Transcript THE SCOTTISH LONGITUDINAL STUDY: A new resource for …

THE VALUE OF LONGITUDINAL
RESEARCH IN POPULATION
GEOGRAPHY
Paul Boyle
(Tom Cooke, Zhiqiang Feng, Vernon Gayle, Elspeth Graham,
Hill Kulu, Paul Norman, Clive Sabel)
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As more engage with the cultural turn there is a risk that
Geography becomes detached from important social issues and
concerns
While Population Geography is regarded as a quantitatively
strong sub-discipline in Geography, it is weak compared to
other disciplines – we face a critical time ahead
Longitudinal data – Population Geographers need to make
better use of existing resources
Longitudinal methods – exciting possibilities for a quantitative
Population Geography of the future
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Theoretically
advanced
research
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Theoretically
weak
research
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Advanced
research
methods
Theoretically
advanced
research
Theoretically
weak
research
Weak
research
methods
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Advanced
research
methods
Population
Geography?
Theoretically
advanced
research
Theoretically
weak
research
Weak
research
methods
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Quantitative
research in
Geography
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It has been argued that an unhealthy divide
has developed within the discipline of
Geography
Geography is gradually ignoring important
social questions for which quantitative
analysis is particularly pertinent
The focus on qualitative methods may have
gone too far?
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“the rise of a post-modern human geography,
with its stress on textuality and texts,
deconstruction, critique, reading and
interpretation, has led human geography into
a theoretical playground where its
practitioners stimulate or entertain
themselves and a handful of readers, but
have in the process become increasingly
detached from contemporary social issues
and concerns.”
Hamnett (2003: 1)
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“there has been a radical shift in the dominant
methodology of much human geographical research.
Quantitative techniques and aggregate social
research have been largely abandoned, in favour of
small scale, interpretative, qualitative, in-depth
methodologies. Analysis of large data sets has
become totally passe, the object of suspicion or even
derision as empiricist. Arguably, methodological
development has been characterised by a shift from
much mindless quantification and measurement to an
unquestioning use of qualitative techniques.”
(Hamnett 2003: 2)
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“The risk is that much of human geography
will cease to be taken seriously in the world
beyond the narrow confines of academe. It
will be seen simply as a corner of the
postmodern theoretical playground, possibly
entertaining to study for a while, but
something which can be safely ignored while
the grown ups get on with the business of
changing the world, often for the worse.”
Hamnett (2003: 1)
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Of course, the extent of this problem is
debated
Some suggest that quantitative research
remains strong in Geography, especially in
Population Geography
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“we strongly take issue with his view that quantitative
techniques and aggregate social research have been
largely abandoned and that geographers no longer
analyse large data sets. Indeed such work remains a
buoyant component of the contemporary discipline,
characterised by much sophisticated analysis of data
sets large and small––certainly not mindless
quantification and measurement but rather an
ordered interrogation.”
Johnston et al. (2003: 157)
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However, while some geographers may
continue to use quantitative analysis, few are
engaging with longitudinal data
Geographers are failing to take advantage of
these ‘jewels in the crown’
Even fewer are adopting sophisticated
longitudinal modelling approaches
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October 2003 –September 2004, 839 cohort
data sets downloaded from the archive (346
users)
cohort
NCDS
BCS70
NCDS & BCS70
MCS
Datasets downloaded
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missing
CLS
Geography
Sociology, social policy etc
paediatrics and
development
discipline
Education
Psychology
Data library service
Government departments
Economics
Applied statistics and
maths
Health & epidemiology
Business and management
Applied social research
Other
0
20
40
60
80
100
Count
Disciplines of individuals downloading datasets
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There are few quantitatively trained young
Population Geography researchers
Despite ESRC’s strong emphasis on the
training of quantitative researchers
• Various training for early- or mid-career
researchers
• PhD students get extra £3000 for quantitative
techniques
• Aiming to influence training in undergraduate
degrees
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The value of
longitudinal
methods
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Cross-sectional data
• Single point in time
• Pooling cross-sectional surveys to examine change
through time (e.g. GHS)
• Measures aggregate, not individual, change
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Longitudinal data
• Multiple points in time
• Panel study with repeated measurements (e.g.
BHPS)
• Cohort studies chart the development of groups
from a particular time point (e.g. birth cohorts)
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The value of longitudinal data analysis
• Permits insights into the processes of change
(mobilities and transitions)
• Age, period and cohort effects
• Direction of causality
• State dependence
• Residual heterogeneity
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Age, period, cohort effects
Age
16
17
Age
18
19
20
21
(Cohort 1)
16
17
18
19
(Cohort 2)
16
17
(Cohort 3)
Age
Age
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Period
Cohort
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Direction of causality
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Cross-sectional data show that the
unemployed have poorer health than the
employed, but what is the direction of effect?
Unemployment
Poor health
Unemployment
Poor health
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“As far as I'm concerned I'm in good shape. I
still love my football. I'm not retiring and my
leaving is in no way health related.”
(Houllier, May 2004)
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Person A
Month
Health status
Employment
status
1
17
Employed
2
1
Employed
3
1
Employed
4
1
Unemployed
5
1
Unemployed
6
1
Unemployed
7
1
Unemployed
8
1
Unemployed
9
1
Unemployed
10
1
Unemployed
11
1
Unemployed
12
1
Unemployed
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Person B
Health status
Employment status
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Person A
Month
Person B
Health status
Employment
status
Health status
Employment status
1
17
Employed
17
Employed
2
1
Employed
17
Employed
3
1
Employed
17
Employed
4
1
Unemployed
17
Unemployed
5
1
Unemployed
17
Unemployed
6
1
Unemployed
10
Unemployed
7
1
Unemployed
16
Unemployed
8
1
Unemployed
5
Unemployed
9
1
Unemployed
4
Unemployed
10
1
Unemployed
3
Unemployed
11
1
Unemployed
2
Unemployed
12
1
Unemployed
1
Unemployed
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Person A
Month
Person B
Health status
Employment
status
Health status
Employment status
1
17
Employed
17
Employed
2
1
Employed
17
Employed
3
1
Employed
17
Employed
4
1
Unemployed
17
Unemployed
5
1
Unemployed
17
Unemployed
6
1
Unemployed
10
Unemployed
7
1
Unemployed
16
Unemployed
8
1
Unemployed
5
Unemployed
9
1
Unemployed
4
Unemployed
10
1
Unemployed
3
Unemployed
11
1
Unemployed
2
Unemployed
12
1
Unemployed
1
Unemployed
Person A unemployed for 9 months, health score 1
Person B unemployed for 9 months, health score 1
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Person A
Month
Person B
Health status
Employment
status
Health status
Employment status
1
17
Employed
17
Employed
2
1
Employed
17
Employed
3
1
Employed
17
Employed
4
1
Unemployed
17
Unemployed
5
1
Unemployed
17
Unemployed
6
1
Unemployed
10
Unemployed
7
1
Unemployed
16
Unemployed
8
1
Unemployed
5
Unemployed
9
1
Unemployed
4
Unemployed
10
1
Unemployed
3
Unemployed
11
1
Unemployed
2
Unemployed
12
1
Unemployed
1
Unemployed
Person A unemployed for 9 months, health score 1
Person B unemployed for 9 months, health score 1
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State dependence
Past behaviour
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Current behaviour
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State dependence
Unemployed, t-1
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?
Unemployed, t
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State dependence
Unemployed, t-1
?
Unemployed, t
?
Employed, t-1
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Residual heterogeneity
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The possibility of substantial variation
between similar individuals due to
unmeasured or unmeasurable variables
• Data collection instruments fail to capture the full
complexity of social life
• No way of accounting for omitted explanatory
variables in cross-sectional analysis
• Techniques exist for accounting for omitted
explanatory variables if we have data for an
individual at more than one time point
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Making better use
of the data we
already have
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The UK has a wealth of longitudinal data
Considerable value in routinely collected
administrative data
An under-utilised resource, which has been
yet to be properly and consistently harnessed
Relatively cheap and, often, comprehensive in
coverage
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For many reasons, the public are rightly
apprehensive about the general erosion of
privacy of information
•
•
•
•
•
Surveillance society…
Identity cards…
Identity theft…
Use of DNA material…
Links to insurance cover…
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protection of people’s privacy …
versus
… creation of bona fide and valuable
knowledge about population and
society
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“Despite my background as a civil libertarian...
I question the primacy of individual freedom
(and its associated concepts – autonomy,
privacy, and liberty) as the prevailing social
norm. Freedom is a powerful and important
idea, but I think scholars have given
insufficient attention to equally strong values
that are captured by the notions of
partnership, citizenship, and community....”
Lawrence Gostin 2000 Public Health Law (University of California Press)
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26th June 2006
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19th June 2007
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We should adopt a culture of data sharing
(ESRC are leading the way)
Carefully controlled settings can provide
access to data in imaginative ways
Linking data is not risky if careful procedures
for data access are in place
• Safe settings
• Remote access
• Special license
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The Scottish
Longitudinal Study
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Modelled on England and Wales LS
Provides linked data from the Scottish Census
and administrative records
Sampling based on 20 ‘semi-random’
birthdays (5.5% of the Scottish population)
Initial sample drawn from the 1991 Census
(around 274,000 members)
Similar sample drawn from 2001 Census
Link vital events information in the
intervening period
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Data sources
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Census
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• 1991 Census, 2001 Census
• Including data on occupation,
economic activity, social class,
housing, ethnicity, age, sex,
marital status, household
composition, health, education,
country of birth, migration,
workplace, religion etc.
• Information on SLS member and
other household members
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Population data
• Immigration
• Emigration
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Vital statistics
• Births (SLS birthdate)
• Births (to sample
members)
• Stillbirths
• Infant mortality
• Deaths
• Widow(er)hoods
• Divorces
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Health data
• Cancer registrations
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Data sources
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Census
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• 1991 Census, 2001 Census
• Including data on occupation,
economic activity, social class,
housing, ethnicity, age, sex,
marital status, household
composition, health, education,
country of birth, migration,
workplace, religion etc.
• Information on SLS member and
other household members
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Population data
• Immigration
• Emigration
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Vital statistics
• Births (SLS birthdate)
• Births (to sample
members)
• Stillbirths
• Infant mortality
• Deaths
• Widow(er)hoods
• Divorces
• Marriages
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Health data
• Cancer registrations
• Hospital episodes
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The future…?
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Linkage of additional data…
• Educational data (school census and exam results)
• Small-area geographical estimates of income and
health-related behaviours
• And the exciting opportunity to link back through
time
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The SLS and datasets like it have great
potential for population geographers
The large sample allows better geographical
resolution
The integration of census and vital events
information is particularly relevant to topics in
Population Geography
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Research 1:
MND in Finland
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Motor Neurone
Disease in Finland,
1985-1995
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Genetic or
environmental
influences?
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Died
Middle
Born
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Stayed
Moved away
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Cases
102
Controls
82
122
137
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Research 2:
Suburban fertility
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Distinct clusters of low and high fertility, in
urban centres and suburban surrounds
Why is fertility higher in suburban areas?
Suburban compositional, contextual or
selective migration effects?
Explore Finnish fertility histories…
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Research 3:
Migration and
health
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Health
Migration
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Deprivation
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Health
• There is a deprivation gradient
for health status
• Healthy people live in less
deprived locations and vice versa
Migration
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Deprivation
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Health
• Healthy are more likely to
migrate than unhealthy
• Unhealthy elderly are more
likely to migrate than healthy
Migration
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• There is a deprivation gradient
for health status
• Healthy people live in less
deprived locations and vice versa
Deprivation
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Health
• Healthy are more likely to
migrate than unhealthy
• Unhealthy elderly are more
likely to migrate than healthy
Migration
• There is a deprivation gradient
for health status
• Healthy people live in less
deprived locations and vice versa
Deprivation
• More advantaged people tend to migrate towards
less deprived locations?
• Less advantaged people tend to drift into (or be
trapped in) the more deprived locations?
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Age standardised rates of limiting long-term illness:
total population
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Age standardised rates of limiting long-term illness:
all migrants
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Age standardised rates of limiting long-term illness:
long-distance migrants
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Limiting long-term illness
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Conclusion
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A criticism of Population Geography is that it
has not been swept up in the cultural turn as
much as other sub-disciplines within
Geography
For some, this may be a strength
Yet, while Population Geography has retained
a quantitative tradition, these skills are
gradually disappearing
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The quantitative skills among Population
Geographers are weak compared to other
disciplines
We face a critical time ahead
Longitudinal data and methods provide
possibilities that Population Geographers
should take more advantage of
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