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Open Access

Peer-reviewed

Research Article

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

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

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

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

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

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

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

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

Roles Conceptualization, Writing – review & editing

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

Roles Formal analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

Discrimination.

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

Task heterogeneity.

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

Job flexibility.

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

Human capital factors.

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

Expected gender pay gap findings on MTurk

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

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

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

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

Materials and methods

Amazon mechanical turk and cloudresearch..

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

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

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

Analytic sample.

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

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

Database and analytic approach.

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

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

Cluster analysis.

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

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

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

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

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

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

Task selection analyses

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

Advertised hourly pay.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Individual-level factors

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

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

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

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

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

Task heterogeneity

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

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

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

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

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

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

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

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

Other gig economy markets

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

Generalizability

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

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

Potential mechanisms

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

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

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

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

Supporting information

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

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

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

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

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Closing gender gap makes sense ethically and economically.

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World Bank study reveals persistent gender gap in workplaces globally due to legal gaps and ... [+] enforcement issues. The economic benefits of gender equality are vast and urgent.

A World Bank study released earlier this month concludes that a significant gender gap still exists in workplaces in many countries, resulting in lower pay and fewer job opportunities for women. The new report, Women, Business and the Law 2024 , finds that because of inadequate legal protections against sexual abuse, among other harms, the gender gap is even wider than previously reported. The report strengthens the case for seriously addressing the gender gap both as an ethical priority and because societies benefit economically when women have greater job opportunities.

The World Bank’s study, its 10 th annual report on this topic, is the first to examine the economic impact of legal gaps pertaining to personal safety and the costs of childcare. It is based on contributions by more than 2,400 lawyers, judges, academics, civil society representatives and public officials in 190 countries. The report finds that in many countries the threat of personal violence and lack of related legal protections prevents many women from going to work. It also documents deficiencies in laws relating to childcare costs, which often make it prohibitive for women to enter the workforce. Overall, the World Bank concluded that with respect to laws on the books, “women enjoy roughly 64% of the rights of men.”

Even when a country has laws designed to protect women, all too often they are not enforced. The World Bank identified 95 countries that have enacted laws guaranteeing equal pay. But only 35 of those countries have adopted measures to ensure that the laws are being applied. Globally, women earn just 77 cents of each dollar earned by men with comparable jobs. Separately, Oxfam projects that “at the current rate of progress, it will take 170 years to close the gap.”

And while tens of millions of women, especially in less developed countries, have moved into the formal economy in the last 25 years, many lifting themselves out of poverty, millions more still face dire economic conditions. Another report published last year by UN Women concluded that “if current trends continue, more than 340 million women and girls will still live in extreme poverty by 2030, and close to one in four will experience moderate or severe food insecurity.”. One factor contributing to these dire conditions is that 75% of women in less developed states are still working in the informal economy, where they have far fewer legal or social protections.

While the gender gap is most pronounced in less developed countries, it exists almost everywhere. In the United States, for example, the Equal Pay Act mandates equal pay for men and women, but since its enactment six decades ago, this standard is not being enforced uniformly. Recent studies show that today , women in the U.S. are paid 84% of what men make for the same work, a gap that has remained roughly the same for 20 years. African-American women earn even less: 63% of what white men earn for doing the same job.

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The problem is not a lack of training or formal education. Today more than 50% of graduates of four-year U.S. colleges are women . They make up more than 56 percent of law school graduates, and more than 42% of those graduating from the top business schools . An increasing number of women are hired into entry-level professional positions in corporations but all too often they are not given proper mentoring or child-care benefits. Many leave these corporate jobs having failed to advance to the more senior management jobs to which they aspired. According to a 2023 McKinsey & Co. study , “For every 100 men who are promoted from entry-level to manager, only 87 women are promoted, and only 82 women of color are promoted.”

Women face particularly daunting challenges in the world of finance , despite the fact that they typically perform as well as their male peers. A 2015 study from Morningstar on fund managers concluded that there is no meaningful difference in financial performance among firms run men and those run by women. And yet few women control financial firms. According to a 2020 report from Deloitte , “All but six of 111 CEOs at the 107 largest U.S. public financial institutions (including four with co-CEOs) are men.”. The recent exodus of senior women at firms like Goldman Sachs is another reflection of the challenges women face. Women also are vastly underrepresented in many of the highly profitable sectors of the investment world – such as in private equity and hedge funds.

Women are significantly underrepresented in the leadership of the largest U.S. companies as well. Today only 53 companies in the S&P 500 have women CEOs . The path is more promising in the law , but still nowhere near what it should be with women now constituting about a quarter of partners at the top law firms.

There are several factors that contribute to the continuing gender gap in U.S. business. One relates to the challenges they face in balancing work and family. Women who have children usually take parental leave and may opt to work on more flexible schedules. When they make these choices, they risk falling out of step in highly competitive corporate cultures. In 2024, some businesses in the U.S. and Europe are trying to accommodate the realities of working women and modern families. More need to do so.

A second factor that too few business leaders acknowledge or address is unconscious bias. Historically, many business relationships and deals have been forged on the golf course, in social clubs, or at sporting events where relatively few women have been included. It’s past time for companies to ensure that these spaces do not exclude women or to develop new venues for building business relationships.

Achieving gender equality is the right and ethical thing to do. But it’s also the smart thing to do from a purely economic perspective . As an International Monetary Fund study found in 2018, “closing the gender gap could increase GDP globally by an average of 35 percent.”. Oxfam estimates that in developing countries alone, gender inequality cost countries $9 trillion a year in lost earnings, a massive figure that would significantly boost the economies of those countries.

Making women truly equal is an achievable goal, and one that will benefit everyone in society. Attention to these issues needs to be a priority for men and women; it is long overdue.

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Mind the gap: exploring the impact of the gender wage gap towards women's academic success and career aspirations

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The gender pay gap in the USA: a matching study

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• Volume 33 , pages 271–305, ( 2020 )

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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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• 26 March 2021

Assessing the gender gap in academia

• Nicolò Romano

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Gender disparities in opportunity are deeply rooted in academia, and affect countries as diverse as Italy and Norway. A comparative study published in the Journal of Informetrics shows that, in both countries, it is harder for female professors to reach the highest academic ranks than it is for their male colleagues, despite being just as productive. But it is still harder for Italian women than for Norwegian ones, most likely because of differences in welfare systems, and cultural factors.

The study analysed 1 the scientific performance of Italian and Norwegian university professors, comparing gender patterns across countries, research fields and academic positions. The authors say they chose to compare the two countries because they differ widely in how family responsibilities are shared between men and women.

Using data from publicly available databases on academic staff, the authors looked at publications by 36,000 professors from 2011 to 2015, considering researchers and professors who held formal faculty positions for at least three years and with at least one publication over the period. Overall women represent 33.8% of academic staff included in the sample in Italy and 33.9% in Norway. But women in Italy are more concentrated in lower academic ranks, representing 47.2 % of assistant professors, 35.2 % of associate professors and only 18.3 % of full professors. In Norway, the corresponding figures are 41.5 %, 46.5 %, and 26.1 %, respectively.

“We designed a new indicator to better portray research productivity and not simply production over time” says Giovanni Abramo from the Institute for System Analysis and Computer Science (IASI-CNR) at the National Research Council in Rome, one of the authors. The indicator embeds both output data (the number of publications and citations) and input (number of co-authors, average yearly salary, years of experience). “In scientific research,” adds co-author Ciriaco Andrea D’Angelo from Università di Roma Tor Vergata, “usually those who have more resources, like money and time, tend to achieve better results. Indicators such as the h-index do not account for this and we wanted a more accurate one”.

According to the indicator, men outperform women overall. Their average performance is 37% higher than women’s in Italy, and 32% higher in Norway. But, the difference is mostly due to the fact that men are overrepresented in the top 10% performers, and that in both countries full professors — the group where women are least present — have the highest performance. Differences in performance tend to fade away in the remaining 90% of performers, and they even reverse when only full professors are taken into account. “As a matter of fact, performances of men and women do not differ much, except in the top performing groups,” the authors write.

The researchers also simulated how university staff should look like if academic ranks were aligned with research performance, and found that, all else being equal, there would be 9% more female full professors in Italy and 6.5% more in Norway.

“We alone cannot explain the causes of these phenomena since complex cultural and relational factors are involved. We just looked at data, but sociologists of science and gender studies experts will certainly have lot of work to do,” D’Angelo says.

For Ilenia Picardi, a researcher in sociology at Università Federico II in Naples who studies gender gaps in academia, the study’s findings confirm concerns about the fairness of the career system, and suggest that the productivity of young female researchers is particularly affected by motherhood: early stages in research career often overlap with a crucial period when women, more than men, devote much time to childcare and family.

Picardi also notes that structural and cultural factors influence research performance but often remain “unnoticed” by indicators. “In Norway, over 90% of children go to kindergarten, while only 24% of children do so in Italy”. She notes. “Different welfare states and cultural views do indeed affect research performance.” Giulia Quattrocolo, an Italian researcher who is a group leader at the Kavli Institute for Systems Neuroscience, agrees. “In Norway we have longer maternity leaves and mandatory paternity leave,” she says. “Gender equality is viewed as important and work schedules are more flexible for mothers and fathers.”

Yet, it would be “extremely reductionist” if gender equality in research only accounted for motherhood, Picardi notes. “What we need is an open debate on the mechanisms responsible of gender inequalities, such as intrinsic biases in performance indicators.”

doi: https://doi.org/10.1038/d43978-021-00037-2

Abramo G., Aksnes D.W., D’Angelo C.A., Journal of Informetrics , 15 (2021).

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• v.8(8); 2022 Aug

The gender gap in higher STEM studies: A systematic literature review

Sonia verdugo-castro.

a GRIAL Research Group, Department of Didactics, Organization and Research Methods, Research Institute for Educational Sciences, Universidad de Salamanca, Salamanca, Spain

Alicia García-Holgado

b GRIAL Research Group, Computer Science Department, Research Institute for Educational Sciences, Universidad de Salamanca, Salamanca, Spain

Mª Cruz Sánchez-Gómez

c GRIAL Research Group, Department of Didactics, Organization and Research Methods, Universidad de Salamanca, Salamanca, Spain

Associated Data

Data associated with this study has been deposited at Verdugo-Castro, S., García-Holgado, A., & Sánchez-Gómez, M. C (2021). Code repository that supports the research presented in the paper ‘The gender gap in higher.

STEM studies: A Systematic Literature Review’ (v1.0) [Data set]. Zenodo. https://zenodo.org/record/5775211 .

The development of science, technology, engineering, and mathematics (STEM) requires more qualified professionals in these fields. However, gender segregation in higher education in this sector is creating a gender gap that means that for some disciplines female representation does not even reach 30% of the total. In order to propose measures to address the phenomenon, it is necessary to understand the possible causes of this issue.

A systematic literature review and mapping were carried out for the study, following the PRISMA guidelines and flowchart. The research questions to be answered were (RQ1) What studies exist on the gender gap in relation to the choice of higher education in the STEM field; and (RQ2) How do gender roles and stereotypes influence decision-making related to higher education? The review of peer-reviewed scientific articles, conferences texts, books and book chapters on the European education area was applied. A total of 4571 initial results were obtained and, after the process marked by the PRISMA flowchart, the final results were reduced to 26. The results revealed that gender stereotypes are strong drivers of the gender gap in general, and the Leaky Pipeline and Stereotype Threat in particular. To narrow the gender gap, it is necessary to focus on influences from the family, the educational environment, and the peer group, as well as from the culture itself. Positive self-concept, self-efficacy, self-confidence, and self-perception need to be fostered, so that the individual chooses their studies according to their goals.

Gender gap; STEM; Gender; Stereotypes; Diversity; Higher education.

1. Introduction

The science, technology, engineering and mathematics (STEM) field is experiencing a shortage of skilled workers ( Codiroli Mcmaster, 2017 ), yet it is experiencing a great deal of technological development ( Winterbotham, 2014 ). In addition, the STEM education sector suffers from under-representation of gender diversity, namely of women ( García-Holgado et al., 2019a , García-Holgado et al., 2019b , García-Holgado et al., 2019c ; Jacobs et al., 2017 ). This situation invites reflection on the cause of gender segregation in scientific and technical higher education.

With regard to motivation as a vector for deciding which higher education studies to pursue, studies have been published, such as that of Guo et al. (2018) , in which it is pointed out that women prefer to opt for professions related to people, their care and education, while men prefer to opt for the fields of things. However, beyond the simple explanation of what they prefer, it is necessary to detect what modifies and conditions the motivation, and therefore the final decision.

Gender stereotypes in the STEM education sector are related to Stereotype Threat ( Corbett and Hill, 2015 ) and the Leaky Pipeline, which lead to the loss of equal representation in the sector.

Stereotype Threat is a social phenomenon that occurs when the person concerned fears confirmation of the negative stereotyping of the group to which they belong ( Cheryan et al., 2017 ). Given that the STEM sector has been socially ascribed to men ( Blackburn, 2017 ; Nosek et al., 2009 ), women may fear rejection in the field of study and careers. One of the consequences of Stereotype Threat is when erratic stereotypical thoughts lead the affected persons to doubt their abilities, deteriorating their self-confidence, despite having optimal performance results ( Correll, 2001 ).

This situation of loss of a sense of belonging can erode women's self-efficacy ( Hall et al., 2015 ), and eventually lead to the phenomenon of the Leaky Pipeline ( Berryman, 1983 ).

Understanding the factors involved in the process of deciding which higher education studies to pursue will shed light on how to enable the retention of women ( Reiss et al., 2016 ). Such retention is essential to avoid further loss of human capital, given that female participation rates in STEM studies are worryingly low.

In addition, to combat the gender gap, the different social and cultural factors involved, as well as gender stereotypes, which, as pointed out by authors such as Bian et al. (2017) , can be observed from the age of six, must also be taken into account in the frame of reference. However, taking as a reference authors such as Ceci et al. (2014) , the need to pay attention to solid environmental influences is reaffirmed. The latter authors ( Ceci et al., 2014 ), in their study, concluded that early sex differences in spatial and mathematical reasoning do not necessarily stem from biological bases, that the gap between the average mathematical ability of females and males is narrowing, and that sex differences show variations over time and across nationalities and ethnicities. Thus, all this points to the need to pay attention to environmental and contextual factors that modulate the impact on the gender gap.

On a biological basis, there is controversy in the literature. While some authors argue that the gender gap is not biologically based ( Bian et al., 2017 ; Blackburn, 2017 ; Borsotti, 2018 ; Cantley et al., 2017 ; Codiroli Mcmaster, 2017 ), other authors do suggest that differences between men and women in career and lifestyle preferences are to some extent due to biological influences ( Stewart-Williams and Halsey, 2021 ).

Therefore, as Ceci et al. (2014) point out, gender discrimination has historically been a potential reason for the under-representation of women in scientific academic careers. Today, however, attention must also be paid to the barriers girls and women face to full participation in scientific and technical fields ( Ceci et al., 2014 ).

Although segregation does not occur in 100% of the countries in the world, there is a widespread trend of gender segregation in tertiary studies. As an example, about STEM higher education, during 2018, in France, 28,857 men (74.55%) studied tertiary Physics studies, compared to 9,850 women (25.45%). The same was true in Spain with 73.23% male representation, in Greece with 70.51% and in Austria 78.32%. In the disciplines of Mathematics and Statistics, for example, in the UK, 63.05% of the representation was male, as in France with 70.41%. And in Sweden, in Exact Mathematical Sciences 66.06% of the students were male. Also, in 2018, 81.67% of students in ICT studies in the European Union were male. For example, in Spain, 86.92% of students in Software disciplines were male. Moreover, during 2018, 73.53% of students in Engineering, Manufacturing and Construction disciplines in the European Union were male. For example, in Germany, 82.02% of Engineering students were male. And finally, 81.93% of Electronics and Automation students in Turkey were male, as was the case in Architecture with 69.07% of men ( European Institute of Gender Equality, 2018 ).

To explore the factors involved in horizontal gender segregation in the STEM education sector, a review of the existing literature is proposed through a Systematic Literature Review on the gender gap in STEM education in the European Union.

After searching and reading other reviews, it was decided to develop the Systematic Literature Review.

First, Canedo et al. (2019) address the barriers that women face in software development projects. The authors aim to find mechanisms to encourage women's interest in the field of software development projects. In turn, Gottfried et al. (2017) present a literature review on how friends and familiar social groups play a role in the likelihood that high school students do or do not pursue advanced studies in mathematics and science. Also, Wang & Degol (2013) address motivational pathways towards STEM career choices, in relation to gender; they do so using Expectancy Value Theory as a framework. Finally, Yazilitas et al. (2013) focus on micro-level and macro-level patterns linked to the unequal representation of students of both genders in STEM.

After reading the reviews, it was decided to continue with the review process of the present study, given that they did not respond to the research questions posed for the research. Canedo et al. (2019) focus their attention on software development projects; however, they do not address other STEM fields and do not propose to analyse the social, academic, and personal factors involved in segregation. On the other hand, Gottfried et al. (2017) base their study on the influence of friends and family on the decision to study mathematics and science, however, the spheres of technology and engineering are not included, and the perspective is not open to another classification of elements, such as personal and academic. Similarly, Wang & Degol (2013) propose to discover the motivations towards the choice of careers, although they do so from a psychological perspective, and the study is outdated as it was published in 2013. Finally, Yazilitas et al. (2013) also start from a psychological perspective. Nonetheless, in order to answer the research questions of the review presented here, it is necessary to take an educational perspective and not only a psychological one, because socio-educational elements are addressed.

In deciding to continue the process, the PRISMA model was used. The aim of the work was to identify what work has been or is being developed on the subject, and to understand the influence of gender stereotypes on the segregation process. The aim was to answer what are the objectives pursued in the existing studies, what are the methodologies and scientific methods used, whether specific instruments and/or data collection techniques have been used for the study of the gender gap in STEM studies, as well as what are the results obtained in the studies. Also, it aimed to know the relationship between the gender gap in STEM studies and the cultural and social patterns surrounding gender.

This paper is organised in six blocks. The first is the introduction, followed by the planning of the research in the second block (materials and methods), then the results of the mapping in the third block, and the results of the Systematic Literature Review and the discussion in the fourth block. The fifth section contains the conclusions. Finally, the sixth section describes the threats to the validity of the study.

2. Materials and methods

Systematic Literature Review (SLR) allows for the identification, evaluation and interpretation of all available research relevant to a particular research question, thematic area, or phenomenon of interest ( Kitchenham, 2004 ). The systematic literature review process is divided into three phases: planning the review, conducting the review and writing the report ( Kitchenham and Charters, 2007 ). Along with the Systematic Literature Review, a systematic mapping can be carried out, which entails the same phases as outlined above ( Petersen et al., 2015 ).

In the work presented, an SLR and a systematic mapping of the gender gap in higher education in the Science, Technology, Engineering, and Mathematics (STEM) sector have been carried out. In this work, the systematic mapping is presented as a complementary element to the Systematic Literature Review. The procedure followed is the PRISMA flowchart and guidelines ( Moher et al., 2009 ).

The review and mapping process was divided into a set of phases or steps. These phases range from the systematic review of other SLRs related to the gender gap in STEM higher studies–to determine the need to carry out the present study–, to the results obtained after carrying out the review. The phases followed were: (1) systematic review of other SLRs, (2) definition of the research questions for the SLR and mapping, (3) definition of the inclusion and exclusion criteria, (4) definition of the search strategy, (5) definition of the quality criteria, (6) data extraction, (7) results, and (8) data analysis and report writing.

The complete detailed explanation of each step of the systematic literature review presented in this article is contained in supplementary material 1. Each element has been detailed in supplementary material 1, simplifying the information in this document to facilitate the wording of the explanatory steps of the review.

2.1. Identifying the need for a review

Before conducting a systematic review or mapping of the literature it is necessary to examine whether there is a real need for the review. It should be determined whether a systematic review already exists that answers the research questions posed and can support the research. There is no scientific reason to conduct a systematic review or mapping that has been done before, unless there is a clear bias in the review or it is outdated and new studies have been published since the existing review was completed ( Petticrew and Roberts, 2005 ). To find out whether there are previous reviews or mappings that answer the research questions posed in the study, a search for existing systematic reviews and mappings should be conducted. For this part of the analysis, the following research question is posed: Do SLRs or mappings exist that answer the research questions of this study?

Finally, 107 documents were identified in Scopus with this equation of terms, 36 of them related to reviews and mappings. After reviewing the 36 documents, only 2 met the indicated criteria. On the other hand, in Web of Science, 49 documents were identified with the search string stated. Of the 49 documents, 9 were associated with a literature review or mapping, and, after examining the documents, only 2 met the criteria. Of the four final articles, one of them followed the SLR methodology, one of them partially followed the SLR methodology and the other two did not follow the SLR methodology.

From the review of the four final papers, it was concluded that none of them answered the research questions that were posed for this study. This is because they focus on other elements related to the gender gap ( Canedo et al., 2019 ; Gottfried et al., 2017 ), in addition to the fact that two of them are outdated, as they are publications from 2013 ( Wang and Degol, 2013 ; Yazilitas et al., 2013 ). Nine years have passed since 2013, which means almost a decade left unaddressed in these reviews.

Detailed information on this section of the systematic literature review and on the inclusion and exclusion criteria, search strategy, search strings, and criteria for quality assessment can be found in supplementary material 1.

2.2. Research questions

Once the actual need to carry out the SLR of the present study was determined, the process began. The first phase was to review the research questions and the mapping questions. First, two research questions (RQ) were defined:

• • RQ1: What studies exist on the gender gap in relation to the choice of higher education in the STEM field?
• • RQ2: How do gender roles and stereotypes influence decision-making related to higher education?

Secondly, eight mapping questions (MQ) have been defined:

• • MQ1: Which databases publish studies in relation to the gender gap in the STEM education sector?
• • MQ2: Which keywords are applied in the studies?
• • MQ3: How are the studies distributed per year?
• • MQ4: What kind of methodologies and methods do the studies apply?
• • MQ5: In which countries do the studies take place?
• • MQ6: With which population are the studies conducted?
• • MQ7: What instruments or data collection techniques have been validated?
• • MQ8: What kind of data collection instruments or techniques are used?

Based on the research questions defined, the PICOC method proposed by Petticrew and Roberts (2005) was used to define the scope of the review:

• • Population: Gender gap in the STEM sector.
• • Intervention: Studies conducted, and proposals related to the gender gap in the STEM education sector
• • Comparison: No comparison.
• • Outcomes: Results of studies conducted in relation to the gender gap in the STEM education sector.
• • Context: Students integrated in the European educational field, especially in the STEM sector, with a special focus on EQF levels 5, 6, 7, and 8 (European Qualifications Framework for Lifelong Learning).

Universal human factors condition the gender gap in STEM higher education. Since as known from the scientifically accepted SCCT model of Lent et al. (1994) , motivations and outcome expectations condition the decision on which higher education studies to pursue. However, the gender gap is not only influenced by intrinsic factors but also by extrinsic elements. Cultural patterns marked by stereotypes and gender roles present themselves differently, depending on the local culture ( Bourdieu, 1980a , 1980b , 1984 ). Since the gender gap is a sociological phenomenon that responds to socio-cultural rules, the gender gap index does not occur equally in all world geographical regions ( García-Holgado et al., 2019c ; World Economic Forum, 2021 ).

In this sense, it is of scientific interest to analyse the gender gap in developed geographical areas which implement measures to alleviate segregation where the gap is manifest. For this purpose, global gender gap reports have been consulted to determine the gender gap index situation in the different world regions.

According to the World Economic Forum (2021) , each country is in a particular situation concerning closing the gender gap. According to the World Economic Forum (2021) , the geographical areas of Eastern Europe and Western Europe are in a worse situation in terms of closing the gender gap than areas of North America such as Canada and the United States. In the global ranking of gender gap indices, updated to 2021, Canada ranks 24th out of 156, and the United States ranks 30th out of 156. In 2021 Canada closed 77% of the gender gap and the United States 76%. Meanwhile, other Eastern and Western European countries are in less favourable positions. In 2021 Hungary was ranked 99 out of 156, with 69% of the gender gap closed; Greece was ranked 98 out of 156, with 69% of the gender gap closed; Romania was ranked 88 out of 156, with 70% of the gender gap closed; Malta was ranked 84 (70%); the Czech Republic ranked 78th (71%); the Slovak Republic ranked 77th (71%); Poland ranked 75th (71%); Italy ranked 63rd (72%); Luxembourg ranked 55th (73%); Estonia ranked 46th (73%); Croatia ranked 45th (73%); Slovenia ranked 41st (74%), and Bulgaria ranked 38th (75%).

Also addressing gender segregation in the vertical sense, according to the World Economic Forum (2021) , the low presence of women in top positions demonstrates the persistence of a “Glass Ceiling” even in some of the most advanced economies. While in the United States women occupy the 42% of senior and management positions, in other countries such as Sweden they occupy the 40%, in the United Kingdom the 36.8%, in France the 34.6%, in Germany the 29%, in Italy and the Netherlands the 27%.

On the other hand, as far as the gender pay gap is concerned, developed countries still have a gap to close, e.g., France has 39% of the gap to close, Denmark has 38% of the gap to close, while the United States has 35% of the gap to close.

Therefore, given the results of the reports, it has been decided to analyse the scientific production on the gender gap in higher STEM studies in the European Union. Although it is a geographical area that is on the way to reducing the gender gap, there are still high rates to be closed.

2.3. Data mining

Regarding the data extraction, the metadata of the publications obtained from the search was downloaded from the databases in CSV format. The raw datasets are available in Zenodo ( Verdugo-Castro et al., 2021 ). The phases of defining the protocol, searching and extracting the initial data from the databases were carried out by all the authors of this publication. The search results are current as of 10 November 2021. Subsequent filtering of the successive phases was done by peer review among the authors. The data mining process is an iterative and incremental process. The process was done through different phases ( Figure 1 ). The process is described through the PRISMA flowchart ( Moher et al., 2009 ).

PRISMA flowchart of the Systematic Literature Review. Source: Created by the authors.

First, the results were identified, following the application of search strings in the two selected databases. The results of the databases were downloaded in CSV format. Then, all results were organised in a spreadsheet in Google Sheets. The spreadsheet was configured to automatically detect duplicate titles to facilitate their search and removal. After removing the duplicate items, the data extraction stages began with the application of different filters ( http://bit.ly/3a4gRM5 ).

• • First stage: On a second sheet of Google Sheets, three items were analysed to see if the publication was related to the study objective and the research questions. This phase allowed us to define the candidates for reading. These three elements were the title, the abstract and the keywords ( http://bit.ly/39lO0DX ).
• • Second stage: The documents resulting from the previous phase were then dumped onto a third sheet. On this third sheet of Google Sheets, the inclusion and exclusion criteria were applied. To proceed to the next stage, each publication had to meet all the inclusion criteria ( http://bit.ly/39lO0DX ).

During the first phase, 2794 items were removed, and during the second phase, 698 items were removed. A total of 3492 items were eliminated between the first and second phases. The reasons for discarding these publications were:

• o The publication's subject matter did not have a clear relationship to the gender gap in the STEM education sector.
• o The study addressed the gender gap in STEM fields at the employment or business level but, not in the educational field.
• o The study addressed gender segregation in education, but from the perspective of female teachers, not female students.
• o The study addressed educational elements not related to the gender gap. For example, academic performance and grades.
• o The research was not carried out in European Union countries or regions.
• o The publication was not open access or available through University of Salamanca databases subscriptions.
• • Third stage: The third stage of the process focused on the eligibility of publications. The publications selected in the previous stage were read again. This time they were read with the aim of answering the quality questions ( http://bit.ly/36fnBpi ). In total, there were 10 questions, each of which was answered with one of the following options: yes (1), no (0), partial (0.5). Each answer corresponded to a score, so that the sum of the answers gave each paper a score between 0 and 10. Those papers with a score equal to or higher than 6 were selected for the final stage.

At the quality stage, 196 items were discarded if they did not reach the minimum cut-off score of 6. While all publications were related to the gender gap in the STEM education sector in an EU country or region, the reasons for exclusion were as follows:

• o The objectives of the publication were not clearly aligned with the gender gap in STEM. In some cases, the approach to segregation was collateral and superficial.
• o Some research did not propose methodological approaches of interest at qualitative, quantitative or mixed levels.
• o Other research did not propose intervention proposals (four of the ten quality questions are linked to socio-educational proposals).
• o Some studies do not take into account the limitations encountered throughout the research.
• o The publication does not answer at least one of the two SLR research questions.

Finally, 26 items made it to the final phase. Each selected paper was analysed in detail to obtain the answers to the research and mapping questions.

3. Results of the systematic mapping

The results to the systematic mapping questions are presented below.

3.1. MQ1: which databases publish studies in relation to the gender gap in the STEM education sector?

About three quarters of the publications are indexed in Scopus, compared to 23% of those indexed in Web of Science.

3.2. MQ2: which keywords are applied in the studies?

As presented in Table 1 , the most frequently used keywords are gender, STEM, and stereotypes.

Table 1

Results to the MQ2.

3.3. MQ3: how are the studies distributed by year?

As shown in Figure 2 , the years with the highest number of publications are 2018 and 2017.

Results to the MQ3.

3.4. MQ4: what kind of methodologies and methods do the studies use?

It can be seen from Figure 3 that there is a preponderance of studies based on quantitative paradigms, although qualitative designs and mixed approaches are emerging. Complete information on this question can be found in Table 1 of Supplementary Material 2 linked to this article.

Results to the MQ4.

3.5. MQ5: in which countries are the studies carried out?

As presented in Figure 4 and 9 studies were carried out in Germany; 5 in Spain, 3 in the UK and Ireland, 2 in areas such as Italy, Portugal, Denmark, Belgium and Finland, and only one study in other regions, such as Slovenia, Norway, Scotland, Latvia, Estonia and the Czech Republic.

Results to the MQ5.

3.6. MQ6: with which population are the studies conducted?

As shown in Figure 5 , the samples with which the studies have been carried out are primarily university students and secondary school students. Studies have also been carried out with primary school students and secondary school and university students. Finally, in one study, there have been samples of primary, secondary, and university education; and in another study, the sample has been female graduates.

Results to the MQ6.

Complete information on this question can be found in Table 2 of Supplementary Material 2 linked to this article.

Table 2

Results for the MQ7 and MQ8.

3.7. MQ7: what data collection instruments or techniques have been validated? And MQ8: what kind of data collection instruments or techniques are proposed?

Table 2 provides information on what kind of techniques or instruments have been used to collect the data and which of them have been validated.

4. Results of the systematic literature review and discussion

The qualitative analysis of the resulting papers in the systematic literature review has been organised into two main blocks (4.1. and 4.2.). Since there are two research questions to be answered for SLR, the first research question is answered in the first block (4.1. IQ1: What studies exist on the gender gap in relation to the choice of higher education in the STEM field?), and the second block answers the second research question (4.2. IQ2: How do gender roles and stereotypes influence decision-making related to higher education?).

In turn, a grouping strategy has been followed to classify the results thematically and facilitate their understanding. After reading all of them, the main themes studied in the papers were identified as categories, and the results of the papers were organised based on these categories. Finally, eight main themes have been identified, four to answer the first research question and four to answer the second SLR research question.

In the first block, in which the first SLR research question is answered, the main themes are Socio-educational projects and proposals (4.1.1.), study of gender differences (4.1.2.), initiatives in secondary and university education (4.1.3.) and Active methodologies and intervention initiatives (4.1.4.). On the other hand, in the second block, in which the second research question of the SLR is answered, the main topics are Social Cognitive Career Theory (SCCT) and early intervention (4.2.1.), educational institutions and the learning process (4.2.2.), perceptions of male-dominated domains (4.2.3.) and social structures and contextual influences (4.2.4.).

The first research question addresses what studies exist on the gender gap in relation to the choice of higher education in the STEM field. In this sense, it is possible to identify studies on gender differences, socio-educational proposals, and initiatives that can be organised by educational levels, in this case, secondary and university, and also by typology, active methodologies, and intervention initiatives.

On the other hand, the second question addresses how gender roles and stereotypes influence decision-making related to higher education. In this line, the SCCT model ( Lent et al., 1994 ) explains the relationship between social stereotypes and the decision taken. However, the question can also be answered regarding the influence of education as an institution, social and contextual influences, and the perception of socially androcentric spaces.

Figure 6 visually presents the main ideas of the results for the two research questions.

Main ideas of the results for the two research questions.

4.1. IQ1: what studies exist on the gender gap in relation to the choice of higher education in the STEM field?

4.1.1. socio-educational projects and proposals.

The IRIS project, Interests and Recruitment in Science, arises to study the factors that determine young people's choices ( Henriksen et al., 2015 ). The aim is to gain a better understanding of how young people evaluate STEM as an option for their educational choices, as achievement in science and technology is only one of many factors that influence their choices.

In terms of specific intervention groups, Heybach and Pickup (2017) allude to a socio-educational approach in the UK. A group called STEMettes ( STEMettes, 2021 ) is working to combat what they consider to be a culture in which girls do not imagine women doing "science stuff" while they are mothers.

In the framework of project design for the improvement of diversity and gender inclusion, there are different technology companies that follow a gender perspective trend, such as LinkedIn, Salesforce, Intel, Google, Microsoft and IBM. In this line, Peixoto et al. (2018) propose an initiative based on robotics, as an inclusive tool, to combat the gender gap.

Also, the Girls4STEM project led by the School of Engineering of the University of Valencia (ETSE-UV) in Spain aims to increase and retain the number of female students, applying its intervention with students aged 6 to 18, their families and teachers ( López-Iñesta et al., 2020 ).

Another project worth mentioning is 'Increasing Gender Diversity in STEM' ( Ballatore et al., 2020 ). The aim is to investigate the gender difference in the self-perception of female students about their career choice. In order to find out the self-perception, a web application for students called ANNA tool was designed and used.

Finally, the project Science and Technology as Feminine, promoted by the Spanish Association of Science and Technology Parks (APTE), aims to raise awareness of the under-representation of women in STEM fields and promote girls' inclusion in scientific and technical careers ( Davila Dos Santos et al., 2021 ).

4.1.2. Study of gender differences

From the study by Kang et al. (2019) it was found that during the transition period from primary to secondary school there were gender differences in relation to interest in and preferences for science subjects, and in relation to future career prospects. Preferences were mostly in biology for girls and physics and chemistry for boys. Furthermore, it was concluded that teachers are agents of change involved in the educational process, so it is necessary for them to take care of the material they use and the way they communicate with students. Perhaps by conveying to girls the fact that science careers can respect people's personal time, they might retain their interest in science.

Also, an element to pay attention to is self-efficacy and, for this, Brauner et al. (2018) work from mental models. The study was carried out in Germany and a socio-educational approach was proposed, in which the subjects were participants in robotics courses to increase vocational interests and interest in computer science. From the results it can be concluded that the participants drew predominantly male STEM people in rather isolated situations. The people drawn are perceived to look nerdy , although they are also perceived as quite attractive and intelligent. Even so, the mood of the people in the pictures was perceived as slightly negative. It was concluded that girls reported significantly lower levels of technical self-efficacy and lower interest in computer science than boys. However, it is of deep concern that this effect emerges so early and can be measured empirically at the age of 11 or 12 years. The study by Brauner et al. (2018) shows that gender differences with respect to mental models, self-efficacy and interest have already developed by the age of 12.

Furthermore, in the line of socio-educational applications, the research by Wulff et al. (2018) is based on the performance of the Physics Olympiad in Germany in 2015. The aim was to generate motivation in young men and women in the field of physics. To this end, the aim was to develop physical identity for both men and women. After the Olympiad, the return rate for the following year for female participants was 60% (62% for males), while the return rate for non-participating females was 28% (39% for males).

Finally, the study by Reich-Stiebert and Eyssel (2017) tested the effect of gender-typicality of academic learning tasks on HRI (Human-Robot Interaction) and showed that the gender of the robot had no influence on the participants' objective learning performance. That is, participants' learning was neither positively nor negatively affected by learning with a "male" or "female" robot. This fact could be exploited to reduce gender-related performance disparities and contribute to equal opportunities for male and female students in higher education.

4.1.3. Initiatives in secondary and university education

One innovation introduced by the education system is presented in the study by Görlitz and Gravert (2018) . It analyses the potential of redesigning the secondary school curriculum in Germany to achieve increased enrolments in higher STEM degrees. The results suggest a positive and robust increase in the likelihood of choosing STEM as a university major for males, although there is no effect for females. One cause could be the acquired roles of men and women.

Another proposal in Germany is that of Finzel et al. (2018) , who aim to motivate secondary school female students to consider Computer Science as a possible option. The latest measure has been the introduction of the make IT mentoring programme in 2014. The programme was designed to provide female students with information about Computer Science and to include measures that consider self-concept and gender stereotypes correlated with a negative image of women in Computer Science. Within make IT , participants should be supported to achieve a more realistic self-assessment and positive feedback of their own abilities.

In addition, Ertl et al. (2017) work on self-concept. From their research they conclude that students who reported a higher number of favourite STEM subjects at school have a higher self-concept, while higher levels of school support and teacher stereotyping indicate a lower and less positive self-concept in STEM. Regarding the impact of stereotypes, STEM female students mentioned that they were pursuing an atypical career path and that their social environment was surprised by this type of career choice.

4.1.4. Active methodologies and intervention initiatives

Continuing with the proposals, mentoring is proposed as a measure to reduce the gender gap in STEM. Stoeger, Hopp, et al. (2017) conducted their study in Germany and aimed to compare the effectiveness of individual versus group online mentoring in STEM. This was done within the framework of CyberMentor , an online mentoring programme in STEM for gifted girls designed to increase participation rates of talented girls in STEM. In terms of results, the proportion of communication about STEM topics was higher in group mentoring than in individual mentoring. Girls in group mentoring showed a higher amount of STEM-related networking compared to girls in individual mentoring. Finally, group mentoring mentees reported an increase in elective intentions in STEM, while individual mentoring mentees reported no significant differences.

In addition, to work on interest and attitudes towards mathematics, Cantley et al. (2017) work from Collaborative Cognitive Activation Strategies, and from the Izak9 resource. Following the study there was a small increase in girls' enjoyment of mathematics in both the Republic of Ireland and Northern Ireland. However, boys' enjoyment increased marginally in the Republic of Ireland and decreased marginally in Northern Ireland.

In terms of attitudes, Borsotti (2018) empirically investigates the main socio-cultural barriers to female participation in the software development degree programme at the IT University of Copenhagen in Denmark (ITU). The results reveal that almost all respondents attributed the gender gap to a greater extent to the existence of stereotypes.

On outreach interventions, Sullivan et al. (2015) aim to help secondary school girls develop an optimal view of the role of computers in society and to learn some of the key computer skills, including computer programming. It examines CodePlus, a programming club based on the Bridge 21 model, which was established in three all-girls schools. Students worked together on activities including computational thinking, computers in society and programming using Scratch. The results obtained in the Sullivan et al. (2015) study are: (1) there was no gender difference in expected and actual mathematics grades, (2) boys played computer games for much longer than girls, (3) girls spent more time using computers for homework, while boys spent more time using computers to look up general non-school related information, (4) boys demonstrated significantly higher levels of self-efficacy than girls, (5) boys were also more likely to study computer science at university than girls and were more confident about being accepted into a computer science degree. The comparisons demonstrate clear differences in how girls view themselves in terms of computer science ability.

On the other hand, Salmi et al. (2016) found that after visiting science, technology and engineering exhibitions with students, girls were in a better position to decide about their future because they experienced more autonomy than boys. This study also revealed that girls had higher attitudes towards science than boys. However, for the engineering factor, boys' attitudes were significantly more positive than girls'. Motivations are also explored in the study by Olmedo-Torre et al. (2018) . In this case, they study the differences between the motivations of female STEM students, forming two groups: (1) Computing, Communications, and Electrical and Electronic Engineering studies (CCEEE women), and (2) other STEM studies (non-CCEEE women). The female respondents considered social stereotypes (31.47%) and immediate environment (14.5%) as the main reasons for the low enrolment of women in STEM studies. Surprisingly, the third reason (11.03%) is that women do not like engineering. In addition, CCEEE women were less likely than non-CCEEE women to consider themselves more able than men in physics, chemistry, mathematics, computer science and graphic expression.

Also, Botella et al. (2019) aim to increase the number of female students by providing them with support, in order to prevent them from giving up in the early stages. The work programme of the School of Engineering of the University of Valencia (ETSE-UV) is organised around four main actions: (1) providing institutional encouragement and support, (2) increasing the professional support network, (3) promoting and supporting leadership and (4) increasing the visibility of female role models. Two other elements to study are identity as a scientist and scientific capital. The study by Padwick et al. (2016) is developed for this purpose within Think Physics (Northumbria University, Newcastle) ( Think Physics, 2016 ). Through collaboration with industry, agencies and schools, Think Physics ( Think Physics, 2016 ) addresses the gender imbalance and under-representation of lower socio-economic groups in the physics, engineering, and computing sectors.

Furthermore, continuing with the analysis of capital, Stoeger et al. (2017) study whether the level of educational capital and the learning capital of students are related to STEM Magnet schools. The findings show that more and more girls are choosing STEM magnet school options as part of their studies. Interestingly, however, this general trend is not followed when choosing higher STEM studies. Cincera et al. (2017) also address scientific understanding, applying a programme to enhance the acquisition of scientific skills. However, there was no significant change in either the girls' or the boys' group.

Meanwhile, the study conducted in Portugal by Martinho et al. (2015) seeks to identify gender differences with respect to cooperation and competitiveness. The results reveal that women are more cooperative than men and men are more competitive than women. Thus, one of the socially assigned gender roles is manifested.

However, the gender gap also concerns communities and industries. González-González et al. (2018) present good practices from communities and industries. Laboratorial, which has a "Talent Fest", stands out. There is also Microsoft, which offers mentoring to young women, for the development of their digital skills. Finally, there is also the Women at Google initiative, which aims to increase the presence of women in the company and encourage them to feel more empowered.

Also, Herman et al. (2019) aim to promote the re-entry into the STEM labour market of women who abandoned their careers, through a blended learning programme. The Badged Open Course (BOC) was developed in 2016 to support women returning to STEM careers after a long period of time.

Finally, as is known from the updated indices published in the latest report of the World Economic Forum (2021) , the different countries included in the rankings still have a percentage of the gender gap to close. However, given the results obtained in the systematic review of the literature, it is striking that in those countries where initiatives have been implemented to alleviate the gender gap, the gender gap continues to persist. This finding is consistent with the conclusions obtained in the study by Stoet and Geary (2018) . The authors concluded in their research that, paradoxically, countries with lower gender equality indexes had relatively more female graduates in STEM disciplines than those with higher gender equality indexes. As noted by the same authors ( Stoet and Geary, 2018 ), this finding is noteworthy since, following other authors such as Williams and Ceci (2015) , countries with higher gender equality indexes are those that offer girls and women more educational and empowerment opportunities and generally promote women's participation in STEM fields. In line with Stoet and Geary's (2018) argument, it is not only social and cultural factors that play a role, but also the individual choices and attitudes that students make, which may be influenced by other factors such as socioeconomic status. In this sense, and in agreement with other authors ( Stoet and Geary, 2018 ; M.-T. Wang and Degol, 2013 ), students should base their educational decisions on their potential, regardless of the educational field to which the decision is directed.

4.2. IQ2: how do gender roles and stereotypes influence decision-making related to higher education?

4.2.1. social cognitive career theory (scct) and early intervention.

According to Heybach and Pickup (2017) in order to suppress gender roles and stereotypes that foster the gender gap it is necessary to move away from androcentrism, and the stereotypical belief that the rational mind is male and the passive nature is female. This would move away from the binary logic, in which occupations have either a female or male profile. The STEM workforce should be empowered, preventing gender roles and stereotypes from increasing the Leaky Pipeline ( Heybach and Pickup, 2017 ). To retain girls and women, the Stereotype Threat must be lessened. Girls and women grow up thinking that they should be dedicated to caring for the family, and scientific thinking is also thought to be masculine in nature. To eradicate these erratic beliefs Heybach and Pickup (2017) propose female role models as a possible solution, in order to increase interest.

For their part, Peixoto et al. (2018) indicate that efforts to retain women and girls in STEM focus on secondary education and/or university. However, it is more relevant to work from an early age. From an early age, it is already evident that boys identify more with the concept of science than girls. Stereotypical perceptions of what STEM is lead boys to feel that scientists can be similar to them at higher rates than girls.

Kang et al. (2019) also point to boys' and girls' interests as a key element, as career aspirations may begin around the age of 11 or 12. Academic and extracurricular experiences and science education are conditioning elements. In addition, the Social Cognitive Career Theory (SCCT) points out that attention should be paid to the expectations of results, since they are a major source of interest.

Other authors who also argue the importance of addressing the gender gap from an early age are ( Brauner et al., 2018 ). They point out that self-efficacy plays an important role in decision-making. This in turn relates to the locus of control of Causal Attribution Theory. Considering that gender, ethnicity, and other distinguishing characteristics may also interfere with decision-making, one must again turn to SCCT. This theory points out that different elements need to be addressed in order to reduce segregation: self-efficacy, outcome expectations, personal goals, career interests, career path choices, performance, and perceived achievements.

However, it is not only a question of interests, self-efficacy, and outcome expectations. According to Cantley et al. (2017) attention should also be paid to attitudes. When the transition from primary to secondary school takes place, students' attitudes towards mathematics become more negative. Attitudes are influenced by interest and enjoyment. For this reason, Cantley et al. (2017) propose to work from Cognitive Activation Teaching Strategies, since they are related to the intrinsic motivation of the person.

4.2.2. Educational institutions and the learning process

Padwick et al. (2016) point out that an important and involved element is science capital. Children with higher science capital are more likely to choose higher STEM studies than those with lower science capital.

Also, Stoeger, Greindl, et al. (2017) , who report on STEM magnet schools and non-STEM magnet schools, assume that gender stereotypes can be observed at the age of six. This fact implies that STEM magnet schools could play an important role in increasing participation in STEM studies.

In this line, Salmi et al. (2016) emphasise the difficulty of changing attitudes after primary education, since they are formed at an early age. Salmi et al. (2016) focus on cognitive, motivational, and learning aspects, because motivation and attitudes precede intention. Therefore, if positive attitudes towards the STEM sector can be generated at an early age and motivational elements are introduced, a behavioural approach to science and engineering can be generated.

In terms of motivation, according to Görlitz and Gravert (2018) those who choose to take mathematics and science classes in secondary education are more likely to specialise in these areas at university.

In addition, scientific identity and agency play a role in decision-making. In accordance with Wulff et al. (2018) agency and scientific identity, tinged with social roles, are a possible source of underrepresentation. Elements such as stereotypes, lack of interest, motivation or sense of belonging may explain the underrepresentation of young women in domains such as Physics.

4.2.3. Perceptions of male-dominated domains

In the sense of identity, as Borsotti (2018) points out computer science has been socially constructed as a masculinised domain, resulting in stereotypical perceptions and beliefs, low self-efficacy on the part of women and girls, and biased assessment in STEM subjects.

To address this, according to Sullivan et al. (2015) exposure to computer science, at home or at school, and encouragement from family and peers are the main factors influencing girls' decisions to pursue higher education in computer science. Other factors include self-perception, self-confidence, self-efficacy, scientific understanding, parenting strategy, stereotypes, and biases that girls and women must combat, and the barriers girls face when working in male-dominated environments.

In this regard, Ertl et al. (2017) also consider that negative perceptions, stereotypical beliefs and Stereotype Threat reinforce dysfunctional attribution patterns, which ultimately lead to a lower proportion of women, especially in the areas of technology and engineering. The authors also focus on self-concept as a key element to avoid the gender gap, based on Expectancy-Value Theory.

4.2.4. Social structures and contextual influences

Olmedo-Torre et al. (2018) insist on the relevance of the perception of the immediate environment. It is important to involve families and teachers in the search for a solution. According to Botella et al. (2019) gender roles and patterns and stereotypes installed in the family and in society about relevant careers for both men and women have an impact on the future education of boys and girls, and on their career choices. There are proposals to address these obstacles, such as the promotion of female role models in STEM fields, academic counselling, teacher mentoring, internship opportunities and career and skills development.

Furthermore, picking up on the idea of mentoring, according to Finzel et al. (2018) the probability of choosing higher studies in computer science is lower for women than for men. However, the low proportion is not due to a lack of competence of female students, as they are not less qualified. Instead, the presence of gender stereotypes and the absence of female role models are possible reasons for the low representation of women in computer science. Therefore, mentoring programmes are proposed to encourage the development of higher education in STEM.

In terms of real-world initiatives, Reich-Stiebert and Eyssel (2017) propose an intervention with robots. They aim to investigate whether "female" gendered robots could effectively support learning in STEM disciplines, and whether "male" gendered robots could support learning in linguistic and literary studies. After conducting the study, it can be concluded that the female agent tends to be more effective regardless of the gender of the participants.

Moreover, Henriksen et al. (2015) indicate that the challenge for future research is to further explore the social structures, discourses, curricular components, etc., that impede women's participation in the fields of science, where they have so far had only a small representation.

In addition to all of the above, the educational factor leads to the employment factor. According to González-González et al. (2018) , the problem of educational segregation extends to professional life. Finally, Cincera et al. (2017) point out that an optimal response to segregation is to encourage interactive learning through multimedia applications, in order to attract students' attention to science.

5. Conclusions

5.1. methodologies and methods and population groups.

According to the literature, the methodologies and methods that can be applied in gender gap studies in the STEM education sector may differ. Mixed models ( Herman et al., 2019 ; Padwick et al., 2016 ) and multi-method approaches ( Borsotti, 2018 ; Brauner et al., 2018 ; Ertl et al., 2017b ; Finzel et al., 2018 ; Henriksen et al., 2015 ; Olmedo-Torre et al., 2018 ) can be used. Quantitative studies ( Cantley et al., 2017 ; Cincera et al., 2017 ; Görlitz and Gravert, 2018 ; Kang et al., 2019 ; Reich-Stiebert and Eyssel, 2017 ; Salmi et al., 2016 ; Stoeger et al., 2017 ; Stoeger et al., 2017 ; Sullivan et al., 2015 ; Wulff et al., 2018 ), or qualitative studies ( Botella et al., 2019 ; Martinho et al., 2015 ) can also be applied. On the other hand, another type of study is based on the review of initiatives ( González-González et al., 2018 ; Heybach and Pickup, 2017 ; Peixoto et al., 2018 ).

However, what is most interesting is to know which population groups are of scientific interest in investigating this topic of study. The literature reveals that it is of interest to investigate from early ages to the working stages ( González-González et al., 2018 ; Herman et al., 2019 ) through primary education ( Padwick et al., 2016 ; Salmi et al., 2016 ; Sullivan et al., 2015 ), secondary ( Brauner et al., 2018 ; Cincera et al., 2017 ; Kang et al., 2019 ; Wulff et al., 2018 ) and university ( Ertl et al., 2017b ; Henriksen et al., 2015 ; Martinho et al., 2015 ; Olmedo-Torre et al., 2018 ; Reich-Stiebert and Eyssel, 2017 ; Stoeger et al., 2017 ). Moreover, as revealed in the literature, it is not only interesting to focus on one age group. Research can be conducted with students and women who are at different stages of their educational trajectory ( Botella et al., 2019 ; Cantley et al., 2017 ; Finzel et al., 2018 ; Görlitz and Gravert, 2018 ; Stoeger et al., 2017 ), such as students in primary, secondary and university education simultaneously.

5.2. Measurement and assessment resources

It is helpful to know what resources can be used to carry out studies in which the gender gap in the STEM education sector is studied and measured. Among the resources are gender gap measurement and assessment tools. After consulting the literature, it is noted that some instruments are aimed at detecting scientific identity, such as the Aspires Questionnaire ( Padwick et al., 2016 ). There are also instruments for measuring attitudes towards science, such as: Deci-Ryan motivation, Situation motivation test, Science attitudes, Future educational plans, Raven test, Knowledge test and School achievement ( Salmi et al., 2016 ).

On the other hand, Sullivan et al. (2015) have used an adaptation of the Papastergiou questionnaire to measure perceptions and self-efficacy concerning Computer Science. Along the lines of motivation, the Aiken Scale ( Cantley et al., 2017 ) is helpful and validated for measuring interest in mathematics. In addition, Wulff et al. (2018) , who conducted a Physics Olympiad, used: Content interest physics and Situational interest, for the measurement of interest. In the context of the IRIS project, Henriksen et al. (2015) used the validated IRIS Q questionnaire.

However, not all possible resources are quantitative instruments. Focus groups ( Henriksen et al., 2015 ) and qualitative interviews ( Borsotti, 2018 ; Martinho et al., 2015 ) can also be applied to approach knowledge through discourses. Another qualitative strategy is analysing through drawings ( Brauner et al., 2018 ).

Cincera et al. (2017) used the SEI Questionnaire to close the reflection on data collection resources adapted from the NoS instrument. Kang et al. (2019) validated an instrument based on PRiSE and PISA within the MultiCO project. Olmedo-Torre et al. (2018) applied the validated survey "Survey for engineering students and graduates", collecting quantitative and qualitative data. Finally, Stoeger et al. (2017) applied the Questionnaire of Educational and Learning Capital (QELC) to analyse educational and learning capital.

5.3. Possible initiatives

On the other hand, another of the original contributions of this work is the systematisation of possible initiatives to implement aimed at closing the gender gap in the STEM education sector. In this sense, Peixoto et al. (2018) propose an initiative based on robotics as an inclusive and motivational measure to encourage interest from the school stage. Along the same lines, Sullivan et al. (2015) carried out outreach interventions through programming in secondary education.

In terms of proposals that worked positively in the studies, to boost interest and motivation in physics from secondary education, Wulff et al. (2018) applied a Physics Olympiad with boys and girls. Continuing also in the context of secondary education, a proposal that has generated positive effects is the redesign of the curriculum to promote STEM disciplines ( Görlitz and Gravert, 2018 ). Also, to motivate female secondary school students to consider Computer Science as a possible field of study, Finzel et al. (2018) conducted a mentoring programme called make IT. In the same line, Stoeger et al. (2017) conducted a mentoring-based study within the context of the CyberMentor programme.

Using different methodologies, Cantley et al. (2017) promoted the enjoyment of mathematics through Collaborative Cognitive Activation Strategies.

In the university environment, the School of Engineering of the University of Valencia (ETSE-UV) promotes actions to increase the number of female students ( Botella et al., 2019 ). The actions are institutional support, increasing the support network, promoting leadership, and promoting female role models.

Finally, initiatives should not only be promoted in schools and universities. As advocated by González-González et al. (2018) , communities and businesses should also promote good practices. Finally, along the same lines, Herman et al. (2019) promote the re-entry of STEM women into the labour market through a Blended Learning programme.

In this way, it is concluded that it is worth investing resources and efforts in proposals based on scope interventions. According to the professional or training stage, applying one type of initiative or another will be more appropriate, as has been seen among those discussed above.

5.4. Impact of stereotypes

Measures and interventions could combat the effects of segregation, including the "Leaky Pipeline" phenomenon and the Stereotype Threat. These stereotypes are perpetuated over time. One of the socially acquired roles is that of family care for women, as demonstrated by Weisgram and Diekman (2015) .

However, it is inappropriate to think that intervention measures should focus exclusively on women and girls. The gender gap is a system-wide problem. Education, business and society, and family and social actors are indispensable elements to be mentioned ( Craig et al., 2019 ; Fisher and Margolis, 2003 ; Lehman et al., 2017 ; Sax et al., 2017 ). However, it remains striking that initiatives heavily target women and girls.

The scientific vocation is considerably affected by stereotypes. These stereotypes must be fought to deconstruct them. Investing efforts to close the gender gap should not be a matter of quotas or public image. As presented in a study by the Harvard Business Review ( Hewlett et al., 2013 ), organisations that have a more diverse and inclusive workforce tend to be more innovative and experience greater market growth than companies that do not adopt such a philosophy.

However, action should not be delayed until secondary or university education. Authors such as Kang et al. (2019) –and accordance with Nurmi (2005) – confirm that career aspirations begin at the age of 11–12 years. Therefore, it is necessary to act from an early age, as supported by Brauner et al. (2010) , Miller et al. (2018) and Wang (2013) .

In this sense, girls generally prefer more family and contact-oriented occupations than boys, as Konrad et al. (2000) point out. Thus, women have continuously shown less interest in science and STEM occupations, especially in engineering ( Ceci and Williams, 2010 ; Diekman et al., 2010 ).

In addition to personal goals, outcome expectations and interests, other constructs such as self-concept, motivation, attitudes, performance, and self-efficacy should be addressed. By enhancing scientific and confident identity and self-confidence in the discipline, positive self-knowledge can be enhanced. Moreover, if people have gains in agency ( Bandura, 1977 ), they will feel more prepared to engage in what they really want to do.

5.5. Other segregation types

Finally, while the work presented in this paper focuses on horizontal segregation in women's entry and persistence in STEM fields, horizontal segregation is not the only form of segregation that exists. It is also essential to recognise the existence and impact of vertical segregation ( Corbett and Hill, 2015 ). The latter type prevents or hinders promotion within the field, resulting in the Glass Ceiling phenomenon. Vertical segregation manifests mainly in the labour sector once women are immersed in the labour market. This phenomenon occurs because of the obstacles and barriers women face that make it difficult to progress at the same rate as their male counterparts ( Cotter et al., 2001 ; de Welde and Laursen, 2011 ; Zeng, 2011 ). When the Glass Ceiling occurs in the academic and scientific space, it is accompanied by the Scissors Effect ( Wood, 2009 ).

Perceived barriers include the lack of female role models and references, gender bias, hostile work environment, lack of natural work-family balance, unequal growth opportunities based on gender, and the gender pay gap ( Botella et al., 2019 ; ISACA, 2017 ).

As can be seen, the two types of segregation, vertical and horizontal, share a common trigger: perceived barriers in the environment and context. For this reason, it is essential to work on these barriers to reduce them until they are eradicated.

6. Threats to the validity of the study

The systematic review and mapping presented in this paper, just like any other research method, may suffer from threats to its validity, as well as some limitations. Two categories of threats are identified: construct validity and validity of conclusions.

To preserve the validity of the construct, a series of measures were applied to maintain the objectivity of the results. These measures were: to review previous SLRs to confirm the need to carry out the presented study, and to follow systematised and documented phases marked by inclusion, exclusion, and quality criteria, with the ultimate aim of mitigating possible biases. On the other hand, although a search protocol has been defined, this does not guarantee that all publications related to the subject are included. In order to weigh up this threat, searches have been carried out in the two main research databases, namely Web of Science and Scopus.

In addition, for the validity of the conclusions, the data extraction process has been described step by step and documented by means of different spreadsheets which are available from the links: http://bit.ly/3a4gRM5 , http://bit.ly/39lO0DX and http://bit.ly/36fnBpi .

The main limitation encountered in the research was the initial management of the large volume of results obtained from the equation of terms. The initial starting point was 4571 results, which meant that the start of the process took longer than desired.

Finally, as a future prospect, it is proposed to make systematic updates of the literature presented, with the aim of identifying new proposals for intervention, as well as methodological approaches to the factors influencing the gender gap.

Declarations

Author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades under a FPU fellowship (FPU017/01252). This work has been possible with the support of the Erasmus+ Programme of the European Union in its Key Action 2 "Capacity-building in Higher Education". Project W-STEM "Building the future of Latin America: engaging women into STEM" (Reference number 598923-EPP-1-2018-1-ES-EPPKA2-CBHE-JP). The content of this publication does not reflect the official opinion of the European Union. Responsibility for the information and views expressed in the publication lies entirely with the authors.

Data availability statement

Declaration of interest's statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

This research work has been carried out within the PhD Programme of the University of Salamanca in the field of Education in the Knowledge Society ( http://knowledgesociety.usal.es ), and this research was supported by the Spanish Ministry of Science, Innovation and Universities with a grant for the training of University Teachers (FPU017/01252). Also, the authors would like to thank Elena P. Hernández Rivero (Language Centre-USAL) for translation support.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

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Gender Pay Gap Thesis Statement

Gender equality: the pinnacle concept that American society is not-so desperately trying to achieve. Many Americans have convinced themselves that gender equality was remedied by the Nineteenth Amendment and the Second Feminist Movement, and have not considered the thousands of steps that are left on the journey. In recent years, a matter of public interest has been the gender wage gap, stating that women are earning significantly less money than men for doing an equivalent amount of work. Critics of the effort to “break the glass ceiling” claim that a pay gap does not exist, and that if it does, it is because women either do not work as hard, have to tend to their families, or hold lower paying jobs. However, the gender pay gap has been proven to exist in a variety of different forms, …show more content…

The gender pay gap is a significant issue in the United States because it promotes institutional and internal sexism and the unfair treatment of human beings. An infamous statistic about the wage gap has been the 77 cent statistic, stating that for every dollar a man earns, a woman earns 77 cents. The statistic is calculated by, “...dividing the median earnings of full-time, year-round, working women by the median earnings of full-time, year-round, working men, all rounded to the nearest $100” (Glynn 2). This, however, represents males and females from all occupations, causing opponents to argue that, because it does not represent the gap between people who have the same job, a wage gap does not exist. Nonetheless, multiple studies have proven that a gender pay gap does exist within the United States.

Summary Of Equal Pay Day By Dana Ford

The main purpose of the article, “Equal Pay Day: When, where and why women earn less than men” by Dana Ford, is to inform the audience about the pay gap between genders that still exists in the United States today. To emphasize on the subject of gender pay gap, Ford shows the reader how race, age, and even the state the woman lives in could affect how big or small the pay gap is. While the speaker, Dana Ford, may use a negative tone toward the issue, this newdesk editor is also aware of the progress in equality in the past 50 years. Ford states that “The good news is that the gender pay gap is getting smaller. In 1964, women on average were paid 59% of what men were paid.

Separate And Unequal Analysis

In the United States, women have been fighting for their equality since the beginning. First, it was the women’s suffrage movement that was catching everyone’s eye. Recently, the fight against the gender wage gap has come to many people’s attention and is finally making an

Exploratory Essay

There are 3,418,059,380 women in the world (Geohive.com, 2015) and yet, women, in 2010, got paid a staggering 19% difference in wage on a universal standpoint (Economist, 2011). Such contributing factors as this (wage), has created an overwhelming notion of gender inequality leading to such things as segregation in the workforce across the globe. Ethos is universally known as the ethical appeal, convincing one of a person’s character (Courses.durhamtech.edu, 2015). The staggering numbers of economic contributions of women compared to men has however, highlighted that there are fewer women to men ratios in the workforce due to the where we live, maternal implications (pregnancies), upbringing and education.

Women In Today's Wage Gap

Canadian women earned 87 cents to every dollar made by men in 2015, according to Statistics Canada in a statement released on International Women’s Day. This statement was released to show how today’s wage gap has improved compared to the 77 cents women made to every man’s dollar in 1981 (CBC News). It’s meant to represent an improvement and is supposed to be a good thing, yet it is not. Why? Because this statistic should not even exist in the first place.

Wage Gap Among Women

One concept that can potentially solve this problem is comparable worth, or pay equity. This is a simple, bias-free tool used to determine how much a worker should make, based off experience, qualifications, skills, etc. If this concept were legally mandated, businesses would be forced to pay women what they deserve. This intuitive program could help to finally close the gender wage gap. Women do not need to be victims of oppression in the workplace any longer; it is time to embrace solutions like this and fight for

Wage Gap In America

The underlying problems concerning the gender wage gap, need to be brought to the forefront of the government. America has improved drastically regarding women’s equality, but there are important issues with stereotyping and assuming women are not as proficient as men in certain occupations, that leaves this nation flawed. These matters can be resolved by setting stipulations into major

Gender Wage Gaps

It is said that because of the Equal Pay Act of 1963, the gender wage gap no longer exists. Studies today show that the gender wage gap is still very much alive. In the 6th edition of Women’s Voices, Feminist Visions: Classic and Contemporary Readings written by Susan M. Shaw and Janet Lee, Shaw and Lee explain, “the gender wage gap is an index of the status of women’s earnings relative to men’s and is expressed as a percentage and is calculated by diving the median annual earnings for women by the median annual earnings for men” (Shaw and Lee 497). Data from the U.S. Bureau of Labor and Statistics in 2010 showed the ratio of women’s to men’s annual earnings were 77%. This means for every dollar a man made, a woman made 77 cents.

Corning Glass Works V Brennan Summary

The year the Equal Pay Act was passed into law (1963) the wage gap between a man and women working full time was 41 cents with women making 59 cents for every dollar a man earned. Since then, the income disparity has decreased by almost 50 percent. In 2014, the wage gap was 21 cents with women making 79 cents for every dollar a man earned (The Wage Gap Over Time). This 20 cent decrease in the wage gap since 1963 shows how significant of a difference the Equal Pay Act and its enforcement through Corning Glass Works v Brennan, along with other court cases, have been. The current 21 cent wage gap today shows that the issue of unequal pay based on sex still exists, and that more needs to be done to close this gap.

Persuasive Essay On Pay Gap

Shining some much-needed sunlight on the gender wage gap will make a difference for every one of us, men and women, right now.” (www.nytimes.com, 16). “It’s the twenty-first century, and the gender wage gap affects the daily life of women throughout the country, at every economic level, from cashier to CEO. Is it fair? No.

Declaration Of Sentiments By Elizabeth Cady Stanton

When the Equal Pay Act was signed, women were only making 59 cents for every dollar men were making (The Gender Pay Gap). Every year, especially around election time, new statistics are released asserting that despite considerable efforts to close the wage gap between women and men, it still exists. The latest reports state that women generally make 81 cents for every male’s dollar earned today, but the numbers that come out can sometimes be as low as 77 cents on the dollar (Taranto). Statistically, the general consensus is that the wage gap has gotten better, but it is still present. Though the wage gap has declined, the National Organization of Women reported that at the rate of decline that exists now, the wage gap would not close completely until 2058 in the United States (The Gender Pay Gap).

Argumentative Essay On Gender Pay Gap

It may be 2018, but the gender pay gap is still here, why is that? Women have been and still are getting a lower pay than men to do the same job. Women are doing equal if not more work, but somehow make less. The following paragraphs will explain what is happening today like the fact that over time men 's pay increases more than women 's does. Besides that I will also mention that not just white women make less than men other cultures make even less than them, and I also will share real people speaking up about them being paid less than men.

Gender Pay Gap Essay

Annotated Bibliography Quast, L. (2015, November 22). The Gender Pay Gap Issue Is Fixable -- But May Require Bolder Actions To Overcome. Retrieved from Forbes.com: http://www.forbes.com/sites/lisaquast/2015/11/22/the-gender-pay-gap-issue-is-fixable-but-may-require-bolder-actions-to-overcome/2/ It is reported by the Economic Policy Institute that although women had made tremendous records entering into workforce and gain great successes in education, but their wage is 83% comparing to men. The world forum also released a report in 2015 that women now make as much as men earned a decade ago.

Thesis Statement On Gender Inequality

The fact also arises that women not only suffer from lack of recognition for the work they do in households but also for their work in their jobs. Women work as much as men, if not more. When both paid and unpaid work such as household chores and caring for children are taken into consideration, women work longer hours than men—an average of 30 minutes a day longer in developed countries and 50 minutes in developing countries. This is known as second shift, where women not only work at their jobs but also come back home and complete their household chores. However their contribution remains minimum due to unequal wage pay and lack of consideration given to household chores.

Glass Ceiling Gender

Abstract: There are unequal privileges shared by men and women in the United States workforce. Throughout history human civilization have seen a revolution in the role of women up until modern society, where it is perceived as equal. However, although it might be invisible, an inequality gap still exists and acts as a glass ceiling for women. This research paper will be focusing primarily on the sociological and psychological factors that contribute to this difference in privilege.

Essay On Gender Equality In The Workplace

The United States is currently facing an economical problem that involves males and female differences within the workplace. Males are given bigger and sometimes even better rewards for doing equal amounts of work as their female counterparts. Females are frequently not receiving the same wage even if they can complete the same job of a male. Also, females are less likely to get promoted within their job if they are competing against a male. A source states, “Women are now more likely to have college degrees than men, yet they still face a pay gap in every single education level,

More about Gender Pay Gap Thesis Statement

Related topics.

• Discrimination
• Gender role

The gender gap still exists, but it’s continuing to close

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For years now, I’ve had questions about the often-quoted gender pay gap. Not necessarily doubts, but questions.

The gap theory refers to a difference in men and women’s compensation for working the same jobs, with the same responsibilities and the same seniority for the same number of hours.

Former U.S. Sen. Phil Gramm, along with co-authors John Early and Robert Ekelund, recently wrote a book titled “The Myth of American Inequity.” Recently The Wall Street Journal published an article by the authors explaining their findings — that no such inequity exists.

For starters, the authors agree — and validate — that today’s women in the workplace earn 84 percent of what men earn. But the WSJ article caught my attention because it attempts to explain the “why” of the gap.

First, let’s agree that men and women doing the same work should be paid equally. As with many comparisons, however, the devil is in the details.

Consider one of the book’s key findings:

“The pay gap is the natural economic result of choices men and women make, including how much or how little to work and which occupation to enter. The 84 percent figure is arrived at by dividing the average annual pay for women who work full-time all year by the average annual pay for men working full time for a full year.”

Yet that comparison is misleading because full-time, year-round work is defined so broadly.

How many hours men and women work

The U.S. Census Bureau defines full-time work as 35 or more hours a week. Among full-time workers, a big gender gap exists in how many hours men and women work over those 35 hours.

According to federal statistics, men worked two full hours more per week than women, a two-hour difference that amounts to a quarter of the pay gap.

As for working less than five hours per week, women earned 105 percent of what men earned.

Let’s also agree that all workers gain more earning power as they gain experience. As a result, on average, women over 40 have three fewer years of workplace experience than men of the same age.

It’s also a given that many women drop out of the labor force at some time to give birth and raise children, which alone accounts for about a third of the observed pay gap.

Men and women also make different choices in terms of occupations and education. In general, men tend to choose higher-paying jobs, which is evident in their chosen college majors.

Only one of the highest-earning college majors graduates more women than men, while nine of the 10 lowest-earning majors graduate more women.

Also, men are more likely to select occupations with greater financial risk, jobs that pay commissions on sales. More women are attracted to teaching, while men are more likely to take jobs with physical risk, such as construction, where pay is higher owing to the risk premium.

In fact, men die on the job 12 times more than the rate of women and suffer 50 percent more injuries.

More and more women are becoming CEOs

The good news is that more and more women are becoming CEOs, physicians, lawyers, dentists, engineers and entering other higher-paying professional fields. The increases in recent decades have been astounding, led by a shift in the roles of men and women in the home.

Men are clearly taking on more, though not nearly half, of the responsibilities in the home and taking care of dependent family members.

Indeed, the choices men and women make about how many hours to work, whether and how to share parental and household duties, which occupations to enter, and what type of education to pursue have changed drastically in most of our lifetimes.

As a result of these very positive trends, the gender gap continues to close.

We all make choices in our lives that effect our careers. Stay-at-home dads, which I personally think is a wonderful trend, will find themselves years behind in their paid work experience, just as some mothers do.

It’s an ongoing balance to do what is right for ourselves, our families and our careers.

In my book, that challenge will never change.

Blair is co-founder of Manpower Staffing and can be reached at [email protected] .

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Dissertation on Gender Inequality in the Financial Sector

This research paper focuses on gender inequality amongst senior roles within the financial |Sector. The main purpose of this dissertation is to (1). To highlight the apparent discrimination towards the female gender when employing women in the finance Sector/Industry (2). To demonstrate the gender inequality that arises that prevents women who are working in the financial sector to achieve higher management positions. . To deliver the proposed research objectives and to respond to statements 1 and 2, an exhaustive literature review has been written and formulated by cross-referencing secondary sources that involve both qualitative and quantitative data. Furthermore, primary data has been collected through semi-structured interviews to gather qualitative & quantitative information which aid and support the findings of the dissertation. . The Findings of this report include evidence to support the view that within the financial sector there is a poor level of diversity between genders in senior roles. The board members of the corporations in the FTSE350 count with merely 20% of women. Pay gap for average earners between genders in the financial sector is roughly 40%.. That between the genders of 20% of top earners in the UK the pay gap is much larger. The salary difference between the genders in the roles of a financial manager is up to £45,000 between a male and a female employee. Brokers, company executives and project managers have a pay gap of roughly £20,000 per annum (Page.b,2014). In view of these differentials, it has been found that the government is taking several initiatives to promote equality and diversity of gender, by introducing a charter to address differential treatment between the genders within; finance industry and senior roles; because of the lack of diversity and the pay gap between men and women.

Related Papers

erika valeiras

Human Resource Development International

Mustafa Ozbilgin

Joanna Korczok

The aim of the article is to determine the importance, benefits and ways to enhance a culture of gender diversity in the organization. The article contains an analysis on gender diversity management and focuses on financial services sector including entry-level and senior management female employees’ representation. It is highlighted that building a more diverse workforce is everyone’s responsibility and that diversity should be the highest priority to the company’s strategy. The article will inform practitioners on gender balance matter, best practices and implications for the organizations.

JOURNAL OF THE UNION OF SCIENTISTS - VARNA, ECONOMIC SCIENCES SERIES

aleksandrina pancheva

The studies about the lack of women in boards of financial institutions often cover the &quot;glass limitations&quot;, the built stereotypes, the man&#39;s world of the bankers, etc. This problem directly correlates to another one – the gender pay gap. The intensification of the conflict between men and women about the pays or the financial bonuses is still an ongoing issue, with big financial conglomerates announce over 40% difference in favour of the men. And even though gender discrimination at hiring and pays is illegal, and there are lots of regulations on this matter, the women face both problems in the upper echelons.In attempt to disprove their &quot;lower value&quot;, women look for a way to have a fair appraisal for their work – a chance to reach the top levels (not based on the quotas rules) and narrow the pay gap between them and the men. Is the Theory of the Human Capital valid nowadays? Are there antitheses or at least partial evidence to confute the allegation that wo...

In the UK and other western countries the financial services sector is seen as offering women better career prospects than most other sectors. Unprecedented numbers of well-qualified young women are now achieving promotion to first-line and middle management positions. Companies are represented as progressive employers, committed to promoting equal opportunities. However, a cross-cultural study of three Turkish and six UK banks and high street financial organisations explores how organisational ideologies and cultures operate to perpetuate inequality, based on managers’ gendered conceptions of “the ideal worker”. Favoured staff were identified, sponsored, promoted and rewarded, often based on their personal affinity with senior managers rather than objective criteria. This distinction between favour and exclusion operates not only along the traditional lines of gender, class, age, sexual orientation, religion and physical ability, but also along the new dimensions of marriage, networking, safety, mobility and space. Despite local and cross-cultural differences in the significance of these factors, the cumulative disadvantage suffered by women managers and supervisors in both countries was remarkably similar.

African Journal of Business and Economic Research

Krishna Priya Chakraborty

Gender Equality at workplace refers to the equal rights, responsibilities and opportunities of women and men in employment (UN 2013). Equality does not mean that women and men will become the same but that women's and men's rights, responsibilities and opportunities will not depend on whether they are born male or female. Gender equality implies that the interests, needs and priorities of both women and men are taken into consideration, recognising the diversity of different groups of women and men. Equality between women and men is seen both as a human rights issue and as a precondition for and indicator of, sustainable people-centred development. The aim of gender equality in workplace is to achieve equal treatment for women and men without any discrimination in terms of remuneration, opportunities, empathy, appraisals, retirement etc. at workplace. To achieve gender equality, the workplace has to promote equal pay for work of equal value. The workplace has to remove barriers which acts as a hurdle in promoting equality in gender. Irrespective of the gender, all occupations and industries, leadership should be accessible equally. Women are gender stereotyped regarding the role they have played over the years which influences on their progression in the workplace, leading to problems such as inequality and gender pay gap. This is a theoretical secondary data research paper. The aim of this study is to emphasize the importance of gender equality in workplace and to identify the reasons behind the gender pay gap in workplace. Gender equality in workplace promotes human rights such as being fair and doing the right thing which enhances the productivity of the nation and economic growth. Hence, gender equality plays a very significant role in greater organisational performance and as a core driver of sustainable, long-term economic growth.

Given the increasing number of female graduates and the growing concern in companies regarding gender equality, a combined perspective is presented, consisting of a survey of reported differences in gender compensation, a research on suggested reasons that may justify such inequalities, and a review of the best practices used in companies concerning talent management. It has been found that the educational and occupational choices (the “life and career paths”) of women, especially their frequent choice to interrupt their careers for motherhood reasons, negatively influences their employability, so that they are by and large relegated to lower-paying jobs, which hinders their investments in human capital even more. A series of recommendations have been issued, including the use of flexible compensation policies like teleworking. It is claimed that the companies that accommodate the demands of female workers will gain a competitive edge, although a change in the organizational culture and in the confidence levels of females is required.

The Gender Pay Gap: Why is it still an issue?

joni o sullivan

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Thesis reveals gender gap in reactions to women's sexual assault stories

by Leiden University

Research master student Linda Bomm found in her thesis that men, compared to women, believe female sexual assault survivors less, blame women more, and judge them more negatively—especially if they identify strongly with their male gender.

"When women tell others that they were sexually assaulted, they often face negative social reactions like disbelief, victim-blaming, and negative judgments—or people emphasize that 'not all men' would commit such acts," says Linda Bomm, one of the winners of the FSW Thesis Award.

"These reactions have negative effects on survivors' mental health and can stop them from reporting the assault. In my thesis, I researched the relation between showing such negative reactions and people's gender, their social identification with their own gender, and their psychophysiological threat levels."

How did you research this?

"In two studies (one online, one in the lab with psychophysiological measures), participants reacted to recordings of different women's voices, of which one described being sexually assaulted by a man, and the others described being in a bike accident or mugged by a man."

What did you find?

"We found that men (compared to women) believed the women in the voice recordings less, blamed them more, and judged them more negatively. Compared to women, men also perceived more strongly that men differ from each other, and that men face as much stigma in today's society as women. Men who identified highly with their own gender also saw the woman who talked about being sexually assaulted by a man as less moral than they saw the other women they listened to in the recordings.

"This is remarkable because men's gender identification affected only the morality judgments they made about female sexual assault survivors, not about female survivors of other crimes. The effect was reversed for women . In the lab, we found that when men discussed a woman's sexual assault, they showed higher cardiovascular threat levels than when they discussed a woman's bike accident experience.

"In the spirit of transparently reporting results, I think it is also important to note where we did not find what we expected: We expected men's gender identification to influence their cardiovascular threat levels, and their threat levels to influence their reactions to the disclosed experiences, but we did not find this in our study."

What has been the most valuable lesson you've learned writing your thesis?

"Academically, I would say the most valuable lesson I learned about designing and conducting a study is to stay concise and feasible in your research design . I kept finding new angles to look at the topic or wanting to add more variables to the design, but ultimately had to remind myself that if I ever wanted to finish this project, I had to 'kill my darlings,' as my supervisor put it.

"On a personal level, it may sound cheesy, but the most valuable lesson I learned was that it's worth it to ask for the things you want—I was surprised that my supervisor (Daan Scheepers) was so open to my ideas and supported me in researching this extremely important topic. Also, it's okay to do things in your own time. Ultimately, I worked on my thesis for a year, and while that is longer than most of my fellow students took, it was the right decision for me."

What are your future plans?

"Since October 2023, I'm a Ph.D. student in Political Psychology at the Amsterdam School of Communication Research at the University of Amsterdam. My thesis project really finalized my decision to pursue a career in academia, and so far, it's been very interesting and exciting. I am also working on publishing the thesis in a scientific journal with my thesis supervisor."

What is your message for students who still need to write their thesis, but are finding it stressful or overwhelming?

"Take your time, trust yourself and your own abilities, and dare to propose research topics that you find the most interesting or relevant. That way, it's way easier to get through motivation dips, and sometimes you can be surprised by how open others are to your ideas."

Provided by Leiden University

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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.

Purdue University Research and Teaching Assistantship

Degree type.

• Doctor of Philosophy

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• West Lafayette

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• Labour economics
• Higher education
• Secondary education
• Education policy

• Share full article

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A Museum’s Feminist Artwork Excluded Men. So One Man Took It to Court.

Gender-based discrimination is central to the women-only art installation, in Australia, but one visitor claims it is also illegal.

By Natasha Frost

Reporting from Melbourne, Australia

A wall of vulvas. A performance featuring a recently slaughtered bull. A “poo machine” that replicates the journey of food through the human body.

The Museum of New and Old Art , or MONA, in Hobart, the capital of the Australian state of Tasmania, is no stranger to works that may shock or appall, or the criticism they may draw. But this week, it found itself defending an unusual claim: An artwork, a visitor complained, broke discrimination laws.

The Ladies Lounge — plush green curtains, lavish surroundings, original works by Picasso and Sidney Nolan — is an installation by the American artist and curator Kirsha Kaechele. Opened in December 2020, it is accessible to “any and all ladies,” according to the MONA website — and precisely zero men, other than the solicitous butlers who cater to the women within it.

Like other men, Jason Lau was not allowed to enter the installation when he visited the museum in April 2023. Mr. Lau lodged a complaint with Tasmania’s Anti-Discrimination Commissioner, saying he was discriminated against because of his gender.

The matter was heard by the Tasmanian Civil and Administrative Tribunal in Hobart on Tuesday.

“I visited MONA, paid 35 Australian dollars,” or about $23, “on the expectation that I would have access to the museum, and I was quite surprised when I was told that I would not be able to see one exhibition, the Ladies Lounge,” Mr. Lau said at the hearing, according to reports in the Australian news media. “Anyone who buys a ticket would expect a fair provision of goods and services.”

In an interview, Ms. Kaechele said that she agreed with Mr. Lau, but that his experience of discrimination was central to the work.

“Given the conceptual power of the artwork, and the value of the artworks inside the artwork, his detriment is real,” she said. “He’s at a loss.”

The work was necessarily discriminatory, Catherine Scott, Ms. Kaechele’s lawyer, has acknowledged. But, she argued, denying men access to it still allowed them to experience it, albeit in another way.

During proceedings on Tuesday, Ms. Scott cited a legal exception that states that discrimination may be acceptable if it is “designed to promote equal opportunity for a group of people who are disadvantaged or have a special need because of a prescribed attribute.”

“This case asks the tribunal to appreciate that art may, in fact, promote equal opportunity in a different way, in a way that’s more at a conceptual level,” she said in an interview.

Ms. Kaechele, who is married to David Walsh, the founder of the museum, appeared at the hearing on Tuesday trailed by a phalanx of 25 women in pearls and navy suits, many of them also artists, who silently read feminist texts and posed, crossed their legs and applied lipstick in unison.

In August, another male visitor filed a complaint of gender discrimination over the work, according to a museum spokeswoman. That led to a dialogue with Ms. Kaechele.

“I said, ‘Well, you did get to experience the artwork, because the exclusion of men is the artwork,’” Ms. Kaechele said. “So he appreciated that, he understood, and he dropped the case.”

The Ladies Lounge takes inspiration from male-only spaces in Australia from the past and the present, she said. Australia only permitted women to enter public bars from 1965, and they were often relegated to the so-called “ladies lounge,” a smaller area often selling more expensive drinks.

But discrimination against women is not simply a matter of the historical record. Australia still has a gender pay gap of about 20 percent, women are still underrepresented in leadership and management positions in almost all industries, according to the Australian government , and a number of elite gentlemen’s clubs, like the Melbourne Club, still exclude women from membership.

These clubs exist to connect important men to one another and reinforce patriarchal power structures, Ms. Kaechele said. “In our lounge, we’re just drinking champagne and sitting on the sofa. I don’t think it’s much of a parallel.”

The work was intended to be funny, and its sense of humor derived from the fact that women remain marginalized in Australian life, she added. “It’s meant to illuminate the past and be lighthearted,” she said, “and we can only do that because we’re women and we’re lacking power.”

Mr. Lau, who could not be reached for comment, has asked for a formal apology and for men either to be allowed into the Lounge or to pay a discounted ticket price to account for their loss, which Ms. Kaechele has refused. “I’m not sorry,” she said, “and you can’t come in.”

A decision from the tribunal is expected in the coming weeks.

For MONA and Ms. Kaechele, as the artist, even the potential closure of the exhibit had some advantages, said Anne Marsh, an art historian based in Melbourne.

“Noisy art is good art, noisy feminism is good feminism,” she said. “It gets it on the agenda.”

Natasha Frost writes The Times’s weekday newsletter The Europe Morning Briefing and reports on Australia, New Zealand and the Pacific. She is based in Melbourne, Australia. More about Natasha Frost

Read our research on: TikTok | Podcasts | Election 2024

Regions & Countries

4. emotions, news and knowledge about the israel-hamas war.

Most Americans report having strong emotional reactions to the Israel-Hamas war. Yet, for the most part, Americans are not paying very close attention to news about the conflict. One sign of this limited attention is that only about half of U.S. adults can correctly answer a question that tests their factual knowledge by asking whether the number of deaths in the war, so far, is higher among Palestinians or among Israelis. (The correct answer is that the death toll is higher among Palestinians .)

The fairly low level of attention to the war is also reflected in the relatively high shares of adults who express no opinion on many survey questions.

As has traditionally been true with international affairs , levels of engagement with this war vary greatly across social and demographic groups. In particular, Jewish and Muslim Americans are significantly more likely than other Americans to be following news about the conflict extremely or very closely.

Sadness, anger, exhaustion and fear

An overwhelming majority of U.S. adults (83%) say that hearing or reading news about the Israel-Hamas war makes them feel sad, and about two-thirds (65%) say news about the war makes them feel angry.

Half (51%) report exhaustion when reading or hearing about the war, while 37% say news about the war makes them feel afraid. Altogether, half of Americans say they experience at least three of these four emotions when reading or hearing news about the war.

While most Americans report at least some emotional reactions to the war, Jewish and Muslim Americans stand out, with 43% of Jews and 37% of Muslims reporting all four emotions – sadness, anger, exhaustion and fear – in response to the conflict. Three-quarters of Jews (74%) and two-thirds of Muslims (66%) experience at least three of the four strong emotions.

Women (59%) are much more likely than men (39%) to say they experience at least three of the four emotions.

The differences between men and women are especially wide on fear: 49% of women say reading or hearing news about the war makes them feel afraid, compared with 24% of men.

Differences by age are modest, except for feelings of exhaustion: Adults under 30 are the most likely to say this (60%), compared with 46% of those 50 and older.

A solid majority of Democrats and Democratic-leaning independents (59%) say they feel at least three of the four emotional reactions, while 40% of Republicans and Republican-leaning independents say the same.

These differences are even sharper when considering ideology along with partisanship: 67% of liberal Democrats report experiencing at least three emotional reactions, compared with 39% of conservative Republicans.

Emotional reactions are related to opinions about the conflict.

Those who express sympathy entirely or mostly with the Palestinian people are more likely than those who sympathize with the Israeli people (or who sympathize with both equally) to report experiencing the four emotions.

Attention to news about the war

Most people are not paying close attention to the Israel-Hamas war. The survey finds that 22% of U.S. adults say they have been following news about the war extremely or very closely, while 35% say they are following it somewhat closely, and 43% are following not too or not at all closely.

This level of public attention is basically unchanged from a survey conducted Nov. 27-Dec. 3, 2023 , less than two months after the Oct. 7 attack on Israel by Hamas. In that survey, 26% were following news about the conflict extremely or very closely.

Attention to news about the Israel-Hamas war is comparable in magnitude to attention to news about Russia’s invasion of Ukraine.

One-fifth of Americans report following the Russia-Ukraine conflict extremely or very closely, while 35% are following it somewhat closely. (Americans also describe the Israel-Hamas war and the Russia-Ukraine war as similarly important to U.S. national interests, according to a separate Center survey conducted in January, though slightly more describe the Israel-Hamas war as personally important to them. For more, read “How Americans view the conflicts between Russia and Ukraine, Israel and Hamas, and China and Taiwan.” )

One other point of comparison is news about the 2024 U.S. presidential election. Somewhat more Americans – though still not a majority – report following election news extremely or very closely (32%). Around a third (35%) say they are following the election not too or not at all closely.

Some groups are paying much greater attention to news about the Israel-Hamas war. Jews and Muslims report especially high levels of attention. About six-in-ten Jewish Americans (61%) say they are following the war extremely or very closely, as are 41% of Muslim Americans. In no other religious or nonreligious group analyzed do more than around a quarter report this high level of attention.

Party affiliation

Attention to news about the war is very similar among Republicans and Democrats: 22% and 23%, respectively, say they’re following it extremely or very closely. More of those who describe themselves as either conservative Republicans (26% following extremely or very closely) or liberal Democrats (28%) are paying close attention than are either moderate and liberal Republicans (14%) or moderate and conservative Democrats (19%).

Sympathies in the conflict

U.S. adults who say they sympathize equally with Israelis and Palestinians in the conflict are paying less attention than those who express greater sympathy for one side or the other. Among those who sympathize equally with the Israeli and Palestinian people, 21% are following news about the war extremely or very closely. Among those who sympathize more with the Israeli people, 32% are following extremely or very closely, as are 31% of those who sympathize more with the Palestinian people.

Note: It’s important to highlight that the connections between emotions, news and knowledge discussed in this chapter may be more complicated than a simple case of one thing directly causing another. For example, it could be that people who begin with a strong sympathy for either side in the Israel-Hamas war are more likely to pay attention to news about the war. But it also could be that people who pay close attention to the news about the war are more likely to develop a sympathy for one side or the other. Or, both things may be happening at the same time. Survey research often shows associations without being able to determine causality.

There are sizable differences in attention to the Israel-Hamas war by age. Limited attention to news about the conflict is seen not only in the youngest age group but also among all adults under 50. Just 14% of adults under 30, as well as 14% of those ages 30 to 49, report paying extremely or very close attention to news about the conflict. This compares with 24% of people ages 50 to 64 and 36% of those 65 and older.

Young Democrats are paying a little more attention to the war than are young Republicans, but there is an especially sharp difference in the share who are very disengaged: Republicans under age 30 stand out, with 71% reporting that they are following news about the war not too or not at all closely. Among Democrats in this age group, fewer – 49% – are similarly disengaged.

Knowledge about the conflict

The historical roots of the conflict in Gaza are deep and complicated, so it is unsurprising that many Americans have trouble formulating opinions about the war.

To get a sense of what people know about the situation, we asked the poll’s respondents three factual questions in a multiple choice format. These questions tested whether respondents know that Benjamin Netanyahu is Israel’s current prime minister; that Hamas is the group that attacked Israel on Oct. 7 (the survey asked this question before making any other mention of Hamas); and that more Palestinians than Israelis have died thus far in the current war.

These questions are merely indicators of basic knowledge. One could know all three facts without understanding the complex historical and geopolitical forces at work in the conflict. Nevertheless, it can be informative to look at the interplay between specific knowledge about the current Israel-Hamas war and broader attitudes about the conflict.

A solid majority of respondents (80%) correctly chose Hamas as the group that attacked Israel; alternative choices included Hezbollah, Al Qaeda and the Taliban.

Benjamin Netanyahu was correctly chosen by 62% of Americans as the Israeli prime minister (the alternatives included Rishi Sunak, Benny Gantz and Abdel Fattah el-Sisi). Hardly anyone gave a wrong answer; instead, 35% said they were not sure of the right answer.

And 52% correctly answered that more Palestinians than Israelis have died in the war thus far. Most people are not misinformed: Just 7% said more Israelis have died, and an additional 7% said the number of deaths had been about the same. But 34% said they were not sure of the answer.

Summary index of knowledge

The three questions were combined to form a simple index of knowledge related to the conflict. A little more than four-in-ten people surveyed (43%) answered all three items correctly, while 24% got two out of three. About a third (34%) answered fewer than two items correctly.

Not surprisingly, people who say they’re following news about the conflict extremely or very closely are far more likely to answer all three knowledge questions correctly: 75% could do so, compared with just 34% among those following less closely.

Consistent with their high levels of attention to news about the conflict, American Jews and Muslims were more likely than many other religious groups to correctly answer all three items (76% for Jews, 55% for Muslims).

Among the religiously unaffiliated, 48% got all three questions right – with atheists (69%) and agnostics (60%) particularly standing out.

As with most tests of political knowledge, people with more formal education scored higher than those with less education: 59% of college graduates answered all three questions correctly, compared with 33% of adults with no college degree.

Older respondents generally were more knowledgeable than younger ones. Roughly half of those ages 65 and older (54%) answered all three questions correctly, compared with 31% of those under 30.

And 56% of men got all three questions right, compared with 30% of women. Women were not more likely than men to answer incorrectly, but they were much more likely to indicate they were unsure of the correct answers.

Democrats were a little more knowledgeable, on average, (49% got all three correct) than Republicans (39%), with self-described liberal Democrats the most knowledgeable (60%).

A closer look at knowledge of casualties

Knowledge – or lack of knowledge – about casualties is related to attitudes about the conflict in a few ways. For a start, respondents who do not correctly answer that more Palestinians than Israelis have died are much more likely to decline to answer many opinion questions in the survey .

For example, among those unaware that more Palestinians have died, 59% offered no opinion when asked whether Biden has been favoring one side or the other too much. Among those who knew the balance of casualties, far fewer – 22% – had no opinion on Biden’s approach to the war.

In addition, those who are aware that more Palestinians than Israelis have died in the current war tend to express more pro-Palestinian views on certain questions. One example is that they express more favorable attitudes about the Palestinian people than do respondents who are not aware of the relative number of deaths on each side. Among those who correctly answer this knowledge question, favorable opinions of the Palestinian people outnumber unfavorable opinions by 61% to 36%; among those unaware of the balance of casualties, more have an unfavorable than favorable opinion of the Palestinian people (47% unfavorable, 39% favorable).

Similarly, those aware that more Palestinians have died are about twice as likely to say Biden is favoring the Israelis too much (35%) as to say he’s favoring the Palestinians too much (17%). Among those who do not know that more Palestinians have died, 15% say Biden is favoring the Palestinians too much and 9% say he’s favoring the Israelis too much.

And by 69% to 16%, those who know that more Palestinians have died favor the U.S. providing humanitarian aid to Palestinian civilians in Gaza. Among those who do not know the balance of casualties, 29% favor providing aid and 23% oppose it.

American Jews (79%), Muslims (71%), atheists (80%) and agnostics (72%) were the most knowledgeable religious groups analyzed on this question. As with our knowledge index overall, older adults were more likely than younger ones to correctly answer this question, but the gap was smaller than on the other knowledge items. Democrats were more knowledgeable than Republicans about the death toll in the war (60% and 46%, respectively, answered correctly).

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Table of contents, how americans view the conflicts between russia and ukraine, israel and hamas, and china and taiwan, americans’ views of the israel-hamas war, comparing views of the u.s. and china in 24 countries, views of india lean positive across 23 countries, international views of biden and u.s. largely positive, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .