Unpacking Workplace Microaggressions and Inclusive HR Practices Across Age Groups and Identities

Abstract

Age-related microaggressions are short, everyday comments, actions, or environmental cues that, whether intentional or not, send messages of hostility or exclusion. These remarks or behaviors often target people who do not fit dominant social norms. Although each incident may seem minor on its own, together they can cause serious harm to individuals and organizations. Using cross-sectional data from 1702 Italian in-office employees working for the public administration in the Emilia–Romagna region of Italy (Mage = 50.6, SD = 9.35), we explored the prevalence of age-related microaggressions across different public organizations’ employees in Italy and examined how perceptions differ among demographic groups. Findings reveal that younger employees (ages 23–39) reported significantly higher levels of perceived age-related microaggressions. The perception of age-inclusive HR practices was negatively correlated with microaggressions and positively correlated with job satisfaction, psychological safety, and intrinsic motivation. Additionally, the strength of this association was not uniform; it was strongest for the youngest and oldest employees. These results underscore the importance of attending to demographic differences in the strategic management of human resources. As demographic change transforms workforces globally, age-inclusive HR practices emerge as strategic tools that organizations can use to mitigate subtle discrimination and support employees of all ages.

1. Introduction

Age-related discrimination remains a persistent issue in contemporary organizations, despite the growing emphasis on diversity and inclusion (D&I). In fact, workplaces are transforming to include a wider range of age groups, increasing the risks of age biases and prejudice (Kunze et al., 2011). Traditionally, organizations have centered on explicit biases and discrimination, both of which have been greatly constrained via legislative mandates and organizational policies (Hasyim & Bakri, 2023). For example, through the implementation of formal policies addressing overt forms of discrimination, such as discriminatory hiring practices. Nonetheless, subtle slights persist in organizational settings, often undetected, undermining employee well-being and performance (International Labour Office, 2003). In this study, we specifically address age-related microaggressions. Microaggressions, defined as “brief, intended or unintended, commonplace verbal, behavioral, or environmental indignities that communicate derogatory, hostile, or negative insults and slights toward people who do not classify within the ‘normative’ standard” (Johnson & Johnson, 2023; Sue et al., 2007), are hard to monitor and regulate because they occur informally within daily workplace interactions. The ‘micro’ aspect refers not to the impact on the recipient, which can be significant and cumulative, but to the seemingly small scale of the individual incident and the aggressor’s perception of it as trivial or harmless (Johnson & Johnson, 2023). However, researchers emphasize that the impact is far from ‘micro’, comparing the cumulative effect to “death by a thousand cuts”, leading to substantial psychological and physiological distress over time (Johnson & Johnson, 2023; Smith & Griffiths, 2022). These aspects of microaggressions highlight two main points driving this study. First, whether the combined vulnerability from belonging to multiple marginalized identities makes people more likely to perceive age microaggressions. Second, whether the ambiguous and informal nature of age microaggressions means that organizations need to have specific policies to address them. Specific age-inclusive HR practices offer promising preventative tools for fostering awareness and belonging (Smith & Griffiths, 2022). With this work, we aim to address these two points. After introducing the definition and mechanisms of microaggressions, we used cross-sectional data to explore the conditions under which specifically age-related microaggressions are perceived more frequently within the workplace, specifically in terms of cumulative vulnerabilities (i.e., gender, disability, and age). Then, we look at the potential of HR practices for buffering microaggressions’ perceptions. Specifically, we explore the relationship between age-inclusive HR practices, age-related microaggressions, and work outcomes (i.e., job satisfaction, intrinsic motivation, psychological safety) and whether cumulative vulnerabilities such as gender and disability moderate the relationship between inclusive HR practices and age-related microaggressions.

These questions matter more as the demographic transformation reshapes labor markets worldwide. Population aging is a global phenomenon, and policymakers in many countries aim to retain employees up to and beyond traditional retirement ages (Fasbender et al., 2026). Organizations therefore face workforces that span a wider range of generations than in the past, a shift that makes age diversity a strategic concern for human resource management (Kunze et al., 2011). Within this scenario, the HR function is itself transforming. It is moving from an administrative and compliance-oriented role toward a strategic one, in which inclusion is embedded in the design of recruitment, training, evaluation, and development systems (Eshete & Birbirssa, 2024). The Italian public administration, where employees in the present sample averaged more than fifty years of age, offers an informative setting in which to observe how this transformation unfolds.

This study contributes to the literature in three ways. First, it extends research on microaggressions by focusing specifically on age-related forms, which remain underexplored. Second, it examines how multiple demographic characteristics are associated with differences in the perception of age-related microaggressions. Third, it highlights the role of age-inclusive HR practices as potential organizational resources associated with fewer such experiences and better employee well-being, and shows that this association is not uniform across age groups. Findings can inform future research on workplace age diversity and inclusion.

2. Theoretical Framework

2.1. Microaggressions: Definition and Mechanisms

According to Sue et al. (2007) and Torino et al. (2018), microaggressions appear in three distinct types: microassaults, microinsults, and microinvalidations. Microassaults are more explicit insults, mainly involving verbal and/or nonverbal attacks. These attacks are intended to harm the targeted individual through actions like name-calling and avoidance. Microinsults are characterized by comments or actions that convey rudeness or insensitivity, demeaning a person’s identity or heritage. These are usually subtle and not intended to be malicious by the perpetrator, but they clearly deliver an underlying insult to the recipient. Examples include assuming an older worker is technologically incompetent or expressing surprise at a minority member’s eloquence. Microinvalidations include communications that deny or dismiss thoughts, feelings, or lived experiences and realities of marginalized individuals. This can involve denying the existence of discrimination, minimizing their experiences, or suggesting that discriminated people are too sensitive.

Across conceptualizations, an important feature of microaggressions is their often ambiguous and unintentional nature (Johnson & Johnson, 2023; Smith & Griffiths, 2022). Individuals enacting microaggressions are frequently unaware of their biases or the harm their words or actions inflict, and this lack of awareness makes microaggressions particularly challenging to address directly (Johnson & Johnson, 2023). A meta-synthesis study by Smith and Griffiths (2022) examined subtle slights, including microaggressions, everyday discrimination, and workplace incivility, to identify the salient characteristics of these behaviors. Researchers systematically reviewed 338 papers and found microaggressions usually involve violations related to identity, communicating negative or derogatory messages about a person’s group membership (e.g., age, race, gender) through low-intensity comments or behaviors that happen daily. Additionally, a defining feature of microaggressions is that the perpetrator’s intent is often perceived as ambiguous or unintentional by the target, distinguishing them from clearly malicious acts. Because of these features, microaggressions can have heavy consequences on those who receive them. Meta-analytical findings (Costa et al., 2022) from 141 studies on microaggressions’ frequency showed that they were negatively related to psychological well-being, physical health, and job outcomes such as job satisfaction and turnover intentions, and yet, less is known about precursors of perceived microaggressions. Because microaggressions are subtle, their perception is subjective and may vary across individuals and groups. This makes it particularly important to examine who experiences them and under what conditions.

In the workplace, age is a key dimension of diversity shaping social relations and expectations. Unlike other demographic characteristics, age is dynamic and universal (Johfre & Saperstein, 2023), yet it is also associated with deeply embedded stereotypes and norms. For example, regarding expectations of productivity and competence (Fiske et al., 1999, 2002), older adults are often stereotyped as warm but incompetent (Fiske et al., 2002; Rothermund & De Paula Couto, 2024). This ambivalent stereotype may make them especially vulnerable to subtle forms of bias, including microaggressions. These threats often trigger negative emotions, like stress and anger, and lead to coping behaviors, such as withdrawal or disengagement, as people try to protect their self-concept (Johnson & Johnson, 2023; Peng et al., 2024; Waligóra, 2024). Additionally, modern workplaces are becoming increasingly age-diverse due to demographic changes such as extending working life (Kunze et al., 2011). This growing salience heightens the need to address subtle forms of discrimination that specifically target age, such as age-related microaggressions. However, much of the current research on ageism focuses on clear forms of discrimination, such as in hiring or firing, or on broad age stereotypes, instead of looking at the subtle and everyday moments that make up microaggressions. Thus, even though awareness of microaggressions is growing and the workforce is becoming more age-diverse, there are still major gaps in how we understand age-related microaggressions. Hence, in this study we start by exploring the following research goal:

R1. To determine the prevalence and frequency distribution of perceived age-related microaggressions in a sample of Italian public administration employees.

It is important to notice that people can interpret and be affected by microaggressions differently. Presuming that microaggressions mostly target individuals’ identities (Smith & Griffiths, 2022), it is likely that people who belong to multiple marginalized or systemically discriminated identities (e.g., women, disabled) may experience variability in how they perceive age microaggressions. The 2024 EU Gender Equality Report (European Commission, 2024) suggests how systematic discrimination continues to affect underrepresented groups, manifesting across multiple dimensions. For example, women continue to face persistent barriers in professional settings. There are significant gender differences in employment levels and representation in leadership roles. The most striking finding concerns women who have disabilities. Only 48 percent of them are employed, compared to 54 percent of men who have disabilities and 69 percent of women who do not have disabilities. Furthermore, the report emphasizes that intersectional discrimination creates distinct and complex challenges for individuals who are subject to more than one form of marginalization.

Based on these findings, we expect individuals from underrepresented populations (e.g., women and people who have disabilities) to be more likely to perceive age-related microaggressions in the workplace. In fact, traditional approaches to workplace evaluation often rely on single-axis frameworks that isolate demographic categories such as gender, race, or age. However, such perspectives fail to capture how individuals’ intersecting identities shape their workplace experiences. The MOSAIC framework (Hall et al., 2019) builds on intersectionality theory (Atewologun, 2017; Cho et al., 2013) to provide a process-based explanation of how demographic categories interact and relate to workplace experiences. Rather than treating identity dimensions independently, the MOSAIC framework explains how people cognitively integrate stereotypes from different categories, for example, gender, race, and expected prototypical behaviors (e.g., masculinity). This integration results in either amplified or diluted stereotype content, which then informs expectations and interactions. For instance, a Black woman may be stereotyped differently from a White woman due to the implicit association of Blackness with masculinity, which shifts how her behavior, such as dominance, is interpreted (Livingston et al., 2012). By applying this framework, this study investigates how workers from different demographic backgrounds experience microaggressions at the intersection of multiple identities, such as age, gender, and disability status. The analyses focus on demographic group comparisons as a preliminary step toward understanding these dynamics, recognizing that examining how identity combinations interact statistically remains an important direction for future research.

Overall, we know little about how people from different employee groups (in terms of age, gender, and disability) experience and interpret these moments, especially when they hold intersecting identities, such as older women or younger employees with disabilities. Thus, in this study we further explore the following research goals:

R2. To determine differences in perceptions of age-related microaggressions in the workplace among different demographic groups, particularly in women, people with disabilities, and across age groups.

R3. To examine the risk of being a victim of perceived age-related microaggressions in the workplace based on those demographic characteristics with significant frequency.

2.2. Inclusive Human Resource Practices

While diversity, equity, and inclusion policies are critical for organizational change, they often fail to address subtle slights. The ambiguous and informal nature of microaggressions makes them particularly difficult to address through traditional anti-discrimination policies. For example, Rambe (2024) suggested that these policies often fail to address the informal and interpersonal dimensions of discrimination in the workplace.

Hence, microaggressions remain a challenge, especially in age-diverse workplaces where stereotypes about workers’ competence and abilities persist and erode workers’ well-being. For example, Watermann et al. (2023) showed that age-based exclusionary behaviors negatively influence occupational future time perspective (OFTP), career exploration, and retirement intentions, and that even subtle, microaggressive behaviors lead to long-term consequences such as decreased work engagement.

Additionally, research has shown that individuals holding negative stereotypes about older workers’ competence not only contribute to a discriminatory work environment but also experience negative consequences themselves (Bellotti et al., 2025). Using social identity and social capital theories, Bellotti et al. (2025) found that for younger and middle-aged employees, holding negative age-related stereotypes had a negative relationship with individual (i.e., affective well-being), group-related (i.e., group involvement and group relations), and organizational outcomes (i.e., perceived organizational climate). These effects were mediated by ineffective work interactions, further increasing organizational dysfunction.

Thus, a growing number of studies are supportive of inclusive human resource practices for building workplaces where employees feel valued, respected, and protected from discrimination (Eshete & Birbirssa, 2024; Shore et al., 2018). Strategic human resource management plays an important role in this process by weaving diversity and inclusion into organizational culture and everyday practices. This includes designing fair systems for recruitment, selection, training, and performance evaluation, as well as implementing policies that reduce bias and address explicit and subtle forms of discrimination, such as microaggressions (Eshete & Birbirssa, 2024; Shore et al., 2018). Shore et al. (2018) point to several core elements of inclusive human resource practices, including helping employees feel psychologically safe, involved in their work, respected by others, valued for who they are, and free to express themselves authentically. Instead of merely prohibiting discriminatory actions, inclusive HR policies create a culture of belonging, making all employees, regardless of age or background, feel fairly treated. To this end, human resource practices may focus on specific needs. For example, gender-inclusive policies or accommodations for employees with disabilities can help create a more supportive environment (Rasheed et al., 2024; Van Berkel & Breit, 2024). Similarly, inclusive practices such as age-inclusive HR practices can boost age-inclusive work environments and reduce the likelihood of age-related microaggressions (Boehm et al., 2014). For example, Boehm et al. (2014) found that HR practices focused on age inclusivity, i.e., promoting fairness across generations, help create a workplace climate of psychological safety. These findings suggest that inclusive HR practices do not merely serve as corrective mechanisms but are rather proactively cultivating an organizational culture where employees of all ages feel valued and respected, which reduces explicit and subtle discrimination. Waligóra (2024) expanded the discussion by evaluating how inclusive HR practices affect employee perception of fairness and organizational identification. Their findings showed that employees who experienced fewer incidents of age discrimination showed stronger organizational commitment and engagement.

Age-inclusive HR practices that promote equality and a mutual respect environment among employees create conditions that prevent age-related microaggressions from developing (Russo et al., 2020). For example, Thompson (2020) suggests that HR professionals can learn what microaggressions are, understand their impact, and speak out against biased behavior and policies. These efforts make it less likely that microaggressions will happen (Johnson & Johnson, 2023). Additionally, practices like mentorship, open communication, and ongoing training programs can help prevent age discrimination (Waligóra, 2024). Thus, in this study we aim to explore the following:

R4. To test correlations between perceptions of age-inclusive HR practices, age-related microaggressions, and work outcomes such as job satisfaction, psychological safety, and intrinsic motivation.

Implementing inclusive HR into practice requires careful planning. Some HR practices, even when well-intentioned (e.g., offering flexible work options) can unintentionally widen gaps if they are not designed well or used equally by everyone (European Commission, 2024). For example, when most women use flexible work arrangements, such as digital or remote work, they often face negative career outcomes and greater job insecurity. These findings show that while inclusive HR practices are important, their impact depends on how thoughtfully they are designed and implemented, especially in terms of who the practices are supposed to address. For instance, the intersection of multiple identities may influence how HR practices are perceived and explain different effects on their intended outcomes. Additionally, studies on age-inclusive HR practices remain relatively sparse compared to those examining gender or ethnicity-related policies (Kunz & Ludwig, 2022). Existing literature tends to prioritize age diversity in recruitment and hiring, emphasizing the need to avoid age bias in candidate selection (Kunz & Ludwig, 2022). However, much less attention is given to how age-inclusive practices function after individuals are integrated into the workforce (Boehm et al., 2014; Posthuma & Campion, 2009). This gap is critical, especially given the increasing diversity in organizations and the subtle forms of discrimination that employees may experience throughout their employment journey. This is especially true if we consider the reciprocal impact that being exposed to daily microaggressions can have on the perceptions of HR policies, possibly increasing employees’ skepticism of formal DE&I efforts or perceiving HR policies as performative rather than genuinely inclusive (Mor Barak et al., 2022). In such cases, microaggressions not only erode individual well-being but may also undermine the perceived legitimacy and efficacy of inclusion initiatives. Hence, informed by these findings and the MOSAIC framework, we aim to explore the following research goal:

R5: To investigate whether the relationship between perceptions of age-inclusive HR practices and perceptions of age-related microaggressions varies according to demographic characteristics.

3. Materials and Methods

3.1. Participants and Procedure

Data were collected using a cross-sectional survey design. Participants were employees of several public administration organizations located in the Emilia-Romagna region of Italy. The data were collected as part of a broader organizational assessment conducted by a consulting company on behalf of the Italian Public Administration. Participation was voluntary and anonymous, and the inclusion criterion was active employment within the public administration at the time of data collection. Participants consisted of 1702 Italian in-office employees working for the public administration in Italy, with a mean age of 50.6 years (SD = 9.35), and an age range from 23 to 67. The sample included 567 (33.3%) males and 1110 (65.2%) females, while 25 (1.5%) preferred not to answer. Furthermore, 114 participants, corresponding to 6.7% of the total sample, reported having a disability. We adopted Podsakoff et al.’s (2003) procedural recommendations in that we clarified to participants that there are no right or wrong answers and instructed them to respond honestly.

Harman’s single-factor test was conducted to assess common method bias. A principal component analysis was performed on all 20 items from the five scales included in the study. The single extracted component accounted for 32.7% of the total variance, well below the 50% threshold commonly used to indicate problematic common method variance (Podsakoff et al., 2003). These results do not indicate the presence of a dominant common factor.

As the data were fully de-identified before receipt by the research team, ethical review was waived by the University of Bologna Institutional Review Board. Participants were informed about data processing in compliance with the Italian Legislative Decree No. 196/2003 and EU GDPR 2016/679, and informed consent was obtained from all participants.

3.2. Measures

Variables such as demographics, age-related microaggressions, age-related HR practices, job satisfaction, intrinsic motivation, and psychological safety were assessed using the scales reported below.

Age-related Microaggressions (Racial and Ethnic Microaggressions Scale; Nadal, 2011). Microaggressions were measured using an adapted version of the original Workplace and School Microaggressions subscale, changing “because of my race” with “because of my age”. Responses were given on a 6-point Likert scale (0 = Never, 5 = Five or more times), assessing how frequently participants experienced subtle age discrimination in the workplace over the past six months. A sample item is “My opinion was overlooked in a group discussion because of my age.” Cronbach’s alpha was 0.92. No validated scale measuring age-related microaggressions currently exists in the literature. The REMS was therefore selected for adaptation, as its workplace subscale is structured around professional interpersonal contexts and its item format is sufficiently flexible to accommodate adaptation across discrimination types. To assess the construct validity of the adapted scale, we conducted a confirmatory factor analysis (CFA) using the diagonally weighted least squares (DWLS) estimator, appropriate for ordinal response data (N = 1700). A single-factor model was specified with all five adapted items loading on one latent factor.

Age-Inclusive HR Practices (Boehm et al., 2014). Participants evaluated the extent to which their organization implements age-inclusive HR policies with 6 items on a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree). A sample item is “My company offers equal access to training and further education for all age groups.” Cronbach’s alpha was 0.87.

Job Satisfaction (Cammann et al., 1979; Michigan Organizational Assessment Questionnaire). Participants rated their overall job satisfaction using a 3-item scale, with responses on a 6-point Likert scale (1 = Completely Disagree, 6 = Completely Agree). The sample item is “All in all, I am satisfied with my job.” Cronbach’s alpha was 0.93.

Intrinsic Motivation (MAWS, Motivation at Work Scale; Gagné et al., 2010). Measured with 3 items on a 7-point Likert scale (1 = Absolutely Not, 7 = Absolutely Yes), this scale assessed participants’ motivation to engage in their work for personal fulfillment. A sample item is “I am doing this job because of the moments of pleasure that this job brings me.” Cronbach’s alpha was 0.91.

Psychological Safety (Kerrissey et al., 2021). Assessed using the 3-item version on a 5-point Likert scale (1 = Completely Disagree, 5 = Completely Agree), this measure evaluated employees’ Psychological Safety within their workplace. A sample item is “I feel I can bring up problems and tough issues with the other party.” Cronbach’s alpha was 0.62, and McDonald’s omega was 0.64, which is not uncommon for brief three-item scales in organizational research (Sijtsma, 2009). Item-rest correlations ranged from 0.35 to 0.49, indicating that all items contributed meaningfully to the scale. These values should nonetheless be considered when interpreting correlations involving this measure.

3.3. Analysis Plan

Prior to starting the analyses, we created a new “Age_Groups” variable to be able to answer our research goals R2–R5. Following Kooij et al. (2011), we categorized chronological age into three groups, including younger workers (23–39 years), middle-aged workers (40–54 years), and older workers (55 years and above). This categorization was based on qualitatively distinct work-related profiles associated with different career stages, as Kooij et al. (2011) demonstrated that the direction and magnitude of age-related patterns in the workplace differed meaningfully across these three groups rather than following a simple linear gradient. Age groups were coded as 1 = ages 23 to 39, 2 = ages 40 to 54, and 3 = ages 55 to 67. Gender and disability status were dummy-coded for use in analyses. Gender was coded as 0 = female and 1 = male, and disability status was coded as 0 = having a disability and 1 = not having a disability.

First, we answered R1 (i.e., prevalence and frequency distribution of perceived age-related microaggressions in the sample), looking at the mean score of age-related microaggressions and frequency distributions for each microaggressions scale item. Second, we responded to R2 (i.e., mean differences in age-related microaggression perceptions by demographics such as gender, disability, and age group) by conducting independent-samples t-tests and ANOVA to compare mean microaggressions scores across demographic groups. Third, we explored R3 (i.e., probability of reporting any exposure to age-related microaggressions) by conducting a logistic regression analysis with age groups, gender, and disability as predictors. This complements R2 by shifting from continuous variation in perceived microaggression frequency to the binary question of whether a given demographic profile is associated with any reported exposure at all. Participants who reported “0” (i.e., never experiencing age-related microaggressions in the past six months) were categorized as non-victims, whereas those reporting any experience (scores 1 to 5) were categorized as victims, as the response scale captures frequency of occurrence and any reported experience, however infrequent, reflects exposure to an age-related microaggression over the past six months.

Fourth, we explored R4 (i.e., correlations between age-inclusive HR practices, microaggressions, and work outcomes) through a correlation analysis examining associations among all primary variables. Lastly, to investigate R5 (i.e., whether the association between age-inclusive HR practices and microaggressions varied by age group), we conducted a moderation analysis with age-inclusive HR practices as a continuous predictor, age group as a categorical moderator (reference category: 23–39), and their interaction term. Simple slopes were estimated for each age group to characterize the nature of the interaction. Additionally, as an exploratory analysis, we examined whether age group and gender interacted in predicting perceived microaggressions among participants who identified as female or male (n = 1675).

Finally, to assess whether the demographic findings depended on modeling microaggression experiences as a binary victim/non-victim variable, we re-estimated the model using linear regression with the continuous age-related microaggression score.

All analyses were conducted using SPSS Statistics 27.0 and Jamovi (Version 2.7.6.0).

4. Results

Before the main analyses, we evaluated the construct validity of the adapted age-related microaggression scale through confirmatory factor analysis (CFA). The single-factor model demonstrated excellent fit: CFI = 1.000, TLI = 0.999, RMSEA = 0.042 (90% CI [0.024, 0.062]), SRMR = 0.015. All five items showed strong standardized factor loadings ranging from 0.89 to 0.95 (all p < 0.001), which supports the unidimensionality of the adapted scale.

The mean overall score on the age-related microaggressions scale (i.e., REMS adapted) was 0.46 (SD = 1.05) (

Table 1. Item-Level Frequencies and Descriptive Statistics for Age-Related Microaggressions (N = 1700).

Item	0 (Never)	1	2	3	4	5 (5 or More Times)	M	SD
Micr1. An employer or co-worker was unfriendly or unwelcoming toward me because of my age.	84.4%	6.3%	3.1%	2.1%	0.9%	3.2%	0.39	1.09
Micr2. My opinion was overlooked in a group discussion because of my age.	83.1%	5.8%	4.5%	1.6%	1.3%	3.7%	0.43	1.15
Micr3. I was ignored at work because of my age.	84.8%	5.6%	3.2%	1.5%	1.3%	3.5%	0.39	1.12
Micr4. Someone assumed that my work would be inferior to people of other age groups.	78.5%	6.7%	4.5%	3.2%	1.5%	5.6%	0.59	1.35
Micr5. An employer or co-worker treated me differently because of my age.	80.4%	6.4%	5.1%	2.2%	1.1%	4.9%	0.52	1.27
Total Scale	82.24%	6.16%	4.08%	2.12%	1.22%	4.18%	0.46	1.05

), suggesting a generally low, but present, level of perceived age-related microaggressions. Descriptive statistics for each item indicated that most participants reported no experience of microaggressions, but a meaningful minority did report occurrences. The item-level response distributions are displayed in Table 1. Across all five items, between 78.5% and 84.8% of participants reported never experiencing the age-related microaggression described. However, between 15.2% and 21.5% of participants reported experiencing microaggressions at least once in the past six months. The most frequently reported microaggressions were assumptions of incompetence or inferiority and differential treatment based on age.

To address whether the perception of age-related microaggressions varies significantly among different demographic groups (i.e., R2), we conducted t-tests for gender and disability status, as well as an ANOVA to examine differences across age groups. Findings are displayed in

Table 2. Group Differences in Perceived Age-Related Microaggressions by Demographic Categories (N = 1702).

Group	n	M	SD	t (df)	95% CI	Cohen’s d
Gender				0.32 (1673)	[−0.09, 0.12]	0.02
Women (ref)	1108	0.46	1.02
Men	567	0.44	1.06
Disability Status				1.70 (122.32)	[−0.04, 0.48]	0.18
Disability	114	0.67	1.36
No disability (ref)	1582	0.45	1.01
	n	M	SD	F (df)	95% CI	Post Hoc p (vs. ref)
Age Group				22.96 (2, 1693) ***
23–39	256	0.85	1.36			p < 0.001 (vs. 40–54) p < 0.001 (vs. 55–67)
40–54	761	0.34	0.87			p = 0.111 (vs. 55–67)
55–67	679	0.45	1.07			p < 0.001 (vs. 23–39) p = 0.111 (vs. 40–54)

Note. M = mean; SD = standard deviation; 95% CI = confidence interval for the mean difference; Cohen’s d = standardized mean difference. Ns vary across analyses owing to missing responses on the age-related microaggression measure. Reference (ref) groups are indicated. For age group, post hoc Tukey HSD comparisons: 23–39 > 40–54 (mean diff = 0.51, p < 0.001), 23–39 > 55–67 (mean diff = 0.40, p < 0.001), 40–54 vs. 55–67 (mean diff = −0.11, p = 0.111). Higher scores indicate more frequent perceived age-related microaggressions. For disability status, Welch’s correction was applied as Levene’s test indicated unequal variances. *** p < 0.001.

.

Gender

An independent-samples t-test revealed no significant difference in perceived age-related microaggressions between women (M = 0.46, SD = 1.02, n = 1108) and men (M = 0.44, SD = 1.06, n = 567), t(1673) = 0.32, p = 0.749, 95% CI [−0.088, 0.123], Cohen’s d = 0.017. Thus, perceptions of age-related microaggressions did not significantly differ by gender.

Age Groups

A one-way analysis of variance (ANOVA) was conducted to examine whether perceived age-related microaggressions differed across age groups (23–39, 40–54, and 55–67 years). The results revealed a statistically significant effect of age group on perceptions of microaggressions, F(2, 1693) = 22.96, p < 0.001. Descriptive analyses indicated that younger employees aged 23–39 years (M = 0.85, SD = 1.36, n = 256) reported higher levels of age-related microaggressions than those aged 40–54 years (M = 0.34, SD = 0.87, n = 761) or 55–67 years (M = 0.45, SD = 1.07, n = 679). Post hoc Tukey HSD comparisons confirmed that the 23–39 age group reported significantly more microaggressions than the 40–54 age group (Mdiff = 0.51, SE = 0.07, p < 0.001) and the 55–67 age group (Mdiff = 0.40, SE = 0.08, p < 0.001). No significant difference was identified between the 40–54 and 55–67 age groups (Mdiff = −0.11, SE = 0.05, p = 0.111).

Disability Status

An independent-samples t-test was performed to assess differences in perceived age-related microaggressions between participants with and without a disability. Participants with a disability (M = 0.67, SD = 1.36, n = 114) did not differ significantly from those without a disability (M = 0.45, SD = 1.01, n = 1582), t(122.32) = 1.70, p = 0.092, 95% CI [−0.04, 0.48], Cohen’s d = 0.18. Equal variances were not assumed as Levene’s test was significant (F = 17.20, p < 0.001).

Given our findings, gender, age group, and disability status were retained for further analyses examining demographic predictors. We conducted a binary logistic regression analysis to evaluate whether these variables predicted the likelihood of reporting age-related microaggressions (R3). Age group (reference group: 23–39 years; comparison groups: 40–54 and 55–67 years), gender (reference group: female), and disability status (reference group: having a disability) were entered as independent variables. The overall model showed limited explanatory power (McFadden’s R² = 0.023).

The results detailed in

Table 3. Logistic Regression Predicting Likelihood of Experiencing Microaggressions (N = 1673).

						95% CI
Predictor	Estimate	SE	Z	p	OR	Lower	Upper
Intercept	0.0946	0.249	0.379	0.705	1.099	0.674	1.792
Disability:
Not having a disability–having a disability	−0.0250	0.216	−0.116	0.908	0.975	0.639	1.489
Gender:
Male–Female	−0.2974	0.117	−2.550	0.011	0.743	0.591	0.934
Age Group:
40–54—23–39	−0.9833	0.152	−6.454	<0.001	0.374	0.278	0.504
55–67—23–39	−0.8766	0.154	−5.679	<0.001	0.416	0.308	0.563

Note. N = 1673. Estimate = unstandardized logistic regression coefficient (log-odds); SE = standard error; Z = z-value; p = significance level; OR = odds-ratio; CI = confidence interval. Estimates represent the log odds of being a victim (Victim = 1) relative to a non-victim (Victim = 0). Odds ratios below 1 indicate a lower likelihood of reporting microaggression experiences relative to the reference category. Reference categories: age = 23–39, gender = female, disability = having a disability.

indicated that age group was a significant predictor. Compared to the reference group of employees aged 23–39, those aged 40–54 were significantly less likely to report experiencing microaggressions (OR = 0.37, p < 0.001). Similarly, employees aged 55–67 were also significantly less likely to report microaggressions (OR = 0.42, p < 0.001). Gender also emerged as a significant predictor. Specifically, males were significantly less likely than females to report experiencing microaggressions (OR = 0.74, p = 0.011). In contrast, disability status did not significantly predict the likelihood of reporting microaggressions (p = 0.908). In the continuous model, however, disability status predicted higher reported frequency once age group was accounted for (B = 0.22, p = 0.025), indicating that its association concerns how often microaggressions are reported rather than whether any exposure is reported at all.

The demographic associations differed between the unadjusted and adjusted analyses, and both differences reflect the same underlying pattern. Gender was unrelated to victim status on its own (OR = 0.83, p = 0.089) and became significant once age group entered the model (OR = 0.74, p = 0.011). Men in this sample were concentrated in the youngest age group to a greater extent than women (21.3% versus 11.9%), and younger employees reported microaggressions more frequently, so the unadjusted comparison conflated gender with age composition. Disability showed the mirror image. Employees reporting a disability were older on average (M = 53.1 versus 50.4 years), and their older age profile offset their higher reported frequency, which emerged only after adjusting for age group in the continuous model reported below (B = 0.22, p = 0.025). Age composition therefore masked both associations in the unadjusted comparisons, a pattern consistent with statistical suppression.

The findings suggest that younger employees and female employees are more likely to report experiences of age-related microaggressions in the workplace.

To examine the relationships between perceived age-related microaggressions, age-inclusive HR practices, job satisfaction, psychological safety, and intrinsic motivation (R4), we conducted Pearson’s r correlation analyses. There was a significant negative correlation between perceived age-related microaggressions and perceptions of age-inclusive HR practices, r = −0.26, p < 0.001 (

Table 4. Means, Standard Deviations, Intercorrelations, and Cronbach’s Alpha for Study Variables.

Variable	M	SD	1	2	3	4	5	6
1. Age	50.60	9.35	—
2. Age-related microaggressions	0.46	1.05	−0.11 ***	(0.92)
3. Age-inclusive HR practices	3.66	1.15	−0.10 ***	−0.26 ***	(0.87)
4. Intrinsic motivation	3.40	1.08	0.00	−0.14 ***	0.18 ***	(0.91)
5. Job satisfaction	3.57	1.03	0.03	−0.24 ***	0.30 ***	0.73 ***	(0.93)
6. Psychological safety	3.34	0.90	−0.01	−0.37 ***	0.33 ***	0.26 ***	0.42 ***	(0.62)

Note. Sample sizes (N) for the correlations ranged from 1686 to 1702 due to pairwise deletion of missing data. Cronbach’s alpha reliability coefficients are shown in parentheses on the diagonal. *** p < 0.001.

), indicating that employees who reported more age-inclusive HR practices also reported experiencing fewer age-related microaggressions.

Perceptions of age-inclusive HR practices were also positively correlated with job satisfaction (r = 0.30, p < 0.001), psychological safety (r = 0.33, p < 0.001), and intrinsic motivation (r = 0.18, p < 0.001). Conversely, perceived age-related microaggressions were negatively associated with job satisfaction (r = −0.24, p < 0.001), psychological safety (r = −0.37, p < 0.001), and intrinsic motivation (r = −0.14, p < 0.001).

To investigate R5, we examined whether the association between age-inclusive HR practices and perceived age-related microaggressions was moderated by age group. The overall model was significant, F(5, 1690) = 40.1, p < 0.001, R² = 0.106. The interaction between age-inclusive HR practices and age group was statistically significant, F(2, 1690) = 6.725, p = 0.001, η²p = 0.008, which indicates that this association varied across age groups. Simple slope analyses (

Table 5. Moderating Effect of Age Group on the Relationship Between Age-Inclusive HR Practices and Perceived Age-related Microaggressions (Simple Slope Estimates).

Age Group	N	B	SE	95% CI	β	t	p
23–39	256	−0.324	0.054	[−0.429, −0.219]	−0.354	−6.059	<0.001
40–54	761	−0.158	0.032	[−0.221, −0.094]	−0.172	−4.866	<0.001
55–67	679	−0.311	0.033	[−0.375, −0.247]	−0.339	−9.503	<0.001

Note. N = number of participants in the analysis; reference category for age group: 23–39. B = unstandardized coefficient; SE = standard error; β = standardized coefficient; CI = confidence interval.

) showed that the negative association between age-inclusive HR practices and perceived age-related microaggressions was significant for all three age groups. The association was strongest for employees aged 23–39 (β = −0.354, 95% CI [−0.429, −0.219], p < 0.001) and 55–67 (β = −0.339, 95% CI [−0.375, −0.247], p < 0.001), and was attenuated for employees aged 40–54 (β = −0.172, 95% CI [−0.221, −0.094], p < 0.001). Examination of the interaction coefficients indicated that the slope for the 40–54 group was significantly less steep than for the 23–39 reference group (B = 0.167, SE = 0.063, p = 0.008), whereas the slope for the 55–67 group did not differ significantly from the youngest group (B = 0.014, SE = 0.063, p = 0.827). These findings suggest that the association between age-inclusive HR practices and perceived age-related microaggressions is particularly pronounced for the youngest and oldest employees, and attenuated for middle-aged employees.

As an exploratory analysis, we examined whether the interaction between age group and gender predicted perceived age-related microaggressions (n = 1675). Neither the main effect of gender, F(1, 1669) = 1.797, p = 0.180, η²p = 0.001, nor the Age group × Gender interaction, F(2, 1669) = 0.918, p = 0.399, η²p = 0.001, reached statistical significance. Simple effect analyses confirmed that gender differences in perceived age-related microaggressions were not significant within any of the three age groups. These results suggest that while gender is associated with the likelihood of reporting microaggressions overall, its effect does not differ significantly across age groups in the present sample.

To assess whether the findings depended on the dichotomization of the outcome, we re-estimated the demographic model using linear regression on the continuous microaggression score (0–5) rather than the binary victim classification (N = 1673). The model was significant, F(4, 1668) = 13.70, p < 0.001, R² = 0.032. The age-group differences remained substantial and highly significant, with employees aged 40–54 (B = −0.53, SE = 0.07, p < 0.001) and 55–67 (B = −0.44, SE = 0.08, p < 0.001) reporting lower frequencies than employees aged 23–39. Disability status predicted higher reported frequency (B = 0.22, SE = 0.10, p = 0.025), whereas gender did not reach significance (B = −0.06, SE = 0.05, p = 0.222). The central age finding therefore holds when the full response gradient is modeled directly.

5. Discussion

This study aimed to investigate the prevalence of age-related microaggressions in a sample of public administration employees, explore demographic differences that might trigger vulnerability to microaggressions, and provide a preliminary understanding of how individual (i.e., demographics) and contextual factors (i.e., age-inclusive HR practices perceptions) shape these perceptions. By focusing on microaggressions, we sought to uncover the often-overlooked experiences of employees who may not be captured by studies focusing solely on overt forms of age discrimination. Results indicate that age-related microaggressions persist in our sample, though generally at low frequency. While most employees do not frequently experience such behaviors, the minority that do is still worth investigating. Additionally, results suggest that in the workplace, specific age-related microaggressions such as assumptions of incompetence and differential treatment may happen more commonly.

Examining R2, we found that younger employees (aged 23–39) reported higher levels of perceived microaggressions when compared to middle-aged (40–54) and older workers (55–67). This pattern likely reflects stereotypes about younger employees’ competence and experience (Posthuma & Campion, 2009). Higher perceived age-related microaggressions for younger workers may reflect challenges experienced in the Italian public administration sector, characterized by a mostly older workforce. No significant difference was found based on gender or disability.

When tested through logistic regression (i.e., R3), we found that younger employees were also more likely to report experiences of age microaggressions. This is consistent with the prevalence of age-based stereotypes targeting youth in the public administration sector, where perceptions of inexperience or different generational work ethics can manifest as patronizing or dismissive behaviors, which align with the microaggressions measured in this study. In addition, gender differences emerged, with female employees being more likely to report microaggressions compared to males. This may reflect broader patterns in which women are more frequently exposed to or more attuned to subtle interpersonal discrimination in the workplace. Disability status was not a significant predictor in the logistic regression (OR = 0.98, p = 0.908). These findings highlight the importance of considering demographic differences in the experience and perception of workplace microaggressions, particularly among younger and female employees. It should be noted that the logistic regression model accounted for a limited proportion of variance in microaggression experiences (McFadden’s R² = 0.023), indicating that demographic characteristics alone provide a partial account of who reports exposure to age-related microaggressions. Individual and organizational factors not captured in the current design, such as workplace climate, tenure norms, or sector-specific dynamics, are likely to contribute meaningfully and merit investigation in future research.

An exploratory analysis further examined whether gender differences in microaggression perception varied across age groups; the Age group × Gender interaction was not significant, suggesting that the gender pattern observed in the logistic regression operates similarly regardless of age. The current interpretation suggests that women may be more attuned to subtle interpersonal discrimination, but alternative explanations deserve consideration. One possibility is that women are more frequently targeted by age-related microaggressions because of a well-documented cultural association between femininity and youth. Across large-scale image and language datasets, women are systematically represented as younger than men, an asymmetry that is amplified by media algorithms (Guilbeault et al., 2025). If women are more strongly associated with youthfulness in organizational settings, they may be more exposed to age-based remarks regardless of their actual age, which would produce a gender difference in reported microaggressions without requiring any difference in interpersonal sensitivity. Additionally, the nature of gender-age intersections may not be uniform. Research by Martin et al. (2019) found that agency-related prescriptions target older men more strongly than older women, suggesting that the social penalties associated with age-related expectations do not affect men and women equally across the lifespan. These accounts suggest that the gender difference observed here may reflect differential exposure as much as differential perception. Future research using longitudinal or experimental designs could help disentangle this distinction.

Our analysis of R4 revealed significant negative correlations between age-inclusive HR practices and age-related microaggressions, and between age-related microaggressions and work outcomes such as motivation, psychological safety, and job satisfaction. These findings suggest first that organizations with stronger perceptions of age-inclusive policies, such as equal access to training and promotion opportunities, may provide conditions less conducive to microaggression experiences. Thus, we support prior research emphasizing the role of HR practices in mitigating age discrimination (Boehm et al., 2014; Shore et al., 2018). Further, age-inclusive HR practices were positively correlated with all work outcomes, highlighting that the relevance of inclusive HR practices goes beyond their association with microaggression perceptions to encompass broader indicators of employee well-being and engagement (Eshete & Birbirssa, 2024). Second, the negative relationship between age-related microaggressions and work outcomes underscores that although microaggressions may appear minor individually, their cumulative effects can contribute to psychological and physiological distress over time (Smith & Griffiths, 2022), underscoring their relevance in organizational contexts.

Notably, the protective pattern observed here is consistent with evidence from different national and institutional contexts. Age-inclusive HR practices have been linked to firm-level performance and a positive age-diversity climate in Germany (Boehm et al., 2014), to perceptions of fairness and organizational identification in Poland (Waligóra, 2024), to the retention of older employees in the United Kingdom (Fasbender et al., 2026), and to successful aging at work and the willingness to extend working lives in China (Cui et al., 2025; Peng et al., 2026). The convergence of findings across countries with distinct labor market institutions and cultural norms about age suggests that the value of these practices is not bound to a single national setting. Our results add the Italian public sector to this international picture and indicate that such practices may also operate at the level of everyday interpersonal treatment, complementing the attitudinal and behavioral outcomes documented so far.

The moderation analysis for R5 confirmed that the association between age-inclusive HR practices and perceived microaggressions was significantly moderated by age group, F(2, 1690) = 6.725, p = 0.001. The association was most pronounced for the youngest (23–39, β = −0.354) and oldest (55–67, β = −0.339) employees and attenuated for the middle-aged cohort (40–54, β = −0.172), with the interaction coefficients indicating that the 40–54 group differed significantly from the 23–39 reference group, whereas the 55–67 group did not differ significantly. This finding is consistent with literature suggesting that middle-aged workers may represent the organizational reference group against which every other age-related comparison is made (Finkelstein et al., 2013; Garstka et al., 2004; Posthuma & Campion, 2009). Indeed, age is not only biologically determined but also socially constructed (Johfre & Saperstein, 2023). This normative positioning may shield them from experiencing age-related stereotypes and thus, age-inclusive HR practices may have lower marginal utility. Indeed, inclusion initiatives are generally designed to mitigate disadvantage, and if a group is already structurally aligned with organizational norms, such practices may not produce observable changes in their experiences. This pattern suggests that for the age groups most vulnerable to daily subtle stereotypes, HR practices that actively promote age-inclusivity serve as a particularly strong and meaningful signal of organizational support. Importantly, this interpretation does not imply that middle-aged workers should be overlooked by the company’s policies and procedures. Rather, it suggests that their challenges (e.g., increased work–family responsibilities, career plateauing; Infurna et al., 2020) are less likely to be framed or experienced as age-based exclusion, and therefore less responsive to age-focused inclusion interventions.

5.1. Theoretical Implications

This study has several theoretical implications. First, it extends theoretical understanding of how demographic characteristics shape age-related microaggressions. The MOSAIC framework provides a conceptual lens for interpreting how the intersection of identities from different demographic categories shapes workplace experiences (Hall et al., 2019). The present study takes preliminary steps in this direction by examining group-level differences and the moderating role of age group, while recognizing that a fuller operationalization of intersectional dynamics remains an important direction for future research. The study also extends literature on microaggressions, recognizing their impact in the workplace and especially on younger workers. Indeed, research on forms of workplace ageism showed that both younger and older employees are more likely to be targets of age-based stereotypes (e.g., Abrams et al., 2016; Hanrahan et al., 2017; Kleissner & Jahn, 2020). However, our findings may suggest that in a context where younger workers are underrepresented, age may become more noticeable and susceptible to stereotype-based scrutiny. Younger employees constituted 15% of this sample, in a workforce with a mean age of 50.6 years. Numerical rarity heightens visibility and category-based scrutiny (Kanter, 1977), and younger employees in an aging public sector may therefore be marked by age in ways that their more numerous middle-aged colleagues are not. This pattern also runs counter to an assumption embedded in age discrimination law, most visibly in the United States Age Discrimination in Employment Act, which protects workers aged 40 and above and presupposes a single direction of vulnerability.

Second, the observed negative associations between microaggression frequency and job satisfaction, psychological safety, and intrinsic motivation direct attention to the cumulative impact of microaggressions. While each incident may seem minor on its own, the cumulative effect of these subtle slights can lead to significant psychological and physiological distress over time (Smith & Griffiths, 2022). This underscores the need for organizations to take proactive steps to address microaggressions, even if they appear insignificant in isolation.

Third, the negative relationship between age-inclusive HR practices and perceived age microaggressions suggests that the latter may act as sensitive indicators of inclusivity gaps in an organization’s climate. This aligns with and extends diversity climate theories (e.g., Nishii, 2013) by highlighting that subtle exclusionary behaviors may flourish even in inclusive environments unless HR practices are actively perceived as fair and responsive to demographic differences. The moderation pattern indicates that HR policies may act as organizational signals (Bowen & Ostroff, 2004; Spence, 1973) whose perceived relevance varies across age groups. The association between age-inclusive HR practices and microaggressions was strongest for the youngest and oldest employees and attenuated for the middle-aged group, indicating that such practices are not experienced uniformly across the age distribution. Additionally, despite their correlational nature, the observed negative correlations between microaggressions and motivation, psychological safety, and job satisfaction position microaggressions as a psychosocial mediating mechanism that, if investigated with an appropriate research design, may help explain how HR practices influence broader organizational outcomes.

5.2. Practical Implications

These findings also show some preliminary implications for HR practitioners, policymakers, and organizational leaders. First, the negative association between perceived age-inclusive HR practices and reported microaggression experiences suggests that organizations may reduce the prevalence of subtle age-based discrimination by implementing and clearly communicating such practices to employees of all ages. By designing structured policies that promote fairness and respect across age groups, organizations can create a more inclusive work environment that supports employee well-being and engagement. Nonetheless, their implementation should be tailored to the specific needs of different age groups and identities. For example, younger workers may benefit from mentorship and career development, while older workers might value programs encouraging knowledge sharing across generations (e.g., Anderson, 2019).

Second, the finding that younger employees reported higher levels of age-related microaggressions points to a need for targeted support for this group, especially in contexts where they are underrepresented, such as the Italian public administration sector examined here. To avoid tokenism, we advise organizations to monitor early-career pathways and provide structured onboarding plans to improve employee experiences (Bowers et al., 2023).

Third, the moderation analysis indicated that the negative association between age-inclusive HR practices and microaggression perceptions was most pronounced for the youngest and oldest employees, and attenuated for the middle-aged cohort. This pattern suggests that employees at the margins of the age distribution may be most responsive to such practices, and that organizations should ensure age-inclusivity initiatives are visible and meaningful for employees at all career stages. Their positive association with work outcomes such as satisfaction, motivation, and psychological safety seems to display them as important infrastructures for workforce stability. For example, we recommend implementing training programs that raise awareness about microaggressions and their impact, particularly for managers and leaders. These programs could emphasize the importance of inclusive communication and provide practical strategies for addressing subtle forms of discrimination. Indeed, managers who model inclusive behaviors contribute to establishing standards followed by all organizational members (Nishii & Mayer, 2009).

This study underscores the importance of attending to demographic differences in workplace policies and practices. Organizations may consider a more nuanced approach to diversity and inclusion, recognizing that the relevance of such practices may vary across demographic groups.

5.3. Limitations and Future Research Directions

Despite the interesting avenues for research and the initial practical interest, this study has several limitations. The cross-sectional design limits causal inference regarding HR practices and microaggression prevalence, and other outcomes such as job satisfaction, intrinsic motivation, and psychological safety. Thus, we advise future researchers to address organizational research on microaggressions using longitudinal or experimental designs. For example, experimental studies may manipulate contextual and individual variables to understand which combination of factors may trigger higher microaggressions. Age group moderated the association between age-inclusive HR practices and microaggressions in the present study, and future research could extend this by examining additional moderators such as tenure, contract type, or organizational climate. This design does not allow age effects to be distinguished from cohort effects. Younger respondents may differ from older respondents in how they are treated at work, in their normative expectations about workplace respect, or in their propensity to label ambiguous behavior as a microaggression. Longitudinal or cohort-sequential designs would be required to separate these accounts.

Additionally, the reliance on self-reported data may introduce biases in how microaggressions are perceived and reported. Employees’ perceptions of microaggressions may be influenced by various factors, including their individual experiences, beliefs, and attitudes. As all measures were collected from the same respondents at a single occasion, common method variance cannot be ruled out; the demographic comparisons are less exposed to this concern, as age group, gender, and disability status are reported characteristics rather than perceptual judgments. Nonetheless, the adoption of Podsakoff et al.’s (2003) procedural recommendations contributed to reducing the challenges associated with self-reported measures of perceptions. Future studies should consider using multiple data sources to provide a more comprehensive understanding of microaggressions. Additionally, the psychological safety scale showed a relatively low internal consistency (α = 0.62), which may have attenuated the correlations involving that variable and calls for caution in interpreting those specific associations. Validity evidence for the adapted microaggression scale is currently limited to its factor structure. As no validated measure of age-related microaggressions currently exists, the development and psychometric evaluation of a dedicated instrument remains an important direction for future research.

Respondents reporting a disability constituted a small proportion of the sample (n = 114, 6.7%), which constrains the precision of comparisons involving this group. Although the adjusted analysis identified an association with reported frequency, the limited subsample size warrants caution in interpretation. Designs that oversample employees with disabilities would allow this comparison to be examined with greater precision.

Lastly, the sample comprised Italian public administration employees, which may constrain generalizability to other regions or cultural and organizational contexts. Our data did not include tenure or contract type, which may independently shape microaggression perceptions and inclusive HR practice evaluations and represent important covariates for future research. We recommend that future studies aim for a more diverse sample, including employees from different countries and organizations. Indeed, cultural differences in aging perceptions may shape differences in how age-specific microaggressions are perceived and their impact. Future research could involve comparative studies across countries and sectors, ideally using harmonized measures, to help establish the boundary conditions of the present findings and inform HR strategies suited to an increasingly global workforce.

6. Conclusions

This study provides insight into the prevalence and nature of age-related microaggressions within the workplace, emphasizing the importance of demographic differences and inclusive HR policies. While limitations exist, findings suggest that well-designed HR practices are associated with lower levels of perceived subtle discrimination and better work outcomes. Continued research and organizational efforts are essential to building workplaces where all employees feel valued, respected, and protected from discrimination.

Our findings also challenge the traditional view that age-related discrimination primarily targets older workers, showing instead that younger employees may be particularly exposed to subtle forms of bias in specific organizational contexts. This highlights the need for organizations to move beyond one-size-fits-all diversity initiatives and adopt more nuanced and age-sensitive approaches to inclusion. In this sense, age-inclusive HR practices should not only aim to prevent overt discrimination but also shape everyday interactions, reducing the ambiguity that sustains subtle discrimination and supporting a climate in which all employees, regardless of their position in the lifespan, can contribute and remain engaged.