Quality of life (QoL), encompassing subjective and objective dimensions, is often inadequately captured by solely objective measures like income, CO2 emissions, or life expectancy. While these objective indicators are crucial, they fail to fully reflect the multifaceted nature of community well-being. QoL encompasses physical, material, social, and emotional well-being. Recent decades have seen a rise in the study of subjective QoL's social, behavioral, and environmental dimensions from policy perspectives. Social capital (SC), encompassing cognitive (individual predisposition for societal benefit) and structural (inter-individual interaction) aspects, has garnered significant attention in environmental, economic growth, and social welfare literature. Emerging research recognizes SC's role in subjective QoL. Subjective QoL indicators often measure individual life satisfaction across various aspects (social life, health, environment, wealth, work). Existing research positively correlates SC with subjective QoL concerning health and life satisfaction. However, most studies focus on single QoL domains and are predominantly from developed countries. This study differentiates itself by encompassing both developed and developing countries across all continents, comparing these groups using a multinational survey of 100,956 respondents from 37 countries. The study examines the impact of SC on self-reported life satisfaction and health, and perceived economic inequality, treating these as dependent variables.

Previous research has established a positive correlation between self-reported social capital and both subjective health and life satisfaction. Studies like Herian et al. (2014), Christian et al. (2020), and Elgar et al. (2011) examined the relationship between self-reported SC and health, while Portela et al. (2013), Puntscher et al. (2015), Hoogerbrugge and Burger (2018), Christian et al. (2020), and Li et al. (2021) investigated the link between SC and life satisfaction. This current study builds upon this existing research by considering three key factors: self-reported life satisfaction, health, and perceived economic inequality, acknowledging the interconnectedness of these domains with physical and mental health. A limitation of previous studies is their focus on developed countries, leaving a research gap for developing countries. This study addresses this gap by including a diverse range of nations, enabling a comparative analysis between developed and developing countries.

This cross-sectional study utilizes data from a multinational survey (Chapman et al., 2019) encompassing 100,956 respondents across 37 countries. Country selection was based on data collection feasibility and budgetary constraints. Respondents were randomly selected nationwide, aiming for representation across age and gender categories. Data collection (June 2015 – March 2017) employed both internet and face-to-face surveys, with appropriate translation and quality checks. Subjective QoL was measured using three variables: self-reported life satisfaction, health, and perceived economic inequality, each assessed using a single item on a five-point scale. For comparability, these categorical variables were reclassified into dichotomous outcomes (1 for high satisfaction/good health/high inequality, 0 otherwise). The key independent variable was SC, assessed using items measuring social trust (belief in people/organizations, neighborhood safety) and civic engagement (formal and informal participation). An individual-level SC score was calculated as the arithmetic average of these factors, then aggregated at the country level using a weighted average. Individual-level independent variables included age, gender, SC score, household income (transformed to USD using purchasing power parity), and educational attainment. Country-level variables included average SC and country groups (low-income vs. high-income, based on World Bank GNI per capita data). Multilevel logistic regression models (using MLwiN software) analyzed the effects of individual and country-level characteristics on the dependent variables. Interaction terms examined the effects of average SC with individual educational attainment and income, and SC score with country groups.

Descriptive statistics show that approximately 74.1% reported high life satisfaction, 72.7% good health, and 52.2% reported low economic inequality. Multilevel logit models revealed that the individual-level SC score was positively associated with higher odds of reporting life satisfaction (ORs = 2.33) and good health (ORs = 2.06). Younger age, female gender, higher education, and higher income were also associated with higher life satisfaction. Model 5, which incorporates cross-level interactions, shows that the odds of reporting life satisfaction increased with higher country-level SC across all education groups, with the largest increase for those with tertiary education. Higher income groups were more likely to report satisfaction in countries with low SC, but this gap narrowed with increasing SC. A similar pattern was observed for health, with the exception that the primary education group didn't follow the same pattern of increase. Low-income countries showed a larger increase in life satisfaction and health than high-income countries with increasing SC. Regarding perceived economic inequality, higher education decreased the likelihood of reporting a large gap, and the effect was more pronounced in low-income countries. Low-income countries showed a negative relationship between increased individual SC and perceived economic inequality, while high-income countries showed a positive relationship.

This study demonstrates a positive association between SC and subjective QoL, measured by life satisfaction and health, while also revealing a negative association with perceived economic inequality at the individual level. The findings align with research linking trust (a component of SC) to positive health outcomes, life satisfaction, and economic inequality. The interaction effects highlight the complex relationship between education, income, SC, and QoL. Higher education enhances QoL outcomes and strengthens the negative impact of SC on economic inequality. However, contrary to expectations, low-income countries showed better subjective health and satisfaction than high-income countries, highlighting the significant impact of non-economic factors like SC on well-being in low-resource settings. Income levels had a positive effect on satisfaction and health, but the gap between income groups reduced with higher country-level SC, suggesting that social cohesion can mitigate income disparities’ impact on subjective well-being.

This study provides robust evidence of a positive correlation between SC and life satisfaction and health, and a negative association with perceived economic inequality. Low-income countries displayed superior life satisfaction and health outcomes compared to high-income countries with high SC. The interaction between country groups and individual SC underscores SC’s potential to improve QoL in low-income countries, particularly by reducing perceived economic inequality. High-income countries could benefit from learning from the social mechanisms facilitating higher SC levels in low-income countries. Future research should explore broader QoL dimensions, use longitudinal designs to establish causality, address limitations related to survey design (e.g., single-item measures) and country-level sample size, and consider a wider range of contextual factors to provide further insights into the dynamic interplay between SC and subjective QoL across diverse cultural settings.

While the study controls for several sociodemographic factors, other unmeasured variables might have influenced the results. The use of single items to assess the three QoL domains could limit the depth of analysis, and the cross-sectional design precludes causal inference. Additionally, the relatively small number of countries limits the generalizability of the country-level findings. Finally, this study mainly focused on three domains of subjective quality of life in terms of data availability, however there are more domains within the concept of subjective quality of life that were not covered in this study. Future studies employing longitudinal datasets and more comprehensive measures would help clarify the causal relationships and contextual factors influencing this relationship.