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Journal of Economic Literature

• September 2017

The Gender Wage Gap: Extent, Trends, and Explanations

ISSN 0022-0515 (Print) | ISSN 2328-8175 (Online)

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• J71 Labor Discrimination

Report | Wages, Incomes, and Wealth

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

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

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

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

Introduction and key findings

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

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

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

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

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

Key points include:

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

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

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

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

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

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

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

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

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

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The data underlying the figure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

About the authors

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Peer-reviewed

Research Article

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

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

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

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

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

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

* E-mail: [email protected]

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

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

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

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

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

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

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

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

Editor: Elisa Ughetto, Politecnico di Torino, ITALY

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

Copyright: © 2021 Belingheri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

Data collection

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

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

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

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

Text processing and keyword extraction

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

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

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

Classification of research topics

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

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

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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.

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

Compensation.

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

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

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

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

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

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

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

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

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

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

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

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

Decision-making.

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

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

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

Career progression.

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

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

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

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

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

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

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

Performance.

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

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

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

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

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

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

Organization.

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

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

Human capital.

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

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

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

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

Emerging topics: Leadership and entrepreneurship

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

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

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

Research gaps and conclusions

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

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

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

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

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

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

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

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

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

Supporting information

S1 text. keywords used for paper selection..

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

Acknowledgments

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

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

Lisa toczek.

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

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

Richard Peter

Maria Bohdalova , Comenius University in Bratislava, Slovakia

Associated Data

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

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

1 Introduction

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

1.1 Determinants of the GPG

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

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

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

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

1.2 Theoretical Background

1.2.1 human capital model.

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

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

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

1.2.2 Life Course Approach

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

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

1.3 Aim and Hypotheses

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

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

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

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

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

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

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

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

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

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

2 Materials and Methods

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

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

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

2.2 Measurements

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

2.2.1 Level 1 Variables

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

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

M mean; SD standard deviation.

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

2.2.2 Level 2 Variables

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

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

2.3 Statistical Analysis

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

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

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

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

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

Goodness-of-fit statistics of the GCA.

AIC Akaike’s Information Criterion.

3.1 Descriptive

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

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

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

Income trajectories of men and women.

3.2 Growth Curve Analysis

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

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

L1 = Level 1; L2 = Level 2.

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

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

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

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

3.3 Goodness of Fit

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

4 Discussion

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

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

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

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

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

4.1 Strengths and Limitations

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

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

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

5 Conclusion

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

Acknowledgments

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

Data Availability Statement

Ethics statement.

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

Author Contributions

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

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

Conflict of Interest

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

Publisher’s Note

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

Supplementary Material

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

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			, 2024, vol. 139, issue 3, 1557-1610 The gender ask gap measures the extent to which women ask for lower salaries than comparable men. This article studies its role in generating wage inequality, using novel data from an online recruitment platform for full-time engineering jobs: Hired.com. To use the platform, job candidates must post an ask salary, stating how much they want to make in their next job. Firms then apply to candidates by offering them a bid salary, solely based on the candidate’s résumé and ask salary. If the candidate is hired, a final salary is recorded. After adjusting for résumé characteristics, the ask gap is 2.9%, the bid gap is 2.2%, and the final offer gap is 1.4%. Further controlling for the ask salary explains the entirety of the residual gender gaps in bid and final salaries. To further provide evidence of the causal effect of the ask salary on the bid salary, I exploit an unanticipated change in how candidates were prompted to provide their ask. For some candidates in mid-2018, the answer box used to solicit the ask salary was changed from an empty field to an entry prefilled with the median bid salary for similar candidates. I find that this change drove the ask, bid, and final offer gaps to zero. In addition, women did not receive fewer bids or final offers than men did due to the change, suggesting they faced little penalty for demanding comparable wages. 2024 (external link) (application/pdf) Access to full text is restricted to subscribers. This item may be available elsewhere in EconPapers: for items with the same title. BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text This journal article can be ordered from for this article The Quarterly Journal of Economics is currently edited by , , , and in The Quarterly Journal of Economics from Bibliographic data for series maintained by Oxford University Press ( ). The gender pay gap Research Briefing Work and incomes Brigid Francis-Devine Patrick Brione This briefing paper provides statistics on the size of the gender pay gap in the UK and how it varies by factors such as age, occupation and location. Documents to download The gender pay gap (590 KB , PDF) Supporting documents Data tables Data tables (29 KB , Excel Spreadsheet ) (29 KB , Excel Spreadsheet) The gender pay gap measures the difference between average (median) hourly earnings of men and women, usually shown by the percentage men earn more than women. Note that figures for 2020 especially, but also 2021, should be treated with some caution. Some people were on furlough with reduced pay and figures for 2020 were particularly affected by disruptions to the collection of data from businesses. How big is the gender pay gap? According to the Office for National Statistics (ONS), median hourly pay for full-time employees was 7.7% less for women than for men in April 2023, while median hourly pay for part-time employees was 3.3% higher for women than for men (figures exclude overtime pay). The median is the point at which half of employees earn more and half earn less. It is regarded a better measure of pay of the ‘typical’ employee than taking an average. Because a larger proportion of women are employed part-time, and part-time workers tend to earn less per hour, the gender pay gap for all employees is considerably larger than the full-time and part-time gaps. Median pay for all employees was 14.3% less for women than for men in April 2023. The full-time pay gap has been getting smaller since 1997 and the overall pay gap has also decreased over the period. The part-time pay gap has generally remained small and negative, with women earning more than men on average. Why is there a gender pay gap? The size of the gender pay gap depends on several factors, including: Age: There is little difference in median hourly pay for male and female full-time employees aged in their 20s and 30s, but a substantial gap emerges among full-time employees aged 40 and over. This links to parenthood – the gap between male and female hourly earnings grows gradually but steadily in the years after parents have their first child. Occupation: The gap tends to be smaller for occupation groups where a larger proportion of employees are women; Industry: The pay gap is largest in the financial and insurance industry, and smallest in the accommodation and food services industry; Public and private sector: For full-time workers, the pay gap is slightly smaller in the public sector than the private sector. There is a negligible gender pay gap for part-time workers in the private sector, which contrasts with a large part-time pay gap in the public sector; Region and nation: The full-time gender pay gap is highest in the South East and London and negative in Northern Ireland; Pay: The highest earners have a larger pay gap than the lowest earners. Gender pay gap reporting Since 2017/18, public and private sector employers with 250 or more employees have been required annually to publish data on the gender pay gap within their organisations. They must report the data to the Government, who publishes it. In 2022/23, 79% of reporting employers stated that median hourly pay was higher for men than for women in their organisation, while 13% of employers stated median hourly pay was higher for women. 8% stated that median hourly pay was the same for women as for men. Related Links ONS, Gender Pay Gap 2021 Commons Library, Women and the economy briefing Share this with Facebook Share this with Facebook Twitter Share this with Twitter LinkedIn Share this with LinkedIn Email Share this with Email Related posts Unemployment – international comparisons: key economic indicators. Unemployment: International Comparisons: Data on harmonised unemployment rates for major international economies. Economic situation UK Labour Market Statistics This paper provides the latest statistics and analysis of employment, unemployment, economic inactivity and earnings in the UK. Average Earnings: Key Economic Indicators Average Earnings: Regularly updated data on average weekly earnings, including breakdowns by public and private sectors. 277 IMAGES (PDF) Analysis of the perception about the gender pay gap in the EU Gender Pay Gap as a Multifaceted Social Issue Gender pay gap in U.S. held steady in 2020 Gender Pay Gap as a Multifaceted Social Issue (PDF) Gender Pay Gap New Solutions for an Old Problem: Developing Reporting VIDEO Factors Contributing to the Gender Pay Gap Closing the gender pay gap Navigating the Gender Pay Gap Salary Secrets Revealed! #shorts Gender Pay Gap Myth DESTROYED in Seconds 🤣 Concept of gender pay gap is a ‘load of nonsense’: Caleb Bond Closing the Gender Pay Gap: Empowering Women to Ask for More in the Workplace COMMENTS PDF The Gender Wage Gap: Extent, Trends, and Explanations trends in the US gender wage gap and on their sources (in a descriptive sense). Accounting for the sources of the level and changes in the gender pay gap will provide guidance for understanding recent research studying gender and the labor market. Figure 1 shows the long-run trends in the gender pay gap over the 1955-2014 period based on two The Gender Pay Gap and Its Impact on Women'S Economic Empowerment The findings suggest that the gender pay gap has a significant impact on women's economic empowerment, limiting their financial independence and autonomy. The study also highlights the need for ... The persistence of pay inequality: The gender pay gap in an anonymous Introduction. The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [1, 2].Trends dating back to the 1960s show a long period in which women's earnings were approximately 60% of their male counterparts, followed by increases in ... The Gender Wage Gap Endures in the U.S. A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64. PDF Gender-Based Pay Disparity Study Rather, the research reported in the white paper was driven by our comprehensive review of the literature (Section 4) and currently accepted econometric practices. ... The CONSAD study provided a synopsis of economic research on the gender pay gap as of 2007. However, the synopsis excluded prominent existing studies that could have provided ... Gender Wage Gap: Causes, Impacts, and Ways to Close the Gap This domestic time bur-. den on women results in the lower LFPR seen. 2 Gender Wage Gap: Causes, Impacts, and Ways to Close the Gap. across the globe, though studies show when paid. and unpaid ... Gender wage transparency and the gender pay gap: A survey The gender wage gap refers to the differences between the wages earned by women and men in comparable jobs that generate equal values (OECD 2021). At first glance it seems like a clear and uncontroversial definition; however, applying this definition to data is less straight forward. ... Published articles and research papers on the impact of ... The Gender Wage Gap: Extent, Trends, and Explanations By 2010, conventional human capital variables taken together explained little of the gender wage gap, while gender differences in occupation and industry continued to be important. Moreover, the gender pay gap declined much more slowly at the top of the wage distribution than at the middle or bottom and by 2010 was noticeably higher at the top. Understanding the Gender Earnings Gap: Hours Worked, Occupational This article documents the evolution of the gender earnings gap over the life cycle using data from a cohort of men and women from the National Longitudinal Survey of Youth 1979 (NLSY79). The pattern is similar to the one documented in other datasets: The gender pay gap increases with age. Gender-Specific Wage Structure and the Gender Wage Gap in the U.S The paper is structured as follows: Sect. 2 critically reviews the common conceptualization of the wage structure, Sect. 3 discusses methods of decomposition of the gender wage gap and their ability to identify the contribution of the wage structure to the pay gap, Sect. 4 presents the data and variables used in the analysis, Sect. 5 presents ... PDF The Gender Pay Gap and its Determinants across the Human Capital Research has shown that the gender pay gap increases substantially during this time and is relatively stable thereafter (Manning and Swaffield 2008). These early-career dynamics are the subject of their own literature (discussed ... 2 Our paper estimates conditional average gender gaps across various levels of pre -labor market human capital (i.e. Can minimum wage increases narrow the gender wage gap? Evidence from 1 There are two types of gender wage gaps: the unadjusted gender wage gap (the average difference in pay between men and women) and the adjusted gender wage gap (the difference in pay between women and men after controlling for other determinants of wages, such as job role, education, and experience). Given that we control for other determinants of wages, we examine the effect of minimum wages ... "Women's work" and the gender pay gap The gender pay gap is driven at least in part by the cumulative impact of many instances over the course of women's lives when they are treated differently than their male peers. Girls can be steered toward gender-normative careers from a very early age. ... National Bureau of Economic Research, Working Paper No. 21913. Bureau of Labor ... PDF A Grand Gender Convergence: Its Last Chapter "59 cents on the dollar" and a more recent crusade for pay equality has adopted "77 cents on the dollar." The wage is also a summary statistic for an individual's education, training, prior labor force experience, and expected future participation. The gender gap in wages is a summary statistic for gender differences in work. Workplace Gender Pay Gaps: Does Gender Matter Less the Longer Employees Research indicates men often receive greater merit rewards than women for the same performance. ... Submit Paper. Close Add email alerts. You are adding the following journal to your email alerts ... Auspurg K., Hinz T., Sauer C. (2017). Why should women get less? Evidence on the gender pay gap from multifactorial survey experiments. American ... Gender Pay Gap Find out with our pay gap calculator. In 2019 women in the United States earned 82% of what men earned, according to a Pew Research Center analysis of median annual earnings of full-time, year-round workers. The gender wage gap varies by age and metropolitan area, and in most places, has narrowed since 2000. See how women's wages compare with ... The gender gap in fair earnings increases with age due to higher age Second, research shows that gender pay gaps increase with age. In Germany, gender pay gaps increase sharply after the age of 30, partly due to the unequal distribution of paid and unpaid care work (Cukrowska-Torzewska & Matysiak, 2020; Schäper et al., 2023). Women often have prolonged career interruptions following childbirth and are more ... The persistence of pay inequality: The gender pay gap in an anonymous Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and ... Twenty years of gender equality research: A scoping review based ... 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 ... PDF UNDERSTANDING THE GENDER WAGE GAP Working to eliminate the gender wage gap requires looking beyond these statistics to explain why women's earnings . are lower even when they work full time, all year long. A recent report coauthored by the U.S. Census Bureau and . the Department of Labor's Women's Bureau provides what is currently the most comprehensive examination of the ... The Gender Pay Gap: Income Inequality Over Life Course The gender pay gap has been observed for decades, and still exists. Due to a life course perspective, gender differences in income are analyzed over a period of 24 years. Therefore, this study aims to investigate income trajectories and the differences regarding men and women. ... Hamburg, Germany: HWWI Research Paper, 179. [Google Scholar ... PDF Equal Pay Policies and the Gender Wage Gap: A Compilation of Recent This brief2 compiles recent research on the impact of equal pay laws and policies on the gender wage gap. It presents studies under five topic areas: (1) salary history bans; (2) pay transparency policies; (3) gender and salary negotiations; (4) gender bias in performance management and performance-related pay; and (5) occupational segregation ... PDF The gender pay gap the self-employed. The median, full-time gender pay gap decreased from 9.6% in 2014 to. .4% in April 2015. This is the lowest since the survey began in 1997, although the gap has not changed very mu. h in recent years. Including part-time employees in the overall analysis, the pay gap in 2015 stood at 19.2%, t. The Role of the Ask Gap in Gender Pay Inequality The Role of the Ask Gap in Gender Pay Inequality. Nina Roussille. The Quarterly Journal of Economics, 2024, vol. 139, issue 3, 1557-1610 . Abstract: The gender ask gap measures the extent to which women ask for lower salaries than comparable men. This article studies its role in generating wage inequality, using novel data from an online recruitment platform for full-time engineering jobs ... Gender Pay Gap in India: A Reality and the Way Forward—An Empirical WIDER research project on "Gender wage Gap and its Impact on poverty: Evidence from ... Reilly B., & Dutta P. V. (2005)The gender pay gap and trade liberalisation: Evidence for India(Working Paper No. 32). Poverty Research Unit. Google Scholar ... & Korde R. (2013)Gender pay gap in the formal sector: 2006-2013: Preliminary evidence from ... Gender Wage Gap: Some Recent Evidences from India Gender wage inequality is a chronic socioeconomic malice in developed as well as in developing countries. This paper describes the outcomes of our study on the estimation of gender wage gap in the ... The gender pay gap Median pay for all employees was 14.3% less for women than for men in April 2023. The full-time pay gap has been getting smaller since 1997 and the overall pay gap has also decreased over the period. The part-time pay gap has generally remained small and negative, with women earning more than men on average. essay homework paper phd review speech study thesis