The effect of uncertainty on fuel poverty statistics

1. The effect of uncertainty on fuel poverty statistics Laura Williams, Department of Energy and Climate Change GSS Methodology Symposium, 6th July 2011

2. What is fuel poverty? A household is fuel poor if it needs to spend more than 10 per cent of its income on fuel to maintain an adequate standard of warmth, i.e. if the fuel poverty ratio > 0.1. In England 2008: 3.335 million fuel poor households

3. The fuel poverty model Uncertainty in the inputs leads to uncertainty in the output… INPUTS OUTPUT EHS data Fuel poverty estimate Model Fuel price data Other data

4. Uncertainty analysis This analysis looked at the uncertainty associated with: • Household income • Fuel prices

5. Methodology for estimating uncertainty To estimate the number of fuel poor households: • Using the given data, calculate the fuel poverty ratio for each household. • Sum those with a ratio greater than 0.1. To estimate the impact of uncertainty: • Modify the input data according to its distribution representing the uncertainty. • Using the modified data, calculate the fuel poverty ratio for each household. • Sum those with a ratio greater than 0.1. • Repeat many (typically thousands of) times, i.e. a type of Monte Carlo simulation, in order the create a distribution.

6. UNCERTAINTY IN HOUSEHOLD INCOME

7. English Housing Survey (EHS) Employment Age Insulation Householder features Type, e.g. flat Health Dwellingfeatures Composition Earnings Property age Income information Fuelmix Savings Benefits

8. Uncertainty in EHS income data Uncertainty considered for 5 types of income: • Earnings • Housing benefit • All other benefits • Savings • Other sources (including occupational pensions) Reasons for uncertainty in the incomes reported: • Respondent may not be fully aware of the income of other householders and report incorrect information. • When data are collected in banded amounts (done in order to maximise response rates), e.g. earned income and savings. Under-reporting is not considered as part of the analysis.

9. Uncertainty in EHS income data Information on the absolute uncertainties in reported values of income from the EHS does not exist. Used a study of the Family Expenditure Survey (FES) from the late 1990s which compared FES incomes to the National Accounts. Social security example: Mean: 94.9 % Standard deviation: 1.76% Coefficient of variation: 1.85%

10. Uncertainty in EHS income data Coefficient of variation for each of the 5 income types: i.e. greater uncertainty associated with reported savings than other income sources Can then construct an error distribution using the coefficient of variation

11. UNCERTAINTY IN DOMESTIC FUEL PRICES

12. Uncertainty in fuel prices • The main methodology uses mean gas and electricity prices for each region and method of payment combination. • Gas and electricity price data is sourced from DECC’s Domestic Fuel Inquiry (DFI). • However, this is a simplification of the real situation where actual fuelprices vary in each region due to different tariffs offered by different suppliers. • Used supplementary data from the DFI on the spread of fuel prices paid by households across the country to approximate a simple error distributions. • Examples on the next slide!

13. Uncertainty in fuel prices Variation in domestic bills for direct debit customers, England 2008: Gas Standard electricity

14. RESULTS

15. Results – recap of methodology To estimate the impact of uncertainty: • Modify the input data according to its distribution which represents the uncertainty. • Calculate the fuel poverty ratio for each household. • Sum those with a ratio greater than 0.1. • Repeat many (typically thousands of) times.

16. Headline results • The distribution of possible values for number of households in fuel poverty when incorporating the uncertainty in income and fuel prices: • Mean: approx. 3.343 million households • 95% confidence interval: 3.299 and 3.388 million households • (a range of approximately 88,000 households).

17. Results – more detailed breakdowns • Estimates of the effect of combined income and fuel price uncertainty on a variety of demographic and dwelling characteristics. • Example:

18. Results – more detailed breakdowns • Interval for the ‘lowest 30% of income’ group: 77,000 households. • Interval for the ‘highest 70% of income’ group: 43,000 households. • The narrower range for higher income households is because these households are less likely to be close to the fuel poverty threshold, and so are more robust to the effects of uncertainty.

19. Conclusions • The uncertainty analysis has produced detailed breakdowns of the effect of uncertainty surrounding fuel prices and income on the fuel poverty estimates. • Statistics for those at the highest risk of being in fuel poverty (e.g. lowest 30% of income) are subject to the greatest uncertainty. • Many assumptions have been made therefore the results are best viewed as indicative.

20. A full note on the analysis of uncertainty in the national fuel poverty estimates is available on the DECC website at: http://www.decc.gov.uk/assets/decc/Statistics/fuelpoverty/1609-2008-fuel-poverty-uncertainty.pdf Questions?