Bridging the Pay Gap with Analytics Margrt Vilborg Bjarnadttir - PowerPoint PPT Presentation

Bridging the Pay Gap with Analytics Margrét Vilborg Bjarnadóttir Robert H. Smith School of Business | University of Maryland With David Anderson (CUNY), Cristian L. Dezső (Smith) & David Gaddis Ross (UnFL)

People Analytics Target the right Workforce applicants requirements Identify high- value employees at risk Employee satisfaction and productivity

People Analytics Understanding the organization + sentiment analysis + voice analysis

People Analytics

People Analytics • Increased focus on measuring, reporting and understanding the workforce – diversity, gender pay equity, skills gaps, labor utilization, retention rates, real-time feedback, organizational network analysis

Gender Pay Equity • State/country level legislation

Gender Pay Equity • State/country level legislation

Why Now? • State/country level legislation • Business initiatives

AGE $

Ln(W) = β X AGE wages equals A combination of $ terms that can and should explain wages including ... β female – the adjusted gender pay gap

And then What?

Reducing the Gap • The goal is to close (or reduce) the gap while accounting for operational constraints � � � � δ � � min � � Minimize the cost of raises δ δ i � � � δ � � � � � i � � � � � � � � � � � � � � � � � � � ln s t � c � w � � T � . . s t Reach the target goal F iF i i i � � � 0, i � � � � � � � � � i i Do not hand out negative raises

Reducing the Gap • The goal is to close (or reduce) the gap while accounting for operational constraints � � � � δ � � min � � δ δ i � � � δ � � � � � i � � � � � � � � � � � � � � � � � � � ln s t � c � w � � T � . . s t F iF i i i � � � 0, i � � � � � � � � � i i

Cost Savings Optimization The naive approach

Insights • Who gets a raise? – Employees with Low Wages – Women Who “Resemble” Men – Men Who “Typify” Men

Insights 0 A man who’s raise will positively affect the paygap A woman who’s raise will negatively affect the men paygap women Effect on the pay gap per $

Insights 0 Overpaid Little or no relationship between the residual and pay-gap influence men Underpaid women Effect on the pay gap per $

Ensuring Fairness • Balancing efficiency with fairness – Fairness first • Prioritizing fairness over efficiency – Balanced approach • Weighting fairness and efficiency

Cost Savings Optimization The naive approach Fairness first Balanced approach

Applying Analytics to the Pay Gap • We formulated the demographic pay gap problem as a optimization problem – Provide a provable optimal heuristic to solve it – Insights into which employees influence the gap • Cost savings – The optimization approach saves up to 50% of the cost compared to the naïve method – Fairness models typically save between 10 and 20% Empowering the HR analyst

Real Time Data Driven Decision Making Measurement Know, understand and quantify the problem SALARY MODEL RESULTS close Explanatory Variable Value p-value Significance A gender gap of 5.92% Intercept -0,061

Real Time Data Driven Decision Making Measurement Develop a plan RESULTS

Real Time Data Driven Decision Making Measurement Develop a plan Stay Vigilant Understand in real time the effects of hiring and promotional decisions • How will raises affect my pay-gap? • How is the pay-gap progressing? • During a salary review cycle, how do we optimally balance performance with pay-gap considerations?

Summary • People Analytics have taken off – Need for real-time data-driven decision support – We need to translate the math into user friendly tools • Using smart analytics we can address demographic pay gaps fairly and efficiently

margret@rhsmith.umd.edu • The paper: The unintended consequences of On a Firm's Optimal Response to Pressure for Gender Pay Equity, available at https://ssrn.com/abstract=2798938 • Patent filing: U.S. Provisional Patent Application Number 62/248515 • Business application: PayAnalytics LLC