Cross-national research on subjective well-being has predominantly utilized single-item measures of life satisfaction or global happiness (e.g., “How happy are you?”). These are treated as evaluative indicators because they prompt respondents to make a global assessment of their life as a whole. In contrast, emotional well-being encompasses affective experiences over a specific time frame14. This component is commonly assessed by asking respondents about the frequency of positive and negative emotions experienced during a recent period, such as yesterday or the past week.
The relationship between economic growth and subjective well-being gained prominence with Easterlin’s1 seminal observation that despite substantial economic growth in the United States, average levels of life satisfaction showed no clear upward trend. This finding, later termed the Easterlin Paradox, suggested that beyond meeting basic needs, increases in national income do not translate into improvements in subjective well-being. The Easterlin Paradox has sparked considerable debate and empirical investigation. Several mechanisms have been proposed to explain why economic growth seems to fail to improve well-being. Social comparison theory suggests that well-being depends not on absolute income but on relative position within society15. If economic growth preserves or even increases inequality, the well-being gains from higher absolute income may be offset by unchanged or worsened relative position. Adaptation theory proposes that individuals adjust to improved circumstances, returning to baseline well-being levels despite permanently higher incomes16.
Numerous studies challenge the Easterlin Paradox using global measures of life satisfaction and happiness. Deaton17, using Gallup World Poll (GWP) data, found a strong positive cross-country link between logged GDP per capita and life satisfaction. This effect held across all income levels, with no diminishing returns and possibly stronger effects in wealthy nations, suggesting a robust association with subjective well-being even at high income. Further evidence from Stevenson and Wolfers18,19 and Sacks et al.2, analyzing multiple large-scale, long-term international datasets, established consistent positive associations between subjective well-being and GDP. Their work showed a consistent log income gradient across individuals, countries, and over time, highlighting the importance of increases in absolute income with no saturation point. Inglehart et al.20, using World Values Survey data (1981–2007), also found positive relationships between life satisfaction and national income, with upward happiness trends alongside development in most countries. Analyses with a particular focus on extensive time-series data offer more direct rebuttals of Easterlin’s assertion of no long-term growth-happiness correlation. For example, Veenhoven and Vergunst21 used extensive data (1531 data points, 67 nations, 10–40 years) and found a positive correlation between GDP growth and rising subjective well-being (life satisfaction and happiness). Cai et al.22 showed a similar upward happiness trend with China’s rapid economic growth. Recent European studies find the same pattern. Frech et al.23, analyzing 33 European countries (2002–2018), found that GDP per capita is associated with life satisfaction both between countries and within countries, particularly for the 10% worst-off, affirming a robust growth link with life satisfaction even in wealthy European nations.
While the evidence consistently supports a positive overall relationship between economic growth and life satisfaction, several studies highlight potential nuances, methodological issues, and moderating factors. For example, in a time-series study of ten European nations from the early 1980s to 2018, Easterlin and O’Connor24 found the relationship between long-run changes in happiness and economic growth to be statistically insignificant. Their analysis concluded that these happiness trends were instead principally determined by the generosity of welfare state programs. Mikucka et al.3 emphasized that the relationship between economic growth and life satisfaction varies considerably depending on institutional and social contexts. Their analysis revealed that economic growth unconditionally improved life satisfaction in transition economies but conditionally, dependent on increased social trust and reduced income inequality, in non-transition countries. Further, Bartolini and Sarracino25 demonstrated variability in the correlation between GDP and life satisfaction depending on the time horizon; stronger correlations appeared in the short term but diminished over medium to long-term periods. Despite these nuances, the overarching conclusion from robust cross-national and longitudinal evidence is that higher GDP per capita is reliably associated with increased life satisfaction and happiness, although debates about the precise long-term effects continue due to the limited availability of very long time-series data for a large set of countries.
A few studies have examined economic development and emotional well-being using explicit measures of affect in the Gallup World Poll (GWP). For example, Diener et al.26 found substantially stronger cross-sectional correlations between GDP per capita and life satisfaction (r = .78) than positive affect (r = .29) and negative affect (r = − .04) between 2005 and 2011. In addition, the longitudinal analysis showed that while GDP per capita predicted changes in life satisfaction over time, it did not predict changes in positive or negative affect. Further assessing longitudinal associations in GWP data, Diener and Tay27 reported a significant, albeit modest, positive correlation between changes in GDP and changes in life satisfaction between 2006 and 2013 (r = .08). For specific emotions, their study indicated that changes in GDP had non-significant correlations with changes in enjoyment (r = − .02) and anger (r = − .06) but were significantly negatively correlated with changes in sadness (r = − .10) and stress (r = − .11). Consistent with the general pattern of a stronger association between economic development and life evaluations than with emotional well-being, large-scale panel data analyses by Helliwell et al.28, employing pooled OLS regressions on GWP data (2005–2018), found that log GDP per capita was a strong and positive predictor of life evaluations, while it was not significantly associated with positive affect and negative affect. These findings suggest a fundamental difference in how economic development relates to life satisfaction versus emotional well-being. However, the reliability of previous studies is reduced by several methodological concerns that, so far, have received limited attention.
First, emotional well-being in the GWP is assessed using composite measures constructed from dichotomous survey items (“yes/no” questions about emotional experiences yesterday), whereas life satisfaction is measured with an 11-point numeric scale. This difference in measurement scales could reduce the observed relationship between GDP and emotional well-being due to the lower variance and sensitivity of dichotomous measures compared to numeric scales. Evidence supporting this concern comes from individual-level research. Killingsworth29 found that when emotional well-being was assessed by means of experience sampling using a continuous sliding scale, no saturation point in the relationship with income was observed. In contrast, a saturation point emerged when emotional well-being was measured using dichotomous items identical to those in the GWP, suggesting that the response scale significantly impacted the observed associations.
Second, some emotional well-being items in the GWP have questionable content validity for capturing the valence of affective experiences. For example, items like “Did you laugh or smile a lot yesterday?” may not adequately represent the valence of emotional states, as individuals can experience positive emotions without necessarily expressing them through overt behaviors like smiling or laughing. This concern is particularly critical across different cultural contexts, where emotional expression norms vary significantly30,31. Similarly, the item “Did you experience anger a lot yesterday?” is problematic for measuring negative affect, as anger can serve adaptive functions and may not always be experienced as purely negative, particularly when it motivates corrective action32.
Third, previous studies have typically analyzed positive and negative affect separately rather than estimating an overall affect balance measure. While separate analyses are informative for identifying if economic development is more strongly related to pleasant or unpleasant emotional experiences, they do not directly assess whether economic development is associated with the overall affective quality of daily life. This limits comparability with life satisfaction, which is analyzed as a broad global indicator. This approach may also obscure associations that operate in opposite directions across affect components.
Fourth, the GWP assesses emotional experiences with reference to the previous day, which makes the measures particularly sensitive to short-term fluctuations and situational factors. While country aggregation mitigates idiosyncratic individual variation, it cannot eliminate systematic distortions from shared experiences, such as major news events, holidays, unusual weather, or other fieldwork-period effects that may affect entire populations without reflecting more stable well-being conditions. The volatility of a one-day snapshot is especially consequential in longitudinal analyses, where small to moderate within-country changes over time are central to the research question.
Fifth, these methodological limitations become particularly critical when comparing countries with vastly different cultural norms and developmental stages, especially if measurement equivalence has not been established. The global scope of the GWP encompasses nations ranging from subsistence economies to advanced post-industrial societies. Across these diverse contexts, norms surrounding emotional expression and interpretations of survey questions may differ substantially. Without evidence of measurement invariance, observed cross-national variations in the relationship between GDP per capita and emotional well-being risk reflecting measurement artifacts rather than genuine cultural or developmental differences.
Given these methodological concerns in previous studies, the aim of this study is to analyze the relationship between economic development and emotional well-being, both cross-sectionally and over time, using psychometrically adequate measures. We use multi-item scales measuring emotional well-being with graded response options that are high in content-validity and include items that have been cross-country validated in prior research33,34. To enable direct comparison with previous research on life satisfaction, we analyze a single-item measure of life satisfaction. This comparative approach allows us to assess the extent to which these different indicators of subjective well-being converge or diverge in their relationships with economic development, providing more reliable evidence on whether higher national income is associated with daily emotional experiences alongside cognitive life evaluations. The period covered (2006–2023) encompasses significant economic upheaval, including the 2008 financial crisis, the COVID-19 crisis, and recent inflationary pressures, providing variation in economic conditions that can illuminate the relationship between economic development and subjective well-being over time.