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What Makes People Happy? Evidence from International Data


Individuals’ life satisfaction varies widely across countries. Differences in income explain a large part of this variation, but not all. The purpose of this study is to identify the country-level determinants, in addition to income, that best explain life satisfaction, with the objective of understanding how a country’s policies and developmental strategies may affect the well-being of its residents. To do so, we pool life satisfaction data and key economic, political, social, and environmental variables (including GDP per capita, unemployment rate, level of corruption, social capital, CO2 emissions and particulate matter (PM) concentrations) for a cross-section of countries to calculate the relative contribution of political, social, and environmental variables vis-à-vis economic factors to explain life satisfaction. Regression models indicate that religiosity, social capital, and pollution are among the strongest determinants of differences in life satisfaction. Employing a relative contribution analysis, we find that after individual characteristics, GDP is the most important predictor of life satisfaction, but that country fixed effects remain stubbornly important.

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  1. The group has highlighted the importance of mental health integration for “whole-person care,” supported a minimum wage increase to reduce the negative effect of income disparities on happiness, and promoted the prioritization of economic stability over economic growth to improve well-being.

  2. Jorm and Ryan (2014) review the literature and identify national income, inequality, social welfare, individualism, political stability, democracy, and life expectancy as important predictors of national SWB.

  3. In some specifications, where we include smaller sets of explanatory variables, we observe 96 countries (listed in "Appendix" Table 6).

  4. GDP per capita is included in log form to allow for nonlinear effects of GDP consistent with a diminishing marginal utility of income.

  5. We use the WVS sampling weights to ensure that the samples are representative of each country.

  6. Technically, DA is defined as the squared semi-partial correlation. The level of analysis is defined for all 2(p−2) subset models for which the comparison of each pair of predictors is relevant.

  7. Graham and Nikolova (2015) use the decomposition of explained variance proposed by Field (2003), and Lamu and Olsen (2016) apply a Shapely value decomposition method.

  8. This result may be driven by the education question in the WVS, which asks the age at which individuals finish their full-time education and does not reflect the exact level of education obtained; or as Helliwell (2003) argues using the same dataset, the insignificant association of education might have already be taken into account due to inclusion of income, health and trust.

  9. In a robustness analysis, we investigate impact of income inequality, measured by Gini coefficient, on LS. However due to large number of missing values (more than 50% of the sample), we exclude this variable from our main regressions results. The regression result of the model with a Gini variable showed that the Gini coefficient did not have a statistically significant effect on LS.

  10. The general DS is performed with Stata syntax “Domin.”.

  11. While Sd. DS and DS both convey the same information, excluding the unexplained variation in Sd. DS better reflects the relative importance of the variables in our analysis.

  12. Achieving complete dominance implies that conditional and general dominance, which are weaker criteria of dominance analysis are also obtained (Azen and Budescu, 2003).


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Correspondence to Mona Ahmadiani.

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The first five columns of Table 6 below show the unconditional country averages of self-reported LS data from the WVS. That is the national average of LS (before controlling for individual characteristics) for each wave as well as the average of the four waves. Data is ordered from highest to lowest LS, based on the average across the four waves. The last column shows the conditional country average LS from multi-level regression for years 1995 to 2014.

See Tables 6,

Table 6 Self-reported life satisfaction from the World Values Survey
Table 7 Multilevel regression results


Table 8 Relative importance (Complete Dominance Test)


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Ahmadiani, M., Ferreira, S. & Kessler, J. What Makes People Happy? Evidence from International Data. J Happiness Stud 23, 2083–2111 (2022).

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  • Life satisfaction
  • Subjective well-being
  • International comparisons
  • World Values Survey

JEL Classification

  • I31
  • C31
  • D63