Closing the Gender Pay Gap

What Organizations Can Do To Level Wages

Jun 08, 2020
Technology

SMITH BRAIN TRUST  Women still only earn 80 cents for every dollar a man makes. That wage gap, which can’t be explained by anything but gender, drives much of Margrét Bjarnadóttir’s research. As an assistant professor of management science and statistics at the University of Maryland’s Robert H. Smith School of Business, she has developed fairness-driven algorithms that determine how to best close the gender pay gap. She has also created decision support tools that companies can use to close pay gaps.

In a Q&A on ThriveGlobal, Bjarnadóttir talks about her findings, including one counterintuitive finding: Sometimes giving men in an organization a raise can reduce the measured pay gap. Now she’s broadening her research to address additional challenges, such as how to address multiple demographic pay gaps.

Bjarnadóttir talks about what organizations can do to close the gender wage gap:

1. Measure the gap using real-time data as people are hired, leave or get promoted. “Know your current gap — not the gap you had a year ago,” she says.

2. Develop a plan to close the gap and budget for it.

3. If using performance data to justify raises, make sure that data is not biased.

4. Identify barriers and remove them. “Broaden the definition of equity — it is not only about equal pay, but equal opportunity.”

Read more insights from Bjarnadóttir at ThriveGlobal, including what led her to academia, how she shares her Icelandic heritage in the classroom, and how she loves data-driven decision-making, so much so she created a data model to buy a new MacBook on eBay.

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About the Expert(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>

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Robert H. Smith School of Business
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University of Maryland
Robert H. Smith School of Business
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