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Analysis of Gender Wage Gap and Contributing Features Identification Using Self-Organizing Maps

Abstract

Gender inequality arises when women and men are not treated equally. The gender pay gap is the difference in aggregate wages between men and women. This gap derives from various factors such as differences in educational choice, preferred jobs and industries, and types of positions held. This paper investigates and classifies gender discrimination and pay gaps using a specialized artificial neural network called self-organizing maps. Three different datasets with different attributes were used. The main focus of data classification is on gender and pay attributes. Gender discrimination and wage gaps are discussed in detail, and the proposed model addresses an algorithm for the identification of pay gaps in the dataset. The results are discussed, and contributing attributes are identified.

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