Life History Data in Ageing Studies

1. Life History Data in Ageing Studies James Banks Institute for Fiscal Studies and University of Manchester VIU Summer Institute on Ageing 3-7 June 2019

2. Understanding ageing • We need to understand individual trajectories in order to understand population processes or to inform policy choices • ‘causes and consequences’ • Key dimensions and trajectories over the life-course are integrally linked and dynamically evolving • Physical, biological, psychological, economic, social • Need good microdata to inform research: • Longitudinal • Multidimensional/multidisciplinary • Credible measurements in each dimension • Covering households not just individuals

3. Overview • Background: The ‘HRS family’ ageing studies and the international comparisons agenda • Life HistoryData collection in the HRS family • Exploiting Life History Data. Some examples • Controlling for early life circumstances • Using the whole trajectory – work at older ages • Marriage and health outcomes – Smoking and lung function

4. The ‘HRS’ model for ageing studies • New ageing studies have been starting around the world • US Health and Retirement Study, 1992- • English Longitudinal Study of Ageing, 2002- • Survey of Health Ageing and Retirement in Europe, 2004- • Common principals and values: • Multidisciplinary, not interdisciplinary • Scientific credibility built on detailed measures in every domain • At the individual level and the family/household level • Many sub-dimensions to health, many sub-dimensions to socio-economic position • If research is to focus on ‘mechanisms’ then all have to be captured • Full scientific credibility across disciplines can only come from full public release of data

5. Multidimensional measures Demographics and family Physical Health Incomes, Welfare, Wealth & Debts Psychological and social wellbeing Cognitive function Employment, Pensions Psychosocial factors Consumption, Housing Social and civic participation Health behaviours Expectations …and lots more… Mental Health Blood samples and DNA Physical examination and performance tests Admin data links

6. Internationally comparable data on ageing HRS ELSA SHARE OTHER

7. Increasing supply of comparable data • HRS: Health and Retirement Study; 1992- • ELSA: English Longitudinal Study of Ageing; 2002- • MHAS: Mexican Health and Ageing Study; 2002- • SHARE: Survey of Health, Ageing & Retirement in Europe; 2004- • KLOSA: Korean Longitudinal Study of Ageing; 2006- • JSTAR: Japanese Study of Ageing and Retirement; 2009- • CHARLS: Chinese Health and Retirement Study; 2009/11- • TILDA: The Irish Longitudinal Study on Ageing; 2010- • LASI: Longitudinal Ageing Study in India; 2012/14- • NICOLA: Northern Ireland, 2014/15- • ELSI-B: Brazilian Longitudinal Study of Ageing, 2014/15- • HAGIS: Health and Ageing in Scotland, 2016-

8. More information: http://g2aging.org

9. Anemerging consensus • Country comparisons can be valuable in helping understand causes and consequences of disease as well as links between health, economic outcomes and other dimensions • Facilitated by both more data, and better data • But this has been a relatively recent phenomenon

10. Disease and disadvantage in the US and England: 1 55-64 year olds; controlling for differences in age, sex, education, income, overweight, obese, current and past smoking, heavy drinking Banks, Marmot, Oldfield and Smith, JAMA, 2006

11. Disease and disadvantage in the US and England: 2 40-70 year olds Banks, Marmot, Oldfield and Smith, JAMA, 2006

12. Risk factors only partially explain cross-country differences in health…. US-England-European differences 50-70 year olds, adjusted for age, sex, education, smoking, drinking, BMI, state/country Source: Avendano, Glymour, Banks, Mackenbach; Am J Public Health, 2009

13. An emerging consensus • Cross-country comparisons can be valuable in helping understand causes and consequences of disease as well as links between health and other dimensions • Facilitated by both more data, and better data • But this has been a relatively recent phenomenon • But: We are not interested in cross-country comparisons for their own sake • Key question: What are the effects of social and economic institutions on behaviours and outcomes over the life-cycle? • Much greater variation across countries than within (and within-country) variation is often problematic for identification

14. Disease and disadvantage in the US and England:3 Diabetes Hypertension Source: Banks et al; JAMA 2006

15. Disease and disadvantage in the US and England:3 Diabetes Hypertension Source: Banks et al; JAMA 2006

16. Countries as counterfactuals • It is common to look at US state differences. International comparative analysis is similar. The same identification issues arise, but just to differing matters of degree. • As with state analysis, more robust if use policy changes matched to life-course events or temporal changes • Some identification assumptions are easier to maintain: • Migration / geographic mobility / selection • Integration of markets i.e. spillovers • Others are more more challenging: • Common components to, and trends in, institutions? Adequate controls? • Common data and measurement protocols

17. International comparisons in ageing • Key question is often how does ‘policy’ impact on late-life outcomes? • Many policy changes have long shadows or create extensive transition effects, e.g. pensions, health behaviours • ‘Ex-post’ evaluation: RCTs would take too long to evaluate and trials/study design would be very complicated • ‘Ex-ante’ evaluation: Realistic structural models capturing all dimensions with adequate granularity are still not feasible • Hence quasi-experimental ‘ex-post’ evaluations are common • Life-course health models, and life-cycle economic models, stress the importance of early life factors and then subsequent life-events in driving outcomes at older ages  Cross-country comparisons could be particularly useful

18. The potential power of life-history comparisons • With comparable data on the full life-course we could: • Capture effects of variation in life-time exposures and behaviours • Exploit greater institutional variation if we can look over larger set of generations and country-time periods • But ageing studies typically only start collecting data on respondents at 50, and there are few, if any, internationally comparable prospective cohorts • So HRS-family of studies developed the life-history interview • Link early-life to late-life without waiting for prospective data • Generates longer panel, covering more cohorts, for some dimensions, e.g. labour market analysis (going back to periods before we had longitudinal surveys in many countries

19. Life-history data in ageing studies: Methods

20. A history of histories: 1 • Selected recall questions in large general surveys, • e.g. earnings and other histories in BHPS/PSID etc. • Detailed `autobiographical’ life-histories in small scale studies, • e.g. age 65 year follow up of Boyd Orr cohort (Blane, 2005 IJE) • Combination of two methods into CAPI life-grid calendars • Belli (1998, Memory), Belli, Shay and Stafford (2001, Public Opinion Quarterly) • We consulted heavily with Blane and Belli, and created the ELSA Life History Interview as a supplement to ELSA wave 3 • Combination of life-grid calendar with retrospective recall questions on early life circumstances for a full life-history questionnaire • ELSA ⇢ SHARE ⇢ CHARLS ⇢ HRS • Datasets then supplemented with contextual policy variables given the respondent’s date of birth and age

21. Collecting good `life history’ data • Retrospective module developed for ELSA • ‘45 minute’ face-to-face interview • Take autobiographical recall seriously • A mixed approach of LHC and recall: • Life-History Calendar instrument for key time-lines • Supplemented with follow up questions on: • Specific events identified in calendar • Childhood circumstances • Selected adult life-events • Self-completion questionnaire to cover other dimensions • Implemented as additional interview after ELSA wave 3 • Modified version then implemented in Europe: SHARELIFE

22. The life-grid • Annual - indexed by year and age • News/Events to aid precise recall • Individual’s salient events added as we go along • Five dimensions • Children • Partners • Housing • Employment • Other (e.g. health events) • Interviewers can use dimensions non-linearly

23. Follow-ups on events in grid • Partner (cohabiting) • Marital status at beginning, and subsequently • Reason relationship ended • Housing (resident >6 months) • Ownership/rental status • Employment • Job title • Employee or self-employee • Full or part time (and when switched if switched) • Starting salary

24. Other content • Childhood SES • Living situation at age 10; Parental background indicators • Childhood health • SAH at age 16; Major conditions before age 16 (condition/age first had) • Adult health • Conditions lasting more than one year (condition/age); permanent injury • Smoking history • Gynaecology • Menarchy; Pregnancies; Hysterectomy; Menopause; HRT; • Self-completion questionnaire • Parental bonding; Parental trauma; Difficult life events

26. Further developments • CHARLS – life history plus village history etc. • SHARE • JobEpisodesPanel created from SHARELIFE • SHARELIFE2 and JEP2 • HRS now (2018/19) implementing a similar life historymodule (although administered on paper in self-completion/leave-behind) • Global gateway now making harmonized life-history panel data available

27. A history of histories: 2 • As well as creating full life-history data on more cohorts, the panel has no attrition by construction so is easy to work with • But there are worries over selection and recall bias instead • A number of papers have argued good validity • Havari and Mazzonna, 2016 Eur J Pop • Bingley and Martinello, 2014 WP

28. A history of histories: 3 • Some validation studies have shown retrospective and prospective data from the same individuals can differ, but there is still value in the retrospective • Brown, 2014 Longitudinal and Life Course Studies • Reuben et al, 2016, Journal of Child Psychology and Psychiatry • Newbury et al, 2018, Journal of Psychiatric Research • Ongoing work continues to look into this • Life-history data not perfect but no data are. Better to ask whether the pros outweigh the cons

29. 1. Exploiting data on early life

30. Childhood disease for older cohorts also more prevalent in the US than in England Source: Banks, Oldfield and Smith, 2012

31. Childhood health as an ‘explanation’ for international differences in late life health Pool ELSA and HRS data (N=19,583) and estimate a series of models of the form: H = f(US, age*US, sex*US, Childhood SES*US, Parental Health*US Childhood Health*US) For different measures of late-life health: ‘Major’: Stroke, Cancer, Heart Attack, Heart Failure, Angina, Lung Disease `Minor’: Diabetes, Hypertension, Arthritis `Barker’: Heart Disease and Diabetes related conditions

32. Childhood health as an ‘explanation’ for international differences in late life health H = f(US, US*[age, sex, childhood SES, parental health, height], Childhood Health*US) Summary Table of Estimated US Excess Illness at Older Ages, by Disease category Note: Bold numbers indicate p < 0.01; Light numbers indicate p>0.05 Source: Banks, Oldfield and Smith, 2012

33. Childhood health as an ‘explanation’ for international differences in late life health H = f(US, US*[age, sex, childhood SES, parental health, height], Childhood Health*US) Summary Table of Estimated US Excess Illness at Older Ages, by Disease category Note: Bold numbers indicate p < 0.01; Light numbers indicate p>0.05 Source: Banks, Oldfield and Smith, 2012

34. Childhood health as an ‘explanation’ for international differences in late life health H = f(US, US*[age, sex, childhood SES, parental health, height], Childhood Health*US) Summary Table of Estimated US Excess Illness at Older Ages, by Disease category Note: Bold numbers indicate p < 0.01; Light numbers indicate p>0.05 Source: Banks, Oldfield and Smith, 2012

35. Early life circumstances and late-life outcomes: 1 • Many many ‘HRS family’ papers on this topic, for all kinds of late-life outcomes, and all types early-life or early-adulthood circumstances and exposures • Health • SES • Parental Unemployment • Lone parenthood • Hunger, War,Recessions, … “In this paper we estimate the effect of early life on at older ages” • Lifetime earnings • Physical health • Mental health • Muscle strength • Physical activity • Chewing ability…

36. Early life circumstances and late-life outcomes: 2 • The best of these studies exploit exogenous or quasi-exogenous variation in early life exposures • Kesternich, Siflinger, Smith and Winter, REStat 2014, ‘The effects of World War II on economic and health outcomes across Europe’ • Kesternich, Siflinger, Smith and Winter, EJ Features 2015, ‘Individual behavior as a pathway between early-life shocks and adult health: Evidence from hunger episodes in post-war Germany’ • van den Berg, Pinger and Schoch, EJ 2016, ‘Instrumental variable estimation of the causal effect of hunger early in life on health later in life’ • Avendano, Brugiavini, Berkman, Pasini, Soc Sci Med, 2015, ‘The long-run effect of maternity leave benefits on mental health: Evidence from European countries’ • Bharadwaj, Graff Zavin, Mullins, Neidell, Am J Resp Critical Care Med, 2016, Early-life exposure to the Great Smog of 1952 and the development of Asthma • So there has already been a demonstrable scientific value generated from within and between country comparisons of life-history data

37. 2. Exploiting the full life-history panel

38. Panel data analysis • Fewer studies have exploited the whole life-history trajectories • The majority of those that exist look at life-course events and trajectories as mediators of the early life exposure to late life outcome relationship • Typically code trajectories into a small # of types using sequence or cluster analysis • Doesn’t really use the timing of events in any structural way • Still thinking of life-history variables on the RHS of a late-life outcome regression • Arpino et al 2018, PLOS-One (early-life -> educ-family-work -> health) • Van Hedel et al, 2016, AJPH. (female work-family trajectories -> CVD) • A few are beginning to exploit timing of labour market events in more detail • Avendano, Berkman, Brugiavini, Pasini, 2015, Social Science and Medicine • Antonova, Bucher-Koenen, Mazzonna, 2017, Social Science and Medicine

39. Maternity leave and late-life mental health • Avendano, Berkman, Brugiavini and Pasini, look at effect of paid maternity leave on late-life mental health • Initial 2015 paper exploits variation in maternity leave policies across Europe over time, using timing of childbirth from the life-history grid • Comprehensive maternity leave around the time of first child reduces women’s late-life depression scores by 16% • Follow up paper uses detailed labour market analysis to investigate the role of stress in this finding • Regress stress at each age on childbirth, labour market interruptions (instrumented by policy variation) and rich controls • Being in a childbirth year increases the probability of reporting the most stressful period in life; • Increasing length of maternity leave reduces the risk of reporting stress: 3 months of maternity leave cancels out the stress-inducing effect of a childbirth • Even this still has labour market panel on RHS; what about on LHS?

40. An ELSALIFE SHARELIFE job panel • To demonstrate value we use the full `retrospective panel’ of employment for ELSA and SHARE • Consider all cohorts born 1920-1959 • Create a job spells panel for each member covering all ages from school up to the (first) SHARELIFE interview in 2007 • Merge in contextual data on cohort-country-year specific retirement ages and early retirement ages • Example questions: • What is role of retirement age in late-life work outcomes (i.e. Gruber-Wise)? • Is the labour market becoming more flexible for more recent cohorts? • Note: just select ELSA plus the largest 11 SHARELIFE countries for convenience

41. Social security and retirement around the world Source: Gruber and Wise (eds), 2004, Social Security and Retirement around the world: Micro estimation’,

42. Probability of men working at age 50by year of birth cohort and country SHARELIFE-ELSALIFE Panel (except IE, CH, AU); All respondents born 1920-1959 (n=33,022)

43. Probability of women working at age 50by year of birth cohort and country SHARELIFE-ELSALIFE Panel (except IE, CH, AU); Respondents born 1920-1959 (n=33,022)

44. Aside: Validation of employment histories • We can compare retrospectively reported employment histories to those observed at the time in historical Labour Force Surveys • Look for evidence of selective mortality and/or recall bias • Comparisons crude at this stage because: • Precise age at interview issues • Different period of assessment • Examples from England and Italy • Compare employment rates across single year-of-birth cohorts in selected years • Examples of 1992 and 1977, i.e. going back 15 and 30 years • Much more could be done along these lines

45. England, Male Italy, Male England, Female Italy, Female

46. England, Male Italy, Male England, Female Italy, Female

47. Selective mortality • Little evidence of recall bias: 15 and 30 year recall looks pretty good for all cohorts, perhaps worse for women • Evidence of selective mortality, as expected, particularly in England • n.b. selective mortality would not show itself in Italy if work at older ages is not correlated with life-course health and SES factors • Can see in the ELSA figures for the 1935 cohort and earlier (age >70 at time of data collection) • In general, we would expect healthier than average representatives of the oldest cohorts • They will have been more likely to work and will have had better labour market outcomes in general than those who have died • If anything, we will therefore be understating increases in work across cohorts

48. Gruber-Wise redux SHARELIFE-ELSALIFE Panel (except IE, CH, AU); Respondents born 1920-1959 (n=33,022)

49. Gruber-Wise redux SHARELIFE-ELSALIFE Panel (except IE, CH, AU); Respondents born 1920-1959 (n=33,022)

50. Gruber-Wise redux SHARELIFE-ELSALIFE Panel (except IE, CH, AU); Respondents born 1920-1959 (n=33,022)