The investigation of the adaptation to life events and life trajectories - Balakrishnan, Dilip (Nanyang Technological University, 2023)
This study uses Machine Learning algorithms to investigate two areas of the Subjective Well-Being field - adaptation and and life trajectories. Data from the Household, Income and Labour Dynamics in Australia data set was used for analysis. When investigating adaptation and anticipation, I found that advanced supervised Machine Learning algorithms (LASSO and a combination of Fixed Effects and Random Forests) produced largely similar findings to that of Linear Regression and Fixed Effect models. My results also showed that adaptation was observed for child birth and divorce, but not for unemployment. I also discovered that men and women adapted differently to certain events. Men were not found to adapt to the death of close relatives while women were found to do so. This might have been due to psychosocial reasons. Similarly, while women were not found to adapt to marriage, men were able to adapt to marriage. Furthermore, I used several clustering algorithms, such as K-Means, and found that there were two types of life trajectories (“Calm” and “Turbulent”). The individuals who had Calm life trajectories appeared to have a higher Mean Life Satisfaction, higher Annual Trend in Life Satisfaction and lower Variability in Life Satisfaction than individuals with Turbulent life trajectories. Moreover, I uncovered that individuals with Turbulent life trajectories experienced a greater impact on Life Satisfaction than individuals with Calm life trajectories when experiencing major life events. Possible reasons were that individuals with Calm life trajectories might possess greater hedonic capital and use the flexibility mindset and flexibility sequence to deal with major life events. The implications from this study may be vital for policymakers to design appropriate interventions to help individuals cope with major life events.