Childhood predictors of balance in life: A cross-national analysis of the Global Flourishing Study

doi:10.21203/rs.3.rs-4825071/v1

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Childhood predictors of balance in life: A cross-national analysis of the Global Flourishing Study

https://doi.org/10.21203/rs.3.rs-4825071/v1

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Psychology and related fields have historically been Western-centric, influenced especially by the ideas and values of the United States. Consequently, aspects of life that have been relatively neglected in Western cultures have been similarly overlooked in the literature. This includes balance in life (notwithstanding select instances of interest, like work-life balance). However, amidst a broader movement to redress the Western-centricity of academia is an emergent scholarship on this topic. This paper contributes to this by presenting the most ambitious longitudinal study to date of balance, namely as an item – “In general, how often are the various aspects of your life in balance?” – in the Global Flourishing Study, a five-year (minimum) study investigating the predictors of human flourishing involving (in this first year) 202,898 participants from 22 countries. Specifically, this paper looks at the childhood predictors of balance, using random effects meta-analysis to aggregate all findings in the main text, focusing on three research questions. First, how do different aspects of a child's upbringing predict balance in adulthood, for which the most impactful factor was “Feeling like an outsider growing up” (RR = 0.90; 95% CI [0.87,0.92]), and the least was immigration status (RR = 0.98; 95% CI [0.97, 1.00]). Second, do these associations vary by country, with the effect of being an outsider, for example, being strongest in Australia, and weakest (not different than zero) in Türkiye, South Africa, Nigeria, Poland, Egypt, Hong Kong, Indonesia, and the Philippines. Third, are the observed relationships robust to potential unmeasured confounding, as assessed by E-values, which on the whole was the case. These results shed new light on the cross-cultural and causal dynamics of this overlooked topic, and provide the foundation for further enquiry.

Biological sciences/Psychology/Human behaviour

Health sciences/Health care

balance

wellbeing

flourishing

global

cross-cultural

Global Flourishing Study

Scholars have long sought to understand how people’s childhood affects their outcomes later in life. For the most part though, this scholarship has focused on “negative” (i.e., undesirable) outcomes, such as mental health problems. However, recent decades have seen an emergent interest in more desirable phenomena too, spearheaded by the positive psychology paradigm.

Yet even amidst this “positive turn,” some topics have still been relatively understudied and underappreciated. This includes the notion of balance in life. Despite pockets of interest – most notably in relation to “work-life balance” and a “balanced diet” – one could still argue that its significance overall, across myriad aspects of life, has yet been relatively neglected. This paper aims to help redress this lacuna by focusing on the most ambitious longitudinal study to date on life balance, namely as an item in the Global Flourishing Study. To set the context, this introduction, (1) provides an explanation for why it has tended to be overlooked (namely, the Western-centric nature of academia), and (2) shows that, despite its overall neglect, an emergent body of work is looking into this topic, to which our paper seeks to contribute.

First though, let us offer a working definition of balance as an orientation into the topic. As with many complex concepts, its meaning is contested and debated; moreover, it is usually tied to specific domains (e.g., physiology), rather than defined in the abstract. However, a chapter for the 2022 World Happiness Report analysing new data on balance obtained through the Gallup World Poll (GWP), discussed below, offers a generic definition that they suggest applies across diverse contexts¹: “balance means the various elements which constitute a phenomenon, and/or the various forces acting upon it, are in proportionality and/or equilibrium, often with an implication of stability, evenness, and poise.” These dynamics frequently apply to binary or dyadic phenomena, per its etymology from the Latin bilanx, denoting two (bi) scale pans (lanx). Substantively, these may be poles of a spectrum (e.g., hot-cold) or discrete categories that are frequently linked (e.g., work-life), while temporally these can be synchronic (e.g., neither too hot nor cold) or diachronic (e.g., averaging good work-life balance over a career). Whatever the permutation, in general, balance does not usually mean crude calculation of averages, nor finding a simple mid-point on a spectrum, but skilfully finding the right point or amount. This quality likewise applies when balance is used in reference to relationships among multiple phenomena, such as a “balanced diet.” With this in mind, let us delve into the claim underscoring the value of the present research; namely that, despite the emergence of constructs such as “work-life balance” and a “balanced diet”, the concept overall has been relatively understudied and underappreciated in academia.

Overlooking Balance

In the section below we suggest a considerable literature has emerged on balance over recent decades, perhaps most notably on “work-life balance” and a “balanced diet,” with a Google Scholar search in June 2024 returning 489,000 and 131,000 hits respectively for these phrases. Indeed, there are numerous systematic reviews or meta-analyses just on specific aspects of the constructs, such as – vis-à-vis work-life balance – organisational performance² and particular contexts like Asia³. However, these select usages notwithstanding, the authors of the World Happiness Report chapter argued that the fundamental importance of balance to flourishing has been underappreciated, with one describing it as a “golden thread” running through all aspects of wellbeing but which has nevertheless been “hiding in plain sight”⁴. One indication of the relative weight given to a topic is the number of psychometric scales developed to assess it. In that respect, although one can find scales for some applications of the concept, such as work-life balance⁵ or physical balance⁶, there does not appear to be a scale focusing generally on balance in life. By contrast, for example, one review identified 27 pertaining to resilience alone⁷. Seeking to explain this relative inattention to the importance of balance across life domains, the chapter argues a key reason may be the Western-centric nature of scholarship on flourishing, and indeed of academia generally.

It is increasingly recognized that fields like psychology have tended to be Western-centric, especially in light of Henrich and colleagues’ influential article in Nature which argued that most research in psychology is conducted by and on people in societies they influentially labelled “WEIRD” (Western, Educated, Industrialised, Rich, and Democratic)⁸. Even while this framing itself has been critiqued – such as the tendency to simplistically classify places in a binary way as WEIRD versus non-WEIRD, rather than viewing each element of the acronym is a spectrum on which countries may be situated⁹ – we are on solid ground in noting that most of the world is not as WEIRD as the US, from where the vast majority of work in top journals originates. As such, critics question the extent to which such research is generalizable and universally valid. In response, some scholars might argue that people are relatively similar across cultures and share a common human nature, hence findings from Western contexts can be extrapolated to other locales. However, even if so, most academics would likely agree that the conditions of life can vary dramatically among cultures, so it is difficult to draw conclusions based only on WEIRD contexts. Moreover, the issue with the Western-centricity of scholarship goes beyond just its participants and extends to the scholars themselves, who are also shaped by their location. Since the most influential academics are mostly situated in highly WEIRD societies, the values and traditions in such places are liable to impact their ideas and priorities.

Arguably, this is one reason for the relative underappreciation of balance in academia, namely, that it has been undervalued in the West more broadly, especially relative to Eastern cultures. We do not mean unappreciated of course. Over the centuries many Western scholars have emphasised ideas relating to balance, dating back to classical Greek philosophy, which celebrated concepts such as sophrosyne (which in Plato’s Phaedrus meant the harmonious moderation of the appetitive and spirited aspects of the soul by its rational part), isorropía (denoting equilibrium and poise), and summetría (etymologically connected to “symmetry”). Indeed, among the most enduring ideas from classical Greek thought is Aristotle’s “relative mean,” articulated in his Nicomachean Ethics, whereby the ideal of aretḗ – excellence/virtue in any domain of life – is found by aiming at the mean (mésos) “relative to us.” Although popularly referred to now as the “golden” mean, the qualifier “relative” was important, as the mean could not be determined mathematically or a priori, but only relative to a specific person and their circumstances¹⁰. Regarding character, for instance, he wrote “every virtue of character lies between two correlative faults of vices” (e.g., courage involves avoiding both cowardice and recklessness). However, over time, Western societies have arguably become dominated by modes of thought that tend to inhibit an embrace of principles of balance. By contrast, Eastern societies have cultivated alternative modes that are more conducive to such principles.

This was the observation, for example, of Peng and Nisbett, who argued the West has overall emphasized linear, analytical modes of thought, such as formal either/or logic¹¹. This type of reasoning includes making categorical distinctions between phenomena as being either good or bad. By contrast, the East has instead embraced more holistic, dialectical forms of thinking, including both/and logic. This would allow, conversely, that phenomena can rarely be delineated as intrinsically good or bad; since such judgements are context dependent, a given phenomenon can be both good and bad, depending on the situation. This perspective is exemplified by traditions like Taoism, whose yin-yang motif denotes the interdependency of dark and light – and, by extension, all opposites – in which each relies upon the other for its existence¹². In that respect, “yin-yang balance” has been described as a “unique frame of thinking in East Asia that originated in China but is shared by most Asian countries”¹³. These two modes of thought then give rise to contrasting approaches to flourishing that have been conceptualized as maximizing versus optimizing¹⁴. The former holds that if something is good, we should necessarily seek more of it (and vice versa with bad aspects of existence). This approach may seem like common sense, but it is not the only viable philosophy; indeed, in many instances it may be actively counterproductive. With many beneficial aspects of life – from food¹⁵ to sleep¹⁶ to exercise¹⁷ – rarely is more simply better. Rather, it is usually a case of getting the right amount, striking a fine balance between too little and too much. This we might call the “optimizing” approach, which involves the kinds of holistic, dialectical forms of thinking that are more prevalent in the East. Not all aspects of flourishing are necessarily subject to these optimizing dynamics; one can arguably not be too healthy or too loving, for instance. However, even in these cases, their dynamics are still often defined by optimization. Most elements of health, for instance, involve some kind of ideal balance between deficiency and excess, as denoted by the prefixes “hypo” and “hyper”¹⁸.

Many people in the West of course appreciate such nuances, and likewise have critiqued maximalism¹⁹. The issue though is that there is a fundamental tension and conflict between maximizing and optimizing approaches, which are in some essential sense incompatible. It is therefore consequential for our understanding of flourishing that, overall, the modern West has been animated more by a maximalist than an optimalist ethos. This is the conclusion, for example, of a new analysis titled “Happiness maximization Is a WEIRD way of living”²⁰. A similar study indicated that people in Western cultures tended towards a maximizing stance with respect to outcomes like happiness, pleasure, freedom, health, self-esteem, longevity, and intelligence; by contrast, people from more “holistic” cultures (mainly identified as in the East) leaned more towards an optimizing perspective (which the authors referred to instead as a principle of “moderation”)²¹. Most relevantly, the Western trend towards maximization is evident across academia, with most psychological constructs relating to mental health, for example, operationalised and presented – implicitly or explicitly – in a categorical way as either positive or negative, and the more a person has of the positive qualities the better, and vice versa for negative ones. Significantly, the tendency towards maximalism in the West has implications for our central topic: as intimated throughout this section, balance is at the heart of optimalism, and conversely is antithetical to maximalism. Consequently, with the latter dominating in the West, and scholarship on flourishing being strongly – but of course not exclusively – influenced by Western cultural values, the significance of balance has tended to be underappreciated. That said, the Western-centricity of scholarship is increasingly being redressed, as intimated above. Crucially, this also includes a burgeoning attention to the importance of balance, as we consider next.

Embracing Balance

Despite balance being generally undervalued in academia, there has nevertheless been a considerable amount of relevant scholarship, which a recent review groups into four categories: affective, cognitive, behavioural, and self-other relations⁴. The affective domain, for example, includes work around emotional equanimity, as per constructs like the Peace of Mind Scale²², and relatedly international research on lay perceptions of happiness, where the most prominent psychological definition was inner harmony (with themes of inner peace, contentment, and balance)²³. In relation to cognition, a framework bridging Buddhism and Western psychology identifies various forms of balance²⁴, such as conative (motivation and intention), as reflected in Vallerand’s emphasis on harmonious rather than obsessive passion²⁵. The behavioural category spans the myriad ways people act in the world, including “work-life balance,” together with related notions such as a “balanced lifestyle” (a “satisfying pattern of daily occupation that is healthful, meaningful, and sustainable to an individual within the context of his or her current life circumstances”²⁶), and “energy balance-related behaviours”¹⁸ (i.e., across myriad health-related processes, from exercise to sleep, finding a balance between under- and over-activity). The final category of “self-other” highlights the importance of balance across relationships at all levels of scale, from marriages²⁷ and families²⁸, to whole societies²⁹, and up to the natural world upon which people depend materially and existentially³⁰.

Moreover, work on balance continues to emerge, such as a set of items on this topic introduced into the Gallup World Poll from 2020 onwards by the Global Wellbeing Initiative – a partnership between Gallup and the Japan-based Wellbeing for Planet Earth foundation – whose findings emphasize the importance of balance in life. Regarding the 2020 GWP, the aforementioned chapter in the World Happiness Report showed the significance of that year’s item on balance (“In general, do you feel the various aspects of your life are in balance?”)¹. This had a correlation of .25 with life evaluation (Cantril’s ladder), which was – alongside the item on peace in life (also .25) – higher than any other variable examined to explain life evaluation (with the next highest being household income and having friends to count on, both .22). Likewise, in a regression analysis, balance was the third highest predictor of life evaluation at 0.37 (i.e., compared to people whose lives are not balanced, those whose lives are had 0.37 points higher evaluation, holding all other independent variables constant). Additionally, the data revealed numerous intriguing trends which merit further exploration. For example, despite the association of balance with Eastern cultures, overall, the percentage of people deeming their life in balance was higher in Western (81.0%) than East Asian countries (71.2%) or the rest of the world (69.0%). Another notable pattern pertained to economics, with a correlation of 0.69 between balance and GDP per capita. One can see this in the 2020 balance rankings, with the top ten being all relatively affluent European countries and the bottom ten mostly poor African countries. The chapter thus suggested that balance in life might at least partly be a function of material prosperity. However, these findings deserve more in-depth enquiry, including – most relevantly here, and related to the notion that balance might be linked to prosperity – into the situational factors that might facilitate balance in life.

More specifically, the field has essentially no understanding of its potential childhood predictors. Significantly though, based on decades of research and theorizing in the human-development literature, one might anticipate that childhood conditions would indeed have a meaningful impact on balance later in life. Scholarship in this area views the human being as being comprised of numerous dimensions, the development of which in childhood influences their development and expression in adulthood³¹. One such set of dimensions for example, as related to human flourishing, is articulated in the LIFE model³², which proposes a four-dimensional model of whole-human wellbeing or human flourishing (behavioural, affective, cognitive, moral). The model is based on the work of Ken Wilber³³, who synthesized the work of many dozens of human-development theorists^31,34,35,36. The essence of his synthesis is that successful human development involves making progress along all these dimensions, aided by a nurturing socio-cultural environment – of which the most significant aspect is one’s family situation – leading to flourishing throughout one’s life. Conversely, unsuccessful development means thwarted or skewed progress, often involving adverse childhood experiences that are not adequately navigated, leading to poorer outcomes later in life.

Based on this work, we would expect childhood experiences to indeed impact balance later in life. However, as noted above, there has not been any research into the long-term dynamics of life balance. In fact, there has been relatively little attention to the childhood predictors of “positive” flourishing outcomes more generally. Although scholars have for decades explored how childhood affects outcomes later in life, most research has focused on the aetiology of “negative” (i.e., undesirable) outcomes, such as mental health problems³⁷, which reflects a broader preoccupation with negative phenomena in fields like psychology more broadly. That said, over the past few decades, especially since the emergence of positive psychology in the late 1990s, there has been increasing attention to more “positive” (i.e., desirable) outcomes. As a result, select topics, especially happiness and life satisfaction, have now received considerable study, including the kind of longitudinal work that allows exploration of the childhood roots of such outcomes. One study for example used data from the UK National Child Development Study to study 4,400 children born in 1958 in Britain who have been repeatedly surveyed for 50 years, including on life satisfaction³⁸. They found demographic and socio-economic factors in early childhood had a relatively negligible effect, predicting no more than 1.2% of the variance in adult life satisfaction, whereas more individual characteristics – especially childhood behavioural-emotional problems, and social maladjustment – were found to be “powerful predictors” of such satisfaction.

To date, however, there has been no comparable work on many other desiderata, including balance in life. While this is of course understandable given that the topic has been understudied per se, as outlined above, it still ought to be remedied. To that end, this paper reports on an assessment of balance that has been included in the Global Flourishing Study (GFS), an unprecedently ambitious five-year panel study investigating the predictors of human flourishing across over 200,000 participants from 22 geographically and culturally diverse countries. The particular strength of this annual study is its longitudinal nature. While there are various laudable endeavours researching flourishing in a global context – such as the Gallup World Poll – these are mostly cross-sectional, so cannot provide much insight into causal dynamics. Hence the value of the GFS, which includes a comprehensive battery of items relating to all aspects of flourishing, together with a detailed demographic intake form that specifically asks about respondents’ childhood. Most relevantly, the GFS includes an item on balance adapted from the GWI, which asks people, “In general, how often are the various aspects of your life in balance?” (response options: always; often; rarely; and never).

The analysis is guided by three research questions: (1) how do different aspects of a child's upbringing predict life balance (LB) in adulthood; (2) do these associations vary by country; and (3) are the observed relationships robust to potential unmeasured confounding (as assessed by E-values)? Specifically, we will look at 13 different childhood predictors: (1) age (year of birth); (2) gender; (3) marital status / family structure; (4) age 12 religious service attendance; (5) religious affiliation at age 12; (6) relationship with mother; (7) relationship with father; (8) outsider growing up; (9) abuse; (10) self-rated health growing up; (11) immigration status; (12) subjective financial status of family growing up; and (13) race/ethnicity (when available). We have three main hypotheses: (1) among the childhood predictors, certain ones will show meaningful associations with LB in adulthood; (2) the strength of associations between the predictors and LB in adulthood will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation; and (3) the observed associations between the childhood predictors and LB in adulthood will be robust against potential unmeasured confounding (as assessed through E-values), in some cases suggesting that the observed associations would require strong confounding effects by unmeasured variables to explain away, thus enhancing the credibility of our findings). The study design was pre-registered with the Open Science Framework on November 18th, 2023 (https://osf.io/5uzke).

The description of the methods below has been adapted from VanderWeele and colleagues³⁹. Further methodological detail is available elsewhere^{40,41,42,43,44,45,46,47,48}.

Data

The GFS is a study of 202,898 participants from 22 geographically and culturally diverse countries, with nationally representative sampling within each country, concerning the distribution of determinants of wellbeing. Wave 1 included the following 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, UK, and US. The countries were selected to (a) maximize coverage of the world's population, (b) ensure geographic, cultural, and religious diversity, and (c) prioritize feasibility and existing data collection infrastructure. Data collection was carried out by Gallup. Data for Wave 1 were collected principally during 2023, with some countries beginning data collection in 2022, and exact dates varying by country⁴⁷, as detailed in Supplementary Table S24. Four additional waves of panel data on the participants will be collected annually from 2024–2027. The precise sampling design to ensure nationally representative samples varied by country and further details are available⁴⁷. Survey items included aspects of flourishing such as, besides SWB, health, meaning, character, relationships, and financial stability⁴⁹, plus other demographic, social, economic, political, religious, personality, childhood, community, health, and wellbeing variables. The data are publicly available through the Center for Open Science (https://www.cos.io/gfs). During the translation process, Gallup adhered to the TRAPD model (translation, review, adjudication, pretesting, and documentation) for cross-cultural survey research; for additional details, see the GFS Translation document⁵⁰.

Measures

Childhood Antecedents. 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?” Responses were dichotomized to very/somewhat good versus very/somewhat bad. An analogous variable was used for relationship with father. “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. 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/week, one-to-three times/month, less than once/month, or never. Gender was assessed as male, female, or other. Continuous age (year of birth) was classified as 18–24, 25–29, 30–39, 40–49, 50–59, 60–69, 70–79, and 80 or older. Childhood religious tradition/affiliation was 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; precise response categories varied by country (Johnson et al., 2023). Racial/ethnic identity were assessed in some, but not all, countries, and response categories were unique to each country. For additional details on the assessments see the COS GFS codebook⁵⁰ or Crabtree et al.⁴⁰

Outcome variable. LB is assessed with one question: "In general, how often are the various aspects of your life in balance?" The response options are: always, often, rarely, never. In our analyses, we dichotomized balance in life as always/often [1] vs rarely/never [0]. Also, as a post-hoc sensitivity analysis we provide the equivalent results of Table 2 in the Supplemental Tables with a different dichotomization point (see Tables S24a and S24b).

Analysis

Descriptive statistics for the observed sample, weighted to be nationally representative within country, were estimated for each childhood demographic category. A weighted modified Poisson regression model with complex survey adjusted standard errors was fit within each country of inner peace on all of the aforementioned childhood predictor variables simultaneously. In the primary analyses, random effects meta-analyses of the regression coefficients ^51,52 along with confidence intervals, estimate proportions of effects across countries with effect sizes (risk-ratios) larger than 1.1 and smaller than 0.9, and \(\:{I}^{2}\) for evidence concerning variation within a given demographic category across countries⁵³. Forest plots of estimates are available in the online supplement. 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 since categories/responses varied by country. All meta-analyses were conducted in R⁵⁴ using the metafor package⁵⁵. Within each country, a global test of association of each childhood predictor variable group with outcome was conducted, and a pooled p-value⁵⁶ across countries reported concerning evidence for association within any country. Bonferroni corrected p-value thresholds are provided based on the number of childhood demographic variables ^57,58. For each childhood predictor, we calculated E-values to evaluate the sensitivity of results to unmeasured confounding. 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 association⁵⁹. As a supplementary analysis, population weighted meta-analyses of the regression coefficients were estimated. All analyses were pre-registered with COS prior to data access, with only slight subsequent modification in the regression analyses due to multicollinearity (https://doi.org/10.17605/OSF.IO/ZTM7R); all code to reproduce analyses are openly available in an online repository⁴⁴.

Missing Data

Missing data on all variables was imputed using multivariate imputation by chained equations, and five imputed datasets were used^60,61. To account for variation in the assessment of certain variables across countries (e.g., 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 model to account for missingness to be related to probability of inclusion.

Accounting for Complex Sampling Design

The GFS used different sampling schemes across countries based on availability of existing panels and recruitment needs⁴⁷. 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 elsewhere⁴³.

Data Availability

The study design was pre-registered with the Open Science Framework on November 18th, 2023 (see https://osf.io/5uzke). The datasets generated and/or analysed during the current study are available in the Open Science Framework repository upon submission of pre-registration (https://www.cos.io/gfs-access-data), as is the methodology for the analyses (https://osf.io/pv93c), and all code to reproduce the analyses (https://osf.io/9egpr).

Descriptive Statistics

Table 1 provides the distribution of descriptive statistics (weighted counts and proportions). Participant ages ranged the entire adult lifespan (18–80+). The gender distribution was nearly balanced with 51% female, 48% male, along with a small representation from other gender identities (0.3%). Most participants reported either having a somewhat good or very good relationship with either parent while growing up. The distribution of individuals reporting attending religious services growing up shows that 41% of participants report attending at least once a week and 23% of participants reporting never attending religious services growing up. Counts and proportions for demographic characteristics weighted to be representative of each country’s population are reported on in Supplementary Tables S1a-S22a.

Table 1

Nationally representative descriptive statistics of the observed sample
Characteristic		N = 202,898¹
Relationship with mother
	Very good	127,836 (63%)
	Somewhat good	52,439 (26%)
	Somewhat bad	11,060 (5.5%)
	Very bad	4,642 (2.3%)
	Does not apply	5,965 (2.9%)
	(Missing)	956 (0.5%)
Relationship with father
	Very good	107,742 (53%)
	Somewhat good	55,714 (27%)
	Somewhat bad	15,807 (7.8%)
	Very bad	8,278 (4.1%)
	Does not apply	13,985 (6.9%)
	(Missing)	1,372 (0.7%)
Parent marital status
	Parents married	152,001 (75%)
	Divorced	17,726 (8.7%)
	Parents were never married	15,534 (7.7%)
	One or both parents had died	7,794 (3.8%)
	(Missing)	9,843 (4.9%)
Subjective financial status of family growing up
	Lived comfortably	70,861 (35%)
	Got by	82,905 (41%)
	Found it difficult	35,852 (18%)
	Found it very difficult	12,606 (6.2%)
	(Missing)	674 (0.3%)
Abuse
	Yes	29,139 (14%)
	No	167,279 (82%)
	(Missing)	6,479 (3.2%)
Outsider growing up
	Yes	28,732 (14%)
	No	170,577 (84%)
	(Missing)	3,589 (1.8%)
Self-rated health growing up
	Excellent	67,121 (33%)
	Very good	63,086 (31%)
	Good	47,378 (23%)
	Fair	19,877 (9.8%)
	Poor	4,906 (2.4%)
	(Missing)	530 (0.3%)
Immigration status
	Born in this country	190,998 (94%)
	Born in another country	9,791 (4.8%)
	(Missing)	2,110 (1.0%)
Age 12 religious service attendance
	At least 1/week	83,237 (41%)
	1–3/month	33,308 (16%)
	< 1/month	36,928 (18%)
	Never	47,445 (23%)
	(Missing)	1,980 (1.0%)
Year of birth
	1998–2005; age 18–24	27,007 (13%)
	1993–1998; age 25–29	20,700 (10%)
	1983–1993; age 30–39	40,256 (20%)
	1973–1983; age 40–49	34,464 (17%)
	1963–1973; age 50–59	31,793 (16%)
	1953–1963; age 60–69	27,763 (14%)
	1943–1953; age 70–79	16,776 (8.3%)
	1943 or earlier; age 80+	4,119 (2.0%)
	(Missing)	20 (< 0.1%)
Gender
	Male	98,411 (49%)
	Female	103,488 (51%)
	Other	602 (0.3%)
	(Missing)	397 (0.2%)
Country
	Argentina	6,724 (3.3%)
	Australia	3,844 (1.9%)
	Brazil	13,204 (6.5%)
	Egypt	4,729 (2.3%)
	Germany	9,506 (4.7%)
	Hong Kong	3,012 (1.5%)
	India	12,765 (6.3%)
	Indonesia	6,992 (3.4%)
	Israel	3,669 (1.8%)
	Japan	20,543 (10%)
	Kenya	11,389 (5.6%)
	Mexico	5,776 (2.8%)
	Nigeria	6,827 (3.4%)
	Philippines	5,292 (2.6%)
	Poland	10,389 (5.1%)
	South Africa	2,651 (1.3%)
	Spain	6,290 (3.1%)
	Sweden	15,068 (7.4%)
	Tanzania	9,075 (4.5%)
	Türkiye	1,473 (0.7%)
	United Kingdom	5,368 (2.6%)
	United States	38,312 (19%)
¹n (%); History of abuse was not collected in Israel.

Childhood Experiences Predicting Balance in Life

The meta-analytic estimates of how childhood experiences predict LB are reported in Table 2. These results show an association between all 13 childhood predictors and responses on the LB item, though often these associations are quite modest in magnitude. Childhood experiences associated with endorsing a more frequently experienced balance in life included having a good relationship with either parents, having a sense of a comfortable financial status growing up, being in very good or excellent health growing up, and more frequent attendance at religious services. These factors were, on average across countries, associated with a higher frequency of sense of balance in life as an adult. Immigration status was the only candidate childhood predictor where the global p-value was not significant after controlling for multiple testing (RR = 1.01; 95% CI [0.97,1.05]; global p-value for association in some country = 0.004). For most of these effects, although pooled over countries they were positive, there was commonly no definitive evidence for the effect being statistically different than zero within the country-specific analyses. There were, however, some notable exceptions. For example, immigration status had a moderately strong positive effect in Nigeria (RR = 1.28; 95% CI [1.08,1.51]), which indicates that those born outside of Nigeria more often report having a sense of balance in their life when later living in Nigeria. Another result that stood out was the effect of growing up in a household with a single, never married parent, which on average across countries was negatively related to LB (RR = 0.94; 95% CI [0.92,1.00]). However, within India, the effect was reversed where individuals growing up in household with a single parent were more likely to report a higher frequency of LB in adulthood (RR = 1.21; 95% CI [1.14,1.29]). The forest plots provided in our Online Supplement provide additional evidence for the heterogeneity of these effects across countries (see Figures S1-S27). Further comment on variation in effect sizes and directions across countries is given in the Discussion.

Table 2

Random effects meta-analysis of regressing balance in life on childhood predictors.
				Estimated Proportion of Effects by Threshold
Variable	Category	Risk Ratio	95% CI	< 0.90	> 1.10		Global p-value
Relationship with mother	(Ref: Very bad/somewhat bad)						< .001**
	Very good/somewhat good	1.05	(1.01,1.09)	0.05	0.32	70.6
Relationship with father	(Ref: Very bad/somewhat bad)						< .001**
	Very good/somewhat good	1.04	(1.01,1.06)	0.00	0.14	63.5
Parent marital status	(Ref: Parents married)						< .001**
	Divorced	0.97	(0.94,1.00)	0.09	0.00	64.7
	Single, never married	0.96	(0.92,1.00)	0.23	0.05	75.9
	One or both parents had died	0.95	(0.92,0.98)	0.14	0.00	42.5
Subjective financial status of family growing up	(Ref: Got by)						< .001**
	Lived comfortably	1.03	(1.02,1.05)	0.00	0.00	69.0
	Found it difficult	0.96	(0.94,0.98)	0.00	0.00	48.9
	Found it very difficult	0.94	(0.91,0.96)	0.18	0.00	26.1
Abuse	(Ref: No)						< .001**
	Yes	0.93	(0.91,0.95)	0.10	0.00	33.3
Outsider growing up	(Ref: No)						< .001**
	Yes	0.90	(0.87,0.92)	0.36	0.00	66.2
Self-rated health growing up	(Ref: Good)						< .001**
	Excellent	1.10	(1.07,1.13)	0.00	0.55	81.5
	Very good	1.07	(1.05,1.09)	0.00	0.27	65.6
	Fair	0.93	(0.90,0.95)	0.23	0.00	53.2
	Poor	0.92	(0.87,0.97)	0.36	0.00	46.9
Immigration status	(Ref: Born in this country)						0.004*
	Born in another country	1.01	(0.97,1.05)	0.00	0.09	60.5
Age 12 religious service attendance	(Ref: Never)						< .001**
	At least 1/week	1.06	(1.03,1.08)	0.00	0.14	59.3
	1–3/month	1.06	(1.02,1.09)	0.00	0.14	77.8
	< 1/month	1.04	(1.02,1.06)	0.00	0.00	38.6
Year of birth	(Ref: 1998–2005; age 18–24)						< .001**
	1993–1998; age 25–29	1.01	(0.99,1.04)	0.00	0.00	44.9
	1983–1993; age 30–39	1.02	(0.99,1.04)	0.00	0.00	68.2
	1973–1983; age 40–49	1.02	(0.97,1.06)	0.14	0.14	85.4
	1963–1973; age 50–59	1.06	(1.01,1.11)	0.09	0.50	86.6
	1953–1963; age 60–69	1.11	(1.06,1.17)	0.00	0.55	86.9
	1943–1953; age 70–79	1.12	(1.05,1.19)	0.05	0.50	86.8
	1943 or earlier; age 80+^ǂ	0.53	(0.13,2.18)	0.27	0.59	100.0
Gender	(Ref: Male)						< .001**
	Female	0.98	(0.96,1.00)	0.09	0.00	88.1
	Other^ǂ	0.25	(0.04,1.37)	0.67	0.28	99.9
Note. N = 202,898. p < .05; *p < .004 (Bonferroni corrected threshold); ^ǂGroup is very small (< 0.1% of the observed sample) within several countries leading large uncertainty in this estimate or even complete separation—be cautious about interpreting this estimate; CI = confidence interval; the estimated proportion of effects is the estimated proportion of effects above (or below) a threshold based on the calibrated effect sizes (Mathur & VanderWeele, 2020); I² is an estimate of the variability in means due to heterogeneity across countries vs. sampling variability; the Global p-value corresponds to the joint test of the null hypothesis that the country-specific joint parameter Wald tests (all parameters within variable groups are zero) are all null all 22 countries; and additional details of heterogeneity of effects are available in the forest plots of our Online Supplemental Material.

Sensitivity of Effects to Unmeasured Confounding

Sensitivity to unmeasured confounding was assessed using E-values suggested that some of the observed associations were potentially robust to unmeasured confounding (Table 3). Further, country-specific sensitivity analyses are reported on in the Online Supplement (see Tables S1c-S23c). For example, to explain away the association between excellent (versus good) self-rated health in childhood with adult LB, an unmeasured confounder that was associated with both excellent health and LB with risk ratios of 1.43 each, above and beyond the measured covariates, could suffice but weaker joint confounder associations could not; to shift the 95% confidence interval to include the null, an unmeasured confounder that was associated with both excellent health and LB with risk ratios of 1.34 each, above and beyond the measured covariates, could suffice but weaker joint confounder associations could not.

Table 3

Sensitivity of meta-analyzed childhood predictors to unmeasured confounding.
Variable	Category	E-value for Estimate	E-value for 95% CI
Relationship with mother	(Ref: Very bad/somewhat bad)
	Very good/somewhat good	1.28	1.11
Relationship with father	(Ref: Very bad/somewhat bad)
	Very good/somewhat good	1.23	1.10
Parent marital status	(Ref: Parents married)
	Divorced	1.21	1.01
	Single, never married	1.26	1.05
	One or both parents had died	1.28	1.15
Subjective financial status of family growing up	(Ref: Got by)
	Lived comfortably	1.22	1.15
	Found it difficult	1.25	1.18
	Found it very difficult	1.34	1.24
Abuse	(Ref: No)
	Yes	1.36	1.30
Outsider growing up	(Ref: No)
	Yes	1.47	1.38
Self-rated health growing up	(Ref: Good)
	Excellent	1.43	1.34
	Very good	1.34	1.27
	Fair	1.37	1.27
	Poor	1.41	1.22
Immigration status	(Ref: Born in this country)
	Born in another country	1.10	1.00
Age 12 religious service attendance	(Ref: Never)
	At least 1/week	1.31	1.22
	1–3/month	1.30	1.17
	< 1/month	1.23	1.15
Year of birth	(Ref: 1998–2005; age 18–24)
	1993–1998; age 25–29	1.13	1.00
	1983–1993; age 30–39	1.15	1.00
	1973–1983; age 40–49	1.14	1.00
	1963–1973; age 50–59	1.30	1.11
	1953–1963; age 60–69	1.47	1.31
	1943–1953; age 70–79	1.48	1.27
	1943 or earlier; age 80+^ǂ	3.17	1.00
Gender	(Ref: Male)
	Female	1.18	1.03
	Other^ǂ	7.55	1.00
Note. N = 202,898; the E-value is the minimum strength of the association an unmeasured confounder must have with both the outcome (balance in life) and the predictor, above and beyond all measured covariates, for an unmeasured confounder to explain away an association (VanderWeele & Ding, 2017, p. 269–270); and ^ǂGroup is very small (< 0.1% of the observed sample) within several countries potentially leading to complete separation and large uncertainty in this estimate—be cautious about interpreting this estimate.

The analysis sheds unique light on the childhood predictors of life balance (LB). As indicated above, this outcome has received relatively little attention per se. It is thus unsurprising that there has been barely any research into its childhood predictors, hence the value of our study. In summary, all three of our main hypotheses were supported, often strikingly so. As a reminder, our first was that of the 13 childhood predictors, certain ones will show meaningful associations with LB in adulthood; indeed, every one had a significant association in some country and often pooled across countries. Second, the strength of associations between the predictors and LB will vary by country, reflecting the influence of diverse sociocultural, economic, and health contexts that characterize each nation. Third, the observed associations between the predictors and LB would sometimes be robust against potential unmeasured confounding (as assessed through E-values). Here we shall touch in turn on the predictors, beginning with the one with the strongest impact, namely self-rated health growing up. We discuss this factor in some detail as a way of illustrating the nature and nuances of the data. We then consider the other factors more briefly, referencing and extrapolating the points made in relation to health.

Overall, then, the most impactful factor on average was self-rated health growing up, as assessed on a five-point scale: poor; fair; good; very good; and excellent. Relative to the middle category of “good,” the Risk Ratios (RRs) range from 0.92 for poor (95% CI = 0.87, 0.97) and 0.93 for fair (0.90, 0.95), to 1.07 for very good (1.05, 1.09) to 1.10 for excellent (1.07, 1.13), with all results significant at p < 0.001. An RR can be interpreted as the relative percentage in each category, which in the present paper is calculated in relation to the proportion of people reporting LB. In that respect, although balance was assessed on a four-point scale – never, rarely, often, or always in balance – in our analysis and interpretation we aggregate this into two binary categories, whereby people either have balance (endorsing either “often” or “always”) or do not have it (endorsing either “rarely” or “never”). Thus, taking the RR of 1.10 for excellent health as an example, this means that, compared to people who reported they “only” had good health growing up, the proportion of people with excellent health who have LB is 1.10 times greater than those who do not. Put another way, there is a 10% increase in having LB for those who report excellent health in childhood relative to those who report good health (conditional on all other variables in the model). Another way of interpreting the strength of these results is through the E-value⁵⁹, which pertains to our third main hypothesis. An E-value measures the strength that an “unmeasured confounder” – a variable not included in the analyses – would need to be to “explain away” the observed relationship.

This finding that childhood health is associated with LB in adulthood is unique: we could find no previous study that has explored such a connection, so simply observing it here is a notable addition to the literature. However, it is worth mentioning up front the caveat that we did not actually assess people’s health in childhood itself, but rather their retrospective recollections about their childhood. Crucially, there are indications that people sometimes change their ratings of childhood health over time; one analysis found nearly one half of their sample revised this during a 10-year observation period⁶². Older adults who were relatively advantaged (e.g., with socioeconomic resources and better memory) were less likely to revise it, whereas those with multiple childhood health problems were more likely to do so (either positively or negatively); for example, the development of psychological disorders was associated with more negative revised ratings. As such, we must be somewhat cautious in interpreting our data, given we did not measure health in childhood per se, and acknowledge that recall bias might be present. However, for recall bias to completely explain the observed associations of the childhood predictors with adult LB, the effect of adult LB on the retrospective assessments of the childhood predictors would have to be at least as strong as the observed associations themselves⁶³. Moreover, numerous longitudinal studies have actually measured health in childhood then traced its impact on later outcomes, with a substantial literature showing it does have a substantive effect on myriad aspects of adult life. Given that context, it is reasonable to think – based on our data – that LB is indeed one of the variables affected by it. Much of this existing longitudinal work focuses either on physical or mental health or socio-economic status, with poor childhood health having a long-term detrimental health on these outcomes^{64,65,66,67,68}. Given how expansive and all-encompassing the concept of LB is – spanning all domains of existence, as outlined in the introduction – one can see how issues relating to physical and mental health or socio-economic status would be relevant to our outcome of interest specifically.

Let us now turn to our second hypothesis, namely that we would observe regional differences in the effects of the factors. Many of the studies cited above were in a US context, which indeed is characteristic of the psychological literature as a whole, as elucidated in the introduction. Thus, a particular strength of our research is its multinational reach, covering 22 diverse countries. And, as per our second hypothesis, there was indeed considerable variation in the impact of childhood health. The following are the respective RRs for the four health categories (relative to the middle category of “good”) for the 22 countries (with details for each country provided in the Supplementary Tables), together with the 95% CIs in square parentheses: Argentina (poor = 0.90 [0.74, 1.10], fair = 1.97 [0.88, 1.07], very good = 1.04 [0.99, 1.10], excellent = 1.07 [1.01, 1.13]); Australia (0.79 [0.61, 1.03], 0.93 [0.80, 1.09], 1.00 [0.91, 1.09], 1.13 [1.05, 1.23]); Brazil (0.89 [0.75,1.04], 0.88 [0.82,0.95], 1.04 [1.00,1.10], 1.12 [1.07, 1.17]); Egypt (0.95 [0.82, 1.11], 1.00 [0.92, 1.10], 0.98 [0.93, 1.04], 0.99 [0.93, 1.05]); Germany (1.07 [0.92, 1.24], 0.94 [0.86, 1.01], 1.11 [1.07, 1.15], 1.17 [1.12, 1.22]); Hong Kong (0.74 [0.57, 0.95], 0.84 [0.76, 0.93], 1.06 [1.00, 1.12], 1.05 [0.97, 1.14]); India (0.95 [0.83, 1.09], 0.95 [0.88, 1.01], 1.05 [0.99, 1.11], 1.11 [1.03, 1.19]); Indonesia (0.80 [0.54, 1.19], 0.98 [0.92, 1.04], 1.05 [1.00, 1.10], 1.09 [1.03, 1.15]); Israel (1.13 [0.81, 1.57], 0.96 [0.84, 1.09], 1.04 [0.96, 1.12], 1.08 [1.01, 1.16]); Japan (0.71 [0.65, 0.78], 0.83 [0.79, 0.86], 1.11 [1.09, 1.14], 1.14 [1.11, 1.18]); Kenya (0.94 [0.81, 1.11], 0.92 [0.85, 1.00], 1.11 [1.04, 1.18], 1.10 [1.04, 1.17]); Mexico (0.91 [0.77, 1.08], 0.99 [0.93, 1.06], 1.08 [1.03, 1.13], 1.04 [0.99, 1.09]); Nigeria (1.03 [0.85, 1.26], 1.04 [0.93, 1.16], 1.02 [0.96, 1.08], 0.97 [0.91, 1.04]); Philippines (0.99 [0.86, 1.15], 0.93 [0.85, 1.03], 1.06 [0.94, 1.18], 1.13 [1.04, 1.23]); Poland (0.96 [0.72, 1.29], 0.84 [0.73, 0.97], 1.03 [0.98, 1.08], 1.03 [0.97, 1.09]); South Africa (0.99 [0.85,1.16], 0.90 [0.78, 1.03], 1.03 [0.93, 1.13], 1.01 [0.93, 1.10]); Spain (1.12 [0.96, 1.32], 0.94 [0.80, 1.10], 1.03 [0.98, 1.09], 1.08 [1.02, 1.13]); Sweden (0.80 [0.70, 0.90], 0.92 [0.86, 0.98], 1.17 [1.13, 1.21], 1.23 [1.19, 1.27]); Tanzania (0.94 [0.79, 1.12], 1.01 [0.91, 1.12], 1.08 [0.99, 1.18], 1.13 [1.04, 1.23]); Türkiye (0.81 [0.47, 1.40], 1.03 [0.81, 1.30], 1.23 [1.02, 1.47], 1.31 [1.09, 1.56]); United Kingdom (0.95 [0.75, 1.21], 0.79 [0.67, 0.93], 1.13 [1.05, 1.22], 1.15 [1.07, 1.24]); and United States (0.81 [0.62, 1.07], 0.89 [0.78, 1.02], 1.14 [1.08, 1.21], 1.21 [1.15, 1.28]). As one can see, there are many notable regional nuances. For a start, relative to the difference in RR comparing poor and excellent in the pooled meta-analysis, which was 0.19 (0.91 for poor to 1.10 for excellent health), some nations had a smaller range (with Egypt the narrowest, at 0.05), and others larger (with Türkiye the biggest at 0.50). More research is needed to explore why this regional variation exists, but it will almost certainly involve considerations such as the provision of healthcare in the various countries.

There were also intriguing patterns that are harder to explain and certainly merit further investigation, perhaps most strikingly the fact that, in some countries, the RRs seemed “out of order.” One would expect, based on the overall RRs, that relative to people with “good” childhood health, people with worse health (“poor” or “fair”) would have lower levels of LB (RR < 1.00), while people with better health (“very good” or “excellent”) would have higher levels (RR > 1.00). Indeed, eight countries did conform to this linear escalating pattern (Argentina, Australia, Brazil, Hong Kong, Indonesia, Japan, Sweden, and USA). 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 levels of LB (RR > 1.00), while conversely others with better health (very good and/or excellent) had lower levels (RR < 1.00). In Spain and Israel, for instance, people with poor childhood health had an RR of 1.12 (0.96, 1.32) and 1.13 (0.81, 1.57) respectively: i.e., there, not only does poor childhood health not detract from LB in adulthood, the data suggest it may help, possibly for example by developing resilience. Moreover, the E-values for this particular observation are both 1.50, suggesting these findings are perhaps even moderately robust to potential confounding. 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 LB in adulthood. One could speculate that, at least in some countries, poor childhood health either encourages or compels people to develop certain psychological qualities that might subsequently be conducive to attaining LB. 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. Certainly, this is something that demands more in-depth study.

Let us now consider the other variables. While we do not have the space to discuss these in comparable depth to childhood health, we can nevertheless highlight some notable patterns that merit further study. Indeed, to reiterate, every factor had a significant effect on balance in adulthood. To begin with, family dynamics are important, including having a good relationship with one’s mother (RR = 1.05; 95% CIs = 1.01, 1.09) and father (1.04; 1.01, 1.06), as is having parents who were married compared to either being divorced (0.97; 0.94, 1.00), single or never married (0.96; 0.92, 1.00), or one or both parents having died during childhood (0.95; 0.92, 0.98). The financial situation of the family also matters: relative to people whose families “got by,” those who “lived comfortably” fared better (1.03; 1.02, 1.05), while people did worse whose families found it either “difficult” (0.96; 0.94, 0.98) or “very difficult” (0.94; 0.91, 0.96). These findings accord with a vast existing literature on the importance of these factors for wellbeing, both in childhood itself and moreover in later life. Thus, for example, with respect to the quality of relationships with parents, a considerable literature on attachment styles shows the positive impact of “secure” bonds – generally regarded as the optimal type of attachment – on mental health later in life⁶⁹. So too with marriage: research has consistently shown this to be beneficial for children relative to other possibilities such as divorce/separation, both during childhood itself⁷⁰ and over the life course⁷¹, though one notes that in some situations – such as conflicted or abusive marriages – divorce may indeed be better the option all round⁷². And again, with the financial aspect, research consistently finds that economic security in childhood is associated with better long term mental health prospects⁷³. Until now, however, these factors had not been linked to LB in adulthood, and thus our work extends the literature to encompass this.

Moreover, perhaps of even greater value in this study is the way it highlights regional variation, showing the impact of these factors differs considerably based on socio-cultural dynamics. Thus, the effect of having a good relationship with one’s mother ranged from RR = 0.82 in Egypt (95% CI = 0.71, 0.95) to 1.19 in Spain (1.07,1.33), while the impact of having a good relationship with one’s father ranged from 0.89 in Indonesia (0.77, 1.03) to 1.16 in Poland (1.05,1.28). Likewise, there was considerable variation pertaining to parental marital status, where compared to having parents who were married, the effect of parents: being divorced ranged from 0.85 in Indonesia (0.77, 0.94) to 1.26 in Türkiye (0.95, 1.68); being single or never married ranged from 0.76 in Israel (0.60, 0.95) to 1.21 in India (1.14, 1.29); and one or both parents having died ranged from 0.71 in Türkiye (0.41, 1.24) to 1.18 in Australia (1.00, 1.38). Finally, there was also variation in relation to finances, albeit less so than the other familial dynamics, implying that this factor is somewhat less susceptible to cultural influence. Thus, compared to those whose families “got by” financially, the effect of having “lived comfortably” ranged from 0.97 in India (0.91, 1.03) to 1.11 in Indonesia (1.07, 1.16), while those who found it “difficult” ranged from 0.79 in Türkiye (0.64, 0.98) to 1.04 in India (0.97, 1.11), and those who found it “very difficult” ranged from 0.70 in Türkiye (0.50, 0.98) to 1.04 in Hong Kong (0.86, 1.26). Again, these regional differences are fascinating and deserve further study, and will require in-depth enquiry into cultural dynamics to help explain them and again may perhaps sometimes relate to the potential of adversity to develop resilience. Consider for example the impact of having parents who were single or unmarried, with a 0.45 RR differential between Israel, where such divorce has a markedly negative impact on the likelihood of experiencing LB in adulthood, and India, where it means one is more likely to have LB compared to people whose parents were married. Accounting for such findings will require detailed exploration into the traditions, values and practices pertaining to both marriage and divorce in the respective countries, and likewise in their respective dominant religions (Judaism and Hinduism respectively).

Another important variable is religious attendance at age 12, where not only was this associated with adult LB, but moreover to an increasing degree depending on the frequency of attendance. So, compared to those who never attended, the impact of attending rose from RR = 1.04 for those attending less than once a month (95% CI = 1.02, 1.06), to 1.06 for those attending 1–3 times a month (1.02, 1.09) or weekly (1.03, 1.08). This aligns with an extensive body of work on the positive impact of childhood religious attendance on subsequent physical and mental health⁷⁴. Again though, our study seems to be the first to link this to LB specifically. And once again, perhaps even more striking is the regional variation, where the impact of attending less than once a month ranged from 0.79 in South Africa (0.64, 0.97) to 1.23 in Kenya (0.95, 1.58), of attending 1–3 times a month ranged from 0.82 in South Africa (0.69, 0.97) to 1.33 in Türkiye (1.10, 1.60), and attending weekly ranged from 0.86 in South Africa (0.74, 1.00) to 1.25 in Kenya (1.02, 1.53). We see here some fascinating patterns that warrant closer investigation. Consider for example the discrepancy between South Africa and Kenya (a gap of 0.44 with regard to attending less than once per month, and 0.39 for weekly attendance). When it comes to research on flourishing, Africa receives very little attention, and even when it does, is rarely considered in granular detail, instead often subject to generalizations about the continent as a whole; yet there is considerable variation within the continent across the myriad aspects of flourishing⁷⁵, and this is evident in our findings. Strikingly too, however, the two countries are very similar in terms of religious composition, being predominantly Christian to almost the exact same level (85.5% in Kenya and 85.3% in South Africa)⁷⁶. As such, it would seem that whatever is driving the disparity is most likely not religion per se, but some other factor, which future research can ideally look into.

There is one more set of childhood factors that are impactful for balance in adulthood are adverse experiences, namely, experiencing abuse (RR = 0.93, 95% CI = 0.91, 0.95) and being an outsider growing up (0.90; 0.87, 0.92). These of course connect with a now vast literature on the long-term detrimental impact of Adverse Childhood Experiences, which are documented to negatively impact a panoply of outcomes later in life, ranging from substance use⁷⁷ and food insecurity⁷⁸ to depression⁷⁹ and even frailty in older adults⁸⁰. Thus, to this literature we can also add that such adversities also lower the likelihood of experiencing LB as an adult. Again though, the regional variation is considerable, where the impact of abuse ranges from 0.83 in Poland (0.74, 0.94) to 1.01 in India (0.94, 1.08), while the impact of being an outsider ranges from 0.76 in Australia (0.69, 0.84) to 1.06 in Turkiye (0.86, 1.31). Here with only perhaps slight exceptions, the effects seem more uniform across countries.

Finally, there are three factors that are not necessarily about childhood per se, but are nevertheless relevant to childhood, namely, people’s age, sex, and immigration status. In one sense of course, these are childhood factors (in that they tell us something about people’s childhood), but from another perspective they are factors that pertain to the individual at all life stages. Nevertheless, they are worth briefly noting here. Regarding age, the older the participants, the more likely they are to have LB: compared to people aged 18–24 (i.e., born between 1998 and 2005), the RRs rose from a marginally higher 1.01 (95% CI = 0.99, 1.04) for those aged 25–29 (1993–1998), to 1.12 (1.05, 1.19) for those aged 70–79 (1943–1953), although it dropped considerably to just 0.53 (0.13, 2.18) for those aged over 80 (born in 1943 or earlier), though this latter group was very small, so caution is required in interpreting this data (as indicated by the massive 95% CI of 2.05). These findings could be regarded as reflecting a childhood factor, especially if we interpret the data as being about the time period when people were born, hence being a cohort effect. However, the emergent literature on LB suggests it tends to increase as a function of age¹. As such, it is perhaps more realistic to interpret the findings here as simply being more about the actual current age of the participants. Nevertheless, it is again still interesting to note regional variation, where the RR of the 70–79 age group ranged from 0.79 in Nigeria (0.62, 1.02) to 1.51 in Australia (1.31, 1.74), showing the relationship between age and life balance is not universally observed, and like the other factors here is affected by socio-cultural dynamics.

The penultimate variable is sex, which ranked second last in terms of impact, where compared to men, women had an RR of 0.98 (95% CI = 0.96, 1.00). That said, we should note a very small percentage of the sample stated their gender was neither male nor female but “other”, with this group having considerably lower LB (0.25; 0.04, 1.37). We do need to be cautious in interpreting this finding, as this group was very small (< 0.1% of the observed sample) in several countries, leading to complete separation and large uncertainty in this estimate. Nevertheless, it is a strikingly low RR that does demand further study. There is by now an extensive literature showing that people who identify as LGBTQ + tend to have lower levels of mental health across the lifespan, from youth⁸¹ to older adults⁸². It is perhaps unsurprising that this factor then would also affect LB. It is not certain whether the data here constitutes a childhood factor per se, since the item asks people their current gender, not their gender as a child, and it is possible that some percentage who answered “other” now would not have done so in childhood. That said, even if the latter were the case, it is likely that some relevant dynamics would have manifested during childhood (e.g., some sense of gender dysphoria). Thus, more research will be needed to look into this finding. Also, as with other factors, it will also be important to investigate the regional variation, where the RR for females ranged from 0.87 in Kenya (0.84, 0.91) the Philippines (0.82, 0.93) to 1.08 in Japan (1.06, 1.10), and for those answering “other” ranging from 0.28 in Germany (0.07, 1.08) to 1.43 in Mexico (1.25, 1.64) and Nigeria (1.32, 1.54). It would be helpful to know, for instance, what it is about Mexico and Nigeria that means people who answer “other” tend to be much more likely to have life balance than men or women. Perhaps one possibility is that both have long-standing traditional non-binary roles in some of their cultures – including the Muxes in Mexico⁸³ and the ‘Yan Daudu in Nigeria⁸⁴ – which may be driving at least some of the differences in the data here.

Lastly, the factor with the least impact was immigration status: compared to people born in the country in which they live, those born elsewhere had a RR that was basically equal (1.01; 95% CI = 0.97, 1.05). As with age and sex, this is not necessarily a childhood factor, since it reflects a person’s current immigrant status, not that of when they were a child, though it does indicate that as a child they were born in a country other than the one in which they currently live. Nevertheless, it is still intriguing to note that such status does not seem to have any bearing on LB, which is notable, given that being an immigrant is frequently perceived as presenting challenges that can be detrimental to mental health⁸⁵. That said, research has often found immigrant mental health is “better than expected”⁸⁶, and may even be better than native people, a phenomenon remarked on often enough to have a label – namely, the “healthy immigrant effect” – which “suggests that immigrants have a health advantage over the domestic-born,” though this usually “vanishes with increased length of residency”⁸⁷. In our case, while we didn’t observe this kind of effect, neither were immigrants disadvantaged when it comes to LB. Again though, there were also significant regional disparities, with RR ranging from 0.81 in the Philippines (1.33, 2.00) to 1.45 in Tanzania (0.96, 2.19), so in some countries at least the healthy immigrant effect may play out but the confidence intervals are quite wide.

Although scholars are increasingly appreciative of the importance of LB, this topic has received relatively little attention amidst the proliferation of research into flourishing over the past two decades, particularly when it comes to its childhood predictors. Hence the value of this paper, which is not only the first to explore such predictors, but moreover to examine country-level variations. Using a retrospective assessment of childhood experiences in 22 countries, combined with several important current demographics, we found that most aspects of a child's upbringing that we measured do in fact predict LB in adulthood, though there are important country-level variables that require further research to understand. The most impactful factor was self-rated health growing up, reflecting a significant body of work showing physical health to be an important predictor of a range of subsequent flourishing outcomes (Case et al., 2005), while the least impactful predictor was immigration status. Moreover, all of the significant relationships documented in this study were robust to potential unmeasured confounding, as assessed by E-values. Developmentally, three insights emerge from this dataset. First, this data suggests that, across cultures, some differences in physical flourishing in childhood generate large variation (big disparities in RR) in LB in adulthood. Second, across cultures, differences in some aspects of social flourishing in childhood (especially abuse and being an outsider) generate moderate differences in LB in adulthood. Third, in many cultures, differences in numerous aspects of childhood experience had little to no effect on LB in adulthood. We hope that future research will help to explain the reasons why we found some quite divergent patterns across countries, and more generally that researchers will pay closer attention to LB as a key constituent of human flourishing.

Author Contribution

T.L. wrote the main manuscript text. R.N.P. prepared all tables and figures. B.R.J. and T.V.J. led the overall study on which this paper reports. All authors reviewed the manuscript and contributed edits and additions to the text.

Acknowledgement

This is not an acknowledgement, but a statement we were asked to provide upon submission; I cannot find another place to put it, so am including it here, which I hope is ok?This submission is part of the Global Flourishing collection. We were invited to submit this manuscript, following a peer review offer by Rafal Marszalek.

Data Availability

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No competing interests reported.

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Childhood predictors of balance in life: A cross-national analysis of the Global Flourishing Study

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Introduction

Overlooking Balance

Embracing Balance

Methods

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Analysis

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Accounting for Complex Sampling Design

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Childhood Experiences Predicting Balance in Life

Sensitivity of Effects to Unmeasured Confounding

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Acknowledgement

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