Tuesday, 11 August 2015

The Outcome of Outcome Feminism – Part 1: Factors to be Controlled

Author: Blaise Wilson

The Outcome of Outcome Feminism Series:
Part 1 – Factors to be Controlled: this article
Part 2 - Assumptions: http://egafeminist.blogspot.co.uk/2015/08/outcomeoutcomefem2.html
Part 3 - Freeing women's time and money: http://egafeminist.blogspot.co.uk/2015/09/outcomeoutcomefem3.html
Part 4- Cultural Pressures: http://egafeminist.blogspot.co.uk/2015/09/outcomeoutcomefem4.html
Part 5 - Discrimination: http://egafeminist.blogspot.co.uk/2015/10/outcomeoutcomefem5.html
Part 6 - Discussion of Assumption 6: http://egafeminist.blogspot.co.uk/2015/10/outcomeoutcomefem6.html
Part 7 - Discussion of Assumption 1: http://egafeminist.blogspot.co.uk/2015/11/the-outcome-of-outcome-feminism-part-7.html
Part 8 - The Outcome of Outcome Feminism Conclusion:  http://egafeminist.blogspot.co.uk/2015/11/outcomeoutcomefem8.html
Part 9 - Campaigns and Action: http://egafeminist.blogspot.co.uk/2015/11/outcomeoutcomefem9.html


In this article I discuss the main causes of the Gender Wage Gap in the UK, which is used to measure the Economic Equality of Outcome for Gender.

Although many factors have arisen the major ones are:
  • how the measurement is made 
  • choosing to be the main caregiver to dependants 
  • full or part time work 
  • hours and overtime worked 
  • choice of education 
  • choice of occupation 
  • experience in a chosen occupation 
  • location 
The next article in this series will cover how these factors could be controlled in order to obtain true Equality of Outcome between the sexes.


Following on from my investigation into Equality and its definition I plan to delve further into the topic. The previous article in this series can be found at:  http://egafeminist.blogspot.co.uk/2015/08/egalitarianfeministequality.html

Due to the magnitude of demonstrating what the Outcome of Outcome Feminism may look like, combined with a preference for shorter articles, I have decide to split this topic into sections.

In this part I will concentrate on the Gender Wage Gap and the factors that influence it. On the assumption that in order to create Outcome Equality for the genders, these are the factors that will need to be controlled or made irrelevant in order for all women to earn the same as all men. In a future article I will dive into how this may be achieved and finally I will look at what this means for Equality of Opportunity and make suggestions on how Outcome Feminism can be helped or hindered.

I have chosen to concentrate on the UK Gender Wage Gap, and centre my evidence on reports provided by the UK Government.

The Gender Wage Gap can be broken down into different elements, I will assume that the disparity between the genders should be controlled regardless of which perspective is taken, for example location, age and so forth. This way all men and all women will earn the same, regardless of how you slice the groups.

Wage Gap Factors

I will look at the impacting factors and reasons that drive the Economic Outcome Inequality between men and women in order to demonstrate which factors must be controlled to produce Economic Outcome Equality between all men and all women.

The genders will include all ethnicities at this stage, and it is important to remember that women of colour have be included within the generic term of 'women.' However when considering Economic Outcome Equality between all men and all women, women of colour should be taken as a special case and investigated further due to the stereotypes and historic discrimination against them to ensure they benefit from Equality of Outcome too.

How the Wage Gap is Measured

How you measure the wage gap makes a huge difference in the results. Statistics are renowned for the ability to be shifted about until you get the result you want, if you have a bias towards a certain outcome. Depending on how you slice and dice, or capture the raw data you can pretty much increase or decrease the results to your liking.

Within the UK Patterns of Pay: Estimates from the Annual Survey of Hours and Earnings, 1997 to 2013 [2] the median hourly wage was taken, rather than the mode or mean. It excluded overtime [2, p11-12] and defined Full-Time as people working more than 30 hours a week. [2, p32].

However it also highlighted that the median, although providing a useful comparison, it does not reveal the pay rates for comparable jobs [2, p12] or the standard deviation with those roles.

It included pay before deductions such as National Insurance, student loans and tax [2, p30]. This might have a significant impact on the take home wage as some groups may have additional payments to consider that come straight out of the wages, such as child support or government child care schemes. A comparison of take home wage, including overtime and all deductions would give a clearer idea of living standards and may produce a very different Gender Wage Gap, particularly if government provided welfare was taken into account when looking across all men and all women, both in and out of work.

Limiting the jobs to only those registered on the PAYE scheme makes it quick, easy and reliable to gather data [2, p32], however it this discounts self-employed [2, p28] and cash in hand jobs. This would have an impact by not capturing writers, musicians, artists and similar jobs. It also discounts unpaid work, such as volunteering or being a carer.

This report discounts those affected by absence [2, p30] whereas the Women in the labour market report [5] included women on maternity leave and career breaks in their statistics [5, p19]. This means those who are affected by illness and are forced to take breaks from their career are not included, but this information may have proven useful if comparing men and women with both visible and invisible disabilities or illnesses (e.g. depression).

There are several similar reports. As a result of different boundaries, definitions, methods and raw data each have different results. Here are two examples:

“The AWE and ASHE are not directly comparable on all measures of earnings (for example, despite ASHE providing a more accurate estimate of levels of gross pay, AWE captures bonus payments better). The closest measure that can be derived and compared for these surveys is for mean gross weekly pay (excluding bonuses) for Great Britain. These figures tend to be higher for ASHE than the equivalent AWE figures. In April 2013 the ASHE estimate of mean gross weekly pay (excluding bonuses) for all employees (regardless of whether they were full-time or part-time) was £498, up 2.2% on the previous year. The comparable estimate from the AWE, regular pay (which excludes bonuses and arrears of pay), was £446, up 1.3% from April 2012.” [2, p33] 

“Another source of earnings information is the LFS. This collects information on the earnings and hours of about 15,000 households over each quarter. In addition it collects data on a wide range of personal characteristics, including education level and ethnic origin. This enables the preparation of statistics on levels and distribution of earnings similar to ASHE but with lower precision due to the much smaller sample size.” [2, p34] 

This indicates that sample size is also a key factor in the measurement of the Gender Wage Gap.

How the Gender Wage Gap is measured needs to be identical from year to year, in order to make a direct comparison between the statistics. If this was to be taken as the Measure of Effectiveness for Economic Equality of Outcome for all men versus all women, the method needs to be fully agreed. This will stop changes in the method from distorting the measurement or changing the goal posts in the future.

Percentage of the Gender in the Workforce

How many people are available to work makes a huge difference to the Gender Wage Gap. Since the record began in 1971 there has been an increase in the number of female workers in the UK. This maybe due to shifts in culture and supported by a range of legislation culminating in the Equality Act 2010 [5, p2].

Compared with all women age 16-64 (regardless of personal situation), in 2013 67% of women were in the workforce [5, p1], with a distribution of 55% to 79% depending on location [5, p8]. Of these 13.4 million women in work, 42% were in part-time work, compared to only 12% of men [5, p1].

However, the impact of choosing to have children has a dramatic impact on the careers, and earning potential of women if they decide to put their family before their career. Men who have children are more likely to be in work, whereas women with children are less likely to be employed, especially in full time work [5, p9]. What's more, the age of the child and the relationship status of the mother has a significant impact on the mother's employment status and career choice [5, p9], with 72% of married or cohabiting mothers with dependant children in work verses 60% of single /lone mothers in 2013 [5, p10]. Lone women with children under the age of three were impacted the most [5, p10].

Due to having and raising children the gender wage gap significantly increases when women hit the average age of having their first child, in 2013 this average age was 28 [5, p5]. This causes the gap to widen, reaching a maximum in the age range 40-49. Women's earning peak earlier than men's, reaching a high when they are 30-39 [2, p25]. The gap begins to narrow when the children get older and the women return to the workforce [5, p4].

As previously mentioned, women are far more likely to work part, rather than full time when compared to men. Due to part time work earning 60% less than the full time counterpart [2, p7] and higher paid jobs often having fewer part-time opportunities [1, p12] women are taking home less pay, with fewer opportunities to stay in a high paid career of choice. However as women tend to stay in part-time work for longer in their career [2, p6] women earn more than men when only part-time work is considered with a 2013 median hourly rate of £8.29 compared to men's £7.95 [2, p5]. This also highlights experience within a career as a major factor.

When we look at full-time work, men are working longer hours than women. In 2013 men, on average, worked 40.1 [2, p4] to 44 [5, p19] hours compared to women's 37.4 [2, p4] to 40 [5, p19] hours. This was mostly due to men being more likely to work overtime than women [2, p4].

Overtime (and to a lesser extent incentives/bonuses and premium payments) makes a substantial difference [2, p9], although has not been included within this wage gap analysis, it should be noted that men, on average, boost their earnings with overtime accounting for 6.1% of men's wages compared to only 3.2% for women [2, p9].

To summaries women choosing to work part-time is “likely to be connected with the fact that many women have children and the time taken out of the labour market, combined with career choices they make subsequent to this, may impact on their earnings thereafter.” [2, p26]

Chosen Occupation

In a free-market jobs pay the minimum they can get away with while attracting the best people. This means that some jobs are paid more than others due to their need to attract workers of a certain qualification and/or experience, compensate for dangers, or poor working environment and other inconveniences.

It is worth noting that within occupational groups there is a lot of standard deviation. For example within Sale and Customer Service: “In 2013 the occupation group with the highest median weekly earnings for full-time employees was managers, directors and senior officials, at £765. Sales and customer service occupations were the lowest paid group, at £331 per week.” [2, p20], meaning that time must be taken within these careers to raise in the ranks and be rewarded.

Men tend to work in higher skilled jobs than women, in professions associated with higher levels of pay [5, p17]. Women dominate care, leisure and account jobs (82% women) followed by admin and secretarial roles and sales/customer service occupations while men dominate trade occupations (plumbers, electricians etc), where women only account for only 10% of the workforce, followed by roles within manufacturing [5, p12].

Even for profession occupations women are not earning as much, for a detailed example:

“The lower median earnings of female health professionals were, in part, due to the large number of nurses included in this category. There are about 6 times as many women working in Nursing and Midwifery Occupations as men. The median earnings for female nurses in 2013 were £16.73 per hour in 2013, substantially less than the median earnings of the profession occupations in general.” [1, p13] 

The mining and quarry sector, followed by the electricity, gas, steam and air conditioning supply sector earn the most money [2, p18], whereas the accommodation and food service activities sector has the lowest gross weekly earning in 2013 [2, p18].

However it should also be noted that mining and quarrying, and gas, electricity and water supply are some of the most dangerous jobs in the UK [3, Table 1, p4] which, along with the higher requirement for qualification and experience, add to the increased pay scale. Agriculture, which was the most dangerous uses a large number of low skilled, readily available seasonal migrate workers [4, p132 para 6.27] which significantly affects the offered wages.

As qualifications have a significant impact on what careers are open to an individual it is worth considering what women pick to study at university. There has been an increasing trend of women going into further education [1, p8], which has increased the number of women working in Professional Occupations (which generally require a degree) [1, p11]. However female graduates are marginally more likely to work in slightly lower skilled occupational groups than their male counterparts [5, p14-15].

This is likely due to the choice of degree. Subjects studied at university in which women dominate are related to medicine (nursing, nutrition etc) at 82%, Veterinary Science at 77%, Education at 76%. Whereas the bottom three degrees dominated by women are Engineering and Technology at 15%, Computer Science at 19% and Architecture, Building and Planning at 29%. (The total proportion of female students is calculated on the total number of students, without reference to their subject of study) [6].

With this in mind: “the most common occupation for women was nursing while the most common for men was programmers and software development professionals... programmers and software development professionals earned £20.02 per hour [excluding overtime] while nurses earned on average £16.61 according to the 2012 Annual Survey of Hours and Earnings.” [5, P11], which follows the trend in university degree chosen.

Working in the public or private sector also has an impact on wage. These are made up of very different occupations, with many of the lowest paid jobs (waiters, hairdressers etc.) sitting within the private sector, while higher level professional occupations tend to be held within the public sector [2, p15]. This slants the average wage of the public sector above those in the private sector [2, p15]. This is coupled by some jobs being impacted by a recession more than others, as public sectors tend to be jobs that are more stable but the private sector is more affected by the average available fund of the general population.

Combined with more women in part-time work than full-time, more likely to leave their career in favour of raising children, and choosing lower income professions there is clear reasoning why men take home the higher wages. There could be further physiological reasons based on sexual dimorphism that has a play in the professions each gender prefers which are out of scope for this article.


The final factor is location. Where you work has an impact on the gender wage gap, with those working in London receiving higher wages [2, p23] than other areas.

“The regional earnings distribution differs by sex. While weekly earnings were highest in London for both sexes in 2013, earnings for men were lowest in Northern Ireland, at £477, and for women they were lowest in the East Midlands, at £409” [2, p23].

Although this does not taken the local workforce or standard of living into account [2, p23].

The number of women in a given location in work is impacted by the local ethnic population, and associated traditions, and if the area is linked to a university [5, p7], in which more women will be studying rather than in work.


This is by no means an exhaustive list. Nor does it go into reasons why women tend to be the main carer for dependants (although like it likely to be linked to cultural norms and sexual dimorphism).

These factors indicate that the challenge of obtaining Economic Equality of Outcome for the genders maybe hugely challenging, as it appear personal choice plays a huge role, be it who raises the children or what occupation is chosen and thus what education is sought.

Suggested solutions on how the Gender Wage Gap could be influenced will be discussed in a follow on article.

Although the UK was concentrated on here, the US did a similar analysis and stated: 

“There are observable differences in the attributes of men and women that account for most of the wage gap. Statistical analysis that includes those variables has produced results that collectively account for between 65.1 and 76.4 percent of a raw gender wage gap of 20.4 percent, and thereby leave an adjusted gender wage gap that is between 4.8 and 7.1 percent.

These variables include:
  • A greater percentage of women than men tend to work part-time. Part-time work tends to pay less than full-time work. 
  • A greater percentage of women than men tend to leave the labor force for child birth, child care and elder care. Some of the wage gap is explained by the percentage of women who were not in the labor force during previous years, the age of women, and the number of children in the home. 
  • Women, especially working mothers, tend to value “family friendly” workplace policies more than men. Some of the wage gap is explained by industry and occupation, particularly, the percentage of women who work in the industry and occupation.” [7, p1-2] 
Their conclusions generally support those highlighted here, and thus demonstrates that both the UK and US have a similar problem to solve, assuming the gender wage gap is the key measure and Equal Outcome between the genders is the primary goal.


Although many factors have arisen the major ones are:
  • how the measurement is made
  • choosing to be the main caregiver to dependants
  • full or part time work
  • hours and overtime worked
  • choice of education
  • choice of occupation
  • experience in a chosen occupation
  • location
In the article in this series I will discuss how Equality of Outcome can be achieved by making suggestion on controlling measures the Government may have to implement and some of the wider impacts of chasing Outcome Equality.


[1] UK Additional Gender Pay Gap 2014:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/295833/Analysis_of_the_Gender_Pay_Gap.pdf accessed 09/08/2015

[2] UK Earnings by Gender 1997 to 2013:
http://www.ons.gov.uk/ons/dcp171766_353368.pdf accessed 09/08/2015

[3] UK Deaths in the Workplace 2015:
http://www.hse.gov.uk/statistics/pdf/fatalinjuries.pdf accessed 09/08/2015

[4] Migrant Seasonal Workers:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/257242/migrant-seasonal-workers.pdf accessed 09/08/2015]

[5] UK Women in the labour market 1971 - 2013: http://www.ons.gov.uk/ons/dcp171776_328352.pdf accessed 09/08/2015

[6] The Guardian breakdown of subjects studied by gender:
http://www.theguardian.com/education/table/2010/jul/13/female-students-choice-science-subjects accessed 09/08/2015

[7] US Wage Gap Explained:
http://www.consad.com/content/reports/Gender%20Wage%20Gap%20Final%20Report.pdf accessed 09/08/2015

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