Arts, Faculty of
Psychology, Department of
DSpace
UBCV
Sahakari, Sakshi S.
Master of Arts - MA
University of British Columbia
Tightness-looseness characterizes cultural variation across and within societies through the strength and reinforcement of social norms. Although tightness-looseness has common correlates across cultures, within-country explorations in China and the US have also identified culture-specific aspects to it. The present study maps cultural tightness across a populous nation that remains largely psychologically uncharted: India. Using archival regional data for 28 states and 3 union territories, in Study 1 we construct a tightness-looseness index across India with 10 etic (culturally universal indicators, e.g., access to alcohol) and 9 emic (culturally unique indicators, e.g., caste-related laws) items (α = 0.93). In Study 2 and 3, we examine known influences (e.g., diseases prevalence) and correlates, or potential outcomes (e.g., discrimination), of cultural tightness, as well as underexplored associations such as geography (e.g., surface temperature) and culturally unique norms (e.g., arranged marriages). We tested these associations through specification curve analyses, and machine learning approaches (conditional random forests and SHAP values), with a weight of evidence approach to summarize findings across these complementary analyses. The analyses revealed multifaceted results – some aligning with, and others diverging from prior tightness literature. These findings document tightness-looseness in an overlooked geographic region and contribute to a richer understanding of the construct in diverse cultural contexts.
Tightness-looseness across India by Sakshi S. Sahakari B.A., University of British Columbia, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in The Faculty of Graduate and Postdoctoral Studies (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Sakshi S. Sahakari, 2024ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Tightness-looseness across India submitted by Sakshi S. Sahakari in partial fulfilment of the requirements for the degree of Master of Arts in Psychology Examining Committee: Friedrich M. Goetz, Assistant Professor, Department of Psychology, UBC Supervisor Mark Schaller, Professor, Department of Psychology, UBC Supervisory Committee Member Jason Rights, Assistant Professor, Department of Psychology, UBC Supervisory Committee Member iii Abstract Tightness-looseness characterizes cultural variation across and within societies through the strength and reinforcement of social norms. Although tightness-looseness has common correlates across cultures, within-country explorations in China and the US have also identified culture-specific aspects to it. The present study maps cultural tightness across a populous nation that remains largely psychologically uncharted: India. Using archival regional data for 28 states and 3 union territories, in Study 1 we construct a tightness-looseness index across India with 10 etic (culturally universal indicators, e.g., access to alcohol) and 9 emic (culturally unique indicators, e.g., caste-related laws) items (α = 0.93). In Study 2 and 3, we examine known influences (e.g., diseases prevalence) and correlates, or potential outcomes (e.g., discrimination), of cultural tightness, as well as underexplored associations such as geography (e.g., surface temperature) and culturally unique norms (e.g., arranged marriages). We tested these associations through specification curve analyses, and machine learning approaches (conditional random forests and SHAP values), with a weight of evidence approach to summarize findings across these complementary analyses. The analyses revealed multifaceted results – some aligning with, and others diverging from prior tightness literature. These findings document tightness-looseness in an overlooked geographic region and contribute to a richer understanding of the construct in diverse cultural contexts. iv Lay Summary Social norms guide human functioning in every cultural group. As such, the (1) stringency and (2) enforcement of these norms vary according to the group’s needs for coordination. This idea is captured by the psychology concept of tightness, wherein tighter (versus looser) groups have more stringent and more strictly enforced norms. Applying this to the understudied nation of India, we observed substantive regional diversity in tightness. This variability is influenced by a host of factors relevant to group coordination such as its agricultural practices and surrounding climate. For example, tighter places report lower temperatures and greater crop-farming. Regional levels of tightness also matter for society at large – from informing local infrastructure organization to the well-being of people living in these regions. These findings provide important insights into the diverse experiences of over 1.4 billion Indian residents and highlight the need to consider the role of tightness across different cultural contexts. v Preface This thesis is an original, unpublished work by Sakshi Sahakari, under the supervision of Dr. Friedrich Götz, and in collaboration with Radha Bazaz, Dr. Ara Norenzayan, and Dr. Michele Gelfand. In consultation with my supervisors and collaborators, I was primarily responsible for constructing measures, analyzing data, and writing this thesis. All studies received ethics approval by the Behavioral Ethics Board at the University of British Columbia under the protocol H22-03728. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ............................................................................................................................... viii List of Figures ................................................................................................................................ x Acknowledgements ...................................................................................................................... xi Introduction ................................................................................................................................... 1 Cultural tightness......................................................................................................................... 2 A brief history of cultural tightness .................................................................................... 2 Characterising culture through tightness ........................................................................... 3 Measuring tightness ............................................................................................................ 4 Influences on tightness ................................................................................................................ 5 Threats ................................................................................................................................ 5 Societal complexity and heterogeneity ................................................................................ 6 Geography........................................................................................................................... 7 Implications of tightness ............................................................................................................. 8 Order ................................................................................................................................... 8 Openness ............................................................................................................................. 9 The case for India ...................................................................................................................... 11 The present research .................................................................................................................. 12 Study 1. Regional variability in tightness across India ........................................................... 13 Method ...................................................................................................................................... 13 Contextualizing sample ..................................................................................................... 13 Index creation ................................................................................................................... 14 Construct validation.......................................................................................................... 17 Analytical strategy ..................................................................................................................... 19 Results ....................................................................................................................................... 20 Summary ................................................................................................................................... 22 Study 2. Influences on tightness ................................................................................................. 23 Methods ..................................................................................................................................... 23 Threats .............................................................................................................................. 24 vii Societal complexity and heterogeneity .............................................................................. 25 Geography......................................................................................................................... 26 Control variables .............................................................................................................. 27 Analytical strategy ............................................................................................................ 28 Results ....................................................................................................................................... 31 Threats .............................................................................................................................. 31 Societal complexity and heterogeneity .............................................................................. 34 Geography......................................................................................................................... 34 Summary ................................................................................................................................... 34 Study 3. Correlates of tightness ................................................................................................. 35 Method ...................................................................................................................................... 35 Order correlates ................................................................................................................ 35 Openness correlates .......................................................................................................... 38 Analytical strategy ............................................................................................................ 43 Results ....................................................................................................................................... 43 Order correlates ................................................................................................................ 43 Openness correlates .......................................................................................................... 44 Summary ................................................................................................................................... 47 General Discussion ...................................................................................................................... 48 Bibliography ................................................................................................................................ 60 Appendices ................................................................................................................................... 84 Appendix A. Index items ........................................................................................................... 84 Appendix B. Influences of tightness operationalizations .......................................................... 85 Appendix C. Spatial regression analyses .................................................................................. 88 Appendix D. Unimputed results for influences on Tightness ................................................... 89 Appendix E. Correlate operationalizations ............................................................................... 90 Appendix F. Complete spatial regression and conditional random forest results for each outcome ..................................................................................................................................... 98 Appendix G. SHAP graphs for each outcome ......................................................................... 106 viii List of Tables Table 1. Cultural Tightness Scores for Each Region from Tightest to Loosest ............................ 20 Table 2. Zero-Order Correlations Between Tightness Index and Theorized Correlates .............. 22 Table 3. Specification Curve Analyses and Conditional Random Forest Importance Scores Predicting Tightness in India. ....................................................................................................... 32 Table 4. Specification Curve Analyses and Conditional Random Forest Results for Tightness and Each Correlate. .............................................................................................................................. 46 Table 5. Associations With Cultural Tightness in India, China, and the United States. ............. 49 Table A1. Tightness Index Items. ................................................................................................. 84 Table B1. Operationalizations of Influences on Tightness ........................................................... 85 Table D1. Spatial Autoregression Analysis, Original Conditional Random Forests, and Range of Specification Curve Effect Sizes, Predicting Tightness. ............................................................... 89 Table E1. Operationalizations of Correlates of Tightness. ........................................................... 90 Table F1. Regression Model and Conditional Random Forest Results for Tightness Predicting Substance Use. .............................................................................................................................. 98 Table F2. Regression Model and Conditional Random Forest Results for Tightness Predicting Debt. .............................................................................................................................................. 98 Table F3. Regression Model and Conditional Random Forest Results for Tightness Predicting Corruption. .................................................................................................................................... 98 Table F4. Regression Model and Conditional Random Forest Results for Tightness Predicting Incumbent Voting. ......................................................................................................................... 99 Table F5. Regression Model and Conditional Random Forest Results for Tightness Predicting Trust in Institutions. ...................................................................................................................... 99 Table F6. Regression Model and Conditional Random Forest Results for Tightness Predicting Social Organization. ...................................................................................................................... 99 Table F7. Regression Model and Conditional Random Forest Results for Tightness Predicting Waste Separation. ........................................................................................................................ 100 Table F8. Regression Model and Conditional Random Forest Results for Tightness Predicting Ecological Preservation. ............................................................................................................. 100 Table F9. Regression Model and Conditional Random Forest Results for Tightness Predicting Happiness. ................................................................................................................................... 100 Table F10. Regression Model and Conditional Random Forest Results for Tightness Predicting Institutional Gender Equality. ..................................................................................................... 101 Table F11. Regression Model and Conditional Random Forest Results for Tightness Predicting Gender Equality Attitudes. .......................................................................................................... 101 ix Table F12. Regression Model and Conditional Random Forest Results for Tightness Predicting Religion-Based Discrimination. .................................................................................................. 101 Table F13. Regression Model and Conditional Random Forest Results for Tightness Predicting Caste-Based Discrimination........................................................................................................ 102 Table F14. Regression Model and Conditional Random Forest Results for Tightness Predicting Innovation Enablers. ................................................................................................................... 102 Table F15. Regression Model and Conditional Random Forest Results for Tightness Predicting Innovation. .................................................................................................................................. 102 Table F16. Regression Model and Conditional Random Forest Results for Tightness Predicting Sexual Health Norms. ................................................................................................................. 103 Table F17. Regression Model and Conditional Random Forest Results for Tightness Predicting Abortion Acceptance. .................................................................................................................. 103 Table F18. Regression Model and Conditional Random Forest Results for Tightness Predicting Fertility Norms. ........................................................................................................................... 103 Table F19. Regression Model and Conditional Random Forest Results for Tightness Predicting Arranged Marriage Norms. ......................................................................................................... 104 Table F20. Regression Model and Conditional Random Forest Results for Tightness Predicting Marriage-Related Violence. ........................................................................................................ 104 Table F21. Regression Model and Conditional Random Forest Results for Tightness Predicting Dowry. ......................................................................................................................................... 104 Table F22. Regression Model and Conditional Random Forest Results for Tightness Predicting Child Marriage. ........................................................................................................................... 105 x List of Figures Figure 1. Norm Generation and Index Creation Process. ............................................................ 15 Figure 2. Distribution of Index Items by Emic Versus Etic Categorization. ................................ 16 Figure 3. Tightness at the Regional Level in India. ..................................................................... 21 Figure 4. Imputed SHAP Value Summary of Associations Between Antecedents and Cultural Tightness. ...................................................................................................................................... 33 Figure 5. SHAP Value Summary of Cultural Tightness Predicting Incumbent Voting. ............... 44 Figure G1. SHAP Summary for Predicting Substance Use. ...................................................... 106 Figure G2. SHAP Summary for Predicting Debt. ...................................................................... 106 Figure G3. SHAP Summary for Predicting Corruption. ............................................................ 107 Figure G4. SHAP Summary for Predicting Incumbent Voting. ................................................. 107 Figure G5. SHAP Summary for Predicting Trust in Institutions. .............................................. 108 Figure G6. SHAP Summary for Predicting Social Organization. .............................................. 108 Figure G7. SHAP Summary for Predicting Waste Separation. .................................................. 109 Figure G8. SHAP Summary for Predicting Ecological Preservation. ....................................... 109 Figure G9. SHAP Summary for Predicting Happiness. ............................................................. 110 Figure G10. SHAP Summary for Predicting Institutional Gender Equality. ............................. 110 Figure G11. SHAP Summary for Predicting Gender Equality Attitudes. ................................... 111 Figure G12. SHAP Summary for Predicting Religion-Based Discrimination............................ 111 Figure G13. SHAP Summary for Predicting Caste-Based Discrimination. .............................. 112 Figure G14. SHAP Summary for Predicting Innovation Enablers. ........................................... 112 Figure G15. SHAP Summary for Predicting Innovation. .......................................................... 113 Figure G16. SHAP Summary for Predicting Sexual Health Norms. ......................................... 113 Figure G17. SHAP Summary for Predicting Abortion Acceptance. .......................................... 114 Figure G18. SHAP Summary for Predicting Fertility Norms. ................................................... 114 Figure G19. SHAP Summary for Predicting Spousal Unfamiliarity. ........................................ 115 Figure G20. SHAP Summary for Predicting Marriage-Related Violence. ................................ 115 Figure G21. SHAP Summary for Predicting Dowry. ................................................................. 116 Figure G22. SHAP Summary for Predicting Child Marriages. ................................................. 116 xi Acknowledgements I would like to thank my supervisor, Dr. Friedrich Götz, for his invaluable guidance on this project and over the past two years. Thank you to Dr. Mark Schaller and Dr. Jason Rights for their time and expertise as committee members. A special thanks to Dr. Ara Norenzayan and Dr. Michele Gelfand for their guidance. To my friends, in graduate school and beyond, thank you for enduring my research ramblings, which helped keep me afloat over these years. And of course, to my parents, brother, and sister-in-law, for being my pillars of unending support. 1 Introduction Social norms lie at the heart of human functioning, guiding behaviours, attitudes, and cognitions (Bicchieri et al., 2021). From being codified into larger institutions, to dictating whether it is acceptable for people cut queues and whether grandparents command respect from their grandchildren, these norms persist at every layer of society. While the specific contents of norms vary across time and space, all norms share two important features: their permissiveness, or how much latitude they afford an individual, and the strictness with which they are enforced. This is captured by the concept of tightness-looseness, wherein tighter cultures have stronger social norms and punish deviance strictly, and looser cultures have weaker social norms and a higher tolerance for non-compliance (Triandis, 1989).1 Tightness has been associated with a variety of real-world consequences, including COVID-19 prevalence (Gelfand et al., 2021), discrimination (Harrington & Gelfand, 2014), and leadership (Aktaş et al., 2015). Notably, it has been correlated with such downstream consequences at the regional, within-country level too. In the United States, Harrington and Gelfand (2014) found Tightness related to reduced state-levels of innovation and increased discrimination. Meanwhile across 31 Chinese provinces, researchers found some reversed associations: tighter cultures related to greater innovation and reduced discrimination (Chua et al., 2019). In China, there were also some unique findings such as greater distance from China’s political capital, Beijing, related to increasing cultural looseness (Chua et al., 2019). These findings indicate the necessity of more within-country work to understand the generalizability and culture-specific patterns of Tightness. 1 From this point onwards, the construct will be referred to as Tightness or cultural tightness, instead of tightness-looseness, with looseness being the implied opposite of this spectrum. 2 The present research expands the study of Tightness to a relatively unexplored nation within the realm of psychological research: India. Using archival data, we developed an index characterizing regional differences in Tightness across 31 Indian states and union territories (Study 1). In line with prior literature (e.g., Gelfand et al., 2011; Jackson et al., 2020), we followed this up by using the index to investigate the antecedents (Study 2) and correlates (Study 3), or potential outcomes, of Tightness variations in India. For Studies 2 and 3, we utilized a two-pronged analytical approach with specification curve analyses and conditional random forests. To provide relevant context for these empirical undertakings, the following section describes historical and contemporary conceptualisations of Tightness as well as assessment approaches; and reviews relevant research on the ecological, economic, health-related, social, and political correlates of Tightness. This section ends with an overview of India, its position in the psychological landscape, and its relevance to the study of Tightness. Cultural tightness As mentioned above, cultural tightness is a prominent theory in cultural dynamics which explores the regulation and strength of social norms in societies. The present-day implementation of this theory has evolved from years of interdisciplinary discourse that is outlined below to contextualize our operationalization of the construct. A brief history of cultural tightness The first notable implementation of Tightness came from anthropology when Pelto (1968) characterized thirty traditional societies across the globe based on their social order (e.g., ownership of economic resources). Psychology similarly saw a rise in studies utilizing the construct to distinguish the psychological functioning of small-scale societies (e.g., the Temne of 3 Sierra Leone and Intuits) based on the degree of conformity to social norms (Berry, 1966; Berry, 1967; Witkin & Berry, 1975). Triandis (1972) reintroduced the construct, arguing that Tightness refers to the regulation of social roles and social relationships. Following Triandis’ work on the concept, Morris and colleagues (2000) posited that Tightness refers to the density of social networks, situated within individualism-collectivism. Lastly, another vein of thinking proposed that regulation of social institutions lied at the crux of Tightness (Parboteeah & Cullen, 2003). While viable conceptualizations of the cultural dimension, all of these views failed to account for the multi-level functioning of social norms, and subsequently, Tightness. Addressing this, Gelfand and colleagues (2006), proposed the theoretical conceptualization of Tightness as a multi-level construct that encompasses both the strength of social norms (the number and emphasis), and the strength of social sanctioning (tolerance for deviance from these norms). Within this operationalization, societal agents interact with one another to sustain the Tightness of their society, with effects at every layer of functioning. From decision-making, self-monitoring, and preference for risk-taking amongst individuals; to innovation, order, and cohesion at the organizational or group level, Gelfand and colleagues’ (2006) model accounts for wide-spanning outcomes, making it the most common operationalization of Tightness in the present day. Characterising culture through tightness Gelfand et al.’s (2006) approach has since been widely used, demonstrating the utility of Tightness in studying cross-cultural differences at a mono-level of culture. At the national level, researchers have illustrated Tightness differences across 45 nations and its meaningful relations to psychological processes and societal practices such as social organization and COVID-19 4 outcomes (Gelfand et al., 2011; 2021). Beyond the country-level, researchers have applied the social norm approach to organizations, garnering crucial insights into their culture and functioning (Lee & Kramer, 2016). As outlined above, and of relevance to the present research, the construct has been applied to an under-emphasized unit of cultural analysis: the within-country, regional level. However, to date, only two such investigations have been conducted, in the United States (Harrington & Gelfand, 2014) and in China (Chua et al., 2019). Measuring tightness Researchers vary in their measurement of Tightness. Gelfand and colleagues (2011) developed a 6-item scale assessing the extent to which participant’s perceived social norms, regardless of their content, as pervasive, clearly defined, and reliably imposed within their countries. This includes items such as “In this country, if someone acts in an inappropriate way, others will strongly disapprove” or “There are many social norms that people are supposed to abide by in this country”. Their scale’s stable reliability, generalizability, and efficient measurement (Gelfand et al., 2011) makes it the most common measure of Tightness. Meanwhile, Uz (2015) contends measuring Tightness through the dispersion of attitudes and behaviours. Namely, Uz constructed three measures of Tightness which incorporated the standard deviations on scale items assessing attitudes and behaviours: domain-specific Tightness, domain-general Tightness, and a combination measure. All three measures were reliable and associated with variables theoretically related to cultural tightness, providing a novel assessment tool that goes beyond individuals’ perceptions of Tightness. Another approach to measure Tightness is through legislative indicators, as social norms are often codified into institutional functioning and legislation of a group (Basu, 1998; Dequech, 2009). Hence, legislation around social norms and their institutional sanctioning should indicate 5 the culture’s emphasis on the social norms and their stringent enforcement respectively. Employing this rationale, Harrington & Gelfand (2014) created an index for Tightness consisting of legislative and institutional indicators for the United States. They created an index based on the strength of punishment (e.g., the severity of punishment for violating laws), latitude/permissiveness (e.g., access to alcohol), institutional regulation (e.g., state-level religiosity), and openness to diversity (e.g., the percentage of foreign population). Using this index to map Tightness of the 50 United States of America, the researchers demonstrated the viability of such an archival approach to comprehensively capture cultural variations beyond the collection and subsequent aggregation of large-scale individual data. Influences on tightness Beyond merely capturing cultural differences, researchers have theorized and explored possible influences on cultural tightness. We review this body of research below to guide our exploration of what influences the Tightness of cultures, categorized by variables associated with (1) threats, (2) societal complexity and heterogeneity, and (3) geography. Threats Past research has shown that experiencing ecological and historical/human-made threats influences Tightness (Gelfand et al., 2011). Ecological and human-made threats endanger the group by depleting resources and subsequently increasing the likelihood of non-cooperation (Roos et al., 2015). Supplementing this, research has tied disease prevalence to increased individualism (Fincher et al., 2008; Na et al., 2008), violence (Shrira et al., 2013), and openness to experience (Brown et al., 2016; Schaller & Murray, 2008), all of which are commonly associated with reduced Tightness and coordination. This instigates the need for stronger norms and punishment of deviance to improve social coordination in dealing with these threats and 6 ensuring group survival (i.e., tighter groups, Gelfand et al., 2011; Gelfand et al., 2017). In contrast, cultures that do not face such threats consistently, have lesser need for order and coordination, allowing for weaker norms and lax punishment (i.e., looser groups). Within Tightness literature, nations with a history of ecological threats (population density, resource scarcity, natural disasters, pathogen prevalence) tend to be culturally tighter in the present day (Gelfand et al., 2011; Jackson et al., 2020). Parallelly, in the United States, greater military recruitment, land shortage, and history of slavery is related to tighter states such as Alabama or Tennessee (Harrington & Gelfand, 2014). Supplementing the correlational work, agent-based modelling illustrates that experiencing more threats leads to the “tightening” of cultures on norms of coordination (stronger norms and harsher punishment) and cooperation (individual consensus on cooperation; Roos et al., 2015). Societal complexity and heterogeneity Since societies with complex religious, legal, and agricultural institutions, require large-scale organization (Barry et al., 1959; Pelto, 1968), societal complexity, which takes on different forms, has been associated with Tightness. For instance, across 86 non-industrial societies, greater societal complexity in economic, social and political systems related to tighter cultures (Jackson et al., 2020). Whereas in China, the economic complexity of urban places lent such regions more to cultural tightness (Chua et al., 2019), unlike the typical association of urbanization with openness (Jokela, 2020; Jokela et al., 2015; Schwaba et al., 2018), and – accordingly – looseness in the United States (Harrington & Gelfand, 2014). Relatedly, rice farming requires a lot of collective efforts in all aspects of cultivation such as irrigation efforts and the overall labour required (Bray, 1994; Talhelm & Oishi, 2018), relative to wheat farming that is usually carried out more independently, As such, the greater complexity and 7 interdependent efforts required in rice farming (Talhelm, 2022; Talhelm & Oishi, 2018), have been show to relate to heightened cultural tightness across 32 nations and within China (Talhelm & English, 2020). Societal heterogeneity is another proposed enabler of Tightness, wherein researchers suggested that since regulating norms is more efficient amongst homogenous groups, greater Tightness is associated with reduced heterogeneity. For instance, more religiously and culturally diverse places (Brazil and California) are some of the loosest regions, and more typically homogeneous nations (e.g., Japan) are the tightest (Gelfand et al., 2011; Harrington & Gelfand, 2014). Consistent with this, research has found that greater kinship heterogeneity, or diversity in residence structures, is associated with looser societies (Jackson et al., 2020). Geography The influence of geography, or one’s physical environment, on culture has received much weight in academic discourse (Berry & Annis, 1974; Brislin & Keating, 1976; Freilich, 1967; Oishi & Graham, 2010). Empirical evidence supports the theoretical implications by, for instance, tying rainfall, temperature and precipitation to greater prevalence of religious beliefs (Botero et al., 2014). Most recently, geographical features have been shown to substantially explain human cultural evolutions, on average accounting for 16-20% and up to 68% of variance in culture (Wormley et al., 2023). Given the unique influence of the physical environment on aggression and crime (DeWall & Bushman, 2009; Field, 1992); and perception of threats (Gelfand et al., 2011; Jackson et al., 2019), geography also lends itself to cultural tightness. Corroborating this, a large-scale cross-national study, demonstrated that self-report measures of Tightness were strongly, negatively associated with temperature and rainfall, such that greater rainfall and temperature were 8 associated with looser countries (Wormley et al., 2023). But other measures of Tightness, such as Uz’s (2015) measure, were not related to these geographical variables (Wormley et al., 2023). These mixed findings coupled with the extensive theoretical discourse and empirical evidence, call for more research to understand the nuances of how the physical environment relates to cultural tightness, particularly at the intranational level. Implications of tightness The tightness or looseness of groups results in trade-offs between order and openness (Gelfand, 2021; Gelfand et al., 2006). This implicates several empirical correlates and conceptual consequences that vary along the spectrum of Tightness, which are discussed below to contextualize the present investigation. Order Given the stricter regulation in tighter cultures, Tightness is associated with greater order. At the group-level, Tightness is related to greater organization and improved coordination in direct response to the cultural demands for stringency (Gelfand et al., 2006). For instance, tighter states in the United States, such as Alabama and Texas, are associated with higher incarceration rates and more social stability (fewer divorces, inter-state migrations, etc.; Harrington & Gelfand, 2014). Additionally, research suggests groups adapt their behaviours to the stringency (or lack thereof) of the cultures in order to maintain cultural membership. Particularly, tighter cultures exhibit greater tendencies to hold themselves accountable as the harsher enforcement in their groups leads them to believe that their actions will be punitively evaluated (Gelfand et al., 2011). In contrast, looser cultures have lower felt accountability since there are fewer expectations of the same, so they report reduced self-control in the form of greater openness to and engagement in risk-taking (Gelfand et al., 2011); higher substance use and debt (Harrington 9 & Gelfand, 2014); and greater cheating in the brain teaser game Wordle (Wormley & Cohen, 2022). Research further indicates the order in tighter cultures is reflected in their desire for social organization and punitive, tight institutions. Specifically, cultural tightness is associated with a preference for autocratic leaders in both modern nations such as the United States and France (Gelfand & Lorente, 2021) as well historical, non-industrial societies (Jackson et al., 2020). Additionally, Tightness is related to improved social organization (Gelfand et al., 2011; Harrington & Gelfand, 2014). Related work found that tighter regions reported increased institutional trust amongst Chinese but not American participants, where the reverse relationship was found (McLamore et al., 2023). These mixed findings indicate tighter regions may (or may not) be able to retain trust due to expectations of an institution’s performance, suggesting a need for more work to explore how cultural tightness relates to continued institutional trust. Openness On the other end, Tightness is related to openness, with looser cultures being more conducive for downstream openness. Firstly, situational constraints, or the range of behaviours deemed appropriate in day-to-day situations, can inform openness in terms of reported tolerance and discrimination of marginalised groups (Gelfand et al., 2006; 2011). However, regional Tightness work indicates that this association might be more mixed than previously thought. For instance, in the United States, tighter cultures reported greater discrimination (Harrington & Gelfand, 2014), but in China, tighter regions were associated with greater tolerance of the LGBTQ+ community (Chua et al., 2019). Chua and colleagues (2019) posit that these diverging findings maybe unique to the Chinese context, specifically the paradoxical association between Tightness and urbanization may have facilitated greater exposure to the world in tighter 10 provinces. These differential results suggest the need for more work to understand the association between discrimination and Tightness. Additionally, Tightness relates to problem-solving styles that informs an individual’s creativity (Gelfand et al., 2006), which serves as another form of openness. Specifically, tighter regions are associated with adaptor cognitive styles, where solutions are derived through established procedures (Gelfand et al., 2006; Kirton, 1976; Kirton et al., 1991), whereas looser cultures are tied to innovator problem-solving, characterized by “outside-the-box” thinking (Gelfand et al., 2006; Kirton et al., 1991). As such, researchers have argued that looser regions, with the innovator thinking styles, should be associated with greater creativity. Yet, the empirical research paints a more complex picture. In the United States, as anticipated, tighter regions were associated with fewer utility patents and lower number of fine artists per capita (Harrington & Gelfand, 2014). Meanwhile, in Chinese provinces this relationship was more intricate, as tighter regions were associated with an overall greater number of utility patents but a reduced number of radical, inventive patents (Chua et al., 2019). These differential findings demonstrate the need for further Tightness investigations in different cultural contexts to shed light on such nuances. The extent of openness also manifests in subjective well-being. For instance, in the United States, culturally tight states reported reduced happiness. However, in stark contrast with this, the provinces in China reported greater happiness (Chua et al., 2019). Although there are culture-specific explanations for these (see Chua et al., 2019 for more), together these findings imply that the influence of Tightness on subjective well-being may not be as straightforward as other downstream consequences. 11 The case for India As the country that was once famously observed by Mark Twain as “the country of a hundred nations and a hundred tongues, of a thousand religions and two million gods” (p. 348, 2004), India provides the perfect setting for an intra-national expansion of the theories of Tightness. Prior to colonization, the subcontinent was comprised of several independent kingdoms. Each kingdom encompassed unique cultural repertoires, and economic and social infrastructures (Thapar & Spear, 1965). It was only after the British occupation ended in 1947, that the region was transformed into the single national entity it is known as today, with distinct regional cultures drawing from the historical kingdoms persisting to this day (Thapar & Spear, 1965). In the modern day, the country boasts immense linguistic diversity, with 121 spoken languages, and vast geographical variations, ranging from mountainous terrain to deserts, rainforests, coastal areas, and landlocked plains (Census of India, 2011). This deep-seated diversity has been suggested to translate into regional cultural and psychological variations (Dheer et al., 2015; Singh & Sharma, 2009), with some studies even demonstrating that, compared to the United States and China, India has the greatest within-country cultural diversity2 (Muthukrishna et al., 2020). The initial evidence, coupled with anecdotal and demographical knowledge of India’s diversity, paint a compelling picture of the true potential of Indian cultural diversity which could be uncovered through comprehensive explorations. Beyond India’s regional and cultural diversity, there has been an increasing call for acknowledging the cultural uniqueness of the nation through its study in psychology (Chaudhary & Sriram, 2020; Sinha, 1965). India has received limited attention in cross-national cultural work (e.g., Dhawan et al., 1995; Knafo et al., 2011; Lu, 2022; Savani et al., 2011), and these studies 2 In fact, the within-country cultural diversity of India trumps even the cross-country diversity of the European Union (Muthukrishna et al., 2020). 12 often focus on country-level comparisons or derive intranational comparisons from limited data collection within India. Additionally, with India becoming the most populated nation in 2023 (Hertog et al., 2023), it becomes even more critical to dedicate research towards understanding its culture. Hence in studying Tightness in India, the present work will provide much-needed insights on a country with rich regional variability that is often overlooked in cultural research, and equally importantly, describe the cultural experiences of 1/6th of the world’s population. The present research The present research expands the study of Tightness to India. In Study 1, utilizing archival data, we developed an index characterizing regional variations in Tightness across 31 Indian states and union territories. In doing so, we report statistical properties and evidence for the construct validity of the index. Given prior Tightness literature (Gelfand et al., 2011; Harrington & Gelfand, 2014; Jackson et al., 2020), in Study 2 we followed this by delving into the antecedents. Finally, in Study 3 we examined possible consequences, or correlates. Studies 2 and 3 enable us to draw comparisons to prior findings, both cross-nationally and at the within-country level in China and the United States, and how India may relate to them3. Beyond testing known antecedents and correlates, we contribute to the literature by exploring the geographical influences on cultural tightness (surface temperature, mountainousness, and precipitation), as well as culturally unique correlates of Tightness (e.g., marriage norms, and casteism). Given that this research was done in an understudied cultural context, our investigation was exploratory in nature with no specific predictions. 3 Given the many comparisons, each study is followed by a Summary rather than a discussion of the findings, which are instead elaborated on in the section titled “General Discussion”. 13 Study 1. Regional variability in tightness across India In Study 1, we created a culturally sensitive index of Tightness and applied it to the states and union territories, the equivalent to Canadian provinces, of India. Given prior approaches towards Tightness index construction (Harrington & Gelfand, 2014), we focused on social norms codified into institutions. This was especially relevant to the present study in India, where there has been limited all-encompassing surveys conducted, making large-scale survey work less feasible. Instead, the index was constructed from archival, pre-collected data to capture as many regions as possible. For convergent validity, the associations between the index and theorized correlates were tested. This includes previously tested correlates, such as political orientation (Harrington & Gelfand, 2014), as well as theorized but untested ones, such as censorship. Method Contextualizing sample The emphasis on institutional indicators, leads to a focus on regions instead of individuals to assess culture. That is, instead of collecting individual-level notions of norm regulation, the regions served as observations. This further narrows the scope of our study to regions where institutional variation is possible. India is a quasi-federalist nation, which bestows its constitution a unique configuration where state governments and the central government are interdependent unlike typical federalist countries (e.g., the United States; Constitution of India, 1950). India is currently composed of 36 states and union territories. The latter are recognized as independent regions but directly governed by the central government, so most of them do not have the authority for legislation creation, besides Puducherry, Delhi, and Jammu and Kashmir4. This led 4 India’s self-governing divisions have been revised several times, with the latest recategorization of Jammu and Kashmir, previously a state, as a union territory in 2019 (Government of Jammu and Kashmir, 2019). 14 us to focus on 31 regions that currently or recently had state government’s responsible for creating and regulating legislation. Index creation We generated the indicators through a combined emic-etic approach (Cheung et al., 2011). From a culturally universal (etic) perspective we drew indicators from Tightness literature. For instance, Harrington and Gelfand (2014) included access to alcohol in their United States index. As alcohol consumption is also a moralized issue in India, we included measures of legal drinking ages and the number of “dry days”, or days where alcohol sales are prohibited, in each region. In contrast, from the culturally immersed emic approach we collected indicators unique to India. These were derived through our research team’s lived experiences in India and informal, qualitative interviewers with Indians. For instance, India’s religious diversity and emphasis on religion, along with its complex history with caste have been widely-noted (Agrawal, 2016; Pew Research Center, 2018). Given their integration into Indian society and role in dictating social norms, we included religious beliefs and casteism as indicators. These norms then refined through ratings from our research team to avoid redundancy and conflating tangentially related concepts, see Figure 1 for more details. This culminated in an index consisting of 19 items that characterized Tightness. See Figure 2 for the complete list of indicators, classified into emic and etic items. 15 Figure 1. Norm Generation and Index Creation Process. Note. Figure 1 summarizes the index creation and norm generation process taken on by our research team. The number of items in our sample pool at the end of each stage are denoted by “Nindicators”. For emic norm generation, informal interviews were conducted to identify unique social norms beyond the ones that our team members with lived experience had identified. Psychometric testing particularly involves exploring the Cronbach’s α and the correlations between the separate indicators. For the latter test, given our small sample size, we deemed correlations to be meaningful if they were above |0.1| rather than focusing on significant correlations. Etic indicators from previous literature (Nindicators = 34) Emic norm generation from interviews (Nindicators =126) Regional collection feasibility (Nindicators =110) Blind team ratings (Nindicators = 29) Psychometric properties testing (Nindicators = 19) Final index (Nindicators = 19) 16 Figure 2. Distribution of Index Items by Emic Versus Etic Categorization. These indicators were collected from a variety of sources. From state government documentation we gathered traffic-related fines, and legislation on gambling, drinking, religious conversions and inter-caste marriage support. From the National Crime Records Bureau’s (NCRB; 2022) Crime Statistics report we collected data on arrest records for the different crime rates for deviating from the groups’ norms. Data on appeals approved were taken from the NCRB and the National Jurisdiction Data Grid (2023). Death penalty data were derived from the National Law University’s Annual Death Penalty Report (2021). Finally, religiosity measures Tightness-looseness IndicatorsEmicReported murder cases where the motive was honours killingsBelief in KarmaImportance of religionLaws against religious conversionsStates that have banned beefNumber of dry days instatedReservation laws for Other Backward CastesNumber of inter-caste couples who received government supportGovernment support for relief to victims of casteismEticDeath penalty cases in courtAppeals approved in courtFine amounts for unlicensed drivingFine amounts for disobedient drivingFine amounts for dangerous drivingFine amounts for those not wearing helmets while driving two-wheelersArrest records for suicide abetmentArrest records of people convicted for anti-government/national sentimentLegal drinking ageLaws permitting gambling establishments 17 were taken from Pew Research Center’s India Survey (2021). See Appendix Table A for more details on the index items. Construct validation For convergent validity, the associations between the index and theorized correlates were tested. This included previously tested correlates, namely political orientation, corporal punishment, and suicide prevalence (Harrington & Gelfand, 2014), as well as theorized but untested ones, namely filial obligation, and censorship (Gelfand et al., 2011). We anticipated that, in line with prior research, Tightness would relate to more conservative political orientation, greater corporal punishment, reduced suicide prevalence, greater preference for strict leaders, greater censorship, and greater respect for elders. While building on the validation measures from prior research is a rigorous approach to assess the validation of our measure, we note – at the outset – in the case that our findings do not replicate those of the United States this does not necessarily indicate a lack of construct validity. Rather, it could also reflect real differences in the structure or functioning of Tightness in India compared to the United States. Measures Political orientation. India’s multi-party system allows for more demarcations than the “right-leaning”- “left-leaning” categorization that is often encountered in psychology, where most of the sample comes from the United States (Arnett, 2008; Henrich et al., 2010, Thalmayer et al., 2021) and the political system is binary, unlike the rest of the world. As such, the present study assessed political orientation based on the majority party, defined as each state/union territory’s party with the most seats in the Lok Sabha, the lower house of India’s bicameral 18 Parliament. These data were collected from IndiaVotes (2023), a public elections resource. We coded the parties as right-leaning (1), central (0) or left-leaning (-1).5 Suicide prevalence. Suicide rates were assessed through the NCRB’s Accidental Deaths and Suicide Report (2022). Specifically, the state/union-territory-wise recorded cases of suicide were collected and normalized by the region’s population. Corporal Punishment. Corporal punishment was measured through the India Human Development Survey (2011), where participants were asked to report whether their child had been physically punished by teachers (0=no; 1 =yes). The self-reports were normalized by the total number of participants in each region who answered the question and then aggregated to the regional level. Censorship. Censorship was split into two sub-categories: media and protests. Media censorship was assessed through self-reported exposure to media (from the National Family and Health Survey, 2022) and the number of media outlets or journalists targeted by government (from FreedomHouse’s report in 2021). Protest censorship was assessed through the NCRB’s Crime Statistics report (2022) reported arrests for rioting, unlawful assembly and imputation, assertions prejudicial to national integration. Both variables were normalized by the state population and aggregated to the regional level. Preference for strict leaders. Preference for leaders was measured through Pew Research Center’s India Survey (2021) which asked participants for their preference for leaders with a strong hand (1 = democratic government, 2 = leader with strong hand). In this study, the 5 This process was carried out for both the 2014 and 2019 elections to ensure reliability in the results since the 2014 elections saw a drastic shift in power amongst two of the leading parties (Schakel et al., 2019). 19 number of participants who reported a preference for a strong hand were normalized and aggregated to the regional level. Filial obligation. The filial obligation variable was a composite of the percentage of economically independent older adults and percentage of older adults living alone6 from a report on elderly in India (Ministry of Statistics and Programme Implementation, 2021). All items were reverse coded to denote greater respect, such that higher values would indicate greater dependence6 on one’s family, and normalized by the population of older adults in each region. Analytical strategy Tightness scores were calculated by recoding where necessary such that greater values reflected increased Tightness. These scores were then reweighted items to account for missing values7, and finally standardized. In keeping with prior literature (Baron & Straus, 1989; Harrington & Gelfand, 2014; Vandello & Cohen, 1999), the scores were further transformed (multiplied by 20, and then added to 50) to provide more interpretable scores. To test the construct validity of the index, we calculated zero-order correlations between theorized validation variables and the index. Given the small sample size, we considered any effect size greater that |0.1| to be a meaningful effect instead of basing interpretations on statistical significance (Elleman et al., 2020; Funder & Ozer, 2019). Additionally, to ascertain discriminant validity, following prior research (Rönkkö & Cho, 2022) we consider |0.8| as a threshold, such that correlations greater than |0.8| were deemed highly conceptually similar. 6 These items reflect greater obligation due to the filial expectations: in India, older adults depend on their families as they age for residential and financial security (Chaddha, 2008; Sinha, 1981). 7 As a part of the many recent recategorizations, the state of Telangana was formed in 2014 (Ministry of Law and Justice, 2014). This brough further complexities into India’s judicial system composition at the within-country level and consequently affected the quality of our data as many sources do not have information for Telangana prior to 2014. To address this, we decided to by averaging each region’s score by the number of variables that it had data on. 20 Results The final index items were moderately correlated across the board (rrange = |0.1-0.93|) and were highly internally consistent (α = .93). Table 1 lists Tightness scores for the states and union territories (M = 49.5, SD = 13.36), ranked from tightest to loosest, and Figure 3 visually depicts the Tightness scores on a map of India. Table 1. Cultural Tightness Scores for Each Region from Tightest to Loosest. States and Union Territories Cultural Tightness Uttar Pradesh 89.03 Maharashtra 76.44 Bihar 73.60 West Bengal 69.79 Madhya Pradesh 62.40 Gujarat 58.95 Karnataka 56.13 Rajasthan 54.21 Tamil Nadu 52.62 Andhra Pradesh 52.28 Punjab 51.43 Odisha 50.43 Telangana 50.37 Haryana 49.12 Assam 48.82 Chhattisgarh 48.12 Jharkhand 47.98 Kerala 47.88 Delhi 46.94 Himachal Pradesh 45.16 Jammu and Kashmir 39.62 Manipur 38.56 Meghalaya 38.49 Goa 38.33 Tripura 36.45 Mizoram 36.42 Puducherry 36.07 Nagaland 36.01 Sikkim 36.00 Arunachal Pradesh 35.84 21 Figure 3. Tightness at the Regional Level in India. Note. Darker shades of blue indicate tighter regions. Regarding construct validation, we found that the index correlated as expected with most validation measures besides media censorship (r = -0.52) and protest censorship (r = -0.08) which were negatively associated with the index. See Table 2 for more details on these correlations. 22 Table 2. Zero-Order Correlations Between Tightness Index and Theorized Correlates Variables rtl Media censorship -0.52** Protest censorship -0.08 Filial obligation 0.96*** Corporal punishment 0.92*** Preference for strict leaders 0.25 Election results 2014 0.36* Election results 2019 0.23 Suicide rates -0.21 * p <.05, ** p < .01, *** p < .001 Note. We consider any effect size greater that |0.1| to be a meaningful effect Summary Study 1 indicated that there are considerable regional differences on the constructed index of Tightness. We see a substantial range of scores from Arunachal Pradesh at 35.84 ranking as the most culturally loose, and Uttar Pradesh at 89.03 as the most culturally tight. The top 5 tightest states include Uttar Pradesh, Maharashtra, Bihar, West Bengal, and Madhya Pradesh; whereas the loosest states are Mizoram, Puducherry, Nagaland, Arunachal Pradesh, and Sikkim. The regional variability observed is at par with what is observed in the United States (27.37 – 78.86), which uses the same range of hypothetical scores from 0-100. In terms of construct validation, we see mixed support for our index. In line with prior literature (Chua et al., 2019; Harrington & Gelfand, 2014; Jackson et al., 2020), we see that greater cultural tightness was associated with right-leaning political orientation, greater corporal punishment, preference for strict leaders, greater filial obligation, reduced suicide rates. However, we also see that cultural tightness was negatively related to censorship and that our Tightness index was highly correlated with filial obligation and corporal punishment. Possible explanations for this are explored in the General Discussion section. 23 Study 2. Influences on tightness Following prior literature (Gelfand et al., 2011; Harrington & Gelfand, 2014; Jackson et al., 2020), Study 2 explored potential antecedents of the regional variability in Tightness observed in Study 1. We tested previously investigated antecedents, such as ecological and human-made threats, as well as relatively novel ones, such as geographical elements. We examine these through a two-pronged analysis approach of specification curve analysis and conditional random forest results. Both methodologies are commonly reported in geographical psychology research independently (Götz et al., 2020; Peters et al., 2023; Wei et al., 2017), here we combine them to provide thorough interpretations that consider the combined weight of evidence across the techniques in favour of, or against, a given association. Under specification curve analysis, we also incorporated spatial regressions to address concerns of spatial clustering (Ebert et al., 2022), as done in prior work (Peters et al., 2023). This allows for a thorough inspection of the different associations, while controlling for a range of concerns: underpowered sample sizes (conditional random forests), over-controlling (specification curve analyses), and spatial clustering (spatial regression). We also report SHAP value graphs to supplement the random forest results for accessible interpretation of the findings independent of significance-testing while considering sensitivity of associations to outliers. Given the understudied cultural context, we take on an exploratory approach in testing these associations. Methods The theorized antecedents of Tightness were captured through a criterion of ensuring the data spanned at least 28 of the 31 regions. These variables were also collected from pre-existing available data, and we normalized all the variables where needed over the state population. See Appendix Table B for a complete list of variables collected to assess antecedents of Tightness. 24 Threats Natural disasters. In line with prior research (Harrington & Gelfand, 2014; Jackson et al., 2020), natural disasters were characterized by total number of deaths due to natural disasters (avalanche, exposure to cold, cyclones, tornados, tsunami, earthquake, epidemic, flood, heat stroke, landslide, lightning, torrential rain, forest fire and other natural forces) collected from NCRB’s Accidental Deaths & Suicides report (2022). Disease prevalence. Data were collected from the Indian Health Ministry’s reports (2002 onwards) on region-wise deaths and cases of malaria, dengue, leprosy, tuberculosis, influenza, pneumonia; overall death rate; and infant mortality rate. Population Density. We collected data on population density to assess population pressures as ecological threats, from India’s 2011 Census, which is the latest published census from the Indian government as of 2024. Land usage. Given Jackson and colleagues (2020) measure of land shortage, we tried to replicate this finding with four distinct variables. For direct replication, we operationalized land shortage as land dedicated to agricultural and residential use, such that higher dedicated land would indicate reduced land availability. Next, we utilized data on cropland (specified as cropland relative to the total area in the region), and protected land (land that has been dedicated to and achieved long-term conservation relative to total area in the region) from the World Bank’s India Spatial Database (2023). Lastly, we utilized Willinger and colleagues’ (2023) data on the normalized difference vegetation index by region, to quantify the density of vegetation in each area. Together, these indicated greater land usage for a variety of resources. 25 Military recruitment. Reflecting previous operationalizations (Harrington & Gelfand, 2014), we collected data on military recruitments from 2016 onwards in each region from the Indian Ministry of Defence’s reports. Regional conflicts. As a novel measure of human-made threat, we collated instances of inter-regional disputes to reflect regional conflicts. Crucially, this variable included historical events unique to India. Namely, the end of British occupation was marked with extensive sectarian violence and migrations, as the dissolution of the British rule resulted in the partition of the land into India and Pakistan, which affected several states within present-day India (Bharadwaj & Mirza, 2019; Kaur, 2011; Roy, 2014; Shashkevich, 2019). To capture this, we collected data on the occurrence of regional violence during the Partition mapped onto the modern-day regions (Copland, 2005; Copland, 2002; Pandey, 2001), and data on states that were split up across the nations as a result of the partition (Balasubrahmanyan, 2011; Hajari, 2015; Partition Museum, 2024; Zamindar, 2010). Additionally, we captured the states affected by the reorganization of administrative regional boundaries in 1956 through the States Reorganization Act, which organized the regions along linguistic lines (Ministry of Home Affairs, 1956). These variables were combined such that greater scores indicated more regional conflict. Income Inequality. Income inequality was measured through Gini coefficients, a common measure of income inequality, calculated in prior research at the state and union-territory level (Pandey & Gautam, 2020). Societal complexity and heterogeneity Urbanization. Following prior research (Chua et al., 2019; Harrington & Gelfand, 2014), we considered urbanization as a sign of societal complexity. As such, urbanization was measured 26 as the ratio of urban to rural population by state and union territory, collected from the 2011 Census report. Historical rice farming. As done in other work (Talhelm, 2022; Talhelm & English, 2020), we utilized two variables to assess the extent of rice-farming and its effects of cultural Tightness in India. We used data on the historical percentage of rice paddies by region as our main measure of rice farming, and a binary variable that coded regions as rice or wheat farming regions as our corroboratory measure. These data were taken from prior work which calculated both measures in Indian states and union territories (Talhelm, 2022). Societal heterogeneity. Deviating from Jackson et al.’s (2020) operationalization of heterogeneity, which examined kinship heterogeneity through the presence of uncommon residence patterns, we focused on heterogeneity through demographic diversity. Particularly, we used Census data (2011), to characterize caste-based diversity as the proportion of caste minorities in the region. For religious diversity, we recorded the proportion of the population in each state or union territory that do not identify with as Hindus, the religious majority group in India (Pew Research Center, 2021). Geography Surface temperature. From Willinger et al.’s (2023) data, we derived our measure of average land surface temperature, which has previously been shown to associate with psychological constructs (Van de Vliert, 2017; Wei et al., 2017). Land-water mask. We also utilized Willinger et al.’s (2023) data on land-water mask, the estimated percentage of water coverage across a surface area. Here, higher values indicated greater water coverage (e.g., coastal regions), while lower values were indicative of more land-locked terrains. 27 Mountainousness. Mountainousness was operationalized as the standard deviation of elevation within the state/union territory level from Willinger and colleagues’ (2023) data. A standard deviation of 0 reflects flat land while a larger standard deviation indicates a more hilly, mountainous area. This measure of assessing mountainousness was derived from prior work where it demonstrated relevant predictive utility (Götz et al., 2020; Stieger et al., 2022). Precipitation. The World Bank’s India Spatial Database' (2023) reported the total precipitation in each region as of 2011, which we used as our measure. Higher values indicated greater precipitation. Control variables In keeping with geographical psychology literature (Götz et al., 2018; Rentfrow et al., 2008), we controlled for median age, per capita income, education rates and sex ratio. Median age for each region was derived from the 2011 Census data. For education rates, we collected literacy rates for each state and union territory from the United Nations’ Sustainable Development Goal Index (2023). Sex ratio was measured through the 2011 Census, which reported the ratio of female to male population in each region. Lastly, we utilized two measures to capture income: per capita net state domestic product reported for each region by the Reserve Bank of India’s Handbook of Statistics on Indian States (2021) and economic wellbeing in each region. For the latter, we combined multiple variables that capture economic wellbeing: percentage of population living below the national poverty line as per the United Nations’ Sustainable Development Goal Index (2023), multidimensional poverty index scores reported in India Multidimensional Poverty Index Report (2021), and number of ration cards (equivalent to food stamps in India) allotted in each region according to the National Food Security Act website (2023). 28 Analytical strategy To analyze these data, we took a two-pronged analytical approach such that we conducted two sets of analyses to address different limitations commonly observed in similar research: specification curve analyses and conditional random forests. These are often reported independently in geographical psychological research (Götz et al., 2020; Peters et al., 2023; Wei et al., 2017) but we combine them to allow for a holistic, robust understanding of the relationships by considering the weight of evidence for each association (EFSA Scientific Committee et al., 2017). Here, strong weight of evidence implies relevant effects were found across all three interpretation benchmarks, medium weight of evidence indicating that the expected association was found across two of the three benchmarks, and weak weight of evidence suggesting the effect was detected in only one of the benchmarks. As is common in geographical psychology literature, the aggregation of data to the regional level limits the number of observations for analysis to the number of regions being investigated (N = 31, in our case). As such, we anticipated our analyses to be underpowered and not reflect statistical significance accurately, known as the small-n-large-p problem (Johnstone & Titterington, 2009). To avoid undermining the results as a consequence of this, we focus our interpretations of analyses on the effect sizes and their directionality, a solution previously used in geographical psychology research (Peters et al., 2023). Based on prior work (Elleman et al., 2020; Funder & Ozer, 2019; Peters et al., 2023; Rentfrow et al., 2008), we set a threshold effect size of |0.1|, such that any effect size greater than this threshold would be considered a meaningful effect. Spatial clustering, or dependence amongst observations due to spatial proximity, is a typical consideration when analyses are conducted with regions as the unit of observation. This is 29 a concern with OLS regression models because it violates the assumption of non-independence, leading to biased regression coefficients and likely overestimated effect sizes. Spatial regression models allow us to account for spatial clustering amongst the regions in calculating regression coefficients and effect sizes, making it a common solution in geographical psychology literature (e.g., Atari et al., 2022; Ebert et al., 2021). See Appendix C for more details on these analyses. However, these regression models involve a lot of research degrees of freedom in deciding what controls to use and whether to conduct spatial regression models or not. As such, we reported specification curve analyses, which subsumed the spatial regression models, to provide transparency around specification decisions. Specification curve analyses involve identifying all theoretically-relevant model specifications decisions, and calculating the effect sizes and p-values for each possible combination of specification decisions (Simonsohn et al., 2020). This allows readers to understand and make inferences across all specifications. Although we cannot explicitly model spatial regressions within the specification curve framework, we followed prior research (Peters et al., 2023) by including the spatially weighted dependent variable in our analyses. From this, the median effect size estimates of all possible specifications were drawn and the percentage of these estimates that were greater than an interpretation threshold were calculated for interpretability, as has been done previously (Peters et al., 2023). The slight deviation from prior literature in interpreting the specification curves stems from the current research’s focus on effect sizes rather than significance, given the small sample and subsequent low power. For interpretations, we consider two benchmarks for meaningful relationships: whether the median specification effect size was greater than |0.1| and whether 70% of the specification effect sizes were greater than |0.1|. 30 Lastly, another approach to address the small-n-large-p concern is to use machine learning approaches that go beyond significance-testing. Particularly we used conditional random forests, which has been used in other geo-psychology literature to overcome this limitation (Götz et al., 2020; Stieger et al., 2022). They assess the relative contribution of each predictor by exploring all possible relationships within the model through a recursive partitioning (IJzerman et al., 2018). The random forests compute the importance, or predictive power, of each variable by permutating the values of the predictor to note the loss in predictive accuracy: lesser the loss in accuracy, the greater the importance of the predictor. Given the data-driven, non-parametric approach (Joel et al., 2020), conditional random forests are robust to overfitting, non-linearity, and correlated predictors, making them particularly apt at identifying meaningful predictors. Since conditional random forests cannot accommodate missing values, we report the imputed conditional random forests to provide a holistic understanding of the importance of each predictor. Imputed data were created by 1) creating a subset dataset of the variables with missing values; 2) imputing the missing values in the subset dataset through maximum-likelihood estimation with the EM algorithm, through the norm package in R. From these random forests, we report the ordinal ranking of predictors in addition to the importance scores. Although prior research interprets these importance scores with cutoffs based on the minimum absolute importance score (Götz et al., 2020; Wei et al., 2017), we find that the ordinal rankings provide greater interpretability and more robust inferences. As such, we set a final benchmark of the top 50% ordinally ranked predictors to be meaningfully associated with Tightness. Finally, we supplemented the random forest analyses through visuals computed using SHAP (SHapley Additive exPlanations) values. SHAP values expand on conditional random forests by accounting for interactions between predictors before computing variable importance 31 scores for each predictor (Lundberg & Lee, 2017). The predictors are then rank-ordered by their importance scores, depicted in graphs that essentially illustrate a scatterplot for Tightness and each predictor, with colour-coded data points to highlight the directionality of the relationship (see Figure 4 note for more). Since these account for interactions before ranking the importance of predictors, they are often in-line with conditional random forest results, with discrepancies between the rankings indicating the extent of predictor-interactions. Results Threats Across the threat variables, we found strong weight of evidence for greater military recruitment (β = 0.20, β > .1 = 84.38%, importanceranking = 7th out of 18 predictors) and greater land shortage, including cropland (β = 0.87, β > .1 = 78.12%, importanceranking = 1st), protected land (β = 0.16, β > .1 = 100%, importanceranking = 6th), and land usage (β = 0.20, β > .1 = 75%, importanceranking = 8th), being associated with increased cultural tightness. Meanwhile, we observed medium weight of evidence for reduced vegetation relating to tighter regions (β = -0.16, β > .1 = 56.25%, importanceranking = 3rd), and greater experiences of regional conflicts associated with heightened cultural tightness (β = 0.10, β > .1 = 56.25%, importanceranking = 4th). Finally, we saw weak weight of evidence for population density associated with greater Tightness (β = 0.12, β > .1 = 62.5%, importanceranking = 15th). We did not find any evidence for natural disasters, income inequality, and disease prevalence, related to cultural tightness across the three analyses. Table 3 provides the complete findings for all antecedents. Figure 4 depicts these effects visually with SHAP-values. 32 Table 3. Specification Curve Analyses and Conditional Random Forest Importance Scores Predicting Tightness in India. Note. Results highlighted in green indicate strong weight of evidence, yellow denotes medium weight of evidence, and red suggests weak weight of evidence a Median effect of each predictor on Tightness, independent of which covariates are controlled for. See Appendix Table D for original spatial regressions. b Percentage of effect sizes across all specifications that meet the 0.1 threshold, independent of which covariates are controlled for and whether a spatial regression is used. c Importance scores derived from the imputed conditional random forest, controlling for median age, literacy rates, median income and poverty levels, and the ordinal ranking of importance relative to other predictors. See Appendix Table D for unimputed results. d Similar results found when the binary rice vs. wheat variable was used. Antecedents Median βSCAa β SCA > |.01|b (%) Importance score (ranking)c Threats Cropland 0.87 78.12% 0.15 (1st) Protected land 0.16 100% 0.02 (6th) Army recruitment 0.20 84.38% 0.01 (7th) Land usage 0.20 75% 0.01 (8th) Vegetation -0.16 56.25% 0.11 (3rd) Regional conflict 0.10 53.12% 0.09 (4th) Population density 0.12 62.5 % -0.00 (15th) Disease prevalence 0.04 0% -0.00 (14th) Natural disasters -0.03 12.5% -0.00 (17th) Income inequality 0.06 25% 0.00 (18th) Societal complexity/heterogeneity Historical rice farmingd 0.13 62.5% -0.00 (16th) Caste diversity -0.01 0% 0.00 (9th) Religious diversity -0.02 0% 0.00 (10th) Urbanization 0.00 9.375% -0.00 (13th) Geography Surface temperature -1.32 75% 0.14 (2nd) Precipitation 0.42 96.88% 0.09 (5th) Land water mask 0.06 9.375% 0.00 (11th) Mountainousness -0.08 0% -0.00 (12th) 33 Figure 4. Imputed SHAP Value Summary of Associations Between Antecedents and Cultural Tightness. Note. The y-axis denotes all the predictors that were deemed important through the conditional random forest and SHAP value generation process, ordered by their importance8. The x-axis represents the SHAP-values for predicting Tightness, calculated for each observation9. There are 31 plotted points for each predictor, indicating the spread of the values. Finally, the colour of the points indicates the value of the predictor for each datapoint: orange points are higher and purple ones are lower values. These colours allow us to interpret the directionality of the association. For instance, increasing values of cropland, the most important predictor, correspond to higher SHAP values, suggesting a positive association between cropland and Tightness. 8 SHAP values measure the contribution of each predictor in predicting the outcome of interest, in this case, Tightness. Predictors with low prediction value are excluded from the graph to keep it concise and focused on the most impactful features. 9 In the present research, each observation denotes a region, so for each predictor, there are 31 points. 34 Societal complexity and heterogeneity We saw weak evidence for a positive association between historical rice farming and greater Tightness (β = 0.13, β > .1 = 62.50%, importanceranking = 16th). We found no evidence for the influence of urbanization, religious-based diversity, and caste-based diversity on Tightness. Geography We observed strong weight of evidence suggesting that surface temperature was associated with reduced Tightness (β = -1.32, β > .1 = 75%, importanceranking = 2nd) and precipitation related to more cultural tightness (β = 0.42, β > .1 = 96.88%, importanceranking = 5th). We found no evidence for mountainousness (β = -0.08, β > .1 = 0%, importanceranking = 12th) and land-water mask (β = 0.06, β > .1 = 9.375%, importanceranking = 11th) relating to Tightness. Summary Study 2 provides further support for the utility and validity of the index constructed in Study 1 by replicating certain previously found associations. Specifically, military recruitment and land usage robustly predicted greater cultural tightness, contributing to the research on the threat model of Tightness (Gelfand et al. 2011). Further in line with prior research, we see weak evidence of cultural tightness associated with greater rice farming (Talhelm & English, 2020). We also see support for previously explored predictors with novel operationalizations, with medium evidence for culturally tight regions relating to greater experiences of regional conflict, and lesser vegetated land. Finally, we also contribute to the research on antecedents of Tightness by finding support for an underexplored source of variation: geographical elements. Namely, we found that precipitation and surface temperature are robustly associated with Tightness across India, such that culturally tight regions related to lower surface temperatures and greater precipitation. See the General Discussion section for plausible accounts for the observed results. 35 Study 3. Correlates of tightness In Study 3, we examined the correlates, or potential outcomes, of Tightness. Here again, we used archival, pre-collected data to characterize theoretically-relevant correlates for each region and explored these using the same analytical approach as in Study 2. We attempt to replicate prior work in testing examined correlates, such as debt and discrimination, and expand on this line of research by considering unexplored ones such as incumbent voting and marriage-related norms. Method The theorized correlates were captured through a similar criterion of ensuring the archival data spanned at least 28 of the 31 regions. All variables were normalized by the latest relevant population in the respective state or union territory. See Appendix Table E for a complete list of correlate variables collected. Order correlates Substance use. Substance use was measured through a combination of two variables. First, data from the Magnitude of Substance Use in India report (2019) by the Indian Department of Social Justice and Empowerment was used to characterise dependence on the following substances by region: alcohol, cannabis, opioids, sedatives, cocaine, amphetamine, inhalants, hallucinogens, and drug injections. Next, we also utilized the reported crime rates for possession of drugs for personal use and consumption, as reported in the NCRB’s Crime Statistics (2022). Debt. We were not able to find a measure of debt in India which aligned perfectly with what has been used in prior work (credit card debt; Harrington & Gelfand, 2014). Instead, the debt-to-asset ratio in each state and union territory, from the Debt and Investment Survey (Ministry of Statistics and Programme Implementation, 2019), was used. Debt-to-asset ratio 36 refers to the average amount of debt to the average value of assets per household, providing a different perspective on financial control. We used these values to reflect the amount of debt by region. Corruption. Corruption was assessed through a composite of five variables. From the NCRB’s Crime Statistics reports (2022), we collected data on corruption arrest rates, police disposal of anti-corruption cases, court disposal of anti-corruption cases, and action against people arrested under the Corruption Act. We also collected data from Transparency India (2022), which reports the region-wise right-to-information forms filled. These variables were recoded where needed so that greater scores indicated higher corruption. Incumbent voting. As a proxy for institutional trust, we collected data on incumbent voting. Incumbent voting was calculated by subtracting the number of seats won by the current majority party in the 2014 election for the Lok Sabha from the number of seats won by them in the 2019 election in the Lok Sabha. The data were collected from IndiaVotes (2023), an online election database and the numbers were verified against the general election results reported by the government. This variable provides insight into the change in favourable voting for the party that has the majority vote, because the number of seats won depends on the number of votes gained by the political party. For instance, in Andhra Pradesh, the YSR Congress Party currently holds the majority vote with 22 seats won in 2019 while in 2014 they only had 8, suggesting an increased political favour in the region. Whereas in Bihar, the Bharatiya Janata Party saw a decrease in public favour as they had 17 seats in 2019 but 22 seats in 2014. Trust in institutions. Trust in institutions was operationalized through two measures. The first item, from Pew Research Center’s India Survey (2021), asked participants to report their trust in allopathic, modern, Western medicine over other forms of medicine that are 37 commonly observed and practiced in India: ayurveda, religious rituals, and homeopathy. We aggregated this to the regional level to reflect average trust in public medicine within the region. The second item was derived from the Economic Survey of India (2023), where we considered region-wise deposits into public, commercial banks (as compared to private banks) as a reflection of the trust placed in banking institutions. The two variables were then combined to provide a final measure for average trust in public medicinal and banking institutions. Social organization. Following Harrington and Gelfand’s (2014) operationalization, we assessed social organization as a combination of the following: inmate population per state; crime rates per hundred thousand people for violent crimes, murder, assault, robbery, burglary, and vehicle theft; total police per hundred thousand people, and the percentage of slum-dwelling populations. The crime-related measures were collected from the NCRB’s Crime Statistics 2022 report, while the number of police officers per region was collected from the Ministry of Home Affairs (2020) measure of this, and the slum-dwelling population numbers were taken from the United Nation’s Sustainable Development Goal Index for India (2023). These measures were compiled into a composite such that higher scores indicated greater social organization. Ecological preservation. Although prior research has reported mixed association with cultural tightness (Elster & Gelfand, 2021; Tam & Chan, 2017), we explored this relation through a series of variables collected by the United Nation’s Sustainable Development Goal Index for India (2023): percentage of renewable energy out of total installed generating capacity, disaster preparedness score as per Disaster Resilience Index, CO2 saved from LED bulbs, Disability Adjusted Life Years (DALY) rate attributable to air pollution, forest and tree cover as a percentage of total geographical area, percentage of area covered under afforestation schemes to the total geographical area, percentage of degraded land over total land area, number of cases 38 under Wildlife Protection Act (1972), and percentage increase in area of desertification. These variables were then compiled into create a final ecological preservation variable. Waste separation. Waste separation was assessed through the waste management practices reported by the Government of India’s Swacch Bharat (“Clean India”) website in 2023: the number of door-to-door segregations10, C&D waste recycling, material recovery facilities, solid liquid resource managements, waste to compost plants, and waste to energy plants in each region. Openness correlates Happiness. We collected regional-level average happiness scores from Rajesh Pillania’s India Happiness report (2020). The report calculated happiness scores for 16,590 participants through the UN’s Cantril ladder questions, and five questions on the components of happiness (Pillania, 2020). Institutional gender equality. Institutional gender inequality was measured through aggregated data at the regional level from the United Nations’ Sustainable Development Goal Index (2023) on gender equality. This included the following variables: (1) proportion of women in managerial positions (including women in board of directors), (2) proportion of women who own land, (3) percentage of currently married women who have their demand for modern methods of family planning satisfied, (4) rate of crimes against women, (5) women who have experienced domestic violence, (6) ratio of female to male average wage/salary earnings received among regular wage/salaried employees, (7) percentage of elected women over total seats in the state legislative assembly, and (8) ratio of female to male Labour Force Participation 10 In India, waste management is referred to as waste segregation, in the sense of separating different types of waste. The phrasings for these variables were taken verbatim from the data source to make the data easily identifiable in the sources. 39 Rate (LFPR). The domestic violence and crime rates against women variables were recoded as needed such that greater values indicated higher gender equality. Gender equality attitudes. Individual’s gender equality attitudes were measured through variables collected in Pew Research Center’s India Survey (2021), and through the National Family Health Survey (2022). The numerous attitudinal variables were aggregated to the state/union-territory before they were recorded in our dataset. From Pew Research Center’s India Survey we collected responses to questions regarding the social roles of men/women and opinions on gender equality including: (1) responsibilities in earning money and taking care of children/parents, (2) last rite responsibilities, (3) husband-wife relations, (4) employment rights for men and women, (5) leadership roles, (6) opinions on altering gender makeup, (7) safety of women, (8) equal rights, and (9) discrimination faced by women. Some of these items were collected on a 1-4 Likert scale (completed agree – completely disagree), while the rest were on a 1-4 scale with each number indicating a different target or actor (1 = sons; 2 = daughters; 3 = Both; 4 = depends/neither/others). The variables were recoded where needed such that higher values would indicate greater gender equality attitudes. From the National Family Health Survey, we collected the following variables: (1) married women in employment, (2) women’s control over women's and men's cash earnings, (3) magnitude of women's cash earnings relative to men’s in a household, (4) women's participation in household decision making, (5) women's access to money and credit, (6) women’s freedom of movement, (7) women’s ownership of assets, and (8) total percentage of married women in employment with cash earnings. All measures were recoded prior to collation such that higher values suggest higher equality values. 40 Religious discrimination. Religious discrimination was measured through two forms of criminal activity: religiously motivated (1) murders and (2) protests as reported by the NCRB’s Crime Statistics report (2022). Casteism. Casteism was quantified through reported crime rates related to caste by region including murders, protests, and particularly cases that violate laws intended to protect minority castes, from NCRB’s Crime Statistics report (2022). This includes offences of Untouchability and cases violating the Prevention of Atrocities Act and Protection of Civil Rights Act. Untouchability refers to the social ostracization of individuals due to their membership to certain castes (Human Rights Watch, n.d.), the Prevention of Atrocities was enacted to allow harsher punishments for crimes committed against minority caste members (Human Rights Watch, n.d.), and the Protection of Civil Rights Acts was enacted to allow strong punishment for the preaching or practice of untouchability (Human Rights Watch, n.d.). Innovation. Following prior literature, innovation was operationalized through two variables. The typical measure of innovation/creativity in the Tightness literature is the number of patents produced per region (Harrington & Gelfand, 2014), which we replicated in the present study by collecting data on the number of patents by region, from the India Innovation Index report (2020). In their study on regional variation in Tightness across China, Chua and colleagues (2019) expanded on this association by exploring the differential association between radical innovation (number of innovative patents) and incremental innovation (number of utility patents). In line with that, we included a measure of incremental innovation support from institutions, measured through the five broader categories of indicators: human capital, knowledge workers, business environment, legal/safety, and investment. These scores were also taken from the India Innovation Index (2020). 41 Fertility norms. Since India is the most populated country in the world in 2024 (Hertog et al., 2023), we collected data from the National Family Health Survey (2022) on reported compensation for sterilization. This variable was reverse coded so that higher values indicated greater adherence to the high fertility rates. Another common fertility-related norm in India is the preference for children assigned male sex at birth, which is a major cultural factor shown to influence fertility-related behaviours in India11 (Barman & Sahoo, 2021). To capture this in our measure of fertility norms, we also collected data on the reported female foeticides by region, as well as average self-reported sex preferences for child in the National Family Health Survey (2022), such that higher values aligned with greater preference of children assigned male sex at birth. All three items were combined to provide insight into the fertility norms, such that greater scores indicated greater adherence to fertility norms (male-child preference, and reduced sterilization or contraception use). Abortion norms. Related to the fertility norms, and to replicate Uz’s (2015) work, we explored the relationship between acceptability of abortions and Tightness. To assess acceptability of abortions, we utilized two variables: from Pew Research Center’s India Survey (2021), we collected self-reported stances on the legality of abortions, and from the National Family Health Survey (2022), we collected reported rate of abortions. These variables were combined to create a measure of overall acceptance of abortions, such that higher values indicated greater acceptance of abortions. Sexual health norms. Further relevant to fertility norms, we investigated conformity to sexual health norms. We assessed this through variables in the National Family Health Survey (2022) that captured norms related to menstrual practices (reported bathing practices during 11 Specifically, preference for a child assigned male at birth has led to dramatic increases in female foeticides. This behaviour so prevalent that legislation was introduced to ban sex-determination before birth (Ahmad, 2010). 42 menstruation) and to sexual relations (use of contraceptives, attitudes towards refusing intramarital sexual intercourse, negotiating sexual relations with husband, and husband’s rights when intercourse is refused). Additionally, we collected data on institutional approaches to HIV. From India’s National AIDS Control Organization (2019), we utilized the reported progress of each region on the United Nations’ 95-95-95 strategy towards providing effective HIV combination prevention options (testing and treatment) to 95% of all people at risk of acquiring HIV (Barton-Knott & Hollingdale, 2023). We combined these measures such that higher values would indicate higher sexual health norm adherence. Spousal unfamiliarity. To capture adherence to arranged marriage norms we utilized data from the Indian Human Development Survey (2012) on average self-reported women’s agency in choosing a husband, familiarity with husband prior to marriage, and blood relation to husband12. The items were coded and averaged such that higher values suggest greater unfamiliarity with one’s spouse prior to marriage, before being aggregated to the regional level. Dowry. We assessed dowry prevalence through reported criminal activities in the NCRB Crime Statistics report (2022) related to dowry- arrest records for murders and deaths related to dowry, as well as arrest records for violations of the Dowry Prohibition Act. Marriage-related violence. Marriage-related violence was measured through crimes reported in the NCRB Crime Statistics report (2022) – cases of domestic violence, kidnappings, and child marriage-related kidnappings – and the National Family Health Survey’s self-reported attitudes towards justified domestic violence. All three variables were weighted before being combined into a consolidated measure of marriage-related violence. 12 Consanguineous marriages, the union of men and women of a common ancestor related by blood, are a common historical practice in India (Acharya & Sahoo, 2021), which is why we included it in our measure here. 43 Child marriages. We collected the reported occurrences of child marriages in each state or union territory from UNICEF’s End Child Marriage Progress report (2023), which recorded the number of women who reported first being married before 18. Analytical strategy We employed the same approach in Study 2 to test the collected variables as correlates of Tightness. The same interpretation benchmarks were applied to each analysis, i.e., median specification curve effect sizes above |0.1|, at least 75% of specification curve effect sizes above |0.1|, and an importance ranking at least in the top half of the inputted predictor variables. The same control variables were considered in these analyses (median age, per-capita income, sex ratio, literacy rates, and economic well-being). It’s worth noting that in Study 3, we only had four to five predictors, including Tightness and the control variables, that were considered and inputted. After conducting these analyses, we collated a summary of the findings through a weight-of-evidence approach as in Study 2. Finally, we also depicted the conditional random forest findings in SHAP summary graphs to aid in interpretability. Results Order correlates Across analyses, we found strong weight of evidence for tighter regions relating to reduced incumbent voting (β = -1.85, β > .1 = 100%, importanceranking = 1st). Medium weight of evidence indicated that cultural tightness was associated with reduced debt (β = -0.84, β > .1 = 75%, importanceranking = 3rd), and reduced waste separation (β = -1.24, β > .1 = 75%, importanceranking = 3rd). Finally, the analyses indicated weak evidence of cultural tightness associated with reduced substance use (β = -0.18, β > .1 =25%, importanceranking = 4th), greater trust in institutions (β = 0.15, β > .1 = 62.5%, importanceranking = 3rd), greater social organization 44 (β = 0.13, β > .1 = 56.25%, importanceranking = 3rd), and decreased ecological preservation (β = -0.21, β > .1 = 62.5%, importanceranking = 4th). However, we found no evidence for the relationship between Tightness and corruption (β = 0.03, β > .1 = 50%, importanceranking = 4th). See Figure 5 for an illustration of the strong association between Tightness and incumbent voting. Appendix G include the full set of SHAP illustrations predicting each outcome. Figure 5. SHAP Value Summary of Cultural Tightness Predicting Incumbent Voting. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor of incumbent voting, with tighter regions reporting reduced incumbent voting. Openness correlates We found strong evidence indicating that culturally tight regions were associated with greater religion-based discrimination (β = 1.02, β > .1 = 87.5%, importanceranking = 2nd), and 45 greater gender equality attitudes (β = 0.38, β > .1 = 100%, importanceranking = 2nd). Regarding well-being, we found strong evidence that tighter regions were associated with higher self-reported happiness (β = 0.21, β > .1 = 75%, importanceranking = 1st). Additionally, we saw strong evidence for tighter regions related to reduced spousal unfamiliarity (β = -1.11, β > .1 = 100%, importanceranking = 3rd) but greater marriage-related violence (β = 0.74, β > .1 = 87.15%, importanceranking = 2nd) and child marriage rates (β = 0.58, β > .1 = 93.75%, importanceranking = 2nd). We found medium evidence related cultural tightness with greater casteism (β = 0.40, β > .1 = 75%, importanceranking =3rd). The analyses further provided medium evidence relating Tightness to greater dowry-related practices (β = 0.60, β > .1 = 96.88%, importanceranking = 3rd), and reduced innovation support (β = -0.86, β > .1 = 81.25%, importanceranking = 4th). We also saw weak evidence linking Tightness to greater innovation (β = 0.18, β > .1 =68.75%, importanceranking = 3rd). Lastly, we also observed weak evidence for Tightness associated with greater institutional gender equality, reduced abortion acceptance, and greater adherence to fertility and sexual health norms (see Table 4). 46 Table 4. Specification Curve Analyses and Conditional Random Forest Results for Tightness and Each Correlate. Correlates Median βSCAa βSCA > |.01| (%) b Importance score (ranking) c Order Incumbent voting -1.85 100% 0.42 (1st) Debts -0.84 75% 0.07 (3rd) Trust in institutions 0.15 62.5% 0.16 (3rd) Social organization 0.13 56.25% 0.15 (3rd) Waste separation -1.24 75% 0.18 (3rd) Substance use -0.18 25% 0.36 (4th) Ecological preservation -0.21 62.5% 0.02 (4th) Corruption 0.03 50% -0.03 (4th) Openness Happiness 0.21 75% 0.29 (1st) Gender equality attitudes 0.38 100% 0.22 (2nd) Religion-based discrimination 1.02 87.5% 1.31 (2nd) Caste-based discrimination 0.40 75% 0.10 (3rd) Spousal unfamiliarity13 -1.11 100% 0.08 (3rd) Marriage-related violence 0.74 87.15% 0.13 (2nd) Child Marriages 0.58 93.75% 0.15 (2nd) Dowry 0.60 96.88% 0.11 (3rd) Innovation support -0.86 81.25% 0.01 (4th) Innovation 0.18 68.75 % 0.12 (3rd) Institutional gender equality 0.20 68.75 % 0.26 (3rd) Sexual health norms 0.13 25% 0.16 (3rd) Acceptability of abortion -0.20 59.38% 0.29 (2nd) Fertility norms 0.09 48.88% 0.04 (2nd) Note. Results highlighted in green indicate strong weight of evidence, yellow denotes medium weight of evidence, and red suggests weak weight of evidence. Appendix F include complete spatial regression model and conditional random forest results for each outcome. a Median effect of each predictor on Tightness, independent of which covariates are controlled for. b Percentage of effect sizes across all specifications that meet the 0.1 threshold, independent of which covariates are controlled for and whether a spatial regression is used. c Importance scores derived from the unimputed conditional random forest, controlling for median age, literacy rates, median income and poverty levels, and the ordinal ranking of importance relative to other predictors. 13 When blood relation is parsed out from the variable, the results change direction to more positive (βSCA=0.37). This might be a result of the rapid decline in consanguineous marriages in certain states/union territories (Acharya & Sahoo, 2021). 47 Summary In Study 3 we found that Tightness was robustly associated with previously tested correlates, including but not limited to debt, discrimination, well-being, and innovation. However, the directionality of the associations did not consistently align with what has been shown in prior research. For instance, the relationship between cultural tightness and discrimination was convoluted across the different forms of discrimination considered. We also found mixed support for the relation between Tightness and innovation. But many of the findings still reflect the diverging results seen in the United States and China studies (Chua et al., 2019; Harrington & Gelfand, 2014), highlighting the cultural uniqueness of India. We also observed curious associations between the index and previously untested correlates. For example, cultural tightness was associated with greater marriage-related violence, dowry and child marriage practices, but also reduced spousal unfamiliarity, which reflected arranged marriage norms. Such findings might indicate that our understanding of India’s normative landscape might be shifting. This, and the other findings, are further contextualized in the ensuing General Discussion. 48 General Discussion The present research developed a novel index to capture Tightness across 31 Indian states and union territories. The index revealed substantial differences across the regions along the spectrum of cultural tightness, indicative of discernable within-country cultural diversity (35.84 - 89.03). The regional variability observed was at par with what is observed in the United States (27.37 – 78.86; Harrington & Gelfand, 2014), which uses the same range of hypothetical Tightness scores from 0-100. Further, we found that several theorized antecedents and correlates are strongly associated with cultural tightness (see Table 5). Notably, some of the novel, exploratory variables (e.g., spousal unfamiliarity, and surface temperature) were robustly associated with Tightness. However, our analyses reveal some diverging results from previous research too. Together, the results further validate the study of the construct, but also indicate the need to consider the nuances of cultural Tightness when investigating its functioning in different cultural constructs. Before diving into the correlates of Tightness, it is critical to contextualize our construct validation findings. Firstly, we saw an unexpected negative correlation between our index and censorship. As mentioned above while contextualizing the associations in Study 1, this might be due to the emic-etic nature of the index, which causes Tightness to function differentially in India. Particularly, in India there has been shifting emphasis on certain forms of censorship (online over traditional media; Reporters without Borders, 2023). Additionally, we saw that our Tightness index was highly correlated with corporal punishment and filial obligation. Both constructs aptly represent aspects of tightness – filial obligation for strength of norms and corporal punishment for strength of sanctioning. But neither can capture the construct of Tightness comprehensively – both are necessary, but neither is sufficient on their own. 49 Table 5. Associations With Cultural Tightness in India, China, and the United States. Variables United Statesa Chinab India Antecedents Natural Disasters + 0 Disease Prevalence + 0 Population Density 0 + ~(+) Urbanization - + ~ (-) Income Inequality 0 0 Regional Conflicts + + ~ (+) Military Recruitment + + Correlates Institutional trustc - + + Incumbent voting - + + Debt - - Corruption/Cheatingd - - 0 Discrimination + - ~ (-) Happiness - + + Innovation - (utility patents) + (utility patents) - (invention patents) + (utility patents) - (institutional support) Social Organization + ~ (+) Note: 0 = no evidence; + = positive relation; - = negative relation; ~ = mixed/weak findinge. a As per Harrington & Gelfand’s (2014) findings unless state otherwise in the variables column. b As per Chua and colleague’s (2019) findings unless state otherwise in the variables column. c As per McLamore et al.’s (2023) findings. d As per Wormley & Cohen’s (2022) findings. e Mixed findings in the present study across operationalizations, the directionality is indicated in brackets. Starting with the antecedents, we observed strong evidence that cultural tightness was positively associated with military recruitment, land usage, cropland, and protected land. This suggests that increasing military recruitment within the region, reduced availability of cropland and protected landed, and greater land usage are associated with more culturally tight regions. These findings align with what has been seen in prior Tightness research, where experiencing more threats that endanger group survival leads to greater cultural tightness to ensure coordination in a time of need (Gelfand et al., 2011; Gelfand & Lorente, 2021; Harrington & Gelfand, 2014; Roos et al., 2015). However, the analyses also indicate weak associations between cultural tightness and well-researched antecedents such as natural disasters and disease prevalence, which can be 50 understood through the cultural mismatch hypothesis. According to this hypothesis, the perceptions of threats might be inaccurately diminished or amplified in the group which leads to inefficient reactive tightening or loosening of cultures in response to the threats (Gelfand, 2021). This could subsequently explain why certain threat antecedents were not strongly associated with Tightness in the present study. For instance, research has suggested that environmental events are only perceived as survival threats if the group does not have adequate resources to cope with the event (Van De Vliert et al., 2004). In the context of India, the communication of disaster preparedness in the media has been suggested to exaggerate the country’s ability to cope with natural disasters (Giri & Vats, 2018) relative to its actual capabilities (Thattai et al., 2017; Tripathi, 2015), which might reduce the perception of natural disasters as threats. Similarly, the perceptions of disease prevalence as threatening might be disproportionately lesser than its reality, as seen previously with COVID-19 where greater exposure to the virus made loose culture members fear it less (Gelfand et al., 2021; Gelfand, 2021). Likewise in India, individuals may not perceive diseases as threatening (Tandon et al., 2020), especially given the ceiling effect amongst Indians who are generally more predisposed to contracting diseases and illnesses (John, 2016; Pemberton et al., 2008), which may result in the unexpectedly weak relationship with cultural tightness. These potential explanations require further exploration to better understand the role of mismatches in the role of threats in the tightening of cultures. For the societal complexity and heterogeneity variables, there was weak evidence for historical rice farming related to more culturally tight regions. While this somewhat replicates prior research (Talhelm & English, 2020; Talhelm, 2022), the weak evidence indicates this association might not be as robust as we may have thought. One plausible explanation for this lies in the affordability of resources that make rice farming less interdependent (Talhelm & 51 Oishi, 2018), which could bleed into the reduced need for societal coordination and subsequent Tightness. For instance, India houses one of the world’s largest groundwater irrigation systems (Siebert et al., 2017), so the regions are arguably well-equipped to handle irrigation needs that previously required more collective effort. There are several other features tied to the labour requirements (e.g., field flattening, transplanting; Talhelm & Oishi, 2018) of rice farming, that may have historically prompted a need for group coordination that have shifted with technological advancements which allows for more independent rice farming. This could explain why the association was not as robust in our findings, which future work can explore by collecting contemporary data on rice farming. Amongst the antecedents, two of the strongest predictors of cultural tightness in India were surface temperature and precipitation. Surface temperature was negatively, and robustly, associated with the index. In fact, surface temperature emerged the strongest predictor of Tightness in India amongst the unimputed variables (see Appendix Table D). In real-world metrics, for every 1-degree increase in temperature, there is a 2.69 unit decrease in Tightness, as manifested in Goa’s hot summers and cultural looseness to the culturally tighter and colder weather in the northernmost region of Jammu and Kashmir. Although there is not much work on this association, the powerful influence of surface temperature on cultural tightness in our study replicates existing ideas on temperature’s effects on psychology (Berry & Annis, 1974; Oishi & Graham, 2010) and preliminary findings on the influence of geographical attributes on culture (Wormley et al., 2023). Previously higher temperatures have been related to increased openness (Wei et al, 2017), which is associated with non-conformity, and may contribute to the impressionable effects of temperature on looser cultures. This may be especially true for India, where the scorching temperatures and heat waves overwhelm individual’s routines (Krishnan, 52 2024) and institutional systems (Debnath et al., 2023), akin to temperature’s influence on criminal institutions and aggression (DeWall & Bushman, 2009; Field, 1992), affording the discoordination characteristic of loose cultures. However, given India’s extreme sweltering climate, these effects might be relative and require further probing. Meanwhile, greater precipitation was associated with increasing cultural tightness. In other words, for every millimeter increase in precipitation, there is a 0.30 unit increase in cultural tightness. The precipitation findings might reflect greater perceptions of threat because precipitation in India more typically involves heavy downpours rather than lighter showers that are seen in other countries. Especially since most of the nation is not equipped to handle high rainfall (Sarkar, 2019), which could suggest why higher precipitation presents more coordination challenges and is associated with cultural tightness. Together, the notably robust findings in our study call for further explorations into this association to understand the extent of geography’s influence on cultural tightness. In Study 3, we replicated several associations previously observed between Tightness and order correlates. Particularly, we observed strong and medium evidence that cultural tightness relates to reduced debt and substance use. This aligns with the ideas that members of tighter cultures engage in greater self-control reflecting greater individual-level orderliness because of the top-down expectations of order (Gelfand et al., 2006; Gelfand et al., 2011). We also found evidence for more culturally tight places reporting greater trust in institutions and social organization. This contributes to the line of research that tighter cultures lay greater emphasis on societal order (Harrington & Gelfand, 2014). The finding for trust in institutions aligns with what was seen in China rather than the United States (McLamore et al., 2023), indicating that the shared cultural experiences of rapid development and urbanization might be uniquely allowing 53 for such an association amongst tighter Indian and Chinese regions. These differential findings might be due to government’s (in)ability to meet demands of Tightness in times of need, such as over the COVID-19 pandemic (McLamore et al., 2023). Together, these order-related results paint a nuanced picture of cultural tightness, with some findings indicating more context-sensitive functioning. Although the construct validation replicated previous associations between cultural tightness and political orientations (Gelfand & Lorente, 2021; Gelfand et al., 2011; Harrington & Gelfand, 2014), the results suggested a curious pattern in incumbent voting. Particularly, tighter regions continue to be right-leaning across election cycles, but they reported reduced incumbent voting from the 2014 national elections to that in 2019. One possible explanation for this lies in India’s political context. Firstly, since 2014 the representation of parties in power, or the ones with majority vote in each region, has become less fragmented and more consolidated under the Bharatiya Janata Party (Aiyar & Tillin, 2020; Schakel et al., 2019; Verniers, 2024). It is important to note that the Bharatiya Janata Party is regarded for its autocratic approach and is more right-wing (Biswas, 2021; Haas & Majumdar, 2023; Mody, 2024), which suggests that them gaining more majority votes may still be reflective of a preference for conservatism and stringency in tighter cultures. Such that, tighter places might be voting less favourably for the incumbent parties in 2019 because they are seeking greater Tightness through other parties, such as the Bharatiya Janata Party. This potential explanation requires further research to get deeper insight into people’s attitudes towards these parties and what might have shifted. Curiously, the results revealed that tighter regions were associated with reduced waste separation and ecological preservations practices in place. This deviates from the notion that tighter regions enforce more order in ecological preservation and waste separation. This might 54 instead, be reflecting the limited social emphasis on environmentalism and cleanliness which is reportedly the norm in India (Vigers, 2023). For instance, the government’s initiatives such as the Swacch Bharat (Clean India) Mission, intended to bolster environmentalism and hygiene, have been met with limited success (Curtis, 2019; VerKuilen et al., 2023). Researchers have suggested that these attempts and the general indifference towards environmentalism in India may stem from the unique religious connections to nature (Tomalin, 2002, 2016), which obscures Indians’ attitudes towards ecological preservation. Sachdeva (2017) empirically support this claim, finding that sacred beliefs about the Ganges river led Indian participants to believe that the river is impermeable to pollution. Together, this implies that Indian norms may not lay great emphasis on ecological preservation. Instead, given the domain-specific nature of cultural tightness (Eriksson et al., 2021), our results might be reflecting Tightness just not as anticipated: tighter cultures laying greater emphasis on a lack of environmentalism, to reflect cultural practices. In the openness correlates, we similarly see evidence for replicated associations. Firstly, tighter regions related to heightened happiness. Although this counters prior literature where more constrained regions experience worse well-being outcomes in the United States, (Harrington & Gelfand, 2014; Harrington et al., 2015), the finding is consistent with what was examined in China (Chua et al., 2019). Given that Indians report higher interdependent self-construal (Kapoor et al., 2003; Li et al., 2010), which is associated with improved well-being (Cross et al., 2000; Markus & Kitayama, 1991), and the known well-being benefits of person-environment-fit (Fulmer et al., 2010; Götz et al., 2018), it is plausible that tighter culture members derive well-being benefits from the situational constraints because they align with their beliefs and expectations. Meanwhile individuals in culturally loose regions may feel that situational constraints might not align with their self-construal or expected situational 55 constraints. Hence, like in China, it might be that the cultural context of India influences this finding: while tighter regions in India report greater situational constraints, as proposed by Tightness theory, it does not have the same detrimental effects as in other – more independence-minded – cultures such as the United States. More recent work suggests that the mixed findings might be tapping into a more complex relationship between constraints and subsequent well-being: too much of either freedom or constraints can be detrimental to well-being (Harrington et al., 2015). This could explain the mixed findings we see within each nation, but future explorations of the curvilinear relationship at the intranational level are required to confirm this account. A similar finding that situates the role of Tightness within the Indian cultural context, is that of innovation. Our results suggest that contrary to the United States (Harrington & Gelfand, 2014) but converging with China (Chua et al., 2019), tighter regions are associated with more innovation. This is especially intriguing because we also see, that in line with the United States findings, tighter regions provided lesser support for innovative pursuits. Together, these results suggest that tighter regions are incrementally more innovative. This is possibly a function of problem-solving approaches to innovation which were noted in tighter provinces in China (Chua et al., 2019), and broadly emphasized in Indian innovation practices but rare in the United States as of 2012 (Prakash et al., 2019; Radjou et al., 2012), when Tightness and innovation was studied in the United States (Harrington & Gelfand, 2014). More research is required to confirm what might be driving this in the case of India, but it is beyond the scope of the present study. There were further intriguing results between Tightness and discrimination-related correlates. Namely, although the analyses revealed strong evidence that tighter regions report higher religious discrimination and casteism, in line with prior findings (Harrington & Gelfand, 56 2014; Jackson et al., 2019), these cultures report contradictory efforts towards gender equality, which is more in line with what was seen in tight Chinese provinces (Chua et al., 2019). One account for the mixed findings comes from the notion of domain-specific tightness wherein cultures can regulate social norms associated with certain life domains (Eriksson et al., 2021). This includes gender equality, which can explain why Sweden is overall more culturally tight than the United States (Eriksson et al., 2021), despite being typically regarded as more culturally loose. Consistent with this, researchers have documented greater policy efforts towards fostering more gender-equality in India (Dhar et al., 2022), which does not seem to be the case for religion- and caste-based discrimination (Agrawal, 2016; Human Rights Watch, 1999; Office of International Religious Freedom, 2022). It appears guiding institutions in tighter regions in India are striving to dismantle the deep-seated gender discrimination, due to the push towards gender-based equality, and achieving the desired downstream consequences. Such downstream consequences have been suggested in prior bias-related works as well, where legislation around same-sex marriage in the United States led to reduced implicit and explicit antigay bias (Ofosu et al., 2018; Roy et al., 2023). Whereas with religious and caste-based discrimination we might be seeing associations in line with the United States due to lack of institutional addressal of these associations. Finally, the previously unexplored association between cultural tightness and marriage-related correlates also revealed intriguing associations. Cultural tightness was associated with greater marriage-related violence, dowry and child marriage rates, but with reduced adherence to spousal unfamiliarity. While the positively associated correlates align what prior research suggests about Indian marriage norms (Krishnamoorthy et al., 2020; Parihar, 2023), the finding for spousal unfamiliarity was unexpected given its ability to capture arranged marriage norms. 57 However, it aligns with the cultural shift that arranged marriages practices are currently experiencing (Allendorf & Pandian 2016; Kaur & Singh, 2013; Suri, 2020), with a general move towards providing women more agency in choosing spouses and allowing them to get to know their partners. Similar to the push for gender-equality, it might be that the policy efforts towards equality are leading to domain-specific Tightness around ensuring women have greater agency, thereby increasing spousal familiarity in culturally tight regions. This indicates that our operationalization of marriage norms was more aligned with archaic understandings of these norms, but with the rapid globalization of India, more research is required to capture these norm dynamics and the associated openness. Limitations and future directions The current research presents important, initial evidence around the cultural diversity of Indian regions through cultural tightness, with insights into its predictors and implications as well. However, there are limitations that should be addressed in future research to deepen our understanding of the cultural context and findings. Although the use of archival data facilitated the creation of a comprehensive index to capture as over thirty Indian states and union territories, it affects the quality of our data. First, archival data restricted the ability to capture all thirty-six regions in India, preventing claims of the complete extent of regional cultural diversity within the country. Secondly, these data are highly sensitive to temporality. For instance, much of our index was constructed from legislation which may not be updated to reflect the current social norms. This can be seen in our results for spousal unfamiliarity and gender-based discrimination, which appear to be shifting from earlier notions of how these correlates play out in the Indian context. Thus, our measure for cultural tightness might have been unsuccessful in capturing the updated norms in India as of 2024. Additionally, the data were subject to change due to the 58 sudden reorganization of regions by the government such as the 2014 separation of Telangana from Andhra Pradesh and the recategorization of Jammu & Kasmir as a union territory and its related separation from Ladakh in 2019. This affects the ability to clearly determine the cultural nuances for such regions, which limits the applicability of the present measure of Tightness. Lastly, the quality of our operationalizations hinge on the availability of certain data, which impedes the ability to exactly replicate prior efforts to create a cultural tightness index. For instance, Harrington and Gelfand (2014) included more apt measures in their index (e.g., religious affiliation, ratio of foreign population) which we were not able to replicate, especially since the latest Indian Census is over a decade old. Future research attempting to capture Tightness in India can address these limitations where possible, as more updated data becomes available or by capturing individual-level reports with known scales of Tightness. Another consideration is the underpowered sample size that is commonly found in geographical psychology literature upon aggregating to the regional level (Rentfrow et al., 2008). In our case, the sample dwindled down to 31 observations, given the criterion of legislative control being afforded to the region. We attempted to address this through multiple approaches: 1) presenting multiple, step-wise analyses from specification curves to spatial regressions and only interpreting ones robust across all statistical methods, 2) using established effect size estimates for interpretation, and 3) using machine learning methods that go beyond significance-testing to examine associations between variables of interest. 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Columbia University Press. 84 Appendices Appendix A. Index items Table A1. Tightness Index Items. Items Mean (SD) Death penalty cases in courta 0.00000049 (0.00000035) Appeals approved in courtb 8.14 (0.34) Fine amounts for unlicensed drivingc 2761.29 (118.67) Fine amounts for disobedient drivingc 1190.32 (70.49) Fine amounts for dangerous drivingc 1757.14 (108.71) Fine amounts for those not wearing helmets while drivingc 692.86 (36.82) Arrest records for suicide abetmenta 0.0057 (0.0058) Reported murder cases where the motive was honour killing 0.77 (0.05) Number of people convicted for anti-government/national sentimenta 0.00026 (0.0000019) Belief in Karma (1= no; 2=yes)* 0.06 (0.07) Importance of religion (1-4)* 0.13 (0.15) Laws against religious conversions 0.35 (0.03) States that have banned beef 0.97 (0.04) Legal drinking age 18.03 (0.75) Number of dry days instated 0.52 (0.03) Laws permitting gambling establishments 0.81 (0.002) Reservation Laws for Other Backward Castes by State/UT 28.87 (0.96) Number of inter-caste couples who received government supportb 0.00015 (0.0000056) Government support for relief to victims of casteism (amount)c 5966.43 (214.89) a Variables normalized by the state population. b Variables normalized by the number of appeal cases c Amounts reported in Indian Rupees (or $0.012 USD/$0.016 CAD) 85 Appendix B. Influences of tightness operationalizations Table B1. Operationalizations of Influences on Tightness. Antecedent Database Source Exact Item/Operationalization Years used Natural disasters NCRB Accidental Deaths Distribution of accidental deaths due to avalanche, exposure to cold, cyclones, tornados, tsunami, earthquake, epidemic, flood, heat stroke, landslide, lightning, torrential rain, forest fire and other natural forces (State & UT-wise) 2000 onwards Disease prevalence National Centre for Disease Control Number of deaths and cases of seasonal influenza by state/UT 2010 onwards Central Bureau of Health Intelligence Number of deaths and cases of pneumonia by state/UT 2010 onwards National AIDS Control Organization State/UT wise People living with HIV/AIDS 2019 Niti Aayoga Death rates by state/union territory 2002 onwards Niti Aayoga Infant mortality rate 2002 onwards National Center for Vector Borne Diseases Control Cases and deaths by malaria, and dengue 2017/2018 onwards TBFacts (Tuberculosis Facts) State/UT wise notification of tuberculosis patients 2021 onwards National Health Profile State/UT wise leprosy case detection 2020 onwards Urbanization Census of India Ratio of urban to rural population 2011 Population density Census of India Population Density 2011 86 Military recruitment Ministry of Defence State/UT-wise recruitment in the army 2016 onwards Land shortage Worldbank's India Spatial Databse Number of households with four or more people per room as a share of the total number of households. This indicator is available for total households and households in slums. 2011 Department of Agriculture and Farmers Welfare Land use by State/UT 1998 onwards Ministry of Housing and Urban Affairs State/UT wise sanction of housing 2021 onwards Vegetation Willinger et al. (2023) State/UT-wise normalized difference vegetation index 2000-2008 Crop land Worldbank's India Spatial Database State/UT-wise crop land 2011 onwards Protected land Worldbank's India Spatial Database State/UT-wise protected land 2011 onwards Historical rice farming Talhelm (2022) Percentage of rice crops grown in each state + binary rice vs. wheat variable Income inequality Pandey & Gautam (2020) State-wise Gini coefficients. 2005-2006; 2015-2016 Regional conflicts Department of Personnel and Training List of states affected by states reorganization act. Historical data that refers to the act from 2000 JagranJosh List of interstate water disputes. Historical-present 87 Times of India (newspaper) List of interstate border disputes. Historical-present Copland (2002) Copland, (2005) Pandey (2001) Princely state violence at the time of independence/partition. During British Colonization Partition Museum Balasubrahmanyan(2011) Hajari (2015) Zamindar, (2010) Regions affected by (split up during) the partition of India at the end of British rule During British Colonization Mountainousness Willinger et al. (2023) State/UT-wise mountainousness 2000-2008 Precipitation Worldbank's India Spatial Database State/UT-wise precipitation 2011 onwards Surface temperature Willinger et al. (2023) State/UT-wise surface temperature 2000-2008 Land-water mask Willinger et al. 2023) State/UT-wise land-water mask 2000-2008 Religious Diversity NFHS-5 Proportion of individuals who do not identify as Hindu 2021 Caste Diversity NFHS-5 Proportion of individuals who identify as scheduled caste, scheduled tribe, or backward class 2021 aNiti Aayog is the Indian Government’s official database website. 88 Appendix C. Spatial regression analyses In our analysis, we followed the steps outlined by Ebert and colleagues (2023) to identify the most appropriate spatial regression model through a data-drive approach where spatial dependencies in the outcome variable and a regular OLS linear regression model predicting the outcome are tested. If dependencies were detected, the respective spatial regression model for each outcome of interest was run. If not, a regular OLS linear regression model was reported. To avoid over-controlling, we only included controls if they meaningfully correlated with the predictor(s) or outcome interest. In keeping with the rest of our interpretation benchmarks, anything with a correlation greater than |0.1| was deemed to be a relevant covariate. 89 Appendix D. Unimputed results for influences on Tightness Table D1. Spatial Autoregression Analysis, Original Conditional Random Forests, and Range of Specification Curve Effect Sizes, Predicting Tightness. Antecedents β* βSCA range Original importance scores (ranking) Cropland 1.56*** -0.29 – 1.5 NA Surface Temperature -3.73*** -2.71 – 1.21 0.18 (1st) Vegetation 0.81*** -0.53 – 0.33 0.14 (5th) Regional Conflicts 0.19*** 0 – 0.32 0.14 (3rd) Precipitation -0.44** 0.09 – 0.71 0.14 (4th) Protected Land 0.12** 0.1 – 0.23 NA Army recruitment 0.05 0.07 – 0.35 0.01 (9th) Land Usage 0.23*** -0.07 – 0.35 0.06 (8th) Caste Diversity -0.05* -0.03 – 0.04 -0.00 (17th) Religious Diversity 0.05* -0.08 – 0.04 0.01 (10th) Land Water Mask 0.08 -0.02 – 0.12 -0.00 (19th) Mountainousness -0.05 -0.15 – -0.01 0.01 (12th) Urbanization -0.06 -0.1 – 0.13 -0.00 (18th) Disease Prevalence 0.01 0 – 0.08 0.00 (13th) Population Density 0.12** 0.03 – 0.19 0.00 (15th) Historical Rice Farminga 0.17*** 0.02 – 0.19 0.00 (16th) Natural Disasters 0.07* -0.12 – 0.03 0.00 (14th) Income Inequality -0.06 -0.04 – 0.19 NA *Controlling for median age, income, literacy rates and economic well-being; and the effects of the other antecedent variables. a Similar results found for the binary rice vs. wheat variable. 90 Appendix E. Correlate operationalizations Table E1. Operationalizations of Correlates of Tightness. Group Database Source Exact Item/Operationalization Years Substance Use Department of Social Justice and Empowerment State/UT wise use, dependence and "Quantum of Work" related to alcohol, cannabis, opioids, sedatives, cocaine, amphetamine, inhalants, hallucinogens, and drug injections 2019 NCRB Crime Statistics State/UT wise crime rate for possession of drugs for Personal use/Consumption 2021-2022 Marriage-related violence NCRB Crime Statistics Crimes which violated the Protection of Women from Domestic Violence Act 2021-2022 NCRB Crime Statistics Crimes which violated the Prohibition of Child Marriage Act 2021-2022 NCRB Crime Statistics Reported kidnapping and abduction of women to compel her for marriage 2021-2022 National Family Health Survey Percentage of individuals who agree that it is justified for men to beat their wives 2021-2022 National Family Health Survey Self-reported age at the time of first marriage 2021-2022 Arranged Marriage Norms IHDS & NCAER Whether the wife is related to husband by blood 2014 IHDS & NCAER Who chose the husband 2014 UNICEF End Child Marriage Progress Report on India Percentage of women aged 20 to 24 years who were first married or in union before age 18, by state or union territory 2023 91 IHDS & NCAER How long wife knew husband before marriage 2014 Inter-religious tensions NCRB Crime Statistics Communal/religious motives of murder 2021-2022 NCRB Crime Statistics Communal/religious rioting as an offence against Public Tranquility 2021-2022 Casteism Ministry of Social Justice State-wise cases registered for offences of Untouchability against SCs and STs by Police and their Disposal under the Protection of Civil Rights Act, 2017 Ministry of Social Justice State-wise cases registered during 2019 under the scheduled castes and the scheduled tribes Prevention Of Atrocities Act and Protection of Civil Rights Act 2017 NCRB Crime Statistics Caste conflict rioting as an offence against public tranquility 2021-2022 NCRB Crime Statistics Casteism as a motive for murder 2021-2022 Waste separation Swacch Bharat Website Number of wards 100% door to door segregation 2023 Swacch Bharat Website Number of C&D waste recycling 2023 Swacch Bharat Website Number of material recovery facility 2023 Swacch Bharat Website Number of solid liquid resource management 2023 Swacch Bharat Website Number of waste to compost plants 2023 Swacch Bharat Website Number of waste to energy plants 2023 Gender Equality Attitudes NFHS-5 Total married women in employment 2021-2022 Control over women's and men's cash earnings and relative magnitude of women's cash earnings by state/union territory 2021-2022 92 Women's participation in decision making by state/union territory 2021-2022 Women's access to money and credit and freedom of movement by state/union territory 2021-2022 Ownership of assets by state/union territory 2021-2022 Total % of married women in employment with cash earnings by state/union territory. 2021-2022 Pew India Survey Who is responsible for making decisions about money; taking care of the children; earning money? 2021 In a family, who should have the primary responsibility to care for parents as they age? 2021 Do you agree with the following statements: a wife must obey her husband? 2021 Should sons or daughters be responsible for performing the last rites for their parents? 2021 When there are few jobs, men should have more rights to a job than women? 2021 Do men or women make better leaders? 2021 Is it acceptable to alter the gender makeup of a family using modern methods? 2021 What can the community do to improve women's safety? 2021 How important is it for women to have the same rights as men? 2021 93 Is there a lot of discrimination against women? 2021 Institutional gender equality UN SDG India Index by Niti Ayog (Government) Proportion of women in managerial positions including women in board of directors, in listed companies 2023 Proportion of women who own land 2023 Percentage of currently married women aged 15-49 years who have their demand for modern methods of family planning satisfied 2023 Rate of crimes against women 2023 Women who have experienced cruelty/physical violence by husband or his relatives during the year 2023 Ratio of female to male average wage/salary earnings received among regular wage/salaried employees 2023 Percentage of elected women over total seats in the state legislative assembly 2023 Sex ratio at birth 2023 Ratio of female to male Labour Force Participation Rate (LFPR) (15-59 years) 2023 Innovation enablers India Innovation Report Innovation support through human capital, investment, knowledge workers, business environment, and safety and legal environment. 2020 94 Innovation India Innovation Report Patent applications filed per state/UT 2020 Happiness Pillania India Happiness Report Average happiness ratings by state/UT 2020 Psychological adaptations to everyday constraints Debt and Investment Survey Debt to Asset Ratio across States/Uts 2019 Political Outcomes IndiaVotes 2019 and 2014 general election results - majority voted party 2023 IndiaVotes 2019 and 2014 general election results - number of seats won by majority party in the respective years + the number of seats that the current majority party garnered (2019-2014 seats) 2023 Social Organization NCRB Crime Statistics Inmate population per state 2021-2022 NCRB Crime Statistics States/UT crime rate per lakh population: violent crimes, murder, assault, robbery, burglary, and vehicle theft 2021-2022 Ministry of Home Affairs Total Police per lakh of population 2021 UN Sustainable Development Goals Dashboard Percentage of population living in katch house (slums) 2023 Corruption NCRB Crime Statistics Corruption Arrest rates 2021-2022 NCRB Crime Statistics Police Disposal of Anti-Corruption Bureau, Cases 2021-2022 NCRB Crime Statistics Court Disposal of Anti-Corruption Cases 2021-2022 95 NCRB Crime Statistics Action against Persons Arrested and Departmental Action taken In Offences under Corruption Act 2021-2022 Transparency India State-wise right-to-information forms filled 2022 Ecological Preservation UN Sustainable Development Goals Dashboard Percentage of renewable energy out of total installed generating capacity (including allocated shares) 2023 Disaster preparedness score as per Disaster Resilience Index 2023 CO2 saved from LED bulbs 2023 Disability Adjusted Life Years (DALY) rate attributable to air pollution 2023 Forest + Tree Cover as a percentage of total geographical area 2023 Percentage of area covered under afforestation schemes to the total geographical area 2023 Percentage of degraded land over total land area 2023 Number of cases under Wildlife Protection Act (1972) 2023 96 Percentage increase in area of desertification 2023 Trust in institutions Pew India Survey Average trust in medicine over ayurveda, religious rituals and homeopathy 2021 Ministry of Finance State-wise deposits into commercial, public banks 2023 Sexual Health Norms National Family Health Survey Bathing practices during menstruation 2021-2022 National Family Health Survey Beliefs that wife is justified in refusing to have sex with husband 2021-2022 National Family Health Survey Use of contraceptives 2021-2022 National Family Health Survey Attitudes toward refusing sexual intercourse with husband by state/union territory 2021-2022 National Family Health Survey Ability to negotiate sexual relations with husband by state/union territory 2021-2022 National Family Health Survey Men's attitudes toward a husband's rights when his wife refuses to have sexual intercourse by state/union territory 2021-2022 National AIDS Control Organization Progression on 95-95-95 Strategy 2019 Acceptability of Abortion National Family Health Survey Reported rate of abortion 2021-2022 Pew India Survey Dataset Reported stance on the legality of abortion 2021 Fertility norms National Family Health Survey Compensation provided for sterilization 2021-2022 97 National Family Health Survey Infanticide rates of female fetus 2021-2022 National Family Health Survey Reported sex preference for child 2021-2022 Dowry NCRB Crime Statistics Dowry-related deaths 2021-2022 NCRB Crime Statistics Arrests recorded that violate the Dowry Prohibition 2021-2022 NCRB Crime Statistics Dowry as a motive for murder 2021-2022 98 Appendix F. Complete spatial regression and conditional random forest results for each outcome Table F1. Regression Model and Conditional Random Forest Results for Tightness Predicting Substance Use. Predictors β Importance score Tightness 0.39 0.36 (4th) Median age 2.21 0.47 (2nd) Literacy rates -2.30 0.41 (3rd) Economic wellbeing -0.72 0.48 (1st) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F2. Regression Model and Conditional Random Forest Results for Tightness Predicting Debt. Predictors β Importance score Tightness -1.00** 0.07 (3rd) Median age 2.38*** 0. 51 (1st) Literacy rates -0.67 0.39 (2nd) Economic wellbeing 0.20 0.07 (3rd) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F3. Regression Model and Conditional Random Forest Results for Tightness Predicting Corruption. Predictors β Importance score Tightness -0.13 -0.03 (3rd) Median age 1.99 0.01 (2nd) Literacy rates -1.52 0.04 (1st) Economic wellbeing 0.20 0.04 (1st) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 99 Table F4. Regression Model and Conditional Random Forest Results for Tightness Predicting Incumbent Voting. Predictors β Importance score Tightness -1.99** 0.42 (1st) Median age -3.17* 0.04 (4th) Literacy rates -1.51 0.26 (2nd) Economic wellbeing 0.20 0.10 (3rd) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F5. Regression Model and Conditional Random Forest Results for Tightness Predicting Trust in Institutions. Predictors β Importance score Tightness 0.37 0.16 (3rd) Median age 2.11 0.22 (1st) Literacy rates -1.50 0.19 (2nd) Economic wellbeing -0.34 0.04 (4th) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F6. Regression Model and Conditional Random Forest Results for Tightness Predicting Social Organization. Predictors β Importance score Tightness 0.14 0.15 (3rd) Median age 0.39 0.16 (2nd) Literacy rates 0.10 0.24 (1st) Economic wellbeing -0.09 0.08 (4th) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 100 Table F7. Regression Model and Conditional Random Forest Results for Tightness Predicting Waste Separation. Predictors β Importance score Tightness -1.58** 0.18 (3rd) Median age 1.15 0.30 (2nd) Literacy rates 1.08 0.33 (1st) Economic wellbeing 0.19 0.10 (4th) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F8. Regression Model and Conditional Random Forest Results for Tightness Predicting Ecological Preservation. Predictors β Importance score Tightness -0.25 0.02 (4th) Median age 3.90*** 0.35 (1st) Literacy rates -2.97** 0.20 (2nd) Economic wellbeing 0.04 0.08 (3rd) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F9. Regression Model and Conditional Random Forest Results for Tightness Predicting Happiness. Predictors β Importance score Tightness 0.10 0.29 (1st) Median age -0.26 0.24 (3rd) Literacy rates 1.18*** 0.28 (2nd) Economic wellbeing -0.05 0.14 (4th) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 101 Table F10. Regression Model and Conditional Random Forest Results for Tightness Predicting Institutional Gender Equality. Predictors β Importance score Tightness 0.09 0.26 (2nd) Median age -0.21 0.32 (1st) Literacy rates 1.13 0.32 (1st) Economic wellbeing -0.03 0.16 (3rd) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F11. Regression Model and Conditional Random Forest Results for Tightness Predicting Gender Equality Attitudes. Predictors β Importance score Tightness 0.41 0.22 (2nd) Median age -0.03 0.22 (2nd) Literacy rates 0.08 0.18 (3rd) Economic wellbeing -0.13 0.24 (1st) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F12. Regression Model and Conditional Random Forest Results for Tightness Predicting Religion-Based Discrimination. Predictors β Importance score Tightness 0.81 1.31 (2nd) Median age -1.77 1.37 (1st) Literacy rates 0.84 1.27 (3rd) Economic wellbeing 0.20 1.17 (4th) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 102 Table F13. Regression Model and Conditional Random Forest Results for Tightness Predicting Caste-Based Discrimination. Predictors β Importance score Tightness -0.70 0.10 (3rd) Median age -2.82* 0.28 (1st) Literacy rates 3.38* 0.09 (4th) Economic wellbeing 0.89** 0.16 (2nd) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F14. Regression Model and Conditional Random Forest Results for Tightness Predicting Innovation Enablers. Predictors β Importance score Tightness -0.36 0.01 (4th) Median age 2.06 -0.04 (5th) Literacy rates 1.40 0.04 (3rd) Economic wellbeing -0.43 0.05 (2nd) Per capita income 0.60*** 0.40 (1st) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F15. Regression Model and Conditional Random Forest Results for Tightness Predicting Innovation. Predictors β Importance score Tightness 0.19 0.12 (3rd) Median age 0.53 0.09 (4th) Literacy rates -0.54 0.14 (2nd) Economic wellbeing -0.07 0.06 (5th) Per capita income 0.71*** 0.52 (1st) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. 103 Table F16. Regression Model and Conditional Random Forest Results for Tightness Predicting Sexual Health Norms. Predictors β Importance score Tightness -0.66 0.16 (3rd) Median age .-2.14 0.20 (1st) Literacy rates 2.57 0.19 (2nd) Economic wellbeing 0.46 0.06 (4th) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F17. Regression Model and Conditional Random Forest Results for Tightness Predicting Abortion Acceptance. Predictors β Importance score Tightness 0.14 0.29 (2nd) Median age 1.89 0.27 (3rd) Literacy rates -1.83 0.36 (1st) Economic wellbeing -0.35 0.25 (4th) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F18. Regression Model and Conditional Random Forest Results for Tightness Predicting Fertility Norms. Predictors β Importance score Tightness -0.04 0.04 (2nd) Median age -1.38 0.03 (3rd) Literacy rates 1.41 -0.01 (4th) Economic wellbeing -0.11 -0.03 (5th) Per capita income -0.53*** 0.54 (1st) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 104 Table F19. Regression Model and Conditional Random Forest Results for Tightness Predicting Arranged Marriage Norms. Predictors β Importance score Tightness -0.67 0.08 (3rd) Median age 1.03 0.02 (5th) Literacy rates -0.39 0.07 (4th) Economic wellbeing -0.43 0.13 (1st) Per capita income 0.30 0.11 (2nd) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. Table F20. Regression Model and Conditional Random Forest Results for Tightness Predicting Marriage-Related Violence. Predictors β Importance score Tightness 0.30 0.13 (2nd) Median age -3.23*** 0.06 (4th) Literacy rates 3.65*** 0.08 (3rd) Economic wellbeing -0.03 0.05 (5th) Per capita income -0.09 0.30 (1st) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. Table F21. Regression Model and Conditional Random Forest Results for Tightness Predicting Dowry. Predictors β Importance score Tightness 0.09 0.11(3rd) Median age -2.82 0.20 (2nd) Literacy rates 2.80 0.24 (1st) Economic wellbeing 0.45 0.24 (1st) * p <.05, ** p < .01, *** p < .001 Note. OLS regression model was run because no spatial clustering was detected. 105 Table F22. Regression Model and Conditional Random Forest Results for Tightness Predicting Child Marriage. Predictors β Importance score Tightness 0.09 0.15 (2nd) Median age -1.19 0.04 (5th) Literacy rates 1.03 0.12 (3rd) Economic wellbeing 0.68 0.29 (1st) Per capita income -0.23 0.06 (4th) * p <.05, ** p < .01, *** p < .001 Note. Moran’s test for spatial dependencies indicated significant clustering with the spatial conditional and autoregressive regression model being indicated as the best fit to capture this. 106 Appendix G. SHAP graphs for each outcome Figure G1. SHAP Summary for Predicting Substance Use. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor of substance use. Figure G2. SHAP Summary for Predicting Debt. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor of debt, with tighter regions reporting reduced debt. 107 Figure G3. SHAP Summary for Predicting Corruption. Note. The y-axis denotes importance-ranked predictors, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates value of the predictors for each observation. Here, cultural tightness was the least important predictor. Figure G4. SHAP Summary for Predicting Incumbent Voting. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting reduced incumbent voting. 108 Figure G5. SHAP Summary for Predicting Trust in Institutions. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor of trust in institutions. Figure G6. SHAP Summary for Predicting Social Organization. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the most important predictor, with tighter regions reporting greater organization. 109 Figure G7. SHAP Summary for Predicting Waste Separation. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor of waste separation. Figure G8. SHAP Summary for Predicting Ecological Preservation. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the most least important predictor. 110 Figure G9. SHAP Summary for Predicting Happiness. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting greater happiness. Figure G10. SHAP Summary for Predicting Institutional Gender Equality. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor. 111 Figure G11. SHAP Summary for Predicting Gender Equality Attitudes. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting greater equality attitudes. Figure G12. SHAP Summary for Predicting Religion-Based Discrimination. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor of religion-based discrimination. 112 Figure G13. SHAP Summary for Predicting Caste-Based Discrimination. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting greater discrimination. Figure G14. SHAP Summary for Predicting Innovation Enablers. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-least important predictor of innovation support. 113 Figure G15. SHAP Summary for Predicting Innovation. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-least important predictor of innovation. Figure G16. SHAP Summary for Predicting Sexual Health Norms. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the most important predictor, with tighter regions reporting greater adherence to sexual health norms. 114 Figure G17. SHAP Summary for Predicting Abortion Acceptance. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting reduced abortion acceptance. Figure G18. SHAP Summary for Predicting Fertility Norms. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor: more culturally tight regions reported greater fertility norm adherence. 115 Figure G19. SHAP Summary for Predicting Spousal Unfamiliarity. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor, with more tighter regions reporting reduced spousal unfamiliarity. Figure G20. SHAP Summary for Predicting Marriage-Related Violence. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the second-most important predictor, with tighter regions reporting greater marriage-related violence. 116 Figure G21. SHAP Summary for Predicting Dowry. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the least important predictor of dowry practices. Figure G22. SHAP Summary for Predicting Child Marriages. Note. The y-axis denotes predictors ranked by importance, the x-axis indicating the spread of SHAP values, the impact of a predictor on the prediction, and the colour of the points indicates the value of the predictors for each observation. Here, Tightness is the third-most important predictor, with culturally tight regions reporting greater child marriage practices.
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Goetz, Friedrich M.
Tightness-looseness across India
Text