Using Oaxaca-Blinder and Machine Learning to Decompose the Gaps of Happiness and Financial Satisfaction by Gender

Nguyen Thanh Nam, Nguyen Phuong Anh (), Ngo Ngoc Lan Linh and Pham Quang Huy
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Nguyen Thanh Nam: Hanoi University of Science and Technology, School of Applied Mathematics and Informatics
Nguyen Phuong Anh: Hanoi University of Science and Technology, Department of Financial Management, School of Economics and Management
Ngo Ngoc Lan Linh: Hanoi University of Science and Technology, School of Applied Mathematics and Informatics
Pham Quang Huy: Hanoi University of Science and Technology, School of Applied Mathematics and Informatics

A chapter in Proceedings of the 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023), 2023, pp 47-57 from Springer

Abstract: Abstract In this study, we utilize data from the World Values Survey to provide empirical evidence for variations in levels of happiness and financial satisfaction according on gender. While it is generally observed that women tend to report higher levels of happiness than men on a global scale, the distribution of happiness between genders varies at the country level. In this study, we want to analyse the disparity in satisfaction levels based on visible factors, as well as the differential responses of males and females to these qualities. Research purpose: The primary objective of this study is to examine the disparity in financial satisfaction and happiness across genders and determine the extent to which this gap may be attributed to varying objective circumstances experienced by men and women, as well as the differential responses exhibited by both genders towards the similar objective conditions. Research motivation: In nearly all countries and eras, differences between men and women in relation to the majority of social outcomes have been more prevalent than exceptional. Nevertheless, it is arguable that men and women have never before had such equal access to educational attainment, employment, and civil rights. As these objective aspects of life tend to converge for both sexes, the topic of differences in how they affect personal well-being remains unanswered. Research design, approach, and method: To achieve this objective, this topic proposes combining the Supervised Learning model and the Oaxaca-Blinder model. The contribution of our paper is that application of a decomposition technique in the happiness and subjective well-being literature. The Supervised Learning model, a potent supervised learning technique, will be used to develop a classification model of the individual happiness index. This will enable us to better comprehend the relationship between satisfaction factors and indicators, as well as evaluate their effects on men and women. This algorithm will learn from survey data and identify patterns to classify individual contentment. The Oaxaca-Blinder model (Blinder, 1973; Oaxaca, 1973) will assist us in analysing happiness discrepancies based on financial satisfaction, education, social status, and other characteristics. Through a comparative analysis of these characteristics pertaining to males and females, it is possible to ascertain the extent to which each element contributes to the disparity in happiness observed between the two genders. Main findings: We conclude that women are more “optimistic” than males and tend to value objective aspects of their lives more positively. When considering all of the aspects of life that are typically measured, it is observed that women tend to experience higher levels of happiness than what may be expected. In terms of financial satisfaction, the results of this section indicate that women are happier than men, despite men have superior financial situations and are more contented with them. Practical/managerial implications: This study has the potential to yield significant insights that can drive policymaking and interventions aimed at mitigating the disparity in happiness levels between males and females, so fostering a more egalitarian and successful society for individuals of all genders.

Keywords: Happiness; Financial satisfaction; Gender; Oaxaca-Blinder decomposition; Machine Learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-348-1_6

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