Scholarly Peer Reviewed Articles on Gender Wage Differences in America

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Open Access

Peer-reviewed

Inquiry 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 M. Bates

x

• Published: February 21, 2020
• https://doi.org/10.1371/periodical.pone.0229383

Figures

Abstract

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 yet evident in a labor market characterized past anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in well-nigh five million tasks, we analyze hourly earnings past 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.five% 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 absenteeism of overt bigotry, labor segregation, and inflexible work arrangements, even afterwards feel, pedagogy, and other homo 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 50, 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. PLoS I 15(2): e0229383. https://doi.org/10.1371/journal.pone.0229383

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

Received: March 5, 2019; Accustomed: February five, 2020; Published: February 21, 2020

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

Data Availability: Due to the sensitive nature of some of the information, and the terms of service of the websites used during information collection (including CloudResearch and MTurk), CloudResearch cannot release the full information gear up to make it publically available. The data are on CloudResearch'south 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 assistance researchers access the data set is Tzvi Abberbock, who tin can exist reached at tzvi.abberbock@cloudresearch.com.

Funding: The authors received no specific funding for this piece of 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 Deject Research (previously TurkPrime), the database from which the information were queried. This does non modify our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The gender pay gap, the disparity in earnings betwixt male and female workers, has been the focus of empirical inquiry in the U.s. for decades, as well as legislative and executive activeness under the Obama administration [1, ii]. Trends dating dorsum to the 1960s testify a long period in which women's earnings were approximately sixty% 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 total 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 role of multiple structural and private-level processes that reflect both the almost-term and cumulative effects of gender relations and roles over the life form. Broadly speaking, the drivers of the gender pay gap tin can be categorized every bit: ane) human capital or productivity factors such equally pedagogy, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; three) gender-specific temporal flexibility constraints which can touch on promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, chore assignment, and/or compensation. The latter mechanism is ofttimes estimated past inference as a function of unexplained balance effects of gender on payment later on accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such every bit lawyers [10] and academics of specific disciplines [11]. A recent judge 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 condition, often known every bit the 'maternity penalty' [fifteen].

Not-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 contained workers hired for single projects or tasks frequently on a short-term ground with minimal contractual engagement. "Microtask" platforms such every bit Amazon Mechanical Turk (MTurk) and Crowdflower have get 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 identify user-friendly to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online past the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory piece of work [sixteen]. An estimated 0.4% of U.s. adults are currently receiving income from such platforms each month [17], and microtask piece of work is a growing sector of the service economy in the United States [eighteen]. Although still relatively pocket-size, these emerging labor market place 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 permit researchers to simultaneously account for multiple putative mechanisms idea to underlie the pay gap.

The present report utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains axiomatic when the main causes of the pay gap identified in the literature exercise non apply or can be deemed for in a unmarried investigation. MTurk is an online microtask platform that connects employers ('requesters') to employees ('workers') who perform jobs chosen "Man Intelligence Tasks" (HITs). The platform allows requesters to mail service tasks on a dashboard with a short clarification of the HIT, the compensation being offered, and the time the Hitting is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly attainable to workers. The gender of workers who complete these HITs is not known to the requesters, just was accessible to researchers for the present study (along with other sociodemographic data and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to bear social and behavioral research on MTurk [nineteen].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each job 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 fourth dimension that it takes for that HIT to be completed. Workers employ this data 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 as well 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 take institute that both chore completion time and the pick of tasks influences the gender pay gap in at to the lowest degree some gig economic system markets. For instance, 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 than willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were likewise likely to drive faster than their female counterparts. These findings show that person-level factors similar 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 business relationship simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include bigotry, work heterogeneity (leading to occupational segregation), and job flexibility, as well equally human capital factors such as experience and instruction.

Discrimination.

When employers post their HITs on MTurk they take no fashion 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 up to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker's identity that might be the footing for discrimination cannot factor into an employer'southward decision-making regarding hiring or pay.

Chore heterogeneity.

Another cistron making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed past 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 generally of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only data 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 aforementioned inflexibilities as are often encountered in traditional labor markets. Workers can piece of work at whatever time of the mean solar day or twenty-four hour period 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 unit or other obligations.

Human capital factors.

It is possible that the more experienced workers could learn over fourth dimension how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that tin 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 feel on the platform. Additionally, nosotros business relationship for multiple sociodemographic variables, including age, marital condition, 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 market–e.g., anonymity, self-choice into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to exist 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 job selection that could mimic occupational segregation, we practice account for potential subtle residuum differences in tasks that could differentially concenter male and female person workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher charge per unit. To do this we categorize all tasks based on their descriptions using G-clustering and add together 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 discover higher paying HITs, and if experience is correlated with gender, information technology may atomic number 82 to gender differences in earnings. Theoretically, other factors that may vary with gender could too influence task selection. Previous studies of the pay gap in traditional markets betoken that reservation wages, defined as the pay threshold at which a person is willing to take work, may be lower among women with children compared to women without, and to that of men every bit well [21]. Thus, if women on MTurk are more than likely to have young children than men, they may be more willing to accept available piece of work even if it pays relatively poorly. Other factors such as income, education level, and historic period may similarly influence reservation wages if they are associated with opportunities to notice 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 condition every bit covariates in our models.

Task completion speed may vary past gender for several reasons, including potential gender differences in past experience on the platform. Nosotros examine the estimated bodily 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 marketplace, where known drivers of the gender pay gap either do not utilise or tin be deemed for statistically.

Materials and methods

Information

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 by existing technological solutions such every bit receipt copying, paradigm categorization, and website testing. Every bit of 2010, researchers increasingly began using MTurk for a broad variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers beyond hundreds of academic departments [22]. These research-related HITs are typically listed on the platform in generic terms such every bit, "Ten-minute social science study," or "A study about public opinion attitudes."

Because MTurk was non originally designed solely for inquiry 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 apply. 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 [nineteen]. While the demographic characteristics of workers are not bachelor to MTurk requesters, we were able to retroactively place the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch too 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 written report was IntegReview.

Analytic sample.

Nosotros analyzed the well-nigh 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 information on key demographic characteristics. To exist included in the analysis a HIT had to be fully completed, not just accepted, past the worker, and had to exist accustomed (paid for) by the requester. Although the vast bulk of HITs were open up to both males and females, a small percent 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 whatever HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the assay any HITs that were role of follow-upwards studies in which information technology would be possible for the requester to know the gender of the worker from the prior information collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.iii% of assignments for which gender was unknown with at least 95% consistency across HITs.

Measures.

The main exposure variable is worker gender and the result 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 Striking every bit posted on the dashboard past the requester. We refer to actual pay every bit estimated considering sometimes people work multiple assignments at the same fourth dimension (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 letter factors that could potentially influence earnings on this platform, including marital status, educational activity, 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 lodge with which workers accustomed the Striking in gild to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Assay

Database and analytic approach.

Data were exported from CloudResearch'due south database into Stata in long-course format to represent each task on a single row. For the purposes of this paper, we apply "HIT" and "report" interchangeably to refer to a report put up 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 equally demographic data, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a unmarried written report posted by a requester), and requesters, assuasive for a multi-level modeling analytic approach with assignments nested inside 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 ii dependent variables ane) women'south estimated bodily earnings relative to men's and 2) women's choice of tasks based on advertised earnings relative to men's. We beginning examined the actual pay model, to meet the gender pay gap when including an estimate of task completion speed, so adjusted this model for advertised hourly pay to make up one's mind if and to what extent a propensity for men to select more than remunerative tasks was evident and driving whatever observed gender pay gap. We additionally ran separate models using women'south advertised earnings relative to men's as the dependent variable to examine task selection effects more than directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were counterbalanced across genders. These models also tested for interactions between gender and each of the covariates by adding private 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 whatsoever residual task heterogeneity and gender preference for specific job blazon as the crusade of the gender pay gap, we employ One thousand-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. Nosotros excluded from this clustering whatever tasks which contained sure gendered words (such as "male person", "female", etc.) and any tasks which had fewer than thirty respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or elapsing. The features nosotros amassed on were the presence or absence of 5,140 distinct words that appeared beyond all titles. We then present the distribution of tasks beyond these clusters as well equally average pay by gender and the gender pay gap within each cluster.

Results

The demographics of the analytic sample are presented in Tabular array one. Men and women completed comparable numbers of tasks during the study flow; two,396,978 (48.6%) for men and ii,539,229 (51.4%) for women.

In Table 2 nosotros measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, job selection, and and then 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 by men (t = 17.4, p < .0001), with the mean estimated bodily pay across genders beingness $five.70 per hour.

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 owing to gender differences in the choice of tasks (t = 8.6, p < .0001). Finally, afterwards the inclusion of covariates and their interactions in Model three, 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 Hitting completion speed.

Task selection analyses

Although completion speed appears to business relationship for a meaning portion of the pay gap, of particular interest are gender differences in task pick. Beyond structural factors such as education, household composition and completion speed, chore selection accounts for a meaningful portion of the gender pay gap. Equally 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 department of the newspaper we perform a set of analyses to examine factors that could account for this observed gender difference in job selection.

Advertised hourly pay.

To examine gender differences in chore selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted past male and female workers. We first ran a uncomplicated model (Table three; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay equally the consequence including no other covariates. The unadjusted regression results (Model 4) shown in Tabular array 3, indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised every bit paying 28 cents (95% CI: $0.25-$0.31) less per hr (5.eight%) compared to tasks completed by men (t = 21.8, p < .0001).

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 adulterate to 21 cents (iv.iii%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of chore selection reported in Table 2. Tests of gender by covariate interactions were meaning but 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 amongst unmarried versus currently or previously married women.

To farther 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, besides as mean earnings and the pay gap across all demographic groups, based on the advertised (non actual) hourly pay for HITs selected (hereafter referred to as "advertised hourly pay" and the "advertised pay gap"). The boilerplate task was advertised to pay $iv.88 per 60 minutes (95% CI $four.69, $5.10).

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

To further examine the potential influence of human majuscule factors on the advertised hourly pay gap, Table 5 presents the boilerplate advertised pay for selected tasks by level of feel 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 one,000 HITs, and more than 1,000 HITs. A meaning 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 person and female workers increased with experience, while the gender pay gap decreases. There was some bear witness that male workers have more than cumulative feel with the platform: 43% of male workers had the highest level of feel (previously completing i,001–10,000 HITs) compared to only 33% of women.

Tabular array five also explores the influence of task heterogeneity upon HIT option 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 v,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 notice a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.vi% (consequent with the slightly lower proportion of males on the platform, run across Table ane) and no larger than 50.ii%. As shown in Table 5, the gender pay gap was observed within each of the clusters. These results suggest that residual job heterogeneity, a proxy for occupational segregation, is not probable to contribute to a gender pay gap in this market.

Chore length was defined as the advertised estimated duration of a Hit. Table six presents the advertised hourly gender pay gaps for v categories of Striking length, which ranged from a few minutes to over 1 hr. Again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted additional supplementary analyses to determine if other plausible factors such equally Hit timing could business relationship for the gender pay gap. We explored temporal factors including hr of the day and day of the week. Each completed task was grouped based on the hour and solar day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the mean solar 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).

Give-and-take

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to 5 1000000 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-choice into tasks, relative homogeneity of the tasks performed, and flexible piece of work scheduling–nosotros did not wait earnings to differ by gender on this platform. However, opposite to our expectations, a robust and persistent gender pay gap was observed.

The average estimated bodily pay on MTurk over the course of the examined time period was $v.seventy per hour, with the gender pay differential existence ten.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 job completion speed. Unfortunately, we were not able to directly measure the speed with which workers consummate tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same fourth dimension and multiple HITs tin 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 boilerplate actual hourly rate of $v.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. Nosotros infer however, by the residual gender pay gap after bookkeeping for other factors, that as much equally 57% (or $.32) of the pay differential may be attributable to job 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 accept longer at completing them. There may too be a skill factor related to men'southward greater feel on the platform (see Tabular array five), such that men may be faster on boilerplate at completing tasks than women.

However, our findings too revealed some other component of a gender pay gap on this platform–gender differences in the pick of tasks based on their advertised pay. Because the speed with which workers complete tasks does non touch on these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to exist selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this report and the discussion that follows.

The overall advertised hourly pay was $iv.88. The gender pay gap in the advertised hourly pay was $0.28, or v.viii% of the advertised pay. One time a gender earnings differential was observed based on advertised pay, we expected to fully explicate it by controlling for key structural and private-level covariates. The covariates that we examined included experience, age, income, education, family limerick, race, number of children, chore length, the speed of accepting a task, and 13 types of subtasks. We additionally examined the fourth dimension of day and day of the week as potential explanatory factors. Again, opposite to our expectations, we observed that the pay gap persisted fifty-fifty 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 noesis this is the only report that has observed a pay gap across such diverse categories of workers and conditions, in an bearding 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 piece of work, contributing to a larger gender pay gap in this grouping. This pattern may extend to gig economy markets, in which single mothers may await to online labor markets as a source of supplementary income to help take intendance of their children, potentially leading them to become less discriminating in their choice of tasks and more than willing to work for lower pay. Since female person MTurk workers are twenty% more than probable than men to have children (see Table 1), information technology was critical to examine whether the gender pay gap may be driven by factors associated with family unit composition.

An exam of the advertised gender pay gap among individuals who differed in their marital and parental condition showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do bear upon reservation wages and may thus touch the willingness of online workers to accept lower-paying online tasks), women'southward hourly pay is consistently lower than men's within both unmarried and married subgroups of workers, and amidst workers who practice and exercise not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest amid those workers who are single, and among those who do not accept whatsoever 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 information technology is precisely amid 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 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 into Tabular array 1). However, having examined the gender pay gap separately inside 5 unlike historic period cohorts we institute that the largest pay gap occurs in the 2 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 piece of work in total.

Younger workers are too virtually probable to have never been married or to not take whatever 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 institute for race, instruction, and income. Specifically, a significant gender pay gap was observed inside each subgroup of every 1 of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience

Feel is a factor that has an influence on the pay gap in both traditional and gig economic system labor markets [20]. As noted above, experienced workers may be faster and more than efficient at completing tasks in this platform, merely as well potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for instance, they are better at selecting tasks that will take less fourth dimension 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 comprise the vast majority of the Mechanical Turk workforce, bookkeeping for 67% of all completed tasks (see Tabular array 5). Nevertheless inside this inexperienced grouping, there is a consequent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal consequence on attenuating the gender pay gap.

Chore 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 multifariousness in the kinds of tasks that are available, and men and women may accept been expected to have preferences that influence choices amid these.

To examine whether at that place 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.Nosotros did not notice whatever evidence of gender preference for any of the task types. Within each of the xiii clusters the distribution of tasks was approximately equally dissever 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 one of these 13 clusters.

Some other potential source of heterogeneity is task length. Based on traditional labor markets, 1 plausible hypothesis almost what may drive women'due south 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 probable to piece of work on HITs every bit 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 non observe gender differences in chore choice based on chore 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 prove that scheduling factors do not bulldoze 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 bulk of male person and female Mechanical Turk workers are unmarried, young, and take no children. Thus, while in traditional labor markets task heterogeneity and labor partition is often driven past family and other life circumstances, the cohort examined in this report does not appear to exist affected past these factors.

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

The present findings have of import implications for online participant recruitment in the social and behavioral sciences, and also have 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, equally laboratory-based data drove has slowly and steadily been moving online [16, 24]. Mechanical Turk is by far the well-nigh widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at to the lowest degree some of their human being 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 enquiry participants on anonymous online platforms tends to produce gender pay inequities, and that this happens contained of demographics or blazon of task. While information technology is not clear from our findings what the verbal 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, 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 non 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 information collection platforms have congenital-in tools that tin can permit researchers to easily fix gender pay inequities. Researchers can simply utilise gender quotas, for case, to gear up the ratio of male and female person participants that they recruit. These elementary fixes in sampling practices will not only produce more equitable pay outcomes but are besides nearly probable 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 take relatively like shooting fish in a barrel fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities accept ofttimes remained intractable.

Other gig economy markets

Every bit discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economic system 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 3 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 ameliorate pay, c) feel working for Uber predicted college wages, with men being more than experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by chore heterogeneity, feel, and chore completion speed. To our knowledge, the results presented in the nowadays study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized past a unique population of workers that are almost past definition non a representation of the general population exterior of that labor marketplace. Likewise, Mechanical Turk is characterized past a unique population of workers that is known to differ from the full general population in several means. Mechanical Turk workers are younger, amend educated, less likely to exist 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 nowadays study was not to uncover universal mechanisms that generate the gender pay gap beyond all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should brand this labor market immune to the emergence of a gender pay gap.

Previous theories bookkeeping for the pay gap take identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a part in the emergence of the gender pay gap. This written report examined the work of over xx,000 individuals completing over 5 million tasks, under weather where standard mechanisms that generate the gender pay gap accept been controlled for. Nevertheless, a gender pay gap emerged in this surround, which cannot exist accounted for by structural factors, demographic background, chore 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-minded and other fundamental factors are accounted for. These results bear witness that factors which have been identified to engagement equally giving ascension to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While nosotros cannot know from the results of this written report what the bodily machinery is that generates the gender pay gap on online platforms, we advise that information technology 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 accept a greater propensity to cull less remunerative opportunities relative to men. Information technology may be that these choices are driven by women having a lower reservation wage compared to men [21, 26]. Previous research amidst 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 every bit gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity amongst men [30].

Our results show that even if the bias of employers is removed past 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 atomic number 82 women to undervalue their labor. In turn, women'south experiences with earning lower pay compared to men on traditional labor markets may lower women'southward pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more than likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently go the subject of investigation every bit a potential source for the gender pay gap [3], and the present findings point the importance of such mechanisms existence further explored, particularly in the context of task selection. More inquiry 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 equally well. What these results do testify is that pay discrepancies can emerge despite the absence of discrimination in at to the lowest degree some circumstances. These results should be of particular interest for researchers who may wish to encounter a more equitable online labor market for bookish research, and also advise that novel and heretofore unexplored mechanisms may exist at play in generating these pay discrepancies.

A final note nigh framing: we are enlightened that explanations of the gender pay gap that invoke elements of women'south agency and, more specifically, "choices" risk both; a) diminishing or distracting from important structural factors, and b) "naturalizing" the status quo of gender inequality [xxx]. As Connor and Fiske (2019) contend, causal attributions for the gender pay gap to "unconstrained choices" past women, common as function of human capital explanations, may have the outcome, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women's economical decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences exterior of it (every bit in a higher place), we seek to distance our estimation 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 economic system may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

References

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

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