Since 2016, Glassdoor has conducted an annual review of our own employee compensation as part of our ongoing efforts to promote workplace transparency and pay equity. This pay equity analysis continues Glassdoor’s long tradition of improving transparency and equity in the workplace and is part of Glassdoor’s annual Diversity, Equity & Inclusion Report where we share our progress towards our DE&I goals.
We are pleased to report that we have again found no adjusted pay gap by gender or race/ethnicity at Glassdoor in 2023. This is the eighth consecutive year we've found no statistically significant adjusted gaps by gender (2022, 2021, 2020, 2019, 2018, 2017, 2016). This is also the fifth year we've analyzed pay by race/ethnicity and, similarly, we have found no statistically significant adjusted pay gaps. Each year, we use an approach similar to the methods described in our guide, which outlines how employers can analyze pay equity within their own organizations.
Our Data and Methodology
For our analysis, we collected demographic and payroll data for our full-time employees as of May 9, 2023. We define an employee's total pay as the sum of (1) an employee's semiannual salary, reflecting any changes from our spring review cycle, and (2) any bonuses/commissions they received in the 6-month period covered by the review cycle. Both salary and additional compensation are in semiannual terms to ensure equivalency.
To create a more “apples-to-apples” comparison of pay between similar employees, we estimate adjusted pay gaps in our analysis. To calculate the adjusted pay gap, we estimate a linear model regressing the logarithm of pay on a set of controls, including demographic characteristics like race and gender; job details like title, department, level and tenure; and other factors like geography.
If you'd like to learn more about our approach, we've included three explainers at the bottom of this post to discuss:
- Our Methodology: A more detailed description of our methodology, including who and what is included in our analysis.
- Unadjusted vs. Adjusted Gaps: What is the difference between adjusted and unadjusted pay gaps? What are the benefits and challenges associated with each metric?
- Statistical Significance and Confidence Intervals: What do "statistical significance" and "confidence intervals" mean? How do we use them to interpret results?
Our Findings: No Adjusted Pay Gaps by Gender or Race/Ethnicity among Glassdoor Employees
We have found no statistically significant adjusted pay gaps at Glassdoor in 2023, after controlling for differences between employees including factors like job title, location, tenure and level. The adjusted pay gap provides a more apples-to-apples comparison that assesses whether we are offering "equal pay for equal work".
Chart 1 shows the unadjusted (more on this further below) and adjusted pay gaps by gender and race/ethnicity. After including controls, the unadjusted pay gaps shrink. For women, the unadjusted pay gap is 17.7 percent—meaning women’s pay is 17.7 percent lower than men’s on average—, but after controls, the gap shrinks to a statistically insignificant 0.9 percent. Similarly, the adjusted pay gaps for Asian, Black and Hispanic/Latinx employees shrink to statistically insignificant after including controls. The thin lines on each column are error bars. If the error bar crosses the horizontal axis, then the difference is not statistically significant, and in the chart below, you can see none of the adjusted pay gaps are statistically significant.
Chart 1: No Adjusted Pay Gaps by Gender or Race/Ethnicity at Glassdoor
The unadjusted pay gaps shown in Chart 1 are wider than the adjusted pay gaps because the unadjusted pay gaps don't take into account differences in characteristics that would reasonably determine compensation differences—for instance, job title, level or tenure. In particular, unadjusted pay gaps are driven in large part by differences in representation in higher-paying roles, levels or departments, a trend economists call "occupational sorting." For example, if women are underrepresented in higher-paying fields like engineering, then the unadjusted pay gap will widen. Occupational sorting accounts for over half of the unadjusted gender pay gap economy-wide, according to our research from 2019.
Glassdoor has continued to make progress towards our goals to hire more employees from underrepresented groups, especially into higher-paying technology roles and leadership, which should naturally help mitigate the effect of occupational sorting and, thus, the unadjusted pay gap. Maintaining no adjusted pay gaps while improving representation of underrepresented groups may help us better reduce any unadjusted pay gaps over time.
Conclusion
We're pleased to report that after accounting for differences in jobs, location, tenure and other observable factors, we find no adjusted pay gaps by gender or race/ethnicity among Glassdoor employees in 2023. This remains unchanged from our previous annual analyses since 2016.
At Glassdoor, we believe in the power of transparency to empower employers, employees and job seekers alike to identify and address problems in the workplace. In particular, we believe pay transparency is a powerful tool in the fight for pay equity. In addition to offering pay data to our users on Glassdoor, sharing our own pay equity analysis publicly represents Glassdoor's commitment to hold itself accountable as it continues to advocate for pay equity. To help further workplace transparency more expansively, we urge employers to utilize our free employer guide to examine their own pay practices.
Explainer: Our Methodology
To calculate the adjusted pay gap, we estimate a linear model regressing the logarithm of pay on a set of controls. Log-transforming pay helps to prevent a few high salaries from skewing the average, a common issue in pay data. Our controls include demographic characteristics like race and gender; job details like title, department, level and tenure; and other factors like geography. We do not include performance ratings as a control. The adjusted pay gaps we report are the coefficients estimated for the gender or race dummy variables when including all controls.
Our measure of total pay includes semiannual salary looking forward and bonuses/commissions paid over the prior 6-month period. Because our analysis excludes long-term compensation like LTIPs and RSUs, our final measure of total pay may underreport employees' actual annual total compensation.
Our analysis includes all full-time Glassdoor employees in the U.S. and abroad. However, due to restrictions on collecting demographic information, our race/ethnicity analyses are restricted to non-European employees only. Employees who did not or declined to provide demographic information were included in the analysis as "Decline to Identify". To protect the privacy of smaller groups, we only report gender pay gaps for women compared to men and race pay gaps for Black, Hispanic/Latinx and Asian employees compared to white employees.
Explainer: Unadjusted vs. Adjusted Gap
Our pay equity analysis calculates two key metrics: (1) the unadjusted pay gap and (2) the adjusted pay gap. The unadjusted pay gap is the percentage difference in a group's average pay relative to the reference group. For example, if the average pay for men is $100,000 and the average pay for women is $80,000, the unadjusted pay gap will be 20 percent. That 20 percent gap could be converted into the statement that women make 80 cents for each dollar men make, a popular way to describe the gender pay gap.
One challenge the unadjusted pay gap metric faces is that it doesn't explain why a gap exists. Our past research indicates that an important driver of the gender pay gap is "occupational sorting", the underrepresentation of women in higher-paying roles.
To form a more apples-to-apples comparison, we can use the adjusted pay gap. The adjusted pay gap uses statistical methods to compare pay for employees in similar roles with comparable experiences, levels and backgrounds. This helps give a better understanding whether there is "equal pay for equal work".
The challenge with the adjusted pay gap is that it can statistically remove differences that are driven by gender or race/ethnicity. For example, if women are underrepresented in higher levels of management because they are passed over for promotion on the basis of their gender, controlling for management level may result in the finding that pay within a management level is equitable even if promotion into that level is not. In conclusion, the adjusted pay gap does not guarantee bias does not exist, but does help give a high-level picture of the state of pay equity.
The focus of our pay equity analysis therefore is the adjusted pay gap to ensure we are paying employees equally for equal work. However, we also report the unadjusted pay gap to be transparent about the state of pay at Glassdoor. Because the unadjusted pay gap is in large part driven by underrepresentation of women and minorities in higher-paying roles, our goals to hire more underrepresented groups into those higher-paying roles should help us reduce our unadjusted pay gap over time.
Explainer: Statistical Significance and Confidence Intervals
The adjusted pay gap we report in our analysis is a statistical estimate. As such, unexplained differences in pay can make us less confident in our estimate. Similarly, we may become less confident in our estimate for smaller groups where there is less data to work with.
We consider our estimate to be statistically significant if it falls within a 95 percent confidence interval, meaning that we are 95 percent confident that the value we're estimating actually falls within the interval.
When we say we find no statistically significant pay gap, we mean that our 95 percent confidence interval includes zero percent (or no gap), which means we cannot statistically distinguish the gap we estimate from no gap at all. It does not guarantee no gap exists, but simply means we cannot observe one with the data we have.
As we collect more data and as Glassdoor’s employee population grows, we should be able to more precisely estimate the adjusted pay gap, thereby shrinking the confidence interval. And the smaller the confidence interval, the more confident we can be that there are no adjusted pay gaps.