The Gender Gap in Wages Circa 2000 Peer Reviewed

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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

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

ten

• Published: February 21, 2020
• https://doi.org/x.1371/journal.pone.0229383

Figures

Abstruse

Studies of the gender pay gap are seldom able to simultaneously account for the range of culling putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform inquiry-related tasks, nosotros examine whether gender pay discrepancies are notwithstanding evident in a labor market place characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nigh five million tasks, we clarify hourly earnings by gender, controlling for key covariates which have been shown previously to atomic number 82 to differential pay for men and women. On boilerplate, women's hourly earnings were x.5% lower than men's. Several factors contributed to the gender pay gap, including the trend for women to select tasks that have a lower advertised hourly pay. This report provides evidence that gender pay gaps tin arise despite the absence of overt bigotry, labor segregation, and inflexible piece of work arrangements, even after experience, education, and other homo majuscule factors are controlled for. Findings highlight the demand to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

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

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

Received: March five, 2019; Accustomed: February five, 2020; Published: February 21, 2020

Copyright: © 2020 Litman et al. This is an open admission commodity distributed under the terms of the Creative Eatables 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 information collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically bachelor. The information are on CloudResearch's Sequel servers located at Queens College in the metropolis of New York. CloudResearch makes information available to exist accessed by researchers for replication purposes, on the CloudResearch bounds, in the same way the information were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data prepare is Tzvi Abberbock, who can be reached at tzvi.abberbock@cloudresearch.com.

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

Competing interests: We have read the periodical's policy and the authors of this manuscript have the following potential competing interest: Several of the authors are employed at Deject Research (previously TurkPrime), the database from which the data were queried. This does not alter our adherence to PLOS One policies on sharing information 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, besides as legislative and executive action nether the Obama assistants [1, 2]. Trends dating back to the 1960s testify a long period in which women's earnings were approximately 60% of their male person counterparts, followed by increases in women'south earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [3]. More contempo data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [iv–9].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests information technology is a part of multiple structural and private-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life grade. Broadly speaking, the drivers of the gender pay gap tin exist categorized as: 1) homo capital letter or productivity factors such every bit educational activity, skills, and workforce experience; 2) manufacture or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which tin can bear upon promotions and remuneration; and finally, 4) gender bigotry operating in hiring, promotion, task consignment, and/or bounty. The latter mechanism is often estimated by inference every bit a function of unexplained residual effects of gender on payment after bookkeeping for other factors, an arroyo which is almost persuasive in studies of narrowly restricted populations of workers such as lawyers [x] and academics of specific disciplines [11]. A recent estimate suggests this unexplained gender departure in earnings can business relationship for approximately 40% of the pay gap [iii]. However, more direct estimations of discriminatory processes are also bachelor from experimental evidence, including field inspect and lab-based studies [12–fourteen]. Finally, gender pay gaps take also been attributed to differential discrimination encountered by men and women on the footing of parental status, often known as the 'maternity punishment' [15].

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

In contempo years there has been a dramatic rise in nontraditional 'gig economic system' labor markets, which entail independent workers hired for single projects or tasks ofttimes on a short-term footing 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-scale tasks online at a time and place convenient to the worker. Available tasks tin 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 bounty upon completion of satisfactory piece of work [16]. An estimated 0.4% of US adults are currently receiving income from such platforms each calendar month [17], and microtask work is a growing sector of the service economic system in the United states of america [18]. Although still relatively small-scale, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in means 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 written report utilizes the Amazon Mechanical Turk (MTurk) platform as a example study to examine whether a gender pay gap remains evident when the master causes of the pay gap identified in the literature do not apply or can exist 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 mail tasks on a dashboard with a short description of the HIT, the bounty being offered, and the fourth dimension the Hit is expected to take. When complete, the requester either approves or rejects the piece of work based on quality. If canonical, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, merely was attainable to researchers for the present study (forth with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform unremarkably used to carry social and behavioral research on MTurk [19].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each chore that a worker accepts which tin can and 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 Striking to be completed. Workers use this information to determine what the corresponding hourly pay charge per unit of a chore is probable 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 nosotros refer to equally estimated bodily wages (meet Methods section for details).

Previous studies have institute that both task completion fourth dimension and the selection of tasks influences the gender pay gap in at to the lowest degree some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [xx]. Some of the contributing factors to this pay gap include that male person Uber drivers selected unlike tasks than female drivers, including beingness more willing to work at night and to piece of work in neighborhoods that were perceived to be more unsafe. Male drivers were also likely to drive faster than their female counterparts. These findings testify that person-level factors like chore option, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap considering 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 task flexibility, as well every bit human being uppercase factors such as experience and education.

Discrimination.

When employers mail 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, contained of their gender or other demographic characteristics. Therefore, features of the worker'southward identity that might be the basis for discrimination cannot factor into an employer's decision-making regarding hiring or pay.

Chore heterogeneity.

Another gene making MTurk uniquely suited for the test 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 add-on, the only information that workers accept available to them to choose tasks, other than pay, is the tasks' titles and descriptions. Nosotros additionally classified tasks based on similarity and deemed for possible job heterogeneity effects in our analyses.

Job flexibility.

MTurk is non characterized past the same inflexibilities every bit are often encountered in traditional labor markets. Workers can piece of work at any time of the day or twenty-four hours of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market place for those who are otherwise constrained by family or other obligations.

Man capital factors.

It is possible that the more experienced workers could learn over time how to place higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time judge. 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 feel 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, cocky-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–nosotros did non wait a gender pay gap to be axiomatic on the platform to the same extent every bit in traditional labor markets. However, potential gender differences in job selection and completion speed, which take 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 residue differences in tasks that could differentially attract male and female person workers and indirectly lead to pay differentials if those tasks that are preferentially selected past men pay a college rate. To practise this we categorize all tasks based on their descriptions using Thou-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In improver, if workers who are experienced on the platform are improve able to find higher paying HITs, and if experience is correlated with gender, information technology may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could likewise 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 take young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such every bit income, education level, and age may similarly influence reservation wages if they are associated with opportunities to discover work exterior of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore nosotros consider age, experience on MTurk, pedagogy, 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 feel on the platform. Nosotros examine the estimated bodily pay gap per hour based on Hit payment and estimated bodily completion time to examine the effects of completion speed on the wage gap. We as well examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more straight measure how gender differences in task selection tin 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 nowadays report was to explore whether gender pay differentials ascend within a unique, not-traditional and bearding online labor market, where known drivers of the gender pay gap either do not employ or can be accounted for statistically.

Materials and methods

Data

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 hands be handled past existing technological solutions such equally receipt copying, image categorization, and website testing. Equally of 2010, researchers increasingly began using MTurk for a broad diverseness 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, "10-minute social scientific discipline study," or "A report nearly 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 enquiry tools for scientific use. I 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 business relationship. CloudResearch's functionality has been described extensively elsewhere [19]. While the demographic characteristics of workers are not available to MTurk requesters, nosotros were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates admission to information for each HIT, including pay, estimated length, and title.

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

Analytic sample.

We analyzed the most 5 million tasks completed during an 18-month menses betwixt January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To exist included in the analysis a HIT had to be fully completed, non just accepted, by the worker, and had to exist 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 add-on, 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 data on workers beyond multiple HITs over time. To minimize misclassification of gender, we excluded the 0.three% of assignments for which gender was unknown with at least 95% consistency beyond HITs.

Measures.

The main exposure variable is worker gender and the effect variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated bodily hourly wages are based on the estimated length in minutes and compensation in dollars per Striking equally posted on the dashboard by the requester. Nosotros refer to actual pay every bit estimated considering sometimes people piece of work multiple assignments at the same time (which is immune 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 as well considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital condition, instruction, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included chore length, task cluster (encounter beneath), and the serial lodge with which workers accustomed the Striking in club to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Assay

Database and analytic arroyo.

Data were exported from CloudResearch'due south database into Stata in long-course format to represent each task on a unmarried row. For the purposes of this paper, we use "Hit" and "study" interchangeably to refer to a study put upward on the MTurk dashboard which aims to collect information 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 every bit demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted past 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 assay for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables one) women'south estimated actual earnings relative to men's and 2) women's selection of tasks based on advertised earnings relative to men'south. We first examined the actual pay model, to come across the gender pay gap when including an guess 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'southward advertised earnings relative to men's as the dependent variable to examine task pick furnishings more than directly. The fully adjusted models controlled for the man upper-case letter-related covariates, excluding household income and education which were balanced across genders. These models besides tested for interactions betwixt gender and each of the covariates by adding individual interaction terms to the adapted 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 chore blazon as the crusade 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 utilise to choose the tasks they perform. Nosotros excluded from this clustering any tasks which contained certain gendered words (such as "male", "female person", etc.) and whatever tasks which had fewer than thirty respondents. We stripped out all punctuation, symbols and digits from the titles, then equally to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of five,140 singled-out words that appeared across all titles. Nosotros so present the distribution of tasks across these clusters likewise every bit average pay by gender and the gender pay gap within each cluster.

Results

The demographics of the analytic sample are presented in Tabular array 1. Men and women completed comparable numbers of tasks during the report period; 2,396,978 (48.6%) for men and 2,539,229 (51.four%) for women.

In Tabular array 2 nosotros measure the differences in remuneration between genders, and then decompose whatever observed pay gap into chore completion speed, task pick, and so demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on boilerplate, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed past men (t = 17.iv, p < .0001), with the hateful estimated actual pay across genders existence $5.70 per 60 minutes.

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 pick of tasks (t = eight.half dozen, p < .0001). Finally, later the inclusion of covariates and their interactions in Model 3, the gender pay differential was further adulterate to 32 cents (t = half-dozen.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 meaning portion of the pay gap, of particular interest are gender differences in task selection. Across structural factors such as education, household limerick 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 practise is an important question to explore. In the next department of the paper we perform a gear up of analyses to examine factors that could account for this observed gender divergence in task selection.

Advertised hourly pay.

To examine gender differences in job pick, we used linear regression to straight examine whether the advertised hourly pay differed for tasks accepted by male person and female workers. We starting time 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 result including no other covariates. The unadjusted regression results (Model 4) shown in Tabular array three, 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 (five.8%) compared to tasks completed by men (t = 21.viii, p < .0001).

Model v examines whether the remuneration differences for tasks selected by men and women remains pregnant 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 pregnant (t = 9.ix, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Tabular array 2. Tests of gender by covariate interactions were pregnant simply 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 choice we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, also every bit hateful earnings and the pay gap across all demographic groups, based on the advertised (not bodily) 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 hr (95% CI $4.69, $v.x).

The design beyond demographic characteristics shows that the advertised hourly pay gap betwixt genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table iv, but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hr for workers age xviii–29), and decreased linearly with age, failing to $0.xiii per 60 minutes among workers age 60+. Advertised houry gender pay gaps were axiomatic across all levels of educational activity and income considered.

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

Table v as well explores the influence of job heterogeneity upon Hitting option and the gender gap in advertised hourly pay. K-ways clustering was used to group HITs into 20 clusters initially based on the presence or absenteeism of five,140 distinct words appearing in Hitting titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.three% of submitted work assignments (HITs).

The themes of all clusters likewise as the average hourly advertised pay for men and women inside each cluster are presented in the 2nd console of Table five. The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did non observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.vi% (consistent with the slightly lower proportion of males on the platform, see Tabular array i) 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 marketplace.

Task length was defined as the advertised estimated duration of a HIT. Table half-dozen presents the advertised hourly gender pay gaps for 5 categories of HIT length, which ranged from a few minutes to over 1 hour. Once again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted boosted supplementary analyses to make up one's mind if other plausible factors such as HIT timing could account for the gender pay gap. Nosotros explored temporal factors including hour of the day and solar 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 solar day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

Discussion

In this report we examined the gender pay gap on an anonymous online platform beyond an eighteen-calendar month menstruum, during which close to 5 meg tasks were completed by over twenty,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such every bit anonymity, cocky-choice into tasks, relative homogeneity of the tasks performed, and flexible piece of work scheduling–we did non expect earnings to differ past gender on this platform. However, opposite to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the form of the examined time catamenia was $5.70 per hr, with the gender pay differential being 10.five%. 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 assay of gender differences in actual earned pay volition be affected by gender differences in task completion speed. Unfortunately, we were non able to direct measure the speed with which workers consummate tasks and account for this factor in our analysis. This is because workers have the ability to have multiple HITs at the same fourth dimension and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual fourth dimension that many workers spend working on tasks is probable less than what is indicated in the metadata bachelor. For this reason, the estimated average bodily hourly rate of $5.70 is likely an underestimate and the gender gap in bodily pay cannot be precisely measured. We infer all the same, by the residue gender pay gap later accounting for other factors, that every bit much as 57% (or $.32) of the pay differential may be attributable to job completion speed. In that location are multiple plausible explanations for gender differences in task completion speed. For case, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also exist a skill gene related to men's greater experience on the platform (see Table v), such that men may exist faster on average at completing tasks than women.

However, our findings also revealed some other 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 consummate tasks does not touch these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on boilerplate to exist selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the chief 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 five.viii% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, nosotros expected to fully explain information technology by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family unit composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the fourth dimension of day and day of the week equally potential explanatory factors. Once again, reverse to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, dissever 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 xc sub-groups examined. These findings allows us to dominion 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 simply study that has observed a pay gap across such various categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for well-nigh all variables that are traditionally implicated every bit causes of the gender pay gap.

Individual-level factors

Individual-level factors such every bit parental status and family composition are a common source of the gender pay gap in traditional labor markets [15]. Unmarried mothers have previously been shown to accept lower reservation wages compared to other men and women [21]. In traditional labor markets lower reservation wages pb unmarried mothers to exist willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economic system markets, in which single mothers may expect to online labor markets equally a source of supplementary income to assist take care of their children, potentially leading them to go less discriminating in their choice of tasks and more willing to piece of 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 amongst individuals who differed in their marital and parental condition showed that while married workers and those with children are indeed willing to piece of 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'due south 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 amidst those workers who are single, and among those who do not take any children. This observation shows that information technology is non possible for parental and family status to account for the observed pay gap in the present study, since information technology is precisely amongst unmarried individuals and those without children that the largest pay gap is observed.

Age was some other factor that we considered to potentially explain the gender pay gap. In the nowadays 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 (run across Table 1). Even so, having examined the gender pay gap separately within five different age cohorts we institute that the largest pay gap occurs in the two youngest accomplice groups: those between 18 and 29, and betwixt 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also about likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consequent in showing that the largest pay gap does non sally from factors relating to parental, family unit, or historic period-related person-level factors. Similar patterns were plant for race, teaching, 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

Experience is a gene that has an influence on the pay gap in both traditional and gig economic system labor markets [20]. Every bit noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more than remunerative tasks compared to less experienced workers if, for case, they are better at selecting tasks that will accept less time to complete than estimated on the dashboard [20]. On MTurk, men are overall more experienced than women. However, experience does not business relationship for the gender gap in advertised pay in the present study. Inexperienced workers contain the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table five). Yet inside this inexperienced group, there is a consequent male person earning advantage based on the advertised pay for tasks performed. Further, decision-making for the issue of experience in our models has a minimal effect on attenuating the gender pay gap.

Job heterogeneity

Another of import source of the gender pay gap in both traditional and gig economy labor markets is job 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 yet some variety in the kinds of tasks that are bachelor, and men and women may take been expected to accept preferences that influence choices among these.

To examine whether in that location 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 nearly pay. The clustering algorithm revealed 13 categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did non discover any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally carve up betwixt men and women. Thus, there is no show that women as a grouping have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each i of these xiii clusters.

Some other potential source of heterogeneity is job 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 exist a gender pay gap.

Even so, we did non observe gender differences in task selection based on job duration. For instance, 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 boilerplate compared to longer tasks.

Additional show 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 accept no children. Thus, while in traditional labor markets task heterogeneity and labor division is oft driven past 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 enquiry

The nowadays findings have important implications for online participant recruitment in the social and behavioral sciences, and also take theoretical implications for agreement the mechanisms that give rise to the gender pay gap. The last 10 years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based information drove has slowly and steadily been moving online [xvi, 24]. Mechanical Turk is by far the near 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 claiming and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings conspicuously reveal for the beginning 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 equally those observed in other more traditional labor markets, later on 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. Dissimilar traditional labor markets, online data collection platforms have built-in tools that can let researchers to easily fix gender pay inequities. Researchers can only utilise gender quotas, for example, to prepare the ratio of male and female participants that they recruit. These simple fixes in sampling practices volition not but produce more equitable pay outcomes simply are also most probable advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results bespeak to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economic system 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 than women. However, different in the present study, the gender wage gap on Uber was fully explained by iii factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more than likely than women to drive in congested locations which resulted in improve pay, c) experience working for Uber predicted higher wages, with men beingness more experienced. Thus, opposite to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, feel, and task completion speed. To our cognition, the results presented in the present report are the get-go to evidence that the gender wage gap tin emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition non a representation of the general population outside of that labor market place. Likewise, Mechanical Turk is characterized past 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 probable to be married or take children, less likely to be religious, and more likely to have a lower income compared to the full 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 environs, characterized past 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 add-on to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the piece of 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. Yet, a gender pay gap emerged in this environment, which cannot be deemed for by structural factors, demographic background, job preferences, or discrimination. Thus, these results reveal that the gender pay gap can sally—in at to the lowest degree 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 engagement 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 written report what the actual mechanism is that generates the gender pay gap on online platforms, we propose that it may be coming from exterior of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else beingness 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 amidst 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 [thirty].

Our results show that even if the bias of employers is removed by hiding the gender of workers every bit 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. Information technology may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women'due south experiences with earning lower pay compared to men on traditional labor markets may lower women's pay expectations on gig economic system markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more probable than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, take recently go the subject of investigation equally 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 than enquiry 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 tin sally despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to come across a more equitable online labor market for academic research, and likewise suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note most framing: we are enlightened that explanations of the gender pay gap that invoke elements of women's agency and, more specifically, "choices" chance both; a) diminishing or distracting from important structural factors, and b) "naturalizing" the status quo of gender inequality [xxx]. As Connor and Fiske (2019) debate, causal attributions for the gender pay gap to "unconstrained choices" past women, common as part of man capital letter explanations, may take the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. Past explicitly locating women's economical decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of information technology (as to a higher place), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote farther 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 data

References

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Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0229383

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