Modelling short-distance residential moves using linked data: An application of the Northern Ireland Longitudinal Study (NILS) Myles Gould (UoL) Ian Shuttleworth & Paul Barr (QUB)

Outline The project – Scope and rationale Research questions The data – Northern Ireland Longitudinal Study (NILS) Analytical strategy (ML models) Model Results – Answers to 3 questions about residential moves through sectarian space Conclusion

The Project & Research Questions

Analysis of address changes within NI ( ) Residential segregation – moving apart or moving together? – understanding migration patterns – which areas are gaining and losing population through migration after the 2001 Census? All part of wider project – who moves, how far, from/to where, and what influences moves – exploring how far internal migration in NI sifts the population with regard to residential segregation (community background & socio-economic deprivation) Today’s focus: residential moves through sectarian space (with particular focus on community background) The Project

Research Questions With respect to residential moves across sectarian space... 1.Are individuals more likely to move in areas where they are in the minority? 2.Is there evidence of longer distance moves in areas where individuals are in the minority? 3.Do Catholics and Protestants move to different areas? 4.Does it matter? [i.e. Our Conclusion] – How far does migration redistribute the population and does it lead to greater residential segregation?

The Data & Analytical Strategy

Information on post-census 76,741 moves from health card registrations ( ) provide response(s) – individual characteristics from 2001 Census Modelled information on moves between 890 SOAs within N.Ireland – analyse information here on origin OAs – built from OAs (c.1,800-2,500 residents) Project selection criteria: – were in NI in 2001 and had a census record – were still in NI in 2007 (e.g. not dead or left NI) – were aged 25-74; exclude student age group (who bias results) & very old The Data: Northern Ireland Longitudinal Study

Modelled Hierarchal Data Structure Level 2: Origin SOAs Level 1: NILS Members The Data: Northern Ireland Longitudinal Study

Analytical Strategy (ML Models) Approach: Complex modelling, simple graphing/presentation Properly handle spatial clustered data Get purchase on within-area and between-area variability Precision weighted estimation Individual variables: sex, religion, marital status, SES, age, limiting long-term illness, housing tenure – can include as overall main effects (as here) – allow different, differentials between SOAs - complex between-area random variation (done but not presented here today) Ecological level-2 variables: %Catholic, deprivation score, density Can also model cross-level interaction between individual & ecological variables (presenting today)

Different Modelling Strategies Source: Tacq (1986) cited in Snijders & Bosker (1999) Multilevel models Macro modelMicro model Cross-level interaction Macro-level effect on micro-level response Main effects only

Results

1. Are individuals more likely to move in areas where they are in the minority? Catholics are less mobile (less likely to make one or more moves) than Protestants everywhere Catholics, ceteris paribus, are less likely to move in areas where they are in the majority Protestants are slightly more likely to move in highly-Catholic areas Overall, cross-level interactions between individual community background and ecological religion are small but highly statistically significant Logit Propensity to move Predicted Ratio %Cath Origin/Destination %Catholic Predicted Probability to move Average 42.9% 42.9%≈0%99.9% Average 42.9% 42.9%≈0%99.9%71.4%14.4% 71.4%

2. Is there evidence of longer distance moves in areas where individuals are in the minority? Protestants in most places tend to move greater distances than Catholics Protestants who lived in highly Catholic areas tended to travel further than those who were in highly Protestant places Catholics in highly Catholic areas tend to move shorter distances than Catholics in Protestant areas Cross-level interactions are large and statistically significant Average 42.9% 42.9%≈0%99.9%71.4%14.4%42.9%≈0%99.9%71.4%14.4%

Ratio %Catholic Receive : Sending – Main Effects Variable/CharacteristicEffect Age* (Base is )+ve (except 55-64) Gender: Male* (Base is Female)+ve Religion: Protestant*; Other/None combi.* (Base is Catholics)–ve (large) Education–ve Illness (Base is not ill) +ve Tenure: Rented*; Private Rented (Base is Owner occupied)+ve Social Economic Status: Professionals; Self-employed*; Not working*, Student (Base is Routine Occupations) +ve Social Economic Status: Intermediate; Lower supervisor; –ve Marital status: Separated; Widowed (Base is Married)+ve Marital status: Single*, Remarried; Divorced–ve % Catholics*-ve Long distance move* (Base is short move)+ve Log MDM* (social deprivation)+ve (v. large) Log Density*-ve * = statistically significant

Catholics are more likely to move to a ‘more catholic area; But some Protestants also move to ‘more catholic areas’ 3. Do Catholics and Protestants move to different areas?

Catholics are less likely to move to a ‘less catholic area’ but some still move; Protestants more likely to move to ‘less catholic areas’ 3. Do Catholics and Protestants move to different areas?

Catholics are always more likely to go to more Catholic areas than Protestants Others/nones lie somewhere between Catholics and Protestants Everybody in highly-Protestant areas are more likely to move to more Catholic areas – a function of the balance of opportunities for moves At the other end of the scale, in highly-Catholic areas, everyone moves to more Protestant (e.g. less Catholic) areas but Protestants are more likely to move to less Catholic areas than Protestants Logten Ratio %Catholics receiving over sending Predicted Ratio %Catholics receiving over sending %Catholic Logten Ratio %Catholics receiving over sending Predicted Ratio %Catholics receiving over sending %Catholic Difference %Catholics receiving compared to sending %Catholic Average 42.9% Moving from and to same sort of area (ratio=1) Moving to more Catholic Moving to less Catholic

Conclusions

Conclusion (1): Does it matter? Clear evidence for communal differentials in: a)migration propensity b)distance moved and c)the types of SOA to which Catholics and Protestants move Even 12 years after the Belfast/Good Friday Agreement of 1998 communal/national identity remains an important factor shaping short-distance migratory moves in NI

Conclusion (2): Does it matter? But this is very different from saying that the segregation is increasing as the two communities ‘move apart’. This is because: – Although Catholics are more likely than Protestants to move to more Catholic SOAs some Protestants still move to more Catholic areas – Catholics move to areas that are typically only ‘slightly more Catholic’ – most moves are short-distance to very similar areas [& vice versa?] – Moves to less Catholic areas tend to cancel out moves to ‘more Catholic areas’ – Differences in migration propensities and distances moved (eg long distance moves for Protestants who live in SOAs more than 80% Catholic) tend to be numerically less noteworthy in changing the demographic composition of SOAs because of the relatively small numbers living at the ends of the distribution (eg Catholics living in Protestant areas and vice versa)

Conclusion (3): Does it matter? Most moves tend to be short distance and most to and from very similar SOAs in terms of religious composition Given the already existing social geography/sectarian geography of NI, migration of the type and level seen since 2001 is unlikely to redistribute the population to lead to either more segregation or integration More moves, particularly over a long distance, are needed to lead to either of these two outcomes

Conclusion With regard to community background, migration did not redistribute the population fundamentally This suggests that ‘normal’ migration will on its not desegregate the NI population The other implication is that to increase/decrease segregation more individuals need to move and to move further Was the population system much more dynamic when biggest increases in residential segregation seen?

Conclusion: Other ML Data Structures for Modelling Spatial-Temporal Data Source: Gould et al (1997)

Acknowledgements The help provided by the staff of the Northern Ireland Longitudinal 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