USING NEURAL NETWORKS TO PREDICT WAGES BASED ON WORKER SKILLS

Otto James, Chaodong Han and Stella Tomasi
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Otto James: College of Business & Economics, Towson University, USA
Chaodong Han: College of Business & Economics, Towson University, USA
Stella Tomasi: College of Business & Economics, Towson University, USA

Studies in Business and Economics, 2021, vol. 16, issue 1, 95-108

Abstract: This paper describes the results of our Neural Network (NN) models that predict annual wages based on the combination and levels of 35 different skills possessed by wage earners. These models can estimate the value of skills for skill-based compensation systems. They can be used by employers to determine how much to compensate different combinations of skills and by employees to estimate what they should be paid. Finally, governments can use the models to support the development and analysis of labor policies. We collect and integrate official U.S. Government data on 35 general job skills with the annual wage data for over 900 standard occupations. The skills data is then used as inputs to train an artificial intelligence (AI) neural network (NN) models. The resulting NN models train to above 70 percent accuracy in predicting annual wage levels based on the combination and levels of 35 different skills. This research makes use of authoritative U.S. Government data in new ways that can be used to better understand the connections between general skills and their relationships to wages. The need for these types of analytical tools is all the more important as changes in the job market have been severely impacted by the COVID 19 pandemic and are increasingly at risk of being replaced by automation.

Keywords: Neural Networks; Occupational Skills; Skill-based Pay; Salary Prediction (search for similar items in EconPapers)
Date: 2021
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