*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.
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Table of contents, how the gender pay gap increases with age, mothers with children at home tend to be less engaged with the workplace, while fathers are more active, employed mothers earn about the same as similarly educated women without children at home; both groups earn less than fathers, progress in closing the gender pay gap has slowed despite gains in women’s education, gender pay gap differs widely by race and ethnicity, broader economic forces may impact men’s and women’s earnings in different ways, what’s next for the gender pay gap.
The gender pay gap – the difference between the earnings of men and women – has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap has narrowed this century contrasts sharply with the progress in the preceding two decades: In 1982, women earned just 65 cents to each dollar earned by men.
There is no single explanation for why progress toward narrowing the pay gap has all but stalled in the 21st century. Women generally begin their careers closer to wage parity with men, but they lose ground as they age and progress through their work lives, a pattern that has remained consistent over time. The pay gap persists even though women today are more likely than men to have graduated from college. In fact, the pay gap between college-educated women and men is not any narrower than the one between women and men who do not have a college degree. This points to the dominant role of other factors that still set women back or give men an advantage.
One of these factors is parenthood. Mothers ages 25 to 44 are less likely to be in the labor force than women of the same age who do not have children at home, and they tend to work fewer hours each week when employed. This can reduce the earnings of some mothers, although evidence suggests the effect is either modest overall or short-lived for many. On the other hand, fathers are more likely to be in the labor force – and to work more hours each week – than men without children at home. This is linked to an increase in the pay of fathers – a phenomenon referred to as the “ fatherhood wage premium ” – and tends to widen the gender pay gap.
Related: Gender pay gap in U.S. hasn’t changed much in two decades
Family needs can also influence the types of jobs women and men pursue , contributing to gender segregation across occupations. Differential treatment of women, including gender stereotypes and discrimination , may also play a role. And the gender wage gap varies widely by race and ethnicity.
Pew Research Center conducted this study to better understand how women’s pay compared with men’s pay in the U.S. in the economic aftermath of the COVID-19 outbreak .
The study is based on the analysis of monthly Current Population Survey (CPS) data from January 1982 to December 2022 monthly files ( IPUMS ). The CPS is the U.S. government’s official source for monthly estimates of unemployment . For a quarter of the sample each month, the CPS also records data on usual hourly earnings for hourly workers and usual weekly earnings and hours worked for other workers. In this report, monthly CPS files were combined to create annual files to boost sample sizes and to analyze the gender pay gap in greater detail.
The comparison between women’s and men’s pay is based on their median hourly earnings. For workers who are not hourly workers, hourly earnings were computed as the ratio of usual weekly earnings to usual weekly hours worked. The samples include employed workers ages 16 and older with positive earnings, working full time or part time, including those for whom earnings were imputed by the Census Bureau . Self-employed workers are excluded because their earnings are not recorded in the CPS.
The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.
“Mothers” and “fathers” refer to women and men 16 and older who have an own child younger than 18 living in the household.
The U.S. labor force, used interchangeably with the workforce in this analysis, consists of people 16 and older who are either employed or actively looking for work.
White, Black and Asian workers include those who report being only one race and who are not Hispanic. Hispanics are of any race. Asian workers include Pacific Islanders. Other racial and ethnic groups are included in all totals but are not shown separately.
“High school graduate” refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). “Some college” include workers with an associate degree and those who attended college but did not obtain a degree.
Younger women – those ages 25 to 34 and early in their work lives – have edged closer to wage parity with men in recent years. Starting in 2007, their earnings have consistently stood at about 90 cents to the dollar or more compared with men of the same age. But even as pay parity might appear in reach for women at the start of their careers, the wage gap tends to increase as they age.
Consider, for example, women who were ages 25 to 34 in 2010. In that year, they earned 92% as much as men their age, compared with 83% for women overall. But by 2022, this group of women, now ages 37 to 46, earned only 84% as much as men of the same age. This pattern repeats itself for groups of women who were ages 25 to 34 in earlier years – say, 2005 or 2000 – and it may well be the future for women entering the workforce now.
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. This general pattern has not changed in at least four decades.
The increase in the pay gap coincides with the age at which women are more likely to have children under 18 at home. In 2022, 40% of employed women ages 25 to 34 had at least one child at home. The same was true for 66% of women ages 35 to 44 but for fewer – 39% – among women ages 45 to 54. Only 6% of employed women ages 55 to 64 had children at home in 2022.
Similarly, the share of employed men with children at home peaks between the ages of 35 to 44, standing at 58% in 2022. This is also when fathers tend to receive higher pay, even as the pay of employed mothers in same age group is unaffected.
Parenthood leads some women to put their careers on hold, whether by choice or necessity, but it has the opposite effect among men. In 2022, 70% of mothers ages 25 to 34 had a job or were looking for one, compared with 84% of women of the same age without children at home. This amounted to the withdrawal of 1.4 million younger mothers from the workforce. Moreover, when they are employed, younger mothers tend to put in a shorter workweek – by two hours per week, on average – than other women their age. Reduced engagement with the workplace among younger mothers is also a long-running phenomenon.
Fathers, however, are more likely to hold a job or be looking for one than men who don’t have children at home, and this is true throughout the prime of their working years , from ages 25 to 54. Among those who do have a job, fathers also work a bit more each week, on average, than men who do not have children at home.
As a result, the gender gap in workplace activity is greater among those who have children at home than among those who do not. For example, among those ages 35 to 44, 94% of fathers are active in the workforce, compared with 75% of mothers – a gap of 19 percentage points. But among those with no children at home in this age group, 84% of men and 78% of women are active in the workforce – a gap of 6 points.
These patterns contribute to the gap in workplace activity between men and women overall. As of 2022, 68% of men ages 16 and older – with or without children at home – are either employed or seeking employment. That compares with 57% of women, a difference of 11 percentage points. This gap was as wide as 24 points in 1982, but it narrowed to 14 points by 2002. Men overall also worked about three hours more per week at a job than women in 2022, on average, down from a gap of about six hours per week in 1982.
Parenthood affects the hourly earnings of employed women and men in unexpected ways. While employed mothers overall appear to earn less than employed women without children at home, the gap is driven mainly by differences in educational attainment between the two groups. Among women with similar levels of education, there is little gap in the earnings of mothers and non-mothers. However, fathers earn more than other workers, including other men without children at home, regardless of education level. This phenomenon – known as the fatherhood wage premium – is one of the main ways that parenthood affects the gender pay gap among employed workers.
Motherhood does have important effects on the potential earnings of women. Women who experience breaks in their careers after becoming mothers sacrifice at least some of their earnings . Some mothers may never work for pay after having children, passing on earnings altogether. But it is difficult to know what the earnings of mothers might have been and, as a result, it is hard to know for certain what the full effect of motherhood is on women’s earnings. Estimates suggest that motherhood may account for much of the current shortfall in the earnings potential of women overall. 1
Among employed men and women, the impact of parenting is felt most among those ages 25 to 54, when they are most likely to have children under 18 at home. In 2022, mothers ages 25 to 34 earned 85% as much as fathers that age, but women without children at home earned 97% as much as fathers. In contrast, employed women ages 35 to 44 – with or without children – both earned about 80% as much as fathers. The table turns for women ages 45 to 54, with mothers earning more than women with no children at home. Among those ages 35 to 44 or 45 to 54, men without children earned only 84% as much as fathers.
But these patterns in the earnings of employed mothers and women with no children at home are influenced greatly by differences in education levels between the two. Among employed women ages 25 to 34, some 61% of women without children at home had a bachelor’s degree or higher level of education in 2022, compared with 37% of mothers. It follows that among women ages 25 to 34, those without children at home (a more highly educated group, on average) earned more than women with at least one child at home. Conversely, employed mothers ages 45 to 54 were more likely than other women to have at least a bachelor’s degree – 58% vs. 42%. For that reason, mothers ages 45 to 54 earned more than women without children. 2
When the earnings of mothers are compared with those of women without children at home who have the same level of education, the differences either narrow or go away. Among employed women ages 25 to 34 with at least a bachelor’s degree, both mothers and women without children at home earned 80% as much as fathers in 2022. Among women ages 25 to 34 with a high school diploma and no further education, mothers earned 79% as much as fathers and women with no children at home earned 84% as much. The narrowing of the gap in earnings of mothers and women without children at home after controlling for education level also extends to other age groups.
Thus, among the employed, the effect of parenthood on the gender pay gap does not seem to be driven by a decrease in mothers’ earnings relative to women without children at home. Instead, the widening of the pay gap with parenthood appears to be driven more by an increase in the earnings of fathers. Fathers ages 25 to 54 not only earn more than mothers the same age, they also earn more than men with no children at home. Nonetheless, men without children at home still earn more than women with or without children at home.
Although there is little gap in the earnings of employed mothers and women with no children at home who have the same level of education, there is a lingering gap in workplace engagement between the two groups. Whether they had at least a bachelor’s degree or were high school graduates, mothers ages 25 to 34 are less likely to hold a job or be looking for one. Similarly, younger mothers on average work fewer hours than women without children at home each week, regardless of their education level. The opposite is true for fathers compared with men without children at home.
The share of women with at least a bachelor’s degree has increased steadily since 1982 – and faster than among men. In 1982, 20% of employed women ages 25 and older had a bachelor’s degree or higher level of education, compared with 26% of employed men. By 2022, 48% of employed women had at least a bachelor’s degree, compared with 41% of men. Still, women did not see the pay gap close to the same extent from 2002 to 2022 as they did from 1982 to 2002.
In part, this may be linked to how the gains from going to college have changed in recent decades, for women and men alike. The college wage premium – the boost in earnings workers get from a college degree – increased rapidly during the 1980s. But the rise in the premium slowed down over time and came to a halt around 2010. This likely reduced the relative growth in the earnings of women.
Although gains in education have raised the average earnings of women and have narrowed the gender pay gap overall, college-educated women are no closer to wage parity with their male counterparts than other women. In 2022, women with at least a bachelor’s degree earned 79% as much as men who were college graduates, and women who were high school graduates earned 81% as much as men with the same level of education. This underscores the challenges faced by women of all education levels in closing the pay gap.
Notably, the gender wage gap has closed more among workers without a four-year college degree than among those who do have a bachelor’s degree or more education. For example, the wage gap for women without a high school diploma narrowed from 62% in 1982 to 83% in 2022 relative to men at the same education level. But it closed only from 69% to 79% among bachelor’s degree holders over the same period. This is because only men with at least a bachelor’s degree experienced positive wage growth from 1982 to 2022; all other men saw their real wages decrease. Meanwhile, the real earnings of women increased regardless of their level of education.
As women have improved their level of education in recent decades, they’ve also increased their share of employment in higher-paying occupations, such as managerial, business and finance, legal, and computer, science and engineering (STEM) occupations. In 1982, women accounted for only 26% of employment in managerial occupations. By 2022, their share had risen to 40%. Women also substantially increased their presence in social, arts and media occupations. Over the same period, the shares of women in several lower-paying fields, such as administrative support jobs and food preparation and serving occupations, fell significantly.
Even so, women are still underrepresented in managerial and STEM occupations – along with construction, repair and production, and transportation occupations – when compared with their share of employment overall. And there has been virtually no change in the degree to which women are over represented in education, health care, and personal care and services occupations – the last of which are lower paying than the average across all occupations. The distribution of women and men across occupations remains one of the drivers of the gender pay gap . But the degree to which this distribution is the result of personal choices or gender stereotypes is not entirely clear.
Looking across racial and ethnic groups, a wide gulf separates the earnings of Black and Hispanic women from the earnings of White men. 3 In 2022, Black women earned 70% as much as White men and Hispanic women earned only 65% as much. The ratio for White women stood at 83%, about the same as the earnings gap overall, while Asian women were closer to parity with White men, making 93% as much.
The pay gap narrowed for all groups of women from 1982 to 2022, but more so for White women than for Black and Hispanic women. The earnings gap for Asian women narrowed by about 17 percentage points from 2002 to 2022, but data for this group is not available for 1982.
To some extent, the gender wage gap varies by race and ethnicity because of differences in education, experience, occupation and other factors that drive the gender wage gap for women overall. But researchers have uncovered new evidence of hiring discrimination against various racial and ethnic groups, along with discrimination against other groups, such as LGBTQ and disabled workers. Discrimination in hiring may feed into differences in earnings by shutting out workers from opportunities.
Changes in the gender pay gap are also shaped by economic factors that sometimes drive men’s and women’s earnings in distinctive ways. Because men and women tend to work in different types of jobs and industries, their earnings may respond differently to external pressures.
More specifically, men’s earnings essentially didn’t change from 1982 to 2002. Potential reasons for that include a more rapid decline in union membership among men, a shift away from jobs calling for more physical skills, and global competition that sharply reduced employment in manufacturing in the 1980s. At the same time, women’s earnings increased substantially as they raised their level of education and shifted toward higher-paying occupations.
But in some ways, the economic climate has proved less favorable for women this century. For reasons that are not entirely clear, women’s employment was slower to recover from the Great Recession of 2007-2009. More recently, the COVID-19 recession took on the moniker “ she-cession ” because of the pressure on jobs disproportionately held by women . Amid a broader slowdown in earnings growth from 2000 to 2015, the increase in women’s earnings from 2002 to 2022 was not much greater than the increase in men’s earnings, limiting the closure in the gender pay gap over the period.
Higher education, a shift to higher-paying occupations and more labor market experience have helped women narrow the gender pay gap since 1982. But even as women have continued to outpace men in educational attainment, the pay gap has been stuck in a holding pattern since 2002, ranging from 80 to 85 cents to the dollar.
More sustained progress in closing the pay gap may depend on deeper changes in societal and cultural norms and in workplace flexibility that affect how men and women balance their careers and family lives . Even in countries that have taken the lead in implementing family-friendly policies, such as Denmark, parenthood continues to drive a significant wedge in the earnings of men and women. New research suggests that family-friendly policies in the U.S. may be keeping the pay gap from closing. Gender stereotypes and discrimination, though difficult to quantify, also appear to be among the “last-mile” hurdles impeding further progress.
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March 14 is Equal Pay Day. Here are five fast facts about the gender wage gap.
Learn more about equal pay in the United States .
Wendy Chun-Hoon is the director of the U.S. Department of Labor’s Women’s Bureau. Follow the agency on Twitter: @WB_DOL .
Brecha Salarial por Género: 5 Datos
El 14 de marzo es el Día de la Igualdad Salarial. Consulte estos 5 datos sobre la brecha salarial de género.
Aprenda más sobre la igualdad salarial en Estados Unidos.
Wendy Chun-Hoon es la directora de la Oficina de la Mujer del Departamento de Trabajo de EE.UU. Siga a la agencia por Twitter: @WB_DOL .
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Mosomi, J.N. 2018. Distributional changes in the gender wage gap in the post-apartheid South African labour market. . ,Faculty of Commerce ,School of Economics. http://hdl.handle.net/11427/30000
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NZ Media Releases
ANZ NZ joins other companies in publicly reporting the organisation’s gender pay gap.
2022-03-08 09:51
ANZ Bank New Zealand (ANZ NZ) today joins other companies in publicly reporting the organisation’s gender pay gap - the high-level indicator of the difference between what women and men earn in a company.
ANZ NZ Chief Executive Officer Antonia Watson said the move was an important step towards creating an equitable, diverse and inclusive workplace.
“When we look across all roles in ANZ New Zealand, on average women are paid 22.4 per cent less than men,” Ms Watson said.
“For comparable roles, men and women in ANZ New Zealand are paid about the same.”
According to Statistics New Zealand the average gender pay gap in the country is 9.1 per cent.
“What ANZ New Zealand’s numbers show is that more of the senior and managerial roles are held by men. That’s not good enough and I take it as my responsibility to bring this down.
“Companies can’t shy away from the gender pay gap. We need to be transparent and hold ourselves to account if we are to drive much greater representation of women in leadership and higher paying roles.”
Representation of women at the manager, senior manager, and executive levels at ANZ New Zealand is 45.8, 38.8 and 34.6 per cent respectively. They are up from 44.5, 29.7 and 32.7 per cent in early 2020. We have a gender balanced Board and executive team.
“While those numbers are heading in the right direction, there is still a lot to be done.
“There are a number of initiatives underway to address the gender pay gap, including empowering our leaders to look for women who might not have the confidence to put themselves forward for a more senior role or an opportunity for development.”
Ms Watson acknowledged that creating an equitable, diverse and inclusive workplace wouldn’t be easy. ANZ NZ was also committed to improving the ethnicity pay gap and was looking to report on that in 2023.
ANZ NZ measures gender pay using two methods:
You can find more information on ANZ New Zealand’s gender pay gap here and MindTheGap’s Public Pay Gap Registry here .
For media enquires contact Briar McCormack 021 280 1173
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This Dissertation is brought to you for free and open access by the Walden Dissertations and Doctoral Studies Collection at ScholarWorks. It has been ... narrowing the gender wage gap according to published government reports (United States Government Accountability Office, 2011; United States Department of Labor, 2011;
The gender pay gap refers to the difference in average earnings between men and women in the workforce. ... Breaking the Patriarchal Mold: A Journal on Feminism, Singleness, and Women's ...
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.
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 ...
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 results in Table 6, columns 2 and 3, show that on average female academics earning below the median value (column 2) experience an increase in log earnings by 0.50 percentage points (a nominal fall of £223 or a 22.8 per cent fall in the gender pay gap). 1 More importantly, academics earning above the median annual earnings (column 3 ...
Blau and Kahn (2017) report that the gender gap in years of education has reversed from − 0.2 to + 0.2 between 1981 and 2011 for the USA. The gap in years of work experience fell from 7 in 1981 to 1.4 years in 2011. In consequence, the role of these traditional factors in the gender wage gap has shrunk.
A 10% increase in the minimum wage significantly reduces the gender wage gap by 1.24% for workers in low violation rate region (less than the median). The effect of minimum wages on the gender wage gap for workers in high violation rate region (greater than the median) is statistically insignificant. The results are omitted due to space ...
The Gender Wage Gap: Extent, Trends, and Explanations Francine D. Blau and Lawrence M. Kahn NBER Working Paper No. 21913 January 2016 JEL No. J16,J24,J31,J71 ABSTRACT Using PSID microdata over the 1980-2010, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably over this period.
Human capital theorists (e.g., Becker 1985; Polachek 1981; Tam 1997) argue that occupational wages are a gender-neutral function of the labor market.From this perspective, differences in occupation-level wages are driven primarily by the human capital investments that workers make (e.g., education and training) as well as the skills and tasks required on the job.
The gender wage gap is unadjusted and is defines as the difference between median earning of men and women relative to median earnings of men. (OECD). For decades now, working women are getting paid less than men. On average women are paid 80 cents for every dollar paid to men.
The principle of equal pay for work of equal value has radical potential but uneven application and impact. As one strand within the multiplicity of measures required to impede the reproduction of gender pay gaps, its strengths lie in an expanded notion of equality and capacity to challenge gendered norms embedded in wage-setting practices.
The gender wage gap remains about $1.07 per hour with the addition of these explanatory variables, suggesting that compensating differentials contribute at most only minimally to the gender wage gap. In Model 6, controlling for state, month, and year fixed effects has a very small effect on the gender wage gap, narrowing the gap by 7 cents per ...
This dissertation explores the relationship between human capital and the gender pay gap in the context of the United Kingdom (UK). The paper looks at the gender pay gap variations between nations within the UK and attempts to determine whether these variations are attributable to differences in human capital alone. Past literature is
University Women, in 2011, this gender wage gap was an estimated 23 percent, which means for. every dollar earned by men, only 77 cents were earned by women (p. 3). Understandably, this. value for the raw wage gap is often interpreted "as a clear indication of overt wage.
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 ...
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-
by the gender wage gap in this thesis. The gender wage gap is defined as the situation whereby men have the same occupation as women and work in the same industry as women, but in which men earn more after controlling for age, education, tenure and the number of hours worked per week. Productivity is then also assumed to be the same.
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 ...
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 ...
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).
a book of federal rules and regulations. This dissertation studies two of the primary wage di erential topics: gender wage gaps and public wage gaps. The purpose of this chapter is to familiarize readers with the evolution of empirical techniques used to measure wage di erentials, as well as to introduce some of the theories regarding the
The gender pay gap - the difference between the earnings of men and women - has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap ...
The largest identifiable causes of the gender wage gap are differences in the occupations and industries where women and men are most likely to work. Women are 2 out of every 3 full-time workers in occupations that pay less than $30,000 per year, and fewer than 1 in 3 full-time workers in jobs paying an average of $100,000 or more.
Gender inequality in the labour market is real and it matters. This thesis examines gender wage inequality in the post-apartheid South African labour market from 1993 to 2015 and finds that there is a substantial median wage gap of between 35% and 23% since the end of apartheid. This gap is unexplained by differences in human capital characteristics and it is not declining over time.
ANZ NZ was also committed to improving the ethnicity pay gap and was looking to report on that in 2023. ANZ NZ measures gender pay using two methods: The gender pay gap, which represents the overall average salary gap between men and women; and; The pay equity gap, which compares the average pay of men and women doing the same or similar roles.