Book Chapters

• Introduction

  The main aim of this dissertation is to improve our understanding of pathways and drivers of ethnic and socioeconomic neighborhood change over time. This dissertation specifically focuses on the following research questions: (1) What trajectories of neighborhood change can be identified over time? (2) To what extent can neighborhood change be explained by population change and housing stock characteristics?

  This dissertation contributes to the literature in two ways. First, there is an empirical gap in the literature on how typical the neighborhoods that change are, and the prevalence and extent of change across all neighborhoods (Tunstall, 2016; Zwiers et al., 2017). The literature has been dominated by the assumption that gentrification and decline are wide-spread developments that quickly transform cities. However, a small body of research argues that neighborhoods are rather ‘slothful’ and that significant neighborhood change is rare and may take several decades (Hulchanski, 2010; Meen et al., 2013; Tunstall, 2016). Prior studies on neighborhood change have been limited by a short-term perspective, often reducing neighborhood change to the difference between two points in time. The lack of longitudinal analyzes of neighborhood change is an important lacuna that affects urban planning and neighborhood policies. This dissertation aims to add to the literature on longitudinal studies on neighborhood change, both theoretically and methodologically. By analyzing neighborhood trajectories over longer periods of time, this dissertation provides insight into different trends over time and the prevalence and rate of change. The innovative methodologies used in this dissertation contribute to broadening the scope of statistical methods for the analysis of longitudinal neighborhood change. This dissertation employs new visualization techniques to illustrate the various pathways of change. In addition, it uses advanced statistical models that allow for causal analysis and the identification of non-linear patterns of neighborhood change.

  Second, this dissertation focuses on several determinants of neighborhood change. In the literature, residential mobility has long been seen as the most important driver of neighborhood change. However, residential mobility is influenced by structural factors such as the quality of the housing stock, local housing markets, and government policy (Meen et al., 2013; Nygaard & Meen, 2011). This dissertation will show how the housing stock and urban restructuring programs shape patterns of residential mobility and neighborhood change. Moreover, this dissertation argues that residential mobility is not the sole driver of neighborhood change by also analyzing other types of population change, such as demographic change and in-situ change. An emerging body of research has argued that neighborhoods can change without any significant in- and out-migration (Bailey, 2012; Finney & Simpson, 2009; Teernstra, 2014). Processes of natural growth and ageing, as well as changes in the socioeconomic status of the sitting population, can change the population composition and status of neighborhoods. This dissertation contributes to a better understanding of the role of housing stock characteristics and population dynamics in shaping the spatial distribution of different ethnic and socioeconomic groups.

  The main aim of this dissertation is to improve our understanding of pathways and drivers of ethnic and socioeconomic neighborhood change over time. This dissertation specifically focuses on the following research questions: (1) What trajectories of neighborhood change can be identified over time? (2) To what extent can neighborhood change be explained by population change and housing stock characteristics?

  This dissertation contributes to the literature in two ways. First, there is an empirical gap in the literature on how typical the neighborhoods that change are, and the prevalence and extent of change across all neighborhoods (Tunstall, 2016; Zwiers et al., 2017). The literature has been dominated by the assumption that gentrification and decline are wide-spread developments that quickly transform cities. However, a small body of research argues that neighborhoods are rather ‘slothful’ and that significant neighborhood change is rare and may take several decades...

  The main aim of this dissertation is to improve our understanding of pathways and drivers of ethnic and socioeconomic neighborhood change over time. This dissertation specifically focuses on the following research questions: (1) What trajectories of neighborhood change can be identified over...

  Merle Zwiers

  31-42

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• The Global Financial Crisis and neighborhood decline

  The Global Financial Crisis (GFC), which started in 2008, has had a major impact on many Western European and North American countries. In the years preceding the crisis, many countries in the Global North experienced rising house prices, accompanied by an expansion of mortgage financing (Wachter, 2015). As the financial market has become increasingly global, the collapse of the subprime mortgage market and house price bubble in the United States (US) has had repercussions on a global scale (Martin, 2011). While there were significant differences between impacted countries in the timing and macroeconomic processes underlying the GFC, the characteristics of the subsequent economic recession have been similar: stagnating economic growth, a sovereign debt crisis, and rising unemployment (Aalbers, 2015). Many governments have responded to the declining economy and growing unemployment levels with the implementation of major budget cuts for social provisions (Peck, 2012). This has contributed to both relative and absolute growth in the number of economically disadvantaged households and has exacerbated poverty on both sides of the Atlantic. While the average income of the top 10% of the populations of OECD countries was essentially unaffected by the crisis, the average income of households in the lowest income decile experienced an annual decline of 2% between 2007 and 2010 (OECD, 2013a). In many countries, the GFC has also had a major impact on the housing market, evidenced by a large drop in home prices and declining sales of both existing and new-build housing (Van Der Heijden et al., 2011).

  Today, many countries are slowly recovering from the first shocks of the GFC and the economic recession that followed. However, in many Southern European countries, unemployment rates continue to be very high and, although unemployment is declining in places like the United States and Germany, long-term unemployment appears to be a persistent problem in many countries (OECD, 2014; Shierholz, 2014). Similarly, despite graudual stock market recoveries and some modest increases in house prices, repercussions from the GFC and economic recession persist in all countries. In many countries, the GFC has had predictable effects on the supply side of the housing market - the willingness of banks to lend money to prospective owners has generally declined. In some countries, investors in real estate became more selective, avoiding projects with too much risk; in the United States, in contrast, investors of another ilk have bought large numbers of foreclosed, real estate owned (REO) properties with the main goal of making a profit (e.g. Mallach, 2010b). Regeneration and restructuring initiatives have been put on hold throughout Western Europe (Boelhouwer & Priemus, 2014; Raco & Tasan-Kok, 2009; Schwartz, 2011). While some governments, such as the United Kingdom and the Netherlands, implemented stimulus programs to generate more (affordable) housing in the years after the crisis, recent budget cuts have put an end to these programs (Scanlon & Elsinga, 2014; Schwartz, 2011).

  The demand side of the housing market has also changed. Banks have tightened lending terms, making it more difficult for many households to obtain a mortgage (Goodman et al., 2015). As a result, there is more demand for private rentals and social or public housing. The GFC has affected employment on both sides of the Atlantic, in terms of either high unemployment levels or a shift toward more casualized labor contracts such as zero hour or temporary employment contracts (Aalbers, 2015; Puno & Thomas, 2010). This has led to financial strain and housing affordability problems for many households (JCHS, 2015). In the United States, households that are behind on their mortgage payments, and that are unable to obtain a mortgage modification with their lender, are faced with displacement due to foreclosure. This results in a large group of residents with badly damaged credit ratings who are in search of post-foreclosure housing in nearby areas (Martin, 2012). In other countries where the option of foreclosure is often not available, households that are unable to pay their rent or mortgage often have to move to cheaper dwellings and less attractive neighborhoods, while others have to stay put, because moving is too expensive or alternatives are not available, or because negative equity makes it impossible for them to move.

  All of these developments have contributed to rising inequality in the Global North, particularly in terms of income and housing (e.g. Immervoll et al., 2011; Bellman & Gerner, 2011). The GFC therefore raises questions about the future development of neighborhoods, especially because inequality tends to have specific spatial outcomes including increased segregation, increased spatial concentrations of low-income groups, and negative neighborhood effects (e.g., European Commission, 2010; Glaeser et al., 2009; Van Eijk, 2010; Zwiers & Koster, 2015). While there has been little research on the effects of the GFC at the neighborhood level, the evidence described above suggests that the effects are distributed unevenly across urban areas (Foster & Kleit, 2015;   Batson & Monnat, 2015). As households in the bottom income decile have experienced the sharpest drop in income, the effects of the GFC are likely to be felt most acutely in the most disadvantaged neighborhoods (see also Rugh & Massey, 2010; Thomas, 2013).

  In view of these concerns, this article sets out to identify factors that affect neighborhood decline in the aftermath of the GFC. Many economists have pointed to structural changes in national housing markets and to the changing role of states as important consequences of the GFC (e.g. Wachter, 2015), yet, few researchers analyze how these changes play out at the neighborhood level. Similarly, housing researchers have identified multiple drivers behind neighborhood decline, but many of them focus on within-neighborhood processes at the expense of developments at higher scale levels (Van Beckhoven et al., 2009). In this paper, we aim to bridge this gap by presenting 10 hypotheses on how changes at different geographical scales affect neighborhood decline. Our goal is not to create the next ideal-type model of neighborhood decline processes; rather, we seek to further the intellectual debate on neighborhood decline call for more research on the spatial consequences of the GFC, specifically on neighborhoods as an important territorial dimension of increasing inequality.

  Our hypotheses mainly pertain to the Global North. Although these countries have very different political, economic, and social structures, research on neighborhood change in different contexts in the Global North has often led to broadly similar findings. This suggests that many of the underlying processes of neighborhood change are comparable across countries. In the same vein, the increasingly global nature of financial and housing markets (Aalbers, 2015) yields similarities in the effects of the GFC and the economic recession between countries. However, the effects of the GFC are mediated by national policies, local (housing market) circumstances, and intra-neighborhood processes, meaning that the GFC has different outcomes in different places.

  The next section of this article presents a short discussion of definitions of neighborhoods and neighborhood decline. We then highlight important elements from existing studies to formulate 10 hypotheses about the effects of the GFC and the economic recession on neighborhood decline. These hypotheses are divided over three sections, each with a different geographical focus. The conclusion brings our arguments together and calls for more contextualized longitudinal research. 

  The Global Financial Crisis (GFC), which started in 2008, has had a major impact on many Western European and North American countries. In the years preceding the crisis, many countries in the Global North experienced rising house prices, accompanied by an expansion of mortgage financing (Wachter, 2015). As the financial market has become increasingly global, the collapse of the subprime mortgage market and house price bubble in the United States (US) has had repercussions on a global scale (Martin, 2011). While there were significant differences between impacted countries in the timing and macroeconomic processes underlying the GFC, the characteristics of the subsequent economic recession have been similar: stagnating economic growth, a sovereign debt crisis, and rising unemployment (Aalbers, 2015). Many governments have responded to the declining economy and growing unemployment levels with the implementation of major budget cuts for social provisions (Peck, 2012). This has...

  The Global Financial Crisis (GFC), which started in 2008, has had a major impact on many Western European and North American countries. In the years preceding the crisis, many countries in the Global North experienced rising house prices, accompanied by an expansion of mortgage financing...

  Merle Zwiers, Gideon Bolt, Maarten van Ham, Ronald van Kempen

  43-60

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• The path-dependency of lowincome neighborhood trajectories: An approach for analyzing neighborhood change

  Socio-spatial polarization is increasing in large cities throughout Europe (Tammaru et al., 2016). Socio-spatial polarization refers to the process where the gap between the rich and the poor is increasing, which is translated into spatial segregation along ethnic or socioeconomic lines. In the European context, this has resulted in distinctive spatial patterns in large cities where the rich are increasingly located in historic city centres, while the poor reside in the more disadvantaged outer-city neighborhoods (cf. Hulchanski, 2010; Van Eijk, 2010). Despite substantial government investments to counteract such socio-spatial polarization over the past few decades, this process seems to be persistent, though it varies over time and between places (Bailey, 2012).

  In most of the studies on socio-spatial polarization the continuous dynamic character of neighborhoods is neglected, reducing neighborhood change to comparing two points in time. However, neighborhoods are constantly changing in their population composition as the result of residential mobility and demographic events, thereby changing the aggregate status of neighborhoods. Many studies investigating neighborhood change focus on exceptional cases of gentrifying or declining neighborhoods (Bailey, 2012; Bailey et al., 2013; Bailey & Livingston, 2007; Clark et al., 2006; Finney, 2013; Hochstenbach & Van Gent, 2015; Jivraj, 2013; Van Ham et al., 2013). Although these studies have provided important insight in the drivers behind neighborhood change, they are typically limited to time-specific case-studies in particular cities. As a result, we do not know if neighborhoods with similar characteristics experience similar processes of change over time – or if processes of gentrification or downgrading are the exception to the rule. In addition, we have limited understanding of how processes of gentrification and downgrading affect other neighborhoods. As neighborhoods do not operate in a societal and policy vacuum, changes in one neighborhood are likely to affect other neighborhoods as well. It has, for example, been argued that processes of urban restructuring or gentrification are likely to lead to new concentrations of deprivation in other neighborhoods through the displacement of low-income groups (Bolt et al., 2009). As such, the upgrading of one neighborhood might go hand-in-hand with the deterioration of another neighborhood (Bråmå, 2013; Musterd & Ostendorf, 2005a).

  In addition, many studies in this field rely on percentile shifts and point-in-time measures to analyze change, neglecting the possibility that development over time might be more non-linear than linear or need much more time to take effect (see also Van Ham & Manley, 2012). Because the physical structure of neighborhoods hardly changes, neighborhoods can maintain their overall status over longer periods of time (Meen et al., 2013; Tunstall, 2016). However, selective mobility and demographic events lead to a constantly changing population composition (Van Ham et al., 2013). In this paper we argue that to fully understand processes of neighborhood change, the next step in neighborhood research is to focus on detailed neighborhood trajectories and to identify typologies of neighborhood change over longer periods of time. Analyzing interrelated neighborhood trajectories and understanding why some neighborhoods are more prone to change than others is therefore highly relevant to the debate on spatial manifestations of inequality and neighborhood development.

  In this paper, we present an approach for analyzing neighborhood change by focusing on long-term neighborhood change combined with a detailed analysis of neighborhood trajectories. Focusing on the trajectories of low-income neighborhoods in the Netherlands over the period 1971-2013, we analyze the role of physical characteristics in neighborhoods change. In the Dutch context, neighborhood and housing quality is often related to the debate on neighborhood change, however, few empirical studies try to analyze to what extent physical characteristics are related to today’s spatial patterns. Different starting positions in terms of housing quality can have long-lasting effects on neighborhood status through processes of path-dependency (Meen et al., 2013). In addition, because the Dutch government has invested heavily in urban restructuring by changing the share of owner-occupied and social-rented dwellings in particular neighborhoods, we analyze the effect of demolition and construction on the different neighborhood trajectories. Changes to the housing stock generate mobility processes and may thus affect neighborhoods in both direct and indirect ways.

  To analyze neighborhood trajectories we use a combination of methods. Sequence analysis allows for the analysis of complete pathways through time and is therefore a promising method for longitudinal neighborhood research. Sequence analysis is gaining popularity in the social sciences and is increasingly used by researchers interested in patterns of socio-spatial inequalities (e.g. Coulter & Van Ham, 2013; Hedman et al., 2015; Van Ham et al., 2014). However, sequence analysis is ultimately a descriptive method and its potential for explaining trajectories is limited. Researchers have therefore developed a methodological framework that combines sequence analysis and a treestructured discrepancy analysis, allowing for the analysis of the relationship between covariates and sequences (Studer et al., 2011). As such, this framework can provide insight in how different covariates affect neighborhood trajectories in different ways. To our knowledge, this paper offers the first empirical application of this combination in the field of urban research, constituting a new approach towards researching neighborhood dynamics and a move towards the visualization and analysis of complex trajectories. In this paper, we only highlight the most important aspects of the combination between sequence analysis and a tree-structured discrepancy analysis. Based on our presentation, researchers should be able to get a basic understanding of both methods (for a full understanding of sequence analysis researchers are referred to Gabadinho et al., 2011; for a tree-structured discrepancy analysis to Studer et al., 2011).

  The remainder of this paper is organized as follows. We start with expounding our approach for analyzing neighborhood change. We then move to describe the combination of sequence analysis and the tree-structured discrepancy analysis in more detail. In the data and method section, we elaborate on the structure of the dataset and the methodological choices made. We then discuss the substantive results and reflect on the applicability of the methods for neighborhood research.

  Socio-spatial polarization is increasing in large cities throughout Europe (Tammaru et al., 2016). Socio-spatial polarization refers to the process where the gap between the rich and the poor is increasing, which is translated into spatial segregation along ethnic or socioeconomic lines. In the European context, this has resulted in distinctive spatial patterns in large cities where the rich are increasingly located in historic city centres, while the poor reside in the more disadvantaged outer-city neighborhoods (cf. Hulchanski, 2010; Van Eijk, 2010). Despite substantial government investments to counteract such socio-spatial polarization over the past few decades, this process seems to be persistent, though it varies over time and between places (Bailey, 2012).

  In most of the studies on socio-spatial polarization the continuous dynamic character of neighborhoods is neglected, reducing neighborhood change to comparing two points in time. However, neighborhoods are...

  Socio-spatial polarization is increasing in large cities throughout Europe (Tammaru et al., 2016). Socio-spatial polarization refers to the process where the gap between the rich and the poor is increasing, which is translated into spatial segregation along ethnic or socioeconomic lines. In...

  Merle Zwiers, Reinout Kleinhans, Maarten van Ham

  61-78

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• The effects of physical restructuring on the socioeconomic status of neighborhoods: Selective migration and upgrading

  Many European and North American governments have a long tradition of urban restructuring programs to regenerate deprived neighborhoods. The combination of lowquality housing and a variety of socioeconomic problems, such as high crime rates and high unemployment rates, was thought to negatively affect the larger urban area and its residents. On the city level, concentrations of poverty were considered to be detrimental to the economic prosperity of urban regions by reducing the attractiveness of the area to businesses and higher income groups. On the individual level, neighborhood deprivation was thought to have a negative impact on the individual life chances of residents through a lack of network resources and negative role models. Urban restructuring policies therefore aimed to break up concentrations of poverty and to counteract negative neighborhood effects by changing the spatial distribution of disadvantaged residents (VROM, 1997).

  In many European countries, the main tool of urban restructuring was housing diversification. Through the demolition or sales of low-quality social housing and the construction of more expensive owner-occupied or private-rented dwellings, policymakers aimed to create a socioeconomic mix of residents in deprived neighborhoods. The in-migration of middle- and high-class households in these neighborhoods was thought to lead to a process of socioeconomic upgrading (Kleinhans, 2004). It was implicitly assumed that these middle- and higher income groups would act as role models and network resources for the original residents, thereby improving theirindividual life chances (Andersson & Musterd, 2005). The socioeconomic upgrading of previously deprived neighborhoods was also thought to have positive spillover effects on nearby neighborhoods, by improving the housing market position, reputation, and attractiveness of the larger geographical area (cf. Deng, 2011; Ellen & Voicu, 2006).

  Many scholars have since been critical about urban restructuring. Some have criticized urban restructuring policies for being a form of state-led gentrification (Uitermark & Bosker, 2014). Similar to other processes of gentrification, state-led gentrification arguably leads to displacement as the demolition and sales of social housing forces disadvantaged residents to relocate elsewhere (Boterman & Van Gent, 2014; Uitermark & Bosker, 2014). In addition, the construction of more expensive dwellings stimulates exclusionary displacement, making it financially difficult for low-income residents to move into the neighborhood (Boterman & Van Gent, 2014; Marcuse, 1986). Others have been critical about the effectiveness of urban restructuring in actually achieving neighborhood change (e.g. Lawless, 2011; Permentier et al., 2013; Tunstall, 2016; Wilson, 2013). It has been argued that although urban restructuring has led to a physical upgrading of neighborhoods and a diversified population composition as a result of selective migration, it has failed to improve the lives of disadvantaged residents and it did not lead to significant changes in the socioeconomic status of neighborhoods (cf. Bailey & Livingston, 2008; Jivraj, 2008; Permentier et al., 2013; Tunstall, 2016; Wilson, 2013). 

  The present study focuses on the extent to which urban restructuring has stimulated socioeconomic neighborhood change as a result of changes in the population composition in the 31 largest Dutch cities. While many studies have extensively analyzed the effects of urban restructuring on individual outcomes (e.g. Bolt & Van Kempen, 2010b; Manley et al., 2012; Miltenburg, 2017), it has been much more difficult to identify the effects of urban restructuring on area-based outcomes (Lawless, 2011). First, urban restructuring programs were both people-based and place-based programs that entailed a number of different interventions over time that also differed between neighborhoods and cities in size and scope. This implies that it has been difficult to ‘measure’ urban restructuring and to identify control neighborhoods with similar socioeconomic characteristics that did not experience any urban restructuring (Lawless, 2011). The present study overcomes this limitation by focusing on the share of demolished and newly constructed dwellings as the main indicator of urban restructuring. We use propensity score matching to compare neighborhoods that experienced physical restructuring to neighborhoods with similar socioeconomic characteristics that did not, allowing us to analyze the causal effect of policy on socioeconomic neighborhood change.

  Second, many studies investigating the effects of physical restructuring have focused on relatively large administrative areas, which means that the effects have to be large to change the trajectory of the entire neighborhood. We therefore analyze neighborhood change on a relatively low spatial scale, i.e. 500 by 500 meter grids, which allows us to better capture the effects of very localized demolition and new construction.

  Third, research has shown that significant changes take time to have effect (Meen et al., 2013; Tunstall, 2016; Zwiers et al., 2017; Zwiers et al., 2018a). Prior studies on urban restructuring in the Netherlands have been limited by a relatively short-time perspective, ranging from one to six years (e.g. Permentier et al., 2013; Wittebrood & Van Dijk, 2007), while it is possible that the effects of physical restructuring will only be visible over a much longer period of time. We therefore focus on neighborhood change over a 15-year period, providing insight in the effects of physical change over and beyond the course of the restructuring programs and the extent to which restructured neighborhoods have been successful in maintaining and attracting middle- and highincome groups over time.

  This study focuses on neighborhood socioeconomic change in the 31 largest Dutch cities between 1999 and 2013. We compare changes in the median neighborhood income between restructured neighborhoods, control neighborhoods, adjacent neighborhoods, and all other neighborhoods. We find that restructured neighborhoods have experienced the highest increase in the median neighborhood income. We analyze to what extent these changes can be explained by a changed population composition or neighborhood change in-situ. Changes to the housing stock as a result of urban restructuring seems to attract and maintain middle- and high-income households in previously deprived neighborhoods. However, these effects are very local and do not extent to adjacent neighborhoods. These findings contribute to our understanding of long-term neighborhood change and illustrate that large-scale shocks such as physical restructuring can change the trajectory of a neighborhood.

   

   

   

  Many European and North American governments have a long tradition of urban restructuring programs to regenerate deprived neighborhoods. The combination of lowquality housing and a variety of socioeconomic problems, such as high crime rates and high unemployment rates, was thought to negatively affect the larger urban area and its residents. On the city level, concentrations of poverty were considered to be detrimental to the economic prosperity of urban regions by reducing the attractiveness of the area to businesses and higher income groups. On the individual level, neighborhood deprivation was thought to have a negative impact on the individual life chances of residents through a lack of network resources and negative role models. Urban restructuring policies therefore aimed to break up concentrations of poverty and to counteract negative neighborhood effects by changing the spatial distribution of disadvantaged residents (VROM, 1997).

  In many European countries, the...

  Many European and North American governments have a long tradition of urban restructuring programs to regenerate deprived neighborhoods. The combination of lowquality housing and a variety of socioeconomic problems, such as high crime rates and high unemployment rates, was thought to...

  Merle Zwiers, Marteen van Ham, Reinout Kleinhans

  79-94

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• Trajectories of ethnic neighborhood change: Spatial patterns of increasing ethnic diversity

  The share of ethnic minority residents has been increasing in many major European cities during the past two decades and these cities are experiencing increasing ethnic diversity (Vertovec, 2007). For example: In 1999, non-western ethnic minorities, such as Turks, Moroccans, Antilleans, and Surinamese, comprised 8.5% of the Dutch population. By 2015, the share of the same groups had increased to 12.1%, which, in absolute numbers, means that the number of ethnic minorities in the Netherlands has increased by almost 700,000 people in 16 years (Statistics Netherlands, 2017). About 62.5% of this increase in the number of ethnic minorities is the result of natural growth (Statistics Netherlands, 2017). Geographically, members of ethnic minorities tend to be overrepresented in large cities because of the services and the availability of affordable housing (cf. Borjas, 1999) and the presence of immigrant networks (Logan et al., 2002). Studies on ethnic segregation have focused on the question of how ethnic minorities are sorting into different neighborhoods in these cities and to what extent they live together or apart from the native population (e.g. Bolt & Van Kempen, 2010a; Johnston et al., 2009; 2010; Poulsen et al., 2011). Although segregation is most often viewed as a condition of neighborhoods and cities at a certain point in time, ethnic segregation is not a static phenomenon but is a dynamic process that develops through time without a specific end point (Johnston et al., 2010). An emerging body of research is therefore focused on investigating segregation from the perspective of the changing ethnic population composition in neighborhoods (e.g. Johnston et al., 2009; Poulsen et al., 2011). Analyzing what types of neighborhoods experience change in the ethnic population composition and identifying the drivers of these changes is crucial to our understanding of processes of ethnic segregation.

  There are two main drivers of ethnic neighborhood change. The first is residential mobility. The selective moving behavior of different ethnic groups can affect ethnic neighborhood change in different ways. Studies on segregation have argued that ethnic heterogeneity in neighborhoods stimulates the out-mobility of the native (majority) population to more White neighborhoods (e.g. Clark & Coulter, 2015; Kaufmann & Harris, 2015). ‘White avoidance’ theories, however, argue that the native population avoids ethnically diverse areas in the first place (Clark, 1992; Quillian, 2002). In both cases, the moving behavior of the native population affects the ethnic population composition in neighborhoods. With regards to the residential mobility of ethnic minorities, studies on spatial assimilation have argued that as ethnic minorities become more assimilated into the host society over time, they tend to move away from concentration areas developing similar residential mobility patterns as the native population (Bolt & Van Kempen, 2010a; Sabater, 2010; Simpson & Finney, 2009; Simpson et al., 2008). However, there is evidence that indicates that ethnic minorities are less likely to leave and more likely to move into ethnically concentrated neighborhoods (e.g. Bolt & Van Kempen, 2010a), as a result of a lack of financial resources (Clark & Ledwith, 2007), institutional constraints (Galster, 1999; Musterd & De Winter, 1998), or specific ethnic preferences (Bolt et al., 2008).

  A small body of research highlights a second driver and has argued that ethnic neighborhood change is the result of both residential mobility and demographic change (Finney & Simpson, 2009; Simpson, 2004; 2007; Simpson & Finney, 2009). The share of ethnic minorities in a particular neighborhoods can change without residential mobility. Demographic events such as birth and deaths can influence ethnic neighborhood change in different ways. The relatively young age structure of many migrant groups often implies higher fertility rates when compared with the majority population (Finney & Simpson, 2009). When ethnic minorities have disproportionally more children than natives, the share of ethnic minorities in a neighborhood increases irrespective of mobility patterns. Similarly, higher mortality rates among the native population as a result of ageing might lead to high natural decline among natives, thereby reducing the share of the native population in a neighborhood (Finney & Simpson, 2009; Simpson & Finney, 2009).

  Residential mobility and demographic change are important drivers of ethnic neighborhood change, which affect ethnic segregation. In the context of growing ethnic diversity in many cities, it is important to question the extent to which this growth is evenly distributed over neighborhoods within these cities. Are there, for instance, particular neighborhoods that experience above average increases in their share of ethnic minorities, and if so, is this increase driven by selective sorting processes or natural growth? Or are ethnic minorities increasingly integrated, showing more variation in their residential mobility patterns over time? The present study aims to answer these questions by analyzing full trajectories of ethnic neighborhood change in the four largest cities in the Netherlands between 1999 and 2013. We employ a Latent Class Growth Model (LCGM) to categorize neighborhoods based on their unique growth trajectories of the ethnic population composition over time. This modelling strategy offers an empirical contribution to segregation research by categorizing patterns of ethnic neighborhood change, contributing to our understanding of diverging processes of ethnic segregation over time. Theoretically, this paper bridges two important fields of literature on the drivers behind ethnic segregation: residential mobility and natural growth. By integrating these theories, we seek to better understand the relative impact of both mechanisms on various levels of ethnic neighborhood change.

  The share of ethnic minority residents has been increasing in many major European cities during the past two decades and these cities are experiencing increasing ethnic diversity (Vertovec, 2007). For example: In 1999, non-western ethnic minorities, such as Turks, Moroccans, Antilleans, and Surinamese, comprised 8.5% of the Dutch population. By 2015, the share of the same groups had increased to 12.1%, which, in absolute numbers, means that the number of ethnic minorities in the Netherlands has increased by almost 700,000 people in 16 years (Statistics Netherlands, 2017). About 62.5% of this increase in the number of ethnic minorities is the result of natural growth (Statistics Netherlands, 2017). Geographically, members of ethnic minorities tend to be overrepresented in large cities because of the services and the availability of affordable housing (cf. Borjas, 1999) and the presence of immigrant networks (Logan et al., 2002). Studies on ethnic segregation have focused on the...

  The share of ethnic minority residents has been increasing in many major European cities during the past two decades and these cities are experiencing increasing ethnic diversity (Vertovec, 2007). For example: In 1999, non-western ethnic minorities, such as Turks, Moroccans, Antilleans, and...

  Merle Zwiers, Maarten van Ham, David Manley

  95-114

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• Intergenerational continuity of ethnic segregation: Socio-spatial assimilation of third generation immigrants in the Netherlands

  Ethnic segregation continues to be a persistent feature of Western European cities (e.g. Jivraj & Khan, 2015; Lymperopoulou & Finney, 2017; Zwiers et al., 2018a). Ethnic segregation is often understood as the result of a lack of socio-spatial assimilation and is thought to have hampering effects on social integration, mobility, and interethnic contact, thereby posing a threat to inclusive diverse societies (Kaplan & Douzet, 2011; Monkkonen & Zhang, 2013; Van Ham & Tammaru 2016). In 2016, the four largest ethnic groups in the Netherlands – Moroccans, Turks, Surinamese, and Antilleans – comprised 7.6% of the total population (Statistics Netherlands, 2017). This relatively small share of the population tends to be overrepresented in particular (deprived) neighborhoods where they comprise half of the population (Zwiers et al., 2018a). Patterns of ethnic segregation of these four ethnic groups have remained relatively stable in the Netherlands over the past few decades (Zwiers et al., 2018a). Ethnic segregation is often viewed as the result of a process of assimilation that develops over the course of generations (Peach, 1996). As the first generation tends to concentrate in particular neighborhoods after recent immigration, the second generation generally shows more spatial dispersal and movement to more mixed neighborhoods (e.g. Massey, 1985). Indeed, many studies confirm that second generation immigrants show more spatial mobility into non-concentration neighborhoods as a result of socioeconomic assimilation (e.g. Bolt & Van Kempen, 2010a; South et al., 2005). However, these findings only apply to a small share of immigrants, as most immigrants continue to lag behind in educational and labor market outcomes compared to the native population (Huijnk & Andriessen, 2016; Statistics Netherlands, 2016b). It has been argued that this lack of socioeconomic assimilation inhibits their socio-spatial mobility, explaining the persistence of ethnic concentration neighborhoods in the Netherlands (Bolt & Van Kempen 2010a).

  In the Dutch context, this idea of gradual social and economic assimilation over the course of generations is implicitly captured in the official definition of ethnicity (Kooiman et al., 2012). In the Netherlands, an individual is considered to be an ethnic minority when he/she has at least one parent abroad, distinguishing between those born abroad themselves (first generation) and those born in the Netherlands (second generation) (Statistics Netherlands, 2016a). According to this definition, third generation immigrants who are born in the Netherlands and whose parents are both born in the Netherlands, but with one or more grandparents from an immigrant background, are defined as native Dutch (Statistics Netherlands, 2016c). Behind this definition of ethnic group membership lies the assumption that third generation immigrants are socially, economically, and culturally integrated into Dutch society. According to the spatial assimilation hypothesis, this would be reflected in spatial integration as well, meaning that the third generation would predominantly live in non-concentration and more ethnically mixed neighborhoods, leading to decreasing levels of ethnic segregation. However, to date, there are no studies that have analyzed the socio-spatial behavior and outcomes of third generation immigrants in the Netherlands.

  The official definition of ethnicity also has important empirical consequences. Because third generation individuals are not included as minority group members in the definition of ethnicity, they tend to ‘disappear’ in official statistics (cf. Kesler & Schwartzman, 2015). As a result, it is unclear how the residential mobility behavior of third generation immigrants will affect ethnic segregation. When second generation immigrants have children, the share of immigrants in a neighborhood will decrease as these children are officially defined as native Dutch. In addition, when third generation immigrants move into ethnic concentration neighborhoods, statistically, this would be interpreted as an inflow native Dutch, decreasing the share of immigrants in a neighborhood. Third generation immigrants might, however, still be very different from the native Dutch population in cultural, social, and economic terms. Neighborhoods with high shares of third generation immigrants might be considered as ethnically diverse - or even ethnic concentration - neighborhoods by other residents, thereby affecting the neighborhood preferences and/or residential mobility behavior of other ethnic groups (cf. Schelling, 1971). Processes of ‘White flight’ or ‘White avoidance’ in response to the residential mobility behavior of third generation immigrants might have additional effects on ethnic segregation (Crowder & South, 2008; South & Crowder, 1998).

  The main aim of the present study is to explore the extent to which the definition of ethnicity influences conclusions about ethnic segregation by focusing on the residential patterns of third generation immigrants in the 31 largest Dutch cities between 1999 and 2013. The analysis consists of two parts: first, aggregate statistics of the share of third generation immigrants in different types of neighborhoods are analyzed which shows that ethnic concentration neighborhoods will most likely see the largest increase in the share of third generation immigrants over time. Second, I focused on third generation home-leavers and their spatial mobility behavior which contributes to our understanding of intergenerational processes of socio-spatial assimilation. My findings show that third generation immigrants continue to be overrepresented in ethnic concentration neighborhoods which raises questions about the assumed unidirectional process of socio-spatial assimilation. Ethnic segregation seems to be the continuing trend among third generation immigrants. The official definition of ethnicity in the Netherlands, which assumes socio-spatial assimilation, seems to mask the persistent intergenerational continuity of ethnic segregation.

  Ethnic segregation continues to be a persistent feature of Western European cities (e.g. Jivraj & Khan, 2015; Lymperopoulou & Finney, 2017; Zwiers et al., 2018a). Ethnic segregation is often understood as the result of a lack of socio-spatial assimilation and is thought to have hampering effects on social integration, mobility, and interethnic contact, thereby posing a threat to inclusive diverse societies (Kaplan & Douzet, 2011; Monkkonen & Zhang, 2013; Van Ham & Tammaru 2016). In 2016, the four largest ethnic groups in the Netherlands – Moroccans, Turks, Surinamese, and Antilleans – comprised 7.6% of the total population (Statistics Netherlands, 2017). This relatively small share of the population tends to be overrepresented in particular (deprived) neighborhoods where they comprise half of the population (Zwiers et al., 2018a). Patterns of ethnic segregation of these four ethnic groups have remained relatively stable in the Netherlands over the past few...

  Ethnic segregation continues to be a persistent feature of Western European cities (e.g. Jivraj & Khan, 2015; Lymperopoulou & Finney, 2017; Zwiers et al., 2018a). Ethnic segregation is often understood as the result of a lack of socio-spatial assimilation and is thought to have...

  Merle Zwiers

  115 - 130

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• Conclusion

  This dissertation consisted of five chapters, one theoretical and four empirical chapters. These chapters are complete research papers, each with their own research question, theoretical framework, empirical analyses, results and discussion section. All papers have been published in peer-reviewed journals or are currently under review. The main findings from these chapters are summarized below. Section 7.3 then reflects upon this dissertation’s main contributions to the literature and provides some suggestions for future research. The following section (7.4) discusses the limitations of this dissertation. Section 7.5 concludes with a discussion of the policy implications.

  This dissertation consisted of five chapters, one theoretical and four empirical chapters. These chapters are complete research papers, each with their own research question, theoretical framework, empirical analyses, results and discussion section. All papers have been published in peer-reviewed journals or are currently under review. The main findings from these chapters are summarized below. Section 7.3 then reflects upon this dissertation’s main contributions to the literature and provides some suggestions for future research. The following section (7.4) discusses the limitations of this dissertation. Section 7.5 concludes with a discussion of the policy implications.

  This dissertation consisted of five chapters, one theoretical and four empirical chapters. These chapters are complete research papers, each with their own research question, theoretical framework, empirical analyses, results and discussion section. All papers have been published in...

  Merle Zwiers

  131-146

  • Download PDF