Corporate report

DWP single departmental plan: 2015 to 2020 headline indicators technical detail

Updated 20 April 2017

This corporate report was withdrawn on 14 December 2017

This has been replaced by another version.

Our single departmental plan sets out our 5 objectives for this Parliament including how we will fulfill our commitments.

Our plan includes 11 headline indicators to help measure progress against each objective. Indicators within the plan will be updated with new data as it becomes available. Technical detail on how each indicator is calculated is set out below.

1: The overall employment rate

Indicator description

This indicator shows of the 16 to 64 year old population, what proportion are in employment.

It uses data from the Labour Force Survey published monthly by the Office for National Statistics as 3 month rolling averages, approximately 6 weeks after the period.

Technical description

The indicator is the proportion of 16 to 64 year olds who are in employment. Data is seasonally adjusted so quarterly comparisons can be made.

Rationale

One of our key objectives, as set out in the single departmental plan is to run an effective welfare system that enables people to achieve financial independence by:

• providing assistance and guidance into employment
• ensuring the health, employment and benefit system appropriately promotes and supports work
• ensuring young people are earning or learning
• increasing the number of people from an ethnic minority background in employment
• supporting older people to remain in work

Formula

This indicator is measured by dividing the number of 16 to 64 year olds who are in employment by the total number of 16 to 64 year olds. This shows, of the 16 to 64 year old population, what proportion are in employment.

Start date

The data is already published and is available on a comparable basis back to 1971.

Good performance

Generally a statistically significant increase in the indicator will demonstrate an improvement in the labour market allowing for changes in the population. External factors such as economic conditions will need to be taken into account when interpreting changes to this measure.

Behavioural impact

Minimal – indicator is not a target.

Comparability

Data is seasonally adjusted and available monthly on a rolling quarter basis, and therefore quarter on quarter comparisons can be made. It is based on Labour Force Survey data which uses internationally agreed concepts and definitions, so is internationally comparable.

It is not possible to compare over-lapping quarters (consecutive data points) as this is effectively the same as comparing single month figures which is not considered to be robust.

Collection frequency

Monthly.

Time lag

6 weeks after the reference quarter.

Data source

The Labour Force Survey is published monthly by the Office for National Statistics in the Labour Market Statistics bulletin.

Type of data

National Statistic, based on survey data.

Robustness and data limitations

Analyses are National Statistics produced to high professional standards. The quality of National Statistics products is assessed on a regular basis by the independent UK Statistics Authority.

Along with other users, DWP are represented on groups that monitor the quality and relevance of the underlying data (Labour Force Survey Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group).

The data are seasonally adjusted, however it is a more reliable indicator of performance to compare changes over the course of a year rather than quarter. The confidence intervals around the indicator are published each month.

The latest confidence interval for a single quarterly estimate of the indicator is +/- 0.4 percentage points. The confidence interval for a quarterly change is +/- 0.3 percentage points and for a year on year change is +/- 0.5 percentage points.

The survey data is based on a random sample of 45,000 households, used for a wide range of National Statistics and is considered to provide robust estimates of labour market outcomes.

Collecting organisation

Office for National Statistics.

Return format

Percentage.

Geographical coverage

Data is published at a national, regional and local level; however local level data is often based on small sample sizes.

Therefore, local data may be useful for point estimates to get an idea of how the employment rate is doing but is less robust for making comparisons over time or between areas.

How indicator can be broken down

Data is published by:

• gender
• age (16 to 17, 18 to 24, 25 to 34, 35 to 49, 50 to 64 and 65+)
• nationality
• country of birth
• education status for those aged 16 to 24

Further guidance

Published in the monthly Labour Market Statistics Statistical Bulletin on the ONS website. It is available in excel format.

2: The employment rate for disabled people

Indicator description

This indicator shows what proportion of the 16 to 64 year old Equality Act 2010 disabled population are in employment.

It uses data from the Labour Force Survey published quarterly by the Office for National Statistics, approximately 6 weeks after the period.

The data are seasonally unadjusted and therefore only year on year comparisons should be made.

Technical definition

People with a physical or mental impairment that has a ‘substantial’ and ‘long-term’ negative effect on their ability to do normal daily activities are defined as disabled under the Equality Act 2010.

The indicator is the proportion of 16 to 64 year old people with a disability who are in employment.

Rationale

As set out in the single departmental plan, one of DWP’s key objectives is to run an effective welfare system that enables people to achieve financial independence by providing assistance and guidance into employment by:

• ensuring the health, employment and benefit system appropriately promotes and supports work
• ensuring young people are earning or learning
• increasing the number of people from an ethnic minority background in employment
• supporting older people to remain in work

This indicator aligns with these objectives and will provide a measure of the success of our employment policies and programmes in further reducing the employment rate gap between disabled and non-disabled people – in line with the commitment in the Spending Review 2015 for a white paper.

Formula

This indicator is measured by dividing the number of 16 to 64 year old Equality Act 2010 disabled who are in employment by the total number of 16 to 64 year old Equality Act 2010 disabled.

This shows, of the 16 to 64 year old Equality Act 2010 disabled population, the proportion that are in employment.

Start date

The data are already published and are available on a consistent basis back to Quarter 2 2013.

Good performance

Generally, a statistically significant increase in the indicator will demonstrate an improvement in the labour market performance of disabled people.

External factors such as economic conditions will need to be taken into account when interpreting changes to this measure.

The latest estimates for the confidence interval for a single quarterly estimate of the indicator is +/- 1.0 percentage points. The confidence interval for a year on year change is +/- 1.3 percentage points.

The confidence intervals are not published by ONS.

Behavioural impact

Minimal, the indicator is not a target.

Comparability

Data are seasonally unadjusted and available on a quarterly basis so only year on year quarterly comparisons can be made.

It is based on Labour Force Survey data which uses internationally agreed concepts and definitions.

Collection frequency

Quarterly.

Time lag

6 weeks after the reference quarter.

Data source

The Labour Force Survey published quarterly by the Office for National Statistics.

Type of data

National Statistic, based on survey data.

Robustness and data limitations

Analyses are National Statistics produced to the high professional standards. The quality of National Statistics products is assessed on a regular basis by the independent UK Statistics Authority.

Along with other users, DWP are represented on groups that monitor the quality and relevance of the underlying data (Labour Force Survey Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group).

The data are seasonally unadjusted, so only year on year quarterly comparisons can be made. The latest estimates for the confidence interval for a single quarterly estimate of the indicator is +/- 1.0 percentage points.

The confidence interval for a year on year change is +/- 1.3 percentage points. The confidence intervals around the indicator are not published by ONS.

The survey data is based on a random sample of 45,000 households, used for a wide range of National Statistics and is considered to provide robust estimates of labour market outcomes.

In April 2013, changes were made to the wording of the disability questions in order to bring the LFS into line with the Government Statistical Service (GSS) Harmonised Standards for questions on disability and also enable the LFS estimates to be consistent with the definitions used in 2010 Equality Act.

Due to the definitional changes, these estimates cannot be compared with estimates for previous years which were based on a definition relating to the Disability Discrimination Act (DDA).

Collecting organisation

Office for National Statistics.

Return format

Percentage.

Geographical coverage

United Kingdom.

How indicator can be broken down

The employment rate for people with a disability is published, but cannot be broken down further from published data.

Further guidance

Labour market status of disabled people is published quarterly by the Office from National Statistics and is available in excel format on the ONS website.

3: Percentage of young people (18 to 24 year olds) not in full-time education who are in employment

Indicator description

This indicator shows of the 18 to 24 year olds not in full-time education, what proportion are in employment. It uses data from the Labour Force Survey published monthly by the Office for National Statistics approximately 6 weeks after the period.

Rationale

The indicator aligns with our objective outlined in our the single departmental plan to:

• run an effective welfare system that enables people to achieve financial independence by providing assistance and guidance into employment
• ensure the health, employment and benefit system appropriately promotes and supports work
• ensure young people are earning or learning; increasing the number of people from an ethnic minority background in employment
• support older people to remain in work

The indicator reflects one part of the government’s wider commitment to improving the proportion of young people engaged in a positive activity, employment, education or training.

It recognises that DWP’s role within this is to improve labour market outcomes for those not in full-time education.

Formula

This indicator is measured by dividing the number of 18 to 24 year olds not in full-time education who are in employment by the total number of 18 to 24 year olds who are not in full-time education.

This shows, of the 18 to 24 olds not in full-time education, what proportion are in employment.

Start date

The data is already published and is available on a comparable basis back to 1992.

Good performance

Generally a statistically significant increase in the indicator will demonstrate an improvement in the labour market position of young people but external factors such as economic conditions will also need to be taken into account.

Behavioural impact

Minimal, indicator is not a target.

Comparability

Data is seasonally adjusted and available monthly on a rolling quarter basis and therefore quarter on quarter comparisons can be made.

It is based on Labour Force Survey data which uses internationally agreed concepts and definitions, so is internationally comparable.

It is not possible to compare overlapping quarters (consecutive data points) as this is effectively the same as comparing single month figures which is not considered to be robust.

Collection frequency

Monthly.

Time lag

6 weeks after the reference quarter.

Data source

The Labour Force Survey published monthly by the Office for National Statistics in the Labour Market Statistics bulletin.

Type of data

Survey.

Robustness and data limitations

Analyses are National Statistics produced to high professional standards.

The quality of National Statistics products is assessed on a regular basis by the independent UK Statistics Authority.

Along with other users, DWP are represented on groups that monitor the quality and relevance of the underlying data (Labour Force Survey Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group).

Although the data are seasonally adjusted, it is a more reliable indicator of performance to compare changes over the course of a year rather than quarter.

There is no published data for the confidence intervals around the indicator. However, by looking at the variation in the raw data and by applying a design factor provided by the Office for National Statistics, it is estimated that the confidence interval for a single quarterly estimate of the indicator is +/- 1.4%.

The confidence interval for a year-on-year change is wider, because it is based on 2 independent estimates and so subject to 2 ‘sources of uncertainty’.

The confidence interval for a year on year change is approximately +/- 2%.

The survey data is based on a random sample of 45,000 to 50,000 households, used for a wide range of National Statistics and is considered to provide robust estimates of labour market outcomes.

Collecting organisation

Office for National Statistics.

Return format

Percentage.

Geographical coverage

Only national data is published.

How indicator can be broken down

Data is published by gender.

Further guidance

Published in the monthly Labour Market Statistics Statistical Bulletin on the ONS website, available in excel format.

4: Numbers on key out of work benefits

Technical definition

This indicator measures the non-seasonally adjusted number of people aged 16 to State Pension age claiming:

• Jobseeker’s Allowance
• Universal Credit and not in employment
• Employment and Support Allowance
• Incapacity Benefit
• Severe Disablement Allowance
• Income Support (as a lone parent or in the ‘other’ category)
• Pension Credit (under State Pension age)

There will be upward pressure on this indicator during the rollout of Universal Credit, as UC specifically aims to bring more people into the full conditionality regime during this Spending Review period.

Assessing performance against this indicator will need to take account of this effect.

The equalisation of State Pension age between 2010 and 2020 will change the composition of this group by including women between 60 to 64. Further rises to age 66, 67 and 68 for men and women will follow.

The ‘Others on Income Related Benefit’ category, the majority are PC claims for people aged under State Pension age.

The remainder are on Income Support, some are disablement-related, with either the claimant, their partner or their child receiving a disability premium, but without falling in to the ‘Employment and Support Allowance and incapacity benefits’ group. There are some remaining cases in other small groups

The groups that comprise the key-out-of-work benefits are based on the concept of Statistical Groups presenting each person by the main reason they are in contact with the department.

The total will include a small number of people who are in work.

Rationale

Reflects the government’s economic and social objective of helping more people into employment and less dependent on the state.

Linked to welfare reform policy objectives and the development and delivery of Universal Credit and the policy objectives of the Work Programme to help more people off benefit into employment.

The indicator aligns with our objective outlined in our the single departmental plan to run an effective welfare system:

• that enables people to achieve financial independence by providing assistance and guidance into employment
• by ensuring the health, employment and benefit system appropriately promotes and supports work
• by ensuring young people are earning or learning
• by increasing the number of people from an ethnic minority background in employment
• by supporting older people to remain in work.

Formula

Claimant count, the published table includes both UK figures (seasonally adjusted) and GB figures (not seasonally adjusted) published by ONS.

The indicator uses the GB, not seasonally adjusted figure for comparability with statistics for other benefits.

Prior to May 2013 the Claimant Count is Jobseeker’s Allowance only. The experimental Claimant Count series includes Universal Credit (UC) claimants from May 2013.

Universal Credit figures are taken from DWP Experimental Statistics.

From May 2013 to October 2013, the Universal Credit figures represent all claimants of Universal Credit, including those in work and those not seeking work. This caused a slight overcount in the number of unemployed people.

From November 2013 onwards, the Universal Credit figures represent those claimants who had not worked in the reference period.

This is an improved estimate of unemployed Universal Credit claimants at a point in time. It still slightly overstates the Claimant Count number, as it includes some non-jobseekers who are not in employment, but this does not affect the Out of Work benefit indicator, as these non-jobseekers should still be counted.

ESA, IB, lone parents on IS, others (IS others and PC) are GB figures, not seasonally adjusted.

It should be remembered when using this data that the coverage of the Claimant Count UK seasonally adjusted figure is slightly different to that for the other key out-of-work benefits.

Whereas figures for Claimant Count UK cover the entirety of the United Kingdom, the coverage of all other figures is all cases in Great Britain and overseas.

These statistics exclude cases in Northern Ireland.

Please see the Northern Ireland Department for Social Development website for information about benefits in Northern Ireland.

The key out-of-work benefits shown in this table are chosen to best represent a count of all those benefit recipients who cannot be in full-time employment as part of their condition of entitlement.

Those claiming solely bereavement benefits or Disability Living Allowance (DLA) are not included as these are not out-of-work or income based benefits. DLA is paid to those needing help with personal care.

These people can, and some will, be in full-time employment. If DLA claimants are also in receipt of JSA, IS or ESA/incapacity benefits in addition to DLA, they will be counted under the relevant statistical group.

In addition, we exclude those claiming solely carer’s benefits or claiming carer’s benefits alongside Income Support, as the department does not pursue active labour market policies for this group.

Carer’s benefits are paid to those with full-time caring responsibilities.

Worked example:

Claimant Count (UK Seasonally adjusted) = 797.12

Claimant Count (GB not seasonally adjusted) = 762.75

ESA and incapacity benefits = 2521.16

Lone parents on Income Support (IS) = 441.62

Other (IS others and PC) = 110.31

Start date

Data already published since February 1997.

Indicator type

Impact indicator.

Good performance

A decrease would demonstrate whether improvement has been achieved. As this series is based upon 100% administrative data, year on year movement of any magnitude can be considered as a change in this indicator.

Seasonal movements in benefit claiming patterns affect the quarterly series, hence, year on year comparisons are needed. Economic conditions will also affect the performance of the indicator.

It may be difficult to measure good performance using this indicator, as those brought in to full conditionality by Universal Credit will increase the numbers on out-of-work benefits.

Behavioural impact

Focusing on reducing pure benefit numbers may reduce DWP’s concern with job entry and employment outcomes.

Comparability

Benefits data isn’t generally compared across countries because of differences in the structure and coverage of the welfare system.

Collection frequency

Quarterly in February, May, August and November.

Time lag

The table is published approximately 5.5 months after the reporting point.

However, the department also produces early estimates of the ‘Employment and Support Allowance and incapacity benefits’ and the ‘lone parents on IS’ groups.

Whilst these estimates are missing the ‘others’ group and are estimates rather than final National Statistics, they can provide a useful early view.

The ONS publish the Claimant Count 1 month after the reporting point.

Data source (which data collection it comes from)

The number of people claiming JSA is measured using 100% administrative data from the monthly non-seasonally adjusted claimant count published by the ONS.

The Universal Credit figures used within this are taken from DWP Experimental Statistics.

The number of people claiming other out of work benefits is measured using 100% administrative data from the Work and Pensions Longitudinal Study (WPLS).

Type of data (whether it is an official statistic, national statistic, survey, MI)

The Claimant Count is an Experimental Statistic. All other data used in the indicator are National Statistics.

Robustness and data limitations

Data is produced in line with ‘quality and methods’ outlined in the DWP’s Statistical Summary Quality Statement.

This relates to ‘Get Britain working’ (reducing numbers on benefit is proxy for more people in work) ‘welfare reform’ (where overall reduction in number and spend on benefit is key measure).

Collecting organisation

The raw data are collected by DWP as part of the process for assessing and paying benefits. The claimant count is published by the ONS.

The other benefit data are published by DWP.

Return format

The table is published as an Excel spreadsheet showing numbers in thousands to 2 decimal places (ie to the nearest 10).

Geographical coverage

The UK seasonally adjusted claimant count can be broken down to government office region level.

All other series can be broken down to Lower Super Output Area (LSOA).

This must be done separately for the 2 series:

• GB non-seasonally adjusted claimant count
• Other benefit series

How indicator can be broken down

The claimant count can be broken down by gender. Breakdowns by age and duration can be added by opting to remove clerical (non-computerised) claims.

• Official labour market statistics

Benefit data can also be broken down by:

• partner flag
• benefit combination
• duration of oldest current claim
• number of children
• age of youngest child dependent
• Tabulation Tool: Working Age Client Group

Equality group breakdowns are available for: age bands, ethnicity and gender:

• Excel: number of people on key out of work benefits
• CSV: number of people on key out of work benefits

Further guidance

The main table is in Excel format, further breakdowns are available as HTML and Excel downloads.

5: Number of eligible employees in a pension scheme sponsored by their employer.

Technical definition

This indicator measures the number of employee jobs where the individual is:

• aged at least 22 and under State Pension age
• earning above the earnings threshold for automatic enrolment
• participating in a pension scheme sponsored by their employer

An individual may have more than 1 job.

Rationale

It is estimated that millions of people are not saving enough for their retirement.

A key element of the government’s pensions reform outlined in the DWP business plan is the introduction of automatic enrolment whereby employers will be required to automatically enrol all eligible workers into a workplace pension.

This began in October 2012. Automatic enrolment is intended to overcome the barriers to saving by harnessing inertia – ie rather than having to decide to save in a workplace pension, an employee has to make an active decision not to save (opt out).

The incentive to save is reinforced by a mandatory minimum employer contribution.

The Coalition Agreement set out the government’s commitment to go ahead with the implementation of automatic enrolment. It is estimated that around 9 million people will be newly saving, or saving more, in a workplace pension as a result of the reforms.

This indicator supports 1 of our key objects as detailed in the single departmental plan, to increase saving for, and security in, later life.

Formula

Using the ASHE; all employee jobs (including those affected by absence) with an employer sponsored pension (Defined Benefit, Defined Contribution, Group Personal Pension, Stakeholder Pension and unknown pension type) and age = > 22 and age < SPa and annual gross earnings > earnings threshold.

The £9,440 threshold (in 2013 to 14 earning terms) has been applied in 2014. The £8,105 threshold (in 2012 to 2013 earning terms) has been applied in 2013 and deflated by average weekly earnings (AWE) from 2003 to 2012.

Start date

Data available in a consistent format from 2005.

Indicator type

Impact indicator.

Good performance

An increase of at least 100,000 employee jobs, based on unrounded data would demonstrate that an improvement has been achieved.

Behavioural impact

No behavioural impact.

Comparability

Not an internationally recognised indicator that can be used to make comparisons.

Collection frequency

Annually, data collected relates to specific reference data in April.

Time lag

Pensions data relating to April are generally released in the February of the following year.

Data source (which data collection it comes from)

ASHE. This is an ONS Survey.

Type of data (whether it is an official statistic, national statistic, survey, MI)

Survey data – data not published in this format, produced by custom analysis of the ASHE. The indicator is based on around 140,000 employee jobs.

Robustness and data limitations

No known quality issues. Analyses are National Statistics produced to high professional standards, good fit between data and indicator being measured.

The department has oversight of the controls operated by the ONS as it is represented on the survey’s cross government user group.

An indication of the quality of estimates is included as part of tables provided by the ONS. This is measured by its coefficient of variation, which is the ratio of the standard error of an estimate to the estimate.

More information can be found at Annual Survey of Hours and Earnings Pension tables, 2014 provisional results (ONS).

Collecting organisation

Data are collected and processed by the ONS. Analysis is performed by DWP analysts.

Return format

The unit of measurement is number of employee jobs.

Geographical coverage

Great Britain.

How indicator can be broken down

Equality group breakdowns available for: age and gender:

• Excel: number of eligible employees in a pension scheme sponsored by their employer
• CSV: number of employees in a pension scheme sponsored by their employer

Further guidance

Methodology and guidance for ASHE can be found at the Annual Survey of Hours and Earnings methodology and guidance (ONS).

The ASHE releases published by ONS can be found at All releases of Annual Survey of Hours and Earnings (ONS).

The ASHE publications focus on employee jobs rather than individuals, so there are 2 separate records for an individual with 2 jobs. The ASHE publications also cover the UK whereas this indicator is only for GB.

6: The percentage of pensioners with a low income

Technical definition

This indicator measures the percentage of pensioners with incomes below 60% of median income in any particular year after deducting housing costs.

Incomes are equivalised to adjust for family size and composition, so different household types can be compared in a reliable way.

The After Housing Costs (AHC) low income measure is preferred for pensioners, as around 3 quarters of pensioners own their own home. Particularly when pensioners’ incomes are compared to those of younger individuals the AHC measure allows for more meaningful comparisons of income, both between groups and over time.

Rationale and context

The government wants all pensioners to have a decent and secure income in retirement.

Key strategies include the “triple guarantee” which means the Basic State Pension will increase annually by the highest of earnings growth, price increases or 2.5%.

In addition, government is protecting important benefits for older people, including:

• free eye tests
• free NHS prescriptions
• free bus passes
• free TV licenses (for those aged 75 and over)
• Winter Fuel Payments

We have introduced automatic enrolment into a workplace pension (whereby instead of choosing whether to join a scheme, individuals have to opt out actively) to make the decision to save the default.

The incentive to save is reinforced by a mandatory minimum employer contribution. The new State Pension will be implemented for new pensioners from April 2016.

These reforms will deliver a simpler flat-rate State Pension for future pensioners, with the full level set above the basic level of means-tested support to provide a clear foundation for retirement saving.

This indicator helps measure one of our key objects as detailed in the single departmental plan, to increase saving for, and security in, later life.

Formula for low income estimate

Produce an ‘after housing costs’ equivalised income for all individuals, calculate the median and then look at how many pensioners fall below a threshold of 60% of that median.

Worked example: The equivalised median real income ‘After Housing Costs (AHC)’ in 2013 to 2014 was £386 a week for all individuals. Thus the 60% of median income threshold was £232 a week.

A pensioner with a household income of £232 or less per week would therefore be described as ‘low income’ using this measure.

Start date

Data published in the ‘Households Below Average Income’ series first published in 1988, sourced from the Family Resources Survey (FRS) since 1994.

Indicator type

Impact indicator.

Good performance

Generally a statistically significant decrease in the indicator will demonstrate that an improvement has been achieved, but external factors such as wider economic conditions also need to be taken into account.

The confidence interval range for the 2013 to 2014 data is 12.8% to 14.8% which results in a 95% confidence interval of around +/- 1.0 percentage points.

This means that we can be 95% sure that between 12.8 and 14.8% of pensioners are in relative low income after housing costs (AHC) .

For 2013 to 2014, our central estimate is 13.8%.

Behavioural impact

Direction of indicator informs policies affecting pensioner poverty.

Comparability

Measuring poverty using a 60% of equivalised median income is widely used internationally as a way of measuring relative poverty.

The preferred measure for pensioners differs from the most commonly used international measures, as it takes incomes after housing costs have been deducted.

This however is appropriate in the UK context as most pensioners own their own home.

Collection frequency

Annual Households Below Average Income.

Time lag

Around 1 year after the end of the survey period.

Data source (which data collection it comes from)

Family Resources Survey (FRS) with quality assurance and analysis completed by DWP, in collaboration with Other Government Departments. The Institute for Fiscal Studies also independently quality assures the underlying data.

Type of data (whether it is an official statistic, national statistic, survey, MI)

National Statistics. Survey data.

Robustness and data limitations

Data are National Statistics produced to high professional standards. The quality of National Statistics products is assessed on a regular basis by the independent UK Statistics Authority (UKSA).

In 2013 to 2014, full interviews were completed in around 20,000 households in the UK. Measure does not include care home residents as the sample used for the survey consists of private households only.

Relative to administrative records, the FRS is known to under-report benefit receipt, however the FRS is considered to be the best source for looking at benefit and tax credit receipt by demographic characteristics not captured on administrative sources, and for looking at total benefit receipt on a benefit unit or household basis.

The FRS also collects information on other (non-benefit) income sources.

Confidence intervals can be calculated for the headline statistics. Relative low income is the headline poverty measure, but there are other measures of income that might be used including absolute low income and material deprivation.

The data source, the FRS, is sponsored by the DWP, and has been designed to meet departmental needs for understanding of the household income distribution and changes over time.

Collecting organisation

A consortium of the ONS and the National Centre for Social Research under contract to DWP.

Return format

The main unit and format of measurement is: percentage, but the numbers of individuals represented are also published and presented to the nearest 100,000.

Geographical coverage

Regional – using a 3 year average to get a sufficient sample size to produce reliable analysis.

How indicator can be broken down

There are a wide range of additional breakdowns available. Any material collected on the Family Resource Survey can be broken down by low income or other distributions, subject to sample size constraints.

Equality group breakdowns are available for: age bands, families with someone disabled and ethnic group rate of pensioner poverty.

Further guidance

Data is an SAS data file. Results are published in report format.

7:OBR 7:OBR assessment of government meeting the Welfare Cap

Technical definition

This indicator shows whether the government is meeting the conditions of the welfare cap over the 5 years of the medium-term Annually Managed Expenditure forecast.

Rationale

We are responsible for ensuring that the welfare system is affordable.

The welfare cap is a limit on the amount of Annually Managed Expenditure that government can spend on welfare (other than the State Pension and unemployment-related benefits).

Meeting the conditions of the welfare cap is therefore an indication that welfare spending is being kept under control.

This indicator supports 1 of our key objectives as set out in our single departmental plan, to create a fair and affordable welfare system which improves the life chances of children.

Formula

The indicator compares Autumn Statement AME forecasts for capped spending with the cap set in the 2015 summer Budget.

Start date

Comparisons between the welfare cap and forecast spending within it were first made by the Office for Budget Responsibility at Autumn Statement 2014.

Good performance

Meeting the conditions of the welfare cap indicates that spending is being kept under control and is therefore affordable.

Behavioural impact

None.

Comparability

The level of the welfare cap is fixed at the beginning of each Parliament. Performance against the cap will therefore be measured against the same baseline at Autumn Statements from 2015 to 2019, unless a change is approved by the House of Commons.

The cap will be extended by a year each time the forecast period is extended, and the figure for each new year thus added will act as the baseline for comparison in future years.

The 2014 Autumn Statement considered performance against the welfare cap set at Budget 2014, and so is not directly comparable with performance from 2015 onwards.

Collection frequency

The Office for Budget Responsibility reports on performance against the welfare cap annually at the time of the Autumn Statement.

Time lag

The welfare cap is a forward-looking measure based on forecasts for the next 5 financial years.

Data source

Forecasts of social security benefit and tax credit expenditure produced by the Department for Work and Pensions, HM Revenue and Customs, Department for Business, Innovation and Skills and Northern Ireland Department for Social Development for the Office for Budget Responsibility’s autumn Economic and Fiscal Outlook.

Type of data

Forecast.

Robustness and data limitations

Forecasts by their nature contain an element of uncertainty. Uncertainties in welfare spending forecasts centre on the economy and areas of reform.

The OBR’s Forecast Evaluation Report 2015 reported an error of around 0.75% in the Budget 2014 forecasts for spending within the welfare cap for the year ahead, and 3% in the Budget 2013 forecasts for the same year.

Collecting organisation

Office for Budget Responsibility.

Return format

£ billion.

Geographical coverage

UK.

How indicator can be broken down

DWP published forecasts show spending by individual benefit for Great Britain. Figures are also split between children, people of working age and pensioners, and by statistical group.

8:Percentage of children in workless households

Lead Analyst: Mike Howe DfE.

Technical definition

The number of children living in workless households as a proportion of all children. A workless household is a household that includes at least 1 person aged 16 to 64, where no-one aged 16 or over is in employment.

‘Children’ refers to all children under 16.

Data are not seasonally adjusted and therefore only year on year comparisons are meaningful.

Rationale

Our objective is to improve life chances of children by introducing new measures on worklessness, educational attainment, family stability, drug and alcohol dependency and debt. Worklessness is one of the root causes of poverty.

This indicator supports one of our key objectives as set out in the Single Departmental Plan, to create a fair and affordable welfare system which improves the life chances of children.

Formula

Number of children living in workless households/number of children living in all households = proportion of children living in workless households.

Worked Example: Using latest April to June 2015 data: 1,431,000 / 12,118,000 = 11.8%.

Start date

Already published in public domain. There is a time series of data from 1996 for April to June data and from 2004 for October to December data.

Indicator type

Impact indicator.

Good performance

An improvement would be indicated by a statistically significant fall in the indicator.

The magnitude of the fall required for a statistically significant change depends on the sampling variability around both the current and previous data points.

For example, for there to have been a statistically significant fall between April to June 2014 and April to June 2015, the proportion of children living in workless households had to fall by at least 0.9 percentage points.

As the Labour Force Survey (LFS) is a sample survey, it is subject to a margin of uncertainty, as different samples give different results.

Therefore small changes over time in estimated indicators may not be statistically significant (i.e. they may have arisen from the different samples by chance).

The threshold of a year-on-year change greater than 0.9 percentage points for a change between 2014 and 2015 is based on the confidence interval of the change rather than the single estimate.

Significant changes in the indicator may be observed more easily over a longer time period. For example, 2 consecutive year-on-year changes, neither of which are statistically significant, may combine to show a significant change over the 2 year period.

Similarly, looking at a series of estimates over time will aid interpretation of trends.

The proportion of children living in workless households is calculated from the number of children living in workless households and the total number of children (as shown above), so changes to either of these figures affects the indicator.

It is therefore important to look at why there has been a change to the indicator: whether there is a change in the number of children living in workless households or a change in the total number of children (or both).

For example, it would be possible for there to be a fall in the proportion of children living in workless households at the same time as an increase in the number of children living in workless households, if the total number of children rose at a greater rate than the number of children living in workless households.

In the year to October to December 2014, the number of children increased, but the number of children in workless households fell.

In addition, external factors impacting on the prevalence of parental worklessness, for example general economic conditions, will affect this indicator but are outside of the department’s control.

Behavioural impact

Monitoring children in workless households may result in a focus on only whether their parents are working or workless, with little attention on other factors such as their earnings levels or the quality of their employment.

Comparability

Eurostat (the EU statistics Agency) publishes data on the proportion of children living in jobless households in EU countries (including the UK).

The data is not directly comparable with the ONS published figures as it uses a different age definition for children (defined as aged 0 to 17) and adults (aged 18 to 59) to enable comparisons between different countries.

Collection frequency

Published around every 6 months for Quarter 2 (April to June) and Quarter 4 (October to December).

April to June and October to December figures are not directly comparable as the data are not seasonally adjusted; only year on year comparisons are meaningful.

Time lag

Generally around 2 to 4 months after end of reference quarter.

Data source (which data collection it comes from) Household Labour Force Survey (HLFS).

National Statistics.

Robustness and data limitations

As the HLFS is a sample survey, it is subject to a margin of uncertainty as different samples give different results – for example the confidence interval around the estimate for April to June 2015 is ±0.6 percentage points.

See Table K of working and workless households, 2015 – table showing quality measures.

The survey data is based on a sample of around 45,000 households. However, only about a third of these households contain children, so small sample sizes can limit analysts’ ability to identify statistically significant trends.

Data are National Statistics produced to high professional standards.

The quality of National Statistics products is assessed on a regular basis by the independent UK Statistics Authority. DWP is involved in the quality assurance process for the HLFS by assessing results for plausibility.

Along with other users, DWP are represented on groups that monitor the quality and relevance of the underlying data (LFS Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group) and are able to feed in any concerns about the data collection process.

Collecting organisation

Office for National Statistics (ONS).

Return format

Percentage terms.

Geographical coverage

National (UK).

Estimates are also available for:

• Great Britain
• England (including regional breakdown)
• Scotland
• Wales
• Northern Ireland

How indicator can be broken down

Equality group breakdowns are available for age, gender and ethnicity:

• proportion of children living in workless households
• CSV: proportion of children living in workless households.

The headline data can also be broken down by:

• whether all members of the household are unemployed, all are inactive, or a combination of the 2 – the data can further be compared with households where all members are working, and with households containing both working and workless members
• household type, ie whether children are living in a couple household, lone parent household, or other type of household
• region or county of the UK
• housing tenure

These further breakdowns are published by ONS and are in the public domain.

Further guidance

This indicator is published in Excel and CSV format.

9:Customer and claimant satisfaction of DWP Services

Technical definition

This indicator measures data from the DWP Claimant Service and Experience Survey to generate a pan-departmental score of overall customer satisfaction with the department’s services.

This is a quarterly survey, with data collected from 14,198 interviews between July 2014 and May 2015.

The indicator measures the proportion of the following claimants who had meaningful contact with the department in the 3 months prior to the fieldwork, who are either fairly or very satisfied with the service they received:

• Jobseeker’s Allowance (JSA)
• Employment and Support Allowance (ESA)
• Income Support (IS)
• Disability Living Allowance (DLA)
• Attendance Allowance (AA)
• Carers Allowance (CA)
• State Pension (SP)
• Pension Credit (PC)
• Personal Independance Payment (PIP)
• Universal Credit (UC)

This indicator supports 1 of our key objectives as set out in the single departmental plan to deliver outstanding services to our customers and claimants.

Rationale

We’re committed to continuously reviewing and refining our processes to improve productivity, efficiency and service levels to our customers and claimants.

This indicator supports 1 of our key objectives as set out in our single departmental plan to deliver outstanding services to our customers and claimants.

Formula

The indicator is derived from a combination of the satisfaction scores from claimants who had meaningful contact or interaction with DWP services, from all quarters and benefit groups.

Satisfaction is assessed through a single item relating to general perception of satisfaction – the proportion of respondents who report to be ‘fairly satisfied’ or ‘very satisfied’ with the DWP service they received.

The score is weighted to reflect the extent of contact from different benefit groups; for example if JSA claimants have a greater amount of contact than other benefit claimants, then the overall satisfaction score will be more heavily influenced by the score for this group.

Start date

Data on service satisfaction is already published and is available from 2003.

In 2009 to 2010 Pension, Disability and Carers Service (PDCS) moved to a combined survey to include both pensions and disability carers.

However comparisons longitudinally are limited due to changes in sample design, questionnaire and business structures.

From 2010 to 2011, data was combined from the PDCS and Jobcentre Plus (JCP) surveys to generate a pan-departmental score which was available from Autumn 2011. The data for the 2014 to 2015 indicator was first published in July 2015.

Good performance

An increase in the proportion of satisfied respondents would indicate an improvement in DWP service. However comparisons with previous years is limited due to changes in sample design and methodological approach.

Behavioural impact

None.

Comparability

Direct comparison to other countries cannot be made due to differences in welfare policy and delivery.

Direct comparison to previous years can also not be made due to changes in methodology and sample design, including the addition of new benefit groups.

The satisfaction measure could potentially be compared to other organisations if overall satisfaction of those organisations is measured with the same question item and response scale.

Collection frequency

Fieldwork runs every quarter, with findings available internally. Published scores are available annually (on a financial year basis).

Time lag

Overall Satisfaction score for the financial year is available approximately 1 month following the conclusion of the final quarter of fieldwork (concludes in May).

More detailed findings are published in an annual report approximately 6 months later (December).

Data source

DWP Claimant Service and Experience Survey.

Type of data

Survey data.

Collection organisation

TNS BMRB.

Return format

Percentage.

Geographical

National, UK.

How indicator can be broken down

The overall satisfaction score can be broken down (base size permitting) by:

• region
• benefit group
• type of transaction
• age
• gender
• ethnicity
• disability
• sexual orientation
• marital status
• religion
• whether claimants have children or not and English as a second language.

Sample approach

The survey took its population as all claimants who had contact with the service in the 3 months prior to each data collection rather than all claimants in receipt of benefits.

The rationale for this was that claimants who had no recent contact would not be able to provide useful information about the current state of the service, if they were able to offer any opinions at all.

The vast majority of JSA and UC, claimants would have had contact with the service in the 3 months prior to data collection, given the conditions of these benefits to regularly meet with JCP services. Therefore it is practical to draw a representative sample of live claimants and subsequently screen out respondents who report no contact within the previous 3 months.

For ESA claimants, contact of live claims is less frequent compared to JSA and UC, and depends on the nature of the claim. Therefore the survey sample includes a higher proportion of people who have made a new claim for ESA to ensure a consistent response rate. Only a small minority of IS, DLA, AA, CA, SP, PC and PIP claimants would have contacted the service in the 3 months prior to fieldwork. As a result of this it would be an impractical and expensive exercise to screen out respondents.

Instead claimants were identified on databases held by DWP if:

• they had made a claim for a new benefit
• they had had a renewal
• there had been some other change of circumstance that could be identified on the system (bank details, address, marital status etc).

This means that claimants who have had contact, but their enquiry did not result in a change to their records would not be included.

Implications of differences in the sampling approach

Differences between the sampling approach for the 2 groups means that for JSA, UC and ESA claimants who have made contact, which has not resulted in an administrative change to their records, would be screened in. (Note – screening during an interview cannot be 100% effective and it is possible that some claimants were screened in or out erroneously).

This means that interpretation of the Claimant Satisfaction of DWP Services Indicator needs to take into account the differences in what constitutes ‘contacting claimants’ for the 2 groups. This factor did not impact on the construction of the weighting of the Claimant Satisfaction of DWP Services Indicator.

Survey content

For the purpose of the Claimant Satisfaction of DWP Services Indicator, responses to the following question are used.

Question

So, thinking about all the services provided by Jobcentre Plus/the Pension Service/the Disability and Carers Service, overall how satisfied or dissatisfied are you with the service. Are you…?

• 1 Very satisfied
• 2 Fairly satisfied
• 3 Fairly dissatisfied
• 4 Very dissatisfied
• 5 Don’t know

The question is administered through 1 questionnaire for all respondents. (Note – although the survey asks respondents about the service received through these agencies it should be noted that Jobcentre Plus and the Pension Disability and Carers service ceased to have executive agency status in October 2011. Nevertheless, some respondents may still recognise the agency brands and using these would aid recollection and understanding of questions).

Previously a separate questionnaire was administered to respondents in contact with JCP and those in contact with PDCS; however they were worded and measured consistently. In addition the previous questionnaires and the current combined one are similar in design as they:

• focus the respondent to provide feedback on a specific contact
• place the overall performance measure towards the end of the survey

As a result the questionnaire assesses overall satisfaction, after the respondent has been through the process of retracing their most recent contact.

While key measures are asked of all respondents (eg measures relating to the Department for Work and Pensions Customer Charter) and 1 questionnaire is administered to all respondents, it should be noted that many items will not be asked of all respondents (eg job search activities for JSA and ESA (and some IS) claimants).

Weighting

The survey results have been weighted to be representative of contacting DWP claimants rather than the overall benefit recipient population.

This means that claimants on benefits for which there is more regular contact with the department are present in larger numbers than those on benefits for which there is less contact but greater numbers (e.g. State Pension).

The procedure for arriving at the DWP weight mainly involves calculating the population of contacting claimants for each benefit and then applying a weight to respondents within each benefit according to the number of contacting claimants.

The JSA, ESA and UC figures are calculated by taking the total benefit caseloads for these claimants, and then asking a screening question in the survey to arrive at an accurate figure for how many of these are contacting claimants. This provides proportions of each benefit caseload that are contacting claimants. This represents an efficient approach as the majority of JSA, ESA and UC claimants have regular contact with JCP, and so only a minimal number of claimants will be screened out as non-contacting claimants.

For IS, DLA, AA, CA, SP, PC and PIP claimants, figures are calculated by taking the total number of claimants of each benefit who show up on DWP databases as having had a change of record over the previous 6 months, and using this as a proportion of the total caseload of each benefit to calculate provisional numbers of contacting claimants.

A further screening question is asked in the survey to ensure all participants who the DWP database suggests have had contact actually did.

The proportion of claimants who are screened out is then applied to the earlier figure to arrive at final figures for contacting claimants for these benefits.

These are combined to create a weighting ratio to represent DWP contacting claimants. As stated earlier, there is a slight difference in the way in which contacting claimants are defined between those claiming JSA, ESA or UC, and those claiming IS, DLA, AA, CA, SP, PC or PIP. As such, the latter claimants who contacted DWP but this contact did not result in a change of their record (eg to make an enquiry only), would not be included in the survey.

It should be noted that when looking at the population of contacting claimants over time there have been some changes to the nature of contact and the benefits included.

For example, UC and PIP are new benefits which were not included in last year’s survey.

10:Benefit processing times

Technical definition

The annual aggregate measure is derived from monthly data relating to processing times for working age benefit claims cleared within planned timescales. Specifically, the percentage of working age claims processed within 2014 to 2015 timescales:

• JSA –10 days
• ESA – 16 days
• IS – 5 days

The aggregate measure is constructed using a weighting of the performance for each benefit using the total number of claims processed for each benefit over any particular year.

The measure is reported at national level and 2015 to 2016 data will be available from May 2016.

Rationale

We’re aiming to continuously improve our services to all our customers and claimants. A key outcome is to reduce the time taken to process new claims.

This indicator supports 1 of our key objectives as set out in the Single Department Plan to deliver outstanding services to our customers and claimants.

Formula

Benefit Processing Indicator (BPI) = Clearance volumes of JSA + ESA + IS / Total Claims Processed volumes for JSA + ESA + IS.

Aggregate Planned Clearance % = [(JSA Volume Processed) * (JSA Planned Timescale %) + (ESA Volume Processed)*(ESA Planned Timescale %) + (IS Volume Processed) * (IS Planned Timescale %)] / Total Claims Processed volumes JSA + ESA + IS.

The Aggregate Planned Clearance % was then projected back to form a standard of comparison year on year.

Start date

1 April 2010.

Good performance

Performance improves year on year and is greater than the planning assumption.

Behavioural impact

Clearance time measures are used in operations to manage performance and customer satisfaction.

Comparability

The measure definition indicator insures that there is comparison over time.

Collection frequency

Monthly.

Time lag

2 weeks.

Data source

DWP MISP data system.

Type of data

Management Information, DWP Management Information System Programme (MISP).

Robustness and data limitations

Data collection is automated via MISP system and checks are in place to ensure accuracy of data extraction from system.

Collecting organisation

DWP.

Return format

Percentage.

Geographical coverage

National, UK.

How indicator can be broken down

Benefit type (Employment and Support Allowance, Job Seekers Allowance and Income Support) and also by year.

11: Benefit overpayments (arising from fraud and error) as a percentage of overall benefit expenditure

Technical definition

This indicator measures the estimates of the levels of overpayment and underpayment, as a percentage of benefit expenditure, due to fraud and error across the benefit system in Great Britain.

The net overpayments (the amount of benefits overpaid minus the amount of overpayments recovered) value is calculated as the monetary value of fraud and error overpayments minus the monetary value of overpayments recovered in the same financial year.

The net overpayments rate is calculated by dividing the net overpayments value by the total benefit expenditure for that year.

Recoveries refer to money recovered in the same financial year as the overpayment estimates, regardless of the period the debt is from.

This includes debt recovered by both the department and Local Authorities (for Housing Benefit only).

Some recoveries have no associated overpayments for the same period, as these benefits are no longer administered by the department.

This is because the debt relates to expenditure from previous years. When this occurs, we subtract the recoveries from the total monetary value of fraud and error.

Rationale

This is our primary indicator for levels of fraud and error in the benefit system.

This indicator supports one of our key objectives as set out in our single departmental plan, to deliver outstanding services to our customers and claimants and is a measure used in the Annual Report and accounts.

The indicator is important for DWP assurance on the impact of anti-fraud and error activity and has been used as the benchmark measure for the fraud and error strategy.

Read about uses and users of the DWP fraud and error in the benefits systems statistics.

Formula

The indicator is calculated from sample data, adjustments are made and data is grossed up to provide an estimate for the whole benefit population.

Worked example:

Fraud and error preliminary 2014 to 2015 estimates

The estimate of total overpayments due to fraud and error across all benefits is £3.2 billion. This is 1.9% of the total benefit expenditure, which was estimated to be £168.1 billion in 2014 to 2015.

The estimate of total underpayments due to fraud and error across all benefits is £1.4 billion; this is 0.9% of the total benefit expenditure in 2014 to 2015.

These estimates are subject to statistical sampling uncertainties as detailed in the ‘Robustness and data limitations’ section below.

Start date

June 2013.

Indicator type

Impact indicator.

Good performance

A statistically significant decrease in the percentage of overpayments and underpayments would demonstrate improved performance but economic conditions and overall expenditure would also need to be taken into account.

The estimates are based on a random sample of the total benefit caseload and are therefore subject to statistical uncertainties.

This uncertainty is quantified by 95% confidence intervals around the central estimate. These 95% confidence intervals show the range within which we would expect the true value of fraud and error to lie.

The general reader can broadly estimate whether changes are significant by seeing how much the confidence intervals overlap. If they do not overlap then the differences between estimates are generally significantly different and indicate a real change in the estimates over time.

If confidence intervals do overlap then the difference between estimates are generally not significantly different, indicating that any changes are more than likely to be due to sampling variation rather than real change.

As detailed in the ‘Latest data’ section above, the levels of overpayments and underpayments of Fraud and Error in the benefit system have both remained level over time since 2005 to 2006.

Behavioural impact

This indicator should not lead to perverse incentives as it is based on a random sample of the live caseload, independently reviewed for fraud and error.

Comparability

The indicator is a recognised standard. It is recognised by the UK Statistics Authority and the National Fraud Authority.

Comparable statistics are available for:

• Northern Ireland, Northern Ireland Fraud and Error in the Benefit System Social Security Agency annual reports
• HM Revenue and Customs Tax Credits, HMRC estimates of Error and Fraud in Tax Credits

Collection frequency

Published twice a year (May and November).

Time lag

7 to 8 month time lag from the end of the measurement period to when the report is published (for example, the period ending March 2015 was published in November 2015).

Type of data (whether it is an official statistic, national statistic, survey, MI)

National Statistics.

Collecting organisation

DWP (Statistical Services Division in the Information, Governance and Security directorate in Professional Services) produce these statistics.

The Performance Measurement branch carry out the survey fieldwork and the Fraud and Error Measurement Analysis branch analyse the results to produce and publish the fraud and error estimates.

Return format

Data is shown as a percentage and is also available in monetary value terms (£).

Geographical coverage

National, Great Britain level: England, Scotland and Wales.

How indicator can be broken down

Equality group breakdowns, for benefit overpayments and underpayments split by error type (fraud, customer error and official error) are available by gender and age band for the following income related benefits:

• Income Support
• Jobseeker’s Allowance
• Pension Credit
• Housing Benefit
• Employment and Support Allowance
• Excel: monetary value of fraud and error in the benefit system
• CSV: monetary value of fraud and error in the benefit system

The fraud and error estimates can also be used for:

• Obtaining an estimate for the amount over and under paid in total and by benefit, and broken down into fraud, claimant error and official error, across the benefits administered by the DWP and Local Authorities
• Obtaining estimates for the amount over and under paid by benefit, broken down into the types of fraud, claimant error and official error, across Income Support, JSA, ESA, PC and Housing Benefit.

Further guidance

Current and historical national statistics publications for these estimates are available in the Fraud and Error in the Benefit System reports.

This page also contains links to further detailed documentation including guidance on:

• Quality and Methodology
• Uses and Users
• Ad hocs and Pricing
• Variance and Confidence Intervals.

The results data is published in reports and Excel spreadsheets on the above page.

The source data is stored in an SQL database and analysis data is stored in SAS datasets.

Historical net overpayment data

	2014/15	2013/14	2012/13	2011/12	2010/11
Monetary value of overpayments	£3,020m	£3,360m	£3,510m	£3,360m	£3,240m
Recoveries	£930m	£850m	£830m	£780m	£680m
Net overpayments	£2,090m	£2,520m	£2,680m	£2,580m	£2,560m
% net overpayments	1.2%	1.5%	1.6%	1.6%	1.7%

*Numbers may not sum due to rounding.

Data sources and how data is collected

Recoveries. We obtain recovery data from the Debt Management system. This data includes recovery amounts for all benefits, and is broken down by benefit type and error classification.

We obtain data on Housing Benefit overpayment recoveries from the DWP publication Housing Benefit recoveries and fraud national statistics.

Monetary value of fraud and error overpayments. These are taken from the published national statistics.

What is included and excluded in the data?

Recoveries of overpayments for most benefits are included in the net overpayment calculation. Even if the department no longer administers a certain benefit, if the overpayment is recovered in subsequent years, it is included in the calculation. Several sources of “recoveries” are excluded from the calculation:

• administrative and civil penalties, these are penalties that are paid rather than a recovery of the overpayment value
• Social Fund loans and Short Term advances, these do not relate to overpayments
• Direct Payment After Death, these are not included in the Monetary Value of Fraud and Error (MVFE) calculation

Also, where we have negative recovery values, due to a claimant successfully appealing against a recovery and being refunded the amount, we set the total recovery value to zero.

How do we quality assure our data?

As the fraud and error estimates are published national statistics, the net overpayment estimates go through a rigorous quality assurance process.

The finalised estimates are scrutinised by a quality assurance group bringing together the measurement team, other analysts, the error checking teams and operational staff.

What is our uncertainty estimate?

The fraud and error monetary value estimates are based on a random sample of all benefit cases and so are subject to statistical uncertainties (sampling error) the estimates based on the sample may differ from the values based on the whole benefit caseload (the true value).

This statistical uncertainty is quantified by the estimation of 95% confidence intervals around the central estimate.

The confidence intervals show the range within we would be 95% sure the true value of fraud and error lies. In other words, if we took 100 samples, we would expect the result of 95 of them to lie within the confidence intervals.

Confidence intervals are presented alongside the central estimates in our tables.

If a difference between 2 estimates is ‘statistically significant’, it means that this difference is likely to be real and not due to sampling variation.

The calculation to determine whether a change is statistically significant is complex and takes into account the width of the confidence intervals.

As a rough guide, if confidence intervals do not overlap, then the change is generally statistically significant. It is important to note that the recoveries data is based on actual amounts rather than a sample.

Therefore, the net overpayments calculation does not affect the uncertainty around the monetary value of fraud and error estimate (ie the confidence intervals for the net overpayment value and the monetary value of fraud and error are exactly the same).

Impact of net overpayments indicator

The net overpayments indicator is only affected by the values in the calculation: the monetary value of fraud and error overpayments published as a national statistic and the monetary value of overpayment recoveries.

Can net overpayments data be broken down further?

Yes, by benefit.

The net overpayment value for individual benefits is calculated in the same way as the overall net overpayment value, overpayment recoveries are subtracted from the fraud and error monetary value for overpayments.

As the definitions of error classification (fraud, claimant error, and official error) differ between overpayments and recoveries, the net loss estimate can only be calculated for total fraud and error.

Further information

Read more about DWP research and how it supports our policies and services.

Read about statistics at DWP. The list of latest statistics from DWP allows you to refine the results using keywords and other filters.

Read the latest list of publications and research from DWP.

Our tabulation tool allows you to download and customise DWP statistics.

You can find Health and Safety statistics on the Health and Safety Executive (HSE) website.

Read previous DWP business plan transparency indicator measures. The previous indicator measures are also on the National Archives website.