Homophily horizons and ethnic mover flows among homeowners in Scotland

Abstract

This article analyses mover flows in Glasgow and the role of ethnic homophily, the tendency for movers to be drawn to areas with similar ethnicities to their own. We look at how homophily affects the spatial relocation patterns of homeowners in Glasgow from Scottish, Indian, Pakistani and Chinese descent, and focus on the extent to which homophily extends beyond the immediate locality to surrounding neighbourhoods. Our interest is in estimating the "homophily horizon"how far the gaze of homophily reaches in mover location decisions. Using a simple Schelling-type theoretical model, we argue that homophily horizons are potentially important in shaping the long-term social structure of cities as they may profoundly affect how potent the overall sorting tendencies of the housing market are in driving segregation. In principle, the more distant the homophily horizon, the more quickly the housing market will tend towards segregation, other things being equal. We adopt Folch and Rey's use of the local centralization index to capture the influence of surrounding neighbourhoods in shaping mover flows and neighbourhood dynamics. Our estimation combines ethnic mover flows derived from surname analysis of house buyers from the house transactions recorded in Registers of Scotland data. Our results show that the presence of the own ethnic group in the local surroundings is important for explaining mover flows, and that homophily is a local phenomenon.

Segregation in the Twenty First Century: Processes, Complexities and Future Directions

“…We have said little in this special issue, however, about non‐spatial aspects of segregation which have the potential to become increasingly important as social media and technology make trans‐spatial communication increasingly easier and more immersive. Social network analysis (SNA) offers a potentially fruitful way to create a more capacious unified conceptual and empirical framework for considering both spatial and non‐spatial connections, incorporating also more subtle aspects of segregation such as perceived homophily (Dean & Pryce ) and homophily horizons (Bakens & Pryce ). The problem with purely spatial approaches to segregation is that they overlook non‐spatial connections between neighbourhoods and between individuals; connections that are difficult to analyse using traditional methods.…”

“…We also need to understand the implications of and for long term dynamics of migration and segregation. Bakens and Pryce (), for example, note how different preferences for homophily (particularly homophily horizons) could have profound impacts on the long‐run segregation trajectories of cities. The impacts of these processes on life outcomes have important implications for inequality across different social classes and ethnic groups.…”

Do segregated housing markets have a spillover effect on housing prices in nearby residential areas?

“…The individual incentives, and perceptions of difference, can lead collectively to segregation, and thus individual disorganised behaviour can translate into collective outcomes. Bakens and Pryce (2019) and Savage (2010) studied how homophily affects homeowners' patterns of spatial relocation. Homophily horizons are probably crucial in creating the social structure of cities in the long term and driving segregation in the housing market (Bakens and Pryce, 2019).…”

Measuring the changing pattern of ethnic segregation in England and Wales with Consumer Registers

“…Consumer Registers do not record any demographics but names can be used to infer age, sex and ethnicity (Longley et al, 2015). Names-based classifications have been proved to be viable for ethnicity related studies (Bakens and Pryce, 2018;Lan et al, 2018;Longley et al, 2015). In contrast to previous names-based segregation studies, the freely available Ethnicity Estimator tool (indicators.cdrc.ac.uk/ethnicity-and-names/ee/) has been calibrated with the 2011 Census population data and demonstrate a success rate of 88% (Kandt and Longley, 2018), which performs better than other algorithmic comparators such as Onomap (Kandt and Longley, 2018).…”

“…These vicissitudes have previously been overcome by using algorithmic classifications of individual given-and family-name pairings to establish a consistent benchmark for analysis (Mateos et al, 2011(Mateos et al, , 2009. Names-based classification of ethnicity has been applied in previous research (Bakens and Pryce, 2018;Lan et al, 2018;Longley et al, 2015). However, unlike the classification that we use here, such tools do not validate their results with reference to the self-assignments (e.g.…”