*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.
In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.
Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.
To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).
Predictor: Gender Adjustment: None | ||||
Women | -0.28 | 0.016 | -0.34, -0.27 | < .0001 |
Predictor: Gender Adjustment: covariates | ||||
Women | -0.21 | 0.014 | -0.23, -0.18 | < .0001 |
*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.
Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.
To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).
Total HITs | Mean HITs per Worker | Mean Advertised Hourly Pay | Mean Gender Gap in Advertised Hourly Pay | ||||
---|---|---|---|---|---|---|---|
Male | Female | Male | Female | Male | Female | ||
N = 2,396,978 (48.6%) | N = 2,539,229 (51.4%) | 241 | 206 | $4.87 CI: $4.86 - $4.87 | $4.59 CI: $4.58 - $4.60 | -$0.28 CI: -$0.25, -$0.31 | |
18–29 | 733,449 | 602,078 | 203.28 | 165.77 | $4.95 CI: $4.94 - $4.96 | $4.63 CI: $4.62 - $4.64 | -$0.31 CI: -$0.26. -$0.37 |
30–39 | 935,663 | 905,114 | 242.65 | 208.26 | $4.93 CI: $4.92 - $4.94 | $4.68 CI: $4.67 - $4.69 | -$0.25 CI: -$0.20, -$0.31 |
40–49 | 399,718 | 456,955 | 269.90 | 217.29 | $4.82 CI: $4.80 - $4.83 | $4.55 CI: $4.54 - $4.57 | -$0.26 CI: -$0.18, -$0.34 |
50–59 | 202,425 | 375,498 | 306.24 | 258.96 | $4.65 CI: $4.64 - $4.67 | $4.51 CI: $4.50 - $4.52 | -$0.14 CI: -$0.04, -$0.24 |
60+ | 125,723 | 199,584 | 356.16 | 255.55 | $4.30 CI: $4.28 - $4.31 | $4.43 CI: $4.41 - $4.44 | -$0.13 CI: $0.02, -$0.23 |
< 20k | 645,605 | 694,642 | 232.73 | 207.73 | $4.96 CI: $4.95 - $4.97 | $4.67 CI: $4.66 - $4.68 | -$0.28 CI: $0.22, -$0.35 |
20-39k | 684,893 | 766,424 | 250.14 | 207.48 | $4.90 CI: $4.89 - $4.91 | $4.60 CI: $4.59 - $4.61 | -$0.30 CI: -$0.24, -$0.36 |
40-59k | 529,075 | 516,939 | 248.98 | 202.40 | $4.84 CI: $4.83 - $4.85 | $4.57 CI: $4.56 - $4.58 | -$0.26 CI: -$0.20, -$0.33 |
60-79k | 274,803 | 283,948 | 240.63 | 217.42 | $4.78 CI: $4.76 - $4.79 | $4.54 CI: $4.53 - $4.55 | -$0.23 CI: -$0.16, -$0.31 |
80-99k | 116,851 | 125,550 | 224.28 | 190.81 | $4.71 CI: $4.69 - $4.73 | $4.44 CI: $4.42 - $4.47 | -$0.26 CI: -$0.14, -$0.39 |
100k+ | 145,751 | 151,726 | 211.54 | 200.70 | $4.74 CI: $4.72 - $4.76 | $4.47 CI: $4.46 - $4.49 | -$0.27 CI: -$0.17, -$0.36 |
Never married | 1,390,328 | 940,558 | 242.26 | 189.25 | $4.97 CI: $4.96 - $4.97 | $4.66 CI: $4.65 - $4.67 | -$0.30 CI: -$0.25, -$0.35 |
Married | 824,711 | 1,225,612 | 230.30 | 214.42 | $4.74 CI: $4.73 - $4.75 | $4.57 CI: $4.56 - $4.58 | -$0.16 CI: -$0.11, -$0.21 |
Previously married | 181,939 | 373,059 | 284.72 | 229.43 | $4.70 CI: $4.69 - $4.72 | $4.46 CI: $4.45 - $4.48 | -$0.23 CI: -$0.13, -$0.34 |
0 | 1,583,991 | 1,129,463 | 237.34 | 195.07 | $4.94 CI: $4.94 - $4.95 | $4.68 CI: $4.67 - $4.69 | -$0.26 CI: -$0.21, -$0.30 |
1–2 | 626,125 | 979,470 | 247.19 | 212.65 | $4.74 CI: $4.73 - $4.75 | $4.53 CI: $4.52 - $4.54 | -$0.21 CI: -$0.15, -$0.27 |
3+ | 186,862 | 430,296 | 248.49 | 224.58 | $4.67 CI: $4.66 - $4.69 | $4.49 CI: $4.65-$4.50 | -$0.18 CI: -$0.10, -$0.27 |
No College degree | 1,262,163 | 1,405,325 | 245.65 | 214.32 | $4.90 CI: $4.90 - $4.91 | $4.59 CI: $4.59 - $4.60 | -$0.31 CI: -$0.26, -$0.35 |
College degree | 854,543 | 850,904 | 241.53 | 201.54 | $4.87 CI: $4.87 - $4.88 | $4.63 CI: $4.62 - $4.64 | -$0.24 CI: -$0.19, -$0.29 |
Post-college degree | 280,272 | 283,000 | 218.45 | 184.61 | $4.69 CI: $4.68 - $4.71 | $4.46 CI: $4.44 - $4.47 | -$0.23 CI: -$0.15, -$0.31 |
White | 1,830,078 | 1,981,698 | 244.50 | 207.51 | $4.87 CI: $4.86 - $4.88 | $4.59 CI: $4.58 - $5.00 | -$0.28 CI: -$0.24, -$0.31 |
Asian | 210,613 | 135,706 | 220.77 | 204.99 | $4.93 CI: $4.91 - $4.95 | $4.59 CI: $4.57 - $4.61 | -$0.34 CI: -$0.21, -$0.47 |
Black | 155,652 | 255,258 | 238.36 | 211.13 | $4.78 CI: $4.76 - $4.80 | $4.57 CI: $4.55 - $4.58 | -$0.21 CI: -$0.10, -$0.32 |
Hispanic | 165,820 | 116,016 | 235.54 | 195.64 | $4.87 CI: $4.85 - $4.89 | $4.68 CI: $4.66 - $4.70 | -$0.19 CI: -$0.05, -$0.33 |
The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.
To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.
Analytic Sample | Total HITs | Mean No. of HITs | Mean Hourly Advertised Pay | Mean Gender Pay Gap | |||||
---|---|---|---|---|---|---|---|---|---|
Male | Female | Male | Female | Male | Female | Male | Female | ||
0–100 | 9% | 12% | 280,198 | 404,357 | 62.01 | 61.27 | $4.87 CI: $4.85 - $4.88 | $4.61 CI: $4.59 - $4.62 | -$0.27 CI: -$0.24, -$0.30 |
101–500 | 27% | 33% | 816,473 | 1,074,898 | 284.33 | 277.86 | $5.13 CI: $5.12 - $5.14 | $4.82 CI: $4.81 - $4.83 | -$0.31 CI: -$0.28, -$0.34 |
501–1000 | 21% | 21% | 645,805 | 699,215 | 716.01 | 719.46 | $5.32 CI: $5.31 - $5.34 | $5.07 CI: $5.06 - $5.08 | -$0.25 CI: -$0.19, -$0.31 |
1001–10000 | 43% | 33% | 1,301,602 | 1,077,372 | 1650.48 | 1513.35 | $5.34 CI: $5.33 - $5.35 | $5.16 CI: $5.15 - $5.17 | -$0.18 CI: -$0.12, -$0.24 |
Evaluating, Rating, Perceptions | 27.50% | 28.19% | 804,730 | 872,473 | 1144.1 | 893.08 | $4.97 CI: $4.96 - $4.97 | $4.62 CI: $4.61 - $4.62 | -$0.35 CI: -$0.32, -$0.38 |
Short surveys which mention time duration | 4.04% | 3.88% | 118,114 | 120,061 | 1127.94 | 918.04 | $5.37 CI: $5.36 - $5.38 | $5.17 CI: $5.16 - $5.19 | -$0.20 CI: -$0.17, -$0.22 |
Academic, research studies | 12.85% | 12.51% | 376,102 | 387,022 | 1177.98 | 938.72 | $5.47 CI: $5.46 - $5.49 | $5.23 CI: $5.21 - $5.24 | -$0.25 CI: -$0.21, -$0.28 |
Surveys about attitudes and beliefs, opinions and experiences | 1.68% | 1.65% | 49,084 | 50,914 | 1068.29 | 841.51 | $5.74 CI: $5.71 - $5.76 | $5.48 CI: $5.53 - $5.57 | -$0.26 CI: -$0.30, -$0.22 |
Consumer surveys, purchases, behaviors, marketing | 21.37% | 21.66% | 625,585 | 670,137 | 1122.11 | 882.55 | $5.18 CI: $5.17 - $5.19 | $4.91 CI: $4.90 - $4.92 | -$0.27 CI: $0.24, -$0.30 |
Social attitudes | 3.73% | 4.05% | 109,234 | 125,394 | 1060.93 | 805.97 | $4.16 CI: $4.15 - $4.18 | $3.86 CI: $3.85 - $3.87 | -$0.30 CI: -$0.27, -$0.34 |
Games | 1.73% | 1.67% | 50,640 | 51,790 | 1110.96 | 886.83 | $5.55 CI: $5.52 - $5.59 | $5.25 CI: $5.22 - $5.28 | -$0.30 CI: -$0.25, -$0.36 |
"Answer a survey about…" | 3.28% | 3.37% | 95,960 | 104,411 | 1088.95 | 860.83 | $4.77 CI: $4.76 - $4.78 | $4.63 CI: $4.61–4.64 | -$0.15 CI: -$0.12, -$0.17 |
Decision making | 6.20% | 5.81% | 181,448 | 179,731 | 1174.91 | 951.17 | $5.33 CI: $5.32 - $5.34 | $5.18 CI: $5.17 - $5.19 | -$0.15 CI: -$0.12, -$0.18 |
“Short survey” | 7.81% | 7.58% | 228,640 | 234,674 | 1131.87 | 897.98 | $5.63 CI: $5.62 - $5.64 | $5.52 CI: $5.51 - $5.53 | -$0.11 CI: -$0.09, -$0.14 |
“Short study“ | 2.27% | 2.33% | 66,428 | 72,243 | 1120.43 | 874.02 | $5.59 CI: $5.55 - $5.63 | $5.23 CI: $5.20 - $5.27 | -$0.36 CI: -$0.29, -$0.42 |
Psychology studies | 1.70% | 1.76% | 49,711 | 54,424 | 1135.55 | 903.52 | $4.80 CI: $4.78 - $4.82 | $4.60 CI: $4.58 - $4.62 | -$0.20 CI: -$0.15, -$0.25 |
Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).
The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.
Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.
Analytic Sample | Total HITs | Mean No. of HITs | Mean Hourly Advertised Pay | Mean Gender Pay Gap | |||||
---|---|---|---|---|---|---|---|---|---|
Advertised Duration (minutes) | Male | Female | Male | Female | Male | Female | Male | Female | |
0–5 | 24% | 23% | 580,969 | 595,793 | 752.17 | 617.50 | $6.77 CI: $6.75–6.79 | $6.47 CI: $6.45 - $6.49 | -$0.29 CI: -$0.25, -$0.35 |
5–10 | 32% | 30% | 761,543 | 772,963 | 798.10 | 655.79 | $5.23 CI: $5.22 - $5.23 | $5.06 CI: $5.06 - $5.06 | -$0.17 CI: -$0.14, -$0.19 |
10–30 | 38% | 39% | 908,853 | 991,595 | 805.00 | 645.52 | $4.51 CI: $4.50 - $4.51 | $4.25 CI: $4.24.—$4.25 | -$0.26 CI: -$0.22, -$0.30 |
30–60 | 5% | 6% | 126,051 | 156,033 | 775.28 | 610.07 | $3.55 CI: $3.54 - $ 3.56 | $3.21 CI: $3.20 - $3.23 | -$0.33 CI: -$0.28, -$0.39 |
60+ | 1% | 1% | 19,562 | 22,845 | 822.89 | 655.63 | $3.75 CI: $3.71 - $3.79 | $3.34 CI: $3.31 - $3.38 | -$0.40 CI: -$0.31, -$0.50 |
Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).
In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.
The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.
However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.
The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.
Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.
An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.
Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.
Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.
Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.
Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.
To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.
Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.
However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.
Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.
The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.
This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.
As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.
Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.
Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.
While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].
Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.
More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.
A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.
Funding statement.
The authors received no specific funding for this work.
Kadie Philp, commissioner and CAO of Ontario’s Pay Equity Commission, says “structural and unconscious bias influences the way we value the contributions made by women and men in the labour market.” Jessica Lee
Is the work performed by a daycare worker as valuable as that of an electrician? Is a restaurant server’s work as valuable as an IT professional’s?
More to the point: Is the work historically or stereotypically done by women as valuable as the work historically or stereotypically done by men?
These are questions Ontario’s pay equity process answers. Seeking to redress systemic discrimination in wages, the process of pay equity is designed to close gender wage gaps by providing equal pay for work of equal value. While the gender wage gap in Canada is slowly narrowing, many women in Canada today continue to be paid less than men. Even with gains made in equity for women in the workplace (the wage gap decreased from 16 per cent in 2007 to 12 per cent in 2022), jobs predominantly held by women, such as librarians or child-care workers, persist in being lower paid than those typically held by men.
This disparity is due in part to biases around the “comparable worth” of different jobs, says Kadie Philp, commissioner and CAO of Ontario’s Pay Equity Commission .
“Women have historically been clustered in specific types of jobs, and those jobs have been undervalued for various reasons, some linked to early structures of the labour market during the industrial revolution and others linked to prescribed gender roles that saw women’s participation in the labour market as an extension of their unpaid domestic work,” says Ms. Philp, who also hosts a podcast called Level the Paying Field .
“Through data analysis, we can see how both structural and unconscious bias influences the way we value the contributions made by men and women in the labour market.”
For example, research has shown that when women move into a formerly male-dominated sector, wages decrease, says Ms. Philp. She cites one study that found when women moved into computer design, wages in that job class fell 34 per cent.
Ms. Philp says the real and significant cost of comparable worth bias is that companies are “losing out on attracting and retaining talent by not recognizing and rewarding their contributions equitably.”
Dr. Anthony Greenwald, professor emeritus of the University of Washington and co-author of Blind Spot, Hidden Biases of Good People, has been studying the concept of bias for decades. He says that implicit biases, or unconsciously held negative beliefs against a specific social group, are typically developed in early childhood. “People don’t acquire them voluntarily and aren’t aware they are acquiring them,” he says.
To help people educate themselves on their own implicit biases, Dr. Greenwald and a colleague co-created the Implicit Association Test in 1998. Users are asked to make a series of choices as rapidly as possible. The result they receive is based on the speed of the response. It has since been taken by millions of users and used in multiple research studies.
“Later research showed what it was measuring was importantly related to discriminatory judgement and behaviour,” says Dr. Greenwald, a recent guest on the Level the Playing Field podcast.
While his research also found that implicit bias is impossible to change, he says that one way to mitigate its effects is for organizations to apply an epidemiological-like study to examine their gender wage gap. The process he outlines leads to uncovering disparity based on bias and correcting it.
“You take a large group of people and sort them by age, race or ethnicity to see if there are differences. Almost always, you will find differences,” he says. “The problem is that most employers don’t do it routinely.”
Ms. Philp says she feels many employers have made substantive efforts in trying to address systemic or implicit discrimination in their compensation, promotion and hiring practices. But, like Dr. Greenwald, she says “companies could be doing better in leveraging the data they have.”
If disparities are found related to race, gender, socioeconomic status or age, companies can correct them, says Ms. Philp. “That is exactly what the pay equity process does.”
System-wide efforts to overcome bias in comparable worth have been ongoing, including the establishment of federal and provincial pay equity legislation in a number of provinces including Ontario and Quebec . Ms. Philp notes that over the past 20 years, the gender wage gap has narrowed in every province in Canada, ranging from six to 11 per cent.
“[Gains] haven’t been huge, but it shows there is traction and attention is being paid to this in the labour market,” she says.
One way for companies to address pay equity is for companies to use comparable worth to evaluate jobs and determine their value to a company or business, Ms. Philp says. This involves a gender-neutral ranking of the value of comparable skills across occupations, regardless of sex. If jobs traditionally held by women are determined to have comparable worth to jobs held primarily by men, they should be paid commensurably.
Another way to address gender wage gaps is for provinces to introduce pay transparency legislation, which would require salaries be included on any publicly advertised job posting. So far, B.C. has introduced this legislation in 2023 and Ontario is poised to introduce such a requirement in 2024.
Laura Williams, managing partner of Williams HR Law LLP, says while there may be some initial bristling by companies at salary disclosure requirements, it has potential to be an important piece in reducing gender bias in pay.
Companies generally do not have their compensation practices in order, Ms. Williams says, “so they’re flying by the seat of their pants, and this has become a challenge.”
Pay transparency laws will hold employers’ feet to the fire, causing companies to become a “lot more methodical around setting their pay [rates].”
And, she adds, pay transparency has the potential to improve pay equity because it will assist in the pay negotiation process, an area where men have traditionally been stronger negotiators.
“I believe it will make a change. I don’t think it will be the be all and end all,” says Ms. Williams.
Ms. Philp says she has seen results in some jurisdictions that require strict reporting of any pay disparities that are found during pay equity analysis and posting of those results. “Certainly where there is rigorous reporting requirements, the wage gap is closing more effectively,” she says.
But she is hopeful that greater awareness of pay equity will change the landscape, rather than harsher penalties, as more companies voluntarily move in the right direction.
“We have to celebrate those successes and thank the companies and leaders who have committed to this agenda, so we can have a vibrant and healthy and fairly compensated labour market.”
Advertising feature produced by Globe Content Studio with Pay Equity Office. The Globe’s editorial department was not involved.
The average male in Hawaiʻi out-earned the average female by 50%, a smaller margin than the 69% gap observed nationally, but according to a new University of Hawaiʻi Economic Research Organization ( UHERO ) blog , the margin is “still very large.” UHERO cites data from the American Community Survey spanning 2015 to 2022.
“These aggregate figures, however, overlook crucial factors such as educational attainment and occupation, which significantly impact earnings,” wrote UHERO Research Economist Rachel Inafuku, the author of the blog. “An accurate measure of the gender pay gap needs to consider whether women with similar skill levels and educational backgrounds earn less than their male counterparts.”
When examining earnings across education levels from 2015 to 2022, lifetime earnings are consistently higher for men than women, even within the same level of education. The smallest discrepancy is found among individuals with professional or doctoral degrees, where men earn 33% to 35% more than women holding the same degree.
The most substantial gap surfaces among those who attended college but didn’t complete their degree: men in this group earn 63% more than their female counterparts. For a woman to match the lifetime earnings of a man with some college experience but no degree, she would need to attain at least a master’s degree. At every educational tier, women in the subsequent degree bracket fail to surpass the earnings of men at the previous education level.
Among the 130 occupations examined in UHERO ’s sample, men earned more than women in 82% of these careers, while 18% of these professions saw women earning more. Occupations such as financial managers, chief executives, and pharmacists exhibited some of the lowest ratios of female to male earnings, indicating that women working these jobs earn much less than men. Conversely, roles like hosts/hostesses, bartenders, and paralegals show the highest ratios of female to male earnings.
The gender pay gap has been a focal point in economics especially within the past year when Claudia Goldin won the Nobel Prize for uncovering key drivers of gender differences in the labor market. While female labor participation increased substantially over the past century, Goldin found that the earnings gap between men and women in the U.S. hardly closed over a long period of time.
Several national studies have found that women are more likely to prioritize home activities, such as reducing their work hours to care for their children, which often results in career sacrifices.
“Here in Hawaiʻi , the data aligns with Goldin’s findings,” Inafuku wrote. “Throughout their mid to late 20’s, men earn slightly more than women. After that, this pay differential widens as men’s wages grow at a much faster rate than women’s, and the gap continues over the course of a worker’s career.”
Read the entire blog on UHERO ’s website .
UHERO is housed in UH Mānoa’s College of Social Sciences .
If required, information contained on this website can be made available in an alternative format upon request. Get Adobe Acrobat Reader
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IMAGES
COMMENTS
The CONSAD study estimated the gender wage gap using data from the 2007 CPS ORG files. The CPS ORG is a monthly survey, administered by the U.S. Census Bureau, of 50,000 to 60,000 households; it provides data on approximately 105,000 persons ages 16 and older. These interviews are conducted
2007). While the gender pay gap is gradually becoming smaller and smaller, however there is still much to be achieved. Scholars, economists, and politicians provide different explanations of why the gender pay gap exists. One of the major factors contributing to the gender pay gap is discrimination. Women are facing discrimination for numerous ...
trends in the US gender wage gap and on their sources (in a descriptive sense). Accounting for the sources of the level and changes in the gender pay gap will provide guidance for understanding recent research studying gender and the labor market. Figure 1 shows the long-run trends in the gender pay gap over the 1955-2014 period based on two
The gender wage gap refers to the differences between the wages earned by women and men in comparable jobs that generate equal values (OECD 2021). At first glance it seems like a clear and uncontroversial definition; however, applying this definition to data is less straight forward. We highlight three fundamental challenges here.
The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates.
The IPWRA analysis estimates (for October 2017 to March 2018) a gender pay gap of about 15% and a gap in hourly wages from working part time (compared to full time) of about 27%. For those individuals who are both a female and a part-time worker, the gap compared with that for full-time males was estimated at 31%.
Masters Thesis Mind the gap: exploring the impact of the gender wage gap towards women's academic success and career aspirations. The gender pay gap still exists, even though many companies have passed laws supporting gender equality and made efforts to lessen gender disparities in the workplace. The issue of the gender wage gap impacts the ...
This brief2 compiles recent research on the impact of equal pay laws and policies on the gender wage gap. It presents studies under five topic areas: (1) salary history bans; (2) pay transparency policies; (3) gender and salary negotiations; (4) gender bias in performance management and performance-related pay; and (5) occupational segregation ...
This report examines wages on an hourly basis. Technically, this is an adjusted gender wage gap measure. As opposed to weekly or annual earnings, hourly earnings ignore the fact that men work more hours on average throughout a week or year. Thus, the hourly gender wage gap is a bit smaller than the 79 percent figure cited earlier.
The gender pay gap is a prominent topic of discussion among policymakers and academics. Various governments use pay transparency policies as an instrument to address gender pay inequality. Our study adds to this literature, suggesting that such a low-cost intervention may lead to considerable reductions in the gender pay gap.
May 2016 Abstract. Irrespective of professional experience and educational background, gender pay disparity. is a problem in the federal government. Women have to overcome salary barriers, such as. agency segregation, position segregation, and invisible barriers known as the glass. ceiling and the glass wall.
Separate and unequal: Occupation-establishment sex segregation and the gender wage gap. American Journal of Sociology, 101, 329-365. Crossref. Web of Science. Google Scholar. Petersen T., Saporta I. (2004). The opportunity structure for discrimination. American Journal of Sociology, 109, 852-901. Crossref.
entile) for the same groups was. ry.how to achieve gender equality First the go. d news: the gender gap has nar-rowed. The ratio of median earnings increased from 0.56 to 0. 8 in the three decades prior to 2010. This narrowing of the gap in pay reflects the converging economic roles of men and women, a reality that is among the grandest social and.
Beyond the Average Gender Pay Gap: ... Ying-Fen Lin A thesis submitted to the University of She eld for the Degree of Doctor of Philosophy in the Department of Economics December, 2013. Abstract Despite repeated commitments to promote gender equality in the United Na-
The gender pay gap has been observed for decades, and still exists. Due to a life course perspective, gender differences in income are analyzed over a period of 24 years. ... Thesis. Jun 2021 ...
The findings suggest that the gender pay gap has a significant impact on women's economic empowerment, limiting their financial independence and autonomy. The study also highlights the need for ...
1 Introduction. In the European Union (EU) in 2019, women's average gross hourly earnings were 14.1% below the earnings of men ( Eurostat, 2021a ). The gender pay gap (GPG) has existed for decades and still remains to date. According to Eurostat GPG statistics, the key priorities of gender policies are to reduce the wage differences between ...
and 2010, OECD countries' gender wage gap decreased by an average of only 1.2 percentage points between 2010 and 2018 (OECD, 2020).2 The gender wage gap has implications for women, their families, and the societies of which they are a part. As more women have entered the workforce, their earnings have become
The median weekly earnings, the dependent variable, for men is $674, while for women it is $480, with a pay gap of 71.2%. Among the individual level factors, women. were more likely to be older at 39.4 years, compared to 38.6 years for men and women. were likely to have more education (13.65 years) than men (13.29).
The gender pay gap and the mental well-being of women Name student: Michelle Lemmen Student ID number: 428983 Supervisor: Dr. A.C. Gielen Second assessor: E.S. Zwiers Date final version: 03/07/2018 Abstract This paper is about a study on the existence of an association between a gender wage gap and the mental health of women in Germany.
A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64.
7 In literature, the econometric estimations of the relative importance of gender-specific factors, on one hand, and the wage structure, on the other, in explaining international differences and overtime changes of the gender pay gap are based on a decomposition method first developed by Chinhui Juhn, Kevin M. Murphy, and Brooks Pierce ...
Introduction. The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [1, 2].Trends dating back to the 1960s show a long period in which women's earnings were approximately 60% of their male counterparts, followed by increases in ...
[This, by the way, is why AI (like ChatGPT) is so concerning: there is not a name attached to the information, so there is no way to check sources, thus, it does not aid critical thinking.] WNBA ...
Today, the gender pay gap remains around 75-80 cents per dollar on average, and hasn't budged in a decade. Despite this reality, a recent Glassdoor survey of adults in seven countries found the majority don't even believe a gender pay gap exists at their company—despite of mountain of economic research showing otherwise.
Philp notes that over the past 20 years, the gender wage gap has narrowed in every province in Canada, ranging from six to 11 per cent. "[Gains] haven't been huge, but it shows there is ...
The gender pay gap has been a focal point in economics especially within the past year when Claudia Goldin won the Nobel Prize for uncovering key drivers of gender differences in the labor market. While female labor participation increased substantially over the past century, Goldin found that the earnings gap between men and women in the U.S ...
Gender pay gap: men earned nearly 25pc more a week than women in Ireland last year. Men earned €47,187 a year, almost 21pc more than women earned in a year, at €39,039. Anne-Marie Walsh.
Among Japan's 47 prefectures, Tochigi has the largest wage gap between men and women, while Kochi has the smallest, according to a Japanese government list. The list, released at a government meeting Monday, is based on regular pay shown in a 2023 labor ministry survey. It shows the percentage of women's wages to the income of men, which is set at 100. Women in Tochigi earned 71.0 pct compared ...