essay about gender gap

Closing the equity gap

Jeni Klugman

Caren Grown and Odera Onyechi

Why addressing gender inequality is central to tackling today’s polycrises

Nonresident Senior Fellow, Africa Growth Initiative, Global Economy and Development, Brookings Institution

As we enter 2023, the term “ polycrisis ” is an increasingly apt way to describe today’s challenges. 1 Major wars, high inflation, and climate events are creating hardship all around the world, which is still grappling with a pandemic death toll approaching 7 million people.

Faced with such daunting challenges, one might well ask why we should be thinking about the gender dimensions of recovery and resilience for future shocks. The answer is simple: We can no longer afford to think in silos. Today’s interlocking challenges demand that sharp inequalities, including gender disparities, must be addressed as part and parcel of efforts to tackle Africa’s pressing issues and ensure the continent’s future success.

“We can no longer afford to think in silos. … Gender disparities, must be addressed as part and parcel of efforts to tackle Africa’s pressing issues and ensure the continent’s future success.”

The burdens of the pandemic have been unequally borne across regions and countries, and between the poor and better off. Inequalities exist around gender—which can be defined as the “socially constructed roles, behaviors, activities, attributes and opportunities that any society considers appropriate for men and women, boys and girls” and people with non-binary identities. 2 As Raewyn Connell laid out more than two decades ago, existing systems typically distribute greater power, resources, and status to men and behaviors considered masculine . 3 As a result, gender intersects with other sources of disadvantage, most notably income, age, race, and ethnicity.

This understanding is now mainstream. As recently observed by the IMF, “The gender inequalities exposed by the COVID-19 pandemic follow different paths but almost always end up the same: Women have suffered disproportionate economic harm from the crisis.” 4 Among the important nuances revealed by micro-surveys is that rural women working informally continued to work through the pandemic , but with sharply reduced earnings in Nigeria and elsewhere. 5 And as the burden of child care and home schooling soared, rural households headed by women were far less likely than urban households to have children engaged in learning activities during school closures.

Important insights emerge from IFPRI’s longitudinal panel study (which included Ghana, Kenya, Niger, Nigeria, Senegal, and Uganda) covering income loss, coping strategies, labor and time use, food and water insecurity, and child education outcomes. 6

Among the especially adverse impacts for women were greater food and water insecurity compared to men, including worrying about insufficient food and eating less than usual, while a large proportion of women also did not have adequately diverse diets. Moreover, many women had to add hours to their workday caring for sick family members, and their economic opportunities shrank, cutting their earnings and widening gender income gaps.

While today’s problems seem daunting, there remain huge causes for optimism, especially in Africa. Over the past three decades, many African countries have achieved enormous gains in levels of education, health, and poverty reduction. Indeed, the pace of change has been staggering and commendable. As captured in the Women Peace and Security Index , which measures performance in inclusion, justice, and security, 6 of the top 10 score improvers during the period 2017-2021 were in sub-Saharan Africa. [GIWPS.2022. “Women Peace and Security Index” Georgetown Institute for Women, Peace and Security.] The Democratic Republic of Congo was among top score improvers since 2017, as the share of women with financial accounts almost tripled, to 24 percent; and increases exceeding 5 percentage points were registered in cell phone use and parliamentary representation. In the Central African Republic, improvements were experienced in the security dimension, where organized violence fell significantly, and women’s perceptions of community safety rose 6 percentage points up to 49 percent.

Looking ahead, efforts to mitigate gender inequalities must clearly be multi-pronged, and as highlighted above—we need to think outside silos. That said, two major policy fronts emerge to the fore.

Ensure cash transfers that protect against poverty , are built and designed to promote women’s opportunities, with a focus on digital payments. 7 Ways to address gender inequalities as part of social protection program responses 8 include deliberate efforts to overcome gender gaps in cell phone access by distributing phones to those women who need them, as well as private sector partnerships to subsidize airtime for the poorest, and to make key information services and apps freely available . 9 Programs could also make women the default recipient of cash transfer schemes, instead of the head of household. Furthermore, capacity-building initiatives can be built into program design to give women the skills and capabilities needed to successfully manage accounts and financial decisionmaking. 10

Reducing the risk of violence against women. Women who are not safe at home are denied the freedom from violence needed to pursue opportunities that should be afforded to all. In 2018, 10 of the 15 countries with the worst rates of intimate partner violence were in sub-Saharan Africa—in descending order of average intimate partner violence these were, the Democratic Republic of Congo, Madagascar, Congo, Equatorial Guinea, Zambia, Ethiopia, Liberia, South Sudan, Djibouti, and Uganda.

“As the burden of child care and home schooling soared, rural households headed by women were far less likely than urban households to have children engaged in learning activities.”

Conflicts and crises multiply women’s risk of physical, emotional, and sexual violence . During the pandemic, risk factors like economic stress were compounded by service closures and stay-at-home orders, which increased exposure to potential perpetrators. 11 Several governments responded by strengthening existing help services , including police and justice, supporting hotlines, ensuring the provision of psychological support, and health sector responses. 12 Examples of good practice included an NGO in North-Eastern Nigeria, which equipped existing safe spaces with phone booths to enable survivors to contact caseworkers.

However, given the high levels of prevalence and often low levels of reporting, prevention of gender-based violence is key. Targeted programs with promising results in prevention include community dialogues and efforts to change harmful norms, safe spaces, as well as possibilities to reduce the risk of violence through cash plus social protection programs. These efforts should be accompanied by more systematic monitoring and evaluation to build evidence about what works in diverse settings.

Finally, but certainly not least, women should have space and voices in decisionmaking. This case was powerfully put by former President Sirleaf Johnson in her 2021 Foresight essay, which underlined that “ economic, political, institutional, and social barriers persist throughout the continent, limiting women’s abilities to reach high-level leadership positions .” 13 Persistent gender gaps in power and decision-making, not only limits innovative thinking and solutions, but also the consideration of more basic measures to avoid the worsening of gender inequalities. Overcoming these gaps in power and decision-making requires safeguarding legal protections and rights, investing in women and girls financially, and opening space for women in political parties so that women have the platforms to access high-level appointed and competitive positions across national, regional, and international institutions. 14

Strengthening fiscal policy for gender equality

Senior Fellow, Center for Sustainable Development, Global Economy and Development, Brookings Institution

Research Analyst, Center for Sustainable Development, Global Economy and Development, Brookings Institution

It is often said that women act as “shock absorbers” during times of crisis; this is even more so in the current context of climate change, the COVID-19 pandemic, and increased geopolitical conflict. These three global crises have simultaneously stretched women’s ability to earn income and intensified their unpaid work. Well-designed fiscal policy can help cushion the effects of these shocks and enable women and their households to recover more quickly.

Over 60 percent of employed women in Africa work in agriculture, including in small-scale food production; women are the primary sellers in food markets, and they work in other sectors such as informal trading. At the same time, women are an increasing share of entrepreneurs in countries such as Ghana and Uganda, even as they face financial and other constraints to start and grow their firms. [Africa Gender Innovation Lab (GIL). 2020. “Supporting Women Throughout the Coronavirus Emergency Response and Economic Recovery.” World Bank Group. ] In addition to earning income for their households, women bear the major responsibility for unpaid domestic activities such as cooking; collecting water and fuelwood; caring for children, elderly, and other dependents—so women are more time-poor than are men.

African women and entrepreneurs have been impacted disproportionately more than men by the triple shocks mentioned earlier. Extreme weather events disrupt food production and agricultural employment, making it harder for women to earn income . 15 16 17 The pandemic and conflict in Ukraine further intensified women’s paid and unpaid activities . 18 19 Beyond climate change and the war in Ukraine, localized conflicts and insecurity in East and West Africa exposes women and girls to gender-based violence and other risks as they seek to support their families and develop new coping strategies. 20 21 22

“Responding to these shocks necessitates a large infusion of resources. In this context, fiscal policy can be deployed more smartly to advance gender equality and create an enabling environment for women to play a greater role in building their economies’ recovery and resilience.”

Responding to these shocks necessitates a large infusion of resources. In this context, fiscal policy can be deployed more smartly to advance gender equality and create an enabling environment for women to play a greater role in building their economies’ recovery and resilience. Public expenditure supports critical sectors such as education, health, agriculture, social protection, and physical and social infrastructure, while well-designed tax policy is essential to fund the public goods, services, and infrastructure on which both women and men rely.

Gender-responsive budgets, which exist in over 30 countries across the continent, can be strengthened. Rwanda provides a good model for other countries. After an early unsuccessful attempt, Rwanda invested seriously in gender budgeting beginning in 2011. 23 24 The budget is focused on closing gaps and strengthening women’s roles in key sectors—agriculture, education, health, and infrastructure—which are all critical for short- and medium-term economic growth and productivity. The process has been sustained by strong political will among parliamentarians. Led by the Ministry of Finance, the process has financed and been complemented by important institutional and policy reforms. A constitutional regulatory body monitors results, with additional accountability by civil society organizations.

However, raising adequate fiscal revenue to support a gender budget is a challenge in the current macro environment of high public debt levels, increased borrowing costs, and low levels of public savings. Yet, observers note there is scope to increase revenues through taxation reforms, debt relief, cutting wasteful public expenditure, and other means. 25 26 We focus here on taxation.

Many countries are reforming their tax systems to strengthen revenue collection. Overall tax collection is currently low; the average tax-to-GDP ratio in Africa in 2020 was 14.8 percent and fell sharply during the pandemic, although it may be rebounding. 27 Very few Africans pay personal income tax or other central government taxes, 28 29 and statutory corporate tax rates (which range from 25-35 percent), are higher than even the recent OECD proposal for a global minimum tax 30 so scope for raising them further is limited. Efforts should be made to close loopholes and reduce tax evasion.

As countries reform their tax policies, they should be intentional about avoiding implicit and explicit gender biases. 31 32 33 34 Most African countries rely more on indirect taxes than direct taxes, given the structure of their economies, but indirect taxes can be regressive as their incidence falls primarily on the poor. Presumptive or turnover taxes, for example, which are uniform or fixed amounts of tax based on the “presumed” incomes of different occupations such as hairdressers, can hit women particularly hard, since the burden often falls heavily on sectors where women predominate. 35 36

Property taxes are also becoming an increasingly popular way to raise revenue for local governments. The impact of these efforts on male and female property owners has not been systematically evaluated, but a recent study of land use fees and agricultural income taxes in Ethiopia finds that female-headed and female adult-only households bear a larger tax burden than male-headed and dual-adult households of property taxes. This is likely a result of unequal land ownership patterns, gender norms restricting women’s engagement in agriculture, and the gender gap in agricultural productivity. 37

“Indirect taxes can be regressive as their incidence falls primarily on the poor. Presumptive or turnover taxes … can hit women particularly hard, since the burden often falls heavily on sectors where women predominate.”

Going forward, two key ingredients for gender budgeting on the continent need to be strengthened. The first is having sufficient, regularly collected, sex-disaggregated administrative data related to households, the labor force, and other survey data. Investment in the robust technical capacity for ministries and academia to be able to access, analyze, and use it is also necessary. For instance, the World Bank, UN Women, and the Economic Commission for Africa are all working with National Statistical Offices across the continent to strengthen statistical capacity in the areas of asset ownership and control, work and employment, and entrepreneurship which can be used in a gender budget.

The second ingredient is stronger diagnostic tools. One promising new tool, pioneered by Tulane University, is the Commitment to Equity methodology, designed to assess the impact of taxes and transfers on income inequality and poverty within countries. 38 It was recently extended to examine the impact of government transfers and taxes on women and men by income level and other dimensions. The methodology requires standard household-level data but for maximum effect should be supplemented with time use data, which are becoming more common in several African countries. As African countries seek to expand revenue from direct taxes, lessons from higher income economies are instructive. Although there is no one size fits all approach, key principles to keep in mind for designing personal income taxes include building in strong progressivity, taxing individuals as opposed to families, ensuring that the allocation of shared income (e.g., property or non-labor income) does not penalize women, and building in allowances for care of children and dependents. 39 As noted, corporate income taxes need to eliminate the many breaks, loopholes, and exemptions that currently exist, 40 and countries might consider experimenting with wealth taxes.

In terms of indirect taxes, most African countries do not have single-rate VAT systems and already have zero or reduced rates for basic necessities, including foodstuffs and other necessities. While it is important to minimize exempted sectors and products, estimates show that goods essential for women’s and children’s health (e.g., menstrual health products, diapers, cooking fuel) should be considered part of the basket of basic goods that have reduced or zero rates. 41 And while African governments are being advised to bring informal workers and entrepreneurs into the formal tax system, 42 it should be noted that this massive sector earns well below income tax thresholds and already pays multiple informal fees and levies, for instance in fees to market associations. 43 44

Lastly, leveraging data and digital technologies to improve tax administration (i.e., taxpayer registration, e-filing, and e-payment of taxes) may help minimize costs and processing time, and reduce the incidence of corruption and evasion.32 Digitalization can also be important for bringing more female taxpayers into the net, especially if digital systems are interoperable; for instance, digital taxpayer registries linked to national identification or to property registration at the local level. However, digitalization can be a double-edged sword if privacy and security concerns are not built-in from the outset. Women particularly may need targeted digital financial literacy and other measures to ensure their trust in the system. Recent shocks have worsened gender inequality in Africa. It is therefore important now, more than ever, to invest in strengthening fiscal systems to help women and men recover, withstand future shocks, and reduce gender inequalities. While fiscal policy is not the only tool, it is an important part of government action. To be effective and improve both budgeting and revenue collection, more and better data, new diagnostic tools, and digitalization will all be necessary.

• 1. Martin Wolf. 2022.“How to think about policy in a policy crisis”. Financial Times.
• 2. WTO. 2022. “Gender and Health”. World Health Organization.
• 3. Connell RW. 1995. “Masculinities”. Cambridge, UK. Polity Press.
• 4. Aoyagi, Chie.2021.“Africa’s Unequal Pandemic”. Finance and Development. International Monetary Fund.
• 5. WB.2022. “LSMS-Supported High-Frequency Phone Surveys”. World Bank.
• 6. Muzna Alvi, Shweta Gupta, Prapti Barooah, Claudia Ringler, Elizabeth Bryan and Ruth Meinzen-Dick.2022.“Gendered Impacts of COVID-19: Insights from 7 countries in Sub-Saharan Africa and South Asia”. International Food Policy Research Institute.
• 7. Klugman, Jeni, Zimmerman, Jamie M., Maria A. May, and Elizabeth Kellison. 2020. “Digital Cash Transfers in the Time of COVID 19: Opportunities and Considerations for Women’s Inclusion and Empowerment”. World Bank Group.
• 8. IFPRI.2020. “Why gender-sensitive social protection is critical to the COVID-19 response in low-and middle-income countries”. International Food Policy Research Institute.
• 9. IDFR.2020. “Kenya: Mobile-money as a public-health tool”. International Day of Family Remittances.
• 10. Jaclyn Berfond Franz Gómez S. Juan Navarrete Ryan Newton Ana Pantelic. 2019. “Capacity Building for Government-to-Person Payments A Path to Women’s Economic Empowerment”. Women’s World Banking.
• 11. Peterman, A. et al.2020. “Pandemics and Violence Against Women and Children”.Center for Global Development Working Paper.
• 12. UNDP/ UN Women Tracker.2022. “United Nations Development Programme. COVID-19 Global Gender Response Tracker”. United Nations Development Programme. New York.
• 13. McKinsey Global Institute .2019. “The power of parity: Advancing women’s equality in Africa”.
• 14. Foresight Africa. 2022. “African Women and Girls: Leading a continent.” The Brookings Institution.
• 15. One recent study in West, Central Africa, East and Southern Africa found that women represented a larger share of agricultural employment in areas affected by heat waves and droughts, and a lower share in areas unaffected by extreme weather events. Nico, G. et al. 2022. “How Weather Variability and Extreme Shocks Affect Women’s Participation in African Agriculture.” Gender, Climate Change, and Nutrition Integration Initiative Policy Note 14.
• 16. Carleton, E. 2022. “Climate Change in Africa: What Will It Mean for Agriculture and Food Security?” International Livestock Research Institute (ILRI).
• 17. Nebie, E.K. et al. 2021. “Food Security and Climate Shocks in Senegal: Who and Where Are the Most Vulnerable Households?” Global Food Security, 29.
• 18. Sen, A.K. 2022. “Russia’s War in Ukraine Is Taking a Toll on Africa.” United States Institute of Peace.
• 19. Thomas, A. 2020. “Power Structures over Gender Make Women More Vulnerable to Climate Change.” Climate Change News.
• 21. Kalbarczyk, A. et al. 2022. “COVID-19, Nutrition, and Gender: An Evidence-Informed Approach to Gender Responsive Policies and Programs.” Social Science & Medicine, 312.
• 22. Epstein, A. 2020. “Drought and Intimate Partner Violence Towards Women in 19 Countries in Sub-Saharan Africa During 2011-2018: A Population-Based Study.” PLoS Med, 17(3).
• 23. Stotsky, J. et al. 2016. “Sub-Saharan Africa: A Survey of Gender Budgeting Efforts. IMF Working Paper 2016/512.
• 24. Kadama, C. et al. 2018. Sub-Saharan Africa.” In Kolovich, L. (Ed.), Fiscal Policies and Gender Equality (pp. 9-32). International Monetary Fund (IMF).
• 25. Ortiz, I. and Cummins, M. 2021. “Abandoning Austerity: Fiscal Policies for Inclusive Development.” In Gallagher, K. and Gao, H. (Eds.), Building Back a Better Global Financial Safety Net (pp. 11-22). Global Development Policy Center.
• 26. Roy, R. et al. 2006. “Fiscal Space for Public Investment: Towards a Human Development Approach.”
• 27. ATAF, 2021.
• 28. Moore, M. et al. 2018. “Taxing Africa: Coercion, Reform and Development. Bloomsbury Publishing.
• 29. Rogan, M. 2019. Tax Justice and the Informal Economy: A Review of the Debates.” Women in Informal Employment: Globalizing and Organizing Working Paper 14.
• 30. African Tax Administrative Forum (ATAF). 2021. African Tax Outlook 2021.
• 31. Stotsky, J. et al. 2016. “Sub-Saharan Africa: A Survey of Gender Budgeting Efforts.” IMF Working Paper 2016/512.
• 32. Coelho, M. et al. 2022. “Gendered Taxes: The Interaction of Tax Policy with Gender Equality.” IMF Working Paper 2022/26.
• 33. Organisation for Economic Co-operation and Development (OECD). 2021. Gender and Capital Budgeting.
• 34. Grown, C. and Valodia, I. 2010. Taxation and Gender Equity: A Comparative Analysis of Direct and Indirect Taxes in Developing and Developed Countries. Routledge.
• 35. Joshi, Anuradha et al. 2020. “Gender and Tax Policies in the Global South.” International Centre for Tax and Development.
• 36. Komatsu, H. et al. 2021. “Gender and Tax Incidence of Rural Land Use Fee and Agricultural In¬come Tax in Ethiopia.” Policy Research Working Papers.
• 38. Lustig, N. 2018. “Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty.” Brookings Institution Press.
• 39. Grown, C. and Valodia, I. 2010. “Taxation and Gender Equity: A Comparative Analysis of Direct and Indirect Taxes in Developing and Developed Countries.” Routledge.
• 40. Cesar, C. et al. 2022. “Africa’s Pulse: An Analysis of Issues Shaping Africa’s Economic Future.” World Bank.
• 41. Woolard, I. 2018. Recommendations on Zero Ratings in the Value-Added Tax System. Independent Panel of Experts for the Review of Zero Rating in South Africa.
• 42. It is important to distinguish between firms and individuals that are large enough to pay taxes but do not (which include icebergs, e.g., which are registered and therefore partially visible to tax authorities but do not pay their full obligations) and ghosts, e.g., those which should register to pay but do not and there invisible to tax authorities) and firms and individuals that are small and potentially but not necessarily taxable such as street vendors and waste pickers. Rogan, M. (2019). “Tax Justice and the Informal Economy: A Review of the Debates.” Women in Informal Employment: Globalizing and Organizing Working Paper 14.
• 44. Ligomeka, W. 2019. “Expensive to be a Female Trader: The Reality of Taxation of Flea Market Trad¬ers in Zimbabwe.” International Center for Tax and Development Working Paper 93.

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5 Powerful Essays Advocating for Gender Equality

Gender equality – which becomes reality when all genders are treated fairly and allowed equal opportunities –  is a complicated human rights issue for every country in the world. Recent statistics are sobering. According to the World Economic Forum, it will take 108 years to achieve gender parity . The biggest gaps are found in political empowerment and economics. Also, there are currently just six countries that give women and men equal legal work rights. Generally, women are only given ¾ of the rights given to men. To learn more about how gender equality is measured, how it affects both women and men, and what can be done, here are five essays making a fair point.

Take a free course on Gender Equality offered by top universities!

“Countries With Less Gender Equity Have More Women In STEM — Huh?” – Adam Mastroianni and Dakota McCoy

This essay from two Harvard PhD candidates (Mastroianni in psychology and McCoy in biology) takes a closer look at a recent study that showed that in countries with lower gender equity, more women are in STEM. The study’s researchers suggested that this is because women are actually especially interested in STEM fields, and because they are given more choice in Western countries, they go with different careers. Mastroianni and McCoy disagree.

They argue the research actually shows that cultural attitudes and discrimination are impacting women’s interests, and that bias and discrimination is present even in countries with better gender equality. The problem may lie in the Gender Gap Index (GGI), which tracks factors like wage disparity and government representation. To learn why there’s more women in STEM from countries with less gender equality, a more nuanced and complex approach is needed.

“Men’s health is better, too, in countries with more gender equality” – Liz Plank

When it comes to discussions about gender equality, it isn’t uncommon for someone in the room to say, “What about the men?” Achieving gender equality has been difficult because of the underlying belief that giving women more rights and freedom somehow takes rights away from men. The reality, however, is that gender equality is good for everyone. In Liz Plank’s essay, which is an adaption from her book For the Love of Men: A Vision for Mindful Masculinity, she explores how in Iceland, the #1 ranked country for gender equality, men live longer. Plank lays out the research for why this is, revealing that men who hold “traditional” ideas about masculinity are more likely to die by suicide and suffer worse health. Anxiety about being the only financial provider plays a big role in this, so in countries where women are allowed education and equal earning power, men don’t shoulder the burden alone.

Liz Plank is an author and award-winning journalist with Vox, where she works as a senior producer and political correspondent. In 2015, Forbes named her one of their “30 Under 30” in the Media category. She’s focused on feminist issues throughout her career.

“China’s #MeToo Moment” –  Jiayang Fan

Some of the most visible examples of gender inequality and discrimination comes from “Me Too” stories. Women are coming forward in huge numbers relating how they’ve been harassed and abused by men who have power over them. Most of the time, established systems protect these men from accountability. In this article from Jiayang Fan, a New Yorker staff writer, we get a look at what’s happening in China.

The essay opens with a story from a PhD student inspired by the United States’ Me Too movement to open up about her experience with an academic adviser. Her story led to more accusations against the adviser, and he was eventually dismissed. This is a rare victory, because as Fan says, China employs a more rigid system of patriarchy and hierarchy. There aren’t clear definitions or laws surrounding sexual harassment. Activists are charting unfamiliar territory, which this essay explores.

“Men built this system. No wonder gender equality remains as far off as ever.” – Ellie Mae O’Hagan

Freelance journalist Ellie Mae O’Hagan (whose book The New Normal is scheduled for a May 2020 release) is discouraged that gender equality is so many years away. She argues that it’s because the global system of power at its core is broken.  Even when women are in power, which is proportionally rare on a global scale, they deal with a system built by the patriarchy. O’Hagan’s essay lays out ideas for how to fix what’s fundamentally flawed, so gender equality can become a reality.

Ideas include investing in welfare; reducing gender-based violence (which is mostly men committing violence against women); and strengthening trade unions and improving work conditions. With a system that’s not designed to put women down, the world can finally achieve gender equality.

“Invisibility of Race in Gender Pay Gap Discussions” – Bonnie Chu

The gender pay gap has been a pressing issue for many years in the United States, but most discussions miss the factor of race. In this concise essay, Senior Contributor Bonnie Chu examines the reality, writing that within the gender pay gap, there’s other gaps when it comes to black, Native American, and Latina women. Asian-American women, on the other hand, are paid 85 cents for every dollar. This data is extremely important and should be present in discussions about the gender pay gap. It reminds us that when it comes to gender equality, there’s other factors at play, like racism.

Bonnie Chu is a gender equality advocate and a Forbes 30 Under 30 social entrepreneur. She’s the founder and CEO of Lensational, which empowers women through photography, and the Managing Director of The Social Investment Consultancy.

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About the author, emmaline soken-huberty.

Emmaline Soken-Huberty is a freelance writer based in Portland, Oregon. She started to become interested in human rights while attending college, eventually getting a concentration in human rights and humanitarianism. LGBTQ+ rights, women’s rights, and climate change are of special concern to her. In her spare time, she can be found reading or enjoying Oregon’s natural beauty with her husband and dog.

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The Gender Gap Is Taking Us to Unexpected Places

By Thomas B. Edsall

Mr. Edsall contributes a weekly column from Washington, D.C., on politics, demographics and inequality.

In one of the most revealing studies in recent years, a 2016 survey of 137,456 full-time, first-year students at 184 colleges and universities in the United States, the U.C.L.A. Higher Education Research Institute found “the largest-ever gender gap in terms of political leanings: 41.1 percent of women, an all-time high, identified themselves as liberal or far left, compared to 28.9 percent of men.”

The institute has conducted freshmen surveys every year since 1966. In the early days, until 1980, men were consistently more liberal than women. In the early and mid-1980s, the share of liberals among male and female students was roughly equal, but since 1987, women have been more liberal than men in the first year of college.

While liberal and left identification among female students reached a high in 2016, male students remained far below their 1971 high, which was 44 percent.

Along parallel lines, a Knight Foundation survey in 2017 of 3,014 college students asked: “If you had to choose, which do you think is more important, a diverse and inclusive society or protecting free speech rights.”

Male students preferred protecting free speech over an inclusive and diverse society by a decisive 61 to 39. Female students took the opposite position, favoring an inclusive, diverse society over free speech by 64 to 35.

Majorities of both male and female college students in the Knight survey support the view that the First Amendment should not be used to protect hate speech, but the men were more equivocal, at 56 to 43, than women, at 71 to 29.

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What does gender equality look like today?

Date: Wednesday, 6 October 2021

Progress towards gender equality is looking bleak. But it doesn’t need to.

A new global analysis of progress on gender equality and women’s rights shows women and girls remain disproportionately affected by the socioeconomic fallout from the COVID-19 pandemic, struggling with disproportionately high job and livelihood losses, education disruptions and increased burdens of unpaid care work. Women’s health services, poorly funded even before the pandemic, faced major disruptions, undermining women’s sexual and reproductive health. And despite women’s central role in responding to COVID-19, including as front-line health workers, they are still largely bypassed for leadership positions they deserve.

UN Women’s latest report, together with UN DESA, Progress on the Sustainable Development Goals: The Gender Snapshot 2021 presents the latest data on gender equality across all 17 Sustainable Development Goals. The report highlights the progress made since 2015 but also the continued alarm over the COVID-19 pandemic, its immediate effect on women’s well-being and the threat it poses to future generations.

We’re breaking down some of the findings from the report, and calling for the action needed to accelerate progress.

The pandemic is making matters worse

One and a half years since the World Health Organization declared COVID-19 a global pandemic, the toll on the poorest and most vulnerable people remains devastating and disproportionate. The combined impact of conflict, extreme weather events and COVID-19 has deprived women and girls of even basic needs such as food security. Without urgent action to stem rising poverty, hunger and inequality, especially in countries affected by conflict and other acute forms of crisis, millions will continue to suffer.

A global goal by global goal reality check:

Goal 1. Poverty

In 2021, extreme poverty is on the rise and progress towards its elimination has reversed. An estimated 435 million women and girls globally are living in extreme poverty.

And yet we can change this .

Over 150 million women and girls could emerge from poverty by 2030 if governments implement a comprehensive strategy to improve access to education and family planning, achieve equal wages and extend social transfers.

Goal 2. Zero hunger

The global gender gap in food security has risen dramatically during the pandemic, with more women and girls going hungry. Women’s food insecurity levels were 10 per cent higher than men’s in 2020, compared with 6 per cent higher in 2019.

This trend can be reversed , including by supporting women small-scale producers, who typically earn far less than men, through increased funding, training and land rights reforms.

Goal 3. Good health and well-being

Disruptions in essential health services due to COVID-19 are taking a tragic toll on women and girls. In the first year of the pandemic, there were an estimated 1.4 million additional unintended pregnancies in lower and middle-income countries.

We need to do better .

Response to the pandemic must include prioritizing sexual and reproductive health services, ensuring they continue to operate safely now and after the pandemic is long over. In addition, more support is needed to ensure life-saving personal protection equipment, tests, oxygen and especially vaccines are available in rich and poor countries alike as well as to vulnerable population within countries.

Goal 4. Quality education

A year and a half into the pandemic, schools remain partially or fully closed in 42 per cent of the world’s countries and territories. School closures spell lost opportunities for girls and an increased risk of violence, exploitation and early marriage .

Governments can do more to protect girls education .

Measures focused specifically on supporting girls returning to school are urgently needed, including measures focused on girls from marginalized communities who are most at risk.

Goal 5. Gender equality

The pandemic has tested and even reversed progress in expanding women’s rights and opportunities. Reports of violence against women and girls, a “shadow” pandemic to COVID-19, are increasing in many parts of the world. COVID-19 is also intensifying women’s workload at home, forcing many to leave the labour force altogether.

Building forward differently and better will hinge on placing women and girls at the centre of all aspects of response and recovery, including through gender-responsive laws, policies and budgeting.

Goal 6. Clean water and sanitation

In 2018, nearly 2.3 billion people lived in water-stressed countries. Without safe drinking water, adequate sanitation and menstrual hygiene facilities, women and girls find it harder to lead safe, productive and healthy lives.

Change is possible .

Involve those most impacted in water management processes, including women. Women’s voices are often missing in water management processes. 

Goal 7. Affordable and clean energy

Increased demand for clean energy and low-carbon solutions is driving an unprecedented transformation of the energy sector. But women are being left out. Women hold only 32 per cent of renewable energy jobs.

We can do better .

Expose girls early on to STEM education, provide training and support to women entering the energy field, close the pay gap and increase women’s leadership in the energy sector.

Goal 8. Decent work and economic growth

The number of employed women declined by 54 million in 2020 and 45 million women left the labour market altogether. Women have suffered steeper job losses than men, along with increased unpaid care burdens at home.

We must do more to support women in the workforce .

Guarantee decent work for all, introduce labour laws/reforms, removing legal barriers for married women entering the workforce, support access to affordable/quality childcare.

Goal 9. Industry, innovation and infrastructure

The COVID-19 crisis has spurred striking achievements in medical research and innovation. Women’s contribution has been profound. But still only a little over a third of graduates in the science, technology, engineering and mathematics field are female.

We can take action today.

 Quotas mandating that a proportion of research grants are awarded to women-led teams or teams that include women is one concrete way to support women researchers. 

Goal 10. Reduced inequalities

Limited progress for women is being eroded by the pandemic. Women facing multiple forms of discrimination, including women and girls with disabilities, migrant women, women discriminated against because of their race/ethnicity are especially affected.

Commit to end racism and discrimination in all its forms, invest in inclusive, universal, gender responsive social protection systems that support all women. 

Goal 11. Sustainable cities and communities

Globally, more than 1 billion people live in informal settlements and slums. Women and girls, often overrepresented in these densely populated areas, suffer from lack of access to basic water and sanitation, health care and transportation.

The needs of urban poor women must be prioritized .

Increase the provision of durable and adequate housing and equitable access to land; included women in urban planning and development processes.

Goal 12. Sustainable consumption and production; Goal 13. Climate action; Goal 14. Life below water; and Goal 15. Life on land

Women activists, scientists and researchers are working hard to solve the climate crisis but often without the same platforms as men to share their knowledge and skills. Only 29 per cent of featured speakers at international ocean science conferences are women.

 And yet we can change this .

Ensure women activists, scientists and researchers have equal voice, representation and access to forums where these issues are being discussed and debated. 

Goal 16. Peace, justice and strong institutions

The lack of women in decision-making limits the reach and impact of the COVID-19 pandemic and other emergency recovery efforts. In conflict-affected countries, 18.9 per cent of parliamentary seats are held by women, much lower than the global average of 25.6 per cent.

This is unacceptable .

It's time for women to have an equal share of power and decision-making at all levels.

Goal 17. Global partnerships for the goals

There are just 9 years left to achieve the Global Goals by 2030, and gender equality cuts across all 17 of them. With COVID-19 slowing progress on women's rights, the time to act is now.

Looking ahead

As it stands today, only one indicator under the global goal for gender equality (SDG5) is ‘close to target’: proportion of seats held by women in local government. In other areas critical to women’s empowerment, equality in time spent on unpaid care and domestic work and decision making regarding sexual and reproductive health the world is far from target. Without a bold commitment to accelerate progress, the global community will fail to achieve gender equality. Building forward differently and better will require placing women and girls at the centre of all aspects of response and recovery, including through gender-responsive laws, policies and budgeting.

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Economic Inequality by Gender

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

By: Esteban Ortiz-Ospina , Joe Hasell and Max Roser

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

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

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

Related topics

Women's Employment

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

Women’s Rights

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

Maternal Mortality

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

See all interactive charts on economic inequality by gender ↓

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

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

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

The gender pay gap measures inequality but not necessarily discrimination

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

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

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

The implication is that observing differences in pay between men and women is neither necessary nor sufficient to prove discrimination in the workplace. Both discrimination and inequality are important. But they are not the same.

In most countries, there is a substantial gender pay gap

Cross-country data on the gender pay gap is patchy, but the most complete source in terms of coverage is the United Nation's International Labour Organization (ILO). The visualization here presents this data. You can add observations by clicking on the option 'add country' at the bottom of the chart.

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

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

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

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

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

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

However, the ILO data shows similar trends.

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

The gender pay gap is larger for older workers

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

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

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

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

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

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

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

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

Olivetti and Petrongolo (2008) show that this pattern holds in the data: unadjusted gender wage gaps across countries tend to be negatively correlated with gender employment gaps. That is, the gender pay gaps tend to be smaller where relatively fewer women participate in the labor force .

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

Why is there a gender pay gap?

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

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

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

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

Differences in human capital

The adjusted pay gap.

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

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

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

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

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

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

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

Several points stand out here.

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

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

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

When comparing the contributing factors in 1980 and 2010, we see that education and work experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important. 6

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

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

For example, suppose that women are indeed discriminated against, and they find it hard to get hired for certain jobs simply because of their sex. This would mean that in the adjusted specification, we would see that occupation and industry are important contributing factors – but that is precisely because discrimination is embedded in occupational differences!

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

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

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

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

Looking beyond worker characteristics

Job flexibility.

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

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

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

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

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

The motherhood penalty

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

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

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

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

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

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

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

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

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

Ability, personality, and social norms

The discussion so far has emphasized the importance of job characteristics and occupational choice in explaining the gender pay gap. This leads to obvious questions: What determines the systematic gender differences in occupational choice? What makes women seek job flexibility and take a disproportionate amount of unpaid care work?

One argument usually put forward is that, to the extent that biological differences in preferences and abilities underpin gender roles, they are the main factors explaining the gender pay gap. In their review of the evidence, Francine Blau and Lawrence Kahn (2017) show that there is limited empirical support for this argument. 11

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

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

In contrast, the evidence does suggest that social norms and culture, which in turn affect preferences, behavior, and incentives to foster specific skills, are key factors in understanding gender differences in labor force participation and wages. You can read more about this farther below.

Discrimination and bias

Independently of the exact origin of the unequal distribution of gender roles, it is clear that our recent and even current practices show that these roles persist with the help of institutional enforcement. Goldin (1988), for instance, examines past prohibitions against the training and employment of married women in the US. She touches on some well-known restrictions, such as those against the training and employment of women as doctors and lawyers, before focusing on the lesser known but even more impactful 'marriage bars' that arose in the late 1800s and early 1900s. These work prohibitions are important because they applied to teaching and clerical jobs – occupations that would become the most commonly held among married women after 1950. Around the time the US entered World War II, it is estimated that 87% of all school boards would not hire a married woman and 70% would not retain an unmarried woman who married. 12

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

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

Many other studies have found similar evidence of bias in different labor market contexts. Biases also operate in other spheres of life with strong knock-on effects on labor market outcomes. For example, at the end of World War II only 18% of people in the US thought that a wife should work if her husband was able to support her . This obviously circles back to our earlier point about social norms. 15

Strategies for reducing the gender pay gap

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

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

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

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

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

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

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

How well do biological differences explain the gender pay gap?

Across the world, women tend to take on more family responsibilities than men. As a result, women tend to be overrepresented in low-paying jobs where they are more likely to have the flexibility required to attend to these additional responsibilities.

These two facts – documented above – are often used to claim that, since men and women tend to be endowed with different tastes and talents, it follows that most of the observed gender differences in wages stem from biological sex differences. But what’s the broader evidence for these claims?

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

• There is evidence supporting the fact that statistically speaking, men and women tend to differ in some key aspects, including psychological attributes that may affect labor-market outcomes.
• There is no consensus on the exact weight that nurture and nature have in determining these differences, but whatever the exact weight, the evidence does show that these attributes are strongly malleable.
• Regardless of the origin, these differences can only explain a modest part of the gender pay gap.

Some context regarding the gender distribution of labor

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

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

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

“Boys are better at maths”

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

Let's look at the data.

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

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

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

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

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

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

More importantly for the question at hand, gender gaps in 'hard skills' are not large enough to explain the gender gaps in earnings. In their review of the evidence, Blau and Kahn (2017) concludes that gaps in test scores in the US are too small to explain much of the gender pay at any point in time. 26

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

“It’s about personality”

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

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

A recent article from the 'Heterodox Academy ', which was produced specifically in the context of the Google Memo, provides a fantastic overview of the evidence on this topic and the key points of contention among scholars.

For the purpose of this blog post, let's focus on the review of the evidence presented in Blau and Kahn (2017) – their review is particularly helpful because they focus on gender differences in the context of labor markets.

Blau and Kahn point out that, yes, researchers have found statistical differences between men and women that are important in the context of labor-market outcomes. For example, studies have found statistical gender differences in 'people skills' (i.e. ability to listen, communicate, and relate to others). Similarly, experimental studies have found that women more often avoid salary negotiations , and they often show a particular predisposition to accept and receive requests for tasks with low promotability. But are the origins of these differences mainly biological or are they social? And are they strong enough to explain pay gaps?

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

Here is a concrete example: Leibbrandt and List (2015) did an experiment in which they assessed how men and women reacted to job advertisements. 29 They found that although men were more likely to negotiate than women when there was no explicit statement that wages were negotiable, the gender difference disappeared and even reversed when it was explicitly stated that wages were negotiable. This suggests that it is not as much about 'talent', as it is about norms and rules.

“A man should earn more than his wife”

The experiment in which researchers found that gender differences in negotiation attitudes disappeared when it was explicitly stated that wages were negotiable, emphasizes the important role that social norms and culture play in labor-market outcomes.

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

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

Of particular relevance in the context of labor markets, social norms also often take the form of specific behavioral prescriptions such as "a man should earn more than his wife".

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

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

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

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

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

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

Why do gender norms and identity matter?

Does it actually matter if social norms and culture are important determinants of gender roles and labor-market outcomes? Are social norms in our contemporary societies really less fixed than biological traits?

The available research suggests that the answers to these questions are yes and yes. There is evidence that social norms can be actively and rapidly changed.

Here is a concrete example: Jensen and Oster (2009) find that the introduction of cable television in India led to a significant decrease in the reported acceptability of domestic violence towards women and son preference, as well as increases in women’s autonomy and decreases in fertility. 35

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

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

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

To conclude: The evidence suggests that biological differences are not a key driver of gender inequality in labor-market outcomes; while social norms and culture – which in turn affect preferences, behavior, and incentives to foster specific skills – are very important.

This matters for policy because social norms are not fixed – they can be influenced in a number of ways, including through intergenerational learning processes, exposure to alternative norms, and activism such as that which propelled the women's movement. 36

How are women represented across jobs?

Representation of women at the top of the income distribution.

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

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

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

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

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

The two charts present the key figures from the study.

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

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

The two charts allow us to answer the initial questions:

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

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

Representation of women in management positions

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

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

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

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

Representation of women in low-paying jobs

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

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

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

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

How much control do women have over household resources?

Women often have no control over their personal earned income.

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

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

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

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

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

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

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

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

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

Land ownership is more often in the hands of men

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

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

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

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

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

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

Gender differences in access to productive inputs are often large

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

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

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

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

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

Interactive Charts on Economic Inequality by Gender

Acknowledgements.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

Copy the code below to embed this chart on your website.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

About the authors

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Open Access

Peer-reviewed

Research Article

Twenty years of gender equality research: A scoping review based on a new semantic indicator

Contributed equally to this work with: Paola Belingheri, Filippo Chiarello, Andrea Fronzetti Colladon, Paola Rovelli

Roles Conceptualization, Formal analysis, Funding acquisition, Visualization, Writing – original draft, Writing – review & editing

Affiliation Dipartimento di Ingegneria dell’Energia, dei Sistemi, del Territorio e delle Costruzioni, Università degli Studi di Pisa, Largo L. Lazzarino, Pisa, Italy

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Engineering, University of Perugia, Perugia, Italy, Department of Management, Kozminski University, Warsaw, Poland

Roles Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation Faculty of Economics and Management, Centre for Family Business Management, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

• Paola Belingheri, 
• Filippo Chiarello, 
• Andrea Fronzetti Colladon, 
• Paola Rovelli

• Published: September 21, 2021
• https://doi.org/10.1371/journal.pone.0256474
• Reader Comments

9 Nov 2021: The PLOS ONE Staff (2021) Correction: Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLOS ONE 16(11): e0259930. https://doi.org/10.1371/journal.pone.0259930 View correction

Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences. However, existing literature reviews fail to provide a comprehensive and clear picture of what has been studied so far, which could guide scholars in their future research. Our paper offers a scoping review of a large portion of the research that has been published over the last 22 years, on gender equality and related issues, with a specific focus on business and economics studies. Combining innovative methods drawn from both network analysis and text mining, we provide a synthesis of 15,465 scientific articles. We identify 27 main research topics, we measure their relevance from a semantic point of view and the relationships among them, highlighting the importance of each topic in the overall gender discourse. We find that prominent research topics mostly relate to women in the workforce–e.g., concerning compensation, role, education, decision-making and career progression. However, some of them are losing momentum, and some other research trends–for example related to female entrepreneurship, leadership and participation in the board of directors–are on the rise. Besides introducing a novel methodology to review broad literature streams, our paper offers a map of the main gender-research trends and presents the most popular and the emerging themes, as well as their intersections, outlining important avenues for future research.

Citation: Belingheri P, Chiarello F, Fronzetti Colladon A, Rovelli P (2021) Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLoS ONE 16(9): e0256474. https://doi.org/10.1371/journal.pone.0256474

Editor: Elisa Ughetto, Politecnico di Torino, ITALY

Received: June 25, 2021; Accepted: August 6, 2021; Published: September 21, 2021

Copyright: © 2021 Belingheri 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: All relevant data are within the manuscript and its supporting information files. The only exception is the text of the abstracts (over 15,000) that we have downloaded from Scopus. These abstracts can be retrieved from Scopus, but we do not have permission to redistribute them.

Funding: P.B and F.C.: Grant of the Department of Energy, Systems, Territory and Construction of the University of Pisa (DESTEC) for the project “Measuring Gender Bias with Semantic Analysis: The Development of an Assessment Tool and its Application in the European Space Industry. P.B., F.C., A.F.C., P.R.: Grant of the Italian Association of Management Engineering (AiIG), “Misure di sostegno ai soci giovani AiIG” 2020, for the project “Gender Equality Through Data Intelligence (GEDI)”. F.C.: EU project ASSETs+ Project (Alliance for Strategic Skills addressing Emerging Technologies in Defence) EAC/A03/2018 - Erasmus+ programme, Sector Skills Alliances, Lot 3: Sector Skills Alliance for implementing a new strategic approach (Blueprint) to sectoral cooperation on skills G.A. NUMBER: 612678-EPP-1-2019-1-IT-EPPKA2-SSA-B.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The persistent gender inequalities that currently exist across the developed and developing world are receiving increasing attention from economists, policymakers, and the general public [e.g., 1 – 3 ]. Economic studies have indicated that women’s education and entry into the workforce contributes to social and economic well-being [e.g., 4 , 5 ], while their exclusion from the labor market and from managerial positions has an impact on overall labor productivity and income per capita [ 6 , 7 ]. The United Nations selected gender equality, with an emphasis on female education, as part of the Millennium Development Goals [ 8 ], and gender equality at-large as one of the 17 Sustainable Development Goals (SDGs) to be achieved by 2030 [ 9 ]. These latter objectives involve not only developing nations, but rather all countries, to achieve economic, social and environmental well-being.

As is the case with many SDGs, gender equality is still far from being achieved and persists across education, access to opportunities, or presence in decision-making positions [ 7 , 10 , 11 ]. As we enter the last decade for the SDGs’ implementation, and while we are battling a global health pandemic, effective and efficient action becomes paramount to reach this ambitious goal.

Scholars have dedicated a massive effort towards understanding gender equality, its determinants, its consequences for women and society, and the appropriate actions and policies to advance women’s equality. Many topics have been covered, ranging from women’s education and human capital [ 12 , 13 ] and their role in society [e.g., 14 , 15 ], to their appointment in firms’ top ranked positions [e.g., 16 , 17 ] and performance implications [e.g., 18 , 19 ]. Despite some attempts, extant literature reviews provide a narrow view on these issues, restricted to specific topics–e.g., female students’ presence in STEM fields [ 20 ], educational gender inequality [ 5 ], the gender pay gap [ 21 ], the glass ceiling effect [ 22 ], leadership [ 23 ], entrepreneurship [ 24 ], women’s presence on the board of directors [ 25 , 26 ], diversity management [ 27 ], gender stereotypes in advertisement [ 28 ], or specific professions [ 29 ]. A comprehensive view on gender-related research, taking stock of key findings and under-studied topics is thus lacking.

Extant literature has also highlighted that gender issues, and their economic and social ramifications, are complex topics that involve a large number of possible antecedents and outcomes [ 7 ]. Indeed, gender equality actions are most effective when implemented in unison with other SDGs (e.g., with SDG 8, see [ 30 ]) in a synergetic perspective [ 10 ]. Many bodies of literature (e.g., business, economics, development studies, sociology and psychology) approach the problem of achieving gender equality from different perspectives–often addressing specific and narrow aspects. This sometimes leads to a lack of clarity about how different issues, circumstances, and solutions may be related in precipitating or mitigating gender inequality or its effects. As the number of papers grows at an increasing pace, this issue is exacerbated and there is a need to step back and survey the body of gender equality literature as a whole. There is also a need to examine synergies between different topics and approaches, as well as gaps in our understanding of how different problems and solutions work together. Considering the important topic of women’s economic and social empowerment, this paper aims to fill this gap by answering the following research question: what are the most relevant findings in the literature on gender equality and how do they relate to each other ?

To do so, we conduct a scoping review [ 31 ], providing a synthesis of 15,465 articles dealing with gender equity related issues published in the last twenty-two years, covering both the periods of the MDGs and the SDGs (i.e., 2000 to mid 2021) in all the journals indexed in the Academic Journal Guide’s 2018 ranking of business and economics journals. Given the huge amount of research conducted on the topic, we adopt an innovative methodology, which relies on social network analysis and text mining. These techniques are increasingly adopted when surveying large bodies of text. Recently, they were applied to perform analysis of online gender communication differences [ 32 ] and gender behaviors in online technology communities [ 33 ], to identify and classify sexual harassment instances in academia [ 34 ], and to evaluate the gender inclusivity of disaster management policies [ 35 ].

Applied to the title, abstracts and keywords of the articles in our sample, this methodology allows us to identify a set of 27 recurrent topics within which we automatically classify the papers. Introducing additional novelty, by means of the Semantic Brand Score (SBS) indicator [ 36 ] and the SBS BI app [ 37 ], we assess the importance of each topic in the overall gender equality discourse and its relationships with the other topics, as well as trends over time, with a more accurate description than that offered by traditional literature reviews relying solely on the number of papers presented in each topic.

This methodology, applied to gender equality research spanning the past twenty-two years, enables two key contributions. First, we extract the main message that each document is conveying and how this is connected to other themes in literature, providing a rich picture of the topics that are at the center of the discourse, as well as of the emerging topics. Second, by examining the semantic relationship between topics and how tightly their discourses are linked, we can identify the key relationships and connections between different topics. This semi-automatic methodology is also highly reproducible with minimum effort.

This literature review is organized as follows. In the next section, we present how we selected relevant papers and how we analyzed them through text mining and social network analysis. We then illustrate the importance of 27 selected research topics, measured by means of the SBS indicator. In the results section, we present an overview of the literature based on the SBS results–followed by an in-depth narrative analysis of the top 10 topics (i.e., those with the highest SBS) and their connections. Subsequently, we highlight a series of under-studied connections between the topics where there is potential for future research. Through this analysis, we build a map of the main gender-research trends in the last twenty-two years–presenting the most popular themes. We conclude by highlighting key areas on which research should focused in the future.

Our aim is to map a broad topic, gender equality research, that has been approached through a host of different angles and through different disciplines. Scoping reviews are the most appropriate as they provide the freedom to map different themes and identify literature gaps, thereby guiding the recommendation of new research agendas [ 38 ].

Several practical approaches have been proposed to identify and assess the underlying topics of a specific field using big data [ 39 – 41 ], but many of them fail without proper paper retrieval and text preprocessing. This is specifically true for a research field such as the gender-related one, which comprises the work of scholars from different backgrounds. In this section, we illustrate a novel approach for the analysis of scientific (gender-related) papers that relies on methods and tools of social network analysis and text mining. Our procedure has four main steps: (1) data collection, (2) text preprocessing, (3) keywords extraction and classification, and (4) evaluation of semantic importance and image.

Data collection

In this study, we analyze 22 years of literature on gender-related research. Following established practice for scoping reviews [ 42 ], our data collection consisted of two main steps, which we summarize here below.

Firstly, we retrieved from the Scopus database all the articles written in English that contained the term “gender” in their title, abstract or keywords and were published in a journal listed in the Academic Journal Guide 2018 ranking of the Chartered Association of Business Schools (CABS) ( https://charteredabs.org/wp-content/uploads/2018/03/AJG2018-Methodology.pdf ), considering the time period from Jan 2000 to May 2021. We used this information considering that abstracts, titles and keywords represent the most informative part of a paper, while using the full-text would increase the signal-to-noise ratio for information extraction. Indeed, these textual elements already demonstrated to be reliable sources of information for the task of domain lexicon extraction [ 43 , 44 ]. We chose Scopus as source of literature because of its popularity, its update rate, and because it offers an API to ease the querying process. Indeed, while it does not allow to retrieve the full text of scientific articles, the Scopus API offers access to titles, abstracts, citation information and metadata for all its indexed scholarly journals. Moreover, we decided to focus on the journals listed in the AJG 2018 ranking because we were interested in reviewing business and economics related gender studies only. The AJG is indeed widely used by universities and business schools as a reference point for journal and research rigor and quality. This first step, executed in June 2021, returned more than 55,000 papers.

In the second step–because a look at the papers showed very sparse results, many of which were not in line with the topic of this literature review (e.g., papers dealing with health care or medical issues, where the word gender indicates the gender of the patients)–we applied further inclusion criteria to make the sample more focused on the topic of this literature review (i.e., women’s gender equality issues). Specifically, we only retained those papers mentioning, in their title and/or abstract, both gender-related keywords (e.g., daughter, female, mother) and keywords referring to bias and equality issues (e.g., equality, bias, diversity, inclusion). After text pre-processing (see next section), keywords were first identified from a frequency-weighted list of words found in the titles, abstracts and keywords in the initial list of papers, extracted through text mining (following the same approach as [ 43 ]). They were selected by two of the co-authors independently, following respectively a bottom up and a top-down approach. The bottom-up approach consisted of examining the words found in the frequency-weighted list and classifying those related to gender and equality. The top-down approach consisted in searching in the word list for notable gender and equality-related words. Table 1 reports the sets of keywords we considered, together with some examples of words that were used to search for their presence in the dataset (a full list is provided in the S1 Text ). At end of this second step, we obtained a final sample of 15,465 relevant papers.

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https://doi.org/10.1371/journal.pone.0256474.t001

Text processing and keyword extraction

Text preprocessing aims at structuring text into a form that can be analyzed by statistical models. In the present section, we describe the preprocessing steps we applied to paper titles and abstracts, which, as explained below, partially follow a standard text preprocessing pipeline [ 45 ]. These activities have been performed using the R package udpipe [ 46 ].

The first step is n-gram extraction (i.e., a sequence of words from a given text sample) to identify which n-grams are important in the analysis, since domain-specific lexicons are often composed by bi-grams and tri-grams [ 47 ]. Multi-word extraction is usually implemented with statistics and linguistic rules, thus using the statistical properties of n-grams or machine learning approaches [ 48 ]. However, for the present paper, we used Scopus metadata in order to have a more effective and efficient n-grams collection approach [ 49 ]. We used the keywords of each paper in order to tag n-grams with their associated keywords automatically. Using this greedy approach, it was possible to collect all the keywords listed by the authors of the papers. From this list, we extracted only keywords composed by two, three and four words, we removed all the acronyms and rare keywords (i.e., appearing in less than 1% of papers), and we clustered keywords showing a high orthographic similarity–measured using a Levenshtein distance [ 50 ] lower than 2, considering these groups of keywords as representing same concepts, but expressed with different spelling. After tagging the n-grams in the abstracts, we followed a common data preparation pipeline that consists of the following steps: (i) tokenization, that splits the text into tokens (i.e., single words and previously tagged multi-words); (ii) removal of stop-words (i.e. those words that add little meaning to the text, usually being very common and short functional words–such as “and”, “or”, or “of”); (iii) parts-of-speech tagging, that is providing information concerning the morphological role of a word and its morphosyntactic context (e.g., if the token is a determiner, the next token is a noun or an adjective with very high confidence, [ 51 ]); and (iv) lemmatization, which consists in substituting each word with its dictionary form (or lemma). The output of the latter step allows grouping together the inflected forms of a word. For example, the verbs “am”, “are”, and “is” have the shared lemma “be”, or the nouns “cat” and “cats” both share the lemma “cat”. We preferred lemmatization over stemming [ 52 ] in order to obtain more interpretable results.

In addition, we identified a further set of keywords (with respect to those listed in the “keywords” field) by applying a series of automatic words unification and removal steps, as suggested in past research [ 53 , 54 ]. We removed: sparse terms (i.e., occurring in less than 0.1% of all documents), common terms (i.e., occurring in more than 10% of all documents) and retained only nouns and adjectives. It is relevant to notice that no document was lost due to these steps. We then used the TF-IDF function [ 55 ] to produce a new list of keywords. We additionally tested other approaches for the identification and clustering of keywords–such as TextRank [ 56 ] or Latent Dirichlet Allocation [ 57 ]–without obtaining more informative results.

Classification of research topics

To guide the literature analysis, two experts met regularly to examine the sample of collected papers and to identify the main topics and trends in gender research. Initially, they conducted brainstorming sessions on the topics they expected to find, due to their knowledge of the literature. This led to an initial list of topics. Subsequently, the experts worked independently, also supported by the keywords in paper titles and abstracts extracted with the procedure described above.

Considering all this information, each expert identified and clustered relevant keywords into topics. At the end of the process, the two assignments were compared and exhibited a 92% agreement. Another meeting was held to discuss discordant cases and reach a consensus. This resulted in a list of 27 topics, briefly introduced in Table 2 and subsequently detailed in the following sections.

https://doi.org/10.1371/journal.pone.0256474.t002

Evaluation of semantic importance

Working on the lemmatized corpus of the 15,465 papers included in our sample, we proceeded with the evaluation of semantic importance trends for each topic and with the analysis of their connections and prevalent textual associations. To this aim, we used the Semantic Brand Score indicator [ 36 ], calculated through the SBS BI webapp [ 37 ] that also produced a brand image report for each topic. For this study we relied on the computing resources of the ENEA/CRESCO infrastructure [ 58 ].

The Semantic Brand Score (SBS) is a measure of semantic importance that combines methods of social network analysis and text mining. It is usually applied for the analysis of (big) textual data to evaluate the importance of one or more brands, names, words, or sets of keywords [ 36 ]. Indeed, the concept of “brand” is intended in a flexible way and goes beyond products or commercial brands. In this study, we evaluate the SBS time-trends of the keywords defining the research topics discussed in the previous section. Semantic importance comprises the three dimensions of topic prevalence, diversity and connectivity. Prevalence measures how frequently a research topic is used in the discourse. The more a topic is mentioned by scientific articles, the more the research community will be aware of it, with possible increase of future studies; this construct is partly related to that of brand awareness [ 59 ]. This effect is even stronger, considering that we are analyzing the title, abstract and keywords of the papers, i.e. the parts that have the highest visibility. A very important characteristic of the SBS is that it considers the relationships among words in a text. Topic importance is not just a matter of how frequently a topic is mentioned, but also of the associations a topic has in the text. Specifically, texts are transformed into networks of co-occurring words, and relationships are studied through social network analysis [ 60 ]. This step is necessary to calculate the other two dimensions of our semantic importance indicator. Accordingly, a social network of words is generated for each time period considered in the analysis–i.e., a graph made of n nodes (words) and E edges weighted by co-occurrence frequency, with W being the set of edge weights. The keywords representing each topic were clustered into single nodes.

The construct of diversity relates to that of brand image [ 59 ], in the sense that it considers the richness and distinctiveness of textual (topic) associations. Considering the above-mentioned networks, we calculated diversity using the distinctiveness centrality metric–as in the formula presented by Fronzetti Colladon and Naldi [ 61 ].

Lastly, connectivity was measured as the weighted betweenness centrality [ 62 , 63 ] of each research topic node. We used the formula presented by Wasserman and Faust [ 60 ]. The dimension of connectivity represents the “brokerage power” of each research topic–i.e., how much it can serve as a bridge to connect other terms (and ultimately topics) in the discourse [ 36 ].

The SBS is the final composite indicator obtained by summing the standardized scores of prevalence, diversity and connectivity. Standardization was carried out considering all the words in the corpus, for each specific timeframe.

This methodology, applied to a large and heterogeneous body of text, enables to automatically identify two important sets of information that add value to the literature review. Firstly, the relevance of each topic in literature is measured through a composite indicator of semantic importance, rather than simply looking at word frequencies. This provides a much richer picture of the topics that are at the center of the discourse, as well as of the topics that are emerging in the literature. Secondly, it enables to examine the extent of the semantic relationship between topics, looking at how tightly their discourses are linked. In a field such as gender equality, where many topics are closely linked to each other and present overlaps in issues and solutions, this methodology offers a novel perspective with respect to traditional literature reviews. In addition, it ensures reproducibility over time and the possibility to semi-automatically update the analysis, as new papers become available.

Overview of main topics

In terms of descriptive textual statistics, our corpus is made of 15,465 text documents, consisting of a total of 2,685,893 lemmatized tokens (words) and 32,279 types. As a result, the type-token ratio is 1.2%. The number of hapaxes is 12,141, with a hapax-token ratio of 37.61%.

Fig 1 shows the list of 27 topics by decreasing SBS. The most researched topic is compensation , exceeding all others in prevalence, diversity, and connectivity. This means it is not only mentioned more often than other topics, but it is also connected to a greater number of other topics and is central to the discourse on gender equality. The next four topics are, in order of SBS, role , education , decision-making , and career progression . These topics, except for education , all concern women in the workforce. Between these first five topics and the following ones there is a clear drop in SBS scores. In particular, the topics that follow have a lower connectivity than the first five. They are hiring , performance , behavior , organization , and human capital . Again, except for behavior and human capital , the other three topics are purely related to women in the workforce. After another drop-off, the following topics deal prevalently with women in society. This trend highlights that research on gender in business journals has so far mainly paid attention to the conditions that women experience in business contexts, while also devoting some attention to women in society.

https://doi.org/10.1371/journal.pone.0256474.g001

Fig 2 shows the SBS time series of the top 10 topics. While there has been a general increase in the number of Scopus-indexed publications in the last decade, we notice that some SBS trends remain steady, or even decrease. In particular, we observe that the main topic of the last twenty-two years, compensation , is losing momentum. Since 2016, it has been surpassed by decision-making , education and role , which may indicate that literature is increasingly attempting to identify root causes of compensation inequalities. Moreover, in the last two years, the topics of hiring , performance , and organization are experiencing the largest importance increase.

https://doi.org/10.1371/journal.pone.0256474.g002

Fig 3 shows the SBS time trends of the remaining 17 topics (i.e., those not in the top 10). As we can see from the graph, there are some that maintain a steady trend–such as reputation , management , networks and governance , which also seem to have little importance. More relevant topics with average stationary trends (except for the last two years) are culture , family , and parenting . The feminine topic is among the most important here, and one of those that exhibit the larger variations over time (similarly to leadership ). On the other hand, the are some topics that, even if not among the most important, show increasing SBS trends; therefore, they could be considered as emerging topics and could become popular in the near future. These are entrepreneurship , leadership , board of directors , and sustainability . These emerging topics are also interesting to anticipate future trends in gender equality research that are conducive to overall equality in society.

https://doi.org/10.1371/journal.pone.0256474.g003

In addition to the SBS score of the different topics, the network of terms they are associated to enables to gauge the extent to which their images (textual associations) overlap or differ ( Fig 4 ).

https://doi.org/10.1371/journal.pone.0256474.g004

There is a central cluster of topics with high similarity, which are all connected with women in the workforce. The cluster includes topics such as organization , decision-making , performance , hiring , human capital , education and compensation . In addition, the topic of well-being is found within this cluster, suggesting that women’s equality in the workforce is associated to well-being considerations. The emerging topics of entrepreneurship and leadership are also closely connected with each other, possibly implying that leadership is a much-researched quality in female entrepreneurship. Topics that are relatively more distant include personality , politics , feminine , empowerment , management , board of directors , reputation , governance , parenting , masculine and network .

The following sections describe the top 10 topics and their main associations in literature (see Table 3 ), while providing a brief overview of the emerging topics.

https://doi.org/10.1371/journal.pone.0256474.t003

Compensation.

The topic of compensation is related to the topics of role , hiring , education and career progression , however, also sees a very high association with the words gap and inequality . Indeed, a well-known debate in degrowth economics centers around whether and how to adequately compensate women for their childbearing, childrearing, caregiver and household work [e.g., 30 ].

Even in paid work, women continue being offered lower compensations than their male counterparts who have the same job or cover the same role [ 64 – 67 ]. This severe inequality has been widely studied by scholars over the last twenty-two years. Dealing with this topic, some specific roles have been addressed. Specifically, research highlighted differences in compensation between female and male CEOs [e.g., 68 ], top executives [e.g., 69 ], and boards’ directors [e.g., 70 ]. Scholars investigated the determinants of these gaps, such as the gender composition of the board [e.g., 71 – 73 ] or women’s individual characteristics [e.g., 71 , 74 ].

Among these individual characteristics, education plays a relevant role [ 75 ]. Education is indeed presented as the solution for women, not only to achieve top executive roles, but also to reduce wage inequality [e.g., 76 , 77 ]. Past research has highlighted education influences on gender wage gaps, specifically referring to gender differences in skills [e.g., 78 ], college majors [e.g., 79 ], and college selectivity [e.g., 80 ].

Finally, the wage gap issue is strictly interrelated with hiring –e.g., looking at whether being a mother affects hiring and compensation [e.g., 65 , 81 ] or relating compensation to unemployment [e.g., 82 ]–and career progression –for instance looking at meritocracy [ 83 , 84 ] or the characteristics of the boss for whom women work [e.g., 85 ].

The roles covered by women have been deeply investigated. Scholars have focused on the role of women in their families and the society as a whole [e.g., 14 , 15 ], and, more widely, in business contexts [e.g., 18 , 81 ]. Indeed, despite still lagging behind their male counterparts [e.g., 86 , 87 ], in the last decade there has been an increase in top ranked positions achieved by women [e.g., 88 , 89 ]. Following this phenomenon, scholars have posed greater attention towards the presence of women in the board of directors [e.g., 16 , 18 , 90 , 91 ], given the increasing pressure to appoint female directors that firms, especially listed ones, have experienced. Other scholars have focused on the presence of women covering the role of CEO [e.g., 17 , 92 ] or being part of the top management team [e.g., 93 ]. Irrespectively of the level of analysis, all these studies tried to uncover the antecedents of women’s presence among top managers [e.g., 92 , 94 ] and the consequences of having a them involved in the firm’s decision-making –e.g., on performance [e.g., 19 , 95 , 96 ], risk [e.g., 97 , 98 ], and corporate social responsibility [e.g., 99 , 100 ].

Besides studying the difficulties and discriminations faced by women in getting a job [ 81 , 101 ], and, more specifically in the hiring , appointment, or career progression to these apical roles [e.g., 70 , 83 ], the majority of research of women’s roles dealt with compensation issues. Specifically, scholars highlight the pay-gap that still exists between women and men, both in general [e.g., 64 , 65 ], as well as referring to boards’ directors [e.g., 70 , 102 ], CEOs and executives [e.g., 69 , 103 , 104 ].

Finally, other scholars focused on the behavior of women when dealing with business. In this sense, particular attention has been paid to leadership and entrepreneurial behaviors. The former quite overlaps with dealing with the roles mentioned above, but also includes aspects such as leaders being stereotyped as masculine [e.g., 105 ], the need for greater exposure to female leaders to reduce biases [e.g., 106 ], or female leaders acting as queen bees [e.g., 107 ]. Regarding entrepreneurship , scholars mainly investigated women’s entrepreneurial entry [e.g., 108 , 109 ], differences between female and male entrepreneurs in the evaluations and funding received from investors [e.g., 110 , 111 ], and their performance gap [e.g., 112 , 113 ].

Education has long been recognized as key to social advancement and economic stability [ 114 ], for job progression and also a barrier to gender equality, especially in STEM-related fields. Research on education and gender equality is mostly linked with the topics of compensation , human capital , career progression , hiring , parenting and decision-making .

Education contributes to a higher human capital [ 115 ] and constitutes an investment on the part of women towards their future. In this context, literature points to the gender gap in educational attainment, and the consequences for women from a social, economic, personal and professional standpoint. Women are found to have less access to formal education and information, especially in emerging countries, which in turn may cause them to lose social and economic opportunities [e.g., 12 , 116 – 119 ]. Education in local and rural communities is also paramount to communicate the benefits of female empowerment , contributing to overall societal well-being [e.g., 120 ].

Once women access education, the image they have of the world and their place in society (i.e., habitus) affects their education performance [ 13 ] and is passed on to their children. These situations reinforce gender stereotypes, which become self-fulfilling prophecies that may negatively affect female students’ performance by lowering their confidence and heightening their anxiety [ 121 , 122 ]. Besides formal education, also the information that women are exposed to on a daily basis contributes to their human capital . Digital inequalities, for instance, stems from men spending more time online and acquiring higher digital skills than women [ 123 ].

Education is also a factor that should boost employability of candidates and thus hiring , career progression and compensation , however the relationship between these factors is not straightforward [ 115 ]. First, educational choices ( decision-making ) are influenced by variables such as self-efficacy and the presence of barriers, irrespectively of the career opportunities they offer, especially in STEM [ 124 ]. This brings additional difficulties to women’s enrollment and persistence in scientific and technical fields of study due to stereotypes and biases [ 125 , 126 ]. Moreover, access to education does not automatically translate into job opportunities for women and minority groups [ 127 , 128 ] or into female access to managerial positions [ 129 ].

Finally, parenting is reported as an antecedent of education [e.g., 130 ], with much of the literature focusing on the role of parents’ education on the opportunities afforded to children to enroll in education [ 131 – 134 ] and the role of parenting in their offspring’s perception of study fields and attitudes towards learning [ 135 – 138 ]. Parental education is also a predictor of the other related topics, namely human capital and compensation [ 139 ].

Decision-making.

This literature mainly points to the fact that women are thought to make decisions differently than men. Women have indeed different priorities, such as they care more about people’s well-being, working with people or helping others, rather than maximizing their personal (or their firm’s) gain [ 140 ]. In other words, women typically present more communal than agentic behaviors, which are instead more frequent among men [ 141 ]. These different attitude, behavior and preferences in turn affect the decisions they make [e.g., 142 ] and the decision-making of the firm in which they work [e.g., 143 ].

At the individual level, gender affects, for instance, career aspirations [e.g., 144 ] and choices [e.g., 142 , 145 ], or the decision of creating a venture [e.g., 108 , 109 , 146 ]. Moreover, in everyday life, women and men make different decisions regarding partners [e.g., 147 ], childcare [e.g., 148 ], education [e.g., 149 ], attention to the environment [e.g., 150 ] and politics [e.g., 151 ].

At the firm level, scholars highlighted, for example, how the presence of women in the board affects corporate decisions [e.g., 152 , 153 ], that female CEOs are more conservative in accounting decisions [e.g., 154 ], or that female CFOs tend to make more conservative decisions regarding the firm’s financial reporting [e.g., 155 ]. Nevertheless, firm level research also investigated decisions that, influenced by gender bias, affect women, such as those pertaining hiring [e.g., 156 , 157 ], compensation [e.g., 73 , 158 ], or the empowerment of women once appointed [ 159 ].

Career progression.

Once women have entered the workforce, the key aspect to achieve gender equality becomes career progression , including efforts toward overcoming the glass ceiling. Indeed, according to the SBS analysis, career progression is highly related to words such as work, social issues and equality. The topic with which it has the highest semantic overlap is role , followed by decision-making , hiring , education , compensation , leadership , human capital , and family .

Career progression implies an advancement in the hierarchical ladder of the firm, assigning managerial roles to women. Coherently, much of the literature has focused on identifying rationales for a greater female participation in the top management team and board of directors [e.g., 95 ] as well as the best criteria to ensure that the decision-makers promote the most valuable employees irrespectively of their individual characteristics, such as gender [e.g., 84 ]. The link between career progression , role and compensation is often provided in practice by performance appraisal exercises, frequently rooted in a culture of meritocracy that guides bonuses, salary increases and promotions. However, performance appraisals can actually mask gender-biased decisions where women are held to higher standards than their male colleagues [e.g., 83 , 84 , 95 , 160 , 161 ]. Women often have less opportunities to gain leadership experience and are less visible than their male colleagues, which constitute barriers to career advancement [e.g., 162 ]. Therefore, transparency and accountability, together with procedures that discourage discretionary choices, are paramount to achieve a fair career progression [e.g., 84 ], together with the relaxation of strict job boundaries in favor of cross-functional and self-directed tasks [e.g., 163 ].

In addition, a series of stereotypes about the type of leadership characteristics that are required for top management positions, which fit better with typical male and agentic attributes, are another key barrier to career advancement for women [e.g., 92 , 160 ].

Hiring is the entrance gateway for women into the workforce. Therefore, it is related to other workforce topics such as compensation , role , career progression , decision-making , human capital , performance , organization and education .

A first stream of literature focuses on the process leading up to candidates’ job applications, demonstrating that bias exists before positions are even opened, and it is perpetuated both by men and women through networking and gatekeeping practices [e.g., 164 , 165 ].

The hiring process itself is also subject to biases [ 166 ], for example gender-congruity bias that leads to men being preferred candidates in male-dominated sectors [e.g., 167 ], women being hired in positions with higher risk of failure [e.g., 168 ] and limited transparency and accountability afforded by written processes and procedures [e.g., 164 ] that all contribute to ascriptive inequality. In addition, providing incentives for evaluators to hire women may actually work to this end; however, this is not the case when supporting female candidates endangers higher-ranking male ones [ 169 ].

Another interesting perspective, instead, looks at top management teams’ composition and the effects on hiring practices, indicating that firms with more women in top management are less likely to lay off staff [e.g., 152 ].

Performance.

Several scholars posed their attention towards women’s performance, its consequences [e.g., 170 , 171 ] and the implications of having women in decision-making positions [e.g., 18 , 19 ].

At the individual level, research focused on differences in educational and academic performance between women and men, especially referring to the gender gap in STEM fields [e.g., 171 ]. The presence of stereotype threats–that is the expectation that the members of a social group (e.g., women) “must deal with the possibility of being judged or treated stereotypically, or of doing something that would confirm the stereotype” [ 172 ]–affects women’s interested in STEM [e.g., 173 ], as well as their cognitive ability tests, penalizing them [e.g., 174 ]. A stronger gender identification enhances this gap [e.g., 175 ], whereas mentoring and role models can be used as solutions to this problem [e.g., 121 ]. Despite the negative effect of stereotype threats on girls’ performance [ 176 ], female and male students perform equally in mathematics and related subjects [e.g., 177 ]. Moreover, while individuals’ performance at school and university generally affects their achievements and the field in which they end up working, evidence reveals that performance in math or other scientific subjects does not explain why fewer women enter STEM working fields; rather this gap depends on other aspects, such as culture, past working experiences, or self-efficacy [e.g., 170 ]. Finally, scholars have highlighted the penalization that women face for their positive performance, for instance when they succeed in traditionally male areas [e.g., 178 ]. This penalization is explained by the violation of gender-stereotypic prescriptions [e.g., 179 , 180 ], that is having women well performing in agentic areas, which are typical associated to men. Performance penalization can thus be overcome by clearly conveying communal characteristics and behaviors [ 178 ].

Evidence has been provided on how the involvement of women in boards of directors and decision-making positions affects firms’ performance. Nevertheless, results are mixed, with some studies showing positive effects on financial [ 19 , 181 , 182 ] and corporate social performance [ 99 , 182 , 183 ]. Other studies maintain a negative association [e.g., 18 ], and other again mixed [e.g., 184 ] or non-significant association [e.g., 185 ]. Also with respect to the presence of a female CEO, mixed results emerged so far, with some researches demonstrating a positive effect on firm’s performance [e.g., 96 , 186 ], while other obtaining only a limited evidence of this relationship [e.g., 103 ] or a negative one [e.g., 187 ].

Finally, some studies have investigated whether and how women’s performance affects their hiring [e.g., 101 ] and career progression [e.g., 83 , 160 ]. For instance, academic performance leads to different returns in hiring for women and men. Specifically, high-achieving men are called back significantly more often than high-achieving women, which are penalized when they have a major in mathematics; this result depends on employers’ gendered standards for applicants [e.g., 101 ]. Once appointed, performance ratings are more strongly related to promotions for women than men, and promoted women typically show higher past performance ratings than those of promoted men. This suggesting that women are subject to stricter standards for promotion [e.g., 160 ].

Behavioral aspects related to gender follow two main streams of literature. The first examines female personality and behavior in the workplace, and their alignment with cultural expectations or stereotypes [e.g., 188 ] as well as their impacts on equality. There is a common bias that depicts women as less agentic than males. Certain characteristics, such as those more congruent with male behaviors–e.g., self-promotion [e.g., 189 ], negotiation skills [e.g., 190 ] and general agentic behavior [e.g., 191 ]–, are less accepted in women. However, characteristics such as individualism in women have been found to promote greater gender equality in society [ 192 ]. In addition, behaviors such as display of emotions [e.g., 193 ], which are stereotypically female, work against women’s acceptance in the workplace, requiring women to carefully moderate their behavior to avoid exclusion. A counter-intuitive result is that women and minorities, which are more marginalized in the workplace, tend to be better problem-solvers in innovation competitions due to their different knowledge bases [ 194 ].

The other side of the coin is examined in a parallel literature stream on behavior towards women in the workplace. As a result of biases, prejudices and stereotypes, women may experience adverse behavior from their colleagues, such as incivility and harassment, which undermine their well-being [e.g., 195 , 196 ]. Biases that go beyond gender, such as for overweight people, are also more strongly applied to women [ 197 ].

Organization.

The role of women and gender bias in organizations has been studied from different perspectives, which mirror those presented in detail in the following sections. Specifically, most research highlighted the stereotypical view of leaders [e.g., 105 ] and the roles played by women within firms, for instance referring to presence in the board of directors [e.g., 18 , 90 , 91 ], appointment as CEOs [e.g., 16 ], or top executives [e.g., 93 ].

Scholars have investigated antecedents and consequences of the presence of women in these apical roles. On the one side they looked at hiring and career progression [e.g., 83 , 92 , 160 , 168 , 198 ], finding women typically disadvantaged with respect to their male counterparts. On the other side, they studied women’s leadership styles and influence on the firm’s decision-making [e.g., 152 , 154 , 155 , 199 ], with implications for performance [e.g., 18 , 19 , 96 ].

Human capital.

Human capital is a transverse topic that touches upon many different aspects of female gender equality. As such, it has the most associations with other topics, starting with education as mentioned above, with career-related topics such as role , decision-making , hiring , career progression , performance , compensation , leadership and organization . Another topic with which there is a close connection is behavior . In general, human capital is approached both from the education standpoint but also from the perspective of social capital.

The behavioral aspect in human capital comprises research related to gender differences for example in cultural and religious beliefs that influence women’s attitudes and perceptions towards STEM subjects [ 142 , 200 – 202 ], towards employment [ 203 ] or towards environmental issues [ 150 , 204 ]. These cultural differences also emerge in the context of globalization which may accelerate gender equality in the workforce [ 205 , 206 ]. Gender differences also appear in behaviors such as motivation [ 207 ], and in negotiation [ 190 ], and have repercussions on women’s decision-making related to their careers. The so-called gender equality paradox sees women in countries with lower gender equality more likely to pursue studies and careers in STEM fields, whereas the gap in STEM enrollment widens as countries achieve greater equality in society [ 171 ].

Career progression is modeled by literature as a choice-process where personal preferences, culture and decision-making affect the chosen path and the outcomes. Some literature highlights how women tend to self-select into different professions than men, often due to stereotypes rather than actual ability to perform in these professions [ 142 , 144 ]. These stereotypes also affect the perceptions of female performance or the amount of human capital required to equal male performance [ 110 , 193 , 208 ], particularly for mothers [ 81 ]. It is therefore often assumed that women are better suited to less visible and less leadership -oriented roles [ 209 ]. Women also express differing preferences towards work-family balance, which affect whether and how they pursue human capital gains [ 210 ], and ultimately their career progression and salary .

On the other hand, men are often unaware of gendered processes and behaviors that they carry forward in their interactions and decision-making [ 211 , 212 ]. Therefore, initiatives aimed at increasing managers’ human capital –by raising awareness of gender disparities in their organizations and engaging them in diversity promotion–are essential steps to counter gender bias and segregation [ 213 ].

Emerging topics: Leadership and entrepreneurship

Among the emerging topics, the most pervasive one is women reaching leadership positions in the workforce and in society. This is still a rare occurrence for two main types of factors, on the one hand, bias and discrimination make it harder for women to access leadership positions [e.g., 214 – 216 ], on the other hand, the competitive nature and high pressure associated with leadership positions, coupled with the lack of women currently represented, reduce women’s desire to achieve them [e.g., 209 , 217 ]. Women are more effective leaders when they have access to education, resources and a diverse environment with representation [e.g., 218 , 219 ].

One sector where there is potential for women to carve out a leadership role is entrepreneurship . Although at the start of the millennium the discourse on entrepreneurship was found to be “discriminatory, gender-biased, ethnocentrically determined and ideologically controlled” [ 220 ], an increasing body of literature is studying how to stimulate female entrepreneurship as an alternative pathway to wealth, leadership and empowerment [e.g., 221 ]. Many barriers exist for women to access entrepreneurship, including the institutional and legal environment, social and cultural factors, access to knowledge and resources, and individual behavior [e.g., 222 , 223 ]. Education has been found to raise women’s entrepreneurial intentions [e.g., 224 ], although this effect is smaller than for men [e.g., 109 ]. In addition, increasing self-efficacy and risk-taking behavior constitute important success factors [e.g., 225 ].

Finally, the topic of sustainability is worth mentioning, as it is the primary objective of the SDGs and is closely associated with societal well-being. As society grapples with the effects of climate change and increasing depletion of natural resources, a narrative has emerged on women and their greater link to the environment [ 226 ]. Studies in developed countries have found some support for women leaders’ attention to sustainability issues in firms [e.g., 227 – 229 ], and smaller resource consumption by women [ 230 ]. At the same time, women will likely be more affected by the consequences of climate change [e.g., 230 ] but often lack the decision-making power to influence local decision-making on resource management and environmental policies [e.g., 231 ].

Research gaps and conclusions

Research on gender equality has advanced rapidly in the past decades, with a steady increase in publications, both in mainstream topics related to women in education and the workforce, and in emerging topics. Through a novel approach combining methods of text mining and social network analysis, we examined a comprehensive body of literature comprising 15,465 papers published between 2000 and mid 2021 on topics related to gender equality. We identified a set of 27 topics addressed by the literature and examined their connections.

At the highest level of abstraction, it is worth noting that papers abound on the identification of issues related to gender inequalities and imbalances in the workforce and in society. Literature has thoroughly examined the (unconscious) biases, barriers, stereotypes, and discriminatory behaviors that women are facing as a result of their gender. Instead, there are much fewer papers that discuss or demonstrate effective solutions to overcome gender bias [e.g., 121 , 143 , 145 , 163 , 194 , 213 , 232 ]. This is partly due to the relative ease in studying the status quo, as opposed to studying changes in the status quo. However, we observed a shift in the more recent years towards solution seeking in this domain, which we strongly encourage future researchers to focus on. In the future, we may focus on collecting and mapping pro-active contributions to gender studies, using additional Natural Language Processing techniques, able to measure the sentiment of scientific papers [ 43 ].

All of the mainstream topics identified in our literature review are closely related, and there is a wealth of insights looking at the intersection between issues such as education and career progression or human capital and role . However, emerging topics are worthy of being furtherly explored. It would be interesting to see more work on the topic of female entrepreneurship , exploring aspects such as education , personality , governance , management and leadership . For instance, how can education support female entrepreneurship? How can self-efficacy and risk-taking behaviors be taught or enhanced? What are the differences in managerial and governance styles of female entrepreneurs? Which personality traits are associated with successful entrepreneurs? Which traits are preferred by venture capitalists and funding bodies?

The emerging topic of sustainability also deserves further attention, as our society struggles with climate change and its consequences. It would be interesting to see more research on the intersection between sustainability and entrepreneurship , looking at how female entrepreneurs are tackling sustainability issues, examining both their business models and their company governance . In addition, scholars are suggested to dig deeper into the relationship between family values and behaviors.

Moreover, it would be relevant to understand how women’s networks (social capital), or the composition and structure of social networks involving both women and men, enable them to increase their remuneration and reach top corporate positions, participate in key decision-making bodies, and have a voice in communities. Furthermore, the achievement of gender equality might significantly change firm networks and ecosystems, with important implications for their performance and survival.

Similarly, research at the nexus of (corporate) governance , career progression , compensation and female empowerment could yield useful insights–for example discussing how enterprises, institutions and countries are managed and the impact for women and other minorities. Are there specific governance structures that favor diversity and inclusion?

Lastly, we foresee an emerging stream of research pertaining how the spread of the COVID-19 pandemic challenged women, especially in the workforce, by making gender biases more evident.

For our analysis, we considered a set of 15,465 articles downloaded from the Scopus database (which is the largest abstract and citation database of peer-reviewed literature). As we were interested in reviewing business and economics related gender studies, we only considered those papers published in journals listed in the Academic Journal Guide (AJG) 2018 ranking of the Chartered Association of Business Schools (CABS). All the journals listed in this ranking are also indexed by Scopus. Therefore, looking at a single database (i.e., Scopus) should not be considered a limitation of our study. However, future research could consider different databases and inclusion criteria.

With our literature review, we offer researchers a comprehensive map of major gender-related research trends over the past twenty-two years. This can serve as a lens to look to the future, contributing to the achievement of SDG5. Researchers may use our study as a starting point to identify key themes addressed in the literature. In addition, our methodological approach–based on the use of the Semantic Brand Score and its webapp–could support scholars interested in reviewing other areas of research.

Supporting information

S1 text. keywords used for paper selection..

https://doi.org/10.1371/journal.pone.0256474.s001

Acknowledgments

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see http://www.cresco.enea.it/english for information).

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Gender Inequality Essay

500+ words essay on gender inequality.

For many years, the dominant gender has been men while women were the minority. It was mostly because men earned the money and women looked after the house and children. Similarly, they didn’t have any rights as well. However, as time passed by, things started changing slowly. Nonetheless, they are far from perfect. Gender inequality remains a serious issue in today’s time. Thus, this gender inequality essay will highlight its impact and how we can fight against it.

  About Gender Inequality Essay

Gender inequality refers to the unequal and biased treatment of individuals on the basis of their gender. This inequality happens because of socially constructed gender roles. It happens when an individual of a specific gender is given different or disadvantageous treatment in comparison to a person of the other gender in the same circumstance.

Get the huge list of more than 500 Essay Topics and Ideas

Impact of Gender Inequality

The biggest problem we’re facing is that a lot of people still see gender inequality as a women’s issue. However, by gender, we refer to all genders including male, female, transgender and others.

When we empower all genders especially the marginalized ones, they can lead their lives freely. Moreover, gender inequality results in not letting people speak their minds. Ultimately, it hampers their future and compromises it.

History is proof that fighting gender inequality has resulted in stable and safe societies. Due to gender inequality, we have a gender pay gap. Similarly, it also exposes certain genders to violence and discrimination.

In addition, they also get objectified and receive socioeconomic inequality. All of this ultimately results in severe anxiety, depression and even low self-esteem. Therefore, we must all recognize that gender inequality harms genders of all kinds. We must work collectively to stop these long-lasting consequences and this gender inequality essay will tell you how.

How to Fight Gender Inequality

Gender inequality is an old-age issue that won’t resolve within a few days. Similarly, achieving the goal of equality is also not going to be an easy one. We must start by breaking it down and allow it time to go away.

Firstly, we must focus on eradicating this problem through education. In other words, we must teach our young ones to counter gender stereotypes from their childhood.

Similarly, it is essential to ensure that they hold on to the very same beliefs till they turn old. We must show them how sports are not gender-biased.

Further, we must promote equality in the fields of labour. For instance, some people believe that women cannot do certain jobs like men. However, that is not the case. We can also get celebrities on board to promote and implant the idea of equality in people’s brains.

All in all, humanity needs men and women to continue. Thus, inequality will get us nowhere. To conclude the gender inequality essay, we need to get rid of the old-age traditions and mentality. We must teach everyone, especially the boys all about equality and respect. It requires quite a lot of work but it is possible. We can work together and achieve equal respect and opportunities for all genders alike.

FAQ of Gender Inequality Essay

Question 1: What is gender inequality?

Answer 1: Gender inequality refers to the unequal and biased treatment of individuals on the basis of their gender. This inequality happens because of socially constructed gender roles. It happens when an individual of a specific gender is given different or disadvantageous treatment in comparison to a person of the other gender in the same circumstance.

Question 2: How does gender inequality impact us?

Answer 2:  The gender inequality essay tells us that gender inequality impacts us badly. It takes away opportunities from deserving people. Moreover, it results in discriminatory behaviour towards people of a certain gender. Finally, it also puts people of a certain gender in dangerous situations.

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

The gender gap in pay has remained relatively stable in the United States over the past 20 years or so. In 2022, women earned an average of 82% of what men earned, according to a new Pew Research Center analysis of median hourly earnings of both full- and part-time workers. These results are similar to where the pay gap stood in 2002, when women earned 80% as much as men.

As has long been the case, the wage gap is smaller for workers ages 25 to 34 than for all workers 16 and older. In 2022, women ages 25 to 34 earned an average of 92 cents for every dollar earned by a man in the same age group – an 8-cent gap. By comparison, the gender pay gap among workers of all ages that year was 18 cents.

While the gender pay gap has not changed much in the last two decades, it has narrowed considerably when looking at the longer term, both among all workers ages 16 and older and among those ages 25 to 34. The estimated 18-cent gender pay gap among all workers in 2022 was down from 35 cents in 1982. And the 8-cent gap among workers ages 25 to 34 in 2022 was down from a 26-cent gap four decades earlier.

The gender pay gap measures the difference in median hourly earnings between men and women who work full or part time in the United States. Pew Research Center’s estimate of the pay gap is based on an analysis of Current Population Survey (CPS) monthly outgoing rotation group files ( IPUMS ) from January 1982 to December 2022, combined to create annual files. To understand how we calculate the gender pay gap, read our 2013 post, “How Pew Research Center measured the gender pay gap.”

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

In addition to findings about the gender wage gap, this analysis includes information from a Pew Research Center survey about the perceived reasons for the pay gap, as well as the pressures and career goals of U.S. men and women. The survey was conducted among 5,098 adults and includes a subset of questions asked only for 2,048 adults who are employed part time or full time, from Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used in this analysis, along with responses, and its methodology .

The  U.S. Census Bureau has also analyzed the gender pay gap, though its analysis looks only at full-time workers (as opposed to full- and part-time workers). In 2021, full-time, year-round working women earned 84% of what their male counterparts earned, on average, according to the Census Bureau’s most recent analysis.

Much of the gender pay gap has been explained by measurable factors such as educational attainment, occupational segregation and work experience. The narrowing of the gap over the long term is attributable in large part to gains women have made in each of these dimensions.

Related: The Enduring Grip of the Gender Pay Gap

Even though women have increased their presence in higher-paying jobs traditionally dominated by men, such as professional and managerial positions, women as a whole continue to be overrepresented in lower-paying occupations relative to their share of the workforce. This may contribute to gender differences in pay.

Other factors that are difficult to measure, including gender discrimination, may also contribute to the ongoing wage discrepancy.

Perceived reasons for the gender wage gap

When asked about the factors that may play a role in the gender wage gap, half of U.S. adults point to women being treated differently by employers as a major reason, according to a Pew Research Center survey conducted in October 2022. Smaller shares point to women making different choices about how to balance work and family (42%) and working in jobs that pay less (34%).

There are some notable differences between men and women in views of what’s behind the gender wage gap. Women are much more likely than men (61% vs. 37%) to say a major reason for the gap is that employers treat women differently. And while 45% of women say a major factor is that women make different choices about how to balance work and family, men are slightly less likely to hold that view (40% say this).

Parents with children younger than 18 in the household are more likely than those who don’t have young kids at home (48% vs. 40%) to say a major reason for the pay gap is the choices that women make about how to balance family and work. On this question, differences by parental status are evident among both men and women.

Views about reasons for the gender wage gap also differ by party. About two-thirds of Democrats and Democratic-leaning independents (68%) say a major factor behind wage differences is that employers treat women differently, but far fewer Republicans and Republican leaners (30%) say the same. Conversely, Republicans are more likely than Democrats to say women’s choices about how to balance family and work (50% vs. 36%) and their tendency to work in jobs that pay less (39% vs. 30%) are major reasons why women earn less than men.

Democratic and Republican women are more likely than their male counterparts in the same party to say a major reason for the gender wage gap is that employers treat women differently. About three-quarters of Democratic women (76%) say this, compared with 59% of Democratic men. And while 43% of Republican women say unequal treatment by employers is a major reason for the gender wage gap, just 18% of GOP men share that view.

Pressures facing working women and men

Family caregiving responsibilities bring different pressures for working women and men, and research has shown that being a mother can reduce women’s earnings , while fatherhood can increase men’s earnings .

Employed women and men are about equally likely to say they feel a great deal of pressure to support their family financially and to be successful in their jobs and careers, according to the Center’s October survey. But women, and particularly working mothers, are more likely than men to say they feel a great deal of pressure to focus on responsibilities at home.

About half of employed women (48%) report feeling a great deal of pressure to focus on their responsibilities at home, compared with 35% of employed men. Among working mothers with children younger than 18 in the household, two-thirds (67%) say the same, compared with 45% of working dads.

When it comes to supporting their family financially, similar shares of working moms and dads (57% vs. 62%) report they feel a great deal of pressure, but this is driven mainly by the large share of unmarried working mothers who say they feel a great deal of pressure in this regard (77%). Among those who are married, working dads are far more likely than working moms (60% vs. 43%) to say they feel a great deal of pressure to support their family financially. (There were not enough unmarried working fathers in the sample to analyze separately.)

About four-in-ten working parents say they feel a great deal of pressure to be successful at their job or career. These findings don’t differ by gender.

Gender differences in job roles, aspirations

Overall, a quarter of employed U.S. adults say they are currently the boss or one of the top managers where they work, according to the Center’s survey. Another 33% say they are not currently the boss but would like to be in the future, while 41% are not and do not aspire to be the boss or one of the top managers.

Men are more likely than women to be a boss or a top manager where they work (28% vs. 21%). This is especially the case among employed fathers, 35% of whom say they are the boss or one of the top managers where they work. (The varying attitudes between fathers and men without children at least partly reflect differences in marital status and educational attainment between the two groups.)

In addition to being less likely than men to say they are currently the boss or a top manager at work, women are also more likely to say they wouldn’t want to be in this type of position in the future. More than four-in-ten employed women (46%) say this, compared with 37% of men. Similar shares of men (35%) and women (31%) say they are not currently the boss but would like to be one day. These patterns are similar among parents.

Note: This is an update of a post originally published on March 22, 2019. Anna Brown and former Pew Research Center writer/editor Amanda Barroso contributed to an earlier version of this analysis. Here are the questions used in this analysis, along with responses, and its methodology .

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

• Gender & Work
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Carolina Aragão is a former research associate focusing on social and demographic trends at Pew Research Center .

Among young U.S. workers without a college degree, men and women hold very different types of jobs

Half of latinas say hispanic women’s situation has improved in the past decade and expect more gains, a majority of latinas feel pressure to support their families or to succeed at work, for women’s history month, a look at gender gains – and gaps – in the u.s., women have gained ground in the nation’s highest-paying occupations, but still lag behind men, most popular.

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The Need for Eliminating The Gender Wage Gap to Improve Society

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

Result of the feminization of poverty, gender pay gaps on the example soccer`s team, a study of gender inequality in hong kong: review of literature, the effects of gender inequality on society and the economy, the legal dilemma behind equal pay for equal work in india, reflection of gender inequality in different spheres, gender discrimination in the workplace: challenges and solutions, gender hierarchies, stereotypes, and the fight for equality.

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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• Published: 04 September 2024

Does women’s higher education reduce wage inequality? Evidence from Palestine using repeated cross-sectional data

• Najiba Morar 1 , 2 &
• Sameera Awawda 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1133 ( 2024 ) Cite this article

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Despite the increase of the share of highly-educated women, gender wage gap remains an ongoing issue in developing countries. The increase in women’s education would provide them with more job opportunities resulting in higher employment rate amongst women and, thus, lower gender pay gap. In Palestine, the share of women with high education is 62% while their labor force participation rate is only 18%. This paper examines the effects of gender higher education on wage inequality in the Palestinian context. The study applied the Mincer equation to study the determinants of wage, while the decomposed Gini coefficient is used to measure the contribution of education and other factors to overall wage inequality. The study used data from the labor force survey (LFS) which is conducted by the Palestinian Central Bureau of Statistics (PCBS) covering the period from 2010 to 2020. Results show that those with higher education have relatively higher wages as compared to those with only high education or with school education. Results also show that gender wage inequality has increased during the study period (2010–2020), but the contribution of both gender and education differences to the overall wage inequality has decreased. In general, the gender pay gap remains a crucial issue in the Palestinian context with a persisting decreasing pay gap over time across all education levels. Policymakers shall orient efforts towards investing in women’s education, thus increasing their empowerment in the labor market, which in turn would improve the level of development and economic growth in the country.

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Introduction.

The human capital theory suggests that education affects individual earnings, countries’ economic growth, and level of development. It highlights the undeniable importance of education as an investment in personal and societal economic growth and development (Gregorio and Lee, 2002 ; Kao et al., 1994 ). Empirical evidence shows that education is a key driver of social mobility as it reduces gender inequality, especially the inequality of opportunities (Adnan, 2015 ; Asadullah and Yalonetzky, 2012 ; Jacobs, 1996 ). The expansion of education has been often viewed as a critical policy instrument for combating rising income inequality over the medium term, not only by promoting economic growth but also by breaking the intergenerational transmission of poverty and reducing inequality of opportunity (Coady and Dizioli, 2018 ).

Different studies (Autor et al., 2005 ; Menezes-Filho et al., 2006 ) emphasized that higher education enhances productivity and allows individuals to earn higher incomes, thus reducing the gap between high and low earners (compression effect), as identified by Knight and Sabot ( 1983 ). On the other hand, higher education may worsen income inequality by creating a “skill-biased” technological change, where skilled workers expect a higher wage premium in the labor market, therefore, a greater concentration of income among the higher-skilled and educated individuals (composition effect) (Menezes-Filho et al., 2006 ). The interplay between these effects is complex. Abdullah et al. ( 2015 ) argued that while an initial increase in the number of educated workers might increase inequality (the composition effect), after reaching a certain threshold, the increased supply of skilled workers decreases the wage premium for higher-skill workers, thereby lowering income inequality (the compression effect). Their meta-regression analysis of 64 empirical studies reveals that education affects both tails of the income distribution: it reduces the income share of top earners and increases the share of bottom earners.

The impact of gender and higher education on wage variations has received a great deal of attention in the empirical literature. Women’s higher education leads to lower levels of income inequality or a decrease in the gender wage gap (Seneviratne, 2020 ). The increase in women’s education and the changes in the occupational structure would provide more significant opportunities for skilled women to enter professional jobs and higher-end female-dominated occupations (Harkness, 2010 ). This suggests that addressing wage inequality requires improving women’s education, addressing systemic biases, and promoting equal opportunities for career advancement (Bradley, 2000 ; Farkas et al., 1997 ). Fan and Sturman ( 2019 ) showed that there is a gender wage gap among newly graduated students with the same level of education although the share of women with higher education is greater than that of men in the study sample. Other studies focus on the gender wage gap for particular sectors. For example, Sridadia and Prihantonob ( 2020 ) applied the Mincer equation to measure the gender wage gap in Indonesia. Their results revealed that the gender wage gap is higher in the industrial sector as compared to the service sector. Interestingly, other studies found that the field of study has a significant impact on wage inequality, with those involved in numeracy (scientific) fields of education having higher wages compared to those involved in literacy fields of education (Bol and Heisig, 2021 ). This is explained by the fact that those with a scientific background have more skills compared to those with a literacy background, thus they have higher wages. This result is also confirmed elsewhere (e.g., Altonji et al., 2012 ; Kirkeboen et al., 2016 ).

It is worth noting that the impact of education on wage inequality depends on various factors, including government policies (Abdullah et al., 2015 ). While education subsidies can increase opportunities for poor children, public spending on education, particularly higher education, often disproportionately benefits middle- and upper-class families. This underscores the need for targeted policies to ensure that educational expansion benefits those most in need. This perspective aligns with the findings of Gregorio and Lee ( 2002 ), who reviewed empirical literature across countries using a panel data set from 1960 to 1990 at five-year intervals. Their results emphasized the strong impact of educational factors on income inequality, with higher educational attainments playing a more significant role in income distribution. Those findings illustrate that education plays an indispensable role in shaping wage distribution. Other factors affecting income inequality have also been considered in the empirical literature, such as socioeconomic and sociodemographic factors (e.g., Pattayat et al., 2023 ; Hovhannisyan et al., 2022 ; Wu et al., 2021 ). Moreover, wage distribution has a close association with many other factors, including political upheaval, policy decisions, economic stability, and technological progress (Coady and Dizioli, 2018 ). It is commonly argued that education inequality is positively correlated with income inequality; however, the impact of education might be positive or negative on income inequality depending on time and situation (Abdullah et al., 2011 ). Therefore, policies that reduce wage inequality must consider the multiple channels through which education affects income distribution and address the underlying structural and institutional factors contributing to inequality (d’Hombres et al., 2012 ; Ramadan et al., 2015 ).

Today, women worldwide experience significant barriers to labor force participation and struggle to access better employment opportunities. Unfortunately, the situation is not different in the occupied Palestinian territory. Women encounter numerous challenges that prevent them from reaching certain job positions in both the public and private sectors. Moreover, a research by Albotmeh and Irsheid ( 2013 ) suggested that women face disparities in working conditions, such as inadequate health and safety standards and job security, which in turn force them to work in the informal sector. These disparities also result in wage discrimination between men and women, even with the same qualifications and level of education. According to the International Labour Organization (ILO), the worldwide annual women’s labor participation rate was 45% in 2020, whereas in Palestine, it was about 18%. The employment rate for women was 4% in Palestine, which is lower than the average global level of 6%. Women’s potential labor force rate was 5% globally and 21% in Palestine (ILO, 2016 ). Al Habeel et al. ( 2011 ) indicated that the Palestinian situation is unique because of the Israeli occupation, which significantly influenced gender roles and identity formation.

The Palestinian labor market confronts structural imbalances as it depends on job opportunities in Israel, which are generally restricted to men. This, in turn, may create disparities in local market opportunities for men and women (Khattab, 2002 ). Cultural differences regarding women’s skills also create obstacles to working in the industrial and service sectors (Hilal et al., 2008 ). Male workers dominate both the private and government sectors. The market generally favors hiring men over women, considering their reproductive roles in the household. There are more female graduates compared to male graduates, but there are still fewer employment opportunities for women. Women often face denial of employment due to cultural differences rather than qualifications. Fear of harassment also deters women from seeking jobs (Harkness, 2010 ). Recently, Daoud and Shanti ( 2016 ) emphasized that the government sector in the West Bank has experienced a slight improvement in women’s participation rates, but the participation rate in the private sector remains low. Furthermore, gender discrimination based on political opinions has also emerged in the public sector (Alkafri, 2011 ).

To the best of the researchers’ knowledge, there is extensive literature on wage inequality and education. However, no previous attempt has been made to assess the impact of women’s higher education on earnings and income inequality in Palestine. Moreover, previous studies relied only on the Mincer equation, the Gini index, or other methods. Therefore, this study aimed to investigate the impact of women’s higher education on wage inequality in Palestine using a mixed methodology of the Mincer earnings function and the Gini index. Namely, this study tries to answer the following questions: What is the impact of women’s higher education on wages? And What is the contribution of education to wage inequality? Based on the empirical evidence, women’s attainment of higher education is negatively associated with the gender wage gap. Accordingly, the study tested the following main hypothesis: (i) Women’s higher education in Palestine would reduce gender wage inequality, and that (ii) the contribution of higher education to total wage inequality is decreasing over time.

In this paper, the Mincer earnings function measured the rate of returns on education, while the Gini index measured the contribution of inequality in education (part of the inequality of opportunities) to the overall wage inequality. Total wage inequality has been decomposed using the Gini index to analyze the impact of higher education on reducing wage inequality over the years. It is noteworthy that this study utilized data from the Palestinian labor force survey for the period 2010–2020. The Mincer earnings function is a single-equation model that explains wage income as a function of schooling and labor market experience (Mincer, 1974 ). The equation has been examined using different datasets from various countries (Martins and Pereira, 2004 ; Dakić and Savić, 2017 ). Typically, the logarithm of earnings is modeled with a list of explanatory variables, including educational attainment and experience (Waseema, 2022 ; Gregorio and Lee, 2002 ; Martins and Pereira, 2004 ). The Mincer equation has undoubtedly contributed to the advancement of labor and educational economics. It helps understanding the factors affecting wages, the rate of return on education, enrollment in education, the effects of wage discrimination, and the value of on-the-job training and labor market experience (Martins and Pereira, 2004 ). The Gini index has been widely used to measure income inequality (Trapeznikova, 2019 ). Furthermore, many studies have used the decomposed Gini index to measure the different sources of inequality contributing to overall income inequality (Ramadan et al., 2015 ). Wagstaff et al. ( 2003 ) provide a decomposition of the concentration index (the bivariate version of the Gini index) based on regression analysis applied to health data in Vietnam. This approach computes the share of inequality of each cofactor to the overall inequality of the dependent variable. The method has been widely applied in empirical research to measure inequalities in health (e.g., Doroh et al., 2015 ; O’Donnell, 2012 ) as well as income inequalities (e.g., Devkota et al., 2017 ; Zhong, 2011 ).

The remainder of this article is structured as follows. Section two discusses the methods including the data used and the analytical approach. Section three provids descriptive statistics of the data used, the analysis of wage determinants, and the decomposition of wage inequality. Section four displays the main results, while Section five concludes and provides some limitations and policy implications.

We used the Palestinian Labor Force Surveys (LFS), conducted by the Palestinian Central Bureau of Statistics (PCBS) on an annual basis from 2010 to 2020. The LFS covers the two main Palestinian regions (the West Bank and the Gaza Strip), and it includes individuals 10 years of age or older who are out of the labor force, unemployed, or employed in any economic sector. The datasets are harmonized for all years, that is the bulk of the questions as well as the structure of each question are similar in all LFS series. The LFS also provides data on demographic characteristics, such as gender, age, level of education, marital status, locality (place of residence: rural, urban and refugee camps), and region (West Bank and Gaza Strip). The survey also includes a question about the employment type which is composed of four categories: employer, self-employed, wage employee, or unpaid family member. For the current study, we choose a sub-sample focusing on wage employees only which forms about 65% to 70% of total employed individuals. We first estimate the wage equation based on (Mincer, 1974 ) as follows

where \(w\) is the average daily wage obtained by each individual; \(d\) is the set of binary education variables (schooling, high education, and higher education), the level of experience is proxied by the age of the individual, and \(y\) is the set of other explanatory variables. The level of education is the main cofactor in the model which is a represented in three binary variables: (i) School: if the highest level of education obtained by each individual is secondary school; (ii) high education: if the highest level of education is diploma or bachelor, and (iii) higher education: if the highest level of education is masters or PhD. Other variables include gender; locality type (whether living in urban, rural or refugee camps), and marital status (whether married or not). Equation 1 is estimated using the ordinary least square method. It is worth noting that the Mincer equation is one of the most widely used approaches in the empirical evidence that captures the impact of schooling and experience as mentioned at the outset. Thus, it is suitable for the estimation of the effect of women’s higher education on wage differences in Palestine.

Then, total inequality in wages is measured using the Gini index. Furthermore, the contribution of each factor to overall wage inequality is measured using the decomposed Gini index built based on regression analysis (Eq. 1 ). Inequality in wages due to variation in education is referred to as legitimate inequality. Wage inequality due to all other variables—classified as variables beyond individuals’ control—is referred to as illegitimate inequality. The decomposed Gini index of wages can be written as follows (Wagstaff et al., 2003 ):

where \(G\left(w\right)\) is the Gini index of wages measuring the overall inequality in the variable; \(G\left({d}_{k}\right)\) is inequality in the education variables/categories ( \({d}_{k},{k}=\mathrm{1,2}\) and \(3\) ); \(G\left({{age}}_{m}\right)\) is the inequality in experience (age); \(G\left({y}_{j}\right)\) is inequality in other contributing factors \({y}_{j}\) ( \(k=1,\ldots ,J\) ); \({\mu }_{\varepsilon }\) is the mean value of error; \({\mu }_{w}\) is the mean value of wages, and \(G\left(\varepsilon \right)\) is inequality in the error term (unexplained inequality). The contribution of education to overall inequality in wages is calculated as \(\mathop{\sum }\nolimits_{k=1}^{3}{\alpha }_{k}G\left({d}_{k}\right)\) where \({\alpha }_{k}={\beta }_{k}{\mu }_{k}/{\mu }_{w}\) ( \({\mu }_{k}\) is the mean value of \({d}_{k}\) ). Similarly, the contribution of each other explanatory variable \({y}_{j}\) to overall wage inequality is \({\alpha }_{j}G\left({y}_{j}\right)\) where \({\alpha }_{j}={\gamma }_{j}{\mu }_{j}/{\mu }_{j}\) ( \({\mu }_{j}\) is the mean value of \({y}_{j}\) ). The Gini index is the commonly-used measure of income inequality which is easily measurable and decomposable based on regression analysis. The decomposition of the Gini index shows which cofactor is more important than others in terms of its contribution to overall wage inequalities.

This section summarizes main data regarding wages across gender and education groups. Then it provides the main results of the regression analysis and the decomposed Gini index.

Descriptive analysis

Table 1 provides data on the average daily wage—measured in New Israeli Shekel (ILS) – based on gender and level of education. The table also shows the gender wage gap—a widely recognized occurrence that describes the disparity in average earnings between men and women in the workforce. Typically, it is measured as the percentage difference between the average hourly or daily wage of men and women, relative to men’s earnings. Two main observations are worth highlighting. First, the average daily wage for men is higher than that of women for all levels of education across all the years. Secondly, the average daily wage is increasing with the level of education for both men and women. For example, in 2010 the average daily wage was 48.79 ILS for women and 60.72 ILS for men with school education resulting in a wage gap of 19.64%. The gender wage gap has increased to 42.41% for those having only a school education. Regarding men and women with high education, the wage gap has decreased from 21.96% in 2010 to 16.27% in 2020 which is still relatively high. As for the higher education group, generally, the wage gap slightly decreased during the period 2011–2019 from 20.21% to 19.88%. It’s important to note that the wage gap in higher education is exceptional because, in 2010 and 2020, men’s wage was 5.97% and 4.67% respectively lower than women’s wages.

Table 2 explains the percentage of male and female employment status. It shows that the percentage of employed males decreased by 3.52% points in 2020 as compared to 2010. However, the unemployment rate remained at 12% with little variation throughout the years. Also, the share of men who are out of the labour force is stable (46.37% in 2010 as compared to 46.58% in 2020). However, the share of women who are out of the labour force has slightly decreased from 87.4% in 2010 to 86.4% in 2020. On the other hand, the unemployment rate among women has increased from 3.13% in 2010 to 5.29% in 2020.

Results presented in Table 3 shows that the male unemployment rate and employment rate negligibly changed from 2010. However, the unemployment rate among females has increased over time reaching almost 39% in 2020 as compared to 24.85% in 2010.

Wage determinants

The results of this section and subsequent section are provided for 2010, 2015, and 2020. Detailed results are available upon request. Table 4 presents the determinants of wages in Palestine for the years 2010, 2015, and 2020. The results indicate that males generally have higher wages than females, with a decreasing wage gap between 2015 and 2020. In 2020, males earned 23.3% higher than females in terms of average wage, compared to 29.8% in 2010 holding other factors constant. Age was found to have an inverted U shaped effect on wages. However, this effect has decreased in 2020 as compared to 2015. Additionally, being married was found to have a positive effect on wages with married individuals having higher wages of 8.3% in 2010 as compared to non-married individuals. This wage difference has decreased to 5.5% and 3.5% in 2015 and 2020, respectively. Finally, results showed that individuals living in rural areas have higher wages than those living in urban areas. Those living in rural and urban areas have higher wages than those living in refugee camps (50.8% and 14.2% respectively).

Regarding the education variable, individuals with higher education earn significantly more than those with lower levels of education. Specifically, individual with higher education earn wages that is 112.5% Footnote 1 higher than those with high education and 176.7% with only school education in 2010 as shown in Table 4 . In 2020, these two differences have been decreased to 92.1% and 116.3%, respectively. Regarding the difference in wage between individuals with high education and school education, results in Table 4 show that the difference has decreased from 64.2% in 2010 to 24.2% in 2020. Although the wage gap still exists, these results indicate that the wage gap has been decreasing between all education groups.

Decomposition of inequality

According to Table 5 , the overall level of education wage inequality has increased from 32.2% in 2010 to 39.3% in 2020. Regarding the decomposition of the Gini index of wages in Palestine in 2020, the contribution of education as the main factor in the analysis is 29% of which 4.6% are due to differences in higher education. The overall contribution of education to wage inequality has decreased from 37.3% in 2010 to 29.0% in 2020. Moreover, the contribution of the variance in higher education has decreased from 9.4% to 4.6% for the same period. As for school education, the share of its inequalities to overall wage inequality was 18.9% in 2010 and decreased to 13% in 2020. Whereas the inequality of high education is responsible for 11.4% of total inequality in 2020. This share has decreased over years from 18.9% in 2010 to 17.2% in 2015. This means that the legitimate inequality due to differences in education is relatively high but decreasing over time. Gender inequalities explain 7.8% of total inequality of wages in 2020, compared to 12% in 2010, and 2015. The locality factors contribute to 30.0% to total wage inequality in 2020 which has tripled as compared to 2010 (11.7%).

This study investigates the role of women’s higher education in reducing wage inequality in the Palestinian territory. We used the LFS conducted between 2010 and 2020 to estimate wage determinants as well as wage inequality, where gender and level of education were the main cofactors. Further, total wage inequality has been decomposed using the standard Gini index to explore the contribution of higher education to total inequality. Many results of this research are worth highlighting. First, results show that males still have higher wages as compared to females but the wage gap has decreased over the period under consideration (2010–2020). Second, education remains a significant factor in determining wages across time, with higher levels of education leading to higher wages. However, the magnitude of the effect decreases over time, particularly for those with higher education (master’s or PhD degrees). These two results can be explained by the fact that the percentage of educated women has increased over time. For example, the percentage of women with high and higher education has increased from 13.55% in 2010 to 23.25% in 2020. This could also be explained by the extensive work on bridging the gender gap policies or societal attitudes towards gender equality over time. It is evident from the Palestinian government work especially the Ministry of Women’s Affairs in collaboration with UNESCO who worked together to improve 97 policies on gender equality and women’s empowerment during the period of 2011–2017 (Hirsh et al., 2020 ). Further, job opportunities in Palestine have been relatively enhanced for skilled women, i.e., women with higher levels of education. This in line with the results of Hillis et al. ( 2018 ) who found a statistically significant impact of master’s and PhD degrees on the gender wage gap in Palestine by using the ordinary least square method. They showed that Skilled women are often found in specific occupations and sectors more than skilled men. In 2013, only 2.9% of full-time workers in medium-sized private enterprises were women. In 2015, 48% of skilled women worked as teaching professionals, compared to 15.2% of skilled men. The education sector employed 55% of skilled women in 2015. Although the gap has been decreased over time, the gender wage gap still an issue in the Palestinian context. This might be because women are overrepresented in lower-paying fields and underrepresented in higher-paying sectors. This concentration of women in certain occupations limits their access to higher-paying jobs and reduces their potential for upward mobility (Morrar, 2022 ; Loewenthal and Miaari, 2020 ; Calì and Miaari, 2018 ; ILO, 2016 ).

Third, another important result is related to the impact of locality type on wages. Results show that individuals in rural areas have higher wages as compared to individuals living in urban areas and refugee camps. This might not be a surprising result in the Palestinian context because many Palestinian “men” living in rural areas choose to work either in urban areas or in Israel where wages are significantly higher. Fourth, the overall wage inequality has increased over time from 32.2% in 2010 to 39.3% in 2020. Interestingly, the part of inequality due to gender differences or education differences has been decreasing over time. This implies that despite their importance in interpreting wage inequality, education and gender differences are not the main determinants of inequality. Other important factors that contribute to wage inequality with increasing share over time include the locality type which has been previously demonstrated. Moreover, some sensitivity analysis (available upon reasonable request) shows that the shares of gender and education to overall inequality are similar to the results obtained in Table 5 . This validates our conclusions regarding the decreasing effect of both variables on wage inequality and confirms our hypothesis regarding the impact of women’s higher education on wage inequalities. Another important result regarding the contribution of education to overall wage inequality is related to the contribution of each level of schooling to total inequality. As shown in the previous section, in general, the contribution of higher education to total wage inequality is lower than the contribution of other levels of education for all years. This might reflect the higher wage gap among individuals with low levels of education.

The Mincer earnings function was used to measure the rate of returns on education, while the Gini index measured the contribution of inequality in education (part of the inequality of opportunities) to the overall wage inequality. Results showed that there is a persisting but decreasing gender pay gap over time, however male wages have consistently been higher than those of female at all levels of education. Moreover, the contribution of higher education to overall inequality is smaller than the contribution of other levels of education (high and school education) for all years. Therefore, addressing this inequality requires a concerted effort from policymakers, employers, and society as a whole to create a more equitable workplace and to invest in higher education, particularly for marginalized women. This also requires the promotion and enforcement of gender equality in the workplace, and the enhancement of social protection system in the labour market. Promotion of gender diversity in leadership can also create a more inclusive work environment. At the educational level, it is important to adopt and advocate policies and action plans (develop and support educational programs, mentorship and career development, etc.) which can bridge the skills gap and reducing gender disparities in sectors that traditionally dominated by men and to help women to advance in their careers and achieve higher-paying positions.

Empirical evidence demonstrated that developing countries investing in women’s education, thus increasing their empowerment in the labor market, are likely to witness an increase in economic growth over time. This has important implications for countries where income inequality and human capital levels are often low. Policymakers should focus on the provision of quality education for women to help them secure high-paying jobs like men, resulting in a reduction in wage inequality. This evidence supports the efforts of the Palestinian Authority regarding the national employment strategy (NES). The main goal of the NES is to enhance the strategic development of the Palestinian labor market through different mechanisms, including promoting both women’s participation in the labor market and the education system in the country. This may require augmenting the government budget share allocated to the education sector. In addition, the government can provide funds or loans for female students to pursue higher education degrees either in the country or abroad. Creating more job opportunities in partnership with the private sector is another approach to enhance income equity.

Some limitations of the current study are worth stating. Generally, factors affecting wages are not limited to those presented in Table 4 . Other factors may include religion, traditions, ethnic background, age at marriage, occupational segregation, access to professions, ability, institutional discrimination, etc. The authors conducted some sensitivity analyses by adding and removing some variables, such as region and occupation. It is concluded that the results are robust. For example, the contribution of higher education to overall inequality in 2020 is about 4.6% under different specifications of Eq. 1 . However, information regarding other variables, such as age at marriage and access to professions, is absent from the LFS used in this study. Another limitation is related to the sample size of women with higher education. This might be a source of bias. As shown in Table 1 , the wage gap was negative in two periods. This is a misleading result since wages are higher for men. Overall, Future research may consider such types of limitations by developing new detailed qualitative indicators that could help provide an in-depth analysis of the missing variables that account for societal and other economic factors that may affect wage inequality in Palestine. In addition, further studies regarding women’s higher education should account for issues related to women’s opportunities, such as access to higher education, college challenges and experiences, family responsibilities, and job matching issues.

Data availability

The authors used data from the Palestinian Central Bureau of Statistics (PCBS) under license for this study and under the condition that they were not allowed to publish the raw data. However, the Palestinian Central Bureau of Statistics website, https://www.pcbs.gov.ps/pcbs_2012/PressEn.aspx , displays all the published reports for that data used, especially the labor market reports, including all labor force key indicators.

This percent has been calculated as the exponential of the difference between the coefficients of higher education and high education. Similar calculations have been done for the other education groups.

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This work has been completed thanks to the funding provided by the research committee at Birzeit University, the grant number is 60/2021.

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Theoretical Framework: The two authors contributed equally to this section. Literature Review: The first author contributed 70%, and the second author contributed 30%. Data Analysis: The first author contributed 70%, and the second author contributed 30%. Writing: The two authors contributed equally to this section.

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Morar, N., Awawda, S. Does women’s higher education reduce wage inequality? Evidence from Palestine using repeated cross-sectional data. Humanit Soc Sci Commun 11 , 1133 (2024). https://doi.org/10.1057/s41599-024-03620-2

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The Gender Wage Gap Across Life: Effects of Genetic Predisposition Towards Higher Educational Attainment

IZA Discussion Paper No. 17255

42 Pages Posted: 4 Sep 2024

Alex Bryson

UCL ; National Institute of Economic and Social Research (NIESR)

University College London

David wilkinson.

Using two polygenic scores (PGS) for educational attainment in a biomedical study of all those born in a single week in Great Britain in 1958 we show that the genetic predisposition for educational attainment is associated with labour market participation and wages over the life- course for men and women. Those with a higher PGS spend more time in employment and full-time employment and, when in employment, earn higher hourly wages. The employment associations are more pronounced for women than for men. Conditional on employment, the PGS wage associations are sizeable, persistent and similar for men and women between ages 33 and 55. A one standard deviation increase in the PGS is associated with a 6-10 log point increase in hourly earnings. However, whereas a 1 standard deviation increase in the PGS at age 23 raises women's earnings by around 5 log points, it is not statistically significant among men. These associations are robust to non-random selection into employment and to controls for parental education. Our results suggest that genetic endowments of a cohort born a half century ago continued to play a significant role in their fortunes in the labor market of the 21st Century.

Keywords: gender wage gap, employment, educational attainment, polygenic score, National Child Development Study

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