Individual well-being, geographical heterogeneity and social capital

Individual well-being, geographical heterogeneity and social capital Isabel Neiraa Fernando Brunab Marta Portelac Adela García-Aracild a University of Santiago de Compostela and INGENIO (CSIC-UPV), Quantitative Economics Department, Faculty of Economics and Business. Av. Xoan XXIII s/n, 15782 Santiago de Compostela, Spain. Phone: +34 8818 11547. E-mail: isabel.[email protected] (corresponding author). b University of A Coruña, Economics and Business Department. Campus de Elviña s/n, 15071 A Coruña, Spain. Phone: +34 981167000. Fax: + 34 9811 67070. E-mail: [email protected] c University of Santiago de Compostela, Department of Finance, Faculty of Business Administration. Av. de Afonso X O Sabio s/n, 27002 Lugo, Spain. Phone: +34 8818 24453. E-mail: [email protected] d INGENIO (CSIC-UPV), Spanish Council for Scientific Research and Universitat Politècnica de València, Ciudad Politécnica de la Innovación. Camino de Vera s/n, 46022 Valencia, Spain. Phone: +34 96 387 7048. E-mail: agar[email protected] Abstract This paper argues the relevance of analysing the origins of contextual effects to explain subjective wellbeing (SWB). Using the 2012 European Social Survey, the study applies social capital indicators to distinguish between-context and between-individual heterogeneity in three multilevel models of happiness and life satisfaction. Five indicators of social capital at individual and regional level are used to measure the trust, networks and norms dimensions of social capital. Random intercept and random slope hierarchical models are used to control for unexplained regional variability. The possibility of aggregated subjective perceptions conditioning, or interacting with, the effects of individual perceptions is also examined. The results show that the regional means of the social capital indicators are useful in explaining not only average levels of SWB (between-context heterogeneity) but also differences in the importance individuals attribute to their social capital (between-individual heterogeneity). The paper also proposes a research agenda to expand the frontier on contextual effects in the new science of well-being. Keywords: Happiness, life satisfaction, multilevel models, between-context, between-individual, European regions. JEL codes: C51, C81, I31, Z13. Acknowledgments: We are grateful to the participants of the Workshop of Socioeconomics and Economic Sociology (A Coruña, 2016) and to anonymous referees for their helpful comments. The research for this paper was conducted as part of the research project ref. EDU-2013-45177-R ‘Education, Employability and Empowerment of the Youth’ (3E4Youth), funded by the National R&D Programme of the Spanish Ministry of Economy and Competitiveness. The views expressed in this paper are not necessarily the views of that organization. Cover Page Individual Well-Being, Geographical Heterogeneity and Social Capital Abstract This paper argues the relevance of analysing the origins of contextual effects to explain subjective well-being (SWB). Using the 2012 European Social Survey, the study applies social capital indicators to distinguish between-context and between-individual heterogeneity in three multilevel models of happiness and life satisfaction. Five indicators of social capital at individual and regional level are used to measure the trust, networks and norms dimensions of social capital. Random intercept and random slope hierarchical models are used to control for unexplained regional variability. The possibility of aggregated subjective perceptions conditioning, or interacting with, the effects of individual perceptions is also examined. The results show that the regional means of the social capital indicators are useful in explaining not only average levels of SWB (between-context heterogeneity) but also differences in the importance individuals attribute to their social capital (between-individual heterogeneity). The paper also proposes a research agenda to expand the frontier on contextual effects in the new science of well-being. Keywords: Happiness, life satisfaction, multilevel models, between-context, between-individual, European regions. JEL codes: C51, C81, I31, Z1 Abstract 1 1 Introduction The empirical literatures in psychology, sociology and economics have paid increasing attention to subjective well-being (hereafter SWB) 1 in recent years. Based on different methodologies, research results show variations in SWB across different geographical settings or contexts. Analysis of contextual factors, both economic (e.g., gross domestic product per capita or unemployment rates) and non-economic (e.g., social capital) is considered increasingly relevant (Manski, 1993; Westlund et al., 2010; Pittau et al., 2010; Ballas and Tranmer, 2012; Aslam and Corrado, 2012; Han, 2015) As Duncan et al. (1998) indicate, the existence of contextual differences in SWB, considered as regional differences in this paper, does not necessarily imply the existence of effects directly associated with the general living environment. The differences may be attributable to the fact that specific types of people who are more likely to be happy or unhappy due to individual characteristics are more commonly found in particular places. Compositional effects (individual) must be distinguished from contextual effects (regional) of the socioeconomic environment. This distinction suggests that the individuals’ SWB can stem from processes operating at several levels, a lower microlevel compositional effect (characteristics of people within the region) and a higher macrolevel effect (differential characteristics of people between regions). “The key question is not whether variations between different settings exist but what is their origin” (Duncan et al., 1998). Multilevel (hierarchical or mixed) modelling is the proper technique for analysing the origins of these variations. Contextual effects are associated with a word that has been used ambiguously in several literatures: heterogeneity. The term is most often used to describe a particular type of heterogeneity, between-context heterogeneity, which takes into account regional differences in the dependent variable. The traditional empirical approach controls these regional differences out, through dummy variables (fixed effects), instead of explaining them. This approach removes the regional variances, losing important information (Bell and Jones, 2015). Alternatively, this information can be incorporated in random 2 intercept multilevel models, as Rampichini and d'Andrea (1997), Pittau et al. (2010), Aslam and Corrrado (2012) and Han (2015) do for SWB, considering the effects of regional (level-two) variables. Moreover, regional heterogeneity may follow complex patterns in what Duncan et al. (1998) call between-individual heterogeneity, a term that refers to the effects of individuals’ (level-one) explanatory variables of SWB. Between-individual heterogeneity can be modelled through random slopes or cross-level interactions. Studying higher-level economic variables, Pittau et al. (2010) estimate a random slopes model of SWB, and Schyns (2002) and Ballas and Tranmer (2012) analyse interactions of individually and geographically aggregate determinants of SWB. No previous paper has, however, focused on 1 “Subjective well-being is the scientific name for how people evaluate their lives” (Diener, 2016). Gasper (2004) provides a framework to clarify the meaning of SWB. The term is related to other concepts, such as quality of life, analysed by Veenhoven (2000). Different disciplines and schools approach the concepts of SWB, life satisfaction and happiness in different ways. In the economics literature, these concepts are often considered as interchangeable synonyms (e.g., Frey and Stutzer, 2002; Layard, 2005). The empirical literature usually measures the general concept of SWB through survey questions about life satisfaction and happiness, which register the cognitive and affective dimensions of SWB, respectively. Throughout this paper, we use these two indicators to measure SWB. 2 The word random refers to an estimation algorithm able to use the information provided by the estimation residuals for individuals and groups (contexts) of individuals. Considering the residual variances within (individual) and between (regional) enables better identification (estimation) of individual and contextual effects. Manuscript Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 comparative analysis of between-context and between-individual heterogeneity in an SWB model with geographical hierarchy. Aslam and Corrrado (2012) consider some between-individual heterogeneity of social and economic variables when estimating their model for two different subsamples of regions, although they do not explicitly model this variable. Yuan (2016) studies random intercept and random slope models with interactions between social capital and income, but the study’s higher-level variables refer to households, not geographical units. This paper underscores the importance of studying the origins of both between-context and betweenindividual heterogeneity in the empirical analysis of SWB. The paper focuses on alternative ways of modelling the compositional and contextual (regional) effects of social variables on Europeans’ SWB. Specifically, it focuses on three dimensions of social capital conditioning individuals’ feelings and behaviour: trust, networks and norms. These three dimensions have not been considered together in previous multilevel research on social capital. We use information derived from the sixth wave of the European Social Survey (ESS), conducted in 2012, to study contextual effects through individual perceptions averaged geographically at the regional level. Because our empirical approach is multilevel, exploiting data at individual and regional levels, individuals are considered as nested into a geographical social environment that conditions their feelings and behaviour. This spatial context creates a vertical dependency on individuals’ SWB. Recently, the multilevel literature has been converging with the tradition of spatial econometrics 3 , which studies horizontal dependencies between geographical spaces (Corrado and Fingleton, 2012; Pierewan and Tampubolon, 2014; Dong and Harris, 2015; Dong et al., 2016). These horizontal and vertical spatial relationships are still not well understood, and our study focuses on the vertical ones. The contributions of this paper are the following. First, we provide evidence for the relevance of analysing the origins of heterogeneity in the empirical research on SWB, distinguishing between results for happiness and life satisfaction. Second, the paper develops a way to measure the three dimensions of social capital using principal components analysis of ESS questions, which has been proven useful in the estimation of three multilevel modelling specifications. Third, the methodological section of the paper summarizes several issues that have not been emphasized sufficiently in the empirical literature on SWB and proposes an agenda for further research. The main results of our estimations show that the contextual effects of different dimensions of social capital affect SWB by different mechanisms. In the dimension of trust, the institutional component measured at the regional level seems to affect individuals’ perceptions of the importance of individual institutional trust for happiness and life satisfaction, while the social component of trust at the regional level seems to exert only a direct influence on life satisfaction. The regional aggregation of emotionally linked networks appears to affect the positive evaluation of individual networks for happiness. Conversely, organizational linked networks are shown to reduce the indicators of SWB. These results illustrate additional possibilities for expanding the research frontier of the science of well-being. The paper is structured as follows. Section 2 introduces the conceptual framework on SWB and social capital, and their contextual-regional relevance. Section 3 describes the data and methodological approach. Section 4 reports the results of the estimation of three multilevel models for life satisfaction and happiness. Section 5 3 See Stanca (2010), Puntscher et al. (2014) and Fazio and Lavecchia (2013) for spatial econometrics analysis of variables related to the present paper. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 3 discusses some implications of our main findings, and Section 6 summarizes the conclusions. The paper includes two appendices with additional empirical details. 2 Subjective Well-being, Social Capital and Geography 2.1 Defining SWB and Social Capital Following the contemporary literature (e.g., Stanca, 2010; Portela et al., 2013; Puntscher et al., 2015), we focus on happiness and life satisfaction as indicators of SWB. Related to pleasant emotions (often short-term) or feeling good, happiness may represent an affective dimension of SWB. Life satisfaction is more closely related to cognitive judgments about feeling fulfilled in life or living a good life. Although the measurable effects of individual and regional determinants of individual well-being depend on the indicator used as a proxy of SWB, this paper uses individuals' responses to survey questions about happiness and life satisfaction as dependent variables in its estimations. Analysis of well-being draws on a number of disciplines to determine life satisfaction and happiness at the individual level in relation to economic and social factors shaping individual behaviour and feelings. These factors include income or unemployment (Easterlin, 1974, 2001; Clark and Oswald, 1994), health status, marriage, friendship, beauty and others (Frey and Stutzer, 2002; Layard, 2005). Of these, this paper focuses on social capital (Portela et al., 2013; Puntscher et al., 2015; Han, 2015). The concept of social 4 capital has been developed by Bourdieu, Coleman and Putnam. Bourdieu’s definition of social capital emphasizes the existence of “network(s) of more or less institutionalized relationships… which provide each of its members with the backing of collectively-owned capital” (1986, pp. 248–249). Whereas Bourdieu focuses on the existence of social networks, Coleman defines social capital by its function. “It is not a single entity, but a variety of different entities having two characteristics in common: they all consist of some aspect of social structure, and they facilitate certain actions of individuals who are within the structure” (Coleman, 1990, p. 302). Encompassing the approaches of Bourdieu and Coleman, Putnam (1993, p. 167) sees social capital as “features of social organization, such as trust, norms and networks, that can improve the efficiency of society by facilitating coordinated actions”, or the “connections among individuals’ social networks and the norms of reciprocity and trustworthiness that arise from them” (Putnam 2000, p. 19). This meaning of social capital is closely related to the concept of sense of community in the field of community psychology, defined by McMillan and Chavis (1986) as “a feeling that members have of belonging, a feeling that members matter to one another and to the group, and a shared faith that members’ needs will be met through their commitment to be together”. Although the concepts of social capital and sense of community have been used in different literatures, Pooley et al. (2005) suggest the possibility of combining the concepts to enhance our understanding of community. The main limitation 5 of the concept of social capital is its multidimensional character, which makes it difficult to define and operationalise. The concept is, however, widely used in empirical research on different phenomena. Its constraints “should stimulate and enrich the debate from a theoretical and applied perspective. From a socio-economic point of view, there is a widespread perception that we are just at the beginning – and 4 Among the several available surveys on social capital, Maleckia (2012) presents a summary of the regional perspective emphasized in this paper. 5 Criticisms of the concept of social capital are reviewed by Fine (2010), Bjørnskov and Sønderskov (2013), Inaba (2013) and Andriani and Christoforou (2016). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4 probably inside a dark room – where theoretical and empirical frameworks are not clearly developed yet” (Andriani and Christoforou, 2016). Our paper contributes to this debate by providing an empirical framework for analysing social capital that combines the three dimensions emphasized by the theories presented above—trust, networks and norms. In the dimension of trust, we follow Paxton (1999), distinguishing between trust in society as a whole and trust in institutions. Following Putnam’s approach, we categorize networks as informal (exchanges with friends, relatives and colleagues) and formal (participation in work meetings and other professional organizations). With regard to norms, we consider collective actions aimed at mutual benefit, such as collection of signatures, participation in lawful public demonstrations, boycotting certain products or businesses, etc. While not identical, such social activism is related to the idea of civic engagement stressed by the OECD's (2016) Better Life Initiative. The very concept of social capital implies that individuals’ feelings and behaviour are conditioned by the social contexts in which the individuals are embedded. Among these possible social contexts, we focus on the geographical aspect. 2.2 The Effects of the Social Capital Dimensions on SWB: A Geographical Approach The traditional empirical literature has used microdata to make inferences about the individual-level relationship between SWB and a wide range of socioeconomic and demographic characteristics. As mentioned above, individual characteristics create compositional effects. In addition to individual characteristics, Manski (1993) discusses how to model different individuals’ propensity to behave depending on exogenous characteristics of their community. This paper approaches these contextual effects from a geographical perspective, viewing individuals as affected by the social conditions in their spatial context. Contextual national economic determinants of SWB have been analysed by Veenhoven (2009) using aggregate indicators and by Schyns (2002) using multilevel techniques. Some studies (cited in the introduction), examine regional social and economic contextual factors of SWB in a multilevel setting, at times with contradictory results. For instance, Inglehart et al. (2008) find that, at the level of society, economic conditions seem to influence life satisfaction more strongly than happiness. In contrast, Puntcher et al. (2015) show that their indicator of strong ties (close relationships with family and friends) in European regions is statistically significant for happiness but not for life satisfaction. In focusing on the trust, networks and norms dimensions of social capital, however, the relevance of regional variables depends on the indicators used to proxy these dimensions (Scrivens and Smith, 2014). The dimension of trust has received the most study. Higher trust seems to imply higher SWB, at both individual and aggregate level (e.g., Helliwell and Putnam, 2004; Rodríguez-Pose and von Berlepsch, 2014). The effects of social networks on SWB depend on type of network and aggregation level. Aslam and Corrado (2012) show positive effects of informal networks (personal relationships) at individual but not aggregate level. Conversely, Han (2015) finds no significant effect at individual level. Furthermore, Rodríguez-Pose and von Berlepsch (2014) find negative effects of formal (organizational) networks on SWB. For social norms, differences in critical measurement hinder comparison of results in the few existing studies of this dimension (Bjornskov, 2006; Leung et al., 2011; Rodríguez-Pose and von Berlepsch, 2014). The significance and sign of the effects of norms on SWB is still uncertain. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 5 In order to treat the individual and regional factors that affect SWB, the empirical approach developed in the next section is multilevel. Perceptions of SWB are conditioned not only by the characteristics of the individual but also by how these characteristics define the individual’s behaviour and emotions depending on the context in which he/she lives. As discussed above, multilevel modelling can address the origin of between-context and between-individual heterogeneity (Duncan et al., 1998), capturing the latter through random slopes and crosslevel interaction terms, permitting the effects of the individual social capital variables to differ by region. This paper focuses on alternative specifications for modelling vertical dependencies among the data of nested observational units in terms of individuals and regions, as we show in the following section. 3 Empirical Approach 3.1 Data To analyse the role of social capital at both individual and aggregate levels to explain Europeans’ SWB, we use data from the ESS, developed to enable systematic study of social and demographic trends across Europe (ESS, 2012). Data were collected during 2012 for the sixth wave of the ESS, in 30 countries from some 55,000 individuals. Due to data availability issues, our analysis covers 24 European countries, disaggregated into 249 regions. The regional classification follows Eurostat’s Nomenclature of Territorial Units for Statistics (NUTS), which determines four aggregation levels, from countries (NUTS-0) to the smallest harmonized territorial units (NUTS-3). 6 As the ESS does not provide homogeneous NUTS-level disaggregation across countries, one limitation of our study of contextual regional effects is the use of regions defined at different NUTS aggregation levels (see the enclosed figures), as in Aslam and Corrado (2012). To avoid terminological confusions, our multilevel level-one (micro) data correspond to individuals’ ESS responses and our level-two (macro) data to regional averages of individuals’ responses defined at three different NUTS aggregation levels (NUTS 1, 2 and 3). As discussed above, we use happiness and life satisfaction as dependent variables to capture the affective and cognitive dimensions of SWB, respectively. The ESS provides information on happiness levels based on the question: “Taking all things together, how happy would you say you are?” For life satisfaction, the ESS asks: “All things considered, how satisfied are you with your life as a whole nowadays?” The responses range on a scale from zero (extremely unhappy/dissatisfied) to ten (extremely happy/satisfied). Given that the dependent variables are ordinal, the natural approach would be to study them through a multilevel ordered logit or probit model (Rampichini and d'Andrea, 1997; Yuan, 2016). We assume a linear relationship between the SWB indicators and their determinants, however, because using ordinality or cardinality makes little practical difference. 7 The dependent variables are not standardized here because standardization tends to reduce individual and regional variability (Heck and Thomas, 2008), which this paper attempts to model. Figure 1 shows the spatial distribution of the regional averages of the dependent variables. The darker colour indicates higher happiness/life satisfaction. As stated in Section 2.1, some overlap occurs between happiness and life satisfaction indicators. Since the correlation between happiness and life satisfaction is 0.72, the estimation results presented below for both variables are generally similar, although we highlight some relevant differences. 6 See http://ec.europa.eu/eurostat/web/nuts/overview. 7 Our tests on the practical consequences of the linear hypothesis confirm the conclusions of Frey and Stutzer (2002), Ferreri-Carbonell and Frijters (2004), Pittau et al. (2010), Rodríguez-Pose and von Berlepsch (2014), Aslam and Corrado (2012) and Yuan (2016). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 6 [FIGURE 1 ABOUT HERE] [FIGURE 2 ABOUT HERE. Figures 1 and 2 positioned together] Among the possible determinants of SWB, we focus on social capital. As discussed in Sections 2.1 and 2.2, the concept’s multidimensionality makes it difficult to synthesize in a single variable. Among the possible ways to measure the trust, networks and norms dimensions of social capital, this paper chooses separate principal components analysis (PCA) of individual-level data for ESS questions related to each of the three dimensions (see Appendix A, Tables A1 to A3, for more details). The results of the PCA for the trust dimension of social capital show two underlying components, which we call institutional and social (interpersonal) trust. For the network dimension, we also obtained two components, labelled informal (support) and formal (organizational) networks. For the third dimension, norms, the PCA produces a single component, civic engagement (sociopolitical activism). Analysis of the interrelationships among these five components is left for further research using alternate measurement approaches. The regional social capital variables are defined as the average values of the components obtained through PCA of the individual data. 8 In the models below, this means that average value is repeated for all individual observations in the same region. Figure 2 maps the spatial distribution of the regional means for the five PCA components of social capital described above. Additionally, our multilevel analysis of the individual and regional social capital determinants of SWB is controlled by many socio-demographic individual factors, such as age, gender, education, political orientation, health and income (see Appendix B). 3.2 Methodology This paper presents three different specifications for analysis of the contextual (regional) effects of social capital dimensions on SWB. They do not exhaust the possibilities offered by multilevel level modelling but illustrate alternative mechanisms to model the origin of regional differences in SWB. The first specification captures between-context heterogeneity, as in Aslam and Corrado’s (2012) model, but focuses on our five social capital indicators. The other two specifications also capture between-individual heterogeneity, through random slopes and interaction terms. These last two models include hierarchical dependence on level-one variables, since belonging to one region or another may generate different perceptions of the importance of the individual social capital variables. Some readers may choose to skip the following technical details and go to the end of the section. We follow Snijders and Bosker (2012) for general description of the models, 9 with slight changes in notation. An indicator of the SWB of individual 𝑖 nested in region 𝑗 (𝑌 𝑖𝑗) is supposed to depend on individual (level-one) control variables (𝐶𝑖𝑗 ) and social capital variables (𝑋𝑖𝑗 ). The index for individuals (𝑖 = 1, … , 𝑛𝑗) in these variables starts over for each regional group. As the values of a level-two variable do not depend on individual 𝑖, level-two variables have only the group 𝑗 index (𝑋 𝑗). When the coefficients are modelled, a subscript of 00 indicates the overall intercept, a subscript of 10 parameters of level-one variables (individuals), and a subscript of 01 coefficients for level-two variables 8 Sabatini (2006) and Portela et al. (2013), among others, follow a similar approach. Puntscher et al. (2016) compare alternative aggregation methods, whose relevance in a multilevel setting is also an issue for further research. 9 These authors, among others, explain the assumptions relevant to the models, not reproduced here for the sake of brevity. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 7 (regions). The models below introduce two random terms, 𝑈0𝑗 for regional intercepts and 𝑈1𝑗 for regional slopes of the individuals’ social capital variables. These random effects are latent variables. They force the estimation algorithm to consider the regional residuals in order to model regional dependence in the level-one values of 𝑌 𝑖𝑗 (random intercepts) or the effects of the level-one values of 𝑋𝑖𝑗 on 𝑌 𝑖𝑗 (random slopes). We focus here on interpreting three alternate ways of capturing compositional and contextual effects and do not discuss the portion of regional variability in the SWB indicators that remains unexplained in each case. 10 Model I: Withinand Between-Group Model The individual level (micro) model for region 𝑗 captures the compositional effects through the following equation with three 𝛽 coefficients: 𝑌 𝑖𝑗 = 𝛽0𝑗 + 𝛽1𝑗𝑋𝑖𝑗 + 𝛽2𝑗𝐶𝑖𝑗 + 𝜖𝑖𝑗 (1) The regional intercepts 𝛽0𝑗 allow for between-context heterogeneity, specified as a latent regression model in which a common intercept 𝛾00 is added to regional intercepts that cannot be observed without error 𝑈0𝑗 . Additionally, observable contextual effects are captured by the regional means (Mundlak, 1978). Therefore, the macro (regional), or level-two, model is as follows: 𝛽0𝑗 = 𝛾00 + 𝛾01𝑋 𝑗+ 𝑈0𝑗 (2) The total error of this model is decomposed into two random effects at individual (𝜖𝑖𝑗) and regional (𝑈0𝑗) levels, with variances of 𝜎𝜖 2 and 𝜎𝑈0 2, respectively. Substituting equation (2) into (1) and reordering, we obtain a random intercept model, which includes both observable and non-observable contextual effects. The within (intra)-group regression model for region 𝑗 becomes: 𝑌 𝑖𝑗 = 𝛾00 + 𝛽1𝑗𝑋𝑖𝑗 + 𝛾01𝑋 𝑗+ 𝛽2𝑗𝐶𝑖𝑗 + 𝑈0𝑗 + 𝜖𝑖𝑗 (3) where the systematic (non-random) part of the intercept is 𝛾00 + 𝛾01𝑋 𝑗. Taking the regional average on both sides of equation (3), we get the following between-group regression model: 𝑌  𝑗= 𝛾00 + (𝛾01 + 𝛽1𝑗)𝑋 𝑗+ 𝛽2𝑗𝐶𝑗+ 𝑈0𝑗 + 𝜖𝑗 (4) To confirm explicitly that 𝛽1𝑗 captures the relative effects of individual 𝑋𝑖𝑗 with respect to regional averages 𝑋 𝑗, we rewrite equation (3) through within-group centring. The coefficient of 𝑋 𝑗 in equation (4) is conserved using the mean-centred level-one explanatory variable, as in this equation: 𝑌 𝑖𝑗 = 𝛾00 + 𝛽1𝑗(𝑋𝑖𝑗 − 𝑋 𝑗) + (𝛾01 + 𝛽1𝑗)𝑋 𝑗+ 𝛽2𝑗𝐶𝑖𝑗 + 𝑈0𝑗 + 𝜖𝑖𝑗 (5) We choose to estimate equation (5) in order to stress its statistical equivalence to equation (3), an issue insufficiently highlighted in the existing multilevel SWB literature. To establish notation for estimable coefficients in the final specifications, we rename them as 𝛾00 = 𝛾00, 𝛾10 = 𝛽1𝑗 and 𝛾01 = 𝛾01 + 𝛽1𝑗, while 𝛿10 replaces the unmodelled 𝛽2𝑗 of the individual control variables. Our Model I thus follows the withinand between-group specification utilized by Aslam and Corrado (2012): 𝑌 𝑖𝑗 = 𝛾00 + 𝛾10(𝑋𝑖𝑗 − 𝑋 𝑗) + 𝛾01𝑋 𝑗+ 𝛿10𝐶𝑖𝑗 + 𝑈0𝑗 + 𝜖𝑖𝑗 (6) In this type of mean-centred specification, when both 𝛾01 and 𝛽1𝑗 are positive, the estimated effects of relative individual social capital (𝛾10) will be lower than the estimates for the regional mean (𝛾01). 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Yuan, H. (2016). Structural social capital, household income and life satisfaction: The evidence from Beijing, Shanghai and Guangdong-Province, China. Journal of Happiness Studies, 17(2), 569-586. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 17 Appendix A: Data [TABLES A1 TO A3 ABOUT HERE] Appendix B: Control Variables in the Determinants of Europeans’ SWB [TABLES B1 TO B2 ABOUT HERE] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 18 LIST OF FIGURES Figure 1. European distribution of regional means of happiness and life satisfaction (2012) Figure 2. European distribution of regional means of five measures of social capital (2012) . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 19 TABLES Tables in Sections 3.2 and 4.1 Table 1. Three multilevel models with contextual effects and random intercepts Model Specification Effects I 𝑌 𝑖𝑗 = 𝛾00 + 𝛾10(𝑋𝑖𝑗 − 𝑋 𝑗) + 𝛾01𝑋 𝑗+ 𝛿10𝐶𝑖𝑗 + 𝑈0𝑗 + 𝜖𝑖𝑗 Withinand between-group model II 𝑌 𝑖𝑗 = 𝛾00 + 𝛾10𝑋𝑖𝑗 + 𝛾01𝑋 𝑗+ 𝛿10𝐶𝑖𝑗 + 𝑈0𝑗 + 𝑈1𝑗𝑋𝑖𝑗 + 𝜖𝑖𝑗 Random slopes for individuals III 𝑌 𝑖𝑗 = 𝛾00 + 𝛾10𝑋𝑖𝑗 + 𝛾01𝑋 𝑗+ 𝛾11𝑋𝑖𝑗𝑋 𝑗+ 𝛿10𝐶𝑖𝑗 + 𝑈0𝑗 + 𝜖𝑖𝑗 Cross-level interactions without random slopes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 20 Table 2. Social capital in three multilevel models of Europeans’ happiness (27,532 individuals from 249 regions) Variables Model I Model II Model III Individual social capital (𝑋𝑖𝑗 − 𝑋 𝑗 in Model I and 𝑋𝑖𝑗 in Models II and III): 𝛾10 Trust: Institutional 0.2314*** 0.2454*** 0.2325*** (0.0111) (0.0195) (0.0111) Trust: Social 0.3339*** 0.3517*** 0.3362*** (0.0113) (0.0157) (0.0113) Networks: Informal 0.3032*** 0.3249*** 0.3049*** (0.0109) (0.0173) (0.0109) Networks: Formal -0.0306** -0.0495*** -0.0453*** (0.0101) (0.0114) (0.0120) Norms: Civic engagement 0.0108 0.0183 0.0097 (0.0105) (0.0118) (0.0120) Regional means (𝑋 𝑗): 𝛾01 Trust: Institutional 0.4323*** 0.1246 0.1857 (0.1014) (0.0754) (0.0989) Trust: Social 0.4890*** 0.1597* 0.1503 (0.0937) (0.0719) (0.0919) Networks: Informal 0.4582*** 0.0465 0.1410 (0.1059) (0.0818) (0.1035) Networks: Formal -0.7953*** -0.4795*** -0.7805*** (0.1807) (0.1191) (0.1753) Norms: Civic engagement -0.0211 0.0182 -0.0091 (0.1340) (0.0880) (0.1302) Interaction individual-region (𝑋𝑖𝑗 ∗ 𝑋 𝑗): 𝛾11 Trust: Institutional -0.1136*** (0.0256) Trust: Social -0.0191 (0.0234) Networks: Informal -0.1535*** (0.0290) Networks: Formal -0.0669 (0.0346) Norms: Civic engagement -0.0166 (0.0276) Variance of random effects Individuals (𝜎𝜖 2) 2.3499*** 2.2754*** 2.3469*** Regions (𝜎𝑈0 2) 0.1880*** 0.1479*** 0.1738*** Slopes of 𝑋𝑖𝑗 (𝜎𝑈1 2) Trust: Institutional 0.0536*** Trust: Social 0.0236*** Networks: Informal 0.0357*** Networks: Formal 0.0043*** Civic engagement 0.0046*** -2 Log Likelihood 104,172.1 103,724.5 104,143.1 Note: Standard errors are in parentheses. * Significant at 5% level; ** at 1% level; *** at 0.1% level. The estimated overall intercept (𝛾00) is not presented. The 𝛾01 coefficients in column (1) cannot be compared to those in columns (2) and (3), as explained in Section 3.2. Appendix B provides the results for the individual control variables. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 21 Table 3. Social capital in three multilevel models of Europeans’ life satisfaction (27,532 individuals from 249 regions) Variables Model I Model II Model III Individual social capital (𝑋𝑖𝑗 − 𝑋 𝑗 in Model I and 𝑋𝑖𝑗 in Models II and III): 𝛾10 Trust: Institutional 0.3851*** 0.3990*** 0.3833*** (0.0125) (0.0224) (0.0126) Trust: Social 0.4010*** 0.4210*** 0.4051*** (0.0128) (0.0177) (0.0128) Networks: Informal 0.2556*** 0.2734*** 0.2578*** (0.0123) (0.0176) (0.0123) Networks: Formal -0.0415*** -0.0590*** -0.0580*** (0.0114) (0.0123) (0.0136) Norms: Civic engagement 0.0289* 0.0330* 0.0277* (0.0119) (0.0133) (0.0136) Regional means (𝑋 𝑗): 𝛾01 Trust: Institutional 0.6403*** 0.1770* 0.2169 (0.1205) (0.0892) (0.1196) Trust: Social 0.7004*** 0.3377*** 0.3081** (0.1114) (0.0853) (0.1111) Networks: Informal 0.5209*** 0.1429 0.2814* (0.1258) (0.0975) (0.1250) Networks: Formal -1.0651*** -0.5990*** -1.0527*** (0.2153) (0.1400) (0.2127) Norms: Civic engagement 0.1784 0.1293 0.1785 (0.1596) (0.1037) (0.1579) Interaction individual-region (𝑋𝑖𝑗 ∗ 𝑋 𝑗): 𝛾11 Trust: Institutional -0.1719*** (0.0290) Trust: Social 0.0227 (0.0266) Networks: Informal -0.0455 (0.0328) Networks: Formal -0.0774* (0.0391) Norms: Civic engagement -0.0191 (0.0313) Variance of random effects Individuals (𝜎𝜖 2) 3.0048*** 2.9138*** 3.0011*** Regions (𝜎𝑈0 2) 0.2719*** 0.2211*** 0.2630*** Slopes of 𝑋𝑖𝑗 (𝜎𝑈1 2) Trust: Institutional 0.0713*** Trust: Social 0.0287*** Networks: Informal 0.0293*** Networks: Formal 0.0036*** Civic engagement 0.0054*** -2 Log Likelihood 111,134.8 110,693.6 111,114.7 Note: See note to Table 2. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 22 Tables in Appendix A [Tables A1 to A3 positioned together in Appendix A] Table A1. Rotated component matrix of the trust dimension of social capital: loadings Items Components Institutional trust Social trust Most people can be trusted, or you can’t be too careful 0.827 Most people try to take advantage of you, or try to be fair 0.816 Most of the time people are helpful, or mostly looking out for themselves 0.782 Trust in country’s parliament 0.830 Trust in legal system 0.770 Trust in the police 0.642 Trust in politicians 0.840 Trust in political parties 0.835 Trust in the European Parliament 0.800 Trust in the United Nations 0.751 % of total variance 44.09 23.54 Note: KMO statistic = 0.877. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 23 Table A2. Rotated component matrix of networks dimension of social capital: loadings Items Components Informal networks Formal networks Work in a political party or action group during last 12 months 0.645 Work in another organization or association during last 12 months 0.793 Involved in work for voluntary or charitable organizations 0.687 How often you meet with friends, relatives or colleagues socially 0.786 Take part in social activities compared to others of the same age 0.727 People with whom you can discuss intimate and personal matters 0.658 % of total variance 27.67 25.85 Note: KMO statistic = 0.667. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65