identify and analyse causes of the gender pay gap essay

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• Introduction

Causes of the gender pay gap

The cost of the gender pay gap, beyond binary, the bottom line.

Why we still have a gender pay gap in 2024

The gender wage gap continues to be a subject of study—mainly because it still exists even after women have made decades of progress in education and the workforce . So, why does the disparity in pay between men and women still exist?

• Societal expectations of caregiving prompt many women to take lower-paying jobs that offer flexibility.
• Even when women who are married to men earn the same as their spouses, they often spend more time on housekeeping and caregiving.
• On average, women who have children give up 15% of their wages to provide care, resulting in $295,000 in losses over a lifetime.

There’s no one cause of the gender pay gap. The Government Accountability Office (GAO) has found that women continue to be underrepresented in management positions . Also, the agency’s findings persistently show that a large portion of the gender pay gap is “unexplained” but might be “due to factors not captured in the data we analyzed, such as non-federal work experience, discrimination, and individual career choices, among others.”

Discrimination. The unjust or prejudiced views of other people or groups can be hard to quantify, since the government doesn’t analyze potential historical or systemic reasons for the pay gap, according to the GAO report. In addition to the conclusion by the U.S. Department of Labor (DOL) that 70% of the gap is “unexplained,” it’s also generally acknowledged that the pay gap is wider for women of color.

For every $1 paid to non-Hispanic white men, for example, Black women earned 67 cents, and Hispanic women were paid 57 cents. So even though it’s expected that equal pay laws and antidiscrimination laws cut down on pay disparity, it appears a large portion of the “unexplained” pay gap is attributable to discrimination, whether it’s based on sex, race, or both.

Career choice. The occupations workers find themselves in also contribute to gaps in pay, with some experts positing that women might simply “choose” to go into lower-paying careers. Claudia Goldin , who won the Nobel Memorial Prize in Economic Sciences in 2023 for her work on the gender pay gap, has noted that just 10% to 33% of the gender pay gap is based on choice of occupation.

And “choice” might be the wrong word:

• Women are still expected to do the lion’s share of caregiving, meaning they often need to take lower-paying jobs that have more time flexibility, Goldin says.
• Women and “women’s work” continue to be undervalued in society, according to the Labor Department. When women enter a career field in greater numbers, the average pay for that occupation starts to decrease.
• Caregiving often results in women being forced out of the workforce, at least for a time, leading to increased chances for missed raises and promotions.
• About 63% of women with children under the age of 6 are employed in some capacity, compared to 71% of those with children age 6 or older. For men, there’s almost no difference in employment rates based on the ages of their children.

For women, the gender wage gap can be costly in a variety of ways:

• Lower starting salaries mean lower bases from which to get raises.
• Taking time out for caregiving duties leads to missed opportunities for higher pay and leads to gaps in contributing to retirement accounts .
• Less money saved for retirement is a problem when women have a longer life expectancy and are more likely to live in poverty during retirement .
• Some estimates indicate that the employment-related costs of women’s caregiving roles amount to about $295,000 for those born from 1981 to 1985 (in 2021 dollars adjusted for inflation ).

What about breadwinning women? Women who earn as much or more than their spouses in heterosexual marriages face nonmonetary costs, such as mental health issues or relationship challenges. The Pew Research Center found:

• In marriages where earnings were equal, women still spent more time on housework and caregiving, while men took more time for leisure.
• In marriages where wives were the primary earners, husbands’ leisure time increased significantly (compared with egalitarian marriages), while the time they spent on caregiving and housework stayed about the same.
• As the gender gap closes, male partners are less likely to pick up the slack at home, leaving more work for women.

In same-sex relationships , these discrepancies are less pronounced because gay and lesbian couples typically adhere less to stereotypical gender roles.

Disparity in wages doesn’t exist only between cisgender men and women. Those who identify as transgender or nonbinary earn 30% to 40% less than the typical worker, according to data from the Bureau of Labor Statistics and the Human Rights Campaign , an LGBTQ+ advocacy organization.

Because they earn less, lesbian, gay, bisexual, and transgender people are more likely to be poorer than their heterosexual counterparts. Research shows that lesbian and bisexual women, as well as lesbian, gay, and bisexual people of color, are especially vulnerable to poverty.

The pay gap between men and women is an entrenched issue with roots in the types of jobs women have typically performed (or were expected to do) and in gender stereotypes. Little progress has been made in closing the disparity in wages during the last 30 years. But the increasing number of women in leadership roles—including Mary Barra, CEO of General Motors ; U.S. Vice President Kamala Harris ; and entertainer Taylor Swift , who has fought for fair pay and whose enormous success has become a symbol of female empowerment—offer some promise that closing the gap may gain greater momentum.

By one estimate, however, the current, meager rate of increase means it will take at least another 35 years for women to reach pay parity. And for many working women, that day won’t come soon enough.

• Women Continue to Struggle for Equal Pay and Representation | gao.gov
• Women in the Workforce: Underrepresentation in Management Positions Persists, and the Gender Pay Gap Varies by Industry and Demographics | goa.gov
• [PDF] Understanding the Gender Wage Gap | dol.gov
• Gender Gap | econlib.org
• Gender Pay Gap? Culprit Is “Greedy Work” | news.harvard.edu
• In a Growing Share of U.S. Marriages, Husbands and Wives Earn About the Same | pewresearch.org
• Connecting the Dots: “Women’s Work” and the Wage Gap | blog.dol.gov
• Unpaid Family Care Continues to Suppress Women’s Earnings | urban.org
• Race and the Pay Gap | aauw.org
• Old-Age Poverty Has a Woman’s Face | un.org
• Getting a Job: Is There a Motherhood Penalty? | gap.hks.harvard.edu
• Full-Time Trans Workers Face a Wage Gap, Poll Finds | 19thnews.org

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

The data below can be saved or copied directly into Excel.

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.

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

See more work by Jessica Schieder and Elise Gould

What Causes the Wage Gap?

By deborah rho | february 24, 2021.

This post is the fourth in a series that provides deeper context for the findings of the  2020 Status of Women and Girls in Minnesota report , a research collaboration between the  Women’s Foundation of Minnesota  and the  Center on Women, Gender, and Public Policy . The data show that the wage gap between women and white men in Minnesota is twice as large for Hmong, Native American, and Latina women, nearly that for African American women, and 2.5 times greater for Somali women. Here, University of St. Thomas economist Deborah Rho explores the upstream racial and gender inequalities that give rise to the wage gap.  

The headlines are stark: “ How the Pandemic Is Breaking Women ,” “ The Economy Could Lose a Generation of Working Mothers ” and, bluntly, “ Primal Scream .” The COVID-19 pandemic has amplified longstanding inequities in American society, with particularly alarming implications for the progress of gender equality in the labor market.

According to a study by the U.S. Census Bureau , in July 2020, one in five working-age adults said that the reason they were not working was because of the disruption of childcare arrangements due to COVID-19. Of those not working, women were nearly three times as likely as men to not be employed as a result of childcare demands. Despite progress in equality within the household, gender norms and expectations continue to contribute to differences in labor market outcomes of men and women, exemplified by the gender wage gap.

Flexibility vs. Higher Wages

Even before the pandemic, in Minnesota, women made $0.79 for every dollar made by men. While many factors contribute to the gender wage gap, including discriminatory practices , research suggests that time away from employment, occupational clustering, and the time demands of jobs explain much of the difference in wages between men and women. Traditionally, many women dropped out of the labor force for some time in their childbearing years. Though there have been significant changes in this pattern in recent decades, women often do not have the same continuity of work experience as their male counterparts, which contributes to lower wages .

Additionally, women’s expectations about their careers may affect their educational and occupational choices, which greatly affect earnings. Women are overrepresented in low-earning occupations, such as cashiers, administrative assistants, and childcare workers.

Women may be pushed into low-earning occupations through discrimination, which excludes them from higher paying occupations, or socialization, which makes them more likely to seek these jobs.

Women may be pushed into these occupations through discrimination, which excludes them from higher paying occupations, or socialization, which makes them more likely to seek these jobs. One study finds evidence of a “care penalty,” when workers in jobs that require higher levels of caregiving earn lower wages than workers with similar skills in jobs that involve less caregiving. This penalty disproportionately affects women.

Although these are important considerations, recent research suggests that the wage gap can be attributed more to differences in pay within occupation than across occupation. One study finds that only 15 percent of the gender wage gap would be eliminated if men and women were equally represented in each occupation, but 85 percent would be eliminated if they were paid equally within each occupation. This is in part because even within occupations, women disproportionately seek positions that lend themselves to family responsibilities, jobs that are more flexible in the timing of work hours and less likely to have weekend and evening obligations.

These positions pay less than more inflexible jobs within the same occupation , especially in higher paying fields such as law and finance, where employees face many deadlines, develop close relationships with clients, and work in specialized teams. In such jobs, workers are not as easily substituted for one another, which makes flexibility in the timing of work costly to the firm. Within an occupation, men are more likely than women to be willing to take a job with long and inflexible hours and receive the corresponding higher compensation.

The number of hours of work also appear to affect the hourly wages of low- and moderate-income workers. However, rather than receiving a pay premium for working long hours, workers are penalized for working fewer hours. Both men and women experience a large hourly wage penalty for working less than 40 hours a week, but women are more likely to work part-time and therefore are affected to a greater extent.

Both men and women experience a large hourly wage penalty for working less than 40 hours a week, but women are more likely to work part-time and therefore are affected to a greater extent.

To the extent that women are more likely than men to seek particular work arrangements because they are expected to take on greater family responsibilities, it is unlikely that the wage gap will be eliminated until gender equity is attained within the home. For instance, there is growing evidence that parents’ gender attitudes affect the labor supply of daughters and the women their sons marry. Even if parents do not intentionally transmit gender stereotypes within the family, children develop attitudes and norms by observing gender roles at home and in society.

Racial Pay Disparities Compound the Gender Gap

In addition to differences between men and women, a closer look at earnings reveals dramatic disparities in wages across race. Economics research has typically considered the gender wage gap separately from racial wage gaps, but this may miss important dynamics between gender and race in the labor market. While on average, a white woman in Minnesota earns $0.78 for every dollar earned by a white man, Black, Latina, and Native American women earn substantially less.

Minnesota Cents on the Dollar

Average Wage and Salary Income Relative to White Men

Source: 2020 Status of Women and Girls in Minnesota . CWGPP analysis of American Community Survey, 2013-17. Average earnings of full-time, year-round workers age 16 and over in Minnesota.

Black and brown women are not only affected by a gender wage penalty but by racial disparities in the labor market. This difference is more troublesome given that African American and Native American mothers are more likely to be the primary breadwinner in the family and more likely to be single parents.

A Significant Portion of Minnesota’s Mothers Are the Primary Breadwinner

Source: 2020 Status of Women and Girls in Minnesota . Percent of mothers with children under 18 who are either single with earned income or earn more income than their spouse or partner. CWGPP analysis of the American Community Survey, 2013-17.

There is much evidence of unfair treatment of minority workers. Researchers have conducted experiments in which they send fictitious resumes to real employers using the name of the applicant to signal race. The quality of the resume is held constant so any differences in callback rates across race can be interpreted as discrimination. One such study found that white sounding names received 50 percent more callbacks for interviews than African American sounding names. Using a similar methodology, my coauthor and I found that in the Twin Cities, employers were less likely to call back applicants with names that sounded African American or Somali American than they were to call back those that sounded white.

In the Twin Cities, employers were less likely to call back applicants with names that sounded African American or Somali American than they were to call back those that sounded white.

Experiments like these are designed to parse out whether unequal labor market outcomes are a result of employer discrimination or because of differences across groups in characteristics such as level and quality of education, which are themselves impacted by discrimination. In the United States, compared to white students, Black and Hispanic students are more likely to drop out of high school and less likely to attend college given that they graduate. Unsurprisingly, disparities in “premarket factors” such as education also contribute to income inequality.

Early Childhood Investments Can Level the Field

There are no simple solutions to these longstanding disparities. But among family policies, access to high quality early childhood programs can be one way to help address both gender and racial inequality. A growing body of research has established that early childhood environments greatly influence later life outcomes. Numerous studies show that programs that have targeted disadvantaged children in their earliest years have had large positive impacts on earnings and education. Given that children of color are more likely to be living in poverty, programs that focus on quality care and education for children of low-income families will contribute to diminishing the racial wage gap.

In addition to benefiting the children who participate, high quality early childhood programs may also be a way to support working mothers. Affordable childcare is especially important for single mothers to remain in the labor force. Unfortunately, Minnesota is the fifth least affordable state when it comes to center-based childcare. While there is mixed evidence on the impact of government subsidized childcare on maternal labor force participation in general, recent research suggests that such programs increase the employment of single mothers. The expansion of programs that have proven to be valuable to disadvantaged children could contribute to diminishing the gender wage gap by helping mothers stay in the labor market.

The pandemic has revealed the crucial role childcare plays in the careers of parents. In addressing the current crisis, policymakers have an opportunity to invest in better, more equitable early childhood spending that will have an impact for years to come.

Deborah Rho is an associate professor of economics at the University of St. Thomas.

Photo: iStock.com/chabybucko

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Understanding the gender pay gap: definition and causes

Working women in the EU earn on average 12.7% less per hour than men. Find out how this gender pay gap is calculated and the reasons behind it.

Although the equal pay for equal work principle was introduced in the Treaty of Rome in 1957, the so-called gender pay gap stubbornly persists with only marginal improvements being achieved in recent years.

What is the gender pay gap and how is it calculated?

The gender pay gap is the difference in average gross hourly earnings between women and men. It is based on salaries paid directly to employees before income tax and social security contributions are deducted. Only companies of 10 or more employees are taken into account in the calculations. The EU average gender pay gap was 12.7% in 2021 .

Some of the reasons for the gender pay gap are structural and are related to differences in employment, level of education and work experience. If we remove this part, what remains is known as the adjusted gender pay gap.

The gender pay gap in the EU

Across the EU, the pay gap differs widely , being the highest in the following countries in 2021: Estonia (20.5%), Austria (18.8%), Germany (17.6%), Hungary (17.3%) and Slovakia (16.6). Luxembourg has closed the gender pay gap. Other countries with lower gender pay gaps in 2021 are: Romania (3.6%), Slovenia (3.8%), Poland (4.5%), Italy (5.0%) and Belgium (5.0%).

Read about the European Parliament’s fight for gender equality

Interpreting the numbers is not as simple as it seems, as a smaller gender pay gap in a specific country does not necessarily mean more gender equality. In some EU countries lower pay gaps tend to be because of women having fewer paid jobs. High gaps tend to be related to a high proportion of women working part time or being concentrated in a restricted number of professions. Still, some structural causes of the gender pay gap can be identified.

Check out more data on the gender pay gap

Causes of the gender pay gap

Part-time work.

On average, women do more hours of unpaid work , such as childcare or housework.

This leaves less time for paid work. According to figures from 2020 , almost one-third of women (28%) work part-time, while only 8% of men work part-time. When both unpaid and paid work are considered, women work more hours per week than men.

Career choices influenced by family responsibilities

Women are also much more likely to be the ones who have career breaks : in 2018, a third of employed women in the EU had a work interruption for childcare reasons, compared to 1.3% of men. Some career choices made by female workers are influenced by care and family responsibilities .

More women in low-paying sectors

About 24% of the total gender pay gap can be explained by an overrepresentation of women in relatively low-paying sectors, such as care, health or education. The number of women in science, technology and engineering has increased. Women accounted for 41% of the workforce in 2021 .

Fewer and lower-paid female managers

Women also hold fewer executive positions: in 2020 they made up a third (34%) of managers in the EU, although they represent almost half of the employees. If we look at the gap in different occupations, female managers are at the greatest disadvantage: they earn 23% less per hour than male managers.

A combination of factors

Women do not only earn less per hour, but they also perform more unpaid work as well as fewer paid hours and are more likely to be unemployed than men. All these factors combined bring the difference in overall earnings between men and women to almost 37% in the EU (in 2018).

Closing the gap: the benefits

The gender pay gap increases with age throughout the career and alongside increasing family demands, while it is rather low when women enter the labour market. With less money to save and invest, these gaps accumulate and women are consequently at a higher risk of poverty and social exclusion at an older age. The gender pension gap was over 28% in the EU in 2020.

Reducing the gender pay gap creates greater gender equality while reducing poverty and stimulating the economy as women would get more to spend more. This would increase the tax base and would relieve some of the burden on welfare systems. Assessments show that reducing the gender pay gap by one percentage point would increase the gross domestic product by 0.1%.

Parliament's actions against the gender pay gap

In December 2022, negotiators from the Parliament and EU countries agreed that EU companies will be required to disclose information that makes it easier to compare salaries for those working for the same employer, helping to expose gender pay gaps. In March 2023 Parliament adopted those new rules on binding pay-transparency measures . If pay reporting shows a gender pay gap of at least 5%, employers will have to conduct a joint pay assessment in cooperation with workers’ representatives. EU countries will have to impose penalties, such as fines, for employers that infringe the rules. Vacancy notices and job titles will have to be gender neutral. The Council still has to formally approve the agreement for the rules to come into effect.

The proposal for the new rules follows the Parliament’s resolution on the EU Strategy for Gender Equality from January 2021, in which MEPs called on the Commission to draw up an ambitious new gender pay gap action plan with clear targets for EU countries to reduce the gender pay gap over the next five years.

In addition, Parliament wants to make it easier for women and girls to reach top positions and boost gender equality on corporate boards . In November 2022, MEPs approved rules, which aim to introduce transparent recruitment procedures, so that at least 40% of non-executive director posts or 33% of all director posts are occupied by the women by the end of June 2026.

Find out more about what the Parliament does to tackle the gender pay gap

Find out more about equal pay and equal opportunities

• Equal pay for equal work between men and women
• Study: women on board policies in EU countries and the effects on corporate governance

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Gender Wage Gap: Causes, Impacts, and Ways to Close the Gap

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Part of the book series: Encyclopedia of the UN Sustainable Development Goals ((ENUNSDG))

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Economy ; Equality ; Income ; Occupational segregation ; Sustainable Development Goals

Pay inequity between men and women in the same position is defined as inequal pay and is consistently found across the globe in varying degrees and in many different sectors of labor (UN 2015 ). Inequal pay within an organization or within the labor market leads to a difference in average earnings between men and women, which is defined as the gender wage gap . The European Union defines the gender wage gap as “the relative difference in the average gross hourly earnings of women and men within the economy as a whole” (European Commission 2012 ). Statistically, this difference at the time of writing means about 20% lower earnings for women or 83 cents for every US dollar earned by a man (Geiger and Parker 2018 ). This means that over the course of a 40-year career, a woman experiences a lifetime wage gap of $403,440 (NWLC 2018 ).

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Gender Pay Gap

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The Gender Pay Gap: Understanding the Economic and Social Causes and Consequences

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The gender pay gap is a pressing issue that affects individuals and society as a whole, so it is important for economics students to understand it. Despite recent progress, women still earn less than men for the same jobs, leading to economic inequalities and reduced efficiency (see, for example, the recent report released by Moody’s ). Understanding the causes and consequences of the gender pay gap is critical in developing policies that promote fairness and equality.

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Economic Inequality by Gender

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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.

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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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Open Access

Peer-reviewed

Research Article

The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (LL); [email protected] (LB)

Affiliation Department of Psychology, Lander College, Flushing, New York, United States of America

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliation Department of Computer Science, Lander College, Flushing, New York, United States of America

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Roles Conceptualization, Writing – review & editing

Affiliation Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Roles Formal analysis

Affiliation Department of Computer Science, Stern College for Women, New York, New York, United States of America

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

• Leib Litman, 
• Jonathan Robinson, 
• Zohn Rosen, 
• Cheskie Rosenzweig, 
• Joshua Waxman, 
• Lisa M. Bates

• Published: February 21, 2020
• https://doi.org/10.1371/journal.pone.0229383
• Reader Comments

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Citation: Litman L, Robinson J, Rosen Z, Rosenzweig C, Waxman J, Bates LM (2020) The persistence of pay inequality: The gender pay gap in an anonymous online labor market. PLoS ONE 15(2): e0229383. https://doi.org/10.1371/journal.pone.0229383

Editor: Luís A. Nunes Amaral, Northwestern University, UNITED STATES

Received: March 5, 2019; Accepted: February 5, 2020; Published: February 21, 2020

Copyright: © 2020 Litman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Funding: The authors received no specific funding for this work.

Competing interests: We have read the journal's policy and the authors of this manuscript have the following potential competing interest: Several of the authors are employed at Cloud Research (previously TurkPrime), the database from which the data were queried. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination.

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity.

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility.

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors.

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch..

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample.

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach.

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis.

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

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https://doi.org/10.1371/journal.pone.0229383.t001

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

https://doi.org/10.1371/journal.pone.0229383.t002

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay.

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

https://doi.org/10.1371/journal.pone.0229383.t003

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

https://doi.org/10.1371/journal.pone.0229383.t004

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

https://doi.org/10.1371/journal.pone.0229383.t005

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

https://doi.org/10.1371/journal.pone.0229383.t006

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Task heterogeneity

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

S1 table. distribution of hits, average pays, and gender pay gaps by hour of day..

https://doi.org/10.1371/journal.pone.0229383.s001

S2 Table. Distribution of HITs, average pays, and gender pay gaps by day of the week.

https://doi.org/10.1371/journal.pone.0229383.s002

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Gender Pay Gap for Women: The Main Causes

The gender pay gap is the analysis of wages earned by women compared to men without including positions. Despite several ways to calculate the pay gap, all the results point to the fact that women are paid less than men. The gap has been perceived to be wider for women of color. The pay gap in the labor industry is a pertinent issue because diversity is becoming part and parcel of every organization and every nation; therefore, dealing with this backlash is a challenge to many. There is a fight for inclusivity in employment as more women are empowered and educated globally. Some multinationals employ people from all backgrounds, and compensation has to be harmonized. Pay Gap is, therefore, the elephant in the room that needs to be addressed to understand its root causes and the parties that need to spring to action to bridge the gap.

Statistics of the global gender pay gap

Several countries have different gender pay gaps as developed nations harmonize the issue through labor policies while developing countries still suffer at large. In Australia, the average weekly wages for men are $1837, while women earn $1575.50 (Workplace Gender Equality Agency, 2021). These statistics iterate that women make less than men by $261.50 every week. This is just a general overview of what the gap in Australia means and how women are affected. Further statistics on the various labor sectors for either public servants or the private sector might create revelations worth a lawsuit. When factored in by region, Western Australia has the highest gender pay gap of 21.9% compared to Eastern Australia’s 7% (Workplace Gender Equality Agency, 2021). However, the public service has tried to harmonize this gap with a 7.3% variation compared to the private sector’s 23.5% (Workplace Gender Equality Agency, 2021). Other nations might be worse than what Australia has portrayed.

One of the places where the gender pay gap has been reported in the US is the higher learning institutions. Women have held most low-paying jobs in the higher education sectors, with men being offered high-paying jobs. In a study on the representation of women in institutions of higher learning, it was reported that the position most women are offered is that of the human resource manager (Bichsel &McChesney, 2017). There has so far been no woman by 2017 to be given the post of chief officer in any top university; this means that women still earned less than men. Despite women being outnumbered 9:1 in the top positions, they made far better than their male counterparts (Bichsel &McChesney, 2017). This begs for the question, are only women who are paid less? Men are also subject to the gender pay gap debate and should not be left out of the discussion.

It appears that closing the gender pay gap looks like a lifetime effort. The world economic forum suggests that the gap can only be completed in the 52 years in Europe even if all the countries embark on sustainable development goal of equal pay with sanctions (World Economic Forum, 2021). The forum further suggests taking the US 62 years and the Latin America and the Caribbean 69 years. In all other regions that have been reported to have human rights issues, it is estimated that it would take over a century. For example, it would take the middle east and northern Africa 142 years to close the gap. Only in south Asia, it is believed that the gap would be completed in close to two centuries to come. In sub-Saharan Africa, where the countries are the third world, it will take 122 years for women’s pay to be at the same level as men’s.

Causes of the gender pay gap

With such a disparity witnessed in different regions with different economic and cultural setups, the causes of the gender pay gap can be narrowed to cultural and racial diversity. To further highlight this, women are underrepresented in most positions; hence they naturally earn lower than men. Culture plays a more significant role than any other cause for the increasing gender pay gap. To begin with, some industries are male-dominated while others are female-dominated; this disparity can be linked with culture. Men do not want to associate themselves with ‘women industries’ while women do not associate themselves with ‘male industries.’ The female-dominated industries and jobs perceived to be female attract lower pay (Workplace Gender Equality Agency, 2021). This is part of the cultural divide between gender roles where women are below men in society and should be submissive.

Gender-defined roles are also disadvantageous on the part of women as they take more time away from work. Take the example of a woman who is eight months pregnant; she is considered disabled because pregnancy comes with many limitations (Equal Employment Opportunity, 2021). Taking time away from the workplace means that it is difficult for women to advance their careers. They would be primarily concentrating on the welfare of their newborns, which is also a gender-defined role. If women do not grow their careers, their pay will stagnate while men who have a one-month paternity leave rise. Therefore, women are still held to the confines of gender-defined roles where they look after their families as men march out to provide and advance their careers. It is, therefore, difficult to find women in the comfort of their homes taking care of their families while sitting at the top of the food chain in the labor industry.

The issue of stagnated careers for women brings to light the empowerment of women. There are few women in top positions, thus showing a lack of charge. Empowerment spans the sectors of education caused by a cultural divide and lack of economic development that results in a lack of resources for financing education (Barroso & Brown, 2021). With little or no empowerment for women in society, their pay would still be less. They will be forced to work part-time jobs with fewer hours of duty hence low income. In reality, it would be challenging to employ a woman with little education to positions that enumerate handsomely; the employer’s hands, in this case, are tied. Therefore, women will still be behind men in pay since men are more educated than females. Bridging this gap would thus help reduce the pay gap that exists for women in most workplaces.

Racial Disparity

Today, Black women work in an assortment of occupations and enterprises at every unique level. However, many Black ladies stand up to the very misperceptions about their work that have been framed at the convergence of racial and sexual orientation inclinations for quite a long time (Reese, 2018). Therefore, Black ladies face unreasonable assumptions, unique difficulties, and one-sided presumptions concerning where they fit in the working environment that contrasts with the insights held regarding ladies from other racial and ethnic gatherings just as men. Individuals of color have needed to explore and now and again face contending, imperfect, or fragmented stories about their hard-working attitude, family obligations, and general esteem that impact choices regarding what they ought to acquire (Frye, 2019). At the point when sexism and bigotry cross in the working environment, the impact is obliterating.

A further look at the racial disparities in the gender wage gap brings to light the plight of immigrant workers and how they are paid in the US. Every year several immigrants come into the US for greener pastures with the promise of better work opportunities. These immigrants come from different places, including but not limited to the Latino from Mexico, Chinese, and Indians. These employment opportunities they are promised make them believe they would be paid better. It has been reported that 5.4 million immigrant women living in America are undocumented out of the total 23.2 million immigrants (American Immigration Council, 2020). Because these women are undocumented and would do anything to make ends meet, employers take advantage of this to pay them below the minimum wage, which is better than their countries of origin. Therefore, undocumented women have no choice but to work with the bit of low-paying jobs they get.

Bridging the gender pay gap; a public policy perspective

The gender pay gap is an issue of public policy because reducing it is suitable for the general public. Policies, therefore, have to be developed to ensure that the sustainable goal of gender equality in pay is achieved by 2030. One of the policies that can help bridge the wage gap is the Paycheck Fairness Act which prohibits discrimination on compensation based on sex (United States Congress, 2021). The Act also bans workplace policies where employers direct employees not to reveal their salaries to colleagues. Employers often did these to ensure there were no protests on how employees were paid. There is a high likelihood that employees would abandon their tools if they conversed about their salaries and realized there are a lot of discrepancies. The ActAct, therefore, infringes privacy while protecting the fundamental right of discrimination that causes the gender wage gap.

During recent debates, one thing that has come when the gender pay topic arises is women’s empowerment in society—empowering women spans the economy, education, and cultural sectors. Economic empowerment of women makes them realize the strength of their gender and rights. Having the financial means to create jobs gives women power over several other things. Women can now set the enumeration standards in the market that other businesses can emulate. Economic empowerment also gives the female gender a seat in the room where economic policies are formulated. Where women are leaders or are placed in top positions, junior employees, especially their fellow women, are rewarded handsomely (UN Women, 2018). Therefore, it would be difficult for women who are economically empowered to subject their fellow women to the same awful experience they went through when they did not.

Educationally when women are empowered, they are provided with the knowledge to decide between right and wrong and make policies for a better future for the girl child. Being educated refers to a lot of things; first, it means getting the required academic qualifications. Secondly, it means creating awareness about the gender pay gap and situations that cause it, including how to reduce the gap. Educating women will ensure that they are more qualified to apply for the top positions that have for a long time been a preserve for men. Their salaries would thus be higher due to the competition they create for such posts. Informing women about their rights, such as maternity leaves, can help a lot. In 2018, there was a campaign by Bill and Melinda Gates that aimed at providing income for women having parental leaves (Clifford, 2019). This helped them remain in financial control even during their leaves, thus earning them economic empowerment.

The next step in ensuring the gender pay gap for women is reduced is to open up opportunities for women to allow for a diverse representation. The possibilities for women should start with political seats where several women are encouraged to apply for public office. The current vice president for America is a woman, Kamala Harris, who was chosen as a running mate to the surprise of many. An excellent example of where opportunities should open up for women is in the Supreme Court, where out of nine justices, only three are women (Supreme Court of the United States, 2021). The pay cannot, however, be complained about as the justices are paid based on experience. Junior associates are paid less than chief justices regardless of gender. This means that Jr., Associate Justice Clarence Thomas, is paid less than Associate Justice Sonia Sotomayor.

Lastly, the government should thump up its efforts to reduce the gender pay gap by introducing several policies aimed at doing so. One of the policies that can be introduced allows women on maternity leaves to work from home for up to a year. Usually, when women are given maternity leaves, they are not expected to work until the maternity leave expires. Some employers often fire their employees during such leaves, while some deny them essential services. Enabling the employees to work from home allows them to continue with the employment and gain more experience until they are ready to return to the office. Cultural interventions for this issue include defining the traditional gender roles that have kept women behind the curtains, thereby derailing their progress in the career world. Once through with maternity leave, a woman should go back to work and not be forced to stay at home to take care of the child.

In conclusion, the gender pay gap for women is a public policy issue because it affects the general public. The gender pay gap for women is also an issue that cannot be dealt with immediately; instead, gradual efforts should be implied from a collective responsibility perspective. The disparity existing for women’s pay is mainly caused by racism, discrimination by sex, and cultural norms. It can, however, be reduced by empowering women economically, education-wise and helping them define the cultural norms that have led to the disparity. Racism and discrimination by sex can be handled by introducing public policies directed towards curbing the said vices. With the said efforts put in place, society’s women will almost be equal to men, and at least, the efforts made to empower them will not be in vain.

American Immigration Council. (2020). Immigrant women and girls in the United States . Web.

Barroso, A., & Brown, A. (2021). Gender pay gap in U.S. held steady in 2020 . Pew Research Center. Web.

Bichsel, J., & McChesney, J. (2017). The gender pay gap and the representation of women in higher education administrative positions: the century so far. Research Report . College and University Professional Association for Human Resources. Web.

Clifford, C. (2019). Bill Gates’ Foundation says 52-week paid leave isn’t doable after all, but will give new parents $20,000. CNBC. Web.

Equal Employment Opportunity. (2021). Is pregnancy covered under the Americans with Disabilities Act? SHRM. Web.

Frye, J. (2019). Racism and sexism combine to shortchange working Black women . Center for American Progress. Web.

Reese, C. C. (2018). The status of public sector pay equity for women of color in the United States. Review of Public Personnel Administration , 39 (4), 594-610. Web.

Supreme Court of the United States. (2021). Justices. Web.

UN Women. (2018). Facts and figures: economic empowerment . Web.

United States Congress. (2021). H.R.7 – 117th Congress (2021-2022): Paycheck fairness act . Congress.gov | Library of Congress. Web.

Workplace Gender Equality Agency. (2021). Australia’s gender pay gap statistics 2021 . Welcome | WGEA. Web.

World Economic Forum. (2021). Global gender gap report 2021 . Web.

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The Issue of Pay Gap in The Women's U.s. Soccer Team

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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 reporting: Understand what it is, if you need to report and why

Learn about the gender pay gap, find out which employers need to report on it and understand why it's important

This guide is designed to help employers understand more about the gender pay gap and find out if they need to report on it. It provides a summary of the regulations, which organisations they apply to, and what happens if you don’t report your gender pay gap figures. It explains what the gender pay gap is, what causes it, why it needs to be tackled, and why gender pay gap reporting has been introduced.

There has been a government consultation since gender pay gap reporting was introduced, looking at mandatory reporting of ethnicity pay data and, although there is not a legal requirement, some organisations are already reporting. We strongly encourage you to also report on ethnicity pay gaps. For more information, see the CIPD guide on Ethnicity pay reporting .

The CIPD provides information on the legislation relating to gender pay gap reporting in our dedicated member resource. This legal information provided is for guidance only and if your organisation is following related legal proceedings, you should seek further legal advice from a specialist solicitor. 

For guidance on how to calculate and publish your gender pay gap report refer to our guide on calculation and publication

What is the gender pay gap.

The gender pay gap is a measure of labour market or workplace disadvantage, expressed in terms of a comparison between men’s and women’s average (median) hourly rates of pay. It’s about pay, but also about other factors, such as occupational segregation, or the fact that in the main it’s women who look after children and other dependants.

Gender pay gap reporting doesn’t specifically ask who earns what, but what women earn compared with men. It provides a framework within which gender pay gaps can be surfaced, enabling us to constructively consider why they exist and what to do about them.

The gap can be measured in various ways, and it’s important to understand how, in any specific context, the gap is being measured. A gender pay gap can be expressed as:

• A positive measure, for example, a gap of 13.9% – this indicates the extent to which women earn, on average, less per hour than their male counterparts.
• A negative measure, for example, a gap of −9.2% – this indicates the extent to which women earn, on average, more per hour than their male counterparts. This may happen, for example, if you employ a high proportion of men in low-paid, part-time work, and/or your senior and higher-paid employees are women.

Example: A negative gender pay gap

Avocet Care employs 305 staff across six residential homes providing specialist dementia care. The employees are predominantly female. The highest-paid employees are highly qualified nursing and managerial staff, only three of whom are male. At each home four to five men are employed as maintenance staff and drivers. These jobs are relatively low paid.

Avocet’s mean and median gender pay gap calculations show gaps in favour of women, and its pay quartiles show the predominance of women in all four quartiles. The company does not pay bonuses.

In its narrative Avocet points to the predominance of women in the care home sector, and to the shortage of suitably qualified male carers. It also sets out what action it is taking to recruit more men into its caring and nursing roles, and explains that, given its mixed-sex client profile, attracting more men is a business priority.

To fully understand the gender pay gap, we need to think about it in three different ways:

• As a measure of labour market disadvantage – for example, throughout the economy, women are concentrated in lower-paid jobs.
• As a measure of workplace disadvantage – for example, women in your organisation are concentrated in lower-paid jobs; this is where the government wants you to act. Taking steps to reduce the gap at workplace level will help narrow the gap at national level.
• As a measure of the difference between the individual earnings of a man and a woman – a difference doesn’t automatically mean that the woman is missing out on equal pay. To be entitled to equal pay, a woman must be employed by the same employer, on the same terms and conditions, and the work that she does has to be equal to that being done by her male colleague. And even then, there may be an acceptable reason for the pay difference, such as location. However, it’s also important not to lose sight of the fact that unequal pay may be contributing to the gender pay gap.

Gender pay gaps are the outcome of economic, cultural, societal and educational factors. Some argue that they reflect personal choice but, although the decision to seek paid employment may be an individual choice, that choice is strongly influenced by matters outside of the individual’s control, such as the availability and affordability of childcare, and it is still the case that the choices available to women are more constrained than those available to men. The key influences on the gender pay gap are summarised in figure 1.

Unpaid caring responsibilities

The cost of childcare has been identified as a particular problem that affects women’s participation in the labour market. A 2017 report from Working Families found that childcare costs account for a significant proportion of family expenditure and that the high cost of childcare has a great influence on whether parents, particularly mothers, choose to either give up work or reduce their working hours. And, in so far as the care of adults is concerned, women are more likely than men to be carers.

Women as unpaid carers: A survey carried out by Carers UK in 2022 found that 80% of carers are female .

Between 2000 and 2015, time spent caring for adults by people aged over 50 has increased, but there is concern that there may not be enough unpaid carers to meet future demand. Factors such as increasing female employment, fewer children and higher divorce rates among men over 60 years may affect the future availability of children to provide unpaid care for their elderly parents. In 2015/16, an estimated 345,000 unpaid carers aged 16-64 in England, predominantly women, left employment to provide care.

In 2022, 600 people a day, on average, left work to take on caring responsibilities, and 75% of carers still in employment worry about juggling work and care.

Occupational segregation

Despite half a century of equalities legislation, the UK labour market remains highly segregated, with men dominating some types of job and women others; many women are concentrated in the ‘five Cs’ of caring, cleaning, catering, clerical and cashiering, all of which tend to be lower paid. In terms of the gender pay gap, the problem with occupational segregation is not that men and women are doing different types of work, but that segregation is associated with these jobs being valued differently. The introduction of the National Minimum Wage and the National Living Wage provides a wage floor for the lowest-paid jobs but does nothing to challenge any underlying undervaluation of the work.

In terms of gender pay gap reporting, a lot of attention has been paid to vertical segregation – jobs in the higher echelons of an organisation being dominated by men – but horizontal segregation also contributes to the gender pay gap. Horizontal segregation occurs lower down the hierarchy and manifests as men and women doing distinctly different types of work, with the ‘male’ jobs being paid more than the ‘female’ jobs. When the reverse is true – the ‘female’ jobs being paid more than the ‘male’ jobs – a negative gender pay gap may arise. In the first two years of gender pay gap reporting, some employers have been paying increasing attention to the impact of horizontal segregation and are looking to find ways of tackling it.

Pay discrimination

In terms of the gender pay gap’s contribution to actual inequalities in pay, horizontal occupational segregation presents a high risk of equal pay claims, as does a high mean bonus gap. We look at this later in What your measures tell you , but it would be sensible to take account of the risk of equal pay claims being brought. In 2017/18, the Employment Tribunal received 35,558 equal pay claims . In 2018/19 the figure fell to 26,860, but as the reports only provide the headline figures for the number of cases filed, it is not possible to form a view as to why there has been a substantial drop.

Part-time working

Looking only at part-time employees, we see a negative gender pay gap, with median pay for part-time employees being higher for women than for men . However, hourly rates of pay for part-time work tend to be lower than for full-time work and, with such a high percentage of women working part-time, their low hourly rates of pay mean that the gender pay gap for all employees is greater than that for full-time employees alone. Seventy-one per cent of part-time workers are women; 38% of women work part-time, compared with 14% of men. And whereas men tend to work part-time at the beginning and end of their working lives, women do so in their middle years.

As well as the moral case for making access to work and progression opportunities more equal for men and women, the economic benefits of closing the gap are considerable. Because of this, the government considers that the rate of progress is too slow and has committed to closing the gap within a generation. Gender pay gap reporting is one way of fulfilling that commitment. We look at the benefits in more detail in the section 'Why should the gender pay gap be addressed?’.

Promoting pay transparency

Pay transparency, which provides people with the information to assess the fairness of the way in which pay is allocated, is increasingly being demanded by regulators and the public. For some time now, companies have been required to disclose their directors’ pay, while public bodies must disclose the pay of their senior officers.

The pay transparency afforded by gender pay gap reporting helps to illuminate the structural drivers of inequality, such as occupational segregation or the unequal distribution of family responsibilities.

It also prompts employers to examine structural or cultural barriers within the organisation that may be contributing to the pay gap and, ideally, tackle them. In other words, addressing the factors that are creating a ‘glass ceiling’, preventing women from progressing to the most senior roles.

Gender pay gap reporting is also consistent with the kind of transparency that has long been required by the equal pay legislation, namely that everyone involved in a pay system should know how it operates. This means employees and their managers knowing what an employee must do to earn each component of their pay packet. For example, what does an employee have to do to earn their salary, or why does one employee receive a particular allowance, but another employee doesn’t?

Putting the kind of transparency afforded by the equal pay legislation alongside gender pay gap reporting means we have the information needed to uncover the causes of gender pay inequality. An example of how, in the context of gender pay gap reporting, these two kinds of transparency complement each other would be to know why you are paying someone a bonus, information which could help you to explain the bonus gap reported in your gender pay gap report.

At whole economy level, the gender pay gap is calculated from data drawn from the Annual Survey of Hours and Earnings (ASHE), which is carried out by the Office for National Statistics (ONS). ASHE is based on a 1% sample of employee jobs, drawn from HMRC Pay As You Earn records. ASHE collects information on the levels, distribution and make-up of earnings and hours paid. Results are produced by gender and by various industrial, occupational and geographic breakdowns, as well as by public and private sectors and by age group.

In the absence of an annual report on the overall gender pay gap in the UK (such as, for example, that produced by Belgium ), ASHE is the key official source of information on the gender pay gap in the UK, but to get a full picture of women’s earnings relative to men’s, it’s important to read the annual survey in its entirety, and not just the section on the gender pay gap. Knowing, say, that average earnings in the private sector are lower than those in the public sector, and that in 2018 earnings growth was greater for full-time than for part-time workers , helps to put the gender pay gap into context.

In April 2023, the UK’s gender pay gap for full-time employees was 7.7%, meaning that average pay for full-time female employees was 7.7% lower than for full-time male employees, or for every £1 a full-time male employee earned, a full-time female worker earned 92.3 pence.

Among all employees, the gender pay gap decreased to 14.3% from 14.9% in 2022. The Office for National Statistics notes that during the COVID-19 pandemic period, earnings estimates were affected by changes in composition of the workforce and the impact of the Coronavirus Job Retention Scheme (furlough) making interpretation difficult. Additionally, data collection disruption and lower response rates mean that, for 2020 and 2021, data were subject to more uncertainty and should be treated with caution.

Source:  The gender pay gap 2023

How the ONS estimates the UK gender pay gap

The ONS estimates the gender pay gap based on hourly earnings, excluding overtime, and bases its calculations on median rather than mean earnings.

• Hourly earnings are used because they take account of the fact that men are proportionally more likely than women to work full-time. At ages 16–21, men’s jobs are split almost equally between full-time (52.1%) and part-time (47.9%), but, between the ages of 30–39 and 40–49, more than 90% of men’s jobs are full-time (93.7% and 92.8% respectively). For women, only 67.5% (ages 30–39) and 64.1% (ages 40–49) hold full-time jobs.
• Overtime is excluded because, as it is still in the main women who bear the day-to-day responsibility for looking after children or dependent relatives, they are less likely than men to work overtime.
• The ONS prefers median rather than mean earnings because the median is not affected by extreme values. However, as the mean gap captures the fact that the upper end of the earnings distribution is dominated by men, the mean is an important measure of women’s labour market disadvantage.

Women’s patterns of paid work differ from those of men, and this can put them at a disadvantage, but men’s and women’s work experience is converging – the proportion of men working part-time, for example, rose from around 7% in 1992, to 13% in 2010 and to 15.2% in 2021. And for women, full-time employment has grown more quickly than part-time employment.

While the baseline measurement for both ASHE and gender pay gap reporting is of hourly earnings, it’s also possible to calculate the gender pay gap by weekly, monthly and annual earnings, and by occupation, age, ethnicity and disability status, and to analyse the gap at various points in the earnings distribution. You probably feel that the six measures you are being asked to produce are more than enough, but it’s worthwhile bearing in mind that the deeper down you drill into your people and pay data, the more likely you are to recognise what it is you need to do to take effective action to reduce your gender pay gap.

Over the past 30 years the gender pay gap in full-time employment has narrowed, but the pace of improvement has been uneven and there’s still a way to go. Knowing where the sticking points are may help you deal with your organisation’s own gender pay gap.

As can be seen from figure 2 above, the gender pay gap has decreased markedly over time, but what the figure doesn’t show is that the extent to which it has done so has varied across different age groups. The gender pay gap is small or negative for employees in their 20s or 30s but widens considerably for older age groups.

The gender pay gap within different groups of occupations also varies considerably, and in different ways for different occupations. The pay gap has been consistently high for those in the skilled trades, and for managers and directors. It has been consistently lower than the national average for professional and associate professional occupations, because, with increased attendance at universities, there have been proportionately more women entering professional and associate professional occupational groups. However, a lack of flexible working arrangements on offer at senior levels more generally is a factor affecting women’s progression opportunities.

In addition to the moral and social justice case for gender equality, there are further national and organisational benefits of seeking to close the gender pay gap (figure 3).

With women outperforming men educationally, the case for ensuring their skills are fully utilised is incontestable. In addition, failing to tackle a gender pay gap is likely to cause damage to your organisation’s reputation in the eyes of both current and potential clients and employees.

The economy: Gender equality, economic growth, pay and pensions

A key source of evidence on the economic dimensions of the gender pay gap is the report of the 2016 inquiry into the gap by the Women and Equalities Committee (WEC). The WEC found that the UK’s 19.2% gap (2016) was not only an equality issue, it also represented a significant loss to UK productivity and, in the face of an ageing workforce, a skills crisis, the need for a more competitive economy, and that the gap needed to be addressed. The WEC concluded that tackling the underlying causes of the gender pay gap would not only increase productivity and address skills shortages, but it would also improve the performance of individual organisations.

Several studies support the WEC’s conclusion:

• In its evidence to the inquiry, the former UK Commission for Employment and Skills (UKCES) quoted research suggesting that the underutilisation of women’s skills costs the UK economy between 1.3% and 2% of GDP every year. The UKCES also suggested that eradicating the full-time gender pay gap would contribute an additional £41 billion of spending into the economy each year.
• McKinsey’s 2016 report , The Power of Parity: Advancing women’s equality in the United Kingdom, suggested that even partial progress towards parity had the potential to add as much as £150 billion to GDP by 2025, over and above the business-as-usual scenario – in fact, an estimated 6.8% more. This would be the equivalent of raising GDP growth by 0.7% per year for the next 10 years.
• The Gender Pensions Gap Report 2022 study showed that while women’s expected retirement income is increasing and the gender gap is shrinking, women’s pension wealth is only 33.5% of men’s, or for every £1 a man had in his pension pot, a woman had just 33.5 pence. Closing the gap by bringing women’s earnings up to the level of men’s would increase the likelihood of women being able to provide for their own pensions, thereby reducing both pensioner poverty and the welfare support needed to counter it.

The workplace: Gender equality, talent and reputation

At an organisational level, promoting gender equality is part of being a good employer, one that strives to achieve fairness. Being open about your gender pay gap and how you’re tackling it increases employee confidence in you as an employer, and in your pay and reward processes.

Organisations with gender-diverse profiles at senior levels make a better financial return than those who do not. McKinsey’s Diversity Matters research has shown that for every 10% increase in gender diversity in a UK company’s executive team, earnings before interest and taxes rose by 3.5%. But the national ratio of women in leadership relative to men is poor (there are currently two male managers for every female manager), with the UK lagging behind comparable economies such as the United States, Sweden and Canada.

Women make up around half the talent pool, so attracting and retaining them is central to future success. Women are better qualified than ever before with girls still doing better than boys at both GCSE and A level in England, Wales and Northern Ireland.

Even before gender pay gap reporting was introduced, employees and job seekers were taking pay gap data into account when applying for a job or considering whether to stay in one. Now, with gender pay gap reports available on the government’s gender pay gap viewing service site, school-leavers, graduates and older workers looking to change jobs are all now able to access information about your gender pay gap, and they are sure to do so.

CIPD guidance on reward

For more on employee attitudes to reward, take a look at the CIPD’s factsheet on reward and pay . For women, an employer’s record on equality, inclusion and diversity is especially important. PwC’s report, The Female Millennial: A new era of talent , shows that young women seek out employers with a strong record on equality, diversity and inclusion. Eighty-five per cent of female millennials surveyed said an employer’s policy on equality, diversity and workforce inclusion was important when deciding whether or not to work for them. Being open about your gender pay gap, and proactive in tackling its causes, will reduce the likelihood of your organisation being seen as a second- or third-choice employer.

What do I have to report and when?

Regulations introduced in 2017 require public, private and voluntary sector organisations, with 250 or more employees, to report annually on their gender pay gap using a specified ‘snapshot date’ relevant to their sector (see figure 5).

The snapshot date will always be 31 March for public authorities, and 5 April for all other employers, in any year in which they have 250 or more relevant employees. This date is:

• the date which determines who counts as an employee for the purposes of gender pay gap reporting
• the date used to determine employees’ hourly pay (your gender pay gap calculations are based on hourly pay, as defined by the Regulations)
• the date from which you have a year to publish your gender pay gap report.

Most employers will know if they have 250 or more employees on the relevant snapshot date (figure 5). Those whose headcount hovers around or varies above and below the 250 threshold, is so close to 250 that the definition of employee used in the Regulations means they may end up hitting 250, or includes a large number of non-standard employees employees (such as agency workers, or self-employed workers), will need to check if the Regulations apply. It’s also important to note that the definition of employee used to work out if you are a relevant employer may not be one you are used to using, and that duration of employment is not taken into account.

If your organisation employs fewer than 250 people, it is still a useful discipline for you to calculate the size of your organisation’s gender pay gap and think about what action may be required. We encourage all employers of whatever size to calculate and publish their pay gaps. And if your employee numbers cross over the threshold or the government should at any time lower the 250-employee reporting threshold, if you’re already collecting and analysing the data, you will be ahead of the game.

There are six different measures (figure 6) of the gender pay gap and each provides a slightly different take, but each is more meaningful if read alongside the others and in the context of your overall HR and payroll policies and practices, such as training and development, or recruitment and selection. It is likely that your recruitment practices, for example, will impact on starting salaries, which will in turn feed into both your mean and median gender pay gap figures, while the way in which you manage performance may well feed into your bonus pay gap. You will also want to read each year’s figures alongside those of previous years, both to measure progress and to gain a greater understanding of your gender pay gap.

As with the snapshot date, the deadline varies depending on the sector (see figure 5 above). If you decide to publish before the deadline, which we would encourage, you may find it helpful to stick to the same date every year to ensure consistency, and help your readers gain a clearer understanding of any gender pay gap.

You must publish the required data on the UK Government’s gender pay gap reporting service website . For private and voluntary sector employers, the information will have to be accompanied by a statement confirming its accuracy, signed by a director or equivalent, which includes their name and job title. Once you have published your report on the reporting service website, it automatically appears on the government’s viewing service website, where any interested person is able to access it. The viewing service website also lets people know if a report is late.

You must also publish your pay data on your own organisation’s website in a manner that is accessible to employees and the public, and you will have to ensure that it remains there for at least three years.

We look in detail at how to calculate employee earnings in this guide . Here, you simply need to know that your calculations will be based on:

• gross ordinary pay (including basic pay, piecework pay, shift premiums, paid leave pay and allowances)
• bonus pay (personal, team bonuses and so on)

Paid in the relevant pay period (pay period including the snapshot date) and by the snapshot date (31 March for public sector, 5 April for businesses and charities).

What you say about your gender pay gap, and where and how you choose to say it, is of paramount importance. While the reporting process makes publication of your figures and the sign-off of those figures compulsory, you also have the option of including an accompanying narrative and an action plan. Communication is also about how you inform your employees and the wider world about your organisation’s gender pay gap report. We look at this in the section: How to communicate your gender pay gap .

Narratives and action plans

Although there is currently no legal obligation, the government strongly encourages employers to produce a voluntary accompanying narrative that provides context, explains any pay gaps, and sets out what actions will be taken. We also encourage you to produce a narrative, and the Regulations may change in the future.

The government’s reporting site enables you to include a link to where your full report appears on your own site. The government’s viewing service site (which mirrors the reporting site) provides readers with a link headed ‘See what this employer has to say about their gender pay gap’; clicking on this takes readers through to where your report appears on your own site.

The Regulations do not require you to publish an action plan either, or even to draw one up, but the government encourages you to do so, as do the CIPD. Uploading an action plan on your website, alongside a narrative, lets people know what action you are planning to take to address the gap, and that you are serious about doing so. In addition to helping you tackle the gender pay gap itself, drawing up an action plan will help you to answer questions about what you are doing.

We also recommend that you draw up a communications plan well before you publish any data, to ensure that you tell the story you want to tell and are ready to respond to questions. We look at this in the 'How to communicate your gender pay gap' section .

• the Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017 – these apply to public bodies
• the Equality Act 2010 (Gender Pay Gap Information) Regulations 2017 – these apply to private and voluntary sector organisations.

Who should perform the calculations?

Payroll software should do most of the work for you, so do engage with your software provider. You will also want, as with any major HR or payroll project, to ensure that you have the right skills on board to interpret your figures and understand the causes of any gap, communicate effectively to your various stakeholders, and plan how you will address your gap. Larger organisations may want to create a team that includes people with knowledge of the organisation’s payroll and HR systems, a communications expert, and someone with an understanding of statistics. The Royal Statistical Society has noted that with the introduction of gender pay gap reporting, the government is, in effect, asking HR professionals to take on some important statistical tasks, and one of the aims of this guide is to support HR professionals in obtaining, analysing and taking action on their data.

If you think you are at risk of equal pay claims

Despite some overlap, the gender pay gap is a different issue to equal pay, and the two should be considered independently. Reducing a gender pay gap does not necessarily reduce the risk of equal pay claims. The gender pay gap regulations and equal pay regulations provide more information on each.

What happens if an employer doesn't report on their gender pay gap?

Failure to comply amounts to a breach of the Equality Act 2010 and would therefore open an organisation up to action by the Equality and Human Rights Commission (EHRC). The EHRC have a series of actions and penalties that they can impose on organisations depending on the type of business and nature of the breach. We provide focused legal guidance on each of these stages in our dedicated Gender pay gap law page .

Where can I find more information?

Cipd sources.

Employment law: Equal pay: UK employment law Topic page: Flexible and hybrid working Topic page: Recruitment Topic page: Reward Factsheet: Induction

External sources

Acas Business in the Community Equality and Human Rights Commission Equal Pay Portal

Gov.UK Gender pay gap reporting service Gender pay gap viewing service website Equality Act 2010 (Gender Pay Gap Information) Regulations 2017 Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017

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Introduction Of The Identify And Analyse Causes Of The Gender Pay Gap Assignment

The gender pay gap in hospitality industry, gaps in employment, expectations in highly paid roles, unconscious bias.

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The conversion of a matrilineal society into a patriarchal society and the vague notion of division of labour subdued the position of females in the social sphere as well as in the workplace. According to Morchio and Moser (2021), in the modern 21st century, the gender pay gap has appeared to be one of the major causes that inhibited the seamless inflow of productivity in the industrial arena. In most cases, the prevalent gap between men and women from the perspective of wage and recognition is culminating directly in economic inequality and overall imbalance in the economic equilibrium. Segovia-Pérez et al. (2019) added that the gender pay gap in hospitality is tarnishing the reputation of the industry as a whole and further thwarting the path of seamless ESG practices. The gender pay gap can be defined as a discriminatory pay scale between men and women based on their gender orientation and stereotypical notions.

In essence, the prevalence of the gender pay gap seems to be a complete foil to the principle of equal pay for equal work that is espoused in organisations to streamline an inflow of equality. As per data, in developed nations like the UK, 2.8% of the wage gap is traced among men and women in 2020 (ONS.GOV, 2020). Although the gap in remuneration is considered a major stumbling block, the existence of women has come under acute stakes. Dating back to history, there is evidence of women taking control of the jobs usually termed as masculine, and they not only managed to accomplish the tasks but proved their superiority. In the hospitality sector, the soaring rate pay gap is detrimental to employee retention and endangers the quality of service delivered to the upscale clientele and loyal consumer base. Encompassing the persistent pay gap in the remuneration scale, a concerning study has its thrust on identifying particular pain points of the pay gap in the hospitality sector.

Over the decades' relentless research works and experiments are going on to analyse and specify the areas of vulnerability in hospitality that are fuelling discrimination among employees based on gender. Arbelo et al. (2021) mentioned that the inequitable distribution of wealth among the existing heterogeneity in the hospitality sector persists in varied forms. Recently in the UK media, unequal distribution of remuneration and career opportunities among men and women are becoming a highly debated topic. Calinaud et al. (2021) claimed that despite the trend of reshuffling the workforce to create a diverse one, women in hospitality and other sectors often remain far away from achieving executive positions. This author critically developed the counter-argument that the hospitality sector on the globe displays a skyrocketing rate of gender inequality owing to its seasonal nature that fails to offer women necessities like maternal or matrimonial leave. Besides that, a more valid point of argument can be raised as many of the segments of hospitality are completely dominated by male counterparts like chefs, cooks and so on and it might dissuade the women to pursue a senior position as they might have to face unwanted resistance or contingencies from the male colleagues.

Most of the scholars and researchers in their research pieces have articulated three major reasons to be accountable for pushing women away from the hospitality industry. The three major reasons are low pay, unsocial hours and lack of progression in career. A most recent survey has deliberately disclosed the sham pervasive in the hospitality sector that is antagonising the equal participation of individuals in the workplace regardless of their gender, race, creed and others (Dobbin and Kalev, 2022). Even 71% of women are found to be quitting their jobs in the hospitality sector as they are not quite satisfied with their uncertain careers. Moreover, gender-related discrimination in the hospitality industry is speculated to provide impetus to the scarcity of job opportunities congruent with their skills.

Hospitality even during the post-pandemic is regarded as one of the forerunners in driving growth and creating outlets for potential job creation. Mooney and Baum (2019) held the view that despite the massive transition in the arena of hospitality, the future of women in this field appears to be bleak. To expound on the reasons behind the ingrained uncertainties in the sphere of hospitality, the author has further pointed out that unusual working hours and poor work-life balance in some cases are implemented in such a way that they coerce the women to leave their job. MacLeavy (2021) contended that due to common biological factors like childbirth and caregiving responsibilities, the role of women in professional premises is often toned down. With a holistic development of workers regardless of any bias or stereotypes, the organisational authority should be more empathetic, while it is entirely the duty of the employer to offer a secure and flexible work environment to the employees to spur growth and innovation simultaneously. MacLeavy (2021) depicted that common phases of life like marriage and giving birth to a child are viewed as fatalistic for the career development of women in comparison to men as the division of labour is unequal in domestic spaces as well. In terms of delineating in a detailed manner, it is worth mentioning that often couples logically decide that the lowest earner would bear the familial duties more than the high-earned ones. From this perspective, such unconscious mistakes in the domestic sphere reinforce the pay gap in the labour market and consciously or unconsciously women fall prey to it.

Del Boca et al. (2020) came up with the notion that due to childbearing, women are bound to take care of children more than their male partners. In workplaces, it is to be speculated that after having children, women would not be able to invest most of their efforts and time in their employers. Litman et al. (2020) suggested that to reduce the latent pay gap in the labour market considering the biological conditions of the women and their familial duties, for both men and women, the provisions of subsidised child care and sufficient time of parental leave are to be granted.

Tama et al. (2019) found that a high-paid job opportunity is concomitant to extremely high work pressure, as when an employer pays well; the employees remain bound to abide by the rules and job duties. In other words, high-paying jobs do not always guarantee the happiness of individuals on a long-term basis. In the case of women, despite the expectations of employers, they are compelled to fulfil the expectations of society. Webster et al. (2022) identified that adjusted statistics of the workplace often understate the potential for gender discrimination to suppress the earnings of women as a whole. In addition to that, in the hospitality sector, women who are promoted to leading positions are overburdened with work stress that might weigh heavily on the mental health of the women and endanger their security to a large extent. Even it is to be delineated that in the tech sector and other skill-based sectors, women are suppressed or not given equal opportunity and wages like that of their male co-workers by negating their intellect to be inferior. Costa Dias et al. (2020) discovered that the gender pay gap is driven by the cumulative impact of the course of women's whole lives. Ranging from the different treatment than male peers to imposing forcefully the expectations of society on women formulates the solid seedbed for the gender pay gap. Even expectations are considered to be self-fulfilling prophecies, and in a more rational sense, the women in urban setups to rural areas are prevented from pursuing STEM education that can upskill their skills and make them worthy enough to deal with contemporary expectations of the modern workplace.

In the hospitality sector, the gender pay gap can be due to the unwillingness of women to work night shifts or security concerns, which men usually do not encounter.

Pay equity has become one of the major criteria of the modern workplace as far as employee well-being is concerned. O'Brien (2019) revealed that the simple truth behind the gender gap is that employers are still not aware of the equal positioning of women or else they are reluctant to acknowledge it. Such kind of biases indirectly creates a platform of wage discrimination based on gender and at the same time, employers often perceive that promoting a woman to a higher position or offering them equal pay like that of male colleagues might aggravate the majority of males and it might fuel the disruption within the workplace settings. Sokolova and Sorensen (2021) stressed the fact that employers in a competitive marketplace have a monopsonistic power of wage setting and in the case of women, the ingrained prejudices hinge on the exercise of power. Apart from the notion of profit-making, employers to avoid disruptions in work chose to limit the position of women employees to a certain extent driven by a vague view of wage elasticity. In the hospitality sector, the rising gender gap is a direct consequence of societal expectations and the flawed vision of the professionals that make this particular sphere precarious for the career growth of talented female professionals (Apergis and Lynch, 2022).

Recommendations

The hospitality industry may be able to contribute to the reduction of the wage gap between men and women by, among other things, conducting an analysis of the gender balance within teams, providing training on how to recognise and avoid unconscious bias, establishing gender pay forums, and instituting flexible working policies (Litman et al.,2020).

Because women and their families face the brunt of poverty caused by low tips, raising the tipped minimum wage might potentially have a substantial influence on closing the gender pay gap. Recent recommendations have advocated raising the tipped minimum pay to 70% of the minimum wage in order to ensure that the majority of a worker's income comes from their employer rather than from tips. This would ensure that the minimum wage is met (Morchio and Moser, 2021).

It is common practise in many workplaces to avoid discussing wages with co-workers, thus it is possible that many women are unaware that they are paid less than men for performing the same level of work. For example, ‘The Paycheck Fairness Act’ is an important piece of legislation in the effort to close the wage gap between men and women (MacLeavy, 2021). If passed, this act would make it more difficult for companies to pay male workers more than female workers by reducing pay secrecy and providing women with more powerful tools to combat pay discrimination.

Businesses in the hotel industry can increase their obligations with the assistance of labour unions to include required gender pay audits and the creation of action plans to address discriminatory pay practices. These new responsibilities will go into effect in 2020. Appointing diverse hiring teams in possession of inclusive mind-set can push the women workers to occupy the topmost designations only with the utilisation of talent and credentials. Promoting female leadership can also aid in eliminating the wage gaps and latent bias in the core of the organisational operation. Especially in hotels, female leadership would posit the image of women as capable enough to deal with hectic schedules and excessive work-pressure.

Despite the fact that the wage gap between men and women has been gradually narrowing, there is no guarantee that this trend will continue in the foreseeable future. As a consequence of this, it is essential to investigate potential solutions to the widening gender pay gap as well as alternative avenues via which women might make progress in their professional lives.

Inequality in pay between men and women has a tendency to be more pronounced in settings where overall disparity is greater, hence it is imperative that measures designed to reduce wage discrepancy in general be combined with policies designed to minimise gender pay inequality. Not only are efforts required to reduce the impacts of long-standing gender discrimination and inequality, but they are also required to prevent the formation of new kinds of discrimination in the workplace. To narrate the main issue in detail, it is to be outlined a huge difference lies between theory and practice and it is hindering the equality related legal frameworks to put into practice or to implement effectively. For instance, the prominent ‘The Pay check Fairness Act’ in reality is contrived with a purpose of offering same wage to workers based on their credentials and to exempt from indulging in the practices of discrimination based in gender. In such cases to put an end to these latent biases, the government or self-regulatory authority of hospitality industry would have to mandate submission of the monthly payslips to them in terms of monitoring and strict verification.

Apergis, N. and Lynch, N. (2022) 'The impact of economic freedom on the gender pay gap: evidence from a survey of UK households, Journal of Economic Studies (Bradford), 49(1), pp. 61–76.

Arbelo, A., Arbelo-Pérez, M. and Pérez-Gómez, P., (2021). Heterogeneity of resources and performance in the hotel industry. Journal of Hospitality & Tourism Research, 45(1), pp.68-89.

Calinaud, V., Kokkranikal, J. and Gebbels, M., (2021). Career advancement for women in the British hospitality industry: The enabling factors. Work, Employment and Society, 35(4), pp.677-695.

Costa Dias, M., Joyce, R. and Parodi, F., (2020). The gender pay gap in the UK: children and experience in work. Oxford Review of Economic Policy, 36(4), pp.855-881.

Del Boca, D., Oggero, N., Profeta, P. and Rossi, M., (2020). Women’s and men’s work, housework and childcare, before and during COVID-19. Review of Economics of the Household, 18(4), pp.1001-1017.

Dobbin, F. and Kalev, A., (2022). Getting to diversity: What works and what doesn’t. Harvard University Press.

Litman, L., Robinson, J., Rosen, Z., Rosenzweig, C., Waxman, J. and Bates, L.M., (2020). The persistence of pay inequality: The gender pay gap in an anonymous online labour market. PloS one, 15(2), p.e0229383.

MacLeavy, J., (2021). Care work, gender inequality and technological advancement in the age of COVID?19. Gender, Work & Organisation, 28(1), pp.138-154.

Mooney, S. and Baum, T., (2019). A sustainable hospitality and tourism workforce research agenda: Exploring the past to create a vision for the future. In A research agenda for tourism and development. Edward Elgar Publishing.

Morchio, I. and Moser, C., (2021). The gender pay gap: Micro sources and macro consequences.

O'Brien, A., (2019). Women, inequality and media work. Routledge.

ONS.GOV (2020). Governmental Website . Available at:https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/genderpaygapintheuk/2020 [Accessed on 19 October 2022]

Segovia-Pérez, M., Figueroa-Domecq, C., Fuentes-Moraleda, L. and Muñoz-Mazón, A., (2019). Incorporating a gender approach in the hospitality industry: Female executives’ perceptions. International journal of hospitality management, 76, pp.184-193.

Sokolova, A. and Sorensen, T., (2021). Monopsony in labour markets: A meta-analysis. ILR Review, 74(1), pp.27-55.

Tentama, F., Rahmawati, P.A. and Muhopilah, P., (2019). The effect and implications of work stress and workload on job satisfaction. International Journal of Scientific and Technology Research, 8(11), pp.2498-2502.

Webster, A., Khorana, S. and Pastore, F., (2022). The effects of COVID-19 on employment, labour markets, and gender equality in Central America. IZA Journal of Development and Migration, 13(1), pp.1-43.

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