Research Type: Migration & Mobility

This study will adopt the ‘life course’ approach as a theoretical and methodological framework to examine how demographically selective flows of internal migration by age, religion, social class, gender and health contributes to the process of segregation and desegregation between 1974 and 2015. Using the key components of the life course (cohorts, aging and time periods) as a framework the study will conduct the following investigations;

The free movement of labour across invisible national borders is a central tenant of the European Union (EU) project. With the United Kingdom (UK) voting to leave the EU in 2016, EU nationals living in the UK are now faced with uncertainty over their right to stay, similarly those aspiring to immigrate to the UK face new barriers to entry (Wandsworth et al, 2016).   Given the dependence of the Northern Ireland economy on the supply and retention of European Union (EU) workers, Brexit poses a number of important socio-demographic implications.

Shuttleworth and Green (2010) examined the daily work-related (commuting) expectations of Incapacity Benefit (IB) claimants in NI by means of a DEL-sponsored survey. Lower levels of expected work-related mobility were associated with urban neighbourhood contexts, limited access to private transport, poor educational attainment, and public rented housing. The proposal builds on this work by examining the housing mobility of a similar and closely matched set of NILS respondents to understand the wider dimensions of the mobility/immobility experiences of the long-term ill and economically inactive.

Annual migration flows between local authorities are published as part of estimating the mid-year population. These flows are actually based on movements on the health card registration systems measured by changes of addresses but only the marginal flows are published. The link to the NILS data opens the possibility of estimating the annual cross-flows between areas and by characteristics not measured on health card registration data (such as self-reported health status in the previous Census) using Bayesian models that calibrate different sources of data to be consistent. This has potential implications for local health planning if specific areas are experiencing relatively high inflows of those with poorer health.