gender wage gap uk essay

Merit Sticks to Men: Gender Pay Gaps and (In)equality at UK Russell Group Universities

• ORIGINAL ARTICLE
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• Published: 12 May 2022
• Volume 86 , pages 544–558, ( 2022 )

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• Carol Woodhams   ORCID: orcid.org/0000-0002-9703-1107 1 ,
• Grzegorz Trojanowski 2 &
• Krystal Wilkinson 3  

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Academic studies of gender pay gaps within higher education institutions have consistently found pay differences. However, theory on how organisation-level factors contribute to pay gaps is underdeveloped. Using a framework of relational inequalities and advanced quantitative analysis, this paper makes a case that gender pay gaps are based on organisation-level interpretations and associated management practices to reward ‘merit’ that perpetuate inequalities. Payroll data of academic staff within two UK Russell Group universities ( N  = 1,998 and 1,789) with seeming best-practice formal pay systems are analysed to determine causes of gender pay gaps. We find marked similarities between universities. Most of the variability is attributed to factors of job segregation and human capital, however we also delineate a set of demographic characteristics that, when combined, are highly rewarded without explanation. Based on our analysis of the recognition of ‘merit,’ we extend theoretical explanations of gender pay gap causes to incorporate organisation-level practices.

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Introduction 

The UK Higher Education (HE) sector has historically been male dominated, with evidence of horizontal and vertical segregation (Fagan & Teasdale, 2021 ). Job segregation by gender is also an international phenomenon (Macarie & Moldovan, 2015 ; Peng et al., 2017 ; Rabovsky & Lee, 2018 ). There is evidence for the closing of the HE gender gap internationally in recent decades (Baker, 2016 ) and an improvement in research outputs (Nielsen, 2016 ) and high-level jobs (Fritsch, 2015 ) for female academics. Inequalities persist, however. The causes of gender pay disparities are complex and multi-layered, but analysis of them in the higher education sector, and more generally, is theoretically and empirically incomplete. Smith ( 2009 ), for example, draws on self-report quantitative data to signal a significant gap between men and women academic staff in the UK between and within grades, and explores the implications, but not the causes, of these gaps. Traditionally, theoretical frameworks that explain gender pay differences take investment in one’s own skills and productivity as the starting point (Becker, 1975 ). However, this is a limited view that assumes that skill supply and demand will be fairly rewarded according to the logics of the market. The role of the employer in this link is overlooked.

In the current study we respond to calls to 'bring the firm back into the conceptualisation of inequalities' (Tomaskovic-Devey & Avent-Holt, 2019 , p. 7), drawing on how the relational and social construction of ‘merit’ may be connected to the power and status of workers to influence pay. Krefting ( 2003 ), for example, concludes that women faculty achievements have a lower salary pay-off, which refers to a slower time to tenure, slower time to promotion to full professor, and they earn less than men with comparable backgrounds and accomplishments. Additionally, a range of demographic (Hargens & Long, 2002 ), personal, and institutional factors (Howe-Walsh & Turnbull, 2016 ) have been linked to gender inequality, with the latter including an organisational perception of additional ‘merit’ attributed to men. Though informative, the reliance on qualitative data in these studies limits the generalisability of these findings. The current research rigorously examines ‘lower salary pay-offs’ within men’s and women’s faculty careers, and the potential for subjective and intangible ‘merit’ to be attached to certain bodies (Simpson & Kumra, 2016 ; Thornton, 2013 ).

Most publications on pay differences in HE draw conclusions from national or sectoral datasets meaning that they cannot illuminate patterns at the organisation level (i.e., Madrell et al., 2016 ). By and large, published pay data is usually aggregated, cloaking the role of organisation-level causes such as unequal promotion rates, unequal length of service, faculty specialisms, hours of engagement, types of contracts, the role of qualifications, and ultimately if and how organisational reward practices in relation to ‘merit’, sustain pay gaps. Using internal pay data at the individual employee level linked to personal employment history, we show that it is possible to account for the influence of these factors plus many others. Analysis can isolate the implications of each, building a picture of which characteristics and job patterns are most highly rewarded. These studies are rare due to the challenges of accessing comprehensive individual-level organisation data (for exceptions see Gonäs & Bergman, 2009 ; Travis et al., 2009 ). The current paper aims to enhance our understanding of causes of pay disparities in HE, criticising the effectiveness of organisation equality practice to challenge an institutionalised construction of ‘merit’ in two UK Russell Group universities.

The Russell Group is a catch-all term for 24 universities in the UK renowned for world-class research excellence and academic achievement (university league tables), including the Universities of Oxford and Cambridge (Russell Group, n.d. ). We negotiated access to employee level data; it is not normally available. The paper addresses the following research questions: (1) Is there a gender pay gap at our case- study universities and what factors explain it? (2) What does our analysis reveal about how higher education pay allocation is influenced by perceived ‘merit’?

Determinants of Pay Gaps in Higher Education

Underlying causes of the inequality gap in a whole range of industries, and specifically in higher education, are hotly disputed. Human capital theorists (e.g., Becker, 1975 ) seek to explain disparities in terms of differences in skills and experience between different groups of workers (Jacobsen, 2003 ). Women and men are seen as making different choices around the accumulation and deployment of education and skills linked to perceptions of what will bring the greatest returns, given their family commitments (Toutkoushian et al., 2007 ; Uhly et al., 2017 ). They have different ways of managing the work-life interface (Xiaoni & Caudle, 2016 ), different plans for engagement with paid work over the life-course (Metcalf, 2009 ) and have less work continuity and labour market experience due to part-time employment (Perna, 2005 ). Empirical evidence for the human capital approach specifically in relation to Higher Education comes from studies that account for human capital investment, performance measures, and type of university as explanators, and report gender pay gaps of 22% and 6.8% after controls inserted (Umbach, 2007 ).

Critics draw attention to the limitations of human capital theory, emphasising that preferences are underpinned by the gendered context of HE (Perna, 2005 ). Pay penalties in HE may emerge indirectly from the unequal effects of being segregated into types of institution, academic disciplines, contracts, and work roles that women are better able to manage alongside an uneven division of domestic work – but which have lower prestige and value. Cama et al. ( 2016 ) reported on a range of studies arguing that gender pay gaps cannot be explained by differences in individual, faculty, and institutional attributes, leaving open the possibility that there are organisational, cultural, and Human Resource (HR) effects. Gaps may also emerge because of discretionary pay practices which have the effect of disadvantaging groups in the way that 'merit' is constructed (Elvira & Graham, 2002 ). Typical of the UK HE sector, the two universities in our study formally abide by a framework of 'meritocratic' principles (Littler, 2018 ). Both deploy an objective reward system based on job evaluation plus a range of ‘best’ HR equality measures designed to overcome structural obstacles. We now discuss the potential of these measures to eliminate gender pay gaps, along with feminist critiques.

Recognising ‘Merit’ in Pay Structure Design in the UK’s HE Sector

Academic pay is determined within a market-based allocative system which seeks to reward individual effort, agency, and achievement. In theory, the design of the pay system is to produce standardised pay decisions, pegged to an objective scale, reducing flexibility and managerial discretion (Reskin, 2000 ). The establishment of a sector-wide joint negotiating committee in 2001 included the objective ‘to modernise pay arrangements with the specific aim of promoting equality, transparency and harmonisation to ensure equal pay is delivered for work of equal value’ (UCEA, 2008 : 3 as cited in Perkins & White, 2010 ). Almost all UK institutions, including our research sites, implemented the framework. The assumption is those who are not highly rewarded are not disadvantaged by unjust or discriminatory organisational practices, but rather because of their lack of personal merit (Simpson & Kumra, 2016 ); being abilities, achievements and ‘deservingness’ (Thornton, 2013 ). There are links to be made here with post-feminist governance regimes (Lewis, 2017 ) where the structural inequalities foregrounded in second-wave feminism are said to have been overcome, meaning women’s experience is dictated by their individual merit alone and feminist collective objection or action is redundant.

Critiques of Assumptions of ‘Merit’ in HE

There arelimits to assumptions of the equality of ‘merit’ between genders. Scholars argue that a socially acceptable postfeminist subjectivity requires the simultaneous performance of both ‘ideal worker’ (Acker, 1990 ) masculinity in terms of ambition, drive, and active planning, but also femininity in terms of emotional nurturing behaviour (Hochschild, 1983 ) and personal appearance (Lewis, 2017 ). As men are not required to demonstrate such dual behaviours, it can be argued that standards of ‘merit’ are unequal. Simpson and Kumra ( 2016 ) and Simpson et al. ( 2020 ) observe how narratives of ‘merit’ and ‘deservingness’ intertwine and become a gendered issue – with deservingness relying on subjective evaluations based, in part, on personal values and normative expectations – which stands in contrast to merit, which is typically presented in the HE context as an objective, gender-neutral measure, based upon qualifications and the capacity of the individual to apply them to job-related tasks (Castilla, 2008 , 2012 ; Castilla & Bernard, 2010 ; Simpson et al., 2020 ). Taken together, it is argued, merit fails to ‘stick’ to female bodies. Castilla and Bernard ( 2010 ) term this the ‘meritocracy paradox': that systems that appear to reward skills and effort may involve processes that entrench discrimination. Understandings of ‘merit’ have been and continue to be determined by those at the highest levels of the organisational hierarchy–dominated by men, although there is some interest in the rise of women in positions of power (see Huffman, 2013 ), meaning that the benchmark for success is often based upon masculine traits and the male life-course. Simpson and Kumra ( 2016 ) add that such bias is largely hidden by the desire to see merit in fixed, universal terms (Sen, 2000 ) where it can assuage concerns about unequal allocations of power and authority and provide a discursive mechanism by which inequality is justified.

It follows that merit will also fail to stick to the bodies of other individuals who differ from the white, male, able-bodied ‘ideal worker,’ which has been found in other studies, including those that study the intersectional effects of gender alongside demographic factors such as ethnicity, class, family education history and disability on employment outcomes (Bowleg, 2008 ; Crew, 2020 ; Rickett & Morris, 2021 ; Śliwa & Johansson, 2014 ; Woodhams et al., 2015 ). Whilst an espoused meritocracy, the UK HE sector is responding to significant labour market pressures, which challenge attempts to ensure standard and transparent reward allocation. Government funding has been withdrawn, so the sector is in a period of rapid global reform. To compete for global talent, pressure is brought to bear to ensure that salaries are flexible. For example, in both case study universities, following a selection panel, senior managers debate a salary point to offer based on perceived ‘deservingness’. The full grade range is available including ‘discretionary’ points in ‘exceptional’ circumstances. Pay offers are almost always negotiated (see Gamage et al., 2020 ), maybe with less motivation from female academics (Sarfaty et al., 2007 ). The agreed pay outcome is put to HR for approval and is rarely rejected. Enhanced pay increments can also be negotiated within-role as a retention payment. Subjective assessments of ‘merit’ have potential to undermine equitable outcomes.

Best Practice Equality and the ‘Merit’ Principle

It is recognised that women may be particularly constrained in demonstrating their ‘merit’ due to a range of factors such as additional responsibilities in the home domain, stereotyping and discrimination (Lewis & Simpson, 2010 ; Lips, 2013a , b ). To give them full opportunity to develop, a raft of university initiatives has been introduced (Saltmarsh & Randell-Moon, 2015 ). In our two chosen universities, initiatives cover flexible hours of work and location (Rafnsdóttir & Heijstra, 2013 ) plus a variety of academic contract types, including part-time working, fixed-term working, and term-time working. To assist with social capital development, several women’s leadership and mentoring initiatives have been introduced (see Gallant, 2014 ). Both universities hold Athena Swan awards (Advance HE, n.d. ), an external audit of good diversity practice. At least one department in each holds the highest gold level award. Compulsory training ensures equality and diversity compliance. The modern HE landscape is thus aligned with broader discussions of neoliberal feminism (Rottenberg, 2018 ) viewing the ideal neoliberal feminist subject as a ‘balanced woman’ (Rottenberg, 2014 ) who can manage a professional job role alongside intensive caring responsibilities. Neoliberal structures and cultures emphasise individual competition and merit and suggest the ‘ideal worker’ (Acker, 1990 ) is one unencumbered by responsibilities outside of work. Whilst our female academic subject might note the structures that disadvantage her as a woman (thus differentiating the neo-liberal subjectivity from the postfeminist one), she looks inwardly, guided by these workplace equality initiatives that focus on individual action and adaptation (around working hours and better ‘leaning in’ to organisational structures) to resolve the tension, rather than looking towards collective action to change underlying structures.

There is also criticism from gender scholars concerning interpretation of meritocratic principles within HE, arguing that activities that are seen to be meritorious are those on which men spend more time and have greater success. The highest valued activities when it comes to pay and progression in academia are entrepreneurial research activities (Priola, 2007 ; Thornton, 2013 ), including peer-reviewed publications in high-ranking academic journals and citation figures. There is some evidence that men outperform women in these metrics (Monroe et al., 2008 ), but this is by no means universal (Nakhaie, 2007 ; Nielsen, 2016 ; Shauman & Xie, 2003 ). Female academics tend to spend more time on pastoral work, as they are expected to be nurturing and accommodating to student requests (El-Alayli et al., 2018 ) and undertake the bulk of administration and citizenship activities (Perna, 2005 ). Male academics engage in greater institutional mobility than women academics (Leemann, 2010 ), enabling networking and increased opportunities to collaborate (Loacker & Sliwa, 2015 ). Universities tend to be sites where patriarchal relations and gendered hierarchies of power flourish to the disadvantage of women (Bagilhole & Goode, 2001 ).

Policy Implications

There are significant policy implications in this area. The UK’s Athena Swan, Gender Equality Charter Mark (Madrell et al., 2016 ) and Gender Pay Gap mandatory reporting initiatives are all shedding light on pay gaps at the employer level. These initiatives raise awareness of pay gaps and provide data that is useful in making sectoral comparisons. However, given that reported data is aggregated, there are limitations in their usefulness in illuminating comparative and potentially unfair reward practices at the employee level. Our analysis addresses that gap.

Ethical approval was sought and obtained from the University of Exeter prior to the analysis of this data. Data is secondary in nature. Data is confidential and storage arrangements complied with General Data Protection Regulations.

Sample Characteristics

Tables 1 and 2 provide descriptive statistics for two Russell Group universities that comprised the analysis. The two universities are matched in their gender spilt being 43% and 44% female. Ethnic origin data is categorised into sixteen categories. Nationality data is given in 76 categories in one university and 54 in the other. To ensure viable categories for analytical purposes they were recategorized into White/BME and British/non-British dummy variables. In University 1, 85% of men and 89% of women identify as white. Sixty-six percent of men and 61% of women identify as British. University 2 is matched with corresponding figures of 90%, 89%, 70% and 63%, respectively. Disabled status is self-nominated at the point of recruitment or by updating the self-service HR administration platform. Disabled workers comprise 4% of the workforce in both universities. Sex is given in binary format. Maternity leave taken in the past five years (yes/no) is a dummy variable for women only. The maternity leave variable cannot be added to a fully-fledged Oaxaca-Blinder decomposition as it is meaningfully defined for female academics only. It is not included in the main analyses reported. We add a note below explaining its effects entered in the regression equation.

Grade and seniority are denoted in five hierarchical bands (Associate Lecturer, Lecturer, Senior Lecturer, Reader and Professor, in order of seniority). In both universities, men are significantly more likely to be more senior in higher grades. Men have significantly longer length of service (LOS; 6.18 and 9.25 years for men, compared with 5.41 and 6.84 for women) and significantly more years in the HE sector (8.83 years compared with 8.07 for women) in University 1, but less in University 2 (11.58 years compared with 15.29). Most staff (75% and 92%) hold a doctorate as their highest-level qualification.

The dependent variable is salary. Individual payroll data was obtained for all academics employed by University 1 ( N  = 1,998) and University 2 ( N  = 1,789). Payroll data has greater reliability than self-reported pay (see Leslie et al., 2017 ) and greater validity for investigating the connection of employment histories to pay than aggregated data (van Wanrooy et al., 2013 ). Salary data is taken for a single month (Feb 2018 for University 1 and July 2018 for University 2). To protect the anonymity of the universities we obscure certain features including the organisation’s location in the UK. Support staff are excluded.

The salary structure in both universities is a multi-grade single pay spine linked to tenure and grade and based on a Higher Education Role Analysis job evaluation exercise. Starting salary is based on qualifications, experience, perceived merit, and previous salary. Movement between grades is determined by promotion into a different role. Scheduled pay raises (so-called 'increments') are awarded annually (as of 1 August each year) until the job holder reaches the top of the normal grade range. Each grade, except Professor, then has four to five ‘discretionary’ points that can be used to recognise extra ‘merit’. Professorial salaries are personally negotiated, subject to university-specific banding of pay. Starters and leavers have been removed from the dataset. Full-time equivalent (FTE) pay has been created to remove the effects of part-time working. Both universities award increments during maternity leave.

Salaries are attached to a common UK HE intuitions 51–point pay scale (UCU, 2022 ). There is considerable variation between universities in attaching grades to pay scale points, for example in one university a Reader grade applicant might be appointed between scale point 45 (currently £52,559) and scale point 50 (currently £60,905) and in another, the Reader scale might sit between points 41 and 47. However, internally, a university will always (in theory) appoint staff in the same academic grade to the same range of scale points. University 2 has awarded their female professors a one-off salary uplift (mean of £3,435) following Essex University (BBC News, 2016 ). The uplift was applied in Sept 2016 with reference to the mean of male professorial salaries in the discipline and taking account of length of service.

Analytic Strategy

To examine the first research question on the reasons for gender pay differences, we calculate simple mean gender differences in base pay rates. We then make use of regression analysis, which isolates gender pay differences if all other variables are held constant. This is, of course, hypothetical as men and women are rarely matched, so we use the Oaxaca-Blinder decomposition (OBD) technique (Blinder, 1973 ; Oaxaca, 1973 ). This technique identifies the extent to which pay gaps are due to the different 'endowments' of men and women. Endowments constitute differences between men and women that are meaningful within pay allocation; in other words, their simultaneous distribution across ranks of well-rewarded and less well-rewarded features. For ease of reporting, we have bundled these features into a) demographic (being age, gender, disability, ethnicity and nationality), b) human capital (education, length of service, and length of service in HE), and finally c) segregation and job (faculty of employment, grade & seniority, type of contract, duration of contract, and whether FT or PT). This analytic technique examines which differences and in what proportion men’s and women’s 'endowments' create the gender pay gap.

To address the second research question, we further explore the outcomes of the OBD highlighting the different rates of financial return to endowments, known as 'coefficients' and 'interaction' elements. These elements reveal whether having the same feature, for example a doctorate, results in a differential financial return for men, vis a vis women. Where, and if, this occurs, we consider this to be pay discrimination and indicative of an unbalanced institutionalised interpretation of salary-worthy ‘merit.’

Research Question 1

The mean salary for men academics is £50,050 and £42,192 for women ( t  = 9.21, p  < .001, see Table 1 ) in University 1 and £54,668 and £46,556 in University 2 ( t  = 9.48, p  <  .001, see Table 2 ). Despite differences between universities in pay levels, gender pay differences are consistent. University 1 has a gender disparity of £8,308, or 15.7% and University 2 has £8,112 or 14.8%, favoring men. Table A1 (University 1) and A2 (University 2) in the online appendix provide mean pay based on demographic and job-related characteristics. Based on this initial analysis, we can only draw limited conclusions on ways that job, work, and personal characteristics underlie gender pay differences. To explore further, we first conduct regression analysis and then undertake Oaxaca-Blinder decomposition analyses (Jann, 2008 ).

Tables 3 and 4 give results of pooled and subsample regression analyses. Regression analysis is informative because it shows the effect on pay of a single characteristic isolated from others. The pooled (men and women) sample shows that a significant proportion of pay is explained by factors of horizontal and vertical segregation (i.e., faculty and grade), however segregation is not the only effect. Experience at the university (University 2) and in the HE sector (University 1) is positively correlated with salary, as is age and job family at both universities. Education level is not a strong predictor of wage in this sector, except that in University 2 having an ‘other’ qualification creates a significant disadvantage of £4,140 per year. After inserting all controls, detriments of £1,070 and £1,272 for women are attached to gender.

The origins of the alarming and unexplainable pay difference can be explored first via subsample regression analysis. Regression analysis measures the differences between men and women in their pay as if all other characteristics are equal. Tables 3 and 4 show that employment factors are not equally rewarded, and not always in the expected direction. For example, in both universities, men experience a penalty compared with women for being in a Humanities faculty (-£3,140 compared with -£1,932 in University 1 and -£3,012 compared with -£745 in University 2) with similar patterns in Social Science faculties. Similarly, men are paid less in every grade in University 2, when all other factors are accounted for, and in all except the Professorial grade in University 1. There is also a difference between how men and women are rewarded for length of service at both universities; men being rewarded for short service at both universities. Whilst this is an interesting analysis, it is hypothetical one because it assumes all characteristics other than gender are identical. But gender career differences are dynamic and interactional and regression analysis is imprecise as to whether and to what extent each difference contributes to the actual pay disparity between men and women. For this we turn to an OBD. What follows is an explanation of those findings.

Endowment Effects

Decomposing the pay gap shows consistency between universities. In total, as shown within Tables 5 and 6 , a total of over 81% (£6,335.60) of the gender gap at University 1 and 79% (£6,554.21) at University 2 is attributable to gender differences in bundles of endowments: being demographic, human capital, and segregation/ job characteristics. In other words, most of the pay gap is explained by differences in the way that men and women engage with the jobs, roles, and disciplines that are linked to higher [or lower] pay. A further 12% (£904.51) in University 1 and 11.9% (£978.70) in University 2 per year is due to gender differences in coefficients – i.e. differences in the way these endowments attract reward. The remaining 7% (£563.62) and 8.6% (£706.42) is due to the interaction of gender differences in coefficients and the strength of their effects.

More specifically, most of the pay gap in both universities pertains to job segregation. For example, although like-for-like women are paid more, for example, in a Reader role (as above), the fact that they are underrepresented in Reader and Professorial grades is key. If women academics were as likely to reach the Professor grade as men, the annual gender pay gap would shrink by £5,518.14 at University 1 and £6,825.93 in University 2. Additionally, women are over-represented in the low-paid research-only job family in University 1 and teaching-only job family in University 2, adding to the gender pay gap. Women are over-represented in the lowest-paying faculty (Faculty of Humanities) in University 1 and under-represented in the highest-paying faculty (Faculty of Social Sciences) in University 2. In University 2, women are over-represented in the lower-paying academic grades. Job segregation in seniority and faculty, then, explains over three-quarters of the gender discrepancy in pay in both universities (with Professoriate under-representation solely accounting for over 70%). Differences in demographic and human capital endowments also contribute to the gender disparity in pay. Since women academics are, on average, slightly younger and age has a strong positive association with pay, age constitutes another source of gender pay differences. Differences in LOS at University 1 (men have more service) also helps to explain their higher pay.

Research Question 2

We have seen that segregation (i.e., differences in ways that men and women engage in HE careers), accounts for the majority, but not all the pay difference. There are also uneven gender effects in the financial return to these features, which can be seen in the coefficient and interaction columns of Tables 5 and 6 . For example, whilst women academics being younger and less likely to hold senior academic positions contributed to the pay gap (as above), the coefficient component indicates that age and seniority have a higher return for equal endowments for men academics. Being older benefits men by £288.15 per year in University 1 and £370.33 in University 2, but women 'return' less than half (£142.17 and £170.90 per annum) of this for the exact same feature (i.e. being a year older). This unequal return to age accumulates year-on-year to contribute £5,939.98 / £8,577.29 in favour of men to the gender pay gap. Moreover, we know fewer women academics have reached the Professorial grade, however the coefficient column shows that women in University 1 reap a significantly smaller financial return after achieving it (explaining £243.33 of the gender pay gap) compared to their otherwise-equal male peers. In other words, there seems to be a 'double-whammy' discriminating effect for women: not only are they less likely to possess the characteristics associated with higher pay, even those who do so, are under-paid in comparison. University 2 appears to have staved off these effects, perhaps via their targeted salary uplift in 2016.

The effects of differences in coefficients pertaining to age and seniority are partly offset by gender differences in the effect of the length of service at both universities. Women benefit from longer tenure (reducing the pay gap by £1,889.52 / £1,278.25 pa). Whilst this might seem positive, it indicates that men, because they gain through age, but not length of service, benefit more from increased mobility. Men move more often, and this works to their financial benefit.

Differences in the financial return to demographic features are also important. At University 1, all else being equal, being British is lucrative for men academics but not women (explaining £842.15; more than 10% of the pay gap). At University 2, being white is a benefit for men only, returning an additional £1,792.81 per year into their pay packets. There is also a small, yet statistically significant, gender difference in the effects that disability has on pay in University 1, to the benefit of disabled women; and a larger advantage to women working in Humanities and the Arts in university 2 of £480.13 annually.

Interaction Effects

The aforementioned effects of age and length of service are further strengthened by the significant differences in the effects of interactions of coefficients and endowments in both datasets. For instance, the age interaction component is positive as the returns to age for men tend to be greater, while at the same time they have higher values attached to the age variable.

This paper has analysed payroll data from two UK Russell Group universities with formal payment schemes, based on incremental pay scales and job evaluation. By controlling for human capital, job segregation, and demographic variables, our findings suggest flaws with the way that gender pay differences are regarded and being addressed in academic institutions. The findings help us understand how ‘merit’ is represented within the ostensibly 'objectively determined' pay scales of both universities. As we might anticipate, most ‘merit’ is attributed to seniority and length of service. However, these features are not equally rewarded between men and matched women. The seniority effect is disproportionately advantageous (in pay terms) when attached to men. Men are rewarded for mobility while women are rewarded for loyalty. And a significant proportion of our gender pay gap is linked to features that are not of direct relevance. Men are rewarded in one university for Britishness and the other for whiteness. There are small advantages for women, but these are less numerate and not as financially advantageous.

To elaborate, our findings pertaining to our first research question support previous observations around occupational segregation in explaining pay gaps, i.e., that through conformance to social role (Eagly, 1987 ), individual preference (Hakim, 2000 ) or discriminatory treatment (Lips, 2013a ), women are under-represented in highly-paid academic roles (Doucet et al., 2012 ), and higher-paying grades (Ornstein et al., 2007 ) and over-represented in wage-depressed women-dense disciplines (Reskin & Roos, 2009 ). We show that women and men have different ‘endowments’ (i.e., men are more likely to be older and to be a Professor) that pay out to men’s advantage. Good equality practices such as those within the Athena Swan accreditation, will, if effective, decrease pay differences in relation to these factors. However, our analysis also shows in line with neo-liberal critiques that the benefit of investing in remedies like these will be limited because of organisation-level management practices.

Analysis pertaining to the second research question demonstrated that even if women were to become equally endowed, a significant proportion of the pay gap will be left untouched. Equally endowed women at University 1 earn less like-for-like in the Professorial grade. In both, they earn less each year for equal age. It could be argued that these variations stem from cohort-level differences in human capital, with older women accumulating less quality experience, even if their qualifications and length of service match, however prior literature argues that cohort effects are less significant than life-cycle effects, i.e. ageism in academia (Maguire, 1995 ). It could also be the case that the gender-specific returns to age might result from career breaks stemming from maternity leave periods, however when the maternity leave dummy is included in the regression model the main effect is not significant and other results are upheld. Additionally, length of service is most strongly rewarded if it is short and if the academic is male. Our overall finding is that women have a significant pay penalty, for reasons of segregation (which might also contain discriminatory influences that are hidden from our view), but most importantly because they do not have features in common with older white or British professors who frequently move universities.

There are two inferences here. The first inference in our findings is that pay judgements in academia are made based on an organisational-level understanding of ‘merit’ that ‘sticks’ to certain types of men’s bodies, specifically, white and British older Professors with a record of mobility. This finding supports previous work that shows how these features are of benefit to men. Results of ‘wisdom’ studies show that older men are more likely than older women to be regarded as cognitively ‘wise’ (Ardelt, 2009 ; Baltes et al., 1995 ), and that men, rather than women, inhabit the role of ‘Professor’, not ‘Teacher’, with ease (Miller & Chamberlin, 2000 ). Job mobility is lucrative for academics; however, women feel the need to build and sustain a reputation with their employer to demonstrate competence (Blackaby et al., 2005 ; Booth et al., 2003 ) rather than moving jobs to demonstrate ambition. Women remain on the margins in academia trying to prove their skills whilst men strategize reputation (Krefting, 2003 ). Finally, intersectional ethnic academic women appear to be disproportionately disadvantaged by the combination of ethnicity and nationality and gender in comparison with ethnic men and white women (Eaton et al., 2020 ; McCall, 2005 ).

The second inference points to the failure of formalised payment systems in standardising starting and ongoing salary awards. It might be that women’s actual or perceived inability to negotiate better salary packages into the discretionary grade points is the cause (Dittrich et al., 2014 ). It is well known that negotiation is a complex skill that is deeply ingrained in societal gender roles (Bowles & Babcock, 2013 ); women are less likely to be well-evaluated when they initiate negotiations (Bowles et al., 2005 ) and more likely to receive backlash (Amanatullah & Tinsley, 2013 ; Dannals et al, 2021 ; Rudman, 1998 ; Williams & Tiedens, 2016 ) which may serve to discourage them.

Limitations and Future Research Directions

There are limitations to the generalisability of our work. The paper is based on two cases with reputations for best-practice equality. Both are in the elite research-intensive group. Given the similarities between the two cases, it is highly likely that similar findings would be realised elsewhere in UK universities with a similar best practice-approach and use of standardised national pay and reward structures. However, higher pay gaps and greater wage dispersion has been found in research-intensive universities, so findings may differ in institutions that differently emphasise research output (Bailey et al., 2016 ; Mumford & Sechel, 2020 ). There are also limitations to validity of the data given that we do not have a full set of covariates on productivity/performance and how this might inform promotion and extra-ordinary decisions around base pay. Analysis of social class data, which was not available in this dataset, would add a valuable dimension of understanding for scholars interested in intersectional studies. To further strengthen our understanding of ways that organisations produce and reproduce unequal personifications of a ‘meritorious’ academic in future research projects, we encourage researchers to replicate our methodology in different universities and country contexts, comparing our outcomes with those achieved in organisations with different, and maybe less flexible, reward arrangements. We encourage studies that delve more deeply into the effects of intersecting identities on the causes of gender pay gaps for academics.

Practice Implications

Our findings have specific implications for human resource management professionals and senior leaders in HE and beyond, as they suggest flaws in the ways that gender pay differences are reproduced at the organisational level. In order to tackle the systemic problems highlighted in this paper, we recommend that alongside the typical package of positive action recruitment and promotion measures, such as mentoring (Cullen & Luna, 1993 ), changes are needed around how pay is structured and determined, as both appear to unfairly disadvantage women that are otherwise equally endowed. For example, we recommend the removal of ‘discretionary’ pay points that are typically used in circumstances where staff persistently self-proclaim their ‘merit’ to their managers, creating shorter pay scales which leave less room for managerial subjectivity to choose between pay points. We also recommend stronger guidance on the way that pay is set on appointment. A specific recommendation for University 1 is an immediate salary uplift of the type implemented at University 2. We also recommend positive action measures are extended to recognise the intersectional effects of gender with other disadvantaging personal characteristics such as nationality, ethnicity, and age. Our findings also have implications for academic women working/seeking work in UK HE institutions who may be unaware of their disadvantaged intersectional positioning, due to the principles of ‘meritocratic ideology’ underpinning existing structures and postfeminist/neoliberal feminist discourse. They are encouraged to explore collective forms of agency more akin to second-wave feminist action, such as vocal protest against pay disparities and engagement in trade union action.

Explanations of gender pay gaps are complex and multi-layered. In part, as previously identified in higher education, they result from differences in occupational segregation (Blau & Kahn, 2017 ), which is being tackled in many universities via established equality practice. Our findings, however, indicate additional contributors to pay gaps linked to intersecting features, for example increased age is less advantageous for women, and disability potentially less advantageous for men, and how organisation-level recognition of ‘merit’ sticks to certain bodies, enabled by specific and widespread reward practices. In conclusion we argue that pay structures premised on ‘meritocracy’, and initiatives that aim to level the playing field for academic women under the banner of 'best practice' reinforce postfeminist or neoliberal feminist sensibilities. Women academics, unknowingly complicit, look inwardly for the resolution of disadvantage whilst structures continue to discriminate against them.

However, our primary point here is that salary negotiation involves two parties and responsibility lies with those that carry institutional authority to recognise and reward to ensure that perceived ‘merit’ does not cloud judgement. We contend that our research raises awareness that the organisational space in which resource allocation takes place is influenced by socially defined relational power inequalities (Tomaskovic-Devey & Avent-Holt, 2019 ) that shape perceptions of ‘meritorious’ and ‘deserving’ features.

Data Availability

Data is held by each institution. We have a contract to publish with express agreement, but not to share data.

Code Availability

STATA code available on request.

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Woodhams, C., Trojanowski, G. & Wilkinson, K. Merit Sticks to Men: Gender Pay Gaps and (In)equality at UK Russell Group Universities. Sex Roles 86 , 544–558 (2022). https://doi.org/10.1007/s11199-022-01277-2

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Chelsea Phipps

November 15th, 2021, why we should still be concerned about gender inequality in the uk.

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As anyone who has tried their hand at measuring impact will know, this is no small feat. And when it comes to charitable giving, how should donors decide where their funding can have the most positive influence?

It is this question, amongst others, that I am excited to be working on as part of my mentorship for a Capstone Project team of current MPA students at LSE as we undertake a sector analysis for a client who would like to develop their funding strategy for the gender quality sector in the UK.

With a view toward understanding how to maximise impact, this team’s work will help build a foundational understanding of the sector, its main actors, the main efforts in the field, and where there are gaps. The project will have a particular focus on the role of existing movements in the sector.

Work on this issue is particularly critical now because very few British people view gender inequality as one of the most serious types of inequality in Great Britain . A 2021 survey found that only 23% of Britons consider inequality between men and women to be one of the most serious types of inequality – considerably less than the on average 33% of Europeans who view it as a serious concern in their country. [1] This is despite Great Britain being ranked only 23 rd on the World Economic Forum’s Gender Gap Index. [2]

This is not to say we haven’t made progress. The gender pay gap has decreased, there are more women in higher-paid occupations, and gender inequality in political representation is decreasing. [3] Girls are even outperforming boys at school and women are more likely to enrol in higher education than men. [4]

But while some of the averages on metrics might be improving, progress has been uneven. For some women and girls, particularly those in poorer communities and for women and girls of colour, things have actually become worse. This has been exacerbated by the COVID-19 pandemic, which has led to disproportionate burdens for women.

When people think the job is done and gender equality is a problem for elsewhere, our progress on the issue will stagnate. And the job is far from done :

• Women are less likely to be employed full-time with a rate of 45% compared to 61% of men
• 41% of women provide care for children, grandchildren, older people, or people with a disability compared to 25% of men
• Less than a third of members of parliament are women
• 85% of women cook and/or do housework every day, compared to 49% of men.
• Only 35% of board members for the largest publicly listed companies are women. [5]
• Only 35% of counsellors in England are women. At the current rate, we won’t achieve gender equality in local councils until 2077. [6]

Unsurprisingly , inequalities between genders are even more acute when they intersect with racial inequalities . According to a literature review, The Pay and Progression of Women of Colour , released in September by the Fawcett Society and the Runnymede Trust, in Britain:

• Black girls are twice as likely to be permanently excluded at school compared to white girls.
• Ethnic minority candidates had to send 60% more applications to enter the workforce to receive as many call backs as white people.
• One-third of women of colour say they’ve been unfairly passed over for or denied a promotion at work.
• Women make up only 6% of CEOs of FTSE 100 companies and 35% of civil service permanent secretaries – yet none of these are women of colour.

Achieving gender equality – particularly for those facing intersectional inequalities –isn’t just a question of justice. Increased equality leads to a multitude of positive outcomes for everyone : a stronger economy as women’s participation in work increases and the pay gap decreases, higher levels of peace and stability, better health outcomes given women (particularly women of colour) receive worse medical care than men, more productive and innovative companies, and less poverty. Empowering women also creates a multiplier effect by benefitting multiple generations as women tend to play a greater role in the health and well-being of both children and older generations.

A 2021 survey on what British people think are some of the most serious types of inequality in Great Britain showed that the highest shares of respondents selected income and wealth inequality (56%) and inequality between more and less deprived areas (51%). These forms of inequality are all interconnected: improving gender equality would also help improve wealth and spatial inequalities, the latter of which compound gender inequality.

Among the many culprits for why we haven’t yet achieved gender equality in the UK are social norms, bias, and stereotyping . Survey research has shown that boys and girls as young as the ages of 7-11 have already established gendered career aspirations; for example, boys are 20 times more likely to want to be an engineer or architect. [7] Women and girls – particularly if they are ethnic minorities – also face higher rates of harassment and violence, particularly in workplace or educational settings.

What would it take to change the harmful stereotypes in Britain that help keep inequalities entrenched? To begin to answer this question, the MPA Capstone group will explore the role of movements in achieving progress on gender equality.

We know that social movements have played a key role toward shifting behaviour, attitudes, and policy on many social issues. For example, women in Britain won equal voting rights in 1928 thanks to its famous suffragette movement led by Emmeline Pankhurst. Research has shown that movements have been associated with successful policy outcomes: data from 1975-2005 from studies on 70 countries has shown that there have been more comprehensive government responses to violence against women in countries with stronger women’s movements on the issue. [8]

Movements have the potential to build popular support, to apply political pressure on governments, but also to reframe an issue, including reframing whose issue it is. Widely supported movements can demonstrate that an issue isn’t just a concern of the few, it’s a concern of the many.

But movements are only one of many possible ways of effecting change; what works best differs based on the context. The MPA students will use their research to try to evaluate the impact of different approaches in the sector with a particular focus on determining the extent to which social movements can be a critical lever for change for progressing gender equality in the UK.

I look forward to working with these students to help their client develop a funding strategy that maximises impact on progress toward gender equality. Given what’s at stake and the benefits progress on gender equality would bring, I hope everyone can see why we should all be concerned.

[1] Duffy, B., Murkin, G., Hewlett, K., Benson, R., & Hesketh, R. (2021). Inequalities around the globe: what the world sees as most serious. https://kclpure.kcl.ac.uk/portal/files/149731100/inequalities_around_the_globe_final.pdf

[2] Cowper-Coles, M., Glennie, M., Mendes Borges, A., & Schmid, C. (Oct. 2021). Bridging the gap? An analysis of gender pay gap reporting in six countries.” https://www.kcl.ac.uk/giwl/assets/bridging-the-gap-full-report.pdf

[3] Equality and Human Rights Commission (2019). Is Britain Fairer? The state of equality and human rights in 2018. https://www.equalityhumanrights.com/sites/default/files/is-britain-fairer-accessible.pdf

[4] Cowper-Coles, M., Glennie, M., Mendes Borges, A., & Schmid, C. (Oct. 2021). Bridging the gap? An analysis of gender pay gap reporting in six countries.” https://www.kcl.ac.uk/giwl/assets/bridging-the-gap-full-report.pdf

[5]  European Institute for Gender Equality (EIGE). 28 October 2020. Gender Equality Index 2020. https://eige.europa.eu/publications/gender-equality-index-2020-united-kingdom

[6] Fawcett Society. 2021. We won’t see gender equality in local councils until 2077 – new data shows. https://www.fawcettsociety.org.uk/News/local-council-data-2021

[7] Equality and Human Rights Commission (2019). Is Britain Fairer? The state of equality and human rights in 2018. https://www.equalityhumanrights.com/sites/default/files/is-britain-fairer-accessible.pdf

[8] Horn, Jessica. 2013. Gender and social movements: overview report. BRIDGE. https://www.ludenet.org/projects-files/6/resources/gender-and-social-movements-overview-report-2013-206.pdf

About the author

Chelsea Phipps is a Practitioner in Residence at the LSE Marshall Institute

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The 100 Year Timeline of Gender Pay Inequality

By cn323 | 13 April 2021

Today, April 13th 2021, marks Equal Pay Day , a day that encourages everyone to get out and fight for employment equality. Though the British government now requires companies with more than 250 employees to publish their gender pay gap data, the latest reported figures this month show a worryingly widening gap.

The gender pay gap is an equality measure that shows the difference in average earnings between women and men . The gender pay gap does not show differences in pay for comparable jobs – this refers to unequal pay, which has been illegal for 45 years.

Here, we timeline the 100-year history of pay inequality – both unequal pay and the gender pay gap – in the UK and Kent Business School’s Dr Samantha Evans gives her view on the issue.

WW1: Many women took on jobs while men were deployed in the army. One of the earliest ever recorded strikes was in 1918 by female tram and bus conductors, resulting in a settlement of a bonus pay to make them equal.

1920s and 30s: With women’s suffrage, women’s groups and trade unions encouraged women to demand equal pay and equal unemployment benefit as an election issue.

WW2: The issue of equal pay was again raised during WWII and was demanded by trade unions and women’s organisations from the 1950s onwards.

1968: On 7th June 1968, workers at a Ford plant in Dagenham, East London went on strike because they were being paid 87 per cent less than men doing the same job. Three weeks later, all of the female worker’s salaries were raised to 92 per cent of what was paid to men.

1970: The strikes at Ford contributed to the campaign for equal pay and the passage of the Equal Pay Act (1970) . According to this act, men and women are entitled to equal pay and terms of employment. It was implemented formerly in 1976.  During these years employers often re-graded jobs by changing job titles to evade the Equal Pay Act.

201 0: According to the Equality Act 2010 men and women are entitled to equal pay and conditions if they are doing the same job; like work (work that is the same or broadly similar); work rated as equivalent (different work, but which is rated under a job evaluation scheme as equivalent); or work of equal value (that is, work that requires similar effort, skill and decision-making). Under this law, it is possible to bring a claim up to six years after leaving a job.

July 2013: The Coalition government proposed upfront fees of £1,200 which workers will have to pay for taking employment tribunal cases against their employers. Government statistics showed 75 per cent fewer cases were subsequently brought over three years.

26 July 2017: Changes to the Equality Act came into force in April 2017, which meant that companies with more than 250 employees have been legally required to report their gender pay gap figures by the end of the financial year. Organisations were expected also to reveal the proportion of men and women who receive financial bonuses.

The Supreme Court also ruled that the Government’s employment tribunal fees were illegal.  The Ministry of Justice took steps to abolish the fees in employment tribunals.

2019/2020: The results for 2019 found that it had widened in favour of men, with 78 per cent of the biggest companies in Britain reporting a gap. Covid-19 meant that reporting on the gender pay gap was not able to be achieved in 2020.

Today: The gender pay gap widened across the UK economy in 2020-21, reaching 11.1 per cent (up from 10.6 per cent). Ad agencies recorded a higher-than-average gender pay gap, at 17.8 per cent.

“The fact that employers are still unable to address the gender pay gap in 2021 is symptomatic of the inherent challenges faced by women in the workplace,” explains Dr Samantha Evans, the Athena Swan lead for Kent Business School, an organisation that serves as a body of recognition for the advancement of gender equality in higher education.

“In making a real commitment to equal pay, employers will start to address the systemic discrimination that has meant men are earning more than women.”

To read more expert comments on equality in the workplace click here. 

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Report | Wages, Incomes, and Wealth

“Women’s work” and the gender pay gap : How discrimination, societal norms, and other forces affect women’s occupational choices—and their pay

Report • By Jessica Schieder and Elise Gould • July 20, 2016

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What this report finds: Women are paid 79 cents for every dollar paid to men—despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment. Too often it is assumed that this pay gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves often affected by gender bias. For example, by the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

Why it matters, and how to fix it: The gender wage gap is real—and hurts women across the board by suppressing their earnings and making it harder to balance work and family. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

Introduction and key findings

Women are paid 79 cents for every dollar paid to men (Hegewisch and DuMonthier 2016). This is despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment.

Critics of this widely cited statistic claim it is not solid evidence of economic discrimination against women because it is unadjusted for characteristics other than gender that can affect earnings, such as years of education, work experience, and location. Many of these skeptics contend that the gender wage gap is driven not by discrimination, but instead by voluntary choices made by men and women—particularly the choice of occupation in which they work. And occupational differences certainly do matter—occupation and industry account for about half of the overall gender wage gap (Blau and Kahn 2016).

To isolate the impact of overt gender discrimination—such as a woman being paid less than her male coworker for doing the exact same job—it is typical to adjust for such characteristics. But these adjusted statistics can radically understate the potential for gender discrimination to suppress women’s earnings. This is because gender discrimination does not occur only in employers’ pay-setting practices. It can happen at every stage leading to women’s labor market outcomes.

Take one key example: occupation of employment. While controlling for occupation does indeed reduce the measured gender wage gap, the sorting of genders into different occupations can itself be driven (at least in part) by discrimination. By the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

This paper explains why gender occupational sorting is itself part of the discrimination women face, examines how this sorting is shaped by societal and economic forces, and explains that gender pay gaps are present even  within  occupations.

Key points include:

• Gender pay gaps within occupations persist, even after accounting for years of experience, hours worked, and education.
• Decisions women make about their occupation and career do not happen in a vacuum—they are also shaped by society.
• The long hours required by the highest-paid occupations can make it difficult for women to succeed, since women tend to shoulder the majority of family caretaking duties.
• Many professions dominated by women are low paid, and professions that have become female-dominated have become lower paid.

This report examines wages on an hourly basis. Technically, this is an adjusted gender wage gap measure. As opposed to weekly or annual earnings, hourly earnings ignore the fact that men work more hours on average throughout a week or year. Thus, the hourly gender wage gap is a bit smaller than the 79 percent figure cited earlier. This minor adjustment allows for a comparison of women’s and men’s wages without assuming that women, who still shoulder a disproportionate amount of responsibilities at home, would be able or willing to work as many hours as their male counterparts. Examining the hourly gender wage gap allows for a more thorough conversation about how many factors create the wage gap women experience when they cash their paychecks.

Within-occupation gender wage gaps are large—and persist after controlling for education and other factors

Those keen on downplaying the gender wage gap often claim women voluntarily choose lower pay by disproportionately going into stereotypically female professions or by seeking out lower-paid positions. But even when men and women work in the same occupation—whether as hairdressers, cosmetologists, nurses, teachers, computer engineers, mechanical engineers, or construction workers—men make more, on average, than women (CPS microdata 2011–2015).

As a thought experiment, imagine if women’s occupational distribution mirrored men’s. For example, if 2 percent of men are carpenters, suppose 2 percent of women become carpenters. What would this do to the wage gap? After controlling for differences in education and preferences for full-time work, Goldin (2014) finds that 32 percent of the gender pay gap would be closed.

However, leaving women in their current occupations and just closing the gaps between women and their male counterparts within occupations (e.g., if male and female civil engineers made the same per hour) would close 68 percent of the gap. This means examining why waiters and waitresses, for example, with the same education and work experience do not make the same amount per hour. To quote Goldin:

Another way to measure the effect of occupation is to ask what would happen to the aggregate gender gap if one equalized earnings by gender within each occupation or, instead, evened their proportions for each occupation. The answer is that equalizing earnings within each occupation matters far more than equalizing the proportions by each occupation. (Goldin 2014)

This phenomenon is not limited to low-skilled occupations, and women cannot educate themselves out of the gender wage gap (at least in terms of broad formal credentials). Indeed, women’s educational attainment outpaces men’s; 37.0 percent of women have a college or advanced degree, as compared with 32.5 percent of men (CPS ORG 2015). Furthermore, women earn less per hour at every education level, on average. As shown in Figure A , men with a college degree make more per hour than women with an advanced degree. Likewise, men with a high school degree make more per hour than women who attended college but did not graduate. Even straight out of college, women make $4 less per hour than men—a gap that has grown since 2000 (Kroeger, Cooke, and Gould 2016).

Women earn less than men at every education level : Average hourly wages, by gender and education, 2015

The data below can be saved or copied directly into Excel.

The data underlying the figure.

Source :  EPI analysis of Current Population Survey Outgoing Rotation Group microdata

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Steering women to certain educational and professional career paths—as well as outright discrimination—can lead to different occupational outcomes

The gender pay gap is driven at least in part by the cumulative impact of many instances over the course of women’s lives when they are treated differently than their male peers. Girls can be steered toward gender-normative careers from a very early age. At a time when parental influence is key, parents are often more likely to expect their sons, rather than their daughters, to work in science, technology, engineering, or mathematics (STEM) fields, even when their daughters perform at the same level in mathematics (OECD 2015).

Expectations can become a self-fulfilling prophecy. A 2005 study found third-grade girls rated their math competency scores much lower than boys’, even when these girls’ performance did not lag behind that of their male counterparts (Herbert and Stipek 2005). Similarly, in states where people were more likely to say that “women [are] better suited for home” and “math is for boys,” girls were more likely to have lower math scores and higher reading scores (Pope and Sydnor 2010). While this only establishes a correlation, there is no reason to believe gender aptitude in reading and math would otherwise be related to geography. Parental expectations can impact performance by influencing their children’s self-confidence because self-confidence is associated with higher test scores (OECD 2015).

By the time young women graduate from high school and enter college, they already evaluate their career opportunities differently than young men do. Figure B shows college freshmen’s intended majors by gender. While women have increasingly gone into medical school and continue to dominate the nursing field, women are significantly less likely to arrive at college interested in engineering, computer science, or physics, as compared with their male counterparts.

Women arrive at college less interested in STEM fields as compared with their male counterparts : Intent of first-year college students to major in select STEM fields, by gender, 2014

Source:  EPI adaptation of Corbett and Hill (2015) analysis of Eagan et al. (2014)

These decisions to allow doors to lucrative job opportunities to close do not take place in a vacuum. Many factors might make it difficult for a young woman to see herself working in computer science or a similarly remunerative field. A particularly depressing example is the well-publicized evidence of sexism in the tech industry (Hewlett et al. 2008). Unfortunately, tech isn’t the only STEM field with this problem.

Young women may be discouraged from certain career paths because of industry culture. Even for women who go against the grain and pursue STEM careers, if employers in the industry foster an environment hostile to women’s participation, the share of women in these occupations will be limited. One 2008 study found that “52 percent of highly qualified females working for SET [science, technology, and engineering] companies quit their jobs, driven out by hostile work environments and extreme job pressures” (Hewlett et al. 2008). Extreme job pressures are defined as working more than 100 hours per week, needing to be available 24/7, working with or managing colleagues in multiple time zones, and feeling pressure to put in extensive face time (Hewlett et al. 2008). As compared with men, more than twice as many women engage in housework on a daily basis, and women spend twice as much time caring for other household members (BLS 2015). Because of these cultural norms, women are less likely to be able to handle these extreme work pressures. In addition, 63 percent of women in SET workplaces experience sexual harassment (Hewlett et al. 2008). To make matters worse, 51 percent abandon their SET training when they quit their job. All of these factors play a role in steering women away from highly paid occupations, particularly in STEM fields.

The long hours required for some of the highest-paid occupations are incompatible with historically gendered family responsibilities

Those seeking to downplay the gender wage gap often suggest that women who work hard enough and reach the apex of their field will see the full fruits of their labor. In reality, however, the gender wage gap is wider for those with higher earnings. Women in the top 95th percentile of the wage distribution experience a much larger gender pay gap than lower-paid women.

Again, this large gender pay gap between the highest earners is partially driven by gender bias. Harvard economist Claudia Goldin (2014) posits that high-wage firms have adopted pay-setting practices that disproportionately reward individuals who work very long and very particular hours. This means that even if men and women are equally productive per hour, individuals—disproportionately men—who are more likely to work excessive hours and be available at particular off-hours are paid more highly (Hersch and Stratton 2002; Goldin 2014; Landers, Rebitzer, and Taylor 1996).

It is clear why this disadvantages women. Social norms and expectations exert pressure on women to bear a disproportionate share of domestic work—particularly caring for children and elderly parents. This can make it particularly difficult for them (relative to their male peers) to be available at the drop of a hat on a Sunday evening after working a 60-hour week. To the extent that availability to work long and particular hours makes the difference between getting a promotion or seeing one’s career stagnate, women are disadvantaged.

And this disadvantage is reinforced in a vicious circle. Imagine a household where both members of a male–female couple have similarly demanding jobs. One partner’s career is likely to be prioritized if a grandparent is hospitalized or a child’s babysitter is sick. If the past history of employer pay-setting practices that disadvantage women has led to an already-existing gender wage gap for this couple, it can be seen as “rational” for this couple to prioritize the male’s career. This perpetuates the expectation that it always makes sense for women to shoulder the majority of domestic work, and further exacerbates the gender wage gap.

Female-dominated professions pay less, but it’s a chicken-and-egg phenomenon

Many women do go into low-paying female-dominated industries. Home health aides, for example, are much more likely to be women. But research suggests that women are making a logical choice, given existing constraints . This is because they will likely not see a significant pay boost if they try to buck convention and enter male-dominated occupations. Exceptions certainly exist, particularly in the civil service or in unionized workplaces (Anderson, Hegewisch, and Hayes 2015). However, if women in female-dominated occupations were to go into male-dominated occupations, they would often have similar or lower expected wages as compared with their female counterparts in female-dominated occupations (Pitts 2002). Thus, many women going into female-dominated occupations are actually situating themselves to earn higher wages. These choices thereby maximize their wages (Pitts 2002). This holds true for all categories of women except for the most educated, who are more likely to earn more in a male profession than a female profession. There is also evidence that if it becomes more lucrative for women to move into male-dominated professions, women will do exactly this (Pitts 2002). In short, occupational choice is heavily influenced by existing constraints based on gender and pay-setting across occupations.

To make matters worse, when women increasingly enter a field, the average pay in that field tends to decline, relative to other fields. Levanon, England, and Allison (2009) found that when more women entered an industry, the relative pay of that industry 10 years later was lower. Specifically, they found evidence of devaluation—meaning the proportion of women in an occupation impacts the pay for that industry because work done by women is devalued.

Computer programming is an example of a field that has shifted from being a very mixed profession, often associated with secretarial work in the past, to being a lucrative, male-dominated profession (Miller 2016; Oldenziel 1999). While computer programming has evolved into a more technically demanding occupation in recent decades, there is no skills-based reason why the field needed to become such a male-dominated profession. When men flooded the field, pay went up. In contrast, when women became park rangers, pay in that field went down (Miller 2016).

Further compounding this problem is that many professions where pay is set too low by market forces, but which clearly provide enormous social benefits when done well, are female-dominated. Key examples range from home health workers who care for seniors, to teachers and child care workers who educate today’s children. If closing gender pay differences can help boost pay and professionalism in these key sectors, it would be a huge win for the economy and society.

The gender wage gap is real—and hurts women across the board. Too often it is assumed that this gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves affected by gender bias. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

— This paper was made possible by a grant from the Peter G. Peterson Foundation. The statements made and views expressed are solely the responsibility of the authors.

— The authors wish to thank Josh Bivens, Barbara Gault, and Heidi Hartman for their helpful comments.

About the authors

Jessica Schieder joined EPI in 2015. As a research assistant, she supports the research of EPI’s economists on topics such as the labor market, wage trends, executive compensation, and inequality. Prior to joining EPI, Jessica worked at the Center for Effective Government (formerly OMB Watch) as a revenue and spending policies analyst, where she examined how budget and tax policy decisions impact working families. She holds a bachelor’s degree in international political economy from Georgetown University.

Elise Gould , senior economist, joined EPI in 2003. Her research areas include wages, poverty, economic mobility, and health care. She is a co-author of The State of Working America, 12th Edition . In the past, she has authored a chapter on health in The State of Working America 2008/09; co-authored a book on health insurance coverage in retirement; published in venues such as The Chronicle of Higher Education ,  Challenge Magazine , and Tax Notes; and written for academic journals including Health Economics , Health Affairs, Journal of Aging and Social Policy, Risk Management & Insurance Review, Environmental Health Perspectives , and International Journal of Health Services . She holds a master’s in public affairs from the University of Texas at Austin and a Ph.D. in economics from the University of Wisconsin at Madison.

Anderson, Julie, Ariane Hegewisch, and Jeff Hayes 2015. The Union Advantage for Women . Institute for Women’s Policy Research.

Blau, Francine D., and Lawrence M. Kahn 2016. The Gender Wage Gap: Extent, Trends, and Explanations . National Bureau of Economic Research, Working Paper No. 21913.

Bureau of Labor Statistics (BLS). 2015. American Time Use Survey public data series. U.S. Census Bureau.

Corbett, Christianne, and Catherine Hill. 2015. Solving the Equation: The Variables for Women’s Success in Engineering and Computing . American Association of University Women (AAUW).

Current Population Survey Outgoing Rotation Group microdata (CPS ORG). 2011–2015. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. U.S. Census Bureau.

Goldin, Claudia. 2014. “ A Grand Gender Convergence: Its Last Chapter .” American Economic Review, vol. 104, no. 4, 1091–1119.

Hegewisch, Ariane, and Asha DuMonthier. 2016. The Gender Wage Gap: 2015; Earnings Differences by Race and Ethnicity . Institute for Women’s Policy Research.

Herbert, Jennifer, and Deborah Stipek. 2005. “The Emergence of Gender Difference in Children’s Perceptions of Their Academic Competence.” Journal of Applied Developmental Psychology , vol. 26, no. 3, 276–295.

Hersch, Joni, and Leslie S. Stratton. 2002. “ Housework and Wages .” The Journal of Human Resources , vol. 37, no. 1, 217–229.

Hewlett, Sylvia Ann, Carolyn Buck Luce, Lisa J. Servon, Laura Sherbin, Peggy Shiller, Eytan Sosnovich, and Karen Sumberg. 2008. The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology . Harvard Business Review.

Kroeger, Teresa, Tanyell Cooke, and Elise Gould. 2016.  The Class of 2016: The Labor Market Is Still Far from Ideal for Young Graduates . Economic Policy Institute.

Landers, Renee M., James B. Rebitzer, and Lowell J. Taylor. 1996. “ Rat Race Redux: Adverse Selection in the Determination of Work Hours in Law Firms .” American Economic Review , vol. 86, no. 3, 329–348.

Levanon, Asaf, Paula England, and Paul Allison. 2009. “Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950-2000 U.S. Census Data.” Social Forces, vol. 88, no. 2, 865–892.

Miller, Claire Cain. 2016. “As Women Take Over a Male-Dominated Field, the Pay Drops.” New York Times , March 18.

Oldenziel, Ruth. 1999. Making Technology Masculine: Men, Women, and Modern Machines in America, 1870-1945 . Amsterdam: Amsterdam University Press.

Organisation for Economic Co-operation and Development (OECD). 2015. The ABC of Gender Equality in Education: Aptitude, Behavior, Confidence .

Pitts, Melissa M. 2002. Why Choose Women’s Work If It Pays Less? A Structural Model of Occupational Choice. Federal Reserve Bank of Atlanta, Working Paper 2002-30.

Pope, Devin G., and Justin R. Sydnor. 2010. “ Geographic Variation in the Gender Differences in Test Scores .” Journal of Economic Perspectives , vol. 24, no. 2, 95–108.

See related work on Wages, Incomes, and Wealth | Women

See more work by Jessica Schieder and Elise Gould

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DBS Gender Pay Gap Report 2023

• Disclosure & Barring Service

Published 26 March 2024

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.

This publication is available at https://www.gov.uk/government/publications/dbs-gender-pay-gap-report-2023/dbs-gender-pay-gap-report-2023

1.1. The Disclosure and Barring Service (DBS) has placed equality, diversity, and inclusion as a priority in its 5-year strategy 2020/25 to ensure that its products and services are as accessible and inclusive as possible for both the public and employers. Developing a more diverse workforce, which reflects the public we serve, including having the appropriate gender balance at all levels of the organisation, plays a key role in delivering this priority.

1.2. This 2022/23 report shows that, although we are disappointed that we are not making the progress we had hoped, we have a sound understanding of the drivers for the increase in the mean gender pay gap at DBS, and a robust action plan in place to address these.

1.3. The increased mean gender pay gap to 8.09% from the prior year’s 6.77% predominantly stems from our organisational design (internal structural changes) during 2020-2021, whereby DBS introduced new capabilities and expertise and strengthened existing areas. This recruitment, targeting hard-to-reach audiences, required offering competitive salaries within grade bands, supported by thoroughly evidenced business cases.

1.4. This initiative was crucial for securing the necessary capabilities to achieve our strategy, but it had a significant impact on the gender pay gap. There is a further shift this year to 8.09% related directly to the changing workforce profile at Grade 6 and Senior Civil Service level due to the departure of several female colleagues. In some cases, the roles were subsequently reconfigured or removed from the structure, so the vacancies left by those colleagues were not recruited.

1.5. A notable advancement is the reduction of our median pay gap to just 0.03% (1p), which demonstrates the impact of the carefully considered implementation of our pay and reward strategies. Our approach took into account detailed equality impact assessments, as part of our commitment to pay equity at DBS.

1.6. We are committed to reducing the Gender Pay Gap, and our approach to action planning looks holistically at the changes which will achieve this. Our approach goes beyond focusing solely on pay, ensuring that our working conditions and approach to female-specific issues are considered and addressed. A summary of our action plan is enclosed in section 9.

 2. Background

2.1. Gender pay gap is a comparison of the average pay for all male and female employees, across all jobs. The comparison within the Disclosure and Barring Service (DBS) is shown throughout this report.

2.2. If an organisation has a particularly high gender pay gap, this can indicate that there may be issues to address, and the individual calculations may help to identify what those issues are.

2.3. The Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017 stipulate that all businesses, public sector organisations, and third sector organisations with over 250 employees must publicly report on the average pay differences between their male and female employees.

2.4. The regulations require employers to publish:

• their mean gender pay gap in hourly pay
• their median gender pay gap in hourly pay
• their mean bonus gender pay gap
• their median bonus gender pay gap
• the proportion of male and female employees receiving a bonus payment
• the proportion of male and female employees in each pay quartile

2.5. As a public sector body, we are required to publish this pay gap information within 12 months, taking the ‘snapshot date’ of 31 March annually.

2.6. Our calculations follow the legislative requirements as set out in the Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017.

2.7. This information will be published on the DBS website for a minimum of 3 years and will be published by 31 March 2024.

2.8. This report fulfils DBS’ reporting requirements, analyses the figures in more detail, and updates the action plan produced last year, which sets out what we are doing to close the gender pay gap in our organisation.

2.9. The purpose of gender pay gap reporting is to achieve greater gender equality in terms of pay across the UK and increase pay transparency. This differs from equal pay which deals with differences between male and female employees who carry out equal work.

2.10. DBS recognises that gender identity is not limited to the categories of male and female, and although gender pay gap regulations require DBS to report on all staff as male or female, DBS appreciates that some colleagues may not identify within this gender binary. DBS welcomes and values colleagues of all gender identities.

2.11. To be included as a full-pay relevant employee (this includes part-time employees), the employee must be paid their full usual pay during the pay period in which the snapshot date falls. If the employee is paid less than their usual rate because of being on leave for that period, they should not be counted as a full-pay relevant employee. For example, if an employee is paid Statutory Sick Pay or Statutory Maternity Pay, that is less than their usual pay, they will not be counted as a full-pay relevant employee.

3. Workforce profile

3.1. 1. As of 31st March 2023, DBS employed a total of 1294 employees (by headcount). 803 (62%) of the workforce identified as female and 491 (38%) of the workforce identified as male. This is almost the same profile as at 31st March 2022 when the equivalent figures were 1139 employees, of which 703 (62%) were female and 436 (38%) were male.

3.2. Figures 1 and 2 show a breakdown of the 1,301 relevant employees in DBS on the 31st of March 2023.

Figure 1: DBS workforce information by gender

*Public servants at SCS equivalent grades

Figure 2: DBS workforce numbers by gender

Figures 1 and 2 show that:

• 83% of the workforce is employed at AO, EO, and HEO level
• at AO grade, 60% of employees are female and 40% are male
• at EO grade, 68% of employees are female and 32% are male
• at HEO grade, 66% of employees are female and 34% are male
• at SEO grade, 55% of employees are female and 45% are male
• at Grade 7, 47% of employees are female and 53% are male
• at Grade 6, 50% of employees are female and 50% are male
• for 2023, in Grade 7, Grade 6, and SCS roles, 46% of employees were female at these grades (54 out of 117) - this is a 1 % reduction from 2022 when there were 50 female employees out of a total of 103 (47%)

4. Mean gender pay gap

4.1. As at 31 March 2023, the mean hourly rate for male employees was £16.69 per hour and for female employees, it was £15.34 per hour. This means that male employees earned an average of £1.35 per hour more than female employees, compared to earning an average of £1.10 more per hour than female employees in 2022.

4.2. The mean difference recorded at DBS for 2023 is 8.09%. The gap reduced from 2017 to 2019 from 5.50% to 2.00% but has increased since then, from 2.00% in 2019 to 3.93% in March 2021 and 6.77% in March 2022. This is shown in Figure 3.

Figure 3: Mean pay gap

4.3. We calculated our pay gap using a method set by the Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017. For context DBS’ mean pay gap for 2023 in comparison with other government departments, such as Home Office at 7.0%, (increasing from 6.7% in 2022) and HMRC at 6.0%, (reducing from 6.6% in 2022). The gap at DBS is considerably less than the national public sector mean pay gap, which is 14.3% (Office for National Statistics – provisional 2023 figure).

5. Media gender pay gap

5.1. The median hourly rate of pay for male employees was £13.71 per hour and for female employees, it was £13.70 per hour. This means that male employees earned an average of 1 pence per hour more than female employees, which equates to a difference of 0.03%.  This has decreased from 3.96% last year.

6. Proportion of male and female employees receiving bonuses

6.1. DBS offers in-year reward and recognition awards to employees. These are awarded in the form of vouchers or through salary. Both are included for the purpose of gender pay gap calculations.

6.2. Out of 807 female employees, 395 received a bonus which equates to 49.8%. Out of 494 male employees, 246 received a bonus which equates to 48.9%.

7. Gender bonus gap

7.1. The mean gender bonus gap (difference between male and female employees) is 27.8% with male employees being awarded more and the median gender bonus gap is 0.00%. This is shown in Figure 4.

7.2. This varies year on year on a demand or activity basis and is tested for equality impacts annually. Within the delegated grades rewards and recognition scheme (AO – Grade 6) there is a negative gap of -7%, while the SCS scheme has a gap of 15%.

Figure 4: Mean and median bonus gap

8. hourly pay quartiles.

8.1. DBS employed 1294 employees as at 31st March 2023, with 1254 qualifying as full-pay relevant employees, which means there are 313 employees per pay quartile.

8.2. The hourly rates that represent each quartile are as follows:

• Upper relates to hourly rates of £16.21 and above
• Upper middle relates to the hourly rates of £13.71 to £16.21
• Lower middle relates to the hourly rates of £12.10 to £13.71
• Lower relates to hourly rates up to £12.10

8.3. The gender split per quartile is detailed below:

• In the upper quartile, there are 171 female employees (55%) and 142 male employees (45%)
• In the upper middle quartile, there are 211 female employees (67%) and 103 male employees (33%)
• In the lower middle quartile, there are 192 female employees (61%) and 121 male employees (39%)
• In the lower quartile, there are 193 female employees (61%) and 121 male employees (39%)

8.4. The highest percentage of female employees is in the upper middle quartile (67%), whilst the highest for male employees is in the upper quartile (45%).

8.5. The lowest percentage of female employees is in the upper quartile (55%) and the lowest percentage of male employees is in the lower middle quartile and lower quartile (both 39%).

8.6. The data highlights that, within the 3 lowest quartiles, the proportion of male and female employees compares closely to their workforce representation across all 3 quartiles, with the difference no more than 5%.

8.7. On average, there is a slightly higher proportion of male employees in the upper pay quartiles than female employees compared to their workforce representation (approximately 7%).

8.8. In the highest pay quartile, 51 (45%) of the 114 Grade 7, Grade 6, and SCS staff are female, which is lower than their workforce representation at those grades (62%).

Action Plan Summary

9.1. commitment to pay equality and diversity.

DBS is committed to fostering equality in its pay and reward strategies and beyond. Our organisation consistently monitors equality within the workforce and actively pursues a comprehensive equality, diversity, and inclusion plan.

The below actions aim to ensure fair and equitable practices for all employees with a specific focus for this summary on key factors affecting the gender pay gap. It covers our existing actions, which we believe are having a positive impact, and our new actions added as a consequence of this analysis.

9.2. Impact of Strategic Recruitment and Organisation Design

As part of our strategic organisational design, DBS has engaged in increased recruitment to introduce new capabilities and expertise and strengthen existing areas. This recruitment, targeting hard-to-reach audiences, required offering competitive salaries within grade bands, supported by thoroughly evidenced business cases. While this initiative was crucial for securing the necessary talent, it has also had an impact on the current gender pay gap.

Action completed: We have completed a review of the impact of recent appointments at higher salary business cases and, beyond the initial impact on the pay gap of this large recruitment activity, no further adverse impact is evidenced related to this process.

9.3. Scope for Improvement and Ongoing Actions

Recognising that the increase in our gender pay gap is largely attributable to these recent strategic changes and some smaller contributory factors, DBS acknowledges the need for further improvements.

These are detailed in the actions for 2024-2025 below:

9.4. Theme 1: Equal Pay and Continuous Analysis

DBS will maintain its practice of conducting annual equal pay audits to identify areas for improvement, informing decisions on the annual pay award. Within the frameworks set by HM Treasury and Cabinet Office, DBS has the autonomy to determine pay and reward structures up to Grade 6 through the annual pay remit and similarly through an annual SCS pay remit. These are the frameworks that most directly impact gender pay and equal pay.

The improvement in the median gender pay gap, now at a minimal 0.03%, suggests that our pay strategy is effective for a substantial portion of the workforce. This progress indicates that our recent pay remit initiatives have positively impacted pay equity.

Ongoing and existing actions

• We are committed to transparency in publishing gender pay gap information and learning from best practices in other organisations.
• We will continue to progress our evidence-based and equity and inclusion-focused approach to pay and reward at DBS.
• Continue analysis of job application data as part of wider work on equality, diversity, and inclusion to identify any patterns and appropriately adjust DBS processes.
• An annual analysis of gender pay gap data, with year-on-year comparisons, will be undertaken.
• The DBS Strategic Leadership Team will regularly review this data and monitor the progress of actions within our plan.
• “Adding more deep dive post pay remit analysis and action per grade to the annual pay remit process” - The impacts of our interventions will continue to be tested and analysed in further depth as part of the pay remit process with a greater focus on per grade or group impacts and post remit real terms impact.

9.5. Theme 2: Culture and Visible leadership

The departure of 2 female employees at Senior Civil Servant (SCS) equivalent grade and one female Grade 6 has likely contributed to the skewing of the gender pay gap, particularly given their small number within the organisation. This not only impacts the mean gender pay gap calculation but also has broader implications for organisational culture, succession planning, and the visibility of women in leadership roles.

Senior female colleagues will continue to be supported to spearhead and champion cross-DBS issues as well as topics such as menopause.

Detailed and collaborative examination of staff survey results and focus on delivering an action plan.

“I can’t be what I can’t see” - We will build a visibility plan to ensure that there is visibility of women in leadership roles and that they are featured as part of career workshops.

“Stimulating progression” - DBS is committed to launching a mentoring and coaching scheme via the DBS Academy facility.

9.6. Theme 3: Inclusive Recruitment and Development

DBS is committed to inclusive recruitment, leveraging campaigns and external partnerships. We will monitor Equality, Diversity, and Inclusion (EDI) data post-recruitment activities for continuous improvement.

From a development perspective, there is positive evidence that through mechanisms like Higher Responsibility Allowance, Temporary Promotion and through the DBS Academy we support more female colleagues than male colleagues which will contribute positively over time to the average gender pay gap.

Our advertising language will continue to be critically reviewed, especially for roles at Grade 7 and above, to avoid gender biases and ensure equal appeal to all genders.

Efforts to achieve diverse representation across all protected characteristics in our workforce, at all levels, will continue through targeted recruitment, attraction and development strategies and tested in-depth regularly through detailed equality analysis.

“A compelling and attractive picture of DBS” - As part of our business planning, we commit to ensuring that our positive reward proposition is put forward to best attract more internal and external female colleagues to higher grades roles, featuring the stories of our successful senior female leaders and the DBS offer that provides a flexible and inclusive environment.

“A rich and impactful learning offer” - Stimulating colleague awareness and skills in key areas that will support the achievement of the DBS strategy. This includes ensuring managers have coaching and leadership skills to best support staff to increase both capability and confidence so they can develop the career they want.

10. Conclusion

10.1 The gender pay gap within DBS is 8.09%, increasing from 6.77% last year. DBS compares favourably to the UK mean gender pay gap of 14.3% [1] . While there have been small increases in the DBS gender pay gap over the past 3 years, it now is higher overall than reported in 2017.

10.2 While the gap has shown a marginal increase over the past few years, it is predominantly attributed to changes in the profile of our workforce, especially the higher representation of male employees in senior roles and the increased presence of female employees in junior positions.

10.3 DBS is committed to the action plan set out above to address these trends and ensure fair pay practices across the organisation as part of our strategic priority of equality, diversity, and inclusion.

[1] Source: Office for National Statistics annual survey of hours and earnings (ASHE) – provisional figure for 2023

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Promoting gender-parity in UK laws and their implementation.

Myth: Gender Pay Gap

The myth: Women get paid less than men for the same work due to discrimination.

The term ‘gender pay gap’ is widely used as ‘ proof that society benefits men at the expense of women ’.  When used it is usually not defined and gives the impression that women are paid less than men for the same job.  As the definition below states – this is untrue. 

Definition of Gender Pay Gap

According to the UK government , the Gender Pay Gap is the difference in the average earnings of male and female employees either in a particular organisation, or the average for the whole country.

In 2020 the gap in the UK is around 17%. However, note it is not a pay gap, it’s a Gender Earnings Gap.

What the GPG is NOT:

It’s official:  “The gender pay gap does not show differences in pay for comparable jobs. Unequal pay for men and women has been illegal for 45 years.” https://www.gov.uk/government/news/uk-gender-pay-gap

What the gender pay gap does not account for:

• qualifications
• overtime and/or unsocial hours
• continuity of employment

We can see a gender pay gap in an airline. This is because the highly-paid engineers and pilots are mostly male while the cabin and check-in staff are mostly female. These are choices, not discrimination.

Reasons for the gap

The GPG is NOT caused by unequal wages.  As the government website correctly shows, “The causes of the gender pay gap are complex and overlapping:

• Fewer women choose the most highly-paid careers
• More women work part-time (where pay-rates are lower)
• Fewer women choose to compete for promotion

What about these?

Sometimes the gender pay gap is given as the reason checkout staff at supermarkets (mostly female) are paid less than warehouse staff (mostly male). However, these are just different jobs: male checkout staff are paid less than female warehouse staff .

Sometimes examples are given where an individual female presenter is paid less than an individual male presenter. However, the it is difficult to maintain this line when we are comparing celebrities who negotiate individual salaries. Some female models, actresses, authors are paid more than men – because they have negotiated this. It’s not about discrimination.

Video by Prager University

Christina Hoff Sommers asks: “If, for the same work, women make only 77 cents for every dollar a man makes, why don’t businesses hire only women? Wages are the biggest expense for most businesses. So, hiring only women would reduce costs by nearly a quarter – and that would go right to the bottom line. Don’t businesses want to be profitable? Or, are they just really bad at math?”

She then answers in detail with great graphics.

Video by The Economist

This video, by a mainstream magazine, explains the reasons for the gap.  Although it, correctly, identifies ‘having children’ as a major reason for lower average wages for women, it still describes this as women being ‘held back’, rather than giving motherhood an equal status with a career and simply accepting the financial outcomes.

Video by Christina Hoff Sommers

In this video in the ‘Factual feminist’ series, challenges the assumption that the gap is the result of discrimination.

Satirical interview by Jonathan Pie

Jonathan comes out with all the usual stereotypes and myths and the author puts him right.

Short clip by Shoe0nHead

Clip from Shoe’s last video for those of you tired of repeating the same explanation over and over again to internet feminists who cling on to “muh wage gap” for dear life and wish it was caused by sexism.

It’s a ‘not making my career my first priority’ pay gap

While, on average, more women than men choose lifestyle over career, many men also have these priorities.  If we were to look at ‘ career-minded men ’ v ‘ lifestyle-minded men ’ we would see a similar wage-gap.

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Olivia Colman says she would be paid more if she was a man

Oscar-winning actor says gender pay disparity rooted in outdated idea that male actors draw audiences

Olivia Colman has criticised gender pay disparity in the film industry and said she would be paid more if she was a man, arguing that it is rooted in an outdated idea that male actors draw in the audience.

Speaking about her latest movie, Wicked Little Letters, the Oscar-winning actor said that gender limits her earning potential. “Research suggests that [women have] always been big box office draws,” she told CNN’s Christiane Amanpour programme.

“Don’t get me started on the pay disparity but male actors get paid more because they used to say they draw in the audiences and actually, that hasn’t been true for decades. But they still like to use that as a reason to not pay women as much.

“I’m very aware that if I was Oliver Colman, I’d be earning a fuck of a lot more than I am,” Colman added. “I know of one pay disparity which is a 12,000% difference. Do the maths.”

She did not confirm the production to which this disparity relates.

It is not the first time Colman has pointed out a gender disparity when promoting Wicked Little Letters.

Speaking to the Radio Times last month, Colman pointed out that there’s still a gendered double standard when it comes to obscenities.

“If a woman swears, people act shocked. Fuck off! Women are human – funny, filthy, loving, caring – just like men.”

Wicked Little Letters tells the true story of the Littlehampton poison-pen scandal of 1923, with Colman playing the role of the prim-and-proper Edith Swan.

It is the latest cinematic outing for the actor who is also known for playing Queen Elizabeth II in The Crown and The Favourite, where she stars as the 18th-century monarch Anne.

Colman won the best actress Oscar for her work in The Favourite. She received a second nomination for the same gong in 2022 for her role in The Lost Daughter alongside Wicked Little Letters co-star Jessie Buckley. Colman was also given an actress in a supporting role nomination for The Father in 2021.

This month, Forbes named Adam Sandler as the highest paid actor of 2023 after he raked in $73m (£57.3m) after the success of his 2023 Netflix film Murder Mystery 2 and his standup comedy tour.

Margot Robbie was the second highest earner, achieving $59m after her starring role in the blockbuster Barbie film, which she also produced, but the only other woman to place in the top 10 was Jennifer Aniston, who was Sandler’s co-star in the comedy.

• Olivia Colman
• Gender pay gap

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