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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
Theoretically weak research
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Theoretically advanced
research
Theoretically weak research
Weak research methods
Advanced research methods
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Theoretically advanced
research
Theoretically weak research
Weak research methods
Advanced research methods
Population Geography?
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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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Other
Applied social research
Business and management
Health & epidemiology
Applied statistics and maths
Economics
Government departments
Data library service
Psychology
Education
paediatrics and development
Sociology, social policy etc
Geography
CLS
missing
discipline
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 16 17 18 19 20 21 (Cohort 1)
Age 16 17 18 19 (Cohort 2)
Age 16 17 (Cohort 3)
Age Period Cohort
Age, period, cohort effects
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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
Direction of causality
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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 Person B
Month Health status Employment status
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 A Person B
Month 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 Person B
Month 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 1Person B unemployed for 9 months, health score 1
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Person A Person B
Month 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 1Person B unemployed for 9 months, health score 1
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State dependence
Past behaviour Current behaviour
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State dependence
Unemployed, t-1 Unemployed, t?
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State dependence
Unemployed, t-1
Employed, t-1
Unemployed, t?
?
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Residual heterogeneity
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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Lawrence Gostin 2000 Public Health Law (University of California Press)
“ 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....”
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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 Vital statistics
• Births (SLS birthdate)• Births (to sample
members)• Stillbirths• Infant mortality• Deaths• Widow(er)hoods• Divorces
Health data• Cancer registrations
Census• 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
Population data• Immigration• Emigration
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Data sources Vital statistics
• Births (SLS birthdate)• Births (to sample
members)• Stillbirths• Infant mortality• Deaths• Widow(er)hoods• Divorces• Marriages
Health data• Cancer registrations• Hospital episodes
Census• 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
Population data• Immigration• Emigration
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The future…?
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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Cases Controls
Stayed
Moved away
102
122
82
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 Deprivation
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Health
Migration Deprivation
• There is a deprivation gradient for health status
• Healthy people live in less deprived locations and vice versa
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• Healthy are more likely to migrate than unhealthy
• Unhealthy elderly are more likely to migrate than healthy
Health
Migration Deprivation
• There is a deprivation gradient for health status
• Healthy people live in less deprived locations and vice versa
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• Healthy are more likely to migrate than unhealthy
• Unhealthy elderly are more likely to migrate than healthy
• 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?
Health
Migration Deprivation
• There is a deprivation gradient for health status
• Healthy people live in less deprived locations and vice versa
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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