MASTER’S THESIS

The Influence of Neighbourhoods on Wellbeing and Mental Health Ethnic diversity, neighbourhood deprivation and neighbourhood perceptions in

University of Amsterdam Master’s Thesis Sociology ‘Migration and Ethnic Studies’ Evelien Damhuis (11265949) Supervisor: dr. Sonja Fransen / Second reader: dr. Bram Lancee Amsterdam: July 10, 2017

Acknowledgements

First of all, I would particularly like to thank my supervisor, Sonja Fransen, for her interesting ideas and challenging feedback during my master’s thesis process. Due to her keen supervision, I was able to develop my thesis in the best way possible. I am very grateful for her support and the learning opportunity provided to me. Moreover, I would like to thank my contacts at the municipality of Rotterdam for helping me with my master’s thesis by providing me the opportunity to use their data of the Health monitor 2012. I would like to thank Özcan in particular for his continuing interest in my project. I would also like to take this opportunity to thank Koen Damhuis and Rik Damhuis for critically reading my draft version. Finally, I would like to thank my parents. Without their loving support none of this would have been possible. I am very proud to present to you my master’s thesis.

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The Influence of Neighbourhoods on Wellbeing and Mental Health Ethnic diversity, neighbourhood deprivation, and neighbourhood perceptions in Rotterdam

Abstract. The aim of this study was to gain insights in social contexts influencing wellbeing and mental health, specifically the impact of the residential area. This study added to previous literature by combining three elements, namely the impact of neighbourhood ethnic diversity, the focus on both wellbeing and mental health as outcome measures, and by including both neighbourhood effects and neighbourhood perceptions. Following the contact theory, this study predicted ethnic diversity to positively influence wellbeing and mental health, and moreover that perceived neighbourhood social cohesion and social capital positively mediated this effect. It was also assumed that living in less deprived neighbourhoods and having better neighbourhood perceptions would be beneficial for both wellbeing and mental health. Finally, it was assumed that the impact of neighbourhood characteristics and neighbourhood perceptions on wellbeing and mental health would be stronger for ethnic minorities than for native Dutch residents. Using both individual- and neighbourhood-level data from the municipality of Rotterdam, a multilevel design was created. Findings suggested no impact of neighbourhood ethnic diversity on wellbeing and mental health and negative results were found for the mediation mechanisms. Some evidence was found that suggested that living in less deprived neighbourhoods is beneficial for wellbeing and mental health for both native Dutch and ethnic minority residents. This also holds for neighbourhood perceptions. Finally, evidence was found that both supports and contradicts the assumption that neighbourhood effects and neighbourhood perceptions on wellbeing and mental health is stronger for ethnic minorities than native Dutch. Overall, on the basis of the findings in this study, it could be suggested that it is rather neighbourhood deprivation than neighbourhood ethnic diversity influencing wellbeing and mental health of residents. Finally, this study suggests that individual perceptions of the neighbourhood and individual characteristics better explain wellbeing and mental health of both native Dutch and ethnic minority residents than neighbourhood-level characteristics.

KEYWORDS: wellbeing, mental health, ethnic diversity, neighbourhood deprivation, neighbourhood perceptions, perceived social cohesion, social capital

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Table of contents

1. Introduction 5 2. Theoretical Framework 9 2.1 Wellbeing 9 2.2. Mental health 11 2.3. Neighbourhood effects on wellbeing and mental health 12 2.4. Neighbourhood perceptions on wellbeing and mental health 16 2.5. Differences between native Dutch and ethnic minorities 22 3. Data & Methods 24 3.1. Data 24 3.2. Wellbeing and mental health 24 3.3. Neighbourhood effects 25 3.4. Neighbourhood perceptions 27 3.5. Control variables 29 3.6. Analytical strategy 30 4. Results 34 4.1. Results for wellbeing 34 4.2. Results for mental health 42 4.3. Post-analyses checks 48 5. Discussion & conclusion 49 5.1. Discussion of the findings 49 5.2. Methodological considerations 52 5.3. Conclusion 54 Bibliography Appendices

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1. Introduction

A person’s wellbeing is of central importance to achievements and capabilities in a wide range of life domains. Substantial research has been undertaken examining the links between wellbeing and work, social relations and longevity amongst others (Diener & Ryan, 2009). Individual wellbeing is often understood as a social phenomenon. It is argued that individuals are social beings whose lives are embedded in and shaped by social structures (e.g. Larson, 1996; Keyes & Shapiro, 2004). In recent years, the urban and more specifically the residential environment as possible explanation of one’s wellbeing has received increased attention (e.g. Ettema & Schekkerman, 2016). Still, this residential relevance was acknowledged somewhat earlier. Leyden, Goldberg & Michelbach (2011), for example, described neighbourhoods as ‘the stage’ where interaction between individuals happens and where individuals participate in social activities that can contribute to one’s wellbeing. The existing literature on mental health underscores the argument that individuals are embedded in social structures and that the latter also determines mental health conditions (Stockdale, Wells, Tang, Belin, Zhang & Sherbourne, 2007). For instance, studies found that the residential environment influences depressive disorders (Mair, Diez-Roux, Osypuk, Rapp, Seeman & Watson, 2010; Termorshuizen, Braam & Van Ameijden, 2015). Similar investigations are of great importance, since depressive symptoms can cause multiple unfavourable health outcomes and could have a major impact on daily functioning, comparable to the effects of other major chronic physical diseases (Licht, De Geus, Zitman, Hoogendijk, Van Dyck & Penninx, 2008; Buist-Bouwman, De Graaf, Vollebergh, Alonso, Bruffaerts & Ormel, 2006). Providing insights in (social) factors explaining wellbeing and mental health is therefore relevant. This study is interested in a particular social context influencing wellbeing and mental health, namely that of neighbourhoods. In the literature on neighbourhood effects, various neighbourhood characteristics are argued to be influential on individual wellbeing and mental health. First, the neighbourhood’s ethnic composition is found to be important in explaining wellbeing and mental health. In their article, Veldhuizen, Musterd, Dijkshoorn & Kunst (2015) argue that urban ‘societies’ in Western Europe, including the , have undergone some demographic changes. One of these changes include that cities have become more ethnically diverse. In 2016, more than 20% of the Dutch population had a non-Dutch background. In the four largest cities in the Netherlands (Amsterdam, Rotterdam, The Hague & Utrecht) this percentage is even higher

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(Statistics Bureau, 2017). On the neighbourhood level, this has led to various neighbourhoods becoming more ethnically heterogeneous. This has made neighbourhood ethnic composition an interesting focus area in the literature on neighbourhood effects. Scholars studying the association between the ethnic composition of the neighbourhood and individual health found somewhat contradicting outcomes (e.g. Veldhuizen et al., 2015; Schrier et al., 2014; Termorshuizen et al., 2015; Bécares, Stafford, Laurence & Nazroo, 2011). Focusing on suicide risks, Termorshuizen et al. (2015), for example, argued in their article that having a higher share of individuals from the same ethnic group in the neighbourhood (also referred to as own-ethnic density), has beneficial impacts on suicide risk among non-Western minorities. Furthermore, Knies, Nandi & Platt (2016) for example found that a higher concentration of the own-ethnic group in the neighbourhood is associated with higher life satisfaction. On the contrary, Schrier and colleagues (2014) found no association between neighbourhood ethnic density and psychological distress. More recently, Erdem, Burdorf & Van Lenthe (2017) found that the mental health of individuals residing in high ethnic diverse neighbourhoods tends to be worse than those of residents in low ethnic diverse neighbourhoods. When it comes to the interpretation of studies on neighbourhood’s ethnic composition and wellbeing and mental health, it is important to distinguish between the different contexts in which the studies were conducted. Whereas some research is conducted in the United States (e.g. Mair et al., 2010), other research is conducted in countries in Western Europe (e.g. Erdem et al., 2017). However, urban areas in European welfare states, like the Netherlands, are quite different than in the United States (Bolt & Van Kempen, 2012). Bolt & Van Kempen argue that in the US, for example, there are high concentrations of ethnic minorities in one’s residential area. Such ethnic concentrations are quite rare in the Netherlands, where urban areas are more ethnically diverse. It is therefore that this study will focus on neighbourhood ethnic diversity instead of ethnic density. Besides ethnic composition, a second characteristic that is frequently studied in literature on neighbourhood effects is the association between neighbourhood deprivation and individual health (e.g. Letki, 2008). Letki, for example, found that it is not ethnic diversity that influences health, but rather neighbourhood deprivation. There is, however, no consensus on how neighbourhood deprivation should be defined and operationalized in existing literature (Van Vuuren, Reijneveld, Van der Wal & Verhoeff (2014). Van Vuuren and colleagues did an attempt and defined neighbourhood deprivation as the relatively low physical, social, and economic position of a neighbourhood. Various studies found that neighbourhood deprivation is associated with multiple health indicators, such as poor self-

6 rated health and poor mental health (e.g. Poortinga, Dunstan & Fone, 2008). Whereas some studies have shown that the impact of neighbourhood deprivation on individual health is mainly the result of a concentration of individuals with a somewhat low socioeconomic position in these urban areas, others suggest that there truly is an effect of neighbourhood deprivation on individual health by controlling for individual differences in socioeconomic position (ibid.). This study is also interested in the role of neighbourhood deprivation on wellbeing and mental health. Next to neighbourhood-level characteristics, individual perceptions of the neighbourhood are found to be influential on individual wellbeing and mental health too (Leslie & Cerin, 2008; Poortinga et al., 2008; Stafford & Marmot, 2003). Leslie and Cerin, for example, argue that area-level effects on health have been explored extensively, but that the relation between perceptions of the physical and social neighbourhood environment and health is less clear. Some studies found that ethnic composition of the neighbourhood influences (the perception of ) social cohesion and social capital in the neighbourhood (Cramm, Van Dijk & Nieboer, 2013; Putnam, 2007). Ettema & Schekkerman (2016) emphasize that neighbourhood characteristics and individual neighbourhood perceptions have to be taken into account separately, since these concepts differ both conceptually and in their effects on wellbeing and mental health. This study aims to build on previous literature on neighbourhood effects and individual health in several ways. First, instead of solely looking at risk of mental health problems or individual wellbeing, this study will focus on both. It is relevant to look at as well wellbeing as mental health, since evidence is found that wellbeing and mental health are affected differently by neighbourhood characteristics (e.g. Ettema & Schekkerman, 2016). Furthermore, Cramm and her colleagues (2013) argue that the association between neighbourhood effects and individual health has been researched extensively, but that this effect on wellbeing is investigated to a lesser extent. Second, studies that did look at the effects of neighbourhood characteristics on both mental health and wellbeing (e.g. Ettema & Schekkerman, 2016; Leslie & Cerin, 2008), did not look, to my knowledge, at neighbourhood ethnic diversity as a predictor, which is a focus area in this study. Third, whereas the studies that did look at the ethnic composition of the neighbourhood and its effect on wellbeing and mental health, these studies often only focus on the neighbourhood-level effects on health (e.g. Schrier et al., 2014). This study argues that both individual neighbourhood perceptions and neighbourhood-level characteristics are important to consider.

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Additionally, previous studies found evidence that in Western societies, there are health inequalities between different ethnic minority groups (e.g. Nielsen & Krasnik, 2010; Devillé, Uiters, Westert & Groenewegen, 2006). Devillé and colleagues stated that the prevalence of poor self-reported health of Dutch natives is around 15%, this prevalence is considerably higher among people with a Turkish background (45%), Moroccan background (39%), and Surinamese background (29%). Moreover, according their results, Bécares, Nazroo, Albor, Chandola & Stafford (2012) indicated that the association between neighbourhood effects and individual health varies among various ethnic groups. Important is therefore to consid er differences in effects between different ethnic groups1. According to the Statistics Bureau, around 20% of the Dutch population in 2016 had a ‘non-Dutch’ background2 (2017). A frequent distinction in the Netherlands is between immigrants from ‘western’ and ‘non-western’ descent. Among the Dutch population with a migrant background, 55,3% has a ‘non-western’ background (ibid.). Comparing Dutch cities, Rotterdam has the largest share of ethnic minorities in comparison with other Dutch cities, with 38% of its residents having a non-western migrant background (Statistics Bureau, 2016). With Rotterdam being an interesting ethnic melting-pot, and for reasons on data availability, I opted for the case of Rotterdam. Considering the above, the following research question is sought to be answered:

Focusing on Rotterdam: to what extent do neighbourhood effects and individual perceptions of the neighbourhood influence individual wellbeing and mental health of native Dutch and ethnic minority residents?

This study will be subdivided into five sections. The following section will elaborate on the different concepts and theories employed in this study concerning individual wellbeing and health, neighbourhood effects, and perceptions of neighbourhoods. Subsequently, the data used in this study will be described as well as the operationalization of the concepts. Subsequently, the analytical strategy will be discussed, before presenting and discussing the results of the analyses.

1 Due to data limitations, this study is restricted to distinguish between native Dutch and ethnic minorities. 2 This percentage refers to both persons who live in the Netherlands, but who were born abroad (so-called first-generation immigrants), and persons who live in the Netherlands from whom one or both of the parents were born abroad (so-called second generation immigrants). 8

2. Theoretical framework

This study is interested in explaining individual wellbeing and mental health with neighbourhood effects and neighbourhood perceptions as possible influencers. The neighbourhood effects that this study is particularly interested in are neighbourhood ethnic diversity and neighbourhood deprivation. These concepts will be elaborated on in this chapter after discussing both concepts that are related to the dependent variables in the present study: wellbeing and mental health. Strikingly, in the existing literature there is no consensus on the definition and operationalization of both concepts. This also holds for the concept of neighbourhood deprivation. This study aims to provide a more comprehensive conceptualization and operationalization of these concepts. After discussing wellbeing, mental health, and neighbourhood effects, individual-level neighbourhood perceptions will be discussed. The neighbourhood perceptions that are considered in this study are perceived social cohesion, social capital, and the perceived physical environment in the neighbourhood. Finally, differences in neighbourhood effects and neighbourhood perceptions on wellbeing and mental health between native Dutch and ethnic minorities are argued.

2.1 Individual wellbeing According to Steptoe, Deaton & Stone (2015), people’s wellbeing has become a prominent focus of debates in economics and public policy. Improving the wellbeing of individuals has therefore emerged to one of the main societal aspirations in various European countries. In academia, different disciplines focused on explaining individual wellbeing, including philosophy, psychology, economics, and sociology (Ettema, Gärling, Olsson & Friman, 2010). Psychological wellbeing is generally defined as ‘the degree to which an individual positively evaluates the overall quality of their lives’ (ibid., p. 725). According to Ettema & Schekkerman (2016) the concept of mental health is often related, and sometimes seen as an equivalent of individual wellbeing. However, they argue that effects of neighbourhood characteristics can differ between mental health and wellbeing. Their results suggested that wellbeing was more affected by perceptions of the residential environment, while mental health was more affected by neighbourhood-level effects. Therefore, both concepts will be elaborated and researched separately. In the literature on wellbeing, various conceptualizations exist. Already in 1985, Diener, Emmons, Larsen & Griffin mentioned an increase in the academic attention on wellbeing.

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Specifically, they referred to subjective wellbeing [SWB]. In their article, Diener and colleagues identified three components of SWB: positive affect, negative affect, and life satisfaction. Where the first two referred to a more emotional aspect of wellbeing, the latter referred to a more cognitive aspect. They argued that previous research at that time focused more on the assessment of (positive/negative) affect, and less on the measurement of general life satisfaction. Diener and colleagues developed a multi-item scale to measure life satisfaction, known as the Satisfaction With Life Scale (SWLS). According to Keyes (2006), the focus on individual wellbeing rose after World War II, when more tolerance for the diversity of people and viewpoints developed along with the appreciation of the individual. Keyes (2006) argued that since then, also in the academic literature, scholars increasingly focused on individual’s viewpoints and perceptions, and the individual meaning of life. He underscores different types of wellbeing, such as subjective, hedonic and eudemonic wellbeing. He explained subjective wellbeing as a multifactorial concept that is concerned with various dimensions of individual wellbeing. Hedonic wellbeing is explained as the perceptions on interest in life, happiness, life satisfaction, and positive and negative affect. Keyes argued eudemonic wellbeing to be the evaluation of one’s psychological wellbeing. Diener & Ryan (2009) follow the idea that subjective wellbeing is a rather multidimensional concept which is used to describe overall wellbeing according to individual’s subjective evaluations of their lives. Subjective wellbeing can be measured many ways, for example on a more global scale, such as life satisfaction or how much certain feelings are experienced. Although such measures differ in the academic literature, Diener & Ryan (2009) emphasize that these measures all concern individual wellbeing ‘from the subjective standpoint of the respondent’ (p. 391). More recently, Steptoe and his colleagues (2015) referred to subjective wellbeing as psychological wellbeing. They elaborated on three different aspects of psychological wellbeing: life evaluation (or life satisfaction), hedonic wellbeing, and eudemonic wellbeing. Life evaluation refers to individuals’ thoughts about the quality of their lives. In other terms, how satisfied or happy they are with their lives. Hedonic wellbeing implies the everyday feelings or moods of individuals (e.g. anger, sadness, stress, and happiness, but then the mood not the evaluation of life). Finally, they mention eudemonic wellbeing that focuses on individual judgment about the purpose and meaning of their life. Clearly, in the academic literature, there are many components and dimensions concerning wellbeing, along with various definitions and terms used to describe this concept.

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Bobowik, Basabe & Páez (2015) discuss this complexity of the multi-dimensionality of wellbeing and state that, concerning the structure of wellbeing, ambiguities arose because each conceptualization consists of both strengths and weaknesses. However, the above descriptions of wellbeing do have some components in common. First, wellbeing concerns the individual evaluation of their life and/or emotions. Second, life satisfaction is one recurring feature of wellbeing. This study will therefore refer to individual wellbeing as the individual assessment of their overall life satisfaction.

2.2. Mental health In addition to individual wellbeing, mental health is important to take into consideration in studying neighbourhood effects and neighbourhood perceptions. Even though the concepts of mental health and wellbeing are often related (and mental health is sometimes seen as an equivalent of individual wellbeing), evidence suggests differences in neighbourhood effects on both. Strikingly, many studies on neighbourhood characteristics and mental health neglect elaborating on the concept of mental health (e.g. Leslie & Cerin, 2008; Wandersman & Nation, 1998). This subsection attempts to provide a clearer explanation of the concept of mental health, or at least, how this study will conceptualize mental health. Keyes (2006) argues that mental health combines both the (complete) state of subjective wellbeing (both hedonic and eudemonic wellbeing), and the absence of mental disorders. In their article, Wandersman & Nation (1998) provide an overview of previous studies that looked at the effects of urban neighbourhoods on mental health. Although not going in-depth in explaining the concept of mental health, some features related to mental health are described in their article. These are psychiatric disorders, depression, anxiety, and somatic symptoms. One feature of mental health returns frequently in the three models Wandersman & Nation describe in their article, namely depression. Other scholars also mention various features of mental health. First, Echeverría, Diez- Roux, Shea, Borrell & Jackson (2008), who researched the association between the social and physical living environment on mental health, operationalized mental health using depression measures. Leslie & Cerin (2008), who specifically focus on individual perceptions of the neighbourhood on mental health, mention some aspects of mental health, namely (psychological di)stress, depression, and anxiety. More recently, Erdem and colleagues (2017), used psychological distress as indicator for depression. Looking at racial differences in physical and mental health, Williams, Yu, Jackson & Anderson (1997) described the concept of mental health to be a more overarching concept.

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They use two measures for mental health, namely psychological wellbeing and psychological distress. They referred to psychological wellbeing as the overall satisfaction with life. Williams and colleagues use psychological distress as the sum of the frequency in which respondents felt nervous, sad, hopeless, restless, worthless, and like that everything was an effort. This operationalization is in line with the argumentation of Ettema and Schekkerman (2016), who argued that mental health is sometimes seen as equivalent of wellbeing. However, as argued before, this study will distinguish both concepts. Following the operationalization of many studies described above, this study will refer to mental health as lacking (or holding) depressive symptoms.

2.3. Neighbourhood effects on wellbeing and mental health This study suggests that it is important to distinguish between neighbourhood-level characteristics and individual perceptions of the neighbourhood, since it is argued that there are conceptual differences as well as differences in effects (Ettema & Schekkerman, 2016). In this subsection, the neighbourhood-level effects will be discussed. These are often referred to as the ‘factual’ characteristics of a neighbourhood and are usually based on land use data and official statistics (ibid.). According to Bécares and colleagues (2012), two main domains of neighbourhood effects are important in terms of its impact on individual wellbeing and mental health, namely the neighbourhood’s social as well as its physical environment. First, ethnic diversity will be discussed and subsequently neighbourhood deprivation. 2.3.1. Ethnic diversity The first factor that is assumed to affect wellbeing and mental health is neighbourhood ethnic diversity. Thereby, as explained in the introduction, urban spaces in the United States and Western Europe cannot simply be compared (Bolt & Van Kempen, 2012). Bolt & Van Kempen, for example, state that urban areas with higher shares of ethnic minorities in the US (e.g. poor and ‘black’ ghettos) are very different than ‘ethnic concentrated’ neighbourhoods in, for example, the Netherlands. Such high concentrations of ethnic minorities in one residential area are quite rare in the Netherlands, where the cities are rather ethnically diverse than concentrated. In that sense, Dutch residential areas are not simply comparable to the situation in the US. In line with this argumentation -i.e. that there is less ethnic concentration in cities in Western-Europe, but more ethnic heterogeneity- this study will focus on neighbourhood ethnic diversity. Veldhuizen and her colleagues define ethnic diversity as the degree of ethnic heterogeneity within a neighbourhood (2015). Studies on the association between

12 neighbourhood ethnic diversity and individual health found somewhat contradicting outcomes. A recent study on the relation between ethnic diversity and psychological distress in the four largest cities in the Netherlands (Amsterdam, Rotterdam, The Hague, and Utrecht) found that the mental health of residents in highly ethnic diverse neighbourhoods to be worse than the mental health of residents in low ethnic diverse neighbourhoods (Erdem et al., 2017). On the contrary, focusing on children, Flink and colleagues found that ethnic inequalities in behavioural and emotional problems may be smallest in ethnically heterogeneous neighbourhoods (2013). A study on adolescent’s health that was conducted in Canada, found no significant evidence that living in ethnically diverse neighbourhoods affects health in general (Abada, Hou & Ram, 2007). Although there is no general theory in previous research explaining the relation between ethnic diversity and individual wellbeing and mental health, scholars often follow one of two contradicting theories: the contact theory or the conflict theory (Veldhuizen et al., 2015). These theories are often used in combination with three frequently hypothesized mechanisms of neighbourhood ethnic diversity: social cohesion, social capital, and perceived racial discrimination (e.g. Letki, 2008; Lancee & Dronkers, 2008; Gesthuizen, Van der Meer & Scheepers, 2009; Das-Munshi, Bécares, Stansfeld & Prince, 2010; Bécares et al., 2011; Veldhuizen et al., 2015). For example, Putnam (2007), who conducted research in the United States, found evidence supporting the conflict theory. He found that in ethnically diverse neighbourhoods, people tend to ‘hunker down’, with lower social cohesion and social capital in the neighbourhood. While Putnam not studied these impacts on individual health, other scholars found evidence of these mechanisms affecting individual wellbeing and mental health (e.g. Cramm et al., 2013). On the other hand, researchers in the Netherlands found that having ethnically diverse neighbours increased the inter-ethnic trust in the neighbourhood (Lancee & Dronkers, 2008). The previously mentioned mechanisms and theories will be elaborated more in-depth in the subsection 4.2.1. on perceptions of the neighbourhood. For now, this study chooses to follow the contact theory, assuming positive effects of neighbourhood ethnic diversity. This study opts to follow the contact theory, since Putnam’s study is conducted in the US where neighbourhoods are more ethnically concentrated than neighbourhoods in the Netherlands (Bolt & Van Kempen, 2012), assuming that lower concentrations of ethnic minorities results in contact rather than conflict. This leads to the following hypothesis:

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H1: More ethnic diversity in the neighbourhood positively influences residents’ wellbeing and mental health.

2.3.2. Neighbourhood deprivation Bolt & Van Kempen (2012) emphasized the different contexts between, for example, the United States and western European countries. They argued that ethnic concentrations in urban areas in the U.S. are rather rare in western European countries. Additionally, they stated that relatively deprived areas in the Netherlands are much cleaner and have fewer residents than such areas in the United States. Also, being a welfare state, being poor in the Netherlands is probably less ‘harsh’ than being poor in a more market-driven US (ibid.). According to Knies, Nandi & Platt (2016), neighbourhood deprivation has not previously, at least before their study, been incorporated into models focusing on ethnic differences in life satisfaction. They argue it is a quite new focus field and therefore important here to take into consideration. Not taking ethnic differences into account, simply the association between neighbourhood deprivation and individual health is researched extensively (e.g. Letki, 2008). It is important to study the association between neighbourhood deprivation and wellbeing and mental health, since a previous finding (although focusing health in general) was that it is not neighbourhood ethnic diversity influencing health, but rather neighbourhood deprivation (ibid.). Strikingly, many scholars neglect to elaborate on the concept of neighbourhood deprivation. Verhaeghe and Tampubolon (2012), for example, researched the link between neighbourhood deprivation and self-rated health in England. They did not go in-depth on the clarification of neighbourhood deprivation, but in their operationalization they explained that they use an index for neighbourhood deprivation that was gathered by the Office of the Deputy Prime Minister in 2004. This index focuses on seven domains: income, health, employment, housing, education, crime, and living environment. Various scholars used this Index of Multiple Deprivation [IMD] (e.g. Lang, Llewellyn, Langa, Wallace, Huppert & Melzer, 2008). However, like Verhaeghe and Tampubolon, most scholars neglect to further elaborate on neighbourhood deprivation and the reasons why they have chosen the IMD. Focusing on youth (0-18 years), Van Vuuren, Reijneveld, Van der Wal & Verhoeff (2014) did go somewhat more in-depth on the effects of neighbourhood deprivation. They explained neighbourhood deprivation as the relatively low physical (e.g. graffiti), social (e.g. unemployment), and economical position (e.g. income) of a neighbourhood. They

14 furthermore argue that among scholars, there is no consensus on how to define and operationalize neighbourhood deprivation. Therefore, they analysed 19 studies and their operationalization of neighbourhood deprivation. Their conclusion is that most studies used measures of income/wealth, education, and employment in operationalizing neighbourhood deprivation. A few scholars (also) used measures such as housing and living environment. Although not specifically talking about neighbourhood deprivation, Wandersman & Nation (1998) discuss in their literature overview various neighbourhood characteristics that partially touch upon the above description, and their impact on mental health. They differentiate three conceptual models. First, they discuss the impact of structural neighbourhood characteristics, including socioeconomic status, ethnic composition, residential patterns, and family disruption. Second, they explain the relationship between neighbourhood disorder and mental health. Finally, they talk about the association between environmental stressors and mental health. Wandersman & Nations explain structural characteristics such as the demographic characteristics of a population, which in this case are neighbourhood residents. These include, for example, the percentage of residents living in poverty, ethnic composition, and the percentage of families with high-risk characteristics (e.g. single-parent households). They state that previous research follows the basic assumption that distressed neighbourhoods, that lack economic and social resources, are associated with more social problems that in turn influences mental health. Furthermore, the neighbourhood disorder model refers to social and physical incivilities. Under social incivilities, public drunkenness, corner gangs, and street harassment are explained as possible factors influencing mental health. Abandoned buildings, vandalism, and litter are examples of the physical incivilities. Finally, environmental stressors are discussed as neighbourhood characteristics influencing individual health. They argue noise pollution, crowding, and general pollution to be predictors of mental health. Keeping the idea in mind that there is no clear uniform definition and operationalization of neighbourhood deprivation, previous studies did find effects of living in a deprived neighbourhood on health in general. Pickett & Pearl (2001), for example, reviewed 25 studies that researched the association between the status of the neighbourhood and health. Of these 25 studies, 23 reported a statistically significant association between (at least) one neighbourhood measure and health, controlling for individual characteristics. Some studies, however, suggest that the effect of neighbourhood deprivation on individual health is mainly the result of the concentration of people holding a low socioeconomic position in these

15 neighbourhoods (e.g. Browning and Cagney, 2003). Other scholars did find harmful effects of neighbourhood deprivation on individual health, taking individual socioeconomic characteristics into account (e.g. Jones & Duncan, 1995; Malmström, Johansson & Sundquist, 2001; Fone & Dunstan, 2006). More recently, Poortinga and colleagues (2008) researched the link between neighbourhood deprivation and self-rated health. Their results suggest that there is a statistically significant, negative effect of neighbourhood deprivation on individual health3. However, this effect substantially reduced when controlling for individual socioeconomic status. They found individuals’ perceptions of the neighbourhood to also being an important indicator of individual health. Poortinga, Dunstan and Fone (2008) furthermore argue that there is a well-known problem regarding neighbourhood effect studies, namely that results are open to interpretation. There is a possibility that individual socioeconomic status drives the relation between neighbourhoods and individual health. However, they do suggest that there are also multiple examples of genuine neighbourhood effects, where living in a deprived urban area does harm one’s health. However, since individual characteristics sometimes do change the neighbourhood effects, this study will additionally control for some individual characteristics (see subsection 3.5). For now, following (most) previous studies on neighbourhood deprivation and individual health, the following hypothesis will be tested:

H24: Living in less deprived neighbourhoods has a positive impact on residents’ wellbeing and mental health.

2.4. Neighbourhood perceptions on wellbeing and mental health 2.4.1. Perceived neighbourhood social cohesion & social capital Besides neighbourhood effects, this study argues that also perceptions of the residential environment influence individual wellbeing and mental health. In their research, Ettema & Schekkerman (2016) found that perceptions of the neighbourhood had a higher explanatory power than the ‘factual’ neighbourhood-level characteristics. Leslie & Cerin (2008) also studied the relationship between perceptions of the local environment and mental health.

3 In the study of Poortinga et al. (2008), self-rated health is indicated as individual health in general. Respondents were asked to rate their own health on a 5-point scale. Poortinga et al. made it a dichotomous variable with 1 representing fair/poor health and 0 good/very good/ excellent health. 4 In order to more easily interpret the outcomes later, the hypothesis talks about less neighbourhood deprivation. 16

They focus on both the physical and social environment of the neighbourhood. They argue that neighbourhood perceptions affect the level of satisfaction with one’s residential area, which, in turn, may affect aspects of mental health (e.g. stress, depression, or anxiety). The perceived neighbourhood characteristics that will be discussed here are perceived social cohesion5, social capital, and the perceived physical environment. As explained earlier, perceptions of social cohesion and social capital could act as mechanisms of ethnic diversity. Here, both the direct and mediating effect of both concepts will be discussed. Before turning to the earlier-mentioned theories (conflict theory vs. contact theory), a brief explanation of the concepts of social cohesion and social capital is necessary. Bolt & Van Kempen (2012), for example, explain social cohesion, in its most general meaning, as a kind of ‘glue’ which holds a ‘society’ together. Social cohesion includes various aspects, such as social solidarity, social control, social networks, a strong bond with the place one lives, and a feeling of belonging to each other through a common identity. Forrest & Kearns (2001), for example, define social cohesion as the ‘need for a shared sense of morality and common purpose, aspects of social control and order, the threat to social solidarity of income and wealth inequalities between people, groups and places, the level of social interaction with communities or families, and a sense of belonging to place’ (p. 2128). Lacking social cohesion could lead to extreme social inequality, low levels of place attachment, and low levels of social interaction within communities (ibid.). Additionally, Echeverría and colleagues (2008) described social cohesion as ‘the degree of connectedness and solidarity that exists among people living in defined geographic boundaries’ (p.854). In turn, individual health could be influenced by neighbourhood social cohesion through its promoting role in adopting health-related behaviours (ibid.). Perceptions of neighbourhood social cohesion are thus individual evaluations of the extend of connectedness and solidarity they experience in the neighbourhood. Social capital, on the other hand, is referred to as not solely to a set of social contacts, but also as the means individuals (or households) get out of these contacts (Bolt & Van Kempen, 2012). Putnam (2007) sees social capital as ‘social networks and the associated norms of reciprocity and trustworthiness’ (p. 137). Lancee & Dronkers (2011) argue that it is useful to distinguish between structural and cognitive social capital. Structural social

5 Whereas some studies see neighbourhood social cohesion as a neighbourhood-level characteristic, this study chooses to refer to individual perceptions of neighbourhood social cohesion, following some other scholars on neighbourhood effects and health (e.g. Abada, Hou & Ram, 2007). The discussion of the concept of social cohesion will mostly be equal to earlier research on neighbourhood social cohesion, but the operationalization will be on individual-level perception of neighbourhood social cohesion. 17 capital implies the ‘wires’ in the network, that is the ‘frequency and intensity of the links that contribute to the exchange of resources’ (p. 599). According to Lancee & Dronkers, this also involves a behavioural component. Cognitive social capital, on the other hand, is referred to as the ‘nodes’ in one’s social network, including the attitudes and values (e.g. perceptions of support, reciprocity, and trust) that contribute to resource exchange. Here, however, social capital will be referred to as individual’s social contacts and the means they get out of these contacts. As mentioned earlier, two often used and contradicting theories to explain the link between ethnic diversity and social capital/social cohesion (and in turn mental health), are the contact theory and conflict theory (Veldhuizen et al., 2015). Lancee & Dronkers (2011) discuss both theories. According to the contact theory, more (ethnic) diversity will lead to more inter-ethnic tolerance and more social solidarity. The idea is that individuals of different ethnic groups tend to trust ‘others’ more if they have more contact with people ‘unlike themselves’. The idea is that then initial barriers between the ‘in-group’ and ‘out- group’ will be overcome. Although focusing on prejudice reduction, the first influential intergroup contact hypothesis was introduced by Allport in 1954 (Pettigrew, 1998). Allport discussed four conditions for optimal intergroup contact, namely equal status within the situation, intergroup cooperation, common goals, and support of authorities. Some criticism exists on the first condition, equal status, since this is a rather vague definition and used in different ways. Still, most research support this idea. Furthermore, Allport argued that in order to have good intergroup contact, an active-goal oriented effort is needed. In this way, in order to achieve this goal, people have to cooperate. Therefore, intergroup cooperation is linked with the condition of common goals. Finally, it is argued that explicit social sanctions are necessary for intergroup contact to be more accepted. On the other hand, the conflict theory implies that ethnic diversity strengthens distrust between various ethnic groups and creates more ‘in-group’ solidarity. Meuleman, Davidov & Billiet (2009) talk about increasing negative ‘outgroup’ attitudes, that is, negative attitudes toward persons who are not part of one’s ‘in-group’. These group distinctions can be based on multiple factors, such as ethnicity, but also gender or age. The idea of the conflict theory is that privileges of one’s group are threatened by other groups (ibid.). In other words, negative attitudes toward other groups could be a result of competition of (scarce) goods between groups. These goods can refer to as well material goods (e.g. jobs or welfare-state resources), as immaterial goods (e.g. status or power). This threat is influenced by a context of perceived intergroup competition. According to Meuleman and his colleagues (2009), in

18 previous literature on group conflict, minority group size and economic conditions are used to operationalize competitive conditions. The idea is that the bigger the minority group, the greater the perceived threat, since there would be more ethnic competitors for (scarce) goods then and more potential for mobilization. Also, the idea exists that goods are scarcer when economic conditions are worse. Putnam (2007) also argues that ethnic diversity leads to lower levels of trust between groups. Basically, his argumentation is that when a context is more ethnically diverse, there are more people ‘unlike you’ and less ‘like you’, resulting in fewer people with whom one can identify. Consequently, this results in less trust and fewer social contacts in this context. The above theories clearly show opposite effects of ethnic diversity on perceived social cohesion and social capital. How then do both social cohesion and social capital in turn influence individual health? Although focusing on wellbeing only, Cramm et al. (2013) explain the effects of social capital and social cohesion on individual wellbeing. They found evidence that suggests that neighbourhood social capital and social cohesion are as well significantly as independently associated with wellbeing of elderly residents. They argue that both social capital and social cohesion in the neighbourhood result in higher provision of support to neighbours. This idea that neighbours take care of each other and watch over each other, which is visible in small favours (e.g. support in times of sickness, support in groceries, throwing away garbage), translate into better wellbeing outcomes for older adults. Focusing on mental health, Echeverría et al. (2008) hypothesized that social cohesion influences individual health through, for example, psychosocial processes. They argue that individuals could be provided with meaningful connections, mutual respect, and an increasing sense of purpose and meaning in life among residents, which all positively affects mental health. Their findings support their hypothesis, indicating that (self-reported) social cohesive neighbourhoods positively influences mental health. Abada and colleagues explained how perceived neighbourhood social cohesion can affect (general) health outcomes (2007). They argued that individuals living in specific neighbourhoods are influenced by who they socialize with in terms of values, attitudes, and aspirations. They moreover argued that a lack of social integration in an/(a) (residential) environment may contribute to feelings of hopelessness, which increases the risk of depressive symptoms. In short, perceived social cohesion and social capital in the neighbourhood are argued to have direct implications for wellbeing and mental health. Moreover, both concepts can serve as mediating mechanism for the effect of neighbourhood ethnic diversity on wellbeing

19 and mental health. While in the latter case both conflict and contact theory could be followed, I again choose to go with the contact theory, assuming that ethnic diversity in a neighbourhood could lead to better perceptions of social cohesion and more social capital which in turn affects wellbeing and mental health positively. Again, I assume that ethnic concentrations in Dutch neighbourhoods are not at such high levels that other ethnic groups feel their privileges will be threatened, which could be more likely in the US where ethnic concentrations in neighbourhoods are (much) higher possibly leading to a stronger sense of threat. Considering this, the following hypotheses will be tested (also: see figure 1):

H3: More perceived neighbourhood social cohesion and social capital leads to higher wellbeing and better mental health of residents.

H4: The positive effect of neighbourhood ethnic diversity on wellbeing and mental health of residents is mediated by perceived neighbourhood social cohesion and social capital.

Figure 1. Predicted mediation mechanism of perceived social cohesion and social capital for the effect of ethnic diversity on wellbeing and mental health.

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2.4.2. Perceived neighbourhood physical environment According to Leslie & Cerin (2008), besides the neighbourhood’s social environment, the physical environment is also important in explaining individual health outcomes6. Other scholars also emphasized and researched this association (e.g. Ettema & Schekkerman, 2016; Gidlow, Cochrane, Davey, Smith & Fairburn, 2010). Galster (2010), for example, provides an overview of various mechanisms of neighbourhood characteristics, including three environmental mechanisms: exposure to violence, physical surroundings, and toxic exposure. First, the relation between exposure to violence and mental health was explained. The idea is that if individuals feel that their property or person is in danger this may have an impact on their wellbeing. Second, physical surroundings refer to the idea that deteriorated conditions in the residential area can have negative psychological effects for its residents. These physical surroundings include many conditions, such as area structures, public infrastructure, litter, and graffiti. Bell, Greene, Fisher & Baum (1996), for example, argue that noise (created by public infrastructure) can create psychological distress due to ‘environmental overload’. Finally, toxic exposure implies the exposure to high levels of soil-, air-, and/or water-borne pollution that it is unhealthy for residents. Leslie & Cerin (2008) state in their article that perceptions of the neighbourhood can influence the level of satisfaction with living in a neighbourhood, which in turn influences multiple aspects of mental health. Leslie & Cerin argue the following perceived neighbourhood physical characteristics to be related to self-reported health: crime, green space, access to amenities, traffic load and safety, and recreation areas (2008). Referring to previous studies, they argue that perceived green spaces in the neighbourhood to be beneficial to mental health as well as access to amenities. Perceived characteristics that negatively influence self-reported health are argued to be perceptions of crime and traffic load. A person who perceives his or her neighbourhood as dangerous could create emotional distress. Perceptions of neighbourhood physical disorder, for example, could lead to ideas that neighbourhoods are dangerous. Examples of visible physical disorder are graffiti, litter, and vandalism. In the long run, they argue, signs of crime in the neighbourhood could lead to anxiety and depression. According to Stansfeld, Brown & Haines (2000), traffic load is associated with noise pollution. Stansfeld and colleagues studied the effects of noise on individual health. They argued that the impact of noise is the strongest for outcomes such as ‘quality of life’ rather than

6 Again, some scholars would argue the neighbourhood physical environment to be neighbourhood-level characteristic. However, this study chooses to follow the idea of Leslie & Cerin (2008) who argue the importance of perceived neighbourhood social and physical characteristics. 21

‘illness’. Furthermore, they provide evidence that psychological wellbeing of individuals is reduced in areas exposed to high traffic noise. They state noise pollution may lead to depressiveness and nervousness. Following the idea of Leslie & Cerin, this study argues that positive perceptions of the neighbourhood physical environment (i.e. green spaces, access to amenities, lack of crime, and lack of traffic load) are beneficial for wellbeing and mental health. This leads to the follow ing hypothesis:

H5: Residents who perceive their neighbourhood’s physical environmental more positively will have a higher wellbeing and better mental health.

2.5. Differences between native Dutch and ethnic minorities As mentioned earlier, it is argued that life satisfaction is an important outcome measure, since it is a probable source for inequalities between ethnic groups (Knies, Nandi & Platt, 2016). Moreover, many scholars found that life satisfaction is lower among ethnic minorities than the among the ‘native’ population (e.g. Bobowik et al., 2015; de Vroome & Hooghe, 2014; Koczan, 2013). Also within ethnic groups, differences in life satisfaction are visible (e.g. Amit, 2010). Focusing on Great-Britain, Bécares et al. (2012) also found that the association between individual health (poor self-rated health) and neighbourhood effects varied between ethnic groups and that this association was the weakest for white British people. This subsection aims to find explanations for differences in neighbourhood effects on wellbeing and health. Karlsen, Nazroo & Stephenson (2002) already looked at ethnic inequalities in health, focusing on the social and environmental factors that could influence this. They argued that previous research has focused mainly on biological, genetic, cultural, and behavioural factors and not that much on social/contextual factors. Their findings suggest that ethnic minorities, despite them residing in more deprived areas, perceive the amenities in their residential area more positively than the ‘native’ British people. Bécares et al. (2012) refer to this finding and suggest that this could, for example, reflect ethnic differences in perceptions of objective contexts, different expectations of the context and its amenities, or investment by ethnic minorities in their neighbourhood facilities instead of actual differences in neighbourhood facilities and environment. Their findings confirm this idea, indicating that the association between neighbourhood effects and individual health varies between ethnic groups, with the weakest association for ‘native’ British people.

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Although focusing on physical health among residents in Amsterdam, Agyemang, Van Hooijdonk, Wendel-Vos, Lindeman, Stronks, & Droomers (2007), also discuss ethnic differences in regard to perceptions of the (residential) environment. They state that these perceptions can differ due to, for example, differences in culture, socioeconomic position, migration history, and/or language. They furthermore argue that many of the ethnic minority populations live in disadvantaged neighbourhoods, which is a disadvantage for their health. This study, focusing on Rotterdam, will include both ‘disadvantaged’ and ‘advantaged’ neighbourhoods. Even so, following Agyemang et al., (2007), assuming that ethnic minorities are overrepresented in more ‘disadvantaged’ neighbourhoods, it could be assumed that neighbourhood effects on wellbeing and mental health are stronger for ethnic minorities than for native Dutch. Moreover, this study follows the findings of Karlsen and colleagues (2002), assuming that the effect of neighbourhood perceptions on wellbeing and mental health will be stronger for ethnic minorities than for native Dutch. Considering this, the following hypothesis will be tested:

H6: Neighbourhood effects and the effect of neighbourhood perceptions on wellbeing and mental health are stronger for ethnic minorities than for native Dutch.

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3. Data & Methods

3.1. Data This study will combine individual- and neighbourhood-level data, creating a multilevel design for data analysis. Regarding individual-level data, this study will make use of the ‘Gezondheidsmonitor 2012’ (i.e. Health Monitor 2012) from the municipality of Rotterdam. The Health monitor includes questions on individual characteristics, individual health and wellbeing, as well as individual perceptions of the neighbourhood. Initially, 36,730 persons residing in Rotterdam were asked to participate in the survey. Only 38,4% responded, which is the lowest response rate in comparison with the other 18 municipalities where the mean response rate was 49%7. This resulted in a sample size of 14,113 respondents for the Health monitor 2012 in Rotterdam, of whom 6,352 are men and 7,761 are women. The age of respondents ranges from 17 up to and including 100 years. The survey data is corrected by using big weighting factors from the Dutch Statistics Bureau (Schouten, 2013). This way, the sample reflects the Dutch population. The following subsections will discuss the operationalization of the concepts used in this study. First, the outcome measures (individual wellbeing and mental health) will be elaborated, then the predictors on both the neighbourhood-level and the individual-level. Subsequently, the control variables that will be used in the analyses are discussed. Finally, the analytical strategy of this study will be explained.

3.2. Wellbeing & mental health First, the dependent variables ‘wellbeing’ and ‘mental health’ are both individual-level measures. As explained earlier, wellbeing is conceptualized in this study as the individual evaluation of the overall quality of their lives. In other words, it is concerned with the overall satisfaction of life. Following other scholars, this study uses life satisfaction as proxy for wellbeing (e.g. Bobowik et al., 2015; Ettema & Schekkerman, 2016). The 2012 Health monitor includes the following question: ‘How (dis)satisfied are you, in general, with your current life?’ Respondents answered this question by providing a mark ranging from 1 ‘dissatisfied’ to 10 ‘satisfied’. Psychological distress will be used here as proxy for mental health, again following

7 Unfortunately, it is not clear why the response rate in the city of Rotterdam was this low. In 2008, the response rate was 47,2%. It appears the response rates were higher when age increases. Also, in every age category, women responded more than men. Response rates among native Dutch were higher than each ethnic minority group. The survey was distributed in other languages too (Turkish, English, Moroccan Arabic, and Cape Verdean). Therefore, language barriers cannot serve as explanation for a lower response rate. 24 other scholars (e.g. Williams et al., 1997). The Health monitor included ten questions: ‘How often did you feel 1) very tired without a reason? 2) nervous? 3) so nervous you could not get calm again? 4) helpless? 5) restless or fidgety? 6) so restless you could not sit still anymore? 7) sad or depressed? 8) like everything takes much effort? 9) so sad that nothing could cheer you up? 10) inferior? These questions refer specifically to the previous four weeks and the response categories range from ‘always’ to ‘never’. Before creating a scale, a principal component analysis (pca) was executed. Results show one component with an eigenvalue >1. Following Samuels (2016), the factor loadings here should be at least above 0.3. In this case, all factor loadings are bigger than 0.3. A Cronbach’s alpha was measured to check the reliability of the scale. An often-used rule of thumb in regard to the value of the Cronbach’s alpha is: “_ > .9 – Excellent, _ > .8 – Good, _ > .7 – Acceptable, _ > .6 – Questionable, _ > .5 – Poor, and _ < .5 – Unacceptable (e.g. George & Mallery, 2003). Here, the Cronbach’s alpha is .92, which is, according to the above categorization defined as ‘excellent’. Finally, to check the validity, correlations between the items were measured. According to Hinkle, Wiersma & Jurs (2003), the classification of Pearson correlation coefficients goes as follows: .00 to .03 implies little if any correlation, .30 to .50 implies low correlation, .50 to .70 entails moderate correlation, .70 to .90 implies a high correlation, and .90 to 1.00 implies very high correlation. See appendix 1 for the outputs of the pca, Cronbach’s alpha, and the Pearson correlations. A scale is constructed by taking the means of the items8. Cases were coded as missing values if respondents had missing values on 4 or more items.

3.3. Neighbourhood effects Several neighbourhood-level variables will be merged to the individual-level data from the Health monitor 2012. The Health monitor already listed neighbourhoods in Rotterdam, differentiating 59 neighbourhoods. This neighbourhood distribution (see appendix 2) is the foundation for the neighbourhood characteristics. 3.3.1 Ethnic diversity An ethnic diversity index is used as predictor for individual wellbeing and mental health. Data on residents’ ethnicity in Rotterdam neighbourhoods is collected from the ‘Rotterdam Buurtmonitor’ website (n.d.). From this ethnicity data of 2012, the Herfindahl index of ethnic diversity was calculated for each neighbourhood. Following the formula of Lancee & Dronkers (2008), the subsequent formula is used:

8 This study chose to take the mean of the items instead of letting PCA create a scale, since in this way cases that had missing values on more than 3 items could be excluded. 25

Dc = -((fraction ethnic group 1)2 + (fraction of ethnic group 2)2 +….+ (fraction of ethnic group n)2 )

Here, Dc is the level of neighbourhood ethnic diversity. The ethnic groups that are included are people from Turkish, Moroccan, Antillean, Surinamese, and Cape Verdean descent, along with other non-western immigrants, Western immigrants, other Europeans, and native Dutch. Theoretically, the index ranges from -1 to 0, where ‘-1’ indicates no diversity at all (i.e. there is only one ethnicity in the neighbourhood), and ‘0’ implies total ethnic diversity (i.e. every resident in the neighbourhood has a different ethnicity). See table 1 for further information. 3.3.2. Neighbourhood deprivation Neighbourhood deprivation was explained by Verhaeghe & Tampubolon (2012) as the relatively low physical (e.g. graffiti), social (e.g. unemployment), and economical position (e.g. income) of a neighbourhood. Some scholars used the Index of Multiple Deprivation [IMD], which covers neighbourhood deprivation in various domains, namely: income, health, employment, housing, education, crime, and living environment (e.g. Verhaeghe & Tampubolon, 2012; Lang et al., 2008). Due to limits in data gathering, this study includes: neighbourhood income, physical index, and pollution & hassle. Data on average disposable household income in 2012 was provided to me by the municipality of Rotterdam (Gemeente Rotterdam, 2015). Most data on neighbourhood income was transferred easily. However, some neighbourhoods in the health monitor are combined 9. Therefore, the average income was calculated for these neighbourhoods by first, multiplying average neighbourhood income with the number of households in that neighbourhood, then by summing up these outcomes, and dividing them by the total number of households of these neighbourhoods. For some neighbourhoods (Nieuw-Mathenesse, Blijdorpse Polder, Noord Kethel, Kralingse-bos, and Rijnpoort) there was no information on average disposable household income. In this study, the variable ranges from the least average disposable household income to the most.

9 The combined neigbhourhoods in the Health monitor 2012 are: Stadsdriekhoek + C.S. Kwartier; + Nieuwe Werk + ; Nieuw Mathenesse + Oud Mathenesse + ; Schieven + + + + ; Blijdorp + Blijdorpse Polder; + ; Nieuw + Oud Crooswijk; -Oost + Kralingse-bos; + Kop van Zuid-Entrepot; ’s-Gravenland + ; Zuidrand + Zuidplein + Zuiderpark; Oud-Charlois + ; Strand en Duin + Dorp + Rijnpoort (see appendix 2). 26

The physical Index is one out of three indexes of ‘Wijkprofiel Rotterdam’. Besides the physical index, there are also the safety index and social index. Unfortunately, it was not possible to get data on the safety index. The scores of these indexes are based on feasible facts and numbers, and on the experiences of Rotterdam’s residents. The physical index entails scores on real estate, public spaces, facilities, and environment in neighbourhoods. The average score ranges from 0 to 200, where 0 indicates a ‘bad’ physical neighbourhood environment and 200 a good one. Again, for some neighbourhoods, a mean had to be calculated. There were missing scores for Charlois Zuidrand (therefore, the mean was calculated of , Zuidplein & Zuiderpark), Nieuw-Mathenesse (therefore, the mean was calculated of Oud Mathenesse & Witte dorp), and Landzicht and Spaanse polder (therefore, the mean was calculated for Noord- Kethel, Overschie, Schieveen & Zestienhoven). Moreover, there was a missing score for the neighbourhood . This neighbourhood, therefore, has a missing value on the physical index. See table 1 for the descriptive statistics of this index for this study sample. The last variable that will be used as measurement for neighbourhood deprivation is the score on pollution and hassle. This is an independent score within the social index. I chose to take this score and not the overall score of the social index (as I have done with the physical index), since otherwise there are some overlapping themes with the physical index. I chose pollution and hassle, since this touches upon Index of Multiple Deprivation that focuses on the domains: income, health, employment, housing, education, crime, and living environment, as elaborated on earlier. The score on pollution and hassle indicates a mark that ranges from 0 to 10, with ‘0’ implying a lot of pollution and hassle and ‘10’ (almost) no pollution and hassle. Scores are missing for the neighbourhoods: Blijdorpse Polder (therefore, the mark for Blijdorp is used for both neighbourhoods), and Charlois Zuidrand (therefore, the mean is calculated for Zuiderpark, Zuidplein and Wielewaal).

3.4. Neighbourhood perceptions The predictors on individual-level entail individual perceptions of one’s neighbourhood. As explained earlier, these are considered to be individual evaluations of the residential area, and are based on personal experiences and preferences of one’s neighbourhood (Ettema & Schekkerman, 2016). Such questions are available in the Health monitor (2012). As explained in the previous section, several mechanisms are sought to be studied here. Social capital and perceived social cohesion in the neighbourhood are studied here as possible mediation effects for ethnic neighbourhood composition on individual wellbeing and mental health. Furthermore,

27 the evaluation of the neighbourhood’s physical environment is explained to be a predictor for individual wellbeing and mental health as well.

3.4.1. Perceived neighbourhood social cohesion The 2012 Health monitor provides five statements on perceptions of neighbourhood social cohesion. The statements have Likert scale response categories varying from ‘totally disagree’ to ‘totally agree’. The statements include: ‘People in my neighbourhood help each other’, ‘people in my neighbourhood feel connected to each other’, ‘the people in my neighbourhood are trustworthy’, ‘in general, the people in my neighbourhood do not get along’, and ‘I prefer not to be around the people in my neighbourhood’. All items are first coded in the right direction, so that a higher score equals more perceived social cohesion. Subsequently, a principal component analysis is executed. Results show one component with an eigenvalue higher than 1 with all the factor loadings higher than 0.3. To check the reliability of the scale, a Cronbach’s Alpha of the items was calculated. The outcome is a Cronbach’s alpha 0.82. Also, the validity is checked by providing the correlations of the items. The items are correlated, but none of them too much. See appendix 3 for all output tables. A scale was then created by taking the mean of all items. Cases are indicated as missing if respondents did not provide an answer on 2 or more statements. The scale ranges from 0 to 4, with 0 implying a perception of less social cohesion in neighbourhood and 4 much neighbourhood cohesion. 3.4.2. Social capital Regarding experienced social capital, the questions used refer to social contacts in the neighbourhood and the means individuals get out of such networks. This study chooses to include all questions regarding the means of social networks in the neighbourhood. The first question is ‘when youth is skipping school and loiter around in the neighbourhood, do neighbours do something about this?’ Response categories vary from ‘most probably’ to ‘most probably not’. Having equal response categories, the following question is: ‘if a child acts disrespectfully toward an adult, do neighbours react to that?’ Another question that asked in the survey is: ‘how often do you and other people in your neighbourhood ask each other for advice about personal business, such as raising children or work-related issues?’ The response categories vary from ‘often’ to ‘never’. The following question asked is: ‘If your neighbours are not at home, how often do you watch over their property?’ Response categories range from 1 ‘often’ to 4 ‘never’. Finally, ‘how often do you attend neighbourhood gatherings and/or parties?’ The same response categories apply here as the previous one. The variables were coded in the direction where a higher score equals more social capital in the neighbourhood.

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Results of the principal component analysis show one component with an eigenvalue higher than 1. The factor loadings are all higher than 0.3. A Cronbach’s alpha reliability test shows a Cronbach’s alpha of 0.7, which is acceptable according to George & Mallery (2003). Moreover, to check for validity, a correlation test is carried out to see how much the items correlate. Results show some correlation, but not too much. Appendix 4 provides an overview of the above results. Sequential, a scale is created of the mean scores on the items. Cases were coded as missing values if respondents had missing values on 3 or more items. 3.4.3. Perceived physical environment The last individual-level predictor regarding perceptions of the neighbourhood on individual wellbeing and mental health is the perception of the neighbourhood’s physical environment. The 2012 Health monitor includes two aspects of the physical environment, namely perceived green in the neighbourhood and perceived noise pollution. Regarding perception of the neighbourhood’s green, a mark (ranging from 1 to 10) could be given on the following question: ‘What mark would you give the green in your neighbourhood?’ Furthermore, regarding questions on noise pollution, respondents graded to what extent they experience nuisance from noise of 7 sources (road traffic, train traffic, air traffic, industries, neighbours, mopeds/scooters, and trams/metro). The grades vary from ‘0’ (not bothered at all) to ‘10’ (very bothered). The dataset already provides a dummy variable where 1 implies ‘severe noise pollution’ and 0 ‘no severe noise pollution’ on these 7 sources of noise pollution. However, there are around 5,500 missing values in this variable. Therefore, I choose to exclude noise pollution from the analysis and solely look at the effect of perception of green in the neighbourhood on wellbeing and mental health.

3.5. Control variables Some individual-level control variables will be additionally tested for in this study. First, following other scholars, gender, age, education and marital status are included in the analyses as possible, explanatory individual characteristics (e.g. Knies et al., 2016; Bécares et al., 2011; Agyemang et al., 2007). Knies and her colleagues argue that these individual characteristics are important explanations for life satisfaction. Gender is included with ‘1’ being female and ‘0’ being male. The 2012 Health monitor survey included a question about the respondents’ birthyear. Therefore, the variable age is created by taking 2012 minus the variable birthyear. A slight bias is possible in relation to the actual age, since the exact date of birth is not asked. Furthermore, education was measured by the highest educational level attained. Three dummy variables were created, namely lower education (including: no

29 education, primary school, and lower secondary school), middle education (including : secondary school, and vocational training), and higher education (including: university for applied sciences and university). For marital status, 5 dummy variables are created, categorized as: married, never married, divorced, widowed, and cohabit. In the analyses, lower education and married will serve as reference categories. Furthermore, according to Bécares et al. (2011), number of years living in the neighbourhood could be of importance. This measure will therefore also be included as control variable. Finally, perceived discrimination will be included. Veldhuizen et al. (2015) namely explained that another mechanism that is often used to explain the link between the ethnic composition of neighbourhoods and individual wellbeing (besides social capital and social cohesion) is that of experienced racism or discrimination. Various studies provided evidence that ethnic minority residents who live in neighbourhoods with a higher share of persons from the own-ethnic group experience less racism and discrimination (e.g. Das-Munshi et al., 2010). Other studies, in turn, provided evidence that perceived discrimination has implications for individual health (e.g. Wallace, Nazroo & Bécares, 2016). One question is available in the 2012 Health monitor regarding perceived discrimination: ‘do you ever feel discriminated against because of your religion, skin colour, sexual preference, or age? Response categories are ‘no never’, ‘yes sometimes’, and ‘yes often’. A dummy variable is created where 1 implies ’not discriminated against’ and 0 ‘discriminated against’.

3.6. Analytical strategy This study will conduct various multilevel linear regression analyses in order to measure the effect of the individual- and neighbourhood-level predictors on individual wellbeing and mental health. Since it is hypothesized that there are differences in the effects of the predictors on wellbeing and mental health between native Dutch and ethnic minorities (see subsection 2.5), the analyses will be conducted for each group. Ideally, a distinction would be made between specific ethnic groups. However, the sample for some ethnicities is too small (e.g. 262 respondents with an Antillean background). Therefore, I choose in this study to distinguish between native Dutch and ethnic minorities. The group of ethnic minorities consists of respondents with the following backgrounds: Moroccan, Turkish, Surinamese, Antilleans, other non-western, other-western, and Cape Verdean. First, descriptive statistics are used to provide an overview of the distribution of all variables included in the analyses (see Table 1). These descriptive statistics show some clear

30 differences between native Dutch and ethnic minorities. For instance, the mean scores on wellbeing and mental health are higher among native Dutch than among ethnic minorities. Also, ethnic minorities tend to live in more ethnically diverse neighbourhoods than native Dutch. Furthermore, ethnic minorities live in neighbourhoods with lower average income, lower score on the physical index and lower score on pollution/hassle than native Dutch. Subsequently, multilevel linear regression analyses are to be conducted (first among native Dutch respondents), starting with an intercept-only model to test for significant differences of wellbeing and mental health between neighbourhoods. Thereafter, neighbourhood-level predictors are added to the fixed part of the model and finally, the individual-level predictors and control variables are included in the analysis, thus providing three models. These three models are again tested for among ethnic minority respondents. Additionally, to check if there are already differences in wellbeing and mental health between native Dutch and ethnic minorities, an additional analysis will be conducted, creating a fourth model for both wellbeing and mental health. This last model covers the full sample and includes a dummy variable with 1 ‘native Dutch’ and 0 ‘ethnic minorities’. Furthermore, Sobel tests are conducted for both native Dutch and ethnic minorities to see whether or not the effect of neighbourhood ethnic diversity on wellbeing and mental health could be (partially) explained by perceived social cohesion and social capital. The Sobel test refers to Sobel, who came up with an approximate significance test for the indirect effect of the independent variable on the outcome variable via a mediator (Baron & Kenny, 1986). Baron & Kenny explain that three regression models should be estimated and that the following conditions should be met: first, the predictor must significantly affect the dependent variable in the first analysis; secondly, the predictor should significantly affect the mediator variable in another analysis; finally, the mediator must have a significant effect on the dependent variable in the last analysis. If the effect of the predictor on the dependent variable is less (or zero) in the third analysis than the first, and all variables hold the predicted direction, there is a mediating effect. In this study, following the above criteria, three multilevel mediation analyses will be conducted per outcome variable (i.e. wellbeing and mental health), per group (i.e. native Dutch and ethnic minorities), and per mediating mechanism (i.e. perceived social cohesion and social capital). Following an explanation by professor Andrew Hayes on multilevel mediation analysis (2014), in a model where the predictor is measured at a ‘higher’ (level 2) level and the mediator and dependent variable are both individual-level variables, the effects should all be measured as fixed effects, since there is no variation in the independent variable within level-2 unit.

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Table 1. Descriptive statistics of all the variables (individual-level & neighbourhood-level10) used in the analyses separately for native Dutch and ethnic minorities.

Native Dutch Ethnic Minorities Variables Mean Std. dev. Min Max Mean. Std. dev. Min. Max. Dependent Variables Life Satisfaction 7.85 1.47 1.00 10.00 7.47 1.83 1.00 10.00 Mental Health 3.45 0.66 0.00 4.00 3.24 0.82 0.00 4.00 Independent Variables Social Cohesion 2.61 0.90 0.00 4.00 2.35 0.88 0.00 4.00 Social Capital 1.14 0.66 0.00 3.00 1.00 0.68 0.00 3.00 Green in Neighbourhood 7.08 1.81 1.00 10.00 6.61 2.04 1.00 10.00 Ethnic Diversity -0.41 0.17 -0.70 -0.08 -0.29 0.15 -0.70 -0.08 Neighbourhood Income 31520.74 7617.01 22200.00 58400.00 28288.01 6345.56 22200.00 58400.00 Physical Index 104.79 13.06 72.90 133.30 96.60 13.48 72.90 133.30 Pollution/Hassle 6.95 1.02 4.80 8.60 6.29 1.03 4.80 8.60 Control Variables11 Gender 0.53 0.50 0.00 1.00 0.56 0.50 0.00 1.00 Age 51.85 21.89 17.00 99.00 41.44 19.10 17.00 96.00 Lower Education 0.09 0.29 0.00 1.00 0.17 0.38 0.00 1.00 Middle Education 0.61 0.49 0.00 1.00 0.58 0.49 0.00 1.00 Higher Education 0.30 0.46 0.00 1.00 0.25 0.43 0.00 1.00 Married 0.42 0.49 0.00 1.00 0.35 0.48 0.00 1.00 Cohabit 0.12 0.32 0.00 1.00 0.09 0.29 0.00 1.00 Never Married 0.29 0.45 0.00 1.00 0.41 0.49 0.00 1.00 Divorced 0.08 0.27 0.00 1.00 0.10 0.30 0.00 1.00 Years in Neighbourhood 17.56 15.92 0.00 98.00 12.14 11.75 0.00 98.00 Not discriminated against 0.90 0.30 0.00 1.00 0.63 0.48 0.00 1.00 Valid N 7,323 3,424

10 The neighbourhood-level predictors are notated in bold. 11 In the multilevel regression analyses, ‘lower education’ and ‘married’ are the reference categories. 32

Finally, post-estimation checks are to be conducted, such as checks for multicollinearity and validity. Clark (2013) stated that multicollinearity did not receive the same attention in the context of multilevel modelling (MLM) as in the context of OLS regression. There is only little discussion on the effect of multicollinearity in MLM. This study chooses to follow other scholars who used the variance inflation factor (VIF) in multilevel modelling (e.g. Dickinson & Basu, 2005). It is namely argued that VIF test with Ordinary Least Squares (OLS) regression are conceptually equal to that of multilevel regression. The rule of thumb in most scholarly articles and advanced statistical textbooks is that the VIF cannot be above 10. If it is, there is high multicollinearity (O’Brien, 2007). Furthermore, Pearson correlations coefficients will be explored for all variables included in the analyses to check for validity. Although some concerns exist about Pearson correlations leading to misrepresentation of the actual validity of data, this method is the main method in checking validity. Hinkle, Wiersma & Jurs (2003) offer in their book an overview of the interpretation of the size of correlation coefficients. See section 3.2. for an overview of their classification of the Pearson correlation coefficients. This study will follow this classification. Results of both VIF and correlations will be described in section 4.3.

33

4. Results

Multiple multilevel regression analyses have been conducted in this study in order to provide answers to the earlier argued predictions. This section will describe the outcomes of these analyses extensively. First, the results of the analyses on individual wellbeing will be elaborated for native Dutch and subsequently for ethnic minorities. Then, the results for mental health will be discussed for both groups. What these results involve in regard to the drawn up hypotheses will be discussed in section 5.

4.1. Results for wellbeing 4.1.1. Native Dutch First, among native Dutch, the intercept-only model suggests that the intraclass correlation coefficient (ICC) for wellbeing is 0.0093. This implies that almost 1% of the total variation in individual wellbeing is explained by which neighbourhood respondents live in (see Table 2). Adding neighbourhood-level predictors in the second model, 0.13% of the total variation of wellbeing can be accounted for this model. Looking at the results of the likelihood- ratio test comparing the first and second model, the second model seems to be a better fit than model 1 in explaining individual wellbeing among native Dutch (p<.001). As the results of the fixed part of the model show, average neighbourhood income and pollution/hassle seem to have a significant effect on wellbeing. Both ethnic diversity and the physical index do not explain individual wellbeing, according to the findings. For each unit increase in neighbourhood income, wellbeing increases with (only) 0.000012 (p=.001). The finding on pollution/hassle suggests that each score higher on pollution/hassle12 leads to an increase in wellbeing (b=0.082, p=.035). An overview of these findings can be found in Table 2. In the third model, the individual-level predictors and control variables are added. The likelihood-ratio test suggests this third model to be a better fit in explaining wellbeing than the second model that only includes neighbourhood-level predictors. Interestingly, both neighbourhood income and pollution and hassle do not have a significant effect anymore when individual-level predictors and control variables are included. Looking at the individual-level predictors, both perceived neighbourhood social cohesion and perceived green in the neighbourhood have a significant effect of wellbeing.

12 Note that a higher score on the scale of pollution/hassle implies less pollution and hassle in the neighbourhood. 34

Table 2. Results of the multilevel regression analyses of individual wellbeing for as well native Dutch as ethnic minorities.

Native Dutch Ethnic Minorities Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Variables b13 SE b SE b SE b SE b SE b SE Constant 7.823 7.172 6.969 7.495 6.534 5.654

Neighbourhood-level Ethnic diversity 0.001 0.213 0.180 0.236 -0.196 0.383 0.132 0.372 Income 0.000** 3.65e-06 2.81e-06 4.04e-06 0.000** 7.87e-06 3.11e-06 7.69e-06 Physical index -0.003 0.002 -0.002 0.003 -0.002 0.004 -0.005 0.004 Pollution / hassle 0.082** 0.039 0.015 0.041 0.098 0.064 0.066 0.062

Individual-level (Perceived…) Social cohesion 0.205*** 0.023 0.205*** 0.041 Social capital 0.049 0.031 0.124** 0.052 Green 0.090*** 0.010 0.101*** 0.016

Control variables Gender 0.017 0.033 -0.049 0.060 Age -0.010*** 0.001 -0.002 0.002 Middle education 0.073 0.059 0.354*** 0.086 Higher education 0.187** 0.066 0.595*** 0.099 Never married -0.449*** 0.053 0.009 0.084 Divorced -0.666*** 0.064 -0.739*** 0.106 Widowed -0.720*** 0.061 -0.516** 0.158 Cohabit -0.229*** 0.061 0.266** 0.115 Years in neighbourhood 0.005*** 0.001 -0.002 0.003 Not discriminated 0.373*** 0.054 0.592*** 0.063 ICC (%) 0.93 0.13 0.36 1.12 0.012 0.067 Valid N 7,323 3,424

13 * p<.10, ** p<.05, *** p<.001 35

For perceived social cohesion, each unit increase on this scale results in 0.205 increase in wellbeing (b=0.205, p<.001). For perception of green spaces, each unit increase results in 0.090 increase in wellbeing (b=0.090, p<.001). Social capital does not significantly affect wellbeing. Relevant here is to check the correlation between perceived social cohesion and social capital. The correlation coefficient is .56, which should not have implications for the outcomes. The ICC of the third model is 0.0036, meaning that 0.36% of the total variation of wellbeing can be accounted for both neighbourhood- and individual-level predictors and control variables (see Table 2). This is a slight increase in comparison to the ICC of the second model. Considering the control variables, results show that gender does not significantly affect individual wellbeing among native Dutch. Also, individuals who have middle education as highest completed education do not significantly differ in wellbeing than those with lower education (p=.217). Those who completed higher education score significantly higher on wellbeing than individuals with lower education (b=0.187, p=.005). Individuals who never been married (b=-0.449, p<.001), who cohabit (b=-0.229, p<.001), who are divorced (b=-0666, p<.001) or widowed (b=-0.720, p<.001) score significantly lower on wellbeing than people who are married. Furthermore, being a year older has a significantly, negative effect on wellbeing (b=-0.010, p<.001). Living one year longer in a neighbourhood has a significant, positive effect on wellbeing (b=0.005, p<.001). Finally, people who are not being discriminated against score higher on wellbeing than people who have been discriminated (b=0.373, p<.001).

In this study, it is also hypothesized that the effect of neighbourhood ethnic diversity on wellbeing as well mental health is mediated by perceived social cohesion and social capital (see figure 1). To test this, Sobel tests are conducted by running three analyses (xy; xm; x & m  y), but then two times: one time with perceived social cohesion as mediator and the second time with perceived social capital. The first analysis, measuring the effect of ethnic diversity on wellbeing of native Dutch, while controlling for all control variables, shows that there is a significant, negative effect (b=- 0.417, p<.001; see Table 3). Looking at the results of the second analysis, it seems that neighbourhood ethnic diversity also significantly, negatively affects perceived social cohesion controlling for the control variables (b=-1.373, p=<.001). In the last analysis both the effect of perceived social cohesion and neighbourhood ethnic diversity on wellbeing are tested. Again, controlling for the control variables, perceived neighbourhood social cohesion seems to have a significant, positive effect on individual wellbeing (b=0.261, p<.001). The effect of ethnic diversity, however, is no longer significant (b=-0.073, p=.517).

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Table 3. Fixed effects of multilevel mediation analysis with perceived social cohesion as the mediator1415. Native Dutch Ethnic Minorities Wellbeing Mental Health Wellbeing Mental Health X  Y16 X M  Y X  Y X M  Y X  Y X M  Y X  Y X M  Y Neighbourhood-level Ethnic diversity -0.417 (0.11) -0.073 (0.11) -0.198 (0.05) -0.037 (0.05) -0.527 (0.21) -0.316 (0.21) -0.221 (0.10) -0.147 (0.10)

Individual-level Perceived social 0.261 (0.02) 0.119 (0.01) 0.303 (0.04) 0.102 (0.02) cohesion

Valid N 7,323 3,424

Table 4. Fixed effects of multilevel mediation analysis with social capital as the mediator. Native Dutch Ethnic Minorities Wellbeing Mental Health Wellbeing Mental Health X  Y X M  Y X  Y X M  Y X  Y X M  Y X  Y X M  Y Neighbourhood-level Ethnic diversity -0.417 (0.11) -0.244 (0.11) -0.198 (0.05) -0.134 (0.05) -0.527 (0.21) -0.428 (0.21) -0.221 (0.10) -0.202 (0.10)

Individual-level Social capital 0.238 (0.03) 0.087 (0.01) 0.295 (0.05) 0.053 (0.02)

Valid N 7,323 3,424

14 Only the first and last analysis are notated here (X Y & X M  Y). The criterium that the effect of X  M has to be significant is met each time (and is negative). 15 In each analysis, I controlled for the following individual-level factors: gender, age, education, marital status, years of residence, and perceived discrimination. 16 Unstandardized coefficients are described (with standard error in brackets). Coefficients that are significant (p<.05) are notated in bold, and (p<.10) in italic. 37

This would suggest that there is a mediation effect of perceived social cohesion. However, the direction of the effect of ethnic diversity is not as predicted. That is why no evidence is found for a mediation effect of perceived social cohesion as suggested in this study. Focusing on social capital as mediator, the first analysis that measures ethnic diversity on individual wellbeing again provides evidence for a significant, negative effect, controlling for the control variables (b=-0.417, p=.001). See Table 4 for an overview of the results. Also controlling for all control variables, there is evidence for a significant, negative effect of neighbourhood ethnic diversity on social capital (b=-0.741, p<.001). Finally, including both perceived social capital and ethnic diversity in the analysis, results show a significant, positive effect of social capital on wellbeing (b=0.238, p<.001), and also still a significant effect of ethnic diversity on wellbeing (b=-0.244, p=.032). The level of significance dropped along with the strength of the coefficient. However, equal to the above outcomes, the predicted direction of ethnic diversity deviates from what is earlier hypothesized.

4.1.2. Ethnic minorities Equally to the analyses for native Dutch, the analyses among ethnic minorities commences with an intercept-only model. The results of this model show that the intraclass correlation coefficient (ICC) for wellbeing is 0.0117. This implies that about 1.2% of the total variation in individual wellbeing among ethnic minorities is explained by which neighbourhood respondents live in (see Table 2). In the second model, neighbourhood-level predictors are included. The ICC for this model is 0.00012. This implies a decrease in the ICC, meaning that the second model accounts for 0.012% of the total variation of wellbeing among ethnic minorities. Looking at the neighbourhood-level fixed effects, the results show that only average neighbourhood income significantly affects wellbeing among ethnic minorities (b=0.000, p=.026). With each unit increase in neighbourhood income, there is a (small) increase in individual wellbeing. Neighbourhood ethnic diversity, physical index, and the level of pollution and hassle do not have a significant effect on wellbeing of ethnic minorities, according the findings (see Table 2). The likelihood-ratio test suggests that the second model is a better fit than the first in explaining individual wellbeing of ethnic minorities (p<.001). Subsequently, the individual-level predictors and control variables are added to the third model. The ICC of this model is 0.00067, meaning that as well the neighbourhood- and individual-level predictors as control variables account for 0.067% of the total variation in individual wellbeing among ethnic minorities. The results of the likelihood-ratio test show this

38 third model to be a better fit than the second model that includes only neighbourhood-level predictors (p<.001). Looking more closely at the fixed effects, no big changes in significance are visible for the neighbourhood-level predictors (see Table 2). Furthermore, each of the individual-level predictors significantly affects individual wellbeing. Each unit increase in the scale of perceived neighbourhood social cohesion, there is a 0.205 increase in wellbeing (b=0.205, p<.001). For perceived social capital (b=0.124, p=.017) and perceived green (b=0.101, p<.001) it is also found that the better the perceptions of social capital and green in the neighbourhood, the better the individual wellbeing of ethnic minorities. Looking at the control variables, individuals who never been married do not significantly differ in wellbeing than those who are married. Also gender, age, and numbers of years living in the neighbourhood do not significantly affect the wellbeing of ethnic minorities. People who are widowed score 0.52 lower on wellbeing than people who are married (b=-0.516, p=.001), people who are divorced score 0.74 lower on wellbeing than married people (b=-0.739, p<.001), and individuals who cohabit score 0.27 higher on wellbeing than those who are married (b=0.266, p=.021). Furthermore, both persons with highest completed education being middle and higher education, score higher on individual wellbeing than those who completed lower education (respectively b=0.354, p<.001; b=0.595, p<.001). Finally, individuals who are never been discriminated against score 0.59 higher on wellbeing than persons who have been discriminated against (b=0.592, p<.001).

Also for the ethnic minority group, the Sobel test is conducted for as well wellbeing as mental health, and also for perceived social cohesion and perceived social capital. Here, the outcomes on the Sobel test for wellbeing among ethnic minorities are discussed. First with the mediating variable being perceived social cohesion and secondly with social capital. The first analysis tests the effect of ethnic diversity on wellbeing, controlling for the control variables. The results show that there is a significant, negative effect of ethnic diversity on wellbeing among ethnic minorities in Rotterdam (b=-0.527, p=.011). See Table 3 for an overview of the results. The second analysis looks at the effect of ethnic diversity on the mediation variable, perceived social cohesion. It seems that there is a significant, negative effect of ethnic diversity on social cohesion (b=-0.760, p<.001). Finally, again controlling for the control variables, perceived social cohesion seems to have a significant effect on wellbeing (b=0.303, p<.001), meaning that scoring one unit higher on the social cohesion scale results in a 0.32 increase in wellbeing. Ethnic diversity, on the other hand, is not significant anymore (b=- 0.316, p=.127). These results would suggest that there is evidence for a pure mediation effect

39 of perceived social cohesion. However, the direction of the effect of ethnic diversity is not as predicted earlier in this study and therefore, there is no evidence for a mediating effect of perceived social cohesion as suggested in this study. Looking at perceptions of social capital as the mediator, the first analysis, which includes the control variables and ethnic diversity on wellbeing, has equal results as above (b=- 0.527, p=.011; see Table 4). The second analysis, testing the effect of ethnic diversity on perceived social capital, provides results indicating that there is a significant, negative effect of ethnic diversity on social capital (b=-0.388, p<.001). This implies that the more ethnically diverse the neighbourhood, the lower the perceived social capital. The last analysis, which still controls for the control variables, shows a significant effect of social capital on wellbeing (b=0.295, p<.001). The effect of ethnic diversity on wellbeing remains significant. However, the effect is slightly less significant and has a smaller coefficient (b=-0.428, p=.039). Although the results suggest a mediation effect of perceived social capital, the direction of the effect of ethnic diversity is not as predicted earlier. Therefore, again, no evidence is found for a mediation effect as hypothesized in this study.

4.1.3. Additional analyses for wellbeing As explained in section 3.6, to test whether or not there are differences in wellbeing and mental health between native Dutch and ethnic minorities to begin with, additional analyses are conducted. These analyses consider the full sample (instead of solely native Dutch or ethnic minorities), and a dummy variable is added where ‘1’ is native Dutch and ‘0’ ethnic minorities. Here, the results for wellbeing are elaborated. In subsection 4.2.3. the results for mental health will be described. In this model, as well the neighbourhood-level predictors as the individual-level predictors and control variables are included. The results suggest that for the full sample, no neighbourhood-level predictor has a significant effect on wellbeing (see Table 5). Perceived social cohesion does significantly affect wellbeing (b=0.199, p<.001). Also perceived social capital has a significant effect on wellbeing (b=0.071, p=.009), as well as perceived green (b=0.096, p<.001). Among the control variables, only cohabitation and gender do not seem to significantly affect the wellbeing of residents of Rotterdam. Finally, individuals who are native Dutch seem to score higher on wellbeing than ethnic minorities, the results suggest (b=0.150, p<.001). The intraclass correlation coefficient of this model is 0.0026, which means that the this model accounts for 0.26% of the total variation in wellbeing.

40

Table 5. Results of the additional multilevel regression analyses for wellbeing and mental health for the full sample, including an ethnicity dummy.

Wellbeing Mental Health Variables b17 SE b SE Constant 6.248 2.504

Neighbourhood-level Ethnic diversity 0.128 0.211 0.074 0.088 Income 2.26e-06 3.79e-06 2.61e-06* 1.57e-06 Physical index -0.003 0.002 0.001 0.001 Pollution / hassle 0.033 0.037 -0.002 0.015

Individual-level (Perceived…) Social cohesion 0.199*** 0.021 0.105*** 0.009 Social capital 0.071** 0.027 -0.009 0.012 Green 0.096*** 0.008 0.026*** 0.004

Control variables Gender -0.003 0.030 -0.144*** 0.013 Age -0.007*** 0.001 -0.001** 0.000 Middle education 0.215*** 0.048 0.232*** 0.022 Higher education 0.371*** 0.055 0.355*** 0.024 Never married -0.271*** 0.045 -0.050** 0.020 Divorced -0.698*** 0.055 -0.219*** 0.025 Widowed -0.668*** 0.060 -0.178*** 0.027 Cohabit -0.070 0.055 -0.013 0.024 Years in neighbourhood 0.004*** 0.001 0.002*** 0.001 Not discriminated 0.492*** 0.040 0.287*** 0.018 Native Dutch 0.150*** 0.036 0.059*** 0.016 ICC (%) 0.26 0.16 Valid N 10,747 10,747

17 * p<.10, ** p<.05, *** p<.001 41

4.2. Results for mental health 4.2.1. Native Dutch Regarding individual mental health, the results of the intercept-only model suggest an ICC of 0.0133 meaning that the neighbourhood accounts for 1.33% of the total variation in individual mental health. See Table 6 for the results of an overview of the results of the multilevel regressions analyses for mental health for both native Dutch and ethnic minorities. Adding neighbourhood-level predictors to the analysis for mental health, the ICC decreased to 0.0019 implying that 0.19% of the total variation in mental health among native Dutch can be accounted for the neighbourhood-level predictors. Results from the likelihood- ratio test suggest that the second model, including neighbourhood-level predictors, is a better fit than the first model (p<.001). Looking at the fixed part of the model, results show that neighbourhood ethnic diversity and physical index do not have a significant effect on mental have among native Dutch. Both, average neighbourhood income (b=0.000, p=.001) and pollution and hassle (b=0.036, p=.041) positively affect mental health. Although the effect of neighbourhood income being rather small, the results indicate that the higher the neighbourhood income and the higher the score on pollution and hassle18, the better the mental health of native Dutch. The third model consists of the neighbourhood-level predictors and the individual-level predictors and control variables. Results from the likelihood-ratio test suggest that this model is a better fit in explaining mental health among native Dutch than the second model which only includes neighbourhood-level predictors (p<.001). With the addition of the individual-level predictors and control variables, both neighbourhood income and pollution and hassle do not have a significant effect on mental health anymore (see Table 6). Neighbourhood ethnic diversity and physical index remain insignificant. Among the individual-level fixed effects, perceptions of social capital does not seem to influence mental health of native Dutch. Perceived social cohesion (b=0.110, p<.001) and perceived green in the neighbourhood (b=0.027, p<.001) do significantly affect mental health. This indicates that the perception of more social cohesion and green in the neighbourhood positively affects mental health of native Dutch residents.

18 Again, note that a higher score on this variable indicates that there is less pollution and hassle in the neighbourhood. 42

Table 6. Results of the multilevel regression analyses of individual mental health for as well native Dutch as ethnic minorities. Native Dutch Ethnic Minorities Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Variables b19 SE b SE b SE b SE b SE b SE Constant 3.437 2.976 2.658 3.253 2.763 2.385

Neighbourhood-level Ethnic diversity 0.143 0.099 0.123 0.096 -0.116 0.178 -0.003 0.177 Income 5.71e-06** 3.65e-06 1.31e-06 1.63e-06 0.000** 3.63e- 5.97e-06* 3.61e-06 06 Physical index 0.001 0.001 0.001 0.001 0.001 0.002 0.000 0.002 Pollution / hassle 0.036** 0.01 0.006 0.017 -0.001 0.030 -0.019 0.029

Individual-level (Perceived…) Social cohesion 0.110*** 0.010 0.096*** 0.023 Social capital -0.006 0.014 -0.019 0.023 Green 0.027*** 0.004 0.023** 0.007

Control variables Gender -0.137*** 0.015 -0.157*** 0.027 Age -0.003*** 0.001 0.003** 0.001 Middle education 0.204*** 0.026 0.279*** 0.038 Higher education 0.326*** 0.029 0.388*** 0.044 Never married -0.087*** 0.023 0.004 0.038 Divorced -0.169*** 0.028 -0.328*** 0.048 Widowed -0.146*** 0.027 -0.304*** 0.071 Cohabit -0.046* 0.027 0.040 0.052 Years in neighbourhood 0.003*** 0.001 -0.000 0.001 Not discriminated 0.251*** 0.024 0.316*** 0.028 ICC (%) 1.33 0.19 0.18 1.74 0.14 0.29 Valid N 7,323 3,424

19 * p<.10, ** p<.05, *** p<.001 43

Among the control variables, each variable significantly affects mental health. Individuals that are never married (b=-0.101, p<.001), divorced (b=-0.178, p<.001), widowed (b=-0.178, p<.001), and who cohabit (b=-0.046, p=.088) score significantly worse on the mental health scale than those who are married. Furthermore, females score significantly worse on mental health than men (b=-0.137, p<.001). Also, being one year older has a significant, negative effect on mental health (b=-0.003, p<.001). Individuals who completed middle (b=0.204, p<.001) and higher education (b=0.326, p<.001), significantly score higher on mental health than those who completed lower education. Furthermore, residing one year longer in a neighbourhood significantly increases mental health with 0.003 (p<.001). Finally, people who are not being discriminated against score higher on mental health than people who have been discriminated (b=0.251, p<.001).

Also for mental health as outcome variable, Sobel tests were conducted to find out whether or not the results find evidence for the predicted mediating mechanisms of perceived social cohesion and social capital on the effect of ethnic diversity on mental health. First, the three independent analyses are conducted with perceived social cohesion as mediator. Secondly, the three analyses are executed for perceived social capital as mediator. The results of the first analysis show that, controlling for the control variables, there is a significant, negative effect of ethnic diversity on individual mental health of native Dutch (b=-0.198, p<.001; see Table 3). Looking at the effect of neighbourhood ethnic diversity on perceived social cohesion, the results provide evidence for a significant, negative effect (b=- 1.373, p<.001). The last analysis, again controlling for the control variables, suggests that perceived social cohesion has a positive, significant effect on mental health (b=0.119, p<.001), and that the effect of ethnic diversity on mental health is not significant anymore (b=-0.037, p=.466). However, since the direction of ethnic diversity on mental health and perceived social cohesion is not as predicted earlier in this study, there is no evidence found that there is mediation mechanism of perceived social cohesion on the effect of ethnic diversity on mental health. Focusing on social capital, the results of the first analysis are equal to the outcomes above, since this analysis tests the effect of ethnic diversity on mental health (b=-0.198, p<.001; see Table 4). The results of the second analysis, measuring the effect of ethnic diversity on perceived social capital, provide evidence that there is a significant, negative effect (b=-0.741, p<.001). Finally, again, controlling for the control variables, it seems that social capital has a significant, positive effect on mental health (b=0.087, p<.001). Also, there is still an effect of

44 ethnic diversity on mental health, but with a lower coefficient and lower significance (b=-0.134, p=.011). However, equally to perceived social cohesion, there is no evidence here that suggests a mediating mechanism of perceived social capital, since the direction of ethnic diversity is not as predicted in this study.

4.2.2. Ethnic minorities Also for mental health among ethnic minority groups, an intercept-only model is initially performed. The ICC for the intercept-only model for mental health among ethnic minorities is 0.01117, implying that 1.12% the total variation of mental health could be accounted for this first model. See Table 6 for an overview of the results. Subsequently, the neighbourhood-level predictors are added in the second model. In comparison to the first model, this model seems to be a better fit in explaining mental health among ethnic minorities, following the results of the likelihood-ratio test (p<.001). The ICC of this model is 0.00135, which implies that 0.14% of the total variation in mental health among ethnic minorities could be accounted for this model. Focusing on the fixed part of the model, results show that there is no significant effect of ethnic diversity, physical index, and pollution and hassle on mental health of ethnic minorities. Solely neighbourhood income (b=0.000, p=.001) significantly affects individual mental health slightly. The third model additionally includes individual-level predictors and control variables. According to the results of the likelihood-ratio test, this model is a better fit than the second model (p<.001). In this model, neighbourhood ethnic diversity, physical index, and pollution and hassle remain insignificant. Neighbourhood income, on the other hand, remains having a significant effect on mental health among ethnic minorities (b=0.000, p=.098). Among the individual-level predictors, perceived social capital does not significantly affect mental health (b=-0.019, p=.421). Both perceived neighbourhood social cohesion (b=0.096, p<.001) and perceived green (b=0.023, p=.001) positively, significantly influences mental health. This implies the better the perceptions of social cohesion and green in the neighbourhood, the better individuals’ mental health. Looking at the control variables, never being married, cohabitation, and number of years living in the neighbourhood do not significantly affect mental health among ethnic minorities. Individuals that are widowed (b=-0.304, p<.001) and that are divorced (b=-0.328, p<.001) score lower on the mental health scale than those who are married. Moreover, people who completed middle (b=0.279, p<.001) and higher education (b=0.288, p<.001) seem to have significant better mental health than those who completed lower education. Furthermore, people who are

45 not being discriminated against score 0.316 higher on the mental health scale (b=0.316, p<.001) than persons who have been discriminated against. Finally, females do significantly score lower on mental health than males (b=-0.157, p<.001). The ICC of the third model is 0.00285, which means that this model accounts for 0.29% of the variation in mental health among ethnic minorities.

For mental health among ethnic minorities, the same steps apply as with the Sobel tests that are elaborated earlier this study. The first analysis, while controlling for all control variables, provides evidence that there is a significant, negative effect of ethnic diversity in the neighbourhood on mental health among ethnic minorities in Rotterdam (b=-0.221, p=.025). Secondly, the effect of ethnic diversity on social cohesion is tested. Results show that there is a significant, negative effect of ethnic diversity on perceived neighbourhood social cohesion (b=-0.760, p<.001), meaning that with more neighbourhood ethnic diversity, perceived social cohesion decreases. The last analysis, that includes all control variables, ethnic diversity and perceived social cohesion, shows that there is a significant, positive effect of social cohesion on mental health of ethnic minorities (b=0.102, p<.001). The effect of ethnic diversity on mental health lost its significance (b=-0.147, p=.135). However, since the outcomes contradict the predicted direction of ethnic diversity on perceived social cohesion and mental health, there is no evidence here that suggest a mediating effect of perceived social cohesion. These results are also to be found in Table 3. Subsequently, three analyses are conducted to look at perceived social capital as mediator. First, the same results as above apply, since again the effect of ethnic diversity on mental health is tested (b=-0.221, p=.025; see Table 4). Subsequently, the effect of ethnic diversity on social capital is tested. The results show that there is a significant, negative effect of ethnic diversity on perceived social capital (b=-0.388, p<.001). Finally, controlling for all control variables, the analysis shows that there is a significant effect of perceived social capital on mental health (b=0.053, p=.009). Furthermore, the effect of ethnic diversity on mental health remains significant with a slightly smaller coefficient and weaker significance (b=-0.202, p=.041). However, since again the direction of ethnic diversity is contrary to what is predicted in this study, the results do not provide evidence here that there is a mediating effect of perceived social capital.

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4.2.3. Additional analyses for mental health In section 4.1.3., the results for wellbeing are described concerning the full sample instead of only among native Dutch or ethnic minorities. A dummy variable was included that was coded as ‘1’ native Dutch and ‘0’ ethnic minorities. In this way, it was tested if these groups significantly differ in wellbeing and mental health. Here, these results will be discussed for mental health. See Table 5 for an overview of the results. Interestingly, for mental health, neighbourhood income has a rather small, but significant effect on mental health of the residents of Rotterdam (b=0.000, p=.097). The other predictors do not significantly affect mental health. Looking at the individual-level predictors, perceived social cohesion (b=0.105, p<.001) and perceived green (b=0.026, p<.001) do have a significant effect on mental health. Results suggest that perceived social capital, on the other hand, does not affect mental health. Among the control variables, only cohabitation has no significant effect on mental health. Finally, looking at the dummy variable concerning ethnic groups, results show that native Dutch score significantly higher on the mental health scale than ethnic minorities (b=0.059, p<.001).

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4.3. Post-analyses checks As explained in section 3.6., this study uses VIF measures to test for multicollinearity. According to the main rule on VIF, the score cannot be higher than 10, since this is the threshold for strong multicollinearity. In this study, the VIF test is conducted two times, one time with wellbeing as dependent variable, and one time with mental health. The results of the VIF tests show no VIF score higher than 10. Therefore, the results suggest the criterium for multicollinearity is met. See Appendix 5 for an overview of the outcomes. Furthermore, it is explained earlier that Pearson correlations would be conducted to test for validity. Also, this study argued to follow the classification of Hinkle, Wiersma & Jurs (2003) in regard to the size of the correlation coefficient. The classification was as follows: 00 to .030 implies little if any correlation, .30 to .50 implies low correlation, .50 to .70 entails moderate correlation, .70 to .90 implies a high correlation, and .90 to 1.00 implies a very high correlation. The results of the correlations indicate a few variables that are highly correlated. First, the Pearson correlation coefficient between the neighbourhood-level predictors ethnic diversity and pollution/hassle is -.85. Also, ethnic diversity and the physical index are also highly correlated, with a coefficient of .-75. Although somewhat less correlated, ethnic diversity and neighbourhood income are also quite highly correlated, with a coefficient of -.65. Neighbourhood income and pollution/hassle are also highly correlated (.73), along with neighbourhood income and the physical index (.70), and pollution/hassle and physical index (.80). Besides these neighbourhood-level predictors being highly correlated, two other variable pairs have correlation sizes which stand out in comparison to the other correlations. First, mental health and wellbeing are moderately correlated (.61). Following the argumentation of Ettema & Schekkerman (2016) that mental health is often seen as equivalent for wellbeing, this correlation is rather obvious. However, it was also argued that both concept do differ in relation to neighbourhood-level characteristics and individual-level perceptions of the neighbourhood. Second, as mentioned earlier, perceived social cohesion and social capital are moderately correlated (.56). See Appendix 6 for an overview of all correlation coefficients.

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5. Discussion & Conclusion

5.1. Discussion of the findings This study provided some interesting insights in factors influencing individual wellbeing and mental health. The focus of this research was on the impact of one’s residential area and the perceptions of this area. Here, the findings of this study will be discussed in relation to the drawn up hypotheses. First, the findings for native Dutch on wellbeing and mental health will be elaborated and secondly, these findings will be discussed for ethnic minorities. Regarding the wellbeing of native Dutch, no evidence is found that supported the prediction that neighbourhood ethnic diversity positively affects wellbeing (H1). This also holds for mental health, where too no evidence is found that supports the above hypothesis. The findings indicate that for native Dutch residents, living in more ethnically diverse neighbourhoods does not seem to have an impact on their wellbeing and mental health. This is in line with findings of Abada, Hou, and Ram (2007) who’s results suggested that there is no effect of living in ethnic diverse neighbourhoods on (mental) health. Findings on neighbourhood deprivation show somewhat different results. The model that solely includes the neighbourhood-level predictors showed that both average neighbourhood income and the level of pollution and hassle in the neighbourhood affects the wellbeing of native Dutch. These findings suggest that native Dutch residing in neighbourhoods where the average income is higher, and where there is less pollution and hassle, have a better wellbeing, thus providing (partial) evidence for the second hypothesis (H2). The above findings are in line with findings of Letki (2008). She namely found that it was not neighbourhood ethnic diversity that influences individuals wellbeing, but rather neighbourhood deprivation. However, when neighbourhood perceptions and control variables were included, these findings disappeared and no evidence is found that living in less deprived neighbourhoods is beneficial for residents’ wellbeing (H2). Equal results are found on the impact of neighbourhood deprivation on mental health. This suggest that partial evidence is found for the impact of neighbourhood deprivation on wellbeing and mental health among native Dutch. This is in line with findings of Poortinga and colleagues (2008). Their results namely suggest that there is a negative effect of neighbourhood deprivation on individual health, but that this effect is substantially reduced when controlling for individual socioeconomic status. They also found individuals’ perceptions of the neighbourhood to also being an important indicator of individual health.

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Concerning such individual perceptions of the neighbourhood, findings in this study showed that perceptions of the neighbourhood social cohesion and green spaces positively affect both wellbeing and mental health of native Dutch. However, perceptions of neighbourhood social capital does not seem to have an impact on wellbeing and mental health. Therefore, there is evidence that partially confirms the prediction that more perceived social cohesion and social capital results in higher wellbeing and better mental health (H3). The results do provide enough evidence for the assumption that when residents perceive the physical environment of their neighbourhood more positively, their wellbeing and mental health will be better (H5). These findings are in line with the ideas of Leslie & Cerin (2008), who argued that perceptions of the physical environment may influence the level of satisfaction living in the neighbourhood, which in turn influences aspects of wellbeing and mental health, such as stress, depression, and anxiety. Furthermore, concerning social capital, Letki (2008) found that giving or receiving informal help from neighbours does not lead to individuals perceiving their neighbourhood more positively. This idea can be supported with the above findings. Separate from the above analyses, Sobel tests were conducted to test for the mediating mechanisms of perceived social cohesion and social capital. Results provided no evidence that the effect of more neighbourhood ethnic diversity, that leads to better wellbeing and mental health is mediated by more perceived social cohesion and social capital in the neighbourhood (H4). The contact theory served as theoretical mechanism for this prediction. However, the findings in this study correspond with the conflict theory, since the directions of the findings suggest that more ethnic diverse neighbourhoods leads to perceptions of less social cohesion and social capital in the neighbourhood. The argumentation of Putnam (2007) that ethnic diversity can lead to lower levels of (inter-ethnic) trust in a neighbourhood as well as to more out-group resentment could be supported with the findings of this study20. If there is more out- group resentment could not be concluded from the above findings. However, in line with Putnam, more ethnic diversity in neighbourhoods leads to less perceived neighbourhood social cohesion and social capital. Interestingly, results of these Sobel tests additionally find that neighbourhood ethnic diversity negatively influences wellbeing and mental health, again supporting the conflict theory. However, in the earlier analyses, no effect of ethnic diversity was found when other neighbourhood effects were included. Since here –where no other neighbourhood effects were included- an effect was found of ethnic diversity, and in the earlier analyses -with addition of neighbourhood deprivation indicators-, additional support is found

20 Trust was included as item for the scale of perceived social cohesion. 50 for Letki’s (2008) argumentation that it is rather neighbourhood deprivation than ethnic diversity influencing mental health.

The findings on neighbourhood effects on wellbeing and mental health of ethnic minorities are discussed here. Similar to native Dutch residents, there is no evidence that supports the prediction that neighbourhood ethnic diversity positively influences individual wellbeing and mental health (H1). This does not correspond with what is found in other studies. Furthermore, findings on neighbourhood deprivation provided only limited evidence for the prediction that living in less deprived neighbourhoods positively affects wellbeing and mental health of its residents (H2). For wellbeing, the model that only includes the neighbourhood-level predictors showed that average neighbourhood income has a slight positive impact on residents’ wellbeing. However, once individual-level predictors and control variables were included, this finding disappeared. For mental health, this finding remained. These findings could suggest that average neighbourhood income has somewhat stronger implications for mental health than for wellbeing. Concerning neighbourhood perceptions, evidence is found for both predictions that perceptions of more social cohesion and social capital in the neighbourhood and better perceptions of the neighbourhood physical environment positively influences wellbeing and mental health of ethnic minorities (H3, H5). Interestingly, the findings show that social capital does influence wellbeing of residents, but not mental health. The argumentation of Ettema & Schekkerman (2016) that subjective evaluations of the neighbourhood have a greater impact on wellbeing than on mental health is therefore to some extent applicable here. Overall, the importance of perceptions of the residential area on wellbeing and mental health, as mentioned by various scholars (e.g. Ettema & Schekkerman, 2016; Leslie & Cerin, 2008), can be supported with the findings of this study. Findings of the Sobel tests show that there is no evidence to suggest that perceived social cohesion and social capital serve as positive mediating mechanisms for the effect of neighbourhood ethnic diversity on wellbeing and mental health among ethnic minorities (H4). Equally to the findings for native Dutch, the direction of neighbourhood ethnic diversity does not correspond with what was predicted. My findings provide evidence for the conflict theory rather than the contact theory.

Comparing the outcomes for native Dutch and ethnic minorities, some similarities and differences are found. One similarity is visible in the mediation effects. For both native Dutch

51 and ethnic minorities, perceived social cohesion seemed to have a stronger negative mediating mechanism than social capital. Although this was not a prediction, this finding does provide interesting insights, since it serves as evidence for Putnam’s (2007) argumentation that there is a decrease in trust between neighbours in more ethnically diverse neighbourhoods. Furthermore, it seems that overall, the results suggest neighbourhood perceptions better explain wellbeing and mental health of native Dutch and ethnic minorities than objective neighbourhood-level characteristics. This is in line with what Ettema & Schekkerman (2016) found, namely that subjective evaluations of the neighbourhood have a greater impact on wellbeing than objective neighbourhood characteristics. They argue this could be due to reversed causality, where individuals with a high(er) wellbeing already perceive their living environment more positively. Focusing on these individual neighbourhood perceptions, some evidence is found in the results that suggest that these effects are stronger for ethnic minorities than for native Dutch. This is the for wellbeing, but not for mental health. Therefore, the findings suggest some evidence for the prediction that (neighbourhood effects and) the effect of neighbourhood perceptions on wellbeing and mental health are stronger for ethnic minorities than for native Dutch (H6). Besides these similarities, some differences are found. First, for native Dutch, there is no evidence that more social capital in a neighbourhood positively affects wellbeing and mental health. For ethnic minorities, the results suggest somewhat different, namely that social capital does affect their wellbeing, but not their mental health. A second striking difference is that, for native Dutch, the level of pollution and hassle in the neighbourhood affects the wellbeing as well as the mental health of residents. These results are not found for ethnic minorities. This is not in line with the predication that neighbourhood effects (and the effect of neighbourhood perceptions) on wellbeing and mental health are stronger for ethnic minorities than for native Dutch (H6).

5.2. Methodological considerations This study has provided some interesting insights in the role of neighbourhoods in wellbeing and mental health. The combination of a focus on wellbeing and mental health, ethnic diversity, and on both ‘objective’ neighbourhood-level characteristics and perceptions of the neighbourhood, distinguishes this study from and adds to previous literature on this topic. However, important is to go more in-depth on both strengths and weaknesses of this study. The first strength is that neighbourhoods were defined at the smallest spatial scale possible. It is argued that a smaller spatial scale may better affect the existence and strength of

52 neighbourhood effects on mental health (e.g. Veldhuizen et al., 2015). Another strength is the use of the Herfindahl index as measure for neighbourhood ethnic diversity. This index namely captures complete ethnic diversity in a neighbourhood. As mentioned earlier, Bolt & Van Kempen (2012) argued Dutch cities (and neighbourhoods) are ethnically diverse rather than ethnically concentrated like in the United States. Therefore, this index is a better fit than a measure for ethnic density (i.e. on the proportion of one ethnic group). Furthermore, some limitations of this study need to be clarified. First, the multilevel regression analyses indicated that 57 groups (i.e. neighbourhoods) were being tested. This implies that 2 neighbourhoods were not included in the analyses, since the original number of neighbourhoods was 59. This could seriously influence the outcomes of the analyses. It could be that this unobserved data has resulted in biased estimates for, for example, neighbourhood physical index, neighbourhood income, and neighbourhood pollution and hassle. The physical index, for example, did not have data on the neighbourhood ‘Rozenburg’. This could serve as one explanation why no results are found for neighbourhood physical index, and among ethnic minorities also neighbourhood pollution and hassle. Another explanation for the small (to no) neighbourhood effects could be that these indicators are highly correlated (see Appendix 6). Moreover, although including some factors that can influence differences between neighbourhood residents, individuals themselves could of course differ in various other respects that could be associated with wellbeing and mental health. Therefore, it is possible that there is an overestimation of the role of neighbourhood ethnic diversity and neighbourhood deprivation on wellbeing and mental health. Another limitation of this study is the fact that it was not possible to look at differences in neighbourhood effects between specific ethnic groups. This is due to the fact that some ethnicities were underrepresented in the sample. The total response in Rotterdam was around 38%. However, looking at the response rates for specific ethnic groups, the response rate was 47% among native Dutch, 27% among Turkish-Dutch, 24% among Moroccan-Dutch, and 36% among Surinamese-Dutch. Finally, separate factors were included for neighbourhood deprivation. However, following other scholars, a better fit would be the Index of Multiple Deprivation, (e.g. Lang et al., 2008). This index namely focuses on seven domains: income, health, employment, housing, education, crime, and living environment, and therefore provides a more comprehensive indicator for neighbourhood deprivation. The use of this index would be a recommendation for future research on this subject. In this way, results could be better

53 compared to results in previous literature. This study did, however, attempt to provide a more extensive conceptualization of neighbourhood deprivation, in comparison to other research on this topic. There was namely no consensus in existing literature on the conceptualization and operationalization of this concept.

5.3. Conclusion The aim of this study was to provide insights in the impact of neighbourhoods on individual wellbeing and mental health. It is argued that there is an increasing interest in improving individual wellbeing and mental health in Western European countries, because of their health implications and impact on daily functioning. Important is to look at the role of neighbourhoods in this sense, since neighbourhoods are explained to be ‘stages’ where interaction between individuals happens in various ways that can contribute to one’s wellbeing and mental health (Leyden et al., 2011). Additionally, it was argued that Western European neighbourhoods are becoming more and more ethnically diverse which could have implications for levels of satisfaction of living in such neighbourhoods, and in turn, for wellbeing and mental health (Veldhuizen et al., 2015). Therefore, research on the impact of (ethnic diverse) neighbourhoods on wellbeing and mental is of great importance. This study added to previous literature by combining the focus on neighbourhood ethnic diversity, on both wellbeing and mental health and on both neighbourhood effects and neighbourhood perceptions. The following research question served as starting point for this study: Focusing on Rotterdam: to what extent do neighbourhood effects and individual perceptions of the neighbourhood influence individual wellbeing and mental health of native Dutch residents and ethnic minority residents? In addition, several hypotheses were proposed. First, it was assumed that more neighbourhood ethnic diversity has beneficial implications for wellbeing and mental health. The contact theory was served as theoretical mechanism for the mediating effect of more perceived social cohesion and social capital of the positive effect of neighbourhood ethnic diversity on wellbeing and mental health. Also, it was assumed that the better the perception of the neighbourhood’s physical environment, the better the wellbeing and mental health of the residents. Letki (2008) furthermore stated in her article that it was not neighbourhood ethnic diversity, but rather neighbourhood deprivation that influences mental health, which led to the prediction that residing in less deprived neighbourhood is beneficial for wellbeing and mental health. Finally, it was assumed that there would be ethnic differences in neighbourhood effects (as well neighbourhood-level as individual perceptions) on wellbeing and mental health. The research question and drawn up hypotheses were tested through multilevel modelling, using 54 individual-level data from the Rotterdam Health monitor (2012), and neighbourhood-level data provided to me by the municipality of Rotterdam.

In general, this study finds that neighbourhood ethnic diversity does not influence wellbeing and mental health of its residents. Some aspects of neighbourhood deprivation seem to affect wellbeing and mental health of both native Dutch and ethnic minority residents. Therefore, it could be suggested that, in line with Letki (2008) it is neighbourhood deprivation influencing individual health rather than neighbourhood ethnic diversity. Moreover, this study suggests that neighbourhood perceptions have greater impact on wellbeing and mental health than neighbourhood-level characteristics. A greater focus on how subjective perceptions of the neighbourhood come about is therefore of great importance. I therefore suggest future scholars to go more in-depth on the role of neighbourhood perceptions. Furthermore, this study does not suggest that there are great differences in neighbourhood effects or perceptions of the neighbourhood on wellbeing and mental health between native Dutch and ethnic minorities. Interesting would be, however, to distinguish between various own-ethnic groups instead of taking the group ethnic minorities as a whole into consideration. Finally, this study supports the conflict theory rather than the initially assumed contact theory. Mediation analyses namely showed that neighbourhood ethnic diversity leads to worse perceptions of neighbourhood social cohesion and that neighbourhood ethnic diversity directly is disadvantageous for wellbeing and mental health, taking various individual-level characteristics into account. Interestingly, social capital did not seem to have an equally strong mediating effect as perceived social cohesion. Also, if neighbourhood perceptions are taken into consideration, in addition to neighbourhood- and individual-level characteristics, ethnic diversity does not influence wellbeing and mental health at all. Important for future research is therefore to get a better grip under what circumstances neighbourhood ethnic diversity does and does not influence wellbeing and mental health.

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APPENDIX 1 – RESULTS PCA, CRONBACH’S ALPHA, AND CORRELATIONS FOR ‘MENTAL HEALTH’ Principal Component Analysis

Principal components/correlation Number of obs = 13519 Number of comp. = 8 Trace = 8 Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 5.16023 4.43526 0.6450 0.6450 Comp2 .724971 .287952 0.0906 0.7357 Comp3 .437019 .0193359 0.0546 0.7903 Comp4 .417684 .0627514 0.0522 0.8425 Comp5 .354932 .00837253 0.0444 0.8869 Comp6 .34656 .0362826 0.0433 0.9302 Comp7 .310277 .0619522 0.0388 0.9690 Comp8 .248325 . 0.0310 1.0000

Principal components (eigenvectors) (blanks are abs(loading)<.3)

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Comp6 Comp7 Comp8 Unexplained

mentalh3 0.3365 0.3899 0.8442 0 mentalh4 0.3731 -0.8431 0 mentalh5 0.3507 0.4148 -0.3551 -0.6199 -0.3235 0 mentalh6 0.3311 0.5592 -0.3562 0.5383 0 mentalh7 0.3679 -0.5441 0.3721 0.5618 0 mentalh8 0.3519 -0.6810 0.5960 0 mentalh9 0.3736 0.4767 -0.7061 0 mentalh10 0.3408 -0.3740 0.6725 0.4639 0

Cronbach’s alpha Test scale = mean(unstandardized items)

Average interitem covariance: .4915457 Number of items in the scale: 8 Scale reliability coefficient: 0.9201 Validity Checks:

mentalh3 mentalh4 mentalh5 mentalh6 mentalh7 mentalh8 mentalh9 menta~10

mentalh3 1.0000 mentalh4 0.5872 1.0000 mentalh5 0.6080 0.6149 1.0000 mentalh6 0.5953 0.5564 0.6648 1.0000 mentalh7 0.5441 0.6870 0.5918 0.5106 1.0000 mentalh8 0.5412 0.6373 0.5729 0.5103 0.6442 1.0000 mentalh9 0.5650 0.6934 0.5632 0.5528 0.7325 0.6582 1.0000 mentalh10 0.4953 0.6408 0.5143 0.4763 0.6286 0.5620 0.6563 1.0000

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APPENDIX 2 – NEIGBHOURHOODS IN ROTTERDAM

Buurten Rotterdam Freq. Percent Cum.

1. C.S. Kwartier 385 2.73 2.73 2. 219 1.55 4.28 3. Cool Nieuwe Werk Dijkzigt 249 1.76 6.04 4. 156 1.11 7.15 5. Bospolder 126 0.89 8.04 6. Tussendijken 145 1.03 9.07 7. 149 1.06 10.13 8. 270 1.91 12.04 9. 186 1.32 13.36 10. Nieuw -Mathenesse Oud-Mathenesse Wit 139 0.98 14.34 11. 114 0.81 15.15 12. 354 2.51 17.66 13. Schieveen Zestienhoven Overschie La 386 2.74 20.39 14. 127 0.90 21.29 15. 136 0.96 22.26 16. 187 1.33 23.58 17. Blijdorp Blijdorpse polder 220 1.56 25.14 18. 275 1.95 27.09 19. 173 1.23 28.31 20. 354 2.51 30.82 21. -Zuid 194 1.37 32.20 22. Hillegersberg-Noord 267 1.89 34.09 2 3. Terbregge Molenlaankwartier 280 1.98 36.07 24. 178 1.26 37.33 25. Nieuw-Crooswijk Oud-Crooswijk 157 1.11 38.45 26. Kralingen-West 242 1.71 40.16 27 . Kralingen-Oost Kralingse-Bos 194 1.37 41.54 28. 143 1.01 42.55 29. 145 1.03 43.58 30. Kop van Zuid Kop van Zuid-Entrepot 142 1.01 44.58 31. 193 1.37 45.95 32. Bloemhof 207 1.47 47.42 33. 210 1.49 48.91 34. 142 1.01 49.91 35. 195 1.38 51.29 36. Feijenoord 149 1.06 52.35 37. 171 1.21 53.56 38. Oud-IJsselmonde 139 0.98 54.55 39. 304 2.15 56.70 40. Groot-IJsselmonde 407 2.88 59.58 41. 154 1.09 60.67 42. Pernis 640 4.53 65.21 43. s-Gravenland Kralingseveer 184 1.30 66.51 44. 168 1.19 67.70 45. 184 1.30 69.01 46. 338 2.39 71.40 47. 203 1.44 72.84 48. 138 0.98 73.82 49. 142 1.01 74.82 50. Charlois Zuidrand Zuidplein Zuiderp 198 1.40 76.23 51. 204 1.45 77.67 52. Carnisse 157 1.11 78.79 53. Zuidwijk 202 1.43 80.22 54. Oud-Charlois Heijplaat 171 1.21 81.43 55. 165 1.17 82.60 56. -Noord 276 1.96 84.55 57. Hoogvliet-Zuid 550 3.90 88.45 58. Strand en Duin Dorp Rijnpoort 753 5.34 93.79 64 59. Rozenburg 877 6.21 100.00

Total 14,113 100.00

. APPENDIX 3 – RESULTS PCA, CRONBACH’S ALPHA, AND CORRELATIONS FOR ‘SOCIAL COHESION’

Principal Component Analysis

Principal components/correlation Number of obs = 13069 Number of comp. = 5 Trace = 5 Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 2.95262 1.97592 0.5905 0.5905 Comp2 .9767 .516899 0.1953 0.7859 Comp3 .459801 .0604269 0.0920 0.8778 Comp4 .399374 .187873 0.0799 0.9577 Comp5 .211501 . 0.0423 1.0000

Principal components (eigenvectors)

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Unexplained

cohesion1_~c 0.4891 -0.3594 0.0278 0.4233 -0.6721 0 cohesion2_~c 0.4912 -0.3836 0.0761 0.2642 0.7321 0 cohesion3_~c 0.4792 -0.1681 -0.0040 -0.8556 -0.1004 0 cohesion4 0.3627 0.6412 0.6721 0.0745 -0.0041 0 cohesion5 0.3979 0.5332 -0.7360 0.1160 0.0470 0

Cronbach’s alpha

Test scale = mean(unstandardized items)

Average interitem covariance: .6611292 Number of items in the scale: 5 Scale reliability coefficient: 0.8205

Validity checks

co~1_rec co~2_rec co~3_rec cohesi~4 cohesi~5

cohesion1_~c 1.0000 cohesion2_~c 0.7856 1.0000 cohesion3_~c 0.6206 0.6520 1.0000 cohesion4 0.3206 0.3166 0.3813 1.0000 cohesion5 0.3910 0.3711 0.4361 0.5360 1.0000

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APPENDIX 4 – RESULTS PCA, CRONBACH’S ALPHA, AND CORRELATIONS FOR ‘SOCIAL CAPITAL’

Principal Component Analysis

Principal components/correlation Number of obs = 6754 Number of comp. = 5 Trace = 5 Rotation: (unrotated = principal) Rho = 1.0000

Component Eigenvalue Difference Proportion Cumulative

Comp1 2.41442 1.46373 0.4829 0.4829 Comp2 .950685 .236591 0.1901 0.6730 Comp3 .714094 .131543 0.1428 0.8158 Comp4 .582551 .244298 0.1165 0.9323 Comp5 .338253 . 0.0677 1.0000

Principal components (eigenvectors)

Variable Comp1 Comp2 Comp3 Comp4 Comp5 Unexplained

capital1_rec 0.4920 -0.5048 -0.0752 0.0604 0.7027 0 capital2_rec 0.4960 -0.4808 -0.1535 -0.0499 -0.7048 0 capital3_rec 0.4125 0.2277 0.8163 0.3276 -0.0660 0 capital4_rec 0.4366 0.4342 -0.0212 -0.7844 0.0713 0 capital7_rec 0.3887 0.5231 -0.5514 0.5208 -0.0001 0

Cronbach’s alpha Test scale = mean(unstandardized items)

Average interitem covariance: .289393 Number of items in the scale: 5 Scale reliability coefficient: 0.6983

Validity checks

ca~1_rec ca~2_rec ca~3_rec ca~4_rec ca~7_rec

capital1_rec 1.0000 capital2_rec 0.6589 1.0000 capital3_rec 0.3328 0.3067 1.0000 capital4_rec 0.3008 0.3326 0.3652 1.0000 capital7_rec 0.2586 0.2717 0.2784 0.3960 1.0000

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APPENDIX 5 – VIF SCORES FOR WELLBEING AND MENTAL HEALTH

Wellbeing

Variable VIF 1/VIF

nevermarried 5.53 0.180968 weinig_ver~r 5.02 0.199015 married 4.00 0.250139 index_dive~y 3.90 0.256197 fysieke_in~x 3.22 0.310883 cohabit 2.78 0.360252 age 2.64 0.378471 BUURTINK 2.41 0.415265 divorced 2.00 0.499413 cohesion 1.60 0.625158 capital 1.56 0.641438 OPLEIDING~AG 1.44 0.693102 KAPITAAL8 1.44 0.693325 OPLEIDING_~N 1.39 0.721768 Native_Dutch 1.29 0.776392 green1 1.17 0.857243 not_discri~d 1.16 0.862831 female 1.03 0.967710

Mean VIF 2.42

Mental health

Variable VIF 1/VIF

nevermarried 5.44 0.183977 weinig_ver~r 5.03 0.198995 married 3.92 0.254887 index_dive~y 3.90 0.256522 fysieke_in~x 3.22 0.310083 OPLEIDING~OG 2.75 0.363117 cohabit 2.74 0.365271 age 2.63 0.380051 OPLEIDING_~N 2.57 0.389853 BUURTINK 2.41 0.414668 divorced 1.99 0.502885 cohesion 1.60 0.625087 capital 1.56 0.642032 KAPITAAL8 1.44 0.695434 Native_Dutch 1.29 0.773599 green1 1.16 0.858778 not_discri~d 1.16 0.862155 female 1.03 0.967174

Mean VIF 2.55

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APPENDIX 6 – PEARSON CORRELATION COEFFICIENTS

lifesat mental~h cohesion capital green1 index_~y BUURTINK weinig~r fysiek~x not_di~d married widowed divorced neverm~d cohabit female age OPLEI~AG OPLEID~N OPLEI~OG KAPITA~8 Native~h

lifesat 1.0000 mentalhealth 0.6078 1.0000 cohesion 0.1983 0.2000 1.0000 capital 0.1445 0.1128 0.5626 1.0000 green1 0.1578 0.1270 0.2733 0.2157 1.0000 index_dive~y -0.1079 -0.1135 -0.2629 -0.1992 -0.2567 1.0000 BUURTINK 0.1107 0.1270 0.2557 0.2182 0.2173 -0.6471 1.0000 weinig_ver~r 0.1176 0.1253 0.2809 0.2059 0.2517 -0.8460 0.7307 1.0000 fysieke_in~x 0.0983 0.1207 0.2353 0.1654 0.2635 -0.7505 0.6963 0.7983 1.0000 not_discri~d 0.1706 0.2084 0.1857 0.0896 0.1371 -0.1865 0.1469 0.1856 0.1779 1.0000 married 0.1239 0.0904 0.1501 0.2421 0.1086 -0.1278 0.1007 0.1195 0.0860 0.0628 1.0000 widowed -0.1034 -0.0748 0.0295 -0.0019 0.0654 -0.0540 -0.0139 0.0234 0.0314 0.0547 -0.2440 1.0000 divorced -0.1240 -0.1028 -0.0411 -0.0047 -0.0039 0.0608 -0.0500 -0.0564 -0.0458 -0.0295 -0.2507 -0.0925 1.0000 nevermarried -0.0321 -0.0191 -0.1532 -0.2397 -0.1438 0.1188 -0.0745 -0.1059 -0.0835 -0.1051 -0.5641 -0.2082 -0.2139 1.0000 cohabit 0.0574 0.0458 0.0057 -0.0142 -0.0087 0.0151 0.0112 0.0016 0.0041 0.0379 -0.2824 -0.1042 -0.1071 -0.2410 1.0000 female -0.0192 -0.1156 -0.0064 0.0061 0.0079 -0.0097 0.0058 0.0110 0.0040 -0.0090 -0.0832 0.1304 0.0271 -0.0145 0.0129 1.0000 age -0.0346 -0.0072 0.1483 0.1726 0.1825 -0.1482 0.0458 0.1129 0.1175 0.1458 0.3651 0.3876 0.1634 -0.5904 -0.1773 -0.0299 1.0000 OPLEIDING~AG -0.0984 -0.1557 -0.0617 -0.0277 -0.0323 0.1190 -0.1275 -0.1336 -0.1340 -0.0182 0.0539 0.1170 0.0607 -0.1120 -0.0750 0.0155 0.2042 1.0000 OPLEIDING_~N -0.0216 -0.0198 -0.0458 -0.0299 0.0308 -0.0780 -0.0529 0.0229 0.0073 -0.0146 -0.0098 0.0439 -0.0104 0.0593 -0.1036 0.0121 0.0117 -0.4462 1.0000 OPLEIDING~OG 0.0937 0.1327 0.0939 0.0523 -0.0105 -0.0001 0.1486 0.0704 0.0877 0.0289 -0.0278 -0.1312 -0.0319 0.0154 0.1662 -0.0242 -0.1584 -0.2282 -0.7695 1.0000 KAPITAAL8 0.0099 0.0199 0.0685 0.1103 0.0726 -0.1099 -0.0076 0.0579 0.0245 0.0792 0.2031 0.2125 -0.0045 -0.2163 -0.1787 0.0018 0.5078 0.1078 0.1268 -0.2148 1.0000 Native_Dutch 0.1093 0.1328 0.1346 0.1017 0.1157 -0.3157 0.2038 0.2886 0.2778 0.3206 0.0615 0.0971 -0.0381 -0.1238 0.0384 -0.0231 0.2248 -0.1193 0.0309 0.0514 0.1691 1.0000

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