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

Trajectories of neighborhood change

Neighborhoods represent a scale at which inequalities are reflected in the unequal spatial distribution of ethnic and income groups across urban space. In many cities, the rich reside in high-quality neighborhoods in favorable locations while the poor are concentrated in disadvantaged areas (Hulchanski, 2010; Van Eijk, 2010). However, neighborhoods are not static entities and spatial patterns of socioeconomic and ethnic inequality shift over time as a result of processes of neighborhood change. Neighborhoods can develop in different ways: (1) they can gentrify which is characterized by rising house prices and the replacement of lower income groups by higher income groups (Hochstenbach & Van Gent, 2015; Newman & Wyly, 2006; Slater, 2006); (2) neighborhoods can decline which is indicated by physical deterioration and declining house prices and the succession of higher income groups by lower income groups (Grigsby et al., 1987; Prak & Priemus, 1986; Van Beckhoven et al., 2009); (3) neighborhoods can remain stable in their population composition and/or overall status for longer periods of time (Meen et al., 2013; Tunstall, 2016). There are two empirical gaps in the literature on neighborhood change that this dissertation addresses. First, there has been a lack of longitudinal studies. Many studies on neighborhood change take on a relatively short-term perspective and reduce change to the difference between two points in time. While the literature has been dominated by case-studies on gentrification or decline, fuelling the assumption that gentrification and decline are widespread processes that quickly transform neighborhoods and cities, a growing body of research suggests that neighborhoods are rather ‘slothful’ and that neighborhood change takes time to take effect (Tunstall, 2016; Meen et al., 2013). Overall, we have little insight into the extent to which gentrification and decline are exceptional cases, in addition to, the prevalence and rate of change across all neighborhoods over time (cf. Tunstall, 2016). Second, residential mobility is often seen as the most important driver of neighborhood change. However, residential mobility is shaped by structural factors such as the housing stock, local housing markets, and government policy (Meen et al., 2013; Nygaard & Meen, 2011). Moreover, researchers have argued that residential mobility should be understood in relation to demographic and in-situ change, which can also play an important role in processes of neighborhood change (Bailey, 2012; Finney & Simpson, 2009; Teernstra, 2014). The relative impact of the housing stock and different population dynamics on neighborhood change has however received little attention in the literature to date. This dissertation contributes to the literature on longitudinal neighborhood change, both theoretically and methodologically. Theoretically, it provides insight into diverging pathways of neighborhood change over time, illustrating how different mechanisms interact to shape the urban geography along socioeconomic and ethnic lines. The path-dependent role of the housing stock is analyzed, in addition to the extent to which changes to the housing stock as a result of urban restructuring affect residential mobility and neighborhood change. Moreover, this dissertation investigates patterns of ethnic segregation over time and explores the relative impact of residential mobility and demographic change. Methodologically, this dissertation explores innovative methods for the analysis of neighborhood trajectories, broadening the scope of statistical methods for the field of neighborhood change research. This dissertation uses individual-level administrative data from the System of Social statistical Datasets (SSD) provided by Statistics Netherlands. The SSD contains longitudinal geocoded data on the full Dutch population, as well as information on the built environment. As such, the SSD allows for the analysis of the relationship between the housing stock and population change in processes of neighborhood change. Neighborhoods are operationalized using 500 by 500 meter grids, which are the most consistent low spatial scale over time. Three out of four chapters focused on the 1999 to 2013 time period, while chapter 3 covered the 1971 to 2013 period. This dissertation employed innovative methodologies to analyze trajectories of neighborhood change over time. Chapter 3 presents a combination of sequence analysis and a tree-structured discrepancy analysis that allows for the visualization of neighborhood pathways and its relation to their contexts. Chapter 5 uses a Latent Class Growth Model (LCGM) to categorize neighborhoods based on similarities in the timing and pace of change over time. Both methodologies have proven to be valuable tools for the identification of diverging neighborhood pathways over time. Summary of chapters This dissertation is comprised of five separate, but related papers. Chapter 2 presents a literature review of theories and studies on neighborhood decline. Chapters 3 to 6 are empirical research papers that have their own theoretical framework, empirical analyzes, results and discussion section. All papers have either been published in peer-reviewed journals or are currently under review. The content of chapters 2 to 6 is summarized below. The Global Financial Crisis and neighborhood decline Chapter 2 presents an overview of the literature and theories on the spatial consequences of the Global Financial Crisis (GFC). The impact of the GFC and the economic recession that followed is unevenly distributed between households and individuals, with low-income and vulnerable households being affected the most. As such, it can be expected that the consequences of the GFC are most pronounced in disadvantaged neighborhoods. While many studies have investigated the effects of the GFC on the economy and/or housing markets, only a few studies have focused on the unequal geographical impacts of the GFC (Batson & Monnat, 2015; Foster & Kleit, 2015). This chapter bridges two streams of literature by formulating ten ways in which the GFC might accelerate processes of neighborhood decline. The main goal of this chapter is to further the intellectual debate on neighborhood decline and to call for more longitudinal research on the ways in which the GFC has affected neighborhood trajectories and spatial patterns of increasing inequality. The path-dependency of low-income neighborhoods Chapter 3 presents an innovative longitudinal approach to analyzing neighborhood change and investigates the trajectories of low-income neighborhoods in the 31 largest cities in the Netherlands over the 1971 to 2013 period. Many studies on neighborhood change are limited by relatively short-term perspectives, and/or a focus on specific case-studies of gentrification or decline (e.g. Bailey, 2012; Jivraj, 2013; Hochstenbach & Van Gent, 2015). As such, it is unclear to what extent neighborhoods with similar characteristics experience the same process of change over time – or to what extent gentrification or decline are the exception to the rule. Using sequence analysis and a tree-structured discrepancy analysis, this chapter contributes to the literature by analyzing how housing stock characteristics shape neighborhood trajectories over longer periods of time. The results show that neighborhoods exhibit a high degree of path-dependency. Neighborhoods with high shares of social housing in 1971 display a pattern of increased poverty concentration and neighborhood decline over time. By way of contrast, increases in the share of owner-occupied housing contribute to more upward neighborhood trajectories. The effects of physical restructuring on neighborhoods Chapter 4 analyzes the effects of urban restructuring programs on neighborhood change in the 31 largest Dutch cities. Researchers have been critical about the effectiveness of urban restructuring in actually achieving upgrading neighborhoods (e.g. Lawless, 2011; Permentier et al., 2013; Tunstall, 2016; Wilson, 2013). However, many studies have been faced with methodological limitations with respect to measuring urban restructuring, spatial scale, and time periods. Chapter 4 overcomes these limitations by focusing on the effects demolition and new construction on a low spatial scale over a 15- year period. Using propensity score matching, this chapter finds a positive causal effect of demolition and new construction on neighborhood upgrading. The results indicate that large-scale demolition and new construction leads to socioeconomic upgrading of deprived neighborhoods as a result of attracting and maintaining middle- and high-income households. Urban restructuring appears to have negative spillover effects in terms of an increased share of low-income households in other neighborhoods. Trajectories of ethnic neighborhood change Chapter 5 focuses on trajectories of ethnic neighborhood change in the four largest Dutch cities, Amsterdam, Rotterdam, The Hague and Utrecht, between 1999 and 2013. As the share of ethnic minorities continues to grow in many cities, this raises concerns about increasing levels of ethnic segregation. The literature has been divided on the methods for analyzing ethnic segregation over time and many researchers have relied on single-number indices or typologies based on arbitrary thresholds (e.g. Duncan & Duncan, 1955; Johnston et al., 2010; Massey & Denton, 1993; Peach, 1996; Poulsen et al., 2001). Chapter 5 presents an innovative alternative for the identification of trends in the ethnic population composition over time. Using LCGMs, this chapter finds that neighborhoods show relative stability in the ethnic population composition over time, despite a substantial growth in the ethnic population. Although ethnic minorities are increasingly moving away from concentration neighborhoods, processes of natural growth play an important role in maintaining levels of ethnic segregation. Intergenerational continuity of ethnic segregation Chapter 6 investigates persistent patterns of ethnic segregation over the course of generations. In the literature, it is assumed that ethnic segregation will decrease over the course of generations as later generations will be more socially and economically integrated in society (e.g. Massey, 1985). This assumption is reflected in the official Dutch definition of ethnicity that classifies individuals whose parents are born in the Netherlands, but with one or more immigrant grandparents, as native Dutch. The use of this definition has important empirical consequences and influences conclusions about ethnic neighborhood change. Focusing on the residential patterns of third generation parental home-leavers in the 31 largest Dutch cities between 1999 and 2013, this chapter illustrates that third generation ethnic minorities continue to be overrepresented in ethnic concentration neighborhoods. The intergenerational continuity of socioeconomic disadvantage among ethnic minorities plays an important role in persistent ethnic segregation over time. Findings and conclusions The findings of this dissertation contribute to the field of neighborhood change research in four ways. First, this dissertation has demonstrated that neighborhoods tend to be relatively stable in their socioeconomic and ethnic status over time and that neighborhood change takes several decades to take effect. Second, this dissertation underlines the determining role of the housing stock in processes of neighborhood change. Neighborhoods exhibit a high degree of path-dependency where the initial quality of the built environment is reinforced over time. Chapter 3 has illustrated that the share of social housing is an important determinant of future processes of neighborhood decline. Changes to the housing stock, however, have the ability to alter the trajectories of neighborhoods. Chapter 4 has shown that large-scale demolition and new construction as a result of urban restructuring programs has led to neighborhood upgrading by attracting and maintaining higher income groups. Third, this dissertation has illustrated how different population dynamics interact to maintain the status quo. Chapter 5 and 6 have identified persistent patterns of ethnic segregation over time as a result of socioeconomic disadvantage among ethnic minorities which leads to high residential mobility rates into ethnic concentration neighborhoods. Although residential mobility is often seen as the most important driver of neighborhood change, this dissertation adds to the growing literature on the role of demographic change. The effects of ethnic residential mobility out of concentration neighborhoods on ethnic segregation are mitigated by processes of natural growth. Fourth, this dissertation has explored innovative methods for the analysis of longitudinal patterns of neighborhood change. Sequence analysis in combination with a tree-structured discrepancy analysis allows for a detailed analysis of neighborhood trajectories and the relationship with their contexts. LCGMs enable the identification of diverging neighborhood patterns of change based on timing and pace. Challenges and limitations Despite the contributions to the literature, this dissertation is also faced with several limitations, three of which are highlighted below. First, this dissertation has analyzed patterns of neighborhood change, but has not directly focused on gentrification. While some view urban restructuring as a form of state-led gentrification (e.g. Uitermark & Bosker, 2014), this dissertation sees urban restructuring as fundamentally different from more natural processes of gentrification. The term gentrification has become widely used (and abused) for a wide variety of different and, sometimes unrelated, processes leading to neighborhood upgrading. Future research would benefit from clearly defining gentrification and for analyzing gentrification over longer periods of time. Currently, we have very little insight in the prevalence, rate, and extent of gentrification across neighborhoods and cities and it is unclear to what extent its effects are temporary or long-lasting. Second, this dissertation has limited its focus on the four largest ethnic groups in the Netherlands. However, the spatial distribution of these four ethnic groups is likely to be related to the residential behavior and distribution of other ethnic groups in the Netherlands. Future research would benefit from comparing patterns of segregation across different ethnic groups and the ways they interact to shape the urban geography along ethnic lines. Third, the innovative methods employed in this dissertation enable the analysis of patterns of neighborhood change, however, they are not without limitations. Both methods allow for the identification of groups of neighborhoods that follow similar trajectories over time. However, these methods are faced with a degree of uncertainty around the true number of groups. In addition, a tree-structured discrepancy analysis uses the most significant values of the predictor variables as cut-off points, however, it is unclear to what extent these values can be interpreted as threshold values in processes of neighborhood change. Overall, these limitations reflect the nature of the modelling process and underlines the need to string theoretical reasoning beneath the models. Policy implications This dissertation has underlined the relative stability of neighborhoods over time. Policy makers should keep in mind that neighborhood change takes time to take effect, often exceeding standard policy time periods. Large-scale changes to the housing stock in the context of urban restructuring programs have the ability to generate neighborhood change by stimulating selective residential mobility. However, the positive effects of urban restructuring are limited to the restructured neighborhood. Other neighborhoods appear to suffer from negative spillover effects, illustrated by an increase in the share of low-income households as a result of displacement. The GFC has accelerated the shift towards the marketization of social housing. Some cities aim to stimulate gentrification through the sales of social housing which reduces the size and quality of the social housing stock. The spatial consequences of such policies are however unclear and may take time to take effect. Policy makers should be aware that reducing the size and quality of the social housing stock in large cities complicates the accessibility of cities for low-income groups and can have a major impact on the urban geography of cities and regions. This dissertation has found persistent patterns of ethnic segregation which can be explained by intergenerational ethnic disadvantage. The question remains to what extent spatial patterns of ethnic disadvantage should be targeted by urban (re)development. As studies have shown that ethnic socioeconomic mobility tends to lead to more residential opportunities and spatial dispersal, it could be more beneficial to invest in education and labor market participation. Last, this dissertation has illustrated that official definitions of ethnicity can influence empirical conclusions. Ethnic origin is based on the country of birth of the parents, however, this indicator ignores other aspects of ethnic origin. Later generations of ethnic minorities might still be characterized by other aspects of ethnic origin that play an important role in group inequalities. As society is becoming increasingly diverse, policy makers should be sensitive to ethnic differences and group inequalities that are not directly reflected in official statistics

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

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

Trajectories of neighborhood change

Neighborhoods represent a scale at which inequalities are reflected in the unequal spatial distribution of ethnic and income groups across urban space. In many cities, the rich reside in high-quality neighborhoods in favorable locations while the poor are concentrated in disadvantaged areas (Hulchanski, 2010; Van Eijk, 2010). However, neighborhoods are not static entities and spatial patterns of socioeconomic and ethnic inequality shift over time as a result of processes of neighborhood change. Neighborhoods can develop in different ways: (1) they can gentrify which is characterized by rising house prices and the replacement of lower income groups by higher income groups (Hochstenbach & Van Gent, 2015; Newman & Wyly, 2006; Slater, 2006); (2) neighborhoods can decline which is indicated by physical deterioration and declining house prices and the succession of higher income groups by lower income groups (Grigsby et al., 1987; Prak & Priemus, 1986; Van Beckhoven et al., 2009); (3) neighborhoods can remain stable in their population composition and/or overall status for longer periods of time (Meen et al., 2013; Tunstall, 2016). There are two empirical gaps in the literature on neighborhood change that this dissertation addresses. First, there has been a lack of longitudinal studies. Many studies on neighborhood change take on a relatively short-term perspective and reduce change to the difference between two points in time. While the literature has been dominated by case-studies on gentrification or decline, fuelling the assumption that gentrification and decline are widespread processes that quickly transform neighborhoods and cities, a growing body of research suggests that neighborhoods are rather ‘slothful’ and that neighborhood change takes time to take effect (Tunstall, 2016; Meen et al., 2013). Overall, we have little insight into the extent to which gentrification and decline are exceptional cases, in addition to, the prevalence and rate of change across all neighborhoods over time (cf. Tunstall, 2016). Second, residential mobility is often seen as the most important driver of neighborhood change. However, residential mobility is shaped by structural factors such as the housing stock, local housing markets, and government policy (Meen et al., 2013; Nygaard & Meen, 2011). Moreover, researchers have argued that residential mobility should be understood in relation to demographic and in-situ change, which can also play an important role in processes of neighborhood change (Bailey, 2012; Finney & Simpson, 2009; Teernstra, 2014). The relative impact of the housing stock and differentpopulation dynamics on neighborhood change has however received little attention in the literature to date. This dissertation contributes to the literature on longitudinal neighborhood change, both theoretically and methodologically. Theoretically, it provides insight into diverging pathways of neighborhood change over time, illustrating how different mechanisms interact to shape the urban geography along socioeconomic and ethnic lines. The path-dependent role of the housing stock is analyzed, in addition to the extent to which changes to the housing stock as a result of urban restructuring affect residential mobility and neighborhood change. Moreover, this dissertation investigates patterns of ethnic segregation over time and explores the relative impact of residential mobility and demographic change. Methodologically, this dissertation explores innovative methods for the analysis of neighborhood trajectories, broadening the scope of statistical methods for the field of neighborhood change research. This dissertation uses individual-level administrative data from the System of Social statistical Datasets (SSD) provided by Statistics Netherlands. The SSD contains longitudinal geocoded data on the full Dutch population, as well as information on the built environment. As such, the SSD allows for the analysis of the relationship between the housing stock and population change in processes of neighborhood change. Neighborhoods are operationalized using 500 by 500 meter grids, which are the most consistent low spatial scale over time. Three out of four chapters focused on the 1999 to 2013 time period, while chapter 3 covered the 1971 to 2013 period. This dissertation employed innovative methodologies to analyze trajectories of neighborhood change over time. Chapter 3 presents a combination of sequence analysis and a tree-structured discrepancy analysis that allows for the visualization of neighborhood pathways and its relation to their contexts. Chapter 5 uses a Latent Class Growth Model (LCGM) to categorize neighborhoods based on similarities in the timing and pace of change over time. Both methodologies have proven to be valuable tools for the identification of diverging neighborhood pathways over time

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

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 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.&nbsp

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

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013) / ERC Grant Agreement n. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects).In the last few decades, many governments have implemented urban restructuring programmes with the main goal of combating a variety of socioeconomic problems in deprived neighbourhoods. The main instrument of restructuring has been housing diversification and tenure mixing. The demolition of low-quality (social) housing and the construction of owner-occupied or private rented dwellings was expected to change the population composition of deprived neighbourhoods through the in-migration of middle- and high-income households. Many studies have been critical with regard to the success of such policies in actually upgrading neighbourhoods. Using data from the 31 largest Dutch cities for the 1999 to 2013 period, this study contributes to the literature by investigating the effects of large-scale demolition and new construction on neighbourhood income developments on a low spatial scale. We use propensity score matching to isolate the direct effects of policy by comparing restructured neighbourhoods with a set of control neighbourhoods with low demolition rates, but with similar socioeconomic characteristics. The results indicate that large-scale demolition leads to socioeconomic upgrading of deprived neighbourhoods as a result of attracting and maintaining middle- and high-income households. We find no evidence of spillover effects to nearby neighbourhoods, suggesting that physical restructuring only has very local effects.Publisher PDFPeer reviewe