Still other examples show that even when there are explicit efforts to be fair, underlying gender differences lead to outcomes that are not fair. Decreeing equal space for men’s and women’s public toilets may seem fair, but not only do space requirements differ, the frequency and length of use differs as well – leading to the long lines seen at many public settings. Another example comes from support for academic careers. Even when men and women are given what is intended as equal support to pursue research, women are more likely to be also carrying a burden of child care, meaning that providing equal resources in time and support does not translate into equal levels of assistance – the playing field remains uneven.

Criado Perez does not claim that there are malign intentions behind most of these examples. Rather, what she describes is the result of “the default male”—that time and again, decisions are made by assuming that what works for an average man is sufficient, as if the needs and requirements of men are a good proxy for all humans. And, maddeningly, when a bias is revealed, the response is – implicitly at least – to suggest that the problem is with the person (usually female) who does not conform to the expectations. Women are often treated as a special case, a niche market, a deviation from the norm – rather than half of the human population. The author shares her sense of outrage, and most readers will come to share it, too.

Holding up a mirror

Invisible Women is a powerful book for women and men alike. For women – I am guessing – many examples will not be surprising at all but make clear that one’s personal experience is the norm – and therefore to provide support for confronting bias. For men, the presentation of so many examples, in so many fields, is revelatory. A reader is forced to ask: What about me? Where have I failed to gather the data, or interpreted data in a way that was biased, or made decisions based on assumptions that were not equitable?

Some years ago, I was director of IMD’s Executive MBA program, which consisted of five one-week modules spread over a calendar year, with quite a bit of remote work. As it was designed, in 1998, the prerequisite for admission was completion of our Program for Executive Development, which consisted of two modules, each one five weeks in length, delivered on the IMD campus. In other words, to join our EMBA, which was largely remote, it had been necessary to attend two five-week residential modules.

For the first several years, we had very good enrolment of roughly 60 students per year. I became director in 2009, and over the next years – despite what I thought were significant improvements in the program – enrolment declined steadily. As we investigated, we determined that the problem was the lengthy residential requirement of PED – by 2012, there were fewer managers who could be away for such a length.

We restructured the program such that the residential requirement dropped from ten weeks to four weeks, and within one year our enrolment came back to previous levels – and the gender mix changed significantly as well, going from roughly 10 percent female to close to 35 percent female.

We had not realized that, for our age group – managers roughly 35-45 years of age – our residential requirement was disproportionately discouraging women from taking part. Once we made the change, the problem was obvious – How could we not have seen that? Like so many others, we had designed our program with a standard male in mind – a father and husband, perhaps, but able to absent himself and rely on others.

What to do?

Caroline Criado Perez writes in her Introductory chapter: “at heart, Invisible Women is a call for change.”  Most chapters end with some sense of what can be done, but this is not a book about setting forth policy proposals or mobilizing for action. As the title says, it is about exposing data bias. Its main aim is to raise awareness, to point out the many injustices, to show that they are not isolated and exceptional but constitute a broad pattern that is unmistakable and unacceptable. 

It will also, one would hope, both embolden those who struggle with inequality and inspire others to become more aware of gender biases. Why do we reply on incomplete data sets? Why do we assume that one part of the population will have the same concerns and interests as others? Why do we expect one set of behaviors to be associated with one gender and not the other? As a way to spark insight and self-reflection, Invisible Women is very effective.

Further Reading

Invisible Women left me impressed and wanting to know a lot more. There is less about the sources of bias, about how and why sex differences – based on anatomical and physiological differences, which the author frequently describes – lead to gender differences – the construction of social roles and expectations. That’s a different book, perhaps, but the questions are pertinent. As well, I was left wanting to know the extent to which was described could be found in all human societies or just some, and why some and not others.

Many examples are from English speaking countries or Scandinavia;  there is less from South Asia, East Asia, Africa, Latin America, and the Arab world. These questions are beyond the scope of Invisible Women, whose aim is to expose data bias in its many manifestations. For that purpose, this is an important and persuasive book.