This study offers a unique opportunity to redress three key issues with SWB research that are rarely tackled together (e.g., previous studies may remedy one or two, but rarely all three in combination): (1) we have separate items for three SWB constructs that are often treated as interchangeable but are actually distinct; (2) unlike the majority of SWB research, which offers a relatively fragmented and limited appraisal of its contextual dynamics, the GFS enables analysis of the relationships between SWB and 15 contextual factors (eight relating to childhood, four demographic, and three pertaining to both); and (3) whereas most of the literature is Western-centric, the GFS sample comprises 202,898 participants from 22 geographically and culturally diverse countries, 15 of which could be deemed non-Western. Let us begin by delving into (1). Our findings show both that the three central concepts are closely interlinked yet have subtle differences. Some nations ranked consistently highly, with four featuring in the top five across all constructs: Indonesia (LE = 5th, LS = 1st, and H = 1st ), Mexico (4th, 2nd, 2nd ), Israel (4th, 6th, 3rd ), and Poland (3rd, 4th, 4th ). Conversely, others fared poorly consistently, particularly Türkiye (20th, 22nd, 22nd ) and Tanzania (22nd, 21st, 18th ). That said, other nations had striking disparities among their scores; while Egypt ranked 21st for both LE and H, for instance, it was 3rd for LS. This illustrates the fact that these constructs are not identical, hence the folly of treating them as such, which often happens. Consider for instance the influential World Happiness Report (WHR)21, which since 2012 has ranked nations annually on the same LE metric used here (Cantril’s ladder), based on data in the Gallup World Poll (GWP), with the top ten consistently dominated by the Nordic countries. Thus, while labelled as being about H, the report actually uses data on LE, while it is also sometimes described as being about LS. However, as we argued above and now see here in the data, the constructs are clearly not the same.
Consider Sweden, which ranks second on LE in both our data (behind Israel) and the latest WHR (behind Finland), but is only middle-ranked for LS (9th ) and H (11th ). Thus, while it would be true to describe Sweden as excelling in LE, this would not be so for LS or H. In the introduction we suggested LE might pertain more to confidence in being able to live a good life, rather than the kinds of emotional experiences that receive the label happiness (e.g., pleasure or enjoyment). Given the performance here of Sweden, that interpretation does seem viable. By contrast, LS seems to track H more closely. Indonesia, for example, is ranked first for both H and LS with almost identical scores (8.04 and 7.99), but a considerably lower LE score (6.97, ranked 5th ). That said, these equivalences do not apply across all countries; we already noted for instance that Egypt ranked 21st for both LE and H (5.04 and 6.18), with LS instead the outlier, ranked 3rd (7.96). Thus, even while overall we might tentatively regard H and LS as covering similar ground, and both being quite distinct from LE, we cannot necessarily generalise this across all countries, and more work will be needed to explore cross-cultural nuances in the way these terms are translated, used, and understood in different nations. Moreover, it is worth introducing a note of caution regarding interpreting cross-national differences and making generalizations about how nations are faring based on one sample, even one as relatively large and representative as the GFS seeks to be. After all, such differences may be influenced by matters of translation, cultural norms, interpretation of items and of response scales, and seasonal effects arising from data being collected in different countries at different times of the year.
It is instructive for example to compare LE scores in our data (gathered in mostly in 2023) with those in the latest 2024 WHR21, which aggregates GWP data for 2021, 2022, and 2023, and with GWP data for these years separately, as featured in Supplementary Table S24. In some instances, the scores are very consistent: Israel for example ranks first in our data with 7.33, and is fifth in the WHR with a nearly identical score of 7.34, this being the average of GWP scores of 7.57 in 2021 (ranked 3rd ), 7.66 in 2022 (2nd ), and 6.78 in 2023 (22nd ), with the latter, significantly, based on data collected after the October 7 attacks (namely, between October 17 and December 2), which makes this fall in 2023 understandable. But in other nations the gap is substantial, the largest being Hong Kong, which ranks seventh in our data at 6.85, but is over 1.5 points lower in the WHR at just 5.32 (86th ), with GWP scores of 5.44 in 2021 (74th ), 5.42 in 2022 (84th ), and 5.61 in 2023 (81st ). Even if we allow for yearly variation, it is still striking to see a difference of over 1.00 in the 2023 data alone; indeed, comparing the GFS data with the 2023 GWP, five countries (Indonesia, Hong Kong, South Africa, Kenya, and Egypt) have a differential of over 1.00.
Clearly then, one might draw very different conclusions about how people in these places are faring based on whether one uses our data or that of the GWP. There are various possible avenues of explanation here, including (a) the sampling timeframe, and (b) the participants selected. With regard to (a), there are differences in the timing of data collection by Gallup for the GFS and GWP, with the former having much broader windows (as large as 18 months in Australia), and the latter generally shorter ones (around 3–4 months). This opens up the possibility that the GFS and GWP data were differentially affected by seasonal patterns, as well as by local socio-political events. We already noted, for example, that the 2023 GWP data for Israel was collected after October 7; by contrast, its GFS data was collected between 11/7/22 and 11/23/23; thus, of the latter’s 12.5-month timeframe, only 1.5 months were after October 7, meaning the GFS data would be less affected by that national trauma. Moreover, even apart from uniquely impactful events like the October 7 attacks, Gallup has found that within-country LE tends to regularly fluctuate. While the GWP is just an annual snapshot, they collect LE data on a monthly basis in the US, which sometimes shows considerable variation over just one year. For example, analysing trends over recent years81, the percentage of adults deemed to be “thriving” – which involves scoring 7 or more on Cantril’s ladder today, together with scoring 8 or more on predictions of where one would stand in five years’ time – oscillated from 46.4 in April 2020 (a datapoint which Gallup noted coincided with 30 million unemployment claims being made), up to 53.7 in June (when 4.8 million jobs were added), back to 48.2 in December (when COVID deaths were exceeding 2,500 per day), then back up to 59.2 in June 2021 (with widespread vaccinations and economic reopening). Thus, any assessment of a country’s LE, LS, and H is subject to considerable fluctuation.
That said, some long-term trends within countries are often relatively stable. To use the US again as an example, despite the within-year changes reported by Gallup, the annual trend-lines are relatively consistent, with the following being the LE scores (and rankings) for the US in every WHR: 2012 = 7.27 (11); 2013 = 7.08 (17); [no report for 2014]; 2015 = 7.12 (15); 2016 = 7.10 (13); 2017 = 6.99 (14); 2018 = 6.88 (18); 2019 = 6.89 (19); 2020 = 6.94 (18); 2021 = 6.95 (19); 2022 = 6.97 (16); 2023 = 6.89 (15); 2024 = 6.72 (23). As one can see, levels are generally falling, but significantly doing so quite consistently, rather than veering markedly up and down. Thus, given the stability of at least some countries’ trajectories, variation in means in different samples may also be due to explanation (b) mooted above, namely the participants themselves. For example, the GWP is a one-off sample, whereas the GFS requires participants to commit to a five-year longitudinal involvement (with the GFS analysis restricted to those people who were “retained” from the intake to the annual survey). It is possible that people who do thus commit are more likely to have higher LE than those who just consent to a one-off survey. This explanation is given credibility by the fact that LE scores in the GFS were almost always higher than those in the GWP (with just five exceptions, which were fairly small differentials). One might therefore suspect that the sampling strategy in the GFS does skew slightly higher than the GWP on measures of LE. This interpretation will need more investigation however going forward, as will the other possibilities raised above.
Crucially though, even if we must be cautious about making country-level generalizations about LE, especially when comparing across surveys, the data within the GFS is nevertheless meaningful and internally coherent. In that respect, perhaps of even greater interest here than the country averages and rankings are data relating to the 15 factors analyzed (eight childhood, four demographic, and three pertaining to both), all of which had significant associations with all three constructs, but also substantial variation across countries. Let us briefly summarize the factors in order of the variation observed (i.e., starting with the one with the greatest range between the lowest and highest scoring categories). Beginning with the demographic factors, higher levels of SWB are associated with: (1) being retired (LE = 6.53[90%CI = 6.11,6.94], LS = 7.14[6.82,7.45], H = 7.19[6.94,7.43]), especially relative to those unemployed and looking for a job (5.59[5.31,5.88], 5.97[5.57,6.37], 6.30[5.94,6.66]); (2) being married (6.54[6.15,6.93], 7.12[6.79,7.44], 7.23[6.98,7.49]), especially relative to those separated (5.89[5.54,6.24], 6.31[5.95,6.67], 6.56[6.28,6.84]); (3) attending religious services, especially once a week (6.80[6.33,7.27], 7.40[7.02,7.78], 7.54[7.21,7.87]) relative to those who never attend (6.12[5.78,6.46], 6.55[6.23,6.86], 6.71[6.45,6.96]); and (4) having more education, especially those with over 16 years (6.64[6.36,6.93], 7.00[6.71,7.30], 7.16[6.95,7.36]) compared to those with less than eight years (6.20[5.83,6.56], 6.84[6.49,7.18], 6.95[6.67,7.24]).
Turning to the childhood factors, higher adult SWB is associated with: (1) “excellent” self-rated health (0.40[0.26,0.55], 0.46[0.29,0.62], 0.50[0.34,0.66]) relative to “good,” especially in contrast to those with “poor” health (-0.40[-0.59,-0.22], -0.46[-0.70,-0.22], -0.41[-0.62,-0.20]); (2) a subjective financial status of “living comfortably” (0.29[0.20,0.30], 0.25[0.17,0.33], 0.23[0.16,0.30]) relative those who “got by”, especially in contrast to those who “found it very difficult” (-0.42[-0.55,-0.29], -0.31[-0.46,-0.16], -0.31[-0.42,-0.21]); (3) not experiencing abuse (relative to those who did: -0.25[-0.34,-0.16], -0.39[-0.48,-0.30], -0.33[-0.42,-0.24]); (4) not feeling like an outsider (relative to those who did: -0.16[-0.25,-0.07], -0.29[-0.39,-0.20], -0.28[-0.37,-0.20]); (5) attending religious services, especially once a week (0.22[0.11,0.33], 0.21[0.03,0.38], 0.27[0.14,0.40]), relative to those who never attended; (6) having a good relationship with one’s mother (0.17[0.10,0.23], 0.21[0.12,0.29], 0.25[0.15,0.36]); (7) having a good relationship with one’s father (0.18[0.11,0.24], 0.19[0.10,0.27], 0.13[0.03,0.22]); and (8) one’s parents being married, especially relative to people whose parents were single and never married (-0.14[-0.27,-0.01], -0.13[-0.25,-0.01], -0.13[-0.22,-0.04]).
Finally, there are three factors which could be interpreted and analyzed either as childhood predictors or demographic factors (but which we will report here as the latter): age, sex, and immigration status. Of these, higher SWB is associated with: (1) older age, although the pattern is “U-shaped,” as noted above, with levels fairly high at 18–24 (6.35[6.07,6.62], 6.78[6.43,7.12], 6.96[6.66,7.26]), then gradually falling to their lowest level among those 40–49 (6.18[5.81,6.54], 6.70[6.35,7.06], 6.86[6.59,7.14]), then peaking in those 80+ (6.83[6.42,7.23], 7.17[6.77,7.57], 7.39[7.12,7.66]); (2) being female (6.38[6.07,6.69], 6.89[6.59,7.18], 7.03[6.80,7.26]) rather than make (6.31[5.95,6.67], 6.82[6.49,7.16], 6.98[6.73,7.24]), and especially relative to the very small percentage who report their gender as “other” (5.98[5.70,6.26], 5.91[5.45,6.36], 5.95[5.45,6.44]); and (3) living in one’s country of birth (6.34[6.01,6.68], 6.85[6.54,7.17], 7.01[6.76,7.25] compared to those born elsewhere (6.36[6.05,6.68], 6.81[6.61,7.01], 6.87[6.61,7.14]), although only marginally so, and also only for H and LS, with the reverse trend for LE. Similar association patterns as in unadjusted demographic analyses pertain to the multivariate-adjusted childhood predictor analyses, except for immigration status, for which the unadjusted demographic associations are more pronounced for H, but the adjusted childhood associations are more pronounced for LE and LS.
In many instances patterns were similar for LE, LS, and H, but some differences emerged. For example, childhood relationship with mother and father were similarly associated with adult LE and LS, but the relationship with the mother had a notably stronger relationships with H than that with the father. Being raised by a single parent or in a family in which a parent had died showed somewhat stronger associations with LE than for LS or H, but the experience of abuse or feeling like an outsider in childhood had somewhat stronger associations with LS than for LE or H. While childhood religious service attendance was associated with higher adult LE, LS, and H, this association was somewhat stronger for LE and H than for LS. These differences may merit further investigation.
In some respects, these findings will not be surprising or novel to people, as they mostly corroborate well-established trends in the literature. However, what is especially valuable about the GFS is its cross-cultural dimension. Across all these factors, the patterns observed above are not uniform across nations, but instead are full of nuances and exceptions. This shows that these trends are not inevitable or universal, but instead are contingent on socio-cultural factors. We do not have the space to demonstrate this for all factors, so instead as exemplars will just delve into the two with the greatest variation overall (i.e., across all countries combined) – employment status among the demographics, and self-rated health among the childhood predictors – as a way of illustrating the nuances in the data, which should then also be borne in mind for all factors.
Beginning with the demographic factor with the greatest variation, employment status, faring worst were people who were unemployed and looking for a job (5.59[5.31,5.88], 5.97[5.57,6.37], 6.30[5.94,6.66]), then students (6.41[6.16, 6.66], 6.81[6.47,7.15], 6.94[6.64,7.24]), homemakers (6.26[5.95,6.57], 6.94[6.65,7.23], 7.00[6.75, 7.25]), people with an employer (6.40[6.02,6.78], 6.93[6.59,7.26], 7.09[6.84,7.34]), the self-employed (6.40[6.02,6.78], 6.93[6.59,7.26], 7.09[6.84,7.34]), and, best of all, retirees, though especially in terms of LS (7.14[6.82,7.45]) and H (7.19[6.94,7.43]), with LE slightly lower (6.53[6.11,6.94]), which again makes the point about H and LS being relatively overlapping, and LE quite distinct. These findings align with a now vast literature showing the impact of working patterns across myriad aspects of flourishing, with numerous reviews showing employment generally has a positive impact on SWB82 and unemployment a negative impact83. There are of course caveats, since work can be detrimental to health if overly taxing or stressful84. Overall, though, most research finds at least some benefit to being in work, as did we. Yet it is fascinating to see the greatest levels among retirees, who are also technically “out of work,” which corroborates other research finding a potential boon to SWB associated with retirement, even if the association can be complex85, as discussed further below. There is of course a potential interaction with age, given that SWB generally increases into older age, as noted above. Nevertheless, the data here relating to employment status suggests it is not necessarily being without employment per se that is detrimental to SWB, but rather needing and seeking work yet not being able to find it, with SWB relatively unaffected if people are materially secure and not wanting/needing to work86,87.
Again though, what is particularly valuable about the GFS is the way it reveals national variation, showing the association with these factors differs considerably based on location. Let us again briefly compare all nations, this time in relation to the overall bottom (unemployed and looking for a job), middle (employed for an employer) and top (retirees) categories in terms of average LE, LS, and H (omitting 95% CI levels for reasons of space): Argentina (unemployed = 6.11/6.50/6.88; employed = 6.94/7.31/7.48; retired = 6.71/7.28/7.15); Australia (5.54/5.02/5.71; 6.74/6.66/6.80; 7.36/7.49/7.61); Brazil (5.78/6.34/6.65; 6.83/7.38/7.51; 7.18/7.79/7.82); Egypt (4.78/7.19/6.04; 4.96/7.56/6.06; 4.92/7.43/6.30); Germany (5.46/5.43/5.81; 6.84/7.01/6.97; 6.71/7.06/6.98); Hong Kong (5.63/5.54/5.68; 6.92/7.07/7.23; 6.60/7.20/7.12); India (5.20/6.62/6.25; 5.20/6.71/6.18; 5.87/6.97/6.68); Indonesia (6.41/7.46/7.51; 6.89/7.82/7.92; 7.39/8.45/7.91); Israel (6.60/6.80/6.99; 7.46/7.54/7.84; 6.79/7.03/7.39); Japan (4.24/4.27/4.65; 5.75/5.87/6.05; 6.49/6.69/6.72); Kenya (5.38/5.69/7.29; 5.69/5.96/7.12; 5.48/6.22/7.12); Mexico (6.59/7.19/7.30; 7.25/7.85/7.84; 7.60/8.37/8.06); Nigeria (5.53/6.11/6.93; 5.97/6.54/7.24; 5.99/6.63/7.51); Philippines (6.36/7.28/7.20; 6.41/7.64/7.38; 6.44/7.77/7.51); Poland (6.26/6.61/6.88; 7.14/7.59/7.61; 7.20/7.35/7.34); South Africa (5.89/6.04/6.74; 6.53/6.73/7.22; 5.94/6.57/7.16); Spain (6.05/6.25/6.47; 6.80/6.91/7.07; 6.80/6.81/6.91); Sweden (5.26/4.83/5.19; 7.11/6.97/6.93; 7.72/7.76/7.60); Tanzania (4.49/5.10/6.53; 4.76/5.85/6.98; 3.53/5.45/5.87); Türkiye (4.67/4.67/5.30; 5.15/5.21/5.67; 5.66/5.30/5.73); UK (5.48/5.36/5.79; 6.60/6.55/6.74; 6.91/6.93/7.18); and US (5.33/5.09/5.34; 6.88/6.79/6.93; 7.66/7.60/7.70).
As one can see, many country-level nuances are evident. Just to take the example of retirees, these were not the most prosperous group in all countries, faring less well than people employed for an employer in Argentina (on LE, H), Egypt (LE, LS), Germany (LE, LS), Hong Kong (LE, LS, H), Indonesia (LS, H), Israel (LE, LS, H), Kenya (LE), Poland (LS, H), South Africa (LS, H), Spain (LS, H), and Tanzania (LE, LS, H), and even worse than the unemployed in Kenya (H) and Tanzania (LE, H). To that point, it is also pertinent to note here the regional variation with respect to the factor of age. For a start, no significant associations with age were seen for either LE, LS, or H in Mexico and Spain, and only for some constructs for Argentina, Indonesia, Nigeria, Philippines, Türkiye, and South Africa. Then, not all countries observed the general U-shaped pattern: a general linear decrease in SWB with age was seen across all three constructs in Israel, for LS and H for Poland, and LE for Tanzania. In Israel, for example, the highest scores were for those aged 18–24 (7.71[7.46, 7.97], 8.01[7.79,8.22], 8.08[7.85,8.32]), while the lowest were among those aged 80+ (6.09[5.54,6.64], 6.76[6.05,7.48], 6.97[6.46,7.49]). Moreover, there was also variation in the range of scores within countries, indicating that the relationship with age is significantly conditioned by one’s location. The range was smallest overall in Mexico, with gaps of just 0.18 (LE), 0.56 (LS) and 0.22 (H). By contrast, in Australia, the gaps between those 18–24 (with the lowest scores) and 80+ (the highest) were 1.58, 2.03, and 1.94. It is also notable that variability in SWB was relatively low in younger age groups, but increased substantially from ages 25–29 to 40–49, indicated by a growing percentage of countries showing effects above 0.10 or below − 0.10. This suggests that as people age, their SWB levels diverge increasingly, potentially due to factors such as varying socioeconomic status, healthcare quality, educational opportunities, and cultural values and norms. This variability then plateaued from ages 50 to 79, potentially reflecting a stabilization in life circumstances. However, it rose again in the 80 + group, highlighting diverse aging processes and the potentially substantial impact of socio-cultural factors on SWB in late adulthood. To return then to the question of retirement, all these findings are consistent with prior work suggesting that the relationship between retirement and SWB is complex and inconsistent88, and that factors such as economic resources89 and social relationships90 contribute to retirees’ SWB83 For example, a country’s welfare system may affect the financial resources of those retired, as do non-economic factors such as family relationships (including whether your partner is also retired), health status, and personality, and whether the person chose to retire or not85.
That said, some trends remained robust: those unemployed fared worse than people who are employed in almost all countries (apart from people with an employer in India for H, and self-employed people in Egypt for LE and H, and Philippines for LE). Indeed, they were the lowest category overall in most countries, with the only exceptions, besides those noted in the previous sentence, being homemakers in Tanzania (H) and South Africa (LE), and “other / none of the above” in Argentina (LE, LS, H), Australia (LS), Hong Kong (LE, LS, H), Kenya (LE, H), Nigeria (H), Philippines (H), Poland (H), Tanzania (LE, LS), and UK (LE, LS, H). Other variation concerns the range of values, where some countries had only a narrow difference between the three categories considered above, especially Kenya (0.33, 0.53, 0.17), Egypt (0.18, 0.37, 0.26), and the Philippines (0.08, 0.49, 0.31). By contrast, other countries have a much larger range, suggesting a stronger relationship, especially Australia (1.82, 2.47, 1.90), Japan (2.25, 2.38, 2.07), Sweden (2.46, 2.93, 2.41), and the US (2.33, 2.51, 2.36). All these nuances merit further study to help us better understand the complexities of the overall trend linking employment status to SWB. Indeed, to reiterate, there are regional nuances and complexities across all factors studied here, all of which merit more in-depth investigation, for which this paper can hopefully provide a good foundation.
Turning to the most impactful childhood predictor, self-rated health, we noted above that higher adult SWB was associated with “excellent” health (0.40[0.26,0.55], 0.46[0.29,0.62], 0.50[0.34,0.66)]) relative to “good,” especially in contrast to those with “poor” health (-0.40[-0.59,-0.22], -0.46[-0.70,-0.22], -0.41[-0.62,-0.20]). However, once again, a particular strength of the GFS is the way it highlights regional variation. To illustrate, here are the respective estimates for the two “outer” categories (“poor” versus “excellent”) for LE, LS, and H respectively for all countries (with details for each country in the Supplementary Tables), omitting 95% CIs for reasons of space: Argentina (poor health = -0.56/-0.90/-0.74; excellent health = 0.39/0.35/0.39); Australia (-0.33/-0.31/-0.41; 0.45/0.67/0.57); Brazil (-0.57/-0.75/-0.18; 0.26/0.31/0.41); Egypt (-0.27/-0.07/-0.20; 0.10/-0.07/0.05); Germany (0.45/0.43/0.37; 0.40/0.60/0.68); Hong Kong (-1.44/-1.14/-1.53; 1.37/1.50/1.58); India (-0.99/-0.55/-0.65; -0.17/0.03/0.06); Indonesia (-0.19/-0.49/-0.28; 0.18/0.27/0.33); Israel (0.38/0.15/0.58; 0.51/0.54/0.44); Japan (-0.91/-0.95/-0.85; 0.98/1.10/1.10); Kenya (-0.18/-0.62/-0.55; 0.23/0.08/0.21); Mexico (0.10/-0.13/-0.24; 0.50/0.30/0.45); Nigeria (-0.13/-0.02/-0.27; 0.26/0.20/0.15); Philippines (-0.32/-0.50/-0.42; 0.17/0.14/0.28); Poland (0.41/-0.4/0.43; 0.53/0.70/0.65); South Africa (-0.65/-0.11/-0.32; -0.13/0.12/0.26); Spain (-0.12/0.34/0.27; 0.38/0.46/0.67); Sweden (-0.43/-0.72/-0.55; 0.93/1.06/1.07); Tanzania (-0.56/-0.36/-0.41; 0.14/0.21/0.03); Türkiye (-0.61/-2.64/-1.98; 0.14/-0.07/0.06); UK (-0.59/-0.52/-0.50; 0.37/0.57/0.61); US (-0.44/-0.54/-0.43; 0.72/0.77/0.78).
Evidently, there are again many interesting nuances. For a start, relative to the difference in outcomes comparing poor and excellent childhood health in the pooled meta-analysis (0.80 for LE, 0.88 for LS, and 0.91 for H), there was considerable variation in range, with Egypt the narrowest (0.37/0.14/0.25), suggesting that childhood health differences there have relatively little effect on adult SWB, and Hong Kong the largest (2.81/2.64/3.11), where such differences seem to matter more. Further research is needed to explore why this regional variation exists, but it will almost certainly involve considerations such as economic conditions in the various countries and related issues such as the provision of healthcare and levels of (in)equality. The dynamics might be quite complicated though. It is conceivable that childhood health might have a smaller impact in richer countries, given that such countries may be able to spend more on treating and alleviating poor health, hence lessening its detrimental effects91. On the other hand, richer countries have a tendency towards being more unequal (e.g., a bigger wealth gap between rich and poor), which might exacerbate the effect of poor childhood health, especially in countries without good universal healthcare coverage92,93. Similarly, countries that are relatively unequal may provide myriad opportunities to children with good health that are conducive to adult SWB (such as sporting and artistic programs that foster beneficial qualities, ranging from social capital to creativity), with these same opportunities being less available to those with poorer health and other related issues (such as lower socioeconomic status)94. All these dynamics need further attention.
There were also intriguing patterns that are harder to explain and also merit further investigation, perhaps most strikingly that, in some countries, the effect estimates seemed “out of order.” One would expect, based on the overall estimates, that relative to people with “good” childhood health, people with worse health (“poor” or “fair”) would have lower levels of SWB, while people with better health (“very good” or “excellent”) would have higher levels (a positive RR). Indeed, 13 countries did conform to this linear escalating pattern (Argentina, Australia, Brazil, Hong Kong, Indonesia, Japan, Kenya, Nigeria, Philippines, Sweden, Tanzania, UK, and US). However, in the remaining countries, this pattern was subverted in various ways, where compared to those with good childhood health, some groups with worse health (either poor and/or fair) had higher SWB, while conversely others with better health (very good and/or excellent) had lower levels. For example, people with poor health fared better than those with good health in Germany (LE, LS, H), Israel (LE, LS, H), Mexico (LE), Poland (LE, H), and Spain (LS, H). In these places, not only does poor childhood health not detract from SWB in adulthood, the data suggest it may actually help. We cannot know from our data why this effect is observed, i.e., what is special about these nations where childhood poor health seems to actually facilitate SWB in adulthood. One could speculate that, in these places, poor childhood health either encourages or compels people to develop certain qualities that might subsequently be conducive to SWB in adulthood, including psychological resources (e.g., resilience)95 and social resources (e.g., supportive childhood friends, access to a trusted adult)96. But this of course begs the question, namely, what is it about these countries that this effect is observed, and why are similar effects not found elsewhere. This is something that demands more in-depth study.
It is important also to note that we did not prospectively assess people’s health in childhood, but rather used retrospective assessments of childhood health. Crucially, there are indications people sometimes change their ratings of childhood health over time. One analysis97 found nearly half of their sample revised this during a 10-year observation period: older adults who were relatively advantaged (e.g., socioeconomic resources, better memory) were less likely to revise it, whereas those with multiple childhood health problems were more likely to (either positively or negatively). As such, we must be somewhat cautious in interpreting our data, and acknowledge that recall bias might be present. However, for such bias to completely explain the observed associations of the childhood predictors with adult SWB, the effect of adult SWB on the retrospective assessments of the childhood predictors would have to be at least as strong as the observed associations themselves98. Moreover, numerous longitudinal studies have measured health in childhood then traced its impact on later outcomes, and a considerable literature shows it does have a substantive effect on myriad aspects of adult life99, including mental health100, and even SWB specifically101. Relatedly, there is an extensive literature on the impact of Adverse Childhood Experiences – of which poor health can be seen as an example102 – on adult SWB and mental health103,104. Similarly, research shows the detrimental effects on adult SWB of adverse experiences that are adjacent to poor health, such as physical abuse105, which we also found was negatively associated with adult SWB. As such, in spite of the limitation, some of patterns observed here regarding self-reported childhood health likely indicate real effects.
Before closing, it is worth noting the limitations of the study. First, the assessment of LE, LS, and H were done using one-item measures, which may not capture their full complexity. Future studies can consider using multi-item scales for higher validity and reliability of the measure, such as the five-item Satisfaction with Life Scale for LS106. That said, there is always a trade-off in survey research between depth and breadth: including multi-item scales would limit the number of constructs assessed, and the GFS team decided that, on balance, any limitations of using single item measures are outweighed by the value of including a greater number of constructs55. Second, and relatedly, some caution is needed in interpreting cross-national differences as these may be influenced by various factors, as noted above, such as local, national and international occurrences, as well as seasonal effects arising from data being collected in different countries at different times of the year. The latter consideration, for instance, may contribute to the observation that a country’s SWB can fluctuate, even within a given year, potentially resulting in discrepancies between different SWB assessments, such as between our data and those in the GWP. Third, the present study design was cross-sectional, which precludes conclusions about the directionality of the associations and our demographic analyses should only be interpreted descriptively. The GFS will be a longitudinal study, however, and the second wave of data collection is already underway. Moreover, even with the first wave of data we were able to construct a synthetic longitudinal study by retrospectively assessing childhood experience. Although we adjusted these associations for other potential childhood predictors, there may be residual confounding. Even so, it is possible that some childhood predictors are situated on the causal pathway linking other predictors to current SWB measures (e.g., child adversity being part of a causal chain connecting poor childhood economic conditions to current SWB), so simultaneous adjustment of such predictors may over-adjust potential mediators and provide somewhat conservative effect estimates for the predictors. We also reported E-values to assess the robustness of our findings to unmeasured confounding. Finally, as also noted above, the childhood predictors were assessed retrospectively, making the findings subject to recall bias. However, for recall bias to completely explain away the observed associations would require that the effect of adult SWB on biasing retrospective assessments of the childhood predictors would essentially have to be at least as strong as the observed associations themselves98.