November 1, 2019

To Address the Gender Pay Gap, Look at the Numbers


In September, the US Equal Employment Opportunity Commission (EEOC) chose not to renew an Obama-era policy that helped collect data regarding employee wages and pay. This data had been fundamental in detailing the discrepancies between salaries earned by men and women and proving the gender pay gap actually exists.

The EEOC’s decision highlights just how crucial data and analytics are when addressing the pay gap. Even if the EEOC doesn’t revive this policy or implement new procedures in the future, employers and industry leaders can still use data to identify their own pay gaps and develop solutions to close them.

Data Can Reveal Insights About a Pay Gap and Why It Is Happening

In a hypothetical scenario, leaders at a small software company receive complaints regarding pay discrepancies between male and female employees. To investigate the complaints, company leaders turn to the data they have studiously gathered regarding employee salaries throughout the organization’s history.

The data shows that women in software development roles have been earning 10-20 percent less than men with the same experience and skill set, but the company also finds potential reasons for why the pay gap exists. Most of the veteran developers are men, and they have been with the company since a time when it provided larger raises and bonuses than it does now. However, the company has rarely adjusted its starting salary for entry-level developers, many of whom are now women. Even when those new employees gain as much experience and reach the same skill level as their veteran peers, they are still earning less.

This is just one example of how a pay gap can be identified using data, and it doesn’t reflect why all gaps exist within every company or industry. What the example does show is how data can be a strong starting point in discovering that a gap exists, why it emerged, and what has allowed it to continue.

Data Can Make Employees Aware They’re Underpaid

The earlier example shows a situation where employees themselves have brought issues regarding a pay gap to company leaders. However, women are not always aware a pay gap exists in the first place.

Our modern work culture has determined that it is taboo or inappropriate to discuss one’s salary with colleagues. As a result, many women may never know they are being underpaid by 10-20 percent an hour or $25,000-50,000 per year versus their male counterparts with the same skills and experience. This lack of information only fuels the perpetuation of pay gaps over the years.

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But women can use data to identify specific discrepancies in their pay compared to others in their fields, why these discrepancies exist, and whether they are being unfairly paid compared to their male counterparts. Companies could embrace transparency and provide this data to their own employees, but if that doesn’t happen, women can still gather this information from trusted third-party resources.

Data Can Illustrate the Imbalance Between Compensation and Benefits

In another hypothetical example, a woman is considering a job offer to work in the corporate office of a major eCommerce company. After pulling data on what other newly hired employees have earned for opening salaries in that same job, the woman discovers that her offered salary is roughly $10,000 less than what other new employees have historically earned.

The woman uses this information to negotiate for a higher salary, but the company counters with more paid time off, a more robust health insurance package, and a higher matching contribution to her 401(k). The initial offered salary is still higher than the industry average, and with the additional benefits on the table, the woman decides to take the offer.

This imbalance between salary and benefits can create and nurture gender pay gaps. A woman choosing to receive more vacation days right now versus a higher salary in the long term will only make it more difficult to address and rectify any pay gaps she experiences in the future.

Data Can Be Presented in Misleading Ways, and Employees Should Be Aware

Another hypothetical: In an effort to increase transparency, a large marketing firm publishes a report that indicates women earn just as much as men do on average in the same roles at the company. The report garners praise from employees and industry leaders outside of the organization, and it spreads the “fact” that the company doesn’t have a gender pay gap.

But when reviewing the data more closely, an employee discovers the report only focuses on average pay across the company. It doesn’t take into account the fact that the firm’s top leadership is composed of women who earn 10 times more than the average employee, nor does it consider that some employees hold different job titles despite sharing the same responsibilities.

Data can be presented and packaged in whichever way the person or group analyzing it sees fit. Women need to be cognizant of the data being presented to them. Is it really the best data available? If it isn’t, they can push their leaders to gather or analyze more data, or they can seek information that more fully illuminates their pay status.

Nicole Antoinette Smith is a full-time assistant professor of instruction at Ohio University in the College of Business and a contributing writer for the Online Master’s in Business Analytics blog. She provides management consulting services under Nicole Antoinette Consulting.

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Nicole Antoinette Smith is a full-time assistant professor of instruction at Ohio University in the College of Business, teaching data analytics, information systems, and project management courses. She is also a contributing writer for the Online Master's in Business Analytics Blog. She specializes in data strategy, strategic planning, process improvement, program development, and project management. Nicole Antoinette provides management consulting services under her company (Nicole Antoinette Consulting) using seven proprietary business methodologies, including her trademarked data strategy methodology, dataFonomics.