The cost of living: how data can help tackle the crisis
September 2023
1
About
This report is the result of joint work carried out by the Open Data Institute (ODI) with Mime Consulting, and published in September 2023. If you want to share feedback by email or would like to get in touch, contact charlotte.mcleod@theodi.org.
This work was designed by the ODI, and project-managed by strategic communications agency, Allegory.
We would like to thank the ODI’s Nigel Shadbolt, Roger Hampson, Louise Burke, Emma Thwaites, Resham Kotecha and Lisa Allen for their guidance and support.
3
Summary
4
Housing
Debt
Food
Fuel
The cost of living crisis has had a serious impact on housing costs, leading to large increases in private rental costs and homelessness. The limited availability of open data on homelessness presents an important area for potential improvement that could lead to more research and data collection on hidden homelessness.
Increases in the cost of essential goods and services have forced many families to turn to debt to afford these expenses. Delayed publication schedules, poor geographical granularity, and a lack of data availability on ‘Buy Now Pay Later’ schemes and payday loans disrupt understanding of the true extent of this issue.
With food prices rising substantially since 2021, the number of food parcels delivered by food banks in 2022/23 is at a record high. However, reporting is problematic as there is no universal definition for food insecurity and poor sub-regional data collection. Information on support services is currently disjointed and hard for people to navigate.
Key drivers of the rise in inflation are the increased gas and electricity costs, forcing many people into fuel poverty due to an inability to afford their energy bills. A lag in data releases and a definition for fuel poverty, which excludes many families in difficulty, masks the scale of the crisis currently being experienced.
The cost of living crisis has led to financial pressures which are affecting people’s ability to afford costs related to housing, debt, food and fuel.
This report specifically examines the ‘data needs’ to help us better understand and address the resulting challenges for our society and aims to identify areas for improvement.
5
By looking across multiple datasets, we see several groups are disproportionately impacted across more than one of the four areas we looked at: housing, fuel, and food poverty and debt.
Data challenges
Data tools
We have created two online open data tools:
Four key data challenges have emerged throughout the research:
Support services
Targeting support appropriately requires open data on the location and details of support services. �The implementation and adoption of an open data standard like Open Referral UK would ensure the collection of better quality, more consistent and complete data, connecting those in need to local services.
At risk groups
This may lead to a domino effect, with multiple issues piling up for particular households. These ‘at risk’ groups include young people (aged 16 to 24), single parents, renters, and those belonging to ethnic minorities.
Introduction
The ODI’s founding and current vision is to build a world where data works for everyone, from businesses and governments to communities and individuals. The ODI believes that the need for an open and trustworthy data ecosystem at scale is stronger than ever and the greatest value of all types of data comes when the benefits are shared equitably. Our vision can only be achieved through collaboration across all sectors.
The ODI’s Five Year Strategy outlines the six technology-agnostic guiding principles which underpin the ODI’s role in responding to global trends, capitalising responsibly on opportunities �and addressing market forces outside of the organisation’s control. These include the beliefs that:
6
This report conducts an in-depth exploration of the open data across four domains of the cost of living crisis; housing, debt, food and fuel. This provides insights on those most at risk and, in particular, those who might be missing from official statistics. In particular, this report examines the current data landscape and where there may be gaps in the data infrastructure. A more robust data infrastructure is essential for informing responses to the crisis – and ensuring support is appropriately targeted.
Previous work by the ODI has explored the current data infrastructure which exists for cost of living related topics such as food and fuel poverty, while also highlighting existing data gaps. By examining the current data infrastructure, bringing together a wide range of datasets for the first time in powerful tools such as the ODI’s fuel poverty risk index, valuable insights are drawn together that were previously unavailable. This has helped us to improve our understanding of these topics and identify where resources can be targeted to benefit those most in need.
Note: Due to differences in datasets provided, this report looks at data that covers England, Great Britain and the United Kingdom. �The exact geographical area covered is made clear where relevant.
Open data can play a pivotal role in helping to tackle the current cost of living crisis. Freely accessible data forms the foundations on which researchers and policymakers can draw data-enabled insights. This allows the identification of concerning patterns and trends which can be used to inform interventions and target policies.
This report brings together analysis of open data on food insecurity, fuel poverty, housing costs and debt for the first time, exploring each of the following topics in detail:
Due to discrepancies with published data, this report will look at data that covers England, Great Britain and the United Kingdom.
The current support service landscape is also explored in this report, highlighting the barriers organisations face in the targeting responses and how this could be made more effective through improvements to open data infrastructure.
We have also produced two public facing data tools, facilitating the generation of further data-enabled insights:
Explores the entirety of the combined dataset, mapping each cost of living-related indicator across England. This includes data not previously published, which relevant organisations have shared.
Provides a high-level summary of the most important cost �of living-related statistics in an individual’s local area to help members of the public understand their local picture in relation to regional and England averages.
How might better and more open data help with the cost of living crisis?
7
Throughout this report, we explore data infrastructure and data practices in the context of the cost of living crisis. �We use the term data infrastructure to refer to the following five components:
1. Data assets
Data assets include datasets, identifiers and registers.
2. Data standards and technologies
Data standards and technologies are used to curate and provide access to those data assets. Standards for data are reusable agreements that make it easier for people and organisations to publish, access, use and share better quality data. They are documented, reusable agreements that solve specific problems or meet clearly defined needs. Standards detail the language, concepts, rules, guidance or results that have been agreed. Standards are used when it is important to be consistent, or to repeat processes, make comparisons, and reach a shared understanding.
Standards are used in industries and sectors worldwide to document agreements on physical items, ideas, digital products, processes, and more.
3. Organisations
Organisations govern the data infrastructure. Data governance refers to the exercise of authority, control, and shared decision making (planning, monitoring, and enforcement), over the management of data assets.
4. Guidance and policies
Guidance and policies inform the use and management of data assets and the data infrastructure.
5. People
People include those who are involved in contributing to or maintaining data infrastructure, and those who are impacted by decisions that are made using it.
What do we mean by data infrastructure?
8
What is the cost of living crisis?
The cost of living crisis refers to the fall in ‘real’ disposable incomes (adjusted for inflation and after taxes and benefits) that the UK has experienced since the later months of 2021. While the rate of inflation has slowed in recent months and wages show small signs of recovery, household disposable income remains low and households continue to face financial pressure.
This ongoing pressure has impacted the ability of households to afford costs related to housing, food and fuel and forced many to take on debts. Data published by the Office for National Statistics (ONS) in July 2023 shows that 93% of people living in Great Britain have seen their cost of living increase over the last twelve months. Particular demographic groups have been affected more than others, such as young people (aged 16 to 24), single parents and renters. The greater impact on these groups will be highlighted throughout the report.
9
What is the cost of living crisis?
Housing
Fuel
Food
Debt
The currently available data allows us to explore what we currently know about the cost of living crisis. However, as we explore throughout this report, this picture may not be completely accurate or up to date.
10
Due to the complexity of the cost of living crisis and its multitude of contributing factors, responses must be multifaceted and most importantly, data-enabled. This is to ensure that support is targeted to those who are in greatest need. While there have been multiple responses, including cost of living payments and mortgage repayment support, many are not sufficiently targeted to those who require it the most. For example, following concerns over potential negative impacts of a proposed 80% increase in energy prices, the UK government introduced the Energy Price Guarantee in October 2022. This capped the cost per unit of gas or electricity, limiting annual energy bills to approximately £2,500 for a typical household in Great Britain. This would save households £1,100 by the end of June 2023 compared to undiscounted energy prices. While the Ofgem price cap has fallen below the Energy Price Guarantee, it is remaining in place as a safety measure until March 2024.
However, as households in different income groups use similar amounts �of energy, lower income households use a higher percentage of their income on energy. Therefore, while this guarantee reduces energy costs across the board, its effect on those who require it the most, in lower income households, is least impactful. This emphasises the importance �of using data to inform responses in order to ensure they are most appropriate and effective.
What has been the response to the cost of living crisis?
11
Yet, there have been responses to the cost of living crisis that showcase the positive impact of using open data. For example, the Warm Spaces directory was set up by Creative Collective following the identification of a lack of resources for those facing difficulties in finding warmth throughout the cost of living crisis. This openly available directory:
As many local authorities are beginning to offer grants to help set up warm spaces, the number of warm spaces is likely to grow. Services can easily sign-up to add locations �to this directory, allowing continuous expansion of this resource. While the directory is not fully comprehensive, and there is room for improvement, this is an excellent example of how open data can help tackle the cost of living crisis.
What has been the response to the cost of living crisis?
12
Below we summarise the different methods used to conduct our analysis in this report.
Evidence review – We carried out an evidence review to understand the existing work on the role of data in addressing the cost of living crisis. Evidence reviewed primarily fell into two categories:
Engaging stakeholders – We sought a wide range of perspectives on the use of data about the cost of living crisis, to better understand:
Collection of existing data – Data scoping to identify and process existing open data sources from government departments, the NHS, the Bank of England, charities and private companies.
Analysis of trends, demographic and geographical patterns – To explore what insights the existing data could provide and where there are gaps.
Methodology
Stakeholders engaged:
13
14
Housing
The current open data on housing is relatively detailed. However, there are key groups of people missing from homelessness statistics and less data published on the quality of housing stock owned by private housing associations.
Housing
15
The rising interest rate has had a large impact on mortgage rates, which were three times higher on average in December 2022 compared to the year before. These rising rates are likely to have the largest impact on young people in their 20s and 30s.
How has the cost of living crisis impacted homeowners?
16
Rents in the private sector have been increasing rapidly, putting a strain on household finances. This is particularly a problem for those who receive housing benefits, as the gap between support and rents is widening.
How has the cost of living crisis impacted private renters?
17
1 Northern Ireland has been excluded as data was only provided from 2018 onwards
Almost a quarter of social renters found it difficult or very difficult to pay their rent. Despite the majority of social renters living in properties owned by housing associations, these associations have fewer reporting requirements when compared to local authorities and there is therefore less data published on the quality of this housing stock.
What does the data tell us about social renters and social housing stock?
18
Official statistics on homelessness show a recent increase in those rough sleeping and others experiencing homelessness. However, this is likely an underestimate of homelessness, with many people missing from official statistics.
What do we know about those who are homeless?
19
It is particularly challenging to generate a true estimate of the total number of people who are homeless. The ONS recognises the gaps in the homelessness data and conducted an evidence review in March 2023. In particular, it focused on those who are ‘hidden homeless’; those who are excluded from official statistics and whose experience of homelessness is never captured.
Exactly who is considered ‘hidden homeless’ varies but often includes households who are:
These individuals are challenging to capture in official statistics because their situations are less visible. They may not be engaged with homelessness services or the local authority or may be concerned about their safety if they were visibly homeless.
According to the ONS evidence review, the following groups of people are more likely to experience hidden homelessness:
Spotlight on ‘hidden homelessness’
20
Women are more likely to employ safety strategies, such as sleeping in hospitals or hidden spaces, which mean they are not counted in rough sleeping statistics.
Young people tend to rely on informal support structures and engage in sofa-surfing. As a result, they are less likely to identify as homeless and present to the local authority.
Ethnic minority individuals are less likely to perceive themselves as homeless and tend to rely on family and friends for support. Ethnic minority households are also more likely to experience overcrowding.
Centrepoint
Centrepoint – which is the UK's leading youth homelessness charity – has experienced key data gaps in its work with young people:
21
Opportunities for better and more open data
Current data on housing provides good information on renters and those who have a mortgage. However, it is well established that large numbers of people are left out of official homelessness statistics. More and improved open data may be able to fill some of these gaps.
Centrepoint is calling for the government to report on the number of people who present as homeless or at risk of homelessness to their local authority. Currently, the government only publishes age-disaggregated data about people eligible for a homelessness duty. However, every year around a third of those presenting to homelessness services do not reach the formal assessment stage and therefore do not appear in official statistics, meaning the government is unaware of the level of need in vulnerable young people.
Open data, produced by collaborating with stakeholders and sector experts, could help to fill in the gaps in official statistics on homelessness. This would ensure official statistics are more representative and capture those who are currently missing.
Requiring housing associations to meet the same reporting requirements as local authorities would provide a more complete picture of the state of the housing stock in the social rental sector. Specifically, this would include data on the quality of the housing stock owned by private housing associations.
22
23
Debt
Current data on debt is over three years out of date, due to publishing time lags, and not available at a fine level of geographic granularity. We know very little about high-cost credit loans such as payday loans and Buy Now, Pay Later schemes.
Debt
What do we mean by debt?
As of March 2020, almost half (49%) of households in Great Britain had some form of financial debt, rising to 62% if we include property debt. For many people, this debt is manageable and does not impact quality of life.
We are therefore more interested in debt when it becomes a problem but the definition of ‘problem debt’ is not universal. Citizens Advice, for example, defines a person as being in problem debt if “they are unable to afford their debt repayments”. The ONS, in the Wealth and Assets Survey, has more specific criteria relating to a liquidity or solvency problem. This makes it challenging to compare statistics across datasets.
In this section, we primarily focus on those in problem debt, defined by the relevant source, which includes both those in arrears and who have taken on credit.
24
Official statistics on the debt burden of British households are out of date, masking the impact of the cost of living crisis on household debt.
Debt burden over time
25
1 In April 2016, this survey was moved to a cycle reflecting financial years, which is why the month of reporting changes in 2016
Who is most at risk of being in ‘problem debt’?
Of the StepChange clients, 31% were social renting and 34% were privately renting. This compares to 17% and 20% of the England and Wales population respectively.
Over a quarter (26%) of the StepChange clients were single parents. This compares to just 11.1% of the whole population of England and Wales who are single parents.
59% of StepChange clients were in full- or part-time employment, compared to 57% of the England and Wales population. 10% of clients were unemployed and looking for work.
59% of StepChange clients were aged under 40, compared to 49% of the entire England and Wales population. Just 8% of clients were aged 60 and over.
Age
Employment
Family Composition
Housing
StepChange are a UK debt charity, providing advice and support for those with problem debt. Of the 15,677 clients who accessed advice in June 2023, the following groups stood out as most at risk of being in debt. As official statistics on debt from the ONS are out of date, we have not relied on these to estimate the risk for different groups. Comparison data for England and Wales is taken from the 2021 census.
26
How does debt burden vary across the country?
We know that debt burden and the impact of this debt varies across the country, but we do not have data available at a sufficient level of geographic granularity to provide actionable local intelligence.
27
Local authorities with lowest rates of individual insolvencies | Local authorities with highest rates of individual insolvencies |
City of London | North East Lincolnshire |
Richmond upon Thames | City of Kingston upon Hull |
Westminster | Mansfield |
Kensington and Chelsea | Halton |
Camden | Calderdale |
What do we know about high-cost credit and Buy Now Pay Later schemes?
There are no official statistics published by the government on high-cost
credit and Buy Now Pay Later (BNPL) schemes, despite the serious
consequences these often have for borrowers.
28
Opportunities for better and more open data
There are clear areas for improvement that would increase our understanding of ‘problem debt’ across the country.
Having a unified definition of problem debt would allow for comparisons across datasets and a clearer picture of who is most �at risk. An expansive definition that captures a range of households would ensure that more individuals and families identify their problems with debt and therefore access support before the problem spirals out of control.
A more regulated BNPL sector and greater data published on those who are taking out high-cost credit loans will help to plug current gaps in research and reporting. This should be collected at a regional/local authority level so that it is possible to understand how this impacts people differently across the UK. Without this data, any interventions will be less able to accurately target those most in need.
29
30
Food
There is no universal definition of food poverty, leading to different estimates of the scale of the problem. In addition, information on food support services is currently disconnected, making it challenging for those who need to access this support.
Food
31
Food insecurity and food prices over time
Food costs have clearly grown substantially in the past two years, although this has varied somewhat by food type. However, the data on food insecurity over time is less clear due to definitional differences and the short time period for which data has been collected.
32
Who is most at risk of being food insecure?
Households with disabled adults are more than three times as likely to be food insecure as those without. These households are also much more likely to have used food banks in the past year and constitute 75% of Trussell Trust food bank users.
Households with three or more children were more likely to be food insecure. Single adult households were also more food insecure, with 27% of single adult households food insecure. These households were also the most likely to have relied on food banks.
Social and private renters appear far more likely than owners to be experiencing food insecurity. Strikingly, one in five social renters were found to be food insecure, and 11% had used a food bank in the past 12 months.
18% of households where the head of the household is under 25 are food insecure, and 10% have relied on food banks in the past 12 months. These are both at least double the rate among any other age group. Older households are least likely to be food insecure.
Age
Housing
Family composition
Disability
The 2021/22 Family Resource Survey provides the most comprehensive analysis of the different population groups most at risk of food insecurity across the UK. Survey results are published by household demographic group, which give a clear view on which types of households are most at risk of experiencing food insecurity.
33
How does food insecurity vary across the country?
There is a good range of open data on food insecurity published at a regional level, �but far less by local authority or smaller geographical areas.
34
Food banks
Other food support
Food banks are the primary source of support for many households experiencing food insecurity.
Food banks across the UK are broken down into two categories:
Data on the location of these food banks is published, but not in the same place. �Data on the 1,300+ Trussell Trust food bank locations is published on its website, �while data on other food banks is dispersed. IFAN does publish an equivalent map for members of its network but this is not a comprehensive dataset of all other food banks.
35
What do we know about food support services?
IMAGE GOES HERE
Cover this blue area with your image
Food support provision over time
As the single largest food bank network in the UK, the Trussell Trust data on the level of support provided is the most complete open dataset available. This gives an indication of how demand for food support services has grown in recent years.
Food banks
Other food support
36
1 Pupils are eligible for free school meals if their parents or carers are eligible for specific benefits
Opportunities for better and more open data
Open data could be used to help policymakers, researchers, and charities better
address food insecurity across the country through three key mechanisms:
Analysis of trends over time is currently limited due to changes to definitions over time and a lack of investment in food insecurity tracking pre-2019. As highlighted in the ODI’s Food Insecurity and Data Infrastructure report, more consistent open data over time would allow better tracking of the scale of the problem, as well as evaluation of the impact of food support interventions. As the Food Standards Agency points out in its 2023 report, there is currently no analysis of the impact of free school meals or Healthy Start on food insecurity.
Geographical and demographic granularity in the existing open data is limited. This prevents effective targeting of support in areas with higher need and lower provision of support. More comprehensive and consistent datasets would mean government and charity resources can better understand who is most at risk of food insecurity and
where to target support to specific households, neighborhoods, or demographic groups.
Lists and interactive maps showing the location of food support locations are disparate and incomplete. As part of the Food Insecurity and Data Infrastructure work in 2022,
the ODI brought together a single dataset of almost 4,000 food support locations into a single online tool, with data including an organisations’ target groups and website address. This was the first time that data from multiple food support providers had
been combined in this way. This dataset has been updated as part of this cost of living project, allowing those in need to more easily identify their relevant local services.
37
38
Fuel
Fuel
Government data on fuel poverty is around a year out of date and requires your home to be energy inefficient, which excludes many families and households from official statistics.
39
What is the current picture of fuel poverty �across England?
The West Midlands has the highest proportion of households in fuel poverty, while the South West has the highest average fuel poverty gap. Sub-regional statistics lag by a year but highlight the extent of variation within regions.
40
Who is most at risk of being in fuel poverty?
At 27.8%, households who pay for their electricity using a prepayment meter were much more likely to be in fuel poverty than households who paid by standard credit (17.5%) or direct debit (10.5%).
Over a quarter (26.4%) of single parent households were in fuel poverty. However, the highest fuel poverty gap was for couples under 60 with no dependent children.
A quarter (25%) of households where the oldest member is aged 16 to 24 were in fuel poverty and these households had the highest average fuel poverty gap.
In 2022, 15.9% of households in rural areas were in fuel poverty and it would cost an average reduction of energy bills of £956 to move a rural household out of fuel poverty.
Rurality
Age
Family composition
Payment method
According to government fuel poverty statistics, both the prevalence of fuel poverty and the average fuel poverty gap vary by several property and household characteristics.
41
Since you must be living in an energy inefficient home to be classed �as fuel poor, certain households are likely underrepresented in fuel �poverty statistics.
Who is left out of official fuel poverty statistics?
In England, fuel poverty is measured using the Low Income Low Energy Efficiency (LILEE) indicator
Under this indicator, a household is considered to be fuel poor if:
And
The fuel poverty measures in Scotland, Wales and Northern Ireland do not include this reference to energy efficiency. This makes comparing fuel poverty statistics across the UK nations very challenging.
42
1 In 2022, a household was classed as low income if their post-tax income after they’ve paid their housing and fuel costs was less than £15,385 (60% of the median income for all households)
Fuel poverty over time
There has only been a small change in the proportion of households in fuel poverty from 2021 to 2022 but the average fuel poverty gap has increased substantially. The time lag in the publication of fuel poverty statistics is particularly concerning as we enter a second winter with high energy prices but without direct payment support from the government.
43
Opportunities for better and more open data
Two case studies highlight the role of open data in understanding and reporting on fuel poverty.
Due to concerns about who may be excluded from the official fuel poverty statistics and the year lag of publishing local authority level data, the ODI developed a fuel poverty risk index using open data. This calculates a score that estimates the risk of someone being in fuel poverty for each local authority in England. Building on work by the End Fuel Poverty Coalition in 2021, this takes a more holistic view of fuel poverty and attempts to include those who may be missing from official statistics.
The Data Communications Company (DCC) holds all of the systems data from electricity smart meters in its end-to-end technology system. This includes information, for example, on how regularly a meter is topped up and any low credit alerts. Opening up this data would allow for near real-time, hyper-local information on those who are at risk. DCC is working to make this data available publicly to realise these benefits.
44
45
Support services
Mapping support services from open data
As well as demonstrating the scale and distribution of the impacts of the cost of living crisis, open data can be used to make support services more accessible to those in need, and more effectively direct resources.
As discussed throughout this report, open data on the location of support, and clear details on the services provided, is vital for connecting those in need with appropriate local providers. While some attempts have been made to bring together support service directories, these are piecemeal, incomplete and often duplicative. A few examples of such attempts are:
If local and national organisations made up-to-date data on their services available in a consistent format, then duplication would be avoided, and accurate, complete and reliable service directories could be created. While this will lead to long-term improvements in efficiency, this work needs to be adequately resourced and funded.
As part of this project, an open dataset of cost of living support services has been amalgamated from a variety of existing open sources. This includes data from the Trussell Trust, Citizens Advice, FareShare, and Christians Against Poverty, and covers a broad range of services.
46
Mapping support services from open data: �the role of an open data standard
An open data standard for cost of living support services would help to ensure better quality, more complete data was collected and shared across organisations working to address the cost of living crisis. Defining what information organisations should publish about their services and in what format, would allow easy combination and analysis of organisations’ data.
The Open Referral UK (ORUK) standard for publishing data on community activities and services is a great example of this. This standard has now been adopted by seven local authorities, the Care Quality Commission, London Sport, My Best Life, and one Integrated Care Partnership. Each of these organisations has made the data on their services available through an API, meaning it can be easily accessed, combined with other ORUK standard data, and analysed.
As advocated by the ODI, future open data standards for cost of living support services should build on this work. Organisations providing cost of living support should consider whether they could align with the ORUK standard so their service offerings can be incorporated into ORUK-compliant directories.
47
Example of an open data standard for cost of living support services:
An open data standard should set out what data, including open data, should be collected for cost of living support services. Such a standard would need to be developed in collaboration with relevant stakeholders, but is likely to include:
Good quality and easy to access open data on local services is crucial to supporting efforts to address the cost of living crisis. Work on this open data must be adequately resourced and funded and will likely lead to long-term improvements in efficiency. Such data would have five key benefits to those in need and those attempting to support them:
Mapping support services from open data: benefits of better open data
Local and national government and third sector organisations can quickly identify gaps in provision to target their resources.
48
People can find relevant support service
Targeting of new support to address gaps
Support providers to collaborate
Save time signposting services
Prevent duplication
Including supporting referrals between organisations, which is particularly important as the cost of living crisis has meant that those in need are more often presenting with multiple needs.
By having a single source for information on services, charities and government will not duplicate effort producing service directories for their beneficiaries and residents.
Those in need can more quickly find good quality data on relevant support services.
As people can either self-signpost, or practitioners can use open data to identify appropriate services.
Open data tools and data ethics
Open data tools can present data in an accessible and useful way. Using the same open dataset, the two ODI open data tools (explained later in this slide) present local authority level data in very different ways for different audiences. This demonstrates how publishing more open data can support a wide range of analysis and insights.
Researcher and Policy Maker Tool
This data tool presents a vast array of local authority level data on the cost of living. Users can select from more than 100 indicators and visualise the geographical distribution, alongside support locations. This tool is built from two combined datasets:
Open data tools and data ethics
Local Area Explorer Data Tool
This data tool uses the same open dataset to present insights on the key cost of living issues and support services in a local area. This can be used by individuals, community organisations, and local government to better understand the issues in their area and identify support services.
ODI Data Ethics Canvas
Alongside the tools and open dataset, we have published a data ethics canvas. This identifies potential ethical issues associated with this project and publishing of the open dataset – and sets out mitigations. You can see the Data Ethics Canvas here.
51
Summary
The current data hints at a worsening crisis, with particular groups impacted more severely.
High inflation has placed increased financial pressures on many households across the UK, who are facing a cost of living crisis.
The cost of living crisis refers to the fall in ‘real’ disposable incomes – adjusted for inflation and after taxes and benefits – that the UK has experienced since the later months of 2021. The combination of extreme annual inflation rates – tracked using the Consumer Price Index (CPI) – along with employee wages failing to keep pace, has led to a fall in real household disposable income.
By looking across multiple datasets, several groups emerge as being impacted across more than one category: housing, food and fuel poverty and debt. This may lead to a domino effect, with multiple issues piling up for some households.
18% of households where the head of the household is under 25 are food insecure, and 10% have relied on food banks in the past 12 months. Households where the oldest member is aged 16 to 24 are also the most likely to be in fuel poverty and had the highest average fuel poverty gap of any age group.
Social and private renters appear far more likely than owners to be experiencing food insecurity. Strikingly, one in five social renters were found to be food insecure, and 11% had used a food bank in the past 12 months. They are also more likely to be seeking debt advice.
Over a quarter (26.4%) of single parent households are in fuel poverty. Single parents are also overrepresented in those seeking debt advice, suggesting they are more likely to be overburdened with debt.
Conclusion
52
Existing data has some key gaps and limitations, with four key data challenges emerging throughout the analysis.
Examining one issue in isolation provides only one piece of the puzzle and, to get a more comprehensive picture, datasets need to be combined. For instance, by bringing together official data with data collected from Freedom of Information (FOI) requests and other charities, Shelter developed a more complete account of homelessness in England.
Some people are missing from official statistics. This includes people who may be considered homeless but are not captured in official statistics or those who, because they live in energy efficient homes, are automatically excluded from fuel poverty statistics. As a result, current data doesn’t tell the whole story.
Government datasets are often a year or two years out of date, meaning that the currently available information lags behind people’s real experiences. As the situation is changing rapidly, data can be out of date before it’s published, and can no longer be relied upon to make informed policy decisions.
Several key official statistics are only published at a regional or national level. This masks sub-regional differences. Local authorities and public services are often missing key intelligence to inform their decisions. In addition, there is no uniform approach as to whether datasets are available at an England, Great Britain or United Kingdom level.
Conclusion
53
The cost of living crisis has undoubtedly exacerbated the economic problems that individuals and families face. Whilst the ODI does not claim that data can solve these problems by itself, we believe that the application of data standards, increased availability of data and timely availability of relevant and localised data can improve the way that these problems are addressed and targeted.
While we acknowledge that many of these measures will have a cost attached (mostly for national and local governments) we believe that such costs will likely be balanced against savings made due to better targeting of assistance, more timely interventions and greater ability for individuals to seek help from the right organisations at the right time.
We are making the following recommendations in relation to the better use and application of data:
Publishing a cost of living index that shows data by age, gender, ethnicity and geography. This would highlight the disparities across these classifications and provide useful information for better targeting support including benefits, housing and finance. The index could show housing, food, transport, fuel costs, with ‘baskets of goods’ data weighted by these categories.
Publication of a cost of living help database, which can be used to analyse problems arising from rising or sustained living costs. It should contain the most up-to-date information available and a tool with localised and regionalised information, where charities, welfare organisations and individuals can access contact details of organisations, details of help/benefits available and advice on problems with debt and housing costs.
Standardising and expanding the geographical coverage of data. There is currently limited standardisation of whether official statistics apply to England, England and Wales, Great Britain or the United Kingdom. While some of this is due to devolved powers, the standardisation of data across the nations of the UK would allow for more robust comparison and provide a more complete picture of the cost of living crisis across the UK. Where possible, we also recommend that data is published at the lowest geographic granularity possible, ideally local authority districts.
Data recommendations
54
Collection and publication of data on high-cost debt and BMPL debt at a national (including devolved nations), regional and local level, so that we can understand how this impacts people differently across the UK and allow both government and welfare organisations to identify and address problems by geography, age or type of debt.
Publication of fuel poverty data in full, removing the omission of English homes with an EPC rating of C or above in fuel poverty statistics. In Scotland, Wales and Northern Ireland those in poverty in such homes are included in the statistics. 52.8% of all low-income households in England lived in a property with an EPC of C or above, but all of those were excluded from fuel poverty statistics. This equates to approximately 3.6 million households and paints an inaccurate picture of fuel poverty in England and the UK as a whole.
Publication of more accurate, timely data on homelessness and social housing to better understand those who are at risk and where the need for support lies, including:
Data recommendations
55
Adoption of open standard infrastructure to support the data collection that would inform the provision of services.
The appropriate targeting of support requires timely and accurate data on the location and details of support services. The more complete adoption and implementation of an open data standard, such as Open Referral UK, would ensure the collection of better quality and more consistent data. This would help to connect those in need to local services.
Improving data on food insecurity:
Data recommendations
56
57
Appendix
Glossary
58
High-cost short-term credit | This is a broad term that encompasses several financial products such as overdrafts, loans and rent-to-own schemes. The Financial Conduct Authority has strict criteria on what can be considered high-cost short-term credit, which includes an APR of equal to or more than 100% with a maximum repayment term of 12 months. |
Household Reference Person / Head of Household | This person is sometimes used in official statistics when one person needs to be chosen from the household to be representative. There are specific criteria for establishing who should be the Household Reference Person, which can be found here. |
Open data standard | Open standards for data are “documented, reusable agreements that help people and organisations to publish, access, share and use better quality data” |
References: Report Datasets
59
Data category | Source | Dataset | Geographical coverage | Date published | Most recent year of data | Licence |
Debt | Citizens Advice | England, Wales and Northern Ireland | 2023 | 2023 | Included with permission from Citizens Advice and made available under a CC BY 4.0 license | |
Fuel | Department for Business, Energy and Industrial Strategy | England | 2023 | 2022 | ||
Housing | Department for Levelling Up, Housing and Communities | England | 2023 | 2022 | ||
Housing | Department for Levelling Up, Housing and Communities | England | 2023 | 2022 | ||
Housing | Department for Levelling Up, Housing and Communities | England | 2023 | 2023 |
References: Report Datasets
60
Data category | Source | Dataset | Geographical coverage | Date published | Most recent year of data | Licence |
Food | Department for Work and Pensions | United Kingdom | 2023 | 2022 | ||
Food | The Food Foundation | United Kingdom | 2023 | 2023 | Public data, provided on website | |
Debt | The Insolvency Service | England and Wales | 2023 | 2021 | ||
All | Office for National Statistics | Great Britain | 2023 | 2023 | ||
All | Office for National Statistics | England and Wales | Releases ongoing | 2021 | ||
All | Office for National Statistics | United Kingdom | 2023 | 2023 |
References: Report Datasets
61
Data category | Source | Dataset | Geographical coverage | Date published | Most recent year of data | Licence |
All | Office for National Statistics | United Kingdom | 2023 | 2023 | ||
Debt | Office for National Statistics | Great Britain | 2022 | 2020 | ||
Housing | Office for National Statistics | United Kingdom | 2023 | 2023 | ||
Housing | Office for National Statistics | England | 2023 | 2023 | ||
Food | Trussell Trust | United Kingdom | 2023 | 2023 | Included with permission from Trussell Trust and made available under a CC BY 4.0 license |
Note: As part of this project a more comprehensive combined open dataset has been made available for analysis through the data tools and is downloadable in spreadsheet format. Metadata on the full dataset will also be published alongside this report and the data tools.
References: Bibliography
62
Barclays (2022), ‘Retailer support for regulated ‘Buy Now Pay Later’ products could save 876,000 Brits from problem debt’. |
Bank of England (2023), ‘Money and Credit – June 2023’. https://www.bankofengland.co.uk/statistics/money-and-credit/2023/june-2023 |
Centrepoint (2023), ‘Beyond the numbers: The scale of youth homelessness’. https://centrepoint.org.uk/news/beyond-numbers-scale-youth-homelessness |
Data Communications Company (2021), ‘Data for good’. https://www.smartdcc.co.uk/media/1254/21037-dcc-data-for-good-paper_v8-final.pdf |
Deller, Turner and Waddams Price (2022), ‘Energy poverty indicators: Inconsistencies, implications and where next?’ https://www.sciencedirect.com/science/article/abs/pii/S0140988321004278 |
Department for Energy Security and Net Zero (2023), ‘Energy Price Guarantee’. |
Department for Levelling Up, Housing and Communities (2006), ‘A decent home: definition and guidance’. https://www.gov.uk/government/publications/a-decent-home-definition-and-guidance |
Feeding Britain (2023), ‘Affordable Food Clubs Impact Report March 2023’. https://feedingbritain.org/affordable-food-clubs-impact-report-march-2023/ |
Financial Conduct Authority (2019), ‘Consumer credit – high-cost short-term credit lending data’. https://www.fca.org.uk/data/consumer-credit-high-cost-short-term-credit-lending-data-jan-2019 |
Financial Conduct Authority (2023), ‘Financial Lives 2022 survey – Key findings from the May 2022 survey’. https://www.fca.org.uk/publications/financial-lives/financial-lives-survey-2022-key-findings |
References: Bibliography
63
Food Standards Agency (2023), ‘Household food insecurity in the UK: data and research landscape’. https://www.food.gov.uk/research/household-food-insecurity-in-the-uk-data-and-research-landscape |
Generation Rent (2023), ‘The real reason rents have been rocketing’. https://www.generationrent.org/2023/06/19/the-real-reason-rents-have-been-rocketing/ |
The Guardian (2023), ‘First-time homebuyers now need nearly 10 years to save a deposit, research finds’. |
The Guardian (2022), ‘Where will struggling households turn to after UK clampdown on payday lenders?’ |
House of Commons Research (2023), ‘Gas and electricity prices under the Energy Price Guarantee and beyond’. https://commonslibrary.parliament.uk/research-briefings/cbp-9714/ |
Institute for Fiscal Studies (2023), ‘Freezes in housing support widen geographic disparities for low-income renters’. |
Institute for Government (2022), ‘Cost of living crisis explainer’. https://www.instituteforgovernment.org.uk/explainer/cost-living-crisis |
Joseph Rowntree Foundation (2023), ‘The cost of debt for low-income households in the cost of living crisis’. https://www.jrf.org.uk/blog/cost-debt-low-income-households-cost-living-crisis |
|
References: Bibliography
64
Joseph Rowntree Foundation (2023) ‘Unable to escape persistent hardship: JRF’s cost of living tracker, summer 2023’. https://www.jrf.org.uk/report/unable-escape-persistent-hardship-jrfs-cost-living-tracker-summer-2023 Office for National Statistics (2022), ‘Energy prices and their effect on households’. |
Office for National Statistics (2023), ‘“Hidden” homelessness in the UK: evidence review’. |
Office for National Statistics (2023), ‘How increases in housing costs impact households’. |
Office for National Statistics (2023), ‘Monthly mortgage repayments up 61% for average semi-detached home in the UK’. |
Office for National Statistics (2022), ‘Tracking the price of the lowest-cost grocery items, UK, experimental analysis: April 2021 to September 2022’. |
Office for Statistics Regulation (2022), ‘Cost of Living Crisis: Unpicking and Understanding the data gaps’. |
Open Data Institute (2022), ‘Food insecurity and data infrastructure: report, video and tool’. https://theodi.org/article/food-insecurity-and-data-infrastructure/ |
Open Data Institute (2022), ‘Fuel poverty and data infrastructure: report and fuel poverty risk index’. |
|
References: Bibliography
65
Open Data Institute, nd, ‘What are open standards?’. https://standards.theodi.org/introduction/what-are-open-standards-for-data/ Open Data Institute, nd, ‘What is data infrastructure?’. |
Open Data Institute (2022), ‘Food insecurity and data infrastructure: report, video and tool’. https://theodi.org/article/food-insecurity-and-data-infrastructure/ |
Open Data Institute (2022), ‘Fuel poverty and data infrastructure: report and fuel poverty risk index’ |
Shelter (2023), ‘Alert Briefing: Homelessness and the Cost of Living Crisis’ |
Shelter (2023), ‘Research: Homelessness in England 2022’. |
StepChange (2023), ‘Monthly client data report: June 2023’. |
The Telegraph (2023), ‘A mortgage repayment crisis is looming – and this age group will be hit the worst’. |
Think Digital Partners (2023), ‘DLUHC reveals lack of data sharing on vulnerable people’. |
|
References: Bibliography
66
The Times Money Mentor (2023), ‘Why are mortgage rates so high?’. https://www.thetimes.co.uk/money-mentor/article/uk-mortgage-rates-rise-news-why/ The Trussell Trust (2023), ‘Hunger in the UK’. https://www.trusselltrust.org/wp-content/uploads/sites/2/2023/06/2023-Hunger-in-the-UK-report.pdf Warm Spaces, nd, ‘Warm Spaces Map’ |
Which? (2021), ‘Under Pressure: Who uses Buy Now, Pay Later?’. |