Urban Economic SEGregation: integrating explanatory mechanisms across geographical scales to compare remediatory policies in silico

Project Information

SEGUE

Grant agreement ID: 101039455

DOI

10.3030/101039455

EC signature date 28 April 2022

Start date 1 September 2022

End date 28 February 2029

Funded under

• European Research Council (ERC)

Total cost

€ 1 495 125,00

EU contribution

€ 1 495 125,00

Coordinated by

TECHNISCHE UNIVERSITEIT DELFT
Netherlands

Periodic Reporting for period 1 - SEGUE (Urban Economic SEGregation: integrating explanatory mechanisms across geographical scales to compare remediatory policies in silico)

Reporting period: 2022-09-01 to 2025-02-28

Summary of the context and overall objectives of the project

The uneven concentration of economic resources in cities hampers the well-being and opportunities of poorer citizens and represents a threat to social cohesion. It is considered a major policy challenge by researchers and international institutions alike. My aim with the SEGUE project is to identify and model the combination of major economic, geographic, sociological and demographical drivers of urban economic segregation in order to better understand its dynamics and to better assess possible remediatory policies. The existing literature on urban economic segregation mainly explains it with sociological and intra-city factors (e.g. access to resources, social networks, role models) but also acknowledges that it depends on the evolution of economic inequality. The existing literature on economic inequality, by contrast, mainly focuses on factors operating at the national and individual levels (e.g. selective migration, assortativity, inheritance). My project addresses the gap between these two bodies of literature by first spatialising the national and individual explanations of economic inequality. This means focusing on where people are, live in or move to, because those locations constrain who one can meet, whether romantically, professionally or simply as friends and acquaintances. Second, my project addresses the gap in literature by integrating multiple and multidisciplinary explanations into a simulation model of urban economic segregation. This model will be calibrated using a uniquely rich source of exhaustive and longitudinal individual data from the Netherlands (CBS microdata). Its analysis will produce new insights about the interaction between drivers of economic inequality and segregation in cities and will provide a cost-effective tool to compare policy scenarios to reduce urban economic segregation at different scales of action (local policies, metropolitan or national ones). The results obtained with this project should open new perspectives to study other forms of inequality (gender, race, class) and facilitate the study of economic segregation in other national contexts (e.g. countries with less granular data).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

This project has one aim: to integrate multidisciplinary knowledge about economic inequality and urban segregation to better understand and counter the dynamics of economic segregation in cities, by modelling the main drivers of urban economic segregation and comparing remediatory policies at various geographical scales. I devised three research objectives to address this aim:

1. To uncover the theoretical interactions between the processes generating urban economic segregation at various scales, and particularly the impact of economic inequality on economic segregation.
So far, I reviewed a multidisciplinary and multilingual literature on the relationship between economic inequality and economic segregation. This literature is not well integrated because it spans two very large fields which follow distinct research traditions. Consequently, causal pathways which link economic inequality and economic segregation are not well understood, yet addressing this literature means considering thousands of references, which is unfeasible for most researchers with limited resources. After identifying, screening, reading and analysing the findings of hundreds of (overlooked) research articles, I wrote a new state of the art of our current knowledge base on the causal pathways and theoretical mechanisms involved in the relationship between economic inequality and economic segregation. I did so using state-of-the-art methods of systematic literature reviews and developing them in the process (Cottineau-Mugadza, 2024). In conclusion, I show that variations in economic segregation tend to follow differences in economic inequality in the short term while the reverse causality is more probable in the longer term (i.e. from segregation to inequality). The housing market is the most cited mediator between economic inequality and economic segregation, yet a diversity of theories and mechanisms are mobilized to explain their empirical connections.

2. to “describe” and "animate" this combination of explanatory processes of urban economic segregation using analytical and agent-based models.
So far, we were able to produce detailed indicators of income inequality and segregation (disaggregated by income percentile to analyse the segregation of affluence and poverty), on an annual basis, for all Dutch urban areas – i.e. beyond the situation of well-known capital cities and largely populated cities, after successfully linking datasets describing the entire population residing in the Netherlands in terms of income, wealth, residential location and household composition between 2011 and 2021 (San Millán et al., 2025). We show that inequality and segregation remained stable or decreased in most cities – although large variations exist between cities – and that unequal cities tend to also be more segregated, but patterns vary and the same segregation levels can coexist with diverse inequality metrics.

3. To use these integrated models of urban economic segregation to develop, compare and assess policy scenarios of segregation reduction ex ante.
Work is underway to review relevant policies to reduce urban economic segregation, find empirical cases in the Netherlands where their effectiveness could be measured and evaluated, and design policy scenarios to be used in the ABM, based on this empirical knowledge base. Initial requirements for geospatial population synthesis to initialise agent-based models were outlined in Roxburgh et al. (2025).

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The systematic literature review (Cottineau-Mugadza, 2024) is a novel way to cover such a large literature, to record causal pathways, actors, geographical scales, methods and findings of 80 independents studies of economic inequality and segregation. It highlights trends unknown so far, such as the evolution of indices used to measure inequality and segregation (i.e. the predominance of Gini for inequality and the current consensus over Reardon’s rank-order information theory index for income or wealth segregation) both in the empirical and modelling literature. It informs knowledge about the ways through which inequality translate into segregation (mostly short- to mid-term effects of the housing market and residential relocation); and segregation into inequality (mostly through the long-term effects of the education system and labour market).
Additionally, the empirical analysis (San Millán et al., 2025) demonstrated that it was possible to use state of the art measures of inequality and segregation and to apply them to the richest type of data (i.e. exhaustive longitudinal geolocated individual register data) to produce fine grained detailed analysis of inequality and segregation at multiple scales, for multiple years, comparatively for all cities of the Netherlands, zooming on poverty and affluence. It debunked the myth that economic segregation is rising everywhere, as most cities exhibited stable or declining levels and it revealed the diversity of urban trajectory while the literature tends to restrict our knowledge to national capitals and very large cities, which are quite singular and usually not representative of ordinary cities. This result was not necessarily expected.