micro-level modelling to identify the separate effects of migrant status and other personal...

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 348-25-0004)

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 individualsSource: 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

B) % distribution

of JS change by

down/same/up,

by migrant status

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07

All

Migrants 91-01

Non-migrants 91-01

Non-migrants 81-91-01

Non-migs 91-01 but migs 81-91

Mean change in Job Score, 1991-2001

Source: Calculated from ONS Longitudinal Study. Crown copyright.

B:

A:

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 JS up

% distribution of change in Job Status score 1991-2001, by gender and age in 1991

.0% 20% 40% 60% 80% 100%

All

MalesFemales

Males 16-24Males 25-34Males 35-44Males 45-54Males 55-64

Females 16-24Females 25-34Females 35-44Females 45-54Females 55-64

JS down JS exactly the same JS up


Results for personal characteristics: Job Status in 1991…


0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

20 (r

ef)

co

eff

icie

nt

Job Score in 1991 (20-iles, 1=lowest; 20=highest)

…Gender, Age, Marital status, Dependent child …


-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

mal

e

fem

ale

(ref)

16-1

9

20-2

4

25-2

9

30-3

4

35-4

4

45-5

4

55-6

4 (re

f)

Single

Mar

ried

Remar

ried

Divorc

ed

Wid

owed (r

ef)

DC 91

& 01

DC 01

only

DC 01

only

No DC 9

1 & 0

1 (re

f)

co

eff

icie

nt

Gender Age Marital status

Dependent Child in household 91 and/or 01?

…Birthplace, Immigration year, Ethnicity, Religion…


-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

Born in

UK

co

eff

icie

nt Birth-

place

Year of arrival in UK

Ethnic group Religion

…Illness, Type of working, Social Class, Qualifications…


-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

Wel

l 91

& 01

co

eff

icie

nt

Long-term limiting illnessin 91 and/or 01?

Type of working

Social Class Highest qualification

…Industry of job (top & bottom 10 of 52 sectors)…


-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

Mining coal/lignite; extraction of peat

Tanning/dressing of leather, etc

Manuf apparel;dressing/dyeing fur

Retail trade, except of motor vehicles

Manuf textiles

Other service activities

Manuf pulp, paper and paper products

Manuf food/beverage/tobacco products

Agriculture/hunting/forestry/fishing

Activities membership organisations nec

Manuf chemicals and chemical products

Public admin/defence; compulsory SS

Post and telecommunications

Collection,purification/distri of water

Financial intermediation, etc

Insurance and pension funding, etc

Manuf office machinery and computers

Education

Act auxilliary financial intermediation

Computing and related activities

coefficient

Top 10 and bottom 10 coefficients for Industrial Sectors (out of 52)

…City Region of residence in 2001 (top & bottom 10 of 38)…


-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

ExeterPlymouth

ChesterSwansea

CarlisleShrewsbury

BradfordDerby

HullLincoln

OxfordBirminghamManchester

LiverpoolWorcester

CambridgeNorthampton

CoventryLondon

Reading

coefficient

Top 10 and bottom 10 coefficients for City Regions (out of 38)

Anything left for Migration Status? (A) Moved between the 38 CRs or not? (B) Moved 40km+ between CRs or not?


-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

Stayed in same CityRegion 91/01

Changed City Region91/01 (ref)

Moved <40km and CityRegion stayers

Moved 40km+ betweenCity Regions (ref)

coefficient91/01 Migrant status

Distance of move

(A)

(B)

Summary of results from modelling JS changeThe 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

[email protected] [email protected]

[email protected]

mailto:[email protected]



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