Strict norms and perceived cultural tightness have distinct societal consequences

Strict norms and perceived cultural tightness have distinct societal consequences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Strict norms and perceived cultural tightness have distinct societal consequences Kimmo Eriksson, Irina Vartanova, Pontus Strimling, Brian Haas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8538560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Cultural tightness-looseness theory posits that societies vary in norm strength and deviance tolerance. However, research has conflated two independent dimensions: perceived tightness (shared narratives of social order) and norm strictness (behavioral constraints). Analyzing 25,000 participants across 83 societies, here we show these dimensions are uncorrelated (r = 0.10) and exhibit a double dissociation. Norm strictness stems from historical threats and correlates with lower liberal democracy, whereas perceived tightness is uniquely predicted by population density. Crucially, only perceived tightness predicts coordination benefits: lower COVID-19 mortality, lower homicide rates, and lower obesity. Norm strictness instead predicts higher pandemic mortality, suggesting behavioral rigidity hampers adaptation to novel crises. We provide validated scores for 83 societies, including 15 under-sampled African nations, expanding resources for cross-cultural research. These findings demonstrate that cultural narratives of social order facilitate collective action independently of enforcement, providing a new empirical foundation for understanding societal resilience. Scientific community and society/Social sciences Scientific community and society/Social sciences/Psychology Health sciences/Risk factors cultural tightness-looseness social norms cultural narratives COVID-19 mortality cross-cultural variation Figures Figure 1 Introduction Cultural tightness-looseness theory has become one of the most influential frameworks in the social sciences, offering a parsimonious explanation for societal variation in political institutions, individual psychology, and consumer behavior ( 1 – 4 ). Most critically, the theory has been applied to global public health, with recent findings suggesting that "tighter" nations were better equipped to coordinate collective responses to COVID-19 ( 5 ). Rooted in anthropological observations from the 1960s ( 6 ) and formalized by Triandis ( 7 ) and Gelfand et al. ( 8 ), the theory posits that cultural tightness is a functional adaptation to ecological and historical threats: groups facing threats develop strong norms and low tolerance for deviance to ensure coordination ( 1 ). In this paper, we distinguish between two dimensions of societal tightness that have been conflated in prior work: perceived tightness and norm strictness. Perceived tightness captures metaperceptions—respondents' beliefs about whether their society is generally orderly and rule-oriented. Norm strictness measures normative expectations—the extent to which specific behaviors are judged appropriate or inappropriate across concrete situations. The former reflects beliefs about what kind of society one lives in; the latter reflects judgments about what behaviors should occur. We propose that perceived tightness constitutes a cultural narrative ( 9 ): a shared belief about collective identity concerning social order ( 10 ). Such narratives need not accurately reflect actual normative strength. Rather, they may develop through historical discourse, political rhetoric, or social comparison processes that operate independently of actual behavioral norms. A society that perceives itself as rule-following may mobilize coordination differently than one perceiving itself as rule-breaking, even if their actual norms are similarly strict. This distinction requires reevaluation of prior work on cultural tightness-looseness. The standard measure relies on a 6-item scale asking residents how strong norms are in their society ( 1 ). However, both the original 33-country measure ( 1 ) and the expanded 57-country measure used to study COVID-19 ( 5 , 11 ) derive national scores using within-subject standardization—subtracting each respondent's average rating on other survey items from their perceived tightness ratings. Critically, these other items consisted predominantly of appropriateness ratings for various behaviors; high appropriateness ratings indicate permissive norms. This procedure thus conflates perceived tightness with actual norm strictness, making it impossible to disentangle the effects of cultural narratives from behavioral constraints. The validity of perceived tightness measures has recently come under scrutiny. Minkov et al. ( 12 ) demonstrated that perceived tightness shows weak correlation with self-reported behaviors and fails to predict many theorized consequences, concluding that it may reflect "unfounded national stereotypes." However, we propose that the disconnect between perceptions and behavioral reality reveals two distinct functional dimensions rather than measurement failure. If perceived tightness operates as a cultural narrative independent of norm strictness, their lack of correlation becomes theoretically meaningful. We survey 25,000 participants across 83 societies, including 15 African nations, to independently measure perceived tightness and norm strictness. We find these dimensions are statistically independent (r = 0.10) and exhibit a double dissociation in their origins and consequences. While norm strictness correlates with historical ecological threats and collectivist values, perceived tightness is uniquely predicted by population density. Crucially, only perceived tightness predicts the coordination benefits previously attributed to cultural tightness, such as lower homicide and COVID-19 mortality. These results demonstrate that cultural narratives of social order operate as a distinct functional dimension, facilitating collective action through shared identity rather than behavioral enforcement. By providing validated scores for both dimensions across 83 societies, this study offers a new empirical foundation for understanding societal resilience. Results Validity of perceived tightness measures Gelfand et al. (1) justified their within-subject standardization procedure as necessary to correct for cross-national differences in acquiescence bias—the tendency to agree with survey items regardless of content. However, as discussed above, this procedure contaminated perceived tightness with actual norm strictness. We therefore employed an alternative approach to assess whether acquiescence bias meaningfully distorts our perceived tightness scores: the opposite-items method (13, 14). This method uses responses to pairs of oppositely-worded items to estimate and correct individual-level acquiescence without contaminating the focal measure with other survey content. Utilizing the opposite-items method to calculate bias-adjusted perceived tightness scores, we found this adjustment had minimal impact: the correlation between adjusted and unadjusted scores was r = 0.90, and the mean absolute change was negligible (0.10 scale points on our 1-7 scale; see Supplementary Figure 1). This indicates that acquiescence bias contributes little to cross-societal variation in perceived tightness. The society-level variation in our data reflects substantive cultural differences rather than response artifacts, confirming that within-subject standardization was unnecessary for this purpose while introducing the substantial problem of conflating perceived tightness with norm strictness. Consequently, we utilize unadjusted perceived tightness scores for all subsequent analyses. Independent dimensions of cultural tightness The estimated scores for perceived tightness (Cronbach's α = 0.78) and norm strictness (α = 0.98) in 83 societies are provided in Supplementary Table 1. The central question of this study is whether these different operationalizations of cultural tightness reflect a single underlying dimension or several distinct dimensions. To test this, we examined the correlation between perceived tightness and norm strictness across 83 societies and found it to be negligible and non-significant, r = 0.10, p = 0.37. To check the robustness of this important finding, we performed the corresponding analysis on the raw data from the seminal Gelfand et al. (1) study across 32 societies. Again, the correlation between perceived tightness and norm strictness was negligible, r = 0.05. The consistency of null relationships across datasets from different time periods strongly supports the conclusion that these are independent dimensions. Figure 1 illustrates this result, showing many examples of countries that are simultaneously high on one dimension and low on another. For instance, Rwanda (RWA) and the Democratic Republic of Congo (COD) both score high on norm strictness but diverge sharply on perceived tightness (Rwanda high, Congo low). At the other end, Singapore (SGP) and Costa Rica (CRI) both score low on norm strictness while differing on perceived tightness (Singapore high, Costa Rica low). These striking divergences underscore that a society's actual behavioral constraints cannot be inferred from its residents' subjective perceptions of tightness. Robustness of tightness measures In support of the validity of each tightness dimension, we found that country measures are robust across demographic groups and time. Gender subgroup correlations were remarkably high (r = 0.88 for norm strictness, r = 0.92 for perceived tightness). Student versus non-student subgroup correlations were almost as high (r = 0.75 for norm strictness, r = 0.87 for perceived tightness). Robustness over time was demonstrated by substantial correlations between our 2023–2024 scores and corresponding scores from earlier datasets: r = 0.87 with perceived tightness scores for 46 overlapping countries collected immediately pre-pandemic in 2019/2020 (11), and r = 0.71 with scores from Gelfand et al. (1) data from 2000-2003 for 26 overlapping countries. For norm strictness, the correlation with Gelfand et al. data was r = 0.63. These findings indicate that our dimensions capture society-wide cultural signals that are experienced similarly across demographic groups and that are enduring over time. Distinct ecological and institutional profiles If cultural tightness is multi-dimensional, its dimensions should exhibit distinct profiles of associations with theoretically relevant variables. We examined relationships with ecological threats (child mortality, pathogen prevalence), societal structure (ethnic fractionalization, population density), cultural values (gender egalitarianism), and key institutions (religion, liberal democracy). Table 1 reveals a striking pattern of dissociation between perceived tightness and norm strictness. Similar differential relationships were observed in reanalysis of the raw data from Gelfand et al. (1), see Supplementary Table 2. The results for norm strictness are largely as expected from the theory of cultural tightness-looseness. Thus, consistent with functionalist accounts (1, 3), variables associated with existential threat and societal coordination pressure—high child mortality, pathogen prevalence, and ethnic fractionalization—predicted stricter norms ( r s from 0.39 to 0.75). Cultural values and institutional correlates mirrored this pattern: norm strictness was strongly associated with less gender egalitarian values (r = -0.70), higher religious importance (r = 0.56) and lower liberal democracy (r = -0.62), and lower choice values (r = -0.84). However, perceived tightness displayed a completely distinct profile. Consistent with recent observations (12), perceived tightness was unrelated to ecological threats, ethnic fractionalization, cultural values, or religiosity. Instead, it was uniquely predicted by population density (r = 0.28), suggesting that perceptions of tightness may emerge from experiences of crowding and the salience of social order in dense urban environments. The mechanism linking population density to perceived tightness warrants further investigation. Dense environments may heighten the salience of coordination challenges (e.g., navigating crowded spaces, managing noise), making social order more cognitively accessible and thereby shaping collective narratives. Alternatively, urban density may create greater exposure to diversity, paradoxically strengthening in-group narratives about shared norms to maintain a sense of predictability. The dissociation between norm strictness and perceived tightness extends to their relations with the cultural dimension framework of Minkov and Kaasa (15), which posits individualism-collectivism and flexibility-monumentalism as the two main cultural dimensions. Both of these dimensions were more strongly associated with norm strictness (r = -0.66 and r = -0.44, respectively) than with perceived tightness (r = -0.14 and r = 0.28), suggesting that perceived tightness operates as an additional independent cultural dimension not captured by other frameworks. Table 1. Correlations between tightness dimensions and ecological, structural, and institutional variables. Variable Norm strictness Perceived tightness Ecological & Historical Threats Child mortality 0.75 (p < 0.001) 0.09 (p = 0.44) Pathogen prevalence 0.59 (p < 0.001) 0.13 (p = 0.25) Societal Structure Ethnic fractionalization 0.39 (p < 0.001) -0.16 (p = 0.17) Population density 0.04 (p = 0.75) 0.28 (p = 0.01) Cultural Values & Institutions Gender egalitarian values -0.70 (p < 0.001) -0.18 (p = 0.11) Importance of religion 0.56 (p < 0.001) 0.02 (p = 0.88) Liberal democracy -0.62 (p < 0.001) -0.23 (p = 0.04) Choice values -0.84 (p < 0.001) -0.19 (p = 0.09) Individualism -0.66 (p < 0.001) -0.14 (p = 0.25) Flexibility -0.44 (p < 0.001) - 0.28 (p = 0.02) Note. N ranges from 65 to 83 societies depending on data availability. Entries are Pearson correlation coefficients, boldfaced if statistically significant at p < .05. Unique societal consequences of perceived tightness The preceding analyses show that norm strictness exhibits the profile predicted by functionalist theory: emerging from ecological threats and correlating with collectivist values. As perceived tightness shows a distinct profile unrelated to these factors, the key question is whether it nonetheless has functional significance. We tested whether perceived tightness independently predicts three critical societal outcomes: low COVID-19 mortality, low homicide rates, and low obesity rates. If perceived tightness is merely noise or stereotype, it should not predict these outcomes when controlling for actual norm strictness and key structural factors. Conversely, if perceived tightness functions as a consequential cultural narrative, it should show unique predictive power. First, we examined COVID-19 mortality. Prior research suggests that death rates in 2020, the first year of the pandemic, were lower in tighter societies as a result of societies' initial behavioral coordination before the vaccines (5). To rigorously test this, we modeled the relationship between the two tightness dimensions and mortality, controlling for the most critical intrinsic and structural risk factors identified in epidemiological literature: median age, obesity prevalence, hypertension prevalence, and income inequality (Gini coefficient). We used two versions of the dependent variable: official COVID-19 total mortality in 2020 and, to account for underreporting and broader health impacts, excess mortality estimates in 2020. The results in Table 2 show that, consistent with prior research, perceived tightness was a significant protective factor, predicting both lower official COVID-19 mortality and lower excess mortality. In a striking dissociation, norm strictness instead predicted significantly higher excess mortality. Table 2. Regression of log COVID-19 total mortality and excess mortality on tightness dimensions, controlling for demographic structure, pre-existing health, and socioeconomic vulnerability. Predictor 2020 COVID-19 Mortality 2020 Excess Mortality Perceived Tightness -0.40 (p < 0.001) -0.36 (p = 0.001) Norm Strictness 0.10 (p = 0.27) 0.39 (p < 0.001) Median Age 0.38 (p = 0.001) 0.31 (p = 0.02) Obesity Prevalence 0.30 (p = 0.003) 0.08 (p = 0.48) Hypertension Prev. -0.02 (p = 0.81) 0.19 (p = 0.05) Gini -0.11 (p = 0.25) -0.14 (p = 0.22) Note. N = 78 (data unavailable for Gibraltar, Martinique, Taiwan, Kosovo, and Cuba). Entries are standardized regression coefficients, boldfaced if statistically significant at p < .05. Predictor intercorrelations are reported in Supplementary Table 3. Second, we examined homicide rates, a fundamental indicator of social order and norm enforcement. Perceived tightness predicted significantly lower homicide rates (r = -0.34, p = 0.003, n = 77), an association that remained robust when controlling for leading explanatory variables identified in prior research: national wealth, liberal democracy, and poverty (β = -0.34, p 0.10). The association between perceived tightness and lower homicide rates may reflect the coordinating function of cultural narratives: shared beliefs about social order could facilitate informal social control independently of formal sanctions. Third, we examined obesity rates—a health outcome requiring sustained self-regulation and potentially influenced by cultural norms around body image and self-control. Recent work has identified culture as an important driver, with higher obesity rates in societies that score higher on individualism and lower on flexibility (18). In a multiple regression of obesity rates on these two Minkov cultural dimensions and the two tightness dimensions, perceived tightness was the strongest predictor of lower obesity rates (β = -0.41, p < 0.001), followed by flexibility (β = -0.35, p = 0.001), with no significant association with either norm strictness (β = -0.21, p = 0.13) or individualism (β = 0.18, p = 0.24) (see Supplementary Table 5). These three analyses reveal that it is the cultural narrative of being a tight society, rather than actual behavioral strictness, that predicts several societal benefits: lower pandemic mortality, lower crime rates, and healthier body weight. Perceived tightness thus emerges as the functionally consequential dimension for collective outcomes, operating independently of—and in the case of pandemic mortality, in opposition to—behavioral constraints. Discussion This study demonstrates that cultural tightness-looseness is not a unitary dimension but comprises at least two independent constructs: norm strictness and perceived tightness. Across 83 societies, these dimensions show near-zero correlation (r = 0.10), a finding we replicate in reanalysis of the seminal Gelfand et al. ( 1 ) dataset. This independence suggests that a society's actual behavioral constraints cannot be inferred from its residents' subjective perceptions of order. Societies can exhibit high perceived tightness alongside permissive norms (e.g., Singapore), or strict norms without a corresponding collective narrative of order (e.g., DR Congo). These findings fundamentally reframe the causal logic of cultural tightness-looseness theory. The original model proposed a single path: ecological threat → strong norms → coordination capacity. Our results reveal this conflates two independent paths. The first path follows the functionalist logic: ecological and historical threats produce strict behavioral norms, which correlate with collectivist values and religious institutions. However, this path does not lead to coordination benefits—indeed, norm strictness predicts higher pandemic mortality. The second path is novel: population density → perceived tightness → coordination. Dense environments may heighten the salience of social order, fostering shared narratives about collective rule-following that facilitate coordination when novel challenges arise. Crucially, this second path operates independently of actual behavioral constraints. Societies can achieve coordination through cultural narratives of order without imposing strict norms, and strict norms alone do not confer coordination benefits. This decomposition explains why prior research, which inadvertently combined both dimensions through within-subject standardization, found associations between "tightness" and coordination—the effect was driven entirely by perceived tightness, while the contribution of norm strictness was obscured or even countervailing. The dual-path model also resolves recent debates regarding the validity of the tightness-looseness framework. While perceptions of tightness and behavioral strictness are indeed uncorrelated ( 12 ), this mismatch does not represent measurement failure or "unfounded stereotypes". Instead, perceived tightness operates as a functionally consequential cultural narrative that facilitates coordination through shared identity rather than enforcement. These findings have implications for both research and policy. The widespread use of within-subject standardized scores has systematically confounded these dimensions, and many published findings may require reinterpretation. For policy, our results suggest that public health messaging emphasizing shared identity and collective responsibility may facilitate coordination more effectively than enforcement-focused approaches. Campaigns that reinforce narratives of "who we are as a society" may leverage existing cultural resources for coordination more effectively than introducing new behavioral restrictions ( 19 ). However, how such narratives form and whether they can be deliberately cultivated remains an open question. Our cross-sectional design cannot establish causal relationships, though the high correlation (r = 0.87) between our perceived tightness scores and those collected immediately before the pandemic ( 11 ) rules out the possibility that pandemic experiences shaped these perceptions. Understanding the cultural foundations of societal coordination is increasingly urgent as communities face novel collective challenges, from pandemics to climate change ( 20 ). By providing validated scores on both norm strictness and perceived tightness across 83 societies—with extensive coverage of previously undersampled regions including 15 African nations—this study offers differentiated measures for future investigations into how culture shapes collective action. Methods Participants and societies Data were collected from over 28,000 participants across 92 countries as part of the Global Study of Everyday Norms, a large-scale survey examining cross-cultural variation in social norms. For a detailed description of that study, see Eriksson et al. ( 21 ). For the purpose of the present paper, we excluded nine countries due to small sample sizes (n 0), yielding a final sample of 83 societies with sample sizes ranging from 49 to 1,029 participants per country (median = 257) for a total of 25,048 participants. Participants were recruited primarily through online platforms, with supplementary face-to-face data collection in two countries. The sample included participants from all inhabited continents, with particularly strong representation from previously understudied regions including 15 African countries. Demographics varied by country but globally comprised 57% women, 32% men (remaining 11% are other or missing data), 55% students, 24% non-students (remaining 21% missing data) with indicated ages ranging from 18 to 100 years (median = 22). Although these constitute convenience samples, previous validation of this dataset against representative World Values Survey (WVS) samples confirms that our measures accurately reflect genuine societal-level differences ( 21 ). Specifically, our sample-based measures of choice values (r = 0.72) and religious beliefs (r = 0.81) correlate strongly with representative national data. Further supporting the generalizability of these signals, we found that both perceived tightness and norm strictness were highly robust across demographic subgroups, as reported in the main text. Attrition was minimal; only 1.5% of respondents who reached the tightness items were excluded due to missing data, with no evidence that attrition systematically biased society-level estimates. All participants provided informed consent and reported being 18 years or older. All procedures were approved by relevant institutional review boards as described by Eriksson et al. ( 21 ). Measures Perceived tightness was measured using a six-item scale adapted from Gelfand et al. ( 1 ): ( 1 ) "There are many social norms that people are supposed to abide by in this country," ( 2 ) "In this country, there are very clear expectations for how people should act in most situations," ( 3 ) "People agree upon what behaviors are appropriate versus inappropriate in most situations in this country," ( 4 ) "People in this country have a great deal of freedom in deciding how they want to behave in most situations" [reverse-coded], ( 5 ) "In this country, if someone acts in an inappropriate way, others will strongly disapprove," and ( 6 ) "People in this country almost always comply with social norms." Responses used a 6-point scale from strongly disagree ( 1 ) to strongly agree ( 6 ). Society-level scores were computed by averaging responses across all participants (Cronbach's α = 0.78 at the society level). Of respondents who reached the tightness items, 1.5% missed ≥ 1 item and were excluded from analysis. Norm strictness was operationalized as the overall restrictiveness of behavioral standards, measured through normative assessments of 150 situated behaviors crossing 15 common behaviors with 10 everyday situations. Participants rated each behavior-situation combination on a 6-point scale from extremely inappropriate ( 1 ) to extremely appropriate ( 6 ). To avoid participant fatigue, each respondent rated approximately 50 randomly selected combinations. Society-level norm strictness was calculated as the mean appropriateness rating across all 150 combinations, with lower scores indicating stricter norms (Cronbach's α = 0.98). Demographic subgroup measures of perceived tightness and norm strictness were computed by averaging responses across all men and all women in each society, and across all students and all non-students in 33 societies where both groups were represented. However, participants only rated a subset of norms, and at least one rating for each norm is required to calculate norm strictness. For these reasons, these measures could not be calculated in all subsamples for all societies. Gender-specific scores for norm strictness were calculated in 76 societies and student-status-specific scores were calculated in 27 societies (out of 33 societies with both groups represented). Historical measures of perceived tightness and norm strictness (2000–2003) were obtained by applying the same calculations as above to the raw data from Gelfand et al. ( 1 ). That study used the same perceived tightness scale and included a mostly identical set of everyday norms measured on the same 6-point scale. Gender egalitarian values were measured using a three item scale known as the Equality index ( 22 ), consisting of three items that measure disagreement with traditional gender role statements: "When jobs are scarce, men have more right to a job than women," "On the whole, men make better political leaders than women do," and "A university education is more important for a boy than for a girl." Participants rated each item on a 4-point scale from strongly agree (coded 1) to strongly disagree (coded 4). Missing values (2.8%) were imputed using a multilevel imputation model with predictive mean matching and country as the clustering variable, using information from demographics and other individual-level items in the survey. Ratings were averaged across all participants from a society (society-level 𝛼 = 0.93). Choice values were measured using Welzel's ( 22 ) Choice index, consisting of justifiability ratings for homosexuality, abortion, and divorce. The items are phrased “For each of the following actions, please indicate whether or not you think it is wrong,” with a five-point scale coded from 1 to 5 (Always wrong, Mostly wrong, Sometimes wrong, Rarely wrong, Not wrong at all). External data sources Control and validation variables from other sources included ecological and historical threats (child mortality and natural disaster mortality from the Environmental Sustainability Index; Yale Center for Environmental Law and Policy, 2005; historical pathogen prevalence from Murray and Schaller ( 23 )), importance of religion from Gallup ( 24 ), liberal democracy from V-Dem ( 25 ), and ethnic fractionalization from Alesina et al. ( 26 ). For COVID-19 mortality analyses, we controlled for the most critical intrinsic and structural risk factors identified in epidemiological literature. To address intrinsic population risk, we included median age, a proxy for the demographic age structure that was consistently the most powerful predictor of mortality ( 27 ), alongside national obesity and hypertension prevalence to account for the syndemic effect of non-communicable diseases ( 28 ). To account for structural socioeconomic vulnerability, we included the Gini coefficient, which has been identified as a more direct and robust predictor of adverse outcomes than absolute national wealth ( 29 , 30 ). COVID-19 mortality data for 2020, including both official death rates and excess mortality estimates, were sourced from Our World in Data. For analysis of homicide rates (per 100,000 population, World Bank), we selected controls based on prior work on antecedents of homicide rates ( 16 , 17 ): national wealth (GDP per capita, World Bank), liberal democracy ( 25 ), and poverty rate (World Bank). For analysis of obesity rates sourced from Our World in Data, we selected controls based on prior work on cultural variation in obesity ( 18 ): individualism and flexibility ( 15 ) and national wealth (GDP per capita, World Bank). Statistical analysis The statistical analysis was conducted in R version 4.4.3. A major methodological concern in cross-cultural research involves acquiescence bias: the tendency to agree with survey items regardless of content, which varies systematically across cultures ( 31 ). Previous tightness-looseness studies used within-subject standardization that may have distorted results ( 13 ). We implemented the opposite-items method ( 14 ), which requires including pairs of items with opposite content. Specifically, our survey included both "People in this country have a great deal of freedom in deciding how they want to behave in most situations" and its opposite "People in this country have very little freedom in deciding how they want to behave in most situations." A respondent without acquiescence bias should show equal agreement and disagreement with these opposite items, yielding a mean response at the scale midpoint (3.5 on our 6-point scale). Individual acquiescence bias was estimated as the deviation of each participant's mean response to the opposite items from this midpoint. Bias-adjusted scores were calculated by subtracting each individual's estimated acquiescence bias from all their scale responses before aggregating to the society level. This procedure was completed for 83 societies where both opposite items were included. All correlational results use the Pearson correlation coefficient. To assess the robustness of our results for norm strictness to the specific selection of normative behaviors, we utilized a double-bootstrap procedure (1,000 iterations) in which both societies and norm strictness items were resampled with replacement; the results of this robustness check are reported in Supplementary Table 6. All multiple regression analyses use standard OLS regression. Declarations Data availability All society-level scores, including measures for perceived tightness and norm strictness for 83 societies, are available at OSF ( https://osf.io/xv2g6/ ). Code availability The R code for data processing, bias adjustment, and statistical analysis is available at OSF ( https://osf.io/xv2g6/ ). Competing interests The authors declare no competing interests. Author contributions KE, PS, and BWH conceived the study. IV performed the analysis. KE wrote the manuscript. All authors reviewed, edited, and approved the manuscript. 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J Epidemiol Glob Health 10(3):204–208 Fischer R (2004) Standardization to account for cross-cultural response bias: A classification of score adjustment procedures and review of research in JCCP. J Cross Cult Psychol 35(3):263–282 Additional Declarations There is NO Competing Interest. Supplementary Files NCommsCulturaltightnessin83societiesSupplement.docx Supplementary Material Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8538560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":574028797,"identity":"f497e64a-8d99-4aaf-b501-c03287dbf0a0","order_by":0,"name":"Kimmo 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13:06:26","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88997,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8538560/v1/b25c7a434769370630d0a50a.html"},{"id":100409373,"identity":"59bf7463-dd4c-40d0-a357-846b1608b56e","added_by":"auto","created_at":"2026-01-16 13:07:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170360,"visible":true,"origin":"","legend":"\u003cp\u003eThe dimensions of perceived tightness and norm strictness are independent across 83 societies. Labels are ISO country codes.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8538560/v1/e0644445a3765d5d73fe0cc7.jpeg"},{"id":100414834,"identity":"4fb8a43b-ab65-432d-8692-15748885830c","added_by":"auto","created_at":"2026-01-16 13:20:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":844959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8538560/v1/2e5a9b20-2a19-4b94-9f6b-d7d0c42ec7de.pdf"},{"id":100408680,"identity":"aed2bac4-5611-4f3c-a47d-ea6f86a1f653","added_by":"auto","created_at":"2026-01-16 13:06:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1892172,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"NCommsCulturaltightnessin83societiesSupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8538560/v1/1ac1b1e27d859d320ed018a6.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Strict norms and perceived cultural tightness have distinct societal consequences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCultural tightness-looseness theory has become one of the most influential frameworks in the social sciences, offering a parsimonious explanation for societal variation in political institutions, individual psychology, and consumer behavior (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Most critically, the theory has been applied to global public health, with recent findings suggesting that \"tighter\" nations were better equipped to coordinate collective responses to COVID-19 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Rooted in anthropological observations from the 1960s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) and formalized by Triandis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and Gelfand et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the theory posits that cultural tightness is a functional adaptation to ecological and historical threats: groups facing threats develop strong norms and low tolerance for deviance to ensure coordination (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this paper, we distinguish between two dimensions of societal tightness that have been conflated in prior work: perceived tightness and norm strictness. Perceived tightness captures metaperceptions\u0026mdash;respondents' beliefs about whether their society is generally orderly and rule-oriented. Norm strictness measures normative expectations\u0026mdash;the extent to which specific behaviors are judged appropriate or inappropriate across concrete situations. The former reflects beliefs about what kind of society one lives in; the latter reflects judgments about what behaviors should occur.\u003c/p\u003e \u003cp\u003eWe propose that perceived tightness constitutes a cultural narrative (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e): a shared belief about collective identity concerning social order (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Such narratives need not accurately reflect actual normative strength. Rather, they may develop through historical discourse, political rhetoric, or social comparison processes that operate independently of actual behavioral norms. A society that perceives itself as rule-following may mobilize coordination differently than one perceiving itself as rule-breaking, even if their actual norms are similarly strict.\u003c/p\u003e \u003cp\u003eThis distinction requires reevaluation of prior work on cultural tightness-looseness. The standard measure relies on a 6-item scale asking residents how strong norms are in their society (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, both the original 33-country measure (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and the expanded 57-country measure used to study COVID-19 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) derive national scores using within-subject standardization\u0026mdash;subtracting each respondent's average rating on other survey items from their perceived tightness ratings. Critically, these other items consisted predominantly of appropriateness ratings for various behaviors; high appropriateness ratings indicate permissive norms. This procedure thus conflates perceived tightness with actual norm strictness, making it impossible to disentangle the effects of cultural narratives from behavioral constraints.\u003c/p\u003e \u003cp\u003eThe validity of perceived tightness measures has recently come under scrutiny. Minkov et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) demonstrated that perceived tightness shows weak correlation with self-reported behaviors and fails to predict many theorized consequences, concluding that it may reflect \"unfounded national stereotypes.\" However, we propose that the disconnect between perceptions and behavioral reality reveals two distinct functional dimensions rather than measurement failure. If perceived tightness operates as a cultural narrative independent of norm strictness, their lack of correlation becomes theoretically meaningful.\u003c/p\u003e \u003cp\u003eWe survey 25,000 participants across 83 societies, including 15 African nations, to independently measure perceived tightness and norm strictness. We find these dimensions are statistically independent (r\u0026thinsp;=\u0026thinsp;0.10) and exhibit a double dissociation in their origins and consequences. While norm strictness correlates with historical ecological threats and collectivist values, perceived tightness is uniquely predicted by population density. Crucially, only perceived tightness predicts the coordination benefits previously attributed to cultural tightness, such as lower homicide and COVID-19 mortality. These results demonstrate that cultural narratives of social order operate as a distinct functional dimension, facilitating collective action through shared identity rather than behavioral enforcement. By providing validated scores for both dimensions across 83 societies, this study offers a new empirical foundation for understanding societal resilience.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidity of perceived tightness measures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGelfand et al. (1) justified their within-subject standardization procedure as necessary to correct for cross-national differences in acquiescence bias\u0026mdash;the tendency to agree with survey items regardless of content. However, as discussed above, this procedure contaminated perceived tightness with actual norm strictness. We therefore employed an alternative approach to assess whether acquiescence bias meaningfully distorts our perceived tightness scores: the opposite-items method (13, 14). This method uses responses to pairs of oppositely-worded items to estimate and correct individual-level acquiescence without contaminating the focal measure with other survey content.\u003c/p\u003e\n\u003cp\u003eUtilizing the opposite-items method to calculate bias-adjusted perceived tightness scores, we found this adjustment had minimal impact: the correlation between adjusted and unadjusted scores was r = 0.90, and the mean absolute change was negligible (0.10 scale points on our 1-7 scale; see Supplementary Figure 1). This indicates that acquiescence bias contributes little to cross-societal variation in perceived tightness. The society-level variation in our data reflects substantive cultural differences rather than response artifacts, confirming that within-subject standardization was unnecessary for this purpose while introducing the substantial problem of conflating perceived tightness with norm strictness. Consequently, we utilize unadjusted perceived tightness scores for all subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndependent dimensions of cultural tightness\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimated scores for perceived tightness (Cronbach\u0026apos;s \u0026alpha; = 0.78) and norm strictness (\u0026alpha; = 0.98) in 83 societies are provided in Supplementary Table 1. The central question of this study is whether these different operationalizations of cultural tightness reflect a single underlying dimension or several distinct dimensions. To test this, we examined the correlation between perceived tightness and norm strictness across 83 societies and found it to be negligible and non-significant, \u003cem\u003er\u003c/em\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e = 0.37. To check the robustness of this important finding, we performed the corresponding analysis on the raw data from the seminal Gelfand et al. (1) study across 32 societies. Again, the correlation between perceived tightness and norm strictness was negligible, \u003cem\u003er\u003c/em\u003e = 0.05. The consistency of null relationships across datasets from different time periods strongly supports the conclusion that these are independent dimensions.\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates this result, showing many examples of countries that are simultaneously high on one dimension and low on another. For instance, Rwanda (RWA) and the Democratic Republic of Congo (COD) both score high on norm strictness but diverge sharply on perceived tightness (Rwanda high, Congo low). At the other end, Singapore (SGP) and Costa Rica (CRI) both score low on norm strictness while differing on perceived tightness (Singapore high, Costa Rica low). These striking divergences underscore that a society\u0026apos;s actual behavioral constraints cannot be inferred from its residents\u0026apos; subjective perceptions of tightness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRobustness of tightness measures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn support of the validity of each tightness dimension, we found that country measures are robust across demographic groups and time. Gender subgroup correlations were remarkably high (r = 0.88 for norm strictness, r = 0.92 for perceived tightness). Student versus non-student subgroup correlations were almost as high (r = 0.75 for norm strictness, r = 0.87 for perceived tightness). Robustness over time was demonstrated by substantial correlations between our 2023\u0026ndash;2024 scores and corresponding scores from earlier datasets: r = 0.87 with perceived tightness scores for 46 overlapping countries collected immediately pre-pandemic in 2019/2020 (11), and r = 0.71 with scores from Gelfand et al. (1) data from 2000-2003 for 26 overlapping countries. For norm strictness, the correlation with Gelfand et al. data was r = 0.63. These findings indicate that our dimensions capture society-wide cultural signals that are experienced similarly across demographic groups and that are enduring over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDistinct ecological and institutional profiles\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf cultural tightness is multi-dimensional, its dimensions should exhibit distinct profiles of associations with theoretically relevant variables. We examined relationships with ecological threats (child mortality, pathogen prevalence), societal structure (ethnic fractionalization, population density), cultural values (gender egalitarianism), and key institutions (religion, liberal democracy). Table 1 reveals a striking pattern of dissociation between perceived tightness and norm strictness. Similar differential relationships were observed in reanalysis of the raw data from Gelfand et al. (1), see Supplementary Table 2.\u003c/p\u003e\n\u003cp\u003eThe results for norm strictness are largely as expected from the theory of cultural tightness-looseness. Thus, consistent with functionalist accounts (1, 3), variables associated with existential threat and societal coordination pressure\u0026mdash;high child mortality, pathogen prevalence, and ethnic fractionalization\u0026mdash;predicted stricter norms (\u003cem\u003er\u003c/em\u003es from 0.39 to 0.75). Cultural values and institutional correlates mirrored this pattern: norm strictness was strongly associated with less gender egalitarian values (r = -0.70), higher religious importance (r = 0.56) and lower liberal democracy (r = -0.62), and lower choice values (r = -0.84).\u003c/p\u003e\n\u003cp\u003eHowever, perceived tightness displayed a completely distinct profile. Consistent with recent observations (12), perceived tightness was unrelated to ecological threats, ethnic fractionalization, cultural values, or religiosity. Instead, it was uniquely predicted by population density (r = 0.28), suggesting that perceptions of tightness may emerge from experiences of crowding and the salience of social order in dense urban environments. The mechanism linking population density to perceived tightness warrants further investigation. Dense environments may heighten the salience of coordination challenges (e.g., navigating crowded spaces, managing noise), making social order more cognitively accessible and thereby shaping collective narratives. Alternatively, urban density may create greater exposure to diversity, paradoxically strengthening in-group narratives about shared norms to maintain a sense of predictability.\u003c/p\u003e\n\u003cp\u003eThe dissociation between norm strictness and perceived tightness extends to their relations with the cultural dimension framework of Minkov and Kaasa (15), which posits individualism-collectivism and flexibility-monumentalism as the two main cultural dimensions. Both of these dimensions were more strongly associated with norm strictness (r = -0.66 and r = -0.44, respectively) than with perceived tightness (r = -0.14 and r = 0.28), suggesting that perceived tightness operates as an additional independent cultural dimension not captured by other frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eCorrelations between tightness dimensions and ecological, structural, and institutional variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"541\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorm strictness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived tightness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcological \u0026amp; Historical Threats\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChild mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.75\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 (p = 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePathogen prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13 (p = 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocietal Structure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthnic fractionalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.39\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.16 (p = 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (p = 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.28\u003c/strong\u003e (p = 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultural Values \u0026amp; Institutions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender egalitarian values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.70\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.18 (p = 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImportance of religion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.56\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 (p = 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiberal democracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.62\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.23 (p = 0.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChoice values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.84\u0026nbsp;\u003c/strong\u003e(p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.19 (p = 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividualism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.66\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14 (p = 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFlexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.44\u003c/strong\u003e (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003cstrong\u003e0.28\u003c/strong\u003e (p = 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eN ranges from 65 to 83 societies depending on data availability. Entries are Pearson correlation coefficients, boldfaced if statistically significant at p \u0026lt; .05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUnique societal consequences of perceived tightness\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preceding analyses show that norm strictness exhibits the profile predicted by functionalist theory: emerging from ecological threats and correlating with collectivist values. As perceived tightness shows a distinct profile unrelated to these factors, the key question is whether it nonetheless has functional significance. We tested whether perceived tightness independently predicts three critical societal outcomes: low COVID-19 mortality, low homicide rates, and low obesity rates. If perceived tightness is merely noise or stereotype, it should not predict these outcomes when controlling for actual norm strictness and key structural factors. Conversely, if perceived tightness functions as a consequential cultural narrative, it should show unique predictive power.\u003c/p\u003e\n\u003cp\u003eFirst, we examined COVID-19 mortality. Prior research suggests that death rates in 2020, the first year of the pandemic, were lower in tighter societies as a result of societies\u0026apos; initial behavioral coordination before the vaccines (5). To rigorously test this, we modeled the relationship between the two tightness dimensions and mortality, controlling for the most critical intrinsic and structural risk factors identified in epidemiological literature: median age, obesity prevalence, hypertension prevalence, and income inequality (Gini coefficient). We used two versions of the dependent variable: official COVID-19 total mortality in 2020 and, to account for underreporting and broader health impacts, excess mortality estimates in 2020. The results in Table 2 show that, consistent with prior research, perceived tightness was a significant protective factor, predicting both lower official COVID-19 mortality and lower excess mortality. In a striking dissociation, norm strictness instead predicted significantly higher excess mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Regression of log COVID-19 total mortality and excess mortality on tightness dimensions, controlling for demographic structure, pre-existing health, and socioeconomic vulnerability.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"544\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020 COVID-19 Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020 Excess Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Tightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.40 (p \u0026lt; 0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.36 (p = 0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorm Strictness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10 (p = 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.39 (p \u0026lt; 0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMedian Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.38 (p = 0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.31 (p = 0.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObesity Prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.30 (p = 0.003)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08 (p = 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension Prev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02 (p = 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.19 (p = 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11 (p = 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14 (p = 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eN = 78 (data unavailable for Gibraltar, Martinique, Taiwan, Kosovo, and Cuba). Entries are standardized regression coefficients, boldfaced if statistically significant at p \u0026lt; .05. Predictor intercorrelations are reported in Supplementary Table 3.\u003c/p\u003e\n\u003cp\u003eSecond, we examined homicide rates, a fundamental indicator of social order and norm enforcement. Perceived tightness predicted significantly lower homicide rates (r = -0.34, p = 0.003, n = 77), an association that remained robust when controlling for leading explanatory variables identified in prior research: national wealth, liberal democracy, and poverty (\u0026beta; = -0.34, p \u0026lt; 0.001; see Supplementary Table 4) (16, 17). In contrast, norm strictness showed no significant relationship with homicide rates in any model (all ps \u0026gt; 0.10). The association between perceived tightness and lower homicide rates may reflect the coordinating function of cultural narratives: shared beliefs about social order could facilitate informal social control independently of formal sanctions.\u003c/p\u003e\n\u003cp\u003eThird, we examined obesity rates\u0026mdash;a health outcome requiring sustained self-regulation and potentially influenced by cultural norms around body image and self-control. Recent work has identified culture as an important driver, with higher obesity rates in societies that score higher on individualism and lower on flexibility (18). In a multiple regression of obesity rates on these two Minkov cultural dimensions and the two tightness dimensions, perceived tightness was the strongest predictor of lower obesity rates (\u0026beta; = -0.41, p \u0026lt; 0.001), followed by flexibility (\u0026beta; = -0.35, p = 0.001), with no significant association with either norm strictness (\u0026beta; = -0.21, p = 0.13) or individualism (\u0026beta; = 0.18, p = 0.24) (see Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003eThese three analyses reveal that it is the cultural narrative of being a tight society, rather than actual behavioral strictness, that predicts several societal benefits: lower pandemic mortality, lower crime rates, and healthier body weight. Perceived tightness thus emerges as the functionally consequential dimension for collective outcomes, operating independently of\u0026mdash;and in the case of pandemic mortality, in opposition to\u0026mdash;behavioral constraints.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that cultural tightness-looseness is not a unitary dimension but comprises at least two independent constructs: norm strictness and perceived tightness. Across 83 societies, these dimensions show near-zero correlation (r\u0026thinsp;=\u0026thinsp;0.10), a finding we replicate in reanalysis of the seminal Gelfand et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) dataset. This independence suggests that a society's actual behavioral constraints cannot be inferred from its residents' subjective perceptions of order. Societies can exhibit high perceived tightness alongside permissive norms (e.g., Singapore), or strict norms without a corresponding collective narrative of order (e.g., DR Congo).\u003c/p\u003e \u003cp\u003eThese findings fundamentally reframe the causal logic of cultural tightness-looseness theory. The original model proposed a single path: ecological threat \u0026rarr; strong norms \u0026rarr; coordination capacity. Our results reveal this conflates two independent paths. The first path follows the functionalist logic: ecological and historical threats produce strict behavioral norms, which correlate with collectivist values and religious institutions. However, this path does not lead to coordination benefits\u0026mdash;indeed, norm strictness predicts higher pandemic mortality. The second path is novel: population density \u0026rarr; perceived tightness \u0026rarr; coordination. Dense environments may heighten the salience of social order, fostering shared narratives about collective rule-following that facilitate coordination when novel challenges arise. Crucially, this second path operates independently of actual behavioral constraints. Societies can achieve coordination through cultural narratives of order without imposing strict norms, and strict norms alone do not confer coordination benefits. This decomposition explains why prior research, which inadvertently combined both dimensions through within-subject standardization, found associations between \"tightness\" and coordination\u0026mdash;the effect was driven entirely by perceived tightness, while the contribution of norm strictness was obscured or even countervailing.\u003c/p\u003e \u003cp\u003eThe dual-path model also resolves recent debates regarding the validity of the tightness-looseness framework. While perceptions of tightness and behavioral strictness are indeed uncorrelated (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), this mismatch does not represent measurement failure or \"unfounded stereotypes\". Instead, perceived tightness operates as a functionally consequential cultural narrative that facilitates coordination through shared identity rather than enforcement.\u003c/p\u003e \u003cp\u003eThese findings have implications for both research and policy. The widespread use of within-subject standardized scores has systematically confounded these dimensions, and many published findings may require reinterpretation. For policy, our results suggest that public health messaging emphasizing shared identity and collective responsibility may facilitate coordination more effectively than enforcement-focused approaches. Campaigns that reinforce narratives of \"who we are as a society\" may leverage existing cultural resources for coordination more effectively than introducing new behavioral restrictions (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, how such narratives form and whether they can be deliberately cultivated remains an open question. Our cross-sectional design cannot establish causal relationships, though the high correlation (r\u0026thinsp;=\u0026thinsp;0.87) between our perceived tightness scores and those collected immediately before the pandemic (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) rules out the possibility that pandemic experiences shaped these perceptions.\u003c/p\u003e \u003cp\u003eUnderstanding the cultural foundations of societal coordination is increasingly urgent as communities face novel collective challenges, from pandemics to climate change (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). By providing validated scores on both norm strictness and perceived tightness across 83 societies\u0026mdash;with extensive coverage of previously undersampled regions including 15 African nations\u0026mdash;this study offers differentiated measures for future investigations into how culture shapes collective action.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and societies\u003c/h2\u003e \u003cp\u003eData were collected from over 28,000 participants across 92 countries as part of the Global Study of Everyday Norms, a large-scale survey examining cross-cultural variation in social norms. For a detailed description of that study, see Eriksson et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For the purpose of the present paper, we excluded nine countries due to small sample sizes (n\u0026thinsp;\u0026lt;\u0026thinsp;25 with non-missing values on used variables), the omission of one or more items for tightness measures, or data quality concerns (correlation between tightness opposite-items\u0026thinsp;\u0026gt;\u0026thinsp;0), yielding a final sample of 83 societies with sample sizes ranging from 49 to 1,029 participants per country (median\u0026thinsp;=\u0026thinsp;257) for a total of 25,048 participants. Participants were recruited primarily through online platforms, with supplementary face-to-face data collection in two countries. The sample included participants from all inhabited continents, with particularly strong representation from previously understudied regions including 15 African countries. Demographics varied by country but globally comprised 57% women, 32% men (remaining 11% are other or missing data), 55% students, 24% non-students (remaining 21% missing data) with indicated ages ranging from 18 to 100 years (median\u0026thinsp;=\u0026thinsp;22).\u003c/p\u003e \u003cp\u003eAlthough these constitute convenience samples, previous validation of this dataset against representative World Values Survey (WVS) samples confirms that our measures accurately reflect genuine societal-level differences (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Specifically, our sample-based measures of choice values (r\u0026thinsp;=\u0026thinsp;0.72) and religious beliefs (r\u0026thinsp;=\u0026thinsp;0.81) correlate strongly with representative national data. Further supporting the generalizability of these signals, we found that both perceived tightness and norm strictness were highly robust across demographic subgroups, as reported in the main text. Attrition was minimal; only 1.5% of respondents who reached the tightness items were excluded due to missing data, with no evidence that attrition systematically biased society-level estimates.\u003c/p\u003e \u003cp\u003eAll participants provided informed consent and reported being 18 years or older. All procedures were approved by relevant institutional review boards as described by Eriksson et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003ePerceived tightness was measured using a six-item scale adapted from Gelfand et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e): (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \"There are many social norms that people are supposed to abide by in this country,\" (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \"In this country, there are very clear expectations for how people should act in most situations,\" (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \"People agree upon what behaviors are appropriate versus inappropriate in most situations in this country,\" (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) \"People in this country have a great deal of freedom in deciding how they want to behave in most situations\" [reverse-coded], (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) \"In this country, if someone acts in an inappropriate way, others will strongly disapprove,\" and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) \"People in this country almost always comply with social norms.\" Responses used a 6-point scale from strongly disagree (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to strongly agree (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Society-level scores were computed by averaging responses across all participants (Cronbach's α\u0026thinsp;=\u0026thinsp;0.78 at the society level). Of respondents who reached the tightness items, 1.5% missed\u0026thinsp;\u0026ge;\u0026thinsp;1 item and were excluded from analysis.\u003c/p\u003e \u003cp\u003eNorm strictness was operationalized as the overall restrictiveness of behavioral standards, measured through normative assessments of 150 situated behaviors crossing 15 common behaviors with 10 everyday situations. Participants rated each behavior-situation combination on a 6-point scale from extremely inappropriate (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to extremely appropriate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). To avoid participant fatigue, each respondent rated approximately 50 randomly selected combinations. Society-level norm strictness was calculated as the mean appropriateness rating across all 150 combinations, with lower scores indicating stricter norms (Cronbach's α\u0026thinsp;=\u0026thinsp;0.98).\u003c/p\u003e \u003cp\u003eDemographic subgroup measures of perceived tightness and norm strictness were computed by averaging responses across all men and all women in each society, and across all students and all non-students in 33 societies where both groups were represented. However, participants only rated a subset of norms, and at least one rating for each norm is required to calculate norm strictness. For these reasons, these measures could not be calculated in all subsamples for all societies. Gender-specific scores for norm strictness were calculated in 76 societies and student-status-specific scores were calculated in 27 societies (out of 33 societies with both groups represented).\u003c/p\u003e \u003cp\u003eHistorical measures of perceived tightness and norm strictness (2000\u0026ndash;2003) were obtained by applying the same calculations as above to the raw data from Gelfand et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). That study used the same perceived tightness scale and included a mostly identical set of everyday norms measured on the same 6-point scale.\u003c/p\u003e \u003cp\u003eGender egalitarian values were measured using a three item scale known as the Equality index (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), consisting of three items that measure disagreement with traditional gender role statements: \"When jobs are scarce, men have more right to a job than women,\" \"On the whole, men make better political leaders than women do,\" and \"A university education is more important for a boy than for a girl.\" Participants rated each item on a 4-point scale from strongly agree (coded 1) to strongly disagree (coded 4). Missing values (2.8%) were imputed using a multilevel imputation model with predictive mean matching and country as the clustering variable, using information from demographics and other individual-level items in the survey. Ratings were averaged across all participants from a society (society-level \u0026#120572; = 0.93).\u003c/p\u003e \u003cp\u003eChoice values were measured using Welzel's (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Choice index, consisting of justifiability ratings for homosexuality, abortion, and divorce. The items are phrased \u0026ldquo;For each of the following actions, please indicate whether or not you think it is wrong,\u0026rdquo; with a five-point scale coded from 1 to 5 (Always wrong, Mostly wrong, Sometimes wrong, Rarely wrong, Not wrong at all).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExternal data sources\u003c/h2\u003e \u003cp\u003eControl and validation variables from other sources included ecological and historical threats (child mortality and natural disaster mortality from the Environmental Sustainability Index; Yale Center for Environmental Law and Policy, 2005; historical pathogen prevalence from Murray and Schaller (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)), importance of religion from Gallup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), liberal democracy from V-Dem (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and ethnic fractionalization from Alesina et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor COVID-19 mortality analyses, we controlled for the most critical intrinsic and structural risk factors identified in epidemiological literature. To address intrinsic population risk, we included median age, a proxy for the demographic age structure that was consistently the most powerful predictor of mortality (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), alongside national obesity and hypertension prevalence to account for the syndemic effect of non-communicable diseases (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To account for structural socioeconomic vulnerability, we included the Gini coefficient, which has been identified as a more direct and robust predictor of adverse outcomes than absolute national wealth (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). COVID-19 mortality data for 2020, including both official death rates and excess mortality estimates, were sourced from Our World in Data.\u003c/p\u003e \u003cp\u003eFor analysis of homicide rates (per 100,000 population, World Bank), we selected controls based on prior work on antecedents of homicide rates (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e): national wealth (GDP per capita, World Bank), liberal democracy (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and poverty rate (World Bank).\u003c/p\u003e \u003cp\u003eFor analysis of obesity rates sourced from Our World in Data, we selected controls based on prior work on cultural variation in obesity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e): individualism and flexibility (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and national wealth (GDP per capita, World Bank).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted in R version 4.4.3.\u003c/p\u003e \u003cp\u003eA major methodological concern in cross-cultural research involves acquiescence bias: the tendency to agree with survey items regardless of content, which varies systematically across cultures (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Previous tightness-looseness studies used within-subject standardization that may have distorted results (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We implemented the opposite-items method (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), which requires including pairs of items with opposite content. Specifically, our survey included both \"People in this country have a great deal of freedom in deciding how they want to behave in most situations\" and its opposite \"People in this country have very little freedom in deciding how they want to behave in most situations.\" A respondent without acquiescence bias should show equal agreement and disagreement with these opposite items, yielding a mean response at the scale midpoint (3.5 on our 6-point scale). Individual acquiescence bias was estimated as the deviation of each participant's mean response to the opposite items from this midpoint. Bias-adjusted scores were calculated by subtracting each individual's estimated acquiescence bias from all their scale responses before aggregating to the society level. This procedure was completed for 83 societies where both opposite items were included.\u003c/p\u003e \u003cp\u003eAll correlational results use the Pearson correlation coefficient. To assess the robustness of our results for norm strictness to the specific selection of normative behaviors, we utilized a double-bootstrap procedure (1,000 iterations) in which both societies and norm strictness items were resampled with replacement; the results of this robustness check are reported in Supplementary Table\u0026nbsp;6. All multiple regression analyses use standard OLS regression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll society-level scores, including measures for perceived tightness and norm strictness for 83 societies, are available at OSF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/xv2g6/\u003c/span\u003e\u003cspan address=\"https://osf.io/xv2g6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe R code for data processing, bias adjustment, and statistical analysis is available at OSF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/xv2g6/\u003c/span\u003e\u003cspan address=\"https://osf.io/xv2g6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eKE, PS, and BWH conceived the study. IV performed the analysis. KE wrote the manuscript. All authors reviewed, edited, and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are grateful to all researchers who collected data for the Global Study of Everyday Norms. A large language model (Claude) was used to generate editorial suggestions for this paper. This work was supported by the Knut and Alice Wallenberg Foundation (grant no. 2022.0191, recipient PS).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGelfand MJ et al (2011) Differences between tight and loose cultures: A 33-nation study. Science 332(6033):1100\u0026ndash;1104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelfand MJ, Harrington JR, Jackson JC (2017) The strength of social norms across human groups. 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A validation of the revised Minkov-Hofstede model of culture with World Values Survey items and scores for 102 countries. J Int Manage 28(4):100971\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumayer E (2003) Good policy can lower violent crime: Evidence from a cross-national panel of homicide rates, 1980\u0026ndash;97. J Peace Res 40(6):619\u0026ndash;640\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePridemore WA (2011) Poverty matters: A reassessment of the inequality\u0026ndash;homicide relationship in cross-national studies. Br J Criminol 51(5):739\u0026ndash;772\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkaliyski P et al (2022) The weight of culture: Societal individualism and flexibility explain large global variations in obesity. Soc Sci Med 307:115167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Bavel JJ et al (2022) National identity predicts public health support during a global pandemic. 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J Cross Cult Psychol 41(1):99\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshanloo M, Gebauer JE (2020) Religiosity's nomological network and temporal change: Introducing an extensive country-level religiosity index based on Gallup World Poll data. Eur Psychol 25(1):26\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoppedge M et al (2025) V-Dem [Country\u0026ndash;Year/Country\u0026ndash;Date] Dataset v16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23696/vdemds25\u003c/span\u003e\u003cspan address=\"10.23696/vdemds25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Varieties of Democracy Project\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlesina A, Devleeschauwer A, Easterly W, Kurlat S, Wacziarg R (2003) Fractionalization. J Econ Growth 8(2):155\u0026ndash;194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown PA (2023) Country-level predictors of COVID-19 mortality. Sci Rep 13:9263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Mouhayyar C, Jaber LT, Bergmann M, Tighiouart H, Jaber BL (2022) Country-level determinants of COVID‐19 case rates and death rates: An ecological study. Transbound Emerg Dis 69(4):e906\u0026ndash;e915\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam MF, Wildman J, Rahim HA (2023) Income inequality and its association with COVID-19 cases and deaths: a cross-country analysis in the Eastern Mediterranean region. BMJ Glob Health 8(11):e012271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJawad Hashim M, Alsuwaidi AR, Khan G (2020) Population risk factors for COVID-19 mortality in 93 countries. J Epidemiol Glob Health 10(3):204\u0026ndash;208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer R (2004) Standardization to account for cross-cultural response bias: A classification of score adjustment procedures and review of research in JCCP. J Cross Cult Psychol 35(3):263\u0026ndash;282\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cultural tightness-looseness, social norms, cultural narratives, COVID-19 mortality, cross-cultural variation","lastPublishedDoi":"10.21203/rs.3.rs-8538560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8538560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCultural tightness-looseness theory posits that societies vary in norm strength and deviance tolerance. However, research has conflated two independent dimensions: perceived tightness (shared narratives of social order) and norm strictness (behavioral constraints). Analyzing 25,000 participants across 83 societies, here we show these dimensions are uncorrelated (r\u0026thinsp;=\u0026thinsp;0.10) and exhibit a double dissociation. Norm strictness stems from historical threats and correlates with lower liberal democracy, whereas perceived tightness is uniquely predicted by population density. Crucially, only perceived tightness predicts coordination benefits: lower COVID-19 mortality, lower homicide rates, and lower obesity. Norm strictness instead predicts higher pandemic mortality, suggesting behavioral rigidity hampers adaptation to novel crises. We provide validated scores for 83 societies, including 15 under-sampled African nations, expanding resources for cross-cultural research. These findings demonstrate that cultural narratives of social order facilitate collective action independently of enforcement, providing a new empirical foundation for understanding societal resilience.\u003c/p\u003e","manuscriptTitle":"Strict norms and perceived cultural tightness have distinct societal consequences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:07:07","doi":"10.21203/rs.3.rs-8538560/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"877f197e-7278-4073-84d4-bfbb52fc00ce","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61080448,"name":"Scientific community and society/Social sciences"},{"id":61080449,"name":"Scientific community and society/Social sciences/Psychology"},{"id":61080450,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-22T14:36:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:07:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8538560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8538560","identity":"rs-8538560","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}