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Department of Economics

College of social & behavioral science, main navigation, research in gender.

Gender inequalities have been persistent and pervasive in all economies. Until recently, the importance of gender inequalities was not well-appreciated in economic theory or policies. Today, gender-equitable approaches to economic policy-making are becoming increasingly common. In 2015, with the adoption of Sustainable Development Goals, the United Nations declared achieving gender equality as a central goal in economic development outcomes to be achieved by 2030.

Commitment to Gender Equality

We are one of a handful of Ph.D.-granting economics departments in the world with a core strength in feminist economics. We use gender as a central category of analysis alongside, class and race.

TOPICS OF RESEARCH

Our feminist economics research focuses on theory, empirical work and economic policy, addressing the causes and consequences of gender inequalities in economic life and development of economic policies that aim to eradicate gender inequalities. Topics include

  • Gender and macroeconomics
  • Gender and international trade
  • Gender differences in income and time poverty
  • The interface of paid and unpaid work
  • Gender differences in labor market behaviors and outcomes
  • Causes and consequences of violence against women
  • Care policies
  • Role of institutions in promoting gender equality

COURSE WORK THROUGH A GENDER LENS

Since 1995 we have offered undergraduate and graduate students the opportunity to study economics through a gender lens.

Undergraduate and Master's Level

  • ECON 5170/6170
  • ECON 5560/6560/GNDR 5560 
  • ECON 2040/GNDR 2040

Ph.D. Field Courses

  • ECON 7150
  • ECON 7180

Faculty Focusing on Gender Equality

We would love to hear from you! Our faculty would be happy to share more about their current projects.

Diksha Arora

Diksha Arora

Diksha Arora's current research focuses on the relationship between climate change, macro policy and gender disparities in access to decent work in Latin America. She has also conducted research examining gendered time poverty and its impact on household income and resilience to climate change, and gender-based constraints to adoption of climate-smart practices. 

Besides academic research, she has worked with local- and national-level policymakers in several Latin American countries to promote uptake of gender considerations in their local development programs and climate-change adaptation policies. She has also worked with several international organizations such as the World Bank, FAO and CGIAR. Most recently, her work with World Bank and Canada Caribbean Resilience Fund helped promote gender mainstreaming in disaster risk management policies in Caribbean countries.

gunseli berik

Günseli Berik

Günseli Berik ’s research examines gender inequalities in livelihood and well-being outcomes—earnings, working conditions, training, population sex ratios, time use in the household. Her empirical research has focused on Turkey, Taiwan, Korea, Bangladesh, the US, and Utah. In recent research she examines aggregate well-being measures that incorporate unpaid work and the environment; street harassment in South Asia; and the feminist project in economics. She served as editor of the journal  Feminist Economics  between 2010 and 2017. 

haimanti bhattacharya

Haimanti Bhattacharya

Haimanti Bhattacharya's research falls under the broad rubric of applied microeconomics and has three specific themes: environment, resource & food, psychology & economics, and gender. Her gender research examines different aspects of violence against women, including the relationship between engaging in paid work and spousal violence. She has also engaged with interdisciplinary research teams to examine perceptions and implications of sexual violence against women. The main geographic focus of her research is India.

pavitra govidan

Pavitra Govindan

Pavitra Govindan is a behavioral and experimental economist specializing in the topics of social norms and behavioral change, gender differences in self-promotion, and role of institutions in promoting gender diversity.  She has conducted lab experiments with university students, lab-in-the-field experiments in rural India, and online experiments on Qualtrics and Amazon Mechanical Turk. She has been a faculty member in the Economics department at the University of Utah since 2018.

eunice han

Eunice Han is a labor economist, specializing in labor relations and educational policy. Her research focuses on workers’ well-being and inequality. Because the goals of labor unions are aligned with these topics, many of her studies examine the relationship between unions and labor market outcomes in both the private and public sectors. In particular, she is interested in understanding gender differences in employment, labor earnings, and other labor market conditions, as well as identifying tools to close the gender gap.

codrina rada

Codrina Rada

Codrina Rada is a macroeconomist with an interest in issues of growth and income distribution. She uses theoretical and empirical tools to study trends in income inequality in modern economies and the effect of rising inequality on economic activity within the context of global economic integration. Her gender research uses computable general equilibrium (CGE) framework to examine the impacts of gender inequality on food security in Mozambique and Ethiopia.

catherine ruetschlin

Catherine Ruetschlin

Catherine Ruetschlin studies labor market inequalities and public policy. Her current research is focused on markets for childcare and the labor market for childcare workers. In 2021 and 2022, she worked with Utah’s Department of Workforce Services Office of Child Care to evaluate access to and affordability of childcare services across the state. She also contributed to a forthcoming interdisciplinary study with the US Department of Veterans Affairs examining the labor market challenges facing female veterans. Catherine has taught at the University of Utah since 2018.  

codrina rada

Sarah Small

Sarah Small’s research falls under the broad umbrella of feminist economics. Her current research focuses on a variety of topics including intrahousehold bargaining, care work, the occupational crowding hypothesis, and history of feminist economic thought. Much of her work aims to understand how households allocate unpaid labor in the United States, especially within couples facing differences in income, union membership, and business ownership. She also studies how economics courses can be made more inviting to women. Before joining the University of Utah in 2022, she was a Feminist Economics Fellow and a postdoctoral researcher at the Center for Women and Work at Rutgers University. 

Current PhD Student Research

Latest department publications.

Utah 2021 Child Care Market Rate Study

Author: Catherine Ruetschlin and Yazgi Genc

Published: Utah Department of Workforce Services Office of Child Care

The Routledge Handbook of Feminist Economics

Author: Günseli Berik and Ebru Kongar (PhD, 2003, U of Utah)

Published: Routledge International Handbooks , 2021

Gender norms and intra-household allocation of labor in Mozambique: a CGE application to household  and agricultural economics

Authors:  Diksha Arora  and Codrina Rada

Published: Agricultural Economics, 2020

The Effects of Teachers' Unions on the Gender Pay Gap among US Public School Teachers

Authors: Eunice Han

Published: Industrial Relations A Journal of Economy and Society,2020

What is Eve Teasing? A Mixed Methods Study of Sexual Harassment of Young Women in the Rural Indian Context

Authors:  Haimanti Bhattacharya with Sharon Talboys, Manmeet Kaur, Jim VanDerslice, Lisa Gren, and Steve Alder

Published:  Sage Open , 2017

A Gendered Model of the Peasant Household: Time Poverty and Farm Production in Rural Mozambique

Authors:  Diksha Arora  (Ph.D. 2016, U of Utah) and Codrina Rada

Published:  Feminist Economics , 2017

Rape Myth Acceptance among College Students in the United States, Japan and India

Author: Haimanti Bhattacharya et. al.

Published: Sage Open, 2016

Spousal Violence and Women's Employment in India 

Author: Haimanti Bhattacharya

Published: Feminist Economics , 2015

Utah's Labor Market for Child Care Professionals

Author: Catherine Ruetschlin

Forthcoming: Utah Department of Workforce Services Office of Child Care

The Cost of Quality Childcare in Utah

The gender gap in labor market self-promotion: discrimination, beliefs, and norms: An experiment 

Author: Pavitra Govindan

Forthcoming: Working Paper

Do Meritocracies Increase Females Selecting Into Male-dominated Environments?

Selected Recent Publications from Alumni

Adem elveren.

Ph.D. 2008, Associate Professor at Fitchburg State University

Militarization and Gender Inequality: Exploring the Impact  

Co-author: Valentine M. Moghadam

Journal of Women, Politics & Policy, 2022

Ph.D. 2010, Assistant Professor, Fitchburg State University

Expanding Understanding of Poverty: Time Poverty Revealed Time-Use Data

Harnessing Time-Use Data for Evidence-based Policy, the 2030 Agenda for Sustainable Development and the Beijing Platform for Action: A Resource for Data Analysis , United Nations ESCAP, 2021

Nursel Aydiner-Avsar

Ph.D. 2011, Associate professor  at  Akdeniz University

The Gender Impact of Unemployment on Mental Health: A Micro Analysis for the United States

Forum for Social Economics, 2021

  Chiara Piovani  

Ph.D. 2011, Associate Professor, University of Denver

Gender and Development Programme

Work Time Matters for Mental Health: A Gender Analysis of Paid and Unpaid Labor

Review of Radical Polical Economics, 2021

Ph.D. 2016, PostDoctoral Fellow, Colorado State University

Gender norms and intrahousehold allocation of labor in Mozambique: A CGE application to household and agricultural economics

Agricultural Economics, 2020

Jacqueline Strenio

Ph.D. 2018, Assistant Professor of Economics at Norwich University

Time Heals All Wounds? A Capabilities Approach for Intimate Partner Violence

Feminist Economics, 2020

Emel Memis  

Ph.D. 2007, Associate Professor, Ankara University

“Changes in Global Trade Patterns and Women’s Employment in Manufacturing, 1995-2011”

Feminist Economics, 2018

Ph.D. 2011, Assistant Professor, Istanbul University

Engendering Welfare States: How Fa(i)r are Scandinavian Welfare States

Journal of Economic and Social Thought, 2017

Ph.D. 2011, Associate Professor at University of Wisconsin, Whitewater

“Gender Empowerment and Educational Attainment of US Immigrants and their Home-Country Counterparts”

Feminist Economics, 2017

Ebru Kongar

Ph.D. 2003, Associate Professor at Dickinson College

Gender and Time Use in a Global Context

Co-Author: Rachel Connelly

Palgrave, 2017

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Issue Cover

Article Contents

  • I.Introduction
  • II.Gender equality: where do we stand?
  • III.The role of gender economics in understanding gender inequality
  • IV.Effective interventions to address gender inequality
  • V.Conclusion
  • Acknowledgement

Gender economics: an assessment

  • Article contents
  • Figures & tables
  • Supplementary Data

Almudena Sevilla, Gender economics: an assessment, Oxford Review of Economic Policy , Volume 36, Issue 4, Winter 2020, Pages 725–742, https://doi.org/10.1093/oxrep/graa054

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Concerns about gender equality have jumped to the forefront of public debate in recent years, and Gender Economics is slowly affirming its place as a major field of study. This assessment examines where we are in terms of gender equality. It reviews the theoretical foundations that can explain existing inequalities, and documents the empirical findings supported by the theories, identifying avenues for future research and providing a fruitful framework to think about the effectiveness of policies and interventions targeting gender inequality. In doing so, I provide the foundations against which the contributions in this issue can be placed.

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Economic Inequality by Gender

What is determining the inequality in incomes, jobs, and wealth between men and women?

By Esteban Ortiz-Ospina and Max Roser

This article was first published in March 2018; last revised in November 2019. We'd like to thank Sandra Tzvetkova and Diana Beltekian for great research assistance.

In this topic page we present data and research on economic inequalities between men and women. Whenever the data allows it, we also discuss how these inequalities have been changing over time.

As we show, 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 – rising female labor force participation has been one of the most remarkable economic developments of the last century. In this topic page we present the key facts and drivers behind this important change.

See all interactive charts on economic inequality by gender ↓

The gender pay gap 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 absences 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 one and the same. (You can read about discrimination and 'equal pay for equal work' in our post here ).

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 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.

(NB. By this measure the gender wage gap can be positive or negative. If it is negative, it means that, on an hourly basis, men earn on average less than women. This happens in some countries, such as Malaysia.)

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 UK, for example, the gap went down from almost 50% in 1970 to about 17% in 2016.

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 for the period 2000-2015.

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 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 than 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 understand 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 , 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 scatter plot here shows available ILO estimates on the gender pay gap (vertical axis) vs GDP per capita (on a logarithmic scale along the horizontal axis). 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: “if 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.” 2

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.

Representation of women in senior managerial positions

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.

Firms with female managers

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 add specific countries by using the option 'add country'.

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 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 individual basis, rather than as couples. 3

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.

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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 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.

Control over household resources

Women often have no control over their personal earned income.

The 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) 5

gender economics research topics

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 the next 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 chart we show that this correlation also holds within countries: Women’s control is greater in wealthier households. Household's 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.

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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.

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

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 get 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. 6

Multidimensional indices of gender inequality

Women’s economic opportunity index.

The previous discussion focused on particularly aspects one by one. What is the the picture on economic inequality in the aggregate?

Tracking progress across multiple dimensions of gender inequalities can be difficult, since changes across dimensions often go in different directions and have different magnitudes. Because of this, researchers and policymakers often construct synthetic indicators that aggregate various dimensions.

The Women’s Economic Opportunity Index (WEO) published by The Economist Intelligence Unit , is one such effort to aggregate various aspects of female economic empowerment into a single metric.

The WEO index defines women’s economic opportunity as "a set of laws, regulations, practices, customs and attitudes that allow women to participate in the workforce under conditions roughly equal to those of men, whether as wage-earning employees or as owners of a business." It is calculated from 29 indicators drawing on data from many sources, including the UN and the OECD.

Here is a map showing scores on this index (higher scores denote more economic opportunities for women).

The Gender Inequality Index from the Human Development Report

The Human Development Report produced by the UN includes a composite index that captures gender inequalities across several dimensions, including economic status.

This index, called the Gender Inequality Index, measures inequalities in three dimensions: reproductive health (based on maternal mortality ratio and adolescent birth rates); empowerment (based on proportion of parliamentary seats occupied by females and proportion of adult females aged 25 years and older with at least some secondary education); and economic status (based on labour market participation rates of female and male populations aged 15 years and older).

The map shows scores, country by country.

Historical Gender Equality Index

The Gender Inequality Index from the Human Development Report only has data from 1995. Considering this, Sarah Carmichael, Selin Dilli and Auke Rijpma, from Utrecht University, produced a similar composite index of gender inequality, using available data for the period 1950-2000, in order to make aggregate comparisons over the long run.

This index covers four dimensions:

  • (i) Health, measured by sex rations in life expectancy;
  • (ii) Socio-economic resources, measured by sex ratios in average years of education and labour force participation;
  • (iii) Gender disparities in the household, captured by sex ratios in marriage ages; and
  • (iv) Gender disparities in politics, measured by sex rations in parliamentary seats.

The results from this study are shown in the chart.

As we can see, the second half of the 20th century saw global improvements, and the regions with the steepest increase in gender equality were Latin America and Western Europe.

Interestingly, this chart also shows that in Eastern Europe there was important progress in the period 1950-1980, but there was a reversal after the fall of the Soviet Union.

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, over the last couple of decades most high-income countries have seen sizeable reductions in the gender pay gap .

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 to explain 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 explain 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. 7

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. 8

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.

gender economics research topics

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

The chart here 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. 9

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 cannot be controlled for in a regression), 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 economics research topics

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.

This blog post from 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).

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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 in the data from the US. 11 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). 12

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

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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. 13

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.

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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 improve female labor force participation and reduce 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 emphasised 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. 14

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 standardised 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, behaviour 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 in our blog post dedicated to answer the question 'How well do innate gender differences explain the gender pay gap?' .

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' which 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. 15

The map here highlights that to this day, explicit barriers across the world limit the extent to which women are allowed to do the same jobs as men. 16

However, even after explicit barriers are lifted and legal protections put in their 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. 17

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. 18

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. 19

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. 20

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. 21

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. 22

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 to 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. 23

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 the evidence shows that social norms, too, can be changed.

Definitions & Measurement

Gender pay gap.

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 .

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 one and the same.

How is the unadjusted gender pay gap measured?

Percent differences in average or median earnings.

The gender wage gap is often measured as the difference between average earnings of men and average earnings of women expressed as a percentage of average earnings of men. By this measure the gender wage gap can be negative. This is the definition used by the ILO. (We explore the ILO data above .)

Comparisons of averages can often be misleading because averages are very sensitive to extreme data points. Hence, it is also common to measure gender gaps by comparing earnings for the individuals at the median — or middle — of the earnings distribution. This is the definition used by the OECD. (We explore the OECD data above .)

Ratios of average or median earnings

In addition to percent differences, it is also common to express the gender pay gap as a simple ratio between wages. This is the measure adopted by the United States Census Bureau .

Data Sources

Data hubs dedicated to gender statistics, world bank – gender statistics.

  • Data Source: Multiple sources
  • Description of available measures: Several gender indicators are included in this database. Here are some that we cover in this topic page: Laws mandating equal remuneration for females; Firms with female top managers; Participation of women in purchase decisions; Percentage of men and women (age 15-49) who solely own a land which is legally registered with their name or cannot be sold without their signature; Ownership rights by gender; Percentage of men and women (ages 15+) who report borrowing any money in the past 12 months (by themselves or together with someone else) to start, operate, or expand a farm or business
  • Geographical coverage: Global, by country
  • Link: https://datacatalog.worldbank.org/dataset/gender-statistics

United Nations – Gender Statistics

  • Description of available measures: Minimum Set of Gender Indicators, as agreed by the United Nations Statistical Commission in its 44th Session in 2013.
  • Link: https://genderstats.un.org

OECD – Development Centre’s Social Institutions and Gender Index (SIGI)

  • Description of available measures: This data hub covers cross-country measures of discrimination against women in social institutions (formal and informal laws, social norms, and practices) across 160 countries.
  • Geographical coverage: 160 countries
  • Link: www.genderindex.org

OECD – Gender data portal

  • Description of available measures: The OECD Gender Data Portal includes selected indicators shedding light on gender inequalities in education, employment, entrepreneurship, health and development, showing how far we are from achieving gender equality and where actions is most needed. The data cover OECD member countries, as well as partner economies including Brazil, China, India, Indonesia, and South Africa.
  • Link: http://www.oecd.org/gender/data/

Wikigender statistics

  • Description of available measures: This data hub links several external resources, including the OECD's Gender, Institutions and Development Database, as well as the OECD's Gender data portal
  • Geographical coverage: Global by country
  • Link: www.wikigender.org/statistics /

World Economic Forum - Global Gender Gap Report

  • Description of available measures: The World Economic Forum's data explorer compiles country rankings and profiles according to their Global Gender Gap Index scores. The index is made up of four sub-components including economic participation, education, health, and political empowerment as well as providing a selection of contextual variables - broken down by gender and their combined total - relating to each of the four sub-categories. The explorer enables users to directly compare two countries across all the indicators available.
  • Link: http://reports.weforum.org/global-gender-gap-report-2020/dataexplorer/

Other sources referenced in this article

International labor organization (ilo).

  • Data Source: ILO
  • Description of available measures: Unadjusted gender gap in average hourly wages, Female share of low pay earners
  • Time span: 1990-2016
  • Link: http://www.ilo.org/ilostat/

World Bank – World Development Indicators

  • Description of available measures: Several gender indicators are included in this database. Here are some that we cover in this topic page: Laws mandating equal remuneration for females, Firms with female top managers, Participation of women in purchase decisions.
  • Link: http://data.worldbank.org/data-catalog/world-development-indicators

UN Human Development Report

  • Description of available measures: Gender Development Index, Gender Inequality Index,
  • Link: http://hdr.undp.org/en/data#

Interactive charts on economic inequality by gender

There are some exceptions to this definition. In particular, sometimes self-employed workers, or part-time workers are excluded. You can explore these exceptions using the documentation files containing all the relevant indicator notes.

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.

Atkinson, A.B., Casarico, A. & Voitchovsky, S. J Econ Inequal (2018) 16: 225. https://doi.org/10.1007/s10888-018-9384-z .

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. Available online at: http://siteresources.worldbank.org/INTWDR2012/Resources/7778105-1299699968583/7786210-1315936222006/Complete-Report.pdf

For more discussion of the evidence see page 20 in World Bank (2011) World development report 2012: gender equality and development. World Bank Publications. Available online at: http://siteresources.worldbank.org/INTWDR2012/Resources/7778105-1299699968583/7786210-1315936222006/Complete-Report.pdf

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

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. Available online from https://siteresources.worldbank.org

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. Available online here .

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.

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

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. Available here .

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.

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

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. Available online here .

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Gender Representation in Economics Across Topics and Time: Evidence from the NBER Summer Institute

We document the representation of female economists on the conference programs at the NBER Summer Institute from 2001-2016. Over the period from 2013-2016, women made up 20.6 percent of all authors on scheduled papers. However, there was large dispersion across programs, with the share of female authors ranging from 7.3 percent to 47.7 percent. While the average share of women rose slightly from 18.5% since 2001-2004, a persistent gap between finance, macroeconomics and microeconomics subfields remains, with women consisting of 14.4 percent of authors in finance, 16.3 percent of authors in macroeconomics, and 25.9 percent of authors in microeconomics. We examine three channels potentially affecting female representation. First, using anonymized data on submissions, we show that the rate of paper acceptance for women is statistically indistinguishable to that of men. Second, we find that the share of female authors is comparable to the share of women amongst all tenure-track professors, but is ten percentage points lower than the share of women among assistant professors. Finally, within conference program, we find that when a woman organizes the program, the share of female authors and discussants is higher.

The views expressed are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of New York or the Federal Reserve System. We thank Jim Poterba and NBER staff for providing data on past Summer Institute programs, and special thanks to Alex Aminoff for merging gender identifiers to the NBER submissions data and preparing summary tabulations relating to the 2016 and 2017 meetings. We thank seminar participants in the applied microeconomics workshop at UNC-Chapel Hill and the New York Fed for many helpful comments and suggestions. In addition, special thanks to Jediphi Cabal, Linda Goldberg, Claudia Goldin, Pete Klenow, Anna Kovner, Sydney Ludvigson, and Paola Sapienza for many thoughtful suggestions. Kevin Lai provided stellar research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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  • Published: 15 December 2022

On the robustness of gender differences in economic behavior

  • Helena Fornwagner 1   na1 ,
  • Brit Grosskopf 1   na1 ,
  • Alexander Lauf 2   na1 ,
  • Vanessa Schöller 2   na1 &
  • Silvio Städter 2   na1  

Scientific Reports volume  12 , Article number:  21549 ( 2022 ) Cite this article

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  • Human behaviour
  • Psychology and behaviour

Because of the importance of economic decisions, researchers have looked into what factors influence them. Gender has received a lot of attention for explaining differences in behavior. But how much can be associated with gender, and how much with an individual’s biological sex? We run an experimental online study with cis- and transgender participants that (1) looks into correlational differences between gender and sex for competitiveness, risk-taking, and altruism by comparing decisions across these different subject groups. (2) we prime participants with either a masculine or feminine gender identity to examine causal gender effects on behavior. We hypothesize that if gender is indeed a primary factor for decision-making, (i) individuals of the same gender (but different sex) make similar decisions, and (ii) gender priming changes behavior. Based on 780 observations, we conclude that the role of gender (and sex) is not as decisive for economic behavior as originally thought.

Introduction

Worldwide, humans make economic decisions every day: Should I apply for a new job opportunity in a highly competitive environment? Should I invest in a risky asset or not? How much money should I donate to charities? A vast literature tries to determine the factors that affect decisions in domains such as competitiveness 1 , risk-taking 2 , and altruism 3 . Researchers have looked, among other things, into the role of institutional or market-related features 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , cultural background 12 , 13 , 14 , 15 , 16 , 17 , individual characteristics 18 , 19 , 20 , 21 , 22 , 23 , hormonal 24 , 25 , 26 , 27 , 28 , 29 , or other biological factors, such as genetics, and neurological factors 30 , 31 , 32 , 33 , 34 . Among those factors, gender has received a lot of attention. Over the last few decades, the flourishing research in economics has looked at whether gender is a significant driver of how women and men behave in the domains of competitiveness 35 , risk-taking 2 , 36 , and altruism 3 . We refer to the Supplementary Sect. 14 for a detailed literature review.

But is it really gender that influences behavior? Or, instead, are sex differences causing these observed differences? Or is it a mix of gender and sex? Importantly, sex and gender are two distinct concepts. Whereas sex is defined as “either of the two main categories (male and female) into which humans” are categorized based on their reproductive functions ( www.oed.com , accessed 2021-10-12), gender usually refers to the psychological, behavioral, social, and cultural aspects of being male or female (i.e., masculinity or femininity) 37 , 38 , 39 . For cisgender individuals, the internal gender identity matches and presents itself by the externally determined cultural expectations of the behavior and roles considered appropriate for one’s sex 37 . However, the gender identity of transmen and transwomen and their gender roles are typically not the same as what is associated with their sex assigned at birth 40 . So the question arises: how much of the differences of men and women often found in the economic literature can really be associated with gender as opposed to an individual’s sex?

We investigate this question by using well-known behavioral economic experiments in the domain of competitiveness , risky choices , and altruism . As stated, for these three behavioral traits, gender differences are a common finding. However, existing studies identify gender effects, without controlling for sex. Distinguishing gender from sex effects is practically impossible when only investigating cisgender participants. As a novel approach, we run our experimental study with transmen and transwomen in addition to cismen and ciswomen. We do not use the gender that is attributed to a person by others 38 , 39 , 41 , 42 . Instead, this study utilizes the information on the participants’ self-identification to a particular gender and sex from self-reported categories and established scaling methods from psychological and medical science. The advantage of having this information is that that cisgender and transgender people differ in either their sex or their gender. To illustrate this consider an example: a ciswoman has female sex and feminine gender. A transman has female sex but masculine gender. So differences in the behavior of those two subject groups might be associated with gender instead of sex.

The experimental method is excellent for studying the economic choices we are interested in because of its standardized and validated measures. We have information on the participants’ gender and sex from self-reported categories and established scaling methods. Moreover, instead of just analyzing gender and sex effects correlationally, we elicit the causal impact of gender by exogenously varying gender identities with a priming method.

First, we test how gender correlates with the mentioned choices. By contrasting the behavior of the four different subject groups of cismen, ciswomen, transmen, and transwomen, we obtain insights into how far biology (sex) or the cultural and sociological construct of gender explains differences in economic behavior. Our study is the first investigating competitiveness, risk-taking, and altruism of transmen and transwomen. We hypothesize that if gender is the driving factor, individuals of the same gender (and different sex) make similar decisions, and decisions significantly differ when gender differs (and sex is the same). Second, we concentrate on the causal effect of gender on behavior—an analysis that is rarely done in the literature. The traditional experimental method of randomizing over the variable of interest is not possible with gender. Hence, we need a different approach to elicit causal effects. As our method to test a directional impact of gender, we employ a gender prime: either a masculine or feminine gender identity is subconsciously activated. Priming is an easy-to-implement intervention that has shown to influence individual decision-making in various dimensions. Amongst others, it has been used to activate gender identities or change gender stereotypes 43 , 44 . Those studies’ results are mixed, depending on the objective of the prime (e.g., risk preferences, competitiveness, altruism) and the method of priming (eliciting gender at the beginning of the study or showing pictures).

In our study, we use a word priming method that has shown to be powerful in other contexts 45 , 46 , 47 , and has the advantage that we can easily include a gender-neutral condition by using gender-neutral words. In general, it seems to be the case that different genders react differently to gender priming. Importantly, none of the existing priming studies has recruited transgender subjects as researchers usually rely only on self-reported (binary) gender identities. If cisgender and transgender individuals change their behavior when being primed, this would indicate a causal effect of gender on individual economic decisions. To be more specific, our hypotheses are as follows. First, since our priming affects individuals’ gender identity and not their sex, we anticipate participants with the same gender to react similarly to the respective prime. Put differently, cismen and transmen (ciswomen and transwomen) should adjust their behavior similarly when being primed. Second, we expect reactions to priming to be different when the gender is not the same among the participants. Lastly, the results should be different when participants are primed with their own gender identity instead of their respective other gender identity.

Based on 780 observations from experiments conducted online, our results generally show no correlational or causal effect of gender or sex for competitiveness, risk-taking, and altruism. The only exceptions are that cismen have a higher rate of entering the competition than all other subject groups when primed masculine. They also risk more when primed with a masculine identity compared to the neutral priming condition. In addition, we find that subjects of male sex (i.e., cismen and transwomen) risk more than their female counterparts (ciswomen and transmen). However, these behavioral differences that sometimes point towards gender and sometimes towards sex as explanatory variables do not replicate if we apply different robustness tests, including correcting for multiple hypothesis testing. Thus, we conclude that neither gender nor sex is a consistent main factor influencing the economic decisions measured in this article.

To test our research questions, we set up an online economic experiment. This experiment received ethical approval from the UEBS Research Ethics Committee of the University of Exeter (Ethics application—eUEBS004241; 26.05.2021) and the Ethics Committee of the University of Regensburg (28.04.2021). All research was performed in accordance with the relevant guidelines and regulations. We have obtained informed consent from all participants. The study was preregistered on aspredicted.org (Nr. 68888) before data collection (see https://aspredicted.org/rc9vn.pdf ). We conduct our study (tasks and questionnaires) with oTree 48 on Prolific ( www.prolific.co ). To recruit the different subject groups, we used specific filters provided by Prolific. Prolific was especially well suited to host our study as they have a pool of subjects who registered as being either a transman or a transwoman. We used the Prolific filters on gender identity to recruit our subjects. However, our classification into the subject groups cismen, ciswomen, transmen, and transwomen is based on the self-reported information we elicited with the experimental questionnaire.

Each participant completes six parts and several questionnaires. One part is randomly selected for payment at the end of the experiment. In Part 1, a participant is randomly assigned to either the baseline treatment (NEUTRAL) or a treatment condition that refers to one of the gender priming interventions: FEMININE (primes a feminine gender identity) or MASCULINE (primes a masculine gender identity). Participants are primed by a word search task where different words are used depending on the underlying treatment 46 . The words in FEMININE are: female, woman, she, women, her, girl, hers, lady; in MASCULINE they are: male, man, he, men, him, boy, his, gentleman. In the baseline condition NEUTRAL, participants also solve the word search task, with the following (neutral) words: person, it, people, its, child, theirs, individual, neuter. Participants are shown the words and have two minutes to mark these words in a 10 × 10 grid. In case they find all words, they receive \(\pounds\) 5.

After the word search task, each participant enters the next parts of the experiments, which are the respective economic decision–making parts. As our first decision dimension, we employ monetary incentives to measure competitiveness 49 . We measure the performance in a real effort math task, where the participants are instructed to solve puzzles by finding two two-digit numbers that add up to 100 in 3 × 3 matrices for two minutes. In Part 2, they complete the math task under piece-rate incentives, which means they receive \(\pounds\) 0.50 for every solved puzzle. In Part 3, the same math task is performed under tournament incentives. The participants are divided into groups of four and receive \(\pounds\) 2 for every solved puzzle, but only if they solve more puzzles than every other group member. In Part 4, the participants have to choose, before performing, whether their performance in this part will be paid based on the piece-rate incentives (like Part 2) or according to the tournament rules (like Part 3). Whenever a participant decides on the tournament incentives in Part 4, s/he is classified as competitive and competes against the group member’s performance in the previous Part 3. In all parts, the participants do not receive feedback on how well they perform compared to the other group members until the end of the experiment and have no information on the other group members’ identity or characteristics. Additionally, we measure the participants’ confidence in Part 2 (how well they think they performed compared to the other participants in the session) and Part 3 (how well they think they performed compared to the other group members) with incentivized questions.

Our second decision dimension is the willingness to take risks (Part 5). It is measured using a simple lottery task 50 . Participants receive \(\pounds\) 4 and can invest into a lottery with a 50% chance of success. The invested amount is multiplied by 2.5 in case of success. In case of no success, the invested amount is lost. The participants keep the amount not invested. Risk preferences are measured as the amount a participant invests, where higher investments indicate a higher willingness to take risks. The third decision dimension is altruism (Part 6). We investigate the participants’ altruistic preferences with a dictator game 51 . Participants receive \(\pounds\) 5 and split up this amount between themselves and up to five different charities. Altruism is quantified as the sum donated by a participant.

The post-experimental questionnaire contains (1) a 30-items version of the Bem Sex Role Inventory (BEM) that explores a person’s masculine and feminine self-identification on a continuous scale 52 ; (2) the Transgender Congruence Scale (TCS) 53 which evaluates if and how much someone identifies as transgender; (3) demographic questions, as well as questions on the biological sex, gender, sexual orientation, and whether one self-identifies as transgender; and (4) the Steps to Transition (STT) questionnaire that describes typical steps transgender people undertake in their transition 53 . This questionnaire controls for aspects like legally changing a name, undergoing hormone replacement therapy, having surgery to alter genitalia, or a non-genital surgery like a breast removal. In addition, we include debriefing questions to check if the participants are aware of the study topic and the priming intervention 54 .

The last section of the Supplementary Information provides a detailed description of all instructions and questionnaires (including the screenshots) of our experiment.

Presenting our results, we use the following abbreviations: Brown-Forsythe test (BF), Chi-squared test ( \(\chi ^2\) ), Kruskal-Wallis test (KW), two-tailed Kendall’s rank correlation coefficients test (KTAU), two-tailed Mann-Whitney U test (MWU), Robust Wald test (W), two-tailed Variance Ratio test (VR), Cohen’s d ( d ), and standard deviation (SD). The significance levels are defined as follows: \(p~<~0.05\) (*), \(p~<~0.01\) (**), and \(p~<~0.001\) (***), where a significant result must have at least \(p~<~0.05\) . We summarize multiple p -values by \(p's\) .

Descriptives

We collected a total of \(n~=~780\) observations, out of which 425 are cisgender (214 cismen and 211 ciswomen) and 355 transgender (215 transmen and 140 transwomen; see the Supplementary Subsect. 15.1 for more details). The questionnaire is used to classify one subject into one of the four groups, which asks about their current gender, sex, and whether they self-identify as transgender. We generally find support for the classification into groups according to the guidelines of the American Psychological Association 40 , as the data indicate that for only 5.07% of the transgender individuals their sex changed since birth.

We did a pre-experimental power analysis to calculate the needed sample sizes based on existing work 4 . We used their neutral priming condition to inform our power calculations. Based on their effect size delta of \(-\,0.264\) , the needed observations for \(\alpha ~=~0.05\) and a power of 0.80 are 44 for one subject group in one treatment. Following, it would be enough to have in total \(n~=~528\) . To be more conservative, we preregistered having 72 observations for each subject group in each treatment, resulting in a power of 0.95 (see https://aspredicted.org/rc9vn.pdf for further information). In our particular case of having a non-usual subject group of transgender individuals, we already mentioned in the preregistration that having 72 is very ambitious for transgender individuals, also because of the number of registered transgender individuals on Prolific. We ended up in NEUTRAL with the minimum needed amount of 44 transwomen. Consequently, we had a priori, based on the ex-ante preregistered power calculations and depending on the underlying comparison, at least a power of 0.82. The power increases up to 0.95 for the subject groups with \(n=72\) in one treatment.

As summarized in Supplementary Table 1 , the participants are on average 24.4 years old (SD = 6.60), have an average height in centimeters of 170 (SD = 10.8), and approximately half of them are students (47.2%). Around one third holds a university degree, 69.4% have an income lower than £20,000, and 25.8% report being religious. Our sample consists mostly of participants from the United States, followed by Continental Europe and the United Kingdom. Less than 10% live outside these three mentioned regions. Responses to the BEM classify 28.5% as feminine, 19.4% as masculine, 24.1% as androgynous, and 28.1% as undifferentiated. On the TCS scale ranging from 1 to 5, participants show an average score of 3.67 (SD = 1.1). The average score on the STT, which ranges from 0 to 16, is 4.35 (SD = 4.6). The various subject groups are comparable in several characteristics as indicated by the statistical tests added in Supplementary Table 1 . Descriptive statistics broken down by subject groups are presented in Supplementary Tables 2 and 3 (cisgender) as well as Supplementary Tables 4 and 5 (transgender).

For the outcomes of Part 1, the Supplementary Sect. 2 includes the detailed summarizing descriptives on the participants’ priming. On average, the participants marked 7.45 out of 8 words (SD = 1.53), and 83.97% (i.e., \(n~=~655\) ) marked all words from the list within the given time of two minutes.

Competitiveness

Figure 1 and Supplementary Table 14 summarize the tournament entry rates in Part 4. In order to investigate whether gender and competitiveness are correlated, we focus on the baseline treatment NEUTRAL. No significant variation is reported across the four subject groups ( \(\chi ^2(3)~=~0.408\) , \(p~=~0.939\) ). Similar, when pooling the results by gender (Supplementary Fig. 2 ; cismen + transmen vs. ciswomen + transwomen), tournament entry rates do not differ for feminine and masculine subjects ( \(\chi ^2(1)~=~0.273\) , \(p~=~0.601\) ) and also no difference is found for male and female subjects when pooling the data by sex (Supplementary Fig. 3 ; cismen + transwomen vs. ciswomen + transmen; \(\chi ^2(1)~=~0.028\) , \(p~=~0.867\) ). We compare the differences between the priming conditions (FEMININE and MASCULINE) and the baseline treatment (NEUTRAL) for the causal analysis. Priming does not influence the competition entry rates for any subject group ( \(\chi ^2(1)\) , \(p's~>~0.265\) ), including for cismen when comparing the MASCULINE treatment to the NEUTRAL treatment ( \(\chi ^2(1)\) , \(p~=~0.073\) ). We shall see in the regression analysis that when adding further controls, the impact of MASCULINE priming on cismen becomes significant. Looking at the MASCULINE priming condition only, where the entry rates look very similar for all subject groups except for cismen, the competition entry rate is around 20 percentage points higher for cismen than for all other subject groups ( \(\chi ^2(3)~=~7.991\) , \(p~=~0.046\) ).

figure 1

Tournament entry rates in Part 4 by treatment and subject groups in alphabetical order ( \(n~=~780\) ). The bars show the percentage of participants (between 0 and 100) who chose to compete rather than to perform under piece–rate incentives. The error bars represent the standard errors of the means.

In Supplementary Table 15 , we run Probit regressions for the baseline treatment (NEUTRAL) to disentangle the effects of gender and sex. As our basic regression framework, we have in column (1) just the subject groups and in (2) additionally controls for the performance measures in the real effort task. In column (3), we further take into account the participant’s confidence and willingness to take risks. In column (4), we add the variables age, height, student status, income, religion, and residence, whereas in (5), we control for the outcomes in the TCS and STT. The TCS is interesting in our setting as it accounts for how much individuals feel genuine, authentic, and comfortable with their gender identity and external appearance. Similarly important, the STT measures details about the transition process, especially biological aspects like whether one has had surgery to alter genitalia, a non-genital surgery (like breast removal), or is undergoing hormone replacement therapy. Using joint coefficient tests (see Supplementary Table 15 ), we find neither gender (W, \(\chi ^2(1)\) , \(p's~>~0.437\) ) nor sex (W, \(\chi ^2(1)\) , \(p's~>~0.214\) ) to have a significant effect on competitiveness. We thus conclude that there is no correlation between neither gender nor sex and competitiveness in our study.

To analyze a potential causal effect of gender, we run Probit regressions in Supplementary Table 16 . The non–parametrized analyses are confirmed for ciswomen, transmen, and transwomen. For cismen we find that the gender prime with MASCULINE has a significant impact increasing the competition entry rates in specification (2) (coef = 0.473; 95% CI = 0.036, 0.909; \(p~=~0.034\) ; controlling for performance) and (4) (coef = 0.544; 95% CI = 0.076, 1.012; \(p~=~0.021\) ; controlling for beliefs, risk attitude, and other person-specific covariates). Summing up, only cismen’s competition entry rates seem to be influenced (positively) when priming them with their own gender identity. We do not find a significant impact of gender priming for all other subject groups and priming combinations. We will interpret those results in the Discussion.

Our experimental design does not only allow us to look into the choice to enter a tournament but also into participants’ confidence (i.e., how well they believe they performed in the real effort task when competing, see Supplementary Table 11 ). In NEUTRAL, there is no evidence that subjects of masculine gender have higher performance beliefs than subjects of feminine gender (MWU, \(z~=~-0.912\) , \(p~=~0.362\) ). However, we do find differences between subjects of female and male sex (MWU, \(z~=~-3.470\) , \(p~=~0.001\) ). For priming, no subject group increases or decreases their beliefs when being primed (MWU, \(p's~>~0.177\) ). Regressions in Supplementary Table 12 confirm that beliefs depend on the participants’ sex: male subjects generally have higher confidence in their performance than female subjects (W, F (1), \(p's~<~0.001\) ). And again, confidence does not differ across gender (W, F (1), \(p's~>~0.259\) ). That gender does not play a role in this setting is further confirmed when looking at the causal impact of gender priming on the participants’ confidence. For none of the subject groups, we do find any effect of gender priming on the beliefs when using regression analyses (see Supplementary Table 13 , W, F (1), \(p's~>~0.178\) ).

Another interesting aspect is to see in how far behavior pays off in the competitiveness task. We provide details and various analyses of the performances in the real effort task and the related payoffs of Part 2 to 4 in Supplementary Sect. 3 .

figure 2

Investments into the risky lottery in Part 5 by treatment and subject groups in alphabetical order ( \(n~=~780\) ). The bars show the average investment rate, and the error bars represent the standard errors of the means.

Investment rates in the lottery are depicted in Fig.  2 and stated in Supplementary Table 20 . When applying non–parametric tests, we do not find any differences between the various subject groups within the baseline treatment NEUTRAL (KW, chi-squared with ties =4.712 with 3 d.f., \(p~=~0.194\) ). If anything, transwomen seem to be more risk-taking than transmen in a pairwise comparison (MWU, \(z~=~-1.979\) , \(p~=~0.048\) ). This, however, does not point towards a systematic impact of gender and/or sex when pooling data (Supplementary Figs. 4 and 5 ; gender: cismen + transmen vs. ciswomen + transwomen, sex: cismen + transwomen vs. ciswomen + transmen; MWU, \(p's~>~0.130\) ). Turning to the causal impact of priming, again, we see MASCULINE priming increases the risk attitude for cismen only (MWU, \(z~=~2.075\) \(p~=~0.038\) ) bringing the level of cismen to the one of transwomen in the MASCULINE priming (MWU, \(z~=~0.156\) , \(p~=~0.876\) ). For every other subject group, we do not find any significant impact of gender priming (MWU, \(p~>~0.206\) ).

Joint coefficient tests for the regressions (with and without control variables) in Supplementary Table 21 show the correlational results for our baseline condition. We find no differences in risk-taking of subjects of feminine and masculine gender (W, F (1), \(p's~>~0.132\) ). However, we find a sex effect: male subjects risk more than female subjects (W, F (1), \(p's~<~0.042\) ).

Turning to priming, we have significant differences in risk-taking of cismen when being primed MASCULINE (W, F (1), \(p's~<~0.046\) ; see Supplementary Table 22 ). We find no difference in risk-taking for all other subject groups when primed with a gender (W, F (1), \(p's~>~0.092\) ). The findings are independent of what other control variables are taken into account. The regression analysis for risk attitudes is thus similar to what we found for competition entry rates. When being primed with their own gender, only cismen significantly increase their risk-taking behavior.

figure 3

Donation in Part 6 by treatment and subject groups in alphabetical order ( \(n~=~780\) ). The average donations are indicated by the bars, and the error bars represent the standard errors of the means.

Last, we test for differences in the donation task (see Fig. 3 and Supplementary Table 24 ). Donations in NEUTRAL are not distinguishable across subject groups (KW, chi-squared with ties = 0.434 with 3 d.f., \(p~=~0.933\) ). Neither pooled results for gender nor for sex yield a difference in donation rates (Supplementary Figs. 6 and 7 ; MWU, \(p's~>~0.564\) ). Concerning the causal impact of gender priming, we do not find significant effects for any subject group and any priming condition (MWU, \(p's~>~0.260\) ).

The regression analyses in Supplementary Tables 25 and 26 confirm these findings. Joint coefficient tests for gender or sex do not show significant correlations in the baseline condition (W F (1), \(p's~>~0.580\) ). Moreover, the impact of all priming condition on all subject group remains insignificant, even after controlling for different sets of additional personal covariates (W, F (1), \(p's~>~0.214\) ).

To summarize, we find no correlation between gender or sex on altruism and do not detect any causal impact of gender priming on altruistic behavior in our setup.

Gender and sex differences within priming conditions

As we have shown so far, there is no systematic correlation between gender and behavior in the NEUTRAL treatment. Here we briefly test for gender and sex differences in behavior within the two other priming treatments. Looking at Supplementary Figs. 2 to 7 and analyzing the gender differences with non-parametric tests, we see no difference in competition entry rates across subject groups (FEMININE: \(\chi ^2(1)~=~0.124\) , \(p~=~0.725\) , MASCULINE: \(\chi ^2(1)~=~2.488\) , \(p~=~0.115\) ), risk–taking (FEMININE: MWU, \(z~=~0.584\) \(p~=~0.560\) , MASCULINE: MWU, \(z~=~-0.663\) , \(p~=~0.507\) ), and altruism (FEMININE: MWU, \(z~=~-1.507\) , \(p~=~0.132\) , MASCULINE: MWU, \(z~=~-0.625\) , \(p~=~0.532\) ). Turning to sex differences, the picture slightly changes. First, we see differences between subjects of male and female sex in both priming conditions (FEMININE and MASCULINE) for competitiveness. The differences are close to conventional levels of significance (FEMININE: \(\chi ^2(1)~=~3.808\) , \(p~=~0.051\) , MASCULINE: \(\chi ^2(1)~=~3.349\) , \(p~=~0.067\) ). Second, for risk-taking, we find a significant difference in the MASCULINE treatment only, with subjects of male sex taking more risk than subjects of female sex (MWU, \(z~=~2.558\) \(p~=~0.011\) ). Third, for altruism, we find subjects of female sex having significantly higher scores than those of male sex in the FEMININE treatment (MWU, \(z~=~-2.269\) , \(p~=~0.023\) ). Hence, for risk and altruism we find that only those sexes show higher scores who are primed with the gender identity that they would cisgender-stereotypically be associated with.

Robustness tests

In the remainder of the article, we apply different approaches to test the robustness of our results for comparing behavior across subject groups within NEUTRAL and by subject groups across primings.

Comparing variances instead of means

Recent literature argues that gender differences, for example, in preferences, often remain undetected because the researchers almost exclusively focus on differences in means 2 , 55 . It is suggested that when comparing variance ratios (i.e., the standard effect size measure for variance differences), one reliably finds evidence for greater male variability in cooperation, time, risk, social preferences, and academic grades. Thus, we rerun our analysis based on variance ratios for risk and altruism only, given that competitiveness is measured on a binary scale.

No significant differences in standard deviations of all subject groups within the baseline treatment NEUTRAL (BF(3,255), \(W50~=~2.564\) , \(p~=~0.055\) ) are found for the lottery investment rates. Pooling the results for gender does again show no differences in the variances (VR(143, 114), \(f~=~0.805\) , \(p~=~0.219\) ). Only the investment rate of male subjects has a greater variability compared to females when pooling data based on sex (VR(115, 142), \(f~=~1.5617\) , \(p~=~0.012\) ). This result is in line with a recent meta-analysis, finding a significant difference in variances between men and women of 1.25 2 . Additionally, no causal impact of gender priming between any priming condition for any subject group (VR, \(p's~>~0.100\) ) is reported.

The variances of the donations in NEUTRAL are not distinguishable across subject groups (BF(3, 255), \(W50~=~1.100\) , \(p~=~0.350\) ). The literature reports a variance ratio between men and women of 1.18 2 , which is in line with the variance ratio in our sample of 1.144 between cismen and ciswomen. Neither pooled results for gender nor sex show significant differences in the variances of donation rates (VR, \(p's~>~0.480\) ). Similarly, the donation rates do not differ based on the variances for any subject group when comparing the different priming conditions (VR, \(p's~>~0.343\) ).

Using Cohen’s d s

Cohen’s d can be used with the \(p{-}value\) from a common \(t-test\) to illustrate if an effect size is not only significant but if a significant result is also relevant. One restriction of this approach is that it is only possible to conduct it for pairwise comparisons, which is not fully in line with the main analyses we provide in the “ Results ” section. Moreover, t-tests and their p-values are generally presented together with the Cohen’s d . The p-values tell if the effect is statistically significant, whereas the Cohen’s d s determine the effect size. However, t-tests are usually applied to normally distributed data or in case a dataset is considered to be very large. Nevertheless, we believe that discussing Cohen’s d s adds another valuable robustness test for our results. We consider an effect to be (i) small, when the absolute Cohen’s d is smaller than 0.2, (ii) medium for absolute Cohen’s d between 0.2 and 0.5, and (iii) large if the absolute Cohen’s d is larger than 0.5. In the following, we discuss the Cohen’s d statistics and add the p-values from respective \(t-test\) s only for those that report at least a medium Cohen’s d .

Supplementary Table 17 summarizes the Cohen’s d analyses for competitiveness. When comparing the subject groups in NEUTRAL, we find only small effects ( d \(\in [0.012, 0.101]\) ). The same is true when pooling by gender or sex in NEUTRAL ( d \(\in [0.021, 0.065]\) ). The effects sizes for comparing all four subject groups separately between NEUTRAL and FEMININE ( d \(\in [0.031, 0.187]\) ) and NEUTRAL and MASCULINE ( d \(\in [0.023, 0.115]\) ) are again small. The only exception are cismen, where the difference between NEUTRAL and MASCULINE becomes medium ( d \(=0.303\) ) but is insignificant ( \(p~=~0.072\) ).

The analyses for risk can be found in Supplementary Table 23 . Within NEUTRAL, the effect sizes of comparing cismen or ciswomen with transwomen is medium ( d \(=0.354,0.484\) ) and only significant for the latter comparison ( \(p~=~0.011\) ). Besides, the Cohen’s d is getting large and significant for transmen vs. transwomen ( d \(=0.504\) ; \(p~=~0.008\) ). For all other comparisons, the effects are small between subject groups ( d \(\in [0.017, 0.134]\) ). Pooling by sex reveals a medium, significant effect size ( d \(=0.268\) ; \(p~=~0.032\) ) while the effect size for the gender-wise comparison is small ( d \(=0.143\) ). The effects sizes for each subject group when looking at NEUTRAL vs. FEMININE are small ( d \(\in [0.143, 0.182]\) ), except for the medium insignificant one of transwomen ( d \(=0.335\) ; \(p~=~0. 107\) ). For NEUTRAL vs. MASCULINE, cismen show a medium and significant effect size ( d \(=0.348\) ; \(p~=~0.039\) ), whereas all other subject groups have small or medium, but insignificant Cohen’s d s ( d \(\in [0.013, 0.282]\) ; \(p~>~0.097\) ).

The effect size for the participants’ donations are listed in Supplementary Table 27 . They are small and insignificant within NEUTRAL when comparing by sex, gender, or between subject groups ( d \(\in [0.004, 0.098]\) ; \(p~>~0.600\) ). Similar, the effects sizes for all other comparisons considering the different treatments are small and lack significance ( d \(\in [0.016, 0.188]\) ; \(p~>~0.267\) ). The only slightly medium and insignificant exception ( d \(=0.210\) : \(p~=~0.212\) ) is reported for cismen in NEUTRAL vs. FEMININE.

Using a continuous instead of a categorical gender measure

With just a handful of exceptions 44 , 56 , 57 , 58 , researchers in economics always used a categorical way to measure gender. However, it is more and more discussed that gender might be a continuous characteristic rather than a binary (or categorical) one 59 . Techniques accounting for it include asking different questions 60 or use identity status concerning adherence to actual gender role beliefs 61 . Another method is the BEM sex role inventory 52 . It provides a continuous gender scale, and we conducted it in the post-experimental questionnaire. The BEM is a very accurate predictor for gender and is highly correlated with other continuous gender measures, and single-item measures 58 .

We rerun all regression analyses and include, instead of the subject groups, the variables \(BEM score\!: Feminine\) (defined as the score participants reached on the BEM questions measuring femininity) and \(BEM score\!: Masculine\) (score on masculine questions in the BEM). Results in Supplementary Tables 28 , 30 , and 32 show throughout that neither the feminine nor the masculine score significantly influence how the participants decide in NEUTRAL (W, \(p's~>~0.057\) ). This is not surprising since the BEM scores and the gender categories are highly correlated (feminine: KTAU, Kendall’s score = 21692, \(p~=~0.001\) , masculine: KTAU, Kendall’s score = -18485, \(p~=~0.003\) ), and we did not find correlational gender differences in the baseline condition for neither of the economic decisions we investigate.

Also, for the causal impact of gender priming, no evidence is found for an effect of the BEM score on behavior. Supplementary Tables 29 , 31 , and 33 confirm this with the insignificant variables measuring the two BEM scores ( \(p's~>~0.056\) ), the insignificant interaction terms of the priming condition with the feminine or masculine BEM score ( \(p's~>~0.054\) ), and the insignificant respective joint coefficient tests (W, \(\chi ^2(1)\) / F (1), \(p's~>~0.108\) ).

Controlling for gender congruent upbringing

One limitation of our approach is that the subjects are sorted into distinct gender categories based on their current gender identity. This potentially lacks accounting for psychological, behavioral, social, and cultural experiences that shape a gender identity over time, particularly during adolescence. While we can not fully account for this confound, we can analyze if being raised according to one’s current gender affects our primary outcomes.

In our post-experimental questionnaire, we asked the participants according to which gender their parents treated them. Based on the answers and the self-reported gender, we create the variable gender congruent upbringing (GCU). GCU is equal to 1 if someone was raised according to their current gender identity (or was raised neutrally) and 0 otherwise. How the participants were raised matches the currently reported gender of 32.09% of transmen and 15.00% of transwomen. For cisgender individuals, the variable CGU equals 1 for 99.76%. Due to the lack of variation of CGU for the cisgender sample, we conducted all analyses for transgender individuals only.

We rerun all main regression and include, instead of the different subject groups, the variable GCU. Results in Supplementary Tables 43 and 47 show that whether participants were raised according to their current gender does not significantly influence the participants’ competitiveness and altruism in NEUTRAL ( \(p's~>~0.473\) ). For risk, we see in Supplementary Table 45 a significantly negative coefficient in NEUTRAL for two out of the three regression) ( \(p's~<~0.043\) ). When considering the causal impact of the gender priming, there is again no evidence for an effect of being raised gender-congruent on competitiveness and altruism. Supplementary Tables 44 and 48 show this based on the insignificant coefficient for GCU ( \(p's~>~0.423\) ) and the insignificant respective joint coefficient tests (W, \(\chi ^2(1)\) / F (1), \(p's~>~0.076\) ). For risk (see Supplementary Table 46 ), the coefficients are again significantly negative for NEUTRAL only ( \(p's~<~0.022\) ), because the joint coefficient tests taking the treatments and GCU interactions into account remain insignificant (W, F (1), \(p's~>~0.055\) ).

Controlling for the strength of the priming intervention

To underline the strength of our results concerning the priming, we look at the answers to the survey question “Do you remember any of the words from the word-search puzzle? If not, leave empty.”, which was implemented (not incentivized) at the very end of our experiment. We use the outcome of this question to control for the strength of the priming intervention. It can be assumed that the more words a subject remembered, the more they were still primed towards the end of the study. First, 93.08% of all participants remember at least one out of the eight words. The average number of recognized words is 4.33, and 70.00% of all participants reported at least four words. Thus, it can be assumed that the prime was activated for the majority of participants throughout the experiment.

Second, we rerun the regressions in Supplementary Tables 16 , 22 , and 26 for the three behavioral outcomes. The dummy variables, accounting for the different primings (i.e., the treatment variables), are replaced by \(Rem.\,feminine\,words\) , \(Rem.\,masculine\,words\) , and \(Rem.\,neutral\,words\) , which measure the number of words remembered in each treatment. The only significant and close to significant results found are that cismen in MASCULINE are investing more into the lottery, the more masculine words they remember (see Supplementary Table 35 ; \(p's~<~0.014\) ) and ciswomen in FEMININE are donating more, the more feminine words they remember (see Supplementary Table 36 ; \(p's~<~0.051\) ), compared to the NEUTRAL condition. Moreover, we did a subgroup analysis for those who remembered at least the median amount of priming words (i.e., four words) or less (see Supplementary Tables 37 to 42 ). Overall, when using the remembered words instead of a simple priming variable, our findings in the main “ Results ” section replicate.

Correcting for multiple hypothesis testing

Like other scientists, we face the problem of simultaneously evaluating several hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of tests are false positives. Thus, drawing valid conclusions requires considering the number of performed statistical tests and adjusting the statistical confidence measures accordingly. We employ the free online tool “Multiple Testing Correction” by 62 , available at www.multipletesting.com .

As we perform with our novel pool of transgender individuals a mix of exploratory and confirmatory analysis, the suitable methods for correction are Bonferroni, the Holm (step-down) approach and the Hochberg (step-up) correction which allows for calculating False Discovery Rates (FDR). According to the Multiple Testing Correction, the first significant p -value (values over these thresholds are not considered as significant) is \(p~=~0.0015\) , independent of the used method. So if we—instead of the significance levels explained in the “ Results ” section—define a result as significant if it is at least \(p~<~0.0015\) , all results (non-parametric and findings from regressions) turn out to be insignificant.

This paper applies well-known and extensively used experimental techniques to identify the influence of gender and sex on economic decision-making. First, we separate the impact of gender and sex on economic decisions by collecting data from participants whose gender and sex differ, which is new to the literature. We compare the competitive, risk, and altruistic behavior of four different subject groups—cismen, ciswomen, transmen, and transwomen. Second, we induce either a neutral, feminine, or masculine gender identity by having different priming conditions. Thus, with our experimental setup, we go beyond correlating gender and sex with decisions and try to evoke gender identities through a priming manipulation causally.

While this study is pre-registered and carefully designed following existing literature and the state of the art standards in experimental economics, the findings diverge from previous work. Our results do not show conclusive correlational or causal evidence for gender or sex as determinants of economic decision-making. As described in the main “ Results ” section, we find just a hand full of significant results. These results do generally not replicate when applying different robustness tests, including accounting for multiple hypothesis testing. Thus, the pattern is essentially consistent: gender and sex differences in behavior remain statistically indistinguishable. Besides, we see that cis- and transgender participants do not systematically differ from each other in their behavior. Our overall interpretation of the data is that gender and sex might not matter as much as initially thought. But what can explain these findings?

First, one explanation could be that gender effects might depend on the underlying subject pool. The existing literature has treated gender differences in behavior as a well-established and robust finding. However, the vast majority of these papers use standard student subjects 63 . Studies that use other samples 64 or online samples are generally less likely to report gender differences, especially when controlling for a set of participants’ characteristics 18 , 65 . Moreover, differences in sample size are likely to play a role. We pre-registered a sample that would give us enough statistical power based on existing literature. Still, it remains true that small gender differences in behavior may lie below our minimum detectable effect sizes. The total sample size in this experiment has been constrained by the availability of transgender individuals on Prolific. However, we expect the availability of transgender individuals for future studies to increase, hence allowing for replications of our findings with larger sample sizes.

Second, almost two decades have passed since the first studies that looked into competitiveness, risk, and altruism were published and found gender differences in behavior. One can thus speculate that female empowerment, educational initiatives, and the broader awareness of gender and sex equality in private and professional settings have led to a narrowing of potential behavioral differences in the meantime.

Third, the absence of an effect of gender priming on the behavior of transgender subjects may be rooted in the connotation those subject groups have with gender. For transgender individuals, the concept of gender might be a relatively continuous spectrum whereas for cis-individuals it might be seen as a binary dimension. As such, gender might not be as decisive for transgender as for cisgender individuals. The fact that gender priming seems to work only for cismen but not for ciswomen might hinge on the role gender usually has played for those two subject groups. Whereas for cismen their gender usually comes with advantages and, as such, has a positive connotation, ciswomen might have negative experiences concerning the way society treats them based on their gender.

Despite the partly unexpected findings, we belief that there are several key “takeaways” from this study. For the first time, we present evidence from a sample of cis- and transgender participants in one framework, which allows for both a correlational and a causal approach, and look at how they decide in a competitive context and when making risky or altruistic decisions. Transgender individuals have become a more and more visible part of society. Thus, we think it is crucial to understand their economic behavior. Furthermore, having transgender participants in our sample makes it possible to look deeper into the part that an individual’s gender—as opposed to sex—plays in economic decision-making. In our setting, we shed light on the part of gender effects that can be attributed to biological factors (which refer to a participant’s sex) and other aspects of one’s gender identity. Additionally, we do not measure gender only on a categorical scale; instead, we also apply a continuous gender scale. Our results are qualitatively the same, independent of what gender scale is used. Besides, we use different statistical techniques to analyze our data, which overall point towards the same interpretation of our results. Moreover, we test for the first time if upbringing according to the current gender influences the behavior of transgender individuals. We found that gender-congruent upbringing makes transgender individuals more risk-averse only in the neutral priming condition. For this result, we encourage future research to look into the explanations of this outcome, which would go beyond the original scope of this paper.

Based on our findings, we conclude that the role of gender and sex is not as decisive for economic behavior as previously assumed.

Data and code availability

The dataset generated and analyzed for this research project as well as the custom code that supports the study′s findings are available on OSF ( https://osf.io/tyzjh/?view_only=243e2b6ca1174a8d802f496ce97c6a70 ). The oTree code is available on request.

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Acknowledgements

We acknowledge funding by the International Doctoral Program “Evidence-Based Economics” of the Elite Network of Bavaria, from the program “BayIntAn” of the Bavarian State Ministry of Science and the Arts, and the University of Exeter Business School. The funders have no role in the study design, data collection, analysis, publishing decision, or manuscript preparation.

We want to thank Loukas Balafoutas and Matthias Sutter for their insightful feedback. Moreover, we thank Andreas Roider and Thanee Chaiwat for discussing the project with us, as well as Chanalak Chaisrilak for her support. Moreover, thanks to the European ESA Mentoring group (Anna Dreber, Kim Fairley, Stefanie Huber, Dorothea Kübler, Noemi Peter, and Margaret Samahita) for very helpful comments and suggestions. The constructive discussions at the University of Regensburg’s Department of Economics research seminar are also appreciated.

Open Access funding enabled and organized by Projekt DEAL.

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These authors contributed equally: Helena Fornwagner, Brit Grosskopf, Alexander Lauf, Vanessa Schöller and Silvio Städter.

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Helena Fornwagner & Brit Grosskopf

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H.F., B.G., A.L., V.S., and S.S. contributed equally to all aspects of the project, including, but not limited to the experimental design, project planning, implementation, manuscript writing, and data analysis. The authors declare no competing interests.

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Fornwagner, H., Grosskopf, B., Lauf, A. et al. On the robustness of gender differences in economic behavior. Sci Rep 12 , 21549 (2022). https://doi.org/10.1038/s41598-022-25141-1

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  • Understanding Poverty

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WBG Gender Strategy

World Bank Group Gender Equality Strategy (FY16-23)

Gender equality is central to the World Bank Group’s goals of ending extreme poverty and boosting shared prosperity. No society can develop sustainably without transforming the distribution of opportunities, resources and choices for males and females so that they have equal power to shape their own lives and contribute to their families, communities, and countries.

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Gender equality is a core development objective in its own right. But greater gender equality is also smart economics, enhancing productivity and improving other development outcomes.

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Gender economics in macroeconomic research

By failing to properly take gender interactions into account in research we are limiting today's science. EU-funded research is revealing how economic trends affect genders differently, as for example in the COVID-19 crisis. It is also looking at how the interaction between genders impacts macroeconomic trends.

There is a growing awareness that the failure to take sex, gender and family interactions into account in research has the potential to limit the benefits for today’s science. Most scientific research does not consider sex or gender as variables and treats the male standard as the norm, resulting in potentially inaccurate or incomplete outcomes.

The EU’s six-year GENDERMACRO project, funded by the European Research Council, addressed a number of current topics of interest in macroeconomics. It explicitly integrated gender and family dynamics into the process of evaluating the impact on macroeconomic outcomes, as well as on the results of selected public policy interventions.

‘Most macro models are traditionally based on one gender model, often modelled according to men, so the starting point for our research was that there are gender differences – and that these play a role for the aggregate economy,’ explains Michele Tertilt, the project’s principal investigator and professor at the University of Mannheim in Germany.

‘The family is a foundational unit of society and if we do not take account of interactions within families we risk coming to the wrong conclusions.’

Family matters

‘Men and women generally take different roles in both society and the family with regard to issues such as child rearing, education, human capital, long-term investments, etc. We wanted to look at the interactions within families – husband/wife but also parent/child interactions – and consider to what extent these are important to the economy as a whole,’ says Tertilt.

To analyse this hypothesis, the project built dynamic macro-style models with explicit gender differences. The emphasis was on non-cooperative models of spousal interactions. Using game theory to model family behaviour enables analysis of topics for which cooperation in the family seems questionable (e.g. domestic violence).

By introducing these new models of spousal interaction into macroeconomic models GENDERMACRO was able to provide new insight on a range of applied research questions.

One of the areas examined was the role of female empowerment in economic development and whether transferring money, through development aid, specifically to women is of overall benefit to the economy. The results of the research showed that this is not necessarily the case but depends on the stage of development of the economy in question.

Another area investigated was the impact of the economic cycle on domestic violence. Thanks to detailed data from the Swedish medical system, the GENDERMACRO project confirmed that domestic violence increases during economic recession and decreases during booms. Tracking additional indicators (such as alcohol abuse and depression) enabled a better understanding of the possible mechanisms behind this.

GENDERMACRO also analysed the HIV epidemic in sub-Saharan Africa and the role of gender and family in influencing the impact of public policies introduced to fight the disease. ‘By taking account of behavioural adjustments and indirect impact, we found some quite surprising results, including the existence of thresholds that must be reached for certain interventions to have a positive effect,’ says Tertilt.

Indirectly following on from the GENDERMACRO project, Tertilt and her colleagues applied their approach to the ongoing COVID-19 pandemic. Their research provides some initial results on how this economic downturn is going to affect women and men differently. It also indicates what the main long-term repercussions for gender equality may be in the areas of employment, telework, childcare, home-schooling, employment flexibility, etc. both during the downturn and in the subsequent recovery.

The employment drop related to social-distancing measures has a large impact on sectors, such as care in the community and the hospitality industry, with high female employment. In addition, closures of schools and daycare centres have massively increased childcare needs. This is having a significant impact on women and the effects of the pandemic on working mothers are likely to last for some time.

However, beyond the immediate crisis, there are factors which may ultimately promote gender equality in the labour market. For example, many fathers are now having to take primary responsibility for childcare, which may erode the social norms that currently lead to an unbalanced distribution of the division of labour in housework and childcare.

All of these results reveal that taking gender and family into account in research is important for the quality of research and, further down the line, the quality of public policy interventions. ‘We need to take gender and family out of the black box and integrate it into research so that we can have better-informed science and better-informed policy,’ stresses Tertilt.

Project details

Project acronym GENDERMACRO Project number 313719 Project coordinator: Germany Project participants: Germany Total cost € 1 133 301 EU Contribution € 1 133 301 Project duration February 2013 - January 2019

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

Paola belingheri.

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

Filippo Chiarello

Andrea fronzetti colladon.

2 Department of Engineering, University of Perugia, Perugia, Italy

3 Department of Management, Kozminski University, Warsaw, Poland

Paola Rovelli

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

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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.

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.

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.

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.

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.

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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.

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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.

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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 ).

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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.

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

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).

Funding Statement

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.

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Publications on Gender economics

Structural change and gender sectoral segregation in sub-saharan africa, monetary policy and the gender and racial employment dynamics in brazil, potential impact of daycare closures on parental child caregiving in turkey, notes on intersectional political economy, the long period method, technical change, and gender, gender dimensions of inequality in the countries of central asia, south caucasus, and western cis.

The collapse of the Soviet Union initiated an unprecedented social and economic transformation of the successor countries and altered the gender balance in a region that counted gender equality as one of the key legacies of its socialist past. The transition experience of the region has amply demonstrated that the changes in the gender balance triggered by economic shifts are far from obvious, and that economic expansion and women’s economic empowerment do not always go hand in hand. Therefore, active measures to enhance women’s economic empowerment should be of central concern to the policy dialogue aimed at poverty and inequality reduction and inclusive growth. In this paper, we establish the current state of various dimensions of gender inequalities and their past dynamics in the countries of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan), South Caucasus (Armenia, Azerbaijan, and Georgia), and Western CIS (Belarus, Moldova, and Ukraine), and propose steps aimed at reducing those inequalities in the context of inclusive growth, decent job creation, and economic empowerment.

Time Use of Parents in the United States

What difference did the great recession make.

Feminist and institutionalist literature has challenged the “Mancession” narrative of the 2007–09 recession and produced nuanced and gender-aware analyses of the labor market and well-being outcomes of the recession. Using American Time Use Survey (ATUS) data for 2003–12, this paper examines the recession’s impact on gendered patterns of time use over the course of the 2003–12 business cycle. We find that the gender disparity in paid and unpaid work hours followed a U-shaped pattern, narrowing during the recession and widening slightly during the jobless recovery. The change in unpaid work disparity was smaller than that in paid work, and was short-lived. Consequently, mothers’ total workload increased under the hardships of the Great Recession and declined only slightly during the recovery.

The Economic Crisis of 2008 and the Added Worker Effect in Transition Countries

Time use of mothers and fathers in hard times and better times, the us business cycle of 2003–10.

The US economic crisis and recession of 2007–09 accelerated the convergence of women’s and men’s employment rates as men experienced disproportionate job losses and women’s entry into the labor force gathered pace. Using the American Time Use Survey (ATUS) data for 2003–10, this study examines whether the narrowing gap in paid work over this period was mirrored in unpaid work, personal care, and leisure time. We find that the gender gap in unpaid work followed a U-pattern, narrowing during the recession but widening afterward. Through segregation analysis, we trace this U-pattern to the slow erosion of gender segregation in housework and, through a standard decomposition analysis of time use by employment status, show that this pattern was mainly driven by movement toward gender-equitable unpaid hours of women and men with the same employment status. In addition, gender inequality in leisure time increased over the business cycle.

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gender economics research topics

Illuminating the role of gender in the economy

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gender economics research topics

This chapter discusses the role of gender in economic relations, processes, and outcomes. Gender differences in economic outcomes such as labor force participation and wages have received growing attention from economists in the last several decades – a positive and much needed development in economic thinking. Indeed most readers will know that on average, men are paid more than women for the same work. While such information is important for our understanding of the economy and people’s economic lives, the role of gender in economics goes beyond such descriptive differences in outcomes. We can go further and employ ideas and experiences related to gender to shed light on many underlying mechanisms in both the economy and in economics as a science.

As Paul A. Samuelson and William D. Nordhaus (2010: 4) [1] say, economics is often thought of as “the study of how societies use scarce resources to produce valuable goods and services and distribute them among different individuals.” This definition of economics covers quite a lot of ground: it says that economics strives to understand how and why different people and groups came to be in the economic situation they are in. The definition further implies – since economics seeks to understand the processes or the “how” behind the allocation of resources – that it could help change undesirable distributions. However, as evidenced by Samuelson and Nordhaus’ own book, the discipline sometimes falls short of working to achieve the first goal of understanding economic processes, while the latter possibility to enable change is explicitly left out of the discipline. One major example of economics’ ignorance of the production and distribution of valuable goods and services is its widespread disregard of the role of gender in the economy, which, as this chapter will outline, is a major failing of the discipline. Feminist economics is the branch of economics most engaged with filling this void.  Feminist economics is a sub-­‐field of economics that gained significant traction and recognition in the 1990s and 2000s, although there had been work done as early as the first half of the 20 th century that could today be considered feminist economics. Although it would be convenient to have a clear definition here, it is difficult to define feminist economics in just one way. Instead, this chapter discusses some of the main issues in feminist economics, highlighting how they contribute to a more holistic understanding of economic processes and outcomes than what the mainstream economic models offered by Samuelson and Nordhaus can give us. 

An important starting point in much feminist thought, including that in feminist economics, is an understanding of the idea of “gender.” Here we make a distinction between the categories of biological “sex” and social “gender.” The former term refers to biological differences between people generally related to reproduction, such as hormones, genitalia, and internal reproductive anatomy. Thus, we have categories of “men” and “women”, distinguished by biological difference.

“Gender”, on the other hand, refers to the social assignment of traits and competencies to people based on their biological sex. (Biological sex may also be seen as a social construct, but that is a topic beyond the scope of this chapter.)

People with female biological characteristics, for example, are expected to behave in a “feminine” manner – the definition of which varies across cultures and over time – based primarily on their possession of some of these characteristics. Thus, in contemporary western societies, women are supposed to be the primary caretaker of their children, while men are (still, often) supposed to provide financial resources for their families. At least to some extent, these social expectations can help us explain why women continue to do the majority of the care-­‐ and housework, even when they do as much paid work as their male partners. 

The relevance of gender as a social category in economic processes is not exclusive to economists who consider their work to be feminist; following Akerlof and Kranton (2000) [2] , many economists recognize that identity can play an important role in economic decision-­‐making. Akerlof and Kranton put the concept of identity into a utility function and analyze it in a game-­‐theoretical framework, bringing it more attention and acceptance in the economics profession than feminist economists had ever received, although they had been saying much of the same thing about the role of gender in determining work profiles and other economic circumstances for years before the publication of Akerlof and Kranton’s model. We return to the issue of which academic work is recognized as “valuable” economics again below. Understanding gender as a socially constructed and assigned category helps explain how economic decisions and circumstances are influenced by the expectations assigned to us. As in the example of care work, the fact that men and women often do different types of paid work can be better understood via a lens of gender roles. That most engineers are men while most kindergarten teachers are women must not be completely explained by an analysis of markets distributing work for the price of a wage. We can instead understand these differences, at least in part, by the social assignment of gender-­‐appropriate traits, interests, and competencies for men and women. 

Another early issue in feminist economics was a critique of the simple exclusion of women in economic analysis. Not only were very few economists females, but until about the 1960s, the economic experiences of women were largely left out of economic analysis. Women were not the agents studied in economic models. In part, this ignorance of women’s economic lives was due to the fact that “women’s work” – at that time (and to some extent, still today) considered household and care work – was not considered economically interesting or important. Even when the household – women’s “sphere” – began to be taken into consideration with the rise of New Home Economics (NHE) in the 1960s, the social and economic realities of many women continued to be ignored, this time by an academic framework which assumed and supported structures which justified and perpetuated women’s weaker position in the economy. In particular, a main issue in the New Home Economics models was that a sexual division of labor in the household, in which a man was the income earner while the woman did the unpaid housework and caring labor (these were always heterosexual couples), was the most efficient and therefore best arrangement, based on women’s alleged comparative advantage in the unpaid work. This framework, while it was one of the first to bring women’s existence as economic agents into the field, offers only a limited understanding of the significance of gender in economic processes and outcomes. In particular, it ignores the fact that efficiency in production is actually not the goal of many households; couples and families often make decisions about work based on factors other than maximizing productivity, such as enjoyment and fairness. Further, the NHE framework perpetuates and justifies an image of family life that may not be ideal for all individuals, couples, and families. This justification is in turn often used in the creation and implementation of public policies (such as laws granting longer maternity leave than paternity leave), which then reinforce the sexual division of labor. 

A telling example of the ignorance of women and gender in the discipline of economics is the fact that in their 650+ page long book, Samuelson and Nordhaus discuss gender in just three short sections: in the context of explaining discrimination, in talking about poverty, and to discuss labor force participation. The authors say that the book is read by millions of people who want to understand how the economy works – but talking about the specific economic situation of half of the world’s population in just a few paragraphs of a 650-­‐page long book does not do much justice to fostering an understanding of the whole economy.  In their first of three mentions of gender, talking about discrimination, the authors say that discrimination against women has declined significantly, and that leaving aside the “family gap” (the gap in earnings related to labor market interruptions that occur when people – mostly women – leave their paid jobs to care for children, the elderly, and the sick), “women appear to have approximately the same earnings as equally qualified men” (p. 263). Even if this were statistically the case, framing the discussion in this way ignores the fact that gender-­‐based discrimination continues to exist (even while Samuelson and Nordhaus say that “gender discrimination… is vanishing today” (p. 328)). Thus even achieving the same qualifications as men, such as studying in the same field and having the same amount and extent of labor market experience, probably meant that women faced discrimination along the way to achieving their qualifications. 

In the second example, Samuelson and Nordhaus explain high rates of female poverty as a result of divergent incomes between highly educated/skilled workers from those with less education and skill. The assertion seems to be that women, who are more likely to be in poverty, are in this position because of their lower education and skill. However, women’s completion rates of higher education passed those of men in almost all developed countries in the last several decades. There must, then, be another explanation for women’s higher poverty rates – but Samuelson and Nordhaus do not look for one. Finally, in their discussion of women’s rising labor force participation rates in the last half-­‐century, the authors say that “a change of this magnitude cannot be explained by economic factors alone… one must look outside economics to changing social attitudes toward the role of women as mothers, homemakers, and workers” (p. 252). Samuelson and Nordhaus leave the discussion there, though, and do not say any more about the topic. In this case, we see a clear example of economics being unable and/or unwilling to investigate the “how” in “the study of how societies use scarce resources to produce valuable goods and services and distribute them among different individuals.” Instead, when the models in economics fall short of being able to explain something such as labor force participation, the implication is not that the models ought to be changed, but instead that the topic is simply not economics. It is curious at best that a foundational textbook in the discipline says that understanding how and why the labor force participation has changed is not part of economics – if labor force participation is not part of economics, what should be? 

The difference between simply observing differences by gender instead of looking for the mechanisms behind the existence of the differences, as Samuelson and Nordhaus seem to prefer to do, is a central distinction between feminist economics and what one might call “gender economics.” The latter brings gender into economics by analyzing differences that may exist between men and women, such as gaps in pay, in wealth, or in labor force participation. While feminist economics certainly applauds the recognition of gender-­‐specific differences in the economy, an important goal of feminist economics is to be able to take the analysis further. Unlike Samuelson and Nordhaus, who say that understanding gender gaps would require “look[ing] outside economics,” feminist economists would prefer to see the discipline be expanded to include explanations of why gender-­‐based gaps in economic outcomes exist. Feminist economists thus consider insights from many other fields in their analysis of how and why we see economic differences by gender. For example, they may draw on insights from psychology, sociology, history, demography, and political science to explain the fact that more women now participate in the paid labor force than ever before, such as the relevance of the birth control pill, the feminist movement, and changes in public policy. Often times, feminist economists also use an analysis of power relations to help understand economic phenomena. In this case, part of why more women in developed counties are now working for pay is that their husbands and/or fathers may not legally stop them from doing so. Feminist economics calls for a more interdisciplinary approach to observing and analyzing economic phenomena – one that, upon reading Samuelson and Nordhaus’ book, one would not think is very welcome in the mainstream of the discipline, but which can help us explain economic processes.  

Ignoring insights from other disciplines leads to the fact that economists hardly can or do adapt their models to more closely reflect reality. Similarly, feminist economics are critical of the assumptions made about economic actors or agents, who can seamlessly identify and maximize their utility, but who must have been born with their preferences, because we never hear anything about personal development in economics, as much as it is a part of the human experience and one that affects a person’s economic circumstances. Another point of criticism from feminist economists is the almost exclusive acceptance of mathematical formulations and econometrics as appropriate methodologies for answering questions in economics – as highlighted in the example of the “Economics and Identity” discussed above. As Nelson (1995:138) says, “quality in method is identified primarily with mathematical rigor.” [3]  

The critiques of the exclusivity of methodology, type of agent, and domain of economics are not exclusive to feminist economics. Most heterodox branches of economics have similar qualms with the mainstream of the discipline. A specific contribution of feminist economics regarding these issues, though, is the claim that these topics can also be understood in gendered terms. As Nelson (1995:133) points out, the characteristics of the models, methods, and agents in economics can be described as possessing or striving for “objectivity, separation, logical consistency, individual accomplishment, mathematics, abstraction, and lack of emotion” – attributes typically associated with masculinity. To be sure: this association does not refer to men , but instead to the social construction of the ideal masculinity . These are also not prescriptions of gender made up or supported by feminists or feminist economists, but instead those which exist in social relations. Qualities often associated with femininity, such as subjectivity, emotionality, and interpersonal connectedness, are welcome and wanted in some other social sciences, but economics decidedly rejects such characteristics for its own models, understanding of its agents, and its methods. Thus, while the general critique that economics is too narrow in what it considers to be good science is shared by several heterodox or pluralist schools of thought, feminist economists also offer a gender-­‐specific point of view on the valuation of the approaches used by economists to try to understand the world. It is helpful to understand the discipline in these gendered terms when we recognize that phenomena with “masculine” characteristics are given more credit or acceptance than “feminine” ones. Assertiveness, risk-­‐taking, and leadership – masculine characteristics – are often rewarded on the labor market, while feminine characteristics such as empathy or care are not as highly valued (read: paid). Social expectation and rules dictate that masculine things are more valuable and this gender bias in value judgement may influence how economists decide what and how to study the economy. Being exclusive in methods, agents, and domain of study is dangerous, though, because it leads to a narrow-­‐minded understanding of the economy, setting us up to miss some of what is going on.

Finally, an important element of feminist economics, which is purposely left out of the type of mainstream economics laid out by Samuelson and Nordhaus, is equality. It is not surprising that feminist economists are concerned with equality; the cornerstone of feminist thinking is that all people ought to have equal political, economic, and social rights. In their pursuit of “objectivity,” economists such as Samuelson and Nordhaus decide to not take a normative stand on issues regarding fairness and equality. Instead, they say that these are “political questions that are answered at the ballot box in our democratic societies” (p. 39). Feminist economics criticizes this stand, saying that the structure of the economy may influence political decisions, and that the mainstream economics laid out by Samuelson and Nordhaus is thus not as neutral as it claims to be or appears to want to be. 

Many of the insights of feminist economics can help us understand economic processes. It shows that, unfortunately, the current mainstream of economics cannot adequately explain many economic phenomena. Instead of simply criticizing it and walking away, the goal of feminist economics is to enable the discipline to be able to say even more about how valuable resources are produced and distributed. An analysis of gender in the economy shows that feminist economics can help economics to develop into a field more able to fulfill its stated goals.  

[1] Samuelson, Paul A. and William D. Nordhaus (2010) Economics. 19 th Edition . New York: McGraw Hill Education.

[2] Akerlof, George A. and Rachel E. Kranton (2000) “Economics and Identity.” The Quarterly Journal of Economics 115(3): 715-­‐753. 

[3] Nelson, Julie A. (1995) “Feminism and Economics.” Journal of Economic Perspectives 9(2): 131-­‐148. 

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The intellectual structure of gender equality research in the business economics literature

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  • Published: 12 May 2023

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  • Francisco Díez-Martín   ORCID: orcid.org/0000-0002-9888-833X 1 ,
  • Giorgia Miotto   ORCID: orcid.org/0000-0002-0973-6597 2 &
  • Cristina Del-Castillo-Feito   ORCID: orcid.org/0000-0002-7903-1365 1  

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In both the public and private sectors, gender equality is a major issue faced by modern management. It is also a primary concern for the global sustainable development defined by the UN 2030 Agenda. Gender equality, as a research topic, has been explored from many different social, economic and political sides; nevertheless, gender equality in business economics is still a very promising research field since the everchanging global organisational environment requires frequent updates and polysemic approaches. The aim of this study is to identify and visualise the intellectual structure and dynamics of gender equality research on business economics literature through a bibliometric quantitative literature analysis. Our results found 12 main lines of research. They also identify the trending topics, sources of knowledge, and literature dissemination paths along these lines between 2001 and 2020. This work contributes to the field of gender issues by showing its intellectual structure and providing a research agenda and identifying future research lines and gaps in the area.

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1 Introduction

Gender equality is a major issue in modern management, both in the public and private sectors (Báez et al. 2018 ), and it is a primary concern for the global sustainable development defined by the UN 2030 Agenda (Miotto et al. 2019 ). Gender equality, as a research topic, has been explored from many different social, economic and political perspectives; nevertheless, gender equality in business economics is still a very promising research field since the everchanging global organisational environment requires frequent updates and polysemic approaches (Belingheri et al. 2021 ). The more recent research topics on gender equality in business economics focus on women on boards of directors (Nguyen et al. 2020 ), salary gaps (Wang et al. 2019 ), risk-taking and financial performance impacts (Baixauli-Soler et al. 2017 ; Papanastasiou and Bekiaris 2020 ), CSR and information disclosure (Pucheta-Martínez et al. 2021 ), and family businesses (Kubíček and Machek 2019 ; González et al. 2020 ).

The increasing number of publications on gender issues makes it difficult to monitor the evolution of this field of research. Knowledge accumulation reduces the assimilation capacity of researchers, making it difficult to keep up to date. This has led to the elaboration of several literature reviews on gender issues during the twenty-first century. Most of these literature reviews are focused on the following main topics: gender and entrepreneurship (Moreira et al. 2019 ), women on boards of directors (Terjesen et al. 2009 ; Cabrera-Fernández et al. 2016 ; Kent Baker et al. 2020 ; Nguyen et al. 2020 ), women in international business (Bullough et al. 2017 ), and gender and corporate social responsibility (Amorelli and García-Sánchez 2021 ). We would also like to highlight a literature review about gender equality in business from 2011 that analyses the research in this field from 1995 to 2010 (Broadbridge and Simpson 2011 ).

These literature reviews are focused on specific topics; nevertheless, there is a lack of a multidisciplinary and interconnected overview of the gender equality field that may deeply understand the cause and effect of the different issues involved (Kirsch 2018 ). Existing literature reviews fail to provide a comprehensive, clear picture of what has been studied thus far and, therefore, the most relevant and promising future research lines should occur in this area (Belingheri et al. 2021 ).

In addition, some key issues in understanding the state of the art in this field of research have not been solved, mainly due to the qualitative nature of previous research. Furthermore, several recent studies have not been included in any generic bibliographic analysis about gender issues in business. These previous investigations identify the main lines of research on gender issues; nevertheless, there is no study that classifies the intellectual structure of the research field, the trends that have caught the attention of researchers, or the investigations that have facilitated the dissemination of knowledge connecting different lines of research. The intellectual structure definition is a comprehensive analysis of the domain of a study field; it is a structured way to define the boundaries and the map of discipline (Hota et al. 2020 ).

Knowing the intellectual structure of the field is key to defining research objectives that contribute to current studies, helping to incorporate new research areas into the field, and defining a relevant and updated research agenda (Carayannis et al. 2021 ). Bibliometric techniques are designed to achieve this purpose: systematically design and visualise the intellectual structure and mapping of a research field (Donthu et al. 2021 ; Silva et al. 2021 ). Unlike qualitative literature reviews, such as systematic reviews, bibliometric methods apply quantitative criteria to analyses large amounts of information and to discover knowledge networks and their structure (Zupic and Cater 2015 ). In addition, this methodology reduces the subjectivity grade of qualitative research associated with researcher bias.

To date, a nonbibliometric literature review of gender equality in business economics has been performed. Previous researchers, such as Broadbridge and Simpson ( 2011 ), performed a qualitative literature review, whereas all other literature analyses focused on specific topics linked to gender issues. Taking into consideration the need to improve the theoretical framework of gender equality in business (Hong et al. 2020 ), the aim of this study is to identify and visualize the intellectual structure and dynamics of gender equality research in the business economics literature and academic field. The research questions we would like to answer are as follows:

RQ1. Which are the most relevant research topics in the gender equality field in the business economics discipline?

RQ2. What are the most influential documents in the field of gender equality in business economics?

RQ3. What are the sources of knowledge on gender equality in business economics?

RQ4. How has gender equality research in business economics evolved in the last twenty years?

RQ5. How are the different topics related to gender equality interconnected?

RQ6. Which are the most relevant topics that will define the future research agenda in this field?

The novelty of this study lies in these specific aspects. First, for the first time in this knowledge area, we use a bibliometric method suitable to review a large corpus of documents using a quantitative technique able to perform an objective and unbiased analysis that provides accountable and trustworthy data (Donthu et al. 2021 ; Kumar et al. 2022 ). Second, we provide a holistic perspective of gender equality in the business economics field, avoiding a focus on only one issue, broadening the spectrum of the research and allowing a wider, more inclusive and multidisciplinary assessment (organisational behaviour, people management, legitimacy, etc.). Third, we highlighted the connections of the entire research field of gender equality studies in the business economics literature. Fourth, we define an update and analytic state of the art in terms of gender equality in business economics, and we propose a future, relevant and useful research agenda.

This document is organised as follows: the next section explains the methodology, and we describe the bibliometric techniques and concepts used. Afterword, the results section explains the main lines of research on the field, trends and connections. Finally, the results are discussed, and a research agenda is suggested.

2 Methodology

The study of the intellectual structure of gender equality research in the business economics literature was carried out using bibliometric data. To do so, this study adopts methodological procedures similar to those of previous bibliometric research in the field (e.g. Kumar et al. 2022 ) and implements the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, which consists of three major stages: article assembling, arranging and assessing (Paul et al. 2021 ).

2.1 Assembling

To assemble the corpus of articles on the defined research field, we identify the search keywords related to gender equality in the business economics literature. These keywords are included and organised into the following search string: “gender diversity" or "gender gap" or “gender equality” or “gender parity” or “gender equity”. These search keywords have been chosen taking into consideration previous research on gender equality that refer to all these topics as highly linked and related to an equal sharing of opportunities of progress, properties, paid work, money, decision-making power and time management between men and women (Furlotti et al. 2019 ; Miotto and Vilajoana-Alejandre 2019 ; Mehng et al. 2019 ). These concepts are also included in international reports, indices and institutional policies such as “The 2019 Report on Equality between Women and Men in the European Union” (European Commission 2019 ), the “Sustainable Development Goal 5” indicators (United Nations 2019 ), the “Gender Development Index” (United Nations Development Programme 2020 ), the “Gender Empowerment Measure” (United Nations Development Programme 2020 ) and the “Global Gender Gap Report” (GGI) (World Economic Forum 2019 ).

We use the abovementioned search string through the Web of Science (WoS) document titles, abstracts, and keywords. Even if the Web of Science (WoS) includes les articles tan the Scopus database, in the business economics fields, the percentage of unique and overlapping citations in Scopus and WoS are very similar (Martín-Martín et al. 2018 ). In addition, WoS is the most widely used database in the business economics literature (Zupic and Cater 2015 ), even if scientometric scholars have not yet decided which database is the best one (Pranckutė 2021 ). The search resulted in 22,263 documents.

2.2 Arranging

To arrange the corpus of articles returned from the assembling stage, we applied these filters in the WoS database: research area and publication year. We filtered the corpus of articles taking into consideration the business economic research area. This led to a reduced corpus of 3456 articles. We focused on articles published during the twenty-first century, and the timeframe of the study was 2001–2020. Although the first articles on this subject were published in 1984, from the twenty-first century, there was a high increase, and since then, more than 20 documents have been published annually. This timeframe definition led to a corpus consisting of 3316 documents. Finally, we identified 51 documents whose references were invalid or unreadable. This filter was important because the bibliometric analysis that we apply (co-citations) uses references as the source of analysis. Thus, the final research sample consisted of 3265 documents.

2.3 Assessing

This study applies a bibliometric analysis approach to assess a corpus of 2,816 articles on gender equality. Bibliometric methodology uses quantitative techniques with the aim of summarising large quantities of bibliometric data to show the intellectual structure of a research field (Donthu et al. 2021 ).

Inspired by previous bibliometric research on the business economics field (e.g., Díez-Martín et al. 2021 ), this study performs a bibliometric analysis using science mapping based on cocitation analysis in CiteSpace. Science mapping is a useful technique to explore the relationships between research constituents. It offers an organised visual representation of the characteristics and relationships among different studies in a scientific area (Mukherjee et al. 2022 ). As opposed to the manual analysis of quantitative and qualitative data, science mapping is a more efficient and objective methodology due to automated data analysis (Lim et al. 2022 ; Mukherjee et al. 2022 ).

Cocitation is a science mapping technique (Cobo et al. 2011 ; Mukherjee et al. 2022 ). It defines the frequency with which two papers ‘A’ and ‘B’ are cited together by a third paper ‘C’ (Small 1973 ). The idea behind this approach is that when two papers are cited together, they will probably share similar theories, assumptions, concepts or methods. Co-citation analysis is one of the most widely used methods for bibliometric research in social science disciplines (Zupic and Cater 2015 ) and is useful for uncovering relationships between cocited publications (foundational knowledge) (Mukherjee et al. 2022 ). Cocitation analysis highlights networks between different studies and can detect paradigm shifts, trends and schools of thought from a long-term perspective (e.g. Delgado-Alemany et al. 2022 ). To enrich the assessment of the bibliometric analysis, we used two network metrics (Donthu et al. 2021 ): burstness and betweenness centrality. These indicators provide additional valuable information about the network.

We used CiteSpace software for the cocitation analysis based on previous and well-known reviews of bibliometric software tools (Moral-Munoz et al. 2019 ). CiteSpace is a Java-based scientific detection and visualisation software that analyses the evolution of a research field through bibliometric co-citation (Chen 2006 ). Previous research in business economics used this software to understand the intellectual structure of a body of knowledge (Cruz-Suárez et al. 2020 ; Pascual-Nebreda et al. 2021 ).

Furthermore, this study provides a proposal of a future research agenda and research gaps based on the analysis of the most relevant topics and networks. The next section shows the findings of the study.

In the following two sections, we show the results that answer the research questions. In the first section, we show the main lines of research on gender equality in business economics (RQ1). This section also shows which are the most influential documents on gender equality, identifying the documents that have received more attention by the scholars of this area and that have become trending topics (RQ2). In addition, we describe the sources of knowledge of each main research line (RQ3). The second section describes the evolution of gender equality research in business economics in the last twenty years (RQ4). Furthermore, we highlight the research articles that are the node of connection between the different lines of research related to gender equality (RQ5). Finally, we propose a future research agenda, highlighting the most relevant topics that will define the future research lines in this field (RQ6).

3.1 Main lines of research

The main lines of research on gender issues in the business economics literature are shown in Table 1 (RQ1). We found 12 main research lines. Each research line is a cluster generated by CiteSpace and based on co-citations. To confirm that our clusters are homogeneous between themselves (cohesion) and differentiated from the others (separation), we use the silhouette value. This measure is used to identify the quality of a cluster configuration. Each cluster shows a Silhouette value greater than 0.845, above the recommended 0.7 (Chen et al. 2010 ). In addition, to measure the network quality, CiteSpace uses the modularity Q (from 0 to 1), which identifies the capability of a network to be decomposed into multiple components or clusters (Chen et al. 2010 ). In this study, the gender research network shows a reasonably well-coupled distribution of the clusters, reaching a Modularity Q of 0.7495.

Furthermore, Table 1 shows the number of trending topics of each line of research (RQ2). CiteSpace detects trends (burst documents) by applying the algorithm of Kleinberg ( 2003 ). The burstness identifies the most relevant documents that have been considered a source of a research trend, since they have received a high number of citations during a specific timeframe (Kim and Chen 2015 ; Hou et al. 2018 ). Supplementary material of Appendix 1 shows the results of the burst analysis, which illustrates the trending topics in the research field from 2001 to 2020.

The main lines of research on gender issues in the business economics literature are described below. The order of the description of the clusters is based on the size of the research line. Supplementary material of Appendix 2 identifies the documents included in each cluster. These documents represent the main sources of knowledge on gender equality in business economics (RQ3).

Cluster #1–Risk Management–is the greatest line of research in the field of gender in the business economics literature. It contains the largest number of referenced documents (93). This indicates that most research in this field has focused on the study of how gender risk profiles on boards of directors affect corporate financial performance. According to several authors, the lower risk-taking attitude and the higher risk aversion in firms run by female CEOs have lower leverage, less volatile earnings, and a higher chance of survival than otherwise similar firms run by male CEOs (Cumming et al. 2015 ; Faccio et al. 2016 ). In addition, the inclusion of women on boards of directors may improve fraud control and lower the impact of risky financial operations (Lucas-Pérez et al. 2015 ). Specifically, these papers analyse how gender board composition affects conservativism or risk tolerance in the decision-making process from the financial side (Berger et al. 2012 ; Palvia et al. 2015 ; Hutchinson et al. 2015 ; Bennouri et al. 2018 ). Gender differences and approaches in risk-taking tolerance affect corporate financial performance (Hoogendoorn et al. 2013 ), dividend pay-out policies (Ye et al. 2019 ), forecast accuracy and audit quality (Gul et al. 2013 ), increase ROA and ROE, and significantly decrease Tobin’s Q (Bennouri et al. 2018 ). This line of research has become the second main research trend in the field since 2017, based on seven burst documents (Supplementary material of Appendix 1 shows the results of the burst analysis, which illustrates trending topics in the research field during 2001–2020). Furthermore, the sources of knowledge in cluster #1 show an average year of publication in 2016. This cluster group shows recent and updated articles and research theories.

Cluster #2–Board Performance–represents the second largest areas of research, with more than 80 research papers. The mean year of the investigations on this cluster is 2015. Academics in this area have focused on analysing the relationship between gender diversity on boards of directors and firm performance (Lückerath-Rovers 2013 ; Chapple and Humphrey 2014 ). This line of research has become the main research trend in the field since 2017 (13 burst documents, see Supplementary material of Appendix 1). For example, they analyses the relevance of the morality or legitimacy of gender diverse boards from the point of view of stakeholders’ perceptions (Gregory-Smith et al. 2014 ; Perrault 2015 ). This cluster also analysed quota issues and their usefulness (Seierstad 2016 ) Characteristics such as firm size, type of business, industry, focus on innovation (Cabeza-García et al. 2021 ), size of the board (Strøm et al. 2014 ) or country gender parity have been studied to monitor the impact of gender board diversity and company outcomes (Post and Byron 2015 ).

Researchers on Cluster #3–Quotas and Tokenism–are focused on the presence of women in the companies’ boards of directors and, specifically, on the application of quotas to guarantee the presence of female directors. This practice has been internationally discussed and often adopted. Quota implementation has been considered formally, including quotas in the national legal framework, or informally, as a best practice in several private organizations (Adams and Funk 2012 ). In this context, researchers analyse whether and how organisations should ensure the presence of women in the boardroom and their real impact on governance and performance. (Adams and Ferreira 2009 ; Ahern and Dittmar 2012 ) . Research indicates that women on board performance improves when a critical mass of women is reached, since according to the tokenism literature, women may be reticent to advocate for other women in powerful positions (Torchia et al. 2011 ). For example, with the presence of at least three women and above, CSR indicators improve (Post et al. 2011 ). However, the appointment of women to a board driven by tokenism does not improve corporate performance and results (Abdullah 2014 ). This line of research has been the most trending topic of the field, particularly between 2010 and 2015 (39 burst papers). In fact, the average year of publication of the sources of knowledge in cluster #3 is 2010. Although it is one of the most prolific lines of research in this field and has been trending since 2005, the scientific advances published in this cluster are based on more consolidated and old papers. In other words, advances in this line of research are taking place at a slower rate than those from lines of research with a more recent average year of publication.

Cluster #4–CSR–refers to articles focused on how gender diversity on boards of directors influences CSR performance, strategies and policies. Specifically, the quality and quantity of nonfinancial information and data companies run by women are disclosed compared with organisations managed mainly by men. For example, several authors affirm that diverse and inclusive boards of directors tend to disclose more and better quality information about environmental impact (Frias-Aceituno et al. 2013 ; Amran et al. 2014 ; Liao et al. 2015 ) and that gender diversity has a positive influence on CSR. Researchers suggest that female talent can play a strategic role in enabling firms to manage their social responsibility and sustainable practices appropriately (Setó-Pamies 2015 ), and this CSR output may improve corporate legitimacy (Zhang et al. 2013 ; Díez-Martín et al. 2021 ) and firm value (Fernández-Gago et al. 2016 ). Nevertheless, gender diversity is a key factor for CSR performance if female members are not chosen due to quota allocation since the control of the board of directors’ assignment is negative for the CSR decision-making process (Hafsi and Turgut 2013 ). Others focus their research on finding insights into the link between board diversity and CSR, particularly the importance of linking gender diversity and CSR decision-making processes (Rao and Tilt 2016 ) and the minimum number of women (at least 3) that may make the difference in CSR strategy decisions (Fernandez-Feijoo et al. 2014 ). This is a relatively recent cluster, with an average year of publication of 2016. Seven research trends were created between 2001 and 2020.

Cluster #5–Team Diversity–represents a relatively wide area of research with 76 academic papers. The mean year of the papers in this cluster is 2006; therefore, it includes one of the oldest areas of research within the gender equality field and the most trending before 2010. It generated 15 burst papers between 2001 and 2010. This cluster addresses the topic of team diversity and team outcomes, the differences between group members and their effect on group performance (van Knippenberg and Schippers 2007 ). Researchers have analysed the characteristics or factors that lead firms to appoint more women to top management teams and their outcomes on a firm’s performance. Along this line, they identify the effects of team diversity on firm performance in diverse contexts (Horwitz and Horwitz 2007 ; Joshi and Roh 2009 ). For instance, female top managers’ qualifications are relevant for improving organisational performance (Smith et al. 2006 ). Academics study the elements that motivate the decisions of firms regarding including or not including women on their boards of directors, suggesting that fulfilling internal or external demands has a strong influence (Farrell and Hersch 2005 ; Francoeur et al. 2008 ) and that, in many cases, board diversity is influenced by a firm’s external business environment and requirements (Brammer et al. 2007 ). In other situations, gender diversity in top management positions transcends external factors (Krishnan and Park 2005 ).

Cluster #6–Pay Gap–focuses on the existence of the gender pay gap, the reasons for this issue, and the differences between industries, kind of organisation and countries (Blau and Kahn 2003 , 2006 ; Albrecht et al. 2003 ; Arulampalam et al. 2007 ). The mean year of publication of the sources of knowledge of this cluster is 2005, representing one of the oldest areas of research in the field. It was the second trending line of research before 2010 (10 burst papers). Researchers have shown that the gender gap typically widens towards the top of the wage distribution (the “glass ceiling” effect), and in a few cases, it also widens at the bottom (the “sticky floor” effect) (Albrecht et al. 2003 ; Arulampalam et al. 2007 ). According to these cluster’s papers, the cause of this gap has its rut in the rise of married female labour force participation that occurred in the last century, when several households must decide whether a married woman should work or not, and in most cases, a second salary was necessary to maintain the family (Blau and Kahn 2003 ; Greenwood et al. 2005 ; Attanasio et al. 2008 ). The segregation of women into lower‐paying occupations, industries, and establishments accounts for a sizable fraction of the sex gap in wages (Bayard et al. 2003 ). Nevertheless, women are promoted at roughly the same rate as men but may receive smaller wage increases upon promotion; women are just as likely as men to be promoted but find themselves stuck at the bottom of the wage scale for the new job class (Booth et al. 2003 ). The increase in educational levels contributed decisively towards greater wage inequality (Machado and Mata 2005 ), since higher levels of wage compression (measured in absolute or relative terms) are positively related to training (Almeida-Santos and Mumford 2005 ).

Cluster #7–Competitiveness–research is about the study of the differences between women and men when acting in a competitive environment (Croson and Gneezy 2009 ; Buser et al. 2017 ). There is evidence that demonstrates that women are less inclined to enter competition. They feel less comfortable in a highly competitive environment, and this attitude increases with age (Datta Gupta et al. 2013 ; Andersen et al. 2013 ). Researchers have explained this gender gap by stating that men are more overconfident (Niederle and Vesterlund 2005 ). This attitude affects and limits women’s career progress (Balafoutas and Sutter 2012 ) or the participation of women in science (Fryer and Levitt 2010 ; Moss-Racusin et al. 2012 ). This cluster is very useful for researchers seeking to justify differentiation in gender-biased career orientation and professional progress. Along this line, some results suggest that preferences over uncertainty can be just as important as preferences over competition in driving job-entry choices (Flory et al. 2015 ). This line of research has been the second most trending topic between 2010 and 2015 (24 burst papers). The average year of publication of the sources of knowledge in cluster #7 is 2011.

Researchers on cluster #8–Innovation–try to set a theoretical framework for the relationship between gender and innovation (Agnete Alsos et al. 2013 ) through the analysis of how gender diversity within R&D teams impacts firm innovation but also how gender policies aimed at creating, maintaining, and disrupting institutions (Lawrence et al. 2011 ). The number of papers within this cluster is 23; therefore, it is one of the smallest areas of study within the field. The mean year of the publications is 2011. The results show that innovation is more advanced in higher gender diverse teams (Van Dijk et al. 2012 ; Díaz-García et al. 2013 ). Additionally, the relation between gender and other types of diversity, such as age, education or ethnicity, are also studied when considering the effect on innovation (Østergaard et al. 2011 ). The average year of publication of the sources of knowledge in this cluster is 2011. This line of research generated 7 trending topics between 2010 and 2015.

Cluster #9–Wage Gap Reasons–focuses on the reasons that explain the gender wage gap. The average year of publication of the sources of knowledge in this cluster is 2015, and it generated 3 trending topics between 2010 and 2020. This cluster stresses the idea that the origin of this breach resides in the different roles between women and men in family management and the time dedicated to family care. While convergence between men and women in traditional human capital factors (education and experience) played an important role in the narrowing of the gender wage gap, these factors explain relatively little of the wage gap since women exceed men in educational attainment and have greatly reduced the gender experience gap (Blau et al. 2013 ). Nonetheless, labour-market experience remains an important factor in analysing female wages (Blau and Kahn 2017 ). Women are less likely to work in results-driven companies, with highly variable salaries linked to employees’ objective achievement. Furthermore, women receive only 90% of the firm-specific pay premiums earned by men, and this practice will contribute to the gender wage gap since women are less likely to work at high-paying firms or if women negotiate worse wage bargains than men (Card et al. 2016 ). The salary gender gap would be considerably reduced if firms did not economically reward individuals who laboured long hours, something that is very common, for example, in industries such as the corporate, financial, and legal worlds and less common in technology, science, and health (Goldin 2014 ). To explain women’s shorter time dedicated to work, researchers focus their attention on the analysis of family structure and management: motherhood and children’s education are two factors that explain why women’s income, in middle age, has a gap of up to 32% compared with men’s salary (Angelov et al. 2016 ). Motherhood is one of the most important factors of the gender salary gap (Adda et al. 2017 ). Studies show that, for example, the Motherhood delay leads to a substantial increase in earnings of 9% per year of delay, an increase in wages of 3%, and an increase in work hours of 6 (Miller 2011 ). Likewise, when a woman becomes more likely to earn more than a man, marriage rates decline. In couples where the wife earns more than the husband, the wife spends more time on household chores; moreover, those couples are less satisfied with their marriage and are more likely to divorce (Bertrand and Pan 2013 ).

Cluster # 10–Productivity–research is about gender and productivity. Progress in this line of research is slowing down. The average year of publication of the sources of knowledge in this cluster is 2011, and it generated 3 trending topics before 2010. According to these articles, women progress less and more slowly in their professional careers, and their salaries are lower than those of men for three main reasons: less advanced training, differences in career interruptions (specifically motherhood), and differences in weekly hours (specifically to take care of the kids) (Bertrand et al. 2010 ; Becker et al. 2010 ). The three of them are related to a lack of productivity. The cluster analyses the link between gender and productivity in several industries, environments and countries (Peterman et al. 2011 ; Kilic et al. 2015 ).

Cluster #11–TMT–is one of the smallest areas of research within the gender equality field, including only 9 papers. The mean year of the investigations is 2016; thus, it is one of the most recent topics among the updated research. Within this cluster, researchers explore the effect of female representation in top management teams (TMT) and firm performance (Schwab et al. 2016 ; Jeong and Harrison 2017 ). Several investigations focus on the effect of gender diversity in TMT and financial operations, such as initial public offerings or mergers and acquisitions (Parola et al. 2015 ; Quintana-García and Benavides-Velasco 2016 ). Additionally, the presence of female top managers is positively related to entrepreneurial outcomes in established firms, although these results are weakened in firms with many women among their employees since many times a female top manager is less likely to favour lower-level female employees, as her categorisation as a member of the TMT restricts gender-based favouritism (Lyngsie and Foss 2017 ). Moreover, aspects related to quotas on women on top management teams are also covered, identifying, for example, how the presence of a woman on a top management team (TMT) reduces the likelihood that another woman occupies a position on that team (Dezso et al. 2016 ).

Cluster # 12–Labour Force Access–is the oldest and smallest line of research in this field. The average year of publication of the sources of knowledge in this cluster is 2002. It analyses factors that have caused an increase in women's access to the labour market, and references the revolution that transformed women’s opportunities (Goldin 2006 ). Aspects such as fertility and motherhood are analysed: for example, birth control availability, such as the contraceptive pill, are considered key factors for increasing female employment (Goldin and Katz 2002 ). Moreover, women are currently more educated, attending college and graduate education (Jacob 2002 ; Charles and Luoh 2003 ), and marriage and motherhood ages are later, for example, as a result of the possibility of in vitro fecundation (Gershoni and Low 2021 )., These aspects reduce the gender gap in career achievement. For example, the growing presence of a new type of man–one brought up in a family in which the mother worked–has been a significant factor in the increase in female labour force participation over time (Fernandez et al. 2004 ). Social policies are also considered in the female labour force, and their results in different countries. Nevertheless, for all women around the world, attaining the combination of reproductive empowerment and decent work is a challenge. Career advancement is interrupted by childbearing (Petrongolo 2004 ) despite social protection policies (Finlay 2021 ).

3.2 Connection between lines of research: turning points

In the following section, we show the research network of gender issues in the business economics academic literature (Fig.  1 ). We describe the evolution of the field (RQ4), and we highlight the connections between the main lines of research from 2001 to 2020 (RQ5). To identify the nodes that connect the different research topics, we use betweenness centrality (Bc). This indicator quantifies the number of times that a node acts as the most direct bridge (along the shortest path) between two other nodes (Chen et al. 2009 ).

figure 1

Research network on gender issues in the business economics literature (2001–2020)

To deeply comprehend the research network development, a diagonal observation perspective is recommended, from left to right. In this way, we can better understand how research on gender issues in the business economics literature has evolved. We can see that the research focus shifted from studies about the issues of women's access to the labour market (year 2002) to different topics such as risk management, firms’ performance and CSR.

During the first decade of the twenty-first century, studies on gender focused on analysing the factors that favour women's access to the labour market (cluster #12), the gender pay gap and its causes (cluster #6), and gender diverse working team performances (cluster #5). During these decades, the papers that have contributed the most to the research field, being the main intellectual bridges that connect different approaches in this field, are Arulampalam et al. ( 2007 ) and Smith et al. ( 2006 ). The first paper connects research between clusters #12 and #6 by bridging the gender pay gap and the factors conditioning access to the labour force and career progress, such as the provision of childcare (Bc = 0.13). The second analyses the effects of management diversity and female quotas in the corporate context (Bc = 0.19). This research shows that the positive effects of women in top management strongly depend on the qualifications of female top managers and not on their numbers or quotas, signalling a research diffusion path between clusters #5 and #3.

The second decade of the twenty-first century has seen a growth in the number of lines of research on gender issues. Researchers ponder the consequences of gender quotas and tokenism (cluster #3), relative to women’s productivity in the corporate environment, especially in management positions (cluster #10), and the effects on innovation (cluster #8). They also explore the role that competitiveness plays as a determinant of the gender gap (cluster #7). The research line about productivity is strictly linked to the gender pay gap (cluster #6), since it relates women’s performance and productivity with the salary gap. Nevertheless, the other research lines (#3, #8, #7) all converge into cluster #2 about women on boards and firm performance. Moreover, the most recent and updated research topics (#1, #4 and #11) are linked through cluster #2.

If we consider the evolution of gender topic research related to business economics, gender quotas and tokenism (#3) and woman on board performance (#2) represent the nodes and main line of connection of the actual knowledge network. On the one hand, it is observed that the research lines of the beginning of the century connect with cluster #3. At this stage, Adams and Ferreira (Adams and Ferreira 2009 ) research is a keystone of this cluster and of the whole network (Bc = 0.21). The article affirms that when gender diversity in boards of directors is regulated by female quotas or driven by tokenism, it does not ensure a higher level of efficiency and effectivity of the boards and does not necessarily improve firm performance. On the other hand, it is also observed that the most current lines of research are connected with cluster #2. The keystone article of this cluster is Lückerath-Rovers ( 2013 ), and it investigates the financial performance of Dutch companies both with and without women on their boards (Bc = 0.21). The research shows that the presence of women in top management is a logical consequence of a more innovative, modern, and transparent enterprise, and it may improve stakeholders’ management and reputation; nevertheless, it cannot prove that there is a positive relationship between gender board diversity and a firm’s economic and financial performance.

An interesting node that connects cluster #2 and cluster #7 is represented by Charness and Gneezy’s ( 2012 ) article that demonstrates that women are more conservative about investment, and they appear to be more financially risk averse than men (Bc = 0.10). A different attitude in terms of risk taking and competitiveness may positively and negatively affect companies’ performance if their management teams are more gender inclusive. Cluster #7 represents the link with cluster #9, where researchers, in addition to competitiveness, take into consideration and propose wage gap causes.

4 Research agenda

During the twenty-first century, research on gender issues in the business economics literature has largely advanced. This progress has led to broad and useful knowledge creation and spread, but at the same time, it has also revealed new research gaps that should be addressed. In this paper, we propose a future research agenda (RQ6) based on the actual context. To design this research agenda, we follow the same process as Díez-Martín et al. ( 2021 ). We identified the most relevant and existing gaps based on our reading of the newest trending topic documents (i.e., newest burst documents) and reflection of extant gaps under each major theme.

In Table 2 , we describe the proposed research agenda, identifying the main topics, research questions and primary authors and sources of knowledge.

4.1 Beyond women on board and TMT: the middle management

Many studies analyse the influence of the presence of women on boards of directors on their effects on firm performance (Jane Lenard et al. 2014 ; Liu et al. 2014 ; Nguyen et al. 2020 ). The evolving role of women in society and the application of female quotas imposed by several countries have led researchers to dig into these aspects (Bøhren and Staubo 2014 ; Bertrand et al. 2018 ). Nevertheless, there are very few studies about women in middle management, since the literature on business economics has not yet addressed this topic, probably because it is much easier to obtain information about boards of directors, as the disclosure of this information is mandatory by law in most countries (Kent Baker et al. 2020 ). We learned much about gender diversity on boards and top management; nevertheless, research should better understand the presence and effect of gender diversity in middle management (Ferrary and Déo 2022 ), which is important for daily firm management. We should understand if and how gender diversity in middle management also affects firm performance if inclusive teams are more productive, committed, innovative, risk-taking biased, socially responsible, and accountable. We should examine whether diversity in middle management can make the difference, positively or negatively, or if there are no relevant differences, since strategic decisions depend only on top management teams. Thus, we encourage future research to pursue a better understanding of the role of women in middle management:

What is the gender composition of firms’ middle management?

What are the effects of gender diversity in middle management?

How does gender diversity in middle management affect firm performance?

Are inclusive teams more productive, committed, innovative, risk-taking biased, socially responsible, or accountable?

Can diversity in middle management make the difference, positively or negatively, or there are no relevant differences, since strategic decisions depend only on top management teams?

4.2 Human resources and people management

Few research studies on gender diversity are related to people management. For example, previous studies show that women in the recruiting process tend to increase board gender diversity (Hutchinson et al. 2015 ) or that flexible working schedules and compensation improve firms’ gender inclusion (Goldin 2014 ; Nguyen et al. 2020 ). Nevertheless, there are several aspects related to human resources management that have not yet been covered, such as recruiting process practices and gender diversity; salary gender gap from the people management perspective; working conditions and gender equal career opportunities; the effect of tokenisation at all firm management levels; and external and internal factors that improve or decrease gender equality in management positions. Therefore, we encourage future research to answer the following questions:

How is gender diversity managed and led in recruiting process practices?

How is the gender salary gap managed and dealt with from the people management perspective?

How are human resources departments dealing with the working conditions of women and gender-equal career opportunities?

How can we mitigate the effect of tokenization at all firm management levels?

What are the external and internal factors that improve or decrease gender equality in management positions?

4.3 Organisational behaviour

There are mechanisms that mediate the relationship between gender diversity and firm outcomes (Lucas-Pérez et al. 2015 ). Organisational behaviour variables may affect gender equality teams and firm outcomes (Cabrera-Fernández et al. 2016 ). Corporate leadership and internal communication have a moderating effect on gender issues (Adams 2016 ; Fernández-Temprano and Tejerina-Gaite 2020 ). Future research should focus on the main organisational internal dimensions that may improve gender inclusion and firm performance at the same time (Saitova and di Mauro 2021 ). What are the main soft skills and practices that increase internal gender equality and external competitiveness? At this point, we posit the following research questions:

How does internal organisational management improve gender inclusion and firm performance at the same time?

What are the main soft skills and practices that increase internal gender equality and external competitiveness?

4.4 What about customers?

According to stakeholder management and institutional theory, gender equality policies are very much appreciated and are considered a commitment to the common good (García-Sánchez et al. 2020 ). This alignment with stakeholders’ expectations increases corporate legitimacy (Díez-Martín et al. 2021 ) and access to economic and human resources (Blanco-González et al. 2020 ). Research studies have focused mainly on the impact on specific stakeholders such as shareholders and employees (Perrault 2015 ), ignoring customers. Future investigations should analyse whether gender policies may influence customer behaviours such as purchase intention, brand advocacy, and brand perceived ethicality. Applying behaviour theories (Hegner et al. 2017 ), researchers could understand the relationship between gender diversity and customer behaviour from a different and novel approach. Therefore, we propose the following research questions for future undertaking:

Do gender equality policies influence customer behaviours such as purchase intention, brand advocacy, and brand perceived ethicality?

Is there a relationship between gender diversity in organisations and customer behaviour?

4.5 Wage gap reasons

The gender salary gap is still a global issue (Wang et al. 2019 ). In most Western countries, for example, access to the labour market and to higher education are variables that may not affect the salary gap as in the past since women are as educated as men (Kleinjans et al. 2017 ). Many factors have recently been considered key to explaining the salary gap, such as family caring, motherhood, cultural prejudice, and self-esteem. Nevertheless, a deeper analysis of these aspects should be performed to overcome these obstacles and reduce the salary gap. Examples include children’s education about equal responsibility in family caring, use of technology to improve flexible working schedules for parents, performance evaluation based on results and not on working hours, cultural prejudice that avoids women’s career progress and gender-equal work-life balance opportunities. Therefore, we propose the following research questions:

Does access to the labour market affect the salary gap?

Does access to higher education affect the salary gap?

Which other variables may affect the salary gap: family caring, motherhood, cultural prejudice, self-esteem, etc.

Could children’s education about equal responsibility in family caring reduce the salary gap in the future?

Does the use of technology to improve flexible working schedules for parents, performance evaluation based on results and not on working hours, and a gender-equal work-life balance opportunity help reduce the salary gap?

4.6 Tangible and intangible assets

Most gender issues research focuses on corporate tangible assets such as financial performance and ROI (Reddy and Jadhav 2019 ). Nevertheless, there are very important intangible assets, such as reputation, that may be a very relevant source of competitive advantage (Miotto et al. 2020 ). There are several studies about women’s inclusion on boards of directors and their impact on the media and public opinion (de Anca and Gabaldon 2014 ) and on firm reputation (Bear et al. 2010 ; Navarro-García et al. 2020 ), but there is an unfulfilled research gap about other gender issues in business economics and their impact on external stakeholders’ opinions and expectations. Gender equality policies, if properly communicated, may be a source of positive reputation and corporate legitimacy (Blanco-González et al. 2020 ). In this regard, we call for new research on how organisational gender issue management improves tangible and intangible corporate assets:

Does women’s inclusion on boards of directors have a positive impact on the media, public opinion and firm reputation?

Does gender equality policies impact external stakeholders’ opinions and expectations?

May gender equality policies, if properly communicated, be a source of positive reputation and corporate legitimacy?

How may organisational gender issues management improve corporate reputation and legitimacy?

4.7 Gender in corporate governance and business ethics

Gender diversity and inclusion, specifically about boards of directors’ membership, is one of the most topical corporate governance issues (Nguyen et al. 2020 ). In terms of corporate governance, it has been demonstrated that there are ethical implications that force women to be included in top management positions (Kagzi and Guha 2018 ) and that female corporate leaders are more respectful of the legal framework and behave more ethically than men, decreasing the firm’s negative exposure (Ben-Amar et al. 2017 ). Nevertheless, there are few studies that analyse the relationship between gender issues management and business ethics from a broad and comprehensive perspective. Future research could focus on perceived organisational ethicality and business ethics from a firm gender equality strategy and policies perspective:

What is the relationship between gender issues management and business ethics?

Are perceived organisational ethicality and business ethics connected with the firm gender equality strategy and policies perspective and how?

4.8 Size and geography matter

Many studies have focused on multinational companies and large corporations. These studies have not considered small and medium-sized enterprises. In addition, few studies have compared different countries and the heterogeneous contexts that may affect gender issues, for example, in terms of legal framework, good governance recommendations and women rights development status. Some countries institutionalize gender quotas in private companies, while in others, girls’ right to education is still not ensured.

The priorities in gender issues of some nations are different from others due to institutional and socioeconomic differences (Post and Byron 2015 ). In this multicultural environment, future researchers should test previously raised hypotheses in new contexts. They should take into consideration different kinds of companies: public and private, large and small, in different industries, and from more and less developed countries. The creation of collaborative networks of researchers from different countries working on gender issues in business economics could be a useful and important project to carry out soon. In this regard, we call for new research on:

How are gender issues considered and managed in small- and medium-sized companies?

Is the kind of industry an important variable in terms of gender equality policies?

Are the priorities in gender issues of some nations different from others due to institutional and socioeconomic differences?

In the actual multicultural environment, future researchers should test previously raised hypotheses in new contexts.

How is gender equality perceived in different kinds of organisations, such as public and private, large and small, in different industries, and from more and less developed countries?

How are gender issues in business economics perceived and addressed in different countries?

5 Conclusions

This paper defines and visualises the intellectual structure of the research field of gender in the business economics literature from 2001 to 2020. The intellectual structure definition is a comprehensive analysis of the domain of a study field; it is a structured way to define the boundaries and the map of a discipline (Hota et al. 2020 ; Silva et al. 2021 ; Carayannis et al. 2021 ). The intellectual structure mapping answers the paper’s research question, providing information and details about which are the most relevant research topics in the gender equality field in the business economics discipline? (RQ1. What are the most influential documents in the field of gender equality in business economics? (RQ2.) What are the sources of knowledge on gender equality in business economics? (RQ3.) How has gender equality research in business economics evolved in the last twenty years? (RQ4.) How are the different topics related to gender equality interconnected? (RQ5.) Which are the most relevant topics that will define the future research agenda in this field? (RQ6.)

To date, there are no other studies of this nature for this research field. Our research complements previous qualitative literature reviews applying a quantitative analysis of large volumes of documents. In addition to the use of a systematic and objective bibliometric methodology, the novelty of this research stands on the broader scope and perspective of the analysis of the research field. Previous papers have focused mainly on specific topics, such as women on boards of executives (Kent Baker et al. 2020 ) or gender entrepreneurship (Moreira et al. 2019 ).

This research is based on the quantitative accuracy of a bibliometric review of more than 3000 documents, and its main contribution is the identification of the main research lines, trends and evolution, knowledge sources and extent of gender issues research on business economics. We visualise how the knowledge of this research field is organised, identify past and future challenges, and propose a future relevant research agenda.

The study identifies the most important sources of knowledge on gender issues in business economics from 2001 to 2020 (Supplementary material of Appendix 2). The quantitative applied methodology ensures a high level of objectivity and academic consistency, which provides unique value to the study, being the first one in this field. Previous literature reviews did not achieve such a broad intellectual scope since they were limited by the use of a qualitative and subjective approach (Broadbridge and Simpson 2011 ) or because they focused only on specific topics, such as gender board diversity (Kent Baker et al. 2020 ). The paper organization based on research lines is very useful for researchers that may use this structure as a starting point for their investigations. Moreover, practitioners may have an organised and clear idea of the trending topics about gender issues in business economics and a guideline to follow up on these matters.

Our results highlight the main topics and challenges in gender issues research from 2001 to 2020. We could summarise these topics in the following questions: which are the main factors that influence women's access to work? In what environments, industries, sectors and countries does the gender salary gap persist, and what are the main causes of this issue? Are quota policies helpful? How does gender diversity influence company performance and results? How does gender diversity in management positions affect CSR policies, information disclosure and corporate accountability? How does gender inclusion affect innovation and productivity? How do intrinsic variables (risk profile, competitiveness) influence firms’ results?

These topics have been combined and organized into 12 large research areas, shaping the intellectual structure of gender equality in the business economics academic field. Some of these lines of research confirm previous literature review results and conclusions. For example, researchers have always focused their attention on women on boards’ performance (Cabrera-Fernández et al. 2016 ; Kent Baker et al. 2020 ; Khatib et al. 2021 ), as shown in cluster #2, and this is one of the most relevant topics in the gender issues field. Top managers and CEOs could use this research to make relevant decisions about their boards of executive composition, taking into consideration the impact of gender diversity and inclusion.

Moreover, researchers have worked to analyse the relationship between gender diversity and CSR policies and information disclosure (Pucheta-Martínez et al. 2018 ; Amorelli and García-Sánchez 2021 ), as shown in #4. The gender bias in risk taking attitude and management (cluster #1) have been represented in prior literature reviews and are found to be key factors in entrepreneurship (Moreira et al. 2019 ). Managers should take into consideration the different gender leadership styles according to the type of industry and strategy. For example, some sectors or positions need a riskier style of decision-making progress, while other environments may need a different kind of emotional and social intelligence in their management teams.

Nevertheless, as a novelty of this bibliometric analysis, we identify new relevant research areas such as the wage gap #9, the effect of the gender differences in competitiveness #7, the consequence of a different risk management on firms’ performance #1 (actually the broadest area of research), or the results of gender diversity, not only in the boards of directors but also in the middle management teams #12. To date, middle management has not been considered as important in terms of gender inclusion; nevertheless, in the current competitive and uncertain environment, middle positions need to be cared for as much as top management.

The originality of this bibliometric review also stands on the identification of the main nodes of the knowledge network and connections within gender issues in business economics research. The turning points (Bc papers) highlight the intellectual transition between different research areas. They are useful for enhancing new multidisciplinary and multidimension academic findings and managerial implications.

The burst paper identification (Supplementary material of Appendix 1) highlights the most relevant papers, i.e., the ones that truly focused most of the researchers’ attention and interest during a specific timeframe. We could feature the evolution and challenges that researchers have experienced in this field. In the first two decades of the twenty-first century, research trending topics focused on understanding the reason for the gender pay gap (Blau and Kahn 2003 ; Arulampalam et al. 2007 ). Since 2010, there has been a proliferation of new topics: gender quotas (Nielsen and Huse 2010 ; Torchia et al. 2011 ), performance analysis based on gender (Dezsö and Ross 2012 ), women and innovation (Díaz-García et al. 2013 ), and women and competitiveness (Niederle et al. 2013 ). During the end of the second decade of the twenty-first century, researchers focused their effort to better understand gender firms’ performance based on the gender perspective, specifically to comprehend the existence of very different and, sometimes, contradictory academic results and findings on this topic (Seierstad 2016 ). A very interesting paper about gender and competitiveness is considered a tipping point, identifying the different risk-taking attitudes and management styles between men and women as key factors that may affect organizational competitiveness and performance (Hutchinson et al. 2015 ). Scholars have recently pointed out that during an uncertainty situation, men and women use different mindsets when assessing organisations (Díez-Martín et al. 2022 ). Managers should be aware of the importance of gender inclusivity in their teams since the teams’ composition and dynamics affect companies’ performance and competitiveness.

The analysis of the burst papers also highlights that researchers have overcome some of the main challenges in gender issues since the beginning of the twenty-first century. Currently, research on the gender pay gap and women's access to the labour market is not very relevant. Researchers focus on the gender effects on firms’ performance and their causes.

This research provides important implications for business managers, policymakers, and academics. For business managers, improving their knowledge about the effect of integrating gender policies in businesses can encourage them to develop and implement projects to foster corporate operations and improve efficiency. The broader scope and perspective of our analysis enables the improvement of business decisions related to several aspects. Regarding human resources management, the research has demonstrated gender-biased behaviour that can have an impact on organisations´ performance. Examples include risk-taking attitude, risk tolerance, risky financial operations, fraud control, chance of survival, and behaviour under competitive or uncertainty environments. This knowledge could be considered in employee selection processes or talent management. In addition, considering the effect that diversity in teams has on firm performance (more gender-diverse teams enhance innovation, tend to disclose more and better-quality information about environmental issues, and have a positive influence on CSR), managers could build and manage teams in a more efficient manner. Moreover, business managers that aim to attract diverse talent should consider that women are less motivated to work in results-driven companies based on objective achievement. Regarding stakeholder management, managers should assume that gender equality management in their company could generate implications related to external perceptions about corporate identity and image. Both variables are evaluated by stakeholders who issue legitimacy assessments.

Policymakers have an important role in ensuring gender equality in every area. In the business field, research papers show that inequality in terms of gender is decreasing. However, a salary gap still exists. This situation involves the need to implement policies to support and incentivise gender equality in companies. Nevertheless, many initiatives implemented by policymakers have not achieved the expected results; in fact, many policies related to the establishment of quotas have been questioned and have proven less efficient in reducing the gender gap. Coercive measures are not reaching the required results. In contrast, the most successful policies have resulted from transformative events based on technological innovations that have improved the lives of families. Policymakers could focus their initiatives and resources on enhancing technological innovations with this purpose. Family (management and care) appears to be a key reason for some gender inequalities, such as the wage gap. Thus, policymakers could favour the development of an institutional context that cares for family issues and that could influence organisational behaviour.

For scholars, this research enables us to improve the existing knowledge of gender equality in the business field. The research agenda may be used for the constitution of new theoretical frameworks, as a guide for researchers in future projects, a guideline for relevant topics, a source of innovative methodology, and as a list of potential future collaborators.

Finally, this study presents some limitations, as with any bibliometric review based on co-citations. The analysis is comprehensive, but the chosen filters may limit the scope and dimension of the database. Co-citation analysis is biased on older research, which is more likely to be co-cited. Although the results are obtained through quantitative indicators, researchers’ interpretations may affect the study’s results and conclusions.

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This research was supported by “Ayuda Puente 2019, URJC”. Project V948 “Las políticas de igualdad de género como estrategia de legitimación empresarial”.

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Díez-Martín, F., Miotto, G. & Del-Castillo-Feito, C. The intellectual structure of gender equality research in the business economics literature. Rev Manag Sci (2023). https://doi.org/10.1007/s11846-023-00671-8

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100 Gender Research Topics For Academic Papers

gender research topics

Gender research topics are very popular across the world. Students in different academic disciplines are often asked to write papers and essays about these topics. Some of the disciplines that require learners to write about gender topics include:

Sociology Psychology Gender studies Business studies

When pursuing higher education in these disciplines, learners can choose what to write about from a wide range of gender issues topics. However, the wide range of issues that learners can research and write about when it comes to gender makes choosing what to write about difficult. Here is a list of the top 100 gender and sexuality topics that students can consider.

Controversial Gender Research Topics

Do you like the idea of writing about something controversial? If yes, this category has some of the best gender topics to write about. They touch on issues like gender stereotypes and issues that are generally associated with members of a specific gender. Here are some of the best controversial gender topics that you can write about.

  • How human behavior is affected by gender misconceptions
  • How are straight marriages influenced by gay marriages
  • Explain the most common sex-role stereotypes
  • What are the effects of workplace stereotypes?
  • What issues affect modern feminism?
  • How sexuality affects sex-role stereotyping
  • How does the media break sex-role stereotypes
  • Explain the dual approach to equality between women and men
  • What are the most outdated sex-role stereotypes
  • Are men better than women?
  • How equal are men and women?
  • How do politics and sexuality relate?
  • How can films defy gender-based stereotypes
  • What are the advantages of being a woman?
  • What are the disadvantages of being a woman?
  • What are the advantages of being a man?
  • Discuss the disadvantages of being a woman
  • Should governments legalize prostitution?
  • Explain how sexual orientation came about?
  • Women communicate better than men
  • Women are the stronger sex
  • Explain how the world can be made better for women
  • Discuss the future gender norms
  • How important are sex roles in society
  • Discuss the transgender and feminism theory
  • How does feminism help in the creation of alternative women’s culture?
  • Gender stereotypes in education and science
  • Discuss racial variations when it comes to gender-related attitudes
  • Women are better leaders
  • Men can’t survive without women

This category also has some of the best gender debate topics. However, learners should be keen to pick topics they are interested in. This will enable them to ensure that they enjoy the research and writing process.

Interesting Gender Inequality Topics

Gender-based inequality is witnessed almost every day. As such, most learners are conversant with gender inequality research paper topics. However, it’s crucial to pick topics that are devoid of discrimination of members of a specific gender. Here are examples of gender inequality essay topics.

  • Sex discrimination aspects in schools
  • How to identify inequality between sexes
  • Sex discrimination causes
  • The inferior role played by women in relationships
  • Discuss sex differences in the education system
  • How can gender discrimination be identified in sports?
  • Can inequality issues between men and women be solved through education?
  • Why are professional opportunities for women in sports limited?
  • Why are there fewer women in leadership positions?
  • Discuss gender inequality when it comes to work-family balance
  • How does gender-based discrimination affect early childhood development?
  • Can sex discrimination be reduced by technology?
  • How can sex discrimination be identified in a marriage?
  • Explain where sex discrimination originates from
  • Discuss segregation and motherhood in labor markets
  • Explain classroom sex discrimination
  • How can inequality in American history be justified?
  • Discuss different types of sex discrimination in modern society
  • Discuss various factors that cause gender-based inequality
  • Discuss inequality in human resource practices and processes
  • Why is inequality between women and men so rampant in developing countries?
  • How can governments bridge gender gaps between women and men?
  • Work-home conflict is a sign of inequality between women and men
  • Explain why women are less wealthy than men
  • How can workplace gender-based inequality be addressed?

After choosing the gender inequality essay topics they like, students should research, brainstorm ideas, and come up with an outline before they start writing. This will ensure that their essays have engaging introductions and convincing bodies, as well as, strong conclusions.

Amazing Gender Roles Topics for Academic Papers and Essays

This category has ideas that slightly differ from gender equality topics. That’s because equality or lack of it can be measured by considering the representation of both genders in different roles. As such, some gender roles essay topics might not require tiresome and extensive research to write about. Nevertheless, learners should take time to gather the necessary information required to write about these topics. Here are some of the best gender topics for discussion when it comes to the roles played by men and women in society.

  • Describe gender identity
  • Describe how a women-dominated society would be
  • Compare gender development theories
  • How equally important are maternity and paternity levees for babies?
  • How can gender-parity be achieved when it comes to parenting?
  • Discuss the issues faced by modern feminism
  • How do men differ from women emotionally?
  • Discuss gender identity and sexual orientation
  • Is investing in the education of girls beneficial?
  • Explain the adoption of gender-role stereotyped behaviors
  • Discuss games and toys for boys and girls
  • Describe patriarchal attitudes in families
  • Explain patriarchal stereotypes in family relationships
  • What roles do women and men play in politics?
  • Discuss sex equity and academic careers
  • Compare military career opportunities for both genders
  • Discuss the perception of women in the military
  • Describe feminine traits
  • Discus gender-related issues faced by women in gaming
  • Men should play major roles in the welfare of their children
  • Explain how the aging population affects the economic welfare of women?
  • What has historically determined modern differences in gender roles?
  • Does society need stereotyped gender roles?
  • Does nature have a role to play in stereotyped gender roles?
  • The development and adoption of gender roles

The list of gender essay topics that are based on the roles of each sex can be quite extensive. Nevertheless, students should be keen to pick interesting gender topics in this category.

Important Gender Issues Topics for Research Paper

If you want to write a paper or essay on an important gender issue, this category has the best ideas for you. Students can write about different issues that affect individuals of different genders. For instance, this category can include gender wage gap essay topics. Wage variation is a common issue that affects women in different countries. Some of the best gender research paper topics in this category include:

  • Discuss gender mainstreaming purpose
  • Discuss the issue of gender-based violence
  • Why is the wage gap so common in most countries?
  • How can society promote equality in opportunities for women and men in sports?
  • Explain what it means to be transgender
  • Discuss the best practices of gender-neutral management
  • What is women’s empowerment?
  • Discuss how human trafficking affects women
  • How problematic is gender-blindness for women?
  • What does the glass ceiling mean in management?
  • Why are women at a higher risk of sexual exploitation and violence?
  • Why is STEM uptake low among women?
  • How does ideology affect the determination of relations between genders
  • How are sporting women fighting for equality?
  • Discuss sports, women, and media institutions
  • How can cities be made safer for girls and women?
  • Discuss international trends in the empowerment of women
  • How do women contribute to the world economy?
  • Explain how feminism on different social relations unites men and women as groups
  • Explain how gender diversity influence scientific discovery and innovation

This category has some of the most interesting women’s and gender studies paper topics. However, most of them require extensive research to come up with hard facts and figures that will make academic papers or essays more interesting.

Students in high schools and colleges can pick what to write about from a wide range of gender studies research topics. However, some gender studies topics might not be ideal for some learners based on the given essay prompt. Therefore, make sure that you have understood what the educator wants you to write about before you pick a topic. Our experts can help you choose a good thesis topic . Choosing the right gender studies topics enables learners to answer the asked questions properly. This impresses educators to award them top grades.

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    Gender inequality as a barrier to economic growth: a review of the theoretical literature Open access Published: 15 January 2021 Volume 19 , pages 581-614, ( 2021 ) Cite this article Download PDF You have full access to this open access article Review of Economics of the Household Aims and scope Submit manuscript Manuel Santos Silva &

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    Gender pay gap. 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 ...

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  12. Gender economics in macroeconomic research

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  13. Twenty years of gender equality research: A scoping review based on a

    Abstract 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.

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  22. 100 Best Gender Research Topics

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