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THE VALUE OF LONGITUDINAL RESEARCH IN POPULATION GEOGRAPHY

THE VALUE OF LONGITUDINAL RESEARCH IN POPULATION GEOGRAPHY. Paul Boyle (Tom Cooke, Zhiqiang Feng, Vernon Gayle, Elspeth Graham, Hill Kulu, Paul Norman, Clive Sabel). linking lives through time www.lscs.ac.uk.

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THE VALUE OF LONGITUDINAL RESEARCH IN POPULATION GEOGRAPHY

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  1. THE VALUE OF LONGITUDINAL RESEARCH IN POPULATION GEOGRAPHY Paul Boyle(Tom Cooke, Zhiqiang Feng, Vernon Gayle, Elspeth Graham, Hill Kulu, Paul Norman, Clive Sabel) linking lives through timewww.lscs.ac.uk

  2. 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

  3. Theoretically advanced research Theoretically weak research

  4. Advanced research methods Theoretically advanced research Theoretically weak research Weak research methods

  5. Advanced research methods Population Geography? Theoretically advanced research Theoretically weak research Weak research methods

  6. Quantitative research in Geography

  7. 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?

  8. “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)

  9. “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)

  10. “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)

  11. Of course, the extent of this problem is debated • Some suggest that quantitative research remains strong in Geography, especially in Population Geography

  12. “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)

  13. 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

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

  15. Disciplines of individuals downloading datasets

  16. 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

  17. The value of longitudinal methods

  18. Cross-sectional data • Single point in time • Pooling cross-sectional surveys to examine change through time (e.g. GHS) • Measures aggregate, not individual, change

  19. 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)

  20. 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

  21. Age Period Cohort Age, period, cohort effects Age 16 17 18 19 20 21 (Cohort 1) Age 16 17 18 19 (Cohort 2) Age 16 17 (Cohort 3)

  22. Direction of causality • 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

  23. “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)

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

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

  26. State dependence Past behaviour Current behaviour

  27. State dependence Unemployed, t-1 Unemployed, t ?

  28. State dependence Unemployed, t-1 Unemployed, t ? Employed, t-1 ?

  29. 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

  30. Making better use of the data we already have

  31. 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

  32. 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…

  33. protection of people’s privacy … versus … creation of bona fide and valuable knowledge about population and society

  34. LawrenceGostin 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....”

  35. 26th June 2006

  36. 19th June 2007

  37. 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

  38. The Scottish Longitudinal Study

  39. 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

  40. Data sources • 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 • Vital statistics • Births (SLS birthdate) • Births (to sample members) • Stillbirths • Infant mortality • Deaths • Widow(er)hoods • Divorces • Health data • Cancer registrations

  41. Data sources • 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 • Vital statistics • Births (SLS birthdate) • Births (to sample members) • Stillbirths • Infant mortality • Deaths • Widow(er)hoods • Divorces • Marriages • Health data • Cancer registrations • Hospital episodes

  42. 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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