DESIGN OF A SUBNATIONAL POPULATION ... - geog.leeds.ac.uk€¦ · DESIGN OF A SUBNATIONAL POPULATION PROJECTION MODEL FOR ETHNIC GROUPS Phil Rees, Paul Norman, Pete Boden, Pia Wohland

1

School of GeographyFACULTY OF ENVIRONMENT

ESRC Research Award RES-165-25-0032, What happens when international migrants settle?Ethnic group population trends and projections for UK local areas

DESIGN OF A SUBNATIONALPOPULATION PROJECTION

MODEL FOR ETHNIC GROUPS

Phil Rees, Paul Norman, Pete Boden, Pia Wohland

CSAP Meeting, School of Geography, University of Leeds,14.October 2008

Aims– To project the ethnic populations of local areas

(authorities) in the UK over the next 50 years (project)– To explain the model design being developed

(presentation)

Outline– State space of model (Phil Rees)– Accounting framework (Phil Rees)– Model structure (Phil Rees– Internal migration model (Phil Rees)

Followed by:– Ethnic mortality estimation (Pia Wohland)– New Migrant Databank (Peter Boden)– Ethnic fertility estimation (Paul Norman)

1.Introduction&projectionmodels

2.EthnicMortality

3.Migration

4. Ethnicfertility

2

Source: Dunnell, K. (2007) The changing demographic picture of the UK: NationalStatistician’s annual article on the population. Population Trends 130, 9-21.

STATE SPACEZones (432) (O origins, D destinations)England 352LAs(City of London with Westminster; Isles of Scilly with Penwith)

Wales 22 UAsScotland 32 CAsNorthern Ireland 26 DCs

Ages (102 period-cohorts) (A)Bto0, 0to1, 1to2, …, 99to100, 100+to101+ (102)

Sexes (2) (S)Males, Females

Ethnic Groups (16) (E)16 Groups from the 2001 Census

Time intervals (flexible) (T)1981-1991-2001-2, … , 2005-6, 2006-7, …, 2050-51


2.EthnicMortality

3.Migration

4. Ethnicfertility

Age

Time

x

x+1

x+2

t t+1

lx

lx+1

Survival probabilitiesand survivorshipprobabilities

Lx

Lx+1

sx

px


2.EthnicMortality

3.Migration

4. Ethnicfertility

3


2.EthnicMortality

3.Migration

4. Ethnicfertility

Accounting framework for zones within countries

DESTINATIONS England Wales Scotland

Northern

Ireland Rest of world Deaths Totals Survivors Total Out-Migrants, Imm igrants, Final Populations

C ity of London

Barking and

Dagenham … Isle of Wight

Isle of

Anglesey/Ynys

Môn Gwynedd …

Cardiff/Caerdy

dd Aberdeen City Aberdeenshire : West Lothian Derry City L imavady : Belfast Start Populations

Total Survivors by

origin

Total Survivors by

origin within UK

Total Out-

Migrants to

England

Total Out-

Migrants to

Wales

Total Out-

Migrants to

Scotland

Total Out-

Migrants to

Northern

Ireland

ORIGINS Zone names Zones 1(1) 2(1) … N(1) 1(2) 2(2) … N(2) 1(3) 2(3) … N(3) 1(4) 2(4) … N(4) R D S S *(1) *(2) *(3) *(4)

England City of London 1(1) S1(1)1(1) M1(1)2(1) … M1(1)N (1) M1(1)1(2) M1(1)2(2) … M1(1)N(2) M1(1)1(3) M1(1)2(3) … M1(1)N(3) M1(1)1(4) M1(1)2(4) … M1(1)N(4) E1(1) D1(1) SP1(1)*(*) S1(1) S1(1)UK TOM1(1)*(1) TOM1(1)*(2) TOM1(1)*(3) TOM1(1)*(4)

Barking and Dagenham 2(1) M 2(1)1(1) S2(1)2(1) … M2(1)N (1) M2(1)1(2) M2(1)2(2) … M 2(1)N(2) M2(1)1(3) M2(1)2(3) … M2(1)32(3) M2(1)1(1) M2(1)1(1) … M 2(1)1(1) E2(1) D2(1) SP2(1)*(*) S2(1) S2(1)UK TOM2(1)*(1) TOM2(1)*(2) TOM2(1)*(3) TOM2(1)1(1)

: : : : … : : ; ; : ; ; : ; : : : : : :

Isle of Wight N(1) MN(1)1(1) MN(1)2(1) … SN (1)N (1) MN(1)1(2) MN (1)2(2) … MN (1)N (2) MN(1)1(3) MN(1)2(3) … MN(1)N(3) MN (1)1(4) MN (1)2(4) … MN (1)N (4) EN(1) DN(1) SPN (1)*(*) SN(1) SN(1)UK TOMN(1)*(1) TOMN (1)*(2) TOMN (1)*(3) TOMN (1)*(4)

Wales Isle of Anglesey/Ynys Môn 1(2) M 1(2)1(1) M1(2)2(1) … M1(2)N (1) S1(2)1(2) M1(2)2(2) … M 1(2)N(2) M1(2)1(3) M1(2)2(3) … M1(2)N(3) M1(2)1(4) M1(2)2(4) … M 1(2)N(4) E1(2) D1(2) SP1(2)*(*) S1(2) S1(2)UK TOM1(2)*(1) TOM1(2)*(2) TOM1(2)*(3) TOM1(2)*(4)

Gwynedd 2(2) M 2(2)1(1) M2(2)2(1) … M2(2)N (1) M2(2)1(2) S2(2)2(2) … M 2(2)N(2) M2(2)1(3) M2(2)2(3) … M2(2)N(4) M2(2)1(4) M2(2)2(4) … M 2(2)N(4) E2(2) D2(2) SP2(2)*(*) S2(2) S2(2)UK TOM2(2)*(1) TOM2(2)*(2) TOM2(2)*(3) TOM2(2)*(4)

: : : : … : : : … : : : … : : : … : : : : : :

Cardiff/Caerdydd N(2) M N(2)1(1) MN(2)2(1) … M N(2)N(1) MN(2)1(2) MN (2)2(2) … S N(2)N(2) MN(2)1(3) MN(2)2(3) … M N(2)N(3) MN (2)1(4) MN (2)2(4) … MN (2)N (4) EN(2) DN(2) SPN (2)*(*) SN(2) SN(2)UK TOMN(2)*(1) TOMN (2)*(2) TOMN (2)*(3) TOMN (2)*(4)

Scotland Aberdeen City 1(3) M 1(3)1(1) M1(3)2(1) … M1(3)N (1) M1(3)1(2) M1(3)2(2) … M 1(3)N(2) S1(3)1(3) M1(3)2(3) … M1(3)N(3) M1(3)1(4) M1(3)2(4) … M 1(3)N(4) E1(3) D1(3) SP1(3)*(*) S1(3) S1(3)UK TOM1(3)*(1) TOM1(3)*(2) TOM1(3)*(3) TOM1(3)*(4)

Aberdeenshire 2(3) M2(3)1(1) M2(3)2(1) … M2(3)N (1) M2(2)1(2) M2(3)2(2) … M2(3)N(2) M2(3)1(3) S2(3)2(3) … M2(3)N(3) M2(3)1(4) M2(3)2(4) … M2(3)N(4) E2(3) D2(3) SP2(3)*(*) S2(3) S2(3)UK TOM2(3)*(1) TOM2(3)*(2) TOM2(3)*(3) TOM2(3)*(4)

: : : : … : : : … : : : … : : : … : : : : : :

West Lothian N(3) MN(3)1(1) MN(3)2(1) … MN(3)N(1) MN(3)1(2) MN (3)2(2) … MN (3)N (2) MN(3)1(3) MN(3)2(3) … SN (3)N (3) MN (3)1(4) MN (3)2(4) … MN (3)N (4) EN(3) DN(3) SPN (3)*(*) SN(3) SN(3)UK TOMN(3)*(1) TOMN (3)*(2) TOMN (3)*(3) TOMN (3)*(4)

Northern Ireland Derry City 1(4) M 1(4)1(1) M1(4)2(1) … M1(4)N (1) M1(4)1(2) M1(4)2(2 … M 1(4)N(2) M1(4)1(3) M1(4)2(3) … M1(4)N(3) S1(4)1(4) M1(4)2(4) … M 1(4)N(4) E1(4) D1(4) SP1(4)*(*) S1(4) S1(4)UK TOM1(4)*(1) TOM1(4)*(2) TOM1(4)*(3) TOM1(4)*(4)

Limavady 2(4) M 2(4)1(1) M2(4)2(1) … M2(4)N (1) M2(4)1(2) M2(4)2(2) … M 2(4)N(2) M2(4)1(3) M2(4)2(3) … M2(4)N(3) M2(4)1(4) S2(4)2(4) … M 2(4)N(4) E2(4) D2(4) SP2(4)*(*) S2(4) S2(4)UK TOM2(4)*(1) TOM2(4)*(2) TOM2(4)*(3) TOM2(4)*(4)

: : : : … : : : … : : : … : : : … : : : : : : :

Belfast N(4) M N(4)1(1) MN(4)2(1) … M N(4)N(1) MN(4)1(2) MN (4)2(2) … MN (4)N (2) MN(4)1(3) MN(4)2(3) … MMN(4)N (3) MN (4)1(4) MN (4)2(4) … S N(4)N(4) EN(4) DN(4) SPN (4)*(*) SN(4) SN(4)UK TOMN(4)*(1) TOMN (4)*(2) TOMN (4)*(3) TOMN (4)*(4)

Rest of world Immigrants R I 1(1) I2(1) … IN(1) I1(2) I2(2) … IN(2) I1(3) I2(3) … IN(3) I1(4) I2(4) … IN(4) 0 0 I *(*) 0 0 I *(1) I*(2) I*(3) I*(1)

Totals Populations * FP*(*)1(1) FP*(*)2(1) FP*(*)N (1) FP*(*)1(2) FP*(*)2(2) FP*(*)N (2) FP*(*)1(3) FP*(*)2(3)… FP*(*)N(3) FP*(*)1(4) FP*(*)2(4)

… FP*(*)N(4) E* D* T*(*)*(*) S* S*UK FP*(*)*(1) FP*(*)*(2) FP*(*)*(3) FP*(*)*(4)

Transitions zones Total In-migrants to England Total In-migrants to Wales Total In-m igrants to Scotland Total In-migrants to Northern Ireland

Total

emigrants by

country

Total deaths

by country

Total start

population by

country

Total Survivors by

destination

Total Survivors by

destination

within UK Total intra- and inter-country migrants

Total In-migrants Total In-Migrants from England *(1) TIM*(1)1(1) TIM*(1)2(1) TIM*(1)N (1) TIM*(1)1(2) TIM*(1)2(2)… TIM*(1)N(2) TIM*(1)1(3) TIM*(1)2(3)

… TIM*(1)N (3) TIM*(1)1(4) TIM*(1)2(4)… TIM*(1)N(4) E*(1) D*(1) SP*(1)*(*) S*(1) S*(1)UK M*(1)*(1) M*(1)*(2) M*(1)*(3) M*(1)*(4)

Total In-Migrants from Wales *(2) TIM*(2)1(1) TIM*(2)2(1) TIM*(2)N (1) TIM*(2)1(2) TIM*(2)2(2) TIM*(2)N(2) TIM*(2)1(3) TIM*(2)2(3) TIM*(2)N (3) TIM*(2)1(4) TIM*(2)2(4) TIM*(2)N(4) E*(2) D*(2) SP*(2)*(*) S*(2) S*(2)UK M*(2)*(1) M*(2)*(2) M*(2)*(3) M*(2)*(4)

Total In-Migrants from Scotland *(3) TIM*(3)1(1) TIM*(3)2(1) TIM*(3)N (1) TIM*(3)1(2) TIM*(3)2(2) TIM*(3)N(2) TIM*(3)1(3) TIM*(3)2(3) TIM*(3)N (3) TIM*(3)1(4) TIM*(3)2(4) TIM*(3)N(4) E*(3) D*(3) SP*(3)*(*) S*(3) S*(3)UK M*(3)*(1) M*(3)*(2) M*(3)*(3) M*(3)*(4)

Total In-Migrants from Northern

Ireland *(4) TIM*(4)1(1) TIM*(4)2(1) TIM*(4)N (1) TIM*(4)1(2) TIM*(4)2(2) TIM*(4)N(2) TIM*(4)1(3) TIM*(4)2(3) TIM*(4)N (3) TIM*(4)1(4) TIM*(4)2(4) TIM*(4)N(4) E*(4) D*(4) SP*(4)* S*(4) S*(4)UK M*(4)*(1) M*(4)*(2) M*(4)*(3) M*(4)*(4)

Key to cell colours surviving stayers surviving emigrants final populations

surviving migrants within country deaths survivors

surviving migrants between countries start populations immigrants

4

populations fertility mortalityInternal

migrationInternational

migration

survival

emigration

migrationconditional on

survival within UK

immigration

Mortalityassumptions

Emigrationassumptions

Internal migrationassumptions or

model

Immigrationassumptions

Projected deaths,survivors

Projected survivingemigrants

Projected survivinginternal migrants

finalpopulations

Projected survivingimmigrants

Projected finalpopulations and births

Fertilityassumptions

births

Projectionoutputs

Initialdatabase

scenarios

5

Internal migration model

Because there are a huge number of variables involved in internal

migration, we will need to simplify things.

We cannot estimate the saturated model:

O432 D432 E16 S2 A102

We will start with an independence model to get the projections

underway

O434 + D434 + E18 + S2 +A102

We will then adopt a compromise drawing on other work (van

Imhoff et al. 1997, Raymer et al. 2008) such as

A102S2 + O432D432E7 + E16

age-sex + origin-age, origin-destination-broad ethnicity + detailed

ethnicity


2.EthnicMortality

3.Migration

4. Ethnicfertility

Introduction-aEthnic mortality

1. Introduction &the projectionmodels

2.EthnicMortality

3.Migration

4. Ethnicfertility

Pia Wohland

6

Introduction-a

United Kingdom:

ethnic groups used in estimation or projection modelsbut no ethnic mortality differences recognised


2.EthnicMortality

3.Migration

4. Ethnicfertility

Introduction-a

•United States:Mortality statistics collected by race

2003 life expectanciesWhite men: 75.3Black men: 68.9.White women 80.4Black women 75.9

•New Zealand:life expectancies projections (men/women )European or Other including New Zealand (79.4/ 83.2),Maori (70.4/75.2),Asian (84.0/ 87.2)Pacific (72.8/77.2).

•United Kingdom:Longitudinal study


2.EthnicMortality

3.Migration

4. Ethnicfertility

7


2.EthnicMortality

3.Migration

4. Ethnicfertility

Mortality data by ethnicgroups for the UK?


2.EthnicMortality

3.Migration

4. Ethnicfertility

For example, Burström and Friedlund (2001, p.836) state that,based on a study of 170 thousand respondents to the SwedishSurvey of Living Conditions that“results suggest that poor self-rated health is a strong predictorof subsequent mortality in all sub-groups studied.” Burströmand Friedlund (2001, p.836)

Commentators have suggested differences in the way racial/ethnicgroups interpret questions on health, but McGee et al. (1999,p.45) affirm that“Whatever self-reported health was measuring, it wasnevertheless a strong predictor of mortality among racial/ethnicgroups we studied”.

Self-reported health a good predictor of subsequent mortality

8


2.EthnicMortality

3.Migration

4. Ethnicfertility

Self reported illness in Census 2001

MORTALITY RATES

STANDARDISED MORTALITY RATIOS

POPULATION DATA

REGRESSION ANALYSIS

DEATHS DATA

2001 Vital statistics

Countries & LocalAuthorities

2001 Mid year Estimates


SMR = f(SIR)

2001 , UK Standard

Countries & Local Authorities

2001, UK Standard


STANDARDISED MORTALITY RATIOS BYETHNICITY

2001, UK Standard


LIFE TABLES & SURVIVORSHIP PROBABILITIES BY ETHNICITY

2001 (Calendar Year)


RESIDENTS DATA

2001 Census Tables S16,S65


LIMITING LONG TERM ILLNESSDATA

2001 Census Tables S16,S65


STANDARDISED ILLNESS RATIOS BYETHNICITY

2001, UK Standard


STANDARDISED ILLNESS RATIOS

2001 , UK Standard


RESIDENTS DATA BY ETHNICITY

2001 Census Tables ST 101,107, 207, 318


LIMITING LONG TERM ILLNESS BYETHNICITY

2001 Census Tables ST 101,107, 207, 318


9

Illness data from the census- all group SIR,Vital statistics and mid year estimates- all group SMRSMR as a function of SIR

Gender Nation r2 Intercept Slope

England 0.51 52.1 0.48Females Wales 0.78 43.9 0.37

Scotland 0.69 60.5 0.64Northern Ireland 0.16 71.2 0.26

England 0.63 47.3 0.52

Males Wales 0.56 54.9 0.39

Scotland 0.75 28.3 0.82Northern Irland 0.40 59.9 0.36


2.EthnicMortality

3.Migration

4. Ethnicfertility

SIRs for Indian men

Indian SIR

150 to 200 (1)

102 to 150 (102)

98 to 102 (21)

75 to 98 (168)

30 to 75 (62)

Bangladeshi SIR

200 to 400 (4)

150 to 200 (63)

102 to 150 (227)

98 to 102 (19)

75 to 98 (40)

30 to 75 (1)

SIRs Bangladeshi men

Using illness data from the census- ethnic group SIR

10

SMR Bangladeshi Males

150 to < 175 (4)

130 to < 150 (41)

115 to < 130 (98)

101 to < 115 (146)

99 to < 101 (22)

85 to < 99 (43)

62 to < 85 (0)

Introduction-a

SMR White Bri tis h Males

150 + (0)

130 to < 150 (1)

115 to < 130 (18)

101 to < 115 (75)

99 to < 101 (25)

85 to < 99 (202)

62 to <85 (33)

SMR Indian Males

150 + (0)

130 to <150 (1)

115 to <130 (19)

101 to <115 (79)

99 to <101 (17)

85 to <99 (187)

62 to <85 (51)

SMR Chinese Males

150 + (0)

130 to < 150 (0)

115 to < 130 (0)

101 to < 115 (1)

99 to < 101 (0)

85 to < 99 (58)

55 to < 85 (295)

SMRs forWhiteBritishMales

SMRs forIndianMales

SMRs forBangladeshiMales

SMRs forChinesemales

Ethnic groups SMRsCalculated using ethnicgroup SIR and country

SMR = f(SIR)

SMRs all Males

Ethnic group life tables – using ethnic group SMR andall group mortality rate age distribution

11

http://www.geog.leeds.ac.uk/wpapers/08-04.pdf


2.EthnicMortality

3.Migration

4. Ethnicfertility


2.EthnicMortality

3.Migration

4. Ethnicfertility

Pete Boden

Concept and development and preliminary analysis

New Migrant

databankNew Migrant

databank

12

Uncertainty


2.EthnicMortality

3.Migration

4. Ethnicfertility


2.EthnicMortality

3.Migration

4. Ethnicfertility

13

Alternative sources

Review of alternative sources of international migration data, completed for the GLA in 2006


2.EthnicMortality

3.Migration

4. Ethnicfertility

• ‘Single view’ of alternative statistics

• Clarity of conceptual and measurement differences

• Framework for analysis of trends and patterns inmigration

• Analysis of short-term and long-term migration

• Derivation of ethnic-group migration estimates

• Complement the ongoing programme of work at ONS

New Migrant

databankNew Migrant

databank1. Introduction &

the projectionmodels

2.EthnicMortality

3.Migration

4. Ethnicfertility

Purpose:

14

data sources

• Local Authority District & Unitary Authority (LADUA) statistics, 2001-2008

• Census (2001)

• Total International Migration (TIM)/International Passenger Survey (IPS)

• GP registrations (NHS-Flag4)

• National insurance number registrations (NINo)

• Workers registration scheme (WRS)

• Higher Education Statistics Agency (HESA)

• Labour Force Survey (LFS)

• Work Permits (WP)/Points Based System (PBS)

• National Pupil Database (NPD)

New Migrant

databankNew Migrant

databank


2.EthnicMortality

3.Migration

4. Ethnicfertility

Demonstration

New Migrant

databankNew Migrant

databank


2.EthnicMortality

3.Migration

4. Ethnicfertility

15

Change over timeEngland

All data are Crown copyright. Sources: 100% data extract from the National Insurance Recording System (NIRS): 2006 Mid-yearestimates (ONS, 2007a); GP registration statistics provided by ONS

200,000

300,000

400,000

500,000

600,000

700,000

2001 2002 2003 2004 2005 2006

Mig

ratio

nC

ou

nt

NINO - All NINO - non-Accession

GP Regs TIM - MYE Immig

Census


2.EthnicMortality

3.Migration

4. Ethnicfertility

GP Regs vs TIM estimates, 2002-2006

All data are Crown copyright. Sources: 100% data extract from the National Insurance Recording System (NIRS): 2006Mid-year estimates (ONS, 2007a); GP registration statistics provided by ONS

TIM higher than GP Regs

-20% -15% -10% -5% 0% 5% 10% 15% 20% 25%

England

North East

North West

Yorkshire & Humber

East Midlands

West Midlands

East of England

London

South East

South West

TIM lower than GP Regs1. Introduction &

the projectionmodels

2.EthnicMortality

3.Migration

4. Ethnicfertility

16

West Midlands and Yorkshire & Humber

-

10,000

20,000

30,000

40,000

50,000

60,000

2001 2002 2003 2004 2005 2006 2007

Mig

ration

Coun

t



Census

-

10,000

20,000

30,000

40,000

50,000

60,000

2001 2002 2003 2004 2005 2006 2007

Mig

ratio

nC

ou

nt



Census

West Midlands Yorkshire & Humber



2.EthnicMortality

3.Migration

4. Ethnicfertility

Major Urban district – Yorkshire & Humber

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2001 2002 2003 2004 2005 2006 2007

Mig

ration

Co

un

t



Census

24.0%

17.4%

22.2%

15.0%

21.1%

28.0%

23.9%

0% 5% 10% 15% 20% 25% 30%

NINO non-Acc

NINO Acc

GP Regs

WRS

Census

TIM - MYE

TIM - SNPP 2016

Percentage

Share of Region



2.EthnicMortality

3.Migration

4. Ethnicfertility

17

Rural county – West Midlands

0

500

1,000

1,500

2,000

2,500

3,000

2001 2002 2003 2004 2005 2006 2007

Mig

ratio

nC

ou

nt

1.3%

7.2%

2.6%

16.9%

2.4%

2.0%

3.9%

0% 5% 10% 15% 20%

NINO non-Acc

NINO Acc

GP Regs

WRS

Census

TIM - MYE

TIM - SNPP 2016

Percentage

Share of Region



2.EthnicMortality

3.Migration

4. Ethnicfertility

London Borough

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2001 2002 2003 2004 2005 2006 2007

Mig

ration

Co

unt

6.2%

7.8%

6.2%

1.4%

2.9%

4.3%

3.9%

0% 2% 4% 6% 8% 10%

NINO non-Acc

NINO Acc

GP Regs

WRS

Census

TIM - MYE

TIM - SNPP 2016

Percentage

Share of Region



2.EthnicMortality

3.Migration

4. Ethnicfertility

18

Next steps

Integration of new data - Work Permit statistics, latest NINo andFlag4 data, MYE 2007

Analysis of patterns and trends for UK local authorities

Cluster analysis to identify area ‘types’ where trends and datasetdifferences are similar.

A model for estimating local immigration and emigration..


2.EthnicMortality

3.Migration

4. Ethnicfertility

Introduction-aEthnic fertility


2.EthnicMortality

3.Migration

4. Ethnicfertility

Paul Norman

19

Fertility rates for the projections: needs …

Information on past trends (for LAs)• By all persons, estimate by ethnic group

A range of plausible assumptions (for area types?)• Different fertility scenarios by ethnic group

Measuring fertility (live births)• Age-Specific Fertility Rate (ASFR)

• Total Fertility Rate (TFR)

1.69 = sum ASFRs / 1,000 * 5


2.EthnicMortality

3.Migration

4. Ethnicfertility

Fertility rates in a projection model

ASFRs in a projection: applied to survivingwomen

a.) 9,788 babies = 5,013 boys & 4,774 girls

b.) 7,927 babies = 4,060 boys & 3,867 girls

0

20

40

60

80

100

120

140

<20 20-24 25-29 30-34 35-39 40+

a.) TFR = 1.69

b.) TFR = 1.44


2.EthnicMortality

3.Migration

4. Ethnicfertility

20

Fertility trends vary across space & time

0

20

40

60

80

100

120

140

<20 20-24 25-29 30-34 35-39 40+

Ag

e-S

pe

cif

icF

ert

ility

Ra

tes

(pe

r1

,00

0w

om

en

)

1986

2001

2006

0

20

40

60

80

100

120

140

<2

0

20

-24

25

-29

30

-34

35

-39

40

+

<2

0

20

-24

25

-29

30

-34

35

-39

40

+

<2

0

20

-24

25

-29

30

-34

35

-39

40

+

North West East London

Ag

e-S

pe

cif

icF

ert

ility

Ra

tes

(pe

r1

,00

0w

om

en

)

1986

2001

2006

England & Wales

Government Office Regions


2.EthnicMortality

3.Migration

4. Ethnicfertility

Fertility trends vary across space & time

All persons in Bradford & Leeds: 1982-2006

0

20

40

60

80

100

120

140

160

<20 20-24 25-29 30-34 35-39 40+

AS

FR

s

Bradford

0

20

40

60

80

100

120

140

160

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

1.25

1.50

1.75

2.00

2.25

2.50

<20 20-24 25-29 30-34

35-39 40+ TFR

AS

FR

s TF

Rs

0

20

40

60

80

100

120

140

160

<20 20-24 25-29 30-34 35-39 40+

AS

FR

s

Leeds

0

20

40

60

80

100

120

140

160

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

1.25

1.50

1.75

2.00

2.25

2.50

<20 20-24 25-29 30-34 35-39

40+ TFR

AS

FR

s TF

Rs


2.EthnicMortality

3.Migration

4. Ethnicfertility

21

PopulationTrends 133


2.EthnicMortality

3.Migration

4. Ethnicfertility

Estimate TFRs by ethnic group population?

Child : woman ratios = family size indicatorTFR(Eth) = TFR(AP) * ( CWR(Eth) / CWR(AP) )

But this …

• Under-estimates White TFRs

• Over-estimates Mixed TFRs

Because …

• ‘White’ mother + ‘Black’ father ‘Mixed’ child

• Missed in CWRs


2.EthnicMortality

3.Migration

4. Ethnicfertility

22

Estimate TFRs by ethnic group?

Census SARs can link women & their children

Adjust CWRs

TFR(Eth) = TFR(E&W) * ( SF(Eth) * CWR(Eth) / CWR(E&W) )

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

All groups White Mixed Asian Black Chinese and

OthersUnadjusted Adjusted


2.EthnicMortality

3.Migration

4. Ethnicfertility

Estimate ASFRs by ethnic group?Use survey sources (LFS, GHS, SARs) to estimate / model

General Household Survey

• Age at first birth

• Mother’s country of birth

0

10

20

30

40

50

60

15-19 20-24 25-29 39-34 35-39 40+

0

10

20

30

40

50

60

15-19 20-24 25-29 39-34 35-39 40+

UK Eire Europe South Asia Africa-Carribean RoW

1991

2001


2.EthnicMortality

3.Migration

4. Ethnicfertility

23

Hadwigercurve

Cubic spline

Estimate ASFRs by ethnic group?Curves based on 5 year groups, small numbers(?) & spiky(?)

Smooth, interpolate, then fit to all births by LA

y = -8.76 + (20.1 * age) + (-4.94 * age^2) + (0.33 * age^3)


2.EthnicMortality

3.Migration

4. Ethnicfertility

Estimate ASFRs by ethnic group

Database of LA level ASFRs & TFRs by ethnic group,1980s-2006

Factors on which to focus, by ethnic group

• Trends in TFRs

• Trends in ASFRs, ‘ageing’ of curves

• Convergence to the White group?

Develop some scenarios for the projections

Too complex to make these LA-specific


2.EthnicMortality

3.Migration

4. Ethnicfertility

24

Trends for all LAs in UKk-means classification of LA TFRs & ASFRs across 1982-2006

0

20

40

60

80

100

120

140

160

y06_a15 y06_a20 y06_a25 y06_a30 y06_a35 y06_a40

C1 C2 C3 C4 C5 C6 C7 C8 C9

0

20

40

60

80

100

120

140

160

y82_a15 y82_a20 y82_a25 y82_a30 y82_a35 y82_a40

C1 C2 C3 C4 C5 C6 C7 C8 C9

1.00

1.25

1.50

1.75

2.00

2.25

2.50

y82

_tfr

y83

_tfr

y84

_tfr

y86

_tfr

y87

_tfr

y88

_tfr

y89

_tfr

y90

_tfr

y91

_tfr

y92

_tfr

y93

_tfr

y94

_tfr

y95

_tfr

y96

_tfr

y97

_tfr

y98

_tfr

y99

_tfr

y00

_tfr

y01

_tfr

y02

_tfr

y03

_tfr

y04

_tfr

y05

_tfr

y06

_tfr

C1 C2 C3 C4 C5 C6 C7 C8 C9

ASFRs in1982

ASFRs in2006

TFRs 1982-2006Develop some scenarios for theprojections …

Classification-specific by ethnicgroup


2.EthnicMortality

3.Migration

4. Ethnicfertility

THANKS !

Documents

DESIGN OF A SUBNATIONAL POPULATION ... - geog.leeds.ac.uk€¦ · DESIGN OF A SUBNATIONAL POPULATION PROJECTION MODEL FOR ETHNIC GROUPS Phil Rees, Paul Norman, Pete Boden, Pia Wohland