thesis statement for gender pay gap

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Open Access

Peer-reviewed

Research Article

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

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

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

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

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

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

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

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

Roles Conceptualization, Writing – review & editing

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

Roles Formal analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

Discrimination.

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

Task heterogeneity.

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

Job flexibility.

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

Human capital factors.

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

Expected gender pay gap findings on MTurk

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

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

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

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

Materials and methods

Amazon mechanical turk and cloudresearch..

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

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

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

Analytic sample.

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

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

Database and analytic approach.

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

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

Cluster analysis.

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

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

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

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

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

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

Task selection analyses

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

Advertised hourly pay.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Individual-level factors

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

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

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

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

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

Task heterogeneity

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

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

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

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

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

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

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

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

Other gig economy markets

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

Generalizability

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

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

Potential mechanisms

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

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

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

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

Supporting information

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

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

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

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

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Downloadable Content

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

• Masters Thesis
• Gervacio, Alyzza Colleen A.
• Valiquette L'Heureux, Anais
• David, Ariane
• Political Science
• California State University, Northridge
• Dissertations, Academic -- CSUN -- Political Science.
• Gender Wage Gap
• Higher Education
• Glass Ceiling
• Educational Attainment
• Devaluation
• Occupational Segregation
• Human Capital
• http://hdl.handle.net/20.500.12680/sb397g94d
• by Alyzza Colleen Gervacio

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The gender pay gap in the USA: a matching study

• Original Paper
• Open access
• Published: 05 September 2019
• Volume 33 , pages 271–305, ( 2020 )

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• Katie Meara 1 ,
• Francesco Pastore 2 &
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This study examines the gender wage gap in the USA using two separate cross-sections from the Current Population Survey (CPS). The extensive literature on this subject includes wage decompositions that divide the gender wage gap into “explained” and “unexplained” components. One of the problems with this approach is the heterogeneity of the sample data. In order to address the difficulties of comparing like with like, this study uses a number of different matching techniques to obtain estimates of the gap. By controlling for a wide range of other influences, in effect, we estimate the direct effect of simply being female on wages. However, a number of other factors, such as parenthood, gender segregation, part-time working, and unionization, contribute to the gender wage gap. This means that it is not just the core “like for like” comparison between male and female wages that matters but also how gender wage differences interact with other influences. The literature has noted the existence of these interactions, but precise or systematic estimates of such effects remain scarce. The most innovative contribution of this study is to do that. Our findings imply that the idea of a single uniform gender pay gap is perhaps less useful than an understanding of how gender wages are shaped by multiple different forces.

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1 Introduction

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

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

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

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

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

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

2 Review of literature

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

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

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

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

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

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

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

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

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

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

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

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

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

3.1 Data overview

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

3.2 Sample characteristics

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

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

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

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

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

3.3 Variables

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

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

parttime, 0 if full time and 1 if part time

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

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

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

The following variables were used as covariates only:

married, 1 if married but 0 if not

edyears, number of years of education

hours, the usual number of weekly hours worked

exper, expected experience (explained further below)

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

regional dummy variables

dummy variables for race

occupational dummy variables

sector dummy variables.

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

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

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

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

4 Methodology

4.1 wage decompositions using recentered influence functions.

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

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

4.2 Matching with a single treatment variable

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

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

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

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

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

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

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

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

4.3 Matching with inverse probability weighted regression adjustment (IPWRA)

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

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

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

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

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

Treatment level 0: female = 0 and parttime = 0

Treatment level 1: female = 1 and parttime = 0

Treatment level 2: female = 0 and parttime = 1

Treatment level 3: female = 1 and parttime = 1

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

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

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

4.4 Interpretation of results

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

5 Matching analysis with a single treatment variable

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

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

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

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

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

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

5.2 Treatment effects of gender

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

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

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

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

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

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

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

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

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

6 IPWRA analysis for the full sample

6.1 with gender and part-time working as treatments.

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

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

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

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

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

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

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

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

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

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

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

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

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

6.2 With gender and union membership as treatments

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

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

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

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

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

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

Relative effects compare:

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

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

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

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

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

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

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

6.3 With gender and parenthood as treatments

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

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

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

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

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

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

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

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

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

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

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

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

6.4 With gender and youth as treatments

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

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

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

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

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

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

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

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

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

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

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

7 Conclusions

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

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

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

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

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

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Appendix. Oaxaca RIF decomposition of the gender pay gap

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

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

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Report | Wages, Incomes, and Wealth

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

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

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

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

Introduction and key findings

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

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

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

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

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

Key points include:

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

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

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

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

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

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

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

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

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

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The data underlying the figure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

About the authors

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

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

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Gender Pay Gap

1 exploring the gender pay gap: causes, consequences, and solutions.

Introduction to the Gender Pay Gap: Understanding its basis and significance Pay inequality in the workplace, or what is also known as the gender pay gap, is generally the comparative measure of looking at the position of a woman in the workforce in terms of what she earns for a particular job. There has been […]

2 Unraveling the Complex Roots of the Gender Pay Gap in Modern Society

In the 1920s, women earned the right to vote. In the 1960s, women entered the workforce. In the 1970s, women had Roe vs. Wade passed. It’s 2017, and yet women still don’t get paid the same amount as men. The gender wage gap is a blatant act of sexism in which women get paid 80 […]

3 Tackling the Gender Pay Gap: A Fight for Equality and Fairness

Currently, female employees make 18% less per hour and 36% less per week than their comparable male colleagues, and, astoundingly, in many companies, there is also a bonus pattern that favors men. This is simply not acceptable. Women have the right to be paid equally to their male colleagues. The fact that there is a […]

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4 Unraveling the Mysteries of the Gender Pay Gap in the Modern Workforce

The gender wage gap has been, and still is, a persisting problem in the workforce. At this point in society, it is common knowledge that, on average, men have higher earnings than their women counterparts. A few studies are dissected and explained in this paper, showing why and how the wage gap even exists. The […]

5 The Illusory Gender Pay Gap in Political Knowledge

The Gender Pay Gap Debate: Why Women Prefer “Don’t Know” The Public’s general assumption is that most people don’t know much about politics. Much of prior research concludes that men know more about politics than women. Is the gender gap real? The two assigned articles are essentially the same: they wanted to find out if […]

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

Lisa toczek.

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

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

Richard Peter

Maria Bohdalova , Comenius University in Bratislava, Slovakia

Associated Data

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

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

1 Introduction

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

1.1 Determinants of the GPG

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

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

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

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

1.2 Theoretical Background

1.2.1 human capital model.

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

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

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

1.2.2 Life Course Approach

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

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

1.3 Aim and Hypotheses

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

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

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

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

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

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

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

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

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

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

2 Materials and Methods

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

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

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

2.2 Measurements

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

2.2.1 Level 1 Variables

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

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

M mean; SD standard deviation.

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

2.2.2 Level 2 Variables

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

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

2.3 Statistical Analysis

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

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

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

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

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

Goodness-of-fit statistics of the GCA.

AIC Akaike’s Information Criterion.

3.1 Descriptive

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

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

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

Income trajectories of men and women.

3.2 Growth Curve Analysis

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

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

L1 = Level 1; L2 = Level 2.

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

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

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

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

3.3 Goodness of Fit

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

4 Discussion

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

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

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

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

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

4.1 Strengths and Limitations

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

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

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

5 Conclusion

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

Acknowledgments

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

Data Availability Statement

Ethics statement.

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

Author Contributions

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

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

Conflict of Interest

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

Publisher’s Note

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

Supplementary Material

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

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