Social Networks and Cohesion in the Netherlands: Insights from Combined Administrative and Survey Data

Social Networks and Cohesion in the Netherlands: Insights from Combined Administrative and Survey Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social Networks and Cohesion in the Netherlands: Insights from Combined Administrative and Survey Data Dino Pitoski, Hans Schmeets This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6375519/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Statistics Netherlands (CBS) has recently developed the Whole Population Network file, based on administrative data, which includes over a billion interpersonal relationships among approximately 17 million inhabitants of the Netherlands, spanning each year from 2009 onward. Additionally, over the past decade, CBS has conducted the yearly Social Cohesion and Well-being survey, collecting data from more than 83,000 representatives of the population on topics related to social cohesion, such as social contacts, volunteering, political participation, and trust in others and in institutions. For the purposes of this study, we have constructed a merged dataset from the two, and deployed new indicators of Social Cohesion and of Social Networks per respondent. In the study we describe the merged dataset and present the relationships between person’s social networks (person’s centrality) and 17 indicators of social capital as well as the overarching social capital composite index. We further explore, for both the family and neighbouring network, the relationships with the actual social contacts with family members and neighbours. The study highlights the need for refined network measures to enhance understanding of social interactions. Humanities/Complex networks Social science/Sociology social networks social capital administrative data survey data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Statistics Netherlands (CBS) has recently constructed the Whole Population Network file based on administrative data containing over a billion personal relationships per year among about 17 million inhabitants of the Netherlands (van der Laan 2022a -b; CBS 2023; CBS 2024a-e). These data, referred to as 'Social Networks' in further text, include the characterization of the relationships between individuals based on sub-types of ties from the domains of family, work, neighbourhood, schools, and households. Different characterizations represent different network layers and have been derived for each year in the period from 2009 until 2020. Several studies came out recently that assess these data using extensively the network science methods (Bokányi, Heemskerk, and Takes 2023 ; Kazmina et al. 2023 ; Menyhért et al. 2024 ). In addition to maintaining this dataset, which is based on registers (“administrative data”), CBS has been conducting and maintaining data based on various surveys of Dutch population. Among these is the yearly Social Cohesion and Well-being (Sociale Samenhang en Welzijn or, in further text “SSW”) survey, started in the year 2012, which by now has interrogated over 83,000 randomly sampled individuals of over 15 years of age, as a representative sample of the Dutch population. The survey covers topics related to social contacts, volunteering, involvement in associations, political participation, generalized social trust, trust in institutions and many other aspects concerning social cohesion, including individuals’ subjective well-being. Responses have been gathered via a sequential mixed-mode design: internet and, after two unsuccessful reminders, by telephone or face-to-face, with the response rate of about 55 percent. To reduce potential non-response bias, data have been reweighted by population characteristics such as gender, age, household size, migration background, marital status, income, urbanity and region (for details on the sampling and the design, consult CBS 2024f). The SSW has been the basis for many scientific studies on various topics, such as social capital, religion, voting behaviour, trust, volunteering, the use of language and organ donation (Schmeets and te Riele, 2014 ; Schmeets and Peters, 2021 ; Schmeets and Cornips, 2023 - to name only a few), as well as for numerous reports published by the Statistics Netherlands (see https://www.cbs.nl/en-gb/society ). In this study, we introduce the merged Social Networks and SSW dataset and present key findings derived from integrating network-scientific and statistical methods. By merging comprehensive administrative data with detailed survey responses, this study offers a unique perspective on the interplay between social networks and individual social capital. More specifically, the study aims to explore if and how social network centrality relates to social capital in the Netherlands. We hypothesize that higher network centrality will be associated with more individual social capital. Furthermore, we focus on two specific indicators of social capital: the frequency of contacts with family members, not belonging to their own household, and the contacts with neighbours. More specifically we expect that a higher person’s family and neighbour network results in more contacts with other family members and neighbours respectively. Social Capital and Social Network Research Integrating contemporary network science (Watts 1999 ; Barabási 2002 ; Newman 2010 ), with social capital analysis may reveal how individual connections contribute to overall social capital. There are numerous definitions and measurements of social capital, and social capital is often defined as a building block of social cohesion, or as an attribute that fosters social cohesion (for an overview see Berger-Schmitt, 2002 ; Chan, To and Chan 2006 ; Schiefer and Van der Noll, 2017 ; Moustakas, 2023 ; Tok, Woods and Kong, 2024 ). In another formulation, social capital refers to the networks, norms, and trust that enable cooperation and coordination for mutual benefit. Resources such as knowledge and money enhance an individual's ability to form and sustain relationships that contribute to their social capital. A key theory addressing the influence of these resources is Resource Theory (Bourdieu, 1986 ), which posits that individuals with more resources (such as knowledge, money, time, and social connections) are better positioned to build and leverage social capital. These resources open up more opportunities for social engagement and network participation and can also help foster trust. In the context of participation in social and political activities, the Civic Voluntarism Model (CVM) developed by Verba, Schlozman, and Brady (1995) is particularly relevant. The model highlights the importance of resources like education, income, and time. Those with higher education tend to have more knowledge about society and greater skills for engaging with it. Financial resources facilitate access to social networks and participation in organizational activities, while time is crucial for involvement in more structured activities, such as volunteering or being an active member of a sports or cultural association. Psychological factors, such as interest and the belief that one's contributions are meaningful, also encourage participation. Additionally, being involved in social networks increases the likelihood of being invited to participate in various activities. Thus, Resource Theory and the CVM offer complementary insights into how resources influence trust and societal participation. Social capital is seen both as a community asset (Neira, Vazquez and Portela, 2009) and as an individual attribute - a resource of individuals within the community (Gannon and Roberts, 2018). A widely used definition comes from the OECD (Coté and Healy, 2001 , p. 41; see also Keeley 2007 , pp. 102–105): "networks, together with shared norms, values and understandings, that facilitate cooperation within or among groups". This definition makes it clear that it is about collective connections of people within and among population groups who understand each other's opinions. However, quantifying these social networks in surveys is problematic. Often a proxy is used, such as in the single indicator ‘generalized trust’ or ‘volunteering’, or a composite indicator in which various trust and participation indicators are included (e.g. Putnam, 2000 ; van Beuningen and Schmeets, 2013 ). While the concept of social capital provides a foundational understanding of the qualitative aspects of social networks and the importance of different types of ties in generating social capital (see e.g. Granovetter, 1973 ), contemporary network science offers quantitative tools to measure and analyse the connectedness and importance of individuals as nodes within these networks. Both approaches are complementary, providing a comprehensive understanding of how social networks function and influence various outcomes. By using these methods simultaneously, we can examine how individuals are connected through various relationships such as friendships, professional ties, and kinship, basing primarily on the quantification of the number of connections, which we can subsequently connect with the actual behaviour and the quality of these connections. This integrated approach allows for more nuanced insights into the ways social structures influence individual and collective well-being. Indicator Definitions Indicators derived from SSW data Our analysis uses the social cohesion framework developed by Statistics Netherlands in 2008. This framework measures social capital along two key dimensions—participation and trust—and, partly based on Putnam’s social capital index (Putnam 2000 ), integrates them into a single index (van Beuningen and Schmeets 2013 ; see Fig. 1 in the Appendix). For each of the participation and trust dimensions, the model makes distinctions at the social (micro), organizational (meso) and political (macro) level (see also Halpern, 2005 ; Sharp and Randhawa, 2012 ). On the participation dimension, the social level captures contacts with family, friends and neighbours, and help provided to other people. The organizational level covers volunteering, activities in associations, and paid work, and the political level includes voting in parliamentary elections and participation in other political actions. The trust dimension contains information on trust in other people (social level); trust in the army, police, lawyers, civil servants, media and large companies (organizational level); and trust in parliament (political level). The model thus contains six sub-dimensions, three for participation and trust each, and a total of 17 different indicators. The formative model was identified by Multiple Table Analysis, also called a Hierarchical Components Model (Lohmöller, 1989 ; Tenenhaus et al., 2004 ), and was estimated in R using the PLSPM package (Bertrand, 2024 ). A path weighting scheme was used so weights can be interpreted as regression coefficients. Weights are calculated from the first-order on the two second-order constructs - participation and trust. These, in turn, contribute independently to the social capital index. The addressed 17 indicators are based on the yearly SSW questionnaires in the period 2012–2022 and will be used in the subsequent analyses and use case demonstrations from our merged file. All indicator values are calculated at the individual level. Maintaining Contacts. The survey includes three separate questions to respondents on the frequency of contacts with: i) one or more family members, ii) friends, partners, or very good acquaintances and iii) neighbours, each with the scale from 1 (daily) to 5 (rarely or never). Contacts are meetings in-person, phone-calls, letters, or by using social media. From these we have derived the binary indicators of Maintaining Contacts for each of the three segments, where in each case 1 stands for if the contacts are maintained at least once a week, and 0 if otherwise. Informal Help-Giving. Respondents were asked if they were giving unpaid help to people outside their household, such as the sick, neighbours, family, friends, and acquaintances in the four weeks prior to being surveyed. The one-to-one translation of this binary variable is regarded as an indicator of Informal Help-Giving. Volunteering. The survey investigates whether the respondent has volunteered in the 12 months prior to being surveyed. Based on twelve organizations, such as for schools, sport clubs, and health care organizations, a binary variable was created which is regarded as an indicator of Volunteering. Participation in Associations. Respondents were asked about the frequency of their participation in activities of one or more associations. The newly created binary indicator has values of 1 if the respondent’s participation in activities is at least once per month and 0 if less. Political Action. A set of nine questions was asked regarding respondents’ engagement in political matters in the 5 years period prior to being interviewed, specifically through 1) participating in media events to exert influence, 2) approaching a political party or organization, 3) taking part in meetings or debates organized by governmental bodies, 4) approaching a politician or official, 5) participating in an action group, 6) participating in a protest action, protest march or demonstration, 7) participating in a paper or internet signature campaign, 8) taking part in a political discussion or action on the internet, and 9) making some other (unlisted) political action. The r binary indicator of Political Action has a value of 1 if the respondent engaged in at least one of the actions. Voting Participation. Respondents were asked whether they voted in the most recent Dutch parliamentary elections in 2010, 2012, 2017, or 2021, depending on the date the question was answered. The derived binary indicator of Voting Participation takes a value of 0 (not voted) and 1 (voted). Social trust. The survey asked about the person’s trust in other people in general based on two answer options: (0) you can’t be too careful when dealing with other people or (1) most people can be trusted. Trust in institutions. The survey inquired about trust in: 1) judges, 2) police, 3) the army, 4) the press, 5) civil servants, 6) large companies, and 7) parliament, with the answering scale ranging from 1 to 4, where 1 and 2 stand for highly and quite trustful, respectively, and 3 and 4 stand for little and no trust, respectively. From these questions, we have created binary indicators of Trust in Institutions for each category with a value of 1 if the respondent shows any trust (answering 1 or 2 to the original question), and 0 otherwise. Indicators derived from administrative data (social network indicators) The following are some of the social network indicators we have derived from the Social Networks dataset, calculated at the respondent level, and merged with the SSW dataset into one file. These indicators are also involved in subsequent analyses and use case demonstrations. Degree Centrality. The original Social Networks dataset includes the characterization of relationship between any two individuals based on the sub-types of ties from the domain of family, work, neighbour, school and household ties. We have calculated the degree centrality (Freeman, 1978 ) of the respondent on the binary undirected network abstraction 1 for each layer for each year, which indicator is essentially the count of the number of family members, work colleagues, neighbours, schoolmates and household members the respondent of the SSW survey has in each of the respective network layers. “Binary undirected” is the basic abstraction in which strength and direction of relationship (e.g. the higher weight for closer relatives, the direction father to son, grandfather to father, etc.) are not considered, only the existence (1) or non-existence (0) of the relationship. As correlations between any two-yearly centrality value sets picked from the period were always shown to be close to 1, in the following analyses we mainly used the degree centrality value for 2020 for each respondent. Distance-Weighted Degree Centrality. We have calculated the average and the sum of all direct-flight (great-circle) distances between the municipality of residence of the respondent in the respective period to the municipality of residence of the respondent’s relative in the period. Averaging over all periods for average of distances and for the sum of distances, we have obtained two distance-weighted degree centrality metrics for a respondent in her/his family network. Analysis and Results To answer our research question – if and how social network centrality relates to social capital – we start by investigating how the 17 social capital indicators as well as the 5 social network centralities differ between subpopulations. From the perspective that higher network centrality will be associated with more individual social capital, we expect to find similar patterns. In a next step we will look at the correlations between the 5 centralities and the social capital composite and the single indicators. In Tables 1 and 2 in the Appendix, we detail general individual attributes – gender, age, level of education, income, and migration background – and their relationship with 17 indicators measuring participation and trust, based on SSW data from 2012 to 2022. It clearly shows the variation between all these subpopulations in the levels of participation and trust in Dutch society. More resources, in particular education, results in more participation and trust. This is in line with the Civic Voluntarism Model (Brady, Schlozman and Verba, 1995), according to which volunteering and political participation, expressed in political actions and voting, is highest among the more educated and individuals with a higher income (Quintelier and Hooghe, 2013 ; Ojeda, 2018 ; Barsegyan, Knigge and Maas, 2023 ). Education is also strongly related to the trust indicators. A minority of 40 percent of the group with elementary education say that ‘other people can be trusted’, which gradually increases to 85 percent among the group with a master degree. Also many gender and age differences reveal in participation and trust levels. Females exhibit more contacts with family members and friends, and are more prone to informal help-giving than males. Females also often show more trust in institutions but are less trustful to other people than males. Maintaining contacts with friends is more prevalent among younger individuals, whereas maintaining contacts with neighbours is more common among older individuals, which aligns with expectations. Additionally, a larger percentage of elderly individuals participate in voting, and political action declines with age. And, overall, younger age groups trust others and also trust institutions more than elderly do. Likewise, Dutch natives and migrants differ on many participation and trust indicators. Table 1 Participation by demographic and socioeconomic groups, 2012/2022 Maintaining Contacts - Family Maintaining Contacts - Friends Maintaining Contacts - Neighbours Informal Help-Giving Volunteering Participation in Associations Work Participation Political Action Voting Participation Attributes Indicator as percentage per Gender Man 78.9% 74.0% 59.3% 30.7% 46.7% 44.5% 68.6% 46.6% 82.4% Woman 87.5% 78.4% 59.9% 37.6% 46.7% 42.0% 59.1% 43.4% 81.5% Age 15–24 77.9% 94.9% 45.2% 30.5% 47.6% 55.7% 69.9% 52.3% 76.1% 25–34 88.9% 86.6% 48.2% 31.9% 42.0% 40.9% 86.2% 51.0% 76.2% 35–44 85.9% 76.5% 61.8% 31.4% 54.1% 39.9% 86.4% 49.0% 79.5% 45–54 81.7% 70.5% 60.0% 37.9% 50.8% 40.2% 83.4% 47.4% 82.1% 55–64 82.2% 66.5% 63.3% 41.2% 45.3% 39.1% 67.4% 45.5% 85.8% 65–74 82.9% 69.0% 72.6% 37.0% 48.2% 46.0% 14.3% 37.5% 87.6% 75+ 83.3% 66.2% 72.4% 24.7% 32.6% 41.7% 2.9% 24.8% 84.8% Education Elementary 81.1% 72.8% 64.2% 24.5% 32.2% 33.1% 32.9% 23.4% 66.9% LBO 82.7% 76.4% 64.0% 30.8% 39.5% 40.5% 46.6% 30.2% 75.1% MBO, Havo, Vwo 83.8% 77.1% 60.7% 35.7% 48.1% 43.2% 68.6% 45.4% 81.2% HBO 84.6% 77.4% 56.7% 39.0% 56.9% 49.7% 77.2% 59.8% 90.5% University 82.0% 75.4% 51.6% 35.5% 58.4% 49.2% 82.2% 65.3% 91.2% Income Status First (lowest) quartile 81.8% 78.5% 56.8% 31.0% 41.1% 34.4% 44.3% 40.8% 71.6% Second quartile 84.1% 74.4% 63.4% 32.8% 42.3% 38.6% 53.0% 38.2% 78.8% Third quartile 83.3% 76.0% 60.8% 35.3% 48.9% 45.4% 70.0% 46.0% 84.3% Fourth (highest) quartile 83.5% 76.4% 57.8% 36.5% 51.9% 50.4% 78.5% 51.6% 88.9% Migration Background Native 83.8% 75.9% 60.4% 35.4% 49.4% 46.0% 64.5% 45.7% 85.6% Migrant 81.3% 77.5% 56.6% 29.8% 37.2% 33.3% 61.3% 42.5% 66.8% Total Total 83.3% 76.2% 59.6% 34.2% 46.7% 43.2% 63.8% 45.0% 81.9% Table 2 Trust by demographic and socioeconomic groups, 2012/2022 Social Trust Trust in Army Trust in Press Trust in Police Trust in Parliament Trust in Civil Servants Trust in Large Companies Attributes Indicator as percentage per Gender Male 63.9% 63.8% 36.5% 71.2% 38.3% 44.9% 39.9% Female 58.8% 66.2% 33.2% 74.4% 38.4% 43.9% 38.9% Age 15–24 61.8% 77.8% 32.1% 75.4% 49.6% 56.6% 57.5% 25–34 66.0% 72.8% 33.3% 74.1% 41.2% 48.9% 45.7% 35–44 65.2% 69.4% 36.5% 74.9% 40.4% 46.6% 41.8% 45–54 63.5% 63.2% 36.3% 71.7% 36.9% 42.1% 37.0% 55–64 60.7% 57.3% 35.5% 71.0% 33.3% 38.6% 28.7% 65–74 55.6% 54.4% 35.3% 68.9% 31.0% 35.8% 27.7% 75+ 51.8% 53.9% 34.7% 73.8% 34.1% 41.5% 34.4% Education Elementary 39.6% 57.0% 29.1% 64.7% 30.6% 38.7% 39.2% LBO 46.9% 61.9% 30.0% 67.8% 31.6% 39.8% 41.0% MBO, Havo, Vwo 59.1% 66.0% 32.0% 71.1% 35.6% 42.1% 40.5% HBO 77.2% 67.7% 38.5% 78.5% 48.1% 50.0% 38.3% University 84.6% 69.7% 47.4% 81.5% 57.0% 59.0% 39.1% Income Status First (lowest) quartile 51.1% 61.9% 33.4% 68.2% 35.9% 43.6% 39.8% Second quartile 52.9% 61.6% 31.4% 69.7% 31.5% 40.0% 37.0% Third quartile 62.5% 65.9% 34.2% 73.6% 37.2% 43.8% 38.3% Fourth (highest) quartile 72.6% 68.4% 38.7% 77.1% 45.5% 48.3% 41.9% Migration Background Native 64.0% 65.3% 34.9% 74.0% 37.8% 43.0% 38.7% Migrant 51.4% 63.5% 34.7% 68.2% 40.3% 49.7% 41.9% Total 61.3% 64.9% 34.9% 72.8% 38.4% 44.4% 39.4% In Table 3 we present for the same subpopulations the average centrality degrees. Overall, and not in line with the social capital indicators the subpopulations do not show much variation in the five centrality measures. This is particularly true for the connections with neighbours, ranging from 40 to 45. Males and females exhibit similar levels of family and neighbour connections, but females demonstrate higher work-related connections compared to males. In addition, females show a substantial higher level of connections at schools which indicates that females going into schooling programmes or schools with more attendees than men. Young adults aged 15–24 have high degree centrality in work and school, and they exhibit the highest household connections. Middle-aged individuals, particularly those aged 25–44, show high family connections, with the highest school network connections among those aged 35–44. As regards the educational levels, there is some trace of association with the degree centrality indicators. University-educated individuals have the highest levels of work-related connections and school network connections. Those with HBO education also exhibit high levels of work and school connections. In contrast, individuals with elementary education levels exhibit the lowest levels of connections across these indicators, particularly in school networks. Income status also plays a significant role in degree centrality. Individuals in the highest income quartile show the highest levels of work-related connections and school network connections. Those in the lowest income quartile exhibit lower levels of connections in these areas, although they have relatively high family and household connections. Migration background reveals differences in degree centrality indicators as well. Native individuals have higher levels of family connections and similar levels of work connections compared to migrants. Both groups show similar levels of neighbour and school connections. However, migrants exhibit slightly higher household connections compared to natives. It might be important to note that of the 83,997 respondents, 1,249 were identified as having no family connections whatsoever. This sample represents approximately 1.5% of the total respondent pool. Our closer examination of the demographic profile revealed that this group was predominantly composed of first-generation migrants, which appears logical. The group was distributed across age brackets, with the largest proportion being 65+, likely due to deceased relatives or lack of children. There was no significant gender difference, and the majority had low educational attainment, though not overwhelmingly so. These individuals were excluded due to the very high correlations with the controls in the models which would result in high variance inflation factor values, and due to the extremely small size of the group. Table 3 Social network indicators by sociodemographic subpopulations, 2012/2022 Degree Centrality - Family Degree Centrality - Work Degree Centrality - Neighbours Degree Centrality - School Degree Centrality - Household Attributes Indicator as average number per Gender Male 27.4 80.3 43.7 97.6 2.7 Female 26.7 84.6 43.4 122.9 2.4 Age 15–24 25.7 83.6 44.5 111.7 3.7 25–34 34.0 81.9 43.5 98.2 2.6 35–44 33.6 81.4 44.7 122.2 2.9 45–54 27.5 82.6 44.4 90.4 2.4 55–64 23.9 83.7 43.4 90.0 1.6 65–74 21.9 - 42.1 - 1.5 75+ 17.0 - 40.0 - 3.0 Education Elementary 22.8 79.6 42.2 84.5 2.7 LBO 26.0 79.4 42.9 103.7 2.5 MBO, Havo, Vwo 28.1 81.0 43.5 128.6 2.6 HBO 29.1 85.1 44.2 111.9 2.3 University 26.7 87.3 44.6 115.6 2.4 Income Status First (lowest) quartile 24.1 81.2 41.6 104.1 2.9 Second quartile 26.6 80.8 42.4 104.0 2.5 Third quartile 28.5 82.9 44.1 110.1 2.3 Fourth (highest) quartile 28.0 83.1 45.0 119.7 2.1 Migration Background Native 29.8 81.7 43.5 110.1 2.3 Migrant 16.7 84.6 43.6 110.0 3.3 Total 27.0 82.4 43.5 110.1 2.5 General Associations of Variables In Fig. 2 we list the correlations between the Social Networks indicators and the social capital composite indicator as well as the 17 specific social capital indicators. Generally, the correlations are positive and significant, but rather weak and often not reaching the p-value lower than 0.01, as indicated by a single asterisk (*). Essentially, only two centrality measures show higher levels of significancy (p < 0.01): family and neighbours. However, the connection a person has with its neighbours shows a low correlation (0.12) with social capital, and this is also true for the family networks with social capital (0.11). Unexpectedly, also the correlations between the networks and actual behaviour in terms of contacting family members and neighbours are low. As per other indicators, volunteering, having paid work, and voting in elections show positive correlations with the size of their family, while volunteering and paid work relates to the connections with neighbours. Interestingly, social trust shows weak positive correlations with family and neighbour degree centrality. Apparently, being surrounded by others fosters trust in other people in general. Trust in the army and in parliament also exhibits weak positive correlations with the neighbour network. In sum, the associations between any two variables are generally very weak, with most correlations being close to zero. Notably, the strongest observable relationship emerges between the frequency of maintaining contacts within the family and the individual's actual number of connections in the family network, as indicated by a modest but relatively higher correlation of approximately 0.11. In contrast, there is virtually no association between maintaining contacts with neighbours and the number of neighbours a person has. Due to unexpectedly low correlations especially between connectivity vs. maintaining contacts in both the family and neighbours category, we took a closer look into these categories using some more refined indicators and transformations. In particular, we have used logarithmic transformation with natural logarithms for degree centrality measures. In network research in general, degree distributions following a power-law distribution are commonly found, where a few nodes have very high degrees, while the majority have lower degrees. For the Social Networks of this dataset, the distributions are not strictly following power law, but this is due to how networks, especially those of neighbours in this case, have been abstracted (see van der Laan, 2022b). In addition to Distance-Weighted Degree Centrality in both layers, as defined in the previous section (both in its original form and logarithmically transformed), we incorporated several other measures. These include the natural logarithm of Degree Centrality, the average geographical (crow fly) distance to all family relatives or neighbours of a person based on registered residence addresses in 2020, and the total sum of these distances. To account for potential scale effects, we also applied logarithmic transformations to both the average and total distance measures. However, all such amendments did not substantially improve the correlations. Regressions with (segment) control variables In the following part of the analysis, we delve into the analyses of specific segments of the populations in terms of their general attributes and main social capital components given their social network embeddedness. We perform a set of logistic regressions with dependent variables Maintaining Contacts with Family and with Neighbours. This, and degree centrality variables were recoded before modelling. The Maintaining Contacts variables were recoded as binary indicators with value 1 for person having contact at least once per week, and 0 otherwise. Degree Centrality-Family was split into 15 groups. Specifically, we retained the original values for those with 1 to 5 connections. For those with more extensive networks, we grouped the connections into broader categories: 6 through 9 connections were recoded as 6, 10 through 13 as 7, 14 through 17 as 8, 18 through 21 as 9, 22 through 26 as 10, 27 through 31 as 11, 32 through 39 as 12, 40 through 53 as 13, 54 through 66 as 14, and 67 through 364 as 15. Although arbitrary, this recoding process was guided by prior experimentation with strict decile cut-offs, which proved less sensible. Our approach adheres to power law distribution principles typical of networks, where few individuals have many connections and many have few, while recognizing that it is uncommon for individuals to have only 1 or 2 connections, and that family networks deviate from typical network patterns. As regards the Degree Centrality-Neighbour variable, we found an unusual distribution of the degree centrality values, with a strong frequency peak between 30 and 60. This arises from defining neighbours as the members of a combination of close neighbours and the residents of the closest 10 households within a radius of 200 meters (for a detailed definition, consult CBS, 2024b). This method results in many individuals having similar centrality values, especially in densely populated areas or institutions, thus creating the observed distribution anomaly. On average, a person has 43 neighbours (standard deviation = 7.3) with a range from 13 to 182 2 . We hence did not stick to the former principle as with family centrality but recoded the neighbour degree variable by partitioning the values based on cut-off points for creating 15 equal groups. All other used variables remained as previously defined. The summaries for both models are provided in Table 4 (see Annex), while the description of the models is provided in the sequel. Table 4 Summary of results of logarithmic regression (Maintaining Contacts vs. Degree centrality – Model 1 and Model 2) VARIABLE MODEL 1 - FAMILY MODEL 1 - NEIGHBOURS MODEL 2 - FAMILY MODEL 2 - NEIGHBOURS Odds ratio Confidence limit Odds ratio Confidence limit Odds ratio Confidence limit Odds ratio Confidence limit Degree centrality (reference category: group 1) Degree (2) 1.307 1.040 1.643 1.167 1.087 1.254 1.414 1.093 1.831 1.206 1.115 1.304 Degree (3) 1.377 1.119 1.696 1.165 1.082 1.255 1.541 1.217 1.951 1.246 1.149 1.352 Degree (4) 1.751 1.432 2.142 1.127 1.034 1.229 2.091 1.663 2.629 1.199 1.091 1.318 Degree (5) 1.647 1.348 2.011 1.202 1.120 1.289 2.010 1.602 2.523 1.310 1.213 1.414 Degree (6) 2.156 1.822 2.551 1.202 1.110 1.303 2.603 2.146 3.158 1.318 1.207 1.439 Degree (7) 2.419 2.047 2.860 1.245 1.150 1.348 3.111 2.565 3.772 1.377 1.262 1.502 Degree (8) 2.570 2.174 3.039 1.194 1.102 1.293 3.481 2.868 4.224 1.344 1.231 1.467 Degree (9) 2.802 2.367 3.318 1.177 1.087 1.274 3.687 3.033 4.483 1.295 1.187 1.413 Degree (10) 2.830 2.392 3.349 1.225 1.144 1.312 3.725 3.066 4.526 1.378 1.277 1.486 Degree (11) 3.134 2.642 3.719 1.251 1.150 1.361 4.110 3.372 5.008 1.434 1.308 1.573 Degree (12) 3.222 2.719 3.817 1.183 1.085 1.290 4.174 3.430 5.080 1.385 1.259 1.523 Degree (13) 3.732 3.148 4.425 1.177 1.091 1.270 4.543 3.730 5.532 1.406 1.293 1.529 Degree (14) 3.939 3.275 4.738 1.191 1.103 1.286 4.743 3.838 5.861 1.449 1.331 1.578 Degree (15) 4.705 3.905 5.668 1.204 1.111 1.304 5.522 4.461 6.836 1.517 1.389 1.657 Gender (reference category: male) Female 2.010 1.927 2.097 0.991 0.961 1.022 Age (reference category: 15–24) 25–34 2.474 2.264 2.704 1.289 1.214 1.368 35–44 1.807 1.669 1.956 2.091 1.972 2.217 45–54 1.328 1.239 1.424 1.897 1.796 2.005 55–64 1.547 1.443 1.660 2.166 2.050 2.289 65–74 1.647 1.529 1.774 3.250 3.060 3.452 75+ 1.806 1.644 1.983 3.326 3.086 3.585 Education (reference category: elementary) LBO 1.028 0.946 1.116 0.990 0.927 1.058 MBO, Havo, Vwo 1.116 1.032 1.208 0.910 0.855 0.968 HBO 1.059 0.970 1.155 0.780 0.729 0.835 University 0.922 0.835 1.017 0.640 0.593 0.690 Income (reference category: First (lowest) quartile Second quartile 1.071 0.997 1.150 1.159 1.099 1.224 Third quartile 1.026 0.959 1.098 1.116 1.060 1.175 Fourth quartile 1.091 1.019 1.168 1.029 0.977 1.083 Migration background (reference category: native) Migrant 1.104 1.038 1.175 0.944 0.903 0.986 Models’ strength parameters Nagelkerke R² Cox & Snell R² -2 Log likelihood Wald (Degree) 0.018 0.010 68706.860 841.397 0.001 0.001 106850.568 50.243 0.059 0.034 59212.075 684.601 0.053 0.039 91713.798 143.262 Model 1: Basic model; Maintaining Contacts vs. Degree Centralities In Model 1, we explored the relationships between Maintaining Contacts and Degree Centrality variables within the family network and within the network of neighbours in parallel. The overview is provided in Fig. 3 with boxplots capturing relations between the centrality measures and the social contacts (see Annex for all results). The R Square value (Nagelkerke) for Model(s) 1 suggest that a small percentage of the variance in maintaining social contacts is explained by the degree centrality in the family network, and even less in the neighbourhood network, indicating modest explanatory power (see Annex). However, degree centralities add significant contributions, expressed in the overall Wald values for family (~ 841.4) and neighbours (~ 50.2). Each increase in family degree centrality significantly raises the likelihood of maintaining family contacts. The odds ratio of the second group with one family connection shows that the chance of having at least once a week a family contact is higher compared to the group with maximum of one connection serving as the reference group. Subsequently the odds ratio’s increase, following almost a linear pattern, to 4.6 for individuals with the highest family degree centrality. This suggests that family networks may contribute to the actual contacts with other family members in-person, phone-calls, letters, or by using social media. This model shows clearly the importance of an individual’s family network size in predicting the maintenance of family relationships. As for the neighbours domain, the odds ratios for the other than the baseline group differ only slightly from 1.25 to 1.36 without a clear linear pattern. This suggests that the size of the neighbours in a person’s direct environment is only important if the first cut-off point has been reached. Consequently, having more neighbours does not imply more frequently maintaining contacts with them. Model 2: Adding general attributes In Model 2, we extended the analysis by incorporating controls alongside degree centrality within each network layer to correct for the demographic and socio-economic composition of the 15 groups. In Fig. 4 and Fig. 5 , we provide the odds ratios for degree centrality on maintaining contacts. A slightly larger percentage of the variance in Maintaining Contacts in both domains (Family and Neighbours) is explained by the combined factors, indicating improved explanatory power of Model 2 when compared to Model 1. We further notice that after controlling for gender, age, education, income and migration background, the degree of family centrality clearly relates the frequency of family contacts. The more family connections, the higher the chance that there is at least once per week a family contact. Interestingly, a clearer pattern for the contacts with neighbours emerges, suggesting the importance of taking into account the composition of the 15 neighbour centrality groups. The odds ratios increase gradually from 1.21 to 1.52 indicating that the chance for at least a weekly contact increased by half for the group with the highest centrality compared to the group with the lowest centrality. Additionally, the controls also contribute significantly to the family contacts. Women have more contacts than men, people of intermediary-level education show more contacts than the lowest or highest educated, and the youngest age group has less contacts than all other age groups. Furthermore, the highest income groups have slightly more contacts with their family members than the lowest income group, and the migrants have more contacts than the native population. This model underscores the combined importance of family network size and demographic factors in predicting the maintenance of family relationships. In addition to the presented models, we have performed a few robustness checks by adding more demographic and regional variables into the model, such as household composition, marital status, and urbanity. The inclusion of any of these variables had hardly any effect on the relationship between family centrality and family contacts. Likewise, we added the controls in the model for the weekly contacts with neighbours. While gender is not related to the contacts with neighbours, we notice that such contacts increase for higher age groups. In particular, the 65 + population show a high contact level. Furthermore, such contacts decrease for the higher educated, the middle income groups have more contacts than the lowest income group, and the natives have slightly more contacts than the migrant population. Conclusions and Prospects for Future Research This study investigated the impact of social network centrality measures on individual social capital in the Netherlands. We hypothesized that individuals with higher degree centrality would possess greater social capital. Specifically, we examined two indicators of social capital: the frequency of individuals' contact with family members and neighbours. Our analysis revealed only very weak correlations between the five social network centrality measures and a composite social capital indicator derived from 17 participation and trust indicators. Our primary preliminary conclusion is that social network centrality measures are not suitable substitutes for assessing individual social capital through large-scale surveys. This finding underscores an unfortunate limitation: even the extensive administrative data covering the entire Dutch population cannot come close to substituting for survey data in providing detailed insights into individual social capital, its determinants, and its consequences. Furthermore, we investigated the correlations with the 17 separate indicators and found that these did not improve the associations with centrality metrics. This was also true for the specific associations we anticipated to be stronger: the correlation between family centrality and the frequency of family contacts, and between neighbour centrality and the frequency of contacts with neighbours. These analyses suggest that having a strong network does not necessarily imply intensive use of such networks. On the other hand, regression analyses show that the likelihood of frequently contacting family members and neighbours increases with higher network centralities. That pattern is more prevalent for contacting family members than for contacting neighbours. A possible explanation is that in the SSW survey, the question refers to contacts with direct neighbours, while the measure of neighbour centrality currently includes people within a 200-meter radius. Additionally, we demonstrated that inclusion of demographic and socio-economic controls adds to the understanding of network centralities linked to social contacts, although the increase in explained variance remains low. This suggests that centrality and the included controls are not sufficient predictors of actual social contacts. This could be due to the complexity of social interactions between family members and between neighbours, where factors such as the quality of relationships, social norms, and individual preferences play a significant role. These weak correlations between survey and administrative data suggest that some of the key steps in future research would be to look for the reasons why there is hardly any impact of the social networks on the actual social behaviour, assessed via the representative SSW surveys, even when it comes to relationships with their own family, let alone the neighbours. Future research should aim to address several key areas to build upon these findings. First, enhancing the quality and comprehensiveness of register data, particularly in the abstraction of networks into centrality measures, is crucial. This includes redefining the parameters for what constitutes a neighbourhood, workplace, or school network, ensuring more realistic distributions. Additionally, incorporating more layers and dimensions of relationships will provide a more accurate and detailed picture of social networks. This approach will better capture the diversity and dynamics of networks over time, integrating additional data sources to enrich the existing datasets. Second, as network analysis of centrality is limited without accounting for the weights of relationships, future research should aim to integrate the strength and quality of ties into centrality measures. For example, a parent should be given a higher weight than a cousin. Weighted network analysis can provide a more nuanced understanding of social networks by distinguishing between strong and weak ties, thus offering a clearer picture of how different types of relationships impact social cohesion. Yet, this is not trivial and requires a well-designed network abstraction methodology, evolving from a binary towards a weighted (even directed) approach, particularly when it comes to network layers beyond the basic family layer, which remain problematic at present. Third, the SSW data allow for focussing on specific regions and municipalities. By integrating more information of neighbourhoods such as social and cultural facilities enhancing social contacts could highlight unique cultural or structural factors influencing social cohesion. This would provide a broader context and help identify universal versus context-specific dynamics. Fourth, while the findings of this study are based on a sample from the Netherlands, the external validity of these results, especially in terms of geographic applicability, is limited. Although the dataset is comprehensive and includes a large number of respondents, caution must be exercised when generalizing the results to other geographical contexts, or, ideally, other geographical regions should be explored as well. The unique characteristics of Dutch society, including its social policies, urban infrastructure, and demographic composition, may not fully represent conditions in other countries or regions. However, the large sample size and richness of the data do provide a valuable foundation for understanding social network centrality and social capital within this context. Future studies could extend this approach to other countries or regions to examine whether similar patterns of social network centrality and social capital hold true across diverse geographical settings. Lastly, future research might be dedicated to exploring the relationship between social cohesion and the components of well-being, such as happiness and life satisfaction, and broader wellbeing including physical and mental health, leisure time, work, school, housing, relationships, financial situation, and neighbourhood safety (included in the SSW). Insights from this research could guide policymakers, mental health professionals, urban planners, and educators in fostering environments that enhance social capital, ultimately improving well-being across populations. By addressing these and other domains, we can significantly enhance our understanding of social networks and social cohesion, ultimately contributing to more effective strategies for fostering well-being and community resilience in the Netherlands and beyond. Declarations Funding This work was supported by the ANONYMISED, as part of the project ANONYMISED and as part of the ANONYMISED. Funded by the ANONYMISED. The views and opinions expressed are those of the author and do not necessarily reflect the official views of the ANONYMISED or the ANONYMISED. Neither the ANONYMISED nor the ANONYMISED can be held responsible for them. Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval This study uses secondary, pseudonymized data from Statistics Netherlands (CBS). Data access was provided through CBS’s secure research environment under the Dutch Statistics Act (https://wetten.overheid.nl/BWBR0015926/2018-07-01). All analyses comply with Dutch legislation and the General Data Protection Regulation (EU) 2016/679 (https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng). CBS confirmed that no separate ethics approval was required, as the study involves only pseudonymized secondary data with no direct contact with participants. Informed Consent Informed consent for the Sociale Samenhang en Welzijn (SSW) survey was obtained by CBS from all participants prior to data collection. Respondents were informed about the study purpose, data use, anonymity, and their rights under GDPR. The administrative data from the Person Network of the Netherlands were collected under CBS’s legal mandate. The authors had no interaction with participants and did not collect any new data. Consent procedures were handled entirely by CBS. Consent All participants provided informed consent before participation. Data, Materials and/or Code availability The data examined in this paper is included in the Statistics Netherlands Microdata Catalogue. The data are not available except to the authorized individuals, in accordance with the general data protection regulated by Dutch Criminal Law (https://wetten.overheid.nl/BWBR0001854/2023-10-01) and the Central Bureau of Statistics Act (https://wetten.overheid.nl/BWBR0015926/2018-07-01). Authors’ contributions Conceptualization: Anonymised, Anonymised; Data curation: Anonymised; Formal analysis: Anonymised, Anonymised; Funding acquisition: Anonymised; Investigation: Anonymised, Anonymised; Methodology: Anonymised, Anonymised; Project administration: Anonymised, Anonymised; Software: Anonymised, Anonymised; Resources: Anonymised, Anonymised; Supervision: Anonymised; Validation: Anonymised, Anonymised; Visualization: Anonymised, Anonymised; Writing – original draft: Anonymised, Anonymised; Writing – review & editing: Anonymised, Anonymised. Acknowledgments The authors would like to express their special thanks to the fellow members of Anonymised for their suggestions that helped to improve the structure of this study. References Barabási, A.-L. (2002). Linked: The New Science of Networks . Perseus Publishing. Barsegyan, V., Knigge, A., & Maas, I. (2023). 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(2013). Developing a Social Capital Index for the Netherlands. Social Indicators Research, 113(3), 859-886. doi: 10.1007/s11205-012-0129-2. van der Laan, J. (2022a). A Person Network of the Netherlands . CBS Discussion Paper. Available at: https://www.cbs.nl/-/media/_pdf/2022/20/person_network_netherlands.pdf. Accessed: July 16, 2024. van der Laan, J., et al. (2022b). A Whole Population Network and Its Application for the Social Sciences. European Sociological Review, 39 (1), 145–160. doi: 10.1093/esr/jcac026. Watts, D. J. (1999). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press. Footnotes We used binary undirected networks to simplify the analysis by focusing on the presence or absence of relationships, rather than their strength or direction. This approach, while useful for initial analyses, may overlook the nuances of relationship dynamics. Future studies should consider weighted and directed networks to capture the complexity of social interactions more accurately. For the abstraction of networks, see CBS 2024a-e. To further investigate potential biases, we conducted an analysis where we considered separately the direct neighbour network and the network of extended neighbourhood (as defined in CBS 2024b). We traced a highly unusual distribution of centrality in the network of extended neighbours, where over 90% of respondents were shown to have 20 to 21 extended neighbour relationships, hence we did not distinguish between the two relationships in the metric. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6375519","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453007251,"identity":"1f5c1bb4-e231-41ab-a98a-cdfeb0f1fcc2","order_by":0,"name":"Dino Pitoski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie2RvwqCQBjA7xB0MV3PpWc4EbKgehYlcAt6ghACt3Z7i0bHkw9yOWht9BFsDIK6M7IWL8eG+8H38S0/vn8IaTT/CLSZiDARa2RtyUR/K0QqZS5rA+FUqTD0bmMiw+4UBU41KmtcTLeue4phUczHoWFUNdrMexUPnBXFnBAvTxiseRLMdmYsBkt6FQr2hOCMEHqxUlhnEB/B9oUCA5RzlcIsewjFvQ5UmBgMZ0x2wUrFAzugMSfeIU+ics9Xchc/jxS7OGfu19dCHMw9Bc2tWI5Da1c3zb3/Yi3iQi/kQ9onRmoBfV7XKRqNRqP54gkpLVMEw3jmJQAAAABJRU5ErkJggg==","orcid":"","institution":"Peoplet Ltd. Lindar Istria Croatia","correspondingAuthor":true,"prefix":"","firstName":"Dino","middleName":"","lastName":"Pitoski","suffix":""},{"id":453007252,"identity":"40670a21-45a7-444b-8c9c-215e7c802e87","order_by":1,"name":"Hans Schmeets","email":"","orcid":"","institution":"Centraal Bureau voor de Statistiek","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Schmeets","suffix":""}],"badges":[],"createdAt":"2025-04-04 10:53:21","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6375519/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6375519/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82259953,"identity":"2b5fa7d8-9de3-4e2c-95c3-a5ba644c9ed7","added_by":"auto","created_at":"2025-05-08 11:51:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":667579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchema and measures for the 17 indicators in the Social Capital Composite Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure adapted from Van Beuningen and Schmeets (2013), with adjustments.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/8ffb22bd3e405abadf79f9fb.jpeg"},{"id":82259948,"identity":"833e8bd6-b923-4ac8-a150-30044b8dffe2","added_by":"auto","created_at":"2025-05-08 11:51:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71802,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between SSW and Social Networks indicators\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/9ff43723ba1215a3b7aa1ab4.png"},{"id":82260778,"identity":"9cf24fb4-bd42-4e88-b92d-179ee8a5c196","added_by":"auto","created_at":"2025-05-08 11:59:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaintaining Contacts vs. Degree Centrality – Family vs. Neighbours - odds ratios (Model 1)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/d069b20d05abb29ea1407364.png"},{"id":82259949,"identity":"bc9d0a7f-ea7f-4fa4-91a9-fcdd45cad554","added_by":"auto","created_at":"2025-05-08 11:51:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaintaining Contacts vs. Degree Centrality - Family - odds ratios (model 2)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/5cbfa590e6647826622b2859.png"},{"id":82259946,"identity":"25f5bd29-6021-4baf-ac57-9d2721af530b","added_by":"auto","created_at":"2025-05-08 11:51:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18786,"visible":true,"origin":"","legend":"\u003cp\u003eMaintaining Contacts vs. Degree Centrality - Neighbours - odds ratios (model 2)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/49ba8e95d3bf9cd22bede4c6.png"},{"id":91739371,"identity":"b0a839e6-1129-41c6-97e9-4debf633bba6","added_by":"auto","created_at":"2025-09-19 18:16:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2574346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6375519/v1/1a8826bc-5f97-4cd1-9d4f-cf2085e7c86a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Networks and Cohesion in the Netherlands: Insights from Combined Administrative and Survey Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStatistics Netherlands (CBS) has recently constructed the Whole Population Network file based on administrative data containing over a billion personal relationships per year among about 17\u0026nbsp;million inhabitants of the Netherlands (van der Laan \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e-b; CBS 2023; CBS 2024a-e). These data, referred to as 'Social Networks' in further text, include the characterization of the relationships between individuals based on sub-types of ties from the domains of family, work, neighbourhood, schools, and households. Different characterizations represent different network layers and have been derived for each year in the period from 2009 until 2020. Several studies came out recently that assess these data using extensively the network science methods (Bok\u0026aacute;nyi, Heemskerk, and Takes \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kazmina et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Menyh\u0026eacute;rt et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to maintaining this dataset, which is based on registers (\u0026ldquo;administrative data\u0026rdquo;), CBS has been conducting and maintaining data based on various surveys of Dutch population. Among these is the yearly Social Cohesion and Well-being (Sociale Samenhang en Welzijn or, in further text \u0026ldquo;SSW\u0026rdquo;) survey, started in the year 2012, which by now has interrogated over 83,000 randomly sampled individuals of over 15 years of age, as a representative sample of the Dutch population. The survey covers topics related to social contacts, volunteering, involvement in associations, political participation, generalized social trust, trust in institutions and many other aspects concerning social cohesion, including individuals\u0026rsquo; subjective well-being. Responses have been gathered via a sequential mixed-mode design: internet and, after two unsuccessful reminders, by telephone or face-to-face, with the response rate of about 55 percent. To reduce potential non-response bias, data have been reweighted by population characteristics such as gender, age, household size, migration background, marital status, income, urbanity and region (for details on the sampling and the design, consult CBS 2024f). The SSW has been the basis for many scientific studies on various topics, such as social capital, religion, voting behaviour, trust, volunteering, the use of language and organ donation (Schmeets and te Riele, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schmeets and Peters, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schmeets and Cornips, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e - to name only a few), as well as for numerous reports published by the Statistics Netherlands (see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbs.nl/en-gb/society\u003c/span\u003e\u003cspan address=\"https://www.cbs.nl/en-gb/society\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we introduce the merged Social Networks and SSW dataset and present key findings derived from integrating network-scientific and statistical methods. By merging comprehensive administrative data with detailed survey responses, this study offers a unique perspective on the interplay between social networks and individual social capital. More specifically, the study aims to explore if and how social network centrality relates to social capital in the Netherlands. We hypothesize that higher network centrality will be associated with more individual social capital. Furthermore, we focus on two specific indicators of social capital: the frequency of contacts with family members, not belonging to their own household, and the contacts with neighbours. More specifically we expect that a higher person\u0026rsquo;s family and neighbour network results in more contacts with other family members and neighbours respectively.\u003c/p\u003e"},{"header":"Social Capital and Social Network Research","content":"\u003cp\u003eIntegrating contemporary network science (Watts \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Barabási \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Newman \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), with social capital analysis may reveal how individual connections contribute to overall social capital. There are numerous definitions and measurements of social capital, and social capital is often defined as a building block of social cohesion, or as an attribute that fosters social cohesion (for an overview see Berger-Schmitt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Chan, To and Chan \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schiefer and Van der Noll, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Moustakas, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tok, Woods and Kong, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn another formulation, social capital refers to the networks, norms, and trust that enable cooperation and coordination for mutual benefit. Resources such as knowledge and money enhance an individual's ability to form and sustain relationships that contribute to their social capital. A key theory addressing the influence of these resources is Resource Theory (Bourdieu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), which posits that individuals with more resources (such as knowledge, money, time, and social connections) are better positioned to build and leverage social capital. These resources open up more opportunities for social engagement and network participation and can also help foster trust.\u003c/p\u003e \u003cp\u003eIn the context of participation in social and political activities, the Civic Voluntarism Model (CVM) developed by Verba, Schlozman, and Brady (1995) is particularly relevant. The model highlights the importance of resources like education, income, and time. Those with higher education tend to have more knowledge about society and greater skills for engaging with it. Financial resources facilitate access to social networks and participation in organizational activities, while time is crucial for involvement in more structured activities, such as volunteering or being an active member of a sports or cultural association. Psychological factors, such as interest and the belief that one's contributions are meaningful, also encourage participation. Additionally, being involved in social networks increases the likelihood of being invited to participate in various activities. Thus, Resource Theory and the CVM offer complementary insights into how resources influence trust and societal participation.\u003c/p\u003e \u003cp\u003eSocial capital is seen both as a community asset (Neira, Vazquez and Portela, 2009) and as an individual attribute - a resource of individuals within the community (Gannon and Roberts, 2018). A widely used definition comes from the OECD (Coté and Healy, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, p. 41; see also Keeley \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, pp. 102–105): \"networks, together with shared norms, values and understandings, that facilitate cooperation within or among groups\". This definition makes it clear that it is about collective connections of people within and among population groups who understand each other's opinions.\u003c/p\u003e \u003cp\u003eHowever, quantifying these social networks in surveys is problematic. Often a proxy is used, such as in the single indicator ‘generalized trust’ or ‘volunteering’, or a composite indicator in which various trust and participation indicators are included (e.g. Putnam, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; van Beuningen and Schmeets, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While the concept of social capital provides a foundational understanding of the qualitative aspects of social networks and the importance of different types of ties in generating social capital (see e.g. Granovetter, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), contemporary network science offers quantitative tools to measure and analyse the connectedness and importance of individuals as nodes within these networks. Both approaches are complementary, providing a comprehensive understanding of how social networks function and influence various outcomes.\u003c/p\u003e \u003cp\u003eBy using these methods simultaneously, we can examine how individuals are connected through various relationships such as friendships, professional ties, and kinship, basing primarily on the quantification of the number of connections, which we can subsequently connect with the actual behaviour and the quality of these connections. This integrated approach allows for more nuanced insights into the ways social structures influence individual and collective well-being.\u003c/p\u003e "},{"header":"Indicator Definitions","content":"\u003ch2\u003eIndicators derived from SSW data\u003c/h2\u003e\u003cp\u003eOur analysis uses the social cohesion framework developed by Statistics Netherlands in 2008. This framework measures social capital along two key dimensions—participation and trust—and, partly based on Putnam’s social capital index (Putnam \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), integrates them into a single index (van Beuningen and Schmeets \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the Appendix).\u003c/p\u003e\u003cp\u003eFor each of the participation and trust dimensions, the model makes distinctions at the social (micro), organizational (meso) and political (macro) level (see also Halpern, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sharp and Randhawa, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). On the participation dimension, the social level captures contacts with family, friends and neighbours, and help provided to other people. The organizational level covers volunteering, activities in associations, and paid work, and the political level includes voting in parliamentary elections and participation in other political actions. The trust dimension contains information on trust in other people (social level); trust in the army, police, lawyers, civil servants, media and large companies (organizational level); and trust in parliament (political level). The model thus contains six sub-dimensions, three for participation and trust each, and a total of 17 different indicators. The formative model was identified by Multiple Table Analysis, also called a Hierarchical Components Model (Lohmöller, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Tenenhaus et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and was estimated in R using the PLSPM package (Bertrand, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A path weighting scheme was used so weights can be interpreted as regression coefficients. Weights are calculated from the first-order on the two second-order constructs - participation and trust. These, in turn, contribute independently to the social capital index.\u003c/p\u003e\u003cp\u003eThe addressed 17 indicators are based on the yearly SSW questionnaires in the period 2012–2022 and will be used in the subsequent analyses and use case demonstrations from our merged file. All indicator values are calculated at the individual level.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMaintaining Contacts.\u003c/b\u003e The survey includes three separate questions to respondents on the frequency of contacts with: i) one or more family members, ii) friends, partners, or very good acquaintances and iii) neighbours, each with the scale from 1 (daily) to 5 (rarely or never). Contacts are meetings in-person, phone-calls, letters, or by using social media. From these we have derived the binary indicators of Maintaining Contacts for each of the three segments, where in each case 1 stands for if the contacts are maintained at least once a week, and 0 if otherwise.\u003c/p\u003e\u003cp\u003e \u003cb\u003eInformal Help-Giving.\u003c/b\u003e Respondents were asked if they were giving unpaid help to people outside their household, such as the sick, neighbours, family, friends, and acquaintances in the four weeks prior to being surveyed. The one-to-one translation of this binary variable is regarded as an indicator of Informal Help-Giving.\u003c/p\u003e\u003cp\u003e \u003cb\u003eVolunteering.\u003c/b\u003e The survey investigates whether the respondent has volunteered in the 12 months prior to being surveyed. Based on twelve organizations, such as for schools, sport clubs, and health care organizations, a binary variable was created which is regarded as an indicator of Volunteering.\u003c/p\u003e\u003cp\u003e \u003cb\u003eParticipation in Associations.\u003c/b\u003e Respondents were asked about the frequency of their participation in activities of one or more associations. The newly created binary indicator has values of 1 if the respondent’s participation in activities is at least once per month and 0 if less.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePolitical Action.\u003c/b\u003e A set of nine questions was asked regarding respondents’ engagement in political matters in the 5 years period prior to being interviewed, specifically through 1) participating in media events to exert influence, 2) approaching a political party or organization, 3) taking part in meetings or debates organized by governmental bodies, 4) approaching a politician or official, 5) participating in an action group, 6) participating in a protest action, protest march or demonstration, 7) participating in a paper or internet signature campaign, 8) taking part in a political discussion or action on the internet, and 9) making some other (unlisted) political action. The r binary indicator of Political Action has a value of 1 if the respondent engaged in at least one of the actions.\u003c/p\u003e\u003cp\u003e \u003cb\u003eVoting Participation.\u003c/b\u003e Respondents were asked whether they voted in the most recent Dutch parliamentary elections in 2010, 2012, 2017, or 2021, depending on the date the question was answered. The derived binary indicator of Voting Participation takes a value of 0 (not voted) and 1 (voted).\u003c/p\u003e\u003cp\u003e \u003cb\u003eSocial trust.\u003c/b\u003e The survey asked about the person’s trust in other people in general based on two answer options: (0) you can’t be too careful when dealing with other people or (1) most people can be trusted.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTrust in institutions.\u003c/b\u003e The survey inquired about trust in: 1) judges, 2) police, 3) the army, 4) the press, 5) civil servants, 6) large companies, and 7) parliament, with the answering scale ranging from 1 to 4, where 1 and 2 stand for highly and quite trustful, respectively, and 3 and 4 stand for little and no trust, respectively. From these questions, we have created binary indicators of Trust in Institutions for each category with a value of 1 if the respondent shows any trust (answering 1 or 2 to the original question), and 0 otherwise.\u003c/p\u003e\u003ch3\u003eIndicators derived from administrative data (social network indicators)\u003c/h3\u003e\u003cp\u003eThe following are some of the social network indicators we have derived from the Social Networks dataset, calculated at the respondent level, and merged with the SSW dataset into one file. These indicators are also involved in subsequent analyses and use case demonstrations.\u003c/p\u003e\u003cp\u003e \u003cb\u003eDegree Centrality.\u003c/b\u003e The original Social Networks dataset includes the characterization of relationship between any two individuals based on the sub-types of ties from the domain of family, work, neighbour, school and household ties. We have calculated the degree centrality (Freeman, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) of the respondent on the binary undirected network abstraction\u003csup\u003e1\u003c/sup\u003e for each layer for each year, which indicator is essentially the count of the number of family members, work colleagues, neighbours, schoolmates and household members the respondent of the SSW survey has in each of the respective network layers. “Binary undirected” is the basic abstraction in which strength and direction of relationship (e.g. the higher weight for closer relatives, the direction father to son, grandfather to father, etc.) are not considered, only the existence (1) or non-existence (0) of the relationship. As correlations between any two-yearly centrality value sets picked from the period were always shown to be close to 1, in the following analyses we mainly used the degree centrality value for 2020 for each respondent.\u003c/p\u003e\u003cp\u003e \u003cb\u003eDistance-Weighted Degree Centrality.\u003c/b\u003e We have calculated the average and the sum of all direct-flight (great-circle) distances between the municipality of residence of the respondent in the respective period to the municipality of residence of the respondent’s relative in the period. Averaging over all periods for average of distances and for the sum of distances, we have obtained two distance-weighted degree centrality metrics for a respondent in her/his family network.\u003c/p\u003e"},{"header":"Analysis and Results","content":"\u003cp\u003eTo answer our research question – if and how social network centrality relates to social capital – we start by investigating how the 17 social capital indicators as well as the 5 social network centralities differ between subpopulations. From the perspective that higher network centrality will be associated with more individual social capital, we expect to find similar patterns. In a next step we will look at the correlations between the 5 centralities and the social capital composite and the single indicators.\u003c/p\u003e\u003cp\u003eIn Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in the Appendix, we detail general individual attributes – gender, age, level of education, income, and migration background – and their relationship with 17 indicators measuring participation and trust, based on SSW data from 2012 to 2022. It clearly shows the variation between all these subpopulations in the levels of participation and trust in Dutch society. More resources, in particular education, results in more participation and trust. This is in line with the Civic Voluntarism Model (Brady, Schlozman and Verba, 1995), according to which volunteering and political participation, expressed in political actions and voting, is highest among the more educated and individuals with a higher income (Quintelier and Hooghe, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ojeda, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barsegyan, Knigge and Maas, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Education is also strongly related to the trust indicators. A minority of 40 percent of the group with elementary education say that ‘other people can be trusted’, which gradually increases to 85 percent among the group with a master degree. Also many gender and age differences reveal in participation and trust levels. Females exhibit more contacts with family members and friends, and are more prone to informal help-giving than males. Females also often show more trust in institutions but are less trustful to other people than males. Maintaining contacts with friends is more prevalent among younger individuals, whereas maintaining contacts with neighbours is more common among older individuals, which aligns with expectations. Additionally, a larger percentage of elderly individuals participate in voting, and political action declines with age. And, overall, younger age groups trust others and also trust institutions more than elderly do. Likewise, Dutch natives and migrants differ on many participation and trust indicators.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipation by demographic and socioeconomic groups, 2012/2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaintaining Contacts - Family\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintaining Contacts - Friends\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaintaining Contacts - Neighbours\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInformal Help-Giving\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVolunteering\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParticipation in Associations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWork Participation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePolitical Action\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVoting Participation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndicator as percentage per \u0026lt; Attribute\u0026gt;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e82.4%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoman\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81.5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15–24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e76.1%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25–34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35–44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e79.5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45–54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55–64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65–74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e84.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e66.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e75.1%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBO, Havo, Vwo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81.2%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e90.5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e91.2%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst (lowest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71.6%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e78.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e84.3%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth (highest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e88.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMigration Background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e64.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85.6%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigrant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e66.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e81.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrust by demographic and socioeconomic groups, 2012/2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Trust\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust in Army\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrust in Press\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrust in Police\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrust in Parliament\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrust in Civil Servants\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTrust in Large Companies\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndicator as percentage per \u0026lt; Attribute\u0026gt;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15–24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25–34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.7%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35–44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45–54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.0%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55–64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.7%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65–74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.7%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.4%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.2%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.0%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBO, Havo, Vwo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40.5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst (lowest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e37.0%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth (highest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMigration Background\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.0%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.7%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigrant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.2%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41.9%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.8%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39.4%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e we present for the same subpopulations the average centrality degrees. Overall, and not in line with the social capital indicators the subpopulations do not show much variation in the five centrality measures. This is particularly true for the connections with neighbours, ranging from 40 to 45. Males and females exhibit similar levels of family and neighbour connections, but females demonstrate higher work-related connections compared to males. In addition, females show a substantial higher level of connections at schools which indicates that females going into schooling programmes or schools with more attendees than men. Young adults aged 15–24 have high degree centrality in work and school, and they exhibit the highest household connections. Middle-aged individuals, particularly those aged 25–44, show high family connections, with the highest school network connections among those aged 35–44.\u003c/p\u003e\u003cp\u003eAs regards the educational levels, there is some trace of association with the degree centrality indicators. University-educated individuals have the highest levels of work-related connections and school network connections. Those with HBO education also exhibit high levels of work and school connections. In contrast, individuals with elementary education levels exhibit the lowest levels of connections across these indicators, particularly in school networks. Income status also plays a significant role in degree centrality. Individuals in the highest income quartile show the highest levels of work-related connections and school network connections. Those in the lowest income quartile exhibit lower levels of connections in these areas, although they have relatively high family and household connections.\u003c/p\u003e\u003cp\u003eMigration background reveals differences in degree centrality indicators as well. Native individuals have higher levels of family connections and similar levels of work connections compared to migrants. Both groups show similar levels of neighbour and school connections. However, migrants exhibit slightly higher household connections compared to natives.\u003c/p\u003e\u003cp\u003eIt might be important to note that of the 83,997 respondents, 1,249 were identified as having no family connections whatsoever. This sample represents approximately 1.5% of the total respondent pool. Our closer examination of the demographic profile revealed that this group was predominantly composed of first-generation migrants, which appears logical. The group was distributed across age brackets, with the largest proportion being 65+, likely due to deceased relatives or lack of children. There was no significant gender difference, and the majority had low educational attainment, though not overwhelmingly so. These individuals were excluded due to the very high correlations with the controls in the models which would result in high variance inflation factor values, and due to the extremely small size of the group.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocial network indicators by sociodemographic subpopulations, 2012/2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree Centrality - Family\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree Centrality - Work\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree Centrality - Neighbours\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegree Centrality - School\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDegree Centrality - Household\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIndicator as average number per \u0026lt; Attribute\u0026gt;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15–24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25–34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35–44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45–54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55–64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65–74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBO, Havo, Vwo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst (lowest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth (highest) quartile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigration Background\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigrant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003eGeneral Associations of Variables\u003c/h3\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we list the correlations between the Social Networks indicators and the social capital composite indicator as well as the 17 specific social capital indicators. Generally, the correlations are positive and significant, but rather weak and often not reaching the p-value lower than 0.01, as indicated by a single asterisk (*). Essentially, only two centrality measures show higher levels of significancy (p \u0026lt; 0.01): family and neighbours. However, the connection a person has with its neighbours shows a low correlation (0.12) with social capital, and this is also true for the family networks with social capital (0.11). Unexpectedly, also the correlations between the networks and actual behaviour in terms of contacting family members and neighbours are low. As per other indicators, volunteering, having paid work, and voting in elections show positive correlations with the size of their family, while volunteering and paid work relates to the connections with neighbours.\u003c/p\u003e\u003cp\u003eInterestingly, social trust shows weak positive correlations with family and neighbour degree centrality. Apparently, being surrounded by others fosters trust in other people in general. Trust in the army and in parliament also exhibits weak positive correlations with the neighbour network.\u003c/p\u003e\u003cp\u003eIn sum, the associations between any two variables are generally very weak, with most correlations being close to zero. Notably, the strongest observable relationship emerges between the frequency of maintaining contacts within the family and the individual's actual number of connections in the family network, as indicated by a modest but relatively higher correlation of approximately 0.11. In contrast, there is virtually no association between maintaining contacts with neighbours and the number of neighbours a person has.\u003c/p\u003e\u003cp\u003eDue to unexpectedly low correlations especially between connectivity vs. maintaining contacts in both the family and neighbours category, we took a closer look into these categories using some more refined indicators and transformations. In particular, we have used logarithmic transformation with natural logarithms for degree centrality measures. In network research in general, degree distributions following a power-law distribution are commonly found, where a few nodes have very high degrees, while the majority have lower degrees. For the Social Networks of this dataset, the distributions are not strictly following power law, but this is due to how networks, especially those of neighbours in this case, have been abstracted (see van der Laan, 2022b).\u003c/p\u003e\u003cp\u003eIn addition to Distance-Weighted Degree Centrality in both layers, as defined in the previous section (both in its original form and logarithmically transformed), we incorporated several other measures. These include the natural logarithm of Degree Centrality, the average geographical (crow fly) distance to all family relatives or neighbours of a person based on registered residence addresses in 2020, and the total sum of these distances. To account for potential scale effects, we also applied logarithmic transformations to both the average and total distance measures. However, all such amendments did not substantially improve the correlations.\u003c/p\u003e\u003ch2\u003eRegressions with (segment) control variables\u003c/h2\u003e\u003cp\u003eIn the following part of the analysis, we delve into the analyses of specific segments of the populations in terms of their general attributes and main social capital components given their social network embeddedness. We perform a set of logistic regressions with dependent variables Maintaining Contacts with Family and with Neighbours. This, and degree centrality variables were recoded before modelling. The Maintaining Contacts variables were recoded as binary indicators with value 1 for person having contact at least once per week, and 0 otherwise.\u003c/p\u003e\u003cp\u003eDegree Centrality-Family was split into 15 groups. Specifically, we retained the original values for those with 1 to 5 connections. For those with more extensive networks, we grouped the connections into broader categories: 6 through 9 connections were recoded as 6, 10 through 13 as 7, 14 through 17 as 8, 18 through 21 as 9, 22 through 26 as 10, 27 through 31 as 11, 32 through 39 as 12, 40 through 53 as 13, 54 through 66 as 14, and 67 through 364 as 15. Although arbitrary, this recoding process was guided by prior experimentation with strict decile cut-offs, which proved less sensible. Our approach adheres to power law distribution principles typical of networks, where few individuals have many connections and many have few, while recognizing that it is uncommon for individuals to have only 1 or 2 connections, and that family networks deviate from typical network patterns.\u003c/p\u003e\u003cp\u003eAs regards the Degree Centrality-Neighbour variable, we found an unusual distribution of the degree centrality values, with a strong frequency peak between 30 and 60. This arises from defining neighbours as the members of a combination of close neighbours and the residents of the closest 10 households within a radius of 200 meters (for a detailed definition, consult CBS, 2024b). This method results in many individuals having similar centrality values, especially in densely populated areas or institutions, thus creating the observed distribution anomaly. On average, a person has 43 neighbours (standard deviation = 7.3) with a range from 13 to 182\u003csup\u003e2\u003c/sup\u003e. We hence did not stick to the former principle as with family centrality but recoded the neighbour degree variable by partitioning the values based on cut-off points for creating 15 equal groups. All other used variables remained as previously defined. The summaries for both models are provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (see Annex), while the description of the models is provided in the sequel.\u003c/p\u003e\u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of results of logarithmic regression (Maintaining Contacts vs. Degree centrality \u0026ndash; Model 1 and Model 2)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eVARIABLE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 17.8675%;\"\u003e\n \u003cp\u003eMODEL 1 - FAMILY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMODEL 1 - NEIGHBOURS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMODEL 2 - FAMILY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMODEL 2 - NEIGHBOURS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfidence limit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfidence limit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfidence limit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfidence limit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree centrality (reference category: group 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.040 1.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.087 1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.093 1.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.115 1.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.119 1.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.082 1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.217 1.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.149 1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e1.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.432 2.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.034 1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.663 2.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.091 1.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.348 2.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.120 1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.602 2.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.213 1.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e2.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.822 2.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.110 1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.146 3.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.207 1.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e2.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.047 2.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.150 1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.565 3.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.262 1.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e2.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.174 3.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.102 1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.868 4.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.231 1.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e2.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.367 3.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.087 1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.033 4.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.187 1.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e2.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.392 3.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.144 1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.066 4.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.277 1.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e3.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.642 3.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.150 1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.372 5.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.308 1.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e3.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.719 3.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.085 1.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.430 5.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.259 1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e3.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.148 4.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.091 1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.730 5.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.293 1.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e3.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.275 4.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.103 1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.838 5.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.331 1.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eDegree (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\n \u003cp\u003e4.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.905 5.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.111 1.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.461 6.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.389 1.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (reference category: male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.927 2.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.961 1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (reference category: 15\u0026ndash;24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.264 2.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.214 1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.669 1.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.972 2.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.239 1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.796 2.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.443 1.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.050 2.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.529 1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.060 3.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003e75+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.644 1.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.086 3.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (reference category: elementary)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eLBO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946 1.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927 1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eMBO, Havo, Vwo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.032 1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855 0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eHBO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970 1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.729 0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.835 1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593 0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (reference category: First (lowest) quartile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eSecond quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997 1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.099 1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eThird quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.959 1.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.060 1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eFourth quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.019 1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977 1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigration background (reference category: native)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eMigrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.3228%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.038 1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903 0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u0026rsquo; strength parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 17.7233%;\"\u003e\n \u003cp\u003eNagelkerke R\u0026sup2;\u003c/p\u003e\n \u003cp\u003eCox \u0026amp; Snell R\u0026sup2;\u003c/p\u003e\n \u003cp\u003e-2 Log likelihood\u003c/p\u003e\n \u003cp\u003eWald (Degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\" style=\"width: 17.8675%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e68706.860\u003c/p\u003e\n \u003cp\u003e841.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e106850.568\u003c/p\u003e\n \u003cp\u003e50.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e59212.075\u003c/p\u003e\n \u003cp\u003e684.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003cp\u003e91713.798\u003c/p\u003e\n \u003cp\u003e143.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003ch3\u003eModel 1: Basic model; Maintaining Contacts vs. Degree Centralities\u003c/h3\u003e\u003cp\u003eIn Model 1, we explored the relationships between Maintaining Contacts and Degree Centrality variables within the family network and within the network of neighbours in parallel. The overview is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e with boxplots capturing relations between the centrality measures and the social contacts (see Annex for all results).\u003c/p\u003e\u003cp\u003eThe R Square value (Nagelkerke) for Model(s) 1 suggest that a small percentage of the variance in maintaining social contacts is explained by the degree centrality in the family network, and even less in the neighbourhood network, indicating modest explanatory power (see Annex). However, degree centralities add significant contributions, expressed in the overall Wald values for family (~ 841.4) and neighbours (~ 50.2). Each increase in family degree centrality significantly raises the likelihood of maintaining family contacts. The odds ratio of the second group with one family connection shows that the chance of having at least once a week a family contact is higher compared to the group with maximum of one connection serving as the reference group. Subsequently the odds ratio’s increase, following almost a linear pattern, to 4.6 for individuals with the highest family degree centrality. This suggests that family networks may contribute to the actual contacts with other family members in-person, phone-calls, letters, or by using social media.\u003c/p\u003e\u003cp\u003eThis model shows clearly the importance of an individual’s family network size in predicting the maintenance of family relationships. As for the neighbours domain, the odds ratios for the other than the baseline group differ only slightly from 1.25 to 1.36 without a clear linear pattern. This suggests that the size of the neighbours in a person’s direct environment is only important if the first cut-off point has been reached. Consequently, having more neighbours does not imply more frequently maintaining contacts with them.\u003c/p\u003e\u003ch3\u003eModel 2: Adding general attributes\u003c/h3\u003e\u003cp\u003eIn Model 2, we extended the analysis by incorporating controls alongside degree centrality within each network layer to correct for the demographic and socio-economic composition of the 15 groups. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we provide the odds ratios for degree centrality on maintaining contacts. A slightly larger percentage of the variance in Maintaining Contacts in both domains (Family and Neighbours) is explained by the combined factors, indicating improved explanatory power of Model 2 when compared to Model 1.\u003c/p\u003e\u003cp\u003eWe further notice that after controlling for gender, age, education, income and migration background, the degree of family centrality clearly relates the frequency of family contacts. The more family connections, the higher the chance that there is at least once per week a family contact. Interestingly, a clearer pattern for the contacts with neighbours emerges, suggesting the importance of taking into account the composition of the 15 neighbour centrality groups. The odds ratios increase gradually from 1.21 to 1.52 indicating that the chance for at least a weekly contact increased by half for the group with the highest centrality compared to the group with the lowest centrality.\u003c/p\u003e\u003cp\u003eAdditionally, the controls also contribute significantly to the family contacts. Women have more contacts than men, people of intermediary-level education show more contacts than the lowest or highest educated, and the youngest age group has less contacts than all other age groups. Furthermore, the highest income groups have slightly more contacts with their family members than the lowest income group, and the migrants have more contacts than the native population. This model underscores the combined importance of family network size and demographic factors in predicting the maintenance of family relationships.\u003c/p\u003e\u003cp\u003eIn addition to the presented models, we have performed a few robustness checks by adding more demographic and regional variables into the model, such as household composition, marital status, and urbanity. The inclusion of any of these variables had hardly any effect on the relationship between family centrality and family contacts. Likewise, we added the controls in the model for the weekly contacts with neighbours. While gender is not related to the contacts with neighbours, we notice that such contacts increase for higher age groups. In particular, the 65 + population show a high contact level. Furthermore, such contacts decrease for the higher educated, the middle income groups have more contacts than the lowest income group, and the natives have slightly more contacts than the migrant population.\u003c/p\u003e"},{"header":"Conclusions and Prospects for Future Research","content":"\u003cp\u003eThis study investigated the impact of social network centrality measures on individual social capital in the Netherlands. We hypothesized that individuals with higher degree centrality would possess greater social capital. Specifically, we examined two indicators of social capital: the frequency of individuals' contact with family members and neighbours. Our analysis revealed only very weak correlations between the five social network centrality measures and a composite social capital indicator derived from 17 participation and trust indicators.\u003c/p\u003e\u003cp\u003eOur primary preliminary conclusion is that social network centrality measures are not suitable substitutes for assessing individual social capital through large-scale surveys. This finding underscores an unfortunate limitation: even the extensive administrative data covering the entire Dutch population cannot come close to substituting for survey data in providing detailed insights into individual social capital, its determinants, and its consequences.\u003c/p\u003e\u003cp\u003eFurthermore, we investigated the correlations with the 17 separate indicators and found that these did not improve the associations with centrality metrics. This was also true for the specific associations we anticipated to be stronger: the correlation between family centrality and the frequency of family contacts, and between neighbour centrality and the frequency of contacts with neighbours. These analyses suggest that having a strong network does not necessarily imply intensive use of such networks. On the other hand, regression analyses show that the likelihood of frequently contacting family members and neighbours increases with higher network centralities. That pattern is more prevalent for contacting family members than for contacting neighbours. A possible explanation is that in the SSW survey, the question refers to contacts with direct neighbours, while the measure of neighbour centrality currently includes people within a 200-meter radius.\u003c/p\u003e\u003cp\u003eAdditionally, we demonstrated that inclusion of demographic and socio-economic controls adds to the understanding of network centralities linked to social contacts, although the increase in explained variance remains low. This suggests that centrality and the included controls are not sufficient predictors of actual social contacts. This could be due to the complexity of social interactions between family members and between neighbours, where factors such as the quality of relationships, social norms, and individual preferences play a significant role.\u003c/p\u003e\u003cp\u003eThese weak correlations between survey and administrative data suggest that some of the key steps in future research would be to look for the reasons why there is hardly any impact of the social networks on the actual social behaviour, assessed via the representative SSW surveys, even when it comes to relationships with their own family, let alone the neighbours.\u003c/p\u003e\u003cp\u003eFuture research should aim to address several key areas to build upon these findings. First, enhancing the quality and comprehensiveness of register data, particularly in the abstraction of networks into centrality measures, is crucial. This includes redefining the parameters for what constitutes a neighbourhood, workplace, or school network, ensuring more realistic distributions. Additionally, incorporating more layers and dimensions of relationships will provide a more accurate and detailed picture of social networks. This approach will better capture the diversity and dynamics of networks over time, integrating additional data sources to enrich the existing datasets.\u003c/p\u003e\u003cp\u003eSecond, as network analysis of centrality is limited without accounting for the weights of relationships, future research should aim to integrate the strength and quality of ties into centrality measures. For example, a parent should be given a higher weight than a cousin. Weighted network analysis can provide a more nuanced understanding of social networks by distinguishing between strong and weak ties, thus offering a clearer picture of how different types of relationships impact social cohesion. Yet, this is not trivial and requires a well-designed network abstraction methodology, evolving from a binary towards a weighted (even directed) approach, particularly when it comes to network layers beyond the basic family layer, which remain problematic at present.\u003c/p\u003e\u003cp\u003eThird, the SSW data allow for focussing on specific regions and municipalities. By integrating more information of neighbourhoods such as social and cultural facilities enhancing social contacts could highlight unique cultural or structural factors influencing social cohesion. This would provide a broader context and help identify universal versus context-specific dynamics.\u003c/p\u003e\u003cp\u003eFourth, while the findings of this study are based on a sample from the Netherlands, the external validity of these results, especially in terms of geographic applicability, is limited. Although the dataset is comprehensive and includes a large number of respondents, caution must be exercised when generalizing the results to other geographical contexts, or, ideally, other geographical regions should be explored as well. The unique characteristics of Dutch society, including its social policies, urban infrastructure, and demographic composition, may not fully represent conditions in other countries or regions. However, the large sample size and richness of the data do provide a valuable foundation for understanding social network centrality and social capital within this context. Future studies could extend this approach to other countries or regions to examine whether similar patterns of social network centrality and social capital hold true across diverse geographical settings.\u003c/p\u003e\u003cp\u003eLastly, future research might be dedicated to exploring the relationship between social cohesion and the components of well-being, such as happiness and life satisfaction, and broader wellbeing including physical and mental health, leisure time, work, school, housing, relationships, financial situation, and neighbourhood safety (included in the SSW). Insights from this research could guide policymakers, mental health professionals, urban planners, and educators in fostering environments that enhance social capital, ultimately improving well-being across populations.\u003c/p\u003e\u003cp\u003eBy addressing these and other domains, we can significantly enhance our understanding of social networks and social cohesion, ultimately contributing to more effective strategies for fostering well-being and community resilience in the Netherlands and beyond.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the ANONYMISED, as part of the project ANONYMISED and as part of the ANONYMISED.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunded by the ANONYMISED. The views and opinions expressed are those of the author and do not necessarily reflect the official views of the ANONYMISED or the ANONYMISED. Neither the ANONYMISED nor the ANONYMISED \u0026nbsp;can be held responsible for them.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eEthics approval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study uses secondary, pseudonymized data from Statistics Netherlands (CBS). Data access was provided through CBS\u0026rsquo;s secure research environment under the Dutch Statistics Act (https://wetten.overheid.nl/BWBR0015926/2018-07-01). All analyses comply with Dutch legislation and the General Data Protection Regulation (EU) 2016/679 (https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng). CBS confirmed that no separate ethics approval was required, as the study involves only pseudonymized secondary data with no direct contact with participants.\u003c/p\u003e\n\u003cp\u003eInformed Consent\u003c/p\u003e\n\u003cp\u003eInformed consent for the Sociale Samenhang en Welzijn (SSW) survey was obtained by CBS from all participants prior to data collection. Respondents were informed about the study purpose, data use, anonymity, and their rights under GDPR. The administrative data from the Person Network of the Netherlands were collected under CBS\u0026rsquo;s legal mandate. The authors had no interaction with participants and did not collect any new data. Consent procedures were handled entirely by CBS.\u003c/p\u003e\n\u003cp\u003eConsent\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent before participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData, Materials and/or Code availability\u003c/p\u003e\n\u003cp\u003eThe data examined in this paper is included in the Statistics Netherlands Microdata Catalogue. The data are not available except to the authorized individuals, in accordance with the general data protection regulated by Dutch Criminal Law (https://wetten.overheid.nl/BWBR0001854/2023-10-01) and the Central Bureau of Statistics Act (https://wetten.overheid.nl/BWBR0015926/2018-07-01).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: Anonymised, Anonymised; Data curation: Anonymised; Formal analysis: Anonymised, Anonymised; Funding acquisition: Anonymised; Investigation: Anonymised, Anonymised; Methodology: Anonymised, Anonymised; Project administration: Anonymised, Anonymised; Software: Anonymised, Anonymised; Resources: Anonymised, Anonymised; Supervision: Anonymised; Validation: Anonymised, Anonymised; Visualization: Anonymised, Anonymised; Writing \u0026ndash; original draft: Anonymised, Anonymised; Writing \u0026ndash; review \u0026amp; editing: Anonymised, Anonymised.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their special thanks to the fellow members of Anonymised for their suggestions that helped to improve the structure of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarab\u0026aacute;si, A.-L. 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(1999). \u003cem\u003eSmall Worlds: The Dynamics of Networks between Order and Randomness.\u003c/em\u003e Princeton University Press.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e We used binary undirected networks to simplify the analysis by focusing on the presence or absence of relationships, rather than their strength or direction. This approach, while useful for initial analyses, may overlook the nuances of relationship dynamics. Future studies should consider weighted and directed networks to capture the complexity of social interactions more accurately. For the abstraction of networks, see CBS 2024a-e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e To further investigate potential biases, we conducted an analysis where we considered separately the direct neighbour network and the network of extended neighbourhood (as defined in CBS 2024b). We traced a highly unusual distribution of centrality in the network of extended neighbours, where over 90% of respondents were shown to have 20 to 21 extended neighbour relationships, hence we did not distinguish between the two relationships in the metric.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"social networks, social capital, administrative data, survey data","lastPublishedDoi":"10.21203/rs.3.rs-6375519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6375519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStatistics Netherlands (CBS) has recently developed the Whole Population Network file, based on administrative data, which includes over a billion interpersonal relationships among approximately 17\u0026nbsp;million inhabitants of the Netherlands, spanning each year from 2009 onward. Additionally, over the past decade, CBS has conducted the yearly Social Cohesion and Well-being survey, collecting data from more than 83,000 representatives of the population on topics related to social cohesion, such as social contacts, volunteering, political participation, and trust in others and in institutions. For the purposes of this study, we have constructed a merged dataset from the two, and deployed new indicators of Social Cohesion and of Social Networks per respondent. In the study we describe the merged dataset and present the relationships between person\u0026rsquo;s social networks (person\u0026rsquo;s centrality) and 17 indicators of social capital as well as the overarching social capital composite index. We further explore, for both the family and neighbouring network, the relationships with the actual social contacts with family members and neighbours. The study highlights the need for refined network measures to enhance understanding of social interactions.\u003c/p\u003e","manuscriptTitle":"Social Networks and Cohesion in the Netherlands: Insights from Combined Administrative and Survey Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 11:51:44","doi":"10.21203/rs.3.rs-6375519/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"604cba16-ba54-4cd7-8033-4a995e1d3b59","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48175127,"name":"Humanities/Complex networks"},{"id":48175128,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-09-19T18:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 11:51:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6375519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6375519","identity":"rs-6375519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}