Gender Pay Gaps - Myths

Myths about the Gender Pay Gap

Warning: this post contains mathematics. I apologise to those of you who find this intimidating but I make statements that follow mathematically from the definition of the gender pay gap and it is important that I give my reasoning so that those who are not put off by a bit of straightforward algebra can check it. If you can not read the equations and want to, try this link.

The important conclusion is that an institutional gender pay gap is an incomplete and ambiguous measure of inequality. It is incomplete because the gender pay gap can be small or zero even when the overall proportion of women in the workplace is low. We therefore need to know the proportion of women in the workplace as well as the gender pay gap. It is ambiguous because while if the proportion of women in each salary interval falls as the salary increases then the pay gap is non-zero it is possible for the pay gap to be small or zero and the proportion of women by salary interval to still have undesirable features such as a lack of women at the highest levels. In addition, because the gender pay gap compounds structural factors that are common to both men and women, namely, the salary scale and the number of people in each salary interval, with an inequality factor, namely, how the proportion of women varies with salary, it is difficult to compare different workplaces unless the structural factors are similar.

For these reasons the minimum information required to make sense of a gender pay gap is

• the number of women and the number of men (number rather than proportion since the proportion can be calculated from the numbers and the numbers give an idea of whether the measured gap reflects underlying inequality or just a fluctuation or contingency)
• the average salary for women
• the average salary for men
• the proportion of women by grade or salary

Myth 1: The gender pay gap measures the extent to which women are paid less than men for doing the same job.

There are three contributions to the gender pay gap:
1. Occupational segregation: there are more women in low paid occupations and occupations in which women predominate attract lower pay.
2. Vertical segregation: within an occupation there are more women at lower levels.
3. In some cases women are paid less than men for doing the same job or for work of equivalent value, which is illegal.

There are numerous causes of the gender pay gap, for example, research commissioned by the Government Equalities Office in the UK identifies several factors including differences in years of full time work and the negative effect on wages of having worked part time or taken time out of the labour market to care for a family.

Myth 2: The gender pay gap is a useful indicator of inequality.

The gender pay gap is defined by:

gap = (average pay for men - average pay for women)/(average pay for men)

The average pay for women can be written as:



and the average pay for men as



where is the number of people with salary and is the proportion of them who are women. The number of different salaries (or salary categories) is . The symbol means add all the terms from 1 to together. These formulas work when people are paid on a salary scale or when there are enough people that it makes sense to make a histogram of the number of people in each salary interval.
The total number of people is . The total number of women is , the total number of men is and the overall proportion of women is . This implies that the difference between the average pay for men and the average pay for women can be written as



and the gender pay gap as

.

So, the difference between average pay for men and average pay for women depends on two structural factors, namely, the salary scale and the proportion of jobs at each salary scale point, and an inequality factor, namely, the way in which the proportion of women at each scale point varies with scale point. One implication is that the pay gap will be zero whenever the proportion of women is constant with scale point regardless of what that proportion is. Hence, the pay gap is an incomplete measure of inequality. A workplace with a zero pay gap that has only 10% women is hardly a shining example of gender equality.

From a mathematical point of view we have two equations

, which defines , and



which defines the gap. If we want then we have two equations in  unknowns. This is an under-determined system, unless there are only two steps on the salary scale, so there is the possibility of finding other solutions that give a zero gap besides  for all i. This means that while if  is constant then the gap is zero and if falls systematically as i increases then the gap will be non-zero there could be solutions which have a small or zero gap that nevertheless have undesirable features such as a lack of women at the highest salary levels. The figure below shows an example, which has 220 men and 180 women (45% women among a staff of 400) on an eleven point scale where each point is has a salary 5% greater than the one below starting from £20,000. The average salary for men is £24,923.41 and the average salary for women is £24,901.72, which is a gap of 0.09%. Nevertheless, only 35% of the posts in the top three grades are held by women and only 20% of the posts in the highest grade are held by women. Click here to view the spreadsheet I used to create this figure. The spreadsheet itself is available at this link.

So, as a measure of inequality the gender pay gap is both incomplete and ambiguous.


Myth 3: The overall national pay gap will be eliminated if each workplace eliminates its own pay gap.

Suppose Employer A has a largely female, largely relatively unskilled workforce while Employer B has a largely male, largely skilled or professional workforce. Both employers could eliminate their pay gaps but Employer A would still be paying their predominantly female workforce less on average than Employer B was paying their predominantly male workforce.

Myth 4: The gender pay gap provides a means of comparing inequality across workplaces

As noted in under Myth 2, the gender pay gap depends on two structural factors and an inequality factor. Unless the workplaces have the same salary scale and the same proportion of jobs at each salary scale point it is very difficult to draw conclusions about differences in equality in different workplaces. It would also be helpful if there was agreement on whether to divide by the average salary for men or the average salary for women in the expression for the pay gap. It could also be the case that in the example discussed under Myth 3 that Employer B has a gender pay gap that is hard to eliminate due to a shortage of women with the necessary professional qualifications, for example, in engineering, while Employer A is able to eliminate their gap despite the fact that women working for Employer B have higher average salaries than women working for employer B.

Uk Equality Legislation: Specific Duties Consultation

In a previous post I wrote about different approaches to equality under Teresa Rees’s headings: Tinkering, Tailoring, Transforming. Recent equality legislation in the UK has the potential to be a framework for transforming both workplaces and service delivery to incorporate genuine equality. It also has the potential to create self-sustaining bureaucracies that achieve very little. Which happens depends not on the competence of organisations’ equality and diversity personnel but on the extent to which women, and other groups, avail themselves of the opportunities presented. The consultation on the specific duties is one such opportunity. While responding to consultations can seem like a waste of time, if you do not even attempt to make your views known your voice certainly will not be heard. So, if you live in the UK, download the consultation document from the Government Equalities Office website and respond.

The Equality Act 2010 integrates the former general equality duties that applied to disability, race and sex and extends them to apply to other characteristics such as sexual orientation. The general equality duty requires public authorities, which include universities and research councils, to eliminate discrimination and harassment, advance equality of opportunity and foster good relations between members of different groups. In the context of gender, the Act makes it explicit that advancing equality of opportunity includes removing or minimising disadvantages experienced by women (or men) but not by men (or women), taking steps to meet the needs of women (or men) that are different from those of men (or women), and encouraging women (or men) to participate in public life or any other activity in which participation by women (or men) is disproportionately low. (Note: the Act frames these duties in a way that applies to all characteristics. I have used gender as an example to avoid using the jargon that is required for a more general formulation.)

The specific duties are set by regulation and are intended to provide a framework that ensures that something actually happens. Under previous legislation the specific duties varied. For example, the Race Equality Duty had a detailed prescription for data collection in Higher Education. The Gender Equality Duty required public authorities to gather and use information but had no specific requirements for data collection, other than that the requirement ‘to consider the need to include objectives to address the causes of any gender pay gap’ implies that you actually know what your pay gap is.

The previous specific duties for gender were:

•      To prepare and publish a gender equality scheme, showing how it will meet its general and specific duties and setting out its gender equality objectives.
•       In formulating its overall objectives, to consider the need to include objectives to address the causes of any gender pay gap. 
•       To gather and use information on how the public authority's policies and practices affect gender equality in the workforce and in the delivery of services.
•       To consult stakeholders (i.e. employees, service users and others, including trade unions) and take account of relevant information in order to determine its gender equality objectives.
•       To assess the impact of its current and proposed policies and practices on gender equality.
•       To implement the actions set out in its scheme within three years, unless it is unreasonable or impracticable to do so.
•       To report against the scheme every year and review the scheme at least every three years.

[Source: Gender Equality Duty Code of Practice Gender Equality Duty Code of Practice England and Wales EOC 2006]. It was the responsibility of the Equality and Human Rights Commission (EHRC) to enforce the legislation by issuing guidance and, if necessary, through compliance orders or court orders.

The focus of the proposed new specific duties is on accountability through transparency. Public authorities will be required to publish data that will enable citizens and concerned groups to hold public authorities to account. The EHRC will determine what data should be published though the consultation document mentions the gender pay gap, the proportion of staff from ethnic minority communities and the distribution of disabled employees throughout an organisation’s structure.

Differences from the old specific duty for gender are

• Public authorities will no longer be required to have an equality scheme. Consequently there will no longer be requirements to implement the scheme, to report against the scheme or to review the scheme.
• There will no longer be a specific requirement for consultation but public bodies will be expected to be open about how they have engaged with people.
• There will not be a specific duty requiring equality impact assessments as it is expected that equality impact assessment would form part of normal decision-making. However, the annual publication of equality information will include impact assessments.
• Equality objectives should be reviewed every four years.

Differences from the proposals put forward under the previous government are:

• There will be no national priorities set by the Secretary of State.
• There will be no special focus on procurement as the general and specific duties already apply to all the functions of a public body.
• Public bodies will no longer be required to set out the steps they propose to take in order to achieve equality objectives.

The proposed specific duties are

• Workforce Transparency: Public bodies with 150 or more employees will be required to publish data, to be specified by the EHRC, on equality in their workforces. This is expected to include data on their gender pay gap, the proportion of staff from ethnic minorities and the distribution of disabled employees throughout the organisation’s structure. The data will have to be published at least annually.
• Service Provision: Public bodies will be required to publish data, at least annually, that will enable people to judge how effectively they are eliminating discrimination, advancing equality and fostering good relations through the services they provide.
• Setting objectives: Public bodies will be required, as part of their normal business planning process to set equality outcome objectives that are informed by evidence and that are specific, relevant and measurable. This will enable meaningful scrutiny by citizens and other interested groups. The objectives should be reviewed at least every four years.

The focus on outcomes is welcome. Far too much time and effort has gone into producing plans and then writing reports against those plans in which whatever did happen is presented as though it were what was planned. Trying to minimise the work involved in demonstrating compliance is also welcome. Partly because resources should be directed to achieving aims not demonstrating compliance and partly because equality should be embedded within normal procedures and practices not treated as an optional or externally imposed extra.

My concerns are:

1. Will this ‘meaningful scrutiny by citizens and other interested groups’ actually occur? Are there enough people with the time and resources to carry out this scrutiny? How is it envisaged that such people will hold an institution such as a large, research-intensive university accountable?
2. What data will be required? From a mathematical point of view the institutional gender pay gap is a flawed measure of inequality. However, a lot of people have invested a lot of time and effort into promoting it as a measure of inequality so we are probably stuck with it. The minimum amount of information required to make sense of a gender pay gap is the number of men, the number of women, the average salary of the men, the average salary of the women, and the proportion of women by salary band. It would also be helpful to know if women are disproportionately represented in some occupational groups and, in the context of research and academic staff in universities, whether there are differences by discipline. For workplaces that are large enough for such an exercise to be meaningful, it would be useful to know the proportion of women by salary band and age. This would help distinguish between situations where women are hard done by and something should be done and situations where women were hard done by and something has been done. There is no point in devoting time and resources to fixing something that is not broken. The Equality Challenge Unit, which provides guidance on equality for the higher education sector, has suggestions for the data that higher education institutions should use to inform setting equality objectives in their briefing ‘Revising Gender Equality Schemes’  (January 2010) and the ECU Gender Equality Scheme Self-Assessment Tool.
3. If there is a conflict between presenting data and maintaining the privacy of individuals then privacy should be paramount.
4. Objectives should be realistic and achievable as well as measurable. There is no point aiming for some arbitrary percentage of women among some particular group if that cannot be attained within a reasonable timeframe. It is often forgotten that the most important constraint on how fast the proportion of women among academics can change is the rate at which vacancies occur for them to be appointed to, unless new positions are created. Similarly there is no point aiming to train some proportion of your staff in something-or-other if the resources to deliver the training are not available.
5. What do we mean by measurable? For example, in 2004 women made up 49% of acceptances to Natural Sciences at Cambridge. Five years later in 2009 women made up 40% of acceptances to Natural Sciences at Cambridge (Source: Cambridge University Reporter Undergraduate Admissions Statistics Special Issue, No. 15 2009-2010 and 21 February 2005). Is this a worrying decline or a random fluctuation? Having looked at the numbers, I am inclined to the latter view, though the former is tenable depending on how much the data are tortured. It could also reflect a change in the proportion of acceptances to biological Natural Sciences. Suppose the numbers had been the other way around (i.e. 49% in 2009 and 40% in 2004). Would this be evidence that the University was meeting equality objectives?
6. It is hard to see how an institution could set or achieve equality objectives without consulting with relevant groups. It is very important that institutions should be required to state with whom and how they consulted.
7. Institutions should also be required to state what steps they took to achieve their equality objectives. This would aid the ‘citizens and other interested groups’ to assess whether an institution is building a genuinely equal environment or whether it is just managing the numbers. For example, suppose an institution has reduced its gender pay gap. It would be of interest to know if this had been achieved by making lots of catering assistants and clerical workers redundant or by waiting for other institutions to develop the careers of their female staff and then poaching them. In addition it would be useful to other institutions to help them assess what actions are effective.

The proposed specific duties should enable institutions to embed equality within their organisations. If you have a view on whether or not the proposed specific duties make it more or less likely that this will happen then you should respond to the consultation.

Christchurch Earthquake 2

More on the Christchurch earthquake, also known as the Canterbury earthquake or  the Darfield earthquake...

After much emailing on my husband's part, he and I travelled to Christchurch on Wednesday 8 September to help install sensors to measure accelerations caused by earthquakes in the aftershock sequence following the 7.1 earthquake on 4 September. The project is part of the Quake Catcher Network run from Stanford (qcn.stanford.edu): the Rapid Aftershock Mobilization Program in New Zealand (http://qcn.stanford.edu/ramp/). A Ph.D. Student from Stanford had arrived in Christchurch that morning with 200 sensors in her luggage.

People volunteer to have a sensor in their home for a period of 4-6 weeks. The picture shows one of the sensors. It is secured to the floor using duct tape and glue for a hard floor and duct tape and Velcro for carpet. The cable plugs into a USB port on a computer which has to have BOINC (Berkeley Open Infrastructure for Network Computing)  installed to manage data transfers to the server. Between 8 September and 14 September up to five teams of people from GNS Science, Stanford and the Universities of Auckland and Wellington installed nearly all of the 200 sensors around Christchurch and the surrounding region.

Different parts of the city were affected differently by the shaking. Driving in from the south, we saw very little damage until we reached the central city area where a number of older brick or masonry buildings had been badly damaged. In fact, the three main types of damage were chimneys that either collapsed or became unsafe, older brick or masonry buildings that partially collapsed and problems due to soil liquefaction. When we arrived on 8 September many streets in the central city area were cordoned off. In fact, the serviced apartments where we were staying were inside a cordoned area and we had to be escorted to reception by a soldier. By the time we arrived, water and power had been restored over most of the affected area, though not in some of the most badly affected neighbourhoods and in rural areas. The biggest inconveniences for us were that for the first few days we were not allowed to use the lifts and the internet connections to the rooms were not working properly, possibly because aftershocks were loosening the ethernet cables. By Monday 13 September much of the city was  functioning normally, though a few streets were still closed due to unsafe buildings or continuing demolition.

The response of Christchurch residents to the call for volunteers to host a sensor was amazing. Even those whose houses were undamaged had still had an extraordinarily stressful experience, plus the additional stress of on-going aftershocks, including one of magnitude 5.1 on the morning of 8 September (before we arrived) that caused additional damage.

The GeoNet website has more information about the earthquake, including a video montage of the fault trace reconnaissance and some more on aftershocks. The GNS Science website has more information as well. There is an animation of the aftershock sequence at www.christchurchquakemap.co.nz.

We also recommend the Nobanno Bengali restaurant on the corner of Armagh and Colombo Streets in Christchurch. The food is excellent.

Christchurch Earthquake

The major earthquake near Christchurch , around 350km north of here, woke us at 4.35 this morning. It was strong enough here for me to be concerned about objects falling, though none did. The timing was fortunate, in that not many people were out on the streets. Had they been, there would have been many more casualties. As it is, some people had some amazing escapes. For news stories see TVNZ or BBC.

Tinkering, Tailoring , Transforming

There are two 'big ideas' or explanatory frameworks that made a big impression on me when I first started working in women in SET as opposed to being a woman scientist. One was Teresa Rees's description of different approaches to gender equality as 'Tinkering, Tailoring and Transforming'.

Tinkering refers to an approach based on equal treatment. This is the approach that underlay legislation such as the Sex Discrimination Act (1975) and the Equal Pay Act (1970): it is illegal to treat someone less favourably on the grounds of sex.

The second approach recognizes that equal treatment may not be sufficient to achieve equality: deeply engrained differences make it essential to take action to tackle disadvantages. This is the ethos behind positive action and Rees refers to it as tailoring. The focus of much 'tailoring' activity is adjusting women to accommodate existing structures and processes, for example, 'Women into management' courses.

Transforming sees differences not as a problem to be overcome but as something to be embraced for mutual benefit. In this approach the focus is on adjusting structures and processes to accommodate differences or mainstreaming.

Clearly, institutions must comply with the legal requirement not to discriminate. Also, most people think that equal treatment is fair.

Positive action can be very powerful, especially initiatives that encourage women to advance in their careers while embracing their identities as women. The disadvantage is that it can lead to a focus on 'deficit model' or 'male as norm' approaches in which the problem is seen as being that women are not men with the solution being to 'fix the women' by encouraging them to act more like men. (Note that 'deficit model' is used with two quite different meanings in the literature. For example, Sonnert and Holton, American Scientist 84 (1996) 63-71, see http://www.aps.org/programs/women/workshops/upload/sonnert1.pdf, define the 'deficit model' to mean that women receive fewer chances and opportunities along their career paths as a result of legal, political or social structural obstacles. On the other hand, Carol B. Muller, founder of MentorNet  refers to the 'deficit model' as the assumption that women lack something - ability, experience, interest, inspiration, motivation - that they need to succeed, see Pan-Organizational Summit on the U.S. Science and Engineering Workforce (2003)). Positive action measures can also lead to resentment both among men who feel that women are being given an unfair advantage and among women who feel that they are being labelled as in need of remedial help.

Rees describes the difference between positive action and mainstreaming as

'Rather than helping round women fit into square holes, it makes those holes more adaptable – to take all sizes and shapes.'

The advantage of mainstreaming is that it embeds equality within the organization rather than seeing it as an optional, or externally imposed, extra. The process of embedding equality may lead to resentment: some are unhappy with any measure that goes beyond equal treatment, some will interpret changes as special treatment, and some have a deeply held belief that employees ought to mould themselves to their employer's requirements. The principal disadvantage is that it is difficult to achieve. Inequality results from a large number of interacting factors. Identifying issues and appropriate actions is difficult; monitoring progress on any useful timescale near impossible.

All three approaches are necessary. It is important that people be treated fairly. It is necessary that women should be empowered to succeed within the current structures and processes. We can't wait for them to be fixed. To achieve genuine equality structures and processes have to change.

Yesterday evening I went to a Dunedin AWIS meeting at which three scientists spoke about their careers. One of the things they had been asked to do was comment on things they like and things they dislike about their jobs. Listening to their remarks set me thinking about what were the things I liked and disliked about working in women in SET. So here they are:

Likes (in no particular order)

• The occasions when someone told me, either via a feedback form or in person, that an event I had facilitated made a difference to her life.
• The chance to meet some amazing women. Not just the high-fliers; some of the women with less stellar careers have inspirational stories of courage and persistence.
• The intellectual challenge. Understanding the issues for women in SET involves thinking about interacting people which is a much more difficult problem than the interacting electrons I was accustomed to. However, extracting signal from noise is a problem both activities have in common and there are interesting experimental data and explanatory frameworks in women in SET, see, for example, Virgina Valian’s ‘Why so slow? The advancement of women?’
• The variety and opportunity to develop a wide range of skills: I could be facilitating a personal development course one day, analysing data the next and representing the University at a meeting the following day.
• Being a Springboard trainer.
• Believing that the ultimate aim of what I was doing was to make the University a place where women have an equal opportunity with men to fulfil their potential in SET, even if they also choose to live with a partner and have children. (I also believe that many of the measures that are required for this to happen would make the University a better place for everyone.)

Dislikes (in no particular order)

• The fact that progress happens in very small incremental steps. This is true in science as well but at least you can package your small incremental step in a paper.
• A preference in administration for structure and process over function and outcome.
• A general tendency to keep starting again from scratch instead of learning from and building on what has gone before. In science you usually do a literature search to see what is already known before you start designing experiments. In women in SET it seems to be more normal not just to re-invent the wheel but to re-invent heptagonal wheels.
• The difficulty of obtaining relevant data.
• People who ignore or misinterpret data.
• Poorly defined requests for data or requests that were framed in ways that may have made sense to the requester but certainly didn’t to me.
• Zombie arguments – arguments that continue to surface no matter how many times they are rebutted. (See, for example, Isis on John Tierney.]
• Action Plans. As far as I can tell, the appearance of an action in an ‘Action Plan’ is pretty much a guarantee that it won’t happen. (Reports on action plans become exercises in presenting the things that did happen as though they were the things that were planned to happen - No plan of operations extends with certainty beyond the first encounter with the enemy's main strength (Helmuth von Moltke the Elder).)
• A variant of the Snark syndrome in which if women’s lived experience conflicts with prevailing wisdom on issues for women in SET it is the women’s experience that is discounted.

I have listed more dislikes than likes. That does not reflect my actual experience. I recently read The CEO and the Monk: One Company’s Journey to Profit and Purpose by Robert B. Catell (the CEO), Kenny Moore (the Monk) and Glenn Rifkin (business journalist) (Wiley, 2004). Moore describes sending a note of encouragement to someone who wanted to do something unusual with the quote

‘Don’t ask yourself what the world needs, ask yourself what makes you come alive. And then go do that. Because what the world needs are people who have come alive.’

(according to Wikipedia this quote is attributed to Howard Thurman).

Working in women in SET made me come alive.

The Snark Syndrome

In my local public library I came across 'Women and Science: The Snark Syndrome' by Eileen Byrne, Professor of Education at the University of Queensland. It was published in 1993 and describes the results of a review of research and policy regarding women in science in Australia in in the mid-1980s. The title is taken from Lewis Carroll's 'The Hunting of the Snark':

'Just the place for a Snark!' the Bellman cried,
As he landed his crew with care;
Supporting each man at the top of the tide
By a finger entwined in his hair.

'Just the place for a Snark! I have said it twice:
That alone should encourage the crew.
Just the place for a Snark! I have said it thrice:
What I tell you three times is true'.

Having noted that a great deal of the received wisdom in the area of women in science is still based on assumptions, beliefs and prejudices operating at the level of superstition noted by Hypatia (between 350and 370 – 415 AD), Byrne defines the Snark Syndrome as the assertion of an alleged truth or belief or principle as the basis for policy or practice that neither has a basis in sound empirical research nor is consonant with established theory. She goes on to describe the Snark effect which requires firstly that the educator, teacher or policy-maker has internalized an assertion from hearing it being constantly repeated ('What I tell you three times is true') when the asserted belief is either unfounded or only occasionally and contextually true and secondly that the internalized belief is used to justify and implement major policies.

The research was focussed on the recruitment of women to undergraduate courses in science and engineering and retention to post-graduate, in particular, Ph.D. Courses. The researchers identified ten core factors:

• same-sex role models for women
• the mentor process
• the image of different branches of science and technology (male, female or sex-neutral; socially responsible or systems- and machine-oriented)
• male attitudes to females in 'non-traditional' disciplines; female attitudes (self-esteem, or towards peers)
• single-sex versus co-education
• prerequisites and school patterns of curricular choices as critical filters
• mathematics as a negative critical filter
• careers education and vocational counselling
• women's support networks
• affirmative action projects in science and technology

The research reported in the book covers role-modelling, mentorship, attitudes, image, mathematics as a critical filter and single-sex schooling versus co-education. As well as reviewing previous work the researchers gathered data on women in science and engineering in ten institutions in Australia and also both circulated papers to staff for their response and carried out group interviews with staff.

One of the factors that was particularly affected by the Snark syndrome was role-modelling. They distinguished two hypotheses. The first was:
• same-sex role-modelling is an important influence on breaking the stereotypes of ascribed masculinity and femininity in the vocational setting of curricular choice and of career aspiration.

They concluded that the research tended to support this hypothesis.

The second hypothesis was stronger:
• the acquisition of more female staff in a given discipline will, in itself, result in an increase in female students.

They concluded that this hypothesis is not supported by sound empirical evidence and nor is it consistent with well-grounded rigorous theory. One of their suggestions is that there needs to be a critical mass of women in a particular role for that role to be seen as 'sex-normal'.

However, when they analyzed the views of academic staff they found that, of those who said there were visible women in their discipline, almost all assumed that their mere presence would cause a same-sex modelling process to take place for female students. Furthermore some believed strongly that same sex role models were essential while others argued for the equal value of opposite-sex role-models. The researchers note:

'Both views were frequently described in terms of secure belief without any evidential basis for the belief. The strength of the convictions was inversely correlated with the presence of any factual basis.'

They also noted that role-modelling tended to be confused with mentoring.

Byrne suggests two policy consequences of the widespread but unsupported belief that the presence of women staff would increase enrollments by women. First, it provides an alibi for male inaction:

'It is significant that almost all the proposals put forward both in interviews and in writing also involved women taking on more work, but no traceable expected change on the part of men.'

Secondly, active role-modelling wastes women's scarce time. This does not mean that women should not be visible in the normal course of their work on committees, at public events and forums and as delegates to meetings and conferences. Simply that they should not be asked to participate in additional activities aimed at providing role models for secondary school girls:

'Grants and project money spent on ferrying untypical women to small functions without the context of an overall strategy to attack sex-role stereotyping in books, careers materials and the visual media is likely to be a total waste of scarce public money'.

Byrne also reports some of the reactions to the study:

'This is, of course, exactly the kind of garbage I associate the feminist movement with, and I hope you do not really expect me to waste my time reading it and trying to figure out what all these nonsensical questions mean! It is bad enough that we have to pay tax so that the government can employ people to produce this sort of rubbish; you can't expect me to also spend time on it.'

Policy based on clear definitions, logic and empirical evidence! What will these feminists want next?

The ability to judge the contents of a document without actually reading it is, of course, widespread in academia.

She finishes by telling a story about a conversation during one of the group interviews:

After reading the first four discussion papers in advance, and listening to the group discussion of the issues raised, a Professor from a discipline in which girls were well into the 'abnormal/rubric of exceptions' minority, said: 'Professor Byrne, I have a problem. You are two women directing this project. Do you not think that this invalidates the results?'

After a moment's stunned silence, I replied, 'Professor X, let me be clear what question you are asking. You are saying that because we do not have a mixed-sex research team, our research into these issues is invalid? Presumably you will accept that, then, 90% of scientific research so far is invalid because it has been conducted exclusively by men?'

He shook his head uncertainly.

'I'm sorry. You are saying that because we are women, we are less able or well qualified and need what Simone de Beauvoir termed a “male mediator between us and the Universe”?

He hastily protested that our qualifications and experience were impressive.

'I'm sorry to have misunderstood again. You are saying that because we are women, even if our research is in fact sound, no one will listen to us, simply because we are women?'

As the Professor struggled to come to terms with that, a colleague came to his rescue. 'I think what my colleague is saying, Professor Byrne, is that it would be a pity if so much wide-ranging and substantially funded research on so important an issue, were not influential because …' His voice died away.

I said quietly, 'So you are in fact saying that he believes that however right women are, they cannot be listened to with the same scholarly clout as men?'

That was in 1986, nearly a quarter of a century ago. Have things changed since then? Many policies intended to increase the numbers of women entering, staying in and progressing in SET are still based on the Snark Effect, that is, on internalized beliefs that are unsupported by evidence. In fact, these internalized beliefs are often impervious to facts. If the facts do not support the belief people look for reasons why the facts are wrong. For example, if the figures show that women are just as likely to be promoted as men it must be because the figures are based on an incorrect definition of who is eligible for promotion since it is well known that women are less likely to be promoted than men. I am not sure that the reaction that research on women in science and engineering cannot be objective if it is carried out by women, though of course, it would be objective if carried out by men, has entirely died out, either.