Abstract
Using nationally representative data from 202,898 participants in the Global Flourishing Study, this work examines factors associated with financial well-being across 22 countries. We investigate how demographic factors—including age, gender, marital status, employment status, education, religious service attendance and immigration status—are correlated with financial well-being (as assessed through four dimensions). Additionally, we analyse associations between recalled early-life conditions, such as parental marital status and childhood health, with financial well-being in adulthood. Our findings reveal cross-national differences in levels of financial well-being and its demographic correlates. Early-life conditions were also consistently associated with adult financial well-being, although these associations varied substantially across countries. These results suggest that understanding financial well-being should encompass both current sociodemographic factors and early-life experiences within the unique cultural and socioeconomic contexts of different populations.
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Main
Financial well-being is a multifaceted concept often understood as the ability to meet one’s current and future financial obligations, feel secure about one’s financial future, and have the freedom to make choices that enhance one’s quality of life1. It encompasses both subjective and objective measures of one’s financial situation2.
The approach to financial well-being that combines subjective and objective financial indicators allows for a more comprehensive understanding of how financial circumstances intertwine with individuals’ lives. Although income levels remain critical for financial well-being as they reflect immediate resources, examining individuals’ perceived financial well-being highlights the psychological and behavioural dimensions that objective measures alone may overlook.
Within the financial well-being framework, an important role is attributed to the lack of financial fragility, which builds on the absence of limitations, control over money matters, and of the absence of anxiety in the financial sphere3,4. These attributes often correspond to a person’s ability to secure the financial resources necessary to live comfortably today and in the future5. A strong theoretical foundation for conceptualizing financial well-being, through financial security, the ability to live comfortably on current income, and income level itself, is provided by Maslow’s hierarchy of needs. Basic physiological and safety needs, provided by the security of income, form the base of the hierarchy by alleviating anxiety over immediate ‘deficiency needs’, such as meeting daily expenses, handling near-term purchases and maintaining emergency savings3. Financial security, can thus be positioned as the opposite of financial worry, which relates to negative, repetitive thoughts about the uncertainty of one’s current or future financial situation6,7.
Subjective measures of financial well-being capture perceptions of lifetime achievements and relative deprivation, aspects not fully reflected in objective indicators of socioeconomic status. These subjective indicators may potentially influence or correlate with other key health and well-being outcomes, perhaps even more strongly than objective financial measures8,9. Identifying the key factors associated with financial well-being and economic disparities among individuals has gained substantial traction as a critical area of research. Much of the literature focuses primarily on objective indicators of socioeconomic status, such as personal or family income, wealth, educational attainment and employment or occupation10. However, recent work has also emphasized the importance of individual perceptions of financial well-being11,12.
The challenges involved in improving financial well-being may differ systematically by country due to economic and cultural differences13. People living in low-income countries may be more likely to suffer from low financial well-being, but rumination about the lack of fulfilment of material needs may also be prevalent in high-income countries7. Financial control differs across nations and is often associated with financial well-being, given that it enables the efficient use of financial resources and fosters a sense of personal control over financial matters14. However, the individualistic orientation of much existing research, influenced by Western-centric perspectives, has been questioned for its limited applicability across diverse cultural and socioeconomic contexts15,16. The predominance of studies on various aspects of financial well-being conducted in Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies raises questions about the universality and generalizability of current findings, and suggests a need to incorporate a broader range of cultural and contextual perspectives to provide meaningful conclusions. Examining financial well-being across nations and cultures around the world may inform strategies to promote both financial well-being and other aspects of overall well-being, such as health and quality of life, which are irrevocably intertwined17,18.
In this Article we use data from 22 culturally diverse countries to conduct an exploratory study identifying the set of principal correlates of financial well-being, as well as the likely nature of associations between early-life conditions and financial well-being in adulthood. Our first objective is to examine cross-sectional differences in subjective perceptions of financial situation measured as (1) financial security (the ability to meet normal living expenses), (2) material security (having sufficient resources for basic needs such as safety, food or housing) and (3) subjective financial well-being (comfort with one’s current income).
Our second objective is to study demographic factors relevant to subjective and objective financial situation, specifically age, gender, marital status, employment status, education, religious service attendance and immigration status. Existing research, conducted primarily in high-income countries, suggests that these factors are linked with financial outcomes in multiple ways. For example, we know that, in some countries, financial well-being tends to increase with age11, that there are gender differences in risk-aversion, financial knowledge, and earnings19,20, and that marital status, education and religious participation have been associated with financial well-being21,22,23. It is not clear, however, whether this is true in the multinational context, as relatively little work has been conducted in non-Western and low-income countries.
Similarly, although previous research has provided important insights regarding the associations between religion and financial outcomes24,25, including evidence that certain practices and beliefs are correlated with indicators of wealth and poverty26, it has largely focused on Christianity, providing relatively less insight into other religions. Previous research indicates that the relationship between immigration and financial outcomes is complex, in part because both immigrant populations and host countries are diverse, and there is thus a call for additional cross-national comparative research27. A contextual approach to financial well-being aligns with trends in positive psychology, with the emergence of paradigms such as systems-informed positive psychology and third-wave positive psychology, which emphasize the importance of contextual factors in shaping many aspects of well-being28,29,30.
Our third objective is to examine a set of factors pertaining to early-life conditions that are potentially associated with financial well-being in adulthood. Our study examines how different aspects of a child’s upbringing are associated with financial well-being in adulthood.
More specifically, we assess whether childhood factors such as age cohort, gender, parental marital status, religious service attendance during childhood, quality of relationships with mother and father, feeling like an outsider growing up, experiences of physical or emotional abuse, health, and financial status of the family during childhood are associated with financial well-being in adulthood. A significant body of evidence exists on links between childhood conditions and general well-being31,32, mental and physical health33,34, as well as poverty in adulthood, especially for individuals from low-income families35 or non-white individuals in Western, Educated, Industrialized, Rich, and Democratic countries36, but relatively little is known about the role of non-financial childhood factors in predicting financial well-being later in life.
Childhood is a key developmental phase influencing the moral principles and norms that govern an individual’s attitudes and actions throughout their life37. Foundational work by Cunha and Heckman38 has been instrumental in highlighting the role of early childhood development in forming cognitive and non-cognitive skills, which significantly influence long-term labour market outcomes and overall human capital development. This aligns with Keane and Wolpin’s39 findings on the enduring impact of childhood conditions on career decisions and economic behaviour in adulthood. Childhood circumstances can influence an individual’s susceptibility to negative subjective assessments of financial conditions, potentially serving as a manifestation of a persistent poverty trap. Limited access to resources can induce not only financial hardship, but also psychological distress, including withdrawal and anxiety related to navigating the financial market40,41. Negative family environments during childhood are linked with generally higher levels of anxiety42, which might translate into higher levels of concerns related to financial well-being. Understanding which early-life experiences are linked with adult financial well-being could inform interventions aimed at enhancing financial outcomes among disadvantaged groups.
Beyond examining demographic and childhood correlates of financial well-being in the international context, we also focus on country-specific frameworks. Little empirical research exists outside the United Kingdom and the United States. A recent review found that 65% of the published articles in the consumer studies area (the field with a principal focus on financial well-being) have used US samples13, and only single, selected studies reflected the situation in lower-income countries. This is not surprising given data limitations and the relatively recent focus on subjective aspects of financial status. We address this gap in two ways. First, we allow for heterogeneity in the estimated associations between demographics and childhood correlates of financial well-being across countries. This approach provides insights into how contextual factors including national culture and institutions may interact with individual-level behaviour and can explain the heterogeneity of the obtained results in the international context43. Second, we replicate our primary analysis across four classifications of countries—high income versus low income, collectivist versus individualist, gender egalitarian versus non-egalitarian and secular versus religious—to examine how the associations vary between the predefined groups of countries.
For this study, we use nationally representative data from 202,898 participants from 22 geographically and culturally diverse countries and territories (Argentina, Australia, Brazil, Egypt, Germany, Hong Kong, India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Türkiye, the United Kingdom and the United States) collected under the umbrella of the Global Flourishing Study (GFS).
Descriptive statistics, weighted for national representativeness, are presented in Tables 1, 2 and 3. Financial well-being is examined using four indicators: financial security (FS), material security (MS), subjective financial well-being (SFWB) and household income (HI).
Results
Financial well-being in a cross-national perspective
Table 4 provides ordered means and proportions (highest to lowest), dispersion and Gini coefficients for financial security (FS) and material security (MS), as well as subjective financial well-being (SFWB) for all 22 countries. The results show substantial cross-national variation in financial well-being. Sweden consistently ranked highest in FS (mean, 7.23 on a 0–10 scale, where 0 means ‘worry all of the time’ and 10 means ‘do not ever worry’; 95% confidence interval (CI), 7.17–7.28; s.d., 2.70; Gini, 0.21), MS (mean, 7.50; 95% CI, 7.45–7.55; s.d., 2.57; Gini, 0.18) and SFWB (proportion, 0.95; CI, 0.94–0.95; s.e., 0.002). Australia, Poland, Hong Kong and the United States also ranked high on FS, MS and SFWB. In contrast, the lowest scores on FS and MS were found in Argentina (3.95 and 3.79) and Brazil (4.02 and 3.91), whereas Brazil (0.61) ranked higher on SFWB, with Argentina (0.48) still being a third from the bottom. Standard deviations and Gini coefficients also tended to be higher in these countries, indicating substantial variability of financial well-being in lower-scoring countries. Although, as expected, high-income countries tended to rank higher than lower-income countries, some countries in the latter group scored similarly or higher than their counterparts from the former group (for example, Egypt, the Philippines and Tanzania versus Japan in FS and MS).
FS, MS, SFWB and HI were all positively correlated (Supplementary Table 1). FS and MS exhibited the strongest correlation (r = 0.71), while SFWB showed moderate correlations with FS (r = 0.43) and MS (r = 0.38). Household income exhibited weaker correlations with FS (r = 0.19) and MS (r = 0.18), but was moderately correlated with SFWB (r = 0.37). These patterns were generally consistent across countries, with some variations, such as stronger correlations observed in high-income countries such as the United States, Australia and Germany, and weaker correlations in African countries, including Kenya, Nigeria, Tanzania and South Africa.
Demographic variations in financial well-being
Tables 5 and 6 show meta-analytic means and proportions, pooling estimates across the 22 countries, for all outcomes by demographic categories (for country-specific analyses, see version b of Supplementary Tables 2–23). FS, MS and SFWB were all highest among the oldest age group. Means for the first two were 6.74 (CI, 6.16–7.31) and 7.40 (CI, 7.13–7.67), respectively, and the proportion for SFWB was 0.80 (CI, 0.64–0.90). In contrast, the highest proportions in the top HI quintile occurred in the 30–39, 40–49 and 50–59 age categories (lower among the younger and older).
FS was slightly higher among males (5.79; 95% CI, 5.39–6.19) compared to females (5.39; 95% CI, 4.93–5.86) on average across countries included in the GFS. Similar conclusions held for SFWB and HI, but MS was slightly higher for women.
Married individuals reported the best financial well-being. Although the level of FS (mean, 5.79; 95% CI, 5.30–6.29) was second-highest among groups divided with respect to marital status, they reported the highest MS (mean, 7.23; 95% CI, 6.98–7.49), the highest SFWB (proportion, 0.70; 95% CI, 0.60–0.79), and the largest proportion of married individuals was in the top HI quintile (0.24; 95% CI, 0.19–0.29). In contrast, separated individuals fared worst on FS (mean, 5.13; 95% CI, 4.68–5.57), MS (mean, 6.56; 95% CI, 6.28–6.84) and SFWB (proportion, 0.54; 95% CI, 0.46–0.63). Although widowed individuals were the least likely to be in the top HI quintile (proportion, 0.09; 95% CI, 0.06–0.12), they scored the highest on FS, but ranked lower in the other two indicators.
Retired individuals reported the highest FS (mean, 6.26; 95% CI, 5.77–6.75) and MS (mean, 7.19; 95% CI, 6.94–7.43), whereas the highest SFWB was measured in students (proportion, 0.73; 95% CI, 0.66–0.78). The highest proportion in the top HI quintile was found among individuals employed by an employer (0.27; 95% CI, 0.22–0.32). Unemployed individuals experienced the lowest FS (mean, 4.53; 95% CI, 4.16–4.90) and the lowest SFWB (proportion, 0.48; 95% CI, 0.42–0.54).
All financial variables strictly increased across education categories. Individuals with 16+ years of education fared best in terms of FS (mean, 5.99; 95% CI, 5.56–6.42) and MS (mean, 7.16; 95% CI, 6.95–7.36) compared to those with only up to 8 years of education (FS mean, 5.36; 95% CI, 4.91–5.81; MS mean, 6.95; 95% CI, 6.67–7.24). SFWB was also highest for the former group (proportion, 0.76; 95% CI, 0.67–0.82), as was the highest proportion in the top HI quintile (0.37; 95% CI, 0.30–0.44).
Individuals who attended religious services more than once a week reported the best FS (mean, 6.04; 95% CI, 5.52–6.56) and MS (mean, 7.54; 95% CI, 7.21–7.87) and the highest SFWB (proportion, 0.70; 95% CI, 0.60–0.78). In contrast, the proportion of individuals in the top HI quintile was relatively consistent across the different categories of attendance, with only slight variations and in fact slightly lower for those attending regularly.
There was considerable heterogeneity across countries in many of the reported associations. These country-specific results are discussed in detail in the ‘Discussion’.
Childhood predictors of financial well-being
Tables 7 and 8 provide results from random effects meta-analyses in which we pooled the country-specific analyses by regressing financial well-being on all childhood predictors simultaneously (for country-specific analyses, see version d of Supplementary Tables 2–23).
With regard to FS, there was, on average, an association between parental divorce during childhood and lower FS in adulthood, with an effect size of −0.17 (95% CI, −0.32 to −0.02) compared to the individuals whose parents were married at age 12 years of the respondent.
Respondents whose parents were single or never married, or those who had one or both parents deceased at the age of 12, also reported lower FS with effect sizes of −0.26 (95% CI, −0.45 to −0.06) and −0.21 (95% CI, −0.39 to −0.02) relative to those with married parents, respectively. Subjective financial status during childhood was another important correlate of adult FS. Living comfortably during childhood was positively associated with adult FS, with an effect size of 0.39 (95% CI, 0.29–0.49). Instead, difficult or very difficult financial circumstances at the age of 12 were negatively associated with subsequent FS with effect sizes of −0.30 (95% CI, −0.39 to −0.21) and −0.63 (95% CI, −0.79 to −0.47), respectively, compared to those individuals who ‘got by’.
Excellent health at the age of 12 was positively associated with adult FS with an effect size of 0.37 (95% CI, 0.20–0.54) relative to individuals recalling ‘good health’, while poor health was associated with lower FS with an effect size of −0.48 (95% CI, −0.72 to −0.24).
With respect to MS, there was a positive association between very good or somewhat good relationship with mother during childhood and adult MS, with an effect size of 0.09 (95% CI, 0.02–0.17) compared with a very bad or somewhat bad relationship with mother. In contrast, there was no evidence for an association of MS with father–child relationship quality. Individuals from divorced families reported lower MS in adulthood, with an effect size of −0.21 (95% CI, −0.37 to −0.05) relative to those originating from families with married parents. Respondents with single or never-married parents, or those who had one or both parents deceased, also reported lower MS in adulthood, with effect sizes of −0.25 (95% CI, −0.41 to −0.09) and −0.26 (95% CI, −0.45 to −0.07), respectively, relative to the reference group of individuals from families where parents were married. Participants who lived comfortably during childhood reported a positive effect size of 0.34 (95% CI, 0.25–0.43) compared to those who merely got by, whereas those who experienced difficult or very difficult financial circumstances during childhood showed significant negative associations with MS, with effect sizes of −0.35 (95% CI, −0.45 to −0.25) and −0.68 (95% CI, −0.84 to −0.52), respectively. Experience of abuse during childhood was negatively associated with MS in adulthood, with an effect size of −0.38 (95% CI, −0.48 to −0.29) relative to lack of such experience.
For SFWB, individuals with very good or somewhat good relationships with parents at the age of 12 had a slightly higher chance of reporting high levels of SFWB (relationship with mother: risk ratio (RR) = 1.05 (95% CI, 1.01–1.09); relationship with father: RR = 1.04 (95% CI, 1.01–1.06)), relative to individuals reporting very bad or somewhat bad relationships. For participants whose parents were divorced, a risk ratio of 0.97 (95% CI, 0.94–1.00) was obtained relative to those originating from families with married parents. Coming from a family with single or never-married parents, or with one or both parents deceased, was associated with lower SFWB in adulthood (RR = 0.96; 95% CI, 0.92–1.00 and RR = 0.95; 95% CI, 0.92–0.98, respectively). Living comfortably during childhood was associated with higher SFWB (RR = 1.03; 95% CI, 1.02–1.05) compared with those individuals who ‘got by’, while experiencing an adverse financial situation during childhood was associated with lower SFWB (RR = 0.96; 95% CI, 0.94–0.98 and RR = 0.94; 95% CI, 0.91–0.96, for ‘difficult’ and ‘very difficult’ categories respectively). Experience of abuse during childhood was associated with lower SFWB in adulthood (RR = 0.93; 95% CI, 0.91–0.95) relative to lack of such experience. Participants who felt like outsiders growing up also had lower SFWB, indicated by a risk ratio of 0.90 (95% CI, 0.87–0.92). Furthermore, excellent health at age of 12 was associated with SFWB with a higher risk ratio of 1.1 (95% CI, 1.07–1.13) compared to those with good health, whereas fair or poor health status was associated with SFWB with risk ratios below 1, namely 0.93 (95% CI, 0.90–0.95) and 0.92 (95% CI, 0.87–0.97), respectively.
Regarding the reported household income, participants who reported living comfortably during childhood had a higher probability of being located in the highest HI quintile (RR = 1.15; 95% CI, 1.07–1.24) compared to those who just ‘got by’. Conversely, participants who faced financial difficulties during childhood were less likely to be in the top income quintile as adults. Those who found it difficult or very difficult to get by reported lower risk ratios of 0.90 (95% CI, 0.84–0.95) and 0.89 (95% CI, 0.81–0.97), respectively. Parental marital status also played a notable role. Individuals from divorced families had a lower probability of being in the highest income quintile (RR = 0.93; 95% CI, 0.88–0.99) compared to those whose parents were married. Instead, for those whose parents were single, never married or who were from families with one or both parents deceased, we did not find evidence of an association with income (RR = 0.91; 95% CI, 0.80–1.03 and RR = 0.95; 95% CI, 0.86–1.04, respectively). Excellent health at the age of 12 was associated with a higher likelihood of being in the highest HI quintile (RR = 1.18; 95% CI, 1.11–1.24) compared to those with good health. In contrast, for poor health there was no evidence of association with adult income (RR = 0.59; 95% CI, 0.19–1.79). Feeling like an outsider during childhood was associated with a lower probability of HI in adulthood (RR = 0.91; 95% CI, 0.87–0.97), while experience of childhood abuse did not show such an association with the highest income quintile (RR = 0.97; 95% CI, 0.90–1.03). Religious attendance at the age of 12 did not show an association with HI in any of the examined countries as indicated by the global P value of 0.238.
The associations between childhood conditions and financial well-being indicators rarely differed between countries at different stages of development (version a of Supplementary Table 25). Those whose parents died when they were young found it more difficult to achieve MS (Δeffect size = −0.51, 95% CI, −0.83 to −0.18) and SFWB (Δeffect size = −0.11, 95% CI, −0.19 to −0.03) in lower-income countries than in higher-income ones. In lower-income countries, there were less severe consequences for FS of being subjected to emotional or physical abuse (Δeffect size = 0.15, 95% CI, 0.02–0.28) and a higher likelihood of reaching the highest income quintile (ΔRR = 0.15, 95% CI, 0.02–0.27) than in high-income countries. Similar conclusions can be drawn for growing up as an outsider.
As for the comparison between collectivist and individualistic countries, in collectivist countries, having excellent health during childhood was much less important for achieving FS (Δeffect size = −0.46, 95% CI, −0.73 to −0.19) and MS (Δeffect size = −0.48, 95% CI, −0.71 to −0.25) than in individualistic countries (version b of Supplementary Table 25). Even though it is generally easier to achieve FS and MS in older age, older individuals faced greater challenges in collectivist economies.
In countries classified as religious compared to secular ones, it was harder to achieve FS (Δeffect size = −0.53, 95% CI, −0.94 to −0.12), MS (Δeffect size = −0.62, 95% CI, −0.96 to −0.28) and SFWB (ΔRR = −0.14, 95% CI, −0.25 to −0.03), for individuals who experienced a loss of a parent during childhood (version c of Supplementary Table 25). Contrary to expectations, in more gender-egalitarian countries, we did not note any relative benefits for women in terms of ease of achieving any of the financial well-being outcomes in comparison to less-gender-egalitarian countries (version d of Supplementary Table 25).
Discussion
This study explores the correlates of financial well-being examined from both subjective and objective perspectives, recognizing that these two perspectives may diverge due to psychological factors and other influences not captured by current income, potentially stemming from labour-market friction or country-specific factors. Even though low income is a primary contributor to low financial well-being2, the inability to meet material needs may also be present among those with higher incomes7 who may face financial insecurity stemming from a struggle to maintain one’s current standard of living. As O’Connor and colleagues44 highlight, ‘anyone, regardless of wealth or income, can be vulnerable’. Therefore, examining both subjective and objective aspects of financial well-being is critical.
Links of socioeconomic and demographic factors with financial well-being
Across all countries combined, the results suggest that subjective financial assessments reflected in FS, MS and SFWB, exhibit a U-shaped association with age, consistent with a large body of earlier research on the U-shaped distribution of happiness across life45. Previous studies indicate that younger individuals may be more anxious about financial matters due to a lack of work security, the changing nature of work arrangements, and other factors related to labour market entry7,46.
However, our findings suggest that these concerns may not apply to the youngest adults. As individuals age, the observed U-shaped pattern might be explained by improved financial management skills, reduced financial expectations and increased financial stability due to accumulated wealth and fewer liabilities. Additionally, social safety nets are designed to enhance financial well-being in later life. However, for the highest-income quintile, a different pattern emerged in our meta-analytical results. The most favourable outcomes were observed among middle-aged individuals, indicating an inverted U-shaped association. These findings suggest that the youngest and oldest adults may experience better SFWB even with lower incomes, possibly due to reduced career-related concerns and decreased workplace dissatisfaction during the early and late stages of their careers.
These age-related patterns of SFWB were observed to varying degrees in 13 countries from the GFS sample (Argentina, Australia, Germany, Japan, Mexico, Nigeria, the Philippines, South Africa, Spain, Sweden, Türkiye, the United Kingdom and the United States). In Brazil, however, all four outcomes tended to be higher among older adults, but the association with SFWB was weak. In Egypt, Hong Kong, Indonesia, Israel and Kenya, the associations tended to be weaker, nonlinear and/or inconsistent across age categories. In India, FS and SFWB exhibited nonlinear associations, with an inverse association with HI. Poland and Tanzania, despite their very different contexts, were unique in that all four outcomes were highest among younger individuals.
Gender differences in some financial outcomes were also evident in the meta-analysis, with males reporting more FS and slightly higher SFWB than females. Males were also more likely to be in the top HI quintile, consistent with research highlighting the gender wage gap47 and differences in financial literacy between men and women48. However, the negligible gender differences in MS suggest that financial concerns related to basic needs are more uniformly experienced across genders. This seemingly general pattern was not observed in all countries, however. Our findings extend previous evidence by showing that in Hong Kong, Indonesia and Nigeria, for example, gender differences in any of the examined financial well-being indicators were not observed. In Japan, females reported higher FS and slightly higher MS compared to males, while the gender gap in HI was minimal. In Tanzania, gender differences for FS, MS and SFWB were negligible, though the disparity in HI was more pronounced.
Marital status was associated with financial well-being. In the meta-analysis that combined results from all 22 countries, married individuals reported the highest levels of MS and SFWB, reinforcing previous research on the economic benefits of marriage. These benefits stem from shared resources and financial support49, as well as the ability to divide household tasks, which can reduce both time and mental load50. Conversely, separated and widowed individuals reported lower MS and lower SFWB compared to their married counterparts, highlighting the financial vulnerability associated with marital dissolution. However, there was considerable variation across countries. In Argentina, Hong Kong, India, Indonesia, Kenya, Mexico, the Philippines, South Africa, Sweden and Türkiye, no clear, consistent patterns emerged across the four outcomes. In some countries, including Australia, Germany, Israel, Poland and the United States, married individuals had the best outcomes on all four measures. In others, such as Brazil and Spain, they ranked highest on three out of four. In contrast, in Egypt and Tanzania never-married individuals reported the best outcomes on all four measures. Japan was a unique case, where widowed individuals had the highest FS, MS and SFWB but the lowest HI. In Nigeria, domestic partners had the best outcomes on MS, SFWB and HI, but the worst on FS. In the United Kingdom, separated individuals scored lowest on FS, MS and SFWB, but relatively well on HI.
Not surprisingly, employment status emerged as an important correlate of all aspects of financial well-being in the meta-analysis, suggesting that employment provides not only financial resources but also psychological benefits related to stability of incomes, as noted by other scholars51. Conversely, unemployment was found to be negatively associated with financial well-being, corroborating previous evidence on its negative impact on household financial conditions, often leading to inability to service debts and, in some cases, defaults on loans and mortgages52,53. This finding aligns with the research by Clark and Oswald54 and Diener55, who have extensively documented that employment status and income levels are critical determinants of subjective well-being, with unemployment being associated with lower happiness and life satisfaction. However, our study highlights notable country-specific variations in these patterns. In general, homemakers, the unemployed/job seekers and those in the ‘none of these/other’ category tended to have the worst financial well-being outcomes. In Australia, Brazil, Indonesia, Spain, Sweden, the United Kingdom and the United States, retired individuals had the best outcomes on three or four measures. In India, Japan and Mexico, they led on two of the four. In Argentina, individuals employed by an employer had the highest HI. In Nigeria, Poland and Tanzania, students reported better FS, MS and SFWB, but not HI. In Germany, self-employed individuals had the highest scores across all four measures, whereas in Türkiye, they led in FS, MS and SFWB. Students also reported the best outcomes on three or four aspects of financial well-being in Argentina. Interestingly, in Egypt, individuals in the ‘none of these/other’ category had the best outcomes in MS, SFWB and HI, whereas in South Africa, this group scored highest in FS, MS and SFWB, but lowest in HI.
Consistent with research indicating that education enhances financial literacy and earnings potential, leading to improved financial well-being56, our study found that more years of education were strongly correlated with better financial outcomes across all four measures. Educated individuals were more likely to be in the top income quintile, reflecting the long-term economic benefits of higher education. They also tended to score more highly on SFWB, MS and FS, which may indicate not only reduced concerns about financial and material conditions, but also a stronger sense of agency. Although these patterns were observed in most examined countries, there were notable exceptions. For example, in Brazil, no correlation was found between years of education and FS, in Israel between education and MS, and in Kenya between education and FS and MS. In Hong Kong, Nigeria, South Africa and Türkiye, education was correlated only with HI.
Our meta-analysis revealed that more frequent religious service attendance was associated with higher FS, MS and SFWB. This finding corroborates previous evidence indicating that certain religious beliefs and teachings, such as those promoting thriftiness, moderation and foresight, may help alleviate financial worries and thus improve security26, reduce consumerism and materialism57, and promote saving and positive investment decisions58. Religion may also shift focus away from material outcomes, such as financial status, and towards other important aspects of life, such as family, friends, co-religionists and the broader community. This emphasis on community and relationships fosters support and helping behaviours that could positively influence various aspects of well-being, including financial well-being59. These explanations are consistent with the absent association between religious service attendance and objective financial status reflected in income observed in this study, suggesting that religious involvement primarily links with subjective aspects of financial well-being. As indicated by previous research60,61, the associations between religious service attendance and financial well-being varied across countries. In nations such as Argentina, Australia, Indonesia, Israel, the United Kingdom and the United States, attending religious services more than once a week was associated with the best outcomes for FS, MS and SFWB, but not for HI. In contrast, Germany and Hong Kong showed associations across all four measures. In several countries, including Argentina, Brazil, Israel, Mexico, Sweden and the United States, individuals who attended religious services most frequently had the lowest representation in the top income quintile. In Kenya, those who never attended religious services reported the best outcomes for FS, MS and HI, in Nigeria, non-attendance was associated with the worst outcomes on these same variables, and in Tanzania, those who never attended had the best FS and MS outcomes but the worst SFWB and HI. In contrast, in Poland, attending religious services more than once a week was associated with the worst outcomes across all four measures.
Unlike the meta-analytic results in this study, which showed negligible associations between immigration status and financial well-being, country-specific findings revealed notable associations. For instance, in Australia, immigrants reported better outcomes on FS and MS than native-born individuals, a pattern also found in Brazil for FS. This might be linked to a selective approach to immigration and the higher qualifications of migrants relative to the native population. In contrast, the opposite was observed in Poland and Sweden, where immigrants fared worse. These countries are known to accept immigrants from lower-income countries, which may contribute to these disparities. In Hong Kong, immigrants reported lower SFWB, whereas in India they were less likely to be in the top income quintile. In Israel, immigrants reported worse FS, MS and SFWB, but no differences in HI. Interestingly, in Kenya, immigrants reported lower SFWB, whereas in Spain, they had lower SFWB and HI. In the United States, however, immigrants were more likely to be in the top income quintile.
Childhood correlates of adult financial well-being
This study found that all examined childhood factors were associated with adult financial well-being, reinforcing previous evidence on the enduring impact of early-life conditions on career decisions and economic behaviour39. Positive childhood experiences, such as high-quality parental relationships, were positively linked to adult SFWB, whereas adverse childhood experiences, such as parental divorce and financial difficulties, were associated with lower financial well-being in adulthood. These positive associations align with previous research showing that children raised in warm, supportive parenting environments are more likely to experience greater well-being in adulthood and are less likely to develop antisocial behaviours, even in the face of economic deprivation31,62,63,64. Conversely, the negative associations observed in this study add to existing evidence on the detrimental effects of socioeconomic deprivation and family disruption on emotional well-being34. Experiences of abuse and neglect were similarly found to negatively correlate with financial well-being outcomes, reinforcing earlier findings on their long-term negative consequences for employment, earnings and wealth in adulthood65. These findings underscore the critical role parents play in their children’s lives, providing emotional support66, financial guidance, practical advice and other forms of support67, all of which are essential for shaping future financial behaviours and outcomes. Our findings also suggest that financial control, influenced by early-life experiences and relationships with parents, may be crucial in managing financial resources and promoting FS30,68. This aligns with the foundational work by Cunha and Heckman38, who highlighted the role of childhood development in forming cognitive and non-cognitive skills, which influence long-term labour market outcomes and overall human capital development. Furthermore, research has shown that individuals who developed financial management skills during childhood tend to handle their finances more prudently, avoid excessive debt and save effectively, all of which are likely to promote greater financial well-being48.
The association between religious service attendance at age 12 and adult financial well-being observed in our meta-analysis enhances existing knowledge on the importance of religiosity for financial success23,25. Our findings extend this evidence by showing that childhood religious practice—likely reflecting a child’s family religiosity or religious environment—may also be a key factor in financial well-being in adulthood. This suggests another mechanism through which parental influence during childhood can shape success in later life.
There is, however, substantial heterogeneity across countries in all of these associations between childhood factors and adult financial outcomes (see version d of Supplementary Tables 2–23 for country-specific findings). For example, in Türkiye and Brazil, relatively strong associations were found between positive parental relationships and higher FS, whereas in Sweden and the United States, these associations were less pronounced. Parental divorce and single-parenthood during childhood were particularly correlated with financial security in Argentina and Poland, in contrast to South Africa, where these factors were less linked. Across most countries, subjective financial status during childhood was consistently associated with adult FS, with those who struggle financially as children reporting lower FS as adults. Notably, childhood abuse in India and Tanzania was considerably associated with lower adult FS, whereas these associations were less pronounced in Hong Kong. This might indicate that in countries where the safety nets for children subject to abuse are weaker, the effects of such negative childhood conditions can be more impactful.
MS also varied across countries, with parental relationships and marital status showing different levels of associations. For instance, in Spain and Indonesia, positive parental relationships displayed stronger associations with higher adult MS, whereas in Germany these associations were weaker. Divorce and single-parenthood were strong correlates of lower MS in Australia and the Philippines, but less so in Egypt. Household financial difficulties during childhood were consistently associated with lower MS, particularly in Brazil and Türkiye. Individuals who experienced childhood abuse reported lower MS in India and Tanzania.
The associations between childhood experiences and SFWB in adulthood also varied considerably across countries. In Brazil and the United States, good relationships with parents during childhood were strongly associated with better SFWB, whereas in Germany and Egypt, this association was weaker. Parental marital status showed associations with SFWB. For instance, in the Philippines and Argentina, growing up in a divorced or single-parent family was linked to worse SFWB, unlike in Sweden, where the association was minimal. Financial security during childhood was associated with increased SFWB across most countries, with Australia and India exemplifying this trend.
Differences in the intergenerational transmission of financial status reflected in income level were observed between countries. As predicted by earlier empirical evidence69, in countries with stable and well-developed economic systems, such as the United States and the United Kingdom, strong associations between childhood financial status and achieving the top income quintile were observed, whereas in Brazil and Tanzania, these correlations were weaker. The effect of favourable parental relationships on achieving HI in adulthood was prominent in Hong Kong and Argentina, but in the Philippines and Egypt, this association was less notable. In Türkiye, divorce and single-parenthood were weakly associated with achieving HI, in contrast to Germany, where these factors were more strongly associated with lower-income outcomes.
Because the individualistic and Western-centric focus of much existing research has been criticized for its limited generalizability and applicability across diverse cultural and socioeconomic contexts15,16, our study addressed this issue by taking a cross-national and cross-cultural perspective on financial well-being that included a wide range of diverse countries from around the world. The results indicate considerable variability in FS, MS and SFWB across different countries. Even though low HI is a well-known predictor of FS, dissatisfaction with financial status has been documented also in high-income countries7, implying that cultural, economic and social contexts may considerably influence perceptions of financial outcomes. For instance, individuals from countries with strong social safety nets, such as Sweden, report lower FS and higher SFWB, possibly reflecting the effectiveness of these safety nets in promoting financial well-being in their countries. In contrast, individuals with lower personal incomes from countries with weaker social safety nets tend to exhibit lower FS and lower SFWB, underscoring the detrimental effects of income levels and income inequality on financial well-being70, and also highlighting the potential absence of adequate financial knowledge in households struggling to make informed financial decisions71,72.
The strengths of this study include nationally representative samples; broad cultural, geographical and non-Western coverage; large sample sizes; and multiple financial outcome indicators. The study also has limitations. The demographic analyses are purely descriptive and should not be interpreted causally. Some caution is also needed in interpreting cross-national differences, as these may be influenced by matters of translation, different modes of assessment, differing interpretation of items and also of response scales, and seasonal effects arising from data being collected in different countries at different times of the year. However, the comprehensive, multi-staged process of the GFS questionnaire design, along with subsequent cognitive testing conducted in each GFS country, provide some reassurance that these issues have only a limited impact on the accuracy of the results. A detailed description of the process is provided elsewhere73,74. The childhood predictors assessments may be subject to recall bias.
However, for recall bias to completely explain away the observed associations would require that the effect of adult financial status on biasing retrospective assessments of the childhood predictors would essentially have to be at least as strong as the observed associations themselves75, and some of these were quite substantial. The childhood predictors analyses may also be subjected to unmeasured confounding, although these concerns were at least partially addressed through E-value sensitivity analyses, and in some cases the associations were found to be quite robust. Moreover, the study relies on self-reported measures, which might lead to social desirability bias. Participants may provide responses regarding their financial well-being to align with societal norms or expectations. However, this bias may potentially affect subjective measures only, such as perceived financial security and well-being. Objective indicators, such as household income, should not be affected by this bias. Furthermore, the observed relationships between demographic and childhood correlates and financial well-being may be affected by unobserved heterogeneity resulting from different regional socioeconomic conditions, or cultural norms, which were not directly measured in this study. Another limitation of this study lies in the approach to financial well-being, which places greater emphasis on current money management security and the ability to live comfortably on current income, while giving relatively less attention to expected future financial security and the capacity to meet long-term financial goals.
As a result, our findings may offer a more comprehensive understanding of present financial well-being, but could underrepresent the role of future-oriented financial perceptions and planning in shaping overall financial well-being.
Conclusion
The study highlights the complex interplay between demographic factors, early-life experiences and financial well-being. Factors such as age, marital status, employment, education, religious participation and selected early-life experiences are all associated with objective and subjective domains of financial well-being in adulthood, albeit our findings highlight variations across countries, indicating the need for country-specific efforts to improve financial stability.
Methods
The description of the methods in the following has been adapted76, and more methodological details are available elsewhere74,77,78,79,80. This research complies with all relevant ethical regulations for research involving human participants. Participants in this study were compensated for their participation, with the nominal compensation set to approximately US $5 PPP (purchasing power parity). The exact amount varied between US $3 and US $6 depending on the country. Compensation was provided wherever it was technically feasible. The approach ensured that compensation practices adhered to ethical standards while accommodating the logistical constraints of a diverse, cross-national study.
Study population
We used data from the Global Flourishing Study (GFS), which examines the distribution and determinants of well-being across a sample of 202,898 participants from 22 geographically and culturally diverse countries. No statistical methods were used to predetermine sample sizes, but sample sizes were maximized within the constraints of the available funding for data collection within each country. No exclusion criteria were applied. Ethical approval was granted by the Institutional Review Boards at Baylor University (IRB reference 1841317) and Gallup Inc. (IRB reference 2021-11-02).
Wave 1 of the GFS collected nationally representative data from the following countries and a territory: Argentina, Australia, Brazil, Egypt, Germany, Hong Kong (Special Administrative Region of China, with mainland China included from 2024 onwards), India, Indonesia, Israel, Japan, Kenya, Mexico, Nigeria, the Philippines, Poland, South Africa, Spain, Sweden, Tanzania, Türkiye, the United Kingdom and the United States. These countries were chosen to (1) maximize coverage of the world’s population, (2) ensure geographic, cultural and religious diversity and (3) prioritize feasibility and the existing data-collection infrastructure. Gallup Inc. conducted the data collection primarily in 2023, although some regions began in 2022. Timing varied by country (more information is available in ref. 78). All participants provided informed consent to Gallup, and IRB approval for all data-collection activities was obtained by Gallup81. Four additional waves of panel data will be collected annually between 2024 and 2027. The precise sampling design to ensure nationally representative samples varied by country, and further details are available elsewhere78. The data are publicly available through the Center for Open Science (COS, https://www.cos.io/gfs). The translation process followed the TRAPD model (translation, review, adjudication, pretesting and documentation) for cross-cultural survey research82. Further details about methodology and survey development are documented in the GFS Questionnaire Development Report77, GFS Methodology78, GFS Codebook and GFS Translations documents74.
Each country was weighted to reflect their national demographic characteristics. Age spanned the entire adult lifespan, from 18 to 80+ years. Gender distribution was nearly balanced, with 51% female and 49% male, and a small representation from other identities. Most participants reported having a very good relationship with their mother (63%) and father (53%). About 75% reported that their parents were married growing up. Roughly 35% of respondents lived comfortably growing up, and 41% got by. In the sample, 14% of participants reported experiencing abuse during childhood, and a similar percentage felt like an outsider. Self-rated health during childhood varied: 33% excellent, 31% very good, 23% good, 9.8% fair and 2.4% poor. Most respondents (94%) were born in the country of the survey. Religious service attendance at age 12 was fairly common, with 41% attending services at least once a week. The largest percentage of participants were from the United States (19%) and the smallest from Türkiye (1%). For additional details, see Table 3. Country-specific descriptive statistics are available in versions a and c of Supplementary Tables 2–23.
Measures
Dependent variables
Four outcome variables were used to comprehensively examine financial and material well-being. Financial security (FS) was measured with a single item: ‘How often do you worry about being able to meet normal monthly living expenses?’ This variable was coded 0 = worry all of the time to 10 = do not ever worry, and was treated as a continuous variable in all analyses. Material security (MS) was measured with the following question: ‘How often do you worry about safety, food, or housing?’ This variable was coded 0 = worry all of the time to 10 = do not ever worry, and was analysed as a continuous variable. Subjective financial well-being (SFWB) was measured with the following question: ‘Which one of these phrases comes closest to your own feelings about your household’s income these days: 1 = living comfortably on present income; 2 = getting by on present income; 3 = finding it difficult on present income; and 4 = finding it very difficult on present income’. A dichotomous variable coded 1 if the respondent reported living comfortably or getting by and 0 if they reported difficult or very difficult was constructed and analysed.
Household income (HI) in each country was utilized to create country-specific income quintile brackets. The quintiles are only approximately 20% due to categorization, and we used the highest income bracket that separates the top 20% (top quintile) to create an indicator of high income. This coarse categorization led some top quintiles to be less than or greater than 20% in some countries (for example, Australia ~22%). These brackets are highlighted in version a of Supplementary Tables 2–23.
Variables for demographic variation analyses
Continuous age was classified as 18–24, 25–29, 30–39, 40–49, 50–59, 60–69, 70–79 and 80 or older. Gender was assessed as male, female or other. Marital status was assessed as single/never married, married, separated, divorced, widowed and domestic partner. Employment was assessed as employed, self-employed, retired, student, homemaker, unemployed and searching, and other. Education was assessed as up to 8 years, 9–15 years and 16+ years. Religious service attendance was assessed as more than once per week, once per week, one-to-three times per month, a few times per year or never. Immigration status was dichotomously assessed with ‘Were you born in this country, or not?’ Religious tradition/affiliation was measured with categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-derived, some other religion or no religion/atheist/agnostic. Precise response categories varied by country74. Racial/ethnic identity was assessed in some, but not all, countries, with response categories varying by country. For additional details on the assessments see the COS GFS codebook83 or Crabtree and colleagues77.
Variables for childhood predictor analyses
Relationship with mother during childhood was assessed with the question ‘Please think about your relationship with your mother when you were growing up. In general, would you say that relationship was very good, somewhat good, somewhat bad, or very bad?’ An analogous variable was used for relationship with father. Responses were dichotomized to very/somewhat good versus very/somewhat bad to mitigate convergence issues with estimation of the models. ‘Does not apply’ was treated as a dichotomous control variable for respondents who did not have a mother or father due to death or absence. Parental marital status during childhood was assessed with responses of married, divorced, never married and one or both had died. Family financial status was measured with ‘Which one of these phrases comes closest to your own feelings about your family’s household income when you were growing up, such as when you were around 12 years old?’ Responses were lived comfortably, got by, found it difficult and found it very difficult. Abuse was assessed with yes/no responses to ‘Were you ever physically or sexually abused when you were growing up?’ Participants were separately asked ‘When you were growing up, did you feel like an outsider in your family?’ Childhood health was assessed by ‘In general, how was your health when you were growing up? Was it excellent, very good, good, fair, or poor?’ Immigration status was assessed with ‘Were you born in this country, or not?’ Religious attendance during childhood was assessed with ‘How often did you attend religious services or worship at a temple, mosque, shrine, church, or other religious building when you were around 12 years old?’ with responses of at least once per week, one-to-three times per month, less than once per month or never. Childhood religious tradition/affiliation had response categories of Christianity, Islam, Hinduism, Buddhism, Judaism, Sikhism, Baha’i, Jainism, Shinto, Taoism, Confucianism, Primal/Animist/Folk religion, Spiritism, African-derived, some other religion or no religion/atheist/agnostic; response categories varied by country74. When the category no religion/atheist/agnostic had more than 5% of the within-country sample size, this was used as the reference category. Otherwise, the most prominent religious group was used. Additionally, all religious categories endorsed by less than 3% of the within-country sample size were collapsed into a single religious category. For inclusion in the childhood predictor regression analyses, race/ethnic identity was collapsed as a binary variable of whether an individual was in the most prominent group versus a minority group (race plurality).
Statistical analysis
Analysis strategy for demographic variation
Nationally representative means/proportions for each dependent variable were estimated separately for each country and ordered from highest to lowest along with 95% confidence intervals, standard deviations and Gini coefficients. Variations in means/proportions for each dependent variable across demographic categories were estimated, with all analyses initially conducted by country (version b of Supplementary Tables 2–23). Primary results consisted of random effects meta-analyses of country-specific means/proportions of each dependent variable in each specific demographic category84,85, along with 95% confidence intervals, standard errors, lower and upper limits of a 95% prediction interval across countries, heterogeneity (τ) and I2 for evidence concerning variation within a particular demographic variable across countries86.
Within each country, a global test was conducted to assess the variation in each dependent variable across levels of each demographic variable. A pooled P value87 in the two-tailed test was then calculated across countries to determine whether significant variation existed within at least one country. Bonferroni-corrected P-value thresholds are provided based on the number of demographic variables88,89. We estimated country-specific means/proportions for each dependent variable by religious affiliation/tradition and race/ethnicity, whenever the variables were available, with the results available in version b of Supplementary Tables 2–23, but these variables were not included in the meta-analyses because the observed response categories varied by country. All analyses were pre-registered with the Center for Open Science before data access90,91,92. All code to reproduce analyses are openly available in an online repository93.
Analysis strategy for childhood predictors
A weighted linear regression model with complex survey adjusted standard errors was fit within each country for each dependent variable on all of the aforementioned childhood predictor variables simultaneously. In the primary analyses, random effects meta-analyses of the regression coefficients84,85 along with confidence intervals, lower and upper limits of 95% prediction intervals, heterogeneity (τ) and I2 for evidence concerning variation within a given demographic category across countries86. Meta-analysis was employed as our primary approach to account for cross-national heterogeneity in the associations between childhood predictors and adult financial well-being. By conducting country-specific regressions and then pooling the results, this method allows for variation in effect sizes across countries, capturing cultural, economic and institutional differences while providing insights into both overall patterns and country-level variability. This approach aligns with the nationally representative design of the GFS, ensuring that findings reflect the diversity of contexts without being disproportionately influenced by larger or more populous countries.
Religious affiliation/tradition and race/ethnicity were used within country as control variables, when available, but these coefficients themselves were not included in the meta-analyses, because categories/responses varied by country. All meta-analyses were conducted in R94 using the metafor package95. Within each country, a global test was conducted to assess the link between each childhood predictor variable and outcome. A pooled P value87 in the two-tailed test was then calculated across countries to determine whether significant variation existed within at least one country. Bonferroni-corrected P-value thresholds are provided based on the number of childhood demographic variables88,89. Despite certain reservations to the methodology96,97, for each childhood predictor, we calculated E-values to evaluate the sensitivity of the results to unmeasured confounding (reported in Supplementary Table 24 and, for country-specific analysis, in version e of Supplementary Tables 2–23). An E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association. Moreover, additional analyses were conducted to evaluate potential differences in childhood predictor effects between four groupings of countries. To test the effect of country grouping, we ran a series of meta-regression analyses using country grouping to predict the effects of childhood predictors. These results are included in versions a, b, c and d of Supplementary Table 25. All country-level analyses were pre-registered with the Center for Open Science before data access, with only slight subsequent modification in the regression analyses due to multicollinearity98,99,100.
Analyses on groups of countries were added during the revision process. All code to reproduce analyses are openly available in an online repository93.
Missing data
Missing data on all variables were imputed using multivariate imputation by chained equations, and five imputed datasets were used101,102. To account for variation in the assessment of certain variables across countries (for example, religious affiliation/tradition and race/ethnicity), the imputation process was conducted separately in each country. This within-country imputation approach ensured that the imputation models accurately reflected country-specific contexts and assessment methods. Sampling weights were included in the imputation models to account for specific-variable missingness that may have been related to probability of inclusion in the study.
Accounting for complex sampling design
The GFS used different sampling schemes across countries based on the availability of existing panels and recruitment needs78. All analyses accounted for the complex survey design components by including weights, primary sampling units and strata. Additional methodological detail, including accounting for the complex sampling design, is provided elsewhere81.
All country-specific analyses were conducted in Stata, and all meta-analyses were conducted in R94 using the metafor package95.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
Information related to the Global Flourishing Study as well as the official citation is available at https://doi.org/10.17605/OSF.IO/3JTZ8. Data are available for download at the Center for Open Science (COS) website (https://www.cos.io/gfs) upon completing a pre-registration. All analyses were pre-registered with COS before data access.
Code availability
Code to reproduce the analyses is openly available in the online COS repository at https://doi.org/10.17605/OSF.IO/VBYPE.
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Acknowledgements
We acknowledge and thank Ying Chen and Koichiro Shiba for their help with data analysis. The Global Flourishing Study was generously funded by the John Templeton Foundation (#61665), Templeton Religion Trust (#1308), Templeton World Charity Foundation (#0605), Well-Being for Planet Earth, the Fetzer Institute (#4354), the Well Being Trust, Paul L. Foster Family Foundation and the David & Carol Myers Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of these organizations. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.
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P.B., C.A.M., M.B. and D.W.-B. wrote the paper. M.B. and R.N.P. provided the code for data analysis and conducted the analysis. B.R.J. and T.J.V. acquired funding, coordinated data collection, participated in survey design and supervised the development of the analytic code. All authors contributed to the review and editing of the paper. All authors accepted the final version of the paper.
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T.J.V. reports consulting fees from Gloo Inc., along with shared revenue received by Harvard University in its licence agreement with Gloo related to the commercial use of the Flourishing Index, according to the University IP policy. The other authors declare no competing interests.
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Bialowolski, P., Makridis, C.A., Bradshaw, M. et al. Analysis of demographic variation and childhood correlates of financial well-being across 22 countries. Nat Hum Behav 9, 917–932 (2025). https://doi.org/10.1038/s41562-025-02207-4
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DOI: https://doi.org/10.1038/s41562-025-02207-4
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