the effect of uncertainty on fuel poverty statistics

The effect of uncertainty on fuel poverty statisticsLaura Williams, Department of Energy and Climate Change

GSS Methodology Symposium, 6th July 2011

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

The fuel poverty model

Uncertainty in the inputs leads to uncertainty in the output…

ModelFuel

poverty estimate

INPUTS OUTPUT

EHS data

Fuel price data

Other data

Uncertainty analysis

This analysis looked at the uncertainty

associated with:

1.Household income

2.Fuel prices

Methodology for estimating uncertainty

To estimate the number of fuel poor households:

1. Using the given data, calculate the fuel poverty ratio for each household.

2. Sum those with a ratio greater than 0.1.

To estimate the impact of uncertainty:

1. Modify the input data according to its distribution representing the uncertainty.

2. Using the modified data, calculate the fuel poverty ratio for each household.


4. Repeat many (typically thousands of) times, i.e. a type of Monte Carlo simulation, in order the create a distribution.

UNCERTAINTY IN HOUSEHOLD INCOME

English Housing Survey (EHS)

Householder features

Employment

Income information

Dwelling features

Health

Age

Benefits

Earnings

Insulation

Fuel mix

Savings

Type, e.g. flat

Property age

Composition

Uncertainty in EHS income data

Uncertainty considered for 5 types of income:

1. Earnings

2. Housing benefit

3. All other benefits

4. Savings

5. 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.


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:Year

FES / NA (% - FES total as percentage of National Accounts total)

1985 98.11986 95.21987 95.51988 93.41989 941990 93.31991 93.11992 96.4

Mean: 94.9 %

Standard deviation: 1.76%Coefficient of variation: 1.85%


Aspect of incomeCoefficient of

variation

1. Savings 15.9%

2. Earnings from employment 1.6%

3. Housing benefit 8.7%

4. All other benefits 1.9%

5. Other sources 6.8%

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

UNCERTAINTY IN DOMESTIC FUEL PRICES

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 fuel prices 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!

Uncertainty in fuel prices

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Emids Ea

st NE

N Tha

mes NW

North

SE SW

S

Wes

t Mids

DD

Min

5%

25%

75%

95%

Max

Variation in domestic bills for direct debit customers, England 2008:

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

East

Emids Lond

York

s

Mids NE

Sout

h SE NW SW

Mer

s & N

W

DD

Min

5%

25%

75%

95%

Max

Gas Standard electricity

RESULTS

Results – recap of methodology

To estimate the impact of uncertainty:

1. Modify the input data according to its distribution which represents the uncertainty.

2. Calculate the fuel poverty ratio for each household.


4. Repeat many (typically thousands of) times.

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).

Results – more detailed breakdowns

Total number of households

Estimated number of fuel poor

before uncertainty

Most likely value after addition of uncertainty

Bottom of 95%

confidence interval

Top of 95% confidence

interval

Width of 95%

confidence interval

Width of interval as percentage

of total number

households

Lowest 30% of income 6,502 2,971 2,968 2,929 3,007 77 1.2%

Highest 70% of income 14,906 364 375 354 397 43 0.3%

• Estimates of the effect of combined income and fuel price uncertainty on a variety of demographic and dwelling characteristics.

• Example:

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.

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.

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?

