Micro-level modelling to identify the separate effects
of migrant status and other personal characteristics
on people’s job-status change
Tony Champion, Mike Coombes and Ian Gordon
Paper presented to the British Society for Population Studies Annual Conference,
University of York, 7-9 September 2011
Context – Approach – Job Status metric – Modelling results
Acknowledgements & disclaimer
This presentation reports on part of a project on skills and career
development undertaken for the Spatial Economics Research Centre
funded by ESRC, BIS, CLG and the Welsh Assembly Government
The authors are grateful to Colin Wymer for the map
Census output is Crown copyright and is reproduced with the permission
of the Controller of HMSO and the Queen’s Printer for Scotland
The permission of the Office for National Statistics to use the Longitudinal
Study (LS) is also gratefully acknowledged, as also is the help provided
by staff (notably Christopher Marshall) of the Centre for Longitudinal
Study Information & User Support (CeLSIUS). CeLSIUS is supported
by the ESRC Census of Population Programme (Award Ref: RES 34825-0004)
This presentation has been cleared by ONS (Clearance Number 30112I),
but the authors alone are responsible for the interpretation of the data
Analytical context 1
Dual focus on people and place: whether places differ in how far they help
their residents achieve career progression and hence whether people
benefit much from moving to/from certain places (as hypothesised in
Fielding’s ‘escalator region’ model)
Literature on agglomeration suggests the large labour pools of big cities
improve the matching of supply and demand, hence higher productivity
for cities and improved prospects for career progression for residents
(including in-migrants)
Some large cities – especially London – have achieved strong growth in
knowledge industries, creating more opportunities for high-level
professionals and managers
Linked Census records in the LS (a 1.096% sample) are used to track
people over time, both in terms of their labour market status and their
spatial location (i.e. social and spatial mobility)
Analytical context 2
• This project’s point of departure is Fielding’s ‘escalator region’ (ER)
hypothesis which involves:
- the ER providing faster social mobility than other regions
- people moving from other regions to ‘step on to the escalator’ and
achieving even faster social mobility than the ER’s longer-term residents
- people ‘stepping off the escalator’ later in their lives for a better quality of
life even if seeing a drop in job status
• The project updates and extends elements of Fielding (1992 etc.) by:
- examining 1991-2001 (cf 1971-81 or 1981-91)
- shifting the spatial focus to the urban scale (cf regional)
- using a single-scale measure of job status (cf ‘social groups’)
- adopting micro-level modelling to identify key determinants of career
progression (cf probability of transitioning between ‘social groups’)
• Its main aim is to see how far any other cities can emulate the ‘escalator’
function of London (the core of Fielding’s ER of South East England), but
this paper’s main focus is on gauging the separate effect of migrant status
Aim of and approach to this analysis
Analytical approach: micro-level modelling that attempts to allow for all
the factors that influence people’s occupational mobility alongside
the place where they live
Modelling approach: general linear modelling of a continuous variable
of career development, with the explanatory variables comprising a
set of personal characteristics including migrant status and place
Migrant status/place: people classified by usual residence in 1991 and
2001 by reference to a set of 38 City Regions that constitutes a full
regionalisation of England & Wales (except Berwick)
Measure of career progression: a single Job Status (JS) scale across
the occupational spectrum, based on log of median hourly earnings
of each SOC90 category for mid 1990s derived from modelling LFS
Dependent variable: change in individuals’ JS scores 1991-2001,
scaled in terms of the proportionate change in earnings that they
might expect from any change in occupation (no change = 0)
Population modelled: all ONS Longitudinal Study members aged 16-64
in 1991 (26-74 in 2001) who were in work at both the 1991 and 2001
Censuses and had a valid SOC90 in 1991 and 2001 (N=c130k)
38 City
Regions
based on
best-fit local
and unitary
authorities
Source: derived
by Mike Coombes
Change in JS scores 1991-2001 for all individuals
Source: Calculated from ONS Longitudinal Study. Crown copyright.
Based on rounded data in the histogram, the modal category is little or no change,
but there is a fairly wide spread around this, downward as well as upward.
In terms of unrounded JS scores, no change in JS = 36.8%;
upward shift = 35.6%; and downward shift = 27.7%
A) Mean change in
Job Status score
A:
Mean change in Job Score, 1991-2001
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
All
B) % distribution
of JS change by
down/same/up,
by migrant status
Migrants 91-01
Non-migrants 91-01
Non-migrants 81-91-01
Non-migs 91-01 but migs 81-91
B:
0%
20%
40%
60%
80%
100%
All
Migrants 91-01
Non-migrants 91-01
Non-migrants 81-91-01
Non-migs 91-01 but migs 81-91
JS down
JS exactly the same
Source: Calculated from ONS Longitudinal Study. Crown copyright.
JS up
% distribution of change in Job Status score 1991-2001,
by gender and age in 1991
0%
20%
.
40%
60%
80%
All
Males
Females
Males 16-24
Males 25-34
Males 35-44
Males 45-54
Males 55-64
Females
Females
Females
Females
Females
16-24
25-34
35-44
45-54
55-64
JS down
JS exactly the same
JS up
Source: Calculated from ONS Longitudinal Study. Crown copyright.
100%
Results for personal characteristics: Job Status in 1991…
0.70
0.60
0.40
0.30
0.20
0.10
)
(r
ef
19
20
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
0.00
1
coefficient
0.50
Job Score in 1991 (20-iles, 1=lowest; 20=highest)
Source: Calculated from ONS Longitudinal Study. Crown copyright.
…Gender, Age, Marital status, Dependent child …
0.20
0.15
Dependent Child
in household
91 and/or 01?
0.05
-0.05
Gender
Age
Si
ng
M le
a
R rrie
em
d
ar
r
D ied
iv
W
id orc
ow
e
ed d
(r
ef
)
D
C
91
&
D
0
C
01 1
N
on
D
o
C
ly
D
0
C
91 1 o
nl
&
01 y
(r
ef
)
16
-1
9
20
-2
4
25
-2
9
30
-3
4
35
-4
4
45
-5
55
4
-6
4
(r
ef
)
m
m
a
al le
e
(r
ef
)
0.00
fe
coefficient
0.10
Marital status
-0.10
Source: Calculated from ONS Longitudinal Study. Crown copyright.
…Birthplace, Immigration year, Ethnicity, Religion…
Source: Calculated from ONS Longitudinal Study. Crown copyright.
…Illness, Type of working, Social Class, Qualifications…
Source: Calculated from ONS Longitudinal Study. Crown copyright.
…Industry of job (top & bottom 10 of 52 sectors)…
Top 10 and bottom 10 coefficients for Industrial Sectors (out of 52)
Computing and related activities
Act auxilliary financial intermediation
Education
Manuf office machinery and computers
Insurance and pension funding, etc
Financial intermediation, etc
Collection,purification/distri of water
Post and telecommunications
Public admin/defence; compulsory SS
Manuf chemicals and chemical products
Activities membership organisations nec
Agriculture/hunting/forestry/fishing
Manuf food/beverage/tobacco products
Manuf pulp, paper and paper products
Other service activities
Manuf textiles
Retail trade, except of motor vehicles
Manuf apparel;dressing/dyeing fur
Tanning/dressing of leather, etc
Mining coal/lignite; extraction of peat
-0.10
-0.05
0.00
0.05
0.10
0.15
coefficient
Source: Calculated from ONS Longitudinal Study. Crown copyright.
0.20
…City Region of residence in 2001 (top & bottom 10 of 38)…
Top 10 and bottom 10 coefficients for City Regions (out of 38)
Reading
London
Coventry
Northampton
Cambridge
Worcester
Liverpool
Manchester
Birmingham
Oxford
Lincoln
Hull
Derby
Bradford
Shrewsbury
Carlisle
Swansea
Chester
Plymouth
Exeter
-0.10
-0.05
0.00
0.05
0.10
0.15
coefficient
Source: Calculated from ONS Longitudinal Study. Crown copyright.
0.20
Anything left for Migration Status? (A) Moved between the 38
CRs or not? (B) Moved 40km+ between CRs or not?
coefficient
(A)
91/01 Migrant status -0.10
-0.05
0.00
0.05
0.10
0.15
Stayed in same City
Region 91/01
Changed City Region
91/01 (ref)
(B)
Distance of move
Moved <40km and City
Region stayers
Moved 40km+ between
City Regions (ref)
Source: Calculated from ONS Longitudinal Study. Crown copyright.
0.20
Summary of results from modelling JS change
The most important determinants of 1991-2001 change in JS score (allowing for
the role of all the other variables in the model) are:
• Job status 1991: lowest-JS starters rise fastest, highest rise the slowest
• Gender: more positive (i.e. higher) JS change for men than women
• Age: highest for those aged 16-19 in 1991 (26-29 in 2001), with very regular
reduction with age
• Social class: clear separate effect, with IIIN highest, followed by SC I & II
• Qualifications: clear separate effect, with Higher Degree highest
• Industrial sector: computing highest, mining & tanning lowest
• City Region of residence in 2001: Reading & London highest, Exeter &
Plymouth lowest (but barely 5% range)
Relatively minor effects (mainly <5% range, most <2.5%): Marital status;
Dependent child in household (and change in this); World region of
birth; Year of immigration; Ethnicity; Religion; Long-term limiting
illness (and change in this); Type of working
Allowing for all these variables, what role left for (Internal) Migrant Status?
• Higher rise for inter-CR migrants 91-01 (cf. non-migrants)
• Higher rise for those moving 40km+ (cf. <40km migrants/non-migrants)
• Both these effects small (3% and 1% respectively)
Next steps
• Experiment with alternative and more specific measures of inter-CR
migration (e.g. move to/from London or up/down city-region hierarchy)
• Attempt to separate the ‘step-up’ effect (JS change at time of move)
from a ‘pure escalator’ effect (JS move afterwards), e.g. by looking at
effect of moving in previous decade (or using an alternative data source
that monitors annual change)
• Replace 1991-based personal characteristics with variables that try to
reflect people’s ‘decadal’ experience (e.g. where they change industry
or city region)
• Incorporate interaction terms, e.g. gender with ethnicity
• Do more to identify the types of people who gain most from being in the
‘right place’ and how this reflects on the role of place
Micro-level modelling to identify the separate
effects of migrant status and other personal
characteristics on people’s job-status change
Tony Champion, Mike Coombes and Ian Gordon
tony.champion@ncl.ac.uk
mike.coombes@ncl.ac.uk
i.r.gordon@lse.ac.uk