Wage gaps between gender and ethnic groups receive a lot of media coverage and are a major issue in our society. Identifying and addressing them effectively and accurately shields companies from lawsuits and bad publicity, whilst promoting equality and fairness. For large organizations it can be difficult to identify these gaps as they are spread across thousands or tens of thousands of employees in a variety of roles, locations, and work experiences. Analyzing this data correctly is important as surface level trends don’t always tell an accurate story at the individual level. In this case study, we created a machine learning model to predict expected wages and determine the extent of this international stationary retailer's wage gap.
SFL addresses this issue by developing a predictive model based on one specific demographic based on all possible attributes except the demographic split (job history, location, experience, position). This model is first validated and tuned such that it successfully and accurately predicted salary within that demographic. We then apply this model to other demographic groups in order to see predicted expected salaries. A gap in wages would be evident if the model trained on one race or gender did a poor job of predicting salaries for another race or gender. The discrepancy between the model prediction and actual salary was used to determine the per individual wage gap. This approach also provides a higher level of resolution than simply fitting a model over the whole dataset as it allows identification and quantification of specific groups or types of employees as being unfairly treated.
Almost every company can benefit from this type of analysis. Companies gain highly granular information, down to the individual level, on how their minority employees are being paid relative to other groups. This allows them to address this issues accurately and specifically. Problems that are localized to certain demographics, or certain roles, or employees of certain demographics in certain roles can also be identified. Companies can then correct these issues or if no issues are found be confident that they are doing the right thing. Similar types of modelling can be performed in a vast variety of domains using employee data. This may include skills mappings, optimizing internal transfers and numerous HR related projects.