Abstract
Higher education (HE) staff experience some of the poorest mental health and well-being across occupational groups, with particularly low levels reported among staff from lower socioeconomic status (SES) backgrounds. Combining Needs Theory and Ecological Systems Theory, we investigate how perceptions of policymakers may have contributed to mental health inequalities among HE workers in the United Kingdom (UK) during the COVID-19 pandemic. A survey of UK HE staff (n = 1,116) from 92 universities during a ‘stay at home’ order found that lower subjective SES was linked to poorer mental health and well-being, and to the perception that policymakers were disconnected (perceived inclusion) and did not value (perceived worth) university staff. A structural equation model indicated that these perceptions of policymakers mediated the relationship between subjective SES and mental health and well-being. Greater perceived control by policymakers was also associated with poorer mental health and well-being, though unexpectedly, this effect was not linked to subjective SES. These findings suggest that perceptions of policymakers may worsen socioeconomic inequalities in mental health and well-being through unmet psychological needs.
Within the higher education (HE) sector, poor mental health and well-being among staff has been reported for nearly two decades (e.g., Kinman et al., 2006), with the poorest outcomes often found among those from lower socioeconomic status (SES) backgrounds (Dougall et al., 2021). Recently, the relationship between the HE sector and policymakers has become increasingly strained, due to funding cuts and redundancies (Lough, 2022). During the COVID-19 pandemic, policymakers significantly impacted universities by demanding campus reopenings and encouraging face-to-face teaching, further straining this relationship (McKie, 2020). With over half of university staff now estimated to meet thresholds for probable depression (Wray & Kinman, 2021), urgent action is needed to address this escalating mental health crisis within HE. In the present work, we examine the role of the sociopolitical context as a factor contributing to socioeconomic inequalities in mental health and well-being among HE staff in the United Kingdom (UK), particularly during the COVID-19 pandemic.
Inequalities in mental health and well-being among UK higher education staff
University staff face high levels of stress, with burnout rates considerably higher than in general working populations (Guthrie et al., 2017; Urbina-Garcia, 2020). Not surprisingly, staff report low job satisfaction (Shin & Jung, 2014) and poor quality of working life (Fontinha et al., 2019). Additionally, data suggest poor mental health is nearly twice as prevalent among lower-SES university staff compared to their higher-SES counterparts (Dougall et al., 2021).
Research highlights the critical roles of control (the belief one can influence life outcomes), inclusion (a sense of belonging) and worth (feeling respected) as mechanisms driving socioeconomic disparities in mental health and well-being (e.g., Di Domenico & Fournier, 2014; Dougall et al., 2021, 2023). This aligns with needs-fulfilment theories, which propose that psychosocial needs underpin healthy functioning (Ryan & Deci, 2000; Ryff & Keyes, 1995). A global study of needs theory found autonomy, relatedness and respect to be the only psychosocial needs linked to mental health and well-being, alongside basic needs like food and shelter (Tay & Diener, 2011). Note that autonomy relates to perceived control (the need to feel agency in one’s life), relatedness maps onto inclusion (the need to feel a sense of belonging) and respect aligns with perceived worth (feeling valued). As discussed in more detail below, here we conceptualize these needs as being potentially fulfilled or thwarted not only through the immediate environment (e.g., work colleagues) but also by policymakers through their perceived attitudes and actions.
Emerging evidence links psychosocial needs to differences in mental health and well-being that vary along a socioeconomic status (SES) continuum. SES refers to a person’s or group’s standing in terms of social and material resources, either assessed objectively with aspects such as income, education and/or occupational prestige, or subjectively reflecting a person’s perceived rank (e.g., Adler et al., 2000; American Psychological Association, 2015). Focusing on subjective SES, Dougall et al. (2021) found that SES predicted fulfilment of control-, inclusion- and worth-based needs among UK HE staff, which in turn was linked to mental health and well-being. Lower-SES staff reported feeling less in control, less included and less worthy, which contributed to mental health inequalities. Notably, this research was conducted before the COVID-19 pandemic and focused on needs fulfilment by work colleagues. The present research considers whether perceptions of policymakers in the wider HE policy environment may similarly contribute to these disparities.
Sociopolitical context: policymakers and the HE sector
In recent years, the relationship between UK HE and policymakers has grown particularly tense. By ‘policymakers’, we mean those responsible for UK policies, including government officials, Members of Parliament, Lords, scientific advisers and civil servants. This strained relationship is reflected in public statements criticizing experts (Mance, 2016) and accusing universities of exploiting young people (Hazell, 2020). Additionally, successive HE policies have led to decreased remuneration and funding cuts, particularly in arts programmes, resulting in course closures and staff redundancies (Lough, 2022; UCEA, 2019). Similar conditions are seen in other countries; for example, job losses in Australia and the US are attributed to insufficient government funding (Davies, 2020; Sainato, 2021). For lower-SES staff, academia may offer a path to social mobility, making this sociopolitical context particularly impactful.
Research shows that lower-SES individuals often feel out of place in universities dominated by middle- and upper-class cultures, which subtly exclude those from lower-SES backgrounds (Binns, 2019; Reay, 2021; Stephens et al., 2012). Similarly, the policy environment is dominated by individuals from privileged backgrounds (Carnes, 2018). For instance, 44% of UK politicians attended fee-paying schools, compared to just 7% of the general population (The Sutton Trust and Social Mobility Commission, 2019). This overrepresentation extends to other leadership roles, potentially leading lower-SES HE staff to feel disconnected from policymakers.
Previous research indicates that subjective SES influences how much colleagues fulfil inclusion- and worth-based needs. Given the sociopolitical context of HE, we question whether these needs might also be influenced by policymakers. Specifically, we examine whether SES is linked to the extent to which control-, inclusion- and worth-based needs are fulfilled or thwarted by policymakers, and whether this corresponds to any disparities in mental health and well-being. We draw on Ecological Systems Theory (EST), which suggests that various environments shape experiences across domains, including mental health and well-being (Bronfenbrenner, 1977; Doom et al., 2021). Of note, Dougall et al. (2024) recently reported a meta-review of the literature showing that ecological systems are useful to explain socio-economic disparities in mental health and well-being.
EST holds that well-being is shaped simultaneously by immediate relationships (microsystem), the wider institutional settings that surround those relationships (exosystem) and overarching cultural norms (macrosystem) (Bronfenbrenner, 1977; Neal & Neal, 2013). In the present study, UK policymakers are conceptualized as a source of influence that originates from the exosystem: staff rarely interact with them directly, yet government decisions set the structural conditions under which day-to-day university life unfolds and psychological needs are either met or thwarted.
EST clarifies how distal influences, such as policymakers, can affect HE staff members’ experiences in ways that psychological needs are either met or thwarted. In the current policy climate, control-based needs may be undermined when staff are subjected to funding cuts, course closures and job losses that affect their working conditions but over which they have little say. Such external control, particularly when experienced as imposed or misaligned with one’s interests, likely thwarts experiences of personal autonomy and control (Thibaut & Kelley, 1959). Inclusion may also be affected by cultural and class gaps between policymakers and academic staff, especially for those from lower-SES backgrounds who may already feel out of place in a university setting. Finally, perceived worth may be influenced by political narratives that portray universities as exploitative or out of touch, leaving staff feeling devalued. Thus, EST suggests that policymakers can have a material impact on the conditions that support or undermine HE staff members’ psychological needs related to control, inclusion and worth.
Past work on basic psychological needs has concentrated almost exclusively on interpersonal sources of support — teachers, parents, coaches or direct supervisors — within the microsystem. A recent meta-analysis of more than 4,500 effect sizes shows that the vast majority of studies adopted this focus (Slemp et al., 2024). The present study broadens that perspective by arguing that policy-level actors can similarly affect needs fulfilment and, in turn, staff well-being. In doing so, we integrate Self-Determination Theory (SDT) with an ecological framework and offer a novel account of how distal structures influence mental health and well-being through psychological needs fulfilment and contribute to inequalities.
The COVID-19 pandemic
Universities faced rapid operational changes during the COVID-19 pandemic, dramatically altering teaching and support (Leal Filho et al., 2021). Research suggests this heightened workload worsened staff mental health and well-being (Wray & Kinman, 2022). During the pandemic, policymakers' actions were closely scrutinized as campus reopenings and face-to-face teaching were prioritized, despite staff concerns (Turner, 2021; UCU, 2021). Such actions may have further impacted the extent to which HE staff felt valued and connected with policymakers.
During the pandemic, lower-SES households faced additional stressors, including financial insecurity, increased furlough or redundancy risk, limited ability to work from home, less outdoor space and greater informal caregiving responsibilities (Collinson, 2020; Warren et al., 2021). These pressures may have also affected HE staff, since those from lower-SES backgrounds are more likely to hold temporary or lower-paying roles. Furthermore, even among those with secure positions, SES-related stressors may have added indirect strains. Consequently, lower-SES HE staff may have felt particularly exposed to the control of policymakers.
The present research
Emerging evidence suggests that perceptions of policymakers may be linked to mental health and well-being. Using UK panel data, Wright et al. (2022) found that negative views of government relating to perceived incompetence, inconsistency, cronyism and corruption can be linked with poorer well-being outcomes. The present research extends this line of work by examining these dynamics within the higher education sector. Combining SDT and Ecological Systems Theory, we investigate how perceptions of policymakers may contribute to mental health inequalities among HE workers in the UK, especially during the COVID-19 pandemic, when their actions received public scrutiny.
We focus on subjective SES because it captures individuals’ internalized perceptions of their social standing — an aspect shown to be more proximally tied to psychological outcomes than objective indicators like income or qualifications (e.g., Singh-Manoux et al., 2005; Tan et al., 2020). Subjective SES is particularly relevant in the present context as we seek to establish how signals originating from exosystems are perceived and internalized, shaping individuals’ experiences of inclusion, worth and well-being within HE institutions. In addition, subjective SES is arguably more suitable for studying variations within a relatively homogenous occupational group. Several markers of objective SES are also difficult to harmonize in an internationally recruited university workforce. For example, the Index of Multiple Deprivation (Ministry of Housing, Communities & Local Government, 2019) is a common measure of objective SES that is based on UK postcodes, typically assessed in relation to where respondents grew up. Parental-education questions can similarly be problematic when degrees and qualification frameworks differ. Measures of subjective SES can sidestep these issues by allowing respondents to locate themselves on a continuum that accommodates their idiosyncratic experiences and perceptions.
To ensure our measures suit the HE setting, we use measures developed by Dougall et al. (2023) that capture both eudaimonic and hedonic aspects of mental health and well-being, including items on emotional exhaustion, stress and worry (Guthrie et al., 2017).
EST suggests that HE staff’s mental health and well-being may be influenced not only by staff members’ immediate environment but also by the broader sociopolitical context, which includes policymakers. Thus, we hypothesize that staff members’ subjective SES predicts the extent to which they perceive policymakers as fulfilling control-, inclusion- and worth-based needs, which in turn affects their mental health and well-being. Specifically, we expect that lower subjective SES will be associated with higher perceived control by policymakers and lower inclusion and perceived worth. In turn, we expect that higher perceived control by policymakers and lower inclusion and perceived worth will be linked to poorer mental health and well-being. We tested these predictions in a cross-sectional survey of UK HE staff conducted during COVID-19 restrictions. To contextualize the novel macro-level paths, we also estimate an otherwise identical ‘reference’ model with the familiar sources of needs fulfilment (personal control and ‘other staff’ as sources of inclusion and perceived worth; see Dougall et al., 2021), providing a conceptual benchmark against which the unique contribution of policymakers can be interpreted.
Method
Participants
Of the 1,601 participants who commenced the survey, 1,207 (75%) completed the survey. Two participants (.2%) who completed the survey were excluded for failing a pre-planned attention check. We also removed 89 (7%) responses with incomplete data (see Analytical Strategy section below). The retained sample included 1,116 staff members from 92 universities across the UK. The median number of staff from each university was seven, with numbers ranging from one (2%) to 150 (.1%). Demographic characteristics of retained participants are presented in Table 1. Characteristics of excluded participants are shown in Supplementary Materials Table S1. Respondents’ average age was 43.84 years (SD = 10.89).
Demographic characteristics of retained participant sample (N = 1,116).
Note: Russell Group, pre-92 and post-92 refer to UK university groupings based on historical status and funding structures; Russell Group denotes research-intensive institutions, pre-92 are older universities and post-92 are newer institutions granted university status after 1992.
Rules of thumb for the sample size requirement for Structural Equation Modelling suggest a minimum of 10 participants per parameter (Jackson, 2003). We met this criterion since our final model contained 66 parameters. A further discussion of power analysis can be found in the Analytical Strategy section below.
Procedure
Data were collected between February and March 2021 during a time of COVID-19 restrictions in the UK (for details of restrictions, see Institute for Government, 2022). Survey responses were collected online using Qualtrics (www.qualtrics.com). We recruited UK university staff via posts on social media and through circular emails. Participants could enter a prize draw to win shopping vouchers (up to £50) as compensation.
Materials
Demographic characteristics
Participants indicated their age, gender, ethnicity, staff role at the university (academic vs. non-academic), whether they had self-isolated since the beginning of the academic year (yes/no), the number of people in their household, whether they had responsibilities as a caregiver (yes/no) and their political orientation (from 1 = ‘far left’ to 7 = ‘far right’). These demographic measures served as control variables in further robustness checks reported below and in Supplementary Materials.
Subjective socioeconomic status (SES)
Subjective SES was assessed, first, using the MacArthur ladder ranging from 0 to 10. Higher scores indicate higher subjective SES (Adler et al., 2000). Additionally, three questions measured participants’ economic, social and cultural capital, based on Bourdieu (1986) and as reported in Dougall et al. (2023). Economic capital was operationalized as income, savings, the value of one’s home and wealth. Social capital was measured by the number and status of the people in one’s social network. Finally, cultural capital captured the extent and nature of one’s cultural interests, activities and hobbies (0 = ‘lowest capital’; 100 = ‘highest capital’). Subjective SES was operationalized as the mean of all four measures after dividing scores on the three capital items by 10 to place all items on the same 0–10 metric. Higher composite scores therefore indicate higher perceived rank.
Perceived control
The perceived control scale comprised two questions adapted from Cichocka et al. (2018): ‘policymakers have great control over my life’ and ‘policymakers have great influence on my fate’. In addition, it contained two questions that we created: ‘policymakers are able to decide what happens to me’ and ‘policymakers are able to control the important things in my life’. We asked participants these questions again but substituted ‘policymakers’ with ‘I’ (1 = ‘strongly disagree’; 7 = ‘strongly agree’).
Inclusion
We adapted four items from the scale developed by Mahadevan et al. (2019). Participants indicated the extent to which they thought policymakers ‘would like people like me’, ‘would feel warmly towards people like me’, ‘would be willing to be friends with people like me’ and ‘would be happy for people like me to belong to their social groups’. We also asked participants these questions but substituted ‘policymakers’ with ‘other members of staff’ (1 = ‘strongly disagree’; 7 = ‘strongly agree’).
Perceived worth
We adapted four items from the scale developed by Mahadevan et al. (2019). Participants were asked to indicate their agreement with the following statements: ‘policymakers would think highly of the abilities and talents of people like me’, ‘policymakers would admire people like me’, ‘policymakers would see people like me as important’ and ‘policymakers would look up to people like me’. Again, we asked participants these questions but substituted ‘policymakers’ with ‘other members of staff’ (1 = ‘strongly disagree’; 7 = ‘strongly agree’).
Mental health and well-being
We used the same 12 items employed by Dougall et al. (2023) to measure both eudaimonic and hedonic aspects of well-being (Ryan & Deci, 2001). The scale includes four items measuring subjective well-being in line with the Office for National Statistics (ONS, 2018): ‘Since the start of the academic year in October, how often have you felt satisfied with your life?’, ‘Since the start of the academic year in October, how often have you felt like the things you do in your life are worthwhile?’, ‘Overall, how happy did you feel yesterday?’ and ‘Overall, how anxious did you feel yesterday?’ Two items measured eudaimonic well-being (Waterman et al., 2010): ‘Since the start of the academic year in October, how often have you felt like you had your purpose in life?’ and ‘Since the start of the academic year in October, how often have you felt fulfilled by the activities that you engaged in?’ Two items measured perceived stress (Cohen et al., 1983): ‘Since the start of the academic year in October, how often have you felt nervous and stressed?’ and ‘Since the start of the academic year in October, how often have you felt that you were effectively coping with important changes that were occurring in your life?’ One item measured emotional exhaustion: ‘Since the start of the academic year in October, how often have you felt emotionally exhausted?’; one item assessed problematic worry (Schroder et al., 2017): ‘Since the start of the academic year in October, how often did you worry?’ Finally, two items measured global physical and mental health (Hays et al., 2017): ‘How would you rate your mental health, including your mood and ability to think, since the lockdown began in March 2020?’ and ‘How would you rate your physical health since the lockdown began in March 2020?’ Responses ranged from 0 = ‘not at all/poor’ to 10 = ‘completely/excellent’, as appropriate for all scales. Exploratory Factor Analysis (EFA) suggested the removal of the two items measuring global physical and mental health due to cross loadings. The remaining 10 items loaded on two factors: positive well-being (life satisfaction, happiness, fulfilment, worthwhileness, purpose and coping) and negative well-being (stress, worry, anxiety and emotional exhaustion). Because mental health is a state of well-being characterized by the absence of distress and positive functioning (World Health Organization, 2005), we use the labels ‘positive mental health and well-being’ and ‘negative mental health and well-being’ to refer to these constructs. See Supplementary Materials for complete analyses.
Additional information
We also administered measures intended for other research projects. This included education, occupation of chief income earner, university of employment, type of employment contract (e.g., fixed term or permanent), average number of hours worked each week and self-assessed competence. Further, we collected information about the impact of COVID-19 such as whether the participants were acquainted with someone who had had COVID-19, the duration of any isolation periods and whether their responsibilities as a caregiver had increased due to the pandemic. The present work includes all items pertaining to policymakers and the equivalent items pertaining to ‘other staff members’, subjective SES and mental health and well-being, and no relevant items were excluded. The study was conducted as part of a programme of research, which was pre-registered using AsPredicted (https://aspredicted.org/WC9_5T9).
The presentation of the items within each scale was randomized, and scales were presented as follows: the mental health and well-being scale was randomly presented first or last; the subjective SES scale and the perceived control, inclusion and perceived worth scales were presented at random either before or after the mental health and well-being scale; an attention check appeared at random between the perceived control, inclusion and perceived worth scales. A definition of policymakers was provided. Specifically, before filling in questions pertaining to needs fulfilled by policymakers, participants were instructed to reflect on their experiences of public policies since the start of the academic year. Participants then read, ‘When we say “policymakers” we mean people responsible for creating UK policies and regulations. For example, members of the Government, Members of Parliament, Lords, Scientific Advisers, civil servants, advisory staff, etc.’
Analytical strategy
Data were analysed using R (v4.1.1; R Core Team, 2021). EFA suggests that we have two dependent measures, hence we decided to use Structural Equation Modelling (SEM). The sample size was sufficient to allow for more than 10 participants per parameter (Jackson, 2003). Further, sensitivity analyses indicate that we were adequately powered to reject a misspecified model based on RMSEA fit indices (effect size as the threshold value required to detect ‘good’ model fit = .06, α = .05, n = 1,116 and df = 234, 1-β = >.99) (Moshagen, 2021).
We first tested the measurement model before fitting the hypothesized SEM model, which included subjective SES, mental health and well-being, and the items pertaining to ‘policymakers’. To provide sensitivity analyses, we then analysed the primary model with additional covariates; age, gender, ethnicity, staff role at the university (academic vs. non-academic), whether they had self-isolated since the beginning of the academic year (yes/no), the number of people in their household, whether they had responsibilities as a caregiver (yes/no) and their political orientation (from 1 = ‘far left’ to 7 = ‘far right’). To provide a point of comparison, we also presented a model that is structurally equivalent to the primary model but uses the ‘I’ and ‘other staff members’ items in place of the ‘policymakers’ items. For conciseness, the results of the primary model are reported below, and the additional models are reported in brief, and in more detail in Supplementary Materials.
The proportion of missing data for the items included in our structural equation model was low at .5%. Since this is far below the suggested threshold of 5% (Kline, 2011), and because our method of estimation required complete cases (maximum likelihood estimation with bootstrapping, see below), participants with missing data were removed listwise for all analyses. In our analysis, all latent factors were allowed to correlate freely with each other, and the variance of one indicator in each factor was fixed to 1.0 to scale the latent factor for model identification.
To evaluate model fit, we use a 2-index presentation strategy and report both an absolute index of model fit (RMSEA) and relative indices of model fit (CFI and TLI; Hu & Bentler, 1999). Hu and Bentler suggested the following ‘rules of thumb’ to evaluate model fit: RMSEA close to .06 and CFI and TLI close to .95. Indirect effects were specified within the model syntax.
Data transparency
Data are openly available from OSF at https://osf.io/c8rzv/. Any other materials are available on request from the corresponding author.
Compliance with ethical standards
This study was performed in line with the principles of the Declaration of Helsinki and informed consent was obtained from all participants. The study received ethical approval from the Ethics Committee of the Department of Psychology at Durham University (PSYCH-2020-10-22T17_19_53-cjnc44).
Results
Preliminary analysis
Descriptive statistics, Cronbach’s alpha coefficients and correlations between scale composites are shown in Table 2 (NB: while these statistics provide important insights into the data, the structural equation reported below draws on individual scale items rather than scale composites; bivariate correlations were similar with and without listwise deletion of cases with missing data). As expected, HE staff with lower (vs. higher) subjective SES experienced heightened levels of stress, worry and emotional exhaustion (negative mental health and well-being) and lower life satisfaction, fulfilment and meaning in life (positive mental health and well-being), |rs| = .12 and .33, respectively.
Means, standard deviations, Cronbach’s alphas and correlations with confidence intervals.
Note: M and SD are used to represent mean and standard deviation, respectively. Well-being indicates mental health and well-being. Subjective SES and well-being are measured on a scale from 0 to 10. Perceived control, inclusion and perceived worth are measured on a scale from 1 to 7. Values in square brackets indicate 95% confidence intervals.
***p < .001.
In terms of proportions, among HE staff with lower subjective SES (rated < 5.5, on average, across measures — 27.3% of the sample), 56.1% reported high levels of negative mental health and well-being (rated 7 or above, on average, across measures) compared with 49.2% of HE staff with higher subjective SES (rated ⩾ 5.5, on average — 72.7% of the sample). Only 15.4% of HE staff with lower subjective SES reported high levels of positive mental health and well-being (rated 7 or above, on average, across measures), compared with 28.6% of those with higher subjective SES.
Turning to needs fulfilment, all associations with positive and negative well-being were significant and in the predicted direction. The more respondents felt connected to (inclusion) and appreciated by (perceived worth) policymakers, and the less they felt policymakers were in control, the better staff members’ self-reported mental health and well-being across all measures, |rs| = .17 to .28.
Zero-order correlations also suggest that HE staff with lower (vs. higher) subjective SES felt less connected to (inclusion) and appreciated by (perceived worth) policymakers, rs = .19. In contrast, the proposed link between subjective SES and perceived control by policymakers was not supported, r = −.05.
Main analysis
Measurement model
The process of fitting a measurement model is detailed in Supplemental Materials and summarized in Table 3. A likelihood ratio test suggested that the final model had a good fit, with conservative improvements in RMSEA, CFI and TLI. Retained variables, standardized factor loadings, composite reliability and average variance explained (AVE) for the final model are presented in Table S3 in Supplemental Materials; the full wording of all items is presented in Table S4.
Results of measurement models.
Note: ***p < .001
Structural model
We fitted the structural model specified in Figure 1. Model-fit indices are presented in Table 4. Model fit was adequate — whilst values of RMSEA and CFI passed the threshold indicating good fit, TLI was just approaching the threshold. We explored the modification indices to identify areas of model misfit and decided to correlate the error terms of three sets of items. See Structural Model Revisions section of the Supplementary Materials for details. Adding three paths significantly improved model fit, which now passed the threshold for RMSEA, CFI and TLI. The results of this final model are presented in Table 4.
Path estimates for primary model.
Assessment of structural model fit.
Note: ***p < .001
Indirect effects
We hypothesized that subjective SES would predict mental health and well-being via perceived control, inclusion and perceived worth. As shown in Table 5, this prediction was confirmed for the indirect effect via perceived worth: lower (vs. higher) subjective SES was associated with perceptions of lower perceived worth in the eyes of policymakers, which in turn was linked to lower levels of positive mental health and well-being and higher levels of negative mental health and well-being.
Indirect and total effects of hypothesized mediators in the primary model.
Note: Unstandardized coefficients are presented with bootstrapped 95% confidence intervals. Well-being indicates mental health and well-being.
p < .001, *p < .05
We also found evidence of an indirect effect through inclusion, but only for positive, not for negative, mental health and well-being, thus providing partial support for our hypothesis. Lower (vs. higher) subjective SES predicted lower levels of perceived inclusion vis-à-vis policymakers, which in turn was associated with lower levels of life satisfaction, fulfilment and happiness. Finally, we found no evidence for an indirect effect through perceived control, contrary to our hypothesis. Consistent with the zero-order correlations shown in Table 1, the SEM model yielded no evidence of a significant relationship between subjective SES and perceived control by policymakers. However, there was a significant relationship between policymaker control and mental health and well-being, independent of subjective SES, whereby increased perceived policymaker control was associated with lower fulfilment and meaning in life, and higher stress and worry.
Robustness check
As a robustness check, we examined the above primary model but included a series of control variables: age, gender, ethnicity, whether participants had self-isolated since the beginning of the academic term, the number of people currently living in their household, political orientation, their staff role within the university, and whether they had responsibilities as a caregiver. This model fit the data well, χ2(386, N = 1,116) = 1,218.91, p < .001, RMSEA = .044, CFI = .957, TLI = .948. The pattern of results remained the same as the primary model in terms of statistically significant paths and indirect effects. See Robustness Check in Supplementary Materials for complete results.
Further analyses
To provide a comparison and point of reference, we also analysed the equivalent model using the perceived control items that examine personal control rather than control by policymakers, and the inclusion and perceived worth items that refer to ‘other staff members’ rather than ‘policymakers’. This reference model fits the data similarly well, χ2(234, N = 1,116) = 760.04, p < .001, RMSEA = .045, CFI = .972, TLI = .967, and significant indirect effects were comparable with the policymakers’ model, although there were two notable differences. Firstly, in the reference model we found significant indirect effects via perceived personal control. Secondly, we did not find a significant indirect effect via perceived worth on negative mental health and well-being. See Further Analysis in the Supplementary Materials for a complete account of this model.
Exploratory analyses
Finally, we conducted an exploratory analysis that used comparison group (‘I’ and ‘other staff members’ vs. ‘policymakers’) as a moderator using multigroup SEM. The moderation model fit the data well, χ2(468, N = 1,116) = 1,508.02, p < .001, RMSEA = .045, CFI = .973, TLI = .968. We found support for moderation effects for the subjective SES -> perceived control -> mental health and well-being pathways only, in line with the findings of the individual analyses reported earlier. Subjective SES predicted mental health and well-being through perceived personal control but not through perceived control by policymakers. In contrast, the comparison between ‘other staff members’ and ‘policymakers’ did not moderate the indirect effects via inclusion or perceived worth. This suggests that needs linked to inclusion and perceived worth can be fulfilled both through interpersonal relations at work (other staff members) and through the wider socio-political context (policymakers), both of which can contribute to inequalities in mental health and well-being. See Further Analyses in the Supplementary Materials for a complete account of the moderation results.
Discussion
In this research, we investigated how the sociopolitical context surrounding higher education (HE) may contribute to socioeconomic inequalities in staff mental health and well-being, particularly during the COVID-19 pandemic. Specifically, we examined perceptions of policymakers and the extent to which they are seen to fulfil needs for control, inclusion and worth. In a large cross-sectional survey of HE staff from 92 UK universities, we found that subjective socioeconomic status (SES) significantly predicted both positive and negative mental health and well-being. Furthermore, we observed that perceived worth and inclusion from policymakers mediated the link between subjective SES and mental health and well-being. In other words, HE staff's subjective SES predicted how much they felt respected (i.e., worth) and liked (i.e., inclusion) by policymakers, with perceived worth predicting both positive and negative mental health and well-being, while perceived inclusion predicted only positive mental health and well-being. Unexpectedly, no support was found for a relationship between subjective SES and mental health and well-being via perceived control by policymakers. This overall pattern held when controlling for multiple variables, including political orientation and staff role (academic vs. non-academic).
As a reference and comparison point, we also examined personal control (i.e., autonomy) needs and the extent to which ‘other staff members’ fulfilled inclusion and worth needs. We performed a moderation analysis to compare the mediating pathways for different groups (‘I’/‘other staff members’ vs. ‘policymakers’) representing different ecological levels. The analysis revealed significant indirect effects via perceived personal control but not via perceived control from policymakers, with the latter predicting mental health and well-being independently of subjective SES. COVID-19 rules imposed significant external constraints. Higher education staff share a long-standing sense of low political efficacy (Goodman et al., 2013). Together, these factors may have fostered uniformly low perceptions of control vis-à-vis policymakers, leaving not much variance for variations in SES and accounting for the null result.
Of note, comparisons between mediating pathways also suggested that both colleagues at work and policymakers can satisfy inclusion- and worth-based needs to a similar extent and thereby contribute to mental health and well-being inequalities. These results are consistent with EST, which posits that wider sociopolitical and societal contexts can impact mental health and well-being (Bronfenbrenner, 1977; Doom et al., 2021) and contribute to socio-economic inequalities (Dougall et al., 2024).
Conceptual contributions
The present work shows that in settings where objective socioeconomic differences are narrower, variations in subjective SES can still be meaningfully linked to differences in mental health and well-being, in keeping with the modest association that is typically found between objective and subjective SES (Tan et al., 2020). In relatively homogeneous sectors like UK higher education, subjective SES may more clearly isolate the role of perceived social standing — that is, how individuals view their rank in comparison to locally constructed norms rather than material disparity. The present findings contribute to an emerging body of evidence suggesting that perceived position within a social hierarchy is linked to the satisfaction of basic psychological needs such as inclusion and perceived worth (Dougall et al., 2021). This highlights the importance of accounting for internalized social comparisons even in contexts that appear structurally equal on the surface.
However, our main conceptual contributions relate to the application of SDT and EST. Our work supports EST by demonstrating that exosystem actors — policymakers whom university staff rarely encounter face-to-face — can nonetheless be perceived as satisfying or thwarting core psychological needs for autonomy, inclusion and worth. Crucially, the strength of these perceptions in predicting staff well-being may match that of needs support provided by immediate work colleagues. This evidence confirms EST’s proposition that structurally distant contexts are psychologically consequential, while complementing SDT’s predominant focus on actors at the microsystem such as teachers, parents and line managers. In short, the study illustrates one concrete route through which the macro ‘gets under the skin’ in an institutional setting.
The present findings also illuminate how structural inequality can be internalized: staff who locate themselves lower in the broader status hierarchy are especially likely to feel undervalued and excluded by policymakers, and these appraisals in turn undermine mental health. EST has long asserted that macrosystems shape individual outcomes; our study specifies some of the psychological mechanisms at play. Specially, by integrating SDT’s needs-fulfilment process with EST’s multilevel architecture, we have found initial evidence for a cross-level pathway (subjective status → perceived support originating from the exosystem → well-being) that can now be tested in other sectors and policy domains.
Implications for policy and practice
HE institutions benefit society beyond individual financial returns and economic contributions (e.g., van den Akker & Spaapen, 2017). HE staff play a key role in knowledge creation through research and teaching, contributing to society’s growth and progress. This contribution was highlighted during the COVID-19 pandemic, when HE institutions were involved in vaccine development, designing ventilators, donating supplies and advising as public health experts (Universities UK, 2020). Thus, the poor mental health and well-being of HE staff is a substantial concern (cf. Ford et al., 2011).
There may be ways to reduce SES-related disparities in HE staff well-being. Our findings suggest that strategies to increase perceived worth and inclusion from policymakers could be beneficial. Policy and parliamentary processes could, for example, more clearly acknowledge the role of research evidence to foster stronger engagement (Walker et al., 2019). Additionally, practices such as recognition, feedback and consultation can increase perceived value within a workplace (Geue, 2018). Recognizing and valuing the contributions of HE staff to teaching and research may underscore their value and boost perceived worth. HE institutions themselves can support policy engagement by providing professional development and workload allowances. Furthermore, drawing universities into political ‘culture wars’ through media may erode the relationship between HE and policymakers and may erode perceptions of inclusion and being valued. Our findings suggest that this could negatively impact HE staff mental health and well-being, which may ultimately affect performance and productivity. Finally, policymakers could counter perceptions of lower worth by mandating that universities publish data on the socio-economic make-up of their workforce, including their leadership teams, along with any income disparities. Doing so would provide some recognition and signal to lower-SES staff members that policymakers value their contributions and perspectives (Hernandez et al., 2021; Rickett et al., 2022).
Many government policies impact HE institutions' and staff members' autonomy (Bergan et al., 2020). For instance, public funding decisions can lead to hiring freezes, increased student numbers or changes in research priorities. Globally, policymakers appear to view HE as primarily an economic investment rather than a public good (Molesworth et al., 2011). In the UK, this view has led to the categorization of certain arts degrees as ‘low value’ (Lough, 2022). In Australia, policymakers raised tuition fees for humanities subjects and reduced fees for ‘job-relevant’ courses (Sears & Clark, 2020). Such economic focus may undermine HE staff's sense of autonomy.
It is interesting to note that effect sizes for needs fulfilment related to work colleagues were similar when compared to policymakers. This suggests that there is significant scope for interventions aimed at a macro level, which is often ignored in conversations surrounding HE staff mental health and well-being. As a caveat, it should be noted that effect sizes for the link between subjective SES and mental health and well-being fell into the small-to-medium size range — in keeping with previous studies (Tan et al., 2020) — and only some of this association was explained through needs fulfilment. Given that small effects can accumulate in a context such as mental health (Arango et al., 2018; Funder, & Ozer, 2019), it is hard to judge the overall impact of perceptions of policymakers amongst the population of HE staff.
Limitations
The main limitation of this study is its cross-sectional design, which may also introduce common-source bias. Although we explored subjective SES's influence on perceived control, inclusion, worth, and mental health and well-being, the relationships may be bidirectional or reversed. However, existing research supports the direction examined in this study. For instance, longitudinal studies have shown that inclusion and status mediate the link between SES and well-being (Rubin et al., 2016), and experimental studies suggest similar mediations (Study 3; Yu & Blader, 2019). Given this, our proposed direction appears viable, though future research with experimental, longitudinal or cross-lagged methods could strengthen these findings.
This study also focused on subjective SES due to HE staff's relative homogeneity in education and income, as well as the international background of many HE staff, making objective SES measures less suitable. Dougall et al. (2021) found similar results with subjective and objective SES indicators, though the two are only moderately correlated and not interchangeable (Tan et al., 2020). Future research should aim to include a broader range of SES measures, using a setting or context with greater heterogeneity where objective measures of SES are more suitable.
Furthermore, the present findings are situated within the UK Higher Education (HE) sector during the COVID-19 pandemic, a context that may limit their generalizability. While similar issues have been observed in other Western, English-speaking countries (e.g., Davies, 2020; Sainato, 2021), the extent to which these findings apply to non-Western or non-English-speaking contexts remains uncertain. The role of policymakers in shaping mental health and well-being may vary between cultures, for example due to differences in governance structures, cultural values and societal expectations of authorities (e.g., Helliwell et al., 2018; Hofstede, 2011). On a related note, the target category under study (policymakers) is large and heterogeneous, and opinions towards different sub-groups may vary, both within and between cultures. Future studies should try and differentiate between different targets falling under the category of policymakers.
In the present research, we combined different facets of subjective SES. Navarro-Carrillo et al. (2020) demonstrated that different subjective-status facets (e.g., income-, education-, prestige-based ladders) show partly distinct associations with hedonic and eudaimonic well-being, although they did not test separate mediational pathways. Examining whether different facets of subjective SES predict inclusion- and worth-based needs fulfilment through different psychological routes is therefore an intriguing avenue for future work, but lies beyond the scope of the present paper.
Finally, although the data were collected from staff at 92 UK universities, the distribution of responses across institutions was uneven and precluded a multilevel analysis. Consequently, potential influences at the institutional level, such as specific university policies or culture, could not be examined in our analysis. Future research with more balanced sampling across institutions could enable modelling of such effects, which may further clarify context-specific influences on well-being.
Conclusion
By integrating SDT’s focus on basic psychological needs with EST’s multilevel lens, this study shows that policymakers — though distant — function as tangible sources of need fulfilment for university staff. Staff who feel excluded or devalued by policymakers report poorer mental health, and such perceptions are most common among those who locate themselves lower on the status ladder. Acknowledging the policy sphere as a psychological influence shifts the task of reducing well-being inequalities in higher education beyond local workplace fixes to the broader signals of inclusion, worth and autonomy that government sends to the sector.