the value of longitudinal research in population geography

linking lives through time www.lscs.ac.uk

THE VALUE OF LONGITUDINAL RESEARCH IN POPULATION

GEOGRAPHY

Paul Boyle

(Tom Cooke, Zhiqiang Feng, Vernon Gayle, Elspeth Graham, Hill Kulu, Paul Norman, Clive Sabel)



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


Theoretically advanced

research

Theoretically weak research



research


Weak research methods

Advanced research methods



research


Weak research methods

Advanced research methods

Population Geography?


Quantitative research in Geography


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?


“ 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)


“ 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)


“ 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)


Of course, the extent of this problem is debated

Some suggest that quantitative research remains strong in Geography, especially in Population Geography


“ 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)


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


October 2003 –September 2004, 839 cohort data sets downloaded from the archive (346 users)

cohort

NCDS

BCS70

NCDS & BCS70

MCS

Datasets downloaded


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


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


The value of longitudinal

methods


Cross-sectional data• Single point in time• Pooling cross-sectional surveys to examine

change through time (e.g. GHS)• Measures aggregate, not individual, change


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)


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


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


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


“ 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)


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


Person A Person B



1 17 Employed 17 Employed



4 1 Unemployed 17 Unemployed










Person A Person B















Person A unemployed for 9 months, health score 1Person B unemployed for 9 months, health score 1


State dependence

Past behaviour Current behaviour


State dependence

Unemployed, t-1 Unemployed, t?


State dependence

Unemployed, t-1

Employed, t-1

Unemployed, t?

?


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


Making better use of the data

we already have


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


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…


protection of people’s privacy …

versus

… creation of bona fide and valuable knowledge about

population and society


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....”


26th June 2006


19th June 2007


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


The Scottish Longitudinal

Study


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


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


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


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


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


Research 1:MND in Finland


Motor Neurone Disease in Finland, 1985-1995


Genetic or environmental influences?


Died

Middle

Born


Cases Controls

Stayed

Moved away

102

122

82

137


Research 2:Suburban fertility


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…


Research 3:Migration and

health


Health

Migration Deprivation


Health


• There is a deprivation gradient for health status

• Healthy people live in less deprived locations and vice versa


• Healthy are more likely to migrate than unhealthy

• Unhealthy elderly are more likely to migrate than healthy

Health





• 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





Age standardised rates of limiting long-term illness: total population


Age standardised rates of limiting long-term illness: all migrants


Age standardised rates of limiting long-term illness: long-distance migrants


Limiting long-term illness


Conclusion


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


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

the value of longitudinal research in population geography

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