1. DISSEMINATION AND FUEL POVERTY STATISTICS
Presentation to the Oslo Group
Andrew Ray
Department for Business, Energy and Industrial Strategy
(UK central government ministry)
10 May 2017

2. Dissemination in the UK: Recent Progress
Shorter publications, better infographics
Starting to move away from spreadsheet tables: developing an energy statistics API, data extraction and tabulator tools
A user consultation event on electricity statistics to find out more about what people would like
Focus of talk today is on fuel poverty, to show how what we publish is shaped by policy and delivery.
Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data
Fuel Poverty definition
May need to touch on after housing cost income

3. In England we consider a household to be fuel poor if:
What is Fuel Poverty?
In England we consider a household to be fuel poor if:
they have required fuel costs that are above the national average
were they to spend that amount, they would be left with a residual income below the official poverty line
Households
in fuel poverty face both lower incomes and higher energy needs compared to typical households.
Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data
Fuel Poverty definition
May need to touch on after housing cost income
Annual Fuel Poverty Statistics for England 2014 published 30th June 2016
Based upon English Housing Survey data

4. The International Context
Examples of other ‘objective measures’ from the EU:
Households spending more than 10% of their income on energy – Ireland, the rest of Britain. An unofficial definition in France.
Where average monthly expenditure on energy represents a ‘substantial’ share of average monthly income of the household - Slovakia.
Subjective measures have also been used
No internal consensus on definitions of energy poverty.
Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data
Fuel Poverty definition
May need to touch on after housing cost income
Sources: EU Energy Poverty Handbook launched in 2016
and ‘Manual for Statistics on Energy Consumption in Households’ (Eurostat, 2013 Chapter 7)

5. Depth of FP would reduce if fewer with very high fuel costs.
A relative measure…
14.7%
35.3%
Always half below the median but income changes can alter the % in the FP quadrant.
10.6%
39.4%
Depth of FP would reduce if fewer with very high fuel costs.
Low Income, High Costs
Arrows represent individual household (FP gap)
Not necessarily about getting people out of FP but reducing the inequality in society by reducing the FP gap
Low Income High Costs is a dual indicator, which allows us to measure not only the extent of the problem (how many fuel poor households there are) – which is represented by the shaded quadrant in the bottom left hand corner, but also the depth of the problem (how badly affected each fuel poor household is).
The depth of fuel poverty is calculated by taking account of the fuel poverty gap. This is a measure of the additional fuel costs faced by fuel poor households compared to the non-fuel poor household thresholds – as shown by the vertical blue arrows in the fuel poor quadrant.
Households can move in and out of fuel poverty in a number of ways – high level explanation of crossing vertical and horizontal thresholds and moving diagonally (combination).
However, it is worth noting that as the LIHC indicator is a relative measure, it is difficult to isolate absolute reason for change.

6. Fuel poverty in England 2003-2014
Emphasis on the fairly stable nature of the indicator…...
Explain blue bar fuel poverty level - remained fairly stable
Explain orange dots and trend line represents fuel poverty gap which increased between ; started to come back down again from 2011
Text from previous slide which includes the changes:
In 2014, the number of households in fuel poverty in England was estimated at around 2.38 million, representing approximately 10.6 per cent of all English households. This is The number of households in fuel poverty in England was estimated at 2.38 million (10.6%). This is an increase (1.4%) from 2.35 million households in 2013.
The average fuel poverty gap (the amount needed to meet the fuel poverty threshold), fell from £379 to £371 (2%).
The aggregate fuel poverty gap across all fuel poor households also reduced over this period, from £890 million to £882 million (1%).

7. But we target those with highest energy costs
Aim to eradicate these. FPEER F/G = most non-energy efficient properties after factoring in additional government support
The government has a target to get as many fuel poor homes up to a C FPEER rating by 2030.
The latest Fuel Poverty Strategy outlined interim targets of Band E by 2020, and Band D by 2025.
Chart shows that progress has been made since 2010 with the majority of fuel poor homes now classified as a D rather than an E
The proportion of households in Band E and above has increased over the year from 86.8 per cent in 2013 to 88.4 per cent in 2014, and has seen a steady increase from 78.7 per cent in This shows progress towards the interim target of getting as many households as reasonably practicable to Band E or above by 2020.
Majority of FP HH now classified as band D rather than Band E

8. with interim targets of:
So this year’s publication needs to focus more on the target than on the overall number in fuel poverty…
FUEL POVERTY TARGET
Raise as many fuel poor homes as reasonably practicable to a minimum energy efficiency rating of Band C by 2030;
with interim targets of:
Band E by 2020
Band D by 2025
 As Peter touched on earlier “ ”…..
… and this is important in the fuel poverty context…….
By improving a households energy efficiency up to a Band C, in line with the fuel poverty target, you don’t necessarily make them ‘not fuel poor’ – but what we do achieve is an improvement of that households’ ability to be able to heat their home and potential reduction in the fuel poverty gap which may or may not move a household out of fuel poverty.

9. SE & E have lowest levels
To achieve targets we need to
know where the fuel poor live
LAs with highest levels of FP concentrated in South West, West Midlands and North.
In general LAs in
SE & E have lowest levels
We have been publishing these figures each year down to quite small areas BUT we are reviewing this methodology.

10. However, we only have estimates based on a survey
The English Housing Survey is a continuous national survey of people's
housing circumstances and the condition and energy efficiency
of housing in England.
It is the only survey in England to collect
information on both the occupants and
physical characteristics
of dwellings.
But it only covers about 12,000 out of
22.5 million households
EHS: This is run by DCLG - they collect data (via interview) every year from around households
Around half of these go on to have a physical survey of their property which includes taking measurements of the rooms, any insulation, they type of walls etc.
We use the data from the physical survey to base our fuel poverty estimates on.
English Housing Survey (EHS)

11. The new published figures won’t be detailed enough
If we change to publishing more robust figures for Local Authorities with confidence intervals we will please statisticians but not policy makes.
Is there a way to target fuel poverty interventions in the absence of detailed figures?
Two approaches:
Data science methods to try to classify the fuel poor using existing available data
Legislation to enable additional datasets to be used.
EHS: This is run by DCLG - they collect data (via interview) every year from around households
Around half of these go on to have a physical survey of their property which includes taking measurements of the rooms, any insulation, they type of walls etc.
We use the data from the physical survey to base our fuel poverty estimates on.
English Housing Survey (EHS)

12. Data science pilot
Take the ‘known’ fuel poverty sample in the survey data
Link in a wide range of additional data available nationally on household characteristics
Run machine learning algorithms to try to classify fuel poverty – used TPOT
In theory can then use same approach model to classify all households
Performs better than simple regression models for fuel poverty
But still not as helpful as more relevant real data... 12

13. Data Can we obtain more data?
Within government the data needed to do accurate LIHC targeting exists at address-level, but it is sensitive.
Tax Credits
ESA
JSA
Universal Credit
Pension credits
Housing benefit
Etc…
Household income
Household size
Housing costs
Low Income (equivalised, after bills)
Valuation Office
NEED
Ordnance Survey
EPC Register
Property age
Property size
Property type
Central heating
Fuel type
Boiler info
Etc…
High Costs 13

14. Data sharing legislation
New primary legislation required before tax credit and VOA data can be used.
Proposals were consulted on last year and legislation has just gone through
Aiming for new targeting model in 2018
Will be able to provide more accurate maps and data to energy companies 14

15. Summary
Fuel Poverty annual publication has been improved and made more relevant to the targtes we actually use
But this has raised questions about the local level statistics – are they robust?
In the absence of published statistics that can be used for targeting we will be able to use other means (data linking) to pursue delivery of fuel poverty policies.
However legislation doesn’t necessarily allow us to publish these more detailed numbers