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Abstract

Using data from the 2012–2020 European Social Survey and Schwartz’s theory of basic values, this article maps the values of tech-workers, in order to assess and understand their uniqueness and homogeneity. Consistent with prior, mostly US-focused research, we find that European tech-workers hold a liberal worldview, which values openness to change, individualism, and universalism and devalues conservatism. However, our findings challenge the notion of tech-workers as being a completely distinct or a homogeneous group in terms of their values. While developers appear to be substantively different from other occupations and non-developers working in tech, non-developers hold values similar to those of other occupational elites, such as professionals and managers. The study offers takeaways for research, policy, and education.

Keywords

Developers diversity European Social Survey tech-workers values

When Google’s newly released generative AI tool, Gemini, produced images of people of color in Nazi-era uniforms in response to a request to draw a German soldier in 1943, it ignited a controversy highlighting the pivotal role of people making everyday decisions about the information technologies we use—the tech-workers (Gautam et al., 2024). This controversy adds to a series of values-laden discussions, such as the firing of James Damore, a Google engineer who wrote a memo against the company’s diversity program, and various employee protests across the industry addressing issues from sexual harassment policies to racial inequality (Hicks, 2021; Tan et al., 2023).

These controversies underscore the view of tech-workers as an increasingly influential force affecting the social, political, and economic aspects of both the communities they live in and the society at large (Braman, 2011; Dorschel, 2022b). This focus rests on two main propositions: First, that tech-workers are indeed a distinct group, and, second, that they have a notable impact in the realms of technology, economics, politics, and culture. Researchers often tend to assume the former, while focusing on the latter (Barbrook and Cameron, 1996; Broussard, 2023). In this article, we use the theory of basic human values (Schwartz, 2012) to test the assumption of tech-workers’ uniqueness. This lens allows for generalizable assessment of tech-workers’ values, complementing both qualitative efforts (Avnoon et al., 2024; Dorschel, 2022a; Turner, 2008) and quantitative studies (Brockmann et al., 2019, 2021; Selling and Strimling, 2023), which are often constrained by their geographical focus on the United States or disregard the heterogeneity within the profession by studying mostly the top executives.

Conceptually, we draw on literature about social, political, and cultural roles of tech-workers, and on research about human values and occupations. Empirically, we investigate the value orientations of tech-workers using a representative and geographically diverse dataset from the European Social Survey (ESS) (2022). First, we compare the values of the tech-workers to those of the general population in their respective countries. Second, we explore how values of tech-workers differ from values of other occupational elites. Finally, we examine how values vary among people working in tech, focusing on developers as a unique sub-group.

Tech-workers and their values: literature review

This project weaves together two bodies of literature. First, we build on research about the emerging role of tech-workers as designers of infrastructures of informational power and as an emerging socio-economic and political elite. Second, we draw on literature that interrogates links between working in technological occupations and basic human values. We review these two bodies of literature below and then situate the current project in light of gaps we identify in this literature.

Tech-workers as a distinct and influential social group

Tech-workers are often described as a distinct and influential elite. This uniquely affluent position is assigned not only to the CEOs and founders (Brockmann et al., 2021), but also to those doing the hands-on work of developing the digital infrastructures of the contemporary world (Dorschel, 2022a). While acknowledging the precariousness of certain strata within the industry, Burrell and Fourcade (2021) describe those who design and build technologies as the “coding elite” (p. 215). A beneficiary of privatization of government-sponsored technological development, as well as good education (Binder et al., 2016), the tech elite is socially tight, geographically co-located (Torpey, 2020), and potentially culturally constrained in terms of its worldviews, group (Barbrook and Cameron, 1996; Broockman et al., 2019).

The interest in tech-workers rests on two main observations. First, as a group, tech-workers possess substantive economic and social capital, which sets them apart from the general public (Torpey, 2020). In the United States, for example, the income of high-tech sector employees is on average about twice that of employees in other sectors (Roberts and Wolf, 2018), and they account for an atypically high percentage of ultra-rich (Torpey et al., 2021). Although tech-workers in the EU earn less than their US counterparts, they are still relatively more affluent and enjoy better job security than most (Even et al., 2023). Second, in increasingly digitized societies, tech-workers are the “designers of the next stage of modernity” (Barbrook and Cameron, 1996: 68). They are directly engaged in imagining, building, and maintaining technologies that constitute the infrastructures of informational power. Insofar as institutions rely on information to operate and individuals depend on it for daily life, the power to directly shape the informational environment profoundly “shapes human behavior by manipulating the informational bases” of other forms of power (e.g. instrumental, symbolic; Braman, 2009: 24–27).

Building on those observations, some argue that tech-workers are not only affluent and influential, but also unique in their worldview. In his now classic book, Turner (2008) pointed to the counterculture ideas of the commons and transcendent aspirations, as foundational to cyberculture, even after the technologies upon which it is built have been co-opted by commercial interests and corporate cultures (also see Turner, 2009). Later, Fuchs (2014) posited that software engineers can be considered a “knowledge labour aristocracy” due to their higher wages, autonomy, and privileges, compared to more precarious labor strata. However, he questioned “whether this status results in bourgeois consciousness that is homologous to that of managers and owners” (Fuchs, 2014: 19). Most recently, similar arguments have been evoked by Dorschel (2022b) in his call to study tech-workers as a unique group essential to the understanding of digital capitalism.

Echoing these arguments, there is a growing body of literature calling for a deeper dive into the “hearts and minds of tech-workers” (Dorschel, 2022a: 295). Studying values, as guiding principles for tech-workers’ professional lives, is an important way to address this call. As Friedman and Hendry (2019) observe, “values can be enmeshed in technology” which suggests that “technology in some ways reflects the values of the design team [. . .] either intentionally or not” (p. 32). A small number of studies offer empirical support for that claim. For example, in their analysis of README files on the code-sharing platform GitHub, Levi-Eshkol and Ribak (2024) demonstrated that developers “reveal a sense of agency [and] articulate their socio-technical philosophy” throughout the design process (p. 13). In another study, Avnoon et al. (2024) showed how ethical considerations in algorithm development reflect data scientists’ moral logics: ethical considerations were viewed as a personal (rather than an institutional) endeavor, which can clash with their inherent drive to innovate. Similarly, Hadar et al. (2018) drew a picture of the privacy mindset of tech-workers as a combination of their personal take on pragmatism and the need for constant innovation, against the background of transcendent general ethics. While those studies do not systematically evoke the language of values, they imply that understanding basic human values compositions of those who design and create contemporary information technology is an important research trajectory. A closer interrogation of the tech-workers’ values is the first step toward better understanding of their cultural, political, economic, and technological impact.

The values of tech-workers

There is a rather well-established association between values and occupations (Arieli et al., 2020). For example, past studies have found that routine workers (e.g. bookkeepers and administrators) prioritize stability, creative professions (e.g. artists and engineers) value autonomy (Knafo and Sagiv, 2004), politicians lean toward self-transcendence and power (Weinberg, 2021), and budding entrepreneurs demonstrate openness to change and self-enhancement (Hueso et al., 2020).

The values of tech-workers are often evoked as a basis for normative criticism of the industry, its products, and the power structures it challenges or reifies. Some characterize the entire industry as overwhelmingly favoring technophilia, meritocracy, and solutionism. Such criticism highlights potential implications for strengthening inequality and overlooking the social, cultural, and political repercussions of technological innovation (Barbrook and Cameron, 1996; Turner, 2008). Others, as in the examples above, focus on values in design, arguing that those who build new technologies inadvertently project their worldviews and biases onto the digital artifacts they create, thus making any technology inherently political (Braman, 2011; Friedman and Hendry, 2019; Hallinan et al., 2022; Levi-Eshkol and Ribak, 2024; Shahin et al., 2022; Winner, 1980). Taken together, this body of research mostly aspires to identify values and worldviews that are unique to tech-workers, or, more commonly, it makes assumptions about such values in critiquing the role of (digital) technologies in reifying and challenging established power structures (Braman, 2009; Jakesch et al., 2022; Lessig, 2006). Addressing this lacuna, we build on important recent developments in the literature focused on values of tech-workers as the main object of study.

In research on the values of tech-workers as the object of study, one strand of literature focuses on the ideology of tech-elites as a reflection of the wider tech ecosystem. Many of those studies focus on textual analysis. For example, Haupt (2021) looked at the language Mark Zuckerberg used in public appearances. Through a grounded theory approach, the author reconstructed Facebook’s vision of a better world, as underpinned by digital technology and global connectivity. While this study relied on inductive qualitative text analysis, others used quantitative approaches. For example, Brockmann et al. (2021) worked with Twitter data to analyze attitudes toward meritocracy and democracy among the wealthiest in the tech industry. They observed that tech elites tend to present a more meritocratic and “mission oriented” worldview, compared to the general public and other elites. Lately, Nachtwey and Seidl (2024) leveraged supervised machine learning methods to analyze public statements of tech leaders from the Forbes 400 list to demonstrate the prevalence of the “solutionist” ethos of progress through technology. In addition, in a survey-based study, Broockman et al. (2019) compared the ideologies of tech founders and CEOs, partisan donors, and wealthy Americans. They showed that tech entrepreneurs hold unique ideological combinations supporting liberal redistributive, socially and globally oriented policies (e.g. policies related to social welfare, climate change, and immigration), while strongly opposing regulation. They also compared political attitudes of computer science undergraduates to those of biology majors from the same institution. They found that unique political tendencies, akin to those of the tech elites, are present in aspiring entrepreneurs even before joining the industry.

Another strand of the literature, explicitly ventures beyond tech elites. It analyzes the values of tech-workers doing the day-to-day work of designing, building, and maintaining digital technologies. Neff (2012), famously, demonstrated how tech employees in New York City value personal freedom and autonomy, creativity, risk-taking, and the belief in technology as driving social processes. In a series of more recent semi-structured interviews in the United States and in Germany, Dorschel (2022b) demonstrated that tech-workers employ “a new mode of subjectivation” (p. 297). This “post neo-liberal subjectivity” (Dorschel, 2022a: 1303) evokes values such as self-control, self-rationalization, and self-commercialization, while reflecting critically on one’s class position, expressing progressive views over issues of gender and racial discrimination, and demonstrating thoughtfulness about work–life balance. The resulting shared habitus, rooted in mutual moral codes, also functions as informal mechanisms for social closure and stratification (Dorschel, 2024). As such, values can serve not only as a boundary vis-a-vis other professions, but also to support internal hierarchies within the profession. For example, the prevalent “brogrammer culture,” with its focus on meritocracy and masculinity, might mask underlying gender, class, and race biases in the makeup of the occupation (Benjamin, 2019; Broussard, 2023; Hicks, 2021; Noble and Roberts, 2022; Salter, 2017).

Studies conducted outside of the “Global North” highlight yet another set of value-laden tensions. Di (2023), for example, interviewed tech-workers in the United States and China noting considerable cross-national similarities in their ethical attitudes, and differences in their assessments of the impact of big data on social inequalities. In India, Suri and Abbott (2013) conducted a case study of an IT company, observing how Western corporate norms interact with local cultural values. These scarce studies emphasize the need for geographical diversity in the research of tech-workers.

Complementing these qualitative accounts, Selling and Strimling (2023) used administrative data to study political campaign contributions of tech-workers in the United States. They found that tech-workers tend to support more liberal and anti-establishment candidates. This tendency was driven mainly by belonging to the tech profession rather than working for a company in the tech industry, and it was less pronounced among tech leaders.

Our reading of the literature reveals several lacunas that invite further investigation. First, despite growing recognition of the unique power of tech-workers, there is a general scarcity of research directly addressing the composition of their basic human values. Second, the bulk of research that does address the values of tech-workers, tends to rely on constructs of special interest to authors (e.g. “meritocracy” or “mission-driven” in Brockmann et al., 2021), rather than a general theory of values. This limits comparability with other occupations or across different types of work within the tech industry. Third, existing research typically focuses on elites within tech (e.g. founders, uber-rich) or on tech-workers in very specific geographies primarily, even if not exclusively, in the United States (particularly the Silicon Valley). While such focus is insightful and often practical, it limits the generalizability of the findings as well as the ability to disentangle values heterogeneity within the tech community. Specifically, the claim that “many members of the virtual class in Europe and Asia feel more affinity with their Californian peers than other workers within their own country” (Barbrook and Cameron, 1996: 63), or that regional contexts do not play a role when it comes to tech-workers’ values, should not be assumed but rather empirically tested (Selling and Strimling, 2023).

The current study

In this project, we aim to advance the literature on the values of tech-workers by employing Schwartz’s theory of basic human values (discussed below). This approach allows us to explore the extent to which tech-workers are unique in their value orientations and compare their value orientations with those of other occupations and within the tech sector. We also rely on the European Social Survey, which allows transcending the dominance of US-focused studies. In doing so, we will venture beyond traditional tech-specific theorization and geographical boundaries of prior research.

First, continuing the work of Neff (2012), Dorschel (2022a) and others, we are interested in the value profiles of tech-workers. Adding to prior work, which focused primarily on tech-workers as a single cross-section, we want to examine those values in comparison to the general population. Specifically, we ask (RQ1): How do the basic human values of tech-workers differ from those of the general population?

Second, expanding the work of Brockmann et al. (2021) and Broockman et al. (2019), who focused on the values of particularly influential subgroups within the tech-workers or compared them to other affluent groups, we are interested in the question of uniqueness of the broader class of tech-workers. We want to understand (RQ2): How do tech-workers’ value orientations differ from those of other occupational groups?

Finally, acknowledging that different workers in tech engage in different labor processes and have varying levels of influence on the design of infrastructures of informational power (Braman, 2009; Dorschel, 2022b; Fuchs, 2014), we are interested in understanding the diversity of values among tech-workers. Specifically, we are interested in the heterogeneity in value orientations of those who most directly develop information technologies, and those who maintain and operate them. Thus, we also ask (RQ3): How do the values of developers differ from those of non-developers working in tech?

Methods

Sample

This study uses the European Social Survey (ESS) data—an established, repeated, cross-sectional, and cross-national survey that has been conducted biennially across Europe (and adjacent countries) since 2001 (ESS, 2023). The ESS data are collected mostly through face-to-face interviews, using probabilistic samples that guarantee representativeness in each country unit. The survey measures attitudes, beliefs, and behaviors in over 30 nations and has been used to study the relationship between values and occupations (Långstedt et al., 2023).

The ESS data allow expanding the research beyond the dominant US focus. This is a relevant and important expansion of the context for the study of the values of tech-workers, as the EU accounts for 22% of global R&D development, second only to the United States, which accounts for 28% (National Science Board NSF, 2022). With that, it is important to acknowledge that heterogeneity among and within European countries, particularly in value orientations (Sagiv and Schwartz, 2007), can pose a unique challenge for generalizability—a challenge that we address technically and further discuss below.

We used a consolidated dataset of the ESS (2022) that covers five survey waves for the years: 2012, 2014, 2016, 2018, and 2020 (N = 248,449; see Supplemental Appendix 1 for details about participating countries and total observations by round). In 2020, instigated by the COVID-19 pandemic, the ESS collected data through both face-to-face interviews and self-completed surveys. We chose to exclude the self-completed responses (n = 21,467) to preserve methodological consistency. Furthermore, we removed responses for those below the age of 18 (due to ethical considerations and the fact that they are yet to join the workforce; n = 6669) and one outlier observation of a respondent aged 114. The total number of observations used in the analysis was 220,313. The final sample was 53.5% female, with an average age of 50.6 years (18–104), average education ISCED level of 4.08 (1–7; roughly equivalent to post-secondary non-tertiary education), and 5.2 income decile.

Occupational classification of tech-workers

To identify tech-workers among other occupations we relied on the International Standard Classification of Occupations (ISCO) developed by the International Labour Organization (ILO). ISCO categorizes jobs based on performed tasks and duties. It was adopted by the UN as part of the international family of statistical classifications and is widely used in labor statistics, particularly for comparative analysis (ILO, 2012). The ESS includes four-digit classification of occupation for each respondent, derived by survey administrators from answers to three items pertaining to respondents’ job title, responsibilities, and necessary training. Supplemental Appendix 2 details how we used the four-digit classification to identify both ICT specialists as tech-workers (n = 5363), and “developers” as a subgroup within that category (n = 3008). Tech-workers constitute a relatively small share of the total sample (about 2.7%), they are mostly male, younger, wealthier, and more educated than the general population (see Table 1), which is consistent with patterns observed elsewhere (Eurostat, 2023).

Table 1.

Descriptive statistics—main variables and groups (means; SD in parentheses).

	Total sample	Tech-workers	Developers
Gender (Female; %)	53%	21%	23%
Age (years)	51.44 (17.38)	44.68 (15.34)	43.60 (14.36)
Education (ISCED level)	4.08 (1.81)	5.23 (1.55)	5.68 (1.40)
Income decile	5.26 (2.75)	6.75 (2.55)	7.15 (2.45)
Openness to change	-0.18 (0.67)	0.04 (0.65)	0.1 (0.65)
Conservation	0.13 (0.61)	-0.15 (0.62)	-0.21 (0.62)
Self-enhancement	-0.67 (0.75)	-0.59 (0.72)	-0.59 (0.72)
Self-transcendence	0.63 (0.54)	0.65 (0.52)	0.66 (0.52)
N	220,313	5,363	3,008

Schwartz’s theory of basic human values

To engage with the research questions outlined above, we adopt Schwartz’s theory of basic human values (see Sagiv and Schwartz, 2022, for a review). Schwartz (2014) defines basic values as “desirable transsituational goals, varying in importance, that serve as guiding principles in the life of a person or other social entity” (p. 21). Schwartz’s theory identifies 10 lower-order values which can be examined individually or aggregated into four higher-order values representing two fundamental tensions: between openness to change and conservation, and between self-enhancement and self-transcendence. Regarding the tension between higher-order values of openness to change and conservation, the former is composed of two lower-order values focused on openness to new experiences, namely autonomy of thought and action (self-direction), and novelty and excitement (stimulation); the latter involves three lower-order values, including commitment to beliefs and customs of the past (tradition), adherence to social norms and expectations (conformity), and preference for stability and certainty (security). Unpacking this tension among tech-workers may explain their commonly discussed tendencies toward risk-taking and innovation (Neff, 2012; Turner, 2008).

The tension between higher-order values of self-enhancement with self-transcendence is usually captured by four lower-order values. Self-enhancement values emphasize a preference for control over people and resources (power), and attaining competence and success (achievement); self-transcendence values emphasize concern for others, such as caring for those with whom one has frequent contact (benevolence) or showing concern for the general “other” (regardless of group membership) or the environment (universalism). This tension may help explain tech-workers’ meritocratic tendencies and their doing-well-by-doing-good aspirations, also manifested in the solutionist spirit (Brockmann et al., 2021; Nachtwey and Seidl, 2024).

The original framework also describes hedonism as a lower-order value, which correlates with both openness and self-enhancement constructs. Due to this ambivalence, methodologically-conservative papers have been omitting hedonism when making analysis at an aggregated level (Cieciuch et al., 2019; Lechner et al., 2024)—an approach we adopt for this article.

A number of studies used Schwartz’s theory of values to test associations between values and occupation (e.g. Knafo and Sagiv, 2004). Others explored values as predictors of ethical and political behavior. Caprara et al. (2017) for example, found that values such as universalism predict liberal political orientation, while values such as security predict conservative leanings. Shahin et al. (2022) surveyed how Schwartz’s theory may be used to analyze the values of software engineers in order to understand where value work may, or should, happen in the design process. For example, they observe that the value of self-direction can be associated with prioritizing privacy in design, and the value of universalism with promoting equity.

The possibility to apply Schwartz’s conceptualization of values at different levels of aggregation, offers a degree of empirical flexibility (Sagiv and Schwartz, 2022). Those who analyze the 10 lower-order values typically seek nuance and granularity. In contrast, those who use the four higher-order values, do so because it allows higher reliability, particularly in cross-country comparative studies (Davidov et al., 2008; Lechner et al., 2024). Thus, in this study, we primarily rely on the four higher-order values, but refer to the analysis of the 10 lower-order values when we need to tease out nuances (results based on the analysis of 10 lower-order values including the omitted Hedonism value, are reported in Supplemental Appendix 6).

Operationalizing basic human values in ESS

The ESS has consistently included 21 statements capturing the 10 lower-order constructs according to Schwartz’s theory of basic human values. Following the established practices of working with those data (Schwartz et al., 2015) we first calculated the standardized values of the 10 lower-order constructs: Conformity, Tradition, Benevolence, Universalism, Self-Direction, Stimulation, Hedonism, Achievement, Power, and Security. Each value is measured by asking respondents to indicate the extent to which a statement characterizes them on a scale of one to six, with one corresponding to “very similar to me” and six to “not at all similar to me” (in the analysis we reversed the scale for easier interpretation).

Second, we centered (or ipsatized) the values to the average overall response level for each respondent. Each of the 10 values was then constructed, at the individual level, by averaging the constituent statements, standardized for the mean response rate. The resulting measure represents relative ranking of the particular lower-order value construct, vis-a-vis other values held by that respondent (see Supplemental Appendix 3 for descriptive statistics of the 10 lower-order values).

Third, we calculated the four higher-order value constructs to capture the tension between openness to change and conservation on one axis, and self-enhancement and self-transcendence on the other. To create an aggregate measure for openness to change (M = −0.18; SD = 0.67), we averaged the lower-order constructs of self-direction and stimulation. Similarly, to create a composite measure for conservation (M = 0.12; SD = 0.62) we averaged the values assigned to conformity, tradition, and security; to create a measure for self-transcendence (M = 0.63; SD = 0.54) we averaged the values of universalism and benevolence; and to create a measure for self-enhancement (M = −0.67; SD = 0.75) we averaged the values of achievement and power.

The descriptive statistics of higher-order constructs suggest that tech-workers value openness to change more than the general population, while the opposite dynamic holds for conservation. Tech-workers also value self-enhancement slightly more than people in other occupations, but there is a relatively negligible difference in appreciation of self-transcendence (see Table 1). While indicative, these stylized facts should be treated with caution. They do not account for possible differences between countries and survey rounds, or for interaction between the underlying lower-order constructs, which we discuss below.

Results

We analyzed the data using a series of OLS regressions with fixed effects for country and survey wave (see Supplemental Appendix 4 for per country and per round descriptive statistics). First, we assessed the value profiles of tech-workers compared to those of the general population. Second, we compared the values of the tech-workers to those of other occupational elites. Finally, we repeated the last analysis, while distinguishing developers from non-developers and other occupations.

The value profile of tech-workers

To address RQ1 we employed a series of OLS regressions with identification as a tech-worker as an independent variable, and country and ESS round as fixed effects (see full regression results in Supplemental Appendix 5). In a basic model without the demographics, being a tech-worker was positively associated with openness to change (b = .164, p < .01, d = .242) and self-enhancement (b = .134, p < .01, d = .175), but negatively associated with conservation (b = −.228, p < .01, d = −.367) and self-transcendence (b = −.026, p < .001, d = .048).

In an expanded model we added the demographic factors (age, gender, education, and income), which increased the variance explained. The demographic variables showed statistically significant relationships with values, in directions aligned with extant literature (Schwartz, 2007). For example, openness to change was positively associated with education (b = .044, p < .01), and negatively associated with age (b = −.008, p < .01), and with being a female (b = −.081, p < 0.01). Self-transcendence was positively associated with age (b = .004, p < 0.01), education (b = .021, p < .01), and with being a female (b = .155, p < .01), but negatively associated with income (b = .003, p < 0.01).

The addition of demographics weakened and slightly altered the associations between occupational affiliation and basic human values. Similarly to the basic model, when controlling for demographics, being a tech-worker was positively associated with openness to change (b = .033, p < .01, d = .048) and negatively with conservation (b = −.065, p < .01, d = .104). Also similarly to the basic model, there was a positive association between tech occupation and self-enhancement, but it was no longer significant (b = .016, p > .05, d = 0.021). As we discuss below, this dynamic can be potentially explained by the unique demographic composition of the tech workforce (younger, richer, more educated, and predominantly male).

Contrary to the basic model, where tech occupation was significant and negatively associated with self-transcendence, in the expanded model this relationship was positive, but not significant. Our additional analysis of the lower-order constructs suggests that the aggregate measure of self-transcendence masks a tension between the values of benevolence and universalism: both variables have statistically significant coefficients that offer associations in opposite directions (see Supplemental Appendix 6). In other words, being a tech-worker is negatively associated with in-group prosociality and positively associated with out-group prosociality. Furthermore, the relative contribution of each lower-order construct to the aggregate measure of self-transcendence changes when we account for the unique demographic composition of the tech workforce. In the rest of our sample the two values correlate positively (r = .34), suggesting a distinct composition of these two basic values among tech-workers. This dynamic holds even when focused on other occupational elites, such as managers and professionals (see a detailed discussion below). Managers have negative association with both benevolence (b = −.047, p < 0.01) and universalism (b = −.078, p < 0.01), while professionals have positive association with both (b = −.035, p < 0.01; b = .133, p < 0.01).

Tech-workers versus other occupations

To address RQ2 we ran a set of OLS regressions predicting the four higher-order values, while comparing tech-workers with a range of other occupations. The results highlight two main observations that complement earlier findings about differences between tech-workers and the general public (see full regression table at Supplemental Appendix 7).

First, already in the basic model without any demographic variables, we saw that the tech-workers’ value orientations were different compared to lower-skill-white-collar and blue-collar occupational groups such as technicians, machine operators, clerical or skilled agricultural workers. Compared to tech-workers, people employed in lower-skill-white-collar and blue-collar jobs were more conservative and less open to change; they tended to value self-transcendence and undervalue self-enhancement.

Second, in the basic model, we also observed greater complexity in comparison to other occupational elites, which include high-skill, white-collar occupations such as managers and professionals (Eurofound, n.d.). Compared to tech-workers, professionals exhibited similar patterns to those of the lower-skill-white-collar and blue-collar occupations, but the magnitude of those associations was substantially smaller (except for self-transcendence). Managers demonstrated similar patterns with regard to openness to change and conservation, but not with regard to self-transcendence and self-enhancement (both of which are not statistically significant).

The dynamics described above were more pronounced in the expanded model with socio-demographic controls. In this specification, the orientation toward openness to change, conservation, and self-enhancement among tech-workers was not significantly different from that of professionals. Concurrently, managers appeared to be even more open to change and less conservative than tech-workers, and to score higher on self-enhancement and lower on self-transcendence. These findings further highlight the potential effect of the socio-demographic composition of the tech sector on the values orientations of its members.

Developers versus other occupations

To address RQ3, we relied on ISCO classification to distinguish developers from non-developers and other occupations, and then compare their associations with four higher level values (see Table 2).

Table 2.

Regression analysis predicting four higher-order values, comparing other occupations to developers as a baseline.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variables	Openness to change	Conservation	Self-enhancement	Self-transcendence	Openness to change	Conservation	Self-enhancement	Self-transcendence
Managers	-0.044***	0.100***	-0.019	0.007	0.070***	-0.033***	0.077***	-0.048***
	(0.012)	(0.011)	(0.013)	(0.009)	(0.013)	(0.011)	(0.014)	(0.010)
Professionals	-0.063***	0.063***	-0.098***	0.117***	-0.010	-0.001	-0.000	0.031***
	(0.011)	(0.010)	(0.012)	(0.009)	(0.012)	(0.010)	(0.013)	(0.009)
Technicians	-0.139***	0.169***	-0.147***	0.071***	-0.035***	0.045***	-0.046***	0.009
	(0.011)	(0.010)	(0.012)	(0.009)	(0.012)	(0.010)	(0.013)	(0.009)
Clerical	-0.231***	0.268***	-0.224***	0.087***	-0.090***	0.104***	-0.079***	-0.003
	(0.012)	(0.011)	(0.013)	(0.009)	(0.013)	(0.011)	(0.014)	(0.010)
Services and sales	-0.162***	0.232***	-0.173***	0.037***	-0.040***	0.094***	-0.056***	-0.028***
	(0.011)	(0.010)	(0.012)	(0.009)	(0.012)	(0.011)	(0.013)	(0.009)
Skilled agricultural	-0.350***	0.449***	-0.197***	0.038***	-0.118***	0.172***	-0.016	-0.034***
	(0.014)	(0.012)	(0.015)	(0.011)	(0.015)	(0.013)	(0.016)	(0.012)
Craft	-0.189***	0.271***	-0.079***	-0.043***	-0.055***	0.118***	-0.005	-0.056***
	(0.012)	(0.010)	(0.012)	(0.009)	(0.013)	(0.011)	(0.013)	(0.010)
Machine operators	-0.270***	0.338***	-0.142***	0.003	-0.117***	0.151***	-0.046***	-0.012
	(0.012)	(0.011)	(0.013)	(0.009)	(0.013)	(0.011)	(0.014)	(0.010)
Elementary	-0.295***	0.367***	-0.172***	0.039***	-0.123***	0.162***	-0.030**	-0.020**
	(0.012)	(0.010)	(0.012)	(0.009)	(0.013)	(0.011)	(0.014)	(0.010)
Non-developers	-0.076***	0.063***	-0.026	0.031**	-0.040**	0.022	-0.008	0.039***
	(0.019)	(0.017)	(0.020)	(0.014)	(0.020)	(0.017)	(0.021)	(0.015)
Female					-0.081***	0.088***	-0.165***	0.147***
					(0.003)	(0.003)	(0.004)	(0.003)
Age					-0.008***	0.011***	-0.008***	0.004***
					(0.000)	(0.000)	(0.000)	(0.000)
Education					0.034***	-0.034***	0.001	0.016***
					(0.001)	(0.001)	(0.001)	(0.001)
Income					-0.003***	-0.007***	0.012***	-0.004***
					(0.001)	(0.001)	(0.001)	(0.001)
Constant	-0.288***	0.024	-0.285***	0.418***	-0.117**	-0.234***	-0.018	0.202***
	(0.054)	(0.049)	(0.058)	(0.041)	(0.055)	(0.047)	(0.058)	(0.042)
Observations	197,620	197,669	197,627	197,670	162,852	162,881	162,846	162,880
R ²	0.050	0.086	0.168	0.137	0.110	0.217	0.206	0.161

Standard errors in parentheses; all models include country and ESS-round fixed effects.

p < 0.1. **p < 0.05. ***p < 0.01.

In the basic model developers were extremely open to change and devalued conservation, even when compared to non-developers, who were relatively less open to change (b = −.076, p < .01) and more conservative (b = .063, p < .01). In this sense, non-developer tech-workers were more similar to other occupational elites such as managers and professionals, who also placed relatively less value on openness to change (b = −.044, p < .001; b = −0.063, p < .001, respectively) and relatively more value on conservation (b = .1, p < .001; b = 0.063, p < .001, respectively) compared to developers (also see Figures 1 and 2).

Figure 1.

Openness to change, by occupation (major ISCO groups) compared to developers (dashed reference line); occupation-by-occupation OLS regression.

Figure 2.

Conservation, by occupation (major ISCO groups) compared to developers (dashed reference line); occupation-by-occupation OLS regression.

When comparing other value orientations in the basic model, developers reported a consistently stronger preference toward self-enhancement relative to other occupations (albeit some coefficients were not statistically significant). The differences were less pronounced for self-transcendence. While most other occupational groups (including non-developers) showed strong associations with this value orientation, compared to developers, not all the coefficients were statistically significant (e.g. managers, machine operators); craft workers scored significantly less on self-transcendence compared to developers (b = −.043; p < .01). While not as clear as with openness to change and conservation, this analysis reaffirms the intuition about differences in value orientations between developers and non-developers among tech-workers.

In the expanded model with socio-demographic factors, the basic dynamics regarding openness to change and conservation persisted. Most occupational groups (including non-developer tech-workers) appeared to value openness to change less compared to developers. The only exceptions were managers, who valued openness to change more (b = .07; p < .01) and conservation less (b = −.033; p < .01), and professionals, who did not significantly differ from developers. Similar to analyses presented above, factors such as gender, age, education, and income explained a share of variance in value orientations. As we discuss below, this is another indicator that the demographic makeup of the tech industry may impact its shared values.

Overall, developers appear to be more extreme in their value orientations, compared to non-developers and other occupations. At the same time, non-developers appear to be more similar to other occupational elites such as managers and professionals. All three groups are mostly distinct from other lower-skill-white-collar and blue-collar occupations. While the differences between developers and other occupational elites are dampened with the introduction of socio-demographic variables into the model, they persist, along with the differences between developers and non-developers.

Discussion

We employed Schwartz’s (1992) theory of basic human values to examine the value orientations of tech-workers. We compared the value orientations of tech-workers to those of the general population and other occupational groups, and examined the heterogeneity of values within the tech sector by comparing developers with non-developers working in tech. To our knowledge, this is the first application of a unified theory of values for such an analysis of tech-workers. Our findings reaffirm earlier observations about the unique composition of the values of tech-workers, provide additional details about how tech-workers compare to other occupational elites, add nuance to the interplay of value orientations among the tech-workers, and highlight the potential effect of the socio-demographic composition of the tech industry on its shared values.

First, our findings support prior claims that tech-workers hold a set of values distinct from the general population. Most notably, tech-workers tend to value openness to change and devalue conservation. This finding, based on a European sample, offers support to the generalizability of mostly US-based literature suggesting that tech-workers are more open to change, willing to take risks (e.g. Neff, 2012), and generally more liberal (Brockmann et al., 2021; Selling and Strimling, 2023). Furthermore, we observe that tech-workers tend to value self-enhancement to a greater extent compared to the general population. This finding is consistent with prior research portraying tech-elites as having individualistic and meritocratic tendencies (Brockmann et al., 2019, 2021). Overall, our findings offer a more generalizable support to the view that tech-workers value change, innovation, individualism, and meritocracy, as well as to the idea of shared habitus anchored in values (Dorschel, 2022a).

Second, we observe that the differences described above are more pronounced when comparing tech-workers to lower-skill-white-collar and blue-collar occupations. These observations suggest a potential link between occupational status and values, as the overall value composition of tech-workers seems to be more aligned with that of other occupational elites. In our analysis, both professionals and managers—two occupational groups associated with knowledge-intensive, white-collar jobs—demonstrated value orientations distinct from other occupational groups. At the same time, our findings suggest that professionals and managers are not substantively different from tech-workers in their value orientations. In other words, those findings do not necessarily support the broad claim about tech-workers’ exceptional culture (Turner, 2008), but rather suggest that they are closer to other occupational elites in terms of value orientations.

Third, we add nuance to the interplay of value orientations among tech-workers. Our analysis of the lower-order values composing self-transcendence suggests that, compared to the general population, tech-workers tend to value the out-group orientation (universalism) and devalue the in-group orientation (benevolence). In contrast, these two lower-order values are consistently positively correlated in other occupational groups. One way to interpret such discrepancy is the global or universal orientation of tech-workers, who are focused more on “making the world a better place” rather than improving the wellbeing of their immediate communities. Such interpretation resonates with evidence on Silicon Valley’s philanthropic activity, which tends to support global causes over local initiatives (Zinsmeister and Torres, 2018), the overall globalist orientation of Silicon Valley’s tech elite (Broockman et al., 2019), and criticism of the growing gaps between tech-workers and other residents of cities such as San-Francisco (Walker, 2018).

Furthermore, we observe another type of ambivalence: stronger associations with both self-transcendence (at least through its universalist aspect) and self-enhancement. Prior research based on Schwartz’s theory of basic human values has established that the two values typically negatively correlate, i.e. those who care more about themselves, tend to care less about others (Rudnev et al., 2018). For tech-workers, however, we observe that the two values correlate positively. In other words, tech-workers exhibit a counterintuitive mix of individualistic and prosocial tendencies. In a way, this finding supports prior studies describing tech-workers as a “contradictory class stratum” (Brockmann et al., 2021: 4) or as a class with distinct subjectivity aspiring to be “middle-class wealthy and morally worthy” (Dorschel, 2022a: 1303). It can also explain why tech-workers lean on technology-centered solutionism as a way to bridge the tension between personal wellbeing and social progress (Nachtwey and Seidl, 2024).

Fourth, our findings uncover heterogeneity among tech-workers. When separating developers from other maintenance and supporting roles in tech, we observe developers to be more extreme in their valuing of self-enhancement and openness to change, while devaluing conservation. These tendencies make developers distinct even from other occupational elites, particularly in terms of the tension between openness to change and conservation. Interestingly, isolating developers clarifies the similarity of value orientations of non-developers to other occupational elites such as managers or professionals. These findings add quantitative, generalizable evidence to prior observations about the heterogeneity of values among tech-workers (Dorschel, 2022b; Fuchs, 2014) and emphasize the significance of the values of developers in shaping the foundations of the shared tech habitus.

Finally, our findings highlight the potential impact of the socio-demographic composition of the tech industry on its dominant value orientations. The tech industry has been criticized for consisting of predominantly privileged young males (Broussard, 2023; Hicks, 2021). Indeed, when controlling for socio-demographic variables, the value orientations of tech-workers tend to converge with those of the rest of the sample. This suggests that at least some of the differences between tech-workers and other occupational elites, can be attributed to the distinct socio-demographic composition of this occupation. This is an important finding as it paints value orientations of tech-workers, at least partially, as a function of their socio-demographic background, making them potentially more resistant to change.

Our observations about the socio-demographic composition of tech-workers also point to discussions about value differences as a product of self-selection versus socialization processes in tech. For example, Broockman et al. (2019) observed that higher education students in technological fields exhibit unique political profiles, even before they enter the job market. Similarly, Selling and Strimling (2023) demonstrated that being a tech professional—something usually gained through certain educational choices—is more important in explaining tech-workers’ political orientations than working in a tech company (Broockman et al., 2019; Selling and Strimling, 2023). While neither study claims a causal mechanism, they do suggest that “tech workers’ ideologies seem to have crystallized before joining tech firms” (Selling and Strimling, 2023: 1485) Although the observed contribution of socio-demographics to value differences may lend support to such claims, establishing causality will require a more tailored approach as we discuss below.

Conclusion, limitations, and future research

We set out to test the assumption of tech-workers’ uniqueness through the lens of basic human values. In doing so, we strived to contribute to the debate about the societal impact of tech-workers by systematically analyzing their inherent guiding principles—their values. Our contribution to this debate is twofold.

On one hand, our findings lend generalizable support to earlier observations of tech-workers’ distinctively liberal tendencies. We demonstrate this by expanding the predominant US-focus through the use of a European representative sample. Particularly, we observe that developers, who most directly affect the design of contemporary information technology and set the tone for the shared habitus in the industry, are keen on openness to change, individualism, and universalism. While Schwartz framework does not directly measure solutionism, the combination of exceptionally high predisposition for the new and the risky, for human achievement and universal causes, could explain an uncritical belief in the efficacy of technology to solve global problems. Such tendencies can enable rapid, groundbreaking, and ambitious innovation, but they can also prioritize technological advancement over ethical considerations.

On the other hand, our findings challenge both the idea of tech-workers exceptionality and their assumed homogeneity. Our observations of similarities in value orientations of tech-workers and other occupational elites, may suggest that tech-workers are simply rich and affluent. At the same time, our observations of differences between developers and other tech-workers highlights the heterogeneity of the tech industry. Particularly, observing how developers differ in their value orientations from both other occupational elites and other people working in tech, calls for greater caution in using a generic label to refer to all the people working in the industry.

Employing a unified theory of values and a large-scale, representative sample indeed advances research about the values of tech-workers, but this approach also contains inherent limitations. First, while offering a useful universal conceptualization and operationalization of values that allow for comparison across occupational groups, the same standardization may limit the ability to capture finer nuance or variation in value orientations of participants. Second, the cross-section nature of ESS data poses inherent limitations. Most fundamentally, it does not allow for causal analysis of how specific value compositions come to be or how they may affect technology development.

Future research can address those limitations by integrating both quantitative and qualitative methods to capture both depth and nuance of tech-workers’ value compositions. It should also further interrogate the heterogeneity among the tech-workers (e.g. company stage and size) and other occupational elites (i.e. unpacking the broad categories of managers and professionals). Furthermore, more explicitly comparative work is needed to tease out possible variations in value orientations and tech habitus in different parts of the world.

Future research should also directly tackle causal mechanisms for establishment of value orientations of tech-workers, and the impact of those values on social, political, and technological outcomes. Longitudinal studies may be particularly fruitful for engaging with questions of self-selection versus socialization of tech-workers, or the role of tech-elites, such as venture capitalists and founders, in shaping value orientations in the industry.

Furthermore, given the growing political importance of tech-workers, future research should look into possible links between the values held by tech-workers and their political attitudes and policy preferences. Against the background of growing importance of values, identity, and culture in politics, prior research has suggested that work is an important part of identity in contemporary society, and as such, it is connected to both people’s values and their politics (Kitschelt, 1994; Kitschelt and Rehm, 2014). Specifically, there are studies suggesting that basic human values, as they are conceptualized and operationalized here, are related to one’s political preferences (Caprara et al., 2017). It would be reasonable to expect the unique values profiles of tech-workers, particularly those of developers, to manifest themselves in ideological leanings, political attitudes, and policy preferences. Leveraging the approach presented in this study can be useful in interrogating potential links between tech-workers’ values and their politics. Finally, more critical-design research should explore how the uncovered value patterns of tech-workers manifest themselves in design, configuration, and maintenance decisions.

Finally, in addition to its scholarly contribution, this study offers potential insights for policy development. First, our findings highlight the importance of socio-demographic composition of the tech workforce in shaping the values dominating the industry, and subsequently affecting its ethics and the technologies that the tech-workers create and configure. As such, they lend support to diversity and inclusion efforts in the industry. Second, by identifying developers as a unique subgroup among tech-workers, both in terms of their power and their distinct value orientations, our findings support the growing efforts for ethical education as part of technologists’ training. Third, any policy intervention—whether it is aimed at diversification or ethics education—requires a baseline assessment of the lacuna it is trying to address. Our study offers such a baseline in a given point in time, against which future efforts can be calibrated and their effectiveness can be assessed.

Supplemental Material

sj-docx-1-nms-10.1177_14614448251333343 – Supplemental material for The high-tech elite? Assessing the values of tech-workers using the European Social Survey 2012–2020

Supplemental material, sj-docx-1-nms-10.1177_14614448251333343 for The high-tech elite? Assessing the values of tech-workers using the European Social Survey 2012–2020 by Gilad Be’ery and Dmitry Epstein in New Media & Society

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to thank the HUJI Fund for the Support of Open Access Publishing for supporting the publication of this article in an open-access format.

ORCID iDs

Gilad Be’ery

Dmitry Epstein

Supplemental material

Supplemental material for this article is available online.

Author biographies

Gilad Be’ery is a PhD candidate at the Federmann School of Public Policy at the Hebrew University of Jerusalem and Head of the Economic Reforms Program at the Israel Democracy Institute. His research explores the intersection of economics, society, and technology, with an interest in how these dynamics influence policy debates. His doctoral project focuses on the values and policy preferences of high-tech workers.

Dmitry Epstein is an Assistant Professor in the Department of Communication and the Federmann School of Public Policy at the Hebrew University of Jerusalem. He studies the intersection of information, technology, policy, and society with recent projects focusing on privacy, digital inequality, and internet governance. Dmitry is a co-founder of the Comparative Privacy Research Network.

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