NCeSS 4th International Conference on e-Social Science, Manchester, 18 June 2008

1. NCeSS 4th International Conference on e-Social Science, Manchester, 18 June 2008 Workshop 3: Data Management through e-Social Science Social care data: exploring issues David Bell, Alison Bowes and Alison Dawson

2. This case study will: • Identify some of the substantive issues in research on social care using the example of unpaid care • Outline a microsimulation approach to considering social care issues • Briefly discuss general data-related issues in microsimulation • Describe data management operations that may be required for microsimulation modelling, in particular data fusion • Illustrate potential complexities with data fusion using examples drawn from the Family Resources Survey (FRS) and the Scottish Household Survey (SHS)

3. Example: unpaid care • The continuing importance of unpaid care • The balance of care • Supporting unpaid carers • The dynamics of unpaid caring

4. Key findings to date on unpaid care • The impact of recent policy change • Attitudes to caring • Provision of care, proximity and education • Significance of housing wealth

5. Some key issues • Paying for care • Impact of various co-payment regimes • Impact of changing patterns of wealth • Questions about the best expenditure of resources • Supply of unpaid care • Attitudes • Education • Migration – of parents and children • Delivery of unpaid care • What carers do • How to support carers • Dynamics of unpaid care – need for microsimulation • Population movements • Educational attainment • New types of care provision • Resources for unpaid care

6. Approach • Using a microsimulation model (OPeRA – Older People’s Resource Allocation Model) in the context of Scottish demographic change • drawing on data from various censuses and surveys, including FRS, census data, data on actual caring histories over time (from Welsh survey)

7. Microsimulation: • A microsimulation model is a model which uses simulation techniques and which takes micro level units as the basic unit of analysis when investigating the effects of social and economic policies Dynamic microsimulation: • These models project samples of the population forward in time, enabling, for example the examination of future income distributions under different scenarios

8. EVENTS • Are associated with individuals • Occur over individual lifetimes • May be contingent on prior events • Occur in continuous time • Have a probabilistic element • May have associated costs/benefits Individual Low-level location (e.g. home) High-level location (e.g. local authority) OPERA – Key elements

9. OPERA - Potential outputs • Individual life histories, including record of events • Census on any type of event and calculation of cost by local authority over user-determined period • Preparation of budget for local authority based on current care requirements and costs profile

10. Data related microsimulation issues • What variables/behaviours to simulate? • Over what period? • What level of geographical disaggregation? • Can all relevant information be found in a single sample survey? • If not how do we form encompassing datasets?

11. Data management operations for microsimulation modelling • ‘Cleaning’ data • Checking data • Recoding data • Missing data / case selection • Operationalising variables • Linking data • Matching data files together • Data fusion

12. Data Fusion Common variable(s) Values of unobserved variable(s) are imputed for recipient sample Before data fusion: No values for Var Y After data fusion: Imputed values for Var Y (not true values) • Fusion proceeds by a number of (mostly numerically intensive) procedures. • The objective is to define new variables whose properties differ as little as possible from those of the (unobserved) underlying data.

13. Potential difficulties with data fusion – Examples using FRS and SHS • Family Resources Survey (FRS) • Cross-sectional study, annual sampling frame • Covers GB and N Ireland • Disaggregation possible to Government Office Regions • Observation unit = households/families • Interviews all adults in household • 28,029 households in 2005/06 • 2,016 variables (253 derived) • Scottish Household Survey (SHS) • Cross-sectional study, two year sampling frame • Covers Scotland only • Disaggregation possible to Local Authority areas • Observation unit = individual, households/families • Interviews householder or spouse plus one ‘random adult’ • 31, 013 households, 28,261 random adults in sample to 2005/06 • 2,456 variables (80 ‘administrative’ and derived)

14. SHS response categories WHITE A: Scottish [1] B: Other British [2] C: Irish [3] D: Any other White background [4] MIXED E: Any mixed background [5] ASIAN, ASIAN SCOTTISH OR ASIAN BRITISH F: Indian [6] G: Pakistani [7] H: Bangladeshi [8] I: Chinese [9] J: Any other Asian background [10] BLACK, BLACK SCOTTISH OR BLACK BRITISH K: Caribbean [11] L: African [12] M: Any other Black background [13] OTHER ETHNIC BACKGROUND Any other background [14] Don’t know [15] Refused [16] FRS response categories 1: White – British 2: Any other white background 3: Mixed – White and Black Caribbean 4: Mixed – White and Black African 5: Mixed – White and Asian 6: Any other mixed background 7: Asian or Asian British – Indian 8: Asian or Asian British – Pakistani 9: Asian or Asian British – Bangladeshi 10: Any other Asian/Asian British background 11: Black or Black British – Caribbean 12: Black or Black British – African 13: Any other Black/Black British background 14: Chinese 15: Any other Example 1 – FRS and SHS ‘common’ variables: Ethnicity

15. Example 2 – FRS and SHS ‘common’ variables: Marital status FRS Variable label = Marital status Value label information for MS 1 = Single, never married 2 = Married and living with husband/wife 3 = Married and separated 4 = Divorced 5 = Widowed SHS Variable label = Marital status of the HIH Value label information for hih_stat 1 = Married 2 = Cohabiting (living together) 3 = Single / never been married 4 = Widowed 5 = Divorced 6 = Separated FRS Variable label = De facto marital status Value label information for DVMARDF 1 = Married 2 = Cohabiting 3 = Single 4 = Widowed 5 = Divorced 6 = Separated 7 = Same sex couple

16. Summary • Techniques such as microsimulation can play an important part in considering substantive issues in research on social care. • There are many relevant variables when modelling social care. We identified only a subset of these when considering the example of unpaid care. No single sample survey contains all the variables we would want to use. • We need to identify relevant sample survey datasets and carry out data management operations such as ‘cleaning’ data, operationalising variables and linking datasets prior to modelling. • Metadata provides valuable information that helps us to identify and address practical problems in the data fusion processes necessary to produce better microsimulation models