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LLM - Digital Humanities 15

ERROR: type should be string, got "https://doi.org/10.1007/s12187-022-09993-7\nChild Indicators Research (2023) 16:797-836 https://doi.org/10.1007/s12187-022-09993-7\nChild Indicators Research (2023) 16:797-836 Keywords  Wellbeing · Life Satisfaction · Mental Health · Adolescence · United \nArab Emirates Geographic, Socio‑Demographic and School Type Variation \nin Adolescent Wellbeing and Mental Health and Links \nwith Academic Competence in the United Arab Emirates Jose Marquez1   · Louise Lambert2   · Megan Cutts1 Accepted: 6 November 2022 \n© The Author(s) 2022\n/ Published online: 23 November 2022 Accepted: 6 November 2022 \n© The Author(s) 2022\n/ Published online: 23 November 2022 *\t Jose Marquez \n\t\njose.marquez@manchester.ac.uk\nExtended author information available on the last page of the article *\t Jose Marquez \n\t\njose.marquez@manchester.ac.uk Extended author information available on the last page of the article *\t Jose Marquez 1  Introduction Global attention to the wellbeing and mental health of children and young people \nhas grown (Diener et al., 2018; Thapar et al., 2021). This is partly due to con-\ncerns about negative trends observed in many countries over the last two decades \n(Marquez & Long, 2020; McManus et al., 2019; Mojtabai et al., 2016) and more \nrecent concerns about the impact of the COVID-19 pandemic (Gavin et al., 2021; \nRacine et al., 2021; UNICEF, 2021). In the case of wellbeing, interest was originally driven by an increasing global \nrecognition that children have the right to be heard in all aspects affecting their \nlives (art. 12 UNCRC, United Nations, 1989). This interest was also driven by \nthe need to move beyond “well-becoming” approaches that are concerned with \nchildren’s future outcomes and towards wellbeing approaches that consider \nchildhood as a stage of significance in its own right and not as a mere journey to \nadulthood (Ben-Arieh, 2007). Still, future outcomes remain a key element motivating interest in child well-\nbeing, with concerns often focusing on the wellbeing of adolescents. A reason \nfor this is that adolescence has been identified as a window of opportunity for \nintervention as this is the time when wellbeing begins to decline (Casas and \nGonzález-Carrasco, 2019) and the prevalence of mental health issues increases \n(NHS Digital, 2018). Evidence indicates that most lifetime cases of mental ill \nhealth manifest by the age of 24, with a peak age of onset of 14.5 years (Colizzi \net  al., 2020; Solmi et  al., 2021). Further evidence indicates that wellbeing in \nadolescence is predictive of adult wellbeing, mental health, physical health and \nhealth behaviour, relational, as well as labour market and socioeconomic out-\ncomes (Cavioni et  al., 2021; DeNeve & Oswald, 2012; Goodman et  al., 2015; \nGuzmán et  al., 2020; Kansky et  al., 2016; Marquez et  al., 2022a; Richards & \nHuppert, 2011). Research on the absence of a trade-off between wellbeing and \nacademic outcome (Gutman & Vorhaus, 2012; Lindorff, 2021; Shoshani et  al., \n2016) has also helped advanced wellbeing promotion agendas in school contexts.i )\np\ng p\ng\nWellbeing has traditionally been conceptualised in two ways. In the first, \nhedonic or subjective wellbeing comprises a cognitive component (i.e., life sat-\nisfaction (LS)) and an affective component (i.e., experiencing positive and neg-\native moods, feelings and emotions) (Diener et  al., 2002). Abstract Interest in adolescents’ wellbeing and mental health is growing worldwide, but little \nresearch in this area has been conducted in certain world regions and countries such \nas the United Arab Emirates (UAE). Geographic, socio-demographic and school \ntype differences in adolescent wellbeing and mental health are commonly observed \nin the field, and the UAE is a diverse country where these types of differences have \nbeen found for other outcomes (notably, academic). Yet, no prior national study has \nexplored these differences in terms of wellbeing and mental health in the nation. We address this gap by investigating differences across emirates, gender, socio-\neconomic status, immigrant status, school sector and school curriculum for overall \nlife satisfaction, positive affect, negative affect, meaning and purpose in life, and \ninternalizing difficulties. We use linear regression to analyse cross-sectional data \nfrom the Programme for International Student Assessment (PISA) study from \n2015 and 2018. We find substantial geographic, socio-demographic and school \ntype differences in levels (2018) of wellbeing and mental health -which vary \nacross distinct domains- and declines (2015–2018) of wellbeing. Better wellbeing \nand mental health are observed in the northern emirates and among boys. Better \nwellbeing and poorer mental health are observed among nationals (compared to \nexpatriates) and in public schools (compared to private schools). Despite presenting \nthe best academic outcomes, British schools present the worst wellbeing and \nmental health outcomes. However, results show the absence of a trade-off between \nacademic competence and wellbeing and mental health, with evidence of a small \npositive association with wellbeing. Keywords  Wellbeing · Life Satisfaction · Mental Health · Adolescence · United \nArab Emirates 0123456789)\n1 3 456789)\n3 456789)\n3 J. Marquez et al. 798 1  Introduction This view concerns \nitself with how individuals experience and appraise their own lives. In the sec-\nond, eudaimonic or psychological wellbeing has been conceptualised in terms of \npurpose in life, environmental mastery, autonomy, personal growth, self-accept-\nance, positive relations with others, and optimism (Ryff et al., 2021) and is con-\ncerned with how individuals function and relate to others. In sum, although we \nacknowledge that the term wellbeing/well-being can be used to refer to a range of \noutcomes in different domains (including also objective measures of well-being \nsuch as health status, academic competence, etc.), in this study, we use the term \nwellbeing to refer to subjective/hedonic and psychological/eudaimonic wellbeing. Wellbeing is increasingly being studied in combination with mental health \nin research that, in most cases, defines this in terms of the presence of mental 3 1 3 Geographic, Socio‑Demographic and School Type Variation… 799 ill-health symptoms of internalizing difficulties (depression, anxiety, social anxi-\nety, somatic complaints, etc.) or, less commonly, externalizing difficulties (under-\ncontrolled, impulsive, or aggressive behaviour) (Otto et al., 2021; Petersen et al., \n2018; Trotta et  al., 2020). Wellbeing and mental health have customarily been \nconceptualized as two ends of the same spectrum, but research suggests these are \nindependent constructs that, although related, have different correlates and may \nfollow different trajectories over the life course (Bohlmeijer & Westerhof, 2021; \nKinderman et al., 2015; Patalay & Fitzsimons, 2016, 2018; Sharpe et al., 2016; \nWesterhof & Keyes, 2010). y\nStudying wellbeing and mental health together can provide a more complete \npicture of the emotional and mental states of individuals. Yet, while much has \nbeen learned in the last decades about adolescent wellbeing and mental health in \nmany parts of the world, there is little in others. This is the case of the UAE, a high-\nincome nation in the Gulf Cooperation Council (GCC) region comprised of seven \nemirates and characterised by a diverse socio-demographic profile (See Fig. 1). The national population represents only around 10% of the nation, who differ from \nexpatriates (around 90%) in terms of linguistic, religious and cultural characteristics \n(Chan et al., 2021; Maalouf et al., 2019). This diversity is reflected in outcomes \nfor children and young people, including academic outcomes among a student \npopulation with almost three in four students enrolled in private schools (Buckner, \n2018; Marquez et al., 2022b). 1.1  \u0007Regional, Socio‑Demographic and School Type Variation in Adolescent \nWellbeing and Mental Health Research exploring geographic variation in adolescent wellbeing and mental health \nhas grown in recent years. Although there is a large number of studies investigat-\ning within-country variation (i.e., Islam et al., 2021; Schoon & Henseke, 2022; Van \nPhan and O’Brien, 2019), most research in this area focuses on cross-national dif-\nferences. Evidence from cross-national studies indicate substantial variation across \nsocio-cultural contexts and languages (Campbell et  al., 2021; Marquez & Main, \n2020; Rees et al., 2015). For instance, in an international study of youth wellbeing \nacross 29 nations, Mutumba and Schulenberg (2020) found that national wellbeing \nvariations were in part explained by place of residence, finding higher subjective \nwellbeing in rural areas. Urban–rural differences in mental health and wellbeing \noutcomes have been well documented, with poorer mental health and wellbeing out-\ncomes noted in urban areas (i.e., Buttazzoni et al., 2022; Murphy et al., 2020). Wellbeing and mental health also vary across socio-demographic groups. For \nexample, there is evidence of wellbeing and mental health inequalities during child-\nhood and adolescence in relation to gender, however, the direction of the inequal-\nity shifts throughout development. Boys tend to report higher rates of mental health \ndifficulties than girls in both Western and Middle Eastern countries until puberty \n(Cyranowski, et al., 2000; NHS Digital, 2021; Patton & Viner, 2007), at which time \ngirls record double the rate until the age of 35 to 40 (Cyranowski et al., 2000; Pata-\nlay and Fitzsimons, 2018). In terms of gender differences in adolescent wellbeing, \ngirls tend to present worse outcomes than boys (Bradshaw et al., 2013; Campbell \net  al., 2021; Kaye-Tzadok et  al., 2017), but differences across domains are com-\nmon. For example, a meta-analysis found that overall LS (OLS) tends to be higher \namong boys, but satisfaction in some domains is higher among girls (e.g., satisfac-\ntion with family and school) and higher among males in others (e.g., satisfaction \nwith time-use, self) (Chen et al., 2019). Using PISA data from 73 nations, Campbell \net al. (2021) found that females reported worse outcomes on eudaimonic measures \nof meaning and purpose in life, psychological distress, OLS, and hedonic measures \nof positive affect. f\nLower socio-economic status (SES) has also been associated with lower LS \n(i.e., Chzhen et al., 2016; Elgar et al., 2017; Knies, 2022; Marquez, 2021; Patalay \n& Fitzsimons, 2018; Zaborskis et  al., 2019a, b), although how it is measured \nmatters. 1  Introduction The current study advances our understanding of adolescent wellbeing and \nmental health inequalities in the UAE across emirates, socio-demographic groups \nand school types. Such studies are critical in developing and refining education Fig. 1   The UAE’s seven emirates Fig. 1   The UAE’s seven emirates Fig. 1   The UAE’s seven emirates 3 3 J. Marquez et al. 800 policies, making decisions around resource allocation and responsibilities. This can \nfacilitate that all young people enjoy life with the fewest mental health concerns and \nthe highest wellbeing possible, and that no region, school type or socio-demographic \ngroup is left behind. This study focuses on differences in levels of overall LS (OLS), \npositive affect, negative affect, meaning and purpose in life, and mental health \nsymptoms of internalizing difficulties in 2018, as well as declines in OLS between \n2015 and 2018. We also examine the association between academic competence and \nwellbeing and mental health outcomes. 1.1  \u0007Regional, Socio‑Demographic and School Type Variation in Adolescent \nWellbeing and Mental Health Associations tend to be more robust in studies using measures of material 1 3 Geographic, Socio‑Demographic and School Type Variation… 801 deprivation compared to family income (Gross-Manos, 2017; Main, 2014; Rees \net  al., 2011; Sarriera et  al., 2014; Zaborskis et  al., 2019b) and even more so in \nstudies that use child-derived measures of material well-being compared to those \nderived by adults (Lau and Bradshaw, 2018; Main, 2014). Researchers have found \nthat children and young people with lower SES also have more mental health issues. A review of 52 studies by Reiss (2013) found a two-to-three-time higher likelihood \nof developing mental health issues at lower SES levels, with problems rising as SES \ndeclined. Meta-analyses (i.e., Peverill et al., 2021; Reiss et al., 2019) have confirmed \nthe same, although to varying degrees and for more granular reasons, i.e., parent \neducation (Merz et  al., 2018), parent mental health issues (Amone-P’Olak et  al., \n2011). Migration status is another variable associated with wellbeing, although the \nevidence is mixed. Most find that immigrant adolescents report lower wellbeing \nthan native students (Bradshaw et al., 2011; Casas et al., 2013; Marquez & Main, \n2020; Tang, 2019), but others find no differences or even differences in the opposite \ndirection (Filion et  al., 2018). Contrasting findings seem to be explained by \ndifferences in the host country considered and what aspect of wellbeing is the focus \nof the study. For example, studies exploring academic and wellbeing outcomes have \nfound that native students have better academic performance, but higher (Wang, \n2021) or lower (Rodríguez et al., 2020) scores in positive affect, as well as equal \n(Alivernini et  al., 2020; Rodríguez et  al., 2020) or slightly lower (Wang, 2021) \nscores in LS. Where immigrant adolescents report lower wellbeing, differences \nare often explained by socio-economic characteristics, as well as friendships and \nloneliness, discrimination, religiosity, social participation, acculturation, and school \nrelated factors (e.g., language, bullying, school-related anxiety, fear of failure, grade \nrepetition, etc.) (Berry & Hou, 2017; Dryden-Peterson, 2018; Lee, 2019; Paparusso, \n2021; Schwartz et al., 2013). Differences in LS have been similarly explained: using \ndata from PISA 2015 across 48 countries and economies, Tang, (2019) found that \nimmigrant students reported lower LS, but the gap could be partly explained by \nfactors such as talking to parents, bullying and schoolwork-related anxiety. 1.1  \u0007Regional, Socio‑Demographic and School Type Variation in Adolescent \nWellbeing and Mental Health Research \nexploring migrant differences in adolescent wellbeing in the UAE is non-existent, \nand to the best of our knowledge, only one study has examined this for mental health \n(Shah et al., 2020; described below). Studying this question in the UAE is vital as \nthis is a nation where migrant gaps, particularly in academic outcomes (Marquez \net al., 2022b), emerge in the opposite direction compared to most countries.f Finally, wellbeing and mental health differences across types of schools is \nsubstantial. This is not surprising considering the role school factors play in \nexplaining variation in wellbeing (Marquez & Main, 2020; Marquez et al., 2022c; \nTaylor et al., 2022). In an international comparative study using data from PISA 2015, \nMarquez and Main (2020) found that in 15 of 31 countries, the LS of students differed \nacross types of schools (public, semi-private, private). After controlling for SES and \nother socio-demographic and school characteristics, LS was higher in private or \nsemi-private schools in six countries and lower in nine countries, suggesting that the \nwellbeing impact of attending private schools may differ by country. Studies in the \nUK have found that private schools offer no mental health and wellbeing advantages \n(Henderson et al., 2020; Sullivan et al., 2021), with evidence indicating that attending 1 3 J. Marquez et al. 802 private schools is associated with more psychological distress at age 16 for females \nand predicts it at the age of 42 (Sullivan et al., 2021). Liu and Zhao (2016) found that \nChinese youth attending private schools also reported lower LS, experiencing more \nracial discrimination than their public-school peers.f private schools is associated with more psychological distress at age 16 for females \nand predicts it at the age of 42 (Sullivan et al., 2021). Liu and Zhao (2016) found that \nChinese youth attending private schools also reported lower LS, experiencing more \nracial discrimination than their public-school peers.f While research suggests there is no trade-off between academic and wellbe-\ning outcomes (Gutman & Vorhaus, 2012; Lindorff, 2021; Shoshani et al., 2016), a \nstrong focus on academic performance may undermine wellbeing and mental health. 1.1  \u0007Regional, Socio‑Demographic and School Type Variation in Adolescent \nWellbeing and Mental Health A greater emphasis on exams and performance appears to contribute to lower mental \nhealth in girls (Giota & Gustafsson, 2017; Högberg et al., 2020; Long et al., 2021; \nSonmark et al., 2016) and youth from upper middle-class families in high achieving \nschools (Cho & Chan, 2020; Ebbert et al., 2019a, 2019b; Heller-Sahlgren, 2018; \nLuthar et al., 2020). Fear of failure in high-achieving youth from economically pros-\nperous nations has also been linked to lower LS and wellbeing (Borgonovi & Han, \n2021; Govorova et al., 2020; Wang, 2021). 1 3 1.2  \u0007Mental Health and Wellbeing in the United Arab Emirates Few studies exist relative to youth mental health and wellbeing in the UAE and Gulf \nregion (Alzahrani, 2020; Chan et al., 2021; Lansford et al., 2019; Maalouf et al., \n2019), however, a picture is emerging. Across the Eastern Mediterranean Region \n(EMR), the WHO classification for the Middle East/North Africa, Charara et al., \n(2017, 2018) estimated that depression and anxiety were the third and ninth lead-\ning cause of nonfatal burden of disease. Most nations had higher burdens of mental \ndisorders than global means, particularly for women, with anxiety disorders peak-\ning between 15 and 19 years of age, conduct disorders and ADHD at 10 to 14, and \ndepressive disorders at 40 to 44 years. The increase in burden of mental health was \nmost marked in the Gulf nations, including the UAE. Recently, Chan et al. (2021) \ncalculated pooled prevalence rates for depression (between 26 and 46%) and anxi-\nety (between 17 and 57%) in the Gulf region’s youth, noting their upper limits were \nabove global rates. A study conducted in the UAE’s city of Al Ain on 600 students between the \nages of 12 and 18 found that depressive symptoms were reported in 17% of them \n(Shah et al., 2020), with rates highest in South Asian expatriates, where one in three \nreported depressive symptoms, followed by one in five Emirati nationals. Western \nexpatriate students reported half the rates, but all groups had higher than global \naverages. In another UAE study, Al-Yateem et al., (2020) found that females had \ndouble the rate of anxiety and that low to middle SES youth had greater anxiety \nthan those at the higher end. In terms of wellbeing in childhood or adolescence, \nrecent international studies (OECD, 2017, 2019; Marquez & Main, 2020; Marquez \n& Long, 2020) have provided insights with evidence indicating that between 2015 \nand 2018, the UAE was among the top third countries where the OLS of 15-year-old \nadolescents had declined the most. Potentially related, research on academic outcomes reveal important socio-demo-\ngraphic inequalities in adolescents outcomes in the UAE. For instance, Buckner \n(2018) found that low-income Emirati nationals had worse academic performance 1 3 Geographic, Socio‑Demographic and School Type Variation… 803 on the 2015 and 2018 PISA exams (OECD, 2019) than their higher income peers, \nwith the influence of SES on academic achievement stronger in the UAE than \nelsewhere. 1.2  \u0007Mental Health and Wellbeing in the United Arab Emirates In addition to the three-year private–public academic performance gap \n(Marquez et al., 2022a, b, c), there is also a gender gap. Nationally, girls have out-\nperformed boys in all PISA subjects since 2012 and this gap is also larger than any-\nwhere else (OECD, 2019).i The present analysis, the first of its kind, has special relevance and supports \nthe UAE’s dedication to promoting higher wellbeing across the nation. This is \nespecially relevant for national prosperity as difficulties in childhood predict similar \nissues in adulthood (Auerbach et al., 2018; Johnson et al., 2018; Otto et al., 2021; \nTrotta et al., 2020). Thus, to lay the groundwork for the present analysis, as well as \nfuture studies on how existing inequalities (i.e., wealth, gender) potentially influence \nwellbeing and mental health, we address the following questions: 1. How does the wellbeing and mental health of adolescents in the UAE differ across \nthe nation’s emirates (geographical regions within the country), socio-demographic \ngroups and school types? 2. Do these inequalities vary across distinct domains of wellbeing and mental \nhealth?f 3. Is there a trade-off between academic competence and wellbeing and mental \nhealth outcomes in the UAE? 4. Did the decline in wellbeing between 2015 and 2018 manifest similar group \ninequalities? 2.1  \u0007Participants The sample included 19,277 15-year-old students who participated in the PISA \nstudy 2018 in the UAE. The PISA 2015 sample, used only in Sect. 3.4 (research \nquestion 4), included 13,643. Information about the number of participants across \neach socio-demographic group is reported in Sect. 2.2. For information about the \nPISA study, see PISA’s technical report (OECD, 2018). 2.2  \u0007Measures Our dependent variables included measures of cognitive subjective/hedonic well-\nbeing (OLS), affective subjective/hedonic wellbeing (positive affect, and negative \naffect), psychological/eudaimonic wellbeing (meaning and purpose in life), and \nmental health (internalizing difficulties). Specifically: •\t OLS. Students were asked to rate their satisfaction with life overall on a scale \nwith 0 representing the lowest satisfaction possible and 10, the highest. Data \nwere missing for 9.39% of participants in PISA 2018 (3.98% in PISA 2015). •\t OLS. Students were asked to rate their satisfaction with life overall on a scale \nwith 0 representing the lowest satisfaction possible and 10, the highest. Data \nwere missing for 9.39% of participants in PISA 2018 (3.98% in PISA 2015). 1 3 J. Marquez et al. 804 •\t Positive affect. Participants were asked how often (never, rarely, sometimes, \nalways) they felt happy, lively, proud, joyful, cheerful. A scale was derived sum-\nming up scores from these five items (Cronbach’s alpha 0.8266; 11.38% missing \ndata).f •\t Negative affect. Students were asked how often (never, rarely, sometimes, \nalways) they felt scared, miserable, afraid, sad, cheerful. A scale was derived \nsumming up scores from these four items (Cronbach’s alpha 0.7912; 11.82% \nmissing data). •\t Meaning and purpose in life (Eudaimonic/Psychological well-being). This was \nmeasured by asking about the level of agreement (strongly disagree, disagree, \nagree, strongly agree) with the following: My life has clear meaning or purpose; \nI have discovered a satisfactory meaning in life; I have a clear sense of what \ngives meaning to my life. A scale was created by summing up scores (Cron-\nbach’s alpha 0.8611; 8.86% missing data).fi •\t mental health (internalizing difficulties). Participants were asked to report how \noften over the preceding six months (rarely or never, about every month, about \nevery week, more than once a week, about every day) they had experienced \nsomatic symptoms and symptoms of mental health difficulties: headache, stom-\nach pain, back pain, feeling depressed, irritability of bad temper, feeling nervous, \ndifficulties in getting to sleep, feeling dizzy, feeling anxious. A scale was created \nby summing up scores in each item (Cronbach’s alpha 0.8957; 16.71% missing \ndata). Our independent variables included the following socio-demographic groups: Our independent variables included the following socio-demographic groups •\t Gender. This is a binary variable (0 male, 1 female). The sample includes 49% \nboys and 51% girls in both PISA 2018 and PISA 2015. •\t Gender. This is a binary variable (0 male, 1 female). 2.3  \u0007Analytical Approach To answer our research questions, we conducted linear regression analyses using \nmaximum likelihood estimation. This is the recommended approach to analyse PISA \ndata (OECD, 2009). We also followed recommendations with regards to PISA’s \ncomplex design (OECD, 2018) and the need to account for the probability of the \nstudent being selected to participate in the study. To do this, we used PISA’s rep-\nlicate weights. A sequential approach to estimating these linear regression models \nwas used to examine geographic (emirate) differences, socio-demographic (gender, \nnational/expatriate, SES) differences, and differences across types of schools. This \napproach involved the following steps. For each dependent variable, we ran a series \nof linear regression models, sequentially introducing each independent variable. For \nOLS, first, we used the original PISA 0–10 scale first (Table 1 in Sect. 3.1). Next, in \nSect. 3.2, in order to compare across different wellbeing and mental health scales, we \ncreated a standardized version of the scales on OLS, positive affect, negative affect, \nmeaning and purpose in life, and internalizing difficulties (Table 2 in Sect. 3.2. and \nTables 6, 7, 8, 9 and 10 in Appendix 1). Next, in Sect. 3.3, to consider academic \ncompetence (i.e. average PISA 2018 score in reading, maths, and science) and its \nlinks to wellbeing and mental health, we created an additional model incorporating \nthis variable (Table 3 in Sect. 3.3., and Tables 6, 7, 8, 9 to 10 in Appendix 1). Finally, \nto estimate changes in OLS between 2015 and 2018 in Sect. 3.4, we used a sample \nincluding all the UAE students participating in PISA 2015 and 2018. For each socio-\ndemographic group, we ran a regression model with OLS as the dependent variable \nand cohort (i.e., the year in which the PISA test was taken) as the only independent \nvariable in the model (Table 5 in Sect. 3.4). OLS was the only wellbeing and mental \nhealth variable collected in 2015 and 2018. Levels of missing data were relatively \nlow (see details Sect. 2.2.). We use listwise deletion to deal with missing data, which \nis common in analyses using this data set (Schirripa et al., 2020; Tsai et al., 2018; van \nHek et al., 2018). Analyses were conducted in STATA 15 (StataCorp, 2017). 2.2  \u0007Measures This is a categorical variable including Abu Dhabi (33.46% in PISA \n2018; 25.07% in PISA 2015), Dubai (31.82%; 41.76%), Sharjah (10.98%; \n7.30%), Ajman (7.13%; 5.51%), Umm Al Quwain (UAQ, 2.71%; 3.19%), Ras al \nKhaimah (RAK, 8.56%; 8.33%), Fujairah (5.34%; 8.83%). •\t Emirate. This is a categorical variable including Abu Dhabi (33.46% in PISA \n2018; 25.07% in PISA 2015), Dubai (31.82%; 41.76%), Sharjah (10.98%; \n7.30%), Ajman (7.13%; 5.51%), Umm Al Quwain (UAQ, 2.71%; 3.19%), Ras al \nKhaimah (RAK, 8.56%; 8.33%), Fujairah (5.34%; 8.83%). 2.2  \u0007Measures The sample includes 49% \nboys and 51% girls in both PISA 2018 and PISA 2015. •\t Socio-economic status. PISA’s index of family wealth (see PISA’s technical \nreport: OECD, 2018) was standardized, so that 0 indicates the mean in the UAE \nand 1, the standard deviation. •\t Immigrant background. We created this binary variable by assigning a value of \n0 to nationals (i.e., students born in the UAE and whose father and mother were \nalso born in the UAE) and a value of 1 to expatriates (students who were not \nborn in the UAE or whose father or mother was not born in the UAE). In our \nsample, 44% of participants are nationals and 56% are expatriates (45% and 55% \nrespectively in PISA 2015). •\t School sector. We derived a binary variable that assigns a value of 0 if the stu-\ndent is attending a public school (i.e., publicly operated and funded; 40% of par-\nticipants in PISA 2018, 42% in PISA 2015) and 1 if the student is attending a \nprivate school (i.e., privately operated and funded; 60% of participants in PISA \n2018, 58% in PISA 2015). •\t School curriculum. This is a categorical variable with the following categories: \nMinistry of Education (MoE, 50.08% in PISA 2018; 49.82% in PISA 2015), \nBritish (14.03%; 11.38%), American (13.20%; no data in PISA 2015), Indian \n(12.09%; 4.76%), Technical and Vocational (TVET, 4.30%; no data in PISA \n2015), Other (6.30%; 34.04%). The proportion of students attending private 1 3 1 3 Geographic, Socio‑Demographic and School Type Variation… 805 schools in each type of school is: MoE (22.22%; 28.29%), British (96.33%; \n100%), American (98.73%; no data in PISA 2015), Indian (100%; 82.04%), \nTVET (0%; no data in PISA 2015) Other (97 08%; 95 95%) schools in each type of school is: MoE (22.22%; 28.29%), British (96.33%; \n100%), American (98.73%; no data in PISA 2015), Indian (100%; 82.04%), \nTVET (0%\nd t i PISA 2015) Oth\n(97 08% 95 95%) schools in each type of school is: MoE (22.22%; 28.29%), British (96.33%; \n100%), American (98.73%; no data in PISA 2015), Indian (100%; 82.04%), \nTVET (0%; no data in PISA 2015), Other (97.08%; 95.95%). schools in each type of school is: MoE (22.22%; 28.29%), British (96.33%; \n100%), American (98.73%; no data in PISA 2015), Indian (100%; 82.04%), \nTVET (0%; no data in PISA 2015), Other (97.08%; 95.95%). •\t Emirate. 3.1  \u0007Socio‑Demographic Differences in Overall Life Satisfaction The average OLS score of 15-year-old students in the UAE in 2018 was 6.88, \nbut results from Table 1 show significant geographic variation. Compared to Abu 3 3 806 J. Marquez et al. 3.1  \u0007Socio‑Demographic Differences in Overall Life Satisfaction 3\nTable 1   Output from linear regression models: socio-demographic differences in OLS from 0 to 10\nStandard errors (se); ***p<0 01 **p<0 05 *p<0 1\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nB\nse\nB\nse\nb\nse\nB\nse\nb\nse\nb\nse\nConstant\n6.842***\n0.059\n7.052***\n0.073\n7.228***\n0.082\n7.142***\n0.088\n7.269***\n0.099\n7.440***\n0.077\nEmirate (ref.category: Abu Dhabi): Dubai\n-0.123\n0.083\n-0.130\n0.077\n-0.072\n0.078\n-0.101\n0.080\n0.001\n0.087\n0.131\n0.079\nEmirate (ref.category: Abu Dhabi): Sharjah\n-0.128\n0.172\n-0.107\n0.159\n-0.075\n0.156\n-0.054\n0.160\n-0.023\n0.155\n-0.034\n0.122\nEmirate (ref.category: Abu Dhabi): Ajman\n0.420**\n0.133\n0.421**\n0.141\n0.432**\n0.142\n0.486***\n0.140\n0.516***\n0.153\n0.360**\n0.135\nEmirate (ref.category: Abu Dhabi): UAQ\n0.455**\n0.157\n0.466*\n0.184\n0.369\n0.194\n0.373*\n0.179\n0.311\n0.190\n0.320\n0.191\nEmirate (ref.category: Abu Dhabi): RAK\n0.652***\n0.187\n0.645***\n0.191\n0.581**\n0.191\n0.587**\n0.190\n0.517**\n0.197\n0.387*\n0.182\nEmirate (ref.category: Abu Dhabi): Fujairah\n0.808***\n0.172\n0.803***\n0.187\n0.725***\n0.189\n0.727***\n0.187\n0.646***\n0.193\n0.606***\n0.176\nFemale\n-0.402***\n0.082\n-0.422***\n0.082\n-0.437***\n0.083\n-0.473***\n0.085\n-0.462***\n0.063\nExpatriate\n-0.310***\n0.067\n-0.148*\n0.073\n0.063\n0.076\n0.118\n0.071\nSocio-economic status\n0.226***\n0.038\n0.230***\n0.038\n0.331***\n0.035\nPrivate school\n-0.425***\n0.095\nSchool type (ref.category: MoE): British\n-1.382***\n0.132\nSchool type (ref.category: MoE): American\n-0.884***\n0.096\nSchool type (ref.category: MoE): Indian\n-0.400**\n0.129\nSchool type (ref.category: MoE): TVET\n-1.656***\n0.140\nSchool type (ref.category: MoE): Other\n-0.665***\n0.139\nObservations\n17,467\n17,467\n16,960\n16,941\n16,326\n16,941 Standard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 1 Geographic, Socio‑Demographic and School Type Variation… 807 1 3\nTable 2   Output from linear regression analysis (models 5 and 6): socio-demographic differences in OLS, positive affects, negative affects, meaning and purpose in life, \nand internalizing difficulties (standardized scales)\nStandard errors (se); ***p<0 01 **p<0 05 *p<0 1\nVariable\nOLS\nPositive affects\nNegative affects\nMeaning and purpose \nin life\nInternalizing dif-\nficulties\nB\nSe\nB\nSe\nb\nSe\nb\nse\nb\nse\nConstant\n0.173***\n0.027\n-0.024\n0.025\n-0.408***\n0.031\n0.123***\n0.027\n0.006\n0.035\nEmirate (ref.category: Abu Dhabi): Dubai\n0.046\n0.028\n0.050*\n0.025\n-0.034\n0.027\n0.032\n0.031\n-0.009\n0.031\nEmirate (ref.category: Abu Dhabi): Sharjah\n-0.012\n0.043\n0.063\n0.039\n-0.017\n0.049\n0.071\n0.043\n0.003\n0.038\nEmirate (ref.category: Abu Dhabi): Ajman\n0.126**\n0.047\n0.141***\n0.039\n-0.109**\n0.037\n0.141***\n0.040\n-0.135***\n0.038\nEmirate (ref.category: Abu Dhabi): UAQ\n0.112\n0.067\n0.091\n0.056\n-0.020\n0.066\n0.096\n0.063\n-0.048\n0.038\nEmirate (ref.category: Abu Dhabi): RAK\n0.136*\n0.064\n0.107*\n0.049\n-0.061\n0.057\n0.093*\n0.047\n-0.149**\n0.053\nEmirate (ref.category: Abu Dhabi): Fujairah\n0.213***\n0.062\n0.179***\n0.047\n-0.087\n0.053\n0.263***\n0.050\n-0.159**\n0.058\nFemale\n-0.162***\n0.022\n0.071***\n0.020\n0.348***\n0.023\n0.005\n0.022\n0.383***\n0.023\nExpatriate\n0.041\n0.025\n0.063*\n0.028\n-0.015\n0.024\n0.089***\n0.025\n-0.073**\n0.028\nSocio-economic status\n0.117***\n0.012\n0.148***\n0.012\n-0.081***\n0.014\n0.100***\n0.013\n-0.025\n0.013\nPrivate school\n-0.149***\n0.033\n-0.082**\n0.028\n0.354***\n0.036\n-0.259***\n0.034\n-0.177***\n0.032\nSchool type (ref.category: MoE): British\n-0.486***\n0.046\n-0.331***\n0.044\n0.606***\n0.057\n-0.616***\n0.059\n-0.118**\n0.038\nSchool type (ref.category: MoE): American\n-0.311***\n0.034\n-0.173***\n0.033\n0.483***\n0.034\n-0.407***\n0.036\n-0.138***\n0.040\nSchool type (ref.category: MoE): Indian\n-0.141**\n0.045\n0.033\n0.040\n0.519***\n0.035\n-0.233***\n0.038\n-0.497***\n0.042\nSchool type (ref.category: MoE): TVET\n-0.582***\n0.049\n-0.308***\n0.068\n0.462***\n0.064\n-0.477***\n0.058\n-0.017\n0.050\nSchool type (ref.category: MoE): Other\n-0.234***\n0.049\n-0.124**\n0.042\n0.513***\n0.048\n-0.473***\n0.048\n-0.297***\n0.037\nObservations\n16,941\n16,579\n16,512\n17,034\n15,589 J. 1 3 3.2  \u0007Socio‑Demographic Differences Across Wellbeing and Mental Health \nDomains To compare socio-demographic variations across wellbeing and mental health \ndomains, we conducted the same analysis as in Sect. 3.1. for standardized scales of \nOLS, positive affect, negative affect, meaning and purpose in life, and mental health \n(internalizing difficulties). A summary of this comparative analysis is presented in \nTable 2, which shows the results of the final model (i.e., model 5 for public/private \nschool and model 6 for all other independent variables) for each wellbeing and men-\ntal health variable. Results for models 1 to 6 for each of these standardized scales is \npresented in tables 6, 7, 8, 9 to 10 in Appendix 1. The northern, less-urbanized emirates of Ajman, RAK and Fujairah presented the \nbest wellbeing and mental health outcomes, although for negative affect, this advan-\ntage becomes less clear in RAK and Fujairah when controlling for school sector and \ncurriculum. There are no statistically significant differences in wellbeing and men-\ntal health between Abu Dhabi and Sharjah, whereas compared to Abu Dhabi, UAQ \npresented higher meaning and purpose in life, positive affect and OLS (in model 4, \n0.143, 0.110, and 0.131 s.d. respectively), but these differences become statistically \nnon-significant after controlling for school sector and school curriculum in models \n5 and 6. Finally, compared to Abu Dhabi, Dubai presented lower levels of meaning \nand purpose in life as well as higher levels of negative affect and internalizing dif-\nficulties, although these differences reduce substantially or become non-statistically \nsignificant when controlling for school sector and curriculum. i\nFurther, females reported similar levels of meaning and purpose in life to males, \nslightly higher positive affect (0.071  s.d. in model 6), lower OLS (-0.162  s.d. in \nmodel 6), and much higher levels of negative affect (0.348  s.d. in model 6) and \ninternalizing difficulties (0.383 s.d. in model 6). The gender gap in wellbeing and \nmental health hardly changes after controlling for socio-economic status, immigrant \nstatus and school sector/curriculum.fi As to SES, the relationship with mental health (internalizing difficulties) was not \nstatistically significant, but links with wellbeing emerged. An increase of one stand-\nard deviation in the socioeconomic status scale was associated with higher OLS \n(0.117 s.d. in model 6), higher meaning and purpose in life (0.100 s.d. in model 6), \nhigher positive affect (0.148 s.d. in model 6), and lower negative affect (-0.081 s.d. in model 6). 3.1  \u0007Socio‑Demographic Differences in Overall Life Satisfaction 1 3 1 3 Geographic, Socio‑Demographic and School Type Variation… 809 Compared to schools following the MoE curriculum, OLS was substantially lower \nin TVET schools (-1.656 points), British schools (-1.382 points), American schools \n(-0.884 points), schools following other curriculums (-0.665 points), and Indian \nschools (-0.400 points). 3.1  \u0007Socio‑Demographic Differences in Overall Life Satisfaction Marquez et al. 808 Table 3   Group differences in the average PISA 2018 score (readings, maths, science)\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1\nVariable\nAverage PISA 2018 score \n(reading, maths, science)\nDifference\nb\nse\nGroup comparison\nAbu Dhabi\n410\nDubai\n484\n74***\n7.1\nAbu Dhabi—Dubai\nSharjah\n442\n32***\n8.6\nAbu Dhabi—Sharjah\nAjman\n400\n-10\n7.8\nAbu Dhabi—Ajman\nUAQ\n397\n-13\n10.1\nAbu Dhabi—UAQ\nRAK\n402\n-8\n7.2\nAbu Dhabi—RAK\nFujairah\n398\n-12\n9.0\nAbu Dhabi—Fujairah\nMale\n418\nFemale\n449\n31***\n6.1\nMale—Female\nNational\n389\nExpatriate\n476\n87***\n3.5\nNational—Expatriate\nSocio-economic status\n-\n-1\n1.5\nSocio-economic status\nPublic school\n379\nPrivate school\n469\n90***\n4.8\nPublic school—Private school\nMoE\n388\nBritish\n510\n122***\n5.6\nMoE—British\nAmerican\n445\n57***\n8.6\nMoE—American\nIndian\n494\n106***\n6.8\nMoE—Indian\nTVET\n397\n9\n11.6\nMoE—TVET\nOther\n484\n96***\n13.7\nMoE—Other Table 3   Group differences in the average PISA 2018 score (readings, maths, science) Dhabi, OLS was not statistically different in Dubai and Sharjah, but it was higher \nin the northern, less-urbanized emirates of Ajman, UAQ, RAK and Fujairah. Such differences decrease slightly (and become non-statistically significant for \nUAQ) after controlling for national/expatriate status, and again after controlling \nfor school sector (public/private) and curriculum, although they remain substan-\ntial, ranging from 0.360 points in Ajman to 0.606 points in Fujairah. Further, females reported almost a half-point lower OLS than males (i.e., \n-0.462 points lower in the final model 5 controlling for all other socio-demo-\ngraphic variables). Expatriates reported lower OLS than nationals (-0.310 points \nin model 3), but this gap is half the size after controlling for socio-economic sta-\ntus (-0.148 points in model 4) and becomes non-statistically significant after con-\ntrolling for school sector and curriculum. Socioeconomic status was positively \nassociated with OLS. In the final model 6 with all the control variables included \nan increase of 1 standard deviation in the index of socioeconomic status was \nassociated with an increase of 0.331 points in the 0 to 10 OLS scale. Students attending private schools also reported lower OLS (-0.425 points) than \ntheir peers attending public schools after controlling for all socio-demographic \ncharacteristics (model 6). Differences across school curriculums were even greater. 3.3  \u0007Wellbeing and Mental Health and Academic Outcomes: Is there a Trade‑Off? Considering the results reported in Sect. 3.2 with findings on academic competence \nin the UAE (scores in PISA 2018) reported in Marquez et al. (2022b), some groups \nof students who do better academically seem to present worse wellbeing outcomes. To illustrate, Table 3 presents PISA 2018 score inequalities (average for reading, \nmaths, and science) across our groups of interest. The average PISA score across \nthe UAE was 434 points, and the s.d. 100 points. Table  3 shows that academic \ncompetence is highest in Dubai (484 points on average) and lowest in the northern \nemirates (around 400 points); higher among females (449 points) compared to \nmales (418 points); higher among expatriate students (476 points) compared to \nnational students (389 points); and higher in private schools (469 points) versus \npublic schools (379 points). Academic competence is highest in British schools \n(510 points), followed by Indian schools (494 points), Other schools (484 points), \nAmerican schools (445 points), TVET schools (397), and MoE schools (388 points). Considering the results reported in Sect. 3.2 with findings on academic competence \nin the UAE (scores in PISA 2018) reported in Marquez et al. (2022b), some groups \nof students who do better academically seem to present worse wellbeing outcomes. To illustrate, Table 3 presents PISA 2018 score inequalities (average for reading, \nmaths, and science) across our groups of interest. The average PISA score across \nthe UAE was 434 points, and the s.d. 100 points. Table  3 shows that academic \ncompetence is highest in Dubai (484 points on average) and lowest in the northern \nemirates (around 400 points); higher among females (449 points) compared to \nmales (418 points); higher among expatriate students (476 points) compared to \nnational students (389 points); and higher in private schools (469 points) versus \npublic schools (379 points). Academic competence is highest in British schools \n(510 points), followed by Indian schools (494 points), Other schools (484 points), \nAmerican schools (445 points), TVET schools (397), and MoE schools (388 points). Given that some groups present higher academic competence but lower wellbeing \noutcomes (e.g. females, expatriates, students from Dubai, students attending private \nschools, especially British schools, etc.), we are compelled to ask whether there is a \ntrade-off between academic competence and wellbeing in the UAE? We explored \nthis question estimating an additional model 7 (Tables 6, 7, 8, 9 to 10 in Appendix 1). 3.2  \u0007Socio‑Demographic Differences Across Wellbeing and Mental Health \nDomains The size of the association between socio-economic status and these \nwellbeing variables are similar in models 4 and 5 (i.e., after accounting for school \nsector), but increase substantially in model 6 (i.e., after accounting for school \ncurriculum). Compared to national students, expatriate students reported lower wellbeing (in \nmodel 3, the gaps are -0.109 s.d. for OLS, 0.226 s.d. for negative affect, -0.067 s.d. 1 3 810 J. Marquez et al. for positive affect, and -0.108 s.d. for meaning and purpose in life), but also lower \nlevels of internalizing difficulties (in model 3, -0.196 s.d.). Yet, after controlling for \nsocio-economic status, the wellbeing gaps reduce substantially (and become non-\nstatistically significant in the case of positive affect), and after controlling for school \nsector and curriculum, they become non-statistically significant for OLS and nega-\ntive affect, and reverse direction for meaning and purpose in life (0.089 s.d. in model \n6) and positive affect (0.063 s.d. in model 6). For internalizing difficulties, the gap \nremains similar after controlling for SES and reduces substantially when controlling \nfor school sector and curriculum. Finally, attending private schools is associated with lower OLS (-0.149  s.d.), \nlower meaning and purpose in life (-0.259 s.d.), higher negative affect (0.354 s.d.) \nand lower positive affect (-0.082  s.d.), but also lower internalizing difficulties \n(-0.177  s.d.). Differences across school curriculum are notable. Overall, MoE \nschools present better wellbeing outcomes compared to any other type of school \n(gaps are especially large for negative affect and meaning and purpose in life) and \nsomewhat worse outcomes for internalizing difficulties (although differences are not \nstatistically significant for TVET). In terms of wellbeing, British schools and TVET \nschools present the poorest outcomes, although these differences are less clear for \nnegative affect. In terms of internalizing difficulties, Indian schools present better \noutcomes than any other type. 3.3  \u0007Wellbeing and Mental Health and Academic Outcomes: Is there a Trade‑Off? Model 7 contains the same independent variables as model 6 but adds academic \ncompetence (i.e. average PISA 2018 score in reading, maths, and science). The results \nare summarized in Table 4. (\np\n)\n(\n)\n(\np\n)\nGiven that some groups present higher academic competence but lower wellbeing \noutcomes (e.g. females, expatriates, students from Dubai, students attending private \nschools, especially British schools, etc.), we are compelled to ask whether there is a \ntrade-off between academic competence and wellbeing in the UAE? We explored \nthis question estimating an additional model 7 (Tables 6, 7, 8, 9 to 10 in Appendix 1). Model 7 contains the same independent variables as model 6 but adds academic \ncompetence (i.e. average PISA 2018 score in reading, maths, and science). The results \nare summarized in Table 4. 1 3 Geographic, Socio‑Demographic and School Type Variation… 811 Results show that an increase of 100 points (1 s.d.) on average in the three core \nPISA subjects (reading, maths, and science) is associated with small increases in OLS \n(0.024 s.d.) and positive affect (0.075 s.d.), as well as a small decrease in negative affect \n(-0.047 s.d.). Effects are not statistically significant for meaning and purpose in life and \ninternalizing difficulties. A comparisson between results in models 6 in Table 2 and \nmodels 7 in Table 4 also show that, when adding academic competence to the mod-\nels, the other estimates barely change in most cases, although some small changes are \nobserved in the positive affect and negative affect models for school type and emirate. 3.4  \u0007Socio‑Demographic Differences in Changes to Overall Life Satisfaction \nbetween 2015 and 2018 In view of socio-demographic differences in levels of wellbeing and mental health \nin 2018, we explored whether declines in student OLS between 2015 and 2018 also \nfollowed different patterns across distinct socio-demographic groups. OLS was the only \nwellbeing variable collected in both PISA waves. Table 5 shows how OLS changed on \naverage for each socio-demographic group between 2015 and 2018. On average across \nthe UAE, OLS declined by 0.42 points on the 0 to 10 scale, but this decline was more \npronounced for some groups. The decline was largest in Sharjah (-0.55 points) and \nAbu Dhabi (-0.50 points), followed by RAK (-0.42 points) and Dubai (-0.37 points). Changes were not statistically significant elsewhere. i\nThe decline in OLS was also greater among females (-0.49 points) compared to \nmales (-0.35 points), nationals (-0.48 points) compared to expatriates (-0.41 points), \nand students of higher socio-economic status. With respect to the latter, differences \nwere not statistically significant for those in the first, second, and third quintile, but \nwere substantial for those in the fourth (-0.51 points) and fifth quintile (-0.58 points). i\nFinally, the decline in OLS was similar in size in public schools (-0.43 points) \nand private schools (-0.45 points), but differences across schools following distinct \ncurriculums were indeed substantial. Changes were not statistically significant in MoE \nschools, but the decline in OLS was very large in British schools (-1.06 points). In Indian \nschools (-0.47 points) and schools following other curriculums (-0.45 points), this decline \nwas slightly above the national average. Information about American and TVET schools \nwas not included in PISA 2015, so no comparisons were possible for these schools. 4.1  \u0007Findings The present study revealed important geographic, socio-demographic, and school \ntype differences in trends and levels of adolescent wellbeing and mental health \nin the UAE. First, the northern emirates of Ajman, RAK and Fujairah presented \nbetter wellbeing and mental health outcomes, although these differences became \nsomewhat smaller when controlling for school sector and school type. In these \nemirates, there are fewer expatriate residents, private schools, and schools overall 3 812 J. Marquez et al. 4.1  \u0007Findings 3\nTable 4   Output from linear regression analysis (model 7): socio-demographic differences in OLS, positive affects, negative affects, meaning and purpose in life, and inter-\nnalizing difficulties (standardized scales), after adding academic competence as an additional independent variable\nVariable\nOLS\nPositive affects\nNegative affects\nMeaning and purpose \nin life\nInternalizing dif-\nficulties\nB\nSe\nB\nSe\nb\nSe\nb\nse\nb\nse\nConstant\n0.189***\n0.028\n0.026***\n0.025\n0.439\n0.032\n0.125\n0.027\n0.010\n0.034\nEmirate (ref.category: Abu Dhabi): Dubai\n0.039\n0.028\n0.028\n0.026\n-0.021\n0.028\n0.032\n0.031\n0.003\n0.030\nEmirate (ref.category: Abu Dhabi): Sharjah\n-0.014\n0.043\n0.055\n0.039\n-0.012\n0.048\n0.071\n0.043\n-0.006\n0.038\nEmirate (ref.category: Abu Dhabi): Ajman\n0.129*\n0.047\n0.147***\n0.038\n-0.112**\n0.036\n0.141***\n0.040\n0.137***\n0.038\nEmirate (ref.category: Abu Dhabi): UAQ\n0.112\n0.068\n0.089\n0.057\n-0.019\n0.065\n0.096\n0.063\n0.047\n0.037\nEmirate (ref.category: Abu Dhabi): RAK\n0.135*\n0.064\n0.102*\n0.048\n-0.058\n0.057\n0.092*\n0.046\n0.147**\n0.053\nEmirate (ref.category: Abu Dhabi): Fujairah\n0.212**\n0.062\n0.175***\n0.045\n-0.085\n0.053\n0.263***\n0.050\n0.158**\n0.058\nFemale\n-0.168***\n0.022\n0.054*\n0.020\n0.359***\n0.023\n0.005\n0.022\n-0.388***\n0.023\nExpatriate\n0.029\n0.026\n0.023\n0.029\n0.010\n0.025\n0.088***\n0.025\n0.061*\n0.027\nSocio-economic status\n0.114***\n0.012\n0.142***\n0.012\n-0.077***\n0.014\n0.100***\n0.013\n0.022\n0.013\nPrivate school\nSchool type (ref.category: MoE): British\n-0.505***\n0.046\n-0.391***\n0.043\n0.643***\n0.056\n-0.618***\n0.058\n-0.099*\n0.039\nSchool type (ref.category: MoE): American\n-0.321***\n0.034\n-0.205***\n0.035\n0.503***\n0.035\n-0.408***\n0.036\n-0.128**\n0.041\nSchool type (ref.category: MoE): Indian\n-0.157**\n0.046\n-0.018\n0.040\n0.551***\n0.035\n-0.234***\n0.039\n-0.481***\n0.043\nSchool type (ref.category: MoE): TVET\n-0.588***\n0.050\n-0.326***\n0.063\n0.472***\n0.062\n-0.478***\n0.058\n-0.012\n0.049\nSchool type (ref.category: MoE): Other\n-0.249***\n0.051\n-0.173***\n0.046\n0.544***\n0.051\n-0.474***\n0.050\n-0.281***\n0.038\nAcademic competence (Mean PISA score)\n0.024*\n0.012\n0.075***\n0.013\n-0.047***\n0.012\n0.002\n0.014\n0.024\n0.012\nObservations\n16,941\n16,941\n16,512\n17,034\n15,589 1 Geographic, Socio‑Demographic and School Type Variation… Geographic, Socio‑Demographic and School Type Variation… 813 Table 5   Output from linear \nregression models: socio-\ndemographic differences in \nchanges in OLS (from 0 to 10) \nbetween 2015 and 2018\nSatisfaction with life as a whole \n(0–10 LS scale)\nDifference\n2015\n2018\nB\ns.e\nUAE\n7.31\n6.88\n-0.42\n***\n0.05\nEmirate\nAbu Dhabi\n7.34\n6.84\n-0.50\n***\n0.08\nDubai\n7.09\n6.72\n-0.37\n***\n0.06\nSharjah\n7.27\n6.71\n-0.55\n**\n0.20\nAjman\n7.45\n7.26\n-0.18\n0.19\nUAQ\n7.32\n7.30\n-0.03\n0.21\nRAK\n7.91\n7.49\n-0.42\n***\n0.12\nFujairah\n7.66\n7.65\n-0.01\n0.14\nGender\nMale\n7.45\n7.10\n-0.35\n***\n0.07\nFemale\n7.18\n6.69\n-0.49\n***\n0.07\nImmigrant status\nNational\n7.60\n7.12\n-0.48\n***\n0.07\nExpatriate\n7.12\n6.71\n-0.41\n***\n0.07\nSES\nLowest SES\n6.68\n6.60\n-0.09\n0.13\nMid-low SES\n6.90\n6.71\n-0.19\n0.11\nMid SES\n6.87\n6.87\n-0.01\n0.11\nMid-high SES\n7.26\n6.74\n-0.51\n***\n0.11\nHighest SES\n7.64\n7.07\n-0.58\n***\n0.07\nSchool sector\nPublic school\n7.65\n7.22\n-0.43\n***\n0.06\nPrivate school\n7.12\n6.66\n-0.45\n***\n0.08\nSchool curriculum\nMoE\n7.44\n7.34\n-0.09\n0.05\nBritish\n7.03\n5.97\n-1.06\n***\n0.16\nAmerican\nIndian\n7.30\n6.83\n-0.47\n*\n0.20\nTVET\nOther\n7.24\n6.79\n-0.45\n***\n0.12 Table 5   Output from linear \nregression models: socio-\ndemographic differences in \nchanges in OLS (from 0 to 10) \nbetween 2015 and 2018 Satisfaction with life as a whole \n(0–10 LS scale) (Sheikh Saud bin Saqr Al Qasimi Foundation for Policy Research, 2020); hence, it \nmay be that schools suffer from fewer social comparisons to other schools like those \nranked in bigger urban centres. 4.1  \u0007Findings They may have a more homogenous student body \nand be able to instil a greater sense of belonging and/or identity. Their lead may also \nreflect the geographical advantage of rural areas to mid-size cities (i.e., Buttazzoni \net al., 2022; Murphy et al., 2020; Tomyn et al., 2017), the impact of lower urban \ndensity or quality of place and neighbourhood, or greater access to nature (Carmona, \n2019; Gorski-Steiner et al., 2022; Rentfrow, 2018; Silva et al., 2016; Vanaken & \nDanckaerts, 2018; Ward et al., 2016). In contrast, the emirate of Dubai presented notably lower scores for meaning and \npurpose, higher scores for negative affect and the greatest mental health issues of all \nthe emirates. This is a striking finding given that it is the only emirate to officially 1 3 J. Marquez et al. 814 endorse school wellbeing (OECD, 2021). Its education authority has promoted \nwellbeing as the cornerstone of its efforts and is an item against which schools are \ninspected and classed (see OECD, 2021). Perhaps its highly competitive, perfor-\nmance focused and publicized school ranking scheme which allows for parents to \nmake informed choices as well as school fees to be raised accordingly, has created \nunintended effects on wellbeing (Sheikh Saud bin Saqr Al Qasimi Foundation for \nPolicy Research, 2020; El-Sholkamyl & Al-Saleh, 2017; OECD, 2021). Still, the \nemirate has the highest number of students, private schools and diversity of all, mak-\ning its challenges greater. It also has higher than average rates of bullying (KHDA, \n2021; OECD, 2021), which may be driving the higher rates of mental health issues. Ranking best for academic performance, student anxiety and parental pressure is \nhigher than in other OECD countries and may also contribute (OECD, 2021). Tak-\ning stronger leadership in guiding wellbeing and mental health policy and program-\nming may be beyond its legislative reach and account for the disparity between its \nendorsement of wellbeing versus implementation. Females also reported more mental health issues, lower OLS, higher negative \naffect and somewhat higher positive affect. These findings are consistent with \nprior studies (Al-Yateem et al., 2020; Bradshaw et al., 2013; Campbell et al., 2021; \nCharara et  al., 2017, 2018; Cyranowski et  al., 2000; Hamama & Hamama-Raz, \n2021; Kaye-Tzadok et al., 2017). However, that no gender differences were observed \nfor meaning and purpose in life is inconsistent with findings by Campbell et  al. 1 3 4.1  \u0007Findings The latter finding contrasts with a \nprevious UAE study highlighting higher rates of internalizing disorders (i.e., \ndepression) in South Asian students in the city of Al Ain (Shah et al., 2020). The \nlowest wellbeing is observed in TVET and British schools, and the highest in MoE \nschools, followed by Indian schools. American schools and schools following other \ncurriculums are somewhere in the middle in terms of wellbeing and mental health \nrelative to other types of schools. The low wellbeing of TVET schools may be due \nto their poor public image and status as students and their parents consider them \noptions for low performers (Khan et al., 2018). Why mental health issues are higher in lower performing schools (here, MoE \nschools) suggests school, community, or cultural factors might play a role. Mental \nhealth issues may be unidentified and hence, overly represented (Lansford et  al., \n2019; Maalouf et al., 2019). Alternatively, they may be better tolerated. In Western \ncultures, there is pressure to reduce negative emotional experiences, whereas in \nmany Eastern cultures, distress is accepted and less “psychologized” (e.g., Eloul \net  al., 2009; Huang et  al., 2020). Thus, mental health issues are construed as an \ninconvenience and reflect a dialectical emotional state where they co-exist with and \nare less impactful on wellbeing than they might in other cultural contexts (Lomas, \n2016). Similarly, it may be a third force that is not being captured in the Western \nmeasures being used in PISA (although they have been regionally validated); \nthat is, the unity between mind, body, community, and spiritual aspects of the \nregion (Joshanloo et  al., 2021), which has not yet been captured or investigated \nby education authorities either (OECD, 2021). School factors that produce low \nacademic performance may also fuel mental health concerns, especially as one \ndrives the other (Agnafors et al., 2021; Deighton et al., 2018; Pagerols et al., 2022; \nRiglin et al., 2014). Alternatively, issues identified in UAE public schools, i.e., non-\nnational teachers, low status of teaching, low teacher wellbeing, few resources, and \nlittle parent support (Marquez et al., 2022b) may contribute. In terms of wellbeing, how can the high wellbeing observed in low-performing \nMoE schools be explained? And why is wellbeing so low in high-performing Brit-\nish schools? This question deserves further exploration; however, our study suggests \nthat this is not related to academic outcomes. 4.1  \u0007Findings (2021), who find that in most countries, meaning and purpose in life among 15-year-\nold adolescents is higher in males. Gender differences were larger with regards to \nnegative affect and internalizing difficulties compared to positive affect and OLS.ii ffif\nOur findings also confirm a long-standing truism that for every increase in SES \ngradient, there are parallel increases in wellbeing (Chzhen et al., 2016; Elgar et al., \n2017; Knies, 2022; Marquez, 2021; Patalay & Fitzsimons, 2018; Zaborskis et al., \n2019a, b). However, higher SES was associated with more mental health difficulties, \nwhich contradicts findings from previous literature reviews (Reiss, 2013) and \nmeta-analyses (Peverill et al., 2021; Reiss et al., 2019), and seem to suggest that \nas resources stemming from greater income can buy experiences and time with \nwhich to enjoy life and find it satisfying, the same cannot always be said of those \nsame resources defending against mental health issues, like depression, anxiety, etc. (Killingsworth, 2021), which present a particularly high incidence (above global \nrates) in the GCC region (Chan et al., 2021). Findings around the national/expatriate gaps in wellbeing and mental health \nare compelling. First, expatriates reported lower wellbeing, but also fewer mental \nhealth issues than nationals. Second, after controlling for SES, the gap in wellbeing \nreduces, but the mental health gap remains. The fact that the national/expatriate gap \nin wellbeing, but not mental health, is partly explained by differences in SES aligns \nwith the point discussed above that SES (material well-being) tends to be associated \nwith better wellbeing outcomes but may not necessarily be associated with better \nmental health (see Mogilner et al., 2018), especially in a context of high incidence of \npopulation levels of mental health difficulties (Chan et al., 2021). After controlling \nfor school type (i.e., school sector, curriculum), the wellbeing gaps almost disappear \nfor OLS and negative affect, and reverse direction for meaning and purpose in life. 1 3 815 Geographic, Socio‑Demographic and School Type Variation… The mental health gap reduces as well. This suggests that schools indeed play a role \nin wellbeing and mental health as they explain part of the gaps across the emirates \nand a large proportion of the national/expatriate gaps. Compared to private schools, public schools present higher wellbeing but poorer \nmental health outcomes, and this holds true after controlling for socio-demographic \ncharacteristics. Similarly, the poorest mental health levels are observed in MoE and \nTVET schools, and the best in Indian schools. 4.1  \u0007Findings We found no evidence for a trade-off \nbetween academic and wellbeing and mental health outcomes. In fact, we found a \nsmall positive association with subjective wellbeing, especially with the affective \ncomponent. Higher academic competence was associated with higher OLS, higher \npositive affect, and lower negative affect –although for OLS, the size of the effect \nwas rather small and could potentially be the result of a method effect. Overall, this \nfinding is consistent with previous research reporting (Gutman & Vorhaus, 2012; \nLindorff, 2021; Shoshani et  al., 2016). As happier students make better learners, 1 3 816 J. Marquez et al. schools and education authorities should promote both wellbeing and academic out-\ncomes (Taylor et al., 2022). To promote adolescent wellbeing, more research is needed to understand what pol-\nicy-relevant factors influence it. Research highlights factors such as high-stakes test-\ning, fear of failure, performance anxiety, and sense of belonging to school (Borgonovi \n& Han, 2021; Cho & Chan, 2020; Högberg et al., 2020; Long et al., 2021; Marquez & \nMain, 2020; Sonmark et al., 2016; Wang, 2021) are associated with lower wellbeing, \nwhich might explain some of our results. Further, British schools in the UAE achieve \nthe best education inspection ratings, which are largely defined in terms of academic \noutcomes, and consequently demand the highest school fees (El-Sholkamyl & Al-\nSaleh, 2017). However, our study shows that they also present the worst wellbeing \noutcomes and relatively poor mental health outcomes. To provide the right incentives \nfor wellbeing and mental health promotion, we recommend that education authorities \nincorporate wellbeing and mental health outcomes in their school inspections and that \nthese be made transparent in the rankings. This is vital given the null or positive asso-\nciation between wellbeing and academic outcomes described and the predictive asso-\nciation between wellbeing and mental health in adolescence and later life outcomes \n(Cavioni et al., 2021; DeNeve & Oswald, 2012; Goodman et al., 2015; Guzmán et al., \n2020; Kansky et al., 2016; Richards & Huppert, 2011). Finally, understanding trends can help better understand geographic, socio-demo-\ngraphic and school type differences in adolescents’ wellbeing. As noted in Marquez and \nLong (2020), 15-year-old adolescents’ OLS declined in the UAE between 2015 and 2018, \nas it did in most countries, and the UAE was among the top third of countries presenting \nthe largest decline. 4.1  \u0007Findings Yet, in the present study we found that this decline was larger among \nfemales, national students, students of higher SES and in British schools, as well as in the \nemirates of Sharjah, Abu Dhabi, RAK and Dubai. This may be explained by rising mental \nhealth issues, which serve to drag OLS scores downward (Badri et al., 2022; Lombardo \net al., 2018). Declines in youth wellbeing have been especially pronounced in countries \nthat are more egalitarian, with higher GDP per capita, and where concurrent increases in \nacademic pressure are observed (Borgonovi & Han, 2021; Cosma et al., 2020). Further-\nmore, in a recent study, Marquez et al. (2022c) examined factors associated with declines \nin adolescent OLS between 2015 and 2018 in the UK, the United States, France, Ireland \nand Japan, finding that this decline may be largely due to changes in school well-being \nand, to a lesser extent, material well-being and the use of Information and Communica-\ntion Technologies. They also found differences by gender and across countries, notably \nbetween Western nations and Japan. This highlights again the importance of national stud-\nies on adolescent wellbeing and mental health that account for the specific context and \nsocio-demographic profile of the country as levels and changes over time –and the factors \nassociated with these- not only differ across countries but potentially also across different \nsocio-demographic groups (e.g. gender, ethnicity, migrant status, etc.) within nations. 1 3 4.2  \u0007Limitations and Future Directions We analyzed pre-Covid-19 data. The next PISA edition (2022) will be revelatory \nfor the status of youth wellbeing and mental health, especially as the effects of 1 3 Geographic, Socio‑Demographic and School Type Variation… 817 the pandemic will linger beyond its end (Gavin et al., 2021; Racine et al., 2021; \nUNICEF, 2021). Also, due to the cross-sectional nature of the data and the research \ndesign used, causality cannot be established. Moreover, although levels of miss-\ning data were generally low for all the wellbeing outcomes, these were somewhat \nhigher for mental health symptoms of internalising difficulties (see Sect. 2.2.); thus, \nthe results involving this variable should be interpreted with more caution. Another \nimportant limitation to keep in mind refers to the validity of the Western-derived \nmeasures of wellbeing included in PISA in a non-Western nation such as the UAE \nwhere adolescents may understand and respond to questions on their wellbeing in \nsubstantially different ways compared to their Western peers due to cultural and lin-\nguistic issues. Although the body of research assessing the cross-cultural validity \nof adolescent wellbeing measures is growing (Casas & Gonzalez-Carrasco, 2021), \nmore research is needed to examine the validity of these specific measures of ado-\nlescent wellbeing in the UAE. Overall, while most of our findings coincided with \nthe literature, the picture within the UAE is complex and confirms the need for con-\ntinued broad-based assessments of this sort, using a range of measures to understand \nthe wellbeing and mental health landscape for young people in the country. This is \nespecially the case in the public-school students, which tend to be mostly nationals, \nwhere concurrent mental health issues and high wellbeing operate together and cre-\nate a student profile worth further investigation, in addition to confirming the two-\ncontinua model of wellbeing (Keyes, 2005). We finally echo the OECD’s (2021) \nrecommendation for education authorities to share their data. The KHDA collects \nlongitudinal student wellbeing data in a highly granular form, which can be used to \nunderstand what is driving school, familial and community factors that support well-\nbeing and mental health in the emirate and nation. However, this has not been made \npublic, belabouring efforts and delaying progress. 4.3  \u0007Conclusion In adolescence, wellbeing and mental health should not be considered less important \nthan academic outcomes given their capacity to predict important outcomes later \nin life, including academic outcomes (Cavioni et  al., 2021; DeNeve & Oswald, \n2012; Goodman et al., 2015; Guzmán et al., 2020; Kansky et al., 2016; Richards \n& Huppert, 2011). This stance was further supported by our finding that in the \nUAE, students who reported greater wellbeing also performed better academically. Consequently, schools, parents and relevant stakeholders should take students \nwellbeing and mental health seriously. Our findings on geographic, socio-\ndemographic and school type inequalities in adolescent wellbeing and mental health \nhelp the identification of the more vulnerable groups in the UAE and provide a \nbaseline against which future changes can be measured. Education authorities at the \nnational and emirate level must invest more heavily in public education to address \nacademic performance, while concurrently addressing mental health across all \nschools, as well as improving wellbeing outcomes uniformly. These aims will ensure \nthat the ability of young people to effectively make a living, as well as live happy, \nsatisfactory lives, is maximized. 1 3 818 J. Marquez et al. 4.3  \u0007Conclusion Table 6   Output from linear regression models: socio-demographic differences in OLS (standardized scale)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nConstant\n-0.037\n0.021\n0.037\n0.026\n0.098***\n0.029\n0.068*\n0.031\n0.113**\n0.035\n0.173***\n0.027\n0.189***\n0.028\nEmirate (ref.category: Abu \nDhabi): Dubai\n-0.043\n0.029\n-0.046\n0.027\n-0.025\n0.027\n-0.036\n0.028\n0.000\n0.031\n0.046\n0.028\n0.039\n0.028\nEmirate (ref.category: Abu \nDhabi): Sharjah\n-0.045\n0.060\n-0.038\n0.056\n-0.026\n0.055\n-0.019\n0.056\n-0.008\n0.055\n-0.012\n0.043\n-0.014\n0.043\nEmirate (ref.category: Abu \nDhabi): Ajman\n0.148**\n0.047\n0.148**\n0.049\n0.152**\n0.050\n0.171***\n0.049\n0.181***\n0.054\n0.126**\n0.047\n0.129*\n0.047\nEmirate (ref.category: Abu \nDhabi): UAQ\n0.160**\n0.055\n0.164*\n0.065\n0.130\n0.068\n0.131*\n0.063\n0.109\n0.067\n0.112\n0.067\n0.112\n0.068\nEmirate (ref.category: Abu \nDhabi): RAK\n0.229***\n0.066\n0.227***\n0.067\n0.204**\n0.067\n0.206**\n0.067\n0.182**\n0.069\n0.136*\n0.064\n0.135*\n0.064\nEmirate (ref.category: Abu \nDhabi): Fujairah\n0.284***\n0.060\n0.282***\n0.066\n0.255***\n0.066\n0.256***\n0.066\n0.227***\n0.068\n0.213***\n0.062\n0.212**\n0.062\nFemale\n-0.141***\n0.029\n-0.148*** 0.029\n-0.154*** 0.029\n-0.166***\n0.030\n-0.162***\n0.022\n-0.168***\n0.022\nExpatriate\n-0.109*** 0.024\n-0.052*\n0.026\n0.022\n0.027\n0.041\n0.025\n0.029\n0.026\nSocio-economic status\n0.079***\n0.013\n0.081***\n0.014\n0.117***\n0.012\n0.114***\n0.012\nPrivate school\n-0.149***\n0.033\nSchool type (ref.category: \nMoE): British\n-0.486***\n0.046\n-0.505***\n0.046\nSchool type (ref.category: \nMoE): American\n-0.311***\n0.034\n-0.321***\n0.034 Geographic, Socio‑Demographic and School Type Variation… 819 Geographic, Socio‑Demographic and School Type\nTable 6   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nSchool type (ref.category: \nMoE): Indian\n-0.141**\n0.045\n-0.157**\n0.046\nSchool type (ref.category: \nMoE): TVET\n-0.582***\n0.049\n-0.588***\n0.050\nSchool type (ref.category: \nMoE): Other\n-0.234***\n0.049\n-0.249***\n0.051\nAcademic competence (Mean \nPISA score)\n0.024*\n0.012\nObservations\n17,467\n17,467\n16,960\n16,941\n16,326\n16,941\n16,941 Table 6   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nSchool type (ref.category: \nMoE): Indian\n-0.141**\n0.045\n-0.157**\n0.046\nSchool type (ref.category: \nMoE): TVET\n-0.582***\n0.049\n-0.588***\n0.050\nSchool type (ref.category: \nMoE): Other\n-0.234***\n0.049\n-0.249***\n0.051\nAcademic competence (Mean \nPISA score)\n0.024*\n0.012\nObservations\n17,467\n17,467\n16,960\n16,941\n16,326\n16,941\n16,941\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 Table 6   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nSchool type (ref.category: \nMoE): Indian\n-0.141**\n0.045\n-0.157**\n0.046\nSchool type (ref.category: \nMoE): TVET\n-0.582***\n0.049\n-0.588***\n0.050\nSchool type (ref.category: \nMoE): Other\n-0.234***\n0.049\n-0.249***\n0.051\nAcademic competence (Mean \nPISA score)\n0.024*\n0.012\nObservations\n17,467\n17,467\n16,960\n16,941\n16,326\n16,941\n16,941 Standard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 3 J. 4.3  \u0007Conclusion Marquez et al. 820 Table 7   Output from linear regression models: socio-demographic differences in positive affects (standardized scale)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nSe\nConstant\n-0.034\n0.019\n-0.087*** 0.024\n-0.031\n0.026\n-0.077**\n0.028\n-0.053\n0.031\n-0.024\n0.025\n0.026***\n0.025\nEmirate (ref.category: Abu Dhabi): \nDubai\n0.013\n0.026\n0.015\n0.026\n0.021\n0.026\n0.007\n0.026\n0.033\n0.028\n0.050*\n0.025\n0.028\n0.026\nEmirate (ref.category: Abu Dhabi): \nSharjah\n0.041\n0.046\n0.036\n0.048\n0.049\n0.048\n0.058\n0.050\n0.065\n0.049\n0.063\n0.039\n0.055\n0.039\nEmirate (ref.category: Abu Dhabi): \nAjman\n0.162*** 0.044\n0.161***\n0.042\n0.151***\n0.043\n0.179***\n0.041\n0.193***\n0.042\n0.141***\n0.039\n0.147***\n0.038\nEmirate (ref.category: Abu Dhabi): \nUAQ\n0.123\n0.069\n0.120*\n0.058\n0.105\n0.058\n0.110*\n0.051\n0.098\n0.053\n0.091\n0.056\n0.089\n0.057\nEmirate (ref.category: Abu Dhabi): \nRAK\n0.166**\n0.054\n0.168**\n0.052\n0.149**\n0.051\n0.154**\n0.050\n0.139**\n0.052\n0.107*\n0.049\n0.102*\n0.048\nEmirate (ref.category: Abu Dhabi): \nFujairah\n0.217*** 0.057\n0.219***\n0.051\n0.202***\n0.051\n0.204***\n0.049\n0.189***\n0.050\n0.179***\n0.047\n0.175***\n0.045\nFemale\n0.098***\n0.025\n0.079**\n0.025\n0.071**\n0.025\n0.066*\n0.026\n0.071***\n0.020\n0.054*\n0.020\nExpatriate\n-0.067**\n0.022\n0.017\n0.025\n0.056*\n0.026\n0.063*\n0.028\n0.023\n0.029\nSocio-economic status\n0.119***\n0.012\n0.121***\n0.012\n0.148***\n0.012\n0.142***\n0.012\nPrivate school\n-0.082**\n0.028\nSchool type (ref.category: MoE): \nBritish\n-0.331*** 0.044\n-0.391***\n0.043\nSchool type (ref.category: MoE): \nAmerican\n-0.173*** 0.033\n-0.205***\n0.035\nSchool type (ref.category: MoE): \nIndian\n0.033\n0.040\n-0.018\n0.040\nSchool type (ref.category: MoE): \nTVET\n-0.308*** 0.068\n-0.326***\n0.063 Geographic, Socio‑Demographic and School Type Variation… 821 1\nTable 7   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nSe\nSchool type (ref.category: MoE): \nOther\n-0.124**\n0.042\n-0.173***\n0.046\nAcademic competence (Mean PISA \nscore)\n0.075***\n0.013\nObservations\n17,083\n16,593\n16,579\n15,980\n16,579\n16,579\n16,941\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 3 822 J. Marquez et al. 4.3  \u0007Conclusion Table 8   Output from linear regression models: socio-demographic differences in negative affects (standardized scale)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nConstant\n-0.006\n0.024\n-0.178***\n0.030\n-0.299***\n0.033\n-0.280***\n0.035\n-0.378***\n0.038\n-0.408***\n0.031\n0.439\n0.032\nEmirate (ref.category: Abu \nDhabi): Dubai\n0.158***\n0.038\n0.165***\n0.034\n0.115***\n0.032\n0.121***\n0.033\n0.047\n0.033\n-0.034\n0.027\n-0.021\n0.028\nEmirate (ref.category: Abu \nDhabi): Sharjah\n0.066\n0.081\n0.050\n0.068\n0.017\n0.065\n0.013\n0.066\n-0.014\n0.060\n-0.017\n0.049\n-0.012\n0.048\nEmirate (ref.category: Abu \nDhabi): Ajman\n-0.127**\n0.048\n-0.128**\n0.047\n-0.141**\n0.048\n-0.153**\n0.048\n-0.173***\n0.052\n-0.109**\n0.037\n-0.112**\n0.036\nEmirate (ref.category: Abu \nDhabi): UAQ\n-0.098\n0.083\n-0.109\n0.077\n-0.065\n0.078\n-0.066\n0.077\n-0.009\n0.081\n-0.020\n0.066\n-0.019\n0.065\nEmirate (ref.category: Abu \nDhabi): RAK\n-0.204***\n0.060\n-0.197**\n0.062\n-0.143*\n0.060\n-0.144*\n0.059\n-0.083\n0.062\n-0.061\n0.057\n-0.058\n0.057\nEmirate (ref.category: Abu \nDhabi): Fujairah\n-0.208**\n0.066\n-0.201***\n0.056\n-0.143**\n0.055\n-0.143**\n0.054\n-0.078\n0.054\n-0.087\n0.053\n-0.085\n0.053\nFemale\n0.324***\n0.036\n0.332***\n0.034\n0.335***\n0.033\n0.347***\n0.033\n0.348***\n0.023\n0.359***\n0.023\nExpatriate\n0.226***\n0.025\n0.191***\n0.028\n0.011\n0.029\n-0.015\n0.024\n0.010\n0.025\nSocio-economic status\n-0.051**\n0.016\n-0.052**\n0.016\n-0.081***\n0.014\n-0.077*** 0.014\nPrivate school\n0.354***\n0.036\nSchool type (ref.category: \nMoE): British\n0.606***\n0.057\n0.643***\n0.056\nSchool type (ref.category: \nMoE): American\n0.483***\n0.034\n0.503***\n0.035\nSchool type (ref.category: \nMoE): Indian\n0.519***\n0.035\n0.551***\n0.035\nSchool type (ref.category: \nMoE): TVET\n0.462***\n0.064\n0.472***\n0.062 Geographic, Socio‑Demographic and School Type Variation… 823 1\nTable 8   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nSchool type (ref.category: \nMoE): Other\n0.513***\n0.048\n0.544***\n0.051\nAcademic competence (Mean \nPISA score)\n-0.047*** 0.012\nObservations\n16,999\n16,999\n16,524\n16,512\n15,910\n16,512\n16,512\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 Table 8   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nB\nse\nSchool type (ref.category: \nMoE): Other\n0.513***\n0.048\n0.544***\n0.051\nAcademic competence (Mean \nPISA score)\n-0.047*** 0.012\nObservations\n16,999\n16,999\n16,524\n16,512\n15,910\n16,512\n16,512 Standard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 3 J. Marquez et al. 4.3  \u0007Conclusion 824 Table 9   Output from linear regression models: socio-demographic differences in meaning and purpose in life (standardized scale)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nConstant\n-0.023\n0.019\n-0.039\n0.025\n0.032\n0.030\n0.010\n0.032\n0.079*\n0.036\n0.123***\n0.027\n0.125\n0.027\nEmirate (ref.category: Abu \nDhabi): Dubai\n-0.100**\n0.033\n-0.099**\n0.033\n-0.083*\n0.033\n-0.090**\n0.034\n-0.021\n0.035\n0.032\n0.031\n0.032\n0.031\nEmirate (ref.category: Abu \nDhabi): Sharjah\n0.031\n0.065\n0.029\n0.065\n0.048\n0.064\n0.052\n0.065\n0.073\n0.061\n0.071\n0.043\n0.071\n0.043\nEmirate (ref.category: Abu \nDhabi): Ajman\n0.184***\n0.046\n0.184***\n0.045\n0.187***\n0.047\n0.200***\n0.046\n0.229***\n0.050\n0.141***\n0.040\n0.141*** 0.040\nEmirate (ref.category: Abu \nDhabi): UAQ\n0.165*\n0.069\n0.164*\n0.066\n0.140*\n0.064\n0.143*\n0.062\n0.103\n0.067\n0.096\n0.063\n0.096\n0.063\nEmirate (ref.category: Abu \nDhabi): RAK\n0.203***\n0.051\n0.204***\n0.050\n0.173***\n0.050\n0.176***\n0.049\n0.139**\n0.051\n0.093*\n0.047\n0.092*\n0.046\nEmirate (ref.category: Abu \nDhabi): Fujairah\n0.349***\n0.060\n0.350***\n0.059\n0.317***\n0.059\n0.318***\n0.058\n0.277***\n0.057\n0.263***\n0.050\n0.263*** 0.050\nFemale\n0.030\n0.034\n0.018\n0.034\n0.014\n0.034\n0.004\n0.034\n0.005\n0.022\n0.005\n0.022\nExpatriate\n-0.108***\n0.026\n-0.068*\n0.029\n0.065*\n0.029\n0.089***\n0.025\n0.088*** 0.025\nSocio-economic status\n0.057***\n0.015\n0.059***\n0.015\n0.100***\n0.013\n0.100*** 0.013\nPrivate school\n-0.259*** 0.034\nSchool type (ref.category: \nMoE): British\n-0.616***\n0.059\n-0.618*** 0.058\nSchool type (ref.category: \nMoE): American\n-0.407***\n0.036\n-0.408*** 0.036\nSchool type (ref.category: \nMoE): Indian\n-0.233***\n0.038\n-0.234*** 0.039\nSchool type (ref.category: \nMoE): TVET\n-0.477***\n0.058\n-0.478*** 0.058 Geographic, Socio‑Demographic and School Type Variation… 825 1\nTable 9   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nSchool type (ref.category: \nMoE): Other\n-0.473***\n0.048\n-0.474*** 0.050\nAcademic competence (Mean \nPISA score)\n0.002\n0.014\nObservations\n17,570\n17,570\n17,046\n17,034\n16,415\n17,034\n17,034\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 Table 9   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nSchool type (ref.category: \nMoE): Other\n-0.473***\n0.048\n-0.474*** 0.050\nAcademic competence (Mean \nPISA score)\n0.002\n0.014\nObservations\n17,570\n17,570\n17,046\n17,034\n16,415\n17,034\n17,034\nS\nd d\n(\n) ***\n0 01 **\n0 05 *\n0 1 Standard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 3 J. Declarations Conflict of Interest  We confirm that there are no conflict of interests affecting the submission of this \nmanuscript. Informed Consent  In this research, we exclusively used secondary data collected by the OECD (Programme \nfor International Student Assessment) and publically available here. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, \nwhich permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long \nas you give appropriate credit to the original author(s) and the source, provide a link to the Creative \nCommons licence, and indicate if changes were made. The images or other third party material in this \narticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit \nline to the material. If material is not included in the article’s Creative Commons licence and your \nintended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain \npermission directly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​\nmmons.​org/​licen​ses/​by/4.​0/. 4.3  \u0007Conclusion Marquez et al. 826 Table 10   Output from linear regression models: socio-demographic differences in internalizing difficulties (standardized scale)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nConstant\n0.075*\n0.032\n-0.135*** 0.031\n-0.031\n0.034\n-0.028\n0.034\n0.024\n0.038\n0.006\n0.035\n0.010\n0.034\nEmirate (ref.category: Abu Dhabi): \nDubai\n-0.139*** 0.041\n-0.130*** 0.037\n-0.087*\n0.035\n-0.086*\n0.035\n-0.055\n0.036\n-0.009\n0.031\n-0.003\n0.030\nEmirate (ref.category: Abu Dhabi): \nSharjah\n-0.028\n0.051\n-0.051\n0.043\n-0.019\n0.041\n-0.018\n0.041\n-0.008\n0.041\n0.003\n0.038\n0.006\n0.038\nEmirate (ref.category: Abu Dhabi): \nAjman\n-0.144**\n0.053\n-0.149**\n0.045\n-0.142***\n0.041\n-0.144***\n0.041\n-0.131**\n0.043\n-0.135*** 0.038\n-0.137*** 0.038\nEmirate (ref.category: Abu Dhabi): \nUAQ\n0.027\n0.076\n0.004\n0.039\n-0.038\n0.039\n-0.038\n0.039\n-0.072\n0.043\n-0.048\n0.038\n-0.047\n0.037\nEmirate (ref.category: Abu Dhabi): \nRAK\n-0.107\n0.062\n-0.099\n0.056\n-0.146**\n0.055\n-0.145**\n0.055\n-0.194*** 0.057\n-0.149**\n0.053\n-0.147**\n0.053\nEmirate (ref.category: Abu Dhabi): \nFujairah\n-0.100\n0.069\n-0.096\n0.060\n-0.155*\n0.062\n-0.153*\n0.062\n-0.188**\n0.061\n-0.159**\n0.058\n-0.158**\n0.058\nFemale\n0.395***\n0.030\n0.390***\n0.028\n0.390***\n0.028\n0.387***\n0.029\n0.383***\n0.023\n0.388***\n0.023\nExpatriate\n-0.196***\n0.027\n-0.202***\n0.026\n-0.111*** 0.029\n-0.073**\n0.028\n-0.061*\n0.027\nSocio-economic status\n-0.008\n0.013\n-0.010\n0.014\n-0.025\n0.013\n-0.022\n0.013\nPrivate school\n-0.177*** 0.032\nSchool type (ref.category: MoE): \nBritish\n-0.118**\n0.038\n-0.099*\n0.039\nSchool type (ref.category: MoE): \nAmerican\n-0.138*** 0.040\n-0.128**\n0.041\nSchool type (ref.category: MoE): \nIndian\n-0.497*** 0.042\n-0.481*** 0.043\nSchool type (ref.category: MoE): \nTVET\n-0.017\n0.050\n-0.012\n0.049 Geographic, Socio‑Demographic and School Type Variation… 827 1\nTable 10   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nSchool type (ref.category: MoE): \nOther\n-0.297*** 0.037\n-0.281*** 0.038\nAcademic competence (Mean PISA \nscore)\n0.024\n0.012\nObservations\n16,056\n16,056\n15,601\n15,589\n16,326\n15,021\n15,589\nStandard errors (se); ***p < 0.01, **p < 0.05, *p < 0.1 Table 10   (continued)\nVariable\nModel 1\nModel 2\nModel 3\nModel 4\nModel 5\nModel 6\nModel 7\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nb\nse\nSchool type (ref.category: MoE): \nOther\n-0.297*** 0.037\n-0.281*** 0.038\nAcademic competence (Mean PISA \nscore)\n0.024\n0.012\nObservations\n16,056\n16,056\n15,601\n15,589\n16,326\n15,021\n15,589 3 828 J. 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Marquez et al. 836 Jose Marquez1   · Louise Lambert2   · Megan Cutts1 Louise Lambert \nltlamber@yahoo.com\nMegan Cutts \nmegan.cutts@manchester.ac.uk Louise Lambert \nltlamber@yahoo.com\nMegan Cutts \nmegan.cutts@manchester.ac.uk Louise Lambert \nltlamber@yahoo.com 1\t\nManchester Institute of Education, University of Manchester, Oxford Road, \nManchester M13 9PL, UK 2\t\nCanadian University Dubai, Dubai, UAE 1 3"