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Ethnic Population Projections: Review of Models and Findings

Ethnic Population Projections: Review of Models and Findings. Phil Rees School of Geography, University of Leeds Paper presented at the QMSS2 Seminar on Multi-attribute analysis and projections of ethnic populations, Thorbjørnrud Hotel, Jevnaker, Norway

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Ethnic Population Projections: Review of Models and Findings

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  1. Ethnic Population Projections: Review of Models and Findings Phil Rees School of Geography, University of Leeds Paper presented at the QMSS2 Seminar on Multi-attribute analysis and projections of ethnic populations, Thorbjørnrud Hotel, Jevnaker, Norway Acknowledgements: ESRC Research Award RES-165-25-0032 , colleagues Pia Wohland, Paul Norman and Peter Boden Paper available on: http://www.geog.leeds.ac.uk/research/projects/migrants.html

  2. Outline of the Paper • INTRODUCTION: • context, UK example, aim: to review the field of ethnic population projection, drawing mainly on UK experience • INGREDIENTS: • ethnic group definitions, UK experience, mixed ethnicity, ages, regions and migration, uncertainty • POPULATION PROJECTION MODELS: • from OPCS NCWP projections to JRF region projections via GLA, POPGROUP, Coleman & Scherbov, a new model design separating survivorship and migration • INPUTS TO PROJECTION: • Ethnic populations • Ethnic mortality • Estimation of immigration • Estimating ethnic group internal migration • CONCLUDING REMARKS

  3. CONTEXT • Third Demographic Transition • Changing UK composition • UK population increasing at slow rate, 0.64% in 2006-7 • Variation across regions, highest in South around London • 2001-6 • 2.7% increase in total population • 0.4% decrease in White British population • 23% increase in not White British population • 2001: 87% White British, 13% not White British • 2006: 84% White British, 16% not White British • Highly variable across space: • More ethnic minorities concentration in cities, in south • Greatest growth in ethnic minorities outside core areas

  4. Ethnic definitions: variation over space

  5. T

  6. Additional considerations Recognizing mixed ethnicity Regions and migration

  7. UK ethnic projection studies • 1971 Census OPCS: NCWP, GB • 1980s by OPCS 5 groups for E & W • 1991 Census: London Boroughs by Marian Storkey, John Hollis and others for the GLC/GLA, and for Bradford by Ludi Simpson.

  8. UK ethnic projection studies • Then after the 2001 Census data again on ethnicity had been published we have a further set of local studies by Ludi Simpson and co-workers on local areas in North West England, Leicester and Birmigham. • A projection using five ethnic groups was carried out by Phil Rees and John Parsons for GORS, Wales, Scotland and Northern Ireland in 2006, updated in 2009

  9. UK ethnic estimate and projection studies • Further GLA studies by Baljit Bains, Ed Klodawski and John Hollis • National projection by Coleman and Scherbov including stochastic variants • Ongoing: Phil Rees, Paul Norman, Peter Boden and Pia Wohland) • Ongoing: Ludi Simpson & Nissa Finney • Ongoing: James Raymer

  10. New model design

  11. Inputs to the projection • Base populations • Mortality • Immigration • Internal migration • Emigration • Fertility

  12. White British Chinese Asian or Asian British: Pakistani

  13. Concluding Remarks This paper has reviewed some recent work on ethnic population projection. We have reviewed the requirements of robust ethnic projections, which include proper understanding of the ethnic classifications available for use and the need to specify ages at single year resolution for projections with the greatest value. In choosing a suitable projection model for implementing the projection, it is necessary to understand fully the nature of the migration information available. A trade-off between the ease of computation of single region models and the complexity but greater theoretical rigour of multi-regional models must be arrived at. But the biggest challenge in many countries, including the UK in particular, is the lack of good data on the components of change. This requires innovative thinking about how proxy data and good statistical methods can be used to supply input variables to the projection.

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