The gender gap in tech jobs narrows in advanced economies
This contribution corresponds to the analysis recently carried out by the Financial Times on the gender gap.
Strong demand for IT jobs and flexible arrangements help more women enter the workforce in the US, EU and UK
The proportion of women in tech jobs has increased in advanced economies since the pandemic, helped by strong demand, gender equality policies and greater tolerance for flexible work arrangements.
The notoriously large gender gap in the tech sector has narrowed in the US, EU and UK over the past four years, a Financial Times analysis of official data shows.
However, despite the improvement in gender balance, men still dominate employment in the sector.
This has led to calls for greater diversity in hiring at a time when the industry is developing crucial new artificial intelligence technologies.
“We are at a critical point with respect to AI,” said Athene Margaret Donald, emeritus professor of experimental physics at the University of Cambridge. “You need a diverse workforce to be sure” that finds some way to avoid any bias that is present. “We need more minorities, women and people of color in the room to make sure we get this right.”
In the United States, the proportion of female workers in the technology sector increased from 31% in 2019 to 35% at the end of 2023
According to data from the United States Bureau of Labor Statistics. There were just under 900,000 female workers in computer programming and related services out of a total of 2.5 million last year.
The proportion of women working in computer programming and related services across the EU increased from 23% before the pandemic to 25.2% at the end of 2023, with increases in most EU countries, including Germany, France and Denmark, according to official Eurostat data.
In the United Kingdom, the proportion increased from 29% in 2019 to more than 32% over the past year.
Economists say strong demand for IT jobs since the pandemic has been a major factor.
In the EU, computer programming employed about the same number of people as hosting until 2016, but now it is almost twice as many. In the United States, the number of such jobs increased by 15% in December 2023 compared to the same month in 2019, much more than the overall employment growth of 1.5% during the same period.
The proportion of women in the tech workforce is rising partly because there are now more tech jobs in banks and consumer goods companies, said Bianca Stringuini, head of equality, diversity and inclusion at recruitment group Hays.
These companies “are looking for diverse talent because they need to fill positions and there is still an inherent skills shortage in these areas,” she said.
In the United States, she said, more women are taking on roles in areas such as detecting and testing cyber threats that have traditionally been dominated by men. “We are now starting to see women in all areas of technology as the market grows,” Stringuini said.
More flexible work arrangements have also helped
Facebook has said that candidates who accepted remote job offers were more likely to be women. Companies that only require staff in the office one or two days a week hire 27% more women than those with five-day mandates, according to a report by digital services consultancy Nash Squared.
Tech giants are also improving the gender gap in their workforce
Women accounted for 25.3% of Google’s new tech hires in 2023, up from 20.8% in 2018, according to the company’s diversity report. Overall female representation increased from 30.9 to 34.1% during the same period.
The female share of Microsoft’s workforce increased to 33.1% in 2023, up from 29.2% in 2019
And those in technology positions increased from 21.4% to 26.6% during the same period. Facebook and Apple showed similar trends.
However, the gender gap remains considerably larger in technology than in other large industries. In the United States, just over one in three women work in the computer services sector, compared to 50.3% of women in management consulting services, 65.9% in legal services and 55.6 % in financial services.
“The chasm is gigantic,” said Karen Blake, co-CEO of Tech Talent Charter.
An industry-led membership group that promotes diversity and inclusion. The progression is “too slow and too fragile,” she added. “It feels a little like drops in the oceans.”
According to Donald, women are still discouraged from technological subjects in their early years, which means there are fewer female workers with technological skills. “Our society discourages girls from studying subjects such as physics or computer science practically from birth.”
There is also a risk that the trend could slow or even reverse as hybrid working recedes and big tech companies report widespread layoffs amid cooling demand and AI disruption.
Progress on gender equality in the tech sector is “glacially slow”, said Andy Heyes
UK and Ireland managing director of specialist recruitment company Harvey Nash. He argues that post-pandemic gains were largely due to greater acceptance of working from home, and are now at risk as bosses pressure staff to return to the office. “Equality, diversity and inclusion lose priority in a recession.”
Support G20 actions to reduce the gender gap
This contribution is from the Organization for Economic Co-operation and Development report which has been prepared at a request from the Australian Government. It aims to further strengthen the evidence base supporting the G20 Digital Economy Working Group’s discussions on women’s equal participation in the digital world.
Gender equality is not only a fundamental human right. It is also the cornerstone of a modern, prosperous economy that delivers sustainable inclusive growth.
Recognizing that gender equality is essential to ensuring that men and women can contribute fully to the improvement of societies and economies at large, G20 leaders first committed to the “full economic and social participation of women” in the Cabos in 2012.
They then set the ambitious goal of reducing the gender gap in labor market participation by 25% by 2025 (the 25×25 goal) at the 2014 Brisbane Summit, and committed to implementing a set of policies to improve quality. of women’s employment and the provision of support services.
Since then, as the OECD documents, most G20 countries have made progress, but much remains to be done. In 2016, the gap in the labor market gave participation rates between men and women between 15 and 64 years old around 26% in the G20 economies.
Today, digital transformation offers new avenues for women’s economic empowerment and can contribute to greater gender equality, thanks to the Internet, digital platforms, mobile phones and digital finance.
The services offer “leapfrog” opportunities for all and can help close the gap by giving women the chance to earn additional income, increase their employment opportunities and access knowledge and information.
We need to seize this opportunity to foster greater gender equality in the labor market, drive economic growth and build a more inclusive digital world.
The road ahead is uphill: today around the world, some 327 million fewer women than men have a smartphone and can access mobile Internet.
Women are under-represented in ICT jobs, in senior management and in academia and in certain careers and, as shown in this report, men are four times more likely than women to be IT specialists. TIC.
At 15 years of age, on average, only 0.5% of girls want to become ICT professionals, compared to 5% of boys.
Women-owned startups receive 23% less funding and are 30% less likely to have a positive exit compared to male-owned businesses.
This report explores a range of factors underpinning the digital gender divide, strengthens the evidence base for policymaking and provides policy guidance for consideration by all G20 governments. It has been prepared by the OECD at the request of the Australian Government to support the progress of the G20 2017
Digitalization: policies for a digital future, in particular their goal of supporting women’s equal participation in the digital economy.
It complements the initiative of the Argentine Presidency of the G20 of 2018 to share national policies, actions and practices that have had a significant and measurable impact to build bridges between digital gender and at the same time support the incorporation of the gender perspective in the agenda of the G20 by Argentina.
The report concludes that barriers to access, affordability, lack of education, as well as biases and inherent sociocultural norms, limit the ability of women and girls to benefit from the opportunities offered by digital technology.
Transformation. Furthermore, the relatively lower educational enrollment of girls in disciplines that would enable them to perform well in a digital world, such as science, technology, engineering and mathematics, as well as information and communication technologies, all must join together to prevent the more limited use of digital by women and girls.
Without these tools, increasing gaps and greater inequality could be generated.
Acting now to reverse these trends can pay off: the report reveals that greater inclusion of women in the digital world benefits the economy and increased diversity adds value, both socially and economically.
For example, inventions that emerge from mixed teams are more economically valuable and have greater impact than those in which only men participate.
Get involved: Coordinated political action can help reduce the digital gender divide.
For this it is necessary:
– raise awareness and address gender stereotypes.
– enable improved, safer and more affordable access to digital tools.
– and closer cooperation between stakeholders to remove barriers to the full participation of girls and women in the digital world.
Avoid underlying structural problems
Digital technologies provide new opportunities to advance, but technological solutions cannot address the underlying structural problems that drive the digital gender divide. Concrete policy actions are needed to encourage the full participation and inclusion of women and girls in the digital economy, while addressing stereotypes and social norms that lead to discrimination against women.
It is necessary to resolve the digital gender gap. There is no reason for women to be left behind in the digital world
Transformation. The cost of inaction is high and, in the face of slow growth with increasingly aging societies.
The educational achievements of young women, the economic argument for digital gender equality is clear. Uniting actions to close the gender gap, also in the digital world, can provide new sources of global economic growth, support the implementation of the 2030 Agenda for Sustainable Development and help achieve the G20 goal of having a strong, sustainable and inclusive growth.
Together, we must and can move forward to make digital gender equality a reality.
The STEM gap: women and girls in science, technology, engineering and mathematics
This is a contribution from AAUW, which is a professional group that defines itself from its portal as “We are tenacious and pioneers: we have defended women and girls since 1881 and in the future!
Regarding their mission they say it like this: Gender equality and economic security
We have led the fight for fair wages and economic opportunities for women, and the battle continues: women still receive just 83 cents for every dollar paid to a man, and men continue to dominate the highest positions and highest-paying professions.
Girls and women are systematically kept away from science and mathematics throughout their education, limiting their access, preparation, and opportunities to enter these fields as adults.
Women make up only 34% of the science, technology, engineering and mathematics (STEM) workforce, and men vastly outnumber women majoring in most STEM fields in college.
Gender gaps are particularly high in some of the fastest-growing and highest-paying jobs of the future, such as computing and engineering.
Why so few women in science, technology, engineering and mathematics?
An outstanding equation must be resolved regarding the variables for the success of women in engineering and computing.
Engineering and computer science, two of the most lucrative STEM fields, remain heavily dominated by men. Only 21% of engineering students and 19% of computer science students are women.
Read the AAUW research report to learn ways we can stop driving girls away from math and science, and make these fields more welcoming to women.
Providing women with equal opportunities to pursue (and thrive in) STEM careers helps reduce the gender pay gap, improves women’s economic security, ensures a diverse and talented STEM workforce, and prevents bias in these fields and the products and services. services they produce.
A typical STEM worker earns two-thirds more than those employed in other fields
According to the Pew Research Center, some of the highest-earning STEM occupations, such as computer science and engineering, have the lowest percentages of female workers.
Key factors perpetuating gender gaps in STEM:
– Gender stereotypes: STEM fields are often considered masculine, and teachers and parents often underestimate girls’ math skills as early as preschool.
– Male-dominated cultures: Because fewer women study and work in STEM, these fields tend to perpetuate inflexible, exclusionary, and male-dominated cultures that are neither supportive nor attractive to women and minorities.
– Fewer role models: Girls have fewer role models to inspire their interest in these fields, and they see limited examples of female scientists and engineers in books, media, and popular culture. There are even fewer black women who are role models in math and science.
– Math anxiety: Teachers, who are predominantly women, often have math anxiety that they pass on to girls, often rating them harder for the same work, and assuming that girls must work harder to reach the same level as children.
– The trust gap
The myth of the math brain is one of the most self-defeating ideas in American education: research shows no innate biological cognitive differences between men and women in mathematics.
Many girls lose confidence in math in third grade. Boys, on the other hand, are more likely to say they are good at math in second grade, before differences in performance are evident.
There is a gender gap in math in primary school, but it is really only evident among girls from higher-income, predominantly white areas who achieve significantly higher math results, even compared to girls attending those same schools. .
Girls score higher than boys in math in low-income, predominantly black areas (representing about a quarter of school districts), but their scores remain disproportionately low compared to white children’s scores in high income areas.
Women are underrepresented in the STEM workforce
When students reach college, women are significantly underrepresented in STEM careers; For example, only about 21% of engineering majors are women and only about 19% of computer and information science majors are women.
Nearly 80% of the healthcare workforce are women, but only about 21% of healthcare executives and board members are women, and only about a third of physicians.
And women are more represented in lower-paying fields, such as home health care workers, nurses, and lower-paying specialties, such as pediatricians.
38% of women computer science majors work in the computer science field, and only 24% of engineering majors work in the engineering field.
Men’s annual salaries in STEM are nearly $15,000 higher per year than women ($85,000 compared to $60,828). And Latina and Black women in STEM earn about $33,000 less (on an average of about $52,000 a year).
Economic inequality by gender
How great are the inequalities in wages, jobs and wealth between men and women? What causes these differences?
To give these answers, it is of great interest what Esteban Ortiz-Ospina, Joe Hasell and Max Roser (the latter is the founder of the portal “Our World in Data”) say, who define themselves by focusing on their concern for the following way:
“Poverty, disease, hunger, climate change, war, existential risks and inequality: the world faces many big and scary problems. It is these big problems that our work in Our World in Data focuses on.”
“Thanks to the work of thousands of researchers around the world who dedicate their lives to this, we often have a good understanding of how it is possible to make progress in the face of the big problems we face. “The world has the resources to do much better and reduce suffering in the world.”
The authors are concerned about data on how large wage, employment and wealth inequalities between men and women are, how they have changed over time and what may be causing them.
Although economic gender inequalities remain common and large, they are smaller today than they were a few decades ago.
Women’s employment
Questions we should ask ourselves: – How does women’s participation in the workforce differ between countries?
– How has it changed over time? – What is behind these differences and changes?
Women rights
More questions to ask ourselves:
– How has the protection of women’s rights changed over time?
– How is it different between countries?
– Maternal mortality
-What could be more tragic than a mother losing her life at the moment she gives birth to her newborn?
– Why do mothers die and what can be done to prevent these deaths?
What does the gender pay gap look like across countries and over time?
The “gender pay gap” often appears in political debates, political reports and everyday news. But what is this? What does it tell us? Is it different from one country to another? How does it change over time?
Here we attempt to answer these questions, providing an empirical view of the gender pay gap across countries and over time.
The gender pay gap measures inequality but not necessarily discrimination
The gender pay gap is a metric that tells us the difference in pay (or salary or income) between women and men. It is a measure of inequality and captures a concept that is broader than the concept of equal pay for equal work.
Gross and adjusted wage gap
Wage gaps between men and women reflect differences in many possible dimensions, including workers’ education, experience, and occupation.
When the gender pay gap is calculated by comparing all male workers to all female workers – regardless of differences along these additional dimensions – the result is the “raw” or “unadjusted” pay gap.
In contrast, when the gap is calculated after accounting for underlying differences in education, experience, etc., the result is the “adjusted” wage gap.
Absence of wage gaps because women are excluded
Discrimination in hiring practices can exist in the absence of wage gaps; for example, if women know that they will be treated unfairly and therefore decide not to participate in the labor market.
Similarly, large wage gaps can be observed in the absence of discrimination in hiring practices (for example, if women are treated fairly but apply for lower-paying jobs).
The implication is that observing pay differences between men and women is neither necessary nor sufficient to demonstrate discrimination in the workplace.
Both discrimination and inequality are important. But they are not the same
In most countries, there is a substantial gender pay gap. Cross-country data on the gender pay gap is patchy, but the most comprehensive source in terms of coverage is the United Nations’ International Labor Organization (ILO).
The estimates correspond to the difference between the average income of men and women, expressed as a percentage of the average income of men.
Also the estimates shown correspond to differences between the average hourly earnings of men and women (expressed as a percentage of the average hourly earnings of men) and cover all workers, regardless of whether they work full-time or part-time.
In most countries the gap is positive: women earn less than men, and (ii) there are large differences in the size of this gap between countries.
In most countries, the gender pay gap has decreased over the last two decades
How is the gender pay gap changing over time? To answer this question, the available OECD estimates are considered.
These estimates include OECD member states, as well as some other non-member countries, and are the longest available series of cross-country data on the gender pay gap that we are aware of.
The gender pay gap is unadjusted and is defined as the difference between the average earnings of men and women relative to the average earnings of men. Estimates refer to full-time employees and self-employed workers.
It is observed that the gap is large in most OECD countries, but has been decreasing in the last two decades. In some cases the reduction is notable. In the United States, for example, the gap was reduced by more than half.
These estimates are not directly comparable with those of the ILO, because the wage gap here is measured slightly differently: the OECD estimates refer to percentage differences in average earnings (i.e. the gap here captures differences between men and women in the middle of the income distribution) and cover only full-time employees and the self-employed (i.e., the gap here excludes disparities that arise from differences in hourly wages for full-time workers). part-time and full-time).
However, ILO data shows similar trends, so the conclusion is that in most countries with available data, the gender pay gap has decreased over the last two decades.
The gender pay gap is largest among older workers
The United States Census Bureau defines the wage gap as the relationship between median wages; That is, they measure the gap by calculating the wages of men and women in the middle of the income distribution and dividing them.
By this measure, the gender pay gap is expressed as a percentage (women’s average earnings as a proportion of men’s average earnings) and is always positive. Here, values less than 100% mean that women earn less than men, while values greater than 100% mean that women earn more.
Values closer to 100% reflect a smaller gap.
Ratio of median income of women and men by age, US
First, the series is trending upward, meaning the gap has been narrowing over the past two decades. Second, there are important differences by age.
The second point is crucial to understanding the gender pay gap: the gap is a statistic that changes over a worker’s lifetime. In most rich countries, it is small when formal education ends and employment begins, and increases with age. As we discuss in our analysis of the determinants below, the gender pay gap tends to increase when women get married and when they have children.
The gender pay gap is smaller in middle-income countries, which tend to be countries with low participation of women in the labor force.
Available ILO estimates of the gender pay gap compared to GDP per capita
There is a weak positive correlation between GDP per capita and the gender pay gap. However, the relationship is not really linear. In fact, middle-income countries tend to have the smallest wage gap.
The fact that middle-income countries have low gender pay gaps is largely a result of the selection of women into employment. Olivetti and Petrongolo (2008) explain it this way: “If women who are employed tend to have relatively high-wage characteristics, low female employment rates may be consistent with low gender wage gaps simply because women with relatively high wages Low wages would not have the characteristics of relatively high wages. in the observed salary distribution.”
Olivetti and Petrongolo (2008) show that this pattern holds in the data: unadjusted gender wage gaps across countries tend to be negatively correlated with gender employment gaps. That is, gender pay gaps tend to be smaller when relatively fewer women participate in the workforce.
Thus, rather than reflecting greater equality, the smaller wage gaps observed in some countries could indicate that only women with certain characteristics – for example, without husbands or children – are entering the workforce.
Why does a gender pay gap exist?
In almost all countries, if you compare the salaries of men and women, you find that women tend to earn less than men.
These inequalities have been reducing throughout the world. In particular, most high-income countries have experienced considerable reductions in the gender pay gap over the past two decades.
How did these reductions occur and why do substantial gaps persist?
Before we get into the details, here’s a preview of the main points.
A significant part of the reduction in the gender pay gap in rich countries over recent decades is due to a historic reduction, and often even a reversal, of the educational gap between men and women.
Today, education plays relatively little role in explaining the gender pay gap that still persists in rich countries. In contrast, the characteristics of the jobs that women typically perform remain important contributing factors.
The gender pay gap is not a direct measure of discrimination
However, evidence from different contexts suggests that discrimination is indeed important for understanding the gender pay gap. Similarly, social norms that affect the gender distribution of work are important determinants of wage inequality.
On the other hand, the available evidence suggests that differences in psychological attributes and non-cognitive skills are, at best, modest factors contributing to the gender pay gap.
Differences in human capital and the adjusted wage gap
Differences in earnings between men and women reflect differences in many possible dimensions, including education, experience, and occupation.
For example, if we consider that more educated people tend to have higher incomes, it is natural to expect that the reduction in the wage gap around the world can be explained in part by the fact that women have been catching up with men in terms of income. education and achievements, particularly years of schooling.
In fact, since differences in education contribute in part to explaining differences in wages, it is common to distinguish between “unadjusted” and “adjusted” wage differences.
When the gender pay gap is calculated by comparing all workers, regardless of differences in worker characteristics, the result is the raw or unadjusted pay gap. In contrast, when the gap is calculated after accounting for underlying differences in education, experience, and other factors important to the wage gap, the result is the adjusted wage gap.
The idea of the adjusted wage gap is to make comparisons within groups of workers with more or less similar jobs, seniority, and education. This allows us to discover to what extent different factors contribute to the observed inequalities.
Education and experience have become much less important in explaining gender differences in wages in the US.
Comparing the contributing factors in 1980 and 2010, we see that education and work experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important.
“Unexplained” waste
What is known as the “unexplained” residual has gone down. This means that the observable characteristics of workers and their jobs explain wage differences better today than they did a couple of decades ago.
At first glance, this seems like good news: it suggests that there is less discrimination today, in the sense that differences in income are much more easily explained by differences in “productivity” factors. But is this really the case?
The unexplained residual may include aspects of unmeasured productivity (i.e., unobservable characteristics of workers that could not be taken into account in the study), while the “explained” factors may themselves be vehicles of discrimination.
For example, suppose that women are indeed discriminated against and find it difficult to be hired for certain jobs simply because of their sex. This would mean that in the adjusted specification we would see that occupation and industry are important contributing factors, but that is precisely because discrimination is rooted in occupational differences!
Therefore, while the unexplained residual gives us a first-order approximation of what is happening, we need much more detailed data and analysis to say something definitive about the role of discrimination in the observed wage differences.
Wage differences between men and women around the world are better explained by occupation than by education
The World Development Report shows that today gender pay gaps are much better explained by occupation than by education. This is consistent with the point already made above using data from the United States: as education has expanded radically in recent decades, human capital has become much less important in explaining gender differences in wages.
Justin Sandefur of the Center for Global Development shows that education also does not explain wage gaps if we include workers without income (that is, if we decompose the wage gap after including people who are not employed).
Looking beyond worker characteristics
Labor flexibility
Around the world, women tend to do more unpaid care work in the home than men, and women tend to be overrepresented in low-paid jobs where they have the flexibility to care for these additional responsibilities.
The most important evidence on this link between the gender pay gap and workplace flexibility is presented and analyzed by Claudia Goldin in the article ‘A Grand Gender Convergence: Its Last Chapter’, where she delves into the US data.
There are some key lessons that apply to both rich and non-rich countries.
Women look for jobs compatible with raising children
Goldin shows that when the data on occupational choice are analyzed in some detail, it is clear that women disproportionately seek jobs, including full-time jobs, that tend to be compatible with raising children and other family responsibilities.
In other words, women, more than men, are expected to have temporal flexibility in their jobs. Things like changing work hours and rearranging shifts to deal with emergencies at home. And these are jobs with lower hourly earnings, even when the total number of hours worked is the same.
The importance of workplace flexibility in this context is very clearly illustrated by the fact that, over the past two decades, women in the United States have increased their participation and compensation in only some fields.
In a recent article, Goldin and Katz (2016) show that pharmacy became a highly paid, female-majority profession with a small gender pay gap in the US, at the same time that pharmacies underwent substantial technological changes that made jobs in the field flexible and more productive (e.g., computer systems that increased substitutability among pharmacists).
The data indicates how quickly wages for women in pharmacy have increased, relative to other professions, over the past few decades in the US.
The pain of motherhood
Closely related to work flexibility and occupational choice is the issue of work interruptions due to motherhood. On this front, there is again a large body of evidence supporting the so-called “motherhood penalty.”
Lundborg, Plug, and Rasmussen (2017) provide evidence from Denmark, more specifically from Danish women who sought medical help to achieve pregnancy.
By tracking women’s fertility and employment status through detailed periodic surveys, these researchers were able to establish that women who had successful in vitro fertilization treatment ended up having lower incomes later in life than similar women who, by chance, were treated without success.
Lundborg, Plug, and Rasmussen summarize their findings as follows: “Our main finding is that women who are successfully treated by [in vitro fertilization] consistently earn less from having children.
“We explain the decrease in the annual income of women who work less when they have children and are young and earn less when the children are older. “We explain the decline in hourly earnings, often referred to as the motherhood penalty, by the fact that women move to lower-paying jobs closer to home.”
The fact that the maternity penalty actually refers to “motherhood” and not “fatherhood” is supported by more evidence
A recent study, also from Denmark, followed men and women over the period 1980-2013 and found that after the first child, women’s income fell sharply and never fully recovered. But this was not the case for men with children, nor for women without children.
Please note that these examples are from Denmark, a country that ranks high in gender equality measures and where there are legal guarantees requiring a woman to be able to return to the same job after taking time off to give birth.
Family-friendly policies are only part of the solution
This shows that while family-friendly policies help improve female labor force participation and reduce the gender pay gap, they are only part of the solution. Even where there is generous paid leave and subsidized child care, as long as mothers disproportionately take on additional work at home after having children, wage inequalities are likely to persist.
Ability, personality and social norms
The debate so far has emphasized the importance of job characteristics and occupational choice in explaining the gender pay gap.
This leads to obvious questions:
– What determines systematic gender differences in occupational choice?
– What makes women seek job flexibility and take on a disproportionate amount of unpaid care work?
One argument often made is that, to the extent that biological differences in preferences and capabilities underpin gender roles, they are the main factors explaining the gender pay gap.
In their review of the evidence, Francine Blau and Lawrence Kahn (2017) show that there is limited empirical support for this argument.
To be clear, yes, there is evidence to support the fact that men and women differ on some key attributes that can affect labor market outcomes.
For example, standardized tests show that there are statistical gender gaps in math scores in some countries; and experiments show that women avoid further salary negotiations and often show a particular predisposition to accept and receive requests for tasks with low promotion potential.
However, these observed differences are far from biologically fixed: “gender determination” begins early in life and evidence shows that preferences and abilities are highly malleable. You can influence tastes and you can certainly teach people to tolerate risk, to do calculations or to negotiate salaries.
Furthermore, regardless of their provenance, Blau and Kahn show that these empirically observed differences can typically only explain a modest portion of the gender wage gap.
Social norms and culture key factor in the gender gap
In contrast, evidence suggests that social norms and culture, which in turn affect preferences, behavior, and incentives to foster specific skills, are key factors in understanding gender differences in labor force participation and outcomes. wages.
Discrimination and prejudice
Regardless of the exact origin of the unequal distribution of gender roles, it is clear that our recent and even current practices show that these roles persist with the help of institutional enforcement.
Goldin (1988), for example, examines past prohibitions against the training and employment of married women in the United States. She refers to some well-known restrictions, such as those prohibiting the training and employment of women as doctors and lawyers, before focusing on the lesser-known but even more impactful “marriage prohibitions” that emerged in the late 19th and early 19th centuries. of the XX.
These work bans are important because they applied to teaching and administrative jobs, occupations that would become the most common among married women after 1950.
Around the time the United States entered World War II, it is estimated that 87% of all school boards would not hire a married woman and 70% would not hire a single woman who was married.
The map here highlights that to this day, there are explicit barriers that limit the extent to which women are allowed to do the same jobs as men in some countries.
The question is whether women will be able to hold the same jobs as men by 2025? There are still many doubts about this, and as seen above, depending on which country these women belong to.
Also for any analysis, restrictions on women’s legal capacity and ability to work must be considered.
However, even after explicit barriers are removed and legal protections are established, discrimination and prejudice may persist in less overt ways. Goldin and Rouse (2000), for example, analyze the adoption of “blind” auditions by orchestras and show that by using a screen to conceal a candidate’s identity, fair hiring practices increased the number of women in orchestras by 25% between 1970 and 1996.
Many other studies have found similar evidence of bias in different labor market contexts. Biases also operate in other spheres of life with strong knock-on effects on labor market outcomes.
For example, at the end of World War II only 18% of people in the United States thought that a wife should work if her husband could support her. Obviously, this brings us back to our previous point about social norms.
Strategies to reduce the gender pay gap
In many countries, wage inequality between men and women can be reduced by improving women’s education. However, in many countries, gender gaps in education have closed and we still have large gender inequalities in the workforce. What else can be done?
An obvious alternative is to fight discrimination
But the evidence presented above shows that this is not enough. Changes in public policy and management at the company level are also important: family-friendly labor market policies can help.
For example, maternity leave coverage can contribute by increasing the retention of women during the childbirth period, which in turn increases women’s wages through maintaining work experience and staying in employment. .
Similarly, early education and child care can increase women’s labor force participation (and reduce gender wage gaps) by alleviating the unpaid care work that mothers perform.
Furthermore, the experience of women’s historical advancement in specific professions (e.g., pharmaceuticals in the US) suggests that the gender pay gap could also be significantly reduced if companies did not have the incentive to disproportionately reward workers who They work long hours and on a permanent, unpaid basis.
Of course, changing these incentives is difficult because it requires reorganizing the workplace.
But it is likely to have a major impact on gender inequality, particularly in countries where other measures already exist.
Implementing these strategies can have a positive reinforcing effect. For example, family-friendly labor market policies that lead to greater labor force attachment and wages for women will increase returns to women’s investment in education, so women in future generations will have more likely to invest in education, which will also help reduce gender gaps in labor market outcomes in the future.
However, as powerful as these strategies are, they are only part of the solution.
Social norms and culture continue to be at the center of family decisions and the distribution of work by gender.
Achieving equal opportunity requires ensuring that we change the norms and stereotypes that limit the set of options available to both men and women. It’s difficult, but as the next section shows, social norms can be changed too.
How well do biological differences explain the gender pay gap?
Around the world, women tend to take on more family responsibilities than men. As a result, women tend to be overrepresented in low-paying jobs where they are more likely to have the flexibility to handle these additional responsibilities.
These two facts – documented above – are often used to claim that, since men and women tend to be endowed with different tastes and talents, it follows that most of the observed gender differences in wages derive from biological differences in sex. But what is the broader evidence to support these claims?
In a nutshell, here’s what the research and data show:
– There is evidence to support the fact that, statistically speaking, men and women tend to differ in some key aspects, including psychological attributes that can affect labor market outcomes.
– There is no consensus on the exact weight that nurture and nature have in determining these differences, but whatever the exact weight, the evidence shows that these attributes are strongly malleable.
– Regardless of origin, these differences can only explain a modest part of the gender wage gap.
Some context on the distribution of work by gender
Before we get into the discussion about whether biological attributes explain pay differences across gender roles, let’s get some perspective on the distribution of work by gender.
If we take into account the proportion of women and men dedicated to unpaid care work, including tasks such as caring for children at home, domestic tasks or doing community work, it can be observed throughout the world that there is a radical imbalance in the distribution of Gendered work: Women everywhere do a disproportionate amount of unpaid work.
Proportion of time between women and men dedicated to unpaid care work
Unpaid care work refers to all unpaid services provided within a household for its members, including caring for people, housework and voluntary community work.
Of course, this is closely related to the fact that in most countries there are gender gaps in labor force participation and wages.
“Children are better at math”
It is often argued that differences in the biological attributes that determine our ability to develop “hard skills” such as mathematics are at the heart of the gender pay gap. Are there really big gender differences in math skills? If so, is it due to differences in the attributes we are born with?
Let’s look at the data
Are children better on the math section of the PISA standardized test? One could argue that looking at higher scores is more relevant here, as higher scores are more likely to determine gaps in future career trajectories (e.g. gaps in access to ‘STEM degrees’ at university level).
Data on the proportion of men and women who took the test and scored at the highest level on the PISA test (i.e. level 6) are above the diagonal line in most countries. that marks gender parity. So yes, achieving high scores in math tends to be more common among boys than girls. However, there is enormous variation between countries: the differences between countries are much greater than the differences between the sexes. And in many countries, the gap is effectively non-existent.
Similarly, researchers have found that within countries there is also large geographic variation in gender gaps in test scores. Clearly, these gaps in mathematical ability do not appear to be entirely determined by biological gifts.
In fact, research analyzing PISA results across countries suggests that better social conditions for women are related to better math performance for girls.
Statistical gaps in test scores not only vary substantially across societies, but they also vary substantially over time.
This suggests that social factors play an important role in explaining differences between the sexes.
In the United States, for example, the gender gap in mathematics has narrowed in recent decades. And this reduction occurred as the high school curricula of boys and girls became more similar.
In the United States in 1957, boys took many more math and science courses than girls; but in 1992 there was virtually parity in almost all science and mathematics courses.
The male-to-female ratio of the number of students taking mathematics and science subjects in secondary education in the United States when at a ratio of one indicates parity in the proportion of males and females enrolled in a subject.
What is more important for the question at hand is that the gender gaps in “hard skills” are not large enough to explain the gender gaps in earnings.
In their review of the evidence, Blau and Kahn (2017) conclude that test score gaps in the US are too small to explain much of gender pay at any point in time.
Taken together, then, the evidence suggests that statistical gaps in math test scores are relatively small and are strongly influenced by social and environmental factors.
“It’s about personality”
It is also often argued that biological differences in tastes (e.g., preferences for “people” over “things”), psychological attributes (e.g., “risk aversion”), and interpersonal skills (e.g., the ability to get along with others) are at the center of the gender pay gap.
There are hundreds of studies attempting to establish whether there are gender differences in preferences, personality traits, and “soft skills.” The overall quality and relevance (i.e., internal and external validity) of these studies is the subject of much discussion, as illustrated by the recent debate arising from the Google Memo affair.
A recent ‘Heterodox Academy’ article, which was produced specifically in the context of the Google Memo, provides a fantastic overview of the evidence on this topic and the key points of contention among academics.
For these purposes, let us focus on the review of the evidence presented in Blau and Kahn (2017); Their review is particularly useful because it focuses on gender differences in the context of labor markets.
Blau and Kahn point out that yes, researchers have found statistical differences between men and women that are important in the context of labor market outcomes. For example, studies have found statistical gender differences in “interpersonal skills” (i.e., the ability to listen, communicate, and relate to others).
Similarly, experimental studies have found that women more frequently avoid salary negotiations and often show a particular predisposition to accepting and receiving task requests with low promotion potential.
But are the origins of these differences primarily biological or social? And are they strong enough to explain wage gaps?
The available evidence suggests that these factors can only explain a relatively small fraction of the observed differences in wages. And anyway, they are far from purely biological: preferences and abilities are highly malleable, and “gender determination” begins early in life.
Reaction of men and women to job advertisements
Here is a concrete example: Leibbrandt and List (2015) conducted an experiment in which they evaluated how men and women reacted to job advertisements. They found that while men were more likely to negotiate than women when there was no explicit statement that salaries were negotiable, the gender difference disappeared and even reversed when it was explicitly stated that salaries were negotiable. This suggests that it’s not so much about “talent” as it is about norms and rules.
“A man should earn more than his wife.”
The experiment in which researchers found that gender differences in negotiating attitudes disappeared when salaries were explicitly stated to be negotiable highlights the important role that social norms and culture play in labor market outcomes.
These concepts may seem abstract: what do social norms and culture really look like in the context of the gender pay gap?
The reproduction of stereotypes through everyday positive imposition can be seen in a variety of aspects: a study analyzing 124 prime-time television shows in the US found that female characters continue to play interpersonal roles with romance, family and friends, while the male characters represent work-related roles.
In the realm of children’s books, a study of 5,618 books found that, compared to women, men are represented almost twice as much in titles and 1.6 times more than central characters.
Qualitative research shows that even in the home, fathers are often responsible for enforcing gender norms, especially when it comes to fathers who support the masculinity of their sons.
Of particular importance in the context of labor markets, social norms also often take the form of specific behavioral prescriptions such as “a man should earn more than his wife.”
Consistent with the idea that “a man should earn more than his wife,” the data shows a sharp drop at 0.5, the point at which the wife begins to earn more than the husband.
DISTRIBUTION OF THE WIFE’S SHARE OF INCOME AMONG MARRIED COUPLES IN THE USA – BERTRAND, KAMENICA AND PAN (2015)
This is the result of two factors. Firstly, it is about matching men and women before they get married; “matchings” in which the woman has greater earning potential are less common.
Second, it is the result of decisions made after marriage: Researchers show that married women with higher earning potential than their husbands often stay out of the workforce or take jobs “below their potential.”
The study authors explored the data in more detail and found that in couples where the wife earns more than the husband, the wife spends more time on housework, thus the gender gap in care work. unpaid is even higher.
And these couples are also less satisfied with their marriage and more likely to divorce than couples where the wife earns less than the husband.
This study’s empirical exploration highlights the notable power that gender norms and identity have on labor market outcomes.
Other questions to ask
– Why are gender norms and identity important?
– Does it really matter whether social norms and culture are important determinants of gender roles and labor market outcomes?
– Are the social norms of our contemporary societies really less fixed than biological traits?
The available research suggests that the answers to these questions are yes and yes.
There is evidence that social norms can be actively and rapidly changed.
Here is a concrete example: Jensen and Oster (2009) find that the introduction of cable television in India led to a significant decline in the reported acceptability of domestic violence towards women and preference for sons, as well as increases in women’s autonomy and decreases in fertility.
Of course, television is a small aspect of all the important things that matter for social norms. But this study is important for the debate because it is difficult to study how social norms can be changed.
The television presentation is a unique opportunity to see how a group that is exposed to an engine of social change really changes.
As Jensen and Oster point out, the most popular cable television shows in India feature urban settings where lifestyles differ radically from those in rural areas. For example, many female characters in popular soap operas are more educated, marry later, and have smaller families than most rural women. And, similarly, many female characters in these television shows appear working outside the home as professionals, running businesses, or in other positions of authority.
As the authors note, “the stated desire for the next child to be a boy remains relatively unchanged in areas with no change in cable status, but declines sharply between 2001 and 2002 in villages that gained cable in 2002, and between 2002 and 2003 (but notably not between 2001 and 2002) for those who got cable in 2003. For both attitude measures, the changes are large and striking, and correspond closely to the timing of cable’s introduction.”
Preference for a son decreased in households the year they got cable television.
In conclusion: the evidence suggests that biological differences are not a key driver of gender inequality in labor market outcomes; while social norms and culture – which in turn affect preferences, behavior and incentives to foster specific skills – are very important.
This is important for policy because social norms are not fixed.
They can be influenced in a number of ways, including through intergenerational learning processes, exposure to alternative norms, and activism such as that which fueled the women’s movement. Therefore, it is important to ask ourselves in each country: how are women represented in all jobs?
Representation of women at the top of the income distribution
Despite decreasing in recent decades, a substantial wage gap remains between the average salaries of men and women.