Inequalities in Self-Assessments of Mental and Physical Wellbeing Among Workers in the Tech Industry

Abstract

Although the technology (tech) industry has historically had a reputation for being supportive when it comes to wellbeing initiatives, research has found persistent disparities among the tech industry workforce. Therefore, using an original survey of tech workers worldwide (n = 1207), this paper explores whether gender and identification with racial “minority” status have an impact on self-reported levels of mental and physical wellbeing measures. Controlling for additional demographic variables, our regression model found that workers identifying as racial minorities at work (OR: 2.49; 95%CI 1.80–3.43) were significantly more likely to report lower mental wellbeing scores. Additional analyses found that compared to men who did not identify as a racial minority, minority-identified women (OR: 3.48; 95%CI 2.10–5.76) and men (OR: 2.10; 95% CI 1.40–3.15) were significantly more likely to report worse mental wellbeing, and minority-identified men were significantly more likely to report that work had a negative impact on their physical health as well (OR: 1.78; 95%CI 1.18–2.68). Due to the international scope of our project, our research suggests that demographic disparities in physical and mental wellbeing among tech workers is an ongoing problem on a global scale.

1. Introduction

Particularly in recent decades, discussions around supporting employee wellbeing at work have increased (Ryan et al. 2021; Schulte and Vainio 2010). Although definitions vary slightly across fields, generally, employee “wellbeing” at work is measured by assessing employees’ understandings of their physical and mental health and the impact of the workplace on those assessments (Barrios-Choplin et al. 1997; De Simone 2014; Schulte and Vainio 2010). Perhaps more so than any other sector, the technology (or tech) industry has received attention for its employee wellbeing efforts. For instance, several high-profile tech companies with a global reach (including Google, Apple, and Amazon) have taken steps to implement wellbeing programs in the workplace, including (for example) bringing on-site therapy to the office (della Cava 2018; T. Williams 2023). However, despite these efforts, notable mental and physical wellbeing disparities among tech workers seemingly continue to fall along demographic lines. For example, some research has found that a higher percentage of women tech workers report being burned out at work compared to men (Monahan and Burlacu 2024). In general, those in historically marginalized or underrepresented positions within the industry—due to social class, race, and/or gender identities—may have a more difficult time feeling a sense of wellbeing at work despite industry efforts.

Yet despite its investment in wellbeing, the tech industry is worthy of investigation because recent research argues that the industry is the most powerful and profitable industry across the globe (Neely et al. 2023). The tech industry has exploded in growth since 2005, and the number of global tech jobs is only projected to grow in the next fifteen years (Bradley et al. 2024). At the same time, research has shown that the industry clearly struggles with inequalities among the tech workforce (Neely et al. 2023; Ravenelle 2019; Wynn and Correll 2018). Given the tech industry’s projected growth and its issues with worker equality, it is important to explore the persistence of disparities in more depth and to pinpoint if meaningful wellbeing differences among groups of tech workers fall along demographic lines. Doing so can be beneficial to organizations, as companies that employ diverse groups of employees who feel a sense of inclusion and wellbeing at work are often more successful, innovative, and have healthier organizational cultures compared to their peers (Donovan and Kaplan 2013; Lovell et al. 2020).

Existing theoretical frameworks within sociology have identified both structural and interactional explanations for the continuation of inequalities in the workplace, including how certain organizational procedures and policies, as well as daily interactions at work, can negatively impact the wellbeing of employees (Acker 2006; Alfrey and Twine 2017; Luhr 2023). However, there is a need for more research exploring how experiences of inequality working in the tech industry can have other consequences, such as suboptimal health outcomes for historically marginalized groups of workers, particularly on a global scale. Having this information will illustrate the true scope of the problem and provide insight into possible solutions as the tech industry grows. Therefore, in this paper we use a survey with an international sample of tech industry workers to investigate differences in mental and physical wellbeing assessments among groups of employees. In our analysis, we focus primarily on disparities according gender, racial or ethnic “minority” status, and the intersection of gender and minority status to explore the potential impact of ongoing workplace inequalities on employees’ mental and physical health assessments.

2. Literature Review

2.1. Inequalities in the Tech Industry

Conversations around the importance of employee wellbeing (across a wide range of industries) have gained traction in the last few decades (Geist-Martin et al. 2003). Though approaches vary, “wellbeing” initiatives at work typically involve an attempt to address employees’ physical health concerns, mental health concerns, or some combination thereof (Cook 2021; Lovell et al. 2018; Spence 2015). Given the intense amount of time that many people spend at work, as well as the potential for work to be a source of stress for many people, entities such as the World Health Organization (WHO) have singled out the workplace in particular as a site where health promotion could make a difference when it comes to wellbeing outcomes in the population (Cook 2021; Spence 2015). Wellbeing efforts are also often closely related to diversity, equity, and inclusion (DE&I) initiatives, since race, class, and gender inequalities at work can relate to disparate health outcomes. For instance, research has found that experiences with racism at work can lead to higher burnout among healthcare workers (Kaltiso et al. 2021). However, sociological research projects on inequalities at work (particularly inequalities in the tech industry) have focused less on health outcomes per se and more so on establishing both the existence of inequalities and the ways in which they are perpetuated in workplace contexts.

In explaining the persistence of inequalities, for example, in employee promotions, performance evaluations, attrition rates, and so on, research has found that both structural and interactional factors in the tech industry relate to disparities along the lines of race, class, gender, and sexuality (Neely et al. 2023). For instance, the contingent population of the tech industry workforce is disproportionately made up of women and workers of color (Ravenelle 2019; Schor 2020; Tech Equity Collaborative 2021). These contract and contingent workers typically do not have access to the same career prospects or benefits as their peers in the industry. At the same time, the entrenchment of inequalities among more prestigious positions in the tech industry workforce begins even at the recruiting stage through job seekers’ interactions with the tech companies. For instance, Wynn and Correll’s (2018) research found that women’s interactions with tech company recruiters during their undergraduate years often convey that tech is a “chilly” climate, which functions to diminish women’s interest in tech careers after college (Wynn and Correll 2018).

Additional sociological research has found further issues of gender inequalities related to tech work. For example, research has found the “ideal (tech) worker” in the industry is still constructed as male or masculine (Alfrey and Twine 2017; Mickey 2019, 2022; Neely 2020). For instance, Alfrey and Twine’s (2017) qualitative research on the tech industry found that women who presented as gender-fluid or more masculine reported having a greater sense of belonging at work and were perceived as more competent by their male coworkers. At the same time, research has found that women, especially those who are more feminine-presenting, are often less likely to believe they match the ideal of the “successful” tech worker and are more likely to leave the tech field altogether (Alfrey and Twine 2017; Knappert et al. 2024; Stephan and Levin 2005; Wynn and Correll 2017). In addition, scholars have documented disparities in worker evaluations according to gender, which has an impact on women’s career paths and prospects in the industry (Correll et al. 2020; C. L. Williams 2021). All of these issues can take a toll on women tech workers’ mental health, potentially leading to feelings of burnout, disengagement, and anxiety.

Although a large body of the emerging research on disparities in tech focuses on gender, some research has also explored the experiences of workers from other historically marginalized groups. For instance, team-based work is popular in the tech industry, which can obscure the contributions of individuals and thus disadvantage both women and workers of color (Cech and Waidzunas 2022; Ollilainen and Calasanti 2007). Because women and workers of color risk being perceived negatively if they engage in self-promotion, the obfuscation of their individual contributions via team-based projects can prove problematic and detrimental to their career progress (Neely et al. 2023). Yet at the same time, workers of color face additional barriers regardless of (or in addition to) gender. For instance, research on Black workers in the tech industry has found that social exclusion, racism, discrimination, and sexism (for women) continue to be problems (Franklin 2022; Thomas et al. 2018). Therefore, based on this previous research, our first hypothesis is as follows:

H1: 

Tech industry employees who self-identify as women or as belonging to a racial minority group will be significantly more likely to report elevated levels of anxiety.

At the same time, race adds intersectional considerations to the significance of gender in tech. While white women in the industry tend not to reach the levels of influence and leadership that white men reach in tech organizations, women of color are promoted and recognized even less often than their white peers (Alegria 2019). Other research has found that in addition to being underrepresented (with few exceptions), tech workers of color are routinely confronted with the mental health outcome of high stress from having racist interactions with coworkers. This is the case even among those who are not technically underrepresented, such as Asian workers (Chow 2023; Franklin 2022). Although issues of inequality related to gender and race are well known within the tech industry, progress has been slow partially because the tech industry often does engage with efforts aimed at improvement even if those efforts fall short. For instance, recent research in the U.S. has found that tech workers often see their own organizations as better than most or “above average” when it comes to diversity and equity issues, which can serve to stall initiatives aimed at change (Luhr 2023). Therefore, considering the importance of identity intersections, our second hypothesis is as follows:

H2: 

Compared to men who are not members of racial minority groups, women and men tech industry employees who self-identify as belonging to a racial minority group will be significantly more likely to report elevated levels of anxiety.

The previous findings of sociologists have established that inequalities of gender and race/ethnicity are routinely perpetuated in the contemporary workplace, that these inequalities are typically intersectional, and that the tech industry remains rife with issues despite efforts to improve (Alfrey and Twine 2017; Knappert et al. 2024; Ray 2019). These findings also help explain why diversity, equity, and inclusion (DE&I) efforts aimed at supporting employees who belong to historically marginalized groups often fall short of goals. For example, in the past, many organizations have relied on quick-fix solutions (such as one-time trainings) to address DE&I, which do not speak to structural issues at work (Dalessandro and Lovell 2024). Although sociology has often focused on documenting disparities in the workplace and explaining the potential impacts of these disparities, researchers outside of sociology have more explicitly made connections between ongoing inequalities and disparate health outcomes among tech workers. For instance, some of this research has found that among women who stay in the tech field despite the challenges they face, there are higher rates of burnout and depression compared to men (Ronen and Pines 2008; Simaon et al. 2022). Sociologists have done a thorough job documenting the existence of inequalities in tech, particularly in a U.S. context. However, there is more to learn from a sociological perspective concerning what these disparities might mean for health outcomes among tech workers on a global scale.

2.2. The Impact of Work on Wellbeing Outcomes

Certain fields of research, such as psychology, have already explored linkages between negative health outcomes and factors such as the workplace environment, employee demographics or identities, and social stigma (Arango-Lasprilla et al. 2025; Brouwers 2020; Casey et al. 2024; Norabuena-Figueroa et al. 2025). For example, recent psychological research on teachers has found that gender, age, and socioeconomic status were significant predictors of depression and anxiety (Arango-Lasprilla et al. 2025). However, sociology can add further insight into how the documented cases of inequality translate to disparate mental and physical health outcomes. For instance, scholars of emotions have shown that having to navigate race and gender identities strategically in a workplace environment requires different emotional demands, and takes a different emotional toll, depending on worker identities.

In a U.S. context, research has found that even workers of color in high status positions are held to more demanding emotional standards when compared to their white peers (Wharton 2014; Wilkins and Pace 2014; Wingfield 2009). Similar to findings related to gender, the apprehension around having to carefully navigate race (in particular, racist interactions) with coworkers or clients steers some Black college students on the job market away from lucrative industries (including tech) (Beasley 2011). The emotional consequences of these workplace inequalities can take a toll on mental wellbeing over time, leading to outcomes such as stress and the internalization of negative messages and stereotypes based on identity (Wharton 2014; Wilkins and Pace 2014). Furthermore, while it receives even less attention than mental health, there is likely a connection between suboptimal mental health outcomes and suboptimal physical health outcomes (Lim et al. 2012; Strine et al. 2004). For instance, research has found that poorer mental health outcomes relate to poorer physical health outcomes, particularly for women and racial or ethnic minorities (Chen and Mallory 2021; Harder and Sumerau 2019). Though physical health outcome disparities and the potential relationship physical and mental health disparities have received less attention in the context of work, it is nonetheless important to investigate this issue given the previously documented relationships between demographics and physical health inequalities. Therefore, our last two hypotheses for our research are as follows:

H3: 

Tech industry employees who self-identify as women or as belonging to a racial minority group will be significantly more likely to report that their job has a negative impact on their physical health.

H4: 

Compared to men who are not members of racial minority groups, women and men tech industry employees who self-identify as belonging to a racial minority group will be significantly more likely to report that their job has a negative impact on their physical health.

Though sociological research has done much to establish that identity-based inequalities persist (both independently and when we consider identity or status intersections) in a U.S. context, there is still much more to learn when it comes to both exploring the impact of gender and race inequalities, as well as gender and race intersections, on workers’ health as well as the extent to which patterns documented in the U.S. might be observed on an international scale. Due to globalization, some scholars speculate that patterns of race and gender inequality at work and their impact on certain health outcomes may be more similar across the world than they have been in the past (Rebughini 2021). Therefore, we explored the potential impact of inequalities on mental and physical health assessments among a group of tech workers across the world.

3. Materials and Methods

The data for this study came from a survey conducted by the authors in 2022. The data comprise a convenience sample of employees working full-time at organizations with at least 500 employees across a range of different industries in 20 countries. The focus on 500-plus-employee organizations was a larger goal of the research team. Therefore, the opinions represented here are those of employees at large tech companies. Our approach allowed us to capture the opinions of those working at large tech companies as opposed to smaller firms or startups. Although smaller firms and startups are common in the industry, research has shown that employees working at smaller firms typically have a qualitatively different experience than those working at larger organizations (Green and Whitfield 2009). When it comes to wellbeing, small firms can have different cultural norms around wellbeing compared to larger companies or “big tech” (as large tech companies are sometimes colloquially known). Both because research has found that large tech firms tend to have inequality problems (see Ravenelle 2019), and that large firms are those most likely to hold a disproportionate amount of social and economic power (Govindarajan et al. 2019), it is important to examine the experiences of employees at large firms.

Although 12,532 participants returned a survey, we reduced the sample to 1705 for our analysis since we decided to focus exclusively on employees working in the tech industry. Of the more than 30 different industries represented in our survey, technology workers comprised the largest group. Therefore, even though our sample was one of convenience, the number of tech workers participating in the study was sizable enough to garner meaningful insights. After accounting for missing answers regarding the variables of interest, we further reduced the sample to 1207 for our final analysis. In addition, in our analysis, we condensed countries to regions for simplicity. Here, each continent represents a “region”, and we assigned countries to a region based on their location within a continent.

We crafted the survey questions as part of a larger project investigating a variety of aspects related to the employee experience, and thus focused on a range of topics all related to employees’ experiences in the workplace, including employee feelings of burnout, experiences with leaders, feelings of cohesion and community at work (or lack thereof), and wellbeing based on both mental and physical health assessments. We used what was at that time (2022) the current version of Alchemer software to build the survey and Lucid marketplace (a sample aggregator that contracts with survey panel providers) to administer the survey to potential participants. Lucid is the largest sample aggregator available for online survey respondents and allows for direct-to-respondent sampling via a range of panel providers (Coppock and McClellan 2019). In an effort to maintain data quality, potential respondents had to respond to a series of screener questions designed to identify bad actors and bots and exclude them from the sample. Respondents were compensated anywhere from USD 1 to 4.75 depending on the country and the difficulty of recruiting respondents. Payments were distributed by panel providers and took the form of cash, gift cards, or reward points that could be redeemed for merchandise. In an initial step of recruitment, we shared a survey to the Lucid platform, which then distributed the survey to potential respondents. Respondent participation was voluntary, with no penalty if the respondents chose not to participate or to exit the survey at any time.

After passing the screener questions and informed consent, the respondents were directed to the main survey, which took an average of 30–40 min to complete. In addition to English, we contracted a translation service to translate the larger survey into ten different languages for distribution in countries in which English is not the first language for most potential respondents. Before fielding the larger survey, we tested a pilot survey of 100 responses. We did not collect respondent names or any other identifiable information and conducted our analysis on an anonymized version of the dataset.

Analytic Strategy

We tested our hypotheses using logistic regression models. The models addressing H1 and H3 examined gender and racial minority status separately while the models crafted to address H2 and H4 examined interactions between gender and racial minority status on the outcome variables. For H1 and H2, in order to measure the respondents’ mental health status, the dependent variable we used was a scale developed based on the Generalized Anxiety Disorder questionnaire (GAD-7). The GAD-7, originally developed by Spitzer et al. (2006), is a widely used diagnostic tool for generalized anxiety disorder that has been statistically validated by a range of studies within psychology (Rutter and Brown 2017). Though more conventionally used by psychologists, some sociologists have recently touted the usefulness of examining the concept of “anxiety” to capture mental health since it is increasingly a common, everyday (not fleeting) experience and increasingly widespread across the world (de Courville Nicol 2021; Rebughini 2021). Furthermore, while anxiety is experienced individually, it can also be experienced as a group phenomenon (for example, experienced collectively among certain racial or ethnic groups) (de Courville Nicol 2021).

Given that the GAD-7 is a validated scale, we performed a confirmatory factor analysis of the GAD-7 items on our data to test the validity of the construct. Guidance from Brown (2006) and Hu and Bentler (1999) posits the following criteria to assess model fit: RMSEA (≤0.06), SRMR (≤0.08), CFI (≥0.90), and TLI (≥0.90). Multiple indicators were used to ensure that the model fit was assessed for absolute fit, model parsimony, and fit relative to a hypothesized null model. We found an RMSEA of 0.021, an SRMR of 0.007, a CFI of 0.999, and a TLI of 0.998. Therefore, we found the scale suitable for further analysis. These findings are consistent with previous research in psychology, which has tested the scale extensively. Next, to assess the respondents’ GAD-7 scores, we created a scaled dichotomous variable separating those exhibiting anxiety symptoms “more than half the days” or “every day” (over the last two weeks) for a majority of items from those with lower scores (“several days” or “not at all”).

Since, to our knowledge, the GAD-7 does not have a physical health counterpart, for the H3 and H4 models, the dependent variables consisted of responses to the question “my job has a negative impact on my physical health” (“strongly disagree” to “strongly agree” on a 5-point scale). Originally measured on a Likert scale, we created a dichotomous response variable separating those responding “strongly disagree”, “somewhat disagree”, and “neutral” from those responding “somewhat agree” and “strongly agree”. We chose to convert the two variables measuring health into dichotomous variables to differentiate employees with generally better assessments from those with generally worse assessments, and to explore the potential demographic differences of these groups.

For H1 and H3, the primary independent variables of interest were gender and racial minority status. Regarding gender, although we did capture participants who identified as transgender in our analysis, this group was very small (n = 12, or less than 1% of the sample). Therefore, we grouped together the trans-identified participants with those of cisgender status. Due to their even smaller numbers (n = 7), gender-non-conforming participants were dropped from the analysis. While not ideal, due to their small numbers, we elected to drop this group to reduce the risk of mis-representing their experiences in our findings.

In addition, because our sample covered 20 countries, we examined racial minority status by asking the respondents “In the context of your work, do you consider yourself to be a racial or ethnic minority?” rather than by asking for specific racial or ethnic categories. While the word “minority” is used less often in a U.S. context and is even controversial (Black et al. 2023; Sotto-Santiago 2019), we used it in our survey in an effort to make our question as clear as possible to our international sample. In addition, because some groups can be over-represented in some tech fields at the same time that they may be “minorities” in broader society (such as Asian Americans in a U.S. context, for example) (Luhr 2023), our question aimed to capture the experiences of individuals who feel that their racial/ethnic identities are indeed underrepresented in their workplaces. To examine H2 and H4, we constructed models that included interaction variables measuring the relationship between self-identified gender and racial minority status (with men not identified with racial minority status as the referent group).

In our models, we also controlled for responses to the statement “everyone has the same access to opportunities at my organization” to account for employees’ perceptions of their organizations’ efforts to address equity issues that may relate to disparities in wellbeing outcomes. Lastly, we controlled for the additional variables of generation (age), education level, compensation type (hourly or salaried pay), and geographic region. To ensure that we were not missing important nuance, we tested our models with both the “region” variable and the separate “country” variable and found no statistically significant differences among individual countries. In order to generate statistical results, we used StataMP18.

4. Results

After adjusting our sample based on the completeness of the data, we were able to describe the final demographics of the respondents. These numbers appear in

Table 1. Sample characteristics.

Characteristics	n (%)	Total n (%)
Racial Minority Status
Yes	244 (20)
No	963 (80)	1207 (100)
Gender Identity
Women	463 (38)
Men	744 (62)	1207 (100)
Anxiety Scale Scoring
High	438 (36)
Low	769 (64)	1207 (100)
Work Negatively Impacts Physical Health
Yes	417 (35)
No	790 (65)	1207 (100)
Generation
Baby boomer (1946–1964)	34 (3)
Gen X (1965–1980)	298 (25)
Millennial (1981–1986)	810 (67)
Gen Z (1997–2004)	65 (5)	1207 (100)
Education Level
High school or less	52 (4)
Some college or vocational school	110 (9)
Graduated college	625 (52)
Post-graduate degree	420 (35)	1207 (100)
Compensation Type
Hourly	190 (16)
Salary	1017 (84)	1207 (100)
Geographic Region
Asia	604 (50)
North America	248 (21)
South America	204 (17)
Australia	29 (2)
Europe	75 (6)
Africa	47 (4)	1207 (100)
Opportunities Available to All at Work
Agree	869 (72)
Disagree	338 (28)	1207 (100)

. Notably, men, millennials, and more educated respondents were slightly over-represented in our sample. However, these demographics are consistent with what is currently normal in the tech industry (Cengage Group 2022; Luhr 2023). Minority-identified participants comprised about 20% of the sample.

Next, we examined the impact of racial minority status and gender identity on self-reported levels of anxiety as measured through the GAD-7. While the independent effect of gender was not significant, respondents who reported belonging to a racial minority group had statistically significant greater odds of reporting high anxiety. These results were significant at the p < 0.001 level (see

Table 2. Logistic regression results examining self-reported anxiety levels.

Characteristics	Odds Ratio	95% Confidence Interval
Racial Minority Status
No (ref.)
Yes	2.49 ***	1.80–3.43
Gender Identity
Men (ref.)
Women	1.17	0.89–1.54
Work Has a Negative Impact on Physical Health
No (ref.)
Yes	5.22 ***	4.00–6.86
Generation
Baby boomer (1946–1964) (ref.)
Gen X (1965–1980)	1.68	0.67–4.21
Millennial (1981–1986)	2.47 *	1.01–6.04
Gen Z (1997–2004)	2.97 *	1.04–8.45
Education Level
Graduate college (ref.)
High school or less	1.01	0.52–1.97
Some college or vocational	1.30	0.82–2.06
Post-graduate degree	1.13	0.84–1.51
Compensation Type
Salary (ref.)
Hourly	1.38	0.96–1.99
Region
Asia (ref.)
North America	1.09	0.76–1.55
South America	0.80	0.54–1.20
Australia	2.42 *	1.07–5.48
Europe	1.23	0.70–2.15
Africa	1.35	0.69–2.65
Opportunities Available to All at Work
Disagree (ref.)
Agree	0.58 ***	0.43–0.77

* p < 0.05, *** p < 0.001.

). Regarding physical health impacts, our model showed that racial or ethnic minority status increased the odds of reporting a negative impact of work on physical health, while self-identifying as a woman actually decreased the odds of reporting a negative impact on physical health. However, these results were only significant at the p < 0.05 level (see

Table 3. Logistic regression results examining whether work negatively impacts physical health.

Characteristics	Odds Ratio	95% Confidence Interval
Racial Minority Status
No (ref.)
Yes	1.49 *	1.07–2.07
Gender Identity
Men (ref.)
Women	0.75 *	0.57–0.99
Anxiety Scores
Low (ref.)
High	5.21 ***	3.96–6.86
Generation
Baby boomer (1946–1964) (ref.)
Gen X (1965–1980)	0.57	0.25–1.30
Millennial (1981–1986)	0.55	0.25–1.22
Gen Z (1997–2004)	0.44	0.17–1.16
Education Level
Graduate college (ref.)
High school or less	1.35	0.70–2.61
Some college or vocational	1.20	0.75–1.91
Post-graduate degree	1.17	0.87–1.58
Compensation Type
Salary (ref.)
Hourly	1.86 **	1.29–2.68
Region
Asia (ref.)
North America	0.62 **	0.43–0.88
South America	0.49 **	0.38–0.74
Australia	0.45	0.18–1.09
Europe	1.01	0.58–1.76
Africa	0.35 **	0.16–0.75
Opportunities Available to All at Work
Disagree (ref.)
Agree	0.83	0.62–1.11

* p < 0.05, ** p < 0.01, *** p < 0.001.

). Here, we also saw that high anxiety scores strongly predicted a negative impact on physical health.

Lastly, we constructed two additional models examining the interaction between gender and racial minority status. In both models, men not identifying with a minority racial or ethnic identity served as the referent group. When compared to the referent group, we found that both men and women identifying with racial or ethnic minority status were significantly more likely to report higher anxiety scores at the p < 0.001 level. Women who did not identify with a minority status did not differ from men in this analysis (

Table 4. Logistic regression results examining self-reported anxiety levels (with gender and racial minority status interaction).

Characteristics	Odds Ratio	95% Confidence Interval
Gender and Race Interaction
Non-minority, men (ref.)
Non-minority, women	1.06	0.77–1.45
Minority, men	2.10 ***	1.40–3.15
Minority, women	3.48 ***	2.10–5.76
Work Has a Negative Impact on Physical Health
Yes (ref.)
No	5.25 ***	3.99–6.91
Generation
Baby boomer (1946–1964) (ref.)
Gen X (1965–1980)	1.66	0.66–4.14
Millennial (1981–1986)	2.42	0.99–5.92
Gen Z (1997–2004)	2.90 *	1.02–8.27
Education Level
Graduate college (ref.)
High school or less	1.02	0.52–1.98
Some college or vocational	1.29	0.81–2.05
Post-graduate degree	1.13	0.84–1.53
Compensation Type
Salary (ref.)
Hourly	1.40	0.98–2.01
Region
Asia (ref.)
North America	1.08	0.76–1.53
South America	0.80	0.54–1.20
Australia	2.38 *	1.05–5.38
Europe	1.22	0.69–2.13
Africa	1.35	0.68–2.65
Opportunities Available to All at Work
Disagree (ref.)
Agree	0.59 ***	0.44–0.79

* p < 0.05, *** p < 0.001.

). Regarding the impact of work on physical health, we found that minority-identified men reported that work had a negative impact on their physical health as well. This result was significant at the p < 0.01 level. Measures for both groups of women did not differ from the referent group (see

Table 5. Examining whether work negatively impacts physical health (with gender and racial minority status interaction).

Characteristics	Odds Ratio	95% Confidence Interval
Gender and Race Interaction
Non-minority, men (ref.)
Non-minority, women	0.83	0.61–1.14
Minority, men	1.78 **	1.18–2.68
Minority, women	0.92	0.55–1.53
Anxiety Scores
Low (ref.)
High	5.27 ***	4.00–6.93
Generation
Baby boomer (ref.)
Gen X	0.58	0.25–1.31
Millennial	0.56	0.25–1.25
Gen Z	0.45	0.17–1.19
Education Level
Graduate college (ref.)
High school or less	1.34	0.69–2.60
Some college or vocational	1.21	0.76–1.93
Post-graduate degree	1.16	0.86–1.57
Compensation Type
Salary (ref.)
Hourly	1.83 **	1.27–2.63
Region
Asia (ref.)
North America	0.62 **	0.43–0.89
South America	0.49 **	0.33–0.74
Australia	0.44	0.18–1.10
Europe	1.02	0.58–1.77
Africa	0.35 **	0.16–0.75
Opportunities Available to All at Work
Disagree (ref.)
Agree	0.82	0.61–1.10

** p < 0.01, *** p < 0.001.

).

5. Discussion

An existing body of sociological research supports the claim that within the tech industry, social inequalities rooted in race and gender continue to impact the experiences of individual workers (Alegria 2020; Alfrey and Twine 2017; Neely et al. 2023; Spencer 2023). However, much of this research is qualitative, focused on specific national contexts (such as the U.S.), and often less focused on the specific health consequences of these issues. Therefore, in this paper, we used original survey data to explore how race, gender, and the intersection of gender and racial minority status impact tech workers’ mental and physical wellbeing assessments across the world.

First, when examined as independent categories, we found that racial minority status, but not gender, significantly predicted higher levels of anxiety (our measure of mental health). In addition, we found that those identifying with a racial/ethnic minority group were significantly more likely to report that their job had a negative impact on their physical health. Therefore, we only found partial support for H1, in which we hypothesized (based on past research) that women and minority-identified workers would face higher anxiety scores compared to their peers (see Arango-Lasprilla et al. 2025; Wilkins and Pace 2014). We also found only partial support for H3, in which we hypothesized that women and minority-identified workers would perceive that their work would have a negative impact on their physical health (see Lim et al. 2012; Strine et al. 2004). Instead, we found that women reported a lower negative impact on their physical health compared to men.

However, interacting gender with racial or ethnic minority status revealed additional nuance. Compared to non-minority men, we found that minority-identified men and women, but not non-minority women, were significantly more likely to report higher anxiety levels. This suggests that it is not only gender but also the interaction of gender with racial/ethnic minority status that matters for predicting higher anxiety (or, suboptimal mental health outcomes). Thus, we found support for H2, in which we hypothesized that based on the role of intersectionality, minority-identified women and men would report higher anxiety (see also Alegria 2019, 2020). In addition, regarding the impact on physical health, we found that minority-identified men (but not women) reported worse physical health outcomes related to work. Thus, we found partial support for H4, in which we argued that both minority-identified women and men would report that work negatively impacted their physical health.

Our findings are significant for a few reasons. First, since we attempted to control for organizational culture and climate in our model (via respondent opinions on equality of opportunities), we showed that despite efforts or organizational efforts to account for inequalities, there are still important disparities in health outcomes between historically marginalized groups (due to race/ethnicity and gender) and their peers in the tech industry. This confirms that despite industry efforts, important emotional and physical health differences exist among workers in the industry. Furthermore, one aspect that is unique about our study is that we have shown that these differences also persist on a global scale. Although sociologists have done a thorough job documenting gender and racial/ethnic inequalities in the U.S. tech industry (for examples, see Alegria 2020; Alfrey and Twine 2017; Franklin 2022; Neely 2020), our research further shows that mental and physical health inequalities exist across the world.

Another interesting aspect of our research is that while we found that men and women belonging to racial or ethnic minority groups across the world are suffering poorer health outcomes compared to their peers, minority-identified men in particular are worse off in terms of both mental and physical health outcomes compared to other men. Since previous research has focused so heavily on women’s experiences in tech, this finding suggests that future research should explore further the intersection between race/ethnicity and gender for men in the industry (see also Alegria 2020). In addition, our specific finding that minority-identified men, but not women, believe that their tech jobs significantly impact their physical health outcomes needs further exploration. Our models also found strong relationships between physical and mental health outcomes, yet rather than being distributed equally, men seemed to bear the brunt of physical health impacts. Since tech work is arguably less physical than other types of historically gendered jobs (such as construction or manufacturing work, for example), we need more research to make sense of this outcome. Additional research, both qualitative and quantitative, that explores the employee experience stories of minority-identified men in the global tech industry would help us further understand these nuances.

Indeed, due to the quantitative nature of our analysis, we cannot detail the specific workplace experience stories of our respondents that may have led to our results. However, we know from previous research that in the United States, work (and more specifically, work in the tech industry) can take a disproportionately negative toll on the mental health of historically marginalized workers (Wharton 2014; Wilkins and Pace 2014; Wingfield 2009). We also know that there are connections between mental health and physical health outcomes (Lim et al. 2012; Strine et al. 2004). Our work has shown these connections to be strong for minoritized men especially who are working in the tech industry across the world. Our research shows that it is not just inequalities themselves that are the problem, but that these extant issues can relate to negative health outcomes as well. We hope our work is a jumping off point for more research on the experiences of tech workers internationally, more research on racial or ethnic minority men in the industry, and more research that focuses on the unintended or perhaps surprising consequences or outcomes of persistent inequalities in fields like tech (including health outcomes, which we have explored here).

Our work extends the sociological conversation on the implications of social inequalities in the workplace. While sociological scholars have done a thorough job documenting inequalities at work in the tech industry (see Neely et al. 2023; Ravenelle 2019; Wynn and Correll 2017, 2018), as well as the importance of considering inequalities intersectionally (see Alegria 2019; Alfrey and Twine 2017) and the emotional consequences of these inequalities (Wilkins and Pace 2014), we need even more information from a sociological point of view on the impact of inequalities at work when it comes to wellbeing.

As far as practical recommendations, because our findings suggest that inequalities of wellbeing related to identity statuses are a common problem, tech companies (including those that operate internationally) should take steps now to address this issue as the industry will only continue to grow across the world in the coming years (Bradley et al. 2024). For instance, research has shown that inclusion helps bolster wellbeing (Lovell et al. 2020), and employees across the world are more likely to believe that inclusion is possible when their leaders and coworkers support the cause (Dalessandro and Lovell 2024). Therefore, organizations should support a culture of inclusion to help promote wellbeing. In addition, organizational policies, practices, and programs that help support all employees could help address wellbeing equality issues. For example, a number of organizations have had success with employee recognition programs bolstering wellbeing (Lovell et al. 2020). These programs recognize employees for their individual contributions to team and organizational success, which helps them feel seen and acknowledged for their hard work. Another strategy is supporting employee voice, which other research has shown supports feelings of inclusion and wellbeing (Dalessandro and Lovell 2023). In general, organizations should explore the extent to which inequalities of wellbeing might exist among their workforce populations and take steps to promote greater wellbeing in order to support employees and, by extension, achieve more favorable business outcomes (Donovan and Kaplan 2013).

Limitations

Our research is not without limitations. First, our sample was one of convenience and not generalizable. For instance, because our sample was limited to employees working for large organizations, the views of employees at small companies or start-ups are not represented. Second, because our sample was one of convenience and focused on fulltime employees, it primarily comprised highly educated, salaried workers. Thus, our sample does not thoroughly represent the more contingent members of the tech workforce. Relatedly, we chose not to focus on social class differences due to the difficulty of measuring class across our international context. While controlling for compensation type may address class in an incomplete way, future research might try to capture this concept more fully. Other factors that future research might explore include the experiences of nonbinary or gender-non-conforming participants. Alfrey and Twine’s (2017) qualitative work in the U.S. found that gender-fluid women in the tech industry whose gender presentations were more masculine actually had an easier time in the industry compared to more feminine women. However, we need more information (both qualitative and quantitative) on the extent to which these patterns apply globally. While we could have captured that here, our small sample size presented a challenge to being able to contribute meaningfully to a conversation on the international applicability of these findings. Therefore, future research should explore this issue in more depth outside of the U.S.

Similarly, due to our international scope, we did not ask about the specific racial/ethnic identities of respondents, only whether they identified as a racial/ethnic “minority” at work. While our international scope is a strength, it is limiting in that we do not have information on how specific racial/ethnic identity statuses may impact outcomes. Relatedly, further exploration into cultural or regional factors that may intersect with gender and race are warranted. Additional factors, such as hours worked, specific experiences with discrimination, tenure, job type, and more, can be explored as factors in health outcomes in future research.

Regarding our research methods, our use of logistic regression arguably obscured some of the nuances that could have been captured using other types of methods. However, we elected to use these methods given our larger goal of examining whether notable inequalities exist more generally. Lastly, our study was largely exploratory and due to our research approach, our findings cannot be taken as causal. Future research could expand upon what we have captured here and dive deeper into the causal relationship between, for example, inequality regimes and mental health outcomes through methodological approaches, such as longitudinal studies, mediation models, and contextual analysis.

6. Conclusions

Despite attempts to promote wellbeing in the workplace, sociological research on organizations’ wellbeing efforts demonstrates limited success. Therefore, this paper explored, among a global sample of tech workers, the impact of gender and racial identity on self-reported mental and physical health measures. We found that workers identifying as a racial “minority” at work reported lower mental wellbeing and that minority-identified men were significantly more likely to report a negative relationship between their work and their physical health. While exploratory, our findings indicate that in addition to the importance of seeing issues of wellbeing intersectionally, physical and mental wellbeing disparities among tech workers is an issue that is global in scale.