Impact of key influencing factors on wage outcomes and strategies for maximizing earning potential using regression analysis
Authors
Ghosalkar, Shruti Shounak
Issue Date
2024
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
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Abstract
Employee attrition had been an area of key concern for most organisations globally. Job satisfaction had been based on the wage an employee earns in a job. Knowledge of the worthy skill sets freshers as well as experienced people had or needed to acquire to qualify for a certain job was seen as necessary. The aim of this thesis is to predict the monthly wage in a country a job would pay based on various factors such as country, sector of employment, occupational level, education, experience, soft skills set etc. The data considered here was from a Global Survey for Adult Skills conducted by PIAAC (Programme for the International Assessment of Adult) in 2014, available on OECD (Organisation for Economic Co-operation and Development) website(Organisation for Economic Co-operation and Development, 2014). This global survey spanned over more than 40 countries and accessed the fundamental cognitive and workplace skills essential for individual’s active engagement in society and crucial for economic growth and success. Six European countries (Finland, France, Greece, Ireland, Spain) were selected for the research. Random forest, Linear and XGBoost Regression models were deployed for predicting the wage for the skill sets. XGBoost was preferred due to better performance metric’s values for R-squared and RMSE. Top five influencing factors found from the best model indicated that Denmark had the highest average wage for professionals working as legislators, senior managers, and mangers. Hence, it was suggested to work in Denmark in the above-mentioned fields for better wages.