We all know gender can affect promotions, attrition, and pay—but by how much? In honor of Equal Pay Day, we dug into Pave’s real-time dataset to find out.
In total, our data science team analyzed nearly 700K employee records at private and public companies where self-reported gender information is available. Note that we only compared individuals who identify as either men or women. Due to sample size constraints, individuals identifying as non-binary or other distinctions are not included in this analysis. Additionally, it’s important to remember Pave’s dataset skews heavily toward the technology sector.
Here’s what we discovered.
How large are gender pay gaps?
Let’s start with base salaries.
Our findings show the unadjusted gender pay gap for base salaries is currently 22%. Or, to put things another way, women make 78% of what men do. This result is consistent with data reported by the U.S. government over many decades. However, this unadjusted number does not control for any other variables, such as job family, job level, or location.
Once we normalize results for these factors, the role-adjusted gender pay gap for base salaries is 4%—in other words, women earn 96% of men when working in the same job family, at the same job level, and in the same location.
Looking at role-adjusted gender pay gaps is not an attempt to minimize the impact of pay gaps. Even small pay gaps in annual base salary can have a severe impact on lifetime earnings and retirement savings, and small gaps compound into very big gaps over time.
In general, unadjusted pay gaps are most helpful when examining society-wide questions, such as what career paths are available or welcoming to women, and what policies would make it easier for women to stay in the workforce, while adjusted pay gaps are most helpful when examining the internal workings of an organization’s compensation programs.
Gender Pay Differential - Base Salary
Next, let’s focus on equity compensation, which is a far more complex undertaking.
Given the wide variety in equity philosophies, award vehicles, vesting schedules, and ongoing (or refresh) grant practices, we need to control for more factors when looking at equity. In addition to job family, job level, and location, we also normalized data by company stage of development and private vs public ownership. We also decided to examine the gross actual value of equity holdings vesting over the next 12 months to further smooth out variations in grant practices. In total, 39K employee records had enough data to control for all of these factors.
Once all this work is completed, our analysis shows the role adjusted pay gap for equity is 16%. An adjusted pay gap of this scale suggests companies still have a lot of work to do to strengthen how new hire and ongoing (or refresh) equity awards are determined for women as their careers progress.
Gender Pay Differential - Value of Equity Vesting in Next 12 Months
Representation & Hiring by Gender
As we noted above, unadjusted pay gaps usually highlight society-wide issues, such as the representation of women in the workforce. And, as we can see from analyzing 177K employee records, women are under-represented at every job level except for entry-level individual contributor (P1) roles, with participation rates dropping as job levels increase. Currently, in our dataset, women make up only ~25% of C-suite employees.
If women remain heavily concentrated at lower levels of organizations, this will perpetuate unadjusted pay gaps of 20-30%.
Representation of Women by Level
Another large driver of the gender pay gap is hiring. By exploring data for 149K employees hired in the last 12 months, it’s clear that companies are not hiring women at the same rates as men. Again, hiring for women only outpaces men at entry-level individual contributors (P1) roles, where they made up 53% of new hires.
This means current gender representation rates by level are being maintained, but not materially improved. One slight exception is at the VP-level, where women make up 31.1% of the VP-level population, but 36.2% of new hires.
Hiring of Women by Level
How do performance ratings & promotions vary by gender?
When looking at individual contributor roles across 28K employee records, men receive “exceeds expectations” ratings slightly more often than women at almost every level. The only exception is at the advanced individual contributor (P5) level.
Prevalence of High Performance Rating by Gender Among Individual Contributors
One hypothesis for this trend is that performance reviews are subjective and can sometimes be biased. For example, research in the Harvard Business Review from Paola Cecchi-Dimeglio, a behavioral and data scientist, shows managers can bring a gender bias to reviews, even if unintentionally so. This results in:
- Women are 1.4 times more likely to receive critical subjective feedback than men
- A woman's performance is more likely to be attributed to characteristics rather than skills
When it comes to promotions, however, the data from 49K employee records in Pave’s database shows that women are generally promoted at similar rates to men, across both individual contributor and manager levels.
We even observed slightly higher promotion rates for women in some cohorts. So, while the signals around performance ratings show potential bias, the data on promotions paints a more positive picture for gender equity.
Promotion Rates by Gender & Level
Is turnover higher for men or women?
Our analysis of data from 171K employee records includes a combination of voluntary and involuntary turnover, so there are many variables at play. However, we see clear signals that women turnover at higher rates than men.
These findings are consistent with research that shows that women in the technology sector are more likely to leave their jobs and be impacted by layoffs. Some studies report that women in technology are 65% more likely than men to lose their jobs.
Turnover Rate by Gender & Level
Using Better Data and Tools To Close Gender Pay Gaps
At Pave, we’re on a mission to improve trust and confidence in compensation decisions by providing organizations with better data and tools to manage pay.
While the work of closing gender-based pay gaps is clearly not finished, we can take comfort in the fact that analyzing and understanding pay gaps in real-time across multiple dimensions is easier than ever. The analysis above reflects a small snapshot of how data can be mined within Pave’s ecosystem to assess the quality of compensation decisions.
Additionally, Pave’s Compensation Planning tool allows companies to deploy automation and rule-based guidelines to improve the consistency of decisions, so that, for example, all employees receive similar pay adjustments based on their performance rating. Plus, with the launch of Cycle Insights, companies can now monitor and report on compensation cycles in real-time, with the ability to examine outcomes by gender coming soon.
Learn more about Pave’s end-to-end compensation platform
