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The Enduring Grip of the Gender Pay Gap

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The gender pay gap – the difference between the earnings of men and women – has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap has narrowed this century contrasts sharply with the progress in the preceding two decades: In 1982, women earned just 65 cents to each dollar earned by men.

There is no single explanation for why progress toward narrowing the pay gap has all but stalled in the 21st century. Women generally begin their careers closer to wage parity with men, but they lose ground as they age and progress through their work lives, a pattern that has remained consistent over time. The pay gap persists even though women today are more likely than men to have graduated from college. In fact, the pay gap between college-educated women and men is not any narrower than the one between women and men who do not have a college degree. This points to the dominant role of other factors that still set women back or give men an advantage.

One of these factors is parenthood. Mothers ages 25 to 44 are less likely to be in the labor force than women of the same age who do not have children at home, and they tend to work fewer hours each week when employed. This can reduce the earnings of some mothers, although evidence suggests the effect is either modest overall or short-lived for many. On the other hand, fathers are more likely to be in the labor force – and to work more hours each week – than men without children at home. This is linked to an increase in the pay of fathers – a phenomenon referred to as the “ fatherhood wage premium ” – and tends to widen the gender pay gap.

Related: Gender pay gap in U.S. hasn’t changed much in two decades

Family needs can also influence the types of jobs women and men pursue , contributing to gender segregation across occupations. Differential treatment of women, including gender stereotypes and discrimination , may also play a role. And the gender wage gap varies widely by race and ethnicity.

Pew Research Center conducted this study to better understand how women’s pay compared with men’s pay in the U.S. in the economic aftermath of the COVID-19 outbreak .

The study is based on the analysis of monthly Current Population Survey (CPS) data from January 1982 to December 2022 monthly files ( IPUMS ). The CPS is the U.S. government’s official source for monthly estimates of unemployment . For a quarter of the sample each month, the CPS also records data on usual hourly earnings for hourly workers and usual weekly earnings and hours worked for other workers. In this report, monthly CPS files were combined to create annual files to boost sample sizes and to analyze the gender pay gap in greater detail.

The comparison between women’s and men’s pay is based on their median hourly earnings. For workers who are not hourly workers, hourly earnings were computed as the ratio of usual weekly earnings to usual weekly hours worked. The samples include employed workers ages 16 and older with positive earnings, working full time or part time, including those for whom earnings were imputed by the Census Bureau . Self-employed workers are excluded because their earnings are not recorded in the CPS.

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

“Mothers” and “fathers” refer to women and men 16 and older who have an own child younger than 18 living in the household.

The U.S. labor force, used interchangeably with the workforce in this analysis, consists of people 16 and older who are either employed or actively looking for work.

White, Black and Asian workers include those who report being only one race and who are not Hispanic. Hispanics are of any race. Asian workers include Pacific Islanders. Other racial and ethnic groups are included in all totals but are not shown separately.

“High school graduate” refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). “Some college” include workers with an associate degree and those who attended college but did not obtain a degree.

How the gender pay gap increases with age

Younger women – those ages 25 to 34 and early in their work lives – have edged closer to wage parity with men in recent years. Starting in 2007, their earnings have consistently stood at about 90 cents to the dollar or more compared with men of the same age. But even as pay parity might appear in reach for women at the start of their careers, the wage gap tends to increase as they age.

Consider, for example, women who were ages 25 to 34 in 2010. In that year, they earned 92% as much as men their age, compared with 83% for women overall. But by 2022, this group of women, now ages 37 to 46, earned only 84% as much as men of the same age. This pattern repeats itself for groups of women who were ages 25 to 34 in earlier years – say, 2005 or 2000 – and it may well be the future for women entering the workforce now.

A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64. This general pattern has not changed in at least four decades.

The increase in the pay gap coincides with the age at which women are more likely to have children under 18 at home. In 2022, 40% of employed women ages 25 to 34 had at least one child at home. The same was true for 66% of women ages 35 to 44 but for fewer – 39% – among women ages 45 to 54. Only 6% of employed women ages 55 to 64 had children at home in 2022.

Similarly, the share of employed men with children at home peaks between the ages of 35 to 44, standing at 58% in 2022. This is also when fathers tend to receive higher pay, even as the pay of employed mothers in same age group is unaffected.

Mothers with children at home tend to be less engaged with the workplace, while fathers are more active

Parenthood leads some women to put their careers on hold, whether by choice or necessity, but it has the opposite effect among men. In 2022, 70% of mothers ages 25 to 34 had a job or were looking for one, compared with 84% of women of the same age without children at home. This amounted to the withdrawal of 1.4 million younger mothers from the workforce. Moreover, when they are employed, younger mothers tend to put in a shorter workweek – by two hours per week, on average – than other women their age. Reduced engagement with the workplace among younger mothers is also a long-running phenomenon.

Fathers, however, are more likely to hold a job or be looking for one than men who don’t have children at home, and this is true throughout the prime of their working years , from ages 25 to 54. Among those who do have a job, fathers also work a bit more each week, on average, than men who do not have children at home.

As a result, the gender gap in workplace activity is greater among those who have children at home than among those who do not. For example, among those ages 35 to 44, 94% of fathers are active in the workforce, compared with 75% of mothers – a gap of 19 percentage points. But among those with no children at home in this age group, 84% of men and 78% of women are active in the workforce – a gap of 6 points.

These patterns contribute to the gap in workplace activity between men and women overall. As of 2022, 68% of men ages 16 and older – with or without children at home – are either employed or seeking employment. That compares with 57% of women, a difference of 11 percentage points. This gap was as wide as 24 points in 1982, but it narrowed to 14 points by 2002. Men overall also worked about three hours more per week at a job than women in 2022, on average, down from a gap of about six hours per week in 1982.

Employed mothers earn about the same as similarly educated women without children at home; both groups earn less than fathers

Parenthood affects the hourly earnings of employed women and men in unexpected ways. While employed mothers overall appear to earn less than employed women without children at home, the gap is driven mainly by differences in educational attainment between the two groups. Among women with similar levels of education, there is little gap in the earnings of mothers and non-mothers. However, fathers earn more than other workers, including other men without children at home, regardless of education level. This phenomenon – known as the fatherhood wage premium – is one of the main ways that parenthood affects the gender pay gap among employed workers.

Among employed men and women, the impact of parenting is felt most among those ages 25 to 54, when they are most likely to have children under 18 at home. In 2022, mothers ages 25 to 34 earned 85% as much as fathers that age, but women without children at home earned 97% as much as fathers. In contrast, employed women ages 35 to 44 – with or without children – both earned about 80% as much as fathers. The table turns for women ages 45 to 54, with mothers earning more than women with no children at home. Among those ages 35 to 44 or 45 to 54, men without children earned only 84% as much as fathers.

When the earnings of mothers are compared with those of women without children at home who have the same level of education, the differences either narrow or go away. Among employed women ages 25 to 34 with at least a bachelor’s degree, both mothers and women without children at home earned 80% as much as fathers in 2022. Among women ages 25 to 34 with a high school diploma and no further education, mothers earned 79% as much as fathers and women with no children at home earned 84% as much. The narrowing of the gap in earnings of mothers and women without children at home after controlling for education level also extends to other age groups.

Thus, among the employed, the effect of parenthood on the gender pay gap does not seem to be driven by a decrease in mothers’ earnings relative to women without children at home. Instead, the widening of the pay gap with parenthood appears to be driven more by an increase in the earnings of fathers. Fathers ages 25 to 54 not only earn more than mothers the same age, they also earn more than men with no children at home. Nonetheless, men without children at home still earn more than women with or without children at home.

Although there is little gap in the earnings of employed mothers and women with no children at home who have the same level of education, there is a lingering gap in workplace engagement between the two groups. Whether they had at least a bachelor’s degree or were high school graduates, mothers ages 25 to 34 are less likely to hold a job or be looking for one. Similarly, younger mothers on average work fewer hours than women without children at home each week, regardless of their education level. The opposite is true for fathers compared with men without children at home.

Progress in closing the gender pay gap has slowed despite gains in women’s education

The share of women with at least a bachelor’s degree has increased steadily since 1982 – and faster than among men. In 1982, 20% of employed women ages 25 and older had a bachelor’s degree or higher level of education, compared with 26% of employed men. By 2022, 48% of employed women had at least a bachelor’s degree, compared with 41% of men. Still, women did not see the pay gap close to the same extent from 2002 to 2022 as they did from 1982 to 2002.

In part, this may be linked to how the gains from going to college have changed in recent decades, for women and men alike. The college wage premium – the boost in earnings workers get from a college degree – increased rapidly during the 1980s. But the rise in the premium slowed down over time and came to a halt around 2010. This likely reduced the relative growth in the earnings of women.

Although gains in education have raised the average earnings of women and have narrowed the gender pay gap overall, college-educated women are no closer to wage parity with their male counterparts than other women. In 2022, women with at least a bachelor’s degree earned 79% as much as men who were college graduates, and women who were high school graduates earned 81% as much as men with the same level of education. This underscores the challenges faced by women of all education levels in closing the pay gap.

Notably, the gender wage gap has closed more among workers without a four-year college degree than among those who do have a bachelor’s degree or more education. For example, the wage gap for women without a high school diploma narrowed from 62% in 1982 to 83% in 2022 relative to men at the same education level. But it closed only from 69% to 79% among bachelor’s degree holders over the same period. This is because only men with at least a bachelor’s degree experienced positive wage growth from 1982 to 2022; all other men saw their real wages decrease. Meanwhile, the real earnings of women increased regardless of their level of education.

As women have improved their level of education in recent decades, they’ve also increased their share of employment in higher-paying occupations, such as managerial, business and finance, legal, and computer, science and engineering (STEM) occupations. In 1982, women accounted for only 26% of employment in managerial occupations. By 2022, their share had risen to 40%. Women also substantially increased their presence in social, arts and media occupations. Over the same period, the shares of women in several lower-paying fields, such as administrative support jobs and food preparation and serving occupations, fell significantly.

Even so, women are still underrepresented in managerial and STEM occupations – along with construction, repair and production, and transportation occupations – when compared with their share of employment overall. And there has been virtually no change in the degree to which women are over represented in education, health care, and personal care and services occupations – the last of which are lower paying than the average across all occupations. The distribution of women and men across occupations remains one of the drivers of the gender pay gap . But the degree to which this distribution is the result of personal choices or gender stereotypes is not entirely clear.

Gender pay gap differs widely by race and ethnicity

Looking across racial and ethnic groups, a wide gulf separates the earnings of Black and Hispanic women from the earnings of White men. 3 In 2022, Black women earned 70% as much as White men and Hispanic women earned only 65% as much. The ratio for White women stood at 83%, about the same as the earnings gap overall, while Asian women were closer to parity with White men, making 93% as much.

The pay gap narrowed for all groups of women from 1982 to 2022, but more so for White women than for Black and Hispanic women. The earnings gap for Asian women narrowed by about 17 percentage points from 2002 to 2022, but data for this group is not available for 1982.

To some extent, the gender wage gap varies by race and ethnicity because of differences in education, experience, occupation and other factors that drive the gender wage gap for women overall. But researchers have uncovered new evidence of hiring discrimination against various racial and ethnic groups, along with discrimination against other groups, such as LGBTQ and disabled workers. Discrimination in hiring may feed into differences in earnings by shutting out workers from opportunities.

Broader economic forces may impact men’s and women’s earnings in different ways

Changes in the gender pay gap are also shaped by economic factors that sometimes drive men’s and women’s earnings in distinctive ways. Because men and women tend to work in different types of jobs and industries, their earnings may respond differently to external pressures.

More specifically, men’s earnings essentially didn’t change from 1982 to 2002. Potential reasons for that include a more rapid decline in union membership among men, a shift away from jobs calling for more physical skills, and global competition that sharply reduced employment in manufacturing in the 1980s. At the same time, women’s earnings increased substantially as they raised their level of education and shifted toward higher-paying occupations.

But in some ways, the economic climate has proved less favorable for women this century. For reasons that are not entirely clear, women’s employment was slower to recover from the Great Recession of 2007-2009. More recently, the COVID-19 recession took on the moniker “ she-cession ” because of the pressure on jobs disproportionately held by women . Amid a broader slowdown in earnings growth from 2000 to 2015, the increase in women’s earnings from 2002 to 2022 was not much greater than the increase in men’s earnings, limiting the closure in the gender pay gap over the period.

What’s next for the gender pay gap?

Higher education, a shift to higher-paying occupations and more labor market experience have helped women narrow the gender pay gap since 1982. But even as women have continued to outpace men in educational attainment, the pay gap has been stuck in a holding pattern since 2002, ranging from 80 to 85 cents to the dollar.

More sustained progress in closing the pay gap may depend on deeper changes in societal and cultural norms and in workplace flexibility that affect how men and women balance their careers and family lives . Even in countries that have taken the lead in implementing family-friendly policies, such as Denmark, parenthood continues to drive a significant wedge in the earnings of men and women. New research suggests that family-friendly policies in the U.S. may be keeping the pay gap from closing. Gender stereotypes and discrimination, though difficult to quantify, also appear to be among the “last-mile” hurdles impeding further progress.

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

• It is also worth noting that even if the hourly earnings of mothers are not affected, their weekly or annual pay is reduced in line with the reduction in the hours worked. ↩
• In part, this is because the age of women at first birth varies by educational attainment . Motherhood among women with a bachelor’s degree or higher level of education occurs at an older age than among women without a bachelor’s degree. ↩
• Although Asian men earned about 24% more than White men at the median in 2022, comparisons in this section are drawn with reference to White men. In 2022, White men accounted for about one-third of total employment in the U.S., compared with about 3% for Asian men. ↩

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Report | Wages, Incomes, and Wealth

“Women’s work” and the gender pay gap : How discrimination, societal norms, and other forces affect women’s occupational choices—and their pay

Report • By Jessica Schieder and Elise Gould • July 20, 2016

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What this report finds: Women are paid 79 cents for every dollar paid to men—despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment. Too often it is assumed that this pay gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves often affected by gender bias. For example, by the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

Why it matters, and how to fix it: The gender wage gap is real—and hurts women across the board by suppressing their earnings and making it harder to balance work and family. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

Introduction and key findings

Women are paid 79 cents for every dollar paid to men (Hegewisch and DuMonthier 2016). This is despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment.

Critics of this widely cited statistic claim it is not solid evidence of economic discrimination against women because it is unadjusted for characteristics other than gender that can affect earnings, such as years of education, work experience, and location. Many of these skeptics contend that the gender wage gap is driven not by discrimination, but instead by voluntary choices made by men and women—particularly the choice of occupation in which they work. And occupational differences certainly do matter—occupation and industry account for about half of the overall gender wage gap (Blau and Kahn 2016).

To isolate the impact of overt gender discrimination—such as a woman being paid less than her male coworker for doing the exact same job—it is typical to adjust for such characteristics. But these adjusted statistics can radically understate the potential for gender discrimination to suppress women’s earnings. This is because gender discrimination does not occur only in employers’ pay-setting practices. It can happen at every stage leading to women’s labor market outcomes.

Take one key example: occupation of employment. While controlling for occupation does indeed reduce the measured gender wage gap, the sorting of genders into different occupations can itself be driven (at least in part) by discrimination. By the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

This paper explains why gender occupational sorting is itself part of the discrimination women face, examines how this sorting is shaped by societal and economic forces, and explains that gender pay gaps are present even  within  occupations.

Key points include:

• Gender pay gaps within occupations persist, even after accounting for years of experience, hours worked, and education.
• Decisions women make about their occupation and career do not happen in a vacuum—they are also shaped by society.
• The long hours required by the highest-paid occupations can make it difficult for women to succeed, since women tend to shoulder the majority of family caretaking duties.
• Many professions dominated by women are low paid, and professions that have become female-dominated have become lower paid.

This report examines wages on an hourly basis. Technically, this is an adjusted gender wage gap measure. As opposed to weekly or annual earnings, hourly earnings ignore the fact that men work more hours on average throughout a week or year. Thus, the hourly gender wage gap is a bit smaller than the 79 percent figure cited earlier. This minor adjustment allows for a comparison of women’s and men’s wages without assuming that women, who still shoulder a disproportionate amount of responsibilities at home, would be able or willing to work as many hours as their male counterparts. Examining the hourly gender wage gap allows for a more thorough conversation about how many factors create the wage gap women experience when they cash their paychecks.

Within-occupation gender wage gaps are large—and persist after controlling for education and other factors

Those keen on downplaying the gender wage gap often claim women voluntarily choose lower pay by disproportionately going into stereotypically female professions or by seeking out lower-paid positions. But even when men and women work in the same occupation—whether as hairdressers, cosmetologists, nurses, teachers, computer engineers, mechanical engineers, or construction workers—men make more, on average, than women (CPS microdata 2011–2015).

As a thought experiment, imagine if women’s occupational distribution mirrored men’s. For example, if 2 percent of men are carpenters, suppose 2 percent of women become carpenters. What would this do to the wage gap? After controlling for differences in education and preferences for full-time work, Goldin (2014) finds that 32 percent of the gender pay gap would be closed.

However, leaving women in their current occupations and just closing the gaps between women and their male counterparts within occupations (e.g., if male and female civil engineers made the same per hour) would close 68 percent of the gap. This means examining why waiters and waitresses, for example, with the same education and work experience do not make the same amount per hour. To quote Goldin:

Another way to measure the effect of occupation is to ask what would happen to the aggregate gender gap if one equalized earnings by gender within each occupation or, instead, evened their proportions for each occupation. The answer is that equalizing earnings within each occupation matters far more than equalizing the proportions by each occupation. (Goldin 2014)

This phenomenon is not limited to low-skilled occupations, and women cannot educate themselves out of the gender wage gap (at least in terms of broad formal credentials). Indeed, women’s educational attainment outpaces men’s; 37.0 percent of women have a college or advanced degree, as compared with 32.5 percent of men (CPS ORG 2015). Furthermore, women earn less per hour at every education level, on average. As shown in Figure A , men with a college degree make more per hour than women with an advanced degree. Likewise, men with a high school degree make more per hour than women who attended college but did not graduate. Even straight out of college, women make $4 less per hour than men—a gap that has grown since 2000 (Kroeger, Cooke, and Gould 2016).

Women earn less than men at every education level : Average hourly wages, by gender and education, 2015

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The data underlying the figure.

Source :  EPI analysis of Current Population Survey Outgoing Rotation Group microdata

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Steering women to certain educational and professional career paths—as well as outright discrimination—can lead to different occupational outcomes

The gender pay gap is driven at least in part by the cumulative impact of many instances over the course of women’s lives when they are treated differently than their male peers. Girls can be steered toward gender-normative careers from a very early age. At a time when parental influence is key, parents are often more likely to expect their sons, rather than their daughters, to work in science, technology, engineering, or mathematics (STEM) fields, even when their daughters perform at the same level in mathematics (OECD 2015).

Expectations can become a self-fulfilling prophecy. A 2005 study found third-grade girls rated their math competency scores much lower than boys’, even when these girls’ performance did not lag behind that of their male counterparts (Herbert and Stipek 2005). Similarly, in states where people were more likely to say that “women [are] better suited for home” and “math is for boys,” girls were more likely to have lower math scores and higher reading scores (Pope and Sydnor 2010). While this only establishes a correlation, there is no reason to believe gender aptitude in reading and math would otherwise be related to geography. Parental expectations can impact performance by influencing their children’s self-confidence because self-confidence is associated with higher test scores (OECD 2015).

By the time young women graduate from high school and enter college, they already evaluate their career opportunities differently than young men do. Figure B shows college freshmen’s intended majors by gender. While women have increasingly gone into medical school and continue to dominate the nursing field, women are significantly less likely to arrive at college interested in engineering, computer science, or physics, as compared with their male counterparts.

Women arrive at college less interested in STEM fields as compared with their male counterparts : Intent of first-year college students to major in select STEM fields, by gender, 2014

Source:  EPI adaptation of Corbett and Hill (2015) analysis of Eagan et al. (2014)

These decisions to allow doors to lucrative job opportunities to close do not take place in a vacuum. Many factors might make it difficult for a young woman to see herself working in computer science or a similarly remunerative field. A particularly depressing example is the well-publicized evidence of sexism in the tech industry (Hewlett et al. 2008). Unfortunately, tech isn’t the only STEM field with this problem.

Young women may be discouraged from certain career paths because of industry culture. Even for women who go against the grain and pursue STEM careers, if employers in the industry foster an environment hostile to women’s participation, the share of women in these occupations will be limited. One 2008 study found that “52 percent of highly qualified females working for SET [science, technology, and engineering] companies quit their jobs, driven out by hostile work environments and extreme job pressures” (Hewlett et al. 2008). Extreme job pressures are defined as working more than 100 hours per week, needing to be available 24/7, working with or managing colleagues in multiple time zones, and feeling pressure to put in extensive face time (Hewlett et al. 2008). As compared with men, more than twice as many women engage in housework on a daily basis, and women spend twice as much time caring for other household members (BLS 2015). Because of these cultural norms, women are less likely to be able to handle these extreme work pressures. In addition, 63 percent of women in SET workplaces experience sexual harassment (Hewlett et al. 2008). To make matters worse, 51 percent abandon their SET training when they quit their job. All of these factors play a role in steering women away from highly paid occupations, particularly in STEM fields.

The long hours required for some of the highest-paid occupations are incompatible with historically gendered family responsibilities

Those seeking to downplay the gender wage gap often suggest that women who work hard enough and reach the apex of their field will see the full fruits of their labor. In reality, however, the gender wage gap is wider for those with higher earnings. Women in the top 95th percentile of the wage distribution experience a much larger gender pay gap than lower-paid women.

Again, this large gender pay gap between the highest earners is partially driven by gender bias. Harvard economist Claudia Goldin (2014) posits that high-wage firms have adopted pay-setting practices that disproportionately reward individuals who work very long and very particular hours. This means that even if men and women are equally productive per hour, individuals—disproportionately men—who are more likely to work excessive hours and be available at particular off-hours are paid more highly (Hersch and Stratton 2002; Goldin 2014; Landers, Rebitzer, and Taylor 1996).

It is clear why this disadvantages women. Social norms and expectations exert pressure on women to bear a disproportionate share of domestic work—particularly caring for children and elderly parents. This can make it particularly difficult for them (relative to their male peers) to be available at the drop of a hat on a Sunday evening after working a 60-hour week. To the extent that availability to work long and particular hours makes the difference between getting a promotion or seeing one’s career stagnate, women are disadvantaged.

And this disadvantage is reinforced in a vicious circle. Imagine a household where both members of a male–female couple have similarly demanding jobs. One partner’s career is likely to be prioritized if a grandparent is hospitalized or a child’s babysitter is sick. If the past history of employer pay-setting practices that disadvantage women has led to an already-existing gender wage gap for this couple, it can be seen as “rational” for this couple to prioritize the male’s career. This perpetuates the expectation that it always makes sense for women to shoulder the majority of domestic work, and further exacerbates the gender wage gap.

Female-dominated professions pay less, but it’s a chicken-and-egg phenomenon

Many women do go into low-paying female-dominated industries. Home health aides, for example, are much more likely to be women. But research suggests that women are making a logical choice, given existing constraints . This is because they will likely not see a significant pay boost if they try to buck convention and enter male-dominated occupations. Exceptions certainly exist, particularly in the civil service or in unionized workplaces (Anderson, Hegewisch, and Hayes 2015). However, if women in female-dominated occupations were to go into male-dominated occupations, they would often have similar or lower expected wages as compared with their female counterparts in female-dominated occupations (Pitts 2002). Thus, many women going into female-dominated occupations are actually situating themselves to earn higher wages. These choices thereby maximize their wages (Pitts 2002). This holds true for all categories of women except for the most educated, who are more likely to earn more in a male profession than a female profession. There is also evidence that if it becomes more lucrative for women to move into male-dominated professions, women will do exactly this (Pitts 2002). In short, occupational choice is heavily influenced by existing constraints based on gender and pay-setting across occupations.

To make matters worse, when women increasingly enter a field, the average pay in that field tends to decline, relative to other fields. Levanon, England, and Allison (2009) found that when more women entered an industry, the relative pay of that industry 10 years later was lower. Specifically, they found evidence of devaluation—meaning the proportion of women in an occupation impacts the pay for that industry because work done by women is devalued.

Computer programming is an example of a field that has shifted from being a very mixed profession, often associated with secretarial work in the past, to being a lucrative, male-dominated profession (Miller 2016; Oldenziel 1999). While computer programming has evolved into a more technically demanding occupation in recent decades, there is no skills-based reason why the field needed to become such a male-dominated profession. When men flooded the field, pay went up. In contrast, when women became park rangers, pay in that field went down (Miller 2016).

Further compounding this problem is that many professions where pay is set too low by market forces, but which clearly provide enormous social benefits when done well, are female-dominated. Key examples range from home health workers who care for seniors, to teachers and child care workers who educate today’s children. If closing gender pay differences can help boost pay and professionalism in these key sectors, it would be a huge win for the economy and society.

The gender wage gap is real—and hurts women across the board. Too often it is assumed that this gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves affected by gender bias. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

— This paper was made possible by a grant from the Peter G. Peterson Foundation. The statements made and views expressed are solely the responsibility of the authors.

— The authors wish to thank Josh Bivens, Barbara Gault, and Heidi Hartman for their helpful comments.

About the authors

Jessica Schieder joined EPI in 2015. As a research assistant, she supports the research of EPI’s economists on topics such as the labor market, wage trends, executive compensation, and inequality. Prior to joining EPI, Jessica worked at the Center for Effective Government (formerly OMB Watch) as a revenue and spending policies analyst, where she examined how budget and tax policy decisions impact working families. She holds a bachelor’s degree in international political economy from Georgetown University.

Elise Gould , senior economist, joined EPI in 2003. Her research areas include wages, poverty, economic mobility, and health care. She is a co-author of The State of Working America, 12th Edition . In the past, she has authored a chapter on health in The State of Working America 2008/09; co-authored a book on health insurance coverage in retirement; published in venues such as The Chronicle of Higher Education ,  Challenge Magazine , and Tax Notes; and written for academic journals including Health Economics , Health Affairs, Journal of Aging and Social Policy, Risk Management & Insurance Review, Environmental Health Perspectives , and International Journal of Health Services . She holds a master’s in public affairs from the University of Texas at Austin and a Ph.D. in economics from the University of Wisconsin at Madison.

Anderson, Julie, Ariane Hegewisch, and Jeff Hayes 2015. The Union Advantage for Women . Institute for Women’s Policy Research.

Blau, Francine D., and Lawrence M. Kahn 2016. The Gender Wage Gap: Extent, Trends, and Explanations . National Bureau of Economic Research, Working Paper No. 21913.

Bureau of Labor Statistics (BLS). 2015. American Time Use Survey public data series. U.S. Census Bureau.

Corbett, Christianne, and Catherine Hill. 2015. Solving the Equation: The Variables for Women’s Success in Engineering and Computing . American Association of University Women (AAUW).

Current Population Survey Outgoing Rotation Group microdata (CPS ORG). 2011–2015. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. U.S. Census Bureau.

Goldin, Claudia. 2014. “ A Grand Gender Convergence: Its Last Chapter .” American Economic Review, vol. 104, no. 4, 1091–1119.

Hegewisch, Ariane, and Asha DuMonthier. 2016. The Gender Wage Gap: 2015; Earnings Differences by Race and Ethnicity . Institute for Women’s Policy Research.

Herbert, Jennifer, and Deborah Stipek. 2005. “The Emergence of Gender Difference in Children’s Perceptions of Their Academic Competence.” Journal of Applied Developmental Psychology , vol. 26, no. 3, 276–295.

Hersch, Joni, and Leslie S. Stratton. 2002. “ Housework and Wages .” The Journal of Human Resources , vol. 37, no. 1, 217–229.

Hewlett, Sylvia Ann, Carolyn Buck Luce, Lisa J. Servon, Laura Sherbin, Peggy Shiller, Eytan Sosnovich, and Karen Sumberg. 2008. The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology . Harvard Business Review.

Kroeger, Teresa, Tanyell Cooke, and Elise Gould. 2016.  The Class of 2016: The Labor Market Is Still Far from Ideal for Young Graduates . Economic Policy Institute.

Landers, Renee M., James B. Rebitzer, and Lowell J. Taylor. 1996. “ Rat Race Redux: Adverse Selection in the Determination of Work Hours in Law Firms .” American Economic Review , vol. 86, no. 3, 329–348.

Levanon, Asaf, Paula England, and Paul Allison. 2009. “Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950-2000 U.S. Census Data.” Social Forces, vol. 88, no. 2, 865–892.

Miller, Claire Cain. 2016. “As Women Take Over a Male-Dominated Field, the Pay Drops.” New York Times , March 18.

Oldenziel, Ruth. 1999. Making Technology Masculine: Men, Women, and Modern Machines in America, 1870-1945 . Amsterdam: Amsterdam University Press.

Organisation for Economic Co-operation and Development (OECD). 2015. The ABC of Gender Equality in Education: Aptitude, Behavior, Confidence .

Pitts, Melissa M. 2002. Why Choose Women’s Work If It Pays Less? A Structural Model of Occupational Choice. Federal Reserve Bank of Atlanta, Working Paper 2002-30.

Pope, Devin G., and Justin R. Sydnor. 2010. “ Geographic Variation in the Gender Differences in Test Scores .” Journal of Economic Perspectives , vol. 24, no. 2, 95–108.

See related work on Wages, Incomes, and Wealth | Women

See more work by Jessica Schieder and Elise Gould

Economic Inequality by Gender

How big are the inequalities in pay, jobs, and wealth between men and women? What causes these differences?

By Esteban Ortiz-Ospina, Joe Hasell and Max Roser

This page was first published in March 2018 and last revised in March 2024.

On this page, you can find writing, visualizations, and data on how big the inequalities in pay, jobs, and wealth are between men and women, how they have changed over time, and what may be causing them

Although economic gender inequalities remain common and large, they are today smaller than they used to be some decades ago.

Related topics

Women's Employment

How does women’s labor force participation differ across countries? How has it changed over time? What is behind these differences and changes?

Women’s Rights

How has the protection of women’s rights changed over time? How does it differ across countries? Explore global data and research on women’s rights.

Maternal Mortality

What could be more tragic than a mother losing her life in the moment that she is giving birth to her newborn? Why are mothers dying and what can be done to prevent these deaths?

See all interactive charts on economic inequality by gender ↓

How does the gender pay gap look like across countries and over time?

The 'gender pay gap' comes up often in political debates , policy reports , and everyday news . But what is it? What does it tell us? Is it different from country to country? How does it change over time?

Here we try to answer these questions, providing an empirical overview of the gender pay gap across countries and over time.

The gender pay gap measures inequality but not necessarily discrimination

The gender pay gap (or the gender wage gap) is a metric that tells us the difference in pay (or wages, or income) between women and men. It's a measure of inequality and captures a concept that is broader than the concept of equal pay for equal work.

Differences in pay between men and women capture differences along many possible dimensions, including worker education, experience, and occupation. When the gender pay gap is calculated by comparing all male workers to all female workers – irrespective of differences along these additional dimensions – the result is the 'raw' or 'unadjusted' pay gap. On the contrary, when the gap is calculated after accounting for underlying differences in education, experience, etc., then the result is the 'adjusted' pay gap.

Discrimination in hiring practices can exist in the absence of pay gaps – for example, if women know they will be treated unfairly and hence choose not to participate in the labor market. Similarly, it is possible to observe large pay gaps in the absence of discrimination in hiring practices – for example, if women get fair treatment but apply for lower-paid jobs.

The implication is that observing differences in pay between men and women is neither necessary nor sufficient to prove 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 complete source in terms of coverage is the United Nation's International Labour Organization (ILO). The visualization here presents this data. You can add observations by clicking on the option 'add country' at the bottom of the chart.

The estimates shown here correspond to differences between the average hourly earnings of men and women (expressed as a percentage of average hourly earnings of men), and cover all workers irrespective of whether they work full-time or part-time. 1

As we can see: (i) in most countries the gap is positive – women earn less than men, and (ii) there are large differences in the size of this gap across countries. 2

In most countries, the gender pay gap has decreased in the last couple of decades

How is the gender pay gap changing over time? To answer this question, let's consider this chart showing available estimates from the OECD. These estimates include OECD member states, as well as some other non-member countries, and they are the longest available series of cross-country data on the gender pay gap that we are aware of.

Here we see that the gap is large in most OECD countries, but it has been going down in the last couple of decades. In some cases the reduction is remarkable. In the United States, for example, the gap declined by more than half.

These estimates are not directly comparable to those from the ILO, because the pay gap is measured slightly differently here: The OECD estimates refer to percent differences in median earnings (i.e. the gap here captures differences between men and women in the middle of the earnings distribution), and they cover only full-time employees and self-employed workers (i.e. the gap here excludes disparities that arise from differences in hourly wages for part-time and full-time workers).

However, the ILO data shows similar trends.

The conclusion is that in most countries with available data, the gender pay gap has decreased in the last couple of decades.

The gender pay gap is larger for older workers

The United States Census Bureau defines the pay gap as the ratio between median wages – that is, they measure the gap by calculating the wages of men and women at the middle of the earnings distribution, and dividing them.

By this measure, the gender wage gap is expressed as a percent (median earnings of women as a share of median earnings of men) and it is always positive. Here, values below 100% mean that women earn less than men, while values above 100% mean that women earn more. Values closer to 100% reflect a lower gap.

The next chart shows available estimates of this metric for full-time workers in the US, by age group.

First, we see that the series trends upwards, meaning the gap has been shrinking in the last couple of decades. Secondly, we see that there are important differences by age.

The second point is crucial to understanding the gender pay gap: the gap is a statistic that changes during the life of a worker. In most rich countries, it’s small when formal education ends and employment begins, and it increases with age. As we discuss in our analysis of the determinants below, the gender pay gap tends to increase when women marry and when/if they have children.

The gender pay gap is smaller in middle-income countries – which tend to be countries with low labor force participation of women

The chart here plots available ILO estimates on the gender pay gap against GDP per capita. As we can see there is a weak positive correlation between GDP per capita and the gender pay gap. However, the chart shows that the relationship is not really linear. Actually, middle-income countries tend to have the smallest pay gap.

The fact that middle-income countries have low gender wage gaps is, to a large extent, the result of selection of women into employment . Olivetti and Petrongolo (2008) explain it as follows: “[I]f women who are employed tend to have relatively high‐wage characteristics, low female employment rates may become consistent with low gender wage gaps simply because low‐wage women would not feature in the observed wage distribution.” 3

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, the gender pay gaps tend to be smaller where relatively fewer women participate in the labor force .

So, rather than reflect greater equality, the lower wage gaps observed in some countries could indicate that only women with certain characteristics – for instance, with no husband or children – are entering the workforce.

Why is there a gender pay gap?

In almost all countries, if you compare the wages of men and women you find that women tend to earn less than men.  These inequalities have been narrowing across the world. In particular, most high-income countries have seen sizeable reductions in the gender pay gap over the last couple of decades.

How did these reductions come about and why do substantial gaps remain?

Before we get into the details, here is a preview of the main points.

• An important part of the reduction in the gender pay gap in rich countries over the last decades is due to a historical narrowing, and often even reversal of the education gap between men and women.
• Today, education is relatively unimportant in explaining the remaining gender pay gap in rich countries. In contrast, the characteristics of the jobs that women tend to do, remain important contributing factors.
• The gender pay gap is not a direct metric of discrimination. However, evidence from different contexts suggests discrimination is indeed important to understand the gender pay gap. Similarly, social norms affecting the gender distribution of labor are important determinants of wage inequality.
• On the other hand, the available evidence suggests differences in psychological attributes and non-cognitive skills are at best modest factors contributing to the gender pay gap.

Differences in human capital

The adjusted pay gap.

Differences in earnings between men and women capture differences across many possible dimensions, including education, experience, and occupation.

For example, if we consider that more educated people tend to have higher earnings, it is natural to expect that the narrowing of the pay gap across the world can be partly explained by the fact that women have been catching up with men in terms of educational attainment, in particular years of schooling.

Indeed, since differences in education partly contribute to explaining differences in wages, it is common to distinguish between 'unadjusted' and 'adjusted' pay differences.

When the gender pay gap is calculated by comparing all male and female workers, irrespective of differences in worker characteristics, the result is the raw or unadjusted pay gap. In contrast to this, when the gap is calculated after accounting for underlying differences in education, experience, and other factors that matter for the pay gap, then the result is the adjusted pay gap.

The idea of the adjusted pay gap is to make comparisons within groups of workers with roughly similar jobs, tenure, and education. This allows us to tease out the extent to which different factors contribute to observed inequalities.

The chart here, from Blau and Kahn (2017) shows the evolution of the adjusted and unadjusted gender pay gap in the US. 4

More precisely, the chart shows the evolution of female-to-male wage ratios in three different scenarios: (i) Unadjusted; (ii) Adjusted, controlling for gender differences in human capital, i.e. education and experience; and (iii) Adjusted, controlling for a full range of covariates, including education, experience, job industry, and occupation, among others. The difference between 100% and the full specification (the green bars) is the “unexplained” residual. 5

Several points stand out here.

• First, the unadjusted gender pay gap in the US shrunk over this period. This is evident from the fact that the blue bars are closer to 100% in 2010 than in 1980.
• Second, if we focus on groups of workers with roughly similar jobs, tenure, and education, we also see a narrowing. The adjusted gender pay gap has shrunk.
• Third, we can see that education and experience used to help explain a very large part of the pay gap in 1980, but this changed substantially in the decades that followed. This third point follows from the fact that the difference between the blue and red bars was much larger in 1980 than in 2010.
• And fourth, the green bars grew substantially in the 1980s, but stayed fairly constant thereafter. In other words: Most of the convergence in earnings occurred during the 1980s, a decade in which the "unexplained" gap shrunk substantially.

Education and experience have become much less important in explaining gender differences in wages in the US

The next chart shows a breakdown of the adjusted gender pay gaps in the US, factor by factor, in 1980 and 2010.

When 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. 6

In this chart we can also see that the 'unexplained' residual has gone down. This means the observable characteristics of workers and their jobs explain wage differences better today than a couple of decades ago. At first sight, this seems like good news – it suggests that today there is less discrimination, in the sense that differences in earnings are today much more readily explained by differences in 'productivity' factors. But is this really the case?

The unexplained residual may include aspects of unmeasured productivity (i.e. unobservable worker characteristics that could not be accounted for in the study), while the "explained" factors may themselves be vehicles of discrimination.

For example, suppose that women are indeed discriminated against, and they find it hard to get 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 embedded in occupational differences!

Hence, while the unexplained residual gives us a first-order approximation of what is going on, we need much more detailed data and analysis in order to say something definitive about the role of discrimination in observed pay differences.

Gender pay differences around the world are better explained by occupation than by education

The set of three maps here, taken from the World Development Report (2012) , shows that today gender pay differences are much better explained by occupation than by education. This is consistent with the point already made above using data for the US: as education expanded radically over the last few decades, human capital has become much less important in explaining gender differences in wages.

Justin Sandefur at the Center for Global Development shows that education also fails to explain wage gaps if we include workers with zero income (i.e. if we decompose the wage gap after including people who are not employed).

Looking beyond worker characteristics

Job flexibility.

All over the world women tend to do more unpaid care work at home than men – and women tend to be overrepresented in low-paying jobs where they have the flexibility required to attend to these additional responsibilities.

The most important evidence regarding this link between the gender pay gap and job flexibility is presented and discussed by Claudia Goldin in the article ' A Grand Gender Convergence: Its Last Chapter ', where she digs deep into the data from the US. 8 There are some key lessons that apply both to rich and non-rich countries.

Goldin shows that when one looks at the data on occupational choice in some detail, it becomes clear that women disproportionately seek jobs, including full-time jobs, that tend to be compatible with childrearing and other family responsibilities. In other words, women, more than men, are expected to have temporal flexibility in their jobs. Things like shifting hours of work and rearranging shifts to accommodate emergencies at home. And these are jobs with lower earnings per hour, even when the total number of hours worked is the same.

The importance of job flexibility in this context is very clearly illustrated by the fact that, over the last couple of decades, women in the US increased their participation and remuneration in only some fields. In a recent paper, Goldin and Katz (2016) show that pharmacy became a highly remunerated female-majority profession with a small gender earnings gap in the US, at the same time as pharmacies went through substantial technological changes that made flexible jobs in the field more productive (e.g. computer systems that increased the substitutability among pharmacists). 9

The chart here shows how quickly female wages increased in pharmacy, relative to other professions, over the last few decades in the US.

The motherhood penalty

Closely related to job flexibility and occupational choice is the issue of work interruptions due to motherhood. On this front, there is again a great deal of evidence in support of the so-called 'motherhood penalty'.

Lundborg, Plug, and Rasmussen (2017) provide evidence from Denmark – more specifically, Danish women who sought medical help in achieving pregnancy. 10

By tracking women’s fertility and employment status through detailed periodic surveys, these researchers were able to establish that women who had a successful in vitro fertilization treatment, ended up having lower earnings down the line than similar women who, by chance, were unsuccessfully treated.

Lundborg, Plug, and Rasmussen summarise their findings as follows: "Our main finding is that women who are successfully treated by [in vitro fertilization] earn persistently less because of having children. We explain the decline in annual earnings by women working less when children are young and getting paid less when children are older. We explain the decline in hourly earnings, which is often referred to as the motherhood penalty, by women moving to lower-paid jobs that are closer to home."

The fact that the motherhood penalty is indeed about ‘motherhood’ and not ‘parenthood’, is supported by further evidence.

A recent study , also from Denmark, tracked men and women over the period 1980-2013 and found that after the first child, women’s earnings sharply dropped and never fully recovered. But this was not the case for men with children, nor the case for women without children.

These patterns are shown in the chart here. The first panel shows the trend in earnings for Danish women with and without children. The second panel shows the same comparison for Danish men.

Note that these two examples are from Denmark – a country that ranks high on gender equality measures and where there are legal guarantees requiring that a woman can return to the same job after taking time to give birth.

This shows that, although family-friendly policies contribute to improving female labor force participation and reducing the gender pay gap , they are only part of the solution. Even when there is generous paid leave and subsidized childcare, as long as mothers disproportionately take additional work at home after having children, inequities in pay are likely to remain.

Ability, personality, and social norms

The discussion 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 the systematic gender differences in occupational choice? What makes women seek job flexibility and take a disproportionate amount of unpaid care work?

One argument usually put forward is that, to the extent that biological differences in preferences and abilities 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. 11

To be clear, yes, there is evidence supporting the fact that men and women differ in some key attributes that may affect labor market outcomes. For example, standardized tests show that there are statistical gender gaps in maths scores in some countries ; and experiments show that women avoid more salary negotiations , and they often show particular predisposition to accept and receive requests for tasks with low promotion potential . However, these observed differences are far from being biologically fixed – 'gendering' begins early in life and the evidence shows that preferences and skills are highly malleable. You can influence tastes, and you can certainly teach people to tolerate risk, to do maths, or to negotiate salaries.

What's more, independently of where they come from, Blau and Kahn show that these empirically observed differences can typically only account for a modest portion of the gender pay gap.

In contrast, the evidence does suggest 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 wages. You can read more about this farther below.

Discrimination and bias

Independently 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 instance, examines past prohibitions against the training and employment of married women in the US. She touches on some well-known restrictions, such as those against the training and employment of women as doctors and lawyers, before focusing on the lesser known but even more impactful 'marriage bars' that arose in the late 1800s and early 1900s. These work prohibitions are important because they applied to teaching and clerical jobs – occupations that would become the most commonly held among married women after 1950. Around the time the US entered World War II, it is estimated that 87% of all school boards would not hire a married woman and 70% would not retain an unmarried woman who married. 12

The map here highlights that to this day, explicit barriers limit the extent to which women are allowed to do the same jobs as men in some countries. 13

However, even after explicit barriers are lifted and legal protections put in place, discrimination and bias can persist in less overt ways. Goldin and Rouse (2000), for example, look at the adoption of "blind" auditions by orchestras and show that by using a screen to conceal the identity of a candidate, impartial hiring practices increased the number of women in orchestras by 25% between 1970 and 1996. 14

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 US thought that a wife should work if her husband was able to support her . This obviously circles back to our earlier point about social norms. 15

Strategies for reducing the gender pay gap

In many countries wage inequality between men and women can be reduced by improving the education of women. However, in many countries, gender gaps in education have been closed and we still have large gender inequalities in the workforce. What else can be done?

An obvious alternative is fighting discrimination. But the evidence presented above shows that this is not enough. Public policy and management changes on the firm level matter too: Family-friendly labor-market policies may help. For example, maternity leave coverage can contribute by raising women’s retention over the period of childbirth, which in turn raises women’s wages through the maintenance of work experience and job tenure. 16

Similarly, early education and childcare can increase the labor force participation of women — and reduce gender pay gaps — by alleviating the unpaid care work undertaken by mothers. 17

Additionally, the experience of women's historical advance in specific professions (e.g. pharmacists in the US), suggests that the gender pay gap could also be considerably reduced if firms did not have the incentive to disproportionately reward workers who work long hours, and fixed, non-flexible schedules. 18

Changing these incentives is of course difficult because it requires reorganizing the workplace. But it is likely to have a large impact on gender inequality, particularly in countries where other measures are already in place. 19

Implementing these strategies can have a positive self-reinforcing effect. For example, family-friendly labor-market policies that lead to higher labor-force attachment and salaries for women will raise the returns on women's investment in education – so women in future generations will be more likely to invest in education, which will also help narrow gender gaps in labor market outcomes down the line. 20

Nevertheless, powerful as these strategies may be, they are only part of the solution. Social norms and culture remain at the heart of family choices and the gender distribution of labor. Achieving equality in opportunities requires ensuring that we change the norms and stereotypes that limit the set of choices available both to men and women. It is difficult, but as the next section shows, social norms can be changed, too.

How well do biological differences explain the gender pay gap?

Across 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 required to attend to 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 stem from biological sex differences. But what’s the broader evidence for these claims?

In a nutshell, here's what the research and data shows:

• There is evidence supporting the fact that statistically speaking, men and women tend to differ in some key aspects, including psychological attributes that may 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 does show that these attributes are strongly malleable.
• Regardless of the origin, these differences can only explain a modest part of the gender pay gap.

Some context regarding the gender distribution of labor

Before we get into the discussion of whether biological attributes explain wage differences via gender roles, let's get some perspective on the gender distribution of work.

The following chart shows, by country, the female-to-male ratio of time devoted to unpaid care work, including tasks like taking care of children at home, housework, or doing community work. As can be seen, all over the world there is a radical unbalance in the gender distribution of labor – everywhere women take a disproportionate amount of unpaid work.

This is of course closely related to the fact that in most countries there are gender gaps in labor force participation and wages .

“Boys are better at maths”

Differences in biological attributes that determine our ability to develop 'hard skills', such as maths, are often argued to be at the heart of the gender pay gap. 21 Do large gender differences in maths skills really exist? If so, is this because of differences in the attributes we are born with?

Let's look at the data.

Are boys better in the mathematics section of the PISA standardized test ? One could argue that looking at top scores is more relevant here since top scores are more likely to determine gaps in future professional trajectories – for example, gaps in access to 'STEM degrees' at the university level.

The chart shows the share of male and female test-takers scoring at the highest level on the PISA test (that's level 6). As we can see, most countries lie above the diagonal line marking gender parity; so yes, achieving high scores in maths tends to be more common among boys than girls. However, there is huge cross-country variation – the differences between countries are much larger than the differences between the sexes. And in many countries, the gap is effectively inexistent. 22

Similarly, researchers have found that within countries there is also large geographic variation in gender gaps in test scores. So clearly these gaps in mathematical ability do not seem to be fully determined by biological endowments. 23

Indeed, research looking at the PISA cross-country results suggests that improved social conditions for women are related to improved math performance by girls. 24

Not only do statistical gaps in test scores vary substantially across societies – they also vary substantially across time. This suggests that social factors play a large role in explaining differences between the sexes.

In the US, for example, the gender gap in mathematics has narrowed in recent decades. 25 And this narrowing took place as high school curricula of boys and girls became more similar. The following chart shows this: In the US boys in 1957 took far more math and science courses than did girls; but by 1992 there was virtual parity in almost all science and math courses.

More importantly for the question at hand, 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) concludes that gaps in test scores in the US are too small to explain much of the gender pay at any point in time. 26

So, taken together, the evidence suggests that statistical gaps in maths test scores are both relatively small and heavily influenced by social and environmental factors.

“It’s about personality”

Biological differences in tastes (e.g. preferences for 'people' over 'things'), psychological attributes (e.g. 'risk aversion'), and soft skills (e.g. the ability to get along with others) are also often argued to be at the heart of the gender pay gap.

There are hundreds of studies trying to establish whether there are gender differences in preferences, personality traits, and 'soft skills'. The quality and general relevance (i.e. the internal and external validity) of these studies is the subject of much discussion, as illustrated in the recent debate that ensued from the Google Memo affair .

A recent article from the 'Heterodox Academy ', 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 scholars.

For the purpose of this blog post, let's focus on the review of the evidence presented in Blau and Kahn (2017) – their review is particularly helpful because they focus 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 'people skills' (i.e. ability to listen, communicate, and relate to others). Similarly, experimental studies have found that women more often avoid salary negotiations , and they often show a particular predisposition to accept and receive requests for tasks with low promotability. But are the origins of these differences mainly biological or are they social? And are they strong enough to explain pay gaps?

The available evidence here suggests these factors can only explain a relatively small fraction of the observed differences in wages. 27 And they are anyway far from being purely biological – preferences and skills are highly malleable and 'gendering' begins early in life. 28

Here is a concrete example: Leibbrandt and List (2015) did an experiment in which they assessed how men and women reacted to job advertisements. 29 They found that although men were more likely to negotiate than women when there was no explicit statement that wages were negotiable, the gender difference disappeared and even reversed when it was explicitly stated that wages were negotiable. This suggests that it is not as 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 negotiation attitudes disappeared when it was explicitly stated that wages were negotiable, emphasizes the important role that social norms and culture play in labor-market outcomes.

These concepts may seem abstract: What do social norms and culture actually look like in the context of the gender pay gap?

The reproduction of stereotypes through everyday positive enforcement can be seen in a range of aspects: A study analyzing 124 prime-time television programs in the US found that female characters continue to inhabit interpersonal roles with romance, family, and friends, while male characters enact work-related roles. 30 In the realm of children’s books, a study of 5,618 books found that compared to females, males are represented nearly twice as often in titles and 1.6 times as often as central characters. 31 Qualitative research shows that even in the home, parents are often enforcers of gender norms – especially when it comes to fathers endorsing masculinity in male children. 32

Of particular relevance 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".

The following chart depicts the distribution of the share of the household income earned by the wife, across married couples in the US.

Consistent with the idea that "a man should earn more than his wife", the data shows a sharp drop at 0.5, the point where the wife starts to earn more than the husband.

Distribution of income share earned by the wife across married couples in the US – Bertrand, Kamenica, and Pan (2015) 33

This is the result of two factors. First, it is about the matching of men and women before they marry – 'matches' in which the woman has higher earning potential are less common. Second, it is a result of choices after marriage – the researchers show that married women with higher earning potential than their husbands often stay out of the labor force, or take 'below-potential' jobs. 34

The authors of the study from which this chart is taken 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 household chores, so the gender gap in unpaid care work is even larger; and these couples are also less satisfied with their marriage and are more likely to divorce than couples where the wife earns less than the husband.

The empirical exploration in this study highlights the remarkable power that gender norms and identity have on labor-market outcomes.

Why do gender norms and identity matter?

Does it actually matter if social norms and culture are important determinants of gender roles and labor-market outcomes? Are social norms in 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 decrease in the reported acceptability of domestic violence towards women and son preference, as well as increases in women’s autonomy and decreases in fertility. 35

Of course, TV is a small aspect of all the big things that matter for social norms. But this study is important for the discussion because it is hard to study how social norms can be changed. TV introduction is a rare opportunity to see how a group that is exposed to a driver of social change actually changes.

As Jensen and Oster point out, most popular cable TV shows in India feature urban settings where lifestyles differ radically from those in rural areas. For example, many female characters on popular soap operas have more education, marry later, and have smaller families than most women in rural areas. And, similarly, many female characters in these tv shows are featured working outside the home as professionals, running businesses, or are shown in other positions of authority.

The bar chart below shows how cable access changed attitudes toward the self-reported preference for their child to be a son. As the authors note, "reported desire for the next child to be a son is relatively unchanged in areas with no change in cable status, but it decreases sharply between 2001 and 2002 for villages that get cable in 2002, and between 2002 and 2003 (but notably not between 2001 and 2002) for those that get cable in 2003. For both measures of attitudes, the changes are large and striking, and correspond closely to the timing of introduction of cable."

To conclude: 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 matters 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 propelled the women's movement. 36

How are women represented across jobs?

Representation of women at the top of the income distribution.

Despite having fallen in recent decades, there remains a substantial pay gap between the average wages of men and women .

But what does gender inequality look like if we focus on the very top of the income distribution? Do we find any evidence of the so-called 'glass ceiling' preventing women from reaching the top? How did this change over time?

Answers to these questions are found in the work of Atkinson, Casarico and Voitchovsky (2018). Using tax records, they investigated the incomes of women and men separately across nine high-income countries. As such, they were restricted to those countries in which taxes are collected on an individual basis, rather than as couples. 37

In addition to wages they also take into account income from investments and self-employment.

Whilst investment income tends to make up a larger share of the total income of rich individuals in general, the authors found this to be particularly marked in the case of women in top-income groups.

The two charts present the key figures from the study.

One chart shows the proportion of women out of all individuals falling into the top 10%, 1%, and 0.1% of the income distribution. The open circle represents the share of women in the top income brackets back in 2000; the closed circle shows the latest data, which is from 2013.

The other chart shows the data over time for individual countries. You can explore data for other countries using the 'Change country' button on the chart.

The two charts allow us to answer the initial questions:

• Women are greatly under-represented in top income groups – they make up much less than 50% across each of the nine countries. Within the top 1% women account for around 20% and there is surprisingly little variation across countries.
• The proportion of women is lower the higher you look up the income distribution. In the top 10% up to every third income-earner is a woman; in the top 0.1% only every fifth or tenth person is a woman.
• The trend is the same in all countries of this study: Women are now better represented in all top-income groups than they were in 2000.
• But improvements have generally been more limited at the very top. With the exception of Australia, we see a much smaller increase in the share of women amongst the top 0.1% than amongst the top 10%.

Overall, despite recent inroads, we continue to see remarkably few women making it to the top of the income distribution today.

Representation of women in management positions

The chart here plots the proportion of women in senior and middle management positions around the world. It shows that women all over the world are underrepresented in high-profile jobs, which tend to be better paid.

The next chart provides an alternative perspective on the same issue. Here we show the share of firms that have a woman as manager. We highlight world regions by default, but you can remove them and add specific countries.

As we can see, all over the world firms tend to be managed by men. And, globally, only about 18% of firms have a female manager.

Firms with female managers tend to be different to firms with male managers. For example, firms with female managers tend to also be firms with more female workers .

Representation of women in low-paying jobs

Above we show that women all over the world are underrepresented in high-profile jobs, which tend to be better paid. As it turns out, in many countries women are at the same time overrepresented in low-paying jobs.

This is shown in the chart here, where 'low-pay' refers to workers earning less than two-thirds of the median (i.e. the middle) of the earnings distribution.

A share above 50% implies that women are 'overrepresented', in the sense that among those with low wages, there are more women than men.

The fact that women in rich countries are overrepresented in the bottom of the income distribution goes together with the fact that working women in these countries are overrepresented in low-paying occupations. The chart shows this for the US.

How much control do women have over household resources?

Women often have no control over their personal earned income.

The next chart plots cross-country estimates of the share of women who are not involved in decisions about their own income. The line shows national averages, while the dots show averages for rich and poor households (i.e. averages for women in households within the top and bottom quintiles of the corresponding national income distribution).

As we can see, in many countries, particularly in Sub-Saharan Africa and Asia, a large fraction of women are not involved in household decisions about spending their personal earned income. And this pattern is stronger among low-income households within low-income countries.

Percentage of women not involved in decisions about their own income – World Development Report (2012) 39

In many countries, women have limited influence over important household decisions.

Above we focus on whether women get to choose how their own personal income is spent. Now we look at women's influence over total household income.

In this chart, we plot the share of currently married women who report having a say in major household purchase decisions, against national GDP per capita.

We see that in many countries, notably in Sub-Saharan Africa and Asia, an important number of women have limited influence over major spending decisions.

The chart above shows that women’s control over household spending tends to be greater in richer countries. In the next chart, we show that this correlation also holds within countries: Women’s control is greater in wealthier households. Household wealth is shown by the quintile in the wealth distribution on the x-axis – the poorest households are in the lowest quintiles (Q1) on the left.

There are many factors at play here, and it's important to bear in mind that this correlation partly captures the fact that richer households enjoy greater discretionary income beyond levels required to cover basic expenditure, while at the same time, in richer households women often have greater agency via access to broader networks as well as higher personal assets and incomes.

Land ownership is more often in the hands of men

Economic inequalities between men and women manifest themselves not only in terms of wages earned but also in terms of assets owned. For example, as the chart shows, in nearly all low and middle-income countries with data, men are more likely to own land than women.

Women's lack of control over important household assets, such as land, can be a critical problem in case of divorce or the husband’s death.

Closely related to the issue of land ownership is the fact that in several countries women do not have the same rights to property as men. These countries are highlighted in the map. 40

Gender-equal inheritance systems have been adopted in most, but not all countries

Inheritance is one of the main mechanisms for the accumulation of assets. In the map, we provide an overview of the countries that do and do not have gender-equal inheritance systems.

If you move the slider to 1920, you will see that while gender-equal inheritance systems were very rare in the early 20th century, today they are much more common. And still, despite the progress achieved, in many countries, notably in North Africa and the Middle East, women and girls still have fewer inheritance rights than men and boys.

Gender differences in access to productive inputs are often large

Above we show that there are large gender gaps in land ownership across low-income countries. Here we show that there are also large gaps in terms of access to borrowed capital.

The chart shows the percentage of men and women who report borrowing any money in the past 12 months to start, operate, or expand a farm or business.

As we can see, almost everywhere, including in many rich countries, women are less likely to obtain borrowed capital for productive purposes.

This can have large knock-on effects: in agriculture and entrepreneurship, gender differences in access to productive inputs, including land and credit, can lead to gaps in earnings via lower productivity.

Indeed, studies have found that, when statistical gender differences in agricultural productivity exist, they often disappear when access to and use of productive inputs are taken into account. 41

Interactive Charts on Economic Inequality by Gender

Acknowledgements.

We thank Sandra Tzvetkova and Diana Beltekian for their great research assistance.

There are some exceptions to this definition. In particular, sometimes self-employed workers, or part-time workers are excluded.

This measure can also be negative. This means that, on an hourly basis, men earn on average less than women. It is the case for some countries, such as Malaysia.

Olivetti, C., & Petrongolo, B. (2008). Unequal pay or unequal employment? A cross-country analysis of gender gaps. Journal of Labor Economics, 26(4), 621-654.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865.

For each specification, Blau and Kahn (2017) perform regression analyses on data from the PSID (the Michigan Panel Study of Income Dynamics), which includes information on labor-market experience and considers men and women ages 25-64 who were full-time, non-farm, wage and salary workers.

In 2010, unionization and education show negative values; this reflects the fact that women have surpassed men in educational attainment, and unionization in the US has been in general decline with a greater effect on men.

The full source is: World Development Report (2012) Gender Equality and Development , World Bank.

Goldin, C. (2014). A grand gender convergence: Its last chapter. The American Economic Review, 104(4), 1091-1119.

Goldin, C., & Katz, L. F. (2016). A most egalitarian profession: pharmacy and the evolution of a family-friendly occupation. Journal of Labor Economics, 34(3), 705-746.

Lundborg, P., Plug, E., & Rasmussen, A. W. (2017). Can Women Have Children and a Career? IV Evidence from IVF Treatments. American Economic Review, 107(6), 1611-1637.

Blau, Francine D., and Lawrence M. Kahn. 2017. " The Gender Wage Gap: Extent, Trends, and Explanations. " Journal of Economic Literature, 55(3): 789-865

Goldin, C. (1988). Marriage bars: Discrimination against married women workers, 1920's to 1950's .

The data in this map, which comes from the World Bank's World Development Indicators, provides a measure of whether there are any specific jobs that women are not allowed to perform. So, for example, a country might be coded as "No" if women are only allowed to work in certain jobs within the mining industry, such as health care professionals within mines, but not as miners.

Goldin, C., & Rouse, C. (2000). Orchestrating impartiality: The impact of" blind" auditions on female musicians. American Economic Review , 90(4), 715-741.

Blau and Kahn (2017) provide a whole list of experimental studies that have found labor-market discrimination. Another early example is from Neumark et al. (1996), who look at discrimination in restaurants. In this case, male and female pseudo-job-seekers were given similar CVs to apply for jobs waiting on tables at the same set of restaurants in Philadelphia. The results showed discrimination against women in high-priced restaurants.

The full reference of this study is Neumark, D., Bank, R. J., & Van Nort, K. D. (1996). Sex discrimination in restaurant hiring: An audit study. The Quarterly Journal of Economics, 111(3), 915-941.

Waldfogel, J. (1998). Understanding the "family gap" in pay for women with children. The Journal of Economic Perspectives, 12(1), 137-156.

Olivetti, C., & Petrongolo, B. (2017). The economic consequences of family policies: lessons from a century of legislation in high-income countries. The Journal of Economic Perspectives, 31(1), 205-230.

As we show above, in several nations, such as Sweden and Denmark, a “motherhood penalty” in earnings exists, even though these nations have generous family policies, including paid family leave and subsidized child care.

For a discussion of this mechanism, see page 814, Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Hard skills are abilities that can be defined and measured, such as writing, reading, or doing maths. By contrast, soft skills are less tangible and harder to measure and quantify.

Also importantly: If we focus on gender differences for average , rather than top students, we find that there is not even a clear tendency in favor of boys. ( This interactive chart compares PISA average math scores for boys and girls ).

For more on this see Pope, D. G., & Sydnor, J. R. (2010). Geographic variation in the gender differences in test scores. Journal of Economic Perspectives, 24(2), 95-108.

Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. SCIENCE-NEW YORK THEN WASHINGTON-, 320(5880), 1164.

A number of papers have documented the narrowing of gender gaps in test scores. See, for example, Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance . Science, 321(5888), 494-495.

Blau, Francine D., and Lawrence M. Kahn. 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature, 55(3): 789-865.

Blau and Kahn write: "While findings such as those in table 7 ['Selected Studies Assessing the Role of Psychological Traits in Accounting for the Gender Pay Gap'] are informative in elucidating some of the possible omitted factors that lie behind gender differences in wages as well as the unexplained gap in traditional wage regressions, in general, the results suggest that these factors do not account for a large portion of either the raw or unexplained gender gap."

For a discussion of 'gendering' see West, C., & Zimmerman, D. H. (1987). Doing gender. Gender & Society, 1(2), 125-151.

Leibbrandt, A., & List, J. A. (2014). Do women avoid salary negotiations? Evidence from a large-scale natural field experiment. Management Science, 61(9), 2016-2024.

Lauzen, M. M., Dozier, D. M., & Horan, N. (2008). Constructing gender stereotypes through social roles in prime-time television. Journal of Broadcasting & Electronic Media, 52(2), 200-214.

McCabe, J., Fairchild, E., Grauerholz, L., Pescosolido, B. A., & Tope, D. (2011). Gender in twentieth-century children’s books: Patterns of disparity in titles and central characters. Gender & Society, 25(2), 197-226.

Kane, E. W. (2006). “No way my boys are going to be like that!” Parents’ responses to children’s gender nonconformity. Gender & Society, 20(2), 149-176.

Bertrand, M., Kamenica, E., & Pan, J. (2015). Gender identity and relative income within households. The Quarterly Journal of Economics, 130(2), 571-614.

More precisely, the authors find that in couples where the wife’s potential income is likely to exceed her husband’s (based on the income that would be predicted for her observed characteristics), the wife is less likely to be in the labor force, and if she does work, her income is lower than predicted.

Jensen, R., & Oster, E. (2009). The power of TV: Cable television and women's status in India . In  The Quarterly Journal of Economics , 124(3), 1057-1094.

Regarding intergenerational transmission of gender roles, see Fernández, R. (2013). Cultural change as learning: The evolution of female labor force participation over a century. The American Economic Review, 103(1), 472-500.

For a discussion regarding social activism and its link to the determinants of female labor supply, see for example this study by Heer and Grossbard-Shechtman (1981).

Atkinson, A.B., Casarico, A. & Voitchovsky, S. Top incomes and the gender divide . J Econ Inequal (2018) 16: 225.

The authors produced results for 8 countries, and included earlier results for Sweden from Boschini, A., Gunnarsson, K., Roine, J.: Women in Top Incomes: Evidence from Sweden 1974-2013, IZA Discussion paper 10979, August (2017).

World Bank. (2011). World development report 2012: gender equality and development . World Bank Publications.

The map from The World Development Report (2012) provides a more fine-grained overview of different property regimes operating in different countries.

For more discussion of the evidence see page 20 in World Bank (2011) World Development Report 2012: Gender Equality and Development. World Bank Publications.

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Everything you need to know about pushing for pay equity

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Workers worldwide look forward to payday. But while a paycheck may bring a sense of relief, satisfaction, or joy, it can also represent an injustice—a stark reminder of persistent inequalities between men and women in the workplace.

The gender pay gap stands at 20 per cent , meaning women workers earn 80 per cent of what men do. For women of colour, migrant women, those with disabilities, and women with children, the gap is even greater.

The cumulative effect of pay disparities has real, daily negative consequences for women, their families, and society, especially during crises. The widespread effects of COVID-19 have plunged up to 95 million people into extreme poverty, with one in every 10 women globally living in extreme poverty . If current trends continue, 342.4 million women and girls will be living on less than $2.15 a day by 2030.

What do we mean by equal pay for work of equal value?

Equal pay for work of equal value, as defined by the ILO Equal Remuneration Convention , means that all workers are entitled to receive equal remuneration not only for identical tasks but also for different work considered of equal value. This distinction is crucial because jobs held by women and men may involve varying qualifications, skills, responsibilities, or working conditions, yet hold equal value and warrant equal pay.

In 2020, New Zealand passed the Equal Pay Amendment Bill , ensuring that women and men are paid equally for work that’s different but has equal value, including in chronically underpaid female-dominated industries. 

It is also important to recognize that remuneration is more than a basic wage; it encompasses all the elements of earnings. This includes overtime pay, bonuses, travel allowances, company shares, insurance, and other benefits.

Why does the gender pay gap persist?

The gender pay gap originates from ingrained inequalities. Women, particularly migrant women, are overrepresented in the informal sector. Look around you, from street vending to domestic service, from coffee shop attendants to subsistence farming. Women fill informal jobs that often fall outside the domains of labour laws, trapping them in low-paying, unsafe working environments, without social benefits. These poor conditions for women workers perpetuate the gender pay gap.

Women also do  three more hours of daily care work  than men , globally. This includes household tasks such as cooking, cleaning, fetching firewood and water, and taking care of children and the elderly. Although care work is the backbone of thriving families, communities, and economies, it remains undervalued and underrecognized. Try calculating your daily load with  UN Women’s unpaid care calculator .

The  motherhood penalty exacerbates pay inequity, with working mothers facing lower wages, a disparity that jumps as the number of children a woman has increases. Lower wages for mothers are linked to reduced working time, employment in more family-friendly jobs that tend to be lower paying, hiring and promotion decisions that penalize the careers of mothers, and a lack of programmes to support women’s return to work after time out of the labour market.

Restrictive, traditional gender roles are also spurring pay inequalities. Gender stereotypes steer women away from occupations traditionally dominated by men and push them toward care-focused work that is often regarded as “unskilled,” or “soft-skilled” and therefore, lower paid.

Furthermore, discriminatory hiring practices and promotion decisions that prevent women from gaining leadership roles and highly paid positions sustain the gender pay gap.

Why is pay equity an urgent issue?

Pay equity matters because it is a glaring injustice and subjects millions of women and families to lives of entrenched poverty and opportunity gaps. At the current rate, we risk leaving more than 340 million women and girls in abject poverty by 2030 , and an alarming 4 per cent could grapple with extreme food insecurity by that year.

Women also experience significantly lower social protection coverage than men, a discrepancy that largely reflects and reproduces their lower labour force participation rates, higher levels of temporary and precarious work, and informal employment. All these factors contribute to lower income , savings, and pensions of women and gendered poverty in old age.

What should be done?

As more women are plunged into poverty, the fight for equal pay and pay equity takes on a new sense of urgency because those who earn the least are most damaged by income discrepancy.

In the United States, Black women earn only 63.7 cents , Native women 59 cents , and Latinas 57 cents for every dollar that white men earn. Where money is tight, lower pay can prevent women and families from putting food on the table, securing safe housing, and accessing critical medical care and education—impacts that can perpetuate cycles of poverty across generations.

It is urgent that we put female workers on equal footing as male workers. In a world on the brink of a looming care deficit,  women make up 67 per cent of workers providing essential health and social care services globally . Governments must address underpaid and undervalued jobs in the care sector, including in education, health care and social services, all jobs that women predominantly occupy.

What does the data say about pay equity around the world? 

Unequal pay is a stubborn and universal problem. Despite significant progress in women’s education and labour market participation, progress in closing the gender pay gap has been too slow. At this pace, it will take  almost 300 years to achieve economic gender parity .

Women workers’ average pay is generally lower than men’s in all countries and for all levels of education, and age groups, with women earning on average 80 per cent what men ear n. Women in male-dominated industries may earn more than those in female-dominated industries, but the gender pay gap persists across all sectors.

While gender pay gap estimates can vary substantially across regions and even within countries, higher income countries tend to have lower levels of wage inequality compared to low and middle-income countries. However, estimates of the gender pay gap understate the real extent of the issue, particularly in developing countries, because of a lack of information about informal economies, which are disproportionately made up of women workers, so the full picture is likely worse than what the available data shows us.

Explore  UN Women’s report on the gender pay gap in Eastern and Southern Africa .

Closing the gender pay gap requires a set of measures that push for decent work for all people. This includes measures that promote the formalization of the informal economy, bringing informal workers under the umbrella of legal and effective protection and empowering them to better defend their interests.

Ensuring workers’ right to organize and bargain collectively is an important part of the solution. Women must be involved in employer and union leadership, enabling legislation that establishes comprehensive frameworks for gender equality in the workplace.

Economic empowerment Chief at UN Women Dr. Jemimah Njuki says that, “The gender pay gap requires all stakeholders, including employers, governments, trade unions take full responsibility and work side by side to address these challenges. Women deserve equal pay for work of equal value”.

[Last updated February 2024] 

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Why the Gender Pay Gap Persists in American Businesses

By Molly Mitchell

Women have progressed a lot in terms of workplace gender equity since the days of Rosie the Riveter, but elements of inequity remain stubbornly in place. In 2024, for example, women still earn around 84 cents for every dollar a man earns for the same job on average in the US – almost the same as it was twenty years ago.

The Darden Report recently caught up with Professor Allison Elias , author of “ The Rise of Corporate Feminism ,” to explore the history of this continuing gender pay gap, where things stand today and new research on this dynamic.

Allison Elias’s 2022 book, “The Rise of Corporate Feminism,” was named a Best Summer Book of 2023: Business by the Financial Times .

What is the gender pay gap?

The gender pay gap refers to the difference in earnings between women and men. Specifically, it is the ratio of women’s to men’s median earnings, according to the U.S. Census Bureau, for full-time workers. And importantly, the often-cited 80 percent statistic provides an incomplete picture of women’s experiences in the labor market since the gap is exacerbated for many women of color. Hispanic and Black women experience the largest gaps relative to white, non-Hispanic men.

Why does the gender pay gap happen?

There are many reasons that the gender pay gap exists. Economists label these reasons as supply side (women’s choices) and demand side (employers’ choices), although it can be difficult to untangle the two or categorize them neatly as one or the other.

Traditionally, women have had lower educational attainment, been segregated into jobs that paid lower wages and had less continuous experience in the labor force. But we cannot attribute these trends to women’s choices alone. Legal constraints, economic structures and gender norms have also played a role in shaping women’s preferences and choices. Sociologists may even argue that career preferences emerge in childhood from gender-specific socialization processes.

On the demand side, gender discrimination (at the point of hire and beyond) has contributed to lower pay and fewer promotional opportunities for women. However, it is difficult to measure the extent to which implicit and explicit attitudes of employers account for the wage gap.

Do certain professions/fields experience the gap more than others?

The gender pay gap tends to increase as pay increases, in part because the minimum wage creates a floor for lower earners. People in managerial and professional work, where jobs are more gender integrated, see higher wage gaps than those in jobs requiring a high school degree.

Regarding MBA graduates, the gender wage gap tends to increase over time. Researchers at one top program examined multiple cohorts of MBA graduates 13 years following graduation and found that parenthood impacted women’s earnings more so than men’s. At 13 years out from graduation, women were earning 56 percent of what their male classmates earned. Factors like taking time away from work and working even a few hours fewer per week were found to have tremendous impact on women’s earnings later in their careers. Caregiving responsibilities have a negative influence on women’s earnings (e.g., the motherhood penalty), whereas men have been shown to actually earn more upon becoming fathers! For those in the highest-paid jobs, the returns for what sociologists call overwork are huge, and contribute significantly to sustaining the wage gap.

At a more micro level, we also know from experimental research in social psychology that women receive less credit—and also claim less credit—for their work when engaged in joint tasks with men. I have a recent paper coauthored with Jirs Meuris at Wisconsin where we examine almost two decades of data to demonstrate the effect on the gender wage gap of a job’s interdependence, meaning the extent to which a job requires working on a team or coordinating with others. Over time and across industries and occupations, jobs that are rated as more interdependent, meaning they require two or more people to sequentially complete tasks, have higher gender wage gaps.

This makes sense given what we know from social psychology about credit for joint work: In mixed gender groups, women receive and claim less credit, which could influence reward allocation. But also, we find that the gender of the task matters. The gender wage gap is exacerbated in male-dominated occupations and is lessened in female-dominated occupations.  Rewarding individual contributions in interdependent work settings is more complex and can sustain and worsen gender inequality, particularly in traditionally male settings.

Managers who wish to address this trend should revisit their performance evaluation systems, which were likely designed with independent work in mind. With increasing numbers of employees engaged in interdependent jobs, managers need to find new ways to evaluate individual contributions and rely on multiple sources when determining rewards.

How much progress towards equity have we made? 

Since 1960 the gap has narrowed, although it has hovered around 80 percent for several decades. Despite continuing inequities, women are more likely to graduate from high school, graduate from college and earn master’s degrees. They earn half of all doctorates. In MBA programs, women represent 47 percent of those receiving graduate business degrees from U.S. business schools (in 2020)—a significant increase from less than 5 percent in 1970.

Furthermore, women have gained control over reproduction with the dissemination of a birth control pill, and age at first marriage has continued to rise along with the percentage of women who prioritize career success. These factors are interrelated: investment in education—and interest in career advancement—becomes more attractive for women who have more control over family planning.

While there is much progress in educational attainment, the pay gap is largest in the highest-paid jobs that demand overwork, which economist Claudia Goldin calls “greedy jobs.” Jobs that are highly compensated, such as finance or corporate law, pay disproportionately more on a per-hour basis when they require more time (more than 40 hours a week) and offer less flexibility. A gender pay gap is sustained in these jobs because women are more likely to choose a more flexible path that does not have such high rewards for overwork. Goldin, who recently won the Nobel Prize, calls this issue the “last chapter” in the converging economic roles of men and women.

I have a forthcoming case with economist Peter Debaere about Goldin’s work, which uses protagonists from both of our books, “To America and Back Again” (English for: “Naar jouw Amerika en terug”), and “The Rise of Corporate Feminism,” to illustrate certain historical trends in women’s labor force participation.

Important to note is that even though women in the highest-paid work face the highest wage gap penalties, in general women remain overrepresented in the lowest-paying occupations. And occupations with greater proportions of women tend to pay less even when controlling for educational and skill requirements. Occupational gender segregation intersects with race and ethnicity. As of 2019, white men were overrepresented in jobs with the highest pay (e.g., physician, chief executive, financial investment, pilot, architect) and women (white, Black and Latina), as well as Black and Latino men, were overrepresented in jobs with the lowest pay (e.g., food service, childcare, cashier). So while the gender wage gap is lower among those with less education, occupational segregation remains high in those jobs.

What practical policies or actions are most effective in closing the gender wage gap?

It is difficult to declare one specific remedy for the gender wage gap. Recommendations usually target change at the individual or organizational level while governments are also forwarding interventions. For individuals, there has been much emphasis on women’s propensity (or lack thereof) to negotiate their starting salaries, particularly with the publication and dissemination of “Women Don’t Ask,” a groundbreaking book from 2003.

Recent research using MBA data from management professors Laura Kray, Jessica Kennedy and Margaret Lee suggests that actually women do ask, and the wage gap for this population is no longer an individual-level phenomenon. Instead, organizations and governments should advance solutions, and there is promise in at least two remedies: banning salary history and promoting pay transparency.

Given the historic lack of pay transparency in the private sector, companies are increasingly opting to perform audits to try to ensure pay equity regardless of gender or race. And states are adopting laws to ban an employer’s questions about a candidate’s previous salary, which has been shown to improve salary outcomes for women and underrepresented minorities. Under consideration at the federal level is the Paycheck Fairness Act, which would expand coverage for equal pay and also ban salary history considerations and promote pay transparency.

The University of Virginia Darden School of Business prepares responsible global leaders through unparalleled transformational learning experiences. Darden’s graduate degree programs (MBA, MSBA and Ph.D.) and Executive Education & Lifelong Learning programs offered by the Darden School Foundation set the stage for a lifetime of career advancement and impact. Darden’s top-ranked faculty, renowned for teaching excellence, inspires and shapes modern business leadership worldwide through research, thought leadership and business publishing. Darden has Grounds in Charlottesville, Virginia, and the Washington, D.C., area and a global community that includes 18,000 alumni in 90 countries. Darden was established in 1955 at the University of Virginia, a top public university founded by Thomas Jefferson in 1819 in Charlottesville, Virginia.

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How to Actually Close the Gender Pay Gap

More from our inbox:, it’s not a ‘broken home’, no more moviegoing for me.

To the Editor:

“ Salary Transparency Fails to Fix the Gender Pay Gap ” (Business, July 4) contains numerous examples and anecdotes about the benefits of taking actions to close the gender pay gap.

We agree that transparency is an important component to building inclusive cultures — but it goes beyond knowing what your co-worker earns.

The gender pay gap is a result of pay inequity and unequal representation at all pay levels throughout an organization. Therefore, instituting a pay transparency policy without taking other actions — such as regularly conducting pay equity analyses; banning salary history requests by employers; and evaluating recruitment, promotion, and talent development systems for bias — would still leave an organization with a pay gap.

There is no “one and done” action that will close a company’s gender pay gap. Instituting salary transparency is an “ and” — a policy that should be put in place in addition to taking steps to ensure an equal playing field.

Serena Fong Andrew Grissom New York Ms. Fong is vice president, strategic engagement, and Mr. Grissom is senior associate librarian at Catalyst, a nonprofit promoting gender equity in the workplace.

Re “ A ‘Broken Home’ Didn’t Break Me, or My Kids ” (Opinion guest essay, nytmes.com, July 5):

I am grateful for Joyce Maynard’s tender essay on the long view of divorce. As a child of divorced parents, I, too, wish that my parents hadn’t lined us up on the living room couch and told us that they were getting a divorce. But as the oldest, I felt the burden of their unhappiness on us, and that, too, was too much for children to bear.

Now 40 years later, in a loving marriage of my own and with children and grandchildren, I have one thing to ask of a society so sensitive about language. It’s time to drop the expression “broken home.” Each time I hear the expression, it breaks my heart a little. I want to shout back: “I am not broken. I am strong. And I am loved.”

Erica Brown Silver Spring, Md.

Re “ Sorry, We Aren’t Going Back to the Movies ” (Sunday Review, July 11):

Kara Swisher certainly has it right. Why would anyone want to go back to the movies? To breathe in stale, recirculated air? To eat overpriced, lousy popcorn? To sit near rude people who can’t shut their mouths or turn their phones off? Fuhgedaboutit!

I’m perfectly content to watch movies on my big screen and enjoy all the comforts of home.

An additional plus has been this: By wearing a mask for the last year and a half whenever I ventured out, I not only didn’t get the virus, but also for the first time I can remember I didn’t catch a cold or anything else during this entire period.

So why would I go back to an uncomfortable germ factory when I can enjoy my entertainment with family and friends and open my own bottle of wine or stir (not shake) my own martini?

Steven Morris Mount Pleasant, S.C.

Why the Gender Pay Gap Still Exists

Are today’s working women leaning in.

Posted August 23, 2023 | Reviewed by Lybi Ma

• The gender pay gap exists, women make less than men. One belief is women don’t negotiate for themselves.
• A new series of recently published studies suggests that the belief that women don’t lean in is wrong.
• What factors account for the pervasive gender gap in pay? Front and center is bias and discrimination.

Is there still a gender pay gap? The Pew Research Center estimates that women earn an average of 82 percent of what men are paid for comparable work. The pay gap between what men and women make is real. What are the reasons?

One belief is that men tend to get paid more because they are more likely than women to promote themselves and negotiate for higher pay. This idea that women, compared to men, don’t lean in and advocate for themselves was the topic of popular books by former Facebook COO, Sheryl Sandberg (2013), and Women Don’t Ask: Negotiation and the Gender Divide (Babcock and Laschever, 2003). A new series of studies published in the Academy of Management Discoveries (Kray, Kennedy, and Lee, 2023) suggests that the stereotype that women don’t lean in and negotiate their salaries is wrong.

In this series of studies, women and men, both from the general population, as well as graduates with MBA degrees were asked how much they tried to negotiate higher initial salaries, and how much they asked for raises and promotions later in their careers. The results suggested that women actually engaged in more negotiation than men. Yet, analyses of salaries and career trajectories over time suggested that women were paid less than men (the well-known gender pay gap) and that they were more likely to be turned down for raises and promotions.

Moreover, when people were asked if they believed that part of the gender gap in wages was due to women not negotiating, a significant number of men, and women, believed that it was true (even though the research results debunked the women not leaning in stereotype). Interestingly, men, as opposed to women, were more likely to believe that women’s lack of negotiating led to the pay gap.

If the Gender Pay Gap Is Not Due to Women’s Lack of Negotiation, Why Does It Still Exist?

There may be some other reasons. Typically, women have greater responsibility for household duties, and women are more likely than men to take time out of their career progression to have and raise children. There is also some evidence that women may choose less lucrative career paths, in sectors that tend to be lower paying (for example, education and healthcare). However, the results of these new studies, and earlier research, suggest that simple discrimination and bias against women in the workforce is a primary reason.

What Are Some of the Reasons for Bias?

In positions of leadership , there is still a tendency to view the prototypical leader as a man, and one who has stereotypically masculine, agentic qualities, such as assertiveness , competitiveness, and dominance. Women, as a group, are less agentic and more communal – helpful, nurturing, and kind. In selecting leaders, there is a preference for more agentic qualities, and there is, in many organizations, a preference for a strongman leader.

One psychological reason that may both explain the false belief that women don’t lean in and negotiate for themselves, and may underpin continued gender discrimination in employment is the tendency toward blaming the victim. To rationalize why a pay gap exists, many employers may turn to the false beliefs that women don’t negotiate or stand up for themselves, that women will fall off of their career paths to raise children, or that women aren’t as competitive and high-achieving as men.

In any case, this research demonstrates that the gender pay gap is not because women don’t lean in!

Kray, L., Kennedy, J., & Lee, M. (2023). Now, Women Do Ask: A Call to Update Beliefs about the Gender Pay Gap. Academy of Management Discoveries , (ja).

Sandberg, S. & Scovell, N. (2013). Lean In: Women, Work and the Will to Lead. Knopf.

Babcock, L., & Laschever, S. (2003). Women don't ask: Negotiation and the gender divide . Princeton University Press.

Ronald E. Riggio, Ph.D. , is the Henry R. Kravis Professor of Leadership and Organizational Psychology at Claremont McKenna College.

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The Gender Pay Gap: Income Inequality Over Life Course – A Multilevel Analysis

Lisa toczek.

1 Department of Medical Sociology, Institute of the History, Philosophy and Ethics of Medicine, Faculty of Medicine, University of Ulm, Ulm, Germany

2 Department of Social Medicine, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands

Richard Peter

Maria Bohdalova , Comenius University in Bratislava, Slovakia

Associated Data

The datasets presented in this article are not readily available because the study data contain social security information. Due to legal regulations in Germany, it is not permitted to share data with social security information. Requests to access the datasets should be directed to [email protected] .

The gender pay gap has been observed for decades, and still exists. Due to a life course perspective, gender differences in income are analyzed over a period of 24 years. Therefore, this study aims to investigate income trajectories and the differences regarding men and women. Moreover, the study examines how human capital determinants, occupational positions and factors that accumulate disadvantages over time contribute to the explanation of the GPG in Germany. Therefore, this study aims to contribute to a better understanding of the GPG over the life course. The data are based on the German cohort study lidA (living at work), which links survey data individually with employment register data. Based on social security data, the income of men and women over time are analyzed using a multilevel analysis. The results show that the GPG exists in Germany over the life course: men have a higher daily average income per year than women. In addition, the income developments of men rise more sharply than those of women over time. Moreover, even after controlling for factors potentially explaining the GPG like education, work experience, occupational status or unemployment episodes the GPG persists. Concluding, further research is required that covers additional factors like individual behavior or information about the labor market structure for a better understanding of the GPG.

1 Introduction

In the European Union (EU) in 2019, women’s average gross hourly earnings were 14.1% below the earnings of men ( Eurostat, 2021a ). The gender pay gap (GPG) has existed for decades and still remains to date. According to Eurostat GPG statistics, the key priorities of gender policies are to reduce the wage differences between men and women at both the EU and national levels ( Eurostat, 2021a ). Nevertheless, the careers of men and women differ considerably in the labor market, with women being paid less than men ( Arulampalam et al., 2005 ; Radl, 2013 ; Boll et al., 2017 ). A report from the European Parliament in 2015 about gender equality assessed Germany’s performance in that field as mediocre. The federal government in Germany has already improved laws that focus on gender equality ( Botsch, 2015 ). Regarding Germany, in 2019 the earning difference between men and women were found to be 19.2% ( Eurostat, 2021a ). The reasons behind gender income inequality are complex and have multidimensional explanations.

1.1 Determinants of the GPG

The early 1990s represented a turning point for the participation of women in the labor market ( Botsch, 2015 ). In previous years, women’s participation rate in the workforce has strongly increased, from 51.9% in the year 1980 (West Germany) to 74.9% in 2019 ( OECD, 2021 ). This upward trend represents the increase of women working at older ages ( Sackmann, 2018 ). However, the gender income inequality remains. Different explaining factors of the GPG were found in previous research: patterns of employment, access to education and interruptions in the careers of men and women.

Although there are nearly equal numbers of men and women in the labor market, when considering women’s careers, various gender-specific barriers are occurring. The working patterns were found to have a relevant impact on the GPG in previous research. Atypical employment is increasing and this result in an expansion of the low-wage sector, which mainly affects women in Germany ( Botsch, 2015 ). Additionally, labor market integration of women has mainly been in jobs that provide few working hours and low wages ( Botsch, 2015 ). Moreover, part-time employment represents a common employment type in Germany, which is more frequent among women – as various studies have demonstrated – and explains the GPG significantly ( Boll et al., 2017 ; Ponthieux and Meurs, 2015 ; Boll and Leppin, 2015 ). In addition, the part-time employment occurs more often in occupations characterized by a high proportion of women and low wages ( Matteazzi et al., 2018 ; Boll and Leppin, 2015 ; Hasselhorn, 2020 ; Manzoni et al., 2014 ). Another employment type with few working hours and low pay is a special form of part-time work: marginal work. Marginal work is defined as earnings up to 450 Euros per month or up to 5.400 Euros annually. Also, it is also more common among women than among men ( Botsch, 2015 ; Broughton et al., 2016 ). The marginal part-time work has increased in nearly all EU countries, especially in Germany where it can be found to be above the EU average ( Broughton et al., 2016 ). Besides the working time, occupational status influences the wage differences of men and women. Female-dominated occupational sectors are characterized by lower wages compared to male-dominated ones ( Brynin and Perales, 2016 ). Additionally, in women-dominant industries, remunerations are less attractive and it often entails low-status work in sectors like retail, caregiving or education ( Boll and Leppin, 2015 ; Hasselhorn, 2020 ; Matteazzi et al., 2018 ; Brynin and Perales, 2016 ). Hence, working patterns such as the amount of working time or the occupational status are crucial determinants that contribute to explaining the GPG in Germany ( Blau and Kahn, 2017 ; Boll et al., 2017 ).

The access to education and vocational training are important factors, that influence the GPG. Both influence a first access to the labor market and are considered to be ‘door openers’ for the working life ( Manzoni et al., 2014 ). In Germany, education represents a largely stable variable over time, i.e. only few individuals increase their first educational attainment. Education influences the careers of men and women and can be seen as important an determinant of future earnings ( Boll et al., 2017 ; Bovens and Wille, 2017 ). Although women’s educational attainment caught up with those of men’s in recent years, for men, a higher qualification was still rewarded more than for women ( Botsch, 2015 ; Boll et al., 2017 ). Moreover, in previous research the impact of education on the GPG was not found to be consistent with different influences for men than for women ( Aisenbrey and Bruckner, 2008 ; Ponthieux and Meurs, 2015 ). Manzoni et al. (2014) found out, that the effect of education on career developments were dependent of their particular educational levels. In addition, regardless of the women’s educational catching-up in the last years, looking at older cohorts – born between 1950 and 1964 – women had a lower average level of education than men ( Boll et al., 2017 ).

An increasing GPG over time can also be the result of interruptions in careers, which are found more often for women than for men ( Eurostat, 2021a ; Boll and Leppin, 2015 ). Previous research of Boll and Leppin (2015) has identified explanations for the GPG in Germany by analyzing data from the German Socio-Economic Panel (SOEP) in 2011. They demonstrated that the amount of time spent in actual work was lower for women than for men. Therefore, women gain less work experience than their male counterparts ( Boll and Leppin, 2015 ). Career interruptions not only impact the accumulation of work experience but also the scope of future work. Especially in the period of family formation higher rates of part-time employment among women can be observed ( Boll et al., 2017 ; Ponthieux and Meurs, 2015 ). Moreover, work-life interruptions such as raising children or caring for family members have a major impact on the employment development and are more likely to appear for women than for men ( Ponthieux and Meurs, 2015 ). Although the employment rate of mothers has increased in recent years in Germany, it is still considerably lower than that of fathers ( Federal Statistical Office, 2021 ). Hence, taking care of children is still attributed to mothers, to the detriment of their careers ( Botsch, 2015 ). A recent study, however, found sizable wage differences between men and women who were not parents, refuting the assumption that the GPG applies only to parents ( Joshi et al., 2020 ). Other interruptions in the working lives of men and women are caused by unemployment. Azmat et al. (2006) found that in Germany, transition rates from employment to unemployment were higher for women than for men. Career interruptions have lasting negative effects on women’s wages. Therefore, it can be useful to examine unemployment when analyzing gender inequality in the labor market ( Eurostat, 2021b ).

1.2 Theoretical Background

1.2.1 human capital model.

In previous research, economic theories had been applied to explain the income differences of men and women. Two essential factors could be found: qualification and discrimination. The human capital model claims that qualifications with greater investments can be directly related to higher wages of men and women. The earnings are assumed to be based on skills and abilities that are required through education and vocational training, and work experience ( Grybaitė, 2006 ; Lips, 2013 ; Blau and Kahn, 2007 ). Educational attainment of women has caught up in recent years ( Botsch, 2015 ). However, women’s investments in qualifications were still not equally rewarded as those of men. Therefore, the expected narrowing of the GPG was not confirmed in earlier research ( Boll et al., 2017 ; Lips, 2013 ). Another determinant of the human capital model is work experience. Labor market experience contributes to a large extent to the gender inequality in earnings ( Sierminska et al., 2010 ). Hence, work experience influences the wages of men and women. On the one hand, interruptions due to family life lower especially women’s labor market experience compared to men. On the other hand, part-time employment is more frequent among women with fewer working hours and therefore less work experience. The lesser accumulation of work experience leads to lower human capital and lower earnings for women compared with men ( Blau and Kahn, 2007 ; Mincer and Polachek, 1974 ). Nonetheless, the association of work experience and income is more complex. Regarding the wages of men and women the influence of occupation itself also needs to be considered ( Lips, 2013 ). In the paper of Polachek (1981) different occupations over the careers of men and women were explained by different labor force participation over lifetime. Referring to the human capital model, it is argued that women more likely expect discontinuous employment. Therefore, women choose occupations with fewer penalties for interruptions ( Polachek, 1981 ). However, it should be questioned if working in specific occupations can be defined as a simple choice ( Lips, 2013 ). Besides, part-time employment is found to be more frequent among women, which ultimately leads to few working hours and hence low earnings ( Botsch, 2015 ; Ponthieux and Meurs, 2015 ; Boll et al., 2017 ). Though different working hours cannot be defined as a simple choice either ( Lips, 2013 ).

Earlier criticism about the human capital model discussed that the wage differences of men and women cannot only be explained by the qualification and the labor market experience ( Grybaitė, 2006 ; Lips, 2013 ). Another theoretical approach explaining the GPG refers to labor market discriminations, which effect occupations and wages ( Boll et al., 2017 ; Grybaitė, 2006 ). On the one hand, occupational sex segregation can be associated with income differences of men and women. The different occupational allocation in the labor market of men and women are defined as allocative discrimination ( Petersen and Morgan, 1995 ). In addition, occupations in female-dominated sectors are mostly characterized by low-wages compared to more male-dominated occupations ( Brynin and Perales, 2016 ). On the other hand, even with equal occupational positions and skill requirements women mostly earn less than men, this refers to the valuative discrimination ( Petersen and Morgan, 1995 ). Even within female-dominated jobs a certain discrimination exists, with men being paid more than women for the same occupation. Additionally, employment sectors with a large number of female workers are more likely to be associated with less prestige and lower earnings ( Lips, 2013 ). Achatz et al. (2005) analyzed the GPG with an employer-employee database in Germany. The authors examined the discrimination in the allocation of jobs, differences in productivity-, and firm-related characteristics. They found out that in occupational groups within companies, the wages decreased with a higher share of women in a group. Additionally, a higher proportion of women in a groups resulted in a higher wage loss for women than for men ( Achatz et al., 2005 ).

Although relevant criticism of the human capital model exists, its determinants are still found to be important in explaining the wage differences of men and women ( Boll et al., 2017 ). Nonetheless, income differences of men and women can still be found even with the same investments in human capital. The reason for this could be the occupational discrimination of women ( Brynin and Perales, 2016 ; Achatz et al., 2005 ; Lips, 2013 ). Therefore, the occupational positions can be associated as a relevant factor of the GPG.

1.2.2 Life Course Approach

Besides economic theories, there are other theoretical approaches of explaining the GPG. One of them focusses on the accumulation of disadvantages over the life course: the ‘cumulative advantage/disadvantage theory’ by Dannefer (2003) . It also involves social inequalities which can expand over time. The employment histories of men and women evolve over their working lives and during different career stages, advantages and disadvantages can accumulate. First, this life course perspective considers and underlines the dynamic approach of how factors shape each individual life course. Secondly, it can contribute to explain the different income trajectories of men and women over their working lives ( Doren and Lin, 2019 ; Dannefer, 2003 ; Härkönen et al., 2016 ; Manzoni et al., 2014 ; Barone and Schizzerotto, 2011 ).

The importance of the life course perspective was underlined by some earlier studies. They demonstrated that certain conditions in adolescence or early work-life affected future careers of men and women. Visser et al. (2016) found evidence for an accumulation of disadvantages in the labor market over working life, in particular for the lower educated. The cohort study SHARE had assessed economic and social changes over the life course in numerous European countries in several publications ( Börsch-Supan et al., 2013 ). Overall, education and vocational training, occupational positions and income illustrate parts of the social structure which in turn can demonstrate gender inequality in the labor market ( Boll and Leppin, 2015 ; Hasselhorn, 2020 ; Du Prel et al., 2019 ). Moreover, family events and labor market processes repeatedly affect one another over the life course. The work-family trajectories have consequences on employment outcomes such as earnings ( Aisenbrey and Fasang, 2017 ; Jalovaara and Fasang, 2019 ). Furthermore, the income differences of men and women are not steady but tend to be lower at the beginning of employment and increase with age ( Goldin, 2014 ; Eurostat, 2021a ). Therefore, careers should not be analyzed in a single snapshot, but with a more appropriate life course approach that takes into account factors that influences the wages of men and women over time.

1.3 Aim and Hypotheses

The aim of the present study is to examine income trajectories and to investigate the income differences of men and women over their life course. We are interested in how human capital determinants, occupational positions and the accumulation of disadvantages over time contribute to the explanation of the GPG from a life course perspective.

Focusing on older German employees, our study includes 24 years of their careers and considers possible cumulative disadvantages of women in the labor market compared to those of men. In contrast to Polachek (1981) , who analyzed the GPG as a unit over lifetime, we used a life course approach in regard to the theory of cumulative disadvantages of Dannefer (2003) . Accordingly, we analyze explaining factors of the GPG not only in a single snapshot but over the working careers of men and women. Life course data based on register data and characteristics of employment biographies with information on a daily basis are two additional important and valuable advantages of our study. Existing studies rarely have this information in the form of life course data and when they do, the data is either self-reported and retrospective including possible recall bias, or based on register data which was only collected on a yearly basis. We expect to find differences in the income of men and women over a period of time with overall higher, and more increasing earnings of men than of women.

Hypothesis 1 (H1): The differences of income trajectories throughout working life is expected to demonstrate more income over time among men than among women.

Education and vocational training, and work experience are human capital determinants. They have influence on the earnings of men and women. Although previous research estimated additional important factors contributing to the GPG, human capital capabilities continue to be relevant in explaining the wage differences of men and women ( Blau and Kahn, 2007 ; Boll et al., 2017 ). In our life course approach, we control for human capital determinants due to the information about education and vocational training, and work experience via the amount of working time (full-/part-time) for each year. We expect to find a strong influence of both determinants on the wages of men and women in Germany.

Hypothesis 2 (H2): The income differences between men and women can be explained by determinants of the human capital model.

Previous research found out that factors such as occupational status had an impact on the income differences of men and women ( Blau and Kahn, 2007 ; Boll et al., 2017 ). For a better understanding and explanation of the GPG, gender differences regarding occupational positions must be included to human capital determinants ( Boll et al., 2017 ). We assume that men and women can be found in different occupations, measured via occupational status, and these explain a substantial part of the wage differences between men and women.

Hypothesis 3 (H3): The occupational status of men and women can contribute to the explanation of the GPG.

The life-course approach acknowledges time as an important influence on the wages of men and women. Income differences of men and women can change over time and career stages, while the GPG was found to be lower at the beginning of the employment career and widened with age ( Goldin, 2014 ). Hence, the earning differences between men and women tend to be higher for older employees ( Eurostat, 2021a ; Federal Statistical Office, 2016 ). To account for the influence of age, we additionally included the age of each person in our analysis. Another factor that changes over time and contribute to explain the GPG is part-time work. In general, part-time work result in a disadvantage in pay compared to full-time employment ( Ponthieux and Meurs, 2015 ). However, explanations of the GPG due to different amount of part-time work need to include a special form of part-time work: marginal work. Marginal employment conditions are characterized by low wages and high job insecurities. Also discontinuous employment due to unemployment are characterized by job insecurities and affect the low-paid sector – therefore mainly women ( Botsch, 2015 ). Besides the human capital determinants and occupational positions as important factors explaining the GPG, the region of employment influences the wages of men and women and can also change over the career stages. Evidence from the Federal Statistical Office of Germany in 2014 noticed a divergence of the GPG trend in the formerly separated parts of Germany. The GPG among employees was wider in the Western part (24%) compared to the Eastern part of Germany, where it was found to be 9% ( Federal Statistical Office, 2016 ). Therefore, to examine income differences, the amount of less advantaged employment such as marginal work or periods of unemployment throughout the careers of men and women needs to be considered, as well as the region of employment and the age of a person.

Hypothesis 4 (H4): Factors of the living environment such as regional factors, and social disadvantage work conditions such as marginal work or unemployment, contribute to the income difference between men and women.

Our study about the GPG in Germany adds to earlier research in different ways. First, the accumulation of inequalities over the life course of men and women is known, but only few studies exist that focus on income through life course approach. We can analyze factors that influence the GPG over the careers of men and women due to the availability of social security data with daily information of each person. Besides the wages of men and women, the data additionally contains time-varying information about occupational status, working time and unemployment breaks. Therefore, we use longitudinal data of the German baby-boomers which allow us to measure changes of factors explaining the GPG over time. Second, a relevant contribution of our study is that we can consider different factors contributing to the explanation of the GPG through a life course perspective. The few studies focusing on the GPG over life course included either only determinants of the human capital model ( Joshi et al., 2020 ) or factors of occupational careers ( Moore, 2018 ). Some research included both aspects but had other disadvantages, such as Monti et al. (2020) , who could not analyze temporal evolution of the GPG with the data available. Moreover, previous research on the GPG in Germany could not trace vertical occupational segregation due to missing information of part-time workers, included only data of West Germany and used merely accumulated earnings over time ( Boll et al., 2017 ). Nonetheless, previous research demonstrated the need of analyzing the GPG via life course approach with which the accumulation of advantages and disadvantages for both, men and women, can be considered. Third, due to the usage of a multilevel framework we can examine income trajectories simultaneously at an individual and at a time-related level. Moreover, the influences of time-invariant and time-varying factors can be analyzed regarding differences in earnings of men and women. Hence, the multilevel approach examines income changes between and also within individuals. Furthermore, it acknowledges the importance of the life course perspective with including time as a factor in the model. A recent study also used growth curve modelling to explain gender inequality in the US. However, gender inequality measured through gender earnings was analyzed only across education and race without considering other variables explaining the GPG ( Doren and Lin, 2019 ). To our knowledge, there exists no research on the GPG that covers several essential determinants, hence we aim to fill those research gaps with our study.

2 Materials and Methods

The data were obtained from the cohort study lidA (living at work). The lidA sample includes two cohorts of employees (born in 1959 and in 1965) and was drawn randomly from social security data. LidA combines two major sources of information – register data of social insurance and questionnaire data derived from a survey. The survey was conducted in two waves, 2011 (t 0 ) and 2014 (t 1 ) ( Hasselhorn et al., 2014 ). The ethics commission of the University of Wuppertal approved the study.

In Germany, the social insurance system assists people in case of an emergency such as unemployment, illness, retirement, or nursing care. Employees have to make a contribution to the system depending on their income – except of civil servants or self-employed ( Federal Agency for Civic Education, 2021 ). In our analyses, we included men and women in Germany who participated in the baseline (2011) and in the follow-up (2014), were employed during both waves and subjected to social security contributions. We only included persons who agreed via written consent to the linkage of the survey data to their social security data. Thus, our sample for analysis included 3,338 individuals ( Figure 1 ).

Decision tree – inclusion and exclusion criteria in the sample for analysis.

2.2 Measurements

The social security data of the Institute for Employment Research of the German Federal Employment Agency is based on employers’ reports. The so-called “Integrated Employment Biographies” (IEB) or register data comprises information about individual employment; that is, type of employment, occupational status, episodes of unemployment and income with information about age, gender and education and vocational training. The IEB data are retrieved from employers’ yearly reports submitted to the social security authority ( Hasselhorn et al., 2014 ). The information of the register data was available on a daily basis and contained yearly information from 1993 to 2017 for each person. However, the IEB data contain missing details, especially regarding information that is not directly relevant for social security data and therefore, not of the highest priority for employers’ reports. This is particularly true for data on gender and education and vocational training. As our sample participants consented to the linkage of IEB with questionnaire data, we were able to impute the missing information on these variables with the help of the survey data. All time-varying information in the IEB is coded to the day. Our data have a multilevel structure with time of measurements (Level 1) being nested within individuals (Level 2) and defined as follows.

2.2.1 Level 1 Variables

In our analysis the variable time was based on information about the year of measurement. The starting point represents 1993 and was coded with zero. The outcome variable income was calculated from the IEB data as nominal wages in Euros (€). As time-varying variable, it can be defined as the average daily income per year of each person whose work contributes to social security and/or marginal employment. Information about the work experience due to working time was available for jobs that require social security contribution. To draw this information from the IEB data, the time-varying variable working time was computed with three different types: full- and part-time, part-time, and full-time. The data on occupational status were based on the International Standard of Classification of Occupations 2008 (ISCO-08). This time-varying variable contained information on the occupational status of each job that a person has held over the years. For the multilevel analysis, ISCO-08 was transformed from the German classification KldB 2010 (classification of occupations 2010) of the register data. ISCO-08 is structured according to the skill level and specialization of jobs, which are grouped into four hierarchical levels. Occupational status in our study was defined by the 10 major groups (level one of the classifications ISCO-08), without the group of armed forces who did not appear in our data. Therefore, the nine groups were analyzed: elementary occupations; plant and machine operators and assemblers; craft and related trades workers; skilled agricultural, forestry and fishery workers; services and sales workers; clerical support workers; technicians and associate professionals; professionals; and managers ( International Labour Office, 2012 ). Moreover, information about the number of episodes of marginal work could also be drawn from the register data. Marginal work was defined due to having at least one marginal employment per year. The time periods (episodes) of every marginal employment were counted and added up yearly. Furthermore, the duration of unemployment as time-varying variable was calculated due to information of the register data about the days of unemployment per year. In the register data unemployment is defined as being unemployed or unable to work for up to 42 days, excluding those with sickness absence benefits or disability pensions. The IEB data also provided information on the region of employment, which represents the area in which a company is located (East Germany and West Germany). This time-varying variable was available for each person over the years. A description of the Level 1 characteristics of our sample is provided in Table 2 using the last available information (2017) from the IEB data.

Characteristics of Level 1 variables a for men (n = 1,552) and women (n = 1,786).

M mean; SD standard deviation.

* p < 0.05, ** p < 0.01, *** p < 0.001.

2.2.2 Level 2 Variables

Information about the time-invariant variable education and vocational training was assessed from the survey data in 2011 (baseline). Education and vocational achievements of the sample were grouped in: low, intermediate and high education and vocational training (see Supplementary Table S1 ). The time-invariant variable gender had missing values in the register data. Therefore, we imputed the missing data using information of the survey data. The variable was coded 0 = female and 1 = male. Also based on the survey data, we included the time-invariant variable year of birth with measurements of 1959 and 1965 in the analysis. The characteristics of the Level 2 variables are displayed in Table 1 .

Characteristics of the Level 2 variables a for men (n = 1,552) and women (n = 1,786).

2.3 Statistical Analysis

The characteristics of our sample are displayed in Table 1 and Table 2 . Statistical analyses were performed using either Cramer’s V or by unpaired two sample t -test for numeric variables. Regarding the multilevel analysis, we used a so-called growth curve analysis. It demonstrates a multilevel approach for longitudinal data that model growth or decline over time. For this purpose, all daily information in the IEB were transformed into data on a yearly basis. Level 1 (year of measurements) represents the intraindividual change with time-varying variables. Interindividual changes are determined with time-invariant variables on Level 2 (individuals). Therefore, time of measurements predictors was nested within individuals. We applied a random intercept and slope model, which assumed variations in intercept and slope of individuals over time ( Singer and Willett, 2003 ; Rabe-Hesketh and Skrondal, 2012 ; Hosoya et al., 2014 ). Besides the Level 1 and Level 2 predictors, the cross-level interaction of gender*time interaction was constituted to analyze differences in income slopes of men and women over time ( Rabe-Hesketh and Skrondal, 2012 ).

Level 1 of the two-level growth model is presented below ( Eq. (1) ). y i j measures the income trajectory y for individual i at time j . True initial income for each person is represented with β 0 i . The slope of the individual change trajectory demonstrates β i j . T I M E i j stands for the measure of assessment at time j for individual i (Level 1 predictor). The residual or random error, specific to time and the individual is demonstrated by ε i j .

Eq. 2 and 3 represent the submodels of the Level 2. Eq. 2 defines the intercept γ 00 for individual i with the intercept of z i (illustrating a Level 2 predictor) and residual in the intercept v 0 i . The slope at Level 2 is represented in Eq. 3 with γ 10 and the slope error v 1 i . The effect γ 11 provides information on the extent to which the effect of the Level 1 predictor ( T I M E i j ) varies depending on the Level 2 predictor ( z i ).

To test our hypotheses, we calculated the influence of different variables with adjusting various predictors stepwise into the multilevel analysis. First, we estimated an unconditional means model which describes the outcome variation only and not its change over time (model 1). The next preliminary step was calculating the intraclass correlation coefficient (ICC) of this model 1. It identifies and partitions the two components: within- and between-person variance. The ICC estimates the proportion of total variation of the outcome y that lies between persons ( Singer and Willett, 2003 ). In the next model (model 2), we calculated an unconditional growth curve model which included time as predictor on Level 1. In model 3, the GCA was controlled for gender and time as well as the interaction of both variables. Model 4 was additionally adjusted for human capital determinant: education and vocational training, and working time. The GCA of model 5 was controlled for occupational status. The last model included year of birth, number of episodes of marginal work, duration of unemployment and region of employment (model 6 – fully adjusted model).

In Table 5 , the indices of the Akaike’s Information Criterion (AIC) were used to compare models and explore the best model fit ( Singer and Willett, 2003 ; Rabe-Hesketh and Skrondal, 2012 ). The statistical analyses were performed with IBM SPSS 25.

Goodness-of-fit statistics of the GCA.

AIC Akaike’s Information Criterion.

3.1 Descriptive

Characteristics of Level 2 variables stratified by gender are displayed in Table 1 . 1,552 men and 1,786 women were included in the analyses. It is observed that women significantly differ from men in education and vocational training. Women were less likely than men to have both low and high levels of education and vocational training.

The characteristics of Level 1 variable are represented in Table 2 . Men and women differ significantly in their occupational positions. Also, men had a higher average daily income than women. Part-time jobs are more likely among women as compared to men, who are more likely to be represented in full-time jobs. Moreover, the numbers of episodes of marginal work differ significantly between men and women.

Figure 2 displays the income trajectories over the observation period (1993–2017) among men and women. In 24 years, average daily income per year increased for both. However, men have a higher average income over their life course than women. Over time, a steeper growth of the average daily income per year can be observed for men, compared to the income development of women.

Income trajectories of men and women.

3.2 Growth Curve Analysis

Results of the multilevel analyses with average daily income per year as dependent variable concerning H1 are presented in Table 3 . The ICC of the unconditional means model (model 1) demonstrates that 74% of the total variability in income can be attributed to differences between persons and 26% to the differences within persons. Adding time as a predictor in the multilevel analysis (model 2), the variance components on Level 1 become smaller. Concluding that time accounts for 68% (from 607.34 to 197.12) of the within-person variance in average income. On Level 2, time explains 40% of the variance between persons (interindividual). However, there can be still found significant unexplained results in both levels which suggests that predictors on both levels should be further included. The GCA in model 3 was adjusted for gender (with women as reference group) and the interaction gender*time. The results show a significant effect of gender on the average income over time. The starting place (intercept) lies at 41.74€ with an incremental growth per year of 1.76€. However, regarding women as reference group, men have a higher average income. The significant interaction term also indicates different income development of men and women over time – with men having higher average income trajectory than women. As expected, no relevant change can be found in the within-person variance due to the adding of the Level 2 variable: gender. The variance on Level 2, however, become less concluding that gender accounts for 26% of the variance between persons. Overall, we can verify H1 with these results.

Growth curve models 1 to 3: Estimates of average daily income per year.

L1 = Level 1; L2 = Level 2.

Results of the GCA with average daily income per year as the dependent variable controlled by determinants of the human capital model are presented in Table 4 (model 4). In addition to the multilevel analysis of model 3, model 4 is also adjusted for: education and vocational training, and working time. The results show that the average income is found to be significantly higher for full-time workers and higher educated. There is a social gradient for income regarding education and vocational training – with decreasing levels of education, the income also reduces. People who are working full-time have a higher average income than those who work part-time or full- and part-time. The effect of gender is found to be significant with less average income of women compared to men. Moreover, the income development of men and women over time is still significantly different, with more income growth over time for men than for women. The results of the variance components demonstrate that human capital determinants are explaining 16% of the variance within person and 25% of the variance between persons. However, on both levels there can be still found significant variance and additional variables need to be considered. Our hypothesis 2 can be partially confirmed.

Growth curve models 4 to 6: Estimates of average daily income per year.

Model 5 ( Table 4 ) embeds occupational status to the analysis to find out the contribution of the occupational positions on the earning differences of men and women. Significant differences in the daily average income for each occupational group can be identified. The reference group is represented with the highest occupational group ‘manager’. In nearly all other occupations, manager had the highest average income, except of ‘technicians and associate professionals’. Moreover, the effects of occupational status on income are significant for all ISCO groups except for professionals. However, compared to education and vocational training, occupational status trends are less clear, and a social gradient cannot be identified. The estimated of the fixed effect of gender persists and stays the same, concluding that the occupational position of a person could not influence the effect of gender on income. The increase of income over time can be still found to be significant higher for men than for women. Moreover, including the Level 1 variable, occupational position cannot explain a substantial part of the within-person variance. We can identify occupational positions as significant predictor of the income, but a relevant contribution to explain the GPG cannot be observed. Therefore, we cannot approve hypothesis 3.

The results of investigating the influence of factors of the living environment are presented in Table 4 (model 6). Those, who are born earlier (1959) are found to have a higher average daily income, compared to those born in 1965. Having at least one marginal employment per year influences the average daily income negatively, as does having more unemployed days. Furthermore, average income is influenced by the region of employment, being lower in East Germany than in West Germany. The estimate of gender become a little less, but the average income and the development of income over time still substantially differs between men and women. The factors of living environment account for 10% of the variance between persons. We can only partially accept hypothesis 4.

3.3 Goodness of Fit

Table 5 displays the goodness of fit statistics for the different models of the GCA. The AIC is computed to find the best model fit. Considering the different indices of AIC, model 6 has the best fit.

4 Discussion

This study aimed to examine the income differences of men and women over their life course. We investigated how different factors can explain the GPG over time. Even after extensive control for human capital determinants, occupational factors and various factors of the living environment, the effect of gender on the average daily income persisted. Moreover, the average income development was found to be higher for men compared to women.

The accumulation of inequalities over time can be seen in the difference between men’s and women’s wages. Over the period of 24 years, our results showed that the income development of men increased more compared to women – the GPG widened with time. Due to the availability of life course data, we could consider cumulative disadvantages regarding the earnings of men and women. Moreover, the results of the variance componence also showed the importance of including time to explain the GPG ( Table 3 , model 2). Therefore, we can verify our first hypothesis. The steeper incline of income for men compared to women over time substantiates the presence of GPG in Germany. Goldin (2014) also found a small GPG when people enter the labor market and a widening gap with age. Our findings are also in line with information from the Federal Statistical Office (2016) and Eurostat (2021a) who used representative data and not use cohort specific data of the German working population.

The second hypothesis assumed that human capital determinants (education and work experience) can explain the GPG. The effects of education and vocational training on daily average income significantly differed in our results ( Table 4 , model 4). Findings of Bovens and Wille (2017) also demonstrated that the level of a person’s education determines the income level. Our results also support the previous finding, that education is most often a requirement for the achievement of a certain desired financial situation ( Du Prel et al., 2019 ). Our results also showed that the average income significantly differed considering working time. Full-time workers had higher average income, while men were more likely to work full-time compared to women. Earlier research also showed that part-time work was more frequent among women than among men ( Boll and Leppin, 2015 ; Matteazzi et al., 2018 ; Eurostat, 2021a ). After adjusting for human capital determinants, the unexplained variance was still substantial and the effect of gender remained significant. Hence, H2 can only partially be accepted.

In our third hypothesis, we assumed that the gender differences in occupational position can explain the GPG. We demonstrated that the average income differed according to the occupational status of a person. This is in line with previous findings of Blau and Kahn (2001) who assumed occupation to be an important factor of the financial status of a person. After controlling for occupational status, the effect of gender could still be found to be significant. We cannot accept H3 and therefore cannot confirm results of earlier studies ( Blau and Kahn, 2007 ; Boll et al., 2017 ). In contrast to the results of education and vocational training, we did not observe a clear social gradient of occupational status and income in our analyses. One explanation could be the classification of the occupational status. The ISCO classification is structured hierarchically on four levels. The construction is based on skill level and specialization. In our study, we used the major group structure (level one) with 10 different occupational groups. Using ISCO at level one (major groups) cannot be interpreted as a strict hierarchical order of occupations; instead, it can be considered more of a summary information on occupational status regarding skill level. Moreover, we were only able to generate the major groups of the register data and therefore cannot provide more detailed information about the occupational status. However, ISCO is applied in our study for the purpose of international comparability ( International Labour Office, 2012 ).

The accumulation of disadvantages over time could also be found in our results after controlling for factors such as unemployment or marginal employment. Having (at least one) marginal employment per year influenced the income negatively. We found that discontinuities in employment and interruptions such as unemployment also had a significant negative effect. Average income decreased when the number of days per year of unemployment increased. Furthermore, controlling for the region of employment, people in East Germany had lower daily average income compared to those in West Germany. Regarding the difference between men and women, previous findings also suggested a wider GPG in West Germany than in East Germany ( Federal Statistical Office, 2016 ). However, the GPG in West and East Germany should be compared with caution due to different societal models in the past. Moreover, different labour market characteristics and different infrastructure of childcare facilities lead to a lower GPG in East Germany than in West Germany ( Federal Ministry for Family Affairs, Senior Citizens, Women and Youth, 2020 ). The year of birth was included to eliminate cohort effects, and it was found to influence average income. Men and women born earlier (1959) had higher income than those born in 1965. The fact that they are older and have worked longer in the labor market could be an explanation. The significant effects of gender on the average income and the income trajectories remained after adjusting for these factors. Therefore, hypothesis 4 can only be partially confirmed.

4.1 Strengths and Limitations

Our study has limitations concerning the generalizability of our results due to the database. Our sample includes employees of two age groups (1959 and 1965) in Germany, who are subjected to social security. Thus, the generalizability or extension of the findings to self-employed people, civil servants and other age groups may be limited. The GPG differs considerably between the EU members. The GPG in Germany is one of the widest in the EU, with 19.2% in 2019. Netherlands and Sweden are two EU countries with similar employment rates, but still have lower GPGs with 14.6 and 11.8% ( Eurostat, 2021a ). Efforts to promote gender equality in politics in Germany are limited compared to other EU members. Women are still underrepresented, not only in the political but also in the economic area. Moreover family policy needs to further support full-time employment of women and working mothers ( Andersson et al., 2014 ; Botsch, 2015 ). Therefore, the transfer of our results to other countries should be made with caution. There are some other limitations regarding the IEB data. Information about occupational careers exist from the beginning (1975), but only for persons born in West Germany. Information about people born in East Germany was not available for the period before 1993. Hence, to counteract the systematic bias, we defined 1993 as a cut-off point, when people were either 28 or 34 years old. Additionally, we adjusted our analyses for the region of employment (East/West Germany). Furthermore, information about the marginal work and duration of unemployment were only available from 1999 onwards. Due to the composition of the IEB data, we could not include people who were unwell for long periods of time. Only persons who were unable to work for less than 42 days were included in the data. Regarding the income development of women in our study, Figure 2 shows a decrease between 1997 and 1999. Being in their thirties (32–40 years) and having to raise children at that time can be one possible explanation. Regarding family formation, in 1993 the average age of a mother at birth was 28.4 years ( Federal Statistical Office, 2020 ). At the beginning of our analysis (1993) the average age of both cohorts in the study (28 years; 34 years) is similar to the average age of a mother during that time – especially for the younger cohort. However, our data do not cover information about persons on parental leave or homemakers. Due to the lack of information in the IEB data, implications of family life contributing to a difference in pay for women cannot be included in our analysis. Furthermore, Joshi et al. (2020) could not find a GPG only for parents but also for men and women without children. Therefore, the issue of wage differences between men and women is relevant either way.

Besides these restrictions, our study exhibits several strengths. The study population is highly representative for German employees subject to social insurance contributions, born in 1959 and 1965 and is, therefore, characterized by a high external validity ( Schröder et al., 2013 ). Moreover, the IEB data itself and the nature of the data that the IEB provides, are one important strength of this study. The register data is not subject to possible recall bias. This is a relevant advantage compared to most previous studies that used self-reported data. In addition, the availability of information on a daily basis regarding many variables can be seen as another strength of the study. As a result, income trajectories could be calculated more precisely, compared to many previous studies. Furthermore, in Germany, income is used to calculate the amount of social benefit accruing to each person and therefore represents highly valid information. A further major advantage of our study is represented in our long observation period of 24 years. Only a few studies have applied the life course approach to examine the complexity of the GPG. Our life course data contain various information about employment characteristics which are relevant for the GPG and of high data quality.

Our results showed, even after controlling for relevant factors, that the GPG still persisted. There exist some explanations of the GPG regarding different behaviors of men and women in wage negotiations, which further influence different income developments ( Boll and Leppin, 2015 ). Also, structural disadvantages in the labor market can be a factor explaining the GPG. Individual behavior and labor market structures are not represented in our register data. We can only extract information that is relevant for social security contribution. Nonetheless, previous research of Blau and Kahn (2017) found a larger and more slowly decreasing GPG in the US at the top compared to other levels of the wage distribution. This ‘glass ceiling effect’ describes the reduced career opportunities of women compared to men due to frequent denial of access to leadership positions. Consequently, gender inequality can be found to be greater at the top of the wage distribution. Among European countries, previous studies have found this “glass ceiling effect” in Germany as well ( Arulampalam et al., 2005 ; Boll and Leppin, 2015 ; Huffman et al., 2017 ). However, recent results of Boll et al. (2017) could not confirm the glass ceiling effect in West Germany, thus further research is needed.

5 Conclusion

The gender pay inequalities in the German labor market from a life course perspective exist. Our results demonstrated that human capital determinants continue to be important in explaining the GPG over time. Furthermore, factors of working disadvantages such as marginal work or unemployment are important when trying to explain the income differences of men and women. For further research the availability of more work data over the life course with matching individual data would help to understand the GPG even better.

Acknowledgments

We gratefully acknowledge the support of two staff members of the University Ulm. We would like to thank Gaurav Berry for his support of the data preparation and Diego Montano for his feedback on the statistical analysis.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the ethics commission of the University of Wuppertal. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

LT substantially contributed to the statistical analysis and interpretation of the data, and wrote the manuscript. HB discussed the results and provided critical comments on the manuscript. RP contributed to the obtaining of the funding, interpreting the data, and critically revised the manuscript for important aspects. All authors read and approved the final manuscript.

This work was supported by the German Research Foundation (DFG), grant number 393153877.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsoc.2021.815376/full#supplementary-material .

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Home — Essay Samples — Social Issues — Social Inequality — Gender Wage Gap

Essays on Gender Wage Gap

Gender hierarchies, stereotypes, and the fight for equality, the issue of gender wage gap in america, made-to-order essay as fast as you need it.

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The Impact of Gender on Income Inequality

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Impact of Experience and Education on Womens Wages

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Gender Pay Discrimination in The Us Soccer

The issue of pay gap in the women's u.s. soccer team, result of the feminization of poverty, gender pay gaps on the example soccer`s team, a study of gender inequality in hong kong: review of literature, the effects of gender inequality on society and the economy, the legal dilemma behind equal pay for equal work in india, reflection of gender inequality in different spheres, gender discrimination in the workplace: challenges and solutions.

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men.

Differences in pay are caused by occupational segregation (with more men in higher paid industries and women in lower paid industries), vertical segregation (fewer women in senior, and hence better paying positions), ineffective equal pay legislation, women's overall paid working hours, and barriers to entry into the labor market (such as education level and single parenting rate).

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

The pay gap exists in nearly every profession. Mothers face an even wider pay gap than women without kids. Women with bachelor’s degrees working full time are paid 26% less than their male counterparts. Women face an income gap in retirement.

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Gender pay gap in Great Britain smallest since reporting first enforced

Women still being paid 91p for every £1 a man earns, analysis shows, with gap stubbornly high in public sector

• Women are all over the big screen – but pay gap persists in the UK cultural sector

The gender pay gap has reduced to its lowest level since reporting became mandatory for businesses in 2017. However, women are still being paid just 91p for every £1 a man earns, according to analysis of official government data.

Almost four out of five companies and public bodies are still paying men more than women (78.4%) although the median pay gap reduced slightly from the previous year to 9.1% in 2023-2024, the lowest level since mandatory reporting became law in Great Britain in 2018.

However, the gender pay gap remains stubbornly higher in the public sector at 14.4% with almost nine-in-10 (87.6%) public sector organisations paying men more than women in comparison to just over three-quarters of private companies.

By 3pm on Friday – ahead of the midnight deadline for private companies – a record 10,380 organisations with 250 or more employees had filed data.

However, campaigners have called for further action by the government to tackle pay disparities , including the introduction of fines for companies who do not comply with the law.

The general secretary of the Trades Union Congress, Paul Nowak, said: “Working women deserve equal pay but the gender pay gap is still a huge issue. At current rates of progress, it will take more than 20 years to bring men and women’s pay into line. That is not right … companies must now be required to implement action plans to close their pay gaps and bosses who don’t comply with the law should be fined.

The Equality and Human Rights Commission did not respond to questions on how many relevant companies and bodies had never filed a report despite having a legal obligation to do so but insisted that non-compliance with reporting this data was low, citing only eight known organisations failing to report by the deadline in 2023, and 28 in 2022.

A spokesperson added: “There have been no penalties or fines issued to date. It is important to note that the EHRC does not have the power to issue fines directly, which would be issued via a court order.”

The construction (22.8%), finance and insurance (21.5%), and education (20%) registered the biggest median pay gaps, according to analysis of the sectors reported by each body.

The educational sector’s poor standing is due, in part, to large gaps in Multi-Academy Trusts (MATs); of the worst-performing 100 public bodies with the largest gender pay gap, all but three were academy trusts.

Responding to questions on whether the government should intervene on the large and persistent gender pay gaps in MATs a spokesperson for the Department of Education said schools were responsible for their own decisions on employment issues but were expected to give due consideration to their obligations under the Equality Act 2010.

The accommodation and food, and health and social work sectors reported some of the lowest gender pay gaps, with women earning 0.5% and 1.5% less than their male colleagues, respectively.

Under the Equal Pay Act 1970, it is illegal to pay different amounts to men and women doing the same jobs .

Survey data by the Office for National Statistics, published in November 2023 which covers the wider UK population regardless of the size of the company, shows the gender pay gap declining slowly over time to 7.7% in April 2023 . The data also demonstrated higher disparities among full-time employees in every English region than in Wales, Scotland or Northern Ireland.

A government spokesperson said: “The gender pay gap has been trending downwards since 1997, and the government is committed to ensuring women have equal access to employment, enterprise and investment opportunities.”

• Gender pay gap
• Work & careers

More on this story

Gender pay gap among UK staff at Goldman Sachs at highest level in six years

Olivia Colman says she would be paid more if she was a man

Gender pay gap for UK women aged 40 and over ‘will not close till they are pensioners’

‘It’s going to cost billions’: UK councils face huge bills over equal pay claims

Women still have right to equal pay, says UK government, despite scrapping of EU law

Why, even now, do we think a woman’s work is worth less?

Gender pay gap for women in their 50s won’t close before 2050, claims Labour

Women still paid less than men at four out of five employers in Great Britain

Women who ask for pay rise less successful than men, UK poll reveals

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2023 Gender Pay Gap Analysis in Ireland

Introduction VMware International Unlimited Company is required to annually review and publish its gender pay gap information under the Employment Equality Act 1998 (section 20A) (Gender Pay Gap Information) Regulations 2022. We welcome this legislation that encourages increased transparency around gender pay gaps and are committed to taking long-term action to reduce our gender pay gaps. We are also pleased to report on some improvements this year. Below, we detail our progress, our gender pay gap results for 2023, factors contributing to our pay gaps and measures we will take to help further reduce these.

Gender pay gaps versus equal pay The gender pay gap shows the difference between the average (mean and median) earnings of men and women employees and is always expressed as a percentage of men employees’ earnings. It does not make allowances for differences in job role or seniority and aims to show demographic imbalance in a workforce. Equal pay on the other hand, requires men and women doing the same or similar work to be paid the same. Equal pay in employment has long been a legal requirement in Ireland and is required across our global business.

VMware's findings for 2023

We are pleased to report that various categories of our gender pay gap analysis have shown improvement. Underrepresentation of women in our Ireland business remains the primary factor contributing to our gender pay gaps, although we are pleased to have achieved a hiring increase of women engineering new hires into our talent pipeline (particularly in our Professional 1 and Professional 2 Product Engineering roles). Below are the results of our 2023 gender pay gap analysis:

• The difference between the mean hourly remuneration of men and women employees: 6.7%
• The difference between the mean hourly remuneration of part-time men and women employees: -47.2%
• The difference between the mean hourly remuneration of men and women employees on fixed-term employment contracts: 19.1%
• The difference between the median hourly remuneration of men and women employees: 12.9%
• The difference between the median hourly remuneration of part-time men and women employees: -50.0%
• The difference between the median hourly remuneration of men and women employees on fixed-term employment contracts: -32.4%
• The difference between the mean bonus remuneration of men and women employees: 0.1%
• The difference between the median bonus remuneration of men and women employees: 14.2%
• The % of all men and women employees who were paid a bonus : 97% and 96% respectively
• The % of all men and women employees who received benefits in kind : 93% and 92% respectively
• Lower quartile: 45% women ; 55% men
• Lower middle quartile: 38% women ; 62% men
• Upper middle quartile: 32% women ; 68% men
• Upper quartile: 31% women ; 69% men

Identified Gender Pay Gaps We are encouraged that this year’s findings show various reduced mean and median gender pay gaps, particularly for our full-time, indefinite term employees. However, we acknowledge that gender pay gaps still exist in our Ireland business and that there’s more work to be done. Our analysis of the data suggests that the following factors have materially contributed to this year’s identified pay gaps.

• VMware’s Individual Contributor job level comprises most of the Ireland employee workforce and includes a broad range of roles from interns to senior technical staff. Pay gaps continue to be driven by lower representation of women in the highest paying Individual Contributor roles.
• Individual Contributor roles are comprised of 35% women in comparison to management roles, which encouragingly comprise of 45% women employees. Typically, strong representation of women in management roles would result in a small or negative gender pay gap (i.e., a finding that women’s average remuneration is higher than men’s remuneration), but it doesn’t in VMware’s case because our Individual Contributor level comprises most of our roles in Ireland.
• We’ve assessed that our mean pay gaps are reduced in part due to having more women employees than men employees in our most senior level roles in Ireland. However, VMware acknowledges that there is still further progress to be made to increase representation of women employees across our leadership teams and notably in Ireland, across our Individual Contributor level where there are more women employees in the less senior roles.
• Job Families : Our Customer Management job family in Ireland continues to have disproportionately more men employees than women employees, whereas Business Planning, Finance and HR job families continue to have more women employees. We also found underrepresentation of women employees in our Ireland Product Engineering job family, which is a trend we still see across the Tech industry.

What measures is VMware taking to address its Gender Pay Gap? The factors contributing to VMware’s pay gaps in Ireland indicate that measures are needed to help increase representation of women employees across all levels and departments in our Ireland workforce. Below, we have included details of measures that VMware is taking in Ireland to address its identified gender pay gaps.

To better champion women representation in our Ireland workforce, as well as globally, our ongoing aim is that over 1 in 3 hires (37%) will identify as a woman. To help achieve this:

• all VMware leaders, including those in Ireland, at Senior Director level and above, have had Diversity Equity and Inclusion (“DEI”) goals as part of their management objectives, which have been linked to their bonus objectives. Some of these DEI goals have been focused on increasing representation of women in VMware’s hiring processes by using consistent, objective, inclusive and sex/gender-neutral candidate interview slates;
• all VMware global job requisitions have at least one candidate on the interview slate who identifies as a woman, or the hiring manager will be required to engage in a review process with the relevant VP or above on why it was not possible to meet the candidate interview slate on their requisition. The VP or above is expected to use this review to coach their team member on the ways they can meet this candidate interview slate requirement on future requisitions;
• reports are run to see how many requisitions are in "Guided by Outcomes” (“GO HIRING”) format, which entails a description of the performance outcomes, not the ideal characteristics or qualifications of a candidate;
• all interviewers are encouraged to go through the “GO HIRING” interview training and VMware’s recruitment process follows the structured “GO HIRING” interview format, which aims to be less biased and more inclusive to encourage broader and more diverse representation in our future workforce; and
• VMware advertises many roles as being available on a flexible or remote working basis by default, which can help to increase the number of applications by women.
• Our Power of Difference communities (“PODs”) to address underrepresentation of women across VMware globally, including our Ireland workforce: We continue to recognise that giving people a discussion space, including for issues experienced by women in our Ireland workforce and globally, could help to attract more women candidates in Ireland, and help to enhance women’s employee experience, engagement, professional growth and retention. Our Women@VMware POD continues its aims of encouraging women hired by any VMware location to discuss and share experiences and to provide ideas and suggestions on how VMware can continue promoting opportunity and positive career experience for its women employees.
• Our local initiatives in Ireland to address overall underrepresentation of women in our Ireland workforce: In Ireland, our local women’s Employee Resource Group initiatives aim to increase representation of women in our Ireland workforce, such as our ongoing return to work programme focusing on reskilling or upskilling women who have been out of the workforce for a period of time, to help them transition back to, or to start a career in Tech.This year we also ran a Women Reboot programme which enabled two women to get certifications for a Tech career. We are delighted that these two participants are now employed in the Tech sector.
• Increasing representation of women in Ireland’s technology industry to address underrepresentation of women in our Ireland workforce: We remain clear in our view that it will take time and ongoing commitment to increase representation and retention of women across all parts of our Ireland workforce, which in turn should help further reduce our pay gaps. Accordingly, VMware is continuing its measures at the grassroots level to encourage more women into Tech. In addition to our increased new hires of women in our Ireland engineering organisation, VMware has continued to sponsor iWish for the past 9 years, where it has been involved in showcasing the opportunity of STEM subjects for young women to help encourage more young women to make STEM subject choices for their higher education and beyond. VMware has also continued with its Women in Tech and VMWomen programs. VMware has continued to actively support CoderDojo , another fantastic organisation offering local programming hubs and classes in Cork for 5th and 6th class primary school age girls to help generate early interest and capability in coding amongst girls. VMware further promotes STEM subjects for women in Ireland with its annual competition for students in the local community who are studying STEM at 3rd level by offering a first and runner up prize for the best essay and interview on the theme of Tech as a “Force for Good”. Additionally in Ireland, VMware ran a project from 9th December 2022 with three local schools. 18 young women from these schools were selected to build an application for the benefit of their local community over a period of 12 weeks, with engagement and support from VMware’s employees. The aim was to develop these young women’s – and their peers’ – interest in STEM subjects, particularly in the field of technology engineering. Through this initiative, these young women have acquired technical knowledge and had the opportunity to compete in an App design competition. Our Women at VMware employee initiative also ran various courses intended to promote leadership skills such as our “Influencing with Authority”, “Keys to Succeed in Difficult Conversations” and “Learning & Development Opportunities for New & Existing VMware Leaders” courses. As more women progress in our organisation, these courses aim to help foster and develop their career progression into leadership roles.

Final thoughts We are pleased to see progress for this reporting year with the reduction of some of our gender pay gaps across our Ireland business. However, there is still much work to be done. We remain committed to improving representation, retention and development of our women employees, as well as ensuring equal opportunities for all within our company. Signed: Belinda Hamilton-Crisp, HR Business Partner UK & Ireland On behalf of the board of VMware International Unlimited Company

Pine Gap Military Contractor Signed Away Right to Avoid US Tax

By Tristan Navera

Another defense contractor working at an Australian military base must pay federal income taxes to the US because they waived the right to be taxed by Australia, the US Tax Court said Tuesday.

Robert Diaz and his wife Brittany must pay income taxes for the time he was employed at Joint Defense Facility Pine Gap in central Australia, which is jointly operated by that nation and the US, from 2015 to 2017, the court said. He signed a closing agreement that waived the right to exclude the wages from their income under IRC Section 911(a) ..

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