FoI to Bank of England

Yesterday’s post about staff makeup at the BBC was inspired because I was preparing a Freedom of Information request to the Bank of England (BoE).

The Women’s Room (TWR) (great initiative – encourage all women to join! (I’ve also been on at gender-variant people too)) have been petitioning the BoE to reconsider their decision to remove the only woman currently on English banknotes apart from the queen. As the BoE has given a completely inadequate response, TWR are now fundraising to launch a legal challenge on the basis that BoE has not fulfilled it’s obligations under the Equality Act.

I got to wondering about how many women work for the organisation. It seems most likely that their board of middle-aged, wealthy, white men, simply failed to consider equality issues when making the decision about who to next put on a banknote. Would an organisation with a diverse workforce make the same mistake? It will be interesting to see how much information the BoE holds, and what their employment policy is.

FoI request submitted on 13 June 2013, will provide an update when I receive a response, or after 20 days. Text of request is:

I would like to make a Freedom of Information request for the
information held on the staff makeup at the Bank of England (BOE).
My specific enquiries are as follows:

1) Information held on the number of males and the number of
females employed full time as staff by the BOE, their seniority of
position and salary band

2) Information held on the representation of different age, gender,
ethnicity, religion, and disability within staff and freelancers
(if applicable) of the BOE.

3) Any details of Equality Impact Assessments (EIAs) that were
conducted under the three previous public sector equality duties
(race equality duty, gender equality duty and disability equality
duty), which were in force until April 2011 when the new single
public sector equality duty (PSED) came into force and replaced
them.

4) The BOE Human Resources Policy on Equal Opportunity of
Employment and details of hiring procedures to ensure employment on
basis of merit and equality of opportunity

 

Employment at the BBC

I was perusing BBC Freedom of Information requests (as you do) which are helpfully compiled for all to see. And have compiled these stats:

Staff makeup
Majority (British, English, Scottish, Welsh white): 82.6%
BME: 12.3%
Other white: 5.1%

Disciplinary & grievances cases by ethnicity:
Majority 60%  (78)
BME 35%  (46)
Other white 2%  (2)
Unknown 3%   (4)

BME people are 12% of staff but 35% of grievances? That is a HUGE disparity, and something I hope the BBC is actively working on.

Average salaries by gender:
Male £41,816
Female £36,827
For top two salary bands (£190k+), 38 men and 7 women.

So it seems to me there is a LOT to do on equalities work at the BBC (and I would imagine, most public-sector organisations). I just wonder how we can expect profit-driven private companies to fully enact equality legislation and reduce discrimination, when even our greatest public organisations can’t do it?


Big edit to add actual proper statistical analysis from the lovely Brian (oh how helpful it is to have a statistician to hand!). In his words:

As you suspected the Grievance/Displinary incidence is not independent of the ethnicity of the employee as the statistical Chi-squared test for a contigency table is highly significant (p<0.0001).

Assumptions:
Assume each Grievance/Displinary incident is a different person.
Exclude unknowns
Look at BME vs Other

If anyone would like to look at the data, let me know, but everything you need to run the same tests is available via the links above.

It would be really interesting to find out more about this relationship and try to pick out what it is that gives such a higher incidence of grievance/disciplinary rate for BME staff at the BBC. That’ll be for another day and another FOI, I guess…

A terrible use of data

Last week, I got into a bit of a heated discussion with an admin on the facebook Vegonews[1] page. They had shared a graph, attributed to the website www.diseaseproof.com (though I’ve been unable to find it there), which I think is clearly designed to suggest a causative relationship where the data simply does not show one.

Here is the graph:

Graph attributed to diseaseproof.com which shows percentage of calories from unrefined plant foods and percentage of deaths from heart disease and cancer for the countries Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, "Korea", Thailand and Laos.. It appears to show that as plant food consumption increases, the risk of dying from heart disease and cancer decreases.

Graph attributed to diseaseproof.com which shows percentage of calories from unrefined plant foods and percentage of deaths from heart disease and cancer for the countries Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, “Korea”, Thailand and Laos.. It appears to show that as plant food consumption increases, the risk of dying from heart disease and cancer decreases.

Apart from the scaremongering “KILLER DISEASES” title, the first thing that struck me upon looking at this graph was that the countries on the left generally have a much higher living standard than those on the right, so people in those countries probably live longer, and thus are more likely to develop diseases such as heart disease and cancer, which tend to affect more people later in life. But that was just a hunch, and if I’m critiquing someone else’s use of data, I should probably have my own to counter with. So I headed over to http://esds.ac.uk/international/ and opened up the World Bank macro dataset “World Development Indicators[2]”. After about five minutes of selecting and downloading data, I had the following information:

Country  Life expectancy at birth (years)  GDP per capita, PPP (2005 international $)[3]
Hungary           74       16,958
United States           78       42,297
Belgium           80       32,808
Sweden           81       33,771
Finland           80       31,493
Portugal           79       21,660
Venezuela, RB           74       10,973
Greece           80       24,206
Mexico           77       12,441
Korea, Dem. Rep.           69  ..
Korea, Rep.           81       27,027
Thailand           74        7,673
Lao PDR           67        2,288

As you can see, I also included GDP for comparison. GDP as an indicator of development is massively abused, and is something I think we should be moving away from as much as possible[4], but for a quick exercise such as this, I think it is an acceptable shorthand for “can the average person afford to get enough food?”

I’ve also included both Koreas, as the original graph-designer somehow, astonishingly, neglected to specify which one they meant. Is it the famously secretive, dictatorial North Korea, with a life expectancy of 69 and not enough data for the World Bank to estimate their GDP? (though Wikipedia handily estimates it at $2.4k per capita). Or is it the democratic, high-standard-of-living South Korea, where you can expect to live to the ripe old age of 81?

Here’s my graph:

Graph showing life expectancy and GDP for the same countries as the previous graph,  Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, Korea (South and North), Thailand and Lao. There is generally a higher GDP and life expectancy for the countries on the left, but no strong trend.

Graph showing life expectancy and GDP for the same countries as the previous graph, Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, Korea (South and North), Thailand and Lao. There is generally a higher GDP and life expectancy for the countries on the left, but no strong trend.

Not the most conclusive graph in the world, but then I would say the same for the original, and sadly I’m sure there are many people who took it at face value. I took a couple of quick averages, splitting the countries into left-of-Greece (where we eat too little unrefined plant foods and die of heart disease and cancer) and right-of-Greece (where we eat nothing but vegetables and nobody gets cancer!). (I excluded both Koreas from this).

Left average life expectancy = 78, GDP = $27k

Right average life expectancy = 73, GDP = $7k

So the question becomes – would you rather die of heart disease at 78, or of something else (starvation, diarrhoea, pneumonia) at 73?

But the thing that most grates about this graph is the apparently random selection of countries. If you have enough data points (e.g. countries) then you can select the ones you want to make a relationship look like it exists where it doesn’t. So I undertook a similar exercise, and downloaded data for all 220 countries available from the World Bank on forested area (as a percentage of total land area) and risk of maternal death (% over a lifetime). And behold, I have found a terrible relationship! We must plant trees in order to save the poor mothers!

Graph of forested area as a percentage of total land area and likelihood of maternal death for the countries Brazil, Peru, Panama, Ecuador, Vanuatu, Nepal, India, Madagascar, Ghana, Burkina Faso, Rwanda, Uganda and Mali. The trends appear to show that as forested area decreases, the risk of maternal death increases.

Graph of forested area as a percentage of total land area and likelihood of maternal death for the countries Brazil, Peru, Panama, Ecuador, Vanuatu, Nepal, India, Madagascar, Ghana, Burkina Faso, Rwanda, Uganda and Mali. The trends appear to show that as forested area decreases, the risk of maternal death increases.

(I’d like to say that I didn’t spend a lot of time on this graph, but that would be a lie. It’s actually quite engrossing seeing what you can do once you decide your intention is to abuse the data).

Edited to add:

BadgerBrian points out in the comments that a scatterplot can be a much better visual tool for identifying whether there is a relationship between two variables. His graph here of the GDP and life expectancy of the 12 countries originally mentioned demonstrates this well: there is a strong positive correlation between increasing GDP and increasing life expectancy up until about $25k, then it flattens out (and the USA does a stellar job of having very high income and quite underwhelming life expectancy!) I’ve not been able to get the graph to display in the comments so here it is:

Scatterplot of GDP vs life expectancy for Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, Thailand and Lao. Graph shows positive correlation between the variables up until $25k, where the relationship flattens out

Scatterplot of GDP vs life expectancy for Hungary, USA, Belgium, Sweden, Finland, Portugal, Venezuela, Greece, Mexico, Thailand and Lao.
Graph shows positive correlation between the variables up until $25k, where the relationship flattens out


[1] “Vegonews is about sharing the most up to date information on veg*nism, and to spread the immense benefits of a healthy and natural lifestyle. http://vegonews.com/”

[2] World Bank (2012): World Development Indicators (Edition: April 2012). ESDS International, University of Manchester. DOI: http://dx.doi.org/10.5257/wb/wdi/2012-04

[3] In my original comment on facebook, I used GDP per capita, constant 2000 US$. I’ve changed this for PPP – Purchasing Power Parity, where the dollar amount of GDP is adjusted to reflect how much it actually costs to afford certain products in that particular country.

[4] For example, I argued at an Oxfam meeting last year that income should only be used as an measurement of a broader dimension “livelihoods”, rather than be a dimension itself, and will hopefully be using that in the framework for my PhD.

Intro to cis and why having to write this annoys me

Louise Mensch said yesterday on twitter that ‘cis’ is “an offensive term that I don’t recognise”[1]. This inspired the tag #ThingsMoreOffensiveThanCis which is about equal parts hilarious-things-that-aren’t-offensive and really poignant transphobia.

But, I accept that there are people who may not yet have heard the term ‘cis’ and may want an intro. The briefest definition I can give is: Do you know what ‘trans’ means? It’s the opposite of that[2].

The main reason I’m offended by the constant questioning of ‘cis’ and people calling it an abusive term, is that it suggests that when we talk about gender, cisgender people are automatically ‘normal’, and transgender people are to be singled out. It posits cisgenderism as the default. As many homo- and bisexual people have said over the years to heterosexual people: you’re not normal, you’re just common.

In fact, just about every argument against using ‘cis’ has a homologue in the use of ‘straight’ as the opposite of ‘gay’. If ‘straight’ were to be challenged in any of these ways, it would be seen immediately as homophobic.

These arguments, laid out by CN Lester (and each well-defeated in the space of a tweet) boil down to the following:

1) Cis is a new word and I don’t know it

Well yes. That’s the nature of language, it evolves. You didn’t know what “smartphone” meant ten years ago and yet you’re probably using one now. It’s also not new, having been used since at least 1994 online, and in peer-reviewed work since the late nineties[3]. The reason you don’t recognise it is because you have not been actively involved in debates around gender. Thankfully these discussions are becoming more mainstream now.

2) I don’t ‘identify’ as cis

I have never particularly identified as white[4] but that doesn’t mean that I don’t experience white privilege. I am able to see that my whiteness means that I am not subject to the same racist forces which deny opportunities to people of colour. If you lived in a society where you were regularly being told that you were trans, but did not feel trans, I suspect you would identify strongly as cis. This is again because we have been trained to equate cis with normal. Once you accept that it is not the default state, it becomes easier to identify with this label as a neutral term, without any assumption of shame or pride.

3) You shouldn’t label me without my consent

Why not? I’m sure you have no problem with me labelling you as human, as literate, as an English-speaker. The issue with consent here stems from you not liking the concept of cis. Which goes back to wanting it to be the default state which doesn’t require asserting. Whether you personally identify with the label, get a tshirt printed and go out declaring your cis pride is up to you. The fact that you are cis is just that, a statement of fact.

4) Why can’t I say ‘non-trans’?

Because, again, this suggests that ‘non-trans’ is a default state and thus that trans people are abnormal. How about black and non-black; or woman and non-woman? Do those make sense to you?

5) Why can’t I just be a woman/a man?

Because trans people are often denied that opportunity. And I highly doubt you ever identify as just a woman or a man. What about your sexuality? Your politics? Your job? Your relationship status? Your religion? These are all single facets of your identity, and cis is just another one.

It is important that we are able to distinguish characteristics like this in order to be able to discuss them. There is serious, often deadly, discrimination facing many trans people, and that discrimination needs to be dissected and destroyed. In order to do that we need to be able to separate trans peoples’ experience from cis peoples’. Referring to ‘people’ and ‘trans people’ would be hugely othering[5].

6) It’s all part of your trendy online clique!

As I said in point 1, the word has been around for a long time. But the spread of the internet, forums, blogs and twitter has the specific advantage of helping to level the playing field. Everyone with access to a computer/smartphone and an internet connection (which is not everyone, by a long shot) is able to have their say. Thus the people with the large platforms in traditional media are pulled up when they say offensive things. Things like “cis is an offensive word”.

7) Cis is too hard to explain!

It’s the opposite of trans. If you understand trans then you can explain cis. If you want more, it means someone whose gender identity is the same as the one which was assigned to them at birth.

If you’re into etymology, cis has its roots in Latin; meaning “on this side of”, in comparison with trans meaning “across, on the far side, beyond”.


Really, I can’t think of any reason that someone could be genuinely offended by the term cis, unless they are deeply attached to the idea of trans people being some sort of special kind of sub-set of a given gender. It is simply and literally the antonym of trans. If cis is offensive, then so is trans.

I’m incredibly happy to see how many cis people are standing up now and saying that they recognise the need for this term to become mainstream, and that it is clearly not offensive. I just wish that more of the cis people with the largest platforms would get on board too.



[1] Being a bit facetious, at the time I went with the ‘don’t understand’ meaning of ‘don’t recognise’ and questioned how someone can be offended by something they don’t understand. I still think it’s a valid point. Go ahead and refuse to recognise a word, but you can’t also be offended by it.

[2] This entire post relies on the gender binary. It is definitely more complicated than that, but I’m aiming for an intro here. Apologies to those excluded.

[3] Link is to use of ‘cissexual’ as analogue to ‘transsexual’

[4] Largely due to the fact that every person I’ve seen with a strong white identity is also a massive racist *waves to EDL, BNP, SDL etc.*

[5] Othering = to other, make seem different and sub-human. A tactic used far and wide to justify discrimination.

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European Parliament Info(less) Graphic

I saw this image on facebook, on the European Parliament’s page. They describe it as an “infography” but I would argue that it is just a pretty picture with some statistics written on.

European Parliament poster on gender equality which simply lists the following statistics: Men: CEOs 98%, Executive board members 91%, Employment rate 76%, University graduates 40%, Working parents 90%, Part-time workers 25%, Average salary  € 34,377.00 Women: CEOs 2%, Executive board members 9%, Employment rate 63%, University graduates 60%, Working parents 66%, Part-time workers 75%, Average salary   € 26,390.00

European Parliament poster on gender equality which simply lists the following statistics: Men: CEOs 98%, Executive board members 91%, Employment rate 76%, University graduates 40%, Working parents 90%, Part-time workers 25%, Average salary € 34,377.00
Women: CEOs 2%, Executive board members 9%, Employment rate 63%, University graduates 60%, Working parents 66%, Part-time workers 75%, Average salary € 26,390.00

The point of infographics is that it is easier for our brains to parse information in visual form rather than as text. I would argue this is especially true of the sorts of images that pop up on our news feeds and that  we might only glance at for a few seconds before we move on. It is far less impactful for me to say that only 2.4% of CEOs are women and 97.6% are men, than simply to show this graph:

Stacked bar chart showing CEOs by gender: 2.4% women and 97.6% men

Stacked bar chart showing CEOs by gender: 2.4% women and 97.6% men

So I took it upon myself to copy out their statistics and, in the quickest, dirtiest way possible, put some visuals into their image. I sincerely apologise for how ugly this is, it was achieved using Excel, Paint and Publisher in the shortest time possible (I should really be studying). A designer I am not. But I find it astonishing that any designer would go to the trouble of making the poster and not including the data in a visual format:

European Parliament poster on gender equality with graphical representation of statistics included. For salary, the data point for men (€ 34,377 is taken to be 100%)

European Parliament poster on gender equality with graphical representation of statistics included. For salary, the data point for men (€ 34,377) is taken to be 100%.