The following proposals are listed in the order received. Only the affiliation of the principal investigator is indicated.
We intend to use the data made available through the data challenge on integration of migrants in cities calculating global and local segregation indices for EU and non-EU citizens and for selected countries of origin at the level of municipalities and, if possible, local labour market areas. We intend to analyse only areas that assure significant results. Obviously reference will be made to the existing literature of comparing levels of segregation between metropolitan areas of the same country and between countries, as well as regarding the segregation of immigrants in the countries under study.
The calculation of segregation indices will refer to the approach of Reardon and Sullivan (2004). In particular, consideration will be given to one group, two group and multigroup global and local spatial segregation indices relating to the dimension of spatial evenness (or spatial clustering).
The data made available will allow a comparable analysis of territories and countries of origin, leading to two distinct approaches:
In the first approach segregation indices will be compared according to selected countries of origin (country of citizenship or birth) to analyse if they are similar comparing different countries and urban settings (population size of the areas considered). This analysis will allow us to grasp similarities as well as territorial specificities of the single immigrant communities, and to highlight the effects ascribable to individual foreign communities and the effects linked to the context like the country and the metropolitan area. The focus of this approach would be specifically on the patterns of origin countries within the EU like Romania, Poland, Italy, Portugal, Ireland and UK, and outside of the EU like Albania, Turkey, Ukraine, Morocco, China, India, Peru etc..
In a further approach we intend to analyse possible socio-demographic and socio-economic factors that influence the segregation observed in the metropolitan areas (municipalities or labour market areas). The additional socio-demographic and socio-economic information is derived from data of the 2011 population census round and will comprise information on the demographic structure (gender, age, foreign population), level of education, information regarding economic activity and labour markets. This analysis will also allow to assess the importance of national factors and particularities, like specific policies regarding integration or public housing, compared to local factors.
Based on the results obtained we intend to analyse a selected number of metropolitan areas regarding the measures of segregation for single elements of the available grid. This in-depth analysis will further the understanding of the global and local measures of segregation applied.
The outcomes of the different approaches will allow us to formulate hypothesis regarding the role played by socio-economic differences and policies at the national, regional and local level that favour lower levels of segregation of the different foreign communities. Providing the instruments to identify specific situations (neighbourhoods) of higher segregation within metropolitan areas will also motivate local governments to formulate and implement policies for minorities and aiming at inclusion.
In this project, we intend to examine the correlation of distributed public transport networks and the dispersion of migrant populations in major cities. There is anecdotal evidence regarding the concentration and segregation of migrants in cheaper suburb areas at a local level. While most of these migrants find work in the service industry with areas with a higher population density the economic return for this type of labour does not always allow for the worked to reside in close proximity to where they work. Modern metropolitan spatial trends in employment and housing have shown an inversion of inner-city burrows in major cities from traditional centres for migrant workers to desirable areas for young digital creative workers with a large disposable income. This tread has corresponded with the miniaturisation and mobile nature of their work environment eliminating the traditional movements towards suburban areas closer to the traditional large out of town manufacturing belt. We want to create visual data descriptions using public transport connectivity and the population density of location of the migrant populations. The quantitive data provided in the derived data obtained through a spatial disaggregation of statistics of the 2011 Census, collected from National Statistical Institutes in all cities of eight EU MS (Spain, Germany, Italy, France, Netherlands, Portugal, UK, Ireland) used together with local transport maps and routes connecting workers with the traditional work centres can help build a picture of the normal commuting patterns of these communities. The data research along with studies various aspects of urban diversity, and typical rent values in specific neighbourhoods that show spatial patterns of residential segregation. The objective of visualising this data is to reveal patterns in the data and to bring out certain phenomena of difficult detection if we had to analyse only numerical data.
Using Mapzen's Transitland API to download transit schedule data and Processing with Unfolding Maps to create spatial-temporal visualizations. The Processing sketch will parse the data from D4I database as a CSV file while using the Unfolding Maps library to load map tiles and convert geolocations into screen positions, and movements from origin stop to destination stop using linear interpolation. This will allow us to see the correlation of transport links to the concentration and segregation of migrants outside of inner-city urban area.
Segregation continues to be an important issue as governments in Europe focus on how to integrate the large international flows of new immigrants. An important context for understanding and evaluating changing segregation, and whether and where integration is occurring, is using adequate statistical methods to measure segregation. This application for the EU micro data on migrants has both substantive and technical aims. Substantively, the study will provide a set of segregation measures across cities classified by their cultural settings and their varying sizes. Technically, our study will provide more sensitive measures of the distribution of segregation/integration and specifically how this varies across geographic scale. These approaches will provide new thinking about the scale and trajectory of immigrant integration.
Measuring and modeling patterns of segregation has been a principal activity of studies of residential separation in the urban mosaic. Both an extensive set of indices to measure levels of segregation, and analytic models of the processes whereby segregation comes about, have created an extensive literature on patterns of segregation and their evolution. These studies have included studies of whether or not new immigrants, who often cluster in their initial entry into EU Metropolitan areas, are following an earlier pattern of slow acculturation into the society at large. In other words, do they first cluster, but slowly merge into the larger urban environment and increase mixing across race and ethnicity.
The tools which have been used to examine the patterns of segregation are changing as the research community recognizes that the old administrative units commonly employed (usually tracts as surrogates for neighborhoods) are not sufficiently sensitive to the changing patterns of segregation. Now we can evaluate segregation across multiple scales from the very local to larger communities. We have the tools to draw new neighborhoods, to provide measures of contact and how they change from the local to the global.
A critical factor in the changing patterns of segregation, and the process of assimilation, is the size of new immigrant flows, their locational patterns and the extent to which they acculturate socially. Now with detailed data from EU sources it will be possible to investigate the patterns of segregation and the changes across space. The research with the D 41 data has two aims- to investigate the spatiality of the integration process – (1) how does segregation vary by scale, from macro to meso to micro level measures of isolation, are there concentrations at some scales and integration at others, and (2) how do the trajectories of segregation and integration vary across urban size and national contexts. How does integration/ segregation change when city size changes, how does integration/segregation change with the size of the migration populations and how does it change from one European context to another.
We aim to use the data provided in this challenge to study the relationship between immigrant concentration and ethnic segregation, on the one hand, and anti-migrant sentiment (expressed as electoral support to anti-immigration parties) on the other.
At a policy level, the outcome of the study could be used to support and more accurately target evidence-driven action to counter anti-migrant narratives. Ultimately, this could increase the likelihood of successful integration in cities.
Our study would focus on densely populated European cities. Preliminary analyses by Devillanova – studying electoral responses to immigration shocks in Milan, with collected micro-level data – show that, contrary to beliefs commonly held in the literature, cities are not exempt from immigration-driven political backlashes. Indeed, the presence of immigrants in Milan neighbourhoods tends to bolster the electoral performance of anti-migrant parties, similarly to what happens in less densely inhabited locales.
Our first aim is to test whether this “Milan effect” holds, and whether it is shared across other Italian and European cities. Sufficiently fine granularity is an essential data requirement for studying cities, whilst most available sources only provide aggregation at the NUTS-3 level. Using data on immigrant presence before the recent surge in asylum seekers can provide crucial insights to further transformations occurring in European cities today.
Secondly, we aim at exploring the reasons for such an anti-immigrant backlash. Among other reasons, anti-migrant sentiment may be due to economic motives or in-group / out-group dynamics. To study the former, for Italy we would be able to match the provided data with the registry of all house sales in 2011 (our data range from 2009 to 2012). Using sales prices as a proxy for average income in the area, it would be possible to test whether lower-income neighbourhoods show higher support for anti-migrant parties or, rather, if it is a change in income levels that drives resentment. For instance, relative deprivation theory would predict the latter to be higher in richer neighbourhoods.
Natives might also perceive some cultural / social threat associated with increased ethnic diversity. We aim at using the provided data to develop indexes of ethnic fractionalization to study non-economic motives for anti-migrant sentiment. Specifically, it has been shown that ethnic fractionalisation is associated with lower social capital, which in turn can spur bad governance, higher crime rates, and lower well-being in general. Fractionalisation indexes (accounting for the composition of migrant populations) could be matched with recognized measures of social capital (e.g. blood donations, volunteering, waste recycling rates, etc.). We could also test whether ethnic fractionalization in 2011 affects levels of social capital in later periods, and if so, how districts with low or decreasing social capital may be expected to react to a further “immigration shock”.
Importantly, we could make causal claims on the relationships between economic and/or social conditions and anti-migrant sentiment by relying on an instrumental variable approach such as in Boeri (2015), namely using the ratio of residential square meters per residential building in each of the sampled areas.
Labour market effects of migration might be different at a local, city-level than at aggregate level. By combining the data with information from labour surveys and Gallup’s World Poll, we can obtain descriptive analysis of local labour market changes with migration, and correlates with life satisfaction, jobs, unemployment, attitudes towards migrants. The results will open opportunities for further research, especially on the labour market impact of immigrants at the micro-scale.
Most EU member states face the challenge of integrating immigrants into their labor markets and, more generally, into their societies. This challenge is compounded by the diversity of immigrants’ backgrounds. As part of our research project Mercator Dialogue on Asylum and Migration (MEDAM), we aim to identify the drivers of successful social and economic integration of migrants and to develop research‐based solutions for immigration policies. In a first step, high‐resolution data on the spatial distribution of foreign nationals helps to assess the concentration and clustering of migrants across different origins and host communities. Visual comparisons between major European migrant destinations will showcase potential differences in national integration policies and similarities in patterns of settling decisions among individual communities. Subsequently, connecting survey data from the European Social Survey (ESS) as well as the German Socioeconomic Panel (SOEP) with the spatial concentration data via NUTS3‐regions will allow us to identify correlations between community diversity and various social integration outcomes. We will also pay particular attention to the social integration of the recent refugee migrants. The insights about the role of community diversity for the first integration results could be obtained by linking the spatial data with the recent surveys of refugee migrants (e.g. IAB‐BAMF‐SOEP Survey of Refugees in Germany, 2016). The highly disaggregated spatial data will allow us to perform sensitivity analyses regarding the spatial unit of choice when calculating community diversity, an issue often undermining the credibility of spatial economic research.
In a second part of the project, we focus on the labor market integration of immigrants. While differences between countries are mainly driven by institutional and macroeconomic variation, regional differences within European countries mostly stem from individual and local characteristics. According to existing research, the effect of ethnic communities on the labor market integration of immigrants is ambiguous. Ethnic communities may offer assistance in the job search or act as a labor market traps by limiting contact with the native population and lowering incentives to invest in human capital. To analyze the relationship between local ethnic communities and labor market integration empirically, we merge the spatial concentration data with the German SOEP (holding information on more than 3,500 individual migrants) and Social Security data from Italy (covering more than 14,000 migrants). Besides simply estimating the effect of a migrant’s community of co‐nationals on wages and employment rates, we also investigate the moderating influence of cultural distance between the native and migrant communities. We use linguistic proximity as a proxy for both cultural differences and a measure of integration barriers. We expect the ambiguity of the relationship between community size and labor market integration to vanish once linguistic distance is accounted for. Communities that are linguistically closer to the host community tend to advance integration while the opposite is true for linguistically distant communities.
The diversity of neighbourhoods by ethnicity and country of birth has been explored by multiple researchers, but there remain relatively few studies of the complex spatial patterns of diversity across several countries. Unpicking the residential geographies of migrant groups (referring here to first generation migrants) across numerous urban areas in several European countries will produce invaluable insights. The availability of comparable (within- and cross-country) measures of diversity and neighbourhood mixing could transform our understanding of the population geographies of urban areas in Europe, as well as evidence the outcomes of individual national policies. This paper will build on the investigators’ extensive experience of working with gridded population data for Britain (PopChange project), Northern Ireland, and South Africa, and in measuring segregation and diversity for small areas. The study will entail the construction of innovative local segregation indices (starting with dimensions of evenness and exposure for the majority group against each minority group and between minority groups), and measures of diversity (initially, the reciprocal diversity index, and measures of entropy). Segregation and diversity are a function of spatial scale and these measures will be derived using multiple neighbourhood definitions (for example, using distance decay functions with a range of bandwidth sizes). Variograms will also be estimated for each group in each city, and each set of cities by country, providing a concise summary of the spatial scale of concentrations of each group and sets of groups (using diversity scores for each grid cell). Grid cells of constant size (100m in this case) enable like-for-like comparisons between regions. This, combined with a multi-scale approach to measurement, will enable a systematic assessment of the spatial scales of residential segregation and diversity in each city in each study country. The diversity analyses will be conducted using the largest groups within each study country and, where possible, groups common to each study country. The study will address core questions such as: how do the geographies of individual groups relate to each other and how do these vary between cities and countries? What are the dominant patterns of residential segregation, and how do relative levels of segregation compare between cities? For which groups can these patterns be found? Which urban areas have the largest clusters of diversity? Code in the R programming language which implements the methods we use will be made freely available to allow other researchers to assess the approaches developed.
Migration in Europe is an issue of raising interest. Migrants in Europe distribute across countries and regions following non-random spatial patterns. This phenomenon, which might lead to increase polarization is called chain migration. Chain migration is generally related to the importance that migrants give to the ethnic network in the destination country. In this context, the aim of our study will be to detect statistically significant spatial clusters of migrants in Europe. We aim at analysing the location of the hot spot and cold spots by ethnic group and the existence of overlaps between them. Hot spots are areas where the particularly high concentration of an ethnic group is identified; cold spots are areas with a particularly low concentration of people belonging to a certain ethnic group. The output of the analysis will serve to identify where there is the highest and lowest spatial concentration by ethnic group, and if the ethnic groups are mutually exclusive or complementary. The policy implications of this research are related to the possibility of setting up dedicated place-based policies in each locations showing statistically higher concentration of migrants by ethnicity. Furthermore, with our results policy makers will be able to design policies for each location specific to the prevalent ethnic groups. The second step of our study will look at the spatial distribution of the segregation indices, identifying the main hot spots. The mentioned analysis will be performed through an exploratory spatial data analysis (ESDA), and specifically using a spatial statistic called local Moran. The local Moran allows to identify statistically significant cluster of high values (HH), cluster of low values (LL), outlier in which a high value is surrounded primarily by low values (HL), and outlier in which a low value is surrounded primarily by high values (LH).
- To get a clear empirical overview (including visualizations) focusing on 4 dimensions of migration-related diversity in cities
- Number of different groups of foreign-born origin
- Relative size of foreign-born groups
- Simpson’s Diversity Index, combining richness (number of groups) and equitability (relative size) in one measure.
- Degree of concentration/deconcentration between different areas within cities.
- To compare different configurations of migration-related diversity in different cities.
- To develop (inductively) a typology of configurations of urban diversity
This project contributes to a theoretical and empirical understanding of different types of migration related diversity in cities. The typology (for instance involving superdiverse cities, minority cities, pathway cities, divided cities, majority-minority cities, etc.) will be developed further in more in-depth research regarding the implications of different types of diversity.
- Mapping migration-related diversity at national level
- Map the number of different foreign-born groups, relative size and Simpson’s diversity index for all cities (local administrative units) in the data set.
- Visualize these 3 aspects of diversity using (country) maps.
- Mapping migration-related diversity at urban level
- Select all cities >80.000 from all 8 countries
- Map all aspects of migration-related diversity at the (census) area level in these cities, including the degree of concentration/deconcentration between areas.
- Visualize all 4 aspects of diversity using (city) maps.
- Identify and conceptualize types of urban diversity
- Factor analysis, plotting the relation between the four aspects of diversity (for all cities > 80.000)
- Based on the outcomes of the factor analysis, identify and conceptualize ‘clusters’ of cities in terms of ‘types of migration-related diversity.’
- Dissimilar case study design
- Choose 4 cities per type/cluster (leading to an estimated 16-25 cities)
- Qualitative desk analysis of available studies on these cities, with particular attention to:
- Migration histories
- Economic structures
- Political circumstances
- Urban governance responses Output
- Visualizations of the Simpson’s Diversity Index, the number of identity groups, the percentage of foreign-born population (at national level for all local administrative units, as well as within all cities >80.000), and of the degree of concentration/deconcentration between census areas (all cities over >80000).
- A typology of urban diversities, further developed with around 16-25 in-depth case cities.
- A theory-oriented publication on how the typology was developed and what it means for migration research.
- An edited volume developing the typology with qualitative case studies of the different types in various European cities.
Quite often the integration of migrants is evaluated only from the perspective of objective indicators (e.g. employment, education, etc.), without taking account of other aspects of integration such as assimilation, the feeling of identity or belonging to/being part of society. First, migrants face a number of obstacles not only in entering the labour market but also in having their qualifications recognised or gaining equal access to vocational training or further education to improve their skills. Difficulties are also faced by those migrants who join their family members in Austria and who are exposed to a high risk of staying out of the labour market for long spells. Duration of the migration experience is also crucial and improvements in the employment position and further integration strongly depend on the command of the host-country language. Second, migrants as compared to natives show a higher incidence of not being able to meet their medical needs. Housing, in terms of costs, conditions and space, is another important domain where migrants differ considerably from natives. Furthermore, lack of information with regard to the renting system, discrimination of landlords against migrants as well as limited access to credit make their housing situation more precarious and less advantageous compared to natives. Accordingly, in this study firstly we aim to focus on a broader spectrum of determinants, objective and subjective ones, which might be useful to analyse the relationship between migration and subjective well-being (SWB). In the EU context, current migration policy and recently introduced measures have the purpose not only to regulate migratory movements but also to promote the integration of migrants. Such measures start with better education and targeted language courses for third-country migrants; strengthening of participation in the educational system, vocational training and in activities that better utilise and improve their skills. Promotion of integration in the local surroundings, better housing conditions and raising health awareness are other areas that contribute to a better life quality and consequently to the well-being of individuals. Therefore, the second purpose of this study is to conduct a policy evaluation by comparing individuals affected by any policy intervention with other individuals to whom such an intervention did not apply, but who otherwise have the same characteristics as a group of interest. Thirdly, apart from the analysis of subjective well-being and integration per se, the aim is to analyse the relationship and direction of causation between permanent/temporary migration and integration while considering objective and SWB indicators.
The database provided in this frame will be used to address important research questions as to migration dynamics, labour market integration aspects, integration and well-being of migrants, effectiveness of certain labour market integration programmes. The data and the empirical investigation will improve the understanding of immigration policy, its effects on SWB, labour market and other integration-related domains.
Cities and how they manage immigrant integration have become a hot topic as cities often have a higher immigrant ratio than the nation as a whole, and are the primary reference point of contact and experience for immigrants. Recent scholarship has provided evidence that indeed the urban context, more so than the regional or national, matters for the level of immigrant integration (de Graauw and Andrew 2011; Koopmans 2004; Nicholls and Uitermark 2013; Penninx et al. 2004; Vermeulen 2006; Maxwell 2013). However, comprehensive analyses of how variation in immigrant settlement within and across metropolitan areas influences both local integration and political participation of immigrants have been hampered by the scarcity of comparative, finegrained data on immigrant settlement and immigrant integration.
This project seeks to contribute to the growing literature on immigrant integration in the urban context by asking how geographic patterns of immigrant concentration influence immigrants’ connection to their neighborhood and city, and if they shape their political engagement on either local level? Further, the project examines if differential patterns of engagement are driven by the multi-scalar nature of local integration, i.e. reinforcing or trade-off effects between the neighborhood and city-levels. Thus, the research agenda is structured around three lines of inquiry: (i) local integration; and (ii) political participation, and (iii) interaction between neighborhood and city-level integration and participation.
The first line of research examines if and how immigrants feel onnected to their neighborhood and city as a function of the spatial context of immigrant settlement. The second line of research focuses on the extent to which participation in local political and civil society associations, and attitudes towards urban politics are influenced by group settlement context. The third line of research scrutinizes potential interactions between neighborhood identification and participation on city-level connection and political attitudes. It investigates if under greater spatial concentration neighborhood connection and participation are reinforcing local segregation within 2 cities or facilitate cross-cutting political participation that also leads to greater interest in broader city-based politics.
This project uses survey data from the LOCALMULTIDEM project on immigrants in 5 major cities (Barcelona, Madrid, Milan, Lyon, and London) in 4 countries, which provides rich data on local immigrant integration. However, it needs to be complemented by data on the local concentration of immigrants’ communities in order to enable comparisons across home-countries, neighborhoods, cities, and host-countries. The D4I data would enable the investigation of the three lines of research, but also aid the formulation of suggestions for local administrators and politicians, considering policy interventions to further the integration and participation of immigrants in their city.
The tendency of migrants to concentrate spatially in so‐called ethnic enclaves is a recurrent outcome of contemporary migration patterns and a peculiar manifestation of the way place, culture and social capital influence social behaviour. The development of policies that might improve social cohesion and socio‐economic development in the presence of ethnic enclaves requires a better understanding of three crucial aspects: a) the reasons behind the development of such enclaves which, on the one hand, may be the result of socio‐spatial segregation while, on the other, may occur because of the benefits for migrants of reciprocal proximity and their reliance on ethnic and bounded socioeconomic networks; b) the degree of permeability and interaction between these ethnic enclaves and surrounding local communities, including other migrant groups; and c) the development of trans‐local and transnational networks. The identification and qualification of ethnic enclaves are, as well, still largely open issues. Novel and multidisciplinary methods capable of going beyond traditional measures of spatial segregation are needed because of the inadequacy of the spatial resolution of standard data sources, and because quantitative indicators of the degree of geographic concentration of foreigners with common cultural and linguistic traits is insufficient to highlight the qualitative characteristics associated with ethnic enclaves. To address these issues, this research will apply a variety of methods for the identification, mapping, visualization and storytelling of ethnic enclaves in EU member states by combining geospatial analyses with qualitative research approaches, such as ethnographic observation, participatory photo‐mapping, photovoice, multimodal map‐making . The availability of data with a 100 m by 100 m resolution offers a unique opportunity to experiment with non‐standard measures of ethnic segregation in the form of surface‐based indicators and spatial autocorrelation analysis, needed to overcome long‐standing methodological issues, such as the modifiable area unit problem. Furthermore, the proposed methodology uses a multidisciplinary approach, and participatory action research design, that considers transcultural, ethnographic and historical elements of ethnic enclaves. Participatory design methods, such as hackathons, will also be used to engage local communities and stakeholders in identifying specific economic, social and cultural issues and proposing ideas to address these issues. First, we will provide a mapping of ethnic enclaves in the eight countries covered by the dataset based on the experimentation andintegration of various spatial statistics and mapping techniques. Secondly, a pilot case study of Prato (Italy) will be performed in order to verify and refine the above‐mentioned mapping, and to produce amulti‐layered digital map of greater Prato. This map will include data collected through participatory action research, and data derived from recent studies of ethnic communities in Prato (conducted in collaboration with Monash University Prato Centre). The results will be accessible to all (researchers, policymakers, the informants involved in the project and other stakeholders). The final aim is to design a methodological model which may be applied to other case studies to facilitate a better understanding, visualization and management of the opportunities and barriers which are typical of areas of strong concentration of migrants.
This project compares ethnic segregation of non-EU immigrant groups and majority nationals between five types of rural and urban areas in Germany.
Previous work on ethnic segregation in Europe mostly relied on data on large cities. However, the migrant population in Germany is widely dispersed and a high share lives in rural areas and smaller cities. Little is known about whether the settlement patterns of immigrants in these areas resemble that of larger cities, for example in terms of clustering by nationality and concentration in the city centers.
In smaller cities we can expect either a lower degree of segregation or a similar degree of segregation as in larger cities. On the one hand, living in smaller cities could ease spatial integration because they lack ethnic neighborhoods, there are less co-ethnics in total and housing markets are more relaxed. On the other hand, living in small cities also goes along with higher visibility of non-European immigrants. If this visibility is coupled with stigmatization and exclusion from the majority population, immigrants might settle near their countrymen to avoid discrimination, and hence segregation patterns resemble that of larger cities.
In a first step, this project first analyzes differences in patterns of ethnic segregation between five types of local administrative units (LAU-2) with differing population size: rural (up to 20,000), small cities (20,001 to 100,000), medium cities (100,001 to 400,000), larger cities (400,001 to 1,000,000) and cities with more than one million inhabitants. For this description of segregation patterns we will use a variety of indicators ranging from mere spatial concentration to indices of segregation within municipalities.
After presenting these descriptive statistics, we will explain the spatial integration of immigrants within the five outlined types of regional units: We test whether characteristics of the housing market (e.g. percent homeowners, vacant housing) and labour markets (e.g. unemployment) as well as socio-demographic characteristics (e.g. tax income) are potential predictors of ethnic segregation. For example, a tight housing market could lead to ethnic segregation because it might be easier for migrants to find housing in their communities.
The focus on Germany is motivated by a shortage of research on ethnic segregation in Germany, which is partly due to lacking German-wide data before 2011. Furthermore, the limited comparability of the provided data between countries favor within country research. However, if we find it feasible to compare countries after screening the data, we will extend the research to a Germany-France comparison to assess whether country differences (e.g. a marked rural-urban divide as in France) can help understanding segregation of non-EU immigrants.
The findings provide evidence for decisions on where to steer immigration flows in order to avoid ethnic segregation (rather to rural or urban areas). In addition, it shows which spatial characteristics predict successful spatial integration and thus helps policy makers to lay foundations for programs that limit ethnic segregation.
The recent influx of migrants into Europe poses difficult challenges for receiving states. As migrants are incorporated into urban areas across Europe, important questions remain concerning the e↵ects of neighborhood diversity and residential segregation on the provision of public goods, including education. The Institutions and Political Inequality (IPI) Research Group at the WZB Berlin Social Science Center, with which all the co-authors are affiliated, seeks to address these questions through innovative, methodologically rigorous research and evidence-based policy recommendations. We intend to use D4I data from all available countries for two projects outlined below, both of which speak to important policy challenges faced by governments across the European Union.
“Native Flight” in Urban School Districts:
First, we would like to use the D4I data to investigate parental school choice in urban areas across Europe. Anecdotal evidence suggests that non-migrant “natives” take steps to avoid or seek out catchment area schools due to the ethnic composition of student bodies. Indeed, school segregation has been documented across the EU (e.g., Karsten 2010), but the lack of high-resolution data on the concentration of migrants has made it difficult to precisely show the extent to which schools are more (or less) segregated than we would expect given catchment area demographics. The D4I data will allow us to address this key question, and to identify salient, policy-relevant factors that enter into parents’ decisions vis-`a-vis their local schools.
The Provision of Public Goods in “Migrant Ghettos”:
Second, we plan to use the D4I data to assess the extent to which residential segregation and neighborhood diversity are associated with public goods provision. Academic research and media reports across Europe commonly suggest a link between the concentration of migrants in a neighborhood and the quality and scope of public goods provision (including social services and policing), possibly driven by elected officials’ lack of political responsiveness. This is an urgent policy challenge, both as a matter of fairness and because youth radicalization is frequently described as a downstream e↵ect of the marginalization of so-called “migrant ghettos.” While precise spatial information on public goods is commonly available, high-quality information on the locations of migrant populations has not been. The D4I data fills this gap, and we are excited to use these data to provide a more detailed characterization of the provision of public goods in diverse neighborhoods.
NamSor API classifies personal names accurately by gender, country of origin, or ethnicity. We propose source open residential lists (for example, the white pages or voters list) in a major city (of Spain, Germany, Italy, Netherlands, Portugal, UK or Ireland) and classify them using NamSor Origin. We propose to correlate the name classes with the ec.europa.eu provided micro-census demographics (migrants by country of origin). We propose to identify methods to redress some of the largest biases, with a machine learning model. The resulting model can then be applied to analyse urban segregation in a city where the micro-census data is not available (for example: France), or to measure specific segregation concerns (for example: equal access to quality education).
Social segregation tends to occur in the activities and activity places of individuals under certain conditions depending, for example, on the size and geography of cities, their institutional setup, and migrant populations. In some contexts causally related chain of segregation occurs, where segregation is constantly reproduced. This chain effect of segregation can be explained in terms of the influence of the segregated labor market (work) on residential segregation (home), and of residential segregation on education (school), which in turn affects choices of employment and free-time activity (leisure). Work, home and leisure time activity places form the parts of the physical space where segregation is produced and reproduced. Different studies show that urban public spaces such as pedestrian zones, parks, cultural centers and public transportation corridors have a great integration potential for segregated individuals and communities. Segregation is higher and reproduction of segregation is more evident in cities and neighborhoods with less public space.
By using comparative census data from European cities and additional data sources we attempt to study how the level of segregation and reproduction of segregation is related to the distribution of urban public spaces and their accessibility. We use additional databases considering land use and functions of urban areas, and social status of population groups. The study focus is European cities of different size and ethnic composition.
The research questions are as follows:
1) How is urban public space positioned with respect to majority and minority groups’ activity locations?
2) How is the level of segregation related to urban structure, geography and accessibility of public space?
3) Whether the geography and accessibility of urban public space is related to reproduction of segregation across different generations and activity locations of individuals?
For countries such as Spain, Germany, Italy, France, Netherlands, Portugal, UK, and Ireland, membership in the European Union has had many important impacts, including with regard to the free movement of EU nationals across EU borders. Producing a more accurate picture of intra-EU mobility is not only particularly salient nowadays but also necessary in order to understand the experience and practice of EU citizenship. Following the UK referendum to leave the European Union, issues with regard to free movement and ‘EU immigration’ has permeated political, media, and popular discourses in many Member States. Yet, a clear picture as to the status of EU nationals residing outside of their ‘home’ Member States is needed, especially with regard to their location, as well as their living circumstances within these locations. This is what this project will aim to do. In particular, this analysis will provide crucial baseline data on the emergence of a new cross-border European identity in the context of place-specific pattern of demographic diversification.
In the first instance, we will look at the distribution of non-national EU-born citizens in the various administrative units provided in the data, focussing on the various levels of aggregation of country of birth. This will allow us to compare the proportion of non-national EU-born as well as their concentration, diversity, and segregation in those various areas, with an emphasis on comparing this with other third country nationals in the same location, as well as the ‘local’ population. Where possible, we will also link this information to the children of these intra-EU settles, with specific attention to those born in the country of residence. Secondly, we will link the data to other administrative sources to explore the types of areas in which EU nationals reside, with a focus on measuring the ‘superdiversity’ of these areas. Information about the area distribution in terms of, for example, age, education, employment status, home ownership, and religion will be collected, linked, and examined in order to achieve this. Diversity with regard to these aspects will be examined both individually and concomitantly. This will allow us to explore the circumstances of a group that is often overlooked in integration research, but who is increasingly being put at the forefront of debates in many Member States.
Access to public transport is not only crucial for advanced urban sustainability and better environmental performance, but also for societal inclusion and integration programs since it provides access for marginal social groups to job opportunities, housing, education and other urban services. Therefore, urban strategies about public transport accessibility and the integration of migrants are top priority and inextricable policy themes for the EU’s Urban Agenda.
However, in many EU countries, migration has been a special and controversial political issue, considering that at city-level several planning activities are facing huge implementation and managerial difficulties. Therefore, urban authorities of multiethnic communities need to establish data-driven solutions in their decision-making, if they intend to build powerful local coalitions in their integration programs that often it is claimed to generate a heated debate in the city.
Our proposal deals with the geography of migrants and their access to public transport in selected major European cities (i.e more than 10 case studies). In our knowledge, within Europe, there has not been any attempt to combine big data on migration and public transport in a comparable geography between cities. However, we aim to capitalize on the public transport accessibility method developed by H.Poelman and L.Dijkstra (DG-REGIO, 2015). They have already presented their assessment results for som European cities (mainly in the north), but the migration perspective on the subject is lacking. Their approach for the accessibility indicator uses comparable city boundaries, considers the spatial distribution of the population and analyzes the frequency of public transport. In our study, we take into account the spatial distribution of migrants and for the supply of public transport we utilize several available General Transit Feed Specification (G.T.F.S) datasets, which is often an open data file or it is accessible after an engaged-license agreement. The OpenStreetMap street network dataset will help as to assess the ease of access to public transport stops. We also draw upon to the new EU-OECD city definition for the common city boundaries and the corresponding geodata file is derived from the EUROSTAT’s (open) dataset on 2011 High-Density population clusters.
Consequently, the produced indicators (e.g according to different ethnic groups) allows us to benchmark urban areas against other areas of a similar size. For example, advanced integration actions would be more relevant in cities where there is a chasm in the accessibility results between the domestic populations to the equivalent value of the migrant population. Obviously, this is extremely applicable for allocating funds and actions in the European migration and cohesion policy framework. What is more, in each city the geo-visualized outcome of the accessibility indicator gives us the opportunity to uncover the areas with the most disadvantaged (from the public transport accessibility perspective) ethnic groups that live in the city as well as to indicate the auto-dependent migrant communities. Furthermore, tailored made policy recommendations for each city case (e.g reallocation of bus stops) will be presented in order to highlight the policy efforts that need to be done for better integration and social cohesion results.
Does political representation of immigrants improve social and political outcomes for immigrant populations? Although a wealth of scholarship has examined the factors that explain ethnic minorities’ under(representation) in politics and lower levels of political mobilization, little work has investigated whether higher immigrant minority representation translates into better outcomes in political participation, employment, or integration for these groups. Through analyses of such relationships at the subnational and cross-national level in the Western European context, this study deepens understanding of the conditions under which descriptive representation translates into substantive representation and improves political and economic outcomes for immigrant minorities.
Gaining access to D4I data would be an enormous asset for our study. Complementing our results from cross-national analysis and qualitative interviews in Paris and Berlin to be undertaken in Summer 2018 in Paris and Berlin, analyses of this fine-grained spatial information on immigrants at the city-level would allow us to carefully examine whether and how political representation of immigrants positively effects political and social outcomes for immigrants, and whether and how these outcomes vary across immigrant groups.
We aim to take advantage of the D4I data in two principal ways. First, we examine whether immigrants’ political engagement and participation varies depending on electoral candidates of immigrant-origin across neighborhoods. To determine whether immigrants are more likely to vote in elections with more candidates of immigrant-origin, we use raw data of polls of subnational and national elections (e.g. the Berlin state elections 2016). Using such polls allows for the identification of the exact location of the interview, and contain a wealth of information about socio-economic status of respondents. We will use multi-level regression and poststratification (MRP) to make the data representative on the local level. In a first step, we calculate a multi-level model estimating the relationship between the respondents’ demographic characteristics, macro-variables and an poll answers of interest (for example, which party they voted for). Second, we then predict the probability of answering, for example, Social Democrat based on demographic-geographic “type” (e.g. Probability of Turkish origin males, age 45-65, with high school education in Berlin to saying they voted “Social Democrats”). In the last step, we poststratify, or weight the different type estimates with actual population values. In this example, this allows us to estimate the local level voting patterns of migrants. The same approach would work for any other poll question of interest (see Enns, Lagodny and Schuldt 2016: 51ff).
Second, using the D4I also enables us to examine whether political representation of immigrants leads to better social and economic outcomes for immigrants. Are areas and neighborhoods with more immigrant-origin candidates and representatives more responsive to the needs of immigrant groups? Relatedly, does immigrants’ political engagement and participation vary across socio-economic groups? These findings from the objective, neighborhood-level analysis with D4I data will be complemented with in-depth interviews with immigrants in Berlin and Paris – two cities with rich and varying degrees of immigration - during summer 2018 to gain further insight into the subjective conditions as perceived by these groups.
Although immigrants from Latin America (LA) do not constitute a major group in most countries included in the dataset, they are the main non-EU national group in several Spanish and Portuguese cities and have gained visibility in other places, such as London and several Italian metropolises. Despite this, cross-comparative analysis at the EU level of such population is limited, namely concerning its residential patterns and segregation levels. Because we are dealing with a relatively recent immigrant group, its analysis may contribute to a better understanding of the contemporary segregation patterns in a context of urban fragmentation and housing financialization.
Having this into consideration, we propose to explore the dataset in order to better understand the patterns of concentration and segregation of Latin Americans in the cities of the countries where this population is meaningful, following four basic lines:
- the relationship between scale and segregation, comparing the situation in municipalities with different sizes;
- the relationship between the characteristics of the residential place (metropolis/city centre; suburban; peri-urban and rural) and the segregation levels;
- the comparison between the concentration levels of the Latin Americans and the concentration levels of the autochthonous and the other significant immigration groups in each city;
- the relationship between housing policies and housing indicators and the segregation levels of Latin Americans, using complementary information from the 2011 Census, in particular for Spain and Portugal.
In specific terms, we will analyse Latin Americans as a whole as well as the two largest national LA immigrant groups in each country, if these are meaningful (placed between the largest 20 groups of foreigners). For the case of Spain, the relevance of Latin American population justifies the specific treatment of the data for four or five national immigrant groups. In the case of Portugal, Brazilians are absolutely dominant among the immigrant population coming from the American continent (about 90% of the total), situation that enables the use of the dataset (in the case of Portugal, the aggregation level is the continent).
Despite the limitations associated to the database (e.g. variation in the original national datasets, data gaps, static nature), the harmonization effort put in its construction makes it an instrument with a high potential for the cross-comparative analysis of immigrant segregation in cities of different EU countries. This may help to overcome difficulties experienced in previous researches developed by the team members.
Finally, although focusing a specific group in terms of residential segregation at European level, we will develop a relational analysis (e.g. comparing the cartographic patterns of various immigrant groups; computing dissimilarity indices…) that will provide, not only specific insights for the LA case but also more general elements about immigrants’ concentration in EU cities.
In terms of outputs, we expect to present the results in conferences (posters and oral presentations) in Europe and Latin America and to produce two or three scientific articles. Concerning policy issues, the conclusions on the relationship between scale, housing policy and segregation of a relatively recent immigrant group have the highest potential.
SoBigData is the European Research Infrastructure for Big Data and Social Mining. Within the project, we build exploratories that concentrate on different topics from the social sciences. The exploratory on migration has access to various public and private datasets that enable us to analyse migration from several points of view, including migration flows and stocks, social and economic integration, return of migrants. Migration might generate cultural changes with both long- and short-term effects on the local and incoming population. Migrant integration is generally measured through indicators related to work market integration or social ties (such as mixed marriages). These statistics are currently available with low resolution and not for all countries. In our work, we are observing integration and perception on migration through Big Data. For instance, social network (Twitter) sentiment analysis specific to immigration topics allows us to evaluate perception of immigration, in various European cities. Analysis of retail data (from an Italian supermarket chain) enables us to understand if immigrants are integrated economically but also if they change their habits during their stay. Through analysis of all social data we derive novel integration indices that take into account the activity of both migrant and local population. The effect of multi-culturality on overall sentiment and the phenomenon of superdiversity are also being observed. The studies build on the extensive long-term experience of our team with big data in general, and mobility data in particular.
Our ongoing studies could be significantly enhanced by the D4I dataset, which would be integrated with the datasets currently in our possession. In particular, sentiment analysis on Twitter, related to immigration topics, would be compared with the structure of the population in various districts of European cities, as seen through the D4I data, to understand whether negative/positive sentiment correlates with the level of multinationality of the various areas. With respect to the retail data, the evolution of consumption pattern of foreign-born customers could be influenced by the local size (and composition by country of birth) of the immigrant population. Furthermore, we will develop integration indices that will take into account not only our social data, but also the spatial distribution seen in the D4I dataset. These indices could be then used in the future by policy makers to quantify integration at European level.
The proposed research is a part of a larger project centred around the topic of well-being of people of migrant background and how wellbeing is influenced by intergroup contact and majority attitudes towards immigration. The main research aim of the project is to analyse how the majority group’s attitudes and intergroup contact influence minorities’ subjective well-being across European countries, with the goal of developing ecommendations to improve immigrants’ and majority members’ wellbeing in environments with heterogeneous ethnic composition. Moreover, the project aims to contribute to the research applying Intergroup Contact Theory (Allport 1958, Schuman et al 1985, Wright et al. 1997, Pettigrew and Tropp 2011) by testing the theoretical implications of different forms of contact with empirical, quantitative research.
Minorities' well-being may be affected by both contact and attitudes (Knies, Nandi and Platt 2014, Hewstone 2015). However, while there is increasing interest in the well-being of minorities (Valk and Arpion 2016) and the factors that contribute to it (Oudhof 2007, Verkuyten 2008, Vieno et al. 2009, Safi 2010, Kirmanoglu and Baslevent 2013), the role of the contact with majority and their attitudes towards immigrants and their descends has not been extensively studied in quantitative analyses. This gap is also due to the lack of reliable data combining information on contact, attitudes and wellbeing at small spatial levels.
The proposed research
The proposed study using KCMD Data, will focus on the comparison of the wellbeing of individuals from different ethnic backgrounds living in different neighbourhoods with different ethnic composition in the UK and the Netherlands. The choice of countries is based on their similarity in terms of high foreign population rate, while being different in terms of distribution and segregation patterns. Similarly, the differences in the character of urbanisation of studied countries offer possibility for comparison.
The main research question is ‘How does neighbourhood diversity influence the wellbeing of the majority compared to that of people of various minority backgrounds?
KCMD Data will be paired and matched with local level data from opinion and attitudinal surveys such as ONS Opinion survey (Module Wellbeing, survey conducted in 2011) in the UK or LISS Panel (Effect of perceived social distributions on subjective well-being, data collected between 2010-2014) in the Netherlands.
The research will examine variation in the subjective wellbeing of individuals with different individual-level characteristics (such as socioeconomic and demographic variables), and living in different local contexts as indicated by neighbourhood diversity (based on the KCMD Data), and regional diversity (based on the regional foreign population rate and dispersion of immigrants in the socioeconomic structure based on ISCO qualification of their job), using multilevel regression models.
Subjective well-being will be measured through a question regarding self-evaluated life satisfaction and a derived variable constructed from multiple questions pertaining to satisfaction within certain spheres of life such as health, economy, and state and its policies. The derived variable will more allow for differential weights to be allocated to the importance of certain domains of life satisfaction and thereby allow a more precise interpretation of effects on individual’s well-being. For cross-country comparison, research models will also include country level controls.
KCMD Data will make it possible to nest individuals into smaller local neighbourhood units instead of regions (such as NUTS 1/NUTS 2 units). These smaller areas are more plausibly sites of contact than larger regional areas which might involve unequal dispersion of the minority groups. The use of regions as a proxy for contact is the most problematic limitation of research using datasets lacking information at smaller, neighbourhood, spatial scales. Using these smaller spatial levels, it is also possible to implement different operationalisations of diversity, ethnic group composition, and segregation that will have different implications for contact and, in later stages of the research, attitudes.
The neighbourhood diversity as a proxy for the contact has been explored already, hovewer, the detail the KCMD data and its comparability across countries is novel.
These open a space for cross-country comparison on small scales. The detail information about the country of origin of immigrants provides the means for comparison among groups: variation in the wellbeing of different groups, various effect of the contact with different migrant groups on the wellbeing of majority and its variation across countries.
Additionally, the aforementioned differences in the character of urban areas of the studied countries enable studying how this character influences segregation of immigrant across and within cities.
The results will describe differences in subjective wellbeing of majority and minority groups’ members living in the various areas of the UK and the Netherlands. They will bring more nuanced understanding of the relationship between neighbourhood diversity and perceived wellbeing: the forces that play a role in its shaping, the influence of the immigrants’ background, and the influence of a country that hosts them. These results are crucial for the larger project, that aims to understand what is the role of attitudes in mediating the effect of diversity on wellbeing.
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The number of asylum seekers has been increasing rapidly since 2011 in OECD countries. In 2015 the number of asylum seekers in the OECD reached 1.65 million people, four times the value registered in 2011. As for Netherlands, according to Eurostat data the number of annual asylum applications has shown a continuous increase from 13,065 in 2013 to 44,975 in 2015, followed by a gradual decrease to 15,845 in 2017. From their arrival, and until a decision on their asylum request is taken, asylum seekers are sheltered in reception centres. These reception centres are decentralized throughout the country, as directed by the national government, through COA. Whether this distributional strategy is optimal from the viewpoint of local communities near reception centers is, however, unclear. To investigate this based on actual market behaviour, this paper measures whether the opening or closing of a reception centre affects house prices nearby the centre, while looking also at how this effect differs in different types of areas, such as along the urban--‐rural hierarchy.
While inflows of asylum seekers in a given area can raise housing prices by generating housing demand, such inflows might also lower housing prices due to possible negative attitudes towards the concentration of asylum seekers in a given area, which lower housing demand in areas where asylum seekers are concentrated (Lastrapes, W. D. and Lebesmuehlbacher, T., 2016).
A difference-in-difference approach is used to identify the effect of the presence of reception centres on housing prices. The empirical strategy consists of comparing housing prices in predefined target and control areas before and after the opening and closing of reception centers for asylum seekers. A distance ring dummy is used to identify target and control areas. In addition, the use of distance ring variables is used to measures the distance decay of the external effects. An additional specification is implemented to assess whether the distance decay is linear, concave or convex, like in Van Duijn, M. and Rouwendal, J. and Boersema, R., 2014. Finally, an instrumental variable is used to improve the identification strategy and avoid endogeneity problems, as the dispersal policy is likely to be non--‐random.
The analysis is conducted on data provided by the Dutch Association of Real Estate Agents on transaction prices. Data provide information on the price of the property, year of transaction, location of the sold houses and distance to the reception centre sites controlling by housing and neighbourhood characteristics. Data provided by COA allow to control for all the information related to the reception centres, such as their address, capacity, year of opening and closure.
This paper contributes to the existing literature on hedonic house price models by providing spatial and temporal dimension (Van Duijn, M. et. al 2014) of the external effect of reception centres for asylum seekers and refugees and by looking at how this effect differs along the urban--‐rural hierarchy. It also contributes to the literature on the impact of immigration on housing prices (Gonzales, L. and Ortega, F., 2013; Sá, F., 2015; Saiz, A., 2006 and Sanchis--‐Guarner, R., 2017) and on the impact of asylum seekers’ inflow in receiving countries, which is usually more focused on fiscal budgets (Larsen, B. R., 2011), the labour market (Borjas, G. J. and Monras, J., 2016) and the voting behaviour (Bratti, M. et al., 2017; Steinmayr, A., 2016).
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Objective of the Project
Deriving from the objectives of the Data Challenge, the proposed project Patterns and Correlates of Immigrant-Native Segregation in Europe: Implications for Integration Policies, proposes to study immigrant-native segregation with the aim to find out what are the differences in (a) ethnic geography in European countries and (b) ethnic segregation in larger (200,000+) European cities, and how do these patterns of segregation correlate to important macro (country) and meso (city) level characteristics. The findings of the study would allow to design better policies with regard of immigrant integration policies.
Countries Involved to the Study
The Data Challenge opens small scale data for eight South and West European countries, France, Germany, Ireland, Italy, Netherlands, Portugal, Spain and UK. We will complement it with data from North and East Europe in order to achieve a truly European coverage. The countries included are: Finland, Sweden, Estonia, Czech Republic and Hungary. Comparable data to Data Challenge is available for all these countries, and this data is also available for our us for conducting the comparative study. This implies that our proposal would include a study of 13 European countries. In addition, we consider including Greece, Latvia and Lithuania to the study.
These is additions are possible since we have created a strong network of segregation scholars across Europe (see http://segregationeurope.tudelft.nl/) together with whom we recently published a book Socio-Economic Segregation in European Capital Cities. East meets West (ed. by Tiit Tammaru, Szymon Marcinczak, Maarten van Ham, Routledge 2016) that includes 13 case studies from across the Europe. That is, to the best of our knowledge, the first rigorous comparative study of social segregation levels in Europe.
Many chapters in the book highlight that social and ethnic segregation are tied to each other, and Andersson and Kährik in their chapter on Stockholm in the book Socio-Economic Segregation in European Capital Cities propose the ‘eth-class’ concept for studying segregation. Ethnic segregation is, partly a function of social inequalities, while social segregation is partly a function of ethnically based preferences and discrimination. Getting access to small-scale data on ethnic groups allows us to move forward the ongoing research on the patterns and correlates of with our established network.
Data and analytical strategy
We will arrange a comparative dataset for 13 European countries, small-scale spatial units (grids as described in the Technical report by the Joint Research Centre, JRC) by ethnic/migrant groups. We will aggregate data also into higher-order spatial units, 500 by 500 grids, etc. The exact aggregations will be decided during the study. The database will be complemented by macro (country) and meso (city) level characteristics from Urban Audit and Eurostat. The exact list of variables will be decided within the course of the study in order to address also the key topics of the Data Challenge, including education, housing, access to public services, income and electoral outcomes. Some of the these factors are country level (e.g. Gini Index from Eurostat), some are at the city level (e.g. Share of Journeys to Work by Public Transport by Urban Audit).
The analysis will take place in four steps.
- First, we will use country level data in order to describe the main features of ethnic geography in the 13 countries. We will provide answers to the questions such as How clustered are migrant groups at the national level in each of the case study country? or What are the differences in ethnic geography between the 13 countries? The primary outcome is a series of maps at different spatial aggregation levels.
- Second, we will use data for all 200,000+ plus cities in order to understand the main features of segregation within and between the European cities. We will calculate the classic global measures that would characterize the two main dimension — evenness and exposure — segregation. They will be complemented mappings of the local level geographies of segregation. We will provide answers to the questions such as How segregated are migrant groups within the European cities? or What are the differences in the levels of ethnic segregation between the 13 countries? or How do the local geographies of segregation compare in European cities? The classic measures such as Dissimilarity Index are important since they allow for an extensive comparison of segregation levels also with other cities and across time.
- Third, we extend the classical index-based analysis into the mutli-level modelling framework in order to provide a serve more rigorous answers to the questions posed above. The exact set of cities will be decided during the study but, at minimum, it would include 13 capital cities of the case study countries. We follow the methodology used by David Manley and colleagues in their chapter on London in the book Socio-Economic Segregation in European Capital Cities in order to calculate the multi-level regression-based segregation indices. In essence, the modelling approach will focus on the variance (the spread of the data) for providing a suitable measure through which it is possible to develop a more rigorous understanding of segregation.
- Fourth, we use regression methods to model the relationship between the patterns of segregation in the 200,000+ European cities and key variables reflecting the socioeconomic condition of urban areas. This allows us to understand what are the main correlates of segregation in European cities. The analysis would provide answers to the questions such as What kind of variables correlate to higher levels of ethnic segregation in European cities? or How do the correlates of segregation vary in different parts of Europe? Such studies are common in the U.S. but, to the best of our knowledge, they have never been carried out in the Europe because of the limited availability of data.