The differences in salaries and the career expectations between men and women can’t be explained with just one variable. There are many factors –cultural, psychological– in the nucleus of the economic life of societies, an evident inequality. In the following text the detail of the conditions of this reality are revised.
Women’s salaries are lower than those of men around the world. In some places more than others. The gender gap exists everywhere, regardless even of how we measure it: average salaries, average per hour, average “controlled” by experience. In the whole world the proportion of women business directors, particularly of large businesses open to stocks, is inferior to the proportion of women that participate in the work market. The same occurs with the participation of women in management positions. In some countries the gaps are smaller and in others wider. However I still haven’t found a good example in which there is no gap.
For decades there has been an attempt to explain this phenomenon. It hasn’t been easy because there are multiple causes. And because there isn’t a sole cause, there isn’t only one solution either. But we can start to categorize the explanations of the gender gap into two main groups: one that adduces that the responsibility of the gap lies in institutional and cultural conditions, and biases that would put at a disadvantage even women who would have wanted (and choose) to have similar careers to their male counterparts.
The evidence gathered to this day indicates that both groups of explanations influences the explanation of the global phenomenon of the gender gap. However, the recommendations for policies that emerge from one or another are quite different. It’s because of this that it’s worth treating them separately. The following two sections refer to the two types of analysis mentioned.
The gap is due to the (free) election of women
Different studies indicate that women have more aversion to risk than men. This is an important reason to explain the widely verified fact that they choose more secure jobs and take less risky career options. We as economists know that risk is awarded, which is why it’s understandable that men, more daring than women, also receive a larger return. It is also assumed that in general women are less competitive, and that they have different social preferences to that of their male counterparts.
We know that men and women take on different positions in a segregated work market. Women tend to segregate themselves into activities such as teaching or nursing, while men disproportionately chose activities with higher average salaries.
Even women who access better salaried jobs choose more stable and comfortable career paths, sacrificing promotion opportunities and falling behind in the career ladder compared to their male counterparts. Evidence indicates that women voluntarily chose career paths with less return. And the cause for this can be due to the attributes that we have just mentioned: their high aversion to risk, low level of competitiveness and less desire to negotiate, as well as their different social preferences.
In light of all this, if we wanted to propose a way to close these salary gap, we would have to propose a method that leads towards women choosing what they do not choose today. Or to want what they don’t want today and don’t see as desirable or attractive. Only in this way will women manage to go for different career paths and better salaries.
The question emerges of whether what we are proposing is correct. Is it right to discuss the way in which we can fix women (in a way, so that they seem more like men at least relative to their choices)? The answer is not easy. But in the search and analysis it’s very useful to find a better understanding of the origins of the differences between the sexes that has just been discussed. Are women like this (and prefer what they prefer) because society and education assign an identity for them, or because nature prints it on them? Maybe the key is in sexual hormones, the structure of their cerebral cortex or in the culture in which they are immersed. The political implications between these are quite different.
Apparently, the response lies in culture. Uri Gneezy, in an interesting study published in 2009 that compares two tribes, the Maasai in Tanzania and the Khasi in India, the former with a cultural matriarchy and the latter with a cultural patriarchy, shows how women are less competitive than men in the second case, but surpass men in the first. Western culture is more similar to that of the Khasi, with a wide gender gap in favour of men in competitivity. A recent study for the United States finds that in the group of people belonging to the most competitive quartile, only one in five are women, and in the most competitive decile there are only men.
As the differences would apparently lie in culture, any solution to this would mean an active effort in the education of our children, and in an effort to stimulate women, as Sheryl Sandberg suggests in her bestseller, Lean In, to take more risks, to be involved in competition, and therefore to challenge the conditions that limit our careers and prevent us from shining.
But the cultural change also goes through equalizing women and men in terms of domestic responsibilities. On many occasions women opt for “comfortable” career paths not because they “prefer” them but simply because they have no alternative. Simply put, circumstances impede anything different: someone has to be home to take care of children, someone has to be in charge of house work. We can say that in these cases the differences in feminine and masculine choices aren’t in preferences but in possibilities. In our culture women face a set of choices that are smaller than that of men. Even if we can argue that within their possibilities women choose “freely”, the fact that their set of choices is limited can be considered as a lack of freedom. In conclusion, the pay gaps we see in salaries and careers in men and women are due in part to the difference choices they make. These differences are based on the preferences of gender differences, aversion to risk, competitivity and the will to negotiate, as well as differences in the alternatives available to each. And the explanation to all this lies more in culture than in biological differences between the sexes.
Reforms for the equality of the space of choices between men and women, along with initiatives to stimulate women to compete and to take risks, could help reduce the salary gaps and differences between men and women in prominent positions. They could even make them disappear altogether, as these gaps come about mainly due to the causes described.
“Reforms for the equality of the space of choices between men and women, along with initiatives to stimulate women to compete and to take risks, could help reduce the salary gaps and differences between men and women in prominent positions.”
But it’s not always like this. There are times, as we’ll see in the following section, in which other psychological mechanisms in determined social and institutional contexts have shown to have a determining effect in the gaps and career paths of women. These mechanisms configure restrictions in the advancing of women in the work place, and aren’t due to the exercising of free choices for women.
The gap is built on the base of conscious or unconscious stereotypes and biases of people and institutions.
There are other phenomena that complement the above and that give origin to a different treatment of men and women in the workplace. I’m referring to the existence of stereotypes and biases, that are conscious or unconscious, that lead to differences (that are sometimes frankly discriminating) of women or men in the workplace. These have been widely studied by the conductive economy and are a reality that we should know, in order to face it in everyday life.
Unlike the previous section, the components of the gap that come about because of stereotypes and biases and not the exercising of free choice, require a different reaction from those involved. The challenge in this case is not to work with feminine preferences and the self-imposed restrictions, but instead repairing the institutions, especially its policies of selection and promotion. In this the awareness of biases is vital. It’s only if we are conscious of them will we make the decision to minimise them.
A good starting point for understanding the biases is the classic example of an orchestra. Between 1970 and 2000 the relative participation of women in the most highly acclaimed orchestras in the United States increased seven-fold (from approximately 5 percent to around 35 percent). Claudia Goldin and Cecilia Rouse, Harvard economists, in a study showed that this increase was not coincidental. Rather, it was a product of the introduction of blind auditions, in which musicians played behind a screen. The Boston Symphony Orchestra was the first to ask musicians to audition in this way, and in the 70’s and 80’s the majority of other important orchestras in the United States followed the example. When they did, normally in the preliminary rounds, the probability that a woman would be preselected increased to 50 percent, highly increasing the proportion of women accepted.
The evaluators in charge of the selection of musicians were probably surprised by these results. We often think that we’re making decisions that are objective and fair (based, for example, solely on the quality of the music we’re listening to), but without realizing we include unconscious prejudices and biases into the decision. Conducive psychology and economy have studied phenomena like this and strong evidence points to the fact that humans tend to make biased decisions. And to reach objectivity we trick ourselves and force situations in which biases are not applicable (like blind auditions) because the mistakes that we make that derive from our implicit biases, reduce the wellbeing of everybody (and the quality of music of an orchestra). There is evidence to sustain that some decisions of promotion or salary increase are based on these stereotypes or biases, that are often unconscious.
Any bias in the evaluation of development, as small as it may be, can lead to huge disparities in the representation of a disadvantaged group at the highest level of corporative hierarchy. For example, a computer exercise that stimulated a micro biased in favour of men in the evaluation of development (explaining only 1 percent of the variety of points) led to 65 percent of the positions at the top of the corporative pyramid being taken on by men. Without this micro biased, men and women would have had 50 percent of the positions.
We have to start by understanding that no one is immune to biases. Consider the popular riddle:
A father and his son travel by car and have a serious accident. The father dies and the son is sent to hospital for a complicated urgent operation. They call an eminent doctor. The surgeon enters and says: “I can’t operate him, he’s my son”.
How can this be explained? Our response is intuitive, the story is confusing. After thinking about it we realize that of course, the eminence is the mother of the child. The confusion is not strange or difficult to understand. In Chile, less than 40 percent of doctors are women, and some decades ago the gap was wider. So, it isn’t surprising that when we think of the “eminent doctor”, we think “man”. In this way when we think of “elementary school teacher” we imagine a woman. Economists refer to this phenomenon as a statistical discrimination. People base their evaluation of an individual person on group averages. They do it intuitively, as in this example. They also do it to help in situations in which we don’t have complete information on the relative characteristics of an individual.
Stereotypes serve as general (heuristic) but generally inexact rules, that allow us to process information more easily. We can’t avoid putting people (and other observations) into categories, it’s in our nature. In fact, we rarely do it consciously. So, when we see the sex of a person, gender biases are activated automatically, leading to involuntary or implicit discrimination.
Humans are always using group characteristics when judging an individual. These judgements have real consequences, just as unconscious biases do. For example, the work market penalises women, but recompenses men, for having children. This is partly due to statistical discrimination. Statistically fathers are expected to increase their efforts to provide for their families, while it’s known that mothers, on average, decrease their efforts and even chose to leave the work market when they have children. This evaluation is accurate, on average, but unfair to those who deviate from it.
Stereotypes are useful for decreasing the cogitative burden of complex assessments, for example, about someone’s reliability. But sometimes it doesn’t follow the statistic but instead simply conforms a bias. Many stereotypes don’t adjust to reality. Even those that once did, can lose precision over time. For example, most people still believe that women are worse at maths than men. However, evidence is mixed and changes between countries and populations. In fact, in recent years the gender gap in maths has reversed in various countries, and in the rest of the world it has significantly decreased. However, the reversal of stereotypes hasn’t been as fast.
“Between 1970 and 2000 the relative participation of women in the most highly acclaimed orchestras in the United States increased seven-fold. Claudia Goldin and Cecilia Rouse showed that this increase was not coincidental. Rather, it was a product of the introduction of blind auditions, in which musicians played behind a screen.”
Unfortunately, unlearning stereotypes is basically impossible. Once an initial evaluation has been undertaken based on categories, all new information is interpreted in a biased way, favouring coherence with the initial impression. This process, known as the confirmation bias, is one of the many tricks that our cognitive system imposes on is, making objective evaluation difficult.
Unconscious bias is everywhere and it constantly affects us. Add to this our tendency to apply general (heuristic) rules when faced with uncertainty or incomplete information. As a result, our decisions are inexact, sometimes biased, and frequently lead to the selection and promotion of the wrong people in the professional sphere. These defective selection processes have economic consequences. It’s highly possible that the orchestras mentioned have benefitted from blind auditions, allowing evaluators to choose the best interpreters and generate a better group. The fact that the proportion of women has increased is a secondary effect. Understanding this is important: the main consequence of any policy that looks to avoid the activation of the biases mentioned, is improved productivity. The possible side effect is that there is a better balance between the sexes. The closing of the gap, in this context, doesn’t appear as a consequence of the policy that looked to take on a moral imperative, but instead as a natural outcome of a policy that allowed for the optimization of the processes of selection and promotion.
Recent advances in the understanding of the gender gap concerning salary and representation at the highest level of institutional hierarchy shows that this phenomenon is multidimensional. Any company or State that looks to change this reality should consider the evidence and deal with the different dimensions of the problem. On one hand we should understand that, given the current social and cultural conditions, women are choosing better salaried careers. The need to close the gap caused by the free choice of women in a context of equal opportunities, can be discussed. However, it’s not clear that women will have access to the same alternatives and opportunities as men, especially in societies in which the responsibility of maintaining the home and caring for and emotionally supporting children and the elderly is unequal. On the other hand, conducive psychology and economy have identified other mechanisms that would explain the poor treatment of women in the workplace: biases and stereotypes. To eliminate these inevitable elements of our cognition, we should take on a creative approach, as on many occasions they work unconsciously. A good starting point is recognising that these exist and they play tricks on us. In fact, challenging our biased nature should be a priority for any company manager or director looking to maximise the productivity of their companies.
 In the «Ranking of Women in Direction» (http://www.comunidadmujer.cl/estudios/ranking-mujeres-alta-direccion/) data for Chile in 2018 appears, along with the international comparison. According to this Chile is behind in terms of gender diversity in top positions in direction and management. While in late 2017, 17.3% of directing positions were occupied by women, in developed countries the proportion rises to 20.4% (25% in Europe, 20% in the United States) and in developing countries it falls to 10%. In Chile in 2018 it was 8.2%. In Chile only 4% of businesses have women as CEOs.
 A good synopsis of this is found in Shurchkov, O., & Eckel, C. (2018). «Gender Differences in Behavioral Traits and Labor Market Outcomes». In (Ed.), The Oxford Handbook of Women and the Economy: Oxford University Press.
 Gneezy, U., Leonard, K.L. and List, J.A. (2009). «Gender Differences in Competition: Evidence from a Matrilineal and a Patriarchal Society”. Econometrica, 77(5), 1637-1664.
 Sandberg, S. (2013). «Lean in: Women, work, and the will to lead». New York: Alfred A. Knopf.
 See the recent study (November 2018) on bus drivers in Massachussets by Valentin Bolotnyy and Natalia Emanuel. Available in https://scholar.harvard.edu/files/bolotnyy/files/be_gendergap.pdf
 Goldin, C. and Rouse, C. (2000). «Orchestrating Impartiality: The Impact of “Blind” Auditions on Female Musicians». American Economic Review, 90 (4): 715-741.
 Iris Bohnet, in her 2016 book, What Works. Gender Equality by Design, deepens on these subjects and undertakes enlightening analisis.
 Martell, R. F., Lane, D. M., & Emrich, C. (1996). «Male-female differences: A computer simulation». American Psychologist, 51(2), 157-158
 See for example OECD, PISA 2015 Figure I.5.10 in https://doi.org/10.1787/9789264266490-graph70-en
 In the book, Thinking Fast and Slow, 2011, the Nobel Prize for economy, Daniel Khaneman talks about this and many other aspects of our cognition.
 Iris Bohnet, in her 2016 book, What Works. Gender Equality by Design, profoundly analyses the biases of gender and proposes various ways to resolve them.