Essays on Well-Being Comparisons, Gender Dynamics, and Economic Resilience. This thesis employs big data and computational methods to investigate aspects of human behavior and well-being across four diverse studies. Chapter 1 introduces a new measure of financial vulnerability using survey data to assess the resilience of households in seven EU countries during income shocks, such as those triggered by COVID-19. Using microsimulation techniques, it finds that employment protection benefits significantly reduced vulnerability, though disparities remain for certain groups, notably younger individuals, single parents, women, and non-EU-born individuals. Chapter 2 leverages over 60 million online hotel reviews to explore cultural heterogeneity in self-reported assessments of well-being across nationalities. The study reveals that differences in scale usage for identical experiences correlate with broader life satisfaction scores, highlighting the complex influence of national characteristics on subjective well-being measures and challenging universal comparisons. Chapter 3 narrows this focus to examine the impact of minor events, such as a football match, on well-being reports, showing that responses can shift significantly depending on outcome, thus questioning the stability of subjective assessments in cross-national analyses. Chapter 4 utilizes machine learning and audio analysis to study peer interactions in academic seminars, particularly regarding gender dynamics. Results reveal a pattern where female speakers face more frequent, earlier, and often negatively toned interruptions. These findings contribute to the literature on gender biases in professional settings. Together, these studies demonstrate how computational approaches and high-dimensional datasets can advance economic research, providing new insights into well-being, cultural reporting biases, and gender dynamics. |