and incorporated an additional database, Business Source Ultimate, as recommended by a peer reviewer. Updated searches and search strategies are available in our OSF project.

Selection Process

As a first step of the selection process, searches were uploaded into the reference management software Zotero (Zotero v. 5.0.96.2, Roy Rosenzweig Center for History and New Media, 2021). Subsequently, two authors (LB & MH) merged duplicates based on the availability of data in each version of the duplicate studies. After excluding books and book chapters, the remaining articles were moved into the article screening software Rayyan (Ouzzani et al., 2016) which was used to conduct title/abstract screening of articles. The two authors then independently screened through all titles and abstracts using the blinding mode in the software, which allowed screening without seeing the other reviewer’s decisions. After all articles were screened by both authors, blind mode was turned off and conflicts were collectively resolved. Articles at the abstract and title stage were screened against the PICOS criteria, publication date, conduction date, and language of the publication to decide whether the studies should move on to the full-text screening phase or be excluded from further analyses. We only excluded records where we could confirm that the PICOS were not met: if we were unsure, they were retained.

After finalizing the number of studies to be included into the full-text reading stage, reports were retrieved, and two authors then conducted independent full-text screenings of the retrieved articles. In addition, they independently reported the extracted PICOS data from the included articles and transferred them into a Google sheets spreadsheet. This procedure was not formally blinded, but the authors independently extracted the PICOS data without checking each other’s work, and after independently extracting PICOS data, they collectively resolved any discrepancies.

Using the PICOS framework, studies were either excluded or included into the content coding/data extraction stage, which was conducted collectively and simultaneously among the two aforementioned researchers, whereas the data extracted were reported in the Content Coding Google sheets spreadsheet. Apart from the duplicate resolution in Zotero, each part of screening was conducted manually by the researchers, including coding the data.

Lastly, after going through the references of studies included in the systematic review of correspondence studies by Lippens et al.  (2023) , we included one more eligible study (Capéau et al., 2012) that had previously been excluded in the abstract screening phase.

Data Collection Process

For all studies that passed full-text screening we coded the following: authors, country (where the study was conducted), design, discrimination age (which is the age of applicants in the intervention group), control age (applicants’ age in the control group), job type, discrimination illegal (whether discrimination was illegal in the country where and when the study was conducted), study type, peer-review, and comments (any comments about the study). Two authors collectively, simultaneously, and manually extracted data from each report and reported them in the Content Coding spreadsheet. Data coded included the variables mentioned in the planned protocol section, along with number of participants ( N ), participants in each group ( n ), outcomes (measurement type), and extracted focal tests.

We documented the outcome extractions using RMarkdown (v.2.11.). All of the extracted data is reported in “Materials” on OSF ( https://osf.io/cvn48/ ).

Outcomes Looked for in Data Extraction

Correspondence testing studies have a dependent variable that can take the form of 0 (not invited) or 1 (invited). We planned to extract a two by two frequency table (an example can be found in the preregistration) from each independent comparison. As expected, results were not always reported in this format, but instead as Linear Probability Models (regression coefficients) or as proportions. When possible, we reconstructed this based on reported analysis, tables, or figures (e.g., multiplying a proportion with the number of applications). We closely followed our preregistered protocol in what we extracted, with the exception that we had only defined the appearance of the between-participants table, and not the within-participant table (Yes and No frequencies for Old group and Comparison group), which of course, also includes the “Both invited” and “Neither invited” that are necessary to extract.

For the scenario-based studies we extracted any callback data in the same manner. A more common format for scenario studies are ratings or judgments reported as means and standard deviations. For those, we extracted the mean and standard deviations from the control vs. the older applicant (e.g., in a table). For rankings, rejections vs. invitations were converted into 0 vs. 1 or mean value, depending on the base design. For both study types, when an exact conversion was not possible, we contacted the authors and requested data. Any data we failed to obtain were treated as missing.

Studies can have multiple discrimination outcomes, and we planned to always extract all that matched our criteria and then average them for our main outcome. However, we deviated from our protocol in how we handled multiple outcomes. During the full-text analysis, we noticed that some studies reported differences in types of call-backs (e.g., call-back as an invitation to interview versus call-back as only requesting more information from the applicants). In these instances, we decided to only consider call-backs reported as invitations to interviews or job offers for the quantitative analysis. In case scenario experiments reported multiple eligible outcomes, we prioritized outcomes referring to the likelihood of being hired/selection decisions/job offers, followed by likelihood to be invited to an interview, level of employability and job suitability, resp., when extracting data for synthesis.

Other Variables Sought after in Data Extraction

We coded the country in which the studies were conducted and their status of age discrimination laws and finally, whether the article was peer-reviewed. For the pre-planned subgroup analysis, if available, we started with extracting the reported occupations where the applications were sent in correspondence studies or the types of jobs fictional applicants were hypothetically applying to in scenario experiments, however, this was stopped, as we realized we would not have enough studies for each subgroup to conduct these analyses.

Study Risk of Bias Assessment

We conducted our risk of bias assessment based on the preregistered set of criteria. Because the original assessment tool from Cochrane was ill-suited for these types of studies, we developed an ad-hoc tool for this review.

For the correspondence studies, we assessed risk of bias of individual studies using the following categories: quality of the age manipulation , quality of randomization procedure , quality of callback procedure , quality of the applications , and for the scenario experiments two additional categories were assessed: quality of scenario and quality of the design .

The risk of bias assessment was limited to studies with available outcomes. We systematically differentiated risk of bias indicators for the factors within a study and the potential bias of the entire study. Table 3 shows the bias assessment of individual studies, and a summary of it is available in the Appendix on OSF. The systematic approach to calculate our factor indices can be found in Materials on OSF (“Bias Assessment - Age.csv”; https://osf.io/cvn48/ ).

Within the first category (quality of age manipulation) we assessed whether age was salient on the résumés (i.e., it was stated as a number or date of birth; item 1.1.) and whether the age difference between control and experimental group was large enough according to our preregistered criteria (e.g., 30 – 35 for the comparator; 40 – 49 for the experimental group, etc.; item 1.2). If both responses were negative, we assessed age manipulation as high risk of bias.

For the quality of studies’ randomization procedure we assessed whether age was randomly assigned (item 2.1) and the résumés sent out in randomized order (item 2.2.). To be assessed as low risk of bias, groups either had to be equal in size or the study reported that applicants’ age had been randomized, and applications were randomized, counter-balanced or sent out simultaneously. If item 2.1 received a negative response, the whole category was considered a high risk of bias factor.

The quality of the callback procedure was evaluated by assessing the adequacy of applications in relationship to the job posts applied to (3.1.) and the callback reception (3.2.). Applications were assessed as low bias if the skills on the résumés matched the job requirements and if the callbacks were collected through both phone and email.

We evaluated the quality of the applications by assessing the completeness of the sent applications (4.1.; i.e., résumés included - besides age - name, education and / or work experience) and the application formatting (4.2.; i.e., whether researchers included different formats of résumés to avoid recruiters receiving multiple CVs of identical patterns). It was assumed that in case recruiters received more than one application from the total sum of applications sent per study, they could be influenced by the recognition of similar or equal formatting patterns. The latter item only impacted bias in within-subject designs and the quality application was considered high risk of bias if the second item was high risk of bias.

The fifth category evaluated the quality of the scenario experiment (5.1.), and we assessed whether the presented scenario was realistic enough for the participants. The sixth factor assessed the quality of the design . This factor consisted of two sub-questions in the preregistered protocol, one regarding possible influence of experimenters on the participant, and the other whether the studies were blinded. This was changed post-hoc to a one item (6.1.) question of whether the studies were blinded so that the recruiters (participants) are not aware of the purpose of the design, as the two original sub-questions coincided with the fifth factor.

Due to the assessed relevance of the different factors, we considered a study to automatically be of high risk whenever it scored high in the second factor (quality of the randomization). Otherwise, studies were only assessed as high risk studies if the other factors provided at least two high risk of bias.

Effect Measures

For correspondence testing studies we calculated odds ratios and their variances based on the extracted frequencies. We did this separately for the between-participants and within-participants designs. Effects were calculated for studies included in the quantitative analysis, which all were correspondence studies with within- or between-subject designs. Calculations were done in R (4.1.2), using the metafor package (Viechtbauer, 2010) reported in RMarkdown (v.2.11.).

Synthesis Methods

Whenever studies had used the same type of intervention and comparison, with the same outcome measure type, we synthesized the results using a random-effects meta-analysis using the R package metafor (Viechtbauer, 2010) . Because we only found data for one study using a scenario-based experiment, we could not conduct a meta-analysis for this type of study.

Regardless of the assumption of heterogeneity of the studies, we planned to perform subgroup analyses based on age groups, job type and cultural clusters, provided that we had at least five studies per group. We planned to fit a mixed-effect meta-regression model in metafor (Viechtbauer, 2010) to examine the difference between these subgroups. The model would include all three factors (if they had groups large enough). We planned to use the cut-off of p < .05 for the omnibus test of moderator effects, as well as for the factors. However, because we were not able to obtain at least five studies per group for any of the meta-analyses, we did not conduct the planned subgroup analyses. In case of missing data, it was reported as missing, and we contacted the authors of the original study in order to obtain it.

Reporting Bias Assessment

We planned to assess heterogeneity by estimating tau and I 2 and assess evidence of publication or statistical reporting bias using funnel plots. If we had > 20 studies in a pooled set, we also planned to use PET PEESE (Stanley & Doucouliagos, 2014) and 3-PSM (Iyengar & Greenhouse, 1988) to estimate the effect after adjusting for publication bias. We further planned to examine publication bias and questionable research practices (e.g., p -hacking) through an analysis of the focal tests by using the p -checker ( http://shinyapps.org/apps/p-checker/ ) to calculate an R-index (Schimmack, 2014) and a p -curve. If we had pooled sets of > 20 studies we would further calculate a z -curve (Brunner & Schimmack, 2020) . We planned to conduct a sensitivity analysis if there was a reasonable assumption of studies having a moderate to high risk of bias and if there was disproportionate weighting of studies. To achieve this, we implemented a leave-one-out analysis method. This approach entails conducting the meta-analysis k times, each iteration excluding a different study. Consequently, this enables us to discern which studies (if any) largely influence the average effect size estimate and whether such bias skews the outcomes of the meta-analysis.

We pre-registered that we would extract focal tests, which are the reported significance tests typically used for main inference in a study, in order to conduct a p -curve analysis, R-index and z -curve (if enough studies). However, we did not pre-register which selection rule we would use (i.e., how to decide which one is the focal test). Because most studies did not report focal tests for our age groups, but only an overall effect for age, we extracted either the overall omnibus (test of age discrimination) test or a test for an averagely (e.g., 50 year old) old candidate when there were multiple tests reported. Hence, these focal tests are not directly corresponding to any of our meta-analyses, but simply a way to assess the risk of publication bias in the literature.

We ended up relying only on funnel plots, tau and I 2 , R-index and p -curve, as we did not have enough studies to use PET-PEESE and 3-PSM or z -curve.

Study Selection

Our original searches yielded a total of n = 2,599 reports, of which n = 1,728 can be attributed to sources from our primary databases (PsycInfo, Web of Science, ERIC, and BASE) and n = 871 can be attributed to secondary sources (Google Scholar and manually extracted references from reviews). After excluding duplicates and eliminating all books and certain book chapters based on title/abstract, in Zotero (Zotero v. 5.0.96.2, Roy Rosenzweig Center for History and New Media, 2021), we ended up transferring n = 1,733 studies into the screening process in Rayyan, (Ouzzani et al., 2016) , with n = 1,253 being primary and n = 480 being secondary search results. Rayyan further identified n = 104 duplicates, leaving n = 1629 unique articles, however, we decided to screen all of the imported articles without immediately excluding duplicates suggested by Rayyan, to prevent potential errors made by the software. After screening our articles independently and discussing our decisions to accept or reject a paper for further consideration, we excluded n = 1,580 studies in the screening process and ended up with n = 51 reports to be considered in the full text readings.

After conducting the screening, we found one more relevant study in the references of another review of correspondence studies of hiring discrimination and also included it in the full-text reading phase. However, we only managed to retrieve 49 reports that went further into the full text reading phase. Two reports we were unable to find online, and we received no response from the authors. These 49 reports consisted of 53 studies which were checked against the PICOS criteria. The reference found in review was further included in the full-text reading.

The updated search yielded 3,995 reports from five databases (PsycInfo, Web of Science, ERIC, BASE, and Business Source Ultimate). After the deduplication process, 2,406 abstracts remained for screening. We considered 16 reports for full-text assessment, however, no new studies were included in the review. In the data extraction stage we found that two articles ( Neumark et al., 2016 and Neumark et al., 2019 ) were based on one dataset, so we only included Neumark et al.  (2016) in the analysis stage. Based on the criteria, we included 13 studies into the analysis stage, which were reported in 11 articles. However, final meta-analyses were conducted using eight studies from which we were able to obtain data needed to calculate effect sizes. Studies excluded in the full-text reading stage are available in Table 3 and Table 4 in the Appendix, along with the exclusion reasons ( https://osf.io/cvn48/ ).

Study Characteristics

Studies from 11 articles were eligible to be included into the analyses based on eligibility criteria. However, it proved unfeasible to extract data from certain studies for our data synthesis, thus studies included in the synthesis were the following seven studies: Ahmed et al.  (2012) , Carlsson and Eriksson (2019) , Farber (2019) , Jansons and Zukovs (2012) , Capéau et al.  (2012) and Neumark et al.  (2016, 2019) . Along with that we were able to extract data for one scenario based study (Oesch, 2020) . Below, we provide a narrative summary of the 13 initially included studies. Ahmed et al.  (2012) . In this particular study, the authors decided to conduct a within-subjects correspondence study including 466 job offers in Sweden. The applications were sent to job posts looking for either restaurant workers or sales assistants, with call-back rates being the dichotomous outcome variable. Their intervention group encompassed fictitious applicants aged 46 whereas the comparator was a fictitious applicant aged 31.

Capéau et al.  (2012) conducted a mixed design correspondence study to examine hiring discrimination in Belgium based on age, sex, national origin or a certain physical state (e.g., pregnancy), by measuring call-back rates for fictitious applicants. They sent out 1708 fake resumes, and included ages 35 as comparator, and 23, 47 or 53 as the discriminated counterpart.

Carlsson and Eriksson (2019) conducted a between-subjects correspondence study to test age discrimination in hiring in Sweden, by assessing call-back rates for the fictitious applicants. The authors included age as a continuous variable in the interval of 35 to 70 years, and gender which they signaled via applicants’ names. They assigned names and ages to fictitious applications randomly and sent out triplets of resumes to over 2000 employers, generating a sample of 6066 applications, which were sent to job openings in seven occupations: administrative assistants, truck drivers, chefs, food serving and waitresses, retail salespersons and cashiers, sales representatives, and cleaners.

Challe et al.  (2016) conducted four studies, out of which the first three, within-subjects correspondence studies conducted in France, were included in this review. The first study included fictitious applicants aged 29 as comparators, with the intervention group being fictitious 56-year-old applicants; however, these applicants were also divided into two groups, based on their expected retirement age. Triplets of resumes (29-year-old, 56-year-old closer to retirement, and 56-year-old further from retirement) were sent out to job posts for two occupations: call center agent ( n = 300) and sales assistant (n = 301). The second study aimed to investigate age discrimination in hiring in relation to technological skill obsolescence, and encompassed sending three fictitious applications, with ages of applicants being 32, 42, and 52, to the following occupations: IT project managers and IT developers ( n = 302), and management accountants and accountants ( n = 308). In the third study, the authors intended to examine age discrimination in the context of gender stereotypes around certain occupations. They included fictitious applicants in their 50s (51 and 51) and applicants in their 30s (35 and 36) who were either male or female, and applied to personal service occupations (specifically: home help, cleaning persons, and caretakers). The outcome variable for age discrimination was call-back rates.

Farber (2019) conducted a between-subjects correspondence study in the United States to study age discrimination in hiring. Their intervention consisted of manipulating the age of fictitious applicants (chosen from given age groups: 22–23, 27–28, 33–34, 42–43, 51–52, or 60–61 year-olds) and length of unemployment spell (in weeks: 4, 12, 24, 52). They applied to either low-skill jobs (e.g. receptionist, office assistant) or high-skill jobs (e.g., executive assistant, office manager) ( n = 2122) with the outcome variable being call-back rates.

Jansons and Zukovs (2012) conducted a within-subjects correspondence study in Latvia where they created fictitious resumes, with the intervention group being 55-year-olds and the comparator 35-year-olds. They applied to salesperson jobs ( n = 529) and measured age discrimination by difference in call-back rates between the younger and the older candidate.

Montizaan and Fouarge (2016) conducted a within-subjects scenario (vignette) experiment in the Netherlands to examine age discrimination in hiring, relating to applicants’ and employers’ characteristics. The fictitious applicants were either 35, 45, 55, or 60 years old. Employers ( n = 1100) were presented with two fictitious applications and were asked to choose which of the two presented applicants they would hire. The outcome variable for age discrimination was likelihood to be hired.

Neumark et al.  (2016) conducted a between-subjects correspondence study in the United States. The fictitious applications differed in age (29-31, 64-66) and skill level (high or low) for each occupation they were sent to (sales, security and janitor). The authors sent out 7161 applications and their outcome variable was the call-backs.

Neumark et al.  (2019) conducted a between-subject correspondence study in the United States. Fictitious applications differed in age (29-31, 64-66) and gender. The authors sent out 14,428 applications to 3,607 jobs and their outcome variable was the call-backs.

Oesch (2020) conducted a between-subject scenario experiment in Switzerland. Participants (recruiters, n = 501) had to indicate the likelihood of inviting fictitious candidates to an interview (on a scale from 0 to 10) based on the given resume, and propose an adequate wage for each candidate. The ages of fictitious applicants were 35, 40, 45, 50, or 55 years old. The occupations fictitious candidates were applying to were expert accountant, human resources assistant and building caretaker.

Richardson et al.  (2013) conducted a between-subject scenario experiment in the United States. They recruited 154 participants (students and organization based, n = 102 and 54, resp.). Participants had to assess work-related competencies of fictitious applicants and indicate the likelihood of being hired on a 9-point Likert scale. The age of applicants was taken from a range of 33 to 66 years.

Risk of Bias in Individual Studies

In general, the lowest risk of bias was present in the quality of randomizing applicants’ age to the applications. There is some concern regarding age manipulation, i.e., how age was presented or made salient in the applications. Certain studies opted for making age implicit, through writing school graduation dates (e.g., Neumark et al.) which might impede realizing how old the applicant is intended to be. Application quality in the sense of making the CVs realistic and detailed presented a high potential for bias, however, this might be due to underreporting of details necessary to draw conclusions on the quality. Altogether, the majority of studies present a moderate to high risk of bias, and the results from these studies should be interpreted with caution.

Results of Individual Studies

We present results of syntheses further in text. Meta-analyses which included more than one study are accompanied with forest plots in the text. Other figures and code used to extract data, focal tests, and conduct presented analyses are available in the RMarkdown files in the appended materials. All meta-analyses were conducted using the random-effects model with Restricted Maximum Likelihood (REML) tau estimator, as this is the default in the metafor package. However, for a robustness check, we conducted the meta-analysis with the commonly used Dersimonian and Laird (1986) tau estimator as well, available in the analysis files on OSF ( https://osf.io/zqxga/ ). In general, we found no discrepancies, apart from the meta-analysis of studies on age above sixty-five, where the REML method estimated a higher tau 2 value. All summary statistics were calculated using default methods in the metafor package.

Results of Syntheses

As mentioned previously, we intended to conduct four different types of meta-analyses, containing one of four combinations from either correspondence or vignette studies and either in-between or within-subjects designs. However, due to the availability of our empirical material, we had to omit meta-analyses on scenario-based experiments. The results of the remaining meta-analyses are described below. We present odds ratios with confidence (CI) and prediction intervals (PI), and z and p values.

Age Category - Forty

Our first meta-analysis encompassed all within-subject correspondence studies measuring the hiring disparities between our comparator group and 40 to 49 year old applicants. Via a random-effects meta-analysis ( k =2), the two included studies revealed an effect of applicant age on the hiring decisions held by the participating recruiters against older applicants, with the odds ratio being 0.38 (95 % CI [ 0.25, 0.59 ], 95% PI [ 0.21, 0.70 ], z = -4.36, p < .001). On average, the odds of older applicants receiving a callback were 0.4, and while the upper bound of the confidence interval still suggests lower odds of receiving a callback, the width of the interval implies uncertainty regarding the actual point estimate. Total variance was low (mostly due to a low number of included studies; k =2) tau 2 = 0.05 ( SD of the true effects across studies was tau = 0.22), and the heterogeneity was I 2 = 47.11%.

Our second meta-analysis was set to include all between-subject correspondence studies measuring the hiring disparities between our comparator group and 40 to 49 year old applicants. Via a random-effects meta-analysis, the three studies included revealed an odds ratio of 0.89 (95 % CI [ 0.82, 0.97 ], 95% PI [ 0.82, 0.97 ], z = -2.6, p = .009), meaning they had 0.9 odds of receiving a callback, which, taken along with a narrow confidence interval that borders with no difference, shows a rather low discrimination effect for this age group. Tau 2 and I 2 were zero, however heterogeneity of studies is difficult to assess with such a small number of included studies ( k =3), so this does not imply homogeneity.

Age Category - Fifty

Our third meta-analysis was set to include all within-subject correspondence studies measuring the hiring disparities between our comparator group and 50 to 59 year old applicants. Via a random-effects meta-analysis, the studies on the matter of age discrimination revealed an effect of applicant age on the hiring decisions. The overall effect was OR = 0.41 (95 % CI [ 0.29, 0.58 ], 95% PI [ 0.29, 0.58 ], z = -5.11, p < .001), meaning older applicants had less than half the odds of receiving a callback, and similarly to the analysis of within-subject design for the 40-year old age group, the confidence interval lowers our certainty in the estimate. Once again, tau 2 and I 2 values (0; 0%, resp.) for such a small sample of studies ( k =3) do not provide meaningful assessment of heterogeneity.

Our fourth meta-analysis was set to include all between-subject correspondence studies measuring the hiring disparities between our comparator group and 50 to 59 year old applicants. Via a random-effects meta-analysis we found that on average older applicants had 0.75 odds of receiving a callback, which seems to be a fairly precise estimate based on the narrow confidence interval ( OR = 0.75, 95 % CI [ 0.69, 0.80 ], 95% PI [ 0.69, 0.80 ], z = -7.88, p < .001). As in the former analysis, heterogeneity was not found ( tau 2 = 0; I 2 = 0%).

Age Category - Sixty

Our fifth meta-analysis was set to include all between-subject correspondence studies measuring the hiring disparities between our comparator group and 60 to 65 year old applicants. Via a random-effects meta-analysis, we found on average that older applicants have 0.6 odds of receiving a callback. Confidence interval is relatively narrow, implying a stronger certainty in the precision of the point estimate ( OR = 0.62, 95 % CI [ 0.53, 0.72 ], 95% PI [ 0.46, 0.82 ], z = -5.99, p < .001). Total variance was low ( tau 2 = 0.01, with the SD of the true effects across studies, tau = 0.12), with 75% of it being heterogeneity of true effects ( I 2 = 75.26).

Age Category - Above Sixty-Five

The final meta-analysis included all between-subject correspondence studies measuring the hiring disparities between our comparator group and applicants over 65 years of age. Via a random-effects meta-analysis, we found that on average, older applicants have half the odds of receiving a callback. The confidence interval is extremely wide, so it is uncertain where the true effect size of discrimination for this age group lies, however, the values in the upper bound already suggest the existence of discrimination. ( OR = 0.50, 95 % CI [ 0.29, 0.85 ], 95% PI [ 0.18, 1.34 ], z = -2.55, p = 0.012). Total variance seems relatively low ( tau 2 = 0.18 and the SD of the true effects across studies, tau = 0.43) but the width difference between the confidence and prediction interval shows there is variability in the study effects. Effect heterogeneity accounted for most of that variance ( I 2 = 96.74) which might explain the width of the confidence interval.

Finally, for the one scenario experiment study we obtained data from (Oesch, 2020) we calculated a Hedge’s g effect size of g = 0.02, 95 % CI [ -0.05, 0.09 ] for 40-year-olds, and g = 0.15, 95 % CI [ 0.08, 0.22 ] for 50-year-olds.

Sensitivity Analysis

We conducted a leave-one-out sensitivity analysis and observed that excluding the Carlsson & Eriksson (2019) study from the meta-analysis led to a substantial decrease in the average effect size of age discrimination for the above 65 age group (see Appendix Table 2). This finding suggests that the Carlsson & Eriksson (2019) study increases the overall average effect size in the original meta-analysis for this age group. On the other hand, excluding either Neumark study (2016, 2019) resulted in an increase in the average effect size, indicating that their inclusion in the meta-analysis leads to a weaker effect of age discrimination for the above 65 age group. However, the impact on effect size is more pronounced when excluding the Carlsson and Eriksson study. This can be explained by the fact that their study design differed from the other studies in terms of age range included. Despite the decreased effect size when the study was removed, it still implies the existence of age discrimination.

Sensitivity analysis of other age groups did not show any substantial deviations from the overall effect size estimate which implies robust effect size estimates.

Reporting Biases

Here we present the p -curve and the p -checker results. We found an R-index of 0.91 for the correspondence studies, which implies high power. For the scenario experiments we found an R-index of 1 with 100% success rate. We included studies that we could extract focal tests from and that were reported as significant in the papers. Funnel plots have been conducted to visually assess potential asymmetry in published effects and are available in the RMarkdown files ( https://osf.io/zqxga/ ), however, as we had a small number of studies per analysis, it is hard to assess asymmetry or potential publication bias with few data points. The p -curve shows a strong right skew with more studies having lower p -values, which is consistent with highly powered studies and little publication bias. Taken together, there is little indication of publication bias in this literature.

Certainty of Evidence

We base our certainty of evidence on the GRADE guidelines (Higgins et al., 2021) . First domain considers the study risk of bias, and our assessment shows that the majority of studies suggest a moderate risk of bias. When it comes to imprecision, we generally find narrow confidence intervals and prediction intervals, and see little difference in inference depending on the true effect being in the upper vs. lower bound ( Figure 2 ; Figure 3 ), meaning our average true effect and the deviation of found effects show good precision of evidence. Furthermore, we conducted the leave-one-out sensitivity analyses (available in supplement materials) of the effect size aggregates for between-subject studies examining age discrimination. These analyses show robust findings generally, with the only exception being the removal of Carlsson and Eriksson (2019) , which somewhat increases the effect size and tau. Presumably, the Carlsson and Eriksson (2019) study introduces higher deviation in effects because of the continuous age variable. Furthermore, high heterogeneity of true effects in certain meta-analyses and inability to correctly estimate variance due to low number of studies imply a lower certainty in evidence due to inconsistency. The low diversity of countries where the studies were conducted (i.e., majority, USA and Sweden) also suggests a problem of generalizability. The funnel plots available online (Results. Rmd at https://osf.io/zqxga/ ) and the p -curve ( Figure 4 ) show low risk of publication bias, and as we tried to retrieve gray literature as well, we are confident that reporting bias doesn’t impose a threat to evidence certainty. Overall, we conclude our certainty of evidence to be moderate based on possible problems with imprecision and study risk of bias assessment.

General Report of Results

The results of the present meta-analysis suggest that there is a sizable effect of age discrimination against older applicants in the selection process. The results further suggest that this effect is most likely present already when the applicant’s age is between 40 and 49, and rises gradually with increasing age. Discrimination against older applicants occurs regardless of study design, but the discrimination effects in studies with within-participant designs are noticeably larger, which could be due to the study design, or some other unidentified difference between the samples (e.g., nationality; timing of the studies). With regards to the effect sizes, we argue that the odds of younger applicants being considered over older applicants - even though it varies depending on the age category - is practically large in terms of real-life discrimination. Specifically, we found that older applicants receive 11 to 50 per cent lower odds of being considered over the younger applicant. Lack of available data from the scenario experiments prevents any generalized conclusions, and the discussion will thus only focus on correspondence testing.

The findings from this review indicate considerable levels of ageism in the hiring process. In the context of the ongoing political efforts to extend working lives past the current retirement age, this discrimination is not only likely to have negative economic and health-related consequences for the individual worker, but it might prove largely unfavorable for society as a whole as many qualified candidates are being hindered from (re-)entering the labor market. Comparable meta-analyses on the topic of hiring discrimination against other minority groups usually yield similar results to the findings of our review. Zschirnt and Ruedin (2016) considered the log odds ratios of 34 correspondence studies about ethnic discrimination in OECD countries between 1990 and 2015, finding that on average, ethnic minority applicants receive lower callback rates than majority candidates, with their odds ratios ranging from OR = 0.27 [ 0.17, 0.43 ] to OR = 0.94 [ 0.73, 1.12 ]. The only outlier in this analysis can be seen in Bendick et al.  (1991) , who generated an odds ratio of 2.45 [1.86, 3.22]. However, this is explained by the fact that in their study, Latino applicants had received more qualifications in their applications. Similar results are provided by Flage (2019) , who examined the magnitude of hiring discrimination between hetero- and homosexual applicants in OECD countries. The author finds that the odds ratio in this case is at 0.64; suggesting that the odds for homosexual applicants to receive callbacks are 36 per cent lower than for heterosexual applicants. In this regard, our meta-analyses mirror these findings, with our odds ratios ranging from 0.89 [0.82, 0.97] to 0.50 [ 0.29, 0.85 ], implying that there is an observable difference of discrimination between younger and older applicants.

Lippens et al.  (2023) found a large average effect of hiring discrimination against older applicants ( RR = 0.5804, CI 95% = [ 0.4993; 0.6748 ], p = .018). However, we cannot directly compare their results to ours as we differed in selection criteria, and the combining of different age groups. Furthermore, we consistently compared our older applicants against a comparator of 29 to 35 years old, while their comparator ages varied.

Limitation of Evidence

Following the aforementioned risk of bias assessment, there are certain issues to be considered when evaluating the overall explanatory power of our analysis. In some studies, applications were sent simultaneously (e.g., Ahmed et al., 2012; Neumark et al., 2016 ). On the one hand, it could be argued that time-randomization creates a more realistic replication of the in vivo hiring process and therefore decreases the chances of oversampling. On the other hand, one might also argue that applications, no matter the time of receipt, must necessarily be processed by the recruiter at different points in time and therefore become randomized eventually in practice either way. Either way, as this only concerns two studies, we assume that it does not have any significant influence on our results. The same argumentation applies regarding the age randomization and callback platforms. Although we consider age randomization more important, it was only omitted, i.e., not further described in one study (Challe et al., 2016, p. 3) and thus interpreted as a minor issue, especially considering that group sizes were equal in their experiment. However, we believe that infractions on the quality of the age manipulation and applications play a more central role in our overall assessment and we note that the assumption of the respective risk of bias items had to be rejected across several studies within these two factors.

Another item that was highly critical in regards to risk of bias was age salience. In five studies, age was either not sufficiently salient in the applications or further information was lacking in the paper. Indirect announcements of age (e.g., through high school graduation years, see Neumark et al., 2019 ) might introduce biases if recruiters infer false assumptions about an applicant’s age, for example by disregarding the age at which they picked up college education. This might explain the lower level of age discrimination in some studies.

We also documented the existence of anti-discrimination laws in countries where the studies were conducted, as this might influence the age discrimination effects (see Neumark et al., 2019 ). Although all countries have age discrimination laws which prohibit hiring discrimination based on age, nuances exist (e.g., in Switzerland) and it would be optimal to distinguish which sectors have a higher degree of freedom when making hiring decisions to properly appraise age discrimination effects. Because of the narrow body of evidence, it would be highly recommendable to extend our findings when more data is available. This also encompasses expanding the language barriers this review faces to generate resources that we could not extract. Moreover, the scope of this review is restricted to age discrimination against older people only, and previous findings (Bratt et al., 2018; Finkelstein et al., 2013) suggest that age discrimination is frequently targeted against very young applicants too, since recruiters often appear to be biased against this group, especially between generations. Furthermore, the subgroup analyses were not conducted as planned because of a low number of studies. It would be optimal to extend future reviews in these regards to contribute to a more differentiated understanding of how age discrimination can be influenced by certain contextual factors; particularly regarding the cultural context and the possible moderating role of job tasks and necessary experience on the direction of age discrimination effects. Furthermore, it would be beneficial to conduct a study on the EU level, to correspond to the studies conducted in the US, which were the majority of our sample. Moreover, including countries that don’t belong to the western society would provide even better insight into the state of age discrimination in the workforce and make results generalizable. Finally, we were limited by data availability to provide evidence of age discrimination from scenario-based studies. In general, when comparing the two study types, we would argue that correspondence studies provide more knowledge on discrimination prevalence, and although they are often more practically extensive to conduct, they provide a much more realistic insight into discrimination prevalence and a lower risk-of-bias evidence output. On the other hand, the scenario experiments could prove more useful in revealing information about the recruiters who make the decisions.

Limitations of the Review Process

We also acknowledge certain limitations of our review. In the earlier stages of this review, we exhaustingly screened the available databases and resources for studies; however, we decided to only integrate the first 200 search results for each individual search of Google Scholar, and might therefore have missed a minor number of studies (e.g., gray literature) which could have extended our relevant body of evidence. Moreover, through thorough prior research, we sought to capture all of the essential key terms in our search strategy, nonetheless, we might have overseen studies that could have been found outside of our search terms. What is more, studies were restricted to articles written in English. Especially since we intended to screen for the moderating effect of culture, the inclusion of literature written in other languages would be optimal. In general, we were also limited by the access to available data, especially if studies were less recent. This made it impossible to include some studies that would otherwise have been included after the full text screening phase. With regards to our meta-analyses, it is clear that parts of our individual meta-analyses are underpowered, with a small sample of studies. This partially restricts the validity of our findings. Indeed, the corresponding odds ratios were unexpectedly high in comparison with our findings from the other meta-analyses and these outcomes might thus be biased in terms of their statistical power, especially with certain confidence intervals being wide and bordering no effect (Carlsson & Eriksson, 2019) . Finally, because of time constraints, we conducted the risk of bias assessment for studies individually, meaning that each study was only assessed by one rater. Although the risk of bias was still validated by either the second or third author of this review, we acknowledge that this could have introduced an incremental source of error.

Implications

The evidence from this review suggests that there is an effect of age discrimination in recruitment processes, which tends to increase with age. Given that our results are based on correspondence testing, which looks at discrimination at the initial stage of hiring, results imply a greater disadvantage for older applicants even before having a chance to present themselves. Although the effect might not be substantial, it is crucial for human resources professionals and employers to consider how this bias will affect the labor market. As the retirement age continues to increase, it becomes essential to capitalize on the skills and experience of older employees, for it might not only promote inclusivity at the workplace but increase overall productivity as well.

Furthermore, based on our assessment of study risk of bias, improvements can be made in addressing and reporting age salience and randomization in job applications within studies. Proper age randomization, as emphasized in Heckman’s (1998) critique, is essential in new correspondence experiments to prevent confounding results and undermining a study’s internal validity. Although conducting a meta-analysis of various correspondence studies may mitigate issues raised by Heckman by combining studies with diverse characteristics and samples, ensuring the high quality of original studies is crucial for providing a more accurate estimate of age discrimination effects. Consequently, future correspondence studies should focus on appropriately randomizing age and ensuring age salience is not systematically biased by other applicant characteristics. Moreover, we did not find many scenario experiments in this area, particularly those with well-executed application randomizations and the applicant characteristics. While correspondence tests offer greater external validity, conducting laboratory experiments involving recruiters, which allow for the examination of additional aspects of recruiter reasoning (e.g., motivation, attitudes), could yield valuable insights into the factors contributing to age discrimination in recruitment.

In conclusion, this review aimed to examine the evidence for age discrimination in hiring from the two most commonly used experimental designs, correspondence studies and scenario (vignette) experiments. Based on our findings, we suggest that correspondence studies provide better insight into discrimination prevalence, while scenario experiments might be less ecologically valid, but could be better suited to shed light on other factors relating to discrimination practices, such as recruiter characteristics.

Conceptualization – L.B., M.H., R.C.

Data curation – L.B., R.C.

Formal analysis –L.B., R.C.

Investigation – L.B., M.H.

Methodology – L.B., M.H., R.C.

Project administration – L.B., M.H.

Software – L.B., R.C.

Supervision – R.C.

Validation – R.C., S.S.

Visualization – L.B., M.H.

Writing – original draft – L.B., M.H.

Writing – review & editing – L.B., S.S., R.C.

We have no conflict of interest to declare.

We thank Ida Henriksson for invaluable help with improving our search strategy.

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Original research article, the impact of age stereotypes and age norms on employees’ retirement choices: a neglected aspect of research on extended working lives.

• 1 School of Social Policy, Sociology and Social Research, University of Kent, Canterbury, United Kingdom
• 2 Department of Sociology, VU Amsterdam, Amsterdam, Netherlands

This article examines how older workers employ internalized age norms and perceptions when thinking about extending their working lives or retirement timing. It draws on semi-structured interviews with employees ( n = 104) and line managers, human resource managers and occupational health specialists ( n = 52) from four organisations in the United Kingdom. Previous research has demonstrated discrimination against older workers but this is a limiting view of the impact that ageism may have in the work setting. Individuals are likely to internalize age norms as older people have lived in social contexts in which negative images of what it means to be “old” are prevalent. These age perceptions are frequently normalized (taken for granted) in organisations and condition how people are managed and crucially how they manage themselves . How older workers and managers think and talk about age is another dynamic feature of decision making about retirement with implications for extending working lives. Amongst our respondents it was widely assumed that older age would come with worse health—what is more generally called the decline narrative - which served both as a motivation for individuals to leave employment to maximize enjoyment of their remaining years in good health as well as a motivation for some other individuals to stay employed in order to prevent health problems that might occur from an inactive retirement. Age norms also told some employees they were now “too old” for their job, to change job, for training and/or promotion and that they should leave that “to the younger ones”—what we call a sense of intergenerational disentitlement. The implications of these processes for the extending working lives agenda are discussed.

Introduction

In this article we address how age relations in organisations impact on the willingness of older workers to extend their working lives. Internationally, an important policy phrase has been “live longer, work longer” ( OECD, 2006 ; Street and Ní Léime, 2020 ). Policymakers are trying to stimulate older people to extend their working lives, for example in the context of the United Kingdom (UK) by abolishing mandatory retirement ages, increasing the State Pension Age, and by introducing age discrimination legislation (see e.g., ILC-UK, 2017 , and Lain, 2016 , for an overview). These policies are introduced in response to predicted increased population aging and worries about increasing dependency ratios and the affordability of welfare states. There are various problems with this policy narrative as well as its proposed solutions, including that it appears to be a “one-size-fits-all” approach that ignores the different realities of various groups of older workers (for more detail see e.g., Street and Ní Léime, 2020 ). Another issue is that such policy changes occur in social contexts of considerable ageism.

Ageism is commonplace and embedded at all levels: in public policy narratives when talking about older workers, in popular narratives about baby boomers stealing prosperity from younger generations; in organisational regimes which favor the ideal fit and healthy worker (aka not “the old”) and in workplace banter about older workers being put out to pasture. Although there is a long history of research that shows that negative images of older workers are related to discrimination against these employees (see e.g., Chiu et al., 2001 ; Macnicol, 2006 ; Hurd Clarke and Korotchenko 2016 ; Earl et al., 2018 ), there is less attention to how older workers may themselves make labor market decisions based on internalization of these narratives. Recent reviews have asked for more qualitative research on ageism ( Harris et al., 2018 ) and we seek to begin to address this gap in the literature.

Theoretical Considerations

We are seeking to extend our understanding of various components that are part of ageism. Ageism involves active discrimination, but also stereotyping and age norms. The latter two may operate against people as well as being internalized by those subject to them. It is typical in organisational studies to research ageism as perpetrated by managers against employees (for example, Chui et al., 2001 ; Henkens, 2005 ; for an overview of the workplace literature see Naegele et al., 2018 ). Whilst there is evidence for discriminatory behavior by managers against older (and younger) employees this is a limiting view of the impact that ageism may have in the work setting.

Conceptually we see “age” “as a socially and culturally constructed category” ( Krekula et al., 2018 , p.37; see also Calasanti and Slevin, 2001 ; Calasanti, 2020 ). Regarding older workers, we need to understand how age is constructed and performed in the workplace. Age stereotypes identify what is routinely attributed to particular age groups. Prevalent stereotypes about older workers include that they are “(a) less motivated, (b) generally less willing to participate in training and career development, (c) more resistant and less willing to change, (d) less trusting, (e) less healthy, and (f) more vulnerable to work-family imbalance” ( Ng and Feldman, 2012 , p. 821; see also Posthuma and Campion, 2009 ). In their meta-analysis, Ng and Feldman (2012) only found some evidence for (b), though this does not say why they would be less willing to participate in training and career development. Hurd Clarke and Korotchenko (2016) summarize existing literature as follows: “the research suggests that ageism is often deeply internalized as individuals accept stereotypes that depict later life as a time of poor health, cognitive impairment, dependence, lack of productivity and social disengagement” (p. 1759). Part of this is an internalized health-decline-narrative, which has been referred to as “health pessimism” (see e.g., Brown and Vickerstaff, 2011 ). It has been claimed that because workers themselves believe the stereotypes, many cases of age discrimination go unnoticed ( Laczko and Phillipson, 1990 ). Recent research suggests that stereotypes about motivation, mental and physical health remain very persistent ( Kleissner and Jahn, 2020 ) and age and health perceptions might also have an impact on older workers’ motivations to continue or leave work ( Van der Horst, 2019 ).

Next to age stereotypes, age norms (at which age should you do what?) are also important to take into account. In an employment context, ageist ideas will play out in interpersonal interactions but also institutionally through policies and routine practices ( Martin et al., 2014 ; see also Krekula (2009) on age coding practices). Age norms are frequently normalized (taken for granted) in organisations and condition how people are managed and how they manage themselves. Age norms are related to how people manage themselves because they will inform people’s understanding of their own age and its implications in the work context. Ageism exists through social relations rather than primarily being a characteristic of individual behavior (cf. Van der Horst and Vickerstaff, 2021 :4), which is exemplified by the fact that: “older workers” are only “old” in relation to other presumably “younger workers” and vice versa. The rise of narratives about intergenerational fairness (see Willetts, 2010 ; Wildman et al., 2021 ) may feed into concerns about older workers job blocking younger generations. This may in turn have increased the impact of age norms on labor market considerations in recent years.

Few studies have specifically researched the impact of internalized ageism on older workers but some studies do refer to cases of self-exclusion or what Romaioli and Contarello (2019) in a different context have referred to as a self-sabotage narrative: being “too old for”. Minichiello et al. (2000) show with an Australian sample that “older people may adjust their lives so as to accommodate problems they encounter” and that “older people may simply “drop things out of their life” once access becomes difficult rather than lobby for improved resources” (p. 263), Gaillard and Desmette (2010) showed using a Belgian sample that positive stereotypes of older workers were related to lower early retirement intentions and a higher motivation to learn and develop, and in 2008 that identifying as an “older worker” was related to higher early retirement intentions ( Desmette and Gaillard, 2008 ). Brown and Vickerstaff (2011) suggested that health pessimism may be a factor in retirement planning.

The main aim of this article is a qualitative exploration of the role of internalized age stereotypes and norms in employment decisions of older employees in the United Kingdom. As much is already known about which stereotypes exist, we focus more on how older workers and their managers deploy these stereotypes and age norms when talking about their working lives; we are interested in the social relations of age; how ageism is performed and reproduced through interactions and how this affects thinking about retirement.

Data and Method

This article is based on individual semi-structured face-to-face employee interviews ( n = 104), as well as interviews with line managers, human resource and occupational health managers ( n = 52) divided over four organizations. The organizations were located in different sectors, with varying workforces, and in different regions in the United Kingdom (the South East, North West, West, Wales and the Home Counties; for further details see Table 1 ). Interviewees were selected out of employees aged 50 or over who volunteered to participate using a maximum variation sampling strategy ( Patton, 1990 ; Flyvbjerg, 2016 ). Managers were selected because they had responsibilities for workforces which included some older workers. In this article we concentrate primarily on the interviews with employees. The data were collected between 2014 and 2016 and interviews were held at the work location during working hours, but in a setting that ensured confidentiality. The average length of interview was between 45 and 50 min, they were digitally recorded, and transcribed verbatim.

TABLE 1 . Number of participants by case study organization and employee details.

Employees were interviewed about their retirement plans, experiences of age discrimination at work and their views on policy changes around extending working lives. Questions were open ended encouraging respondents to articulate issues salient to them. Interviews with managers centered on how their organisations managed older workers. The focus in this paper is an analysis of how people talk, the language used, about age and ageing. Though the focus of the interviews was not on internalized age norms and how this affected work decisions, these topics emerged in many interviews when people gave their views on changes in policies, experiences at work, and/or their plans for the future. It may be that the data contains many examples of internalized age-stereotypes because it was not directly questioned. Spedale (2018) notes in her study how the identification as “an older worker was predominantly unconscious and informed by age-related hidden assumptions and taken-for-granted beliefs” (p. 41). By identifying age norms and stereotypes when talking about different topics, the data may contain a more “natural” discussion of age at work. The qualitative data were analyzed thematically. An initial deductive coding frame was developed based on the larger project’s research aims and empirical and theoretical interests. In addition, an inductive open coding approach was taken so that themes and issues could arise from the data. After identifying internalized ageism as an emerging theme, the interviews were thematically recoded in NVivo 12 using the framework for analysis in Table 2 and read and reread for comments on the relationship between ageism and employment decisions (on framework analysis see Ritchie et al., 2003 ).

TABLE 2 . Framework for qualitative analysis.

Our approach here is part of the discursive turn in gerontology ( Previtali et al., 2020 ). Through our focus on talk we hope to both expose the ageist narratives in society and in organizations but also to explore how people actively construct their own understandings of reality. Our purpose is not to attempt to replace other explanations of the dynamics of retirement decisions but rather to add another layer to our understanding. The direct quotations from interviews below are selected as indicative for the identified category. Employee interviewees are identified by gender and age; managerial employees by their role.

In all of the organizations the overwhelming majority of employees said there was little direct age discrimination for example in access to training. There were some individuals who felt they had been passed over for opportunities or targeted for redundancy because of their age, but in general employees agreed with managers that their organizations were not overtly ageist.

I’ve not come across any discrimination, other than the banter around your desk kind of thing, you know. (Male, 57).

I get as much abuse as I think we dish out, the old fart in the office, but no I don’t see any different treatment. (Male, age undisclosed).

As these two quotes demonstrate people think of ageism as about direct discrimination and do not see that age stereotypes and norms are embedded in everyday interactions. Both managers and employees regularly employed ageist stereotypes when talking about older workers or about themselves, what we might refer to as casual or normalized ageism. These reflected the standard negative stereotypes about memory issues, physical capability, productivity, attitudes toward training and development and IT:

I’m just cast as a scatty old lady, you know. (Female, 52).

Although people live longer they don’t necessarily—it’s hard to predict at what point they’re no longer going to be really capable of doing their job, to be blunt. (Male, 56).

I suppose might be that some of the older people might be more kind of dinosaurs in terms of technology and slower to pick up the latest, you know, electronic tools and things. (Male, age undisclosed).

There were also more positive stereotypes about dependability, expertise, knowledge, warmth:

I think as you get older you get more of a sense of responsibility. You don’t like letting people down. You tend to work your way round problems rather than think, oh no, I’m not doing this, I’ll go somewhere else. (Female, 61).

You know, when there’s a problem they come running to us first, we’ll get it sorted. Yeah, I suppose I think they do look at it like that, yeah. (Male, 54).

Managers made explicit comparisons between older and younger workers:

I think certain individuals, as they get older and more established in their role, choose not to pick up on every opportunity that’s put before them, but the excitement comes for us as managers for the younger guys who are, “Yeah, what can I do? Give us more, can I do that, can I do this?” and that keeps that process going. […] if the older guys don’t want to pick up on it, it’s not because it’s not available and we would hold it back for them, it’s definitely available but sometimes their attitude or their energy towards it is less so than the guys further down the chain. (Male, 50 interviewed as an employee but with line manager responsibilities)

The prevalence of age based stereotypes was recognized by some employees and to a degree resisted.

Now training, I think it was perceived, and I think it was a wrong perception, that these people had no experience of working on computers, which is completely wrong because those guys like everybody else were going down Tesco’s and Curry’s buying laptops and desktops and playing around on Facebook and YouTube just like everybody else. (Male, 51).

Sometimes it was not the stereotype itself that was resisted, but the degree to which it would apply to them. They considered themselves as not yet “old” as stereotypes about what it means to be “old” did not apply. Many of the employees interviewed said that they did not “feel” their chronological age and felt that they were valued but at the same time many expressed concern about how others might see them or overlook them:

you do become invisible … but it’s like you are cannon fodder in a way, you’re just there to keep the wheels turning. (Female, 57)

Categorizing Talk About Age

Two conceptual categories developed from the analysis of how managers and employees deployed ageist stereotypes and age norms when talking about work opportunities, retirement timing and extending working lives: 1) the prevalence of a decline narrative, namely the widely held assumption that ageing inevitably brings worsening physical and cognitive health, and 2) the prevalence of an intergenerational narrative. The latter had two dimensions: one about being “too old for” something and the second related to intergenerational disentitlement; the need to step away and privilege younger workers.

Both narratives involve a comparison. The decline narrative conditions how people view the implications of getting old and has a role in how they think about continuing or ending work; here people compare themselves with an imagined future self. The intergenerational narrative is how people place themselves in relation to other generations in the workforce, here people compare themselves (and are compared) to others.

Decline Narrative

In discussing future retirement, the health and mortality of colleagues, family, and friends were constant topics leading to something which may be referred to as the decline narrative ( Gullette, 2004 ) or “health pessimism” (cf. Brown and Vickerstaff, 2011 ). This was expressed repeatedly as not knowing when “one’s time is up” or being able to predict how long decent health would last. For many, this expected age related decline in health translated into a desire to retire in time to enjoy some leisure:

One lady, she retired, she was only retired two months and she passed away. And, you know, you think, I don’t want that to be me. And I know you can never say, but I don’t want to work my whole life just to retire and then die. I’d like to enjoy a bit of free time. (Female, 50).

There was a strong sense of not wanting “to run out of time” and instead wanting to “maximize enjoyment of their remaining years in good health” (cf. Pond et al., 2010 ). In relation to the raising of the state pension age in the United Kingdom some felt that policy might force people to work too long, prejudicing their ability to enjoy retirement, this was especially true for those in manual occupations.

I can understand that you shouldn’t have to retire at 65 or whatever age they want to choose, because there’s lots of people perfectly capable of working and they want to, but I do think we’re in danger of keeping people in work who are not fit, because your bodies do start to wear out a bit and the older you get the more susceptible you are to things going wrong and then what are we going to do with those people, what are they going to do? (Female, 58).

For some others the decline narrative worked the other way around and they saw work as a means for staving off the inevitable decline. Paid work was for them a way to stay active and this would be necessary to stay healthy (longer):

Inside I still feel 35 [laughs], shame that the mirror doesn’t agree with me, but [both laugh], yeah, I mean the job is very physical, so but I look on that as being like keep fit, I’m a great believer in use it or lose it, and I think if I’d have given up work at 60 I’d have been a little old lady by now, probably about three stone heavier and gray haired. (Female, 64).

Not all decisions to stop working or extend working life are related to age stereotypes; some look forward to a period in which they have time for hobbies as they are in a financial position to stop working. Others have more negative reasons to give up their job such as health problems and being unable to continue working. Again others are happy to continue working or are not financially able to retire even though they would prefer to. Next to these push and pull factors, which have been identified in previous research, our data does suggest that the decline narrative also plays a role in how people weigh up the factors encouraging or discouraging continued employment. Many employees talked about a fear of being viewed as old and used pejorative language such as “pottering about”, “being a dinosaur”, “doddery” in describing other older people or their future selves.

Intergenerational Narrative

A second narrative expressed by some of our interviewees is about comparisons between age-groups in the labor market. The interviewees are comparing themselves with younger workers and either consider themselves as now “too old” for certain opportunities, or younger workers more worthy for these opportunities. This comparison can be made implicitly or explicitly. The first dimension of this narrative is the “too old for” (TOF)-narrative, which is based on an implicit comparison, where the older worker now considers themselves “too old for” their job or development:

I’ve spoken to other people and they’ve said it’s a young person’s game. […] multitasking in your head and you’ve got three—, no, 20, 30, 40 tickets coming through and you’re trying to mentally keep hold of it all. […] I’m not a woman I can’t multitask (both laugh). So it would be very hard to keep on doing that. (Male, 52)

TOF was most clearly and commonly expressed in relation to training:

I just feel at 60 now, is that really too old for me to be able to, you know, go on all these courses? And there’s quite a few that they want me to do. (Female, 59).

In the TOF-narrative the younger “other” is implicit. But other times intergenerational comparisons are made more explicitly. Many believed that in straightforward competition organizations preferred younger over older workers and that once you are over 50 opportunities in the labor market diminish markedly:

I continually look online, in the papers, I look in places, but when you are 57 and there’s a 30 year old applying for the same job, they’re not going to take me, are they? They’re not. (Female, 57).

Whilst the lack of opportunities for older people was lamented there was a very strong feeling among many of the interviewees that rising state pension ages and the urge to extend working lives was bad for younger generations:

Give the young people who are out there a chance to get into work, because there’s a lot of people unemployed. And I think the longer we go on, the less chance there is for them to get into work, because there’s less people retiring. That’s how I look at it any road. That’s my point of view. (Male, 60).

I actually have a problem with people working longer cause—, guilt’s not the right word, but there are lots of young people who can’t get jobs, you know. (Male, 69).

A number of employees thought that it was right that opportunities should go to younger people. Age norms were internalized by older workers who expressed the view that they were now ”too old” for training and/or promotion and that they should leave that “to the younger ones”:

I’m not particularly after getting promoted, I’ll leave that for the younger ones. I’m just happy where I am and for me I would rather be in this kind of job. (Female, 56).

I don’t want to improve. I don’t mean I don’t want to improve. I will do what I’m doing. I want to give the chance to the young people. […] I’m very, very sorry, I am not interested. Give the chance to the young people. (Male, 54).

Older workers here are wrestling with it being unfair that older workers may be discriminated against whilst also feeling that they have less entitlement to work when younger groups are unemployed, are still building a career or have young families to support. Many people mentioned their children or grandchildren and how difficult the labor market and work was for them.

With an increasing call for employees to extend their working lives, it is important to explore all the factors that are likely to limit this policy goal. The research reported here focused on a hitherto neglected aspect that of the role of internalized age stereotypes and norms in inhibiting older workers. There is a rich literature on direct discrimination against older workers and to a large extent our managers and employees were thinking about this kind of prejudice in relation to ageism. Age discrimination legislation has been around long enough in the United Kingdom for managers and many employees to know that it is proscribed in law and hence when asked our respondents in the majority said that there was no different treatment based on age.

The language managers used to talk about older workers and the way those older workers framed their own thoughts about, work, extending working lives and retirement tells a rather different story. Age stereotypes were routinely employed with respect to older workers capabilities and potential. Age norms about what was appropriate for different age groups were used to talk about training and development or extending working lives. In this sense ageism was normalized in all of the organizations, taken for granted and to a large extent unexamined. Ageist language did not seem to have the power to shock in the way that overtly racist or sexist language nowadays might.

The decline narrative—that with age comes inevitable physical and cognitive deterioration—was prevalent in how employees talked about extending their working lives and/or retirement. It was a factor in their thinking about the desirability of employment as they aged. This was true for those identifying as in good health as well as those with current health issues. As in other studies many people were concerned to retire early enough to still enjoy some health in retirement ( Pond et al., 2010 ; Brown and Vickerstaff, 2011 ). However, for a minority this decline narrative functioned as an incentive to stay in work as a means of maintaining social and physical activity and staving off the onset of ill-health. This latter view chimed more with the increasingly dominant public narrative of active and healthy aging: that work is good for you and keeps you physically and mentally fit ( Department for Work and Pensions (DWP), 2017 : 9; Moulaert and Biggs, 2012 ; Laliberte Rudman, 2015 ). In doing so of course it still takes the eventual and inevitable decline as its point of departure. The decline narrative has been discussed in the existing literature and our study confirms its ubiquity but we noted that it can play either a positive or a negative role with regard to extending one’s working life.

More distinctive were our findings about the intergenerational narrative. If we conceptualize age as social construct then it focuses attention on the relational aspects of age and how age relations are played out in specific contexts. A rather obvious statement is that older workers are only old in relation to some other younger reference group. However, we could clearly see in the comments of both managers and employees that such comparisons were very much alive in people’s minds. They were employed when they were thinking about career opportunities, training and development or the desirability of extending working lives. This was manifested in the “too old for” narrative, expressing a sense that there is a specific chronology for when things are appropriate in the working life. This is perhaps all the more remarkable in our sample as the majority were in the age category 50–59 (see Table 1 ), with presumably many years still in employment. This self-sabotaging narrative, as Romaioli and Contarello (2019) have characterized it, does lead to older people self-limiting. This means older workers potentially opting out of opportunities that are actually available.

The other dimension of the intergenerational narrative we have dubbed “intergenerational disentitlement”, as there was a strong element in our respondents’ comments that as older workers they were less entitled to training and development and possibly even to a job when compared to younger (potential) colleagues. Here many of our respondents were expressing a tension between a commitment to the fact that age discrimination is unfair and should be resisted whilst nevertheless worrying that by taking a promotion or staying in work they might be denying, by implication a more deserving, younger person. This sense of disentitlement could potentially be an important factor in a situation of redundancies, where both managers and employees may feel that if anyone should go it should be the older workers.

This sense of age-based disqualification for job opportunities might undermine formally equitable processes in the workplace; everyone may be entitled to apply for a job, a redeployment or a promotion but some older workers may define themselves as “too old” or think it should “be left for the younger ones”. Age management policies tend to focus on direct discrimination and formal equality but may do little to tackle underlying and normalized ageism of the sort uncovered here.

Individual decisions about whether to carry on working or retire are as we know complex and constrained. The interaction of health, wealth, marital status, employer action and government policy combine to structure what is possible and what is desirable ( Vickerstaff, 2006 ; Loretto and Vickerstaff, 2012 ; Hasselhorn and Apt, 2015 ; Lain, 2016 ; Phillipson et al., 2019 ). The study reported here seeks to include in the list of dynamic variables in retirement decision making, a full and rounded sense of the impact of ageism. It has added another layer to our understanding. The power of ageism to influence end of working life actions is not limited to direct discrimination, although this still certainly plays a significant role, it also encompasses normalized and taken for granted assumptions about age norms, what is suitable for different age groups and why, as well as internalized stereotypes about older workers abilities and aptitudes.

Limitations of the Current Research and Suggestions for Future Research

We have to acknowledge a note of caution about the generalizability of our findings. By the standards of much qualitative work we had a quite large and diverse data set. Our employee respondents covered a good spread of occupational levels in diverse organizations and the gender balance of the sample reflected the gender composition of the different organizations, with a slight over-representation of female respondents. With the weight and depth of interview material we were able to triangulate responses and have concentrated on oft repeated themes and tropes. The sample was however ethnically homogeneous with the overwhelming majority of our respondents identifying as white British. A more diverse sample including a range of the black and minority ethnic populations in the United Kingdom might have confirmed our findings or uncovered different ways of talking about age and generations. In this article we have not examined the gender differences in ageist talk but rather concentrated on the expressions and themes common to both genders. Further research could usefully delve into the subtle differences in how women and men talk about and experience age.

Our respondents were also interviewed in a particular time and place. We do not seek to diminish the importance of public policy and organizational contexts in setting parameters for what is possible for older workers. It would be interesting to see similar narrative analyses undertaken in different national contexts to see whether internalized ageism is as strong and has the same dimensions as identified here. It is also the case that public narratives of what is right or expected of older populations are in some flux as we shift progressively from a societal view of retirement as an earned right for a long working life to the duty on older people to carry on contributing to economic life. Individuals, with their own dispositions, life experiences and family contexts are wrestling with these changing new messages as are we as researchers. It would be interesting in further research to try to link more clearly the impact of public narratives about greedy baby boomers, intergenerational inequity and healthy aging on narratives in the workplace.

The Main Contributions of This Research

We have addressed the spirit of this special issue by identifying a new pathway in retirement research methodologically and conceptually. In so doing we have added another layer to our understanding of the factors that are in play in disposing early retirement or later working. Although we cannot specify the weight or percentage contribution internalized ageism plays in decisions about paid work we have highlighted that it cannot be ignored as a factor. Methodologically we have demonstrated that in addition to quantitative analyses, case studies of organizational practice, and assessments of the impacts of public policy changes, we need to look at how people talk and think about age in the work setting. Embodied stereotypes and taken for granted age norms make a profound contribution to individual and organizational practices around extending working lives. Conceptually we have tried to deepen our understanding of ageism in the work place. We extended the narrow and limiting focus on discrimination against older workers to investigate other components of ageism, namely how older workers respond to age stereotypes and age norms in how they manage themselves.

Data Availability Statement

The dataset analysed for this study can be found in the UK Data Archive, with reference SN852868. https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=852868 .

Ethics Statement

The original data design and protocols received full ethical approval by the University of Kent. Further ethical review and approval was not required for the current study.

Author Contributions

These authors have contributed equally to this work and share first authorship.

In this paper we use part of the qualitative data from a larger United Kingdom Economic and Social Research Council (ESRC) funded project (Ref. MRC/ESRC ES/L002949/1). The original data design and protocols received full ethical approval. For more information on this larger project, please see ILC-UK (2017) , Phillipson et al. (2019) , and Wainwright et al. (2019) . The re-analysis of interviews which forms the basis of this paper was funded by the ESRC (Ref. ES/S00551X/1).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to members of the original research consortium who undertook the interviews: Joanne Crawford; David Lain; Wendy Loretto; Chris Phillipson, Mark Robinson; Sue Shepherd; David Wainwright and Andrew Weyman. We would like to express our gratitude to those who agreed to be interviewed as part of the study.

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Keywords: ageism, age stereotypes, age norms, older workers, extending working lives, qualitative interviews

Citation: Vickerstaff S and Van der Horst M (2021) The Impact of Age Stereotypes and Age Norms on Employees’ Retirement Choices: A Neglected Aspect of Research on Extended Working Lives. Front. Sociol. 6:686645. doi: 10.3389/fsoc.2021.686645

Received: 27 March 2021; Accepted: 12 May 2021; Published: 01 June 2021.

Reviewed by:

Copyright © 2021 Vickerstaff and Van der Horst. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mariska van der Horst, [email protected]

† These authors have contributed equally to this work and share first authorship

This article is part of the Research Topic

New Pathways in Retirement Research: Innovative Perspectives on Social Inequalities and the Distribution of Transitional Risks

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9 Age Discrimination at Work: A Review of the Research and Recommendations for the Future

Donald M. Truxillo Department of Psychology Portland State University Portland, OR, USA

Lisa M. Finkelstein Department of Psychology Northern Illinois University DeKalb, IL, USA

Amy C. Pytlovany Department of Psychology Portland State University Portland, OR, USA

Jade S. Jenkins Northern Illinois University DeKalb, IL, USA

• Published: 07 April 2015
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People are working longer, with the result that workplaces are becoming older and more age-diverse. For these reasons, workplace age discrimination is of increasing importance. This chapter describes a number of mechanisms that may explain age stereotyping and may lead to workplace age discrimination in the areas of hiring, training, performance appraisal, layoffs and reemployment, and interpersonal treatment. Noting that the growing interest in age at work among researchers and practitioners has led to a number of recent developments, the chapter reviews more recent advances in the field of workplace age discrimination and recommends a number of pathways for future research.

The mean age of the workforce in industrialized countries is increasing ( Eurostat, 2012 ; Toossi, 2007 ) due to increased life expectancies ( Vaupel, 2010 ) and the resulting need to raise retirement ages to keep retirement systems solvent ( European Commission, 2012 ). In addition, the recent economic downturn has induced many people to continue working beyond traditional retirement ages out of financial need. At the same time, many retirees are engaging in “bridge employment” ( Wang, Olson, & Shultz, 2012 ), or working beyond the cessation of their primary employment, in order to earn additional money, maintain social connections, or engage in fulfilling work.

Not only is the mean age of the workforce increasing but also people of different ages are more frequently working side-by-side and on the same teams. On the one hand, this presents the opportunity for people of all ages to be exposed to one another, and perhaps reduce some negative age stereotypes (cf., Harrison, Price, Gavin, & Florey, 2002 ). On the other hand, this exposure may lead to negative outcomes such as “faultlines” between people of different ages (e.g., Van Knippenberg, Dawson, West, & Homan, 2011 ).

These trends have led to a surge of interest in age in the workplace in general, and age stereotyping and discrimination in particular. Recent research has included a number of new lenses on age stereotyping at work, including age-diversity climate ( Böhm, Kunze, & Bruch, 2014 ), interactions among generations ( Iweins, Desmette, Yzerbyt, & Stinglhamber, 2013 ), and metastereotypes ( Finkelstein, Ryan, & King, 2013 ).

In this chapter we take stock of the current literature about workplace age stereotypes and age discrimination and where the field needs to go. We begin by describing the theoretical approaches that have been used to explain workplace age stereotypes and the empirical evidence for whether these theories seem to explain negative workplace decisions, that is, actual discrimination. Next, some of the typical stereotypes of workers of different ages are delineated, followed by the evidence that age is associated with actual workplace discrimination. We conclude with newer research streams used to explain workplace age stereotypes and discrimination and propose some future research directions including the use of workplace interventions.

Theoretical Approaches to Workplace Age Stereotypes

Competence and warmth.

The stereotype content model (SCM) describes competence and warmth as key dimensions for understanding the basis of stereotypes. Competence indicates being capable, skillful, intelligent, and confident; warmth indicates being good-natured, trustworthy, tolerant, friendly, and sincere ( Fiske, Cuddy, Glick, & Xu, 2002 ). Attributions about these qualities depend on social perceptions and structural relations between groups. Groups seen as competing for resources are perceived to be less warm (negative intentions) than allied groups (positive intentions). Further, high-status groups (e.g., rich) are seen to be competent, while low-status groups (e.g., poor) are perceived to be incompetent. These attributions are explained by system justification ( Jost & Banaji, 1994 ) and just-world ( Lerner & Miller, 1978 ) theories, which posit that people rationalize group inequalities by believing social outcomes are fair and deserved.

Cognitive, emotional, and behavioral biases result from these competence and warmth dimensions, as outlined by Fiske and colleagues (2002) in the “behavior from intergroup affect and stereotypes” (BIAS) map. This mapping of responses illustrates how stereotype content impacts emotional and behavioral responses and predicts workplace discrimination depending on where group stereotypes fall on the map. Studies report cross-cultural stereotypes of older individuals as warm and incompetent—although much of the nonworkplace literature is referring to much older people who are beyond retirement age—and these stereotypes can persist even when contrary evidence is presented (e.g., Cuddy & Fiske, 2002 ).

Relational Demography

Relational demography refers to the salient demographic characteristics, such as age, race, gender, tenure, and education level, that influence attitudes and behaviors. The similarity-attraction paradigm proposes that people are attracted to similar others ( Byrne, 1971 ). Further, according to social identity theory ( Tajfel, 1974 ) and self-categorization theory ( Turner, 1987 ), people use group membership to develop their sense of self by categorizing others as members of an in-group (similar) or out-group (dissimilar). Motivation to enhance and maintain self-esteem promotes positive evaluation of similar others and therefore encourages in-group bias. The relational demography phenomenon is related to satisfaction, commitment ( Shore, Cleveland, & Goldberg, 2003 ; Thatcher, 1999 ), performance ( Cummings, Zhou, & Oldham, 1993 ; Thatcher, 1999 ), absenteeism ( Cummings et al., 1993 ), turnover intentions ( Tsui, Egan, & O’Reilly, 1992 ), and actual turnover ( O’Reilly, Caldwell, & Barnett, 1989 ; Wagner, Pfeffer, & O’Reilly, 1984 ), such that age similarity leads to improvement in these variables, and age dissimilarity leads to worsening. In addition, these age differences can lead to workplace discrimination. For example, age differences between supervisors and subordinates have been associated with higher role ambiguity ( Tsui & O’Reilly, 1989 ) and lower supervisor ratings of promotability compared with subordinate-superior dyads of the same age ( Shore et al., 2003 ).

Employees in workgroups usually share more than one demographic attribute (e.g., similar in age, race, and gender), and when multiple attributes are shared (which can also include nondemographic characteristics such as skills, personality, and values), subgroups may form within larger groups and conflict can occur ( Lau & Murnighan, 2005 ). The strength of these divides or faultlines depends on the number of shared characteristics, how the particular similar characteristics align among group members, and the number of potential subgroup possibilities ( Lau & Murnighan, 1998 ). Additionally, contextual variables will influence if, and which, faultlines are activated. Strong faultlines are most likely to emerge when there are multiple similarities within subgroups and few similarities between subgroups ( Lau & Murnighan, 2005 ).

Although diversity has been shown to have positive outcomes for organizations, it can also lead to increased conflict, which predicts decreased satisfaction and performance ( Jehn, 1995 ). A meta-analysis by Thatcher and Patel (2011) demonstrated a negative relationship between age faultlines and both task and relationship conflict. These age faultlines predicted reductions in team performance, cohesion, and satisfaction. Age faultlines also were related to decreased productive energy ( Kunze & Bruch, 2010 ) and turnover ( O’Reilly et al., 1989 ). In short, age faultlines seem to be one possible explanatory mechanism for the effects of age-related differences at work. Moreover, other studies suggest that age faultlines lead to actual discrimination, with a negative impact on group performance ratings and group organizational citizenship behaviors (OCBs; Choi & Sy, 2010 ).

Implicit Stereotypes

Much of the workplace age stereotype research has historically relied on measures asking people to report their attitudes toward in- and out-groups, but these self-report measures of explicit stereotypes may not be capturing the full picture. Empirical evidence illustrates that automatic responses often occur through unconscious processes reflecting implicit stereotypes, and these implicit responses differ from the self-reported explicit responses ( Greenwald & Banaji, 1995 ; Nosek et al., 2007 ; Nosek & Smyth, 2007 ). Measurement of implicit stereotypes has the benefit of not only addressing concerns such as demand characteristics or impression management but also more specifically allowing researchers to uncover stereotypes that exist, even when participants are not cognizant of them. Two methods used to explore implicit stereotypes are priming and the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998 ).

Multiple studies have demonstrated how stereotype priming significantly influences cognitive and physical abilities ( Levy & Banaji, 2002 ). For example, subliminally activated positive and negative stereotypes produced effects relating to memory, attitudes about aging ( Levy, 1996 ), and cardiovascular response to stress ( Levy, Hausdorff, Hencke, & Wei, 2000 ). Though the connection to workplace stereotypes has not been made directly, those effects are clearly workplace relevant.

The IAT assesses a person’s associations between two concepts (e.g., young and old) and two attributes (e.g., good and bad). In an extensive review from nearly 6 years of data, Nosek et al. (2007) reported 80% of participants associated young people with good and old people with bad. Most surprising were results indicating older individuals shared this perception, despite self-reported in-group preferences (see also Nosek, Banaji, & Greenwald, 2002 ). Although implicit age stereotypes may explain perceptions of older and younger workers, current empirical research on the effects of implicit age stereotypes in the workplace, especially whether implicit stereotypes lead to actual workplace discrimination, is minimal.

Job-Age Stereotypes

Another factor impacting the stereotypes experienced by younger and older workers is the categorization of certain jobs or industries as young-typed or old-typed. For example, Gordon and Arvey (1986) reported job stereotyping for occupations including receptionist (20s), high school teacher (30s), and mayor (40s), among others. Other studies revealed age-related job stereotyping for accountants, police, computer scientists, university academics, medical doctors ( Singer, 1986 ) and managerial positions ( Cleveland & Landy, 1987 ).

It is important to note that although age stereotypes are fairly stable, job-age stereotypes are more malleable. They are impacted by the current representation of a particular age group in a job, job-type, or industry ( Gordon & Arvey, 1986 ). As such, job-age stereotypes may be fairly fluid and change over time. Not surprisingly, then, research has demonstrated that manipulating the distribution of different groups within an applicant pool can change perceptions of the age-type for that job, influencing hiring decisions and supervisor perceptions of advancement potential ( Cleveland, Festa, & Montgomery, 1988 ; Shinar, 1978 ).

What Are the Stereotypes of Workers of Different Ages?

Delineating worker age categories.

Age stereotypes are different from other kinds of stereotypes in that age is a continuous variable, not a categorical one. Yet stereotyping is the process of generalizing characteristics about a group of people. Thus, age groups are artificially constructed, that is, they are categories on which to pin our “old” stereotypes, as well as our “young” or even “middle-aged” stereotypes. The boundaries of these age groups, however, seem to be somewhat fluid and contextually dependent ( Pitt-Catsouphes, Matz-Costa, & Brown, 2010 ). For example, the relative age of others in the work context ( Wegge, Roth, Neubach, Schmidt, & Kanfer, 2008 ), as well as the normative age associated with a particular position or industry ( Lawrence, 1988 ; Perry & Finkelstein, 1999 ; Perry, Kulik, & Bourhis, 1996 ) can shift our boundaries about who is included in a particular age category (e.g., older, younger) and thus affected by the associated stereotypes.

Moreover, it has been argued that the particular stereotypes triggered by the category of “older worker” may differ from that of merely “older person” or especially “elderly person” ( Finkelstein & Farrell, 2007 ). Different categories of older people such as “worker” may vary which characteristics of an older person are salient and also lead to different assumptions about them (e.g., Wood & Roberts, 2006 ); information from the job context may further draw our attention to some characteristics over others.

Although the lower boundary of “old” shifts for different professions (and quite likely moves up for each of us as we ourselves climb the ladder of age), a review of the literature indicated that the most common ages seen as the lower boundary for old are 50 or 55 (e.g., Finkelstein et al., 2013 ; Sterns & Doverspike, 1989 ). This is notable, as the legal age in the United States for protection under the Age Discrimination in Employment Act (ADEA) is 40.

What Are the Older Worker Stereotypes?

Reviews and meta-analyses over the last 20 years have examined the most common stereotypes relating to older workers and have identified several general themes (e.g., Bal, Reiss, Rudolph, & Baltes, 2011 ; Finkelstein, Burke, & Raju, 1995 ; Gordon & Arvey, 2004 ; Posthuma & Campion, 2009 ; Posthuma, Wagstaff, & Campion, 2012 ). In brief, the negative stereotypes of older workers include characteristics that fall under categories such as poor performance, resistance to change/technology, more costly, less ability to learn, and shorter tenure ( Posthuma & Campion, 2009 ). Conversely, positive characteristics sometimes associated with older workers include being reliable and/or dependable. Furthermore, older workers seem to be perceived as more conscientious, less neurotic, and higher in OCBs ( Bertolino, Truxillo, & Fraccaroli, 2013 ; Truxillo, McCune, Bertolino, & Fraccaroli, 2012 ). Additionally, in Finkelstein and colleagues’ work ( Finkelstein et al., 2013 ), “experienced” emerged as one of the most common beliefs about older workers (but see Finkelstein, Kulas, & Dages, 2003 , for a discussion of the potentially mixed connotations surrounding this characteristic as it pertains to older workers).

Are They “Accurate?”

Because a stereotype is a generalization, it is not an accurate description for every individual member of a particular group. The question of accuracy of a stereotype, then, has to do with whether there are real differences between groups (on average) on a particular characteristic. Two papers have directly investigated this question. In their large-scale review, Posthuma and Campion (2009) concluded that the accuracy of most older worker stereotypes has either not been supported (e.g., no evidence of lower performance and shorter tenure) or has not been sufficiently investigated (e.g., resistance to change and costliness). In a meta-analysis, Ng and Feldman (2012) only found support for one older stereotype: a lower desire to take part in developmental activities among older as compared with younger workers. However, both desire and ability to learn and develop varies among workers of all ages, and recent work has investigated how to improve training and development activities to support those older workers who struggle with motivation and learning (e.g., Wolfson, Cavanagh, & Kraiger, 2014 ).

What Are the Younger Worker Stereotypes?

Stereotypes of younger workers, who are often referred to as “Generation Y” or “Millennials,” have only recently become a common focus of research ( Deal, Altman, & Rogelberg, 2010 ). Some common themes emerging include negative characteristics such as narcissistic, disloyal, entitled, and overly concerned with feedback ( Twenge, 2006 ). Finkelstein et al. (2013) found more negative views of these younger workers were held by middle-aged workers than by older workers, with the latter noting many positive qualities about younger workers (such as energetic, ambitious, and tech-savvy).

What About “Middle-Aged” Workers?

Recognizing that middle-aged workers had previously been ignored in stereotype research, Finkelstein et al. (2013) included them in their investigation of age stereotypes, where beliefs emerged as almost uniformly positive from both younger and older groups, and included characteristics such as “hardworking” and “stable.” Finkelstein and colleagues noted that this group may be an average standard to which other groups are compared—an age analog to the White male. Perhaps when middle-aged workers are relatively young or old in a workgroup or industry, they are labeled with associated young or old stereotypes by coworkers, although we are not familiar with research that has tested this, and certainly the stereotypes of middle-aged workers and the decisions made about them are ripe for examination.

Effects of Age Stereotypes in Organizations: Workplace Discrimination

Stereotypes about members of age groups may translate into discrimination directed toward older workers or younger workers. Such discrimination may manifest in human resource (HR) decisions such as hiring, training opportunities, performance appraisals, and termination/layoffs. (See Truxillo, Cadiz, & Rineer, 2014 , for a review of how age issues may affect HR functions.) In addition to discrimination in formal HR decisions, overt and subtle forms of discrimination may emerge interpersonally or in the form of different expectations or perceptions of older workers and younger workers.

Age bias in hiring is the most prevalent form of age discrimination in the workplace ( Anti-Ageism Task Force, 2006 ). Interview studies that manipulate job candidate age (so that a candidate with the same qualifications is an older candidate in one condition and a younger candidate in another condition) have often revealed a preference for hiring younger job candidates (e.g., Rosen & Jerdee, 1976b ). Other research has also revealed that older job applicants may receive less favorable responses from employers, have shorter interviews, and receive fewer job offers than younger job applicants ( Bendick, Brown, & Wall, 1999 ). That negative age stereotypes should have effects on hiring decisions more than on other workplace decisions is not surprising: There is relatively little additional information about job applicants, and thus decision-makers will tend to rely more on their stereotypes in making these decisions.

Age and beliefs about aging workers factor into decisions surrounding training opportunities. Organizations may feel that they get a better return on training investments with younger workers because they are perceived as easier to train, more interested in learning new skills, and as likely to have a longer tenure in the organization ( Posthuma & Campion, 2009 ). Older workers are also less likely than younger workers to receive developmental feedback ( Rosen & Jerdee, 1977 ). Some managers may feel that simplifying tasks is a better alternative for improving older workers’ performance than providing them with training opportunities ( Dedrick & Dobbins, 1991 ). Indeed, some older workers may encounter significant pressure to retire as an alternative to receiving additional training and development ( Greller & Stroh, 1995 ).

Performance Appraisal

Despite the fact that performance often improves with age, the belief that older workers do not perform as well as younger workers continues to persist in organizations. Age bias is especially likely to influence performance appraisals when subjective supervisory ratings are used ( Waldman & Avolio, 1986 ). When older workers perform poorly, the cause of this poor performance may be attributed to stable factors perceived as likely to persist over time ( Dedrick & Dobbins, 1991 ). Furthermore, more severe punishments may be recommended for older workers’ poor performance ( Rupp, Vodanovich, & Credé, 2006 ). These findings are particularly interesting given the meta-analytic finding that there are few differences in the performance of older workers ( Ng & Feldman, 2008 ) and that older workers generally tend to have more positive work attitudes ( Ng & Feldman, 2010 ) compared with younger workers.

Layoffs and Reemployment

Perceptions that older workers are more costly, close to retirement, need more training, have greater absenteeism, and use more benefits often factor into decisions to lay off older workers ( McGoldrick & Arrowsmith, 2001 ). Though worker salaries do tend to increase steadily until workers are around 50 years of age ( Hedge, Borman, & Lammlein, 2006 ), other research suggests that older workers are not necessarily more costly or in greater need of training than younger workers. Indeed, compared with younger workers, older workers have lower rates of absenteeism ( Hedge et al., 2006 ) and perform fewer counterproductive work behaviors and more OCBs ( Ng & Feldman, 2008 ). Such patterns of behaviors have the potential to save organizations money and, thus, offset additional costs associated with salary. Nonetheless, when older workers are let go by organizations, their reemployment times are generally longer than for younger workers ( Posthuma & Campion, 2009 ).

Interpersonal Mistreatment

Given that the ADEA extends protections to employees age 40 and up, mistreatment of younger workers may be more overt than the mistreatment of older workers. For example, some research suggests that older workers in leadership positions may dismiss younger workers as lazy, selfish, and less committed to the organization ( Raines, 2002 ). Furthermore, their interactions with younger workers may be characterized by discomfort, disrespect, or distrust ( Myers & Sadaghiani, 2010 ). However, research on the incidence of mistreatment behaviors is mixed. Younger workers have been shown to be more likely than older workers to encounter harassment ( Cortina, Magley, Williams, & Langhout, 2001 ). However, research also suggests that older workers may be more likely than younger workers to be the targets of bullying ( Einarsen & Skogstad, 1996 ).

Moderators: When Are Age Stereotypes More Likely to Lead to Discrimination?

The impact of negative evaluations and decisions informed by age bias may be dependent on certain conditions. One of these conditions is the extent to which the job candidate’s or employee’s age is consistent with the age expectations of the job (job-age stereotype, discussed earlier; e.g., Perry et al., 1996 ). Job-age stereotypes interact with age stereotypes to influence employment decisions (discrimination); that is, younger workers are more likely than old to be hired for “young” jobs, and vice versa (Rosen & Jerdee, 1976a , 1976b ). Additionally, incongruence between age and job age-type correlates negatively with financial rewards, promotion rates, and performance ratings ( Cleveland & Hollmann, 1990 ; Cleveland and Landy, 1983 ).

Research also suggests that the cognitively busy evaluators of job applicants are more likely to rely on age stereotypes when making judgments of job applicants than evaluators who are less cognitively busy ( Perry et al., 1996 ). Further, there are studies that suggest that the influence of age stereotypes is minimized when hiring managers have more experience making hiring decisions ( Singer & Sewell, 1989 ) and when evaluator age bias is low ( Perry et al., 1996 ). Finally, one would expect age stereotypes to be less likely to affect organizational decisions when decision-makers have additional, differentiating information about the employee than just their age (cf. Harrison et al., 2002 ).

Recent Approaches and Potential Research Paths

Our review has described theoretical bases of age stereotypes, stereotype content of older and younger workers, and how these may translate into discrimination regarding workplace decisions. However, given the recent surge in interest in age issues at work, a number of promising new approaches are emerging in the workplace age discrimination literature that could explain the mechanisms underlying it. These approaches also suggest new directions for research.

Age Metastereotyping: What Do Others Think of My Age Group?

Age metastereotyping turns the tables on age stereotyping: Rather than making generalizations about another age group, people make judgments of what they believe another age group thinks of theirs ( Finkelstein et al., 2013 ; Finkelstein, King, & Voyles, 2014 ; Ryan, King, & Finkelstein, 2015 ; Voyles, Finkelstein, & King, 2014 ). In an age-diverse workplace, where age may be a particularly salient characteristic, the stereotypes by which people think that others judge them may have more of an effect on age and job attitudes than the ways that we judge others. Even if these stereotypes are not accurate, they could still have an impact on people’s workplace affect and behavior. As an example, because an older worker may be concerned that her younger counterparts think she must not be interested in learning new things, she might choose to make a point of demonstrating her newly learned job skills when these younger colleagues are around. If they were not applying this stereotype to her in the first place, this may seem to be particularly odd behavior to them, causing further misunderstanding.

A recent empirical investigation into workplace age metastereotypes looked at both metastereotype content (What are the metastereotypes?) and accuracy (Do they match the actual stereotypes?) as perceived by three different age groups: young, middle-aged, and old ( Finkelstein et al., 2013 ). Moreover, the study considered differences in metastereotypes perceived from each of the other age groups (e.g., what older workers think younger workers think of them; what older workers think middle-aged workers think of them). They did indeed find that metastereotypes were sometimes more positive than actual stereotypes, and sometimes more negative. Importantly, both metastereotypes and stereotypes differed based on the age of the source. Although much of the age stereotyping research has operated under the assumption that stereotypes of a group are consistent among different-aged perceivers, this study demonstrated that that idea may be too simplistic.

In addition to the many situational characteristics that make age salient in a workplace situation (e.g., numerical minority) and that could elicit both age stereotypes and metastereotypes, Ryan et al. (2015) argued for the importance of an individual difference variable termed metastereotype consciousness , which is defined as a chronic preoccupation with the stereotypes others hold for their group and may be useful in understanding the role of age metastereotyping at work. In testing a model of affect and behavior of younger workers, they found that metastereotype consciousness related to lower satisfaction with older coworkers, more negative mood, and through mood impacted impression management.

Recently, Finkelstein et al. (2014) proposed a theoretical model describing more fully the antecedents, process, and outcomes by which metastereotypes play a role in workplace affect and behavior. In brief, they suggest that a host of individual differences and situational factors may lead to metastereotype activation in a given context. Metastereotypes can be positive or negative, but either of these valences could produce a positive emotional response (a boost from a positive valence or a challenge from a negative valence) or a negative emotional response (a threat from either). The behaviors resulting from the initial response could include conflict, avoidance, or engagement under different conditions. This model awaits empirical scrutiny, and work is needed both in testing and expanding it, perhaps to include the interplay of stereotypes and metastereotypes in a given workplace episode.

We see many paths for future research on age metastereotypes at work. Only one study ( Finkelstein et al., 2013 ) has empirically measured the content and accuracy of age-based metastereotypes, and this taxonomy should be replicated and extended to more fine-grained age groups (e.g., decades) and new workplace contexts (different job types). Likewise, several antecedents to metastereotype activation have been proposed ( Finkelstein et al., 2014 ; Ryan et al., 2015 ), including situational variables such as age salience and evaluation apprehension and personal variables such as power differences between groups and age prejudice, but these need to be tested. Importantly, more work is needed to understand when age metastereotypes lead to actual workplace discrimination and negative cross-age workplace interactions.

Age-Diversity Climate

Organizational age climate is another recent research area related to age discrimination at work (e.g., Böhm et al., 2014 ; Kunze, Böhm, & Bruch, 2011 ). This research considers not only the effects of organizations’ age-diversity climate on outcomes such as performance and turnover, but also the antecedents that might promote a supportive climate for age diversity. In one study, Kunze et al. (2011) used Fiske’s (2004) tripartite view of bias (stereotyping, prejudice, and discrimination), focusing on age discrimination (i.e., behavior) in particular. What was new to the age literature was their measurement of discrimination as an organization-level variable, that is, climate. Importantly, they also considered any kind of age discrimination, whether against older or younger workers, in their measure. Using a sample of over 8,000 employees from over 128 companies, they found that age diversity in the workplace was related to age-discrimination climate. They also found that an age-discrimination climate was negatively related to company performance through its negative effects on commitment. Extending this work, Böhm et al. (2014) focused on and measured a positive age climate, that is, age-diversity climate (as opposed to age-discrimination climate.) Using a sample of 93 companies and over 14,000 employees, they found that inclusive HR practices (as rated by managers) were related to age-diversity climate, which in turn were related to company performance and employee turnover intentions.

Taken together, these studies suggest two things. First, the age climate within a company is important: Climate appears to relate to important outcomes such as performance and turnover intentions. But perhaps more germane to the present chapter, the results suggest that companies may have an important lever for improving their age climate: treating employees well through their HR practices (see also Kooij, Jansen, Dikkers, & de Lange, 2010 , for a discussion of age and perceptions of HR practices).

This research on organizational age climates suggests a number of possible ways to improve climate and therefore ultimately improve organizational and individual outcomes, although they need to be empirically tested. First, contact between people of different ages in itself may promote more positive attitudes toward people of different ages ( Iweins et al., 2013 ; Kunze et al, 2011 ). In other words, organizations may use greater workplace age diversity as a way to improve the age climate. However, as mentioned earlier, in other cases age diversity can lead to faultlines, tension, and performance decrements. Therefore, research should examine how intentional increases in age diversity within teams and groups can promote positive climate and thus more positive outcomes for organizations, while avoiding potential pitfalls. Moreover, it is important to examine the degree to which a positive age-diversity climate leads to decreased workplace discrimination and which types of discrimination (e.g., training opportunities, promotions) are affected.

Second, Böhm et al. (2014) suggest that certain types of HR practices may lead to improved age climate. That study grouped a range of age “supportive practices” together into bundles without isolating the effects of individual practices and also measured HR practices from the perception of a person in the organization rather than in objective terms. We suggest that future research actively work to implement specific HR practices and policies that support workers in all age groups to examine the effects of such interventions on age diversity climate as well as performance, turnover, and other measures of organizational health.

Third, we challenge organizations to manage their age climate, as is common in many organizations with regard to safety climate (e.g., Christian, Bradley, Wallace, & Burke, 2009 ), and suggest that researchers work to understand how changes in age climate take place over time. Managers from the top down through first-line supervisors can work to communicate support for people of all ages, and the effects of such direct influence on age climate deserves research attention.

Multiple Category Issues

Another recent issue related to workplace decisions is the “multiple category” issue, or the need to consider that many employees fall into multiple categories (e.g., gender, ethnicity, age) that each have stereotypes associated with them. Kulik, Roberson, and Perry (2007) examine this issue from the standpoint of selection systems, applying social cognition theory to predict when such categories will be activated. Kulik et al. (2007) point out two ways of approaching the issue of stereotypes of job applicants who fall into multiple categories, each of which has its own associated negative stereotype. A first and more simplistic approach is to assume that an applicant who belongs to multiple groups, each with its own negative stereotypes, simply accumulates more negative judgments against them (“double jeopardy”). More likely, however, is that an applicant’s different categories may actually conflict with each other for a particular job. An example might be an older, Asian applicant for a software development job in the United States: Their Asian (minority) background may seem consistent with the job they are applying for, but their age may work against them—the decision-maker faces a “multiple-category problem.” Kulik et al. (2007) describe factors, such as individual differences in the decision-maker, which can also influence which categories are activated and used in making a decision. Recently, DeRous, Ryan, and Serlie (2014) explored this issue in two studies of actual HR recruiters examining resumes that were varied by gender and by ethnicity (Arab vs. native Dutch). Their results support Kulik et al.’s propositions, in that the results were affected by characteristics of the applicant (e.g., Arab female versus Arab male), the job (e.g., level of client contact), and the recruiter (prejudice).

Although we are unaware of research that has applied the multiple category problem to age discrimination, we believe that it shows promise for two reasons. First, we know that job-age stereotypes exist ( Perry et al., 1996 ), namely, that there are some jobs that are considered to be more appropriate for older people and others for younger people. Second, the workforce is becoming more diverse, not less, such that decision-makers are more and more likely to face applicants and employees who come from multiple categories (e.g., older White women, younger Asian men). What’s needed, then, is stereotyping research that goes beyond simply comparing the outcomes of older and younger people to also take into account the many categories that a person may belong to and when stereotypes associated with these different categories will be activated ( Kulik et al., 2007 ) and lead to discrimination. We also need to understand how characteristics of the job and the industry come into play (e.g., high tech vs. low tech), as well as characteristics of the decision-maker (their biases, personality, etc.) and under which conditions a particular category is activated and prioritized to affect actual workplace decisions.

Prescriptive Stereotypes: What Older Workers Should Do

In social psychology there is a distinction made between descriptive norms (what people typically do) and prescriptive norms (what people ought to do)—sometimes they coincide, and sometimes they are at odds ( Kallgren, Reno, & Cialdini, 2000 ). Newer stereotyping research is beginning to make a somewhat analogous distinction between descriptive stereotypes (how we believe people in a group actually are) and prescriptive stereotypes (how we believe people in a group should be). Though this has been explored in relation to gender in the past (e.g., Gill, 2004 ), North and Fiske ( 2012 , 2013 ) have only recently applied the concept to age stereotypes, and workplace prescriptive age stereotypes have not been examined.

We already know that typical descriptive stereotypes are very difficult to change, even when people are faced with members of a group who counter the stereotype (they are “exceptions to the rule”) or even large numbers of members who counter the stereotype (observers create subtypes to protect the larger stereotype; Kunda & Oleson, 1997 ). The notion of prescriptive stereotypes introduces yet another barrier to stereotype reduction. If observers’ prescriptive stereotypes are strong enough, they become upset if subgroup members do not stay within their prescribed role in society. For example, it would be hard to deny that women do actually work successfully outside the home, but some people believe that they should not. When it comes to age, one can imagine a teenager’s embarrassment at a parent who listens to the same music or shops at the same stores as they do because older people are not “supposed to” enjoy or be part of pop culture.

Finkelstein (2014) noted that there are not yet examples of research focused on workplace prescriptive age stereotypes beyond a few items in the “succession” dimension of the general older age prescriptive stereotypes scale ( North & Fiske, 2013 ), such as those pertaining to older people needing to make way for younger people in the workplace. It is plausible that prescriptive stereotypes are causing barriers in the modern age-diverse workplace. For example, people may expect to see young people as mentees at work, but not as mentors, and particularly not to older mentees. In this example, both younger and older individuals are out of role expectations, possibly leading to challenges in relationship formation ( Finkelstein, Allen, & Rhoton, 2003 ). Similarly, research has shown that women may be expected to be high on OCB, and the effort to fulfill these expectations may hold them back in their careers ( Bergeron, 2007 , Bergeron, Shipp, Rosen, & Furst, 2013 ). By the same token, it may be that older workers are hindered at work in that they are often expected to have higher OCBs (cf., Bertolino et al., 2013 ).

Similarly, younger workers may be suspected of being more likely to engage in counterproductive work behaviors ( Broadbridge, 2001 ). Workers aware of negative age-relevant expectations may also report more negative job attitudes, poorer mental health, and higher likelihood of turnover ( von Hippel, Kalokerinos, & Henry, 2013 ). Thus, although the research on age prescriptive stereotypes focused exclusively on older people, research is also needed on prescriptive stereotypes that lead to discrimination against younger workers as well (e.g., “young people need to pay their dues before they get ahead”).

Positive Age Stereotypes but Negative Outcomes

It is important to note that findings of workplace discrimination against older workers conflict with reports in which older workers were categorized positively, such as “experienced” (e.g., Finkelstein et al., 2013 ), “conscientious” (e.g., Bertolino et al., 2013 ), and “dependable” ( Posthuma & Campion, 2009 ). Further, some negative older worker stereotypes may be changing with the aging of the baby-boomer generation, as indicated by more recent replications ( Weiss & Maurer, 2004 ) of some of the Rosen and Jerdee ( 1976a , 1976b ) studies. Despite these positive stereotypes of older workers, however, they appear to face more discrimination than their younger counterparts, including longer reemployment times. In other words, workplace decisions about older workers are likely based on more than on just stereotypes of their experience and dependability. This inconsistency between the stereotypes of older workers and apparent discriminatory decisions made about them suggests that research is needed to understand the many complex factors that go into workplace decisions made about older workers. As one example, older workers who are out of work may be seen as “overqualified” (e.g., Erdogan, Bauer, Peiró, & Truxillo, 2011 ) or otherwise less desirable because they are unemployed.

What Are the Relevant Age Categories?

One issue that has challenged workplace age stereotyping and discrimination research for years is the definition of what constitutes a younger, middle-aged, and older worker (e.g., Finkelstein & Farrell, 2007 )—or if even those categories are actually how workers group coworkers’ ages. Very likely, this is a function of a number of contextual factors such as the job in question ( Perry et al., 1996 ), the industry, the organization, the cultural age norms, and the regulations within a particular country. What is considered to be “old” is also probably evolving as people live and work longer. While we can never definitively say what is considered old or young in general, future research would do well to tackle this issue head-on for the particular context in which the research is being done.

The Effects of Implicit Stereotypes on Discrimination

As noted, there has been much interest in the role of implicit stereotypes, but whether implicit age stereotypes affect actual workplace decisions remains underexplored. This seems to be a key issue for understanding not only how age-based decisions work in organizations but also how to combat negative age-related outcomes for workers. In particular, it seems valuable to understand not only if but also when implicit age stereotypes lead to discrimination and the role of implicit stereotypes relative to explicit stereotypes in discriminatory decisions.

Discrimination Against Younger Workers

Though there has been much hype in organizations about “managing Millennials,” there is debate in the literature on two fronts: (1) whether these (or similar) stereotypes have always been applied to younger workers of all generations by their older counterparts (e.g., Parry & Urwin, 2011 ), and (2) whether there is evidence to support these “millennial” qualities (e.g., Costanza, Badger, Fraser, Severt, & Gade, 2012 ). Some of these stereotypes may actually be more negative than those of older workers, an important issue, because most research on workplace age stereotyping and discrimination has focused on older workers, and younger workers are not legally protected from discrimination in many countries. Further, although there seems to be some social awareness that negative stereotypes of older workers are inappropriate and inaccurate in the same vein as gender and racial stereotypes, negative stereotypes of Generation Y seem to be more socially acceptable, and perhaps thus more pervasive, blatant, and dangerous. Therefore, more work is needed to see whether the negative stereotypes of younger people might lead to actual workplace discrimination against them.

Intervention Research: Just Do It

Much of the work we have done to date in the field of age discrimination has been descriptive, that is, focused on understanding which factors can affect age stereotyping at work and how this might affect decisions about older and younger people. But perhaps the greatest need in this area is to be able to provide organizations with best practices for reducing discrimination. The work thus far definitely suggests a number of ways to improve organizational practices, such as improved HR practices (e.g., Böhm et al., 2014 ) and increased positive intergenerational contact (e.g., Iweins et al., 2013 ). But virtually no age discrimination research has examined objective policies and practices or used interventions to understand ways to reduce age discrimination, and such work would greatly benefit both organizational practice and the value provided by our field. Given the challenges of intervention work in organizations, we also urge that the field consider “wise interventions” ( Walton, 2014 ) to aid in the practicality and large-scale dissemination of age supportive-practices. We also welcome intervention research that considers the viability of smaller-scale, individual, behavioral, and attitudinal interventions that focus on both perpetrators and targets of age discrimination at work.

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Age Discrimination in Hiring: Evidence from Age-Blind vs. Non-Age-Blind Hiring Procedures

I study age discrimination in hiring, exploiting a difference between age-revealed and partially age-blind hiring procedures. Under the first hiring procedure, age is revealed simultaneously with other applicant information and job offer rates are much lower for older than for younger job applicants. Under the second hiring procedure, interview selections are based on detailed, age-blind on-line applications, while subsequent interviews are not age-blind. Older applicants are not under-selected for interviews, but after in-person interviews when age is revealed, older applicants still face a much lower job offer rate. This evidence is strongly consistent with age discrimination in hiring.

I am grateful to Ian Burn for helpful comments, research assistance, and collaboration on many projects related to age discrimination. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

The data studied in this paper come from a discrimination case on which I worked as an expert witness (on the plaintiff's side). I was paid on an hourly basis, and hence had no financial stake in the outcome of the case.

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David Neumark, 2024. " Age Discrimination in Hiring, " Journal of Human Resources, vol 59(1), pages 1-34.

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AI ageism: a critical roadmap for studying age discrimination and exclusion in digitalized societies

Justyna stypinska.

1 Freie Universität, Berlin, Germany

2 European New School of Digital Studies, European University Viadrina, Frankfurt (Oder), Germany

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism is presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age. AI ageism can be defined as practices and ideologies operating within the field of AI, which exclude, discriminate, or neglect the interests, experiences, and needs of older population and can be manifested in five interconnected forms: (1) age biases in algorithms and datasets (technical level), (2) age stereotypes, prejudices and ideologies of actors in AI (individual level), (3) invisibility of old age in discourses on AI (discourse level), (4) discriminatory effects of use of AI technology on different age groups (group level), (5) exclusion as users of AI technology, services and products (user level). Additionally, the paper provides empirical illustrations of the way ageism operates in these five forms.

Introduction

Over the last few years, the power of algorithms and artificial intelligence (AI) has become an issue fiercely discussed in society, media, business, and the social sciences. Artificial intelligence and machine learning (ML) systems are frequently pictured as capable of making smarter, faster, better, and presumably more neutral decisions. European Union, along with governments in Europe and around the world, see AI as the biggest promise of the twenty-first century, making positive contributions to multiple aspects of human life from improving healthcare, to climate change mitigation (European Commission 2020 ). At the same time, AI systems entail a number of hazards, such as opaque decision-making, gender-based, or other kinds of bias and discrimination, violation of the right to privacy, promotion of mass social engineering, and limitations to personal freedom (Rahwan 2018 ; Tufekci 2014 ). The scholars of ethical AI express concern about the way those technologies show hidden biases resulting in exclusion and discrimination of members of marginalized groups (Eubanks 2018 ; Mittelstadt et al. 2016 ) and can pose a threat to fundamental human rights and social justice (Aizenberg and van den Hoven 2020 ; Cruz 2020 ).

Particularly, the way sexism and racism operate in AI has attracted significant attention from prominent scholars. Studies have shown how face recognition systems work poorly for women with dark skin (Buolamwini and Gebru 2018 ) and that word embeddings—a framework used for text analysis in machine learning and neural language processes—exhibit female/male gender stereotypes to a disturbing extent (Bolukbasi et al. 2016 ). A seminal study on search engines showed how algorithms systematically retrieved racist and sexist search results, from keywords such as ‘black girl’, which was termed “algorithmic oppression” (Noble 2018 ). Yet, in comparison to the salient and influential research findings on unwanted bias relating to gender and race in AI systems, the category of age—critical to the study of social exclusion and social inequalities—has been largely neglected in existing research. Age bias in AI is only now starting to emerge as a critical problem requiring urgent action, in academic research (Chu et al. 2022 ; Rosales and Fernández-Ardèvol 2019 ) as well as in public policies. The World Health Organization (WHO) expressed concerns that, if left unchecked, AI technologies may perpetuate existing ageism in society and undermine the quality of health and social care that older people receive (WHO 2022 ). The scarcity of scientific investigation of age biases renders the topic of ageism in AI terra incognita .

The aim of this paper is to rectify this gap by pointing to several theoretical aspects and empirical illustrations of manifestations of ageism in AI. There is a plethora of ways AI had been defined, from simple formulations to complex definitions relating to the technological, scientific, and societal implications of the systems. This article follows the understanding of AI as a “socio-technical ecosystem,” which recognizes the interaction between people and technology and how complex infrastructures affect and are affected by society and by human behaviour (Dignum 2022 ). Machine learning can be defined broadly as a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data (Molina and Garip 2019 ) and, more specifically, a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed (Brown 2021 ). This paper provides two main contributions to the debates on bias and fairness of AI, diversity, and inclusion in AI, as well as discussions on AI for social good. Firstly, it proposes a new analytical concept— AI ageism —with the aim of theoretically elucidating the various ways ageism can manifest in AI and creating a roadmap for identification of further critical areas in need of empirical research and policy intervention. Secondly, the paper provides illustrations and identifies areas where the application of AI technologies can become harmful or discriminatory to older populations.

Ethics of AI, which can be considered a critical theory (Waelen 2022 ), is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions AI raises. The concepts of bias and fairness of AI belong to the wider ethical debate among academics, practitioners, and policymakers (Fjeld et al. 2020 ; Kordzadeh and Ghasemaghaei 2021 ; Mittelstadt et al. 2016 ; Tsamados et al. 2022 ), where algorithmic fairness appears to be a “wicked problem” with no clear agreement on problem statement or solution (Woodruff et al. 2018 ). The broad concept of fairness is rooted in philosophy, mathematics, ontology, sociology and law and can be applied for AI and ML mostly by the use of various fairness metrics (Wachter et al. 2020 ). Moreover, the concept of fairness is a situational, evolving, and highly contestable one and can only be understood in reference to the different social groups. Thus, the vast majority of algorithmic fairness frameworks are stipulated with reference to particular  social groups, often requiring a formal encoding of the groups into the dataset and/or algorithm (Hanna et al. 2020 ). Identification of vulnerable social groups is the way technical understanding of bias and fairness in ML (Wachter et al. 2021 ) is linked with the wider societal impact of AI and the variety of interdependencies between different actors in the realms of AI. The societal risk mitigation propositions such as “the society-in-the-loop” (Rahwan 2018 ), which combines the concept of “human-in-the-loop” control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems or the concept of “representational harm” in ML (Barocas et al. 2017 ) reflect the concern of the AI community that the broad societal and marginalized group interests need to be integrated into the AI development processes. Furthermore, due to the lack of diversity among engineers and researchers in AI, and more broadly in digital technologies, the products that are developed and used by billions of users may result in the proliferation of bias on a large scale. Hence, inclusion and diversity in AI are crucial (Chou et al. 2018 ; Zhang et al. 2021 ). Sociologists, along with critical social scientists, can advance the conversation on bias, fairness, transparency, and accountability by transforming it into one of inequality, discrimination, power and hierarchy, and social exclusion, but also social good and social responsibility (Airoldi 2022 ; Zajko 2021 ).

AI for social good and the ageing population

What is “social good”? At minimum, it is a conceptual framework that is filled with discursive content, as mentioned above, fairness, bias, or inclusivity, by various social and political actors. Different approaches to understanding the concept of the social good have been suggested. However, there is still only limited understanding about what constitutes AI for social good (also AI4SG) and different frameworks emerge with the intent to substantiate and advance the development and application of AI for social good. Generally speaking, AI for social good initiatives is successful to that degree they reduce, mitigate, or eradicate a given problem of moral weight. Accordingly, a working definition of AI for social good was coined: “the design, development, and deployment of AI systems in ways that (i) prevent, mitigate, or resolve problems adversely affecting human life and/or the wellbeing of the natural world, and/or enable socially preferable and/or environmentally sustainable developments” (Floridi et al. 2020 , pp. 1773–1774). Against this background, applying artificial intelligence to humanitarian and environmental problems has become a broadly agreed upon coordination point in the discourse of “AI for social good”. The United Nations’ (UN) 17 Sustainable Development Goals (SDGs) has become a leading framework to manage the objectives of such efforts (Tomašev et al. 2020 ). Among ten guidelines to inform future AI4SG initiatives identified by an international collaboration of actors and stakeholders, the issues of fairness and inclusivity are included and reference to the category of age comes across: “Applications of AI need to be inclusive and accessible, and reviewed at every stage for ethics and human rights compliance (…) Unfairness may result in violations of the right to equality, manifesting as inequity in model performance and associated outcomes across race, ethnicity, age, gender, etc.” (Tomašev et al. 2020 , p. 3). Altogether, the questions about the agenda of AI for social good are deeply intertwined with wider ethical and political issues regarding the legitimacy of decision-making with, and about, AI (Floridi et al. 2020 ).

Simultaneously, AI for social good is part of a growing movement 1 that seeks to integrate ethics into AI to achieve more equilibrium between societal needs and technological progress. Previously remarkable attributions of data-driven systems, such as its disruptive potential, automation capabilities, and scaling of revenue are increasingly being codified as insufficient, even naïve, within the ethically aligned regime (Lee and Helgesson 2020 ). Some of the main debates within the movement concern problems such as algorithmic bias (Kordzadeh and Ghasemaghaei 2021 ), fairness vs accuracy (Aler et al. 2022 ), intricacies of keeping society in the loop (Rahwan 2018 ), and explaining results derived from black-boxed algorithms (Kitchin 2017 ). Within this framework, AI for social good implies that it should be rendered bias free and meet the principle of fairness and non-discrimination, in addition to bringing positive social impacts in the application of systems. For example, an algorithm is seen as satisfying the principle of fairness when the calculational outcomes are independent of sensitive variables, which indicates membership in vulnerable categories. The outcomes of those algorithms should be further examined for a non-discriminatory impact on salient social groups (Heinrichs 2022 ).

Furthermore, the ‘social good’ is something which benefits the “general public, being ‘for’ the people, and at the same time, it is something which reflects and respects their wishes, being ‘from’ the people” (Trotta, Lomonaco, Ziosi, 2021 ). This framework focusses on the “receiver” or “user” side of technology and reflects the humanistic approach to regulate AI which puts humans at the centre of AI inspection. With regard to the category of older persons, the categorization ‘for’ people can be understood twofold: firstly, as AI systems serving the particular needs of older people (e.g., in the care sector, transportation, smart housing) the purpose of which is to facilitate, manage, and support the ageing processes of individuals and societies; secondly, it implies AI should be designed according to the principles of universal design—that is, a technology which responds to the needs of all age groups and does not prioritize or exclude any demographic fraction—an ageless AI. To further substantiate the principle of AI ‘from’ people, the focus needs to shift to the diversity of persons engaged in development, design, production, and implementation stages of AI technology. The inclusion of older populations as “relevant social groups” (Pinch and Bijker 1987 ) in the process of construction of technology is an inevitable step into safeguarding the fairness and inclusiveness of the products and services in the field of AI. Actors involved in the social process of construction of technology speak from different backgrounds, experiences, and capabilities—transforming our understanding of the social good into a reflection of a myriad of perspectives. Questions arise: Whose perspectives can be articulated? What articulated perspectives become sanctioned as a legitimate contribution to the discourse? Virginia Dignum ( 2021 ) recently noted that still many stakeholders are not invited to the table, not joining the conversation. I argue that the older population is one group and social category that not only is being excluded from processes of development and deployment of AI, but is also invisible in the debate on ethical, inclusive, and fair AI. For older persons, the concerns include, but are not limited to, discriminatory impacts in health care, housing, employment, banking, and finance issues (Orwat 2020 ). The dearth of research on age biases in AI might be one of the reasons the debates and approaches to fairness and ethics in AI have until now not explicitly recognized that age should be included in the catalogue of protected socio-demographic characteristics.

Ageing population, ageism, and technology

The urgency to investigate ageism in AI stems not only from the technological acceleration, but primarily from the reality of demographic change, which defines the way societies are ageing around the globe. In Europe, for example, the current median age amounts to 43.9 years and is projected to increase to 48.2 years until 2050 (Eurostat 2021 ). WHO estimates that by the end of 2030, the number of people 60 years and older will grow by 56%, from 962 million (2017) to 1.4 billion (2030). This rapid increase created momentum for the UN to proclaim the Decade of Healthy Ageing (2021–2030), where combating ageism is seen as a fundamental strategy for securing healthy and dignified futures for older adults. Simultaneously, the World Health Organization launched a global campaign to fight ageism and issued its very first Global Report on Ageism (WHO 2021 ). Despite decades of research, implementation of anti-discrimination policies and legislation, ageism is still alive and well (Stypinska and Turek 2017 ) or even, as recent studies show, has dramatically intensified during the COVID-19 pandemic (Ayalon et al. 2021 ).

Age is a complex and critical category in the study of social inequalities and social exclusion. The meaning of age goes significantly beyond being a bare number. It is a socially constructed multi-layered concept including biological, psychological, social, and economic dimensions (Marshall and Katz 2016 ; Vincent 1995 ). Further, the process of ageing itself is highly diverse, individualized and context-dependent, which contributes to intensification of inequalities due to accumulation of disadvantages throughout the life course (Ferraro and Shippee 2009 ). Hence, older adults are an extremely heterogenous group with different needs, potentials, and capacities. Ageism, however, is the only prejudice which will inevitably affect everyone, regardless of their gender, race, or other characteristic. Despite its ubiquitous nature, it is still a type of discrimination, which is not recognized as easily as sexism or racism as it often operates in a more subtle, yet corrosive manner. Ageism manifests in all domains of public and private life, and takes on multiple forms and expressions (WHO 2021 ). Therefore, it should not come as a surprise that ageism has found its way to manifest in digital forms, and more precisely in the AI and ML systems and technologies (Chu et al. 2022 ) as recently acknowledged by the World Health Organization’s Policy Brief on Ageism in AI for health (WHO 2022 ).

The definition of ageism has shifted from its basic understanding as “a process of systematic stereotyping and discrimination against people because they are old” (Butler 1975 ) to elaborated and multidimensional conceptualizations reflecting the multifaceted nature of ageism “as negative or positive stereotypes, prejudice and/or discrimination against (or to the advantage of) elderly people on the basis of their chronological age or on the basis of a perception of them as being ‘old’ or ‘elderly’. Ageism can be implicit or explicit and can be expressed on a micro-, meso- or macro-level” (Iversen et al. 2009 , p. 15). Theories of ageism are following the digitalization of the phenomenon, that is, the presence of age biases, stereotypes, prejudice, and discrimination in their digital form. The concept of “visual ageism” for example, responds to this change and describes” the digital media practices of visually underrepresenting older people or misrepresenting them in a prejudiced way” (Ivan et al. 2020 ). Yet, the existing conceptualizations are still too narrow to address the complexity of various ageism manifestations in AI systems, algorithms and automatic decision-making systems. Moreover, they do not capture the reality and nature of human and non-human interactions—key to the era of AI and algorithms.

Social scientists have studied extensively the way older adults use and interact with digital technology (Katz and Marshall 2018 ; Loos et al. 2020 ; Wanka and Gallistl 2018 )  and the way gerontechnology can assist older adults in adapting to ageing processes (Klimczuk 2012 ). The newly emerged theoretical framework of “Socio-gerontechnology” (Peine et al. 2021 ) promises to provide a unique understanding of ageing and technology from a social sciences and humanities perspective and contributes to the development of new ontologies, methodologies, and theories. However, the current ageing research with regard to intelligent technologies has been limited to several themes: the use of social robots and other smart technologies to assist and support active ageing and ageing in place (Pedersen et al. 2018 ), use of digital data in smart mobility (Sourbati and Behrendt 2020 ), and the use of smart technologies (e.g., wearable devices) in tracking and quantifying ageing bodies (Katz and Marshall 2018 ). Yet, the theoretical reflection and empirical analysis on the potential negative impact of these intelligent technologies and algorithmic systems on older persons has only very recently emerged as a necessary research agenda for sociology of ageing and social gerontology (Chu et al. 2022 ), and a thorough and systematic empirical and theoretical programme for the investigation of the phenomenon of ageism in AI is yet to be created.

The understanding of digital inequalities and the main focus has been on the concept of the “digital divide” (Choi et al. 2020 ; Van Dijk and Hacker 2003 ) and its corresponding three levels (Lutz 2019 ). In essence, the strength of the digital divide is that it makes us attentive to the division between digital insiders and excluded groups—outsiders—in terms of technological access and digital skill. In fact, age is the most consistent predictor of basic internet access and use (Hargittai and Dobransky 2017 ). Empirical studies strongly suggest that the older population is a significant part of the group that is systematically excluded from the digital ecosystem due to low digital literacy, internalization of existing negative stereotypes about older people as technically inept, or due to lack of interest (Gallistl et al. 2020 ; Hargittai and Dobransky 2017 ; Köttl et al. 2021 ). But how well do these patterns of digital divide translate from the digital sphere to the realm of AI?

Lutz ( 2019 ) argues that research on the third-level digital divide should include digital traces, algorithmic surveillance, and data-based discrimination into its syllabus. Further, Gran et al. ( 2020 ) suggest that the “digital divide” is facing a new frontier: awareness of algorithms. The authors used cluster analysis to measure levels of “algorithmic awareness” in a representative sample of the Norwegian population. By algorithmic awareness the study understood being aware of the algorithms’ functions and impacts on platforms, in services and search engines. They found out that in total, 41% of respondents expressed no awareness of algorithms, whereas the age group of 60–70 scored 61%, and 70 + scored 74%. In comparison to younger cohorts, the age group of 30–39 scored 15% which is strongly indicative of the age inequality in algorithmic awareness. Their findings show that awareness of algorithms is stronger in younger groups, with older people scoring lowest. Accordingly, this evidence suggests the continuous exclusion of the older population on a group level—an algorithmic divide understood as an extension, or another level of digital divide. The effects of algorithmic divide are believed to threaten to take away the various political, social, economic, cultural, educational, and career opportunities provided by machine learning and artificial intelligence (Yu 2020 ).

Going beyond “bias” in AI: AI ageism as a researcher’s roadmap

Even though conceived of as mathematical formulas, algorithms are neither neutral, fair, nor objective. They reproduce the assumptions and beliefs of those who decide about their design and deployment. The variety of ways in which biases are encoded refers to the ways the technology is designed, the data is encoded, and the way in which people and the wider society interact with each other as well as with the different systems in place (Willson 2017 ). In their seminal work, Friedman and Nissenbaum ( 1996 ) identified three types of bias in computer systems: pre-existing from social institutions, technically created, and emerging bias from the context of use. Furthermore, the domination of AI ethics and fairness research by computer and data scientists is reflected in the use of language. The concept of “bias” originating from computer sciences dominates the scholarly discourses about exclusion in AI (Zajko 2021 ) and poses a risk of reducing the complexity of social inequalities to solely a technical level. Additional risks are presented by the substantial power asymmetries between those with the resources to design and deploy AI systems, and those who are classified, ranked, and assessed by these systems (Whittaker et al. 2019 ).

Therefore, by introducing the concept of AI ageism , I propose to go beyond the usage of “bias” as the dominant epistemological tool for understanding the negative effects of algorithmic models and systems. This concept proposes a broader socio-technical inquiry of different forms of ageism existing in AI understood as a socio-technical ecosystem (Dignum 2022 ). The working definition of AI ageism , open to empirical and theoretical refinement, conveys the following: practices and ideologies operating within the field of AI which exclude, discriminate, or neglect the interests, experiences, and needs of older populations and have or might have disparate impacts on age equality. It includes, but is not limited to, five interrelated forms: (1) age biases incorporated in algorithms and digital datasets (technical level), (2) age stereotypes, prejudices, and ideologies of actors in the field of AI (personal/actor level), (3) invisibility or clichéd representations of category of age and old age in discourses around AI (discourse level), (4) discriminatory effects of use of AI technology on older age groups (group level), (5) exclusion as users of AI technology, services and products (user level). This guiding analytical framework is aimed at delineating lines of research on different manifestations of ageism in AI by focusing on the forms which might be verified through empirical research and serve as an orientation framework for future social study of age and age inequalities in their algorithmic expression. The concept of AI ageism can facilitate the discovery of new data-driven manifestations of ageism and enhance the research by going beyond the limiting concept of “bias” (the technical level), which until now has strongly dominated the discourse and research about social fairness and equality of AI (Zajko 2021 ). The proposed analytical framework reflects on the need for understanding bias in algorithms as a multidisciplinary task (Ntoutsi et al. 2020 ), where it is acknowledged that roots of the problem are not only technological. Although bias in its technical form (level 1) is central to the definition of AI ageism , its full interpretation needs to be complemented by the other forms (2–5), which recognize the complex socio-technical interdependencies of the process of AI creation. Age stereotypes and prejudices are deeply ingrained in the social fabric, in interpersonal relations (2), as well as in discourses, images, and ideologies in the tech industry (3), and these manifestations of AI ageism need research attention to the same extent as the technical bias. This framework acknowledges the divisions on the continuum of the development of AI (from producer to end user), but proposes to go beyond the simple “end-user” perspective as too individualistic and introduces the “group level” as another dimension to which researchers should be particularly sensitive. The attention to this form of ageism—as a discrimination on the group level—reflects the concern about the impact of automating decision-making systems (ADMs), which do not require an active “end-user”, but where decisions are taken on behalf of them, often completely beyond their knowledge and awareness (Barocas et al. 2017 ; Eubanks 2018 ). Although the interdependencies between the forms of AI ageism can be identified analytically (e.g., the relation between the age stereotypes and prejudices of actors and the exclusion of older persons as users due to flawed design), their robustness needs to be tested empirically in a systematic way in the future.

AI ageism in practice: illustrations

The identified dimensions of AI ageism are separated conceptually; however, there are significant interrelations and synergies between them. The proposed working definition should serve as an organizing tool for exploratory purposes, both on empirical and theoretical levels. The illustrations of the five dimensions of AI ageism provided in this section are based on scientific literature review, grey literature, and own preliminary research and observations. Hence, they are not exhaustive in the scope of the problem or the depth of investigation. Their sometimes fragmentary character reflects the unsystematic and incomplete character of research on ageism in AI.

Age bias in algorithms and digital datasets (technical level)

Single studies indicate that age bias can occur in machine learning models or big data approaches. Diaz and colleagues ( 2019 ) analysed the treatment of age-related terms across 15 sentiment analysis models and 10 widely used GloVe word embeddings. Sentiment analysis is often used to measure opinions in product reviews or financial markets, but they are also useful in analysing political opinions expressed on social media. In the case of age-related bias, automated methods of opinion polling may falsely report more negative attitudes towards political issues or financial investments regarding age-related concerns, such as Medicare and Social Security. The study showed evidence that sentiment analysis disclosed significant age biases: sentences with ‘young’ adjectives were 66% more likely to be scored positively than identical sentences with ‘old’ adjectives (Díaz et al. 2019 ). Moreover, it proved that various sentiment analysis methods impact bias in outcomes, particularly that tools validated against social media data exhibit increased bias. Another study found relevant differences in the outcomes of face recognition models for predicting age and gender from photographs (Meade et al. 2021 ). The researchers used convolutional neural net (CNN) which is an advanced deep learning technique to analyse visual imagery. The model was trained on photos of celebrities from IMDb and Wikipedia, where their pictures were matched with their age, as well as data for general public from UTKFace data set. The results showed that age estimation generally performed poorly on older age groups (60 +), an effect which was compounded by gender and race; the age estimation worked disappointingly on older women of colour. Recently, another study showed that, when evaluating systems for facial emotion recognition (FER) using various classification performance metrics, the state-of-the-art commercial systems performed the best when recognizing emotions in younger adults (aged 19–31), and worst for the oldest age group (61–80) (Kim et al. 2021 ). In a similar vein, Korean researchers confirmed age, gender, and racial biases, including the intersectional bias, in popular face recognition models by using the face embedding association test (FEAT) to measure the biased way specific groups are being associated with particular attributes (such as pleasant/unpleasant, likeable/unlikable) (Lee et al. 2022 ).

The use of these ML systems and technologies is becoming widespread in everyday life and the age biases identified in ML models can have a direct or indirect impact on the ageing populations. It can be speculated that the effects on older persons will be predominantly tangible in the areas where for example face recognition systems will inadequately identify the age of the person or the identity of the person due to changes in facial biometric image due to age. Biometric technology has the potential to impact older persons more directly due to the way biological ageing impacts bodily functions. Touch, imaging, speech, and body language will all be impacted due to ageing processes. For instance, risk can relate to the age-linked fading away of fingerprints impacting the accuracy of their recognition (Rosales and Fernández-Ardèvol 2020 ).

In the case of face recognition, the major application where age is explicitly deployed, resides in the realm of age estimation techniques. The age estimation algorithms working on visual data require large datasets for training. Since an algorithm is only as good as the data it works with (Barocas and Selbst 2018 ), this is where ageism in digital datasets becomes apparent. Study of ageism in big data approaches confirms that the most ageist practices in intelligent systems design are related to data set limitations of the representativeness of the studied population and particularly to recruitment procedures that tend to exclude older people (Rosales and Fernández-Ardèvol 2019 ; Sourbati and Behrendt 2020 ). Most of the datasets include radical age cutoffs for their data. For example, The Face and Gesture Recognition Research Network (FG-NET) ageing database contains on average 12 pictures for each of its 82 subjects in varying ages between 0 and 69 years. Other datasets limit the ages at 70 (Tufts-Face-Database), 77 (MORPH), and the list continues. There are some outstanding exceptions to the rule, such as UTKFace dataset, where the photos depict adults up to the age of 116 years. The cutoffs are also visible in training data used for proprietary algorithms developed in the expanding sector of AI industry. For instance, the company YOTI configured its training data set with age brackets of 13–60 years and the highest estimated errors in performance of their algorithm are seen for the age group of 50–60 years. In its White Paper the company admits that, “it seems reasonable to hypothesise that any error will tend to be higher for older people than younger people, because older people will have been exposed to various unpredictable environmental factors for longer” (YOTI 2020 ). However, it is not only the age cutoffs that render the data infrastructures problematic for older populations. The human labelling processes and the classification of data into categories are also highly problematic, as images can be tagged in stereotyping and even offensive ways. In her intensive investigation of the ImageNet database—one of the most powerful visual data infrastructures—Kate Crawford demonstrates that the classifications for human images are, regardless of the supposed neutrality of any particular category, gendered, racialized, ableist, and ageist (Crawford and Paglen 2019 ).

Stereotypes, prejudices, and ageist ideologies in the tech industry (personal/actor level)

The second identified dimension of AI ageism refers to the individual level of ageism among actors in the tech industry. Tech culture is homogenous in terms of age, ethnicity, and gender. It is young, predominantly populated by men of Caucasian or Asian origin, which is associated with the structural discrimination embedded in digital technologies (Wachter-Boettcher 2017 ). Ageism in the IT sector and tech industry is a well-known fact (Cook 2020 ; Marshall 2011 ; Rosales and Svensson 2021 ). In fact, Gullette notes “Silicon Valley can in fact be the most ageist place on the Earth” (Gullette 2017 ). Silicon Valley is the US centre for innovative technology companies and home to 2000 tech companies, the densest concentration in the world. Even more importantly, most of these companies are also leaders in their industries. These include software, social media, and other uses of the Internet, as well as AI. Silicon Valley sets standards for other firms. Companies around the world look to the tech giants to incorporate the same business models and management styles (Galloway 2018 ). Yet, these companies show rampant signs of various types of systemic biases and prejudice (Cook 2020 ; Park and Pellow 2004 ; Shih 2006 ; Wynn 2020 ), ageism being one of them. Large tech companies have phased out older workers over the past few years and continue to discriminate against anyone old enough to remember the 1980s. In 2007, a then 22-year-old Mark Zuckerberg famously admitted that tech companies should not hire people over 30 years because “Young people are just smarter”. Surveys carried out among tech workers only confirm that those blatant ageist statements are in fact the reality for the workers in Silicon Valley. A survey among American tech worker s shows that 76% of respondents say ageism exists in tech globally, whereas 80%of those in their late 40s say they are concerned their age (and ageism attitudes) will affect their careers (Dice 2018 ).

Ageism in the tech sector is specific as it is targeted at persons at a much younger age than in other sectors of the economy, where ageism starts to be experienced by someone as young as 45 years of age (Harris et al. 2018 ). In fact, online survey among tech workers shows that one-fourth of respondents in their early 30s already regard age as a barrier in getting a new job (Dice 2018 ). Another study, carried out among UK tech workers, revealed that on average, across the wider workforce people said they first started to experience ageism at work at an average age of 41 years—while IT and tech workers say they first experienced this at an average age of 29 years (Sevilla 2019 ). Therefore, it is argued that ageism in AI is partially fuelled by age biases prevalent in the tech industry, which are visible both in recruitment and hiring practices, as well as ideologies and beliefs related to concepts such as innovation and progress of the tech industry (Stypińska, Rosales, Svensson, forthcoming). Hence, the concept and ideology of disruptions coming from innovation and business theory can clarify ageism in tech. A disruptive innovation, which AI technology certainly is, is an “innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market-leading firms, products, and alliances” (Rahman et al. 2017 ). Disruptions are what drives innovation, progress, and success in Silicon Valley. These are the stories of “unicorn-startups” 2 with implausible success or established tech giants that started out as the hobby of two geeks in their early 20s in a garage, which hold the collective imagination and frame the way success is understood. Those stories create an ideology that renders anyone over 30 years as incapable of innovation. Since AI research and development is at the forefront of technological innovation globally, it is plausible to assume these ideologies impact the way AI developers, software engineers, data scientists, and other AI practitioners work, think, and solve problems. In fact, studies on ageism in digital platforms indicate that the homophily of the community of software developers, who are predominantly young men of high socio-economic status, contributes to baking the prejudices and biases into the algorithms (Rosales and Fernández-Ardèvol 2020 ).

Ageism in discourses about AI  (discourse level)

Studying discourses about algorithmic systems and processes is crucial to our understanding of the social power of algorithms and AI (Natale 2019 ; The Royal Society 2018 ). Next to the material power, algorithms can exert a discursive power revealing their political entanglements. The way algorithmic systems are spoken about is part of how they are fused into social and organizational structures and how they shape our imagination. Discourse is both constitutive and constituted where it simultaneously shapes, and is shaped by, social structures. Moreover, it is important to investigate how discourses about algorithms shape the broader debates about social change and development, and especially the role innovations play in the processes of development of AI (Beer 2017 ). By discourses on AI ethics, I refer here to the way the principles of AI ethics, such as fairness, bias, inclusivity, and diversity, are conveyed in documents and public debates with a particular focus on which social groups are iterated as those endangered by infringement of those principles.

The majority of initiatives and/or documents for inclusion and promotion of diversity in AI community are targeted at gender and racial minorities. Publications on bias in AI admit that “the most discussed forms of “unfair bias” in the literature relate to particular attributes or groups such as disabilities, race, gender, and sexual orientation” (Silberg and Manyika 2019 ). The most common formulations in the documents were, for example, “the diverse groups in terms of race, culture, gender, and socio-economic backgrounds”, 3 or: “Hiring diverse backgrounds, disciplines, genders, races, and cultures”. 4 The mainstream debates around the issues of AI fairness and inclusivity tend to omit the category of age and older persons. The absence can be observed in two forms: invisibility of old age as object of discussion and lack of representation of older persons as subjects in those discourses. This might be due to several factors, such as a relatively weak social representation of the rights of older persons in the area of AI, or ideologies and stereotypical beliefs about older persons as users or non-users of AI applications held by software producers.

Similar conclusions were drawn by a team of researchers at the University of Toronto who performed an analysis of documents listed in the repository of AI ethics guidelines created by Algorithm Watch, which as of April 2022, contains 173 documents created by governments, private entities, civil society, and international organizations. The search terms used in the analysis were “ageism” and similar notions like “age bias”, “age”, “old/older”, “senior(s)”, and “elderly”. The researchers found that in the 146 analysed documents that were available at that time, only 34 (23.3%) mention ageism as a bias for a total of 53 unique mentions. Out of these, 19 (54.7%) merely listed “age” as part of a general list of protected characteristics next to gender or race (Chu et al. 2022 ). The authors conclude that only 12 (8.2%) of the analysed documents provided somewhat more context about bias against older adults, but often no more than one or two sentences.

Algorithmic discrimination–automatic decision-making (ADM) systems and their outcomes (group level)

This section highlights areas where deployment of AI can result in harm for older persons as a distinct demographic group. Two terms: “algorithmic discrimination” and “discrimination of algorithms” describe the negative outcomes of automated decision-making (ADM) systems or classification systems used in multiple AI applications (Kleinberg et al. 2018 ; Köchling and Wehner 2020 ; Orwat 2020 ). Discrimination occurs when the outcomes/outputs of ADMs infringe on the rights of persons based on their “protected” characteristic, such as gender, race, age, disability, or nationality (Orwat 2020 ). Although not all ADM systems are powered by AI, there is a stable increasing tendency towards more deployment of AI in those solutions (Chiusi et al. 2020b ). Today, ADM systems intertwine with critical moments during a person’s life, for instance in shaping institutional access to higher education, insurance, financial services, and hiring decisions in the labour market (O’Neil 2016 ). For several years automatic decision-making systems have been under scrutiny for their opaque, erroneous, harmful or just false outcomes (Chiusi et al. 2020a , b ; Noble 2018 ; O’Neil 2016 ). These systems can have the purpose of predicting, identifying, detecting, and targeting individuals or communities. ADMs are being increasingly used by private companies (e.g., in recruitment and personnel management) and public sectors (health care, education, social services, law enforcement) (Mittelstadt et al. 2016 ; Orwat 2020 ; Reisman et al. 2018 ).

With regard to ageing populations, the risk of discrimination lies in the way the biased algorithms are being used in practice in those realms of social, economic and cultural life where they could infringe on the rights of older persons. Their increased use in recruitment and hiring practices can threaten the way discrimination cases will be possible to detect (Köchling and Wehner 2020 ). Age discrimination in employment is one of the most wide spread types of discrimination in the labour market (Stypinska and Turek 2017 ) and the attempts to fight it with anti-discrimination legislation are challenging. An investigation by ProPublica revealed that Facebook ads can be and are targeted at precise age groups allowing employers to recruit job applicants that are below a certain age. The category of age can easily be used to create Facebook’s “affinity groups”, used to narrow or refine audiences, which are then used for targeting job advertisements to pre-selected candidates (Ajunwa 2019 ). Moreover, the already famous case of Amazon’s hiring algorithm downgrading the resumes of women gives a hint of what it could mean for older workers. For example, if a company tended to hire candidates who graduated from school (or landed their first job) by a certain date, it might introduce a bias towards younger candidates. The company’s software developers would need to actively monitor the system to ensure that something like that was not happening.

Another example where ADM could negatively impact large groups of older adults is the banking sector. In fact, more than the other ‘protected attributes’, age has the potential to affect credit access in a selective fashion, reducing it for some segments of society, while remaining benign for others. If a mortgage lending model found that older individuals have a higher likelihood of defaulting, it might reduce the lending options based on age leading to excluding older adults from those services (Silberg and Manyika 2019 ). The potential bias that such algorithms may generate against certain groups of people has also been increasingly acknowledged. In fact, in the proposed AI regulation of EU (2021), AI-systems used for credit scoring are designed as ‘high-risk’ and subjected to stringent regulations, which also necessitates further research in this area to collect empirical evidence of how these systems affect older demographic groups. The financial sector needs to be singled out as critical for investigation, since the digital exclusion of older adults has already raised serious concerns and attention. A campaign and a petition signed by more than 600,000 people called "I may be old, but I'm not an idiot" started by a Spaniard Carlos San Juan to stop exclusion of older people by banks emerged as a loud voice of those left behind by rapid digitalization processes (Müller 2022 ).

Documentation of severe social and personal consequences for individuals wronged by the outputs of such systems has raised questions about their fairness and even legality (Richardson 2019 ). A discussion is necessary which kind of ADM systems need to be assessed to what depth, depending on the potential damage for individual and society as a whole (Zweig et al. 2018 ). There should be a systematic assessment of the way in which older populations might be impacted by the increasing deployment of those systems in the private and public sector.

Marginalization and exclusion of older persons as users (user level)

The last form of AI ageism discussed shortly is the exclusion as users. The compounded effects of ever-increasing complexity of digital technology and the already mentioned low algorithmic awareness among older adults (Gran et al. 2020 ) create structures which marginalize or exclude older persons as end users of AI technology. Ageism in technology design is not a new phenomenon. Studies exhibit different patterns in use of digital technology by older adults (Barbosa Neves and Vetere 2019 ; Gallistl et al. 2020 ). They show that older adults have very heterogenous patterns in use and “non-use” of the Internet (Gallistl et al. 2020 ); that older people are prone to self-stereotypes and self-exclusion in use of digital technology (Köttl et al. 2021 ); how older adults use smart watches and augmented reality games (Schlomann et al. 2019 ; Seifert 2020 ). Research suggests that older adults are generally portrayed as frail when described as users of AI assistive technology (Burema 2021 ). However, the question arises whether the data-driven technologies using AI and ML technologies pose any additional risks of harm and ageism?

An example of AI technology where older adults might experience ageism as users is the group of products called “conversational AI” which includes virtual assistants and chatbots. Conversational AI agents are increasingly used by companies for customer services and by consumers as personal assistants. The most famous examples of personal virtual assistants are probably Apple’s Siri, Amazon’s Alexa or Microsoft’s Cortana. Chatbots, used predominantly in customer service, are software applications used to conduct an online conversation via text. This technology is not scripted by humans and responds to human interlocutors using learning and human-guided algorithms (Schiebinger et al. 2011–2020 ). The challenge is that unless corrected for, the virtual assistant also learns and replicates human biases in the dataset (Schlesinger et al. 2018 ). Recent studies showed that virtual assistants and chatbots can exhibit racism and sexism (Schiebinger et al. 2011–2020 ; Cave and Dihal 2020 ). Similar problems might occur when testing virtual assistants and chatbots for their sensitivity to issues of age and ageism. Claims are being made that chatbots and virtual assistant are already ageist and sexist in the way they are profiled (usually as young women), but the question is whether they could also be ageist in their conduct (e.g., treat older customers unfairly or exhibit ageist stereotypes—jokes, etc.). Furthermore, if assistants implemented in a health care application perform more poorly with seniors, it could impact the quality of care provision and ultimately the health of the user. Further examples of areas where older adults might experience ageism as users of AI-driven technology include diverse smartphone applications where incomplete data for older age cohorts might result in inaccurate results (Rosales and Fernández-Ardèvol 2020 ). Further research is needed to reveal other aspects of this form of AI ageism .

Conclusions

The aim of this paper is to turn scholarly attention to ageing populations as a socio-demographic group that can be defined as vulnerable, in relation to data-driven social transformations resulting from increasing use of AI technology in all realms of modern life. By introducing a new concept of AI ageism , this article contributes to the scholarly efforts to advance our knowledge of the harmful ways AI can impact the vulnerable group of older adults. The working definition of AI ageism with its five interrelated forms aspires to embody the complex and multifaceted character of ageism in the realm of AI. Furthermore, I argue that it is essential to go beyond the understanding of inequalities in AI dictated by the narrow use of the term “bias”. As social scientists, we are aware of the structural, institutional, and otherwise “non-quantifiable” forms of injustice and oppression in our social world (Wachter et al. 2020 ; Zajko 2021 ).

The increase in datafication, the advancements of AI such as deep learning and proliferation of operative AI in society, and the lack of knowledge of ageism in AI mutually reinforce the urgency of knowing how the category of age relates to AI and how principles of AI for social good could be implemented here. The COVID-19 pandemic accelerated the processes of digitalization and datafication. Since the beginning of this health crisis, data and advanced digital technologies have played a central role in how we respond and adapt to this situation. As a result, ethical principles such as trust, transparency, accountability, and privacy have been put to the test on a global stage. The current debates on ethical AI happening globally at all levels of stakeholders, from public entities, through large and small companies, AI practitioners and scholars from various disciplines show the urgency of the need to regulate AI with regard to its ethical standing. The landscape of currently drafted regulations, recommendations and guidelines for ethical AI is voluminous and diverse (Hagendorff 2020 ). Concurrently, in the “2021 Pew Research Centre Report”, experts expressed doubt that ethical AI design will be broadly adopted as the norm within the next decade, pointing to several challenges of such an ambitious endeavour, including (among others): the relational character of ethics, the importance of specific context of applying AI, the proprietary, hidden and complex nature of most AI design, the obstacles in governance of ethical AI, as well as the nature and relative power of the actors involved in any given scenario (Rainie et al. 2021 ). It is particularly the relatively low power of representation of ageing populations in the many phases of AI development, as well as their invisibility in the discourses and debates on ethical AI that requires our consideration.

Attempts to create more inclusive, diverse, and fair AI are necessary, even if flawed, inconsistent, or potentially unimplementable, as they have the potential to raise public awareness to these intricate issues. A large portion of the older population is unfamiliar with the complexity of AI, algorithms, or big data (Gran et al. 2020 ) and also do not want to engage with this new technology due to a lack of trust in these developments. To enable informed decisions on their part, communicative efforts must be made to explain various aspects of AI in formats that older age groups can respond to. The theoretical frameworks, as well as the emerging social movement captured under “AI for social good”, thus qualify as such an attempt and open a space for shaping the ways in which AI is and will be used in society. In particular frameworks emphasizing the humanistic approach to AI, such as AI for People, have great potential to include the perspectives of the vulnerable, underrepresented, and precarious social groups and include them in the participatory schemes of AI design. Victoria Dignum, an AI ethicist, notes “the elephant in the room is the huge blind spot we all have about our own blind spots. We correct bias for the bias we are aware of. An inclusive, participatory, approach to design and development of AI systems will facilitate a wider scope” (Dignum 2021 , p.7).

The research on bias in AI has gathered significant momentum. In June 2021, the European Commission Research Program, Horizon Europe, issued a call for proposals to study bias in AI. However, in alignment with the argumentation made in this article, two shortcomings can be observed. Firstly, this call refers primarily to gender and race discrimination, although allows for incorporation of further biases. Secondly, it favours research on technical aspects of the biases with little mention of the socio-cultural or political implications of research on AI biases. Undoubtedly, the research on bias in AI needs to go beyond these two limitations and include not only age and ageing populations as relevant categories for researching digital inequalities, but also pay attention to the way these inequalities can be compounded in an intersectional way. Demographic change and the increasing proportion of older people in the population structure will have grave implications for the way digital and data-driven technologies will be used. Similarly, AI has the potential to transform the way we age and experience old age. Future research and efforts to design ethical AI should bring attention to synergies between these two megatrends and avoid operating in a vacuum or with a limited vision of future.

Open Access funding enabled and organized by Projekt DEAL.

1 Including the work of such organizations or platforms as: Algorithmic Justice League, AI for People, or Algorithm Watch.

2 The term „Unicorn-Startups” refers to those companies with a valuation more than $1 billion.

3 From: “The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems”.

4 From “In pursuit of inclusive AI”, publication of Microsoft.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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