SMITH BRAIN TRUST — The gender pay gap, widely cited for full-time workers in the United States as women earning 79 percent of what men earn, drew 100 businesses to sign on to the White House Equal Pay Pledge in 2016. It also prompted professor Margrét Bjarnadóttir at the University of Maryland’s Robert H. Smith School of Business, with a research team, to develop a gap-solving algorithm that’s based on targeting women whose pay most influences the gap. These women tend to be more like their male colleagues in terms of credentials and career paths.
The first step is to control for legitimate reasons employees receive different wages. It may be due to experience, education, work hours and job roles. In one of their case studies, Bjarnadóttir and her team focused on a company with 506 women and 266 men in 40 job categories. They found an absolute pay gap of 43 percent. But after applying controls, the unexplained “residual” pay gap dropped below 10 percent — the gap potentially attributable to discrimination.
Their optimization approach to closing the gap counters the “naïve” approach by which a company would give every woman a raise of equal proportion, until the gap vanished, Bjarnadóttir says. This can cost more than twice as much as identifying and targeting the women whose pay most influences the gap.
Some practical hurdles, however, stand in the way of maximum efficiency in closing the gap. “Working with multiple companies, we have adjusted the approach to operational realities,” Bjarnadóttir says. For example, in our first test case the algorithm recommended that five women get raises of 50 percent or more. In reality, that wouldn't fly.”
The algorithm can therefore take into account maximum allowable raises for both individuals and groups so that, for instance, no one gets a raise of more than 10 percent, in a year. “Further, we have developed algorithmic ways to develop a balance between fairness and efficiency, ensuring that companies not only close the gap, but do so in a fair and efficient manner,” she says.
Read more: On a Firm’s Optimal Response to Pressure for Gender Pay Equity, by Margrét V. Bjarnadóttir with David Anderson, a Smith School PhD graduate now at the City University of New York; Cristian Dezsö of the Smith School; and David Gaddis Ross at the University of Florida, is a working paper available for review on the Social Science Research Network.
Margrét Bjarnadóttir is an Assistant Professor of Management Science and Statistics in the Department of Decisions, Operations and Information Technologies.
Research interests: Data-driven decision-making, combining operations research modeling with data analytics and developing advanced models to drive decision making through optimization and predictive analytics. Her work has applications in healthcare, sports, people analytics and finance.
Selected accomplishments: Winner of the Smith School’s Top 15% Teaching Award in 2013; and papers published in Operations Research, The European Journal of Operations Research, IIE transactions on Health Care Systems, Pharmaco Economics and more. The idea behind the above-featured research won the Gulleggið, or "The Golden Egg" in 2016 -- beating out over 200 other entrants in Iceland’s biggest business plan competition.
About this series: The Smith School faculty is celebrating Women’s History Month 2017 in partnership with ADVANCE, an initiative to transform the University of Maryland by investing in a culture of inclusive excellence. Daily faculty spotlights support activities from the school’s Office of Diversity Initiatives, culminating with the sixth annual Women Leading Women forum on March 30, 2017.
Other fearless ideas from: Rajshree Agarwal | Ritu Agarwal | Leigh Anenson | Kathryn M. Bartol | Christine Beckman | Margrét Bjarnadóttir | M. Cecilia Bustamante | Rellie Derfler-Rozin | Waverly Ding | Wedad J. Elmaghraby | Rosellina Ferraro | Rebecca Hann | Amna Kirmani | Hanna Lee | Hui Liao | Wendy W. Moe | Courtney Paulson | Louiqa Raschid | Rebecca Ratner | Rachelle Sampson | Debra L. Shapiro | Cynthia Kay Stevens | M. Susan Taylor | Vijaya Venkataramani | Janet Wagner | Yajin Wang | Yajun Wang | Liu Yang | Jie Zhang | Lingling Zhang | PhD Candidates
GET SMITH BRAIN TRUST DELIVERED
TO YOUR INBOX EVERY WEEK