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Criminalizing LGBTQ+ Jamaicans: Social, Legal, and Colonial Influences on Homophobic Policy , Zoe C. Knowles

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Ain't I a Woman, Too? Depictions of Toxic Femininity, Transmisogynoir, and Violence on STAR , Sunahtah D. Jones

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Reproducing Intersex Trouble: An Analysis of the M.C. Case in the Media , Jamie M. Lane

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Penalizing Pregnancy: A Feminist Legal Studies Analysis of Purvi Patel's Criminalization , Abby Schneller

A Queer and Crip Grotesque: Katherine Dunn's , Megan Wiedeman

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"Mothers like Us Think Differently": Mothers' Negotiations of Virginity in Contemporary Turkey , Asli Aygunes

Surveilling Hate/Obscuring Racism?: Hate Group Surveillance and the Southern Poverty Law Center's "Hate Map" , Mary McKelvie

“Ya I have a disability, but that’s only one part of me”: Formative Experiences of Young Women with Physical Disabilities , Victoria Peer

Resistance from Within: Domestic violence and rape crisis centers that serve Black/African American populations , Jessica Marie Pinto

(Dis)Enchanted: (Re)constructing Love and Creating Community in the , Shannon A. Suddeth

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"The Afro that Ate Kentucky": Appalachian Racial Formation, Lived Experience, and Intersectional Feminist Interventions , Sandra Louise Carpenter

“Even Five Years Ago this Would Have Been Impossible:” Health Care Providers’ Perspectives on Trans* Health Care , Richard S. Henry

Tough Guy, Sensitive Vas: Analyzing Masculinity, Male Contraceptives & the Sexual Division of Labor , Kaeleen Kosmo

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Let’s Move! Biocitizens and the Fat Kids on the Block , Mary Catherine Dickman

Interpretations of Educational Experiences of Women in Chitral, Pakistan , Rakshinda Shah

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Incredi-bull-ly Inclusive?: Assessing the Climate on a College Campus , Aubrey Lynne Hall

Her-Storicizing Baldness: Situating Women's Experiences with Baldness from Skin and Hair Disorders , Kasie Holmes

In the (Radical) Pursuit of Self-Care: Feminist Participatory Action Research with Victim Advocates , Robyn L. Homer

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Significance is Bliss: A Global Feminist Analysis of the Liberian Truth and Reconciliation Commission and its Privileging of Americo-Liberian over Indigenous Liberian Women's Voices , Morgan Lea Eubank

Monsters Under the Bed: An Analysis of Torture Scenes in Three Pixar Films , Heidi Tilney Kramer

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Can You Believe She Did THAT?!:Breaking the Codes of "Good" Mothering in 1970s Horror Films , Jessica Michelle Collard

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Gender Trouble In Northern Ireland: An Examination Of Gender And Bodies Within The 1970s And 1980s Provisional Irish Republican Army In Northern Ireland , Jennifer Earles

"You're going to Hollywood"!: Gender and race surveillance and accountability in American Idol contestant's performances , Amanda LeBlanc

From the academy to the streets: Documenting the healing power of black feminist creative expression , Tunisia L. Riley

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Women in Wargasm: The Politics of Womenís Liberation in the Weather Underground Organization , Cyrana B. Wyker

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Opportunities for Spiritual Awakening and Growth in Mothering , Melissa J. Albee

A Constant Struggle: Renegotiating Identity in the Aftermath of Rape , Jo Aine Clarke

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Reforming Dance Pedagogy: A Feminist Perspective on the Art of Performance and Dance Education , Jennifer Clement

Narratives of lesbian transformation: Coming out stories of women who transition from heterosexual marriage to lesbian identity , Clare F. Walsh

The Conundrum of Women’s Studies as Institutional: New Niches, Undergraduate Concerns, and the Move Towards Contemporary Feminist Theory and Action , Rebecca K. Willman

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A Feminist Perspective on the Precautionary Principle and the Problem of Endocrine Disruptors under Neoliberal Globalization Policies , Erica Hesch Anstey

Asymptotes and metaphors: Teaching feminist theory , Michael Eugene Gipson

Postcolonial Herstory: The Novels of Assia Djebar (Algeria) and Oksana Zabuzhko (Ukraine): A Comparative Analysis , Oksana Lutsyshyna

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Loving Loving? Problematizing Pedagogies of Care and Chéla Sandoval’s Love as a Hermeneutic , Allison Brimmer

Exploring Women’s Complex Relationship with Political Violence: A Study of the Weathermen, Radical Feminism and the New Left , Lindsey Blake Churchill

The Voices of Sex Workers (prostitutes?) and the Dilemma of Feminist Discourse , Justine L. Kessler

Reconstructing Women's Identities: The Phenomenon Of Cosmetic Surgery In The United States , Cara L. Okopny

Fantastic Visions: On the Necessity of Feminist Utopian Narrative , Tracie Anne Welser

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The Politics of Being an Egg “Donor” and Shifting Notions of Reproductive Freedom , Elizabeth A. Dedrick

Women, Domestic Abuse, And Dreams: Analyzing Dreams To Uncover Hidden Traumas And Unacknowledged Strengths , Mindy Stokes

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Safe at Home: Agoraphobia and the Discourse on Women’s Place , Suzie Siegel

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Women, Environment and Development: Sub-Saharan Africa and Latin America , Evaline Tiondi

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An IEA-ETS Research Institute Journal

  • Open access
  • Published: 30 July 2019

Trends in gender gaps: using 20 years of evidence from TIMSS

  • Sabine Meinck 1 &
  • Falk Brese   ORCID: orcid.org/0000-0001-8504-6507 1  

Large-scale Assessments in Education volume  7 , Article number:  8 ( 2019 ) Cite this article

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Potential differences in achievement between female and male students have always been an interesting topic in educational research, as well as having policy and economic implications. This study provides an overview of the so-called “gender gap” in mathematics and science knowledge, based on an in-depth analysis of both extremes of student ability distributions. Evidence underpinning debate on gender inequality in education can be explored by analyzing trends in these distributions over the last 20 years. This new approach to gender gap analysis shows that while the gender gaps that existed 20 years ago have persisted, gender equality in education has increased. The persistent trend of an overrepresentation of male students in the group of high-achievers in both mathematics and science is striking, but male and female students are often also unequally represented at the lower end of the ability distributions. Patterns differ between countries and cycles. In many countries, male students constitute the majority of the lower end of the ability distribution, while in others, more female students are failing to achieve, especially at grade eight. Some countries have shown a reversed inequality trend over the last two decades. With the proposed approach in analyzing gender gaps, differences at the tails of the achievement distributions can be investigated even if the gender distribution is skewed. Policymakers could make use of the approach to closely monitor the development of achievement gaps in their countries and initiate measures to tackle potential causes of inequity, leading to gender inequalities regarding educational achievement.

Introduction

Differences in achievement between female and male students, often termed the “gender gap”, have always been of interest, not only in educational research, but also from a political and economic context (UNESCO 2015a ; Hausmann et al. 2009 ). These differences are frequently seen as a matter of inequality (Klasen 2002 ). Achieving strict gender equality in all situations or domains may seem to be a utopian goal. However, laying the foundations of gender equity has become a political issue and is seen as a general measure of justice and fairness, especially in the education context (EGREES 2005 ).

At an international level, gender equality is of high importance, leading UNESCO to declare gender equality as one of the most important goals for education (UNESCO 2015b ), and ultimately to incorporate this aim within the framework of sustainable development goals (United Nations 2018 ). International comparative research is addressing the issue of gender differences continuously, and the topic was prominent in many recently conducted international large-scale assessments in education, including for example the 2015 TIMSS and PISA cycles (Mullis et al. 2016a ; Mullis et al. 2016b ; OECD 2016 ).

Literature review and theoretical framework

Gender equality and equity in education and society.

Gender equality and equity in education is an issue under discussion for more than a century. At the time the right and obligation of schooling was introduced, single-sex schools dominated the educational landscape in many countries. Subjects taught to male and female students differed, reflecting the expected course of life of these children. Consequently, various subjects aimed at a certain gender group; for example cooking would be aimed at girls (Trueman 2015 ). Nowadays, fairly equal opportunities to learn have been established in the vast majority of countries for female and male students. However, the traditional patterns keep influencing in very powerful ways the life course of male and female students. For example, girls—as opposed to boys—still opt more for professions within the social sector and less often for sectors related to the so-called STEM (science, technology, engineering and mathematics) subjects. These patterns can be observed with career and study choices already prior to entering the work force (UNESCO 2017 ).

Moreover, stereotypes related to these traditional understandings of role models persist into the present, and they do influence what happens in the classroom today. Stereotypes affect professional action of teachers, parental influence and expectations, and consequently students’ self-concept, decisions, actions, and achievement. For example, Nguyen and Ryan ( 2008 ) review existing literature regarding negative effects of stereotyping for girls in mathematics, and prove in an experiment the effect of stereotype threats on achievement. Retelsdorf et al. ( 2015 ) found negative associations between teachers’ gender stereotype and boys’ reading self-concept, disadvantaging male students in their reading achievement. They point out that stereotypes can explain the long-term development of self-concepts as a relatively stable personal characteristic. This, in relation with theories on self-fulfilling prophecy, may be one explanation of manifested differences over the course of schooling.

One aspect of gender differences receiving high attention is related to STEM education. UNESCO ( 2017 ) reports on girls’ and women’s education in STEM find that, to date, girls are still underrepresented in choosing STEM disciplines for studying and as their career paths. Also international comparative studies observe similar patterns. The IEA TIMSS-Advanced study on upper secondary students studying advanced mathematics and science conducted in 2015 found (far) more male students in these advanced courses in most of the participating countries (Mullis et al. 2016c ). Further, male students on average achieve significantly higher than girls in—again—most of the countries.

However, countries have tried (in some cases since decades) to counteract gender inequity. UNESCO ( 2017 ) has compiled examples of the various kinds of interventions and programs regarding the gender differences in STEM education and outcomes. The list of examples comprises action targeted at the individual (female) students, for example single-sex workshops for girls to act as scientists led by same-sex tutors in the UK to facilitate girls’ interest in STEM subjects and careers. Other examples are one-week STEM camps (Kenya), where female students carry out experiments and visit companies offering STEM jobs, or “Science, Technology and Mathematics Education (STME) Clinics” (Ghana) which bring together girls in secondary schools with female scientists who could act as role models. Also on education-system level there was action taken: Improvements regarding school safety, education of teachers, smaller mathematics and science classes and better curriculum coverage could be identified (Mullis et al. 2016 ). Finally, at the level of the country or society itself, policies like quotas or financial investments to promote the image of women in STEM jobs are only some examples that have been implemented so far.

Measuring gender equality and equity in education

While the aforementioned studies aim towards showing causes of gender inequity in education, large-scale assessment data cannot provide this type of insight. Rather, figures of equality (i.e., comparing for example the achievement of gender groups) can be used as indicators of gender equity within educational systems, assuming there are no other factors determining differences between the sexes, such as genetic disposition. In other words, gender equity is understood in this context as a synonym for fairness and equal opportunities for female and male students, while gender equality represents an empirically measurable outcome of equity.

But what constitutes equality? As Allison ( 1978 ) states, already the choice of the measure to represent equality can make a difference regarding the perception of equality. Especially in cross-country comparisons, the question of what or who to compare to becomes a deciding factor on the resulting perception of the magnitude of inequality or even the lack of equality. In general terms, there are two types of comparisons.

First, comparisons could be based on absolute figures or distributions, setting or according to some standard across all compared entities, for example education systems. These standards could be related to various aspects of equality, like conditions, or outcomes. UNESCO, for example, classifies concepts for measuring equity in education accordingly (cf. 2018 , 23ff.): ‘Equality of conditions’ would mean that conditions of education are the same for everyone. ‘Equality of outcomes’ entails minimum educational outcomes (e.g., a certain completed level of schooling) for everyone. When ‘education is independent from personal characteristics’, equality is related to the impartial implementation of education. A ‘positive relation between education and ability’ would be a kind of equality where students with higher ability are provided with higher quality education. This would be the case in countries with a tracked school system. Finally, a ‘positive relation between education and being disadvantaged’ (e.g., regarding some criteria like income) would provide higher quality, more or special focused education to disadvantaged students. In international large-scale studies in education, outcome measures often translate to certain benchmarks of achievement or proficiency levels (see, for example, Mullis et al. 2016 , 61ff; Schulz et al., 2017 , 44ff; OECD 2016 , 59ff.). Gender equality is reported in relation to some standard or threshold applied to each country. While giving highly valuable information, this type of comparison has one disadvantage, that is, the overall achievement distributions vary greatly between countries, and there may be very few overlap at the ends of the distributions, leaving few room to compare gender equalities at these ends. For example, if one wishes to compare gender ratios of eights grade students reaching the advanced TIMSS mathematics benchmark in 2015, Footnote 1 results are scanty, as more than two-thirds of the countries have less than 10% of their students reaching this benchmark; a significant number of countries even have no students at all in this category. Switching to the next lower benchmark level (the “high benchmark”) will not help, as the majority of students in high-ranking countries achieve this category, hence, instead of focusing on the distribution tail, one would again rather focus on major population parts.

A different approach would be to compare achievement distributions across countries, using measures of relative equality within countries as comparison criterion. There are several implications of the latter approach: First, there is no need for (agreeing upon) a criterion acceptable and applicable across different education systems and countries. Second, the notion of comparability implies taking into account the country contexts. Looking at relative distributions in specific contexts or situations can provide more detailed insights into variation, especially in cases where international standards do not fit well. Subsequently, such analysis has the potential to reveal tailored options for addressing issues. For example, if gender gaps exist especially at the lower end of the achievement distribution, measures addressing weaknesses of low achieving students (tailored to the affected gender) will be more effective to tackle the issues.

The vast majority of related previous research based on international large-scale assessment data has focused on comparisons of the mean achievement of female and male students, only relatively few studies have addressed gender differences at different levels of achievement. Moreover, according to observations by Halpern et al. ( 2007 ), research focusing on gender differences at the tails of the ability distributions concentrates most on the upper tail (for example, Benbow and Stanley 1983 ). Only little research is concerned with the lower tail of the ability distribution. Research on the upper tail of the distribution looked at the absolute distribution of female and male students (for example, Hedges and Nowell 1995 ; Strand et al. 2006 ).

Baye and Monseur ( 2016 ) looked at differences in the variability of students’ achievement using TIMSS, PIRLS and PISA data spanning more than 20 years. They point out that gender differences at the extreme tails of the achievement distribution are often more substantial than average differences. Males were more frequently among the highest performing students in mathematics and science, but male students also varied more than female students in their level of performance. Bergold et al. ( 2016 ) also identified a higher variability in the achievement of male students, with male students overrepresented as a group among both highest and lowest performing students. The study looked at 17 countries participating in TIMSS and PIRLS in 2011 with fourth-grade students, and at various achievement domains (reading, mathematics and science) simultaneously. Significant variations between country profiles have been acknowledged, suggesting that a single generic model of explaining gender differences may not be reasonable. Both publications (Baye and Monseur 2016 ; Bergold et al. 2016 ) include a comprehensive review of the literature related to gender differences in general and theories on the greater variability of males regarding achievement.

Recently, some research used quantile regression analysis (Davino et al. 2014 ) as another way of differentiating inequalities along ability distributions. For example, Costanzo and Desimoni ( 2017 ) found varying gender inequalities for the different quantiles of the mathematics and reading scores distributions, using data from an Italian study of second and fifths grade students.

Objectives and research questions

The research presented in this paper was stimulated by the following considerations: (1) we acknowledge the fact that gender equity remains an important issue on the political agenda of many countries; (2) many countries have introduced measures tackling gender inequity over the past decades; (3) previous findings suggest that average achievement is not the most comprehensive indicator of gender equality, rather, unequal gender ratios can be observed especially at the tails of the achievement distributions. Consequently, we would like to expand current knowledge by adding a perspective on trends over time regarding gender differences at the tails of the ability distributions. Deviating from approaches used in previous research, we will implement a statistical analysis method that accounts for potentially skewed overall gender distributions within education systems. Focusing on relative ability distributions of female and male students rather than on absolute distributions, for example according to internationally defined proficiency levels, we will be able to identify gender inequalities better for countries where students’ results do not show (enough) variation across these standardized levels. Further, we can compare countries with regard to gender differences even if the average achievement of students varies a lot between these countries. By analyzing the tails of the relative distribution, we will get information on gender inequalities for the highest and lowest achieving students. We argue that these more fine-grained results—compared to overall achievement averages—could be used to develop measures and policies tailored more specifically to these groups of students. Regarding equal opportunities as an aspect of equity, the lower tail of the achievement distribution might be of special interest, as extremely low performance can seriously affect future options in school and later on in life. Finally, the proposed method is robust to skewed overall distributions of female and male students. This is an important aspect when including countries into the analyses were school enrolment is gender-dependent, or for trend analysis if overall gender ratios change over time.

Using TIMSS data, we sought to evaluate whether differences between girls and boys regarding their mathematics and science achievement at fourth and eighth grade changed over the last 20 years. We considered four central research questions.

Considering mathematics and science achievement of fourth and eighth graders, is there an equal gender distribution at the top and bottom end of the achievement distribution within participating countries?

If there is a gender gap, did it change over time? More specifically, did gender gaps change at the fourth grade from 1995 to 2015, and similarly at the eighth grade?

Looking at specific student cohorts in the fourth grade and again 4 years later at the eighth grade by following up the cohort, did the achievement gap widen or narrow?

Are these developments internationally generalizable or can different patterns be observed in different countries or groups of countries?

Data and methods

We analyzed data collected from education systems participating in TIMSS 1995, TIMSS 2015, and at least one additional intermediate cycle of TIMSS. Eighteen education systems at Grade 4 and 20 education systems at Grade 8 satisfied the requirements.

To enable a longitudinal analysis of specific cohorts at country level for addressing research question (3), we reduced the scope of our research further. From the countries included above, we chose only those who participated at grade four in 1995 and 2011, and at grade eight in 1999 and 2015. Consequently, we followed up two cohorts in 11 countries: students who attended grade four in 1995 and grade eight in 1999, and a cohort born 16 years later, with students attending grade four in 2011 and grade eight in 2015. It should be noted that only representative samples of the same cohorts were tested, and not the same students at different ages.

We first identified the 20% best and poorest performers in each country and cycle per grade and subject domain, using the overall mathematics and science achievement scores, by performing a percentile analysis. This analysis resulted in two benchmark scores per population, subject domain, country, and cycle, dividing the best and the poorest performing 20% from the remaining populations (see Fig.  1 ). Readers should be reminded that the achievement levels of these groups differ greatly among countries as shown in Fig. 2 , but these differences are not of interest for this paper. Instead, our focus is purely on the gender gap within and across countries, and time.

figure 1

Identifying the tails of the achievement distributions

figure 2

Ability distributions vary greatly among countries

In a second step, we estimated the differences in percentages of male and female students reaching or failing respective benchmarks resulting from the first step as illustrated in Fig.  3 . Again, separate analyses were run for different grades, subject domains, countries and cycles. The results of these analyses were the relative distributions of female and male students in the groups of “high” and “low” performers. The percentages were computed in a way that allows a direct comparison between the relative distributions even in populations with overall skewed gender distributions (i.e., populations with more female than male students comprising a grade, or vice versa).

figure 3

Estimating relative distributions of female and male students in the groups of ‘high’ and ‘low’ performers

We accounted for the complex sample and assessment design by using sampling weights for the estimation of population parameters, and applying jackknife repeated replication and plausible values for the estimation of standard errors (Foy 2017 ).

General trends in gender gaps

Overall, there are more differences in trends for gender gaps between grades for the same subject than between subjects within grades. Gender gaps for mathematics achievement at grade four and their trends over time are more similar to those for science achievement at the same grade than trends at grade eight. In other words, it seems that, regarding gender differences, grade matters more than subject.

Trends in gender gaps in mathematics achievement at grade four

Twenty percent highest performing students (above 80th percentile).

Overall, there were relatively more boys than girls among the top 20% of students by achievement (Table  1 ). This applies to almost all countries and cycles. In about two-thirds of all observed cases, this difference was significant.

Kuwait was the only exception to this general finding; in 2007 and 2011, female students were significantly overrepresented in this group. Singapore was the only country to possess remarkable gender balance in all cycles from 1995 to 2015. Finally, only in Japan has inequality reduced, with an initially significant gender gap in favor of boys reducing from 2003 (and becoming insignificant in later cycles).

In 2015, in 12 out of 18 countries there was a significant gender gap favoring boys; the same tendency was observed in the other countries, but the differences were not significant. In seven countries (Australia, Czech Republic, Hong Kong (SAR), Hungary, New Zealand, Slovenia, and the United States), a gender gap in favor of boys widened over the last 20 years, starting from a small and mostly insignificant difference in 1995 and 2003 to a significant gap in 2015, posing questions surrounding potential causes of this apparent increase in inequality.

Twenty percent lowest performing students (below 20th percentile)

Overall, fewer significant gender gaps were observed in this group of low-performers (Table  1 ). Moreover, there were no generalizable trend patterns. In The Netherlands, female students were significantly overrepresented in this group compared to their male peers, a gap that has remained fairly constant over the last two decades. In Iran, Kuwait and Singapore, there were significantly more boys represented among these low-performing students in more recent cycles.

A gender gap existing in New Zealand in 1995 (again, with more male students being part of this group) became insignificant in all later cycles.

Trends in gender gaps in mathematics achievement at grade eight

As with grade four, there are relatively higher percentages of boys than girls among the top 20% highest achieving eighth grade students in mathematics (see Table  2 ). Gaps predominantly favor boys, in all significant gaps but one (Thailand). In 1995, there was a significant gender gap in nine countries, whereas, in 2015, this was true for only five countries, showing a reduction somewhat of the gap among the countries considered.

Over the last two decades, relatively constant gender gaps favoring boys can be observed for Italy, Japan, Korea and the United States. In three countries (England, Iran and Israel), gender inequality decreased over the same period. Only in Thailand, there was a tendency to have relatively more girls in this group (gap significant only in 2007).

Overall, the gender gaps in mathematics achievement of the 20% lowest performing students in grade eight differ by country (see Table  2 ). Gender gaps are often smaller than at the upper end of the achievement distribution of this grade. Further, gaps can be observed in both directions, with a higher relative percentage of boys as well as the opposite, a higher percentage of girls.

In 1995, six countries had significant gender differences, with three countries showing relatively more female students in this group, and another three with relatively more boys. Twenty years later, five (but now mostly other) countries still showed significant gender differences, two countries with relatively more girls, and three countries with relatively more boys. No generalizable trend can be observed for this group.

In Hong Kong (SAR), Kuwait, Lithuania, Singapore and Thailand, boys were significantly more likely to be among the low-performing students than girls in more than one cycle. In none of these countries could a tendency towards increased gender equality be observed. While girls were more likely to be in the group of low-performing students in England, Iran, Israel and Korea in the early cycles, this was no longer the case by 1999 or 2003. Russia showed a tendency toward a reversed gender inequality: while boys were overrepresented in this group in earlier cycles of TIMSS, significantly more girls belong to this group in 2015.

Trends in gender gaps in science achievement at grade four

Boys were notably overrepresented in the group of the 20% highest performing students in science achievement at grade four across countries and cycles (see Table  3 ). This finding is consistent with observations related to mathematics. Similarly, Kuwait was again a remarkable exception, with having constantly more female high-achievers (gap significant in 2007 and 2011).

In 1995, 14 countries had significant gender gaps, all in favor of boys. In 2015, this number reduced to nine countries, indicating some reduction of the gap.

There were no clear group patterns for the bottom 20% of students in science achievement at grade four (see Table  3 ). Gender differences were smaller than for the high-achiever group for many countries and while some countries had more boys than girls, others had more girls than boys.

In 1995, eight countries showed significant differences in the relative percentages of male and female students in this group, six with relatively more girls, and two with more boys. In 2015, only three countries out of these eight still showed significant differences, one with a higher percentage of low-performing girls, and two again with relatively more low-performing boys. Kuwait again proved an exception, with remarkably large gender gaps (up to 18% more boys in the group). Differences in all other countries were not significant.

Trends in gender gaps in science achievement at grade eight

Very similar to the findings relating to grade four, the patterns were striking: There were more boys than girls among the 20% highest performing students in science at grade eight in most countries and cycles (see Table  4 ). In 19 out of the 20 countries investigated, significant gender gaps for this group were evident in two or more cycles.

In 1995, 17 countries showed a significant gender gap, all in favor of boys. In 2015, only nine countries had a significant gap, with only one country (Kuwait) having more girls than boys in this group. A tendency towards a reduction of the gender gap can be observed in many countries. In England¸ Iran, Israel, Lithuania, Slovenia and Sweden, the gender differences were significant in earlier TIMSS cycles but, by 2015, they were no longer significant. This suggests these countries improved gender equality among their top-performing eighth grade students in science during the last decade.

Overall, gender inequalities are less pronounced among the 20% lowest performing students compared to the 20% best performing students (see Table  4 ). The differences, however, mostly favor boys, meaning here that there is, in many cases, a higher percentage of female students in this group compared to the percentage of male students.

In 1995, 14 countries had significant gender gaps, 13 countries with a relatively higher percentage of girls, and one country (Kuwait) with a relatively higher percentage of boys. In 2015, only four countries had significant gaps, one with a higher percentage of girls, and three with a higher percentage of boys. This trend shows that the gap favoring boys (a lower percentage of boys in the low-performing group) is closing and even beginning to reverse.

Canada, England, Hong Kong (SAR), Japan, Korea, New Zealand, Russia, Slovenia, Sweden and United States have managed to close a previously existing significant gender gap over the last 20 years. Israel has reversed its gender gap: while relatively more girls belonged to the low-performing group in 1995, boys were overrepresented in 2015. A similar (but insignificant) tendency can also be observed in other countries.

A remarkably large gender gap within the group of low-performing students can be observed in Kuwait: three out of four students are male; 30% of all male students in the country are among the 20% of eighth grade students performing lowest in science, while this is the case for only 9% of all female eighth graders. This difference seems to be stable over the last 20 years.

Trends in gender gaps in mathematics achievement across countries

When looking at the trends in gender differences in mathematics achievement across countries, we can observe differences (i) between the two tails of the mathematics achievement distribution and (ii) between grades four and eight. As Fig.  4 shows, in the majority of countries (12 out of 18) there are gender differences at grade four, either persisting or developed newly since 1995, within the group of 20% highest achieving students in mathematics. In six countries, there is no gap: either there has been none in 1995 already, or a gap existing in 1995 closed in 2015. On the contrary, there is no gender gap (any longer) in 2015 in the majority of countries (15 out of 18) for fourth graders at the lower end of the achievement distribution (20% lowest achieving students in mathematics). A similar pattern exists for students at eighth grade. For students at both tails of the mathematics achievement distribution, there are significant gender differences in the majority of countries (15 out of 20).

figure 4

Changes in gender gaps in mathematics achievement in grades four and eight in the group of the 20% highest achieving and the group of 20% lowest achieving students between 1995 and 2015. Example description of upper left pie chart (grade 4, upper 20% of distribution of math achievement): In five countries, a gender gap remained; in seven countries, a gender gap opened, i.e., developed where there was none before; in six countries, the gender gap closed or there is none in 2015 and there has not been a gap in 1995 neither

Trends in gender gaps in science achievement across countries

Figure  5 shows the change in gender differences in science achievement for the 20% highest and lowest achieving students at fourth and eighth grade in a similar way. While there are some similarities in the overall picture to the findings regarding mathematics achievement, we see also different results. Again, for fourth grade students, differences are found in more countries at the upper tail of the achievement distribution than at the lower end. In half of the countries (9 out of 18), there are persisting or newly developed gender differences for the 20% highest achieving students. For the 20% lowest achieving students, however, gaps have closed or never existed in the majority of countries (15 out of 18). We find rather similar patterns for eighth grade students. Almost half of the countries show gender differences for the 20% highest achieving students (9 out of 20), with mostly persisting gaps, whereas for the 20% lowest achieving students there are no differences in the majority of countries (15 out of 20).

figure 5

Changes in gender gaps in science achievement in grades four and eight in the group of the 20% highest achieving and the group of 20% lowest achieving students between 1995 and 2015. Example description of upper left pie chart (grade 4, upper 20% of distribution of math achievement): In four countries, a gender gap remained; In five countries, a gender gap opened, i.e., developed where there was none before; In nine countries, the gender gap closed or there is none in 2015 and there has not been a gap in 1995 neither

Comparing trends in gender gaps in mathematics and science achievement across countries

Overall, we observe fewer countries with significant gender gaps at the lower tail of the achievement distribution. This holds for both subjects, mathematics and science, as well as for both grade 4 and grade 8. In the upper tail of the achievement distribution, i.e. the 20% highest achieving students, there are more countries with existing gender differences. Here, we find more countries with gender differences for the lower grade students (grade 4) than for the upper grade students (grade 8). However, as the selection of countries included in this analysis is different for grade 4 and grade 8, a direct comparison between results from the two grades is not appropriate.

Trends in gender gaps in mathematics achievement within cohorts

We also examined the gender gaps in mathematics achievement and their trends following up two cohorts from grade four to eight (Table  5 ).

While most of the figures and bars displayed in Tables  5 and 6 resemble information from Tables  1 , 2 , 3 , 4 in a different format, the columns “Gap difference between grade 4 and 8” show the development of gender gaps over 4 years of schooling within the same cohort of students. The first cohort represents students attending grade four in 1995 and grade eight in 1999. The second cohort represents students attending grade four in 2011 and grade eight in 2015.

The first cohort (students attending grade four in 1995) exhibited a significant gender gap favoring boys in England, Iran, Japan and Korea (Table  5 , left upper part). For the first three of these countries, the gap persisted or even widened by grade eight. In Korea, however, the gap decreased over those 4 years and was no longer significant at the eighth grade. Conversely, a gender gap opened up between grades four and eight in the United States. The gap changes were not significant in any country.

In two countries (Hong Kong (SAR) and Korea), the second cohort (fourth graders in 2011) contained significantly more male students among the top-performers at both grades. Australia, Hungary, Slovenia and the United States managed to close an existing gender gap in favor of boys at the fourth grade over the ensuing 4 years: the gaps were no longer significant at grade eight. Finally, Slovenia and England showed a significant change in the gender gaps between grades four and eight. In both cases, more boys belonged to the high-performers at grade four. However, while Slovenia achieved gender equality at grade eight, in England, the inequality gap had reversed, and by 2015 favored girls, with significantly more girls achieving high mathematics scores than in 2011 at grade four.

In the countries considered, there was no general trend in terms of the gender composition of the bottom twenty percent of students in mathematics, nor were there identifiable trends over the years or among cohorts (lower part of Table  5 ).

The picture is very diverse across countries, particularly for the first cohort. Four countries showed significant gender differences in the group allocation; two of them, Iran and Korea, had significantly more girls in this group, while the other two, New Zealand and Singapore, had more boys. These gender gaps reduced over the ensuing 4 years in all countries but Iran, where the gap doubled instead. Moreover, Iran was the only country with a significant gender gap in this group at grade eight in 1999.

Regarding the second cohort, only very few significant gender differences existed in the eleven countries in both grades. In Singapore, significantly more boys belonged to the group of low-performers at fourth grade, and this percentage had doubled by grade eight. A small, yet significant gender difference in the United States with a higher proportion of female students vanished over the years and could no longer be observed at grade eight.

Trends in gender gaps in science achievement between grade four and eight

In contrast to the gender gap trends in mathematics achievement, science achievement showed a very clear and quite generalizable pattern (see upper part of Table  6 ). Male students were overrepresented in the group of the upper 20th percentile of science achievers at grade four in all considered countries in both cycles (1995 and 2011). This overrepresentation was even more pronounced 4 years later at grade eight, with New Zealand being the only country where this group allocation was insignificant at both grades (cohort 1). Further, there was a significant increase in male students in this group in three countries (England, Hungary and the United States). Fortunately, however, the second considered cohort (students that attended grade four 16 years later) painted a less severe picture. While, similarly, relatively more male students were among the top-performers at grade four, 4 years later this was only still true in five countries (Hong Kong, SAR, Hungary, Korea, Singapore and the United States). The gap did not widen significantly in any country.

The gender gap is less pronounced in the group of low performing students (Table  6 , lower part). In 1995, at grade four only four countries showed significant gender gaps. In Hong Kong (SAR), Hungary and Korea more female students were among the low-performing students, but more male students were in this group in New Zealand. While New Zealand reached gender equity 4 years later, the gap persisted in the other countries and widened in England and Iran, again with a higher proportion of girls.

However, the cohort attending grades four and eight 16 years later showed minimal gender gaps at both grades, with the exception of Hungary, where again more girls comprised the lowest percentile of science achievers at eighth grade. Patterns indicate a tendency towards an increase in male students in this group, a yet insignificant trend that should be closely monitored in the future.

Changes over time

Overall, our findings suggest that girls are catching up with boys. In the group of high achieving students in both subject areas, the overrepresentation of boys continued or even extended from grade four to eight from 1990 to 1995. 16 years later, the overrepresentation of boys found at grade four in 2011 rather reduced at grade eight in 2015. For the lower achieving students, at least regarding science achievement, we can observe a similar change. From grade four in 1995 to grade eight in 1999, there was an increase in the overrepresentation of girls in that group. However, there was no such increase from grade four in 2011 to grade eight in 2015. Even more, the overrepresentation of girls at grade four was much lower already in 2011 than that in 1995.

Discussion, conclusions and policy implications

We investigated the gender gaps in mathematics and science knowledge at both extremes of students’ ability distributions. We found that gender gaps that existed 20 years ago have persisted into the present, but also identified encouraging evidence that gender equality in education is increasing. Moreover, data suggests that no general favorable genetic disposition of male students towards mathematics and/or science exists. Otherwise, patterns would be consistent across countries and time. Overwhelmingly obvious is, however, the persisting trend of more male students in the group of high-achievers for both mathematics and science in many educational systems. These subjects have a long history of being more often favored by male students, a situation that fosters gender differences in academic competencies and an underrepresentation of woman in scientific careers. Male and female students may benefit from different teaching approaches and methods to motivate engagement. Several countries have adopted initiatives to address this problem, and the findings indicate that some may have shown success. Similarly, at the lower end of the ability distributions, male and female students are not always equally represented. Patterns differ between countries and cycles. In many countries, male students are overrepresented in this group, while in others, more female students are at risk, predominantly in the upper grades. Policymakers should closely monitor the development of these gaps and initiate measures to tackle gender inequalities. The trends identified in this paper included promising changes in several countries that were able to diminish gender differences in mathematics and science achievement, that have been existing in the past. Furthermore, findings suggest that girls in general are catching up. A closer look at the specific contexts and policy changes might reveal successful measures to counteract gender differences.

This paper adds to existing research into gender gaps in mathematics and science education over the last 20 years, and offers a new approach to gender gap analysis. Investigating the tails of the achievement distributions provide a more differentiated picture of potential gender differences. It thus extends findings of analysis comparing the mean achievement of female and male students. For example, Mullis et al. ( 2016 ) report a decrease of the number of countries without achievement differences between boys and girls in math of fourth grade students. However, our analysis revealed that there is quite some variation between high and low achieving students (cf. Tables  1 and 5 ). For the highest achieving 20% students, the number of countries showing gender differences is rather increasing. For the lowest achieving 20% students, most of the countries included in this analysis showing no gender differences (any longer). This example indicates that the approach can reveal more (detailed) information on gender differences and their changes over time.

Furthermore, rather than looking at groups of students reaching various benchmarks, we focused on the gender composition of the groups of students comprising the 20th highest and lowest achievement percentiles respectively, for each country. This approach overcomes the problem of only very small samples of students reaching the highest or the lowest benchmark in some countries.

Our research revealed trends in these gaps over 20 years of TIMSS, but it does not explain the mechanisms causing these gaps or any of the underlying factors. Further research is needed to understand these mechanisms better and refine implications and recommendations for policy. IEA contextual data is a valuable research resource to uncover such relations. Although this paper focused only on specific countries and cohorts, it may serve as a template for similar analyses of data from other countries and cohorts that have participated in TIMSS, PIRLS or similar large-scale assessments in education.

Finally, the approach of investigating the relative distribution of a characteristic at the tails of an ability distribution within a country or education system could be used for characteristics other than gender as well. As for gender as such a characteristic, we see that in education systems with several (hundreds of) thousands of students in a certain grade or within a certain age group, this characteristic is fairly equally distributed. That might be very different for other characteristics, for example, students’ family background, ethnicity or other student characteristics, for which inequality is perceived as an issue of concern. One of the advantages of the suggested approach is that it is robust in respect to non-equal distributions of such characteristics.

Further research needs

A secondary aim of this paper was to introduce and evaluate a specific approach to identifying (gender) differences in certain outcomes of education (mathematics and science achievement of fourth and eighth grade students). With this more detailed look at tails of ability distributions, the approach could provide information that is more specific and foster the interpretation and explanation of possible inequalities in education. With the analysis presented, trends over time within a set of countries could be identified and support for some common narratives on gender inequalities could be provided, whereas for other narratives we could not find support. In order to investigate possible correlates for changes in or persistence of inequalities, a more detailed look needs to include country, school and classroom contexts, as well as student characteristics. The TIMSS data provides this kind of information and can serve as a valuable source.

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Acknowledgements

We thank Gillian Wilson and Mark Cockle for their thorough editorial review and Adeoye Oyekan for supporting with the analyses.

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SM developed the research questions and design, conducted parts of the statistical analysis and interpretation of results and drafted major parts of the manuscript. FB conducted major parts of the statistical analysis, drafted minor parts of the manuscript and critically revised all other parts of the manuscript. FB was responsible for data compilation, preparation of data analysis, and manuscript revision. All authors have given final approval of the manuscript version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Both authors read and approved the final manuscript.

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Meinck, S., Brese, F. Trends in gender gaps: using 20 years of evidence from TIMSS. Large-scale Assess Educ 7 , 8 (2019). https://doi.org/10.1186/s40536-019-0076-3

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thesis for gender gap

The gender pay gap in the USA: a matching study

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This study examines the gender wage gap in the USA using two separate cross-sections from the Current Population Survey (CPS). The extensive literature on this subject includes wage decompositions that divide the gender wage gap into “explained” and “unexplained” components. One of the problems with this approach is the heterogeneity of the sample data. In order to address the difficulties of comparing like with like, this study uses a number of different matching techniques to obtain estimates of the gap. By controlling for a wide range of other influences, in effect, we estimate the direct effect of simply being female on wages. However, a number of other factors, such as parenthood, gender segregation, part-time working, and unionization, contribute to the gender wage gap. This means that it is not just the core “like for like” comparison between male and female wages that matters but also how gender wage differences interact with other influences. The literature has noted the existence of these interactions, but precise or systematic estimates of such effects remain scarce. The most innovative contribution of this study is to do that. Our findings imply that the idea of a single uniform gender pay gap is perhaps less useful than an understanding of how gender wages are shaped by multiple different forces.

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1 Introduction

This study estimates the gender pay gap in the USA using several different matching estimators. We first justify the use of matching estimators by using an Oaxaca recentered influence function (RIF) model to estimate the gender pay gap. Other authors using a similar approach have found the “unexplained” component of the gender pay gap to be high. Some of these, including Kassenboehmer and Sinning ( 2014 ) and Töpfer ( 2017 ), attribute this to heterogeneity within their sample. A similar analysis in this study also finds a high “unexplained” component, which implies a heterogeneity problem.

Where heterogeneity is an issue, a well-established approach is to use a matching estimator—see, for example, Ñopo ( 2008 ). This study therefore relies on several matching estimators for its core analysis. These are discussed from the methodological perspective later, but matching involves a number of conceptual issues which are central to the approach of this study. A matching approach creates a control group (of males) which, as far as possible, matches the treated group (female) in all relevant characteristics. For the estimator not to be biased, relevant characteristics such as part-time working and union membership must be included as covariates. The result is an estimate of the gap between male and female pay that controls for all relevant observable characteristics, including unionization and part-time work. Estimating a pure “gender” effect on wages is one of the advantages of using a matching estimator, but the process of creating a control group omits other more indirect ways by which women are paid less.

For example, working part-time typically involves a substantially lower hourly rate of pay than working full-time, as this study confirms. A much higher proportion of females work part-time than do males. Likewise, unionized workers exhibit significantly higher hourly pay than non-unionized workers, and females are much less likely to be unionized than males. A matching approach is intended to capture the effect on wages of being female and needs to control for overlapping effects like part-time work or union membership. Methodologically this is sound, but it must be properly understood that there is more to the matter. In terms of hourly pay, females are also disadvantaged by, say, working part-time and being less likely to be unionized. It is proper to ignore such effects in a matching estimate of the pure “gender effect,” but this study emphasizes that such estimates do not capture the full extent of the wage disadvantages faced by females.

The main focus of this study is, within a matching framework, to examine the important interactions between gender and other relevant characteristics. Union membership and part-time work are two of these. The study also considers the effects of parenthood, age, and gender segregation. An important part of the approach taken is the inverse probability weighted regression adjustment (IPWRA) matching estimator. There are important statistical advantages from using an IPWRA estimator (mainly its “double robustness” property), but the key reason for using IPWRA is behavioral more than statistical. The IPWRA estimator can work with two treatment effects and hence estimate the effects of interactions between gender and another variable. For example, consider female and part-time as treatment variables. The IPWRA approach can simultaneously give the following treatment effects on hourly wages: (a) being female, (b) working part time, and (c) both being female and working part time (an interaction effect).

The conceptual relevance of these interactions is not new in the literature, as Blau and Kahn ( 2017 ) make clear, but such interaction effects have not previously been formally estimated in a consistent manner, if at all. The contribution of the paper is to provide clear evidence that a basic matching estimate of the gender pay gap is useful but does not tell the whole story. An analysis which includes not just a “gender only” effect on wages but also interactions between this gender effect and other key covariates (such as part-time work) is a much richer one. This is the main contribution of the study.

Section 2 provides a review of the literature. The data used by the study, which are two samples taken from the US Current Population Survey (CPS) for the period October 2011 to March 2012 and for the period October 2017 to March 2018, are described in Section 3 , and the methodological approach is described in Section 4 . The matching analysis with a single treatment effect is presented in Section 5 and the IPWRA analysis in Section 6 . Section 7 presents the conclusions of the study.

2 Review of literature

Blau and Kahn ( 2017 ) present a comprehensive review of what is now an extensive literature on the gender pay gap in the USA. A number of themes arising in this literature are developed further in this paper. Blau and Kahn ( 2017 ) present detailed empirical evidence to show that some of the core issues have changed since the 1970s. Several of these are of particular relevance for this paper. Firstly, the gender wage gap has fallen dramatically but still remains sizeable. This is perhaps surprising given that the gap in education has been reversed in favor of women. They find that the gender wage gap has fallen from about 36–38% in 1970 to between 18 and 21% in 2010. The analysis presented in this study does not consider long-term changes but does confirm that a substantial wage gap remains.

In their meta-analysis of a total of 263 papers, Weichselbaumer and Winter-Ebmer ( 2005 ) also find evidence of a global reduction of the gender wage gap. At the same time that the gender wage gap was narrowing, the human capital factors used to explain the gap (education and actual work experience) were either moving in favor of women or strongly declining. Beaudry and Lewis ( 2014 ) associate the declining gender wage gap in the USA with changes in the price of skills, related to skill-biased technical change. In another US study, Borghans et al. ( 2014 ) find the decline in the gender wage gap to be associated with a growth in the importance of people skills. In a rare natural experiment, Flory et al. ( 2014 ) link the gap in gender wages to female aversion to competitive work environments.

Blau and Kahn ( 2017 ) report that the gender gap in years of education has reversed from − 0.2 to + 0.2 between 1981 and 2011 for the USA. The gap in years of work experience fell from 7 in 1981 to 1.4 years in 2011. In consequence, the role of these traditional factors in the gender wage gap has shrunk. Together, education and work experience explained about 27% of the gap in 1981 but only around 8% in 2010. A number of other explanatory factors have also reduced in significance, such as the effect of unionization on male wages. Despite this decline, the evidence presented in this study shows that unionization still plays a part in gender wage differences. Blau and Kahn ( 2017 ) show that, in contrast, some other factors have become increasingly important. For example, they find that gender segregation by occupation and industry has become of much greater consequence—accounting for only about 27% of the gap in 1980 but about 49% in 2010. The role of gender segregation is another theme which this study seeks to develop further.

The link between gender segregation and the gender wage gap has long since been made. Polachek ( 1981 ) constructs a model in which female earnings potential depreciates during temporary exits from the labor force while males remaining in the labor force see their earnings potential appreciate from continued skill development. The expectation of interruptions to work experience affects female investment in skills and, hence, occupational choice. Maternity drives women to self-segregate into jobs which are less innovative and less skill driven—occupations that tend to be paid less. Cobb-Clark and Moschion ( 2017 ) provide evidence from Australia that gender differences in educational performance exist at an early stage and vary according to socio-economic status.

A number of studies have tried to assess the extent of occupational segregation in the USA and elsewhere by means of the Duncan and Duncan ( 1955 ) segregation index. Blau and Kahn ( 2013a , b ) find that the segregation index fell from 64.5 in 1970 to 51.0 in 2009. The decline was more rapid in the 1970s than in the 1980s and even more gradual in the following years. As Blau and Kahn ( 2017 ) note, even the diminished value of 51% still represents a high degree of occupational segregation. Unsurprisingly (given the known role that segregation has in explaining the gender wage gap), the high value of the segregation index relative to 2009 confirms that occupational and industry differences by gender still remain sizeable. This study also reports gender segregation indices for the USA with similar findings.

Hegewisch et al. ( 2010 ) find similar evidence of a declining degree of segregation in the USA. Moreover, they link gender segregation to the gender wage gap, finding a negative relationship between the share of women in employment in an occupation and the gender wage gap. Tomaskovic-Devey and Skaggs ( 2002 ) also link gender segregation to the gender wage gap, finding further evidence of the role of industries as a source of wage inequality. Levanon et al. ( 2009 ) consider the view that gender segregation and the gender wage gap are causally related by two sociological processes—devaluation and queuing—using US Census data. Their analysis found some evidence of devaluation (valuing the work of females less) but little evidence of queuing (employers preferring to hire males).

Other studies drew similar conclusions to the USA for other countries. For instance, Barón and Cobb-Clark ( 2010 ) find an important effect of occupational segregation on the gender wage gap in Australia. They find the gender wage gap to be fully explained by productivity characteristics but not fully explained for high-wage workers. Olsen and Walby ( 2004 ) find evidence from the UK that labor market rigidities—including the segregation of women into certain occupations and into smaller, non-unionized firms—were responsible for about 36% of the gender wage gap. Walby and Olsen ( 2002 ) also find both occupational and industrial segregation to have been prevalent in the UK. Livanos and Pouliakas ( 2012 ), in a study of Greece, find that gender segregation with respect to educational subject explained part of the gender wage gap. Pastore and Verashchagina ( 2011 ) find that the gender wage gap more than doubled during the transition from plan to market in Belarus, particularly because women have experienced increasing segregation in low-wage industries.

Polachek ( 1985 ) further extends this link between gender wages and a life cycle view of occupational choice. Polachek ( 2014 ) finds the gender pay gap to be smaller between single men and women and larger between married men and women. This is attributable to his life cycle model of human capital and the resulting different occupational structures between the genders. To the extent that educational choices by women are related to eventual occupational choices, the study of Danish labor markets by Humlum et al. ( 2019 ) suggests that these may also be affected by parental attitudes to labor markets. The role of maternity and aging on female earnings is confirmed by a comparatively recent strand of the literature which focuses on the labor market behavior of young people to try to ascertain at which stage the gender pay gap first arises. Many studies have found little or no gender wage gap among young people. A gap emerges after maternity and widens as workers age. Manning and Swaffield ( 2008 ) provide an early study of this type for the UK. In a study of US MBAs, Bertrand et al. ( 2010 ) attribute a growing gender wage gap that increased with age to career interruptions as well as differences in training and weekly hours of work. More recently, similar findings have been noted for several developing countries—see, for example, Pastore ( 2010 ) and Pastore et al. ( 2016 ). This study provides recent evidence for the USA which confirms the existence of much narrower differences in gender wages for younger than older workers.

Some research has been aimed at locating the gap along the earning distribution to understand whether it is generalized or whether it is attributable to particular groups of individuals with specific skill levels. Blau and Kahn ( 1997 ) find increased demand for highly skilled workers to have widened the gender wage gap. In their study covering 11 countries, Arulampalam et al. ( 2007 ) find evidence of a tendency for the gender pay gap to be concentrated mainly among the low-skill (so-called sticky floor effect) and the high-skill (so-called glass ceiling effect) workers. Examples of the latter include managerial positions, particularly senior management, and many highly paid liberal professions (Goldin, 2014 ). In these types of jobs, not only education and human capital are of importance but also relationships of trust with customers. This makes the role of some individuals hard to substitute and, in consequence, requires flexibility with respect to hours of work—conditions that are often not easily met by women. Olivetti ( 2006 ) provides a new measure of the returns to work experience, using PSID data for the USA. Her analysis shows that there has been a convergence in the rate of returns to work experience by gender, with female returns increasing more rapidly than those of men. This is attributed to the diffusion of new technologies that favor the skills of women more than those of men.

Sulis ( 2012 ), in a study of Italy, found that search frictions, productivity, and discrimination all shaped the gender wage gap. Another issue related to maternity is the prevalence of part-time working by women. Part-time working attracts lower hourly rates of pay and has often been identified as an important contributor to the gender wage gap. Blau et al. ( 2013 ) found that US policies encouraged women to undertake part-time work in lower level jobs. Ermisch and Wright ( 1993 ) provide evidence that women in the UK received lower wages in part-time than in full-time work. Moreover, as noted above, Goldin ( 2014 ) emphasizes the role of flexible working times in highly paid occupations and senior positions. This, in turn, is an argument to support the view that the preference of women for part-time work might tend to exclude women from such types of jobs. The role of part-time working in creating gender wage differences is another focal point of the analysis presented in this study.

Several studies have tried to understand the origins of discrimination and have found evidence that they are related to the persistence of traditional views regarding the gender division of roles in society. Fortin ( 2005 ) finds perceptions of the role of women in the home and in society to have a significant effect on the gender wage gap—that anti-egalitarian views are associated with a higher gender wage inequality. Pastore and Tenaglia ( 2013 ) find evidence of the role that different religious denominations have in favoring or hindering female employment—as a consequence of a different degree of secularization and of views regarding traditional gender roles and the male breadwinner family model.

Gauchat et al. ( 2012 ) examine other potential effects on gender wage inequality in the USA, such as the effects of globalization, finding that it contributes to a reduced gender pay gap. Oostendorp ( 2009 ) finds evidence that the occupational gender wage gap tends to decrease with respect to trade and foreign direct investment in richer countries but found little evidence of any effect in poorer countries. In a study of wages in India, Menon and Van der Meulen Rodgers ( 2009 ) even find the gender wage gap to increase with respect to openness to international trade.

All of the key themes developed by this paper have been previously considered in one way or another by the existing literature. At the heart of the gender pay gap is a sense that women are paid less than men for undertaking essentially the same work. Matching techniques offer the opportunity to better compare like with like, and such comparisons are of considerable importance. But the literature makes clear that female employment is typically not like male employment. For example, gender segregation, part-time working, parenthood, and unionization are all factors which affect differences between male and female wages. The contribution of this paper is to provide systematic and robust evidence on how these factors interact with the core “like for like” gender pay gap. It finds, for example, that being both a female and a part-time worker results in a much greater disadvantage in hourly wages than just being female. In so doing, it implies that the concept of a single gender pay gap is a too simplistic representation of reality.

3.1 Data overview

The study uses two cross-section samples taken from the monthly US Current Population Survey (CPS), the first for October 2011 to March 2012 and the second for October 2017 to March 2018. Since both cross-sections comprise different individuals, it is not possible to formally test for changes between the two periods, but the intention was to check whether key conclusions change between the two periods. The full number of observations for the first sample was 907,775 and for the second 877,776. This sample includes non-responses and individuals who were not in employment at the time. For much of the analysis, the effective sample was necessarily limited to those individuals for whom sufficient information to obtain their usual hourly earnings existed. This amounted to 77,097 individuals for the first sample and 76,308 for the second. It should also be noted that the Stata software automatically removes observations for which there are missing values so the actual number of observations used in any one task may vary from these totals. The first sample (October 2011 to March 2012) comprised 51.6% females and 48.4% males, and the second sample (October 2017 to March 2018) had exactly the same proportions.

3.2 Sample characteristics

Table 1 provides employment rates of males and females for both samples. Participation rates for both males and females increased in the six years between the two samples. In both cases, the proportion of females not in the labor force was about 10% higher than that of males. Lower overall participation rates for females were not the only key difference from males. In both samples, the proportion of females working part time was substantially higher than that of males. In the second later sample, this became more exaggerated with the proportion of females engaged in part-time work being roughly double compared with that of males.

As Blau and Kahn ( 2017 ) note, the existence of gender segregation implies that industry and occupational differences between male and female employment are important contributory factors to gender differences in wages. To assess the extent and evolution of gender segregation, Table 2 reports gender segregation indices for CPS data over a much longer period (March 2005 to March 2018) than those used for the rest of the study. These indices suggest a gradual decline in gender segregation by occupation between March 2005 and March 2018, but the overall degree of segregation by the end still remained substantial. For segregation by industry, there is very little evidence of longer term change. Segregation by industry is lower than that by occupation but still of consequence. It is worth noting carefully that the values of gender segregation indices are necessarily affected by how both “occupation” and “industry” are defined. The narrower the definitions, the more likely one is to observe a greater degree of gender segregation.

These findings are consistent with other studies of gender segregation in US labor markets. Most notably, Blau et al. ( 2013 ) find a value of 51% for occupational segregation in 2009 compared with about 52% in March and September 2009 in this study. The results are also consistent with the findings of Hegewisch et al. ( 2010 ) on occupational segregation. The findings support the view of Blau and Kahn ( 2017 ) that the decline in gender segregation observed in earlier decades has stalled at levels that still represent a high degree of occupational segregation. Available existing evidence on segregation by industry is much more limited so providing such evidence is one of the contributions of this study.

The analysis necessarily used the CPS definitions of both occupation and industry. Detailed definitions of both industry and occupation were used. Due to changes in definitions over the period, the precise number of each varied, but there were at least 600 occupation and 250 industry categories included throughout. It is recognized that such definitions can never be wholly satisfactory and that the results could have been significantly affected by a different alternative set of definitions.

Another relevant feature of the data is that women exhibited lower rates of unionization than men. In the first sample (October 2011 to March 2012), 12.8% of males and 11.4% of females were unionized. In the second sample (October 2017 to March 2018), the comparable proportions were 11.0% for males and 9.9% for females.

3.3 Variables

Much of the analysis was concerned with the effect of gender on wages. For this, the outcome (dependent) variable was the lhwage, the log of usual hourly earnings. For most of the analysis, the key treatment variable was female (0 if male, 1 if female).

The following variables were used mainly as covariates but also served as treatment variables in some instances:

parttime, 0 if full time and 1 if part time

young, 0 if 25 or over and 1 if under 25

parent, 1 if a parent of a child aged under 18 but 0 if not

union, 1 if a union member but 0 if not.

The following variables were used as covariates only:

married, 1 if married but 0 if not

edyears, number of years of education

hours, the usual number of weekly hours worked

exper, expected experience (explained further below)

migrant, 0 if born in the USA but 1 if not

regional dummy variables

dummy variables for race

occupational dummy variables

sector dummy variables.

Both the occupational and sector dummies used the standard CPS definitions. It is recognized that occupations and industries are impossible to define in a wholly satisfactory way and that variations in these definitions could result in quite results for these dummy variables.

To calculate expected experience for each individual in the model, a probit model was used to estimate (separately) the probability of employment at each age starting at 15 and ending at 65. The role of expected experience (and of gender differences in the effect of parenthood) as a determinant of the gender pay gap was first advanced by Polachek ( 1975 ). In this paper, the model of expected experience was of the general form:

where empl is the (0, 1) variable for whether the individual was employed and D is a vector of regional and race dummy variables.

The marginal effects (probabilities) were then used to calculate the probability that each individual would have been in employment at each age from 15 to 65. These were then added together to give the expected experience in years. Given space constraints, the results are not reported here but are available from the authors on request.

4 Methodology

4.1 wage decompositions using recentered influence functions.

Firpo et al. ( 2018 ) offer an extension of the Oaxaca-Blinder wage decomposition using recentered influence functions (RIF). The technique involves two steps, the first of which is to divide the wage distribution into a composition and structure effect using a reweighted procedure (where the weights are estimated). The second step estimates structure and composition effects for each covariate; essentially in a manner similar to that of Oaxaca-Blinder. The key difference is that, using the method developed by Firpo et al. ( 2009 ) and Fortin et al. ( 2011 ), the dependent variable of the regression is replaced by the appropriate RIF. To implement this procedure, we used the oaxaca_rif routine in Stata .

Authors using different data sets than those of this study have used Oaxaca RIF decompositions to estimate the gender pay gap. Some of these, such as Kassenboehmer and Sinning ( 2014 ) and Töpfer ( 2017 ), found a high proportion of unexplained gender differences which they attributed to heterogeneity in their data. Wage decompositions were not a focus of this study. Our main purpose in producing such estimates was to demonstrate that similar problems existed with the two data sets used for this study. The evidence that similar issues exist with the CPS data is intended to support the use of matching estimators in this study. A summary of the results of the Oaxaca RIF analysis is presented in the Appendix . More detailed results are available from the authors on request. The interpretation of the results needs some care. In particular, the “unexplained” component is open to misinterpretation and differing points of view. Further details are not provided here since this study argues that a different methodological approach is more suited to its topic.

4.2 Matching with a single treatment variable

The existing empirical literature emphasizes the need to compare like with like with respect to gender pay differences. Some authors, including Ñopo ( 2008 ) and Frölich ( 2007 ), have advocated the use of matching estimators for this purpose. Both authors propose these techniques as an alternative to the decompositions of the type proposed by Blinder ( 1973 ) and Oaxaca ( 1973 ). For example, Ñopo ( 2008 ) argues that matching addresses the “out of support” problem inherent in Blinder-Oaxaca wage decomposition models. Section 4.1 above argued that a more modern version of wage decompositions using RIF is still subject to heterogeneity issues. Matching approaches are well equipped to deal with heterogeneity issues. In addition, the heart of the matching approach (the selection of a carefully matched control group) has considerable intuitive appeal in any attempt to compare like with like.

A matching approach starts by defining an outcome variable (log of hourly earnings) and a (0, 1) treatment variable (female). It seeks to establish whether a statistically significant difference exists in the log of hourly earnings between the treated (female) group and the untreated (male) group. The procedure selects a control group from the untreated (male) group which is selected to be, as far as possible, identical in all other relevant observable characteristics to the treated (female) group.

A key issue for all matching techniques is the “missing data” problem. For example, the treatment variable (say being female) is observed, but, to compare male and female wages accurately, we would need to know what would have happened if the same individual had been born male. This clearly cannot be observed, and the “missing data” problem is how best to replicate it from an appropriate counterfactual. With a single treatment variable, this means selecting an appropriate control group.

This study uses three different approaches to the selection of the control group. These are propensity score (PS) matching (using kernel density matching), matching by Mahalanobis distance, and coarsened exact matching (CEM). Given the widespread use of the first two matching techniques in the literature, no further explanation is offered here. The CEM technique is a more recent addition to the matching toolbox: see Iacus et al. ( 2012 ). For matching by both propensity score and by Mahalanobis distance, the treated group is not changed and the only “matching” occurs in the creation of a control group. With coarsened exact matching, the process excludes all those observations from the treated group for which a nearly exact match on all covariates cannot be found. CEM sets a maximum difference in the covariates between the treated and untreated groups and removes observations from both groups where no nearly exact match exists. In many respects, this makes it a more rigorous attempt to compare like with like, but, unlike the other approaches, it results in sample size reductions.

Neither PS nor Mahalanobis matching techniques remove those observations from the treated group that are “difficult” to match closely. In consequence, an issue arises of how closely the control group matches the treated group (sometimes referred to as “bias on observables”). For each analysis using both techniques, the match between the two groups was checked using the psmatch2 routine in Stata. The resulting graphs are reported in the separate appendices available from https://www.researchgate.net/publication/331703104_Meara_Pastore_Webster_specification_checks .

A further more intractable problem is the risk of bias on unobservables: an excluded confounding variable may have biased the results. This study uses a large number of covariates in the treatment model in an attempt to reduce this risk (see Section 3 ). However, as King and Nielsen ( 2016 ) have pointed out, doing this can create a risk of a different form of bias: from matching on irrelevant variables. To limit that risk, all covariates included in the probit (treatment) model were first tested for statistical significance in a regression model with the outcome as the dependent variable. These regressions are not reported but details are available from the authors on request.

The approach taken in this study reflects conceptual as well as statistical issues. For matching estimators to be unbiased, they need to include all relevant observables. This means that in estimating the gender pay gap, the technique should control for other covariates that are known to also affect the difference in gender wages. These include the effects of gender segregation, part-time working, unionization, and parenthood. It is, of course, central to the study to estimate the gender wage gap on as close to a “like for like” basis as possible. However, it is also important to recognize that this is an estimate of the direct consequence of gender on wages and that there are other less direct mechanisms that affect gender wages. The approach of this study is to identify how the gender pay gap changes when these “indirect” effects of being female are taken into account.

The CPS data reveal, as expected, that part-time working is more common among females than males and that females are less unionized. The study first uses matching to show that, with the CPS data, there existed a union wage premium and an hourly wage discount for working part time. Next, the study estimated the core (like for like) gender pay gap for both samples. This is estimated firstly with industry and occupation dummies. It was then re-estimated without these dummy variables to identify the effect of gender segregation on the gender pay gap. For the remainder of the matching analysis, the sample was sub-divided into two according to one of the key covariates. These were used to show how the gender pay gap varies between one group and another. For example, the sample was divided into young (under 25) and older workers and the gender pay gap estimated for each. A similar approach was taken for part-time working, union membership, and parenthood. These provided a key insight into how each of these variables influences differences in gender wages.

4.3 Matching with inverse probability weighted regression adjustment (IPWRA)

The IPWRA estimator derived by Cattaneo ( 2010 ) and Cattaneo et al. ( 2013 ) differs from most matching estimators in that it estimates both a treatment model and an outcome model. The treatment model is similar to most matching models. It estimates the probability of the treatment variable (female in this case) being associated with each of a number of characteristics. Many matching models use probit for this purpose. In this study, the IPWRA treatment model used a logit model.

The treatment model gives the probability of, say, observing a female given that one observes a part-time worker. That is, the treatment model is used to assign a sampling probability for each observation. The inverse of this probability is then used to weight each observation in the outcome models. The inverse probabilities are used to address the “missing data” problem. Using these inverse probabilities, in essence, creates a counterfactual to address the missing data issue. The technique next estimates a number of (inverse probability) weighted regression outcome models, one for each treatment level. Each of these produces a series of treatment-specific predicted outcomes, one for each treatment level. The means of these predicted outcomes are then used to estimate the treatment effect.

The IPWRA estimator can be shown to have some important statistical properties. The most important of these is the property of “double robustness”: see Cattaneo ( 2010 ) and Cattaneo et al. ( 2013 ). That is, if either the treatment model or the outcome model is incorrectly specified but the other is correctly specified, then the estimates are still consistent. This means that it is only necessary for one of the two to be correctly specified for the estimator to be consistent. As a corollary, it is necessary to assume that at least one of the treatment or outcome models does not exclude a confounding variable.

Hirano et al. ( 2003 ) have shown that doubly robust estimators (which include IPWRA) exhibit a lower bias than estimators without the double robustness property. Another common problem with matching models is mis-matching on irrelevant variables. King and Nielsen ( 2016 ) point out that IPWRA estimators are less prone to mis-matching on irrelevant observables.

From the perspective of this paper, the reasons for using the IPWRA are not just for the desirable statistical properties of the estimator but also for the questions that it can address. The model is specified to work with a number of discrete treatment levels. This means that it can be adapted to work with more than one treatment variable. For example, suppose that that we have two (0, 1) treatment variables: female and parttime. This can be adapted into four treatment levels:

Treatment level 0: female = 0 and parttime = 0

Treatment level 1: female = 1 and parttime = 0

Treatment level 2: female = 0 and parttime = 1

Treatment level 3: female = 1 and parttime = 1

In this way, it is possible to use the IPWRA to estimate both treatment effects separately and to estimate their joint (interaction) effect when both apply. It is this feature that makes it particularly useful for analyzing the interaction between gender and other related influences such as part-time working, unionization, and parenthood.

In this study, the outcome variable for all IPWRA models was the log of hourly wages. For both the treatment and outcome models, the full set of covariates listed in the preceding section was used. An important assumption of the IPWRA model is known as the overlap assumption. This means that every individual must have a positive probability of receiving each treatment level. For example, it must be possible that union members can be male and can be female. If unions excluded all males or all females, the overlap assumption would be violated. Stata produces graphical checks for the overlap assumption. These are not reported for the IPWRA models in Section 6 but are available in separate appendices available from https://www.researchgate.net/publication/331703104_Meara_Pastore_Webster_specification_checks .

Finally, as with other matching models, the IPWRA analysis assumes that treatments and outcomes are statistically independent (conditional mean independence).

4.4 Interpretation of results

For both the single treatment and the IPWRA matching analysis, the outcome variable is the log of hourly wages. Consequently, the average treatment effect on the treated (ATT) is the difference in the log of wages between, say, females and males. This is often interpreted as the percentage difference in wages. However, the difference in logs is only a linear approximation (by means of a Taylor expansion) of the true percentage difference. This approximation (as can be seen in our results) is only accurate when the difference between the two sets of wages is small. Since the precise percentage difference can readily be derived from the matching output, this is reported together with the relevant ATT throughout this paper, except for the CEM analysis (for which the ATT is estimated differently and correctly reflects the exact percentage difference).

5 Matching analysis with a single treatment variable

5.1 treatment effects of part-time working and union membership.

This section provides a supporting analysis for work to follow on the gender pay gap. Earlier analysis of the CPS data (Section 3 ) has shown that women are less likely than men to be unionized but more likely to be working part time. The purpose of this analysis is to demonstrate that, with the CPS data, both union membership and part-time working have significant effects on wages in their own right.

Table 3 presents matching estimates of the reduction in hourly wages from working part time and the wage premium from being a union member. These are for the full sample and made use of the full set of covariates listed in Section 4 earlier, including industry, occupation, race, and region dummies. Results are for propensity score (kernel density) matching and use a second set of estimates (from matching by Mahalanobis distance) as a robustness check. Since this is a supporting analysis, we do not also provide a set of CEM estimates (as is done with later analysis) in the interests of being concise.

Table 3 shows a statistically significant premium for union membership according to the PS matching estimator. The results (statistically significant at 99% confidence) imply a union wage premium of about 14% for our first sample and about 13% for the second. The Mahalanobis estimates for the first sample are comparable with those of the PS estimator for the first sample (a premium of about 14%) but slightly lower for the second sample (a premium of about 11%). Both estimators support a substantial and statistically significant union wage premium in each sample.

For part-time working, our results consistently show a substantial and statistically significantly lower hourly wage than for full-time working. Propensity score estimates for both our samples are comparable: a part-time discount of about 19% in October 2011 to March 2012 and of about 21% in October 2017 to March 2018. Estimates for matching by Mahalanobis distance are again comparable across the two samples—discounts of about 14% and 16%—but are somewhat lower than those for the propensity score estimator. Nonetheless, both estimators support a conclusion that a substantial disadvantage in hourly wages exists from working on a part-time basis.

This study reported earlier that, for our samples from the US CPS data, women were more likely to work part time and less likely to be unionized. The analysis in this section has shown that, for the same data, both characteristics would contribute to an overall difference between male and female wages that goes beyond the impact of the direct effect of gender alone. This is a key point to be explored further in this study. It implies that a “like for like” comparison of the direct effect of gender on wages is not the only effect that merits consideration.

5.2 Treatment effects of gender

This section focuses on matching estimates for the gender pay gap in the US using both our samples. As discussed earlier, it is important that the matching process makes use of all relevant observed covariates. Not to do so would expose the estimates to an increased risk of bias on unobservables. The resulting estimate is, in consequence, an estimate of the effect on wages of being female with the effects of all other observed covariates controlled by the matching process. Such estimates are unquestionably useful but give rise to two sets of concerns. These are not really statistical but are important for our understanding of gender wage differences. Firstly, we know from the literature that gender wage differences can vary by, for example, age group and that gender segregation affects gender wage differences. It is important to understand these factors. Secondly, the process of matching selects controls (males) which are similar in terms of, say, parenthood, part-time working, or union membership. All of these can affect gender wage differences. In short, there needs to be an estimate of the effect of gender on wages where, as far as possible, like is compared with like. But in so doing, it is important not to neglect other more indirect routes by which gender wage differences occur.

In this section we start by estimating the gender pay gap for both our samples. The main estimate of the gender pay pap quite properly controls for the effect on wages of the concentration of women in lower paid occupations or industries (gender segregation). To identify the effects of gender segregation, we repeat the analysis but without industry or sector dummy variables. Next, we consider the effect of age on the gender wage differences by applying our matching estimates to two sub-samples—young (under 25) and older. Since part-time working results in lower hourly wages (see the preceding section), we then estimate separate gender wage gaps for part-time and full-time workers. Separate gender pay gaps are then estimated for parents and non-parents and for union members and non-members. The purpose of all of these is to provide a much richer analysis and interpretation than just the direct effect of gender on wages.

Table 4 reports the results of this analysis using propensity score (PS) matching (kernel density), Table 5 repeats the analysis for matching by Mahalanobis distance, and Table 6 also repeats the analysis using coarsened exact matching (CEM). The PS matching (Table 4 ) is included since it is the most widely understood matching technique. Matching by Mahalanobis distance (Table 5 ) and matching by the CEM technique (Table 6 ) are both included as robustness checks on the findings of the PS matching analysis.

The PS matching analysis (Table 4 ) produced an estimate of a statistically significant gender pay gap of about 13% for the October 2011 to March 2012 sample and of about 12% for the October 2017 to March 2018 sample. Comparable estimates using (a) Mahalanobis distance (Table 5 ) and (b) CEM (Table 6 ) were (a) 13% and 10.5% and (b) 12% and 14%. In all cases, these estimates were statistically significant at 99% confidence. These estimates represent the gender pay gap resulting from the direct effect of being female. That is, the secondary effects of, for example, part-time working, parenthood, or union membership are included in the controls and not in the estimate.

Table 4 shows the effect of taking into account gender segregation by means of industry and occupation dummy variables. Removing these industry and occupation dummies increased the estimate of the gender pay gap to 15% for the first sample and to 16% for the second. A comparable effect was observed with both the Mahalanobis and CEM estimators (Tables  5 and 6 ). Interpretation of these findings is important. It is not necessary to choose between estimates with industry and occupation dummy variables and those without. Both convey complementary information. To the extent to which the matching was successful in comparing like with like, the estimates for, say, the second sample showed that being female involved hourly wages that were typically 13% less than those for males. Since this estimate controls for differences in industry and occupation, it does not take into account gender segregation. When we allow for the effects of females being more concentrated in lower paid industries and occupations, the comparable estimate is a pay gap of 17%. As with Blau and Kahn ( 2017 ), this supports the conclusion that gender segregation by industry and by occupation is important in understanding gender wage differences.

The next sub-division of the sample was between young (under 25) and older. Previous studies have found the gender pay gap to be smaller or even non-existent for younger workers. With the PS matching (Table 4 ), this study finds a small but statistically significant gender pay gap for young individuals, of about 2% in our first sample and about 3% in the second. Both the Mahalanobis distance matching (Table 5 ) and the CEM (Table 6 ) analysis found no statistically significant gender pay differences (at 95% confidence) for young workers. These findings contrast sharply for the estimates of the gender pay gap for older workers. For each of the three estimators, these were statistically significant and substantially higher than those for young workers. The PS matching estimates (Table 4 ) imply a gender pay gap of about 13% for older workers in the first sample and of about 14% in the second sample. Mahalanobis distance (Table 5 ) and CEM (Table 6 ) yield similar results. The sharp difference in the gender pay gap between young and older workers has some obvious potential implications for the role of marriage and parenthood in gender pay differences. These are discussed further later.

Sub-dividing the sample by part-time and full-time workers produces some further interesting findings. The PS matching analysis (Table 4 ) suggests a statistically significant but small gender pay gap for part-time workers. For this first sample, this was estimated at 3% and, for the second sample, 6%. Both Mahalanobis and CEM techniques (Tables 5 and 6 ) found no statistically significant (at 95%) gender pay difference between male and female part-time workers. The gender pay gap for full-time workers estimated by PS matching (Table 4 ) was statistically significant and substantial for both samples—14% for the first sample and 15% for the second. Both Mahalanobis and CEM techniques produced similar estimates (Tables 5 and 6 ). The finding of no statistically significant gender difference in the hourly wages of part-time workers is of consequence. Evidence presented earlier shows both that a higher proportion of females than males work part time and that part-time working involves its own gap in hourly pay relative to full time. That there is little or no gender pay difference between male and female part-time workers implies that the interaction between gender and part-time effects is of importance. That is, the role of part-time working in the gender pay gap is more through the pay disadvantage of part-time working than any significant gender wage difference between part-time workers. This is further analyzed in the next section.

The division of both samples by parenthood finds a statistically significant gender pay gap for both parents (of children under 18) and for non-parents in both samples, according to all three of the matching estimators used. In every case, the estimated wage gap for parents was substantially greater than that for non-parents. For example, the estimated wage gap for parents using PS matching was about 17% in the first sample and about 18% in the second sample. The comparable estimates for non-parents were 10% and 12%. These findings complement those with respect to age, which imply changes in the gender pay gap at ages consistent with parenthood. They also complement the existing literature which finds a role for parenthood affecting the gender pay gap, not least through its impact on experience and human capital. Again, the role of parenthood is further analyzed in the next section.

The last sub-division of the samples was with respect to union membership. Again all three matching estimators find a statistically significant gender pay gap for both samples and for both union and non-union members. In almost all cases, the estimated gender pay gap for union members is greater than that for non-members. With PS matching, the gender pay gap for union members in the first sample was estimated at about 12% and for non-members at 11%. For the second sample, the comparable estimates were 16% and 13%. These findings imply a contradictory effect of union membership on gender wages. Union membership, as shown earlier, involves a wage premium which, given low female unionization, should widen the gender pay gap. In contrast, the gender pay gap not only exists between male and female union members but also is higher than that for those who are not unionized. This implies that to fully understand the net overall effect of the interaction between unionization and gender on pay, further analysis is needed. This is provided in the next section.

6 IPWRA analysis for the full sample

6.1 with gender and part-time working as treatments.

Table 7 presents the results of the IPWRA analysis with both female and parttime as treatment variables. The two treatment variables were combined to produce the following composite treatment levels:

Treatment level 0—male full time (female = 0 and parttime = 0)

Treatment level 1—female full time (female = 1 and parttime = 0)

Treatment level 2—male part time (female = 0 and parttime = 1)

Treatment level 3—both female and part time (female = 1 and parttime = 1)

The results are divided into two parts—absolute and relative treatment effects. Absolute effects are the treatment effects where the control group is treatment level 0 (comparable male full-time workers). Relative effects compare the other (non-zero) treatment levels with each other. In particular, treatment effects were estimated for:

Treatment level 1 (female full time) relative to treatment level 2 (male part time)

Treatment level 1 (female full time) relative to treatment level 3 (female part time)

Treatment level 2 (male part time) relative to treatment level 3 (female part time).

In a similar manner to the earlier matching analysis, the full set of variables listed in Section 4 was used to construct the relevant treatment and outcome models in each case.

The absolute effects presented in Table 7 produce some interesting findings. Firstly, the gender pay gap between male and female full-time workers was 14% in both the earlier and later of the two samples. These are values consistent with the earlier matching analysis. Secondly, the analysis confirms a substantial gap in hourly pay rates between part-time and full-time workers. The gap in hourly pay between full-time and part-time males was about 24% in both samples. This confirms the earlier findings that part-time working involves a substantial disadvantage in hourly pay rates relative to full-time working. Lastly, the (separate) pay gaps for being female and for working part time re-enforce each other when it comes to the pay gap between part-time women and full-time men. For the earlier sample, this estimated gap in pay was about 27% and for the later sample approximately 28%. This provides clear evidence that the prevalence of part-time working is an important mechanism by which the “like for like” gender pay gap is worsened. That is, it shows that the wage disadvantage of being female is substantially worsened when the prevalence of female part-time working is taken into account.

For the relative effects, female part-time working was found to result in substantially lower hourly wages compared with all female workers. This gap was found to be about 15% in the earlier sample and 16.5% in the later one. This provides evidence that the gap between part-time and full-time rates exists for females as well as for males. Female part-time workers were also found to have statistically significantly lower hourly wages than comparable part-time workers of both genders. However, the gender pay gap among part-time workers was comparatively modest—about 3% in both samples. Finally, part-time males were found to have substantially lower wages than females (both part and full time). This implies that the wage disadvantage of working part time is larger than the disadvantage from being female. This finding emphasizes the importance of including the wage disadvantages of part-time working within the understanding of gender wage differences.

The outcome of the IPWRA analysis of gender and part-time working performs two key functions. Firstly, it shows that the disadvantages of working part time and the prevalence of part-time working among females are both relevant and important for understanding gender wage differences. Secondly, it provides a robustness check on many of the earlier findings of the matching analysis. Since there are also no substantial behavioral differences between the two different time periods, the main findings are not just robust with respect to choice of estimator but also robust with respect to the choice between the two cross-sections.

6.2 With gender and union membership as treatments

Table 8 presents the results of the IPWRA analysis using both gender and unionization as treatments. The following composite treatment levels were used:

Treatment level 0—male non-union (female = 0 and union = 0)

Treatment level 1—female non-union (female = 1 and union = 0)

Treatment level 2—male union (female = 0 and union = 1)

Treatment level 3—both female and union (female = 1 and union = 1)

In this case, the absolute effects are the treatment effects in relation to the control group of non-union males (treatment level 0).

Relative effects compare:

Treatment level 1 (female non-union) with treatment level 2 (male union)

Treatment level 1 (female non-union) with treatment level 3 (female union)

Treatment level 2 (male union) with treatment level 3 (female union).

As before, the full set of variables listed in Section 4 was used to construct the relevant treatment and outcome models. These included industry and occupation dummy variables.

Table 8 finds a gender pay gap between non-unionized females and non-unionized males of about 14% in the earlier sample and around 15% in the later one. Again this is consistent with the preceding estimates of the “like for like” gender pay gap. The results also provide evidence of a substantial union wage premium. Male workers benefited from a union wage premium of approximately 18% in the October 2011 to March 2012 sample and of about 17% in the October 2017 to March 2018 sample. Relative to non-unionized males, the effect of female union membership was to reduce the gender pay gap to about 8% in the earlier sample and about 10% in the later sample. That is, the existence of a union wage premium helps to reduce the overall pay gap for females but does not eliminate it.

The relative treatment effects also produce some interesting and relevant findings. One of these is that there exists a gender pay gap within unionized labor. In the earlier sample, female union members were typically paid about 13% less than comparable males and in the later sample about 16% less. For women, as with men, the results show a union wage premium but this is smaller than that for males. The estimated female wage premium was 8.5% in the earlier sample and about 6% in the later one, both less than one half of the male union wage premium. The estimated gender pay gap between non-unionized females and unionized males is in the order of 40% for both samples.

As with part-time working, the IPWRA analysis shows that a strict “like for like” comparison between male and female wages ignores another indirect mechanism by which female wages are disadvantaged. For both male and female workers, there is a union wage premium, although the premium for women is lower. That females are less likely to be unionized also means that any given union wage premium does less to reduce the overall difference in gender wages. A combination of union premium and gender wage gap leads to very large differences in hourly pay rates between non-unionized females and unionized males.

6.3 With gender and parenthood as treatments

This analysis considers composite treatments derived from the two (0, 1) treatment variables female and parent. The following composite treatment levels were used:

Treatment level 0—male non-parent (female = 0 and parent = 0)

Treatment level 1—female non-parent (female = 1 and parent = 0)

Treatment level 2—male parent (female = 0 and parent = 1)

Treatment level 3—both female and parent (female = 1 and parent = 1)

Absolute treatment effects were in comparison to the control group of treatment level 0 (male non-parents).

Treatment level 1 (female non-parent) with treatment level 3 (female parent)

Treatment level 1 (female non-parent) with treatment level 2 (female parent)

Treatment level 2 (male parent) with treatment level 3 (female parent).

Table 9 presents the results of this analysis. For non-parents, the core (“like for like”) gender pay gap was statistically significant in both the October 2011 to March 2012 and the October 2017 to March 2018 samples (about 10% in the first sample and about 11% in the second). The effect of being a male parent (relative to comparable male non-parents) was estimated to result in a statistically significant wage premium of about 8% in the first sample and about 3% in the second. The (absolute) effect of being both female and a parent implies a wage disadvantage of about 5% compared with male non-parents in the first sample and about 11% in the second.

The relative effects are of particular interest. For females, as with males, the results suggest that a statistically significant wage premium exists for parents in relation to non-parents. This premium was estimated at just under 4% for both samples. Within the sub-sample of all parents, the results show a substantial wage disadvantage from being a female parent (in relation to male parents). This disadvantage was estimated at 14.2% for the first sample and 14.7% for the second. Lastly, the results suggest that the effect of parenthood is to widen the gender pay gap. The estimated treatment effect (in relation to all females) of being a male parent implied a gender wage gap of about 22% in the October 2011 to March 2012 sample and of about 24% in the October 2017 to March 2018 sample.

The finding that parenthood is a further source of wage disadvantage for females is, perhaps, not surprising but important to be supported with evidence. These findings do, however, need careful interpretation. The data include only those females in employment at the time of the relevant surveys. The CPS data identifies parents of children under 18 years at the time of survey. This means that they are not capable of incorporating past adverse effects on human capital for those parents whose offspring are now adults. Despite these limitations, the analysis offers evidence which supports the existing literature which emphasizes the role of female parenthood in understanding the gender pay gap.

6.4 With gender and youth as treatments

Table 10 presents the IPWRA analysis which considers composite treatments derived from the treatment variables female and youth (defined as age under 25). The following composite treatment levels were defined:

Treatment level 0—older male (female = 0 and youth = 0)

Treatment level 1—older female (female = 1 and youth = 0)

Treatment level 2—young male (female = 0 and youth = 1)

Treatment level 3—young female (female = 1 and youth =  1)

Absolute treatment effects were in comparison to the control group of treatment level 0 (older males).

Treatment level 1 (older female) with treatment level 3 (young female)

Treatment level 1 (older female) with treatment level 2 (young male)

Treatment level 2 (young male) with treatment level 3 (young female).

The results presented in Table 10 imply a gender pay gap for those aged 25 or over of about 12% in the October 20011 to March 2012 sample and of 12.5% for the October 2017 to March 2018 sample. For those aged under 25 years, there was also a statistically significant gender pay gap but of much smaller magnitude. For both samples, this was estimated at approximately 3%.

For males, the effect of being young, unsurprisingly, results in statistically significantly lower hourly wages compared with being older. For the earlier sample, the gap was estimated at about 25% and for the later sample at about 22%. For females, the comparable effect was a gap of about 21% for the earlier sample and around 20% for the later one. Given that both being young and being female involve lower hourly wages, it is not wholly surprising that both effects re-enforce each other to create a substantial wage gap between young females and older males. For the earlier sample, this gap was estimated at about 27% and for the later sample at just over 25%.

7 Conclusions

The existing literature on the gender pay gap is extensive and the range of potential causes very numerous. This study has, for example, only touched on a sub-set of the wide range of issues covered by Blau and Kahn ( 2017 ). However, there remains a scope for formal statistical analysis. Not all relevant propositions have been tested. Estimations of the gender pay gap through Oaxaca RIF wage decompositions are still beset with concerns relating to the unexplained component and heterogeneity within the sample. Matching estimators provide a stronger basis for controlling for heterogeneity. In a sense, they provide more reassurance that the “unexplained” gender pay gap is in fact not explained by observable characteristics such as part-time working or parenthood.

Despite the strengths of a matching approach in controlling for covariates other than gender, it is too easy to overlook that some of these are also relevant to understanding gender wage differences. Part of the contribution of this study is that it does not ignore many of the more relevant covariates. It shows that when the concentration of women in lower paid occupations and industries (gender segregation) are taken into account, then the gender pay gap increases. It shows that the gap in hourly wages is much smaller for part-time than for full-time workers and for younger than for older workers and, in some cases, not even statistically significant.

The main contribution of this study is in looking at how these key mechanisms by which females are further disadvantaged interact with the gender effect itself. The IPWRA analysis estimates (for October 2017 to March 2018) a gender pay gap of about 15% and a gap in hourly wages from working part time (compared to full time) of about 27%. For those individuals who are both a female and a part-time worker, the gap compared with that for full-time males was estimated at 31%. This shows that part-time working has as important an effect on gender wage differences as the direct “like for like” gender effect.

The matching analysis also showed the gender pay gap for unionized workers to be higher than that for non-unionized workers. It also showed that unionized workers of both genders benefit from a union wage premium. The IPWRA analysis shows that the net effect of union membership is that female union members face a smaller gender pay gap than other workers. That is, despite the gender pay gap being greater for unionized females than for non-unionized females, the existence of the union wage premium means that they face a lower gender pay gap overall.

This paper used a matching approach to obtain as close as possible a “like for like” estimate of the gender pay gap and then examined how the gender pay gap changes with respect to other influences on gender wage differences such as gender segregation, part-time working, and low female unionization. The extensive literature on gender pay means that these have all been discussed somewhere previously. The contribution of this paper is to provide explicit, soundly based estimates of these interactions. This offers a much richer understanding of the way in which different sources of disadvantage for females interact in the creation of gender pay differences. In some instances, it implies that it might be better not to think of a single gender pay gap but of a series of different pay gaps for different groups.

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Appendix. Oaxaca RIF decomposition of the gender pay gap

  • Robust standard errors are reported for the basic model and bootstrapped standard errors for the reweighted model
  • Q10 = 10th percentile, Q50 = median, and Q90 = 90th percentile
  • Dependent variable = log of hourly wages
  • Covariates:
  • • marital status (0, 1)
  • • expected experience
  • • number of years of education
  • • migrant (0, 1)
  • • parenthood (0, 1)
  • • usual hours of work
  • • part-time (0, 1)
  • • union membership (0, 1)
  • • race dummy variables
  • • region dummy variables
  • • industry and occupation dummy variables
  • Variables used for reweighting:

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Meara, K., Pastore, F. & Webster, A. The gender pay gap in the USA: a matching study. J Popul Econ 33 , 271–305 (2020). https://doi.org/10.1007/s00148-019-00743-8

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

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

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

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  • Published: February 21, 2020
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Table 1

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

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

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

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

Discrimination.

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

Task heterogeneity.

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

Job flexibility.

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

Human capital factors.

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

Expected gender pay gap findings on MTurk

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

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

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

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

Materials and methods

Amazon mechanical turk and cloudresearch..

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

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

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

Analytic sample.

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

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

Database and analytic approach.

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

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

Cluster analysis.

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

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

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

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

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

Task selection analyses

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

Advertised hourly pay.

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

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

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

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

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

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

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

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

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

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

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

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

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

Individual-level factors

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

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

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

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

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

Task heterogeneity

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

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

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

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

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

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

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

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

Other gig economy markets

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

Generalizability

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

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

Potential mechanisms

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

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

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

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

Supporting information

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

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

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

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

  • 1. United States Equal Employment Opportunity Commission, Lily Ledbetter Fair Pay Act of 2009 (2009), available at https://www.eeoc.gov/eeoc/publications/brochure- equal_pay_and_ledbetter_act.cfm, accessed on 11/12/2018.
  • 2. United States Department of Labor (DOL), Office of Federal Contract Compliance Programs (OFCCP), Pay Transparency Nondiscrimination Provision, available at https://www.dol.gov/ofccp/PayTransparencyNondiscrimination.html , accessed on 11/12/2018.
  • View Article
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  • 4. United States Department of Labor (DOL), Bureau of Labor Statistics (BLS) (2016) Women’s earning 83 percent of men’s, but vary by occupation. TED Econ Dly , available at https://www.bls.gov/opub/ted/2016/womens-earnings-83-percent-of-mens-but-vary-by-occupation.htm , accessed on 11/12/2018.
  • 5. Davis A (2015) Women still earn less than men across the board (Economic Policy Institute, 2015), available at http://www.epi.org/publication/women-still-earn-less-than-men-across-the-board/ , accessed on 11/12/2018.
  • 6. “Gender Pay Inequality: Consequences for Women, Families and the Economy” (Joint Economic Committee, 2016). [no author]
  • 7. Hartmann H, Hayes J, Clark J (2014) “How Equal Pay for Working Women would Reduce Poverty and Grow the American Economy” (Institute for Women’s Policy Research, 2014).
  • 8. OECD (2015) In it together: Why Less Inequality Benefits All (OECD Publishing, Paris) available at http://www.oecd.org/els/soc/OECD2015-In-It-Together-Chapter1-Overview-Inequality.pdf , accessed on 11/12/2018.
  • PubMed/NCBI
  • 16. Litman L, Robinson J (In Press) Conducting Online Research on Amazon Mechanical Turk and Beyond. Sage Publications.
  • 17. Farrell D, Greig F (2016) Paychecks, paydays, and the online platform economy: Big data on income volatility. JP Morgan Chase Institute.
  • 18. Kuek SC, Paradi-Guilford C, Fayomi T, Imaizumi S, Ipeirotis P, Pina P, Singh M (2015) The global opportunity in online outsourcing (World Bank Group, 2015) Available at http://documents.worldbank.org/curated/en/138371468000900555/pdf/ACS14228-ESW-white-cover-P149016-Box391478B-PUBLIC-World-Bank-Global-OO-Study-WB-Rpt-FinalS.pdf , accessed on 11/12/2018.
  • 23. Bureau of Labor Statistics, U.S. Department of Labor, Labor Force Statistics from the Current Population Survey, Household Data Annual Averages. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity, on the Internet at https://www.bls.gov/cps/cpsaat11.htm (visited 9/3/18).

Purdue University Graduate School

Essays on Gender Gaps in STEM

This dissertation explores the issue of under-representation of women in STEM fields in high school and the early years of college. One of the major contributors to the persisting gender earnings gap is male-domination in the STEM workforce. Women are under-represented in STEM occupations since they are less likely than men to take advanced STEM courses in high school and to choose STEM majors in college. While the gender STEM gap does not exist at early ages according to most studies, it has been shown that girls start to lag behind boys in Math tests after middle school.

In Chapter 1, I investigate the STEM gender gap in the context of teacher-student gender matching. Using a fixed-effects regression model, and Chilean administrative education data on SIMCE and PSU exams and college application, I explore whether high school girls perform better in Language and Math when they have female teachers, and whether a female Math teacher impacts girls’ preference towards STEM programs when enrolling in college. I find that female teachers improve girls’ overall performance in high school Math exams for all school types, and college entrance exam Math scores for public school girls. However, they negatively affect girls’ probability of choosing STEM majors when enrolling in college. They negatively affect boys’ high school and college entrance exam Language performance and private school boys’ college entrance exam Math performance, but positively affect boys’ college STEM preference. The presence of female Math teachers in high school has negative effects on both boys’ and girls’ college entrance exam Science scores. There is significant heterogeneity in these effects between public, voucher and private schools. The negative preference effect is significant only for

high-performing girls.

Chapter 2 uses restricted NCES data (HSLS:2009 and ELS:2002) and difference-in-difference methodology to explore whether the $4.35 billion federal Race to the Top (RTT) program of 2009 had impacts on overall educational and enrollment outcomes, and gender gaps in these outcomes for high school students in the US. Besides the major objective of making students better prepared for college and future careers, a significant aspect of the RTT program was its emphasis on reducing barriers to women’s entry and success in STEM fields in higher education and the STEM workforce. I find that the program was not successful in fulfilling the major objectives of improving students’ educational outcomes, reducing achievement gaps or improving women’s representation and performance in STEM fields. It prompted students to take fewer and easier courses in high school and increased gender gaps in 12th grade GPA and SAT Math score. While there was a modest reduction in the gender gap in first year college GPA, there were neither any improvements in boys’ or girls’ college STEM credits and grades, nor

any reduction in gender gaps in these outcomes.

In Chapter 3, I use the same restricted NCES data as in Chapter 2, data on state policy obtained from Howell and Magazinnik (2017) and difference-in-difference methodology to explore whether states’ adoption of “college and career ready” common K-12 standards affected the overall educational and enrollment outcomes of high school students in the US and gender gaps in these outcomes. I use the 2009 Race to the Top (RTT) program as a source of exogenous variation, since one of the major policies promoted by the program was the adoption of higher K-12 standards across the US. I find that the tougher standards led to students taking relatively more non-STEM oriented, and thus arguably “easier” courses and increased gender gaps in STEM coursetaking.

Notably, they drove low performing girls out of college education, which resulted in a more competitive college-going female population. This in turn, led girls to outperform boys once enrolled in college, specially in STEM courses. Thus, common standards-adoption whose goal was to improve college and career readiness failed in this endeavor, but made the pool of college-going women more competitive and inadvertently levelled the playing field

for college-bound women.

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Writing help, paraphrasing tool, gender pay gap - free essay samples and topic ideas.

The Gender Pay Gap refers to the relative difference in the average earnings of men and women within the workforce. Essays on this topic could explore the historical evolution of the gender pay gap, its current status across different countries or sectors, and the societal and economic factors contributing to it. Moreover, discussions could extend to the impact of the gender pay gap on economic inequality and suggestions for policies and practices to alleviate the gap and promote gender equality in the workplace. We have collected a large number of free essay examples about Gender Pay Gap you can find in Papersowl database. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

The Gender Pay Gap Situation

Despite numerous feminist movements and policies put in place to promote gender inequality, women still do not get paid as much as men. The gender pay gap is the difference between what the average man and woman makes. Wade (2018) found that full time working women make $0.82 for every dollar that a full time working man makes. The gap has slowly gotten smaller since women were first allowed to work, however, it still persists. According to the Institute for […]

Americanah: Gender Pay Gap in Nigeria and North America

In the book Americanah by Chimamanda Adichie, women's earning potentials are vividly shown based on experiences that Ifemelu and her Aunty Uju have in both Nigeria and North America. These earning potentials affect gender roles and expectations in Nigeria and North America because women are expected more to be the house keepers and mothers rather than ever having a job themselves. Nowadays it is much different as the feminist movement continues to grow across the world. This is presented throughout […]

Gender Wage Gap and Gender Equality

Although men and women have made great strides for gender equality in recent years, the economic pay gap between men and women still persists. The Gender Wage Gap refers to the general gap between what similarly qualified men and women are paid for the same job. It is most commonly measured in the median annual pay of all women who work full time compared to a similar group of men. However, whichever way it is measured, the gender pay gap […]

The Gender Pay Gap in Sport

The gender pay gap, within the United States, is an issue across all places of work and negatively impacts the lives of all women, but the question comes into play in the sports industry. A place where women are encouraged to participate in the same activity as the male competition yet prevented from excelling due to the overbearing male presence. This multi-billion dollar industry is giving the majority of its money to the male athletes and has been since the […]

The Gender Pay Gap

Living in the year 2019 and you would think that after centuries of women being oppressed, there would be some sort of change, a progression that is long overdue. However, the wage gap between men and women is still an ongoing issue that will not be acquired for another hundred years to come. With that in mind, the state of the gender pay gap in America is explained, along with the wage gap in various occupations, and the structural barriers […]

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Why do Women Deserve to be Paid Equally?

In the age of freedom, this is a harsh reality that we are still facing the unequal pay gap in United States. Women are the only bread earners in many American families. Despite their equal work hours, they are paid unequal as compared to their male coworkers. The United States is the largest economy in the world, yet we are struggling for equal income for women. In the age where we are taking stand for LGBTQ’s rights, we must be […]

Gender Pay Gap is a Myth

There have been ongoing arguments about the gender way gap and what are the factors to it. Many assume that it has a lot to do with race or ethnicity and this is not the case at all. Gender wage pay has nothing to do with race or ethnicity because when looking at the graphs from the article “ The Gender Wage Gap: 2017 Earning differences by Race and Ethnicity” from the Institute for Women’s Policy Research it has been […]

The Gender Pay Gap and the Equality

Introduction The gender pay gap and the equality of pay rates have always been topics of discussion in today’s society. Equal pay means that individuals accomplishing the same work should be compensated equivalently in regards to completion. Issues are raised between the earning differences between men and women due to the lack of equal pay between the two genders. By referring back to U.S. history on the subject, we found that the issue dates back over 100 years ago and […]

The Gender Gap in Political Ambition

The gender gap in political ambition has been a topic extensively researched by political analysts and professors for years. The focus of this essay will be to examine why this gender gap exists and how it directly affects the underrepresentation of women who hold public office in the United States. This essay will explore the ways in which young women are politically socialized and factors in early childhood through high school which affect one’s political motivations. This research also seeks […]

What is the Gender Pay Gap

Women have suffered greatly in their bid to be considered equals and this has been from the basic right to exist, vote and to lead companies. Cultures and societies have played a role in how stigmatized the opposite sexes are. Even today, we see many who claim that weaknesses in women prevent them from leading or doing the same work and hence why should they get paid equally. Case and point are the Billie Jean King vs Bobby Riggs, in […]

The Gender Pay Gap Women Face

Income by gender, women in the labor market system versus men.The gender pay gap women face is a result of inequality based on gender, resulting in lower income. An independent variable impacts the dependent variable. As a result, an individuals gender- the independent variable-impacts income-dependent variable. The gender pay gap is an issue and it’s real. Nowadays, being a female in the current U.S culture means that the income I receive will be less than a mans. This culture has shaped […]

Sexism in Shakespeare’s Play Othello

"In the book, Othello written by Shakespeare, there is a main theme of sexism present throughout the book, Although the book was written in the 1600s, and there have been great decreases in sexism around the world, many of these ideas and scenarios are still present to this day. Sexism is defined as prejudice, stereotyping, or discrimination, typically against women, on the basis of sex. Sexism has been present for centuries, in many different forms, such as wage gaps, gender […]

Gender Pay Gap Situation in Bangladesh

Introduction: The difference between the average gross earnings of female and male employees is known as the 'gender pay gap'. ("Gender Equality: Gender Pay Gap", 2019) Most commonly, it refers to the median annual pay of all women who work full time and year-round, compared to the pay of a similar cohort of men. Other estimates of the gender pay gap are based on weekly or hourly earnings, or are specific to a particular group of women. (Vagins, 2019) In […]

The Right to Equality

Imagine a community or city where everyone is treated equally and no one is discriminated against. Everyone in this world deserves to be treated as an equal and nothing less. Equality is the absence of legal discrimination against any one individual, group, class, gender or race. Till this day many races, groups and class are being discriminated. Race and ethnicity is one of the biggest things that cause people to discriminate against others.The global community is not doing enough to […]

Should Everyone Attend College?

For decades there has been an ongoing debate on whether or not everyone should attend College, and if it’ll be beneficial for their overall gross income. On one hand, it is argued that College is a crucial essential path that should indeed be completed in order to reach a designated level of success. While on the other hand, others maintain the belief that it is possible to earn a sufficient income without a College degree. I, however, do believe that […]

Essay about Gender Wage Gap Analysis

Men and women are both capable of successfully occupying the same positions or jobs. Both genders are capable of attaining the same education, working at the same firm, and moving up the ladder while being productive in their occupation. Even though men and women are equal in theses aspects, the wages they are being paid do not show that equality. Men make more than women in the same occupations. This is a significant issue as it allows discrimination to continue […]

Men and Women in the Workplace

The gender wage gap in the economy, as a whole, is defined by “the relative earnings difference between men and women. His female counterparty makes about seventy-seven cents for every dollar a man makes.” Even if a man and a woman have the same background in education and work history, the man will go home with a paycheck higher than the woman. In society, there is a gender wage gap that cannot be remedied by increasing education alone. Inequality results […]

Looking Beyond the Numbers

Mathematicians, scientists, doctors, and countless other professions validate theories by producing a factor that is then analyzed for accuracy. Similarly, in 2019, economists analyzed median income data in the United States and determined what is the most commonly used figure to measure the economic phenomena of gender pay gap, 80.5%. Compared to Estonia at 25.3%, this figure, 80.5%, is frequently misinterpreted as an economic achievement for America. Although most states have implemented laws against gender discrimination and the 1964 Civil […]

Pay Gap by Gender and Race in Seattle WA

Seattle is deeply unsettled the past ten years once a national study unconcealed that the railway line space has one among the biggest genders pay gaps within the country. the foremost goal of this text is to know the sources of the convergence in men’s and women’s earnings within the public and personal sectors similarly because the stagnation of this trend in the new millennium. For this purpose, we tend to delineate temporal changes within the role vie by major […]

A Problem of Social Justice in World

Multiple people are discriminated for their race, their religion, or their sexuality. The idea of entitlement has been an issue in the United States for centuries. Even before the United States became a country in 1776, racial prejudice existed. At first it was the Native Americans' who were looked down on and forced to do the new white settlers dirty work. Then it became African Americans. Whites have been seen to be superior to African Americans for many years, more […]

Women and Men Pay Gap

Imagine you have been working the same job for years, but you learn that your paycheck is less than your co-workers that do the same job as you. Wouldn't that would make you upset? Well, that is what much of women experience with their male co-workers. This is the gender pay gap, and it is sexist. Because the gender pay gap is sexist, the government should put more laws in place for employers, so they pay each gender the same […]

Gender Discrimination in Hollywood

The gender pay gap is also one of the major issues that contribute to discrimination in the industry. This has been a long-standing issue in Hollywood, despite the progression of women right and equality. In the article "The Gender Wage Gap: Cause, Consequences, and Remedies," Yaveline Aly explains some of the major elements that lead to the discrimination between male and female pay gap. In 1963, the Equal Pay Act banished the sperate pay between men and women in the […]

Pink Capitalism for LGBTQ Community

Pink Capitalism, plainly, is the incorporation of the LGBTQ movement and sexual diversity to capitalism and the market economy. It is a targeted inclusion of the LGBTQ community to generate a market focused specifically on them. And even though pride parades sweep away the world and legal turnarounds change our perspectives, it’s hard to deny that discrimination against the LGBTQ community exists, especially in the workplace. Pride parades are about celebrating diversity and inclusion. And while we do celebrate the […]

Hunger Games and in Real Life

The novel The Hunger Games by Suzanne Collins presents themes that are real even in today's society such as inequality. The themes make what would qualify for good writing research topic proposal. This paper presents my writing research topic proposal based on the social issues presented in The Hunger Games. My general/broad topic focusses on inequality The subtopics directly related to the broad topic include income inequality between men and women in the United States, the effects of political inequality […]

Equal Pay Act Analysis

This is a plea to action. The gender wage gap is silent but on going debate. Employees are told to not discuss pay and salary by threat of job security, the threat keeps everyone silent so the pay differnce isn't seen or noticed. In 1963, the Equal Pay Act was introudced. It promised to close the wage gap by essentislly making gender discrimination in wages illegal. For the past 50 years, in the presence of The Equal Pay Act , […]

Education and Women’s Right

In Mary Wollstonecraft’s book, “A Vindication of the Rights of Women”, she covers a broad range of topics in each chapter concerning the equality of women compared to men. To be clear, Wollstonecraft is not indicating that male and female are made mentally and physically the same but wants her readers to understand that both are equal when it comes to acquiring an education and their position in the workplace. This is a point that she debated with opposing theorists’ […]

Equal Rights Ammendment

A potential constitutional reform is to implement the Equal Rights Amendment. If the constitution implemented the Equal Rights Amendment, inequality based on gender would be deemed unconstitutional. The idea of the equal rights amendment is not new and has been thought about for years, but has still not been passed. The Equal Rights Amendment was introduced by Alice Paul in 1923, and many supporters fought for the amendment to be implemented through protests and acts of civil disobedience from 1972 […]

Radical Feminism: when People Go too Far

"Feminism, according to The Merriam Webster Dictionary is, “The theory of the political, economic, and social equality of the sexes” (Merriam Webster Dictionary). It is a noble cause that the majority of people agree with. Sadly, in the present, feminism has a radical group of women convinced that they are oppressed in today's society and demand to be treated above men. It is clear that this group no longer want the equality of the sexes, but rather they desire the […]

France: New Gender Equality Obligations Established

Article Summary In this article, Marion Le Roux and Ji Eun Kaela Kim clarify a set of new obligations that are enforced on employers that aim to promote professional equality between men and women in the workplace. Le Roux and Kim (2019) raise the argument that there are about one-fourth of pay gaps between men and women employees, and they also add that numerous female employees also undergo further kinds of disparate treatment at the workplace (Le Roux and Kim, […]

Sexism in the Workplace Among Minority Women

Women make up less than a quarter of the system. The leadership gap is universal; gender gaps across industries in the U.S have the biggest leadership gap ever found in staffing. This is proven because there is a 15% gap versus leadership representation. Women, specifically, face unequal treatment and pay in comparison to men. The treatment and job positions in the workplace should not be influenced by gender. Gender Is an identification that determines what role an individual will have […]

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What It Will Take to End the Gender Pay Gap

Giving compass' take:.

  • Darreonna Davis explains that education won't close the gender pay gap and suggests policy solutions that will make a difference.
  • What role can you play in supporting evidenced-based interventions to close the gender pay gap?
  • Read about how philanthropy can support family caregiving .

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Education does not solve the pay gap between men and women, data from the  U.S. Census Bureau  found, and the higher-paying the field, the greater the difference. That disparity is unsurprising to experts and advocates, who point to societal norms, policy shortcomings and inflexible working schedules as big parts of the problem.

The gender wage gap is the difference in median annual earnings of women compared to men, and, according to a report published by the  Pew Research Center  earlier this year, it has not changed much in the past 20 years. Pew found that in 2022, women earned 82 cents for every dollar a man earned, compared to 2002, when women earned 80 cents for every dollar a man earned.

The census data shows the gender wage gap persisting in every occupation, even if women make up the majority of workers. Although women make up the majority of social work, elementary education and family and consumer science degree holders, the small portion of men who do hold these degrees typically earn more than the women.

Women with degrees in higher-paying fields saw a larger difference in pay than those in lower-paying fields. For example, women with a social work degree earned $5,710 less than their male counterparts while women with an economics degree earned $22,550 less than a man with the same degree.

And the gap only widens when race and ethnicity are taken into account. Last year, Black and Hispanic women only earned 67.4 percent and 61.4 percent respectively of what White men earned, according to the  Institute for Women’s Policy Research .

The idea that education is not enough to close the gender wage gap was echoed earlier this year by the  U.S. Department of Labor , which found that the gap persisted at every level of educational attainment and remains even with women having more years of education than men. In fact, the gap worsens as women attain higher education.  Data  shows the median weekly earnings difference between men and women with less than a high school diploma is around $150, while the median weekly earnings among women with advanced degrees is around $450 less than their male counterparts.

Both Khattar and Schaller believe the right policies can be put in place to help tighten the gender wage gap. Khattar mentioned the lack of federal paid family and medical leave in the United States, noting that “means that there’s penalties associated financially with taking time off,” which impact women more than men. She also pointed to the  Paycheck Fairness Act , which seeks to limit sex-based wage discrimination; developing pathways for women to enter high-paying jobs; and the  Raise the Wage Act , which would set the federal minimum wage at $17 an hour.

“One of the biggest things that could be done is to increase the minimum wage — and that’s something that could be done at a state level and a federal level — because we know that women, particularly Black women and Latinas, are overrepresented in low-wage work,” she said.

Read the full article about closing the gender pay gap by Darreonna Davis at The 19th. Read the full article

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What local wage gap data can tell us about pay equity in Boston

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A 2023 report from the Boston Women's Workforce Council shows that the gender wage gap in Greater Boston decreased by nine cents over the previous two years, while the racial wage gap increased by three cents since 2021.

Today, Boston Globe Workforce and Income Inequality Reporter Katie Johnston joins The Common to break down this report , and what it can tell us about pay equity in our community.

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Australians will finally see how men and women are paid in the workplace

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Mary Wooldridge is confident that when her agency begins publishing the gender pay gap for large businesses next month, Australia will take a huge step forward for women’s equality.

“We’re optimistic that it will be a significant contributor [to reducing the pay gap],” Wooldridge says. “The publishing of the gender pay gap is the catalyst for a whole lot of work that then needs to happen by individual employers and by industries to shift the outcomes.”

Workplace Gender Equality Agency chief executive Mary Wooldridge is optimistic Australia has taken a meaningful step to narrow the gender pay gap.

Workplace Gender Equality Agency chief executive Mary Wooldridge is optimistic Australia has taken a meaningful step to narrow the gender pay gap. Credit: Paul Jeffers

Federal laws passed in March require the Workplace Gender Equality Agency to release data for private businesses with more than 100 employees – which covers about 5 million workers – from February 27 and for Commonwealth public sector employees next year.

While the agency has been collecting this data for almost a decade, until now it has published only anonymised information about industry sectors. In the first release, the agency will publish employer gender pay gaps by median as well as the gender composition and average remuneration per pay quartile of every large company.

Australians will finally be able to see how men and women are paid in the workplace.

Wooldridge, chief executive at the Workplace Gender Equality Agency, hopes holding businesses to account will spur them to meaningfully address the gender pay gap, which has fallen to 13 per cent, according to the Australian Bureau of Statistics.

‘The legislation has a clear message from the government that this is a priority and that gender equality is not just a nice to have.’ Mary Wooldridge, Workplace Gender Equality Agency chief

The agency calculates the data differently. They have looked at the total remuneration package, which includes base salary, overtime, bonuses and additional payments, and determined the gender pay gap, although at a historic low, is even more stark.

For every $1 on average men earn as part of their total remuneration, women earn 78¢ – or 21.7 per cent less – leaving them annually $26,393 worse off.

The new laws will not be the panacea to ending gender inequality, but Wooldridge believes it will go a long way in addressing the culture.

Australia enshrined “equal pay for equal work” more than 50 years ago, but the gender pay gap is a persistent and complex problem. It is the result of social and economic factors that reduce women’s earning capacity over their lifetime.

For example, female-dominated industries, such as childcare, pay less than the more male-dominated industries of construction. Women disproportionately take time off from work to look after children, or are far more likely to be overlooked for promotions.

But even in the female-dominated industries of healthcare and education, men are more likely to be in senior managerial positions, Woodridge says.

“The legislation has a clear message from the government that this is a priority and that gender equality is not just a ‘nice to have’,” Wooldridge says. “What it requires with transparency and accountability around performance is [that] companies have that light shone on their performance, but also an opportunity then to articulate what they’re doing about it.”

Since 2018, companies in the United Kingdom with more than 250 employees have been required to publish their gender pay gaps. A UK Guardian analysis of the latest data showed the median gap remained wide at 9.4 per cent – the same level as in 2017-18.

Meaningful change will take time to achieve, Wooldridge says, but accountability and transparency will challenge companies to take bigger and bolder steps.

“In addition to publishing the number, employers can publish what we’re calling an employer statement,” she says.

“What are they? What’s driving the gap? And what are their plans to address it? While year one is important, year two and year three’s going to be important because we’ll be able to see progress of companies and the difference between what they say and the outcomes that are delivered.”

AI Group chief executive Tim Piper said companies were generally conscious of addressing the gender pay gap and working towards ending it.

“We are not unsupportive of these moves, but our major concern is the administrative difficulty and the cost that could result because of having to do it,” Piper said.

“Companies must be given the opportunity to invest in it and be a part of it … We need to have as many women involved across the board as possible, so the way to do that is to make sure the gender pay gap is reduced as much as possible.”

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Gender pay gap closing twice as fast in Scotland

Scotland is continuing to lead the way in the UK on closing the gender pay gap, with women in some council areas earning more than men

Scotland will close the gender pay gap by 2034, twice as fast as the UK average, according to new analysis.

On the current trajectory women will earn the same as men on average within 10.8 years in Scotland, compared with 20.9 years nationally, according to official figures analysed by the Scottish Parliament Information Centre.

Kate Forbes, the former SNP leadership candidate, said the figures were “extremely encouraging” and showed that “Scotland continues to lead the way in equal pay in the UK”. The gender pay gap has been lower in Scotland than in the UK since 2003.

• 1 in 17 young people in Scotland not in education, work or training

The gap for average hourly earnings, excluding overtime and the self-employed, was 8.7 per

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Gender pay gap lower in Scotland than rest of the UK

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