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However, this observation is inconsistent with employee observations shared with Perez. Students must, therefore, adjust the pay gap estimates to reconcile employee perceptions with the pay data. Students are provided with a cross-sectional pay data file generated for Elemental's Binary Gender Pay Report. (It is a supplemental Excel file included with the case and teaching note). Informed by employee quotes provided in the case, students perform equity analytics to estimate Elemental's binary gender pay gap. The analyses start with an unadjusted pay gap estimate and then proceed through a series of adjustments. These analyses reveal that although there is no unadjusted gender pay gap across Elemental's four divisions there are distinct pay disparities within individual divisions. A key insight of the case is that aggregating unit pay data at the organization level can mask substantial pay disparities among an organization's constituent units. Students will attribute estimated pay differences to differential treatment of men and women employees (i.e., different behaviors produce different pay) or to disparate impacts of organizational practices (i.e., equivalent treatment produces different pay). More specifically, the instructor can use the equity analytics framework (detailed in the teaching note) to demonstrate which of Elemental's divisions exhibit one of the pay disparities featured in the equity analytics 2 x 2 framework: (1) differential allocations, (2) differential valuations, (3) disparate allocations, or (4) disparate valuations. Each of the four disparity-generating processes necessitates a different approach to closing the pay gap.

However, this observation is inconsistent with employee observations shared with Perez. Students must, therefore, adjust the pay gap estimates to reconcile employee perceptions with the pay data. Students are provided with a cross-sectional pay data file generated for Elemental's Binary Gender Pay Report. (It is a supplemental Excel file included with the case and teaching note). Informed by employee quotes provided in the case, students perform equity analytics to estimate Elemental's binary gender pay gap. The analyses start with an unadjusted pay gap estimate and then proceed through a series of adjustments. These analyses reveal that although there is no unadjusted gender pay gap across Elemental's four divisions there are distinct pay disparities within individual divisions. A key insight of the case is that aggregating unit pay data at the organization level can mask substantial pay disparities among an organization's constituent units. Students will attribute estimated pay differences to differential treatment of men and women employees (i.e., different behaviors produce different pay) or to disparate impacts of organizational practices (i.e., equivalent treatment produces different pay). More specifically, the instructor can use the equity analytics framework (detailed in the teaching note) to demonstrate which of Elemental's divisions exhibit one of the pay disparities featured in the equity analytics 2 x 2 framework: (1) differential allocations, (2) differential valuations, (3) disparate allocations, or (4) disparate valuations. Each of the four disparity-generating processes necessitates a different approach to closing the pay gap.