Transcript Changes to the internal migration methodology used in the English Sub-National Population Projections.

Changes to Internal Migration
methodology for
English Subnational Population
Projections
Robert Fry & Lucy Abrahams
Overview
• Introduction
• Background to subnational population
projections
• Internal migration and the Rogers curve
methodology
• Methodology Review of the Rogers curve
• Analysis of two subnational projections:
• With the Rogers curve
• Without the Rogers curve
• Conclusions
Background
• English subnational population projections
project 25 years into the future
• Use trend data for each component to project
current trends 25 years into future
• Cohort component method
–
–
–
–
–
–
–
–
Starting Population – mid-year population estimates 2006
Remove the armed forces (static population)
Add births
Subtract deaths
Adjust for internal migration
Add net international migration
Add armed forces back in
This process is then repeated to give a 25-year projection
Background
• Developing a new production system for the
English subnational population projections
provided an opportunity to:
• Review & change elements of the methodology
• Build an efficient system with up-to-date software, which
has the ability to cope with methodology changes.
• Focus on internal migration methodology
• Is using the Rogers curve still appropriate for
the 2008-based English subnational
projections?
Internal Migration
• Capture moves within England at the local
authority level (broken down by age & sex)
• Data source: Patient Register data (PR)
• Calculate the probability of moving out of a
local authority (LA):
number of people moving out of an LA
The total number of people living in an LA
Internal Migration
• 5 trend years of data (2002-2006)
• Calculate the out-migration probabilities for
each of the years individually and then take a
five-year average
5 M
OUT , a , g ,i ,T  j
• a = Local Authority

• g = Sex
Pa , g ,i ,T  j
j 1
YRa , g ,i 
• i = Age
5
• T = First year of the projection
•
•
•
•
j = Year
MOUT(a,g,i,T-j)=
P(a, g, i, T-1)=
YR(a, g, i) =
Moves out of a local authority
estimated population in year 1
raw probability of migrating from a
Out-migration Probabilities
• Out-migration Probabilities for Males in Leicester
Out-migration Probabilities
• Out-migration Probabilities of Females in Gloucester
The Rogers Curve
• This out-migration profile was first described
by Andrei Rogers in 1981
• The out-migration profile shows:
• The pre-labour force curve
• The labour-force peak
• The post-retirement curve
• Different models of out-migration
• The Rogers curve with varying numbers of
parameters describes four different models of
out-migration
The Four Models of Out-migration
Current Methodology
•
We apply a 13-parameter curve to the raw
out-migration probabilities:
•
•
•
•
•
The pre-labour force curve
The labour force peak
The retirement peak
The post-retirement peak
Origins of using the Rogers curve
•
•
•
Applied originally to survey data
The model produced more reliable out-migration
probabilities than the survey data
The out-migration profiles of the 1990s were modelled
well by the Rogers curve
Methodology Review: Rogers Curve
• Change to the out-migration profile in many local authorities
Out-migration Probabilities of Females in Mid Bedfordshire
Methodology Review: Rogers Curve
Out-migration Probabilities of Males in Chiltern
Methodology Review: Rogers Curve
• The Rogers curve does not model the data well in
these areas
• A ‘student peak’ appears at age 18/19
• Applying the Rogers curve to the data means we are
not projecting on current trends
• Improving Migration Statistics branch making
improvements to the PR data – using Higher
Education Statistics Authority (HESA) data to capture
more student moves. Use of the Rogers curve would
undo the effects of the additional HESA data
• What impact does removing the Rogers curve have
on the projection?
Investigation
• In theory the current Rogers curve is no
longer suitable for our application
• What effect would its removal have on the
population projections?
Areas with out-migration student
peaks
• What would we expect?
• Lower net migration when raw out-migration
probabilities are used compared to when the
Rogers curve is applied?
• Lower proportion of young adults in
standardised age-profile?
Mid Bedfordshire (Females) –
Out-migration probabilities
Mid Bedfordshire (Females) –
Net internal migration numbers
Mid Bedfordshire (Females) –
Standardised age profile – 2019
Harrow (Males) –
Out-migration probabilities
Harrow (Males) –
Net-migration numbers
Harrow (Males) –
Standardised age profile – 2019
Areas with similar out-migration
probabilities
• What happens in areas where the raw outmigration probabilities are similar to the
Rogers curve probabilities?
• Somewhat dependant on the area. Does the
area typically draw in young adults?
Origin-Destination Matrix
• Out-migration probabilities define how many
people leave an area
• These migrants need a destination
• Origin-Destination matrix is a set of
conditional probabilities giving the probability
of someone moving to a destination
dependant on that person leaving a given
origin
• Generated using the same PRDS data
• No models used
Areas with similar out-migration
probabilities
• Student area = significantly higher numbers
of young adult in-migrants
• Non student area = modest increase in young
adult in-migrants
Nottingham (Males) –
Out-migration probabilities
Nottingham (Males) –
Net internal migration numbers
Nottingham (Males) –
Standardised age profile – 2019
Nottingham (Males) –
In-migration standardised age profile
Plymouth (Males) –
Out-migration probabilities
Plymouth (Males) –
Net internal migration Numbers
Plymouth (Males) –
Standardised age profile – 2019
Plymouth (Males) –
In-migration standardised age profile
West Lancashire (Females) –
Out-Migration Probabilities
West Lancashire (Females) –
Net internal migration numbers
West Lancashire (Females) –
Standardised age profile - 2019
West Lancashire (Females) –
In-migration standardised age profile
What has this initial exploration shown us?
• Differences between projections using the
two sets of probabilities are predictable
• Raw out-migration probabilities produce
results that follow the observed trend data
Conclusions
The use of the Rogers curve in its current form
no longer seems appropriate to use in the
English SNPPs for several reasons:
• We no longer use sample data (fewer
problems establishing firm trends with our
data)
• It no longer fits our current trend data
• HESA data supply
• Using raw out-migration probabilities appears
to improve our projections
Further work
• Further explore differences between use of
raw out-migration probabilities and Rogers
curve (Come to firmer conclusions)
• Look at the possibility of extending the
Rogers curve to include the student peak.
• Look at the possibility of using nonparametric techniques
Further work
• Explore the approach we take in small areas
where the raw data doesn’t establish a trend
(e.g. City of London and Isles of Scilly)
• Expert Panel (October)
• Publication (Spring 2010)
Questions?