research topics on gender pay

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

Leib litman.

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

Jonathan Robinson

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

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

Cheskie Rosenzweig

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

Joshua Waxman

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

Lisa M. Bates

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

Associated Data

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] .

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.

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.

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.

*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

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).

*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

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).

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.

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.

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.

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

Funding statement.

The authors received no specific funding for this work.

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Research Article

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

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

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

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

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

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

* E-mail: [email protected]

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

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

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

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

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

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

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

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

Editor: Elisa Ughetto, Politecnico di Torino, ITALY

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

Data collection

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

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

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

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

Text processing and keyword extraction

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

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

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

Classification of research topics

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

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

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

Evaluation of semantic importance

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

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

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

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

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

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

Overview of main topics

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

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

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

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

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

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

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

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

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

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

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

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

Compensation.

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

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

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

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

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

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

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

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

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

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

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

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

Decision-making.

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

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

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

Career progression.

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

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

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

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

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

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

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

Performance.

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

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

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

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

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

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

Organization.

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

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

Human capital.

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

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

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

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

Emerging topics: Leadership and entrepreneurship

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

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

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

Research gaps and conclusions

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

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

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

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

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

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

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

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

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

Supporting information

S1 text. keywords used for paper selection..

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

Acknowledgments

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

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Higher research productivity = more pay? Gender pay-for-productivity inequity across disciplines

• Open access
• Published: 24 January 2023
• Volume 128 , pages 1395–1407, ( 2023 )

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• Charissa Samaniego 1 ,
• Peggy Lindner 2 ,
• Maryam A. Kazmi 1 ,
• Bobbie A. Dirr 3 ,
• Dejun Tony Kong 4 ,
• Evonzia Jeff-Eke 1 &
• Christiane Spitzmueller   ORCID: orcid.org/0000-0003-0549-0259 1 , 5  

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Gender pay equity for academics continues to be elusive. Adding to scholarship around structural barriers to gender equity in academic settings, we investigate the link between scholarly performance and compensation. We expect high research productivity to be differentially associated with compensation outcomes for men and women. Building on social role theory, we hypothesize that these relationships are contingent upon whether researchers are inside or outside of Science, Technology, Engineering, and Mathematics (STEM). Using the h-index, compensation, and researcher demographics for 3033 STEM and social and behavioral sciences (SBS) researchers from 17 R1 universities, we applied multilevel modeling techniques and showed that cumulative research productivity was more strongly related to compensation for men versus women researchers. However, these effects only held in STEM disciplines but not in SBS disciplines. Based on these results, we recommend that institutions consider changing how pay analyses are conducted and advocate for adding explicit modeling of scientific performance-compensation links as part of routine pay equity analyses.

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Katie Meara, Francesco Pastore & Allan Webster

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The pervasive under-representation of women researchers, specifically in tenured and tenure-earning faculty positions in Science, Technology, Engineering and Mathematics (STEM) (Bilen-Green et al., 2008 ; Lariviere et al., 2013 ; Shen, 2013 ), along with various challenges women face in their academic career progression (Bedi et al., 2012 ; Clauset et al., 2015 ; Edmunds et al., 2016 ; Handelsman et al., 2005 ; Moss-Racusin et al., 2012 ; Quadlin, 2018 ), calls for continued research on gender equity in academic settings. One important form of gender inequity is pay inequity. Academic researchers are expected to be paid equitably based on their research productivity (i.e., pay-for-productivity). Nonetheless, are men and women really paid equally for the same level of research productivity? Or is pay-for-productivity just a myth for women in tenured and tenure track faculty positions? If gender inequity of pay-for-productivity exists, women are likely discouraged to continue their careers in academia, which may help explain the “leaky pipeline” (Clark Blickenstaff, 2005 ) problem seen in STEM as compared to Social and Behavioral Sciences (SBS) disciplines. To date, many studies only examine gender differences in academic salary while controlling for productivity (Bellas, 1997 ; Euwals & Ward, 2005 ; Ginther, Donna K. & Hayes, Kathy J., 2003 ; Umbach, 2007 ) and the results are mixed, leaving gender differences in the strength of the pay-for-productivity relationship unexamined. In other words, it is unclear if the gender pay gap depends on a faculty member’s productivity level. Drawing from theory and research on social roles, we further examine gender differences in pay-for-productivity in STEM and SBS disciplines.

In the present research, we aim to address three questions regarding pay-for-productivity in academic settings: (1) whether, and if so, how strongly, research productivity is positively related to researcher pay (i.e., the intensity of pay-for-productivity), (2) whether productivity is more strongly tied to pay for men than for women (i.e., interaction of gender and pay-for-productivity), and (3) whether gender inequity of pay-for-productivity, if any, is more severe in the STEM disciplines than in the SBS disciplines (i.e., disciplinary difference in gender inequity of pay-for-productivity).

Pay-for-productivity

Pay-for-productivity, from a work motivation perspective, is deemed fair by many workers and motivates them to achieve desired results (Lawler, 1971 ; Maier, 1955 ). Meta-analytic studies suggest performance-contingent pay is among the best methods for boosting performance levels (Rynes et al., 2004 , 2005 ). In academic institutions classified as R1 by the Carnegie Classification of Institutions of Higher Education, research constitutes the most important job responsibility and is a significant factor determining tenure success, promotions, and pay raises across a host of academic disciplines (Fairweather, 2005 ). Thus, besides their intrinsic motivation, academic researchers’ extrinsic motivation to produce research is, to some degree, driven by the extent to which their research productivity is linked to their pay. The University of Arkansas for Medical Sciences introduced a performance-based incentive plan for its College of Medicine in 2005 (Reece et al., 2008 ). With faculty pay directly linked to productivity, performance increased drastically, leading to a total compensation increase of about 20%, in addition to increases in external funding and researchers’ morale and satisfaction (Reece et al., 2008 ).

Some previous studies focused on whether men and women researchers receive equal pay while controlling for factors such as academic ranks, leadership positions (Jagsi et al., 2012 ), and raises (Lindley et al., 1992 ) as proxies for research productivity. Others have controlled productivity by controlling for the number of publications (e.g., number or articles or books; Bellas, 1997 ; Euwals & Ward, 2005 ; Ginther et al., 2003 ; Levin & Stephan, 1998 ; Umbach, 2007 ), without any measure of quality of the publications. In contrast, we explicitly measure research productivity with h-index and investigate whether higher research productivity (and quality) translates into higher pay to the same extent for men and women in academia (i.e., pay-for-productivity). A researcher’s h- index has become one of the most widely used and common metrics to quantify scholarly productivity. Introduced 15 years ago by Hirsch, it refers to the number of publications ( h ) that have received at least h citations each (Hirsch, 2005 ). For example, a researcher who has ten publications with at least ten citations (with all other publications having less than ten citations each), would have an h -index of 10. Although the popularity of this index has skyrocketed, researchers have acknowledged its’ shortcomings including: the susceptibility of inflation due to self-citations (Bartneck & Kokkelmans, 2011 ; Zhivotovsky & Krutovsky, 2008 ), favoring more established researchers (Hirsch, 2005 ), no adjustment for multiple-authorship or order of authors, and no normalization of differential citation practices between disciplines (Alonso et al., 2009 ). Regardless of these drawbacks, the h -index is a single, easily calculable number that incorporates both a measure of quantity in the number of publications, and a proxy for quality in terms of number of citations, and is widely used as a decision-making tool within higher education for hiring and tenure (Barnes, 2017 ; Scruggs et al., 2019 ). Therefore, its effect on compensation should be examined to determine the full utility of this metric.

Hypothesis 1

Research productivity is positively related to researcher salary in STEM and SBS disciplines.

Gender differences in pay-for-productivity

Researchers who identify as men earn around 20% more than their women peers (Carlin et al., 2013 ; Jagsi et al., 2012 ; Lindley et al., 1992 ). Despite shifts in the distribution of men and women researchers in faculty rank, the gender pay gap has not diminished in the last 10 years. In 2020, on average across all disciplines, assistant professors who identify as women make $7605 less than their peers who identify as men, and this difference more than doubles at the full professor level, with women full professors making $19,030 less than full professors who are men ( The Annual Report on the Economic Status of the Profession, 2019–2020 , 2020). Disparities between disciplines may partly explain these gender differences as higher paying disciplines (i.e., biological sciences, engineering, and mathematics) tend to have more researchers who are men versus lower paying disciplines (i.e., English, sociology, and gender studies) with more women researchers(Shulman et al., 2017 ). However, even in disciplines with a high proportion of women, there is still gender pay inequity and thus differences in average discipline pay cannot entirely explain gender pay inequity. One study reported men in disciplines one standard deviation above the mean in representation of women will earn approximately $75,0000 versus women earning $69,000 (Umbach, 2007 ).

Another partial explanation for gender pay inequity has focused on the “productivity puzzle” of women having lower average productivity levels (Cole & Zuckerman, 1984 ; West et al., 2013 ; Xie & Shauman, 1998 ). A plethora of contributing factors have been examined to possibly explain women’s lower productivity levels including family responsibilities (Ceci & Williams, 2011 ; Fox, 2005 ; Hunter & Leahey, 2010 ), resource allocations (Duch et al., 2012 ), and research specialization (Leahey, 2006 ). However, recent analyses of archival data suggest no gender differences in journal acceptance of publications, nor in productivity levels when controlling for structural differences, implying that when given equal resources, men and women publish equally well (Ceci & Williams, 2011 ; Huang et al., 2020 ). While investigating gender differences in productivity levels is an important research topic, in the current study we are not examining why differences may occur, but instead if men and women are paid equitably for their individual productivity level. Research on whether the gender salary gap in academia disappears after controlling for productivity is mixed (Bellas, 1997 ; Euwals & Ward, 2005 ; Ginther et al., 2003 ; Umbach, 2007 ). Only one study to date has examined gender differences in pay-per-performance relationship in specific STEM disciplines (physics, earth science and physiology), and found women were paid more per publication than men, but only for physics (Levin & Stephan, 1998 ). In addition to the data being from the 1970’s, the authors only examined the change in salary in a two-year period, likely missing crucial overall salary differences.

Gender differences in pay-for-productivity can manifest in two ways. First, social role theory grounded expectations for women’s performance may emphasize their communal roles as mentor, rather than their productivity or agentic characteristics (Cejka & Eagly, 1999 ; Koenig & Eagly, 2014 ). In cases where women do not adhere to gender role expectations, social role theory grounded expectations may still lead them to be perceived as less productive and competent and perceived as having lower status than men (England, 1992 ; Heilman, 2001 ); therefore, women are not paid as much as men when they perform well. Second, although women are encouraged to negotiate their salary and other employment terms, compared to men, women researchers’ salary negotiations or requests for salary adjustments are less likely to succeed (Leibbrant & List, 2015 ). Women tend to anticipate backlash for their salary negotiation/request attempts; therefore, they may either opt to not initiate their salary negotiations/requests or lower their aspirations if they decide to do so (Amanatullah & Morris, 2010 ; Amanatullah & Tinsley, 2013 ). Women’s salary negotiation attempts are sometimes viewed as aggressive acts, and frequently invite hostile reactions from others (Rudman et al., 2012 ). Because of gender bias in salary negotiations disfavoring women, we argue that research productivity does not translate into women researchers’ pay as much as men researchers’ pay.

In the current study we focus on research productivity in STEM and SBS fields and examine the gender differences in the strength of pay-per-productivity, that is look at gender differences in the relationship between h-index and salary (not just changes in salary). Looking at gender differences in pay-per-productivity, allows us to examine if gender pay inequity differs across levels of productivity. If women are paid according to stereotypes, then women who have low productivity will be paid the correct amount, but high producing women will be underpaid because they are assumed to be underproductive (i.e., perceived productivity mismatches actual productivity). Thus, we expect that there will be gender salary differences at high performance levels and not at low performance levels.

Hypothesis 2

The link between research productivity and researcher salary is stronger among men researchers than among women researchers. Such that, men are paid more per h-index and gender pay inequity is larger at higher levels of productivity.

STEM vs SBS

Our final inquiry pertains to the disciplinary difference in gender inequity of pay-for-productivity. If this inequity does exist, does it vary across academic disciplines? Specifically, is the hypothesized inequity more severe in disciplines where women are traditionally under-represented than in other disciplines? Women are less likely to enter STEM, feel less welcomed in these disciplines, and are less likely to stay in tenure or tenure-earning positions in these disciplines (Clauset et al., 2015 ; Edmunds et al., 2016 ; Handelsman et al., 2005 ). Furthermore, some evidence suggests that the gender pay gap is larger in STEM disciplines (Umbach, 2007 ; Xu, 2015 ) than in other disciplines, even when researchers control for gender differences in productivity. We postulate women having difficulty to effectively negotiate compensation to be more pronounced in STEM disciplines than in other disciplines such as social and behavioral sciences (SBS) where we expect this gender inequity to be less severe.

In support of our expectations, social role theory (Eagly, 1987 ) suggests that gender roles prescribe what men and women should be like and provide gendered rules and norms based on which behaviors are judged and rewarded or socially sanctioned. Men are expected to be achievement-oriented, competitive, and analytic, whereas women are expected to be warm, considerate, and accommodating (Eagly & Karau, 2002 ; Heilman, 2001 ). Women are not expected to pursue STEM; instead, they are more expected to pursue SBS such as psychology, communication, sociology, etc. (Clark Blickenstaff, 2005 ; Handelsman et al., 2005 ). Women in STEM disciplines violate such gender role expectations and thus face unfavorable evaluations and other social sanctions. In contrast, women researchers in SBS disciplines are less likely to violate gender role expectations and thus may face fewer negative consequences. Such gender role expectations are particularly strong in fields dominated by men such as STEM disciplines as the norms are shaped by men. Women researchers who are achievement-oriented, competitive, and analytic inevitably violate gender role expectations and thus face social sanctions including unfavorable evaluations and social exclusion. These gender role expectations coupled with stereotypes of women as low performers could result in lower female salaries relative to male salaries, but only for high performing women in STEM disciplines, as women with lower productivity are meeting prescriptive gender stereotypes. Thus, we would expect stereotyping of productivity and gender differences in negotiation tactics to affect the salaries of highly productive women in academic STEM disciplines.

Hypothesis 3

The gender difference in the link between research productivity and researcher salary is larger in STEM versus SBS disciplines.

Materials and methods

We collected research productivity and salary data of 3033 tenured and tenure-earning faculty members from 17 universities across the United States. Department chairs were excluded from the analyses. Our criteria for the university selection were based on a study conducted for a National Science Foundation ADVANCE institutional transformation project. The selected data collection sites were large public universities in urban settings that were classified as R1 institutions (i.e., highest research activity by the Carnegie Classification of Institutions of Higher Education). Among these universities, we selected those that made salary data publicly available. In the first step, coders manually searched department websites of all 17 universities, and created a database combining researchers’ gender and discipline information and their demographic information retrieved from their publicly available CVs. In the second step, we used an automated approach to scrape each researcher’s research productivity information ( h -index) from Google Scholar, and collected salary data from websites reporting current 9-month faculty salaries.

The coders utilized a combination of photographs available on departmental websites and names to code each researcher’s gender (1 = woman, 0 = man).

Research productivity

Research productivity was indicated by the h -index in 2019 (Hirsch, 2005 ), which was scraped from each tenured and tenure-earning faculty member’s Google Scholar website. The h -index is the most used metric for research productivity, with h being the number of papers a researcher has authored or co-authored that has accumulated at least h citations (Hirsch, 2005 ).

We collected the 9-month faculty salary data from various websites containing university-published current faculty salaries, as noted earlier.

We controlled for the number of years since the attainment of Ph.D. (i.e., post-Ph.D. years) at the individual level and the following department level controls by utilizing group-mean centering in our multilevel models: proportion of women in department, average department years since the attainment of Ph.D. (i.e., post-Ph.D. department tenure); and mean of h -indices within each department. Our random intercepts multilevel model inherently controlled for the average salary level of the department. We controlled for post-Ph.D. years to ensure that salary increases were attributed to increases in research productivity rather than just researchers’ tenure in their discipline. Our multilevel controls ensured we controlled for university and discipline differences because department averages will be affected by both.

Descriptive statistics

Table 1 presents descriptive statistics and correlations among post-Ph.D. years, the h -index, and salary. Correlations are presented separately for men and women researchers. The average amounts of men and women researchers’ salary were $133,092.40 and $118,459.20, respectively. Women, on average, made 89 cents for every dollar made by men. With 95% confidence, the average salary for men was $10,850.63 to $18,415.71 more than that of women researchers (i.e., 9.16% to 15.55% more than the average salary for women). Gender difference in the h -index may partially explain this gender gap of salary. With 95% confidence, we found that men’s average h -index was 5.32 to 8.33 higher than that of women. The gender difference in the h -index could partially be explained by the gender difference in post-Ph.D. years. Also, with 95% confidence, we found that men had 3.80 to 5.51 more post-Ph.D. years than women.

Multilevel regression analyses

We tested our hypotheses by conducting multilevel regression analyses, given that our data were nested within academic departments (e.g., Psychology department at the University of Houston). We centered gender, post-PhD years, and h-index by their respective group (department) means (Enders & Tofighi, 2007 ) (mean of gender is a proportion). In all reported models, for the sake of parsimony, we did not enter the department means of gender, post-Ph.D. years, and the h -index as predictors because (a) we did not hypothesize the effects of these department means, and (b) inclusion or exclusion of these department means did not change the result patterns, presumably because we group-mean centered. The ICC of salary estimate of 22.47% (i.e., 22.47% of the variance in salary could be explained by cross-department differences) further justified our use of multi-level regression analyses. Department-level salary variability can be explained by both university and discipline differences. Table 2 presents the results of the multi-level regression analyses, with profile confidence intervals being reported in the main text. The baseline model included two control variables: post-Ph.D. years and gender (1 = woman, 0 = man), with the former being a significant predictor of salary ( B  = 2,186.66, t  = 35.22, p  < 0.01).

In line with Hypothesis 1, researchers’ h- index, indicative of their research productivity, was positively related to their salary level (see Model 1, Table 2 ). On average, a one-point increase in the h -index translated into a salary increase of $1,000.46 ( t  = 22.17, p  < 0.01), with its 95% confidence interval [$912.01, $1,088.90]. We did not find support for Hypothesis 2. Specifically, the interaction between gender and the h -index was not significant (Model 2: B  =—120.70, t  = -1.17, p  = 0.24). In other words, pay-for-productivity did not differ significantly between men and women researchers when examining both STEM and SBS discipline simultaneously. Finally, we found support for Hypothesis 3 regarding gender inequity of pay-for-productivity in STEM versus SBS disciplines; the three-way interaction among gender, the h -index, and academic discipline dummy (STEM vs. SBS) was negatively related to researchers’ salary level (Model 3: B  = -397.75, t  = − 1.86, p  = 0.063).

We then probed the two-way interaction between gender and the h -index separately for STEM and SBS disciplines. For the latter, gender inequity of pay-for-productivity was not significant ( B  = 141.80, t  = 0.76, p  = 0.45). However, for the former, pay-for-productivity was unfavorable to women versus men ( B  = -266.66, t  = -2.13, p  = 0.03). On average, in STEM disciplines, men were paid $266.66 (95% confidence interval [$20.95, $512.61]) more than women for each one-point increment in h -index. Figure  1 shows the interaction between gender and the h -index for both STEM (Fig.  1 a) and SBS (Fig.  1 b) disciplines using group mean centered variables. As demonstrated, for STEM disciplines, as h -index increases, predicted salary for men is higher than for women.

Relationship between h-index and salary for STEM and SBS researchers. Plots were generated using group mean centering for h-index and gender. Ranges for both axes have been fixed to allow for comparison

The present research reveals gender inequity of pay-for-productivity in STEM disciplines. Consistent with work motivation theories (Rynes et al., 2004 , 2005 ), we did find that researchers’ salary is coupled with their research productivity as intended, but this pay-productivity coupling was more favorable to men versus women, particularly in STEM disciplines. It is interesting to note that previous research demonstrated high performing women in STEM may need to overcompensate (i.e., build more relationships, acquire more knowledge, or put in more research hours) to achieve the same level of productivity indicators as their male colleagues (Aguinis et al., 2018 ). Thus, not only is the road to becoming a “star” performer more difficult for women, they may not also see the same returns in compensation for their research investments. Women researchers in STEM with a h-index of 49 (one standard deviation above the mean) made around six thousand dollars less than men researchers in STEM with the same h-index. Our study did not follow researchers longitudinally, but we can tentatively extrapolate how a six-thousand-dollar salary gap can add up over the years (i.e., over a ten-year-period this difference would add up to sixty-thousand-dollars). Depending on how their h-index develops over one’s career, a highly productive woman researcher in STEM could experience even more pay inequity.

As with any paper, our study is not without limitations. In contract to studies examining pay differences in non-Western cultural contexts (Takahashi et al., 2018 ) our study focused on North American academics, we expect basic social psychological processes grounded in role theory expectations and gender differences in negotiation behaviors and negotiation outcomes to be similar across cultural contexts. However, in countries where compensation is more strongly driven by federally or locally imposed pay rates, productivity-compensation differences should be weaker across gender. We recommend subsequent research account for cultural contexts and structural differences in compensation structures in academic settings to examine the external validity of our findings across cultural contexts. Also note that in our paper, we aimed to determine linear relationships between productivity and compensation and the moderating role of gender. Hence, for more nuanced analyses, including analyses of star performers’ performance (Aguinis et al., 2018 ) and compensation, or non-linear effects to be determined, we recommend researchers build large, multi-university consortium structures to access large enough data sets to conduct meaningful analyses of a non-linear nature or on subsets (e.g. star performers, faculty of color, faculty with intersectional identities).

Our finding renders support for funding agencies’ (i.e., National Science Foundation) efforts for reducing gender inequity in STEM disciplines (Ceci & Williams, 2011 ) and yet reveals the lingering challenge inherent in these efforts. Given that our analyses relied on archival data, we could not accurately code the race/ethnicity of researchers and thus did not include this demographic factor in our analyses. However, we speculate that pay-for-productivity may further disadvantage those with intersectional identities, such as women of color in STEM disciplines. Given that our focus was on determining whether there is a gender inequity of pay-for-productivity across disciplines, we offer some plausible explanations without testing these explanatory mechanisms. Future research should hence shed light on these possible mechanisms to ultimately identify ways to close gaps. For example, why, when, and how pay-for-productivity relationships are weaker for women in STEM may be a result of fewer women attempting to continuously renegotiate their salary. Alternatively, men may be more likely to seek offers from other institutions and their salary may benefit as a result. Last, it may be possible that women’s attempts to renegotiate their salary based on incremental performance results in negative reactions from administrators at the departmental, college, and university levels.

In our analyses we used the h -index as an indicator of research productivity. We encourage future researchers aiming the productivity-pay link to use broader or supplemental indices of productivity, such as external funding records and total citations. Even though the h -index is a widely known metric for research productivity and is used as a decision-making tool, it is not without weaknesses. For instance, intentional manipulation of the h -index by researchers through self-citations or inclusion of work authored by others may render the metric problematic for exclusive use as a research productivity indicator.

We further urge universities to regularly conduct internal analyses to adjust potential gender inequity of pay-for-productivity. Likewise, professional associations in STEM disciplines should regularly conduct such analyses to reduce the more limited pay-for-performance relationships we observed for women in our study. Notably, we do not intend to assert that the h -index should be treated as the benchmark for research productivity, as it is not problem- or concern-free. However, the h -index is to the measurement of scholarly productivity what democracy is to forms of government: the least problematic. We also urge universities to continuously assess whether high levels of research productivity translate into high pay at similar rates for men and women—the alternative may be to continue to lose women scientists despite high productivity levels and potential. The dearth of women, especially in senior academic/faculty positions in STEM, continues to pose a significant challenge for the science and technology workforce in the twenty-first century. To attract more women to enter STEM disciplines and help them be more engaged and thrive in these disciplines and their organizations, universities should, first and foremost, effectively address the ostensibly “sticky” problem of gender inequity of pay-for-productivity.

Data availability

All data used in this analysis can be found at: https://osf.io/6drsp/?view_only=d92db8841232491baaa107d0bd96c873 .

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Samaniego, C., Lindner, P., Kazmi, M.A. et al. Higher research productivity = more pay? Gender pay-for-productivity inequity across disciplines. Scientometrics 128 , 1395–1407 (2023). https://doi.org/10.1007/s11192-022-04513-4

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Innovation through research is at the heart of everything we do. IGEPS uses rigorous analysis to tackle pervasive and complex problems that impede gender equity in the public sector. We focus on addressing substantive topics targeting underrepresented groups that are beneficial to both scholars and public sector practitioners.

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This research explores the priorities of the gender equity commission in New York City over forty-five years. Archival commission data was organized thematically to understand the history of gender equity and suggest future possibilities for gender equity beyond New York City. In our historical analysis, we see an expansion of the definition of gender and an adoption of an intersectional approach to gender. We identify four historical gender priorities:  sexual harassment and violence, pay equity and economic advancement, health and safety, and gender recognition and celebration . To address systemic issues of gender inequity, we recommend local level administrators embed an intersectional approach in their policies and programming and move away from the commission model to one of a permanent office or agency. These recommendations will better equip municipalities with the resources to increase gender equity, particularly during COVID-19 recovery.

D’Agostino, M. J., & Elias, N. M. (2022). Gender Equity Commission Priorities: An Archival Study and Prospects for the Future. State and Local Government Review, 54(2), 102–119.

Abstract: School closings during COVID-19 exposed an under-addressed gender equity issue in the United States: child care in crisis. To better understand the child care crisis in the current U.S. context, we detail how New York City is addressing child care during COVID-19. We then connect the current approaches to the Lanham Act that was instituted during WWII as a historical parallel. Ultimately, we argue for the adoption of a universal system that is affordable, high-quality, federally-funded with local involvement and discretion, and flexible for primary caregivers seeking care support. This potential system builds on current congressional proposals and should take into account the challenges primary caregivers face in order to disrupt gender imbalances in care, and in turn, produce greater gender equity. COVID-19 is an opportunity to instill lasting change by improving the current U.S. child care model.

Elias, Nicole M. and D’Agostino, Maria J. (2020). Care in Crisis: COVID-19 as a Catalyst for Universal Child Care in the United States. Administrative Theory & Praxis. https://doi.org/10.1080/10841806.2020.1813456

Women in public service face some of the most pressing issues in public administration and policy today. For example, the gender pay gap, gender bias, and gender inequity in policy and administrative decision making. The purpose of this chapter is to detail the value of organized efforts to combat gender disparities by promoting gender competency in both MPA education and the public sector workplace. These avenues for change can be instructive for public administration programs, Master of Public Administration (MPA) students, and public servants. We offer practical means of building gender competency, or the knowledge, skills, and abilities utilized in public organizations for the purpose of promoting sex and gender representation.

Elias, Nicole M. and D’Agostino, M. J. (2020). Changing the Landscape of Public Administration for Women: Organized Efforts to Promote Gender Competency. In Slagle, Derek and Adam Williams (Eds.), Public Affairs Practicum. San Diego, CA: Birkdale Publishers.

Women were 30% of the labor force in 1950 and 48.6% of the workforce today. Women are also currently outpacing men in the attainment of college degrees – 36% of women aged between 25- 29 years have a bachelor’s degree compared to 28% of males in the same age group and have surpassed men in college graduation rates. Despite these growing numbers, women have yet to reach a critical mass in leadership positions. Women represent less than 5% of CEOs in Fortune 500 companies. Out of 195 state heads around the world, only 15 are currently women. Less than 20% of members of the US Congress are women, and women hold only 21% of US Senate seats. Even in the nonprofit world where more than 75% of all workers and volunteers are women, only 45% of women will go on to secure a top position and only 21% of these CEOs will have access to budgets of $25 million or more (Renock, 2017).

Certainly, women have come a long way since first gaining voting rights in 1920. However, we live in interesting times, and challenges remain. Women continue to be stereotyped as unfit for certain jobs because of biological reasons. Women continue to be subject to issues of the glass ceiling and glass cliffs, and inequities persist as women earn 77 cents to a dollar when compared with their male counterparts. Clearly, we have not achieved gender parity in the workplace. Moreover, leadership continues to be viewed as a masculine trait (Eagly & Karau, 2002). The “think manager think male” paradigm is dominant in organizations, continuing to pose challenges for women who aspire to or are currently in leadership roles (Ryan et al., 2016).

Stivers (1993) argued that “these images not only have masculine features but help to keep in place or bestow political and economic privileges on the bearers of culturally masculine qualities at the expense of those who display culturally feminine ones” (p. 84). Indeed, workplaces in the public sector remain gendered (Connell, 2006; Guy & Newman, 2004; Riccucci, 2009; Sabharwal, 2015) challenging the neutrality of public administration. Although Stivers’ work on gender images in 1993 laid the foundation for feminist theorists in public administration, the questions posed in this chapter are: What are some of the challenges women leaders in public administration encounter? What are the gender differences that persist in the field? The chapter will also discuss the implications of research in gender and leadership on scholarship and practice of public administration. Thus, we provide a detailed narrative based on the characterization of women and leadership in the public administration literature and beyond.

D’Agostino, M.J., Sabharwal, M., & Levine, H. (2020). Characterization of Women in Leadership Positions. In Slagle, Derek and Adam Williams (Eds.), Public Affairs Practicum. San Diego, CA: Birkdale Publishers.

In the current United States (U.S.) context, we are facing a constitutional crisis with frequent government shutdowns and new debates surrounding immigration, climate change, budgeting practices, and the balance of power. With competing interests, unclear policy, and inconsistent leadership directives, the question becomes: How do contemporary bureaucrats make sense of this ethically turbulent environment? This collection provides a lens for viewing administrative decision-making and behavior from a constitutional basis, as contemporary bureaucrats navigate uncharted territory.

Ethics for Contemporary Bureaucrats  is organized around three constitutional values: freedom, property, and social equity. These themes are based on emerging trends in public administration and balanced with traditional ethical models. Each chapter provides an overview of a contemporary ethical issue, identifies key actors, institutions, legal and legislative policy, and offers normative and practical recommendations to address the challenges the issue poses. Rooted in a respected and time-tested intellectual history, this volume speaks to bureaucrats in a modern era of governance. It is ideally suited to educate students, scholars, and public servants on constitutional values and legal precedent as a basis for ethics in the public sector.

Elias, Nicole M. and O’lejarski, Amanda M. (Eds). (2020). Ethics for Contemporary Bureaucrats: Navigating Constitutional Crossroads . New York, NY: Routledge Taylor & Francis Group. ISBN 9780367861902

Abstract: The lack of gender equity in the public sector is a critical issue, especially for emergency services. We explore the gendered nature of firefighting and policing at both professional and organizational levels. We assess gender equity by asking the following questions: (1) How have understandings of gender in emergency services evolved over time? (2) What are the normative implications of emergency services’ lack of gender equity? We draw from feminist literature to critique the lack of progress and examine firefighting and policing histories along with the professional ethics codes of the U.S. Fire Administration and the International Association of Chiefs of Police. This analysis demonstrates the potential to foster greater gender equity in emergency services and other public organizations by suggesting means of improving ethics codes that serve as foundations for organizational cultures, policies, and practices

Bishu, Sebawit, McCandless, Sean, and Elias, Nicole M. (2020). Gender in Emergency Services: Foundations for Greater Equity in Professional Codes of Ethics. Public Integrity. https://doi.org/10.1080/10999922.2020.1825179

Popular culture. It is everywhere—from movies, television, music, and literary works to other vehicles for messaging like social media and celebrity influencers. Popular culture frequently provides messages pertinent to social equity, especially about inequities experienced by historically marginalized groups. This special issue explores pop culture’s social equity messaging in the context of public administration. Despite the ubiquity of popular culture’s artifacts and its messages both about and for public administration, it remains under-examined within public administration scholarship. This special issue is an attempt to bring pop culture topics and applications into the discipline. As a starting point, this collection presents seven manuscripts and two reviews that speak to different forms and analyses of popular culture’s messages about and for social equity in public administration.

McCandless, Sean and Elias, Nicole M. (Symposium Co-Editors). (2020). Introduction to the Symposium: Popular Culture, Social Equity, and Public Administration. Public Integrity. https://doi.org/10.1080/10999922.2020.1837506

Abstact: In the discipline of public administration, popular culture remains under-examined in scholarship and under-utilized in pedagogy. However, the field would benefit from greater integration of popular culture to expand understandings of governance, especially in that it provides important representations of and messaging about some of today’s most pressing social equity issues. To contextualize popular culture in public administration, we use critical discourse analysis as a frame to demonstrate how popular culture can inform public administration, especially regarding social equity. We argue that popular culture should be more extensively covered in public administration, because it offers a lens for better understanding intersections of power, equity, and ethics in government.

McCandless, Sean and Elias, Nicole M. (2020). Popular Culture Informing Public Administration: Messages and Prospects for Social Equity. Public Integrity. https://doi.org/10.1080/10999922.2020.1837505

Abstact: Governing in a Global World captures the panorama of women governing around the world. Even though the modern era marks history’s greatest advancements for women, worldwide they hold fewer than 30 percent of decision-making positions in government and are often missing from negotiating tables where policies are made and conflicts resolved. The opening chapters present trends and context for studying women in public service by focusing on path-setters across the globe, the status of women in the world’s executive and legislative bodies, and their participation in public service across several nations. Later chapters examine power, leadership, and representation of women in public service, with several chapters looking at women governing from a regional perspective in the Middle East, Sub Sahara Africa, Latin America, and China. The final chapter presents empirical evidence that shows how policies to increase women’s representation in the public arena reduce gender inequality more than any other policy intervention. Taken together, the chapters illustrate the worldwide importance of, and challenges to, promoting gender equality and women governing.

D’Agostino, M.J. (2018). Women Governing: A Global Perspective, edited with Marilyn Rubin, New York: Routledge.

Abstract: Diversity is an important facet of public administration, thus it is important to take stock and examine how the discipline has evolved in response to questions of representative democracy, social equity, and diversity. This article assesses the state-of-the-field by addressing the following question: How has research on diversity in the field of public administration progressed over time? Specifically, we seek to examine how the focus of diversity has transformed over time and the way the field has responded to half a century of legislation and policies aimed at both promoting equality and embracing difference. We utilize a conceptual content analysis approach to examine articles published on diversity in seven key public administration journals since 1940. The implications of this study are of great importance given that diversity in the workplace is a central issue for modern public management.

Sabharwal, M., Levine, H. & D’Agostino, M.J. (2016). A Conceptual Content Analysis of 75 Years of Diversity Research in Public Administration , Review of Public Personnel Administration, 38(2), 248-267. https://doi.org/10.1177/0734371X16671368

Abstract: The dominant narrative about women’s progress in public administration focuses on identifying the obstacles to that progress and how to overcome them. But to make real progress toward gender equality and social justice, we must rethink our entire approach to research. Understanding the difference women make via narrative inquiry is a necessary change to the prevailing dialectic.

D’Agostino, M.J. (2016). A Narrative Approach to Understanding the Difference Women Make. Administration and Society, Women in Public Administration Symposium, 49(1), 9-19. https://doi.org/10.1177/0095399716641986

D’Agostino, M.J. (2015). The Difference that Women Make: Government Performance and Women-Led Agencies, Administration & Society, 47(5), 532-548. https://doi.org/10.1177/0095399714548267

John Rawls’ A Theory of Justice has served as an important basis for theorizing merit, deservedness, and fairness, and in turn, continues to influence the intellectual development of many disciplines, including political thought, public administration, and the practical application of democratic governance. Yet, Rawls’ failure to account for luck and historical difference renders his work an incomplete framework for pursing the end of justice in public administration. We argue for a more comprehensive treatment of merit, deservedness, and fairness, one that incorporates luck and takes into account social values rooted in historical preference and identity.

Elias, Nicole M. Rishel and Jensen, Courtney E. (2014). Merit, Luck, and Historical Recognition: A More Comprehensive Treatment of Justice in Public Administration. Public Administration Quarterly, 38 (4), 466-487. http://www.jstor.org/stable/24372061

This work explores the meaning of diversity for bureaucratic representation. In light of the United States becoming an increasingly racially and ethnically diverse society, attitudes and approaches toward diversity are likewise shifting. It is important to consider the way we think about and talk about diverse representation, which in turn, contributes to different actions and policies within federal agencies. To evaluate this process of meaning-making, I analyze federal policy seeking to increase representation in the following Executive Orders: 13078, 13163, 13171, 13518, 13548, and 13585. Prime emphasis is devoted to the most recent and comprehensive, Executive Order 13583: Establishing a Coordinated Government-wide Initiative to Promote Diversity and Inclusion in the Federal Workforce issued on August 18, 2011 and the Government-wide Diversity and Inclusion Strategic Plan. This research demonstrates significant implications for management and governance, particularly in the text, discursive practice, and social practice surrounding the meaning of “diversity” purported for the federal bureaucracy.

Elias, Nicole M. Rishel. (2013). Shifting Diversity Perspectives and New Avenues for Representative Bureaucracy. Public Administration Quarterly, 37 (3), 331-373. http://www.jstor.org/stable/24372111

Rishel, Nicole M. (2012). Challenging Technicism: Space for the Individual Bureaucrat in Public Administration Theory and Practice. Administrative Theory & Praxis, 34 (2), 279-286. https://doi.org/10.2753/atp1084-1806340208

The purpose of this paper is to empirically examine the impact of the utilization of organizational practices on the career progression of women to executive positions in state‐level government organizations in the USA.

D’Agostino, M.J. & Levine, H. (2010) The Career progression of women in state government agencies. Gender in Management: An International Journal, 25(1), 22-36.

Diversity, Equity, & Inclusion in the Workplace

In the current context of public-sector workplaces, there is a more concerted effort to address identity and equity through greater inclusion. The aim of the special issue is to present emergent empirical research assessing organizational attributes, policy, and practice for achieving improved workplace inclusion and individual perceptions of inclusive public-sector workplaces. This special issue highlights studies that advance innovative methodologies to address emergent research questions related to understanding, implementing, and assessing inclusive cultures and perceptions of inclusion. The collection of articles advances the understanding and theories of inclusive practices, policies, and organizational attributes. The authors go beyond the rhetoric of inclusion to parse out how inclusion is defined and operationalized in organizations, the impact of inclusive initiatives, and areas of identity that need to be explicitly included. This research provides a pathway for intentionally making the case for inclusion, not just acknowledgment of diversity.

Holmes, M. H., Elias, N. M., D’Agostino, M. J. (2023). Inclusion in Public-Sector Workplaces: Chartin a Path for Theory and Practice. Public Personnel Management, 52(4), 491-497.

Ely and Meyerson’s gendered organizations framework reconceptualizes traditional gender differences defined by biology and lack of structural opportunities, to a complex set of social relations in the workplace. We apply this framework to second-generation gender bias to further understand impediments to women’s career progression in the public sector workplace. In-depth interviews of state-level administrators in U.S. public sector agencies indicate that “narratives” perpetuate second-generation gender bias that is deeply ingrained in organizational practices and policies, especially for women and women of color. This framework can be applied to future studies examining the gendered nature of organizations in different workplace settings. Moving beyond already identified barriers, this study offers a comprehensive framework to understand how second-generation gender bias is central to long-standing workplace inequities.

D’Agostino M, Levine H, Sabharwal M, Johnson-Manning AC. Organizational Practices and Second-Generation Gender Bias: A Qualitative Inquiry into the Career Progression of U.S. State-Level Managers. The American Review of Public Administration. March 2022.

Abstract: This exploratory study questions whether Master of Public Administration programs prepare future public administrators to how gender plays out in negotiations that occur in organizations. Negotiated Order and Second-Generation Bias perspectives provide the theoretical basis to understand that negotiations in organizations may privilege masculine practices. In light of this gender leaning, the classroom is a necessary incubator for understanding gender differences in negotiation. Curricula and survey response data retrieved from NASPAA accredited MPA programs suggest that gender in negotiation is not being addressed in the MPA classroom. Public managers must negotiate for scarce organizational resources including salary, promotion, and other workplace capital. Recognizing that gender in negotiation remains hidden under the shadow of second-generation bias is the first step to the success of future public administrators. We must begin to educate our future public managers with a distinctive negotiation skillset as they navigate the 21st century workplace.

D’Agostino, M.J, Sabharwal, M. and Levine, H. (2019) Gender in negotiation: Preparing public administrators for the 21st century workplace, Journal of Public Affairs Education. https://doi.org/10.1080/15236803.2019.1579594

Elias, Nicole M. (2019) Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ+) Workplace Policy. In: Farazmand A. (Ed.). Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer International Publishing. https://doi.org/10.1007/978-3-319-31816-5_2396-1

Abstract: Sex and gender categories have become more fluid in recent years. With evolving understandings of sexual orientation and gender identity, public administrators are confronted with questions of how to craft policy and make decisions based on new conceptions of sex and gender for transgender employees. Policy and practice is especially challenging in the workplace where sex and gender encompass both personal and professional dimensions. Within the public sector, the federal government is recognized as a leader on these issues, and this work examines federal transgender policy to answer the following questions: 1) how are federal agencies addressing transgender issues in the workplace through formal policy? and 2) what can be done to improve future transgender policy? To gain a better understanding of what constitutes an effective transgender workplace policy, we conducted a qualitative content analysis of nine transgender plans from the following federal agencies: Consumer Financial Protection Bureau, Equal Employment Opportunity Commission, Internal Revenue Service, National Aeronautics and Space Administration, United States Office of Special Counsel, United States Department of Interior, United States Department of Labor, United States Environmental Protection Agency, and United States Office of Personnel Management. Our analysis includes the identification of major themes within the nine policy documents. From this analysis, we propose best practices and future policy directions, as well as suggest ways of expanding the limited scholarship on transgender issues in the public sector.

Elias, Nicole M., Johnson, Rana, Ovando, Daniel, and Ramirez, Julia. (Fall 2017/Spring 2018). Improving Transgender Policy for a More Equitable Workplace. Journal of Public Management & Social Policy, 24(2), 53-81. https://digitalscholarship.tsu.edu/jpmsp/vol24/iss2/7/

Abstract: The federal government lags behind in progressive civil rights policies in regard to universal workplace antidiscrimination laws for lesbian, gay, bisexual, and transgender (LGBT) Americans. The slow progress matters to inclusionary workplace practices and the theory and practice of public administration generally, as recognition of LGBT rights and protection are constitutive of representative bureaucracy and promoting social equity. This study examines the turnover intention rates of self-identified LGBT employees in the U.S. federal government. Using the Office of Personnel Management’s inclusion quotient (IQ), and 2015 Federal Employee Viewpoint Survey (FEVS), we identify links in the relationships between workplace inclusion and turnover outcomes among LGBT individuals. We also examine the impact of agency type on LGBT turnover rates based on Lowi’s agency classification type. Key findings suggest that LGBT employees express higher turnover intentions than those that identify as heterosexuals/straight, and LGBT employees who perceive their agencies as redistributive or communal are less likely to experience turnover intentions. However, an open and supportive workplace environment had a positive impact on turnover, suggesting that to implement effective structural change in an organization’s culture of inclusion, public sector managers must do more than merely “talk the talk.” This finding is also suggestive of LGBT employees’ desire to avoid the stigma of being LGBT and hide their identities. Institutions must heed the invisible and visible identities of their employees to be truly inclusive. Workplace practices that acknowledge the invisible and visible identities of their employees are a positive step toward real workplace inclusion.

Sabharwal, M., Levine, H., D’Agostino, M.J., & Nguyen, T. (2018). Inclusive Work Practices: Turnover Intentions Among LGBT Employees of the U.S. Federal Government. The American Review of Public Administration. https://doi.org/10.1177/0275074018817376

Sex and gender identities are becoming increasingly complex in America, creating new challenges for public administrative agencies. So far, the vast majority of U.S. federal agencies lack comprehensive transgender employee policies – which are currently in place for only nine of approximately 235 federal agencies (including sub-agencies). Yet as the workforce evolves, federal employment policy must accommodate the needs of employees who do not fit traditional sex and gender categories – and particular attention needs to be paid to formulating policies specifying the responsibilities of employers when their employees undergo transitions meant to shift their anatomy or appearance to align with their gender identity.

Elias, Nicole M. (2018). Why U.S. Government Agencies Need Comprehensive Policies for Employees with Various Gender Identities. Policy Brief. Scholars Strategy Network. https://scholars.org/contribution/why-us-government-agencies-need-comprehensive-policies-employees-various-gender

Abstract: Lesbian, gay, bisexual, and transgender (LGBT) identities within the workplace have recently gained greater attention as significant demographic categories. A key question that emerges from the limited scholarship on LGBT employment in the federal government is whether there is a distinction between the experiences of employees within federal security agencies, defined here as the five major agencies that provide civilian support to the defense and military structures of the United States, and employees of other federal agencies. Using data from the 2013 Employee Viewpoint Survey, this article addresses the following questions: How does sexual orientation and/or gender identity as self-reported in the 2013 EVS impact employee perceptions of personal safety and security, job satisfaction, and diversity issues, and how do these perceptions vary between employees of the major security agencies and other federal agencies? The article shows that across the federal government employees are reasonably satisfied with diversity issues in the workplace, with no appreciable difference between those in security and nonsecurity agencies. However, current programs and policies intended to foster and institutionalize diversity are viewed as ineffective and should be improved through new policies and programs.

Federman, Peter S. and Elias, Nicole M. Rishel. (2017). Beyond the Lavender Scare: LGBT and Heterosexual Employees in the Federal Workplace. Public Integrity, 19 (1), 22-40. https://doi.org/10.1080/10999922.2016.1200410

In academia, we often think of networking and mentoring activity as a means to an end. Networking and mentoring can be exciting and considered beneficial in helping to produce opportunities for new research projects, collaborative events, and personal/professional development. Alternatively, these practices could be seen as unavoidable for the tenure file or “necessary evils” to satisfy service requirements or some other obligation. Regardless of your view or the external demands placed on your networking and mentoring activity, we find that networking and mentoring can be positive and rewarding if you are able to be a bit creative and devote some thought and energy to these activities. We are able to do this through the organization we created, Women in the Public Sector at John Jay College (WPS). The purpose of this reflection is to prompt you to think creatively about these seemingly mundane professional activities by detailing our networking and mentoring experiences with WPS.

Elias, Nicole M. Rishel and D’Agostino, Maria J. (2017). Women in the Public Sector: Getting Creative with Networking and Mentoring. In Zavattaro, Staci and Orr, Shannon (Eds.), Reflections on Academic Lives: Identities, Struggles, and Triumphs in Graduate School and Beyond. New York, NY: Palgrave Macmillan.

In an effort to make sense of the work/life balance quandary, this article discusses preliminary results of a broader research project (D‘Agostino and Levine 2009) empirically examining the utilization of work/life practices by women in state-level government in the United States.. The purpose of this research is to examine whether women‘s utilization of work/life practices contributes to their career progression. Therefore, the central research question examines, what is the impact of work/life utilization practices on women’s career progression? Findings indicate that women who have reached executive level positions are more likely to utilize specific practices, such as flexible hours, than others, such as working part time or childcare reimbursement. Furthermore, work/life policies and practices should be framed and marketed to society in general in order to encourage utilization.

Maria D’Agostino. (2011). Making Sense of Women’s Career Progression: Utilization of Work/Life Practices in State Government Agencies. Public Administration and Management, 16(1), 95–.

Sexual Orientation, Gender Identity, & Expression

A growing number of people around the world identify, in some way, as Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+); yet, these voices are noticeably absent from nonprofit research. To address issues of equity and the historic marginalization of LGBTQ+ people both societally and in the nonprofit sector, this manuscript seeks to answer the following questions: Why is it important to include sexual orientation and gender identity and expression (SOGIE) survey questions in nonprofit surveys? What are best practices for including SOGIE survey questions in nonprofit research? We present LGBTQ+ inclusive research strategies and suggested questions for inclusive SOGIE survey design. Though this article focuses primarily on surveying LGBTQ+ populations, it can also be instructive for general population surveys.

Meyer, S.J., Elias, N.M. Rainbow Research: Challenges and Recommendations for Sexual Orientation and Gender Identity and Expression (SOGIE) Survey Design. Voluntas (2022). https://doi.org/10.1007/s11266-021-00436-5

Transgender and gender non-binary (TGNB) individuals face discrimination in healthcare settings and barriers to healthcare access, resulting in health disparities. These inequities are compounded by the intersection of lower socioeconomic status and geography. To understand the differences in how states provide healthcare to TGNB individuals in poverty, we ask: What are state Medicaid programs offering TGNB residents, and how can coverage be more equitable across jurisdictions? To answer these questions, we examine medical services covered by 15 diverse Medicaid programs and compare them to the services recommended by the World Professional Association for Transgender Health (WPATH). Unsurprisingly, the analysis reveals inconsistent TGNB health coverage across states. While some states include coverage for TGNB-related care, some do not, and others place access to services in the hands of medical providers. These coverage disparities leave many TGNB Medicaid recipients across the U.S. without coverage for medically necessary services, prompting equity questions for both research and practice.

Robin J. Kempf, Nicole M. Elias and Alonso J. Rubin-DeSimone JHHSA, Vol. 44 No. 1, 86-108 (2021) https://doi.org/10.37808/jhhsa.44.1.5

Abstract: The recent U.S. Supreme Court ruling in  Bostock v. Clayton County  is a landmark piece of case law that offers fundamental rights to LGBT persons. This essay reflects on how this case arrived at the Supreme Court and its implications for theory and praxis. The overall conclusion is that cautious optimism is warranted.

McCandless, Sean and Elias, Nicole M. (2021). Beyond Bostock: Implications for LGBTQ+ Theory and Practice. Invited Essay. Administrative Theory & Praxis. https://doi.org/10.1080/10841806.2020.1840903

Abstract: LGBTQ+ issues at the local level pose some of the most pressing civil rights challenges in the current U.S. context. This analysis provides insight into what is taking place in major municipalities and how these efforts can be improved to bolster equity and civil rights for LGBTQ+ populations. At a time when identity, language, and public sector values are inherently intertwined and constantly changing, the following question is ripe for analysis: how are major U.S. municipalities addressing the civil rights needs of the LGBTQ+ population? To answer this question, an analysis of government websites from the top 10 U.S. cities by population is conducted, examining the policies, programs, and services that municipalities offer LGBTQ+ residents and the language used to frame these policies, programs, and services as expressions of power, representations of identity, and the website presentation itself.

Elias, Nicole M. (2020). LGBTQ+ Civil Rights: Local Government Efforts in a Volatile Era. Public Administration Review. https://doi-org.ez.lib.jjay.cuny.edu/10.1111/puar.13188

Non-binary gender identity has recently become more widely known and engrained in the public sector. Individuals who identify as non-binary see themselves outside of the traditional male-female system that is the foundation for economic, social, and political institutions. The traditional gender binary system can be discriminatory and exclusive for non-binary individuals. Arguments for adopting inclusive gender identity policy and practice are often rooted in social equity. The Constitution provides a foundation to increase social equity for non-binary gender identity policy and administrative practice. As public administrators take an oath to uphold the values of the Constitution, this expanded treatment of gender equity is highly relevant to understanding our foundational public values and reshaping institutions that remain largely unquestioned. Non-binary identities are increasingly becoming normalized and accepted, and while this is encouraging, it is not enough. This chapter presents a normative argument for adopting non-binary gender policy and practice based on social equity regime values. Then, it offers practical recommendations for non-binary gender identity state-issued identity documents. Altering our very definition of gender is no easy task, especially because gender is an organizing societal structure. Ultimately, non-binary gender inclusion necessitates a serious rethinking of our public values and restructuring of our most fundamental institutions of governance.

Elias, Nicole M. and Saffran, Gwendolyn. Non-binary Gender Identity: Challenging Public Values and Reshaping Institutions. In Elias, Nicole M. and O’lejarski, Amanda M. (Eds). (2020). Ethics for Contemporary Bureaucrats: Navigating Constitutional Crossroads. New York, NY: Routledge Taylor & Francis Group. ISBN 9780367861902

Abstract: Our fundamental understandings and treatments of gender and gender identity within the United States are evolving. Recently, a few countries and several U.S. states have moved away from the binary categories of male and female to include a non-binary gender option for official state documents. This third, gender-neutral option, is usually represented as “X” where “M” for male and “F” for female traditionally appeared. The purpose of this study is twofold; first, to utilize Iris Marion Young’s theory of oppression to help contextualize the historical oppression of non-binary gender identity recognition by the State, and second, to analyze recent efforts by U.S. states to include non-binary gender categories. Using Young’s theory for normative explanation along with the Open Society Foundations’ (OSF) practical recommendations, we present a simple administrative framework for comparing proposed, adopted, and enacted non-binary gender policies across the United States. Tying each OSF best practice to one of Young’s faces of oppression, we are able to assess each law or policies’ effectiveness in dismantling the oppressive binary constructs of society.

Elias, Nicole M. and Colvin, Roddrick. (2020). A Third Option: Understanding and Assessing Non-Binary Gender Policies in the United States. Administrative Theory & Praxis, 42:2, 191-211. https://doi.org/10.1080/10841806.2019.1659046

Our understanding and treatment of gender in the United States has evolved significantly over the past four decades. Transgender individuals in the current U.S. context enjoy more rights and protections than they have in the past; yet, room for progress remains. Moving beyond the traditional male–female binary, an unprecedented number of people now identify as transgender and nonbinary. Transgender identities are at the forefront of gender policy, prompting responses from public agencies at the local, state, and federal levels. Because transgender individuals face increased rates of discrimination, violence, and physical and mental health challenges, compared to their cisgender counterparts, new gender policy often affords legal protections as well as identity-affirming practices such as legal name and gender marker changes on government documents. These rights come from legal decisions, legislation, and administrative agency policies. Despite these victories, recent government action targeting the transgender population threatens the progress that has been made. This underscores the importance of comprehensive policies and education about transgender identities to protect the rights of transgender people.

Elias, Nicole M. (2019). Transgender and Non-Binary Gender Policy in the Public Sector. In: Haider-Markel, Don (Ed). Oxford Encyclopedia of Politics. Oxford University Press. http://dx.doi.org/10.1093/acrefore/9780190228637.013.1168

Abstract: Sex and gender are increasingly complex topics that prompt new policy and administrative responses within public agencies. As the federal workforce evolves, federal employment policy must accommodate the needs of employees who do not fit traditional sex/gender categories. One emerging area of policy targets transgender employees, particularly policy that guides the employer response throughout the transitioning process. This research seeks to answer the following questions: How can transitioning policy and implementation within federal agencies affect employees? and How should transitioning policy be crafted and implemented? This work addresses organizational behavior and management issues by presenting a successful case of a workplace transition. Interviews of an administrator guiding the transitioning process and one of the first federal employees to complete a transition while in a federal field office are conducted. Ultimately, this research explores challenges with emergent policy and suggests avenues for designing and enacting future transitioning policy.

Elias, Nicole M. Rishel. (2017). Constructing and Implementing Transgender Policy for Public Administration. Administration & Society, 49 (1), 20-47. https://doi.org/10.1177/0095399716684888

Elias, Nicole M. (2017). Transgender Rights and Politics: Groups, Issue Framing, and Policy Adoption, Public Integrity, 20:6, 640-644. https://doi.org/10.1080/10999922.2017.1399766

Gender Equity Education

Gender is an important component of Diversity, Equity, and Inclusion (DEI) pedagogy in public affairs, yet gender remains largely absent from public affairs education in three central ways: how courses are structured, the content of courses, and the practice of pedagogy. This article explains the value and need for gender equitable pedagogy in public affairs curricula. We conduct a descriptive analysis of scholarship and best practices from leading think tanks and public advocacy research organizations. Ultimately, this work provides recommendations to strengthen gender equity pedagogy both inside the classroom and in public sector workplaces.

D’Agostino, M. J., Elias, N. M. R., Diez, V., and Krause, E. (2024). Gender Equity in Public Affairs Pedagogy: Structure, Content, and Practice for a More Inclusive Public Sector. Journal of Social Equity and Public Administration, 2(1), 52-70.

D’Agostino, Maria J. and Elias, Nicole M. (Symposium Co-Editors). (2020). #MeToo in Academia: Understanding and Addressing Pervasive Challenges. Viewpoint Symposium in Public Administration Review. https://doi.org/10.1111/puar.13318

Abstract: Sex and gender are evolving identity categories with emergent public policy and administration needs. To respond to the diverse landscape of sex and gender issues in the public sector, greater competency is needed. This research will contribute to the body of work on sex and gender in public administration by asking the following questions: (a) what do graduate students in Master of Public Administration (MPA) programs know about gender competency, (b) have graduate students learned gender competency in their MPA coursework, and (c) how can gender competency in MPA education be further developed and promoted? This study provides a critical analysis of one MPA program, at John Jay College, City University of New York, to begin this line of research. Our e-survey results of a non-random sample of John Jay MPA students demonstrate that many students do not learn about gender competency through their MPA education and that gender competency skills otherwise obtained are limited. To address this, we emphasize the need for incorporating gender competency into MPA education as the first step in equipping future practitioners with skills to promote gender competency in public policy, administrative decision making, and workplace culture. We provide practical means of achieving greater gender competency in MPA curricula and programming and articulate the importance of expanding this research to other MPA programs, MPA faculty and directors, and geographic regions.

Elias, Nicole M. and D’Agostino, Maria J. (2019). Gender Competency in Public Administration Education. Teaching Public Administration, 37(2), 218–233. https://doi.org/10.1177/0144739419840766

Abstract: Student engagement in institutions of higher education has become a central priority for educators and administrators. What “student engagement” means for a diverse student body is an important question for public institutions with justice-related missions. As social welfare policy shifts to allow more recipients of public assistance access to higher education, research regarding their engagement experiences remains scarce. To support a socioeconomically diverse student body, consistent with the Network of Schools of Public Policy, Affairs, and Administration (NASPAA) standards, this project explores the nature of engagement among student recipients of public assistance by asking the following research questions: what forms of engagement with students on public assistance take place? Why is engaging students on public assistance important? How can we foster greater engagement with students on public assistance? To answer these questions, student and faculty focus groups are conducted. From this analysis, we highlight normative implications of engaging a socioeconomically diverse student population and present recommendations for fostering greater engagement.

Elias, Nicole M. and Marrin, Madeleine. (2019). The Importance of Engaging Students on Public Assistance: New Insights and Recommendations for Practice. Teaching Public Administration, 37(3), 341–364. https://doi.org/10.1177/0144739419851149

Abstract: A growing body of literature has documented leadership styles by gender. This study examines if directors of Master of Public Administration (MPA) programs accredited by the Network of Schools of Public Policy, Affairs, and Administration exhibit gender differences in leadership styles. Such differences may affect the implementation of public administration and how effective MPA directors are in achieving positive outcomes. Using a mixed methods approach—specifically, exploratory sequential design utilizing qualitative data and analysis, followed by a quantitative survey—we find that there are some gendered differences among public administration directors. In particular, we find that women directors are significantly more likely than their male counterparts to exhibit traits that resemble transformational leaders. However, we also find that male and female directors converge in terms of other styles of leadership.

Sabharwal, M., Levine, H., D’Agostino, M.J. (2017). Gender Differences in the Leadership Styles of MPA Directors, Journal of Public Affairs Education, 23 (3), 869– 884. https://doi.org/10.1080/15236803.2017.12002293

Our Books & Symposia

Governing in a Global World captures the panorama of women governing around the world. Even though the modern era marks history’s greatest advancements for women, worldwide they hold fewer than 30 percent of decision-making positions and are often missing from negotiating tables where policies are made and conflicts resolved… Learn More →

Explore the gender dimension and expand the dialogue in your classroom through this collection of case studies, empirical studies, and theoretical essays on women’s issues in public administration. This is the first book of its kind written about the female endeavor in public administration from the perspective of female public administrators and academics… Learn More →

In the current United States (U.S.) context, we are facing a constitutional crisis with frequent government shutdowns and new debates surrounding immigration, climate change, budgeting practices, and the balance of power. With competing interests, unclear policy, and inconsistent leadership directives, the question becomes: How do contemporary bureaucrats make sense of this ethically turbulent environment? Learn More →

This ground-breaking Handbook on Gender and Public Administration brings together a rapidly growing new field of study, exploring the emerging contexts of gender and public administration. Capturing the many facets of this dynamic trend, the book explores gender equity and further examines masculinity, intersectionality and beyond binary conceptions of gender. Learn More →

The #MeToo movement, which has rocked politics, media, business and entertainment, is exploding with full force in academia. Recently, Karen Kelsky conducted a crowd sourced survey of sexual harassment in the academy that documents more than 2400 cases. It is important to contextualize #MeToo within the academic community.

The theoretical treatment of sex and gender in public administration and policy pose challenging questions that deserve greater attention. In the history of the field, only three symposia focus on women in the public sector: Nesta Galllas’s (1976) Public Administration Review; Maria D’Agostino and Nicole Elias’s (2017) Administration & Society; and Megan Hatch’s forthcoming symposium in the Journal of Public Affairs Education. These symposia provide a starting point for bringing sex and gender into mainstream public administration literature. Likewise, this Virtual Special Issue is a promising next step for increasing scholarly attention on sex and gender issues in public administration theory.

The first symposium on women in public administration was published in 1976 and focused on three central topics: discrimination against, underrepresentation of, and underutilization of women in public service. Analyses of why conditions of discrimination and underutilization existed, as well as remedies to these challenges, were the crux of the 1976 symposium. Over four decades later, these issues are still pressing and continue to dominate the conversation surrounding women in public administration in the United States. The renewed and continued focus on equal pay, paid family leave, the absence of women in key leadership positions, women’s health care options, and reproductive rights remain center stage in the national policy arena, including the presidential debates.

IGEPS – AWPA Reference Tool

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In partnership with Academic Women in Public Administration (AWPA), we developed a reference tool to diversify resources used in the teaching and practice of public service. This tool promotes work authored by or addressing individuals whose perspectives have not been historically or widely embraced in the literature and teaching of public administration and public policy; including, SOGIE (sexual orientation, gender identity, and expression), racial, ethnic, religious, economic, and more identities.

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Gender Pay Equity: 15 Questions and Answers for You and Your Compensation Committee

Takis Makridis

At this year’s WorldatWork Total Rewards Conference in Dallas, I had the opportunity to participate in a panel discussion on gender pay equity. The session drew north of 400 people, showing just how important this topic is in our field.

Although we had a lot to talk about, we wanted to get the audience involved. So, we spent the first 20 minutes of the session polling the room for questions. Then we dedicated the rest of the time to answering them as we walked through a few prepared slides.

By popular demand—from attendees, that is—here’s an FAQ comprised of those questions. During the panel, there was not enough time to go into detail on each question, so this blog also gives me an opportunity to elaborate a bit more. (For a primer on gender pay equity, see this post .)

By the way, last year we published a similar—but much more detailed—FAQ on CEO pay ratio . If you think something like that would be helpful for gender pay equity, please let me know .

Gender Pay Equity at a High Level

1. how do you balance pay equity with performance.

At least in the United States, the idea of pay equity is integrally tied to ideas of performance and meritocracy. When we examine whether there’s a pay equity problem, what we’re really looking to do is validate that compensation is tied to an acceptable reason. Acceptable reasons include factors like tenure, performance, role, and location. Unacceptable reasons include someone’s gender, ethnicity, race, or some other attribute that doesn’t relate to their work and contribution.

Said differently, it’s okay if two people are paid differently because they do different work, or because one outperforms the other, or because they’re in different states and prevailing wages differ between those states. All those things have to do with the work the person is doing and not their identity.

When we study pay equity, we tend to look at performance, since different levels of performance should merit different levels of remuneration. The rub is that if a widespread gender bias exists, then this bias could show up even in performance ratings. Pay equity analyses are largely about disentangling messy cause-and-effect relationships of this nature: Is lower compensation due to genuinely weaker performance, or is a poor performance evaluation a cover, even subconsciously, for underlying biases? The good news is that with statistics and data modeling, you can attack the problem from multiple vantage points and form reasonable hypotheses as to what is actually taking place.

2. What does it mean to close the pay equity gap?

In its strictest sense, closing the pay equity gap means eliminating differences in pay that cannot be explained by appropriate reasons like role, location, performance, or tenure—providing equal pay for equal work. A pay equity gap exists when there are differences in pay not related to these factors, and further, one class of employees, commonly women, are disproportionately affected.

It’s important to understand that the issue centers on pay equity—today. But in the long run, it’s really about human capital. For example, pay levels might be fully explainable by appropriate factors and yet women or minorities are still underrepresented in leadership positions. This could be due to recruiting problems, promotion issues, skewed levels of attrition, or broader and more structural representation issues at the industry level. Either way, it’s worth understanding the gender, race, and ethnicity progression through the organizational hierarchy and where the unexplained failure points might be.

So today the focus is primarily pay equity. That’s a good place to begin, because it has more concrete data that we can study. But plan for the focus to broaden to overall human capital progression, in which pay equity plays a consistent part.

3. What about attributes besides gender, such as race and ethnicity?

Yes! This topic certainly goes beyond gender. Our presentation happened to focus on gender pay equity, but any potential sources of inequity should be studied. For instance, race is the second-most common factor to look at. What holds some companies back is that they don’t collect very much demographic information, so gender is all they’re able to look at.

Side note: There’s a growing school of thought around the idea of “intersectionality,” which looks at the unique challenges that (for example) black women face. The idea behind intersectionality is that there may be an even more nuanced layer of issues when you combine factors and look at them together instead of in isolation. Fortunately, statistical techniques exist to quite easily test whether there is a unique impact associated with intersectionality cases.

Defining the Importance of the Topic

4. why is it important to correct pay equity gaps.

I really like devil’s-advocate questions. I’m sure the individual who asked this thinks pay equity is important, but wanted more specific reasons beyond simply, “It’s the right thing to do.”

In 1997, McKinsey coined the phrase “war for talent” to describe the emerging economy where companies’ abilities to acquire and retain top talent could be the defining factor in their success. Some 20 years later, these predictions are more acute than ever. Companies still compete by creating better semiconductor chips, better advertisements, better supply chains, or better Six Sigma processes. But it all seems secondary to getting their human capital right.

Getting human capital right helps companies improve on a number of dimensions, including not constricting the labor pool you draw from, ensuring growth and advancement for your top talent, and eliminating arbitrary causes of unnecessary turnover. Pay equity is an essential ingredient to keeping employees motivated. In a recent survey by Randstad US, 78% of employees said a workplace where all employees are treated equally is important to them. In short, we think pay equity is a linchpin to winning the war for talent, and as a result, a key to sustainability. And it’s the right thing to do.

5. How do we persuade top management that gender pay equity is important?

Among tech firms on the East or West Coast, pay equity is a hot-button issue to senior executives, investors, and boards. But that’s not universally true in other industries or geographies. Its importance might seem obvious, but I think pay equity needs thoughtful framing to convey the strategic relevance.

So how can you frame it? One way is as a tool in the war for talent. A pay equity study may reveal that your company:

• Doesn’t have a pay equity problem (many companies don’t).
• Doesn’t have a pay equity problem, but does have a related human capital problem (such as women dropping out of the workforce mid-career).
• Does have a pay equity problem, but it’s not widespread or egregious (suggesting that it’s unintentional, solvable, and that overall compensation systems work well).
• Does have a systemic pay equity problem.
• Has poor representation of women or minorities at senior levels, or even in general.

The first finding is good news that you can share in your talent outreach. The second two findings provide an opportunity to address the issues so that they don’t undercut an otherwise highly effective talent strategy. Indeed, the data sets that reveal a problem can also hold clues about how you can address it. (More on that later.) The second to last finding is rare, but in the unlikely case it exists, identifying and managing the issues proactively will have a major talent benefit while reducing risk. The final problem is more common, and presents a distinct challenge for organizations. This is discussed later in this Q&A.

Another way to frame gender pay equity is through a risk management lens. Consider how organizations audit their information security and financial statements. Reasons include preemptively finding problems and being in a position to give positive assurances to external stakeholders. But a third reason is that should something bad happen, an audit can also show that the company made a good-faith effort to prevent it. The same can be true of pay equity. Even if a prior pay equity study had missed a situation in the data, the existence of the study is itself evidence that management took the matter seriously.

Finally, there’s the trend of compensation committees getting involved with human capital management. Companies are increasingly struggling with succession planning and personnel development at all levels of the organization. A robust pay equity analytics effort can equip senior management with answers when the compensation committee comes calling with questions.

To sum up, of course gender pay equity is about doing the right thing. At a very fundamental level, though, I think it’s about being proactive with the human capital assets of the organization and exercising good stewardship.

Measuring Pay Equity

6. how do you actually measure compensation differences to see if there is a bias.

Here’s how we approach the task at Equity Methods. We start with the hypothesis that compensation should be explainable based on factors like role, tenure, location, performance, education, and so on. We use a statistical technique called multiple regression that quantitatively explains the relationship between a dependent variable (pay) and a series of explanatory variables expected to predict compensation (e.g., role, performance, location, etc.). We also include what is called a “dummy variable” indicating gender, race, or any other area of interest. Dummy variable is a statistical term for the fact that it’s a binary (0/1) variable reflecting a certain trait so that we can test for the presence of systemic bias associated with that trait.

If pay equity exists, we will see no discernible impact of these dummy variables, and all of the dependent variability in compensation will be explained by the other variables. If, however, some of the variation is still predicted by our dummy, this indicates the possibility of systematic pay inequity. More often, however, we find there is not systemic bias but biases that are localized to smaller subgroups, such as individual business units or cost centers. The analysis then flags these groups to be looked at more closely.

By deploying this technique, the regression model can also be run to predict what compensation should be for each individual. For example, the model can be run to say that someone who is (for example) a band 9 vice president, working out of Atlanta, who has received above-average performance ratings and sits in the R&D group of the enterprise business unit, should be paid between $105,000 and $120,000. This model prediction is then used to identify whether any people fall outside the predicted band, and patterns in the outliers can be observed and analyzed.

If people or cohorts fall outside the model predictions, this doesn’t necessarily mean a bias exists. In fact, in an appropriate model, some percentage of employees will be paid outside the bands by construction. But it does mean that this particular model doesn’t explain why they’re paid what they are. These employees may be half men and half women, in which case you are probably fine; however, if women are five times as likely as men to be flagged as outliers, then a problem may exist. That’s why it’s necessary to use multiple models to get a more panoramic view of how compensation works. This is also why dialogue with business unit executives and HR generalists is important.

We like to think of models as ways to identify anomalies that need closer study, since not every dimension of pay strategy can be captured in the underlying data. For instance, variables like education, number of direct reports, and financial health of the cost center are not always readily available. But they could explain differences in pay.

7. How should jobs be aggregated for purposes of modeling? Should they be aggregated?

Before I answer this question, first let’s understand the context. Any serious pay equity analysis needs to look at the job or role a person performs. For example, software developers are usually paid more than business analysts. Job level notwithstanding, the underlying labor markets are different, which will result in different types of offers and pay mixes.

We can take that point to the extreme and say that in one sense, every single person has a distinct role. But that would be silly, since an analysis would fall apart without something to compare it to. So, we need a middle ground where we bundle together like employees while not taking it to the point that we’re analyzing fundamentally different roles together.

In general, statistical models work best when they can sift through large amounts of data in order to tease out nuanced relationships among variables. This is also why multivariate regression approaches work much better than calculating average pay for different groups. The regression model can include variables relating to role so that you gain the benefits of a large dataset without erasing key distinctions in the underlying data.

Also, the best pay equity processes are iterative. Modern computing power allows us to run advanced calculations on large datasets in next to no time at all. We develop multiple models, test them, and see how results converge or differ. As we do this, we assess the statistical efficacy of the different models. Where models show less statistical rigor than expected, we iterate to find an alternative specification that works better. Eventually, we have a suite of models that collectively yield the pay equity insight needed to begin forming conclusions. In other words, there isn’t a hard-and-fast answer to how tightly jobs should be aggregated. Plan to try different levels of aggregation in order to find the right balance and what groupings make the most sense.

8. How do the approaches used in the US differ from pay equity reporting in the UK?

The Equality Act 2010 (Gender Pay Gap Information) Regulations 2017 in the United Kingdom require companies in Great Britain with over 250 employees to disclose certain gender pay gap information on their websites and a government website. The results are public and you can peruse them here .

The UK rules are incredibly prescriptive. The average and median male-to-female pay and bonus pay must be reported. The ratio of males to females who received a bonus must also be disclosed. Finally, companies are instructed to organize their workforce into four equal quartiles based on pay, and disclose the number of males and females in each quartile. Relatively specific definitions and protocols must be followed, and perhaps most importantly, this calculation is not at all robust to the fact that women and men perform different jobs within the organization. In fact, the results show that it is in many ways a better measure of the differences in roles than an indicator of equal pay for equal work.

As we’ve explained, pay equity processes in the US are much different because compensation committees and investors are the ones asking the questions. This leads companies to use state-of-the-art statistical techniques to holistically unpack the complexity of pay relationships. Disclosures, such as UK gender pay reporting, must be both formulaic and simplistic in order to apply across a wide range of companies.

Plan for questions as to why UK-reported results differ from those stemming from an analysis done in the home office. Since the home office project will generally be more rigorous and nuanced, part of its focus should be to explain the rationale behind any differences relative to UK-reported results.

Communication and Legal Privilege

9. how should pay equity processes be communicated internally.

So you’ve done a thoughtful pay equity analysis. What do you tell the organization? The right answer depends on your culture.

Some technology companies have such open cultures that the CEO responds to questions personally and is expected to be very open and transparent on even highly sensitive topics. However, in most cases, I’d say you should worry less about internal “marketing” and more on actual problem-solving. In our observations, public statements of the “We did X, which led to Y,” variety make the analysis more discoverable in any future litigation and start to feel like a PR campaign. But in some corporate contexts, this level of clarity is exactly what is needed.

This doesn’t suggest the right answer is pure silence, either, since shareholders and employees may be asking whether pay equity assessment processes are in place. But even then, basic messages like the following work well: “We absolutely look at pay equity and take the topic seriously. We have recurring processes to do that. Further, we also take preventative steps along the lines of anti-bias training for managers, workforce re-entry programs, and college recruiting initiatives to boost the diversity of entry-level hires.” Customize the specifics, but in our experience, phrasing like this seems to have more credibility while preserving the confidentiality of what is being done.

Regarding legal privilege, analyses like these generally should be commissioned by internal or external legal counsel as part of their effort to give legal advice to their client (the CEO, CHRO, or board). The reason maintaining privilege matters is because many cases won’t have black-and-white answers. As a result, organizations may require time to work through what the results mean and how to act on them. Contextually, a process like this is better kept under privilege than open to discovery should an exogenous lawsuit happen.

10. What is legal privilege and how does it play into things?

In our experience, different attorneys give slightly different viewpoints (again, we’re not attorneys). But the textbook explanation is as follows. In litigation, certain communications between a client and the client’s attorney are privileged (i.e., not discoverable by the opposing side) because they entail the client asking for legal advice. However, if the client then takes that privileged communication, forwards it to their colleague and initiates a separate discussion, then that separate discussion is almost certainly taking place outside the bounds of privilege.

In a pay equity study, generally the client asks their attorney to provide employment law support, of which pay equity is just a part. The attorney engages a quantitative specialist to develop robust statistical models and acts as a go-between for the results. The insights from those models help inform the attorney’s legal advice.

To be clear, many companies perform these studies outside the bounds of legal privilege. It’s a business decision to make based on your own organization’s circumstances and prior approaches to similar matters.

With or without legal privilege, some best practices apply. First, be careful about what you put in writing. Perhaps you see something in an analysis that frustrates you. In that moment, resist the temptation to send an email saying, “I can’t believe we did XYZ!” It’s never a bad time to pick up the phone and talk in person.

Second, tie up loose ends in your “work papers” (i.e., the files you keep on the study). A loose end would be an email or document that says something like the following without resolution: “We should probably correct the pay for these 10 people. What do you think?” Close out any hanging questions like that, or set a time to reassess it via an update to the files.

Finally, document the remediation steps you take. In the event of litigation, you need to show how you took the matter seriously by constantly initiating improvements to pay processes, training programs, and so on.

Remediating Pay Equity Problems

11. what happens if we detect a pay equity problem.

Remediation is an important topic. There are three broad approaches and, of course, many shades of gray in between.

The first approach is to communicate openly within the organization. This may come in a statement such as, “We performed a pay equity analysis and found no evidence of systematic bias. Further differences in pay were random between men and women, and most were easily explained by other factors. We made a total of $X in pay adjustments to 100 employees to remediate anomalous pay below expected levels.” This highly visible approach probably fits 15% to 20% of organizations.

Another way is to make pay adjustments so covertly that only a handful of people know the reason. Under this approach, a study yields suggested pay adjustments and those adjustments are woven into the next upcoming merit cycle, but without telling managers or HR leaders why. In most companies, the reasons behind pay adjustments aren’t fully transparent, which means it’s possible to boost pay adjustments without articulating why. This remediation strategy is typically seen in very large organizations.

The third approach, and usually our preferred one, is to take the results of an analysis to senior business line executives (or the HR generalists supporting them) and pull them into the dialogue. We call this the “Study, Consult, and Act” approach. It preserves discretion while yielding two useful benefits. For one thing, there may be factors that are relevant to the analysis but altogether missing from the data. They can help assess whether that’s the case. This outreach also sends a strong signal from the top that pay equity is a CEO-level priority.

We like the third approach because it’s sustainable. It gets people on board with the mission, marries the mathematical models with on-the-ground context, and opens an ongoing dialogue about pay equity. Making pay tweaks here and there is certainly important, but when done in isolation, it addresses symptoms and not causes.

12. How much should we budget for pay adjustments due to a pay equity problem?

This is an important question, since pay equity is important to every organization, but naturally many organizations have fixed budgets and might find it difficult to implement immediate corrective adjustments. Understandably so, there were skeptics in the room thinking: “It’s great that Salesforce.com can shift budget money around. We probably can’t.”

The good news? Our expectation is that most cases won’t turn out to be budget-busters. That’s one reason why we believe more advanced statistical approaches are necessary to navigate the complexity of pay relationships and present a “measure twice, cut once” answer.

A side benefit of the Study, Consult, and Act approach is that senior management gains early indicators of potential pay biases. This way, if it looks like there will need to be pay adjustments, a dialogue can occur that allows affected parties to begin planning.

In terms of the chronology, a study usually takes six to eight weeks, at which point it’s possible to share how numbers are trending. The socialization process with business line executives usually takes another two or three months, since here the goal is showing them the results so that they can discreetly conduct internal research. After that, it’s time for business line executives to share their perspectives and senior management to make their decisions. All in all, the aim is to not let potential issues linger but to drive a methodical process that creates de facto training to business line executives. The byproduct is that the finance function can have time to digest the financial implications and adjust their budgets.

These processes work only when senior management and the board support them.

Nuances in a Pay Equity Study

13. when doing an analysis, how do you address roles that are predominantly occupied by men.

In our opinion, the starting point is a discussion around why these roles are predominantly occupied by men in the first place.

Take software engineering, a field in which studies suggest the percentage of females is 10% to 15%. The question to ask is whether there’s any valid reason for this. Most would say there isn’t.

Many leading companies have taken these statistics and used them to support overhauls to their recruiting procedures. For example, one high-tech company we work with appointed senior officers to forge relationships with local high schools and universities, creating awareness and excitement among women and minorities about careers in technology. In addition to doing good, they also positioned their organization to be at the forefront of future recruiting.

Of course, there is also the topic of self-selection, such as the assertion that women may simply prefer not to work on an oil rig. Be careful here, since one can easily counter-argue that perhaps the entire reason we don’t see many women working in oil rigs is the presence of structural biases that permeate society. Our suggestion is to devote time to internal dialogue on the topic of representation and your human capital strategy. Perhaps the answer is to show up at the local high schools and begin deconstructing stereotypes that lie behind current representation skews. At any rate, we at least want to raise the concept of self-selection as one that merits further discussion.

Smaller organizations may not have the resources to do what this particular organization did, but that doesn’t mean they’re without options. I’ll use Equity Methods as an example (we have just south of 100 professionals). By overhauling our approaches to campus and experienced-hire recruiting, we’ve significantly leapfrogged the male-female ratio seen in most consulting organizations while also achieving strong ethnic diversity.

What about data that shows women or minorities bailing out at higher rates once they hit a certain level in the organization? Such observations can inform improvements to internal mentoring programs, flex-time tracks, and workforce re-entry processes.

The point is, a multivariate regression model or any cohort analysis might perform worse when comparing the pay of hundreds of men to a handful of women. Nicely-sized datasets are the fuel these models run on. But in these cases where the gender imbalance is high and the model robustness is limited, this in and of itself lends insight and helps focus energy on strategies beyond just compensation.

14. When doing an analysis, how do you think about executives and are they treated differently?

Here I need to give the consultant’s notorious “it depends” answer. We like to include everyone in the analysis. Where we go from there depends on the dialogue and the data.

It’s not unheard of to have pay equity challenges even at executive levels, which we would generally define as the firm’s top 10% in terms of compensation. However, there are generally more unique considerations that need to be looked at and which are not in the data. For example, two business unit executives may have the same band level, live in the same state, and have equal performance ratings—but one earns much more because she manages a considerably larger P&L. If that particular fact isn’t in the HRIS data, a regression model won’t pick it up. Further, as there are fewer employees at each level, the models used lack the power to detect systematic bias.

Another factor with executives is that when problems exist, they’re more generally problems of representation. As a result, the study may trigger a more concentrated focus on helping women or minorities to progress through the career track (as I explain above).

Still, the power of modern computing allows analyses to be sliced multiple ways, so we would suggest including the full population and then being sure to cut the analytics by seniority level to see whether the story differs.

15. It’s not a secret that many women exit the workforce when they have children. How are these events handled in an analysis?

It’s important to start by defining the problem. One way of framing it is that talent leaves because they don’t think it’s possible to excel at work and at raising children at the same time. Another is that talent may wish to re-enter at some point, but it’s not clear how to make this easy and seamless.

However you define it, the first step in solving the problem is to study your data to understand what exactly is taking place. That way, conversations about strategy are grounded in facts. Suppose the data shows a clear trend of women exiting at a certain pay band and age level. What’s the right business response? We know some companies have created more part-time and flex-time roles so that they can help people keep one foot in the pond. This approach of course is easier said than done, since you can also end up with pay equity problems in more customized part-time roles.

Other companies have responded by changing their maternity leave policies so that women don’t feel like they’re forced into a choice at such a pivotal life event. Some companies are also extending paternity leave.

Your company may not in be a position to make wholesale changes to parental leave policies. Even so, you could examine the feasibility of part-time or flex-time opportunities. It’s also worth evaluating workforce re-entry programs, given how many women reach a stage where they do want to come back to work (full-time or part-time) and struggle to make that transition. From a human capital perspective, it makes all the sense in the world to understand how pockets of the labor market are being crowded out, making it harder to compete in the war for talent.

I hope you found this discussion helpful. If you’re among those who asked for this writeup, I’ll be sure to follow up personally.

I mentioned before that we published a more in-depth FAQ on CEO pay ratio. In that publication, we examined CEO pay ratio in a fair amount of detail. Do you think that gender pay equity merits a similar type of publication? If so, what would you like to cover? We think the broader topic of pay equity (extending even beyond gender) is considerably more complicated and meaningful than CEO pay ratio, and we’d like to help advance the dialogue in the industry. Please let me know what you think .

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Research: How to Close the Gender Gap in Startup Financing

• Malin Malmström,
• Barbara Burkhard,
• Charlotta Sirén,
• Dean Shepherd,
• Joakim Wincent

Three ways policymakers, financiers, and other stakeholders can mitigate gender bias in entrepreneurial funding.

A global analysis of previous research over the last three decades shows that women entrepreneurs face a higher rate of business loan denials and increased interest rates in loan decisions made by commercial bankers. Interestingly, the data also reveals that the formal and informal standing of women in a particular society can provide clues to some of the true hurdles to positive change. This article reviews these hurdles, and offers three recommendations for change.

Gender disparities persist in entrepreneurship and statistics reveal the severity of the issue. Globally, only one in three businesses is owned by women . In 2019, the share of startups with at least one female founding member was a mere 20% .

• MM Malin Malmström is a professor of entrepreneurship and innovation at Luleå University of Technology, and a director of the research center Sustainable Finance Lab in Sweden.
• BB Barbara Burkhard is a postdoctoral researcher of entrepreneurship at the Institute of Responsible Innovation at the University of St.Gallen.
• CS Charlotta Sirén is an associate professor of management at the Institute of Responsible Innovation at the University of St.Gallen.
• DS Dean Shepherd is a professor of entrepreneurship, management, and organization at The Mendoza College of Business, University of Notre Dame.
• JW Joakim Wincent is a professor of entrepreneurship and management at the Hanken School of Economics and the Global Center for Entrepreneurship and Innovation at the University of St.Gallen.

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How You Pay Drives What You Choose: Health Savings Accounts versus Cash in Health Insurance Plan Choice

A marked feature of health insurance plan choice is inconsistent choices through the overweighting of premiums relative to out-of-pocket spending. We show that this source of inconsistency disappears when both types of spending come from the same source of designated funds. We focus on the MediSave program in Singapore, whereby residents can pay their health insurance premiums with cash or MediSave funds, but are subject to limits that vary by age and over time. By exploiting variations in those limits, we consistently find that when individuals are able to pay their health insurance premiums with MediSave funds, they are less price sensitive and more willing to enroll in more generous plans—which results in lower spending levels and variance, and lower adverse selection in the market. The results suggest a strong role for mental accounting in insurance decisions.

Lin, Liu, and Yi gratefully acknowledge support from Singapore’s Ministry of Education Academic Research Fund Tier 1 (WBS R-122-000-303- 115). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Gender stereotypes in schools impact on girls and boys with mental health difficulties, study finds

Gender stereotypes mean that girls can be celebrated for their emotional openness and maturity in school, while boys are seen as likely to mask their emotional distress through silence or disruptive behaviours.

Children and teachers who took part in the study said they feared the mental health needs of boys might be missed at school, which makes them an 'at risk' group.

Researchers have warned of the negative impacts on girls where the manifestation of emotional distress such as crying or self-harm could become "feminised and diminished," so taken less seriously.

They have called for increased awareness of the role of gender in mental health services offered in schools and resultant inequalities.

The study was carried out by Lauren Stentiford, George Koutsouris, Tricia Nash and Alexandra Allan from the School of Education at the University of Exeter. They interviewed pupils at two secondary schools in England to ask them: 'Do you think that girls and boys experience mental health in the same way?'

One school was a mixed grammar school in a predominantly white, middle-class rural area and another was a mixed comprehensive school in a predominantly white, working-class urban area. The research took place in autumn 2022.

Researchers spoke to 34 students aged between 12 and 17. Seventeen students identified as female, 12 as male, and 5 as gender diverse. They also interviewed 18 members of staff, including a headteacher, school counsellor, SENCO, and classroom teacher.

The majority -- 43 out of 52 -- felt girls and boys experienced mental health in different ways because of stereotypes that girls are open about their emotions, but boys will hide them.

One pupil, Willow, said: "Girls are more inclined I feel to talk to each other about [mental health] because we're not told to repress our emotions." Another, Kayla, said: "Boys just don't, they barely tell anyone anything that they don't want to talk about because they feel like they'll be looked at and be told the phrase 'man up' or 'boys don't cry."

The phrase 'man up' was referenced multiple times by different staff members and students in both schools.

Participants spoke of persistent and troublesome expectations that boys should not show their emotions.

Dr Stentiford said: "There was a perception that girls are at an advantage over boys in receiving mental health support.

"Students and staff members tended to position girls as above boys in the hierarchy for mental health support because of their perceived emotional openness. Girls were seen as being more emotionally mature than boys and would actively look for help when they needed it.

"There was also evidence of participants understanding emotional distress as manifesting itself differently in girls and boys in school, with girls more likely to cry or withdraw, and boys more likely to engage in off-task or disruptive behaviours such as 'messing around' in class.

"The implications were that girls are seen as more likely to be identified quickly as in need of mental health support, whereas boys could be 'missed' because their disruptive behaviours are misinterpreted. Both girls and boys therefore remain 'trapped' in unhelpful gender stereotypes around mental health.

"The research suggests there is a new and emerging form of gender inequality, set against the context of a perceived growing mental health 'crisis' amongst young people.

"There are dangers around devaluing girls' wellbeing if 'emotional' girls are seen as unfairly advantaged and taking up time and support for mental health difficulties at the expense of boys, who are seen as particularly 'at risk' and a hidden problem."

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Materials provided by University of Exeter . Original written by Kerra Maddern. Note: Content may be edited for style and length.

Journal Reference :

• Lauren Stentiford, George Koutsouris, Tricia Nash, Alexandra Allan. Mental health and gender discourses in school: 'Emotional' girls and boys 'at risk' . Educational Review , 2024; 1 DOI: 10.1080/00131911.2024.2306947

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7 facts about Americans and taxes

Spring reliably brings a whirlwind of number-crunching and form-filing as Americans finish their tax returns. Altogether, the IRS expects to process more than 160 million individual and business tax returns this season.

Ahead of Tax Day on April 15, here are seven facts about Americans and federal taxes, drawn from Pew Research Center surveys and analyses of federal data.

Ahead of Tax Day 2024, Pew Research Center sought to understand Americans’ views of the federal tax system and outline some of its features.

The public opinion data in this analysis comes from Pew Research Center surveys. Links to these surveys, including details about their methodologies, are available in the text.

The external data comes from the U.S. Office of Management and Budget and the IRS Data Book . Data is reported by fiscal year, which for the federal government begins Oct. 1 and ends Sept. 30. For example, fiscal 2024 began Oct. 1, 2023, and ends Sept. 30, 2024.

A majority of Americans feel that corporations and wealthy people don’t pay their fair share in taxes, according to a Center survey from spring 2023 . About six-in-ten U.S. adults say they’re bothered a lot by the feeling that some corporations (61%) and some wealthy people (60%) don’t pay their fair share.

Democrats are far more likely than Republicans to feel this way. Among Democrats and Democratic-leaning independents, about three-quarters say they’re bothered a lot by the feeling that some corporations (77%) and some wealthy people (77%) don’t pay their fair share. Much smaller shares of Republicans and GOP leaners share these views (46% say this about corporations and 43% about the wealthy).

Meanwhile, about two-thirds of Americans (65%) support raising tax rates on large businesses and corporations, and a similar share (61%) support raising tax rates on households with annual incomes over $400,000. Democrats are much more likely than Republicans to say these tax rates should increase.

Just over half of U.S. adults feel they personally pay more than what is fair, considering what they get in return from the federal government, according to the same survey.

This sentiment has grown more widespread in recent years: 56% of Americans now say they pay more than their fair share in taxes, up from 49% in 2021. Roughly a third (34%) say they pay about the right amount, and 8% say they pay less than their fair share.

Republicans are more likely than Democrats to say they pay more than their fair share (63% vs. 50%), though the share of Democrats who feel this way has risen since 2021. (The share among Republicans is statistically unchanged from 2021.)

Many Americans are frustrated by the complexity of the federal tax system, according to the same survey. About half (53%) say its complexity bothers them a lot. Of the aspects of the federal tax system that we asked about, this was the top frustration among Republicans – 59% say it bothers them a lot, compared with 49% of Democrats.

Undeniably, the federal tax code is a massive document, and it has only gotten longer over time. The printed 2022 edition of the Internal Revenue Code clocks in at 4,192 pages, excluding front matter. Income tax law alone accounts for over half of those pages (2,544).

The public is divided in its views of the IRS. In a separate spring 2023 Center survey , 51% of Americans said they have an unfavorable opinion of the government tax agency, while 42% had a favorable view of the IRS. Still, of the 16 federal agencies and departments we asked about, the IRS was among the least popular on the list.

Views of the IRS differ greatly by party:

• Among Republicans, 29% have a favorable view and 64% have an unfavorable view.
• Among Democrats, it’s 53% favorable and 40% unfavorable.

On balance, Democrats offer much more positive opinions than Republicans when it comes to most of the federal agencies we asked about. Even so, the IRS ranks near the bottom of their list.

Individual income taxes are by far the government’s largest single source of revenue, according to estimates from the Office of Management and Budget (OMB).

The federal government expects to collect about $2.5 trillion in individual income taxes in fiscal year 2024. That accounts for nearly half (49%) of its total estimated receipts for the year. The next largest chunk comes from Social Security taxes (including those for disability and retirement programs), which are projected to pull in $1.2 trillion this fiscal year (24%).

By comparison, corporate income taxes are estimated to bring in $612.8 billion, or 12% of this fiscal year’s federal receipts. And excise taxes – which include things like transportation trust fund revenue and taxes on alcohol, tobacco and crude oil – are expected to come to $99.7 billion, or 2% of receipts.

American tax dollars mostly go to social services. Human services – including education, health, Social Security, Medicare, income security and veterans benefits – together will account for 66% ($4.6 trillion) of federal government spending in fiscal 2024, according to OMB estimates.

An estimated 13% ($907.7 billion) will go toward defense spending. Another 13% ($888.6 billion) will repay net interest on government debt, and 10% ($726.9 billion) will fund all other functions, including energy, transportation, agriculture and more.

Related: 6 facts about Americans’ views of government spending and the deficit

The vast majority of Americans e-file their taxes, according to IRS data . In fiscal 2022, 150.6 million individual federal income tax returns were filed electronically, accounting for 94% of all individual filings that year.

Unsurprisingly, e-filing has become more popular since the turn of the century. Fiscal 2000, the earliest year for which comparable data is available, saw 35.4 million individual income tax returns filed electronically (including those filed over the phone). These accounted for just 28% of individual filings that year.

By fiscal 2005, more than half of individual income tax returns (52%) were filed electronically.

Note: This is an update combining information from two posts originally published in 2014 and 2015.

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