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Opium of the people? National identification predicts well‐being over time

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Social group membership and its social‐relational corollaries, for example, social contact, trust, and support, are prophylactic for health. Research has tended to focus on how direct social interactions between members of small‐scale groups (i.e., a local sports team or community group) are conducive to positive health outcomes. The current study provides evidence from a longitudinal cross‐cultural sample (N = 6,748; 18 countries/societies) that the prophylactic effect of group membership is not isolated to small‐scale groups, and that members of groups do not have to directly interact, or in fact know of each other to benefit from membership. Our longitudinal analyses suggest that national identification (strength of association with the nation state in which an individual resides) predicts lower anxiety and improved health; national identification was in fact almost as positively predictive of health status as anxiety was negatively predictive. The findings indicate that identification with large‐scale groups, like small‐scale groups, is palliative, and are discussed in terms of globalization and banal nationalism.
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British Journal of Psychology (2019)
©2019 The British Psychological Society
www.wileyonlinelibrary.com
Opium of the people? National identification
predicts well-being over time
Sammyh S. Khan
1
* , Nicholas Garnett
1
, Daniella Hult Khazaie
1
,
James H. Liu
2
and Homero Gil de Z
u~
niga
3,4
1
School of Psychology, University of Keele, Staffordshire, UK
2
School of Psychology, Massey University, Auckland, New Zealand
3
Media Innovation Lab, Department of Communication, University of Vienna, Austria
4
Facultad de Comunicaci
on y Letras, Universidad Diego Portales, Santiago, Chile
Social group membership and its social-relational corollaries, for example, social contact,
trust, and support, are prophylactic for health. Research has tended to focus on how
direct social interactions between members of small-scale groups (i.e., a local sports team
or community group) are conducive to positive health outcomes. The current study
provides evidence from a longitudinal cross-cultural sample (N=6,748; 18 countries/
societies) that the prophylactic effect of group membership is not isolated to small-scale
groups, and that members of groups do not have to directly interact, or in fact know of
each other to benefit from membership. Our longitudinal analyses suggest that national
identification (strength of association with the nation state in which an individual resides)
predicts lower anxiety and improved health; national identification was in fact almost as
positively predictive of health status as anxiety was negatively predictive. The findings
indicate that identification with large-scale groups, like small-scale groups, is palliative, and
are discussed in terms of globalization and banal nationalism.
Social groups can have a prophylactic effect on peoples’ well-being. Multiple aspects of
social groups endow individuals with this benefit, including social contact (e.g., Cohen,
2004), trust (e.g., Subramanian, Kim, & Kawachi, 2002), and support (e.g., Schwarzer &
Leppin, 1991). However, one of the most remarkable findings in the literature is that
members of groups do not actually have to interact with one another for their well-being
to benefit, merely identifying with the groups to which one belongs is sufficient (see
Haslam, Jetten, Cruwys, Dingle, & Haslam, 2018; Jetten, Haslam, & Haslam, 2012; Khan
et al., 2014).
The social identity approach (for an overview, see Reicher, Spears, & Haslam, 2010)
offers an account for why this is the case. According to this theory, knowing that others
identify with the group(s) to which one belongs leads to a sense of shared social identity,
and as a result, group members are more likely to trust, support, and cooperate with one
another (e.g., Barreto & Ellemers, 2000; De Cremer, 2002; Haslam & Reicher, 2006;
Kramer, Hanna, Su, & Wei, 2001; Reicher & Haslam, 2006; Tyler & Blader, 2000, 2002,
2003). The theoretical approach posits that it is this sense of shared social identity, and
resultant transformation of social relations, which affect well-being, in part via primary
*Correspondence should be addressed to Sammyh S. Khan, School of Psychology, Keele University, Staffordshire ST5 5BG, UK
(email: s.s.khan@keele.ac.uk).
DOI:10.1111/bjop.12398
1
and secondary stress appraisals (Lazarus & Folkman, 1984). From this perspective,
experiences of stress are influenced by the values and norms of the group(s) to which one
belongs (e.g., Levine & Reicher, 1996; St Claire, Clift, & Dumbelton, 2008). On the other
hand, when stress is experienced, the accessibility to social support, afforded by
belonging to and identifying with a group, or groups, serves as a resource for coping (e.g.,
Haslam, O’Brien, Jetten, Vormedal, & Penna, 2005; Haslam & Reicher, 2006; Reicher &
Haslam, 2006). Taken together, the social identity approach to health and well-being is
referred to as the ‘social cure’ (Haslam et al., 2018; Jetten, Haslam, & Haslam, 2011).
The intragroup phenomenon of perceiving and experiencing kinship with people that
one has not interacted with is one that has parallels to the postmodernist notion of nations
as imagined communities (Anderson, 1983). Most citizens of even the smallest countries
will never meet or know most of their fellow citizens, yet they will be bound and act
together on the basis of a shared sense of national identification. The study of national
identity in the field of psychology resonates with the notion of imagined communities.
The social identity approach examines the phenomenon at two levels of analysis: (1) the
representation, contestation, and mobilization of nationalism and national identity by
identity entrepreneurs (e.g., Khan et al., 2017; Reicher & Hopkins, 2001); and (2) the
nature, strength, and implications of subjective identification with the nation (e.g., Blank
& Schmidt, 2003; Fleischmann & Phalet, 2018; Huddy & Khatib, 2007; Kanas &
Martinovic, 2017; Mummendey, Klink, & Brown, 2001; Pehrson, Brown, & Zagefka, 2009;
Pehrson, Vignoles, & Brown, 2009; Verkuyten, 2009); that is, the approach accounts for
both political and individual imaginings of the nation. The focus of the present study is at
the second level of analysis in that it operationalizes national identification as the strength
by which individuals identify with the nation, devoid of ideological connotations; it
arguably represents cognitive attachment to the nation.
Although the psychological literature is rife with empirical accounts of the intergroup
consequences of national identification in a variety of contexts(e.g., Blank & Schmidt, 2003;
Fleischmann & Phalet, 2018; Huddy & Khatib, 2007; Kanas & Martinovic, 2017;
Mummendey et al., 2001; Pehrson, Brown, et al., 2009; Pehrson, Vignoles, et al., 2009;
Verkuyten, 2009), its implications for well-being have largely been ignored. For that matter,
research guided by the social identity approach has tended to focus on the well-being
implications of belonging to and identifying with small- (e.g., family, work, and treatment)
as opposed to large-scale (e.g., national) social categories (for exceptions, see Greenaway
et al., 2015; Khan et al., 2014). However, there exists a marginal body of research
evidencing anassociation between different operationalizations of national attachment and
well-being. For example, Ha and Jang (2015) found that national pride but not national
identity was associated with happiness in a nationally representative sample from South
Korea, whereas Zdrenka, Yogeeswaran, Stronge, and Sibley (2015) observed a positive
relationship between patriotism and quality of life in a nationally representative sample
from New Zealand. These two studies were preceded by two interrelated multi-nation
correlational studies demonstrating that satisfaction with one’s nation predicted satisfac-
tion with life in 128 countries (Morrison, Tay, & Diener, 2011), and that national pride and
nationalism predicted subjective well-being in 31 countries (Reeskens & Wright, 2011).
It is important to highlight that the above studies predominately operationalized
national attachment in terms of its affective (e.g., pride and satisfaction) and ideological
dimensions (e.g., patriotism) as opposed to its cognitive dimension (i.e., identification);
only Ha and Jang’s (2015) research included a measure of national identification, and it
was found to be unrelated to happiness (their main measure of ‘well-being’). These
operationalizations are in line with the literature, which conceptualizes the cognitive,
2Sammyh S. Khan et al.
ideological, and affective dimensions of national attachment as distinct yet interrelated
dimensions differentially associated with political beliefs and attitudes, prejudice, and
happiness (e.g., Blank & Schmidt, 2003; Ha & Jang, 2015; Huddy & Khatib, 2007;
Mummendey et al., 2001; Schatz, Staub, & Lavine, 1999). For example, feeling American
is not contingent on support for a particular political ideology (Huddy & Khatib, 2007),
and conservatives (versus liberals) tend to report higher levels of subjective well-being
(e.g., Napier & Jost, 2008; Schlenker, Chambers, & Le, 2012). One explanation for why
research to date has only uncovered a positive association between the affective and
ideological (as opposed to cognitive) dimensions of national attachment and well-being is
because these dimensions are more proximal to well-being, encapsulating positive
emotions, and positive affect indeed impacts well-being over time (e.g., Fredrickson &
Joiner, 2002).
Furthermore, existing studies on national attachment and well-being have so far only
examined associations cross-sectionally as opposed to longitudinally. The most apparent
limitation of cross-sectional data is that it does not allow for the modelling of change (or
stability) and predictive direction over time. Being able to do so is partic ularly important as
evidence suggests that well-being also impacts the extent to which individuals identify
and engage with social groups (Miller, Wakefield, & Sani, 2017), suggesting a bidirectional
relationship.
Addressing these shortcomings in the literature, the present research set out to
examine the relationship between national identity and well-being. The operationaliza-
tion of national identity specifically captured the cognitive dimension of national
attachment (i.e., national identification), whereas well-being was assessed using clinically
validated measures of mental and general health status. Data were collected longitudinally
between two time-points, 6 months apart. The present research was also conducted
cross-culturally with samples from both WEIRD and non-WEIRD countries (Western,
Educated, Industrialized, Rich, Democratic; Henrich, Heine, & Norenzayan, 2010). To
summarize, the research presented herein set out to examine the occurrence and nature
of the association between national identification and well-being, longitudinally, in a
sample consisting of 6784 participants across 18 countries/societies.
Method
Sample and procedure
Ethical approval for the research was provided by the Ethical approval for the research
was provided by the Massey University Human Ethics Committee (MUHECN NOR
16/31). The total sample consisted of 6,784 participants from 18 countries/societies:
Argentina (N=268), Brazil (N=237), China (N=300), Estonia (N=625), Germany
(N=529), Indonesia (N=224), Italy (N=473), Japan (N=469), New Zealand
(N=489), Philippines (N=108), Poland (N=494), Russia (N=429), South Korea
(N=430), Spain (N=221), Taiwan (N=256), Turkey (N=236), United Kingdom
(N=548), and the United States (N=412). The sampled countries/societies were
selected because they represented a balance between WEIRD and non-WEIRD cultures
(Henrich et al., 2010), which had online penetrations and participant panels enabling
the collection of longitudinal data online. The percentage of females in the country/
society samples ranged from 63% (China) to 43% (Italy), whereas the mean age ranged
from 53 years (UK; SD =14 years) to 37 years (Turkey; SD =11 years). All participants
were citizens of the countries/societies in which they were residing at the time of
participating in the study. The specific sizes and demographic compositions of the
National identification and well-being in 18 countries 3
Table 1. Descriptive and reliability statistics by country/society
Country NMales (%)
Age
GAD-7
Time 1
GAD-7
Time 2
NATID
Time 1
NATID
Time 2 GSRH
Time 1
GSRH
Time 2
M(SD)M(SD)aM(SD)aM(SD)aM(SD)aM(SD)M(SD)
Argentina 268 47.39 43.68 (14.34) 3.28 (1.51) .94 3.27 (1.43) .94 5.28 (1.49) .90 5.35 (1.37) .90 5.40 (1.13) 5.12 (1.14)
Brazil 237 55.27 39.50 (12.24) 3.84 (1.31) .91 4.04 (1.41) .93 4.88 (1.66) .94 4.88 (1.73) .96 5.25 (1.28) 5.18 (1.23)
China 300 37.33 41.74 (12.49) 3.23 (1.08) .90 3.08 (1.22) .95 5.30 (1.24) .92 5.29 (1.27) .92 4.68 (1.13) 4.67 (1.15)
Estonia 625 48.48 50.16 (16.83) 2.88 (1.21) .93 2.74 (1.18) .93 5.78 (1.17) .90 5.88 (1.11) .89 4.71 (1.15) 4.74 (1.16)
Germany 529 54.25 47.84 (13.52) 3.21 (1.43) .93 3.18 (1.43) .94 4.19 (1.57) .92 4.29 (1.59) .93 4.61 (1.45) 4.59 (1.42)
Indonesia 224 45.54 37.65 (9.47) 3.22 (1.26) .93 3.32 (1.27) .94 5.55 (1.17) .91 5.52 (1.18) .92 5.48 (1.07) 5.39 (1.12)
Italy 473 57.29 42.93 (13.24) 3.61 (1.28) .92 3.60 (1.31) .93 4.66 (1.67) .95 4.63 (1.63) .94 4.69 (1.17) 4.61 (1.18)
Japan 469 39.02 46.81 (12.45) 2.83 (1.40) .95 2.77 (1.38) .95 4.85 (1.17) .88 4.85 (1.17) .91 4.01 (1.38) 4.02 (1.34)
Korea 430 48.60 40.62 (12.24) 3.05 (1.30) .93 3.07 (1.35) .95 4.26 (1.32) .92 4.38 (1.37) .93 4.23 (1.20) 4.21 (1.18)
New Zealand 489 54.40 53.16 (15.97) 2.86 (1.28) .93 2.90 (1.31) .94 5.61 (1.42) .93 5.63 (1.38) .94 4.75 (1.48) 4.75 (1.47)
Philippines 108 53.70 36.82 (10.97) 2.96 (1.30) .94 2.93 (1.29) .94 6.16 (1.03) .88 6.14 (1.14) .91 5.25 (1.08) 5.19 (1.21)
Poland 494 53.04 43.90 (14.35) 3.32 (1.47) .94 3.25 (1.43) .94 5.38 (1.48) .92 5.35 (1.55) .94 4.83 (1.35) 4.72 (1.30)
Russia 429 53.61 41.29 (11.95) 3.24 (1.39) .95 3.29 (1.35) .95 5.54 (1.56) .94 5.66 (1.47) .95 4.62 (1.02) 4.55 (1.08)
Spain 221 54.75 45.75 (13.01) 3.30 (1.46) .95 3.29 (1.52) .96 5.02 (1.89) .94 4.80 (1.89) .95 5.03 (1.12) 4.80 (1.13)
Taiwan 256 42.19 39.87 (10.46) 3.40 (1.11) .91 3.31 (1.13) .94 5.43 (1.38) .87 5.47 (1.24) .89 4.25 (1.14) 4.14 (1.11)
Turkey 236 48.31 37.23 (10.98) 3.60 (1.42) .94 3.72 (1.50) .95 5.47 (1.84) .92 5.52 (1.90) .93 5.17 (1.23) 5.18 (1.30)
United Kingdom 548 51.28 53.35 (13.73) 3.04 (1.57) .95 2.96 (1.55) .96 5.16 (1.61) .93 5.21 (1.59) .93 4.71 (1.51) 4.67 (1.48)
United States 412 57.04 55.27 (14.08) 2.58 (1.35) .94 2.57 (1.35) .95 5.63 (1.44) .90 5.58 (1.46) .90 5.18 (1.34) 5.16 (1.33)
4Sammyh S. Khan et al.
samples are presented in Table 1. Data were collected by Nielsen, an international
polling firm (for sampling details, Z
u~
niga & Liu, 2017): The first wave (T1) of data were
collected in September 2015, whereas the second wave (T2) of data were collected in
March 2016. Only cases with complete data at both time-points for all measures were
included in the analyses presented herein. ‘Attrition’ from T1 to T2 (64% on average
across the samples) does not represent participant dropout, but the proportion of
participants that were not contacted at T2 because of participant panel or financial
constraints; that is, it was not either logistically possible to reach participants or it would
have been too costly to recruit participants at two time-points. The survey was back-
translated (Brislin, 1970) into the official language of the countries/societies in which it
was administered.
Measures
National identity (NATID) was measured with four items derived from different studies
assessing different facets of national identity and national identification strength:(1)
Being [nationality] is very important to me’ (Citrin, Reingold, & Green, 1990); (2) ‘To
what extent do you see yourself as a typical [nationality]’ (Huddy & Khatib, 2007); (3)
The term [nationality] describes me well’; and (4) I identify with my nationality
(Postmes, Haslam, & Jans, 2013). Responses to the four items were recorded on a seven-
point Likert scale anchored by the endpoints ‘completely disagree’ and ‘completely
agree’, respectively. Principal Axing Factoring (PAF; oblique rotation) indicated that the
four items loaded onto one factor at both time-points, explaining 76% of the item variance
at T1 and 78% at T2; the items were treated as one factor in the proceeding analyses.
Anxiety was assessed using the Generalized Anxiety Disorder Assessment (GAD-7;
Plummer, Manea, Trepel, & McMillan, 2016; Spitzer, Kroenke, Williams, & Lowe, 2006).
The GAD-7 is a widely used and validated instrument for screening generalized anxiety in
clinical and non-clinical populations (see L
owe et al., 2008). As the name suggests, the
GAD-7 consists of seven items, and these items assess the two main criteria of generalized
anxiety disorder as defined by the fifth edition of the Diagnostic and Statistical Manual of
Mental Disorders (DSM-5): excessive anxiety and worry (e.g., ‘worrying too much about
different things)and difficulties controlling worries (e.g., ‘not being able to stop or
control worrying?’). The items asked participants to indicate how often that they had
been bothered by a specific symptom over the last 2 weeks using a seven-point Likert
scale anchored by the endpoints ‘never’ and ‘always’, respectively. Research into the
psychometric properties of the GAD-7 indicates that its items load onto one factor (L
owe
et al., 2008). PAF (oblique rotation) corroborated that the items loaded onto one factor at
both time-points, respectively, explaining 68% and 72% of the item variance; the items
were entered as one factor in the analyses.
Health status was assessed using two items of general self-rated health (GSRH),
consistent with the approach to measuring self-rated health adopted by DeSalvo et al.
(2006). Although the wording of single-item measures of health status may vary slightly,
they have been found to predict a number of health outcomes on par with or even better
than multi-item and multi-factor instruments, including chronic physical conditions (e.g.,
Macias, Gold,
Ong
ur, Cohen, & Panch, 2015), quality of life (DeSalvo et al., 2006),
mortality (e.g., DeSalvo, Fan, McDonell, & Fihn, 2005), and prospective health
expenditure (e.g., DeSalvo et al., 2009). The first item, GSRH standard (GSRH-S), asked
participants to answer a standard question about their health: ‘How would you say your
health is?’ The second question, GSRH comparative (GSRH-C), accounted for the age
specificity of health and asked participants to answer the following question: ‘Compared
National identification and well-being in 18 countries 5
to others your age, how would you say your health is?’; the GSRH-C question specifically
accounts for appraisals of health being with reference to participants’ health at earlier
stages of their lives, or others that may be perceived as having a better health status due to
their age. Participants responded to both questions on seven-point Likert scales,
respectively, anchored by the endpoints ‘very poor’ and ‘excellent’. PAF (oblique
rotation) indicated that the two items loaded onto one factor, explaining 87% of the item
variance at T1 and 91% at T2; the two items were analysed as one factor.
Age and gender were included as covariate variables in the inferential analysis
presented in the proceeding section.
The descriptive and reliability statistics for the measures in every country/society
sample are presented in Table 1; the correlations between the measures are presented in
Table 2.
Analysis plan
The first phase of the analysis involved a multigroup analysis (MGA) that examined
whether the measures specified as latent variables, that is, NATID and GAD-7, were
metrically invariant across the 18 countries/societies, which would indicate that the
relationships between the observed variables (i.e., the individual items) and the latent
variables (i.e., the variables composed by the items).
The second phase of the analysis involved modelling the bidirectional associations
between the measures across time in a cross-lagged panel model; it also involved
examining whether the associations observed in the cross-lagged panel model were
invariant across the 18 countries/societies. There were multiple advantages of addressing
the research question with a cross-lagged panel model. First, the method enabled the
examination of the effects of national identity upon well-being, and well-being upon
national identity, over time, in the one and same model. Second, it allowed us to examine
reciprocal effects of national identity and well-being over time while controlling for
autoregressive effects. Third, it allowed us to analyse the constructs assessed with
multiple items as latent variables, and thereby account for measurement error. Fourth, it
enabled us to examine whether the associations observed in the model were equivalent
across the 18 countries/societies.
The two phases of the analysis were conducted using the lavaan package (Rosseel,
2012) in R (R Core Team, 2016).
Table 2. Correlation matrix
Measures 2 3 4 5 6 7 8
1. Gender .11*** .02 .03* .01 .00 .14*** .13***
2. Age .01 .00 .21*** .22*** .28*** .29***
3. GSRH Time 1 .78*** .14*** .12*** .30*** .25***
4. GSRH Time 2 .14*** .15*** .26*** .26***
5. NatID Time 1 .77*** .12*** .13***
6. NatID Time 2 .11*** .13***
7. GAD-7 Time 1 .71***
8. GAD-7 Time 2
Note. Males =1; females =2.
*p<.05; ***p<.001.
6Sammyh S. Khan et al.
Results
Testing for metric invariance
The first step of the MGA involved establishing a baseline model; this model is referred to
as the configural model. Parameters are allowed to vary freely between samples in the
configural model, and it is specified in order to establish whether there is a good fit in the
factor between samples before any constraints are imposed. The configural model was
then used as a baseline for comparisons with nested models in which parameters were
increasingly constrained (see Milfont & Fischer, 2010). The comparative fit index (CFI),
the root mean squared error of approximation (RMSEA), and the standardized root mean
squared residual (SRMR) were used to evaluate model fit. Values below .90 for the CFI and
above .10 for the RMSEA and SRMR indicate unacceptable fit between a specified model
and observed data (MacCallum, Widaman, Preacher, & Hong, 2001). We do not rely on the
chi-square value in evaluating model fit because of its sensitivity to large sample sizes
(>200; Kline, 2005). Models A and B tested the factor structures of NATID and GAD-7 at T1
and T2, respectively. Table 3 shows that configural invariance could be observed for the
models.
The second step of the MGA procedure involved constraining the measurement
weights in the models. The fit indices of the constrained models are then compared with
the fit indices of the respective configural models. Cheung and Rensvold (2002) have
recommended that CFI values of <0.010 and RMSEA values of <0.015 between each
progressively constrained model with each respectively preceding model (beginning
with the unconstrained model) suggest that the null hypothesis of invariance should be
accepted. Following these recommendations, Table 3 shows that metric invariance could
be inferred; that is, participants responded comparably to the NATID and GAD-7 items
across the 18 country/society samples.
Cross-lagged panel modelling
The cross-lagged panel model specified the T1 measurements as exogenous variables (i.e.,
the predictor variables), and the T2 measurements as endogenous variables (i.e., the
predicted variables). Every endogenous variable (i.e., T2 variable) was in turn specified to
be predicted by every exogenous variable (i.e., T1 variable). The exogenous variables,
endogenous variables, and error variances for the items across time (e.g., NATID item 1
with NATID item 2, and GAD-7 item 1 with GAD-7 item 2, and so forth) were respectively
Table 3. Measurement invariance models: national identity and anxiety (GAD-7)
df v
2
CFI RMSEA (LCI, HCI) SRMR DCFI DRMSEA
Model A
Unconstrained
(configural)
774 3,481.740 0.955 0.097 (0.093, 1.00) 0.035 0.010 0.001
Measurement weights 927 4,289.726 0.945 0.098 (0.095, 0.101) .066
Model B
Unconstrained
(configural)
774 3,430.011 0.959 0.096 (0.092, 0.099) 0.032 0.008 0.001
Measurement weights 927 4,072.182 0.951 0.095 (0.092, 0.098) 0.054
Note. LCI =lower 90% confidence interval; HCI =higher 90% confidence interval.
National identification and well-being in 18 countries 7
specified to covary freely (see Burkholder & Harlow, 2003; Farrell, 1994). In addition, age
and gender were entered as covariates into the model, specified as exogenous variables
predicting the endogenous variables. This means that any effect observed of a T1 onto a T2
variable would be over and above any influence exerted by age and gender.
Like the tests for measurement invariance, the cross-lagged panel model was analysed
using the lavaan package (Rosseel, 2012) in R (R Core Team, 2016). The fit indices for the
model indicated a good fit between the model specification and the observed data:
v
2
=(270) 5828.512, CFI =0.963, RMSEA =0.055 (90% CI [0.054, 0.056]),
SRMR =0.085. The standardized regression weights and covariances in the model are
shown in Table 4, whereas Figure 1 presents a simplified model illustrating the effects of
the T1 onto the T2 variables; non-significant effects are denoted by dotted lines in the
figure.
Table 4 and Figure 1 show that while national identity was not affected by either
anxiety nor health status over time, national identity had a negative and significant effect
on anxiety and positive and significant effect on health status over time. All of the
autoregressive effects were positive and significant, and there were negative and negative
reciprocal effects between anxiety and health status over time.
Regarding the covariates, age was associated with national identity and anxiety but not
health status, with older participants exhibiting higher levels of national identity and
lower anxiety over time. Female participants reported higher levels of anxiety, yet also
higher health status, over time.
1
Finally, we examined measurement invariance in the cross-lagged panel model. The
same procedure described in the preceding subsection was followed (Milfont & Fischer,
2010) using the lavaan package (Rosseel, 2012) and involved constraining the
Table 4. Unconstrained cross-lagged panel model: regression weights
Estimate SE Z p R
2
National Identity T2
NATD Time 1 0.79 .01 74.43 <.001 .66
GAD-7 Time 1 0 .01 0.33 .741
GSRH Time 1 0 .01 1.13 .260
Age 0.01 0 8.02 <.001
Gender 0.01 .02 0.48 .635
GAD-7 T2
GAD-7 Time 1 0.68 .01 61.77 <.001 .54
GSRH Time 1 0.06 .01 6.48 <.001
NATID Time 1 0.02 .01 2.42 .016
Age 0.01 0 10.82 <.001
Gender 0.09 .02 3.67 <.001
GSRH Time 2
GSRH Time 1 0.76 .01 101.63 <.001 .61
GAD-7 Time 1 0.04 .01 4.54 <.001
NATID Time 1 0.03 .01 4.14 <.001
Age 0 0 0.59 .555
Gender 0.05 .02 2.59 .010
1
The standardised regression weights for every country/society are available in the supplementary materials - https://osf.io/sb
z7x/?view_only=72511614d43f407cbe0cd11601433dda
8Sammyh S. Khan et al.
measurement weights in the model across the country/society samples. The fit indices
resulting from this procedure indicated acceptable fit between the obser ved data and both
the configural model, v
2
=(4,860) 13,183.108, CFI =0.945, RMSEA =0.068; 90% CI
[0.066, 0.069], SRMR =0.091, and constrained model, v
2
=(5,166) 14,465.203,
CFI =0.938, RMSEA =0.069; 90% CI [0.068, 0.071], SRMR =0.099. The CFI and RMSEA
differences of <0.010 and 0.015, respectively, denoted metric equivalence, that is, that the
model was equivalent across countries/societies (Cheung & Rensvold, 2002).
Discussion
The present study examined the longitudinal associations between national identity and
well-being across 18 countries/societies using a cross-lagged panel design. The results
indicated that the relationship between national identity and well-being was unidirec-
tional as opposed to bidirectional: national identity predicted but was not predicted by
anxiety and health status over time. The relationship was upheld while controlling for
participant age and gender. The most striking finding was that national identity had an
almost as palliative effect on health status as anxiety had a detrimental effect on health
status over time.
These findings complement and extend past research that has demonstrated how
identifying with one’s group memberships can be beneficial for well-being. More
specifically, the findings provide empirical evidence in support of the generalizability of
the relationship between identification and health and well-being to large-scale groups.
The benefits of group membership and identification to well-being are thus not isolated to
Figure 1. Cross-lagged panel model. *p<.05; **p<.01; ***p<.001.
National identification and well-being in 18 countries 9
small-scale groups and networks within which members are able to interact with one
another directly; on the contrary, people similarly reap benefits identifying with the
nations to which they belong.
The findings lend themselves to a wide range of interpretations. Most importantly, they
beg the question as to why identifying with large groups, within which it is unlikely, if not
impossible, to meet and interact with every other member, promotes well-being. Billig
(1995) argues that national identity plays a greater part in the day-to-day lives ofpeople than
is readily apparent. He suggests that this is because national identity is perpetuated in banal
everyday practices, for example, while watching an ethnocentric weather report, or while
drinking a particular tea blend popular among locals. This salience begets a sense of security
by intimating how one is embedded in a context within which other (imagined) people also
share the sense ofbelongingness and identity. This shared identity in turn has the potential
to foster trust, cooperation, and support, both in principle and in practice (e.g., Haslam &
Reicher, 2006); that is, theprospect of being supported by fellow citizens is as prophylactic
as the actuality of being supported by fellow citizens (e.g., Haslam et al., 2005).
Other than engendering pro-sociality, group membership and identification fulfil
belongingness needs, which in turn affect well-being (e.g., Baumeister & Leary, 1995;
Greenaway, Cruwys, Haslam, & Jetten, 2015; Greenaway, Cruwys, Haslam, & Jetten, 2016).
These findings have parallels with theorizationsabout the relationship between ontological
security, or insecurity, and the rise of nationalism. Kinnvall (2004) argues that ontological
insecurity, mainly brought upon by rapid globalization and modernity, draws people(s) to
nationalism, as it reduces existential insecurity and anxiety (see also Ariely, 2012). The
findings reported here can be interpreted against this backdrop: Identification with large-
scale groups arguably provides stable anchors in an otherwise rapidly changing world.
However, speculation about what the findings mean, and the processes by which they
are underpinned, warrants some caution. Most importantly, the magnitude of the effects
observed in this study was small, explaining only between two and three per cent of
variance. Without accounting for variables known to interact with both national identity
and well-being (e.g., political ideology and socio-economic status), their robustness
cannot be entirely ascertained. Equally, the limitations of the research design pave the way
for avenues of further research to corroborate and expand on the findings. On one hand,
untangling which individual-level factors interact with the relationship between national
identity and well-being can offer finer accounts of process. According to System
Justification Theory (Jost & Banaji, 1994), the relationship is likely to be influenced by the
extent to which the prevailing societal ethos and status quo within a country are
perceived as being fair. The theory proposes that ‘system justification’ is palliative as it
fulfils epistemic, existential, and relational needs, and research has indeed found it to have
a positive impact on well-being across cultures over time (Vargas-Salfate, Paez, Khan, Liu,
& Gil de Z
u~
niga, 2018). Relatedly, political ideology is also a possible moderator. The
inclusion of wider and more intricate operationalizations of national identification,
capturing different meaning systems that people(s) associate in their identification with
the nation, can determine whether particular sets of beliefs explain the effect, or a greater
proportion of the effect. Research has indeed shown that conservatives tend to
experience higher levels of well-being (e.g., Napier & Jost, 2008; Schlenker, Chambers,
& Le, 2012) and that the endorsement of civic nationalism is more strongly associated with
well-being than ethnic nationalism (Reeskens & Wright, 2011).
Moving beyond subjective individual-level factors, integrating society-level factors
would enable assessment of how the relationship interacts with objective macrolevel
conditions. For example, employing multilevel modelling, Worldwide Governance
10 Sammyh S. Khan et al.
Indicators (WGI) produced by the World Bank could be examined to determine how
societal governance (in terms of, e.g., voice and accountability, government effectiveness,
and control) affects national identification, well-being, and the relationship between the
two. Likewise, development indices, such as the Inequality-adjusted Human Develop-
ment Index (IHDI) produced by the United Nations Development Programme (UNDP),
could be integrated to determine whether, when, and how the health, education, and
income of a country affect the relationship between national identity and health. It would
be reasonable to assume that national identity, at least in part, mediates the effect of
societal indicators, such as GDP, quality of governance, and societal equality (e.g., Diener,
Diener, & Diener, 1995). Likewise, and in line with theorizations presented earlier in this
discussion, it would not be unreasonable to assume that the palliative function of national
identity would be stronger in countries hampered by instability and deficiency; the
relationship between national and life satisfaction has indeed been found to be stronger in
poorer countries (Morrison et al., 2011).
While literature already recognizes the impact of small-group and social-structural
processes on health and well-being, the findings from this study suggest that the health
and well-being of individuals and groups are structured by collective, psychological,
identities and that this phenomenon merits greater attention.
Acknowledgement
This research was supported by Grant FA2386-15-1-0003 from the Asian Office of Aerospace
Research and Development.
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National identification and well-being in 18 countries 15
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