A Tool for Closing the Gender Pay Gap

Quantitative Instrument Helps Firms Find the Right Balance

Feb 28, 2019
As Featured In 
Organization Science

Equal pay for equal work. It’s a simple notion, but one that’s surprisingly hard to implement without buy-in from upper management and quantitative tools for decision support.

In recent research, Margrét Bjarnadóttir and Cristian Dezső from the University of Maryland's Robert H. Smith School of Business and co-authors examine the stubbornly persistent issue of gender pay inequity. Their research comes amid a rise of high-profile lawsuits targeting major employers, and as companies are increasingly turning to consultants and law firms to determine whether their pay practices are putting them in legal jeopardy. 

Writing about their research in Harvard Business Review, the authors say, “In our view, the most common approaches for identifying a pay gap and resolving it are full of pitfalls for the unwary.”

“That’s because it’s a tall order: you have to calculate the gap the right way and figure out how to fix it without ballooning your wage bill, all while truly helping underpaid women, maintaining your incentive structure, and avoiding the creation of new legal liabilities.”

In short, they say, focusing on cost of closing the gap, without considerations of fairness and equity can lead to more problems than it solves, potentially to new legal liabilities, corrupting incentives and at the end of the day, not helping women as much as intended.

The researchers offer a strategy of their own. First, quantify your pay gap using the standard analysis available. And, second, allocate raises as efficiently and reasonably as possible to close the divide. But make sure the raises conform with your HR strategy and norms of fairness.

It’s harder than it sounds, as the research shows that each employees influence on the pay gap is not trivial, in fact the research shows it is “frequently efficient to give certain men raises to reduce the gap, something that surprises even trained statisticians.” 

The authors write, “The mathematical reasons for this are complex, but the intuition is straightforward.” It happens when organizations seek to recalibrate pay based on certain workforce qualifications, such as seniority or education. “Perversely, you are paying women more ‘equitably’ by giving raises to certain men,” they write. “But this doesn’t necessarily mean pay is equitable. It just means you’ve shifted the indicators which drive how pay is determined.”

As a result, efforts solely based on cost efficiency may actually result in distorting, or compressing the pay structure. The researchers say they have observed cases in which “blindly allocating raises as cost-efficiently as possible” would invert the whole wage structure. “This means that you could pay less qualified women (in terms of education, experience, and job responsibilities) more than more qualified women – probably the opposite of what your HR strategy calls for.”

Thankfully, this happened in a simulated settings, not in the real world.

The winds may be changing. Increasingly, employers are taking proactive steps regarding pay equity, not because of regulatory mandates, but because of changing norms, and because offering equal pay can be used both as a recruiting and reputation enhancing tool. Salesforce, SAP, and Adobe all publicly advertise their steps to reach pay parity. And Audi, meanwhile, touted its commitment to pay equity in a commercial during Super Bowl LI. 

Having the decision support tools to optimize the pay decisions, driven by fairness, and avoiding the pitfalls of blind cost optimization can help companies take the right steps in achieving their goals of equal pay for equal work.

Read more: On a Firm’s Optimal Response to Pressure for Gender Pay Equity, by David Anderson, Margrét V. Bjarnadóttir, Cristian Dezső and David Gaddis Ross, is featured in Organization Science and summariezed in Harvard Business Review (Jan. 21, 2019).

About the Author(s)

Margrét Bjarnadóttir
<p>Margrét Vilborg Bjarnadóttir is an associate professor of management science and statistics in the DO&amp;IT department. Bjarnadóttir graduated from MIT's Operations Research Center in 2008, defending her thesis titled "Data-Driven Approach to Health Care, Application Using Claims Data". Bjarnadóttir specializes in operations research methods using large scale data. Her work spans applications ranging from analyzing nation-wide cross-ownership patterns and systemic risk in finance to drug surveillance and practice patterns in health care. She has consulted with both health care start-ups on risk modeling using health care data as well as governmental agencies such as a central bank on data-driven fraud detection algorithms.</p>
Cristian Dezsö
<p>Cristian Dezső is an Associate Professor in the Logistics, Business and Public Policy department at the University of Maryland's Smith School of Business. He holds a Ph.D. in Economics and International Business from New York University's Stern School of Business, an M.A. in economics from the Central European University in Budapest, Hungary, and a B.A. in business from the "Babeș-Bolyai" University in Cluj, Romania. Prior to joining the Smith School, he was an associate with the economic consulting firm Cornerstone Research in Washington DC.</p>

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