Placing NI Residential Segregation in its Geographical Context: Population

Patterns, Residential Moves & History

Ian Shuttleworth (QUB),Myles Gould (UoL) & Paul Barr (Dartmouth College)

Structure• Introduction– Research questions

• Data and analytical approach– Thinking about segregation, moves & distance– Evidence of structuring

• Migration in a stable state? – Results

• Migration in an unstable state?• Conclusion– Comments about the drivers of segregation change

Introduction• Segregation levels in NI did not increase between

1991 and 2001 (Lloyd and Shuttleworth 2009)• Evidence from the NILS shows that migration neither

increased or decreased segregation levels 2001-2007 – predictions: no segregation increase 2001-2011 or else natural increase/decrease are more important

• Yet some theoretical approaches (eg Schelling) based on individual choices and preferences suggest segregation equilibria unlikely – more likely endpoint is complete segregation

Introduction: Some Questions

• How and why have levels of segregation remained constant in NI between 1991 and 2007?

• Why are segregation levels often slow to change?• In what circumstances does migration fail to

redistribute population in such a way as to make major changes in population distributions?

• How can population distributions change so as to increase or decrease segregation levels?

Data and analytical approach• The Northern Ireland Longitudinal Study (NILS);

28% sample of the NI population• Data for migration events 2001-2007 from health

registration data• Population aged 25-74 – excludes students and the

very old• Descriptive analysis plus multilevel modelling

(individuals nested in SOAs)• Presentation today based in part on the NILS data

but also calls upon other sources and analyses too

Data and analytical approach• NILS analysis uses spatial autocorrelation (GeoDa Local

Indicators of Spatial Association - LISA)– classifies SOAs into five classes: random, hi-hi, hi-lo, lo-lo,

lo-hi; for community background and multiple deprivation• This is used to explore how spatial context matters in

shaping probabilities of moving to areas different from that of the starting place

• Basic argument: existing population geographies are important (in conjunction with short-distance housing moves as in the rest of the UK) and set constraints on the ability of migration to drive changes in segregation – see following slides

Further information: Anselin L .(2005) Exploring Spatial Data with GeoDaTM: A Workbook. Spatial Analysis Laboratory, University of Illinois, https://geodacenter.asu.edu/system/files/geodaworkbook.pdf, Chapter 19.

Thinking about segregation, moves & distance

• Have to move large distances to change area with respect to community background

• To change to areas of different social economic status / deprivation needs only relatively short distance moves

Thinking about segregation, moves & distance in NI

Evidence of structuring• NI is somewhat more structured (eg positively

spatially autocorrelated) by community background than other socio-economic variables (Lloyd 2010; 2012); also the NI population falls into bigger spatial aggregations by community background

• This point is apparent in the two maps which show clusters by SOA for (i) community background and (ii) multiple deprivation – more hi-hi and lo-lo clusters for community background than for social deprivation

• Easier to move between different types of places for community background than social deprivation

Evidence of structuring

Community Background Index of Multiple Deprivation

Migration in a stable state?

• Models and descriptive statistics overleaf suggest why segregation levels in NI have remained steady since 1991

• This is because of short distance migration – and NI not an exception in this – in the context of existing population geographies

• These structure the types of moves that are possible, downplaying the role of individual choice, and by default lead to stability

Results: Descriptives

Results: Descriptives

Model 1 : Difference in % CatholicVariable/Characteristic (* = statistically significant) Effect

Age (Base is 25-43 ) +ve (accept 55-64 )Religion: Protestant*; Other*; None* (Base is Catholics) –ve (large)Education* –veIllness (Base is not ill) +veTenure: Rented*; Private Rented* (Base is Owner occupied) +veSocial Economic Status: Self-employed; Not working* (Base is Routine Occupations)

+ve

Social Economic Status: Professionals*; Intermediate*; Lower supervisor; Student

–ve

Marital status: Separated; Widowed (Base is Married) +veMarital status: Single, Remarried*; Divorced –veLong distance move* (Base is short move) -ve (v large)Log MDM* (social deprivation) -ve (large)Log Density* -ve LISA: Lo.Lo*, Low.High*; (base random) +ve (large) LISA: High.High* ; High.Low*; -ve (large)

+ Allow Community background to vary at Level-1 & Level-2

* = statistically significant

Model 2 : MDM DifferenceVariable/Characteristic (* = statistically significant) Effect

Age (Base is 25-43 ) +ve (accept 55-64 )Religion: Protestant*; Other*; None* (Base is Catholics) –ve (large)Education* –veIllness (Base is not ill) +veTenure: Rented*; Private Rented* (Base is Owner occupied) +veSocial Economic Status: Not working* (Base is Routine Occupations) +ve

Social Economic Status: Professionals*; Intermediate*; Lower supervisor; Student, Self-employed; Self-employed*

–ve

Marital status: Separated*; Widowed ; Single*; Divorced (Base is Married) +veMarital status:, Remarried* –veLog MDM* (social deprivation) -ve (v. large)Log Density* -ve LISA: Low.High*; High.High*; (base random) +ve LISA: Lo.Lo*; High.Low* -ve

Random Intercepts at Level-1 & Level-2

Migration in an unstable state?

• Despite stability over much of the past two decades, segregation has increased in the past

• The last large increase was 1971-1991, but there have been others before (eg in the 1920s)

• This pattern has been known as the ‘segregation ratchet’ (Boal 2002) in which disequilibria (eg upsets) are followed by stability

Migration in an unstable state?• Boal (2002) terms these ethnonational ‘earthquakes’ – eg

partition, the start of the troubles• These might move enough people, over long enough

distances, to reshape rather than fall within existing population geographies

• However, other forces may also be important besides politics• Belfast, for example, lost c200,000 people between 1971 and

1991 – a common experience of deindustrialisation and counterurbanisation?

• We also do not know enough about other components of population change such as births and deaths

Conclusion• ‘Normal’ levels (and distances) of migration on their own are

insufficient to reshape segregation levels in NI given already-existing population geographies

• There is perhaps less scope for individual choices and preferences than might be assumed in conceptual frameworks such as the Schelling model as structure is very important

• To overcome geographical structure to change levels of segregation, migration must either be of greater volume and/or move people greater distances

Conclusion• NI is not a particularly immobile society – eg

median migration distances compare with those in other UK regions and other countries such as Sweden

• Overall, then, migration might have only a small redistributive effect depending on the underlying population structure

• Does this explain the similar findings to those seen in NI in Scotland? And the durability of segregation in US cities?

Conclusion

• If so, segregation may well be the result of short-lived intense events/processes

• But once in place, it is hard to undo short if similar drastic events

The help provided by the staff of the Northern Ireland Longitudinal Study/Northern Ireland Mortality Study (NILS) and the NILS Research Support Unit is acknowledged. The NILS is funded by the Health and Social Care Research and Development Division of the Public Health Agency (HSC R&D Division) and NISRA. The NILS RSU is funded by the ESRC and the Northern Ireland ‐Government. The authors alone are responsible for the interpretation of the data and any views or opinions presented are solely those of the author(s) and do not necessarily represent those of NISRA/NILS.

Acknowledgements

For MG/IS Info only: Here’s the model for %Cath

Source file: March 8th 2010GCchk (cld 160310).doc

For MG/IS Info only: Here’s the model for MDMn.b. model simpler that that for Cath Diff

Source: March 9th 2010_mrCL100318nc_ (cld 220310).doc