Abstract
Reades, J., Lees, L., Hubbard, P. and Lansley, G. State-led gentrification in London: using linked consumer and administrative records to track displacement from council estates. Environment and Planning A: Economy and Space. DOI: 10.1177/0308518X221135610.
Author statement regarding the correction to this article: The online-early version of this paper incorrectly described Almeida (2021) as making exclusive use of U.K. census data. In fact, although the data used covers a different period and is quite different in structure and content, it draws from the same underlying CDRC source as our own work alongside standardised geographies and measures from the Office for National Statistics (ONS). We would like to apologise unreservedly to Adam for this mischaracterisation.
That warning was arguably not heeded in Almeida’s (2021) recent quantitative study of the displacement of working-class and Black and Minority Ethnic (BME) residents in London in the 2010s which focused on just three of the capital’s 32 boroughs. This work also relied on the UK decennial census, generating ‘gentrification scores’ for small area census geographies (i.e. the Lower-Layer Super Output Area, discussed later), and it also failed to grapple with the problematic construction of ‘gentrification indexes’ from such data. Cumulatively, such attempts to quantitatively measure displacement suffer from weaknesses in spatial resolution (i.e. the data are not at the household level) and temporal resolution (i.e. the data cannot distinguish between socio-economic change and population change using 10-year snapshots). Indeed, the census asks only about relocation in the preceding year, rendering the intervening nine invisible! Consequently, researchers have failed to meaningfully advance our understanding of displacement (see Preis et al., 2021), with Easton et al.’s (2019) review concluding that, in the UK context, it is ‘time to move beyond conventional census-based measures’.
Generally, census-based attempts to quantitatively measure displacement suffer from weaknesses in spatial resolution (i.e. the data are not at the household level) and temporal resolution (i.e. the data cannot distinguish between socio-economic change and population change using 10-year snapshots). Indeed, the census asks only about relocation in the preceding year, rendering the intervening nine invisible! Consequently, researchers have failed to meaningfully advance our understanding of displacement (see Preis et al., 2021), with Easton et al.’s (2019) review concluding that, in the UK context, it is ‘time to move beyond conventional census-based measures’. Almeida (2021) was first to do this in a report for the Runnymede Trust making use of population churn and ethnicity data from the CDRC. This is the same root source upon which we also rely; however, the analysis was restricted to just three of the capital’s 32 boroughs and, more pertinently, is neither centred on the estate redevelopment processes nor able to ascertain displacement distance or destination because of the resolution chosen and its interaction with privacy concerns.