Statistics and Gender


Statistics, the collation, analysis and ultimately, the interpretation of data, have never been easy – at least for me. They have never reached the level of intuitive and, indeed, have barely climbed past manipulative in my head. And I readily admit to occasional cognitive dissonance even when they are used to support what I already believe. Or, rather, want to believe… I wonder if the sources from which I have accessed the numbers might be those that already pander to my own biases. In the cloud of assertions that cover me, everything is obscure and up for grabs.

I suppose it’s like that for us all, though –we hear what seems important to us and sift clumsily through the rest, filing most of it somewhere else, if at all. Especially if what has been measured is not crystal clear –or at least what has been reported is not. A classic example was that of a survey of shared parental leave in the UK. It was initially reported that only 1% of men were opting for this –much less than the rest of Europe. In fact, however, the figure reported was 1% of all men, not 1% of men who had just had a baby.

We have to examine what we read before we arrive at our conclusions; most of us don’t. Most of us have neither the interest, nor the tools to know if what is presented to us is reasonable, or at least free of bias – especially our own confirmation biases. A lot slips through the net.

A good example of this are the statistics on women and girls: http://www.bbc.com/news/magazine-36314061

‘There is a black hole in our knowledge of women and girls around the world. They are often missing from official statistics, and areas of their lives are ignored completely.’ For example, a record of their participation in the labour force in various countries. The data are often biased towards employment in the formal sector, which in those countries, is where men work. ‘Buvinic [an expert from the Center for Global Development, a think tank] argues that many women get missed out because they consider themselves primarily as housewives, when in reality they work on farms, do part-time jobs and seasonal work or run their own businesses.’

‘There are other problems too, Buvinic says. Not all countries collect statistics on other aspects of women’s lives, such as domestic violence or maternal mortality rates, and when they do collect this data they often do it in different ways, making international comparisons difficult.’ And, ‘There are many statistics that are collected without being broken down by sex, which makes it hard to tell when women are not being treated equally.’ For example, “Until recently, very few banks disaggregated their customer data by sex, leading to difficulties in understanding reasons behind the persistent gender gap in access to and use of financial services,” says Megan O’Donnell, one of Buvinic’s colleagues at the Center for Global Development.’

That I find all of this surprising speaks to my naïveté, I suppose, and yet I have my doubts that many of us would take the time from our busy lives to consider what this neglect might mean. David McNair – Director of Transparency at the One Campaign, a group that fights poverty- even uses the weighted ‘sexist’ epithet and summarizes the problem succinctly: “The reason why it is sexist is that women and girls are disproportionately left out of data collection. They are uncounted, therefore they don’t matter.”

Roughly half the population on the planet doesn’t matter? And it’s the half that has gestated and succoured that other half -the only half that is counted? Even if I try my best not to be an historical revisionist, it does not make sense to me.  Perhaps McNair, again, had the best explanation: “If you have robust data then you can be held to account for your decisions. There are people who have a vested interest in not having that information in the public domain.”

But I suppose we have to look for any encouraging little cracks in the imposing male edifice: ‘Recently the UN’s International Labour Organisation (or ILO) held a conference, where labour statisticians agreed how to start collecting data on unpaid and domestic work, for example time spent cleaning your house. Ten countries have volunteered to take part in a pilot to use this new framework to measure unpaid work.’

Whoa, ten countries have decided to put their toes in the water…? Or rather, their statisticians in the water? How brave. Maybe Macbeth was on to something when he said that ‘tomorrow creeps in this petty pace from day to day.’ It’s the end of his soliloquy that has me worried though: ‘It is a tale told by an idiot, full of sound and fury, signifying nothing.’

Oh, I hope not…

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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