Life is more satisfying when evaluated as a whole: A principal component analysis of Mexicans' subjective well-being

Abstract

For decades, there has been consensus that measures of well-being must move past readily available economic data, and newer measurements of well-being are often obtained through individual questions or questionnaires. Despite being more accessible to compare person to person, recent trends have shown that the former singular global evaluations are held back by their propensity to be heavily influenced by the most current, essential, or frequent event in a person's life, ignoring other aspects of life and differentiating what the survey is recording per respondent. Questionnaires, however, present a secondary problem of creating the exact comparability of individual questions. Most methods are laboriously hand-made or questionnaire-specific, which takes more work on a survey level. The present study proposes using a principal component analysis (PCA) algorithm to index subjective well-being from multidimensional data. This comprehensive model, which considers 30 variables related to different aspects of well-being, offers a robust and holistic approach to well-being evaluation. To prove the validity of this method, we use data from the 2021 BIARE Mexican subjective well-being survey. This approach allows us to explore the importance of the different aspects of happiness that could be useful for policy interventions to maintain and improve Mexican well-being. The results of our study show that the global evaluation of life satisfaction was generally higher than the Happiness Index, suggesting that life is more satisfying when evaluated globally as the aggregated satisfaction or feeling of each aspect of life. These findings hold promise for the development of effective policy interventions to enhance well-being in Mexico and beyond.