Founder of PayAnalytics and the Assistant Professor of Management Science and Statistics at Robert H. Smith School of Business, University of Maryland
Margrét Vilborg Bjarnadóttir, is an entrepreneur and an academic. Dr. Bjarndóttir is the founder of PayAnalytics and the Assistant Professor of Management Science and Statistics at Robert H. Smith School of Business. Dr. Bjarnadóttir holds a B.Sc. degree in Mechanical and Industrial Engineering from the University of Iceland (2001) and a Ph.D. in Operations Research from Massachusetts Institute of Technology (2008). She teaches quantitative modeling and data analytics at the graduate level both in the traditional classroom format as well as online. Before joining the Smith School she was at Stanford’s Graduate School of Business for two years. Dr. Bjarnadóttir’s research focuses on data-driven decision-making, combining operations research modeling with data analytics. In her work she has developed advanced data models to drive decision-making through optimization and predictive analytics. In addition to the main focus of her work, which is health care, she has applied these models to contexts in finance and sports and, most recently, to people analytics. Prior to starting her own company, Dr. Bjarnadóttir advised a number of health care start-ups such as D2Hawkeye, 360Fresh and Benefit Science on cost predictions and risk evaluations. She worked with the parliamentary appointed Special Investigation Commission into the Banking Crash in Iceland and later for the Central Bank of Iceland where she focused on capital control fraud detection in the post-crash era.
Track: Emerging Analytics
Tuesday, April 16, 4:40–5:30pm
Optimal Pay Determination to Reach Diversity Goals
People Analytics are a fast growing field; quantitative methods are becoming main stream in HR departments. There is a great opportunity for the Operation Research Community to play a significant role in how HR decisions are made in the 21st century. In this talk we will review the growing field of People Analytics and take a deep dive into how data driven decision making can support salary decisions, focusing on demographic pay gaps.
The gender pay gap (and other demographic pay gaps) are a topic of discussion in the boardroom, in the media and among policy makers, with multiple new legislation being passed in a number of states as well as across Europe: in Great Britain, France, and Iceland to name a few. While the methodology for determining pay discrimination is known and mostly agreed upon (a log-regression model), how to close a pay gap has remained an open question. Who should get raises and how much? We apply optimization and descriptive analytics to address this knowledge gap. We first describe a cost optimal approach based on statistics and optimization that can meet the “equal pay for equal work” standard for less than half the cost of the naive method of increasing all female workers’ wages equally. In order to balance cost efficiency with fairness we discuss other fairness driven algorithmic approaches that address and close the gender pay gap. These approaches while more expensive than the cost optimal approach can still save significant costs compared to the naïve approach. We further explore the impacts of closing the gap based solely on cost efficiency, which in some cases are surprising, for example we can show that there may exists men within a firm who if they receive salary increases will reduce the gender pay gap. These men strongly typify male employees in terms of traits. We demonstrate the above algorithmic approaches, savings and costs, using real data from our developing partners.