Case study: Reducing Gender Inequality using Analytics

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Case study: Reducing Gender Inequality using Analytics

Is gender equality real? Many studies point in the opposite direction. Women receive lower salaries, are promoted less frequently, and are heavily underrepresented in C-level positions. Does it have to do with job performance or gender biases?

There’s a very limited amount of data about why career prospects are worse for women. Available data collection mechanisms were biased.

Our team started to investigate whether gender biases were real in 5 medium-sized U.S. and international companies. On average, in these companies, the C-level suite was heavily dominated by men, whilst entry-level positions had a 45% women and 55% men breakdown.

Using technology that incentivizes people to provide feedback to each other, we were able to collect tens of thousands of high-quality data points through surveys, one-on-one employee-manager check-ins, and goalsetting tools.

We collected 4 different types of data.

  1. First, 360-degree employee feedback. Employees at companies observe a colleague’s behavior and select the attribute they want to rate them on. A rating from 1 to 10 is entered into the platform, along with a brief description justifying the numerical rating.
  2. Second, employees set and manage goals.
  3. Third, employees answer survey questions about how they are feeling throughout the week.
  4. Fourth, employees and managers do one-on-one monthly check-ins regarding employee performance and morale. Data was collected about hundreds of employees over the course of the quarter.

We collected the data, made it anonymous and ran the analytics. We had data regarding over a dozen important variables, including gender, tenure, position, team, salaries, and the number of promotions/raises received.

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Then, the time came to answer the important question. Do women get fewer promotions and lower salaries due to poor performance or due to bias?

In all the participant companies, performance review and 360-degree feedback results were linked to promotion and compensation. Therefore, in order to understand the real reason behind women receiving lower salaries and being promoted fewer times, we needed to understand whether performance reviews were biased against women. We wanted to compare employees’ real performance (job-related results) to their performance review evaluations.

In order to understand objective results, the team studied the types of goals employees were setting and to what degree they met their goals.

To understand performance review results, the system was averaging 360-degree evaluations that each employee was getting.

We found that men and women were equally as likely to meet goals but men were getting 25% more positive evaluations compared to women in the same position.

We delved deeper to understand patterns of how employees were providing feedback and found that women were providing almost identical performance review scores to men and women whilst 70% of men provided higher evaluations to men than to women.

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This was exacerbated in more senior positions, where approximately 75% of men provided higher reviews to men than to women.

In conclusion, gender biases cannot be eradicated unless we bring radical transparency to how compensation decisions are made, how people evaluate each other, and the relationships between results and performance review scores.

It also shows the importance of comparting how people are rated across gender and seniority levels. These practices will reduce workplace bias, increase perceived fairness and employee happiness – and in the end, increase employee performance.

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