1. Improving the Quality of the HMRC Personal Wealth Statistics Rebecca Ambler and Abeda Malek - HMRC

2. Overview Background Proposed solution Modelling the mortality adjustments Results Involving users Publication Next Steps

3. Background – Previous Method Personal Wealth Statistics produced based on Inheritance Tax (IHT) returns. Prior to ONS’s Wealth and Assets Survey (WAS) main source of data on wealth inequality. Provides a historical time series on wealth inequality.

4. Background – Previous Method: Step 1 Sampled data from IHT returns All estates requiring a grant of representation Grossed for sampling rate Concerns: Not all estates captured on death. Sampling biases. Small samples for large estates.

5. Background – Previous Method: Step 2 Sampled data from IHT returns All estates requiring a grant of representation Grossed for sampling rate Grosse d for mortalit y rates “Identified wealth” Concerns: Method for adjusting the relationship between wealth and mortality out of date.

6. Background – Previous Method: Step3 Sampled data from IHT returns All estates requiring a grant of representation Grossed for sampling rate Grosse d for mortalit y rates “Identified wealth” Adjusted for under- recording and valuation differences “Adjusted wealth” Concerns: Adjustments based on assumptions. Adjustments possibly out of date. Don’t know which estates to adjust.

7. Background – Previous Method: Step 4 Sampled data from IHT returns All estates requiring a grant of representation Grossed for sampling rate Grosse d for mortalit y rates “Identified wealth” Adjusted for under- recording and valuation differences Adjusted for missing estates and trusts “Adjusted wealth” “Marketable wealth” Concerns: Lack of data on missing estates

8. Background - Problems ConcernImpact Not all estates captured on death. Bias to sample (although adjustments attempt to correct). Sampling biases.Bias to sample. Small samples for large estates. Volatility in key data series. Method for adjusting the relationship between wealth and mortality out of date. Potential bias.

9. Background - Problems ConcernImpact Adjustments based on assumptions. Could be inaccurate and lack of transparency. Adjustments possibly out of date. Could be inaccurate. Don’t know which estates to adjust. Biases to measures of wealth inequality. Lack of data on missing estates. Potential bias. Key data series sensitive to assumptions.

10. Solution – Data Sampling had been improved – capturing all estates requiring a grant of representation. Service Level Agreement in place and regular meetings with the data suppliers. While some problems still arising, more are minor.

11. Proposed Solution – Methodology Volatility due to small number of large cases – use a combined 3 year sample. Concerns about adjustments for valuation and under-recording – remove. Concerns about estimates for missing estates – remove. Concerns about adjustments to mortality for levels of wealth – use newly available longitudinal data to revise these.

12. Modelling the Mortality Adjustments – Data Sources English Longitudinal Survey of Ageing (ELSA) - 2006 survey, Dept. of Health, Over 50’s mainly, England only, link between mortality and wealth Wealth and Assets Survey (WAS) - 2006-08 survey, ONS, all adults over 16, all GB Comparisons - Advantages and disadvantages Availability of data - ELSA: October 2010 - WAS: November 2011

13. Modelling the Mortality Adjustments – Regression Model using ELSA Raw Data -UK Data Archive -Waves 1, 2 and 3 -Assumptions Variables -Dependent: Year of Death ( Mortality variable ) -Predictive: Gender, Age and Marital Status Gross Housing Wealth ( Wealth variable) Binary Logistic Regression in SPSS -Merging files, cleaning data

14. Modelling the Mortality Adjustments – Mortality Adjustment Calculation Calculating the mortality adjustments for each gender, age, marital status and wealth category: Log (Mortality) = Gender Indicator + Age Group Indicator + Marital Status Indicator + Wealth Indicator + Constant

15. Modelling the Mortality Adjustments – Regression Outputs

16. Modelling the Mortality Adjustments – Example of Mortality Adjustments

17. Results

19. Involving Users Major change to methodology - consultation to get user views. Mixed response – concerns about timeliness but no alternative proposed. Allowed us to collect views on other issues.

20. Publication New statistics published on HMRC website on 30 th June (http://www.hmrc.gov.uk/stats/personal_wealth/menu.htm)http://www.hmrc.gov.uk/stats/personal_wealth/menu.htm Uses new methodology Developed commentary and added new tables to meet users needs.

21. Publication

25. Next Steps Investigate mortality adjustments for under 45s. Comparison with WAS data. New tables currently published as experimental statistics – investigate quality of those.

26. Questions? For further information please contact: Rebecca Ambler rebecca.ambler@hmrc.gsi.gov.uk rebecca.ambler@hmrc.gsi.gov.uk Abeda Malek abeda.malek@hmrc.gsi.gov.uk Or look on our website http://www.hmrc.gov.uk/stats/personal_wealth /menu.htm http://www.hmrc.gov.uk/stats/personal_wealth /menu.htm