Marcello Morciano Health Economics Group, University of East Anglia

1. Measuring Disability Status and Older People’s Receipt of Disability Benefit in England: A Multi-Survey Latent Variable Structural Equation Approach Marcello Morciano Health Economics Group, University of East Anglia Institute for Social and Economic Research, University of Essex FAMILY RESOURCES SURVEY USER MEETING Royal Statistical Society, London Thursday 10 June 2010

2. Nuffield Project The role and effectiveness of disability benefits for older people Ruth Hancock and Marcello Morciano Health Economics Group, University of East AngliaStephen Pudney and Francesca Zantomio Institute for Social and Economic Research, University of Essex

3. Plan • Why assessing individual’s disability is important? • Public debate around the role of UK-disability benefits • How can we measure disability? • We use a latent factor approach • Does the set of disability indicator used in the analysis matter? • We appraise target efficiency of cash disability benefits in England; • Does three large UK-household surveys provide different results? • We use a structural equation technique • If yes, how much of the unexplained variance is due to survey features? • We use statistical matching technique • Conclusions/Discussion

4. Public Debate around the Long-Term Care (LTC) system in UK

5. Policy debate #1 • “The current LTC system is underfunded, incoherent and unfair” (Keen and Bell, 2009); • Target efficiency of non means-tested disability benefit in England is under debate • “AA might not be the best vehicle to provide financial help to older and disabled people”(Wanless, 2006); • a significant % of those who are in receipt of AA are wealthier and healthier people (Forder and Fernandez, 2009); • “One in five of DLA claimants are in the top two income quintiles*” (HMG, May 2010) A number of different reforms have been proposed bearing in mind their affordability in future years, in the context of population ageing * when DLA is included as income and no account is taken of extra costs of disability.

6. Policy debate #2 • Outgoing labour government set out its aspirations for a National Care Service (HMG 2010) where “[AA] might be integrated in the current care system to create a new offer for individual with care needs” (Green Paper on Social Care, 2009). • The new coalition Government has announced that it will not be implementing but “urgent reform of the social care system is top of its agenda” and it is to establish a commission which will be “tasked with delivering a sustainable settlement, which is a fair partnership between the state and the individual” (HMG, May 2010).

7. How can we measure disability?

8. How can we measure disability? • Difficulties in surveying people with disabilities/impairments • in particular people with cognitive impairments, people who live alone or people in institutions; • (once contacted) difficulties in measuring individual’s disabilities/impairments • Objective tests scores:i.e. external assessment of mobility and cognitive tests; • Self-reported measures:(I)ADL limitations, difficulties in domains of life, presence of particular health conditions, SF-12 or SF-36, etc. • Disability is a multidimensional phenomenon (Manton et. al., 2000); • Set of indicators used in the analysis matters (Van Brakel and Officer, 2008); • Presence of high correlation among indicators; • Self-reported disability measures might be subjected to forms of measurement errors (Bound, 2004; Benitez-Silva et. al., 2004);

9. Does the set of disability indicator used in the analysis matter? measurement issue is more than a statistical curiosity because strong policy conclusions have often been drawn on the basis of different measures APPLICATION:Assess empirically the relation between being in receipt of AA and • Disability status; • SES (mainly economic resources).

10. Attendance Allowance (AA) • Eligibility criteria • Need help with personal care because of severe physical and/or mental disabilities (i.e. help with things such as washing, dressing, eating, using the toilet, or difficulties in communication/understanding); • aged 65+ (if under 65 eligible for DLA); • no means testing; Make a claim to DWP Assessors examine the evidence (Some people who make a claim for AA may be asked to have a medical examination) Receive the Benefit (cover the additional costs due to the disability) 2 weekly rate: Higher £70.35 and Lower £47.10 (tax year 2009-2010); Once in receipt of AA, it might be possible to claim for extra amount of other means-tested benefits with: Housing Benefit; Council Tax Benefit and Pension Credit.

11. Does three large UK-household surveys provide different results?

12. Data We use three large household surveys: • British Household Panel Survey (BHPS); • English Longitudinal Study of Ageing (ELSA); • Family Resources Survey (FRS). Sample selection rules: • Cs 2002/03; • 65+ living in private households in England.

13. Differences across surveys Sample means of SES and AA receipt in FRS, ELSA and BHPS 16.5% AA recipient according official statistics (DWP).12.7-13.8% of those living in private households (MAP2030 estimates) In bold when differences (with respect of FRS) are significant with p < 0.05

14. Data FRS:collects information on difficulties in domains of life* disdif1 : disability difficulty in mobility (moving about) disdif2 : difficulty with lifting, carrying or moving objects disdif3 : difficulty with manual dexterity using hands to carry out everyday tasks disdif4 : difficulty - continence (bladder control) disdif5 : difficulty with communication (speech, hearing or eyesight) disdif6 : difficulty with memory/concentration/learning/understanding) disdif7 : difficulty with recognising when in physical danger disdif8 : difficulty with your physical co-ordination (eg balance Cross-correlations amongst indicators And very detailed information on SES, receipt of Welfare State Support * all items are self-reported and binary (1= if i has difficulty; 0 otherwise)

15. Data BHPS:collects information on (some) ADL limitations and level of difficulties in performing daily activities: Cross-correlations amongst indicators Ahswk* : health hinders doing the housework Astairs* : health hinders climbing the stairs Adress* : health hinders getting dressed Awlk* : health hinders walking more than 10 mins A1stairs : How manages stairs A1around : How manages to get around house A1bed : How manages to get in/out bed A1nail : How manages to cut toenails A1bath : How manages to bath/shower A1road : How manages to walk down road 1: unaided, very easy 2: unaided, fairly easy 3: unaided, fairly difficult 4: unaided, very difficult 5: only with help 6: not at all Income is collected with details; Wealth variables are not available in all waves. Small sample size [65+] * Binary indicators (1= if i has difficulty; 0 otherwise)

16. Data ELSA contains a rich set of self-reported measures of disease, disability, (I)ADL limitations, perceived health status + Scores from test on mobility and cognitive function. walking 100 yards sitting for about two hours getting up from a chair after sitting for long periods climbing several flights of stairs without resting climbing one flight of stairs without resting stooping, kneeling, or crouching reaching or extending your arms above shoulder level pulling or pushing large objects like a living room chair lifting or carrying weights over 10 pounds, like a heavy bag picking up a 5p coin from a table dressing, including putting on shoes and socks walking across a room bathing or showering eating, such as cutting up your food getting in or out of bed using the toilet, including getting up or down using a map to figure out how to get around in a strange place preparing a hot meal shopping for groceries making telephone calls taking medications doing work around the house or garden managing money, such as paying bills and keeping track of ex Cross-correlations amongst indicators & scales Incomes are collected with details but data cleaning process are desirable. * Binary indicators (1= if i has difficulty; 0 otherwise)

17. Method Rather of using summing scales alternative approaches, based on the sample covariance matrix of the indicators, have long been available (e.g., Goldberger & Duncan, 1973; Jöreskog, 1973,1977; Special issue of Journal of Econometrics, 1983; Bollen,1989; Heckman …). • One of the most powerful approach is the latent variable approach

18. The latent variable approach In the surveys we observe functional limitation indicators (y) which is caused by a disability condition η y=f(η) [1] we can express [1] as y= λη [2] Note that: - [2] implies a deterministic relation in which y is caused by λη. - [2] can hold only if no variables beside η can cause y. A possibility is to relax this (strong) assumption adding linearly a stochastic term : y= λη+  [3] We can make the usual assumption in regression analysis that: • E()=0; • Cov (y,)=0 • But… [3] is not identified in a single equation framework because η is unobservable (i.e. latent)

19. Model Measurement equations D1 … Dj z1 Structural equation Behavioural model η AA z2

20. Factor loadings, SEs of functional limitation indicators in measuring the disability index (η)

21. Results Parameter and relative standard error estimates of the structural equation of the latent disability index NOTE: ª variance of D (disability index) constrained to equal 1 for allowing defining the metrics of the latent factor. ¹ Cut-off set to 73, which is the median age observed in the pooled sample of BHPS,ELSA and FRS. ² Cut off set to exp(£513.86), which is the median pre means testing and disability benefit income observed in the pooled sample.

22. Results Parameter and relative standard error estimates of the AA receipt equation

23. Results % of people in receipt of AA by severity of their disability status Note: AA receipt refers to the mean calculated for each survey-specific decile of the latent disability index

24. Results Source: FRS

25. Results Results: • Mobility indicators play a predominant role as indicator of η ; • Age and living standards gradient in disability; • significant and positive relation between receiving AA and disability status: AA is relatively well targeted to disabled people; • significant and negative relation between receiving AA and economic resources: AA mimics a means-tested benefit; • Consistent results in the three surveys considered; • heterogeneity of estimates might arise from sample design features

26. Post-matching Results Cross-survey differences are overcame using statistical matching techniques • Based on mahalanobis distance algorithm (minimising the difference in the Z-vector of commonly observed socio-economic characteristics, thought to influence both disability and AA receipt ) ; • Aimed at creating survey specific sub-samples balanced along the Z. • We obtained 2 matched “control” sub-samples for each baseline (3 X 2 = 6 pair-wise matched couples [12 samples]) • Overall, although matching reduces sample size (=> significance of the estimates) it reduces drastically sample differences (in term of Z).

27. Results Parameter and relative standard error estimates of the structural equation of the latent disability index NOTE: ª variance of D (disability index) constrained to equal 1 for allowing defining the metrics of the latent factor. ¹ Cut-off set to 73, which is the median age observed in the pooled sample of BHPS,ELSA and FRS. ² Cut off set to exp(£513.86), which is the median pre means testing and disability benefit income observed in the pooled sample.

28. Results Parameter and relative standard error estimates of the AA receipt equation

29. Results # 2 • Overall, although matching reduces precision of the estimates results are confirmed • Robustness of our findings is guaranteed by the contemporaneous analysis of 3 data sources + matching technique • When appropriate statistical tools are used to extract the underlying disability state from partial and noisy (self-reported) indicators, despite enormous differences in design and contents, the 3 surveys describe coherent patterns.

30. Questions