Transparency data

DWP business plan transparency measures

Updated 18 August 2015

This document was withdrawn on 19 February 2016

The DWP business plan transprency measures are no longer being updated. Read the the DWP single departmental plan to track the department’s progress towards achieving its priority objectives.

The government is committed to setting new standards for transparency so the public can:

  • more easily see how and where taxpayers’ money is being spent
  • hold politicians, government departments and public bodies to account

The Prime Minister has asked all government departments to publish information about how it meets and measures its objectives and uses its resources to better deliver value for money and reduce the deficit.

The Department for Work and Pensions (DWP) publishes data on its business plan indicators on this page as soon as new data become available. The indicators are reported on formally in the department’s annual report and accounts.

Business plan indicator data before October 2012 is published in the Quarterly Data Summary.

The transparency measures include:

  • ‘input indicator’ measures (DWP productivity)
  • ‘impact indicator’ measures (such as ‘rates of people moving from key out of work benefits’ and ‘rate of disability poverty’
  • other datasets (such as ‘proportion of new JSA applications submitted online’)

Additional breakdowns for people with protected characteristics are published in the DWP Equality Report for all impact indicators (indicators 2 to 13). DWP has a specific duty to publish relevant proportionate information annually to demonstrate it’s compliance with the Public Sector Equality Duty (PSED) which is part of the Equality Act 2010. Information for protected groups will be published in a single PDF on an annual basis.

1. Overall Department for Work and Pensions productivity measure

The DWP productivity measure relates the volume of outputs delivered in each financial year to the volume of inputs consumed in their delivery. An increase in this measure indicates that the department has delivered relatively more outputs for fewer inputs over time.

The measure has been developed in accordance with international convention and the recommendations of the 2005 Atkinson Review ‘Measurement of Government Output and Productivity for the National Accounts’. More detail on the methodology used is available at:

1.1 Latest data (updated July 2015)

The department’s productivity was maintained during the financial year 2014 to 2015.

The following table presents the annual percentage growth of each element of the productivity measure – input, output and productivity – between the financial years 2008 to 2009 and 2014 to 2015.

DWP productivity: annual growth in DWP input, output and productivity

  2008 to 2009 2009 to 2010 2010 to 2011 2011 to 2012 2012 to 2013 2013 to 2014 2014 to 2015 (provisional)
Input 1% 13% -3% -22% -6% -2% -5%
Output 11% 19% 6% -12% -5% -2% -5%
Productivity 10% 5% 10% 12% 1% 0% 0%

Input index

This measures real changes in Departmental Expenditure Limit (DEL) expenditure on staff, goods and services, and depreciation from one year to the next. For example, input increased by 1% in real terms in 2008 to 2009, compared to the 2007 to 2008 level.

Output index

This measures the growth in output, by weighting the growth in individual services by their relative share of the total cost in the previous year. Following convention, total output is represented through an index that combines direct measures of the department’s social security and child maintenance outputs, alongside expenditure on labour market activity, Work Programme and the department’s policy, ministerial and regulatory functions in a cost-weighted sum. The output index measures changes in output volume from one year to the next. For example, output increased by 11% in real terms in 2008 to 2009, compared to the 2007 to 2008 level.

Productivity index

This divides output by input to measure change from one year to the next. For example, in 2008 to 2009 productivity increased by 10% (100 x 111 / 101 = 110) in real terms.

1.2 Technical description

Short title

DWP productivity measure.

Technical definition

Output (cost-weighted activity index) divided by input (deflated expenditure), in-line with National Accounts convention for public service output and productivity measurement.

Rationale

By relating the output that the department produces with the volume of resources used in producing it, the productivity measure provides information on whether the department is delivering relatively more for less, thus demonstrating whether the department is delivering genuine efficiency savings in implementing the spending review settlement.

Formula

Indicator results calculated as follows: Productivity = Output / Input. Productivity is expressed as per cent change from the previous year.

Worked Example: If output increases by 1% whilst input decreases by 3% then the productivity index changes as: 100 x 101/97 = 104.1 – a productivity gain of +4.1%.

Start date

Already published by DWP, with data available from 2002/03 onwards.

Type of indicator

Input indicator.

Good performance

Over the long term the productivity measure should increase to demonstrate that genuine efficiencies have been delivered.

Behavioural impact

No – collecting the data will not have any behavioural impact. The indicator is at too high a level to set operational targets or to influence operational behaviour

Comparability

The measure is now well established in the department, having been developed in accordance with the methodology of the Atkinson Review. The Office for National Statistics (ONS) published a comparison of productivity across the public sector in 2014. The DWP productivity measure is based on this methodology.

Collection frequency

Annually.

Time lag

Final figures available approximately six months after the end of the financial year, provisional figures available earlier.

Data source

Data collected from DWP administrative systems, including Departmental Activity Based Management and Management Information System Programme.

Data type (whether it is an official statistic, national statistic, survey, MI)

DWP administrative systems.

Robustness and data limitations

The DWP productivity measure is now well established in the department, having been developed in accordance with the methodology of the Atkinson Review. The ONS have confirmed it is in line with standard convention and the National Audit Office have described it as ‘an accredited methodology’.

Return format

Expressed as a percent increase in productivity relative to the previous year.

Geographical coverage

Geographical coverage is at National – GB level.

How indicator can be broken down

Productivity measure at national level. It gives an aggregate picture of how efficiently the department delivers its outputs and can be broken down into input and output components. Additional breakdowns are not available or appropriate.

Further guidance

Additional notes on methodology: DWP Productivity Report 2014.

2. Rates of people moving from key out of work benefits

This indicator measures the rate of people moving off Jobseeker’s Allowance (JSA) and Employment Support Allowance (ESA) to any destination.

The indicator reports off-flow rates for cohorts of customers who flow on to each benefit in a given period. For example 94.0% of those starting to receive JSA in April 2014 had stopped receiving the benefit 52 weeks later. The rate of people moving off benefit is calculated after 52 weeks in receipt for JSA and 65 weeks for ESA.

The indicator will be updated monthly, although we would expect only minor changes month on month. There is a reporting lag of up to 14 months for JSA and up to 17 months for ESA due to the elapsed time inherent in the indicator and the time needed to process the data. Data are not seasonally adjusted.

Off-flow rates vary naturally over time, and will be impacted by policy changes, as well as changes in the economy. Over the time period covered by the indicator, the department has enacted welfare reform changes that have changed the composition of both of these benefits. The department has also stopped some employment programmes and introduced new programmes.

2.1 Latest data (updated July 2015)

Rates of people moving off JSA have remained relatively stable over the period. Latest data for March 2015 is 94.0%.

Rates of people moving off ESA have rallied recently. Latest data for March 2015 is 54.6%.

52 week JSA month ending

  Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
2014/2015 92.0% 92.3% 92.8% 92.6% 92.6% 92.9% 93.1% 93.5% 94.1% 94.1% 94.0% 94.0%
2013/2014 89.5% 89.3% 89.7% 89.4% 89.4% 90.5% 90.9% 91.9% 92.2% 92.1% 91.9% 92.2%
2012/2013 87.4% 87.7% 88.3% 88.3% 87.9% 88.9% 89.4% 90.0% 90.3% 90.2% 90.0% 89.9%
2011/2012 90.7% 90.5% 90.5% 89.7% 89.0% 88.8% 88.5% 88.5% 87.7% 87.3% 86.6% 87.2%
2010/2011 89.8% 90.7% 90.9% 90.4% 90.3% 91.0% 91.3% 91.9% 92.0% 91.2% 90.6% 90.8%

65 week ESA month ending

  Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
2014/2015 47.6% 45.7% 45.8% 43.7% 45.7% 47.7% 51.7% 51.54% 50.5% 50.2% 49.4% 51.6%
2013/2014 55.1% 50.2% 49.9% 51.1% 51.3% 50.9% 50.3% 50.0% 48.0% 49.1% 44.3% 53.5%
2012/2013 73.8% 73.8% 73.6% 73.0% 71.9% 66.1% 61.8% 57.4% 54.0% 53.0% 52.2% 58.7%
2011/2012 73.4% 73.2% 73.1% 74.0% 73.8% 73.3% 73.3% 72.7% 73.4% 73.1% 73.7% 72.5%
2010/2011                   73.5% 74.1% 74.0%

2.2 Technical description

Short title

Rates of people moving from out of work benefits

Technical definition

The indicator reports the cohorts of people moving on to JSA and ESA each month to measure how long it takes people to flow off benefit. The percentage of each cohort that have flowed off after 52 weeks (for those in receipt for JSA) and 65 weeks (for those in receipt of ESA) is then calculated. For example 94.0% of those starting to receive JSA in April 2014 had stopped receiving the benefit 52 weeks later. This will be produced for the out of work benefits JSA and ESA and will be expressed publicly as ‘x% of customers left benefit within y weeks.’

Time lag for 52 weeks JSA will be up to 14 months and 17 months lag for 65 weeks ESA due to the elapsed time inherent in the indicator and the time needed to process the data.

Rationale

Jobcentre Plus (JCP) adds value by reducing the time it takes for a customer to move off benefit. The measure directly impacts on the claimant count and will provide useful information to inform whether the department’s spending review settlement is being implemented.

SRP 1 – Working age: Encouraging work and making work pay.

Formula

Indicator results calculated as follows: ‘x% of customers left benefit by y weeks. Therefore ‘90% of customers left benefit by 52 weeks.’ Where: 90 ÷ 100 = 90%

Start date

ESA data available from January 2011, JSA data from April 2010.

Type of indicator

Impact indicator.

Good performance

Generally an increase in the indicator would demonstrate whether an improvement has been achieved. However, the indicator level will be affected by benefit conditionality and operational changes, seasonal variation and to the economic cycle. For example, in a recession, even if JCP is performing well, the off-flow rate is likely to fall. Also, as Incapacity Benefit (IB) claims are re-assessed for ESA the off-flow rate is likely to fall.

Data are not seasonally adjusted.

Behavioural impact

No significant perverse incentives created.

Comparability

It is unlikely the indicator can be used as a recognised standard due to difficulties comparing benefit systems between countries, and we are unaware of other countries having a similar measure.

Collection frequency

Monthly.

Time lag

The indicator’s base data is available approximately 2 months after the event due to allowing a settling period for late reported off flows.

The publication is by definition delayed by the duration applied, for example the 52 week off flow rate for the January 2010 on-flow cohort was not available for publication until March 2011.

Data source (which data collection it comes from)

Data collected from the ESA and Job Seeker’s Allowance Payment System (JSAPS) by DWP.

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

Management Information for performance measurement.

Robustness and data limitations

DWP internal benefit data is known to be very accurate. There are some well documented minor issues regarding clerical claims, but these are small. The Management Information solution went through industry standard, independent testing and user acceptance testing prior to implementation.

Collecting organisation

DWP.

Return format

Unit and format of measurement is: Percentage.

Geographical coverage

GB coverage.

How indicator can be broken down

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

Further guidance

This indicator excludes IB, Severe Disablement Allowance (SDA), Income Support (IS) (lone parents) and IS (others) which are normally defined as ‘out of work benefits.’

Users should be aware that some claimants move from ESA to JSA. These will be shown as an ESA off flow. The reverse is also true.

3. Number of people on key out of work benefits

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

  • Jobseeker’s Allowance (JSA)
  • Employment and Support Allowance (ESA)
  • Incapacity Benefit (IB)
  • Severe Disablement Allowance (SDA)
  • Income Support (IS) (as a lone parent or in the ‘other’ category)
  • Pension Credit (under State Pension age) (PC)

It includes people claiming in Great Britain, including some living overseas.

3.1 Latest data (updated August 2015)

The latest data show that a total of 3.94 million people aged 16 to State Pension age claimed a key out of work benefit in February 2015. This has fallen by around 330,000 from 4.27 million in February 2014.

Due to seasonal movements in benefit claiming patterns only year on year comparisons can be made. Data are based on 100% data.

We will release May 2015 statistics on 11 November 2015 and publish statistics quarterly.

Total numbers claiming a key out of work benefit (millions)

Year February May August November
2015 3.94      
2014 4.27 4.12 4.02 3.91
2013 4.68 4.54 4.41 4.25
2012 4.93 4.80 4.73 4.64
2011 4.86 4.80 4.87 4.83
2010 5.10 4.93 4.87 4.78
2009 4.94 5.04 5.08 5.02
2008 4.35 4.32 4.41 4.54

Read further data about the number of people on key out of work benefits. This includes the history based on the latest data point (annual time series) and the whole data series (full time series page). The cover sheet provides further information, with links to additional data sources and breakdowns.

The data released in August 2015 includes Universal Credit (UC) claimants from May 2013. Before May 2013 the claimant count is Jobseeker’s Allowance only. We have updated the time series to reflect this change to the experimental claimant count.

3.2 Technical description

Short title

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:

  • JSA (from claimant count)
  • ESA
  • IB
  • SDA
  • IS (as a lone parent or in the ‘other’ category)
  • PC (under State Pension age)

The equalisation of State Pension age between 2010 and 2020 will change the composition of this group by including women between 60 and 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 men 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
  • and 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.

Rationale

Reflects the government’s economic and social objective of helping more people into employment and not 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.

SRP 1 – Working age: Encouraging work and making work pay.

Formula

JSA (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. 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 JSA UK seasonally adjusted figure is slightly different to that for the other key out-of-work benefits. Whereas figures for JSA 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: JSA (claimant count) (UK Seasonally adjusted) = 1,480.90 JSA (claimant count) (GB not seasonally adjusted) = 1,447.04 ESA and incapacity benefits = 2,613.10 Lone parents on Income Support (IS) = 679.15 Other (IS others and PC) = 192.19

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.

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 / 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.

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 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)

National Statistics.

Robustness and data limitations

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

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 JCP 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 two 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 two 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.

Benefit data can also be broken down by:

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

Further guidance

Any other relevant information: The main table is in Excel format. The further breakdowns are available as HTML and Excel downloads.

4. Proportion of children living in workless households

This indicator measures the proportion of children who live in workless households. Estimates focus on households in the UK that contain at least one person aged 16-64. Such households are defined as workless if none of the household members aged 16 or over are in employment. Children are defined as those aged under 16.

4.1 Latest data (updated March 2015)

The latest data shows that the estimated proportion of children living in workless households was 12.7% in October to December 2014. This is 0.8 percentage points lower than the 13.5% estimated for October to December 2013 (allowing for rounding).

The decrease over the latest year was not statistically significant, although it did form part of a 1.7 percentage point fall over the latest 2-year period which was statistically significant. The October to December 2014 estimate (12.7%) has a sampling variability of ± 0.7 percentage points. A change over time has estimated sampling variability of approximately ± 0.9 percentage points. The survey sample size is around 45,000 households.

Proportion of children living in workless households in the UK (%). Year on year comparison only

Year Apr-Jun Oct-Dec
2014 12.7% 12.7%
2013 13.8% 13.5%
2012 15.2% 14.4%
2011 15.8% 15.9%
2010 16.2% 16.2%
2009 16.8% 16.0%

Further information is available from:

This indicator is available from Table K, reproduced in the following files:

4.2 Technical description

Short title

Proportion of children living in workless households.

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 one 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

DWP’s vision is to promote high levels of employment by helping those currently out of work into employment. The structural reform priority to help tackle the causes of poverty and improve social justice recognises that work is the best route out of poverty. This indicator aligns with both of these objectives.

SRP 5 – Supporting families: Recognising the importance of family in providing the foundation of every child’s life.

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 October to December 2014 data: 1,535,000 / 12,075,000 = 12.7%

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 October to December 2013 and October to December 2014, 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 2013 and 2014 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, two consecutive year-on-year changes, neither of which are statistically significant, may combine to show a significant change over the two-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 0-17) and adults (aged 18-59) to enable comparisons between different countries.

Collection frequency

Published around every 6 months for Quarter 2 (April-June) and Quarter 4 (October-December). April-June and October-December figures are not directly comparable as the data are not seasonally adjusted; only year on year comparisons are meaningful.

Time lag

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

Data source (which data collection it comes from)

Household Labour Force Survey (HLFS).

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

Impact Indicator

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 October to December 2014 is ±0.7 percentage points. See Table K of the following quality measures published by the Office for National Statistics:

The survey data is based on a sample of around 45,000 households. However, only about a third of these household 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

Unit and format of measurement is: 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:

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 two. 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.

5. Proportion of young people not in full-time education who are not in employment

This indicator shows what proportion of 18 to 24 year olds not in full time education are also not in employment. It is measured by dividing the number of 18 to 24 years olds who are not in employment or full-time education by the total number of 18 to 24 year olds who are not in full time education. It uses data from the Labour Force Survey ( LFS) published monthly by the ONS approximately 6 weeks after the period. The data is seasonally adjusted and therefore quarter on quarter comparisons can be made.

5.1 Latest data (updated May 2015)

From January to March 2015 there were 3.9 million 18 to 24 year olds not in full-time education and, of these, 1.02 million (26%) were not in work. This is a fall of 0.7 points from the previous quarter (October to December 2014) and a fall of 1.9 points on the year.

5.2 Proportion of young people not in full-time education who are not in employment

Year Apr-Jun Jul-Sep Oct-Dec Jan-Mar
2014-2015 26.5% 26.3% 26.7% 26.0%
2013-2014 30.7% 30.4% 28.9% 27.9%
2012-2013 31.6% 30.6% 29.8% 30.3%

Find further detail on the data in the Labour Market Statistics summary of employment, unemployment and inactivity for young people. The indicator is calculated by using the 18-24 data in the ‘levels seasonally adjusted’ tab.

What data are next available and when

We will publish data for April to June 2015 in August 2015.

5.3 Technical description

Short title

Young people not in full time education who are not in employment.

Technical definition

The indicator is the proportion of 18-24 year olds not in full time education who are not in employment. Data is seasonally adjusted so quarterly comparisons can be made.

Rationale

The indicator demonstrates the government’s commitment to improving the proportion of young people engaged in a positive activity – employment, education or training. It recognises the importance of raising participation in education and improving labour market outcomes for those not in full time education and contributes to the department’s Structural Reform Plan commitments on Working age: Encouraging work and making work pay (SRP1) and also a cross-governmental social mobility indicator.

Formula

This indicator is measured by dividing the number of 18-24 year olds who are not in employment or full-time education by the total number of 18-24 year olds who are not in full time education. This shows what proportion of the 18-24s not in full time education are also not in employment. Data is seasonally adjusted and therefore consecutive data periods can be compared.

Start date

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

Indicator type

Impact indicator.

Good performance

Generally a statistically significant decrease 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. 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 ONS, 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 two independent estimates and so subject to two ‘sources of uncertainty’. The confidence interval for a year on year change is approximately +/- 2%.

Behavioural impact

Minimal – indicator is not a target.

Comparability

Data is seasonally adjusted and available monthly on a rolling quarter basis. So comparable quarterly data is available each month. It is based on LFS data which uses internationally agreed concepts and definitions, so is internationally comparable.

Collection frequency

Monthly.

Time lag

Six weeks after the reference quarter.

Data source

(which data collection it comes from)

The LFS published monthly by the ONS in the Labour Market Statistics bulletin.

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

Impact Indicator

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 (LFS 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 the department has estimated that the confidence interval for a single quarterly estimate of the indicator is approximately +/- 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

ONS.

Return format

Percentage.

Geographical coverage

Only national data is published.

How indicator can be broken down

Equality group breakdowns are available for those with/without a disability, ethnicity, gender and religion:

Further guidance

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

6. Proportion of the lowest earning 25 to 30 year olds that experience wage progression 10 years later

This indicator measures the proportion of individuals in the bottom fifth of earners at age 25 to 30 that are 20 or more percentiles higher in the earnings distribution 10 years later.

The Social Mobility Strategy committed DWP to developing an indicator of wage progression, whilst acknowledging that other indicators of labour market success will also form part of a wider suite of indicators of social mobility in adulthood.

6.1 Latest data (updated January 2015)

An initial cohort is formed including all individuals that are aged 25 to 30 and have positive earnings in the chosen start year. An earnings distribution is determined for this cohort, and the bottom quintile become the group of interest. For this initial bottom quintile there are 3 possible outcomes 10 years later:

  • move up the earnings distribution by 20 or more percentiles
  • move along the earnings distribution by less than plus 20 percentiles (includes non-movers and moves down the distribution)
  • ‘evidence indeterminate’, ie drop out of the Annual Survey of Hours and Earnings (ASHE) sample (for example, due to a switch to self-employment, temporary or longer-term unemployment or inactivity, migration or death).

The latest data shows that 12.5% of 25 to 30 year olds in the bottom fifth of earners in 2005 had experienced wage progression by 2014.

The change in the proportion of lowest earners that experienced wage progression between 2004 to 2013 and 2005 to 2014 was not statistically significant, nor has there been any statistically significant change since the first measurement period (2000 to 2009).

Period Percentage of those moving up the earnings distribution by 20 or more percentiles
2001-2010 12.5%
2002-2011 11.7%
2003-2012 12.1%
2004-2013 12.1%
2005-2014 12.5%

6.2 Data which underlies the overall percentage figure

6.3 Technical description

Short title

Proportion of the lowest earning 25-30 year olds that experience wage progression ten years later.

Technical definition

The proportion of individuals in the bottom fifth of earners at age 25-30 that are 20 or more percentiles higher in the earnings distribution 10 years later.

Rationale

This indicator falls under Strategic Reform Priority 2 (Tackling the causes of poverty and making Social Justice a reality) and the cross-governmental priority on social mobility.

The Social Mobility Strategy committed DWP to developing an indicator of wage progression, whilst acknowledging that other indicators of labour market success will also form part of a wider suite of indicators of social mobility in adulthood.

By comparing individuals to their peers through a measure of relative earnings (using an earnings distribution) the indicator is a measure of intra-generational social mobility.

The indicator looks at those who start out in the bottom fifth of earners as the Social Mobility Strategy is focussed on helping those that start out at the bottom to move up. The customer group that will be eligible for in-work support under Universal Credit are likely to be concentrated at the lower end of the wage distribution.

Looking at people aged 25-30 gives a less distorted picture of whether someone has actually been able to progress relative to their peers than looking at those aged 18-24. For younger people just starting off in the labour market wage progression can be very volatile, as they are more likely to be working in jobs that don’t closely match their skills or education.

A movement of 20 of more percentiles represents a substantial movement up the earnings distribution so individuals have experienced a notable improvement in their relative position.

A 10 year period is the best balance between having enough time for individuals to experience meaningful progression and having sufficient years of data to provide a time-series of information (consistent data are available from 1997 onwards).

Formula

The data source used is the ASHE.

The earnings variable of interest is gross hourly earnings excluding overtime where earnings are not affected by absence.

For individuals with more than one job, only their ‘main job’ is included in the analysis (as defined by the job with the greatest gross weekly pay and then the greatest total hours).

An initial cohort is formed including all individuals that are aged 25-30 and have positive earnings in the chosen start year. An earnings distribution is determined for this cohort, and the bottom quintile become the group of interest. For this initial bottom quintile there are 3 possible outcomes 10 years later:

  • move up the earnings distribution by 20 or more percentiles
  • move along the earnings distribution by less than +20 percentiles (includes non-movers and moves down the distribution)
  • ‘evidence indeterminate’: that is drop out of the ASHE sample (due to for, example, a switch to self-employment, temporary or longer-term unemployment or inactivity, migration or death). The indicator reports the proportion of people that started out in the bottom quintile 10 years previously that have moved up the earnings distribution by 20 or more percentiles.

Worked example using ASHE 2004-2013:

  • 4260 people aged 25-30 in 2005 had positive earnings and were in the bottom quintile
  • 530 of these people were known to have moved up the earnings distribution by 20 or more percentiles in 2014 compared to their position in 2005

therefore, 530 / 4260 = 12.5%

Note: Data may not perfectly match due to rounding.

The data are not weighted as the calibration weights within the ASHE dataset are not set up for longitudinal analysis.

Start date

ASHE data is available from 1997, so the earliest 10 year measurement period is 1997 to 2006. However, the ASHE sampling frame was cut in 2007 and 2008, which skews results for 1998 to 2007 and 1999 to 2008 so these data points are not reported.

Indicator type

Impact indicator.

Good performance

An improvement would be indicated by a statistically significant increase in the indicator demonstrating that more lower earners are experiencing wage progression. The magnitude of the increase required for a statistically significant change depends on the sampling variability around both the current and previous data point, for example for there to have been a statistically significant increase between 2004 to 2013 and 2005 to 2014, the proportion of the lowest earners experiencing wage progression would have had to have increased by more than 1.4 percentage points.

Changes to the size of the ‘evidence indeterminate’ group will affect the indicator without there necessarily being a change in the proportion of individuals that experienced wage progression compared to the baseline of only those that had earnings data recorded in the end year of the period.

The policy levers for influencing this indicator sit across government, not just in DWP. Wider factors, for example general economic conditions and skill levels across the workforce, will also influence the indicator.

Behavioural impact

No behavioural impact is expected.

Comparability

This is not an internationally-recognised indicator that can be used to make comparisons.

Collection frequency

Annually. Data collected relate to a specific reference date in April.

Time lag

Provisional data relating to April are generally released in November/December of the same year.

Data source (which data collection it comes from)

ASHE. This is an ONS survey and is a 1% sample of employees in the PAYE system.

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

Survey.

Robustness and data limitations

As ASHE is a sample survey there is a margin of uncertainty with the results, shown by the confidence interval around the point estimate. The 2005 to 2014 data (12.5%) have a confidence interval of ±1.0% percentage points.

ASHE is a survey of approximately 180,000 employees so the self-employed are not included in this measure. The sampling frame excludes individuals that are paid outside the PAYE system and so may be under-representative of very low-paid employees.

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

ASHE replaced the New Earnings Survey (NES) in 2004, with the ASHE method (eg imputation) applied to NES data from 1997 to 2003 (ie excluding the supplementary surveys of job movers and VAT-only businesses). More information can be found in the 2004 ASHE methodology:

In addition, a new questionnaire was introduced in 2005, there was a 20% cut in the sampling frame in 2007 and 2008 (the full sample was reinstated in 2009) and there were methodological adjustments in 2006 and 2007. These changes may lead to discontinuities when comparing data from different time periods. For further information see:

This indicator uses unweighted data as the weighting variables within the dataset are intended for cross-sectional rather than longitudinal analysis, consistent with other longitudinal ASHE outputs within DWP. This may bias estimates as data are not necessarily representative of the employee population (calibrated to the LFS) or adjusted for differences in response rates across firms. More information can be found in the guidance and methodology documents.

Although no weighting is applied to the estimates of the proportion of people experiencing wage progression, a calibration weight is applied to the ASHE dataset when calculating the underlying wage distributions. The switch to SOC2010 occupational coding on the ASHE 2012 dataset and the resulting impact on the calibration weight will affect 2003 to 2012 wage progression estimates by approximately ±0.5%. As this is within the ±1.0% confidence interval for this estimate then it is not judged to have a significant impact on our reporting nor will it compromise the indicator time series.

Further analysis on the impacts of the switch to SOC2010 on ASHE estimates can be found on the ONS website

Collecting organisation

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

Return format

The unit of measurement is a percentage.

Geographical coverage

Great Britain.

How indicator can be broken down

A breakdown by gender is published as part of the Equality report: * DWP equality information 2014: customer data

Further guidance

Methodology and guidance for ASHE can be found at:

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)

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

7. Rate of disability poverty

The indicator measures the percentage of individuals in families with at least one disabled member with a household income which is below 60% of contemporary equivalised median income, before housing costs (BHC).

Data are adjusted for family size and composition, before deducting for housing costs, so different household types are compared in a robust way. Data do not include care home residents as the sample used for the survey consists of the private household population only. Data are for a financial year and published around a year after the financial year ending. A statistically significant change means that a change is likely to be a real effect, rather than just down to normal variation between samples.

The Family Resources Survey definition of disability changed in the year 2012 to 2013 and, as such, comparisons between figures from the year 2012 to 2013 and previous years should be made with caution (see Technical Description).

7.1 Latest data (updated July 2015)

The latest data for 2013 to 2014 shows that there are 20% of individuals in families where at least one member is disabled with incomes below 60% of contemporary equivalised median income before housing costs. The equivalent figure for 2012 to 2013 was 19%.

While the latest estimate rounds to 1 percentage point higher than in 2012 to 2013, the poverty rate in 2013 to 2014 is regarded as unchanged This is because the change to 20% is driven by a move of less than 0.5 percentage points (19.29% to 19.63%). The move from 19% to 20% is therefore also statistically insignificant.

The percentage of individuals in families with at least one disabled member with a household income below 60% of median before housing costs.

Year Rate of disability poverty
2013 to 2014 20%
2012 to 2013 19%
2011 to 2012 18%
2010 to 2011 20%
2009 to 2010 20%

The data from which this indicator is derived can be found by accessing table 7a, page 91 of the Households Below Average Income publication.

Disability Equality Indicators (Office for Disability Issues)

7.2 Technical description

Short title

Rate of disability poverty.

Technical definition

This indicator measures the percentage of individuals in families containing someone who is disabled with incomes below 60% of median income in a particular year. Data are adjusted for family size and composition, before deducting housing costs, so we are comparing different household types in a robust way.

In 2012/13 the Family Resources Survey (FRS) disability questions were revised to reflect new harmonised standards. Disabled people are identified as those who report any physical or mental health condition(s) or illness(es) that last or are expected to last 12 months or more and which limit their ability to carry out day-to-day activities a little or a lot.

However, any changes seen between 2012/13 and 2013/14 are unlikely to be influenced by the question wording as the same was asked of survey respondents in both years.

Between 2012/13 and 2013/14 the overall numbers living in households reporting disability fell by 0.3 million while those not reporting disability rose by 0.4 million.

While the number of pensioners in non-disabled families increased by 0.2 million, the number in disabled families decreased by 0.1 million. Similar is true for children in 2013/14, with the number of children in non-disabled families increased by 0.1 million, and decreased by 0.1 million for those in disabled families.

Rationale

Tackling disability poverty is important in terms of fairness, as individuals in families with at least one disabled member have a higher rate of low-income poverty than individuals in families with no disabled member. Using 60% of contemporary equivalised median as a poverty threshold is what has been done historically and internationally

Formula

We produce a ‘Before Housing Costs’ equivalised income for all individuals, calculate the median and then look at how many people in families containing someone who is disabled fall below a threshold of 60% of the median.

Worked Example.

The equivalised median real income ‘Before Housing Costs’ in 2013 to 2014 was £453 a week for all individuals. Thus the 60% of median income threshold was £272 a week. Individuals in families containing someone who is disabled with an equivalised household income of £271 or less would therefore be deemed to be in poverty.

Start date

Data published in the Households Below Average Income series first published in 1988, sourced from the Family Resources Survey (FRS) since 1994/95

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 need to be taken into account. The confidence interval range for the 2012 to 2013 data is 18.2% to 20.7% which results in a 95% confidence interval of around +/- 1.2 percentage points.

Behavioural impact

No direct behavioural impact is expected

Comparability

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

Collection frequency

Annually http://research.dwp.gov.uk/asd/index.php?page=hbai

Time lag

Around one year after the end of the survey period.

Data source (which data collection it comes from)

Family Resources Survey – with analysis by DWP.

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

National statistic 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.

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 characteristics not captured on administrative sources, and for looking at total benefit receipt on a benefit unit or household basis.

Confidence intervals can be calculated for the headline statistics. Low income is one amongst a group of poverty measures. There are a variety of other measures of poverty that might be used. The data source, the FRS, is sponsored by the DWP, and has been designed to meet its needs.

The latest available figures indicated that 20% of individuals in disabled families were in low income households. The confidence interval suggest the true figure is between 18.4% to 20.8% and therefore the 95% confidence interval for the 2013 to 2014 data is around +/- 1.2 percentage points.

Collecting organisation

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

Return format

Unit and format of measurement is: Percentage.

Geographical coverage

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

How indicator can be broken down

Any material collected on the FRS can be broken down by low income, subject to sample size constraints.

There are a wide range of additional breakdowns including maternity, gender and religion (Northern Ireland only). Data on sexual identity and religion has been added to the survey for all respondents from April 2011.

Equality group breakdowns are available for: age, ethnicity (using a 3 year average) and gender:

Further guidance

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

8. Gap between the employment rates for disabled people and the overall population

This indicator compares the employment rate of disabled people, relative to the employment rate of the total working age population.

This indicator is measured by comparing the seasonally unadjusted employment rate for disabled people as defined in the Equality Act 2010 with the Great Britain unadjusted working age employment rate, both taken from the LFS published quarterly by the ONS.

The 95% confidence interval is ±1.4 percentage points. Data are not seasonally adjusted and therefore year on year comparisons only are meaningful. A statistically significant decrease in the employment rate gap would demonstrate an improvement in equality of opportunity for disabled people.

8.1 Latest data (updated May 2015)

Data for January to March 2015 showed a gap of 26.6 percentage points. This is a decrease of 1.2 percentage points in the employment rate gap from the period 12 months earlier.

Estimates of disability from the Labour Force Survey for 2013 onwards should not be compared directly with earlier years. This is because we:

  • moved to only reporting people who are disabled within the core definition of the Equality Act
  • changed the wording of the disability questions within the survey questionnaire (so that the Labour Force Survey is in line with the GSS harmonised definition for disability)
  Apr-Jun Jul-Sept Oct-Dec Jan-Mar
2014-2015 27.4% 27.2% 27.5% 26.6%
2013-2014 27.2% 28.2% 27.8% 27.8%

Gap in data: estimates for 2013 onwards should not be compared directly with earlier years, due to a change in definitions (see technical description, robustness and data limitations section).

  Apr-Jun Jul-Sept Oct-Dec Jan-Mar
2012-2013 24.6% 24.7% 24.6% 24.4%
2011-2012 24.3% 24.4% 24.4% 24.0%
2010-2011 24.6% 24.9% 23.8% 23.2%

Gap in data: estimates for 2010 onwards should not be compared directly with earlier years, due to a change in reporting behaviour (see Technical Description, Robustness and data limitations section)

  Apr-Jun Jul-Sept Oct-Dec Jan-Mar
2009-2010 26.0% 26.0% 26.0% 24.0%
2008-2009 27.3% 27.4% 26.8% 26.5%
2007-2008 28.0% 27.4% 27.6% 27.6%
2006-2007 27.8% 27.8% 28.7% 27.8%
2005-2006 28.5% 29.0% 28.2% 28.1%
2004-2005 28.8% 28.9% 29.2% 29.1%
2003-2004 30.2% 30.3% 30.0% 30.2%
2002-2003 30.5% 30.3% 30.1% 30.2%
2001-2002 30.5% 30.9% 29.7% 29.6%
2000-2001 31.7% 30.2% 30.2% 30.1%
1999-2000 29.9% 30.3% 30.3% 31.1%
1998-1999 32.6% 32.4% 31.3% 30.5%

When are next data due

The next data is due for release on 12 August 2015.

8.2 Technical description

Short title

Gap between the employment rates for disabled people and the overall population

Technical definition

The indicator measures the difference in the employment rates between the employment rate for disabled people, as defined the Equality Act (EA), and the overall population. This uses the ONS Headline Rate definition of the employment rate, ie between the ages of 16 and 64 for both males and females. Data are not seasonally adjusted and therefore year on year comparisons only.

(1) Calculate the employment rate for disabled people, as defined by the Equality Act (EA) between ages 16 and 64. This is done by dividing number of EA disabled people in the age range who are in employment by the total number of EA disabled people in this age range.

(2) Calculate the employment rate for all people between the ages of 16 and 64, by dividing the number of people in this age range who are in employment by the total number of people in this age range.

(3) Take the difference, in percentage points between the above calculations.

Rationale

Transparency: This data allows DWP to monitor progress towards employment equality for disabled people and allows the public to assess how well the government is performing against the stated commitments.

Fairness: Forms part of the coalition’s government vision for DWP to promote high levels of employment by helping people who are out of work including those in disadvantaged groups to move into work.

Economic Impact: Economical benefits to having people off benefits and into work. Important in terms of equality that disabled people benefit equally from an improving economy and not disproportionally from an economic downturn. SRP3: Enable disabled people to fulfil their potential.

Formula

Overall employment rate: (number of people in employment between 16 and 64 / number of people between 16 and 64), minus:

Employment rate for EA disabled people: (number of EA disabled people in employment between 16 and 64 / number of EA people between 16 and 64).

Worked Example: Overall employment rate: 27.686 million / 38.886 million = 71.2%.

Employment rate for EA disabled: 3.242 million / 7.026 million = 46.1%.

Gap between the employment rates for disabled people and the overall population: 71.2% - 46.1% = 25.1 percentage points

Start date

This data is taken from the LFS though this specific data is not routinely published. It is circulated internally to monitor performance and released ad hoc into the public domain as part of ministerial briefings, PQs etc

Indicator type

Impact indicator.

Good performance

A decrease of more than 1.4 percentage points would show an improvement (a change of less than 1.4 percentage points would not be statistically significant) but economic conditions will also need to be taken into account, particularly as recent research evidence indicates that employment prospects for disabled people are less sensitive to economic conditions than the overall population. This may mean that as the economy improves and overall employment rates increase, the gap between the disabled and the overall employment rates will increase, which would represent a decline in this indicator. Data are not seasonally adjusted and so only year on year comparisons are meaningful.

Behavioural impact

Unlikely to have an impact. This is a high level measure and as such relatively small scale activities such as cherry-picking or parking of customers on employment programmes would be unlikely to have a demonstrable impact on the measure.

Comparatively

The indicator is derived from the LFS. The LFS is used to produce the UK government’s official statistics on employment and unemployment, as per the International Labour Organisation (ILO) definitions and as such would be comparable internationally. Impact indicator 8 is produced using the same methodology.

The disability indicator used in the LFS aligns with the Equality Act definition of disability, which in turns closely aligns with, although may not be strictly identical to, the UN Convention on the Rights of Persons with Disabilities. On the less positive side, there may be issues of comparability in that the definition of Working Age will vary between countries which in turn will lead to variability in the coverage of different countries’ employment rate measures. The definition of Working Age used for indicator 8 is 16-64 for men and women.

Collection frequency

Quarterly.

Time lag

Available approximately 6 weeks after the end of the reference quarter period.

Data source (which data collection it comes from)

LFS: with analysis by DWP.

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

Survey.

Robustness and data limitations

There is no published data for the confidence intervals around the indicator. However, the department has estimated that the confidence interval for a single quarterly estimate of the indicator is +/- 1.0%. The confidence interval for a year-on-year change is wider, because it is based on two independent estimates and so subject to two ‘sources of uncertainty’. The confidence interval for a year on year change is approx. +/- 1.4 %.

The indicator is based on the same data source and definitions as National Statistics on employment among disabled people (Table A08 Labour Market Statistics (ONS), although it currently focuses on a slightly different age group. The indicator is quality assured against the National Statistics estimates. 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 (LFS Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group).

The analysis is based on a random sample of around 45,000 households each quarter, used for a wide range of National Statistics.

The definition of ‘working age’ changed in 2010 to reflect the equalisation of state pension age. The new impact indicator is based on the ONS new headline rate, which includes all people aged 16 to 64.

The fit between the data and the indicator is very good. The LFS is used by ONS to produce National Statistics on UK employment and unemployment rates that conform to ILO standards. The LFS also contains disability data that very closely aligns with the Disability Discrimination Act definition of disability.

Estimates of disability from the LFS for 2010 onwards should not be compared directly with earlier years. There was a change in the reporting behaviour of survey respondents at the start of 2010, mainly reflecting a change in the wording of the survey questionnaire, which is believed to result in more accurate estimates. This will be reflected in future analyses of economic activity of disabled people published by the ONS.

Further changes in 2013 to the wording of the disability questions within the survey questionnaire, and a move to only reporting those who are disabled within the core definition of the Equality Act, have led to a step change in the levels of reported disability and their composition (e.g. proportion in employment). The move to only reporting those who are disabled within the core definition of the Equality Act and the change in the wording of the questions, is to bring the Labour Force Survey in line with the GSS harmonised definition for disability, which is being rolled out across all surveys. This has occurred as a result of the Equality Act 2010. Therefore, estimates of disability from the Labour Force Survey for 2013 onwards should not be compared directly with earlier years.

Collecting organisation

The ONS.

Return format

Unit and format of measurement: Volumes (millions) and expressed in percentage terms.

Geographical coverage

National (UK).

Government office region breakdown has been considered but analysis showed that, due to relatively small sample sizes, confidence intervals were too wide to enable robust time series analysis or comparisons between regions.

How indicator can be broken down

The viability of additional breakdowns would have to be assessed on a case-by-case basis. Sample sizes are too small to enable regional analysis but there may be scope for breakdowns such as gender and broad age groups.

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

Further guidance

Any other relevant information.

It is widely accepted there is a strong connection between the state of the economy and employment rates and this would be expected to have an effect on both of the employment rates used to measure this gap. There are many other factors that can also affect employment rates including the policies of DWP, other government departments, the wider public sector and the third sector. It would not be possible to disentangle the numerous effects and accurately quantify the precise impact any given factor has on employment rates. For this reason we cannot make the direct, provable casual link between the wide range of specific policies included in the DWP Spending Review settlement and a high level economic measure such as the employment rate.

Currently published in Excel.

9. Fraud and error in the benefit system as a percentage of benefit expenditure

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. This indicator is measured using the department’s National Statistics report on fraud and error in the benefit system (UK). Data are published every 6 months (around May and November), each report covering a 12 month measurement period.

Generally a decrease in the indicator (outside of the 95% confidence intervals) will demonstrate that an improvement in reducing fraud and error in the benefit system has been achieved, but economic conditions and overall expenditure will also need to be taken into account.

9.1 Latest data (updated July 2015)

Overpayments

The preliminary estimate for the percentage of total benefit expenditure overpaid in financial year 2014/2015 is 1.9%.

This is a decrease when compared to the 2013/2014, 2012/2013 and 2011/2012 estimates of 2.1%. This is the lowest the estimate has been since comparable measurement began in 2005/2006.

There have been no statistically significant changes in the estimate of total benefit expenditure overpaid since 2005/2006. Therefore the estimated percentage has remained roughly level over time.

Underpayments

The preliminary estimate for the percentage of total benefit expenditure underpaid in the financial year 2014/2015 is 0.9%.

This is the same as the estimates for 2013/2014 and 2012/2013, but is a slight increase when compared to the 2011/2012 estimate of 0.8%. This estimate has remained between 0.8% and 0.9% since 2005/2006.

There have been no statistically significant changes in the estimate of total benefit expenditure underpaid since 2005/2006 and therefore the estimated percentage has remained level over time.

Fraud and error in the benefit system as a percentage of benefit expenditure

Financial Year Overpayments Underpayments
2014 to 2015 (preliminary) 1.9% 0.9%
2013 to 2014 (final) 2.1% 0.9%
2012 to 2013 (final) 2.1% 0.9%
2011 to 2012 (final) 2.1% 0.8%

Find reports for the results in the table at:

Find historical National Statistics reports on fraud and error in the benefit system.

9.2 Technical description

Short title

Fraud and error in the benefit system, as a percentage of 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.

Rationale

This is the primary DWP indicator for levels of Fraud and Error in the benefit system.

It is included in the DWP business plan and is a measure used in the DWP resource accounts and annual report.

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.

The indicator is used as a measure for Structural Reform Priority 6 – Action 6.3: Reduce the level of benefit expenditure overpaid from the current 2.1% to a maximum of 1.7% by 2015 as part of the joint DWP and HM Revenue and Customs Fraud and Error Strategy commitment to reduce annual welfare overpayments by over one quarter over the Spending Review period.

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. The methodology is audited by the National Audit Office every year.

Worked Example:

Fraud and Error preliminary 2014/2015 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 2014/2015.

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 2014/2015.

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

Start date

Published twice a year (May and November).

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 2005/06.

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, the National Audit Office and the National Fraud Authority.

Comparable statistics are available for Northern Ireland and HM Revenue and Customs Tax Credits.

Collection frequency

Published twice a year (May and November).

Time lag

7-8 month time lag from the end of the measurement period to when the report is published (for example, the period ending March 2013 will be published in November 2014).

Data source (which data collection it comes from)

The fraud and error measurement system consists of a random DWP sample survey of approximately 27,000 benefit cases covering Income Support, JSA, ESA PC and Housing Benefit.

The department also undertakes one off National Benefit Reviews (NBRs) for various benefits to estimate the level of fraud and error in a particular financial year using the same process. For those benefits not continuously reviewed or reviewed via NBRs, a proxy measured is used to estimate their rate of fraud and error. Proxy estimates for the unmeasured benefits are either linked to the results of a measured benefit that was likely to have a similar level of fraud and error, or are estimated by applying a rate based on the average value of incorrect payment for all measured benefits.

The survey combines data collated from DWP administrative systems and Local Authority owned Housing Benefit systems with data collected from the claimant during an interview.

The statistics are calculated from the results of a sample survey, which are recorded on an internal DWP database.

For more information on the methods used to produce these estimates and how they are quality assured please refer to:

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

National Statistics

Robustness and data limitations

Data is robust for statistical purposes at national level but the sample is not large enough to be able to produce statistically valid results at regional or local level.

In any survey sampling exercise, the estimates derived from the sample may differ from what we would see if we examined the whole caseload. Where possible, uncertainties have been quantified to give an overall assessment in the form of 95% confidence limits. These confidence limits show the range within which we can be 95% sure that the true value lies. For more details on how confidence levels are calculated please see:

More details on data limitations and the care required in interpreting results are included in the background and methodology document published as part of the of the latest Fraud and Error in the Benefit System National Statistics report.

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:

The fraud and error estimates can also be used for:

Obtaining an estimate for the amount over/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/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 from:

This page also contains links to further detailed documentation including guidance on Quality and Methodology; Uses and Users; Ad hocs and Pricing; and Variance and Confidence Intervals.

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

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

10. Rate of pensioner poverty

This indicator measures the percentage of pensioners in the UK with incomes below 60% of median income in a particular year adjusted for family size and composition and after deducting housing costs. This adjustment allows us to compare different household types in a robust way.

10.1 Latest data (updated July 2015)

The latest data shows that 14% of pensioners (1.6 million) were in relative poverty in the financial year 2013 to 2014. This is based on those with incomes below 60% of contemporary equivalised median income after housing costs. The rate of pensioner poverty was 13% in the financial year 2012 to 2013 and rose to 14% in the year 2013 to 2014. This change was not statistically significant.

The small upward trend is explained by net overall household incomes being supported by, for example:

  • high employment rates
  • a fall in the number of workless families
  • reductions in job-related taxes from changes in the tax-free Personal Allowance

These are less applicable to many pensioner households.

The combined impact has been to keep the median income steady and the relative low-income rate flat but with pensioners benefitting less than the working age population.

While comprising a smaller proportion of most pensioners’ incomes, median income from earnings for pensioners also declined between 2012 to 2013 and 2013 to 2014. This continues a trend seen since 2009 to 2010 and is despite an increase in the employment rate for those aged 65 and over. However the average amounts earned have fallen causing the overall decline in income from earnings.

  2010 to 2011 2011 to 2012 2012 to 2013 2013 to 2014
Rate of Pensioner Poverty 14% 13% 13% 14%

The data from which this indicator is derived can be found by accessing the table 6a, page 75 of­ the Households Below Average Income publication:

Next data due and period covered

Final data for 2014 to 2015 are likely to be published in May or June 2016.

10.2 Technical description

Short title

Rate of pensioner poverty.

Technical definition

This indicator measures the percentage of pensioners with incomes below 60% of median income in a particular year adjusted for family size and composition and after deducting housing costs, so we are comparing different household types in a robust way. The preferred measure of low income for pensioners is based on incomes measured after housing costs, as around three quarters of pensioners own their own home. Considering pensioners’ incomes compared to others after deducting housing costs allows for more meaningful comparisons of income between working age people and pensioners, and between pensioners 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” that the basic State Pension will increase 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 Single Tier 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.

Formula

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 the median.

Worked Example: The equivalised median real income ‘after housing costs’ in 2012/13 was £374 a week for all individuals. Thus the 60% of median income threshold was £224 a week. A pensioner with a household income of £223 or less per week would therefore be described as ‘in poverty’ using this measure.

Start date

Data published in the ‘Households Below Average Income’ series first published in 1988, sourced from the FRS since 1994/95.

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 2012/13 data is 12.2% - 14.3% which results in a 95% confidence interval of around +/- 1.1 percentage points.

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. This measure 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 one year after the end of the survey period

Data source (which data collection it comes from)

Family Resources Survey (FRS) – with analysis completed by DWP

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.

In 2012/13, 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 characteristics not captured on administrative sources, and for looking at total benefit receipt on a benefit unit or household basis.

Confidence intervals can be calculated for the headline statistics. Low income is one amongst a group of poverty measures. There are a variety of other measures of poverty that might be used. The data source, the FRS, is sponsored by the DWP, and has been designed to meet its needs.

Collecting organisation

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

Return format

Unit and format of measurement is: percentage.

Geographical coverage

Regional – using a three-year average to get a sufficient sample size.

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, subject to sample size constraints.

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

Further guidance

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

11. Number of employees in a pension scheme sponsored by their employer

The indicator measures the number of employee jobs (ie excluding the self-employed) where the individual is:

  • aged at least 22 years of age
  • under State Pension age
  • earning above the earnings threshold for automatic enrolment (£10,000 in 2014/15 earnings terms)
  • participating in a pension scheme sponsored by their employer

An individual may have more than one job and may therefore be included in the count more than once depending on their circumstances.

This indicator is measured using data from the Annual Survey of Hours and Earnings (ASHE) which is an ONS Survey. Data are available annually 14 months after the period of data.

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

11.1 Latest data (updated July 2015)

The number of employees in a pension scheme sponsored by their employer increased between 2013 and 2014 by 2.2 million. This continues the reversal of the previous downward trend. This change is statistically significant.

Year Number of employees in a pension scheme sponsored by their employer (million)
2014 13.9
2013 11.7
2012 10.7
2011 11.0
2010 11.4
2009 11.4

Next data and period covered

April 2015 data will be published in April 2016.

11.2 Technical description

Short title

Number of 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 (SPa), earning above the earnings threshold for automatic enrolment and who are participating in a pension scheme sponsored by their employer. An individual may have more than one 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 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 between 8 and 9 million people will be newly saving, or saving more, in a workplace pension as a result of the reforms.

SRP 4 – For retirement: Providing a firm foundation, promoting saving for retirement and ensuring that saving for retirement pays.

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 2013/14 earning terms) has been applied in 2014. The £8,105 threshold (in 2012/13 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 relate to a 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 (CV), which is the ratio of the standard error of an estimate to the estimate. See:

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:

Further guidance

Methodology and guidance for ASHE can be found at:

The ASHE releases published by ONS can be found at:

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

12. Average age people stop working

The indicator measures the ages at which older people withdraw from the labour market and become inactive. It is based on multiplying each age by the probability of leaving the labour market at that age.

We publish data around 6 weeks after the end of each quarterly period. We do not calculate confidence intervals for this indicator. Due to its nature, changes are small and the focus should be on the long term trend. We do not seasonally adjust the data and only year on year comparisons should be made.

12.1 Latest data (updated June 2015)

The latest data, for January to March 2015, shows the average age men stop working was 64.8, the same as a year ago.

The average age women stop working was 63.1, slightly lower than the same quarter in 2013 (although the difference is not statistically significant).

There has been statistically significant change of 0.5 years for women since January to March 2010. This is consistent with evidence that the increasing State Pension age for women has led to older women in employment. This increase is part of the equalisation which will see women’s State Pension age increasing from 60 to 65 between 2010 and 2018. For further detail see Incentives, shocks or signals: labour supply effects of increasing the female state pension age in the UK (Institute for Fiscal Studies).

Men

Financial year Apr-Jun Jul-Sep Oct-Dec Jan-Mar
2014-2015 64.7 64.7 64.7 64.8
2013-2014 64.8 64.8 64.9 64.8
2012-2013 64.8 64.8 64.8 64.7
2011-2012 64.5 64.4 64.6 64.6

Women

Financial Year Apr-Jun Jul-Sep Oct-Dec Jan-Mar
2014-2015 63.1 63.3 63.2 63.1
2013-2014 63.1 63.2 63.3 63.2
2012-2013 62.6 62.7 62.9 63.0
2011-2012 62.7 62.5 62.4 62.4

Find further details on the indicator in chapter 4 of Pension Trends, published by the ONS

Next data due

April to June 2015 data are due in July 2015.

12.2 Technical description

Short title

Average age people stop working.

Technical definition

This indicator measures the ages at which older people withdraw from the labour market and become inactive. This indicator is measured using the ‘average age of withdrawal from the labour market’ statistic which is also published by the ONS, specifically using the ‘static’ methodology, which provides more up to date data than their preferred ‘Duration of Working Life’ measure.

The average age of withdrawal is based on multiplying each age by the probability of exiting the labour market at that age.

Data are not seasonally adjusted so only year on year comparisons are meaningful.

Rationale

Extending working life is one of the main responses to the 2006 Turner report and is an important part of response to demographic ageing and ensuring pensions sustainability.

Tracking changes in average age of withdrawal will provide an indication of how government policies to encourage longer working as well as track wider cultural shifts in working later in life.

SRP 4 – For retirement: Providing a firm foundation, promoting saving for retirement and ensuring that saving for retirement pays.

Formula

The method used for calculating the average age of withdrawal from the labour force is referred to as ‘the static variant’ method. It calculates the conditional probability of an age group remaining economically active in the next period. For example, the conditional probability of a person of age 51 remaining active by the time they reach the age 52, is taken to be the activity rate of a 52 year old divided by the activity rate of a 51 year old. The cumulative probability of remaining active is then calculated for all ages between 50 and 76, from which the proportion of all labour market withdrawals is established for each age.

These proportions are then weighted by their respective age values and then summed to provide estimates of the average age of withdrawal from the labour force.

Start date

Already published annually by the ONS in Pensions Trends (Chapter 4) and is a national statistic.

Pension Trends, Chapter 4: The labour market and retirement (ONS)

Indicator type

Impact indicator.

Good performance

An increase in the average age of withdrawal of more than around 0.5 years would demonstrate an improvement. Confidence intervals are not available due to the nature of indicator. Small changes from one year to another are normal, and the focus should be on longer term trends. We wouldn’t normally expect changes of 0.5 years or more from one year to another – we would expect changes of this magnitude to reflect meaningful change. External factors, such as wider economic conditions should also be taken into account. Data are not seasonally adjusted so only year on year comparisons are meaningful.

Behavioural impact

No

Comparability

Eurostat publishes comparable statistics across the EU. However, the detailed methodology used is slightly different from that used in the business plan indicator, so the exact numbers will differ.

Collection frequency

Data will be published quarterly from April 2003. Previously the data was published from April 2009. The back series data is subjected to retrospective changes to the LFS datasets.

Time lag

Around about 6 weeks after the end of the quarterly period.

Data source

(which data collection it comes from)

Uses ONS LFS data (UK).

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

National statistic, survey data.

Robustness and data limitations

Provides a measure of tracking change in average age of withdrawal over time. The ‘static’ measure we intend to use is the most up to date method, and gives the potential for more regular data.

There are issues in relation to the methodology which uses a static age indicator which assumes that factors affecting economic activity of one cohort are the same as those affecting the activity of the next. It may not be able to capture significant policy shifts such as State Pension age changes. In particular the ‘static’ methodology can overestimate the average exit age of women (by about half a year).

We therefore plan to also publish alongside this the Duration of Working life measure – though this is only available on an annual basis and data is two years lagged, for example 2011 Pensions Trends Report reports the 2009 Duration of Working Life figure.

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 (LFS Steering Group) and the related National Statistics outputs (Labour Market Statistics Theme Group).

The analysis is based on a random sample of around 45,000 households each quarter, used for a wide range of National Statistics.

Collecting organisation

ONS.

Return format

Geographical coverage

UK.

How indicator can be broken down

Equality group breakdowns are available for: ethnic group, gender and religion:

13. Customer and claimant opinion of departmental service levels

This indicator is measured using data from the department’s claimant service and experience survey to generate a score of overall customer satisfaction with the department’s services.

The survey uses data from 14,918 telephone interviews conducted every 3 months between July 2014 and May 2015 with claimants of the following:

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

The indicator measures the percentage of those claimants who had meaningful contact with the department in the 3 months before the interviews and are either fairly or very satisfied with the service they received.

Read a further description of how the indicator has been developed in the Public opinion of the Department for Work and Pensions service levels: technical annex.

13.1 Latest data (updated July 2015)

The fourth departmental score shows the department has achieved an overall customer satisfaction rating of 82%. In April 2014 the survey changed from being run annually to quarterly. Therefore the overall annual score is based on all 4 quarters of data for the financial year 2014 to 2015.

Year Overall customer satisfaction
2014/2015 82%
2013 81%
2012 83%
2011 89%

13.2 Technical description

Short title

Customer and claimant opinion of departmental service levels

Technical definition

The indicator is an overall satisfaction score (either fairly or very satisfied) of the percentage of people who have had meaningful contact with the department in the 3 months before the interviews of each quarter.

This indicator is measured using data from the DWP Claimant Service and Experience survey to generate a pan-departmental score of overall claimant satisfaction with the department’s services. The survey uses data from 14,918 telephone interviews conducted every 3 months between July 2014 and May 2015.

Rationale

The indicator demonstrates DWP’s performance against our Business Plan priority 6 - ‘Controlling costs: Improving services to the public by delivering value for money and reducing fraud and error.’ It provides strategic insight into performance management as it explores the relationship between satisfaction and service delivery.

The indicator has previously been generated from the Jobcentre Plus and Pensions, Disability and Carers’ Service surveys which fed into agency performance management frameworks. These provided robust, national assessments of claimant service in each agency. They allowed the agencies to review performance and improve services accordingly. We aligned the 2 surveys in 2012 and combined them into one survey in 2013. In April 2014 we began running the survey every 3 months, rather than once a year. The survey has ministerial approval.

Formula

The indicator is derived from a combination of the satisfaction scores of people 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 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 2009/10 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 JCP surveys to generate a pan-departmental score which was available from autumn 2011. The data for the 2013 indicator was published in February 2014.

Indicator type

Impact indicator.

Good performance

This is a baseline measure for the department that allows good performance to be agreed and reflected in maintaining or improving the level of satisfaction (depending upon the level of change in the department). Satisfaction ratings are reported at the 95% confidence level and subtle changes which are significant can be detected (for example, 1% change).

Behavioural impact

There are no perverse incentives generated by data collection to determine the indicator. Data is only reported at the benefit level, and includes a random sample of customers, so it is not possible for staff to change behaviour in order to manipulate results.

Comparability

The survey offers limited time series comparability from 2010 to 2011 because this was when the item and sampling strategy was standardised. 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

Quarterly results are collected and used to monitor and inform progress in customer service. We publish results from the survey annually.

Time lag

Headline reporting of the indicator is 3 months from the end of the final quarter of the survey.

Data source (which data collection it comes from)

Survey data collection commissioned from a third party research organisation contracted through the DWP research framework.

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

Survey data.

Robustness and data limitations

The research contractor carrying out the survey is part of the DWP research framework. As such, they have gone through extensive checks to ensure they comply with the departments quality standards and those of the government social research profession.

There is an assurance plan in place as part of the wider project plan, which is agreed prior to contract signing. A series of meetings are conducted between DWP and the contractor to finalise and sign off the details of the research requirements, sampling strategy, data collection, analytic plan and strategy. Potential risks and mitigations are also jointly identified at this stage.

The DWP project manager works closely with the contractor throughout the design, delivery and dissemination of the project. The agreed milestones are central to the delivery of the project and are signed off by the DWP project manager as part of their oversight of the contractor.

Steps taken by the DWP project manager for oversight of the survey include: signing off the final question set, agreeing opt out letters, attending interviewer briefing sessions, observing telephone interviews, regular progress updates and meetings, and monitoring and sense checking the data and analysis. This ensures that quality standards are monitored and the contractor’s processes and controls are adhered to.

The survey items were developed through cognitive testing and a large scale pilot accurately capturing claimant opinions. Significant differences are reported at the 95% confidence interval.

The current survey measures perceptions of benefit claimants and does not include those who have received a service from other agencies of the department (for example, the Health and Safety Executive).

Collecting organisation

External contractor TNS-BMRB.

Return format

Dataset returned in specified format as well as full research report.

Geographical coverage

England, Scotland and Wales.

How indicator can be broken down

Equality group breakdowns are available for: age band, disability, ethnic group and gender:

Satisfaction can be broken down further by:

  • benefit
  • work status
  • disability
  • sexual orientation
  • marital status
  • religion
  • whether claimants have children or not
  • English as a second language

Further guidance

We will publish a full research report in September 2015 for the 2014 to 2015 indicator.

While information is gathered that relates to individual demographics it may not be possible to robustly compare the satisfaction levels of some groups because of the limited number of participants in such groups

14. Number of Incapacity Benefit recipients reassessed and those moving from IB to ESA nationally

The indicator measures progress in reassessing the stock of claimants on those incapacity benefits that preceded the introduction of ESA. It shows the total number reassessed or closing their claim during reassessment and the number of these who moved to ESA. The reassessment process began at national level from April 2011 onwards. Categorisation is by the point at which an IB claimant is referred to Atos Healthcare for a Work Capability Assessment (WCA). Data are released quarterly.

The publication uses the final DWP Decision Maker’s decision, or the recommendation made by the Atos Healthcare Professional, when the Decision Maker’s decision is not available. This provides a more complete analysis.

14.1 Latest data (updated July 2015)

The latest official statistics publication on Incapacity Benefit Reassessment was published on 11 June 2015 for claims referred up until the end of September 2014. It includes action on these claims up to March 2015.

In total, 1,395,290 claimants had begun the Incapacity Benefit reassessment process before the end of September 2014 and had either completed the WCA process or left benefit before the process was complete by March 2015. Of these:

  • 1,084,550 claimants were entitled to the benefit
  • 257,700 claimants were assessed as Fit for Work (FFW) and are therefore not entitled to ESA
  • 53,040 left benefit before the assessment was completed

A further 25,760 claimants who had begun the Incapacity Benefit reassessment process before the end of September 2014 but whose claims were still in progress in March 2015 are not included in the above figures.

This shows that 1,421,050 claimants had started the Incapacity Benefit reassessment process by the end of September 2014. It is estimated that around 1.5 million claimants will have to be migrated, with the remainder expected to leave for other reasons or to reach state pension age before the process is completed. The full roll-out of the Incapacity Benefit Reassessment began nationally on 4 April 2011.

The above figures include the effect of any completed appeals lodged against the outcome of the assessment.

Find more information in ESA: outcomes of Work Capability Assessments: claims made to September 2014 and appeals to March 2015

14.2 Technical description

Short title

Number of people completing or leaving the IB reassessment process.

Technical definition

This indicator records the number of IB claimants that have started the IB to ESA reassessment process and their outcomes. The total includes claimants who start the process but leave benefit before it is completed.

Starting the reassessment process is defined as the point at which an IB claimant is referred for a Work Capability Assessment.

An outcome is defined as: (a) IB recipients who go on to receive ESA (either in the Work Related Activity Group or the Support Group) (b) IB recipients who are found fit for work and so do not qualify for ESA (c) IB recipients who start the assessment process but who then leave benefit before completing their assessment

Rationale

The reassessment of existing incapacity benefits claimants will ensure people receive an appropriate amount of support for their needs. The policy intent is to ensure that all claimants on incapacity benefits are, over time, receiving the same level of financial support and support to return to work if appropriate. The indicator will show the progress of this process.

SRP 1 – Working age: Encouraging work and making work pay

Formula

Indicator results calculated as follows: Monthly cohorts of existing incapacity benefits claims which have begun the reassessment process will be monitored so we know what happens at the assessment including numbers who leave before completing assessment and the outcomes from the Work Capability Assessment. The indicator will be the total number who have completed this process and of these the total who go on to receive ESA.

Start date

The rollout of IB reassessment started in October 2010, with a small trial in Aberdeen and Burnley and was extended nationwide from April 2011. It is estimated that around 1.5 million people will be subject to the process.

Indicator type

This indicator was previously labelled as an ‘Other Key Data Sets’ indicator, that is, it was neither an impact or input indicator.

Good performance

The incapacity benefit reassessments was expected to take 3 years, from April 2011 to the end of March 2014. However, this process was slowed in 2013 to concentrate on processing new ESA claims. It is expected that around 1.5 million people will be subject to the process and will consist of those claimants on IB who do not reach State Pension age or leave the benefit for other reasons before beginning the reassessment process.

A good performance is that the number of cases going through the process is in line with this target, and 1,421,050 claimants – around 95% of 1.5 million – had started the IB Reassessment process by the end of September 2014.

Prior to the start of the reassessment programme it was forecast that 23% of IB claimants undergoing IB Reassessment would be found fit for work. To date, 19% of those completing an assessment have been found fit for work.

Behavioural impact

No, collecting the data should not have any behavioural impact – the indicator is not a target.

Comparability

Unaware of a similar measure in other countries.

Collection frequency

Quarterly.

Time lag

The published data will be approximately 7 months after the claimants began the assessment process, but will include outcomes up to approximately 1 month before publication. Appeals information will be included where available but an appeal may take a year or more from the assessment date to be heard.

Data source (which data collection it comes from)

Data collected from: Internal benefit administration data held by the DWP and assessment data supplied by Atos Healthcare. DWP analysts then collate this data into a larger dataset.

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

Official Statistics.

Robustness and data limitations

Data has undergone internal quality assurance procedures to verify quality including comparison with other available data sources, separate running of data and output computer code and sensitivity analysis to look at changes in outturns for periods (on previous runs of the data) to see if movement fits historic profiles.

The publication uses the final DWP Decision Maker’s decision, or the recommendation made by the Atos Healthcare Professional, when the Decision Maker’s decision is not available. This provides a more complete analysis.

The raw data used to identify benefit claimants Work Capability Assessment process outcomes and statuses, and establish appeals results are: * DWP’s benefit administration datasets covering all claims to ESA (including those going through IB Reassessment) – this is cleaned, checked for error, by the internal data owner. This cleansing means that the base data available at any release is 5 months lagged * Atos Healthcare’s face to face assessment, ESA85, data and limited capability for work questionnaire, ESA50, data – this will cover those cases where the assessment phase is completed

The indicator gives the current status of claims. The relationship between the number of claims processed and the number moving to ESA is likely to change as in progress claims are completed, appeals are heard, and because the claims entering the process so far may not be representative of all IB claims due to be reassessed.

Collecting organisation

DWP and Atos Healthcare – DWP analysts then collate this data into a larger dataset covering the whole process. Data then published by DWP.

Return format

Unit and format of measurement is: Volumes.

Geographical coverage

National - Great Britain.

How indicator can be broken down

Breakdowns by local authority areas are currently available. Further breakdown by health condition, age gender etc may also be available but would require additional resource to develop.

Further guidance

Prior to the start of the IB Reassessment programme the department released an Impact Assessment which details the anticipated costs, benefits and forecast outcomes of IB Reassessment.

That is, excluding the pilot carried out in Autumn 2010.

From Impact Assessment of Employment and Support Allowance (Transitional Provisions, Housing Benefit and Council Tax Benefit) (Existing Awards) Regulations 2010, p11

15. Proportion of new claims to Jobseeker’s Allowance (JSA) submitted online

This indicator measures the proportion of new JSA claims submitted online as a percentage of all JSA new claims received.

For JSA:

  • claims submitted online means – the proportion of claims submitted online which are then recorded in the Jobseeker’s Allowance payment system (JSAPS), where the online marker that was set when the claim was made is still retained
  • all JSA claims means – the total number of JSA claims received and recorded in the JSAPS.

Good performance would be observed through increases in the proportion of customers claiming JSA online.

15.1 Latest data (updated August 2015)

The latest data for July 2015 shows that the proportion of Jobseeker’s Allowance claims submitted online was 86.1%. This is up 0.3 percentage points from the 85.8% achieved in April 2014.

Date Proportion of JSA claims made online (business plan measure)
July 2015 86.1%
June 2015 85.7%
May 2015 85.1%

15.2 Technical description

Short title

Proportion of new JSA applications submitted online.

Technical definition

This indicator measures the proportion of new JSA applications submitted online as a percentage of all JSA new claims received and recorded in the JSAPS within the same month.

‘Submitted online means’ – the proportion of claims submitted online which are then recorded in the JSAPS system, where the online marker that was set when the claim was made is still retained.

‘All JSA new claims’ means – the total number of JSA new claims received and recorded in the JSAPS system.

Rationale

The department is committed to the cross-government aim to make services digital by default. Growing our digital services will enable us to deliver a richer customer experience, make our business more flexible and efficient and enable us to respond better to changing policy requirements. This measure is intended to show the public what the department is doing to improve the service it provides via online channels and help people judge the progress of structural reforms within the department.

SRP 6 – Controlling costs: Improving services to the public by delivering value for money and reducing fraud and error

Formula

The number of claims submitted online which are then recorded in the JSAPS system, where the online marker that was set when the claim was made is still retained divided by the total number of JSA new claims received and recorded in the JSAPS system. Submitted Online and Retaining Online Marker / Total JSA new claims received and recorded in the JSAPS system.

Start date

JCP introduced online applications for contribution-based JSA in August 2009, followed by income-related JSA in August 2010 and from December 2010, Rapid Reclaims to JSA. Data was first published in the Quarterly Data Summary in October 2011.

Indicator type

This indicator was previously labelled as an ‘Other Key Data Sets’ indicator, that is, it was neither an impact or input indicator.

Good performance

Good performance would be observed through increases in the proportion of customers claiming JSA online.

Behavioural impact

The drive to encourage customers to use online services to improve online take-up will result in a behavioural change of staff within the organisation to influence customers to change their method of contact.

Comparability

The measure does not have an internationally recognised indicator that can be used to make comparisons.

Collection frequency

Monthly. The most recent in month figures will be reported monthly.

Time lag

One month.

Data source (which data collection it comes from)

The measure uses Management Information Systems Programme (MISP) data for both the total number of claims and those made online.

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

DWP internal management information.

Robustness and data limitations

For JSA it is known that this measure tends to understate the total number of claims completed online as the online marker (which identifies that a claim has been completed online) can be lost through either manual intervention by call centre staff or technical errors within the Management Information system.

Collecting organisation

DWP management information systems for JSA online.

Return format

Excel.

Geographical coverage

GB coverage only.

How indicator can be broken down

Currently at national level only.

15.3 Priorities for Digital Services

The department is making a greater range of services available online and encouraging claimants and customers to use them because:

  • digital services increase convenience, transparency and control for claimants and customers
  • increasing online self-service enables the department to focus staff time on the claimants and customers who really need it
  • for those looking for work, being online is increasingly essential for finding and getting a job.

The department’s main priority for digital services is to work with claimants and staff to design a compelling, straightforward and attractive online service for Universal Credit, putting online services at the heart of its future business model. The department is preparing for delivering Universal Credit by getting claimants of Jobseeker’s Allowance and other benefits online and encouraging them not just to claim online but also to manage their benefits and jobsearch online.

As well as the 7.1 million people who have already claimed Jobseeker’s Allowance online, more than 4.7 million job searches are conducted every day through the new Universal Jobmatch service which was launched in the autumn of 2012

The department reports performance against the business plan ‘Proportion of new JSA claims submitted online’ indicator in the Annual Report and Accounts and via the business plan transparency measures page – where a technical description is also available providing more technical information about the measurement of this indicator.

The business plan indicator is comprised of a single JSA measure. There are however two measures of JSA available.

Jobseeker’s Allowance

The JSA measure used to measure performance in the business plan indicator:

The business case measure – measures the proportion of JSA applications completed online as a percentage of all JSA new claims received and recorded in the JSAPS within the same month. This percentage figure represents the number of online claims made divided by the total number of claims which are pursued through to a formal decision. This measure is thought to overstate the proportion of JSA claims completed online as the percentage of applications started online is higher than the percentage of claims completed which were started online.

The Management Information System Programme Measure – measures the proportion of JSA applications completed online which are received in JSAPS as a percentage of all JSA new claims received and recorded in JSAPS within the same month. This percentage figure represents the number of JSA claims submitted online and pursued through to a formal decision divided by the total number of claims which are pursued through to a formal decision. This measure is thought to understate the proportion of JSA claims completed online.

Increasing the proportion of JSA claims made online

The department has put into action a comprehensive plan to increase take-up of online claims for JSA, centred around 4 main types of activity:

  • improving the service
  • engaging staff and setting the conditions for local innovation
  • increasing the proportion of claimants with access to the internet and the skills to claim online
  • making changes to process to build awareness of digital services and set the expectation that they are the default channel.

Improving the service

Objective:

  • To reduce the proportion of claimants dropping out between starting and submitting a claim and make the service more attractive and user-friendly.

Already delivered:

  • Changes to GOV.UK to make JSA online easier to find
  • Small changes to the online service to make it easier to use (based on claimant feedback and management information on where claimants tend to leave the service)

Underway:

  • Substantive changes to the online service (based on claimant feedback and management information on where claimants tend to leave the service)

Engaging staff and fostering local innovation

Objective:

Already delivered:

  • Digital Discretionary Fund to support implementation of good operational ideas to drive channel shift
  • digital awards for individuals and teams who make a difference to take-up
  • Digital Delivery Networks across working age operational units charged with using digital services innovatively
  • Digital Champions in every operational office making relationships with partners and ensuring staff and claimants have access to the right information about available help
  • senior digital leads in all areas of Operations to drive energy and engagement
  • ideas and best practice from these initiatives are being captured and considered for national roll-out
  • Digital December communications campaign and activities for staff (including developing their own digital skills)

Increasing the proportion of claimants with access to the internet and the skills to claim online

The vast majority (86%) of JSA claimants already have access to the internet (Research report Developing an online service: Customer research into the benefits and likely uptake of Automated Service Delivery (Jobseeker’s Allowance). But since being online is increasingly important for finding work and for social inclusion more generally, the department is also working to support the remaining 14% of JSA claimants (along with other claimants and customers) to get online.

There are now over 900 Digital Champions in frontline offices across the department, including in every Jobcentre. They work closely with local partners and with and claimants and customers to promote the benefits of getting online and to tell them about sources of supported access and training. The department has also supported campaigns such as Race Online, Spring Online and BBC First Click, and used its employee volunteering programme to support local initiatives to get people online.

Building awareness of digital setting the expectation that it is the default channel

Objective:

  • Ensuring that the department’s business model reflects the move towards digital by default.

Already delivered:

  • Strengthen recorded message for those ringing the new claims telephone line to promote online claims
  • Promote online claims through Rapid Response service for large-scale redundancies
  • Provide posters and business cards for Jobcentres and ask reception staff to promote online claims as the default
  • Promote online claims to those signing off benefit for any subsequent claim

Underway:

  • Amend letters sent to people moving between benefits (where cost effective)

Testing further action

The department also needs to use JSA online to learn what has the greatest impact in getting claimants online. In particular, the department is testing the impact of a number of measures to increase the proportion of JSA claims made online. Three short operational trials ran over the summer, and a further three will be run this autumn. These trials test incentives and disincentives to use different channels, and encourage people who get through to a member of staff on the telephone claim line to go online instead, providing advice about local sources of internet access and offering step-by-step support where necessary. This will not only enable the department to identify which actions may support a step-change in the take-up of online claims for JSA, but also which should be embedded into the business model for Universal Credit.

16. The proportion of State Pension claims completed online - for information

The proportion of State Pension claims completed online is no longer a business plan indicator, but in the interests of transparency the department is continuing to publish the data.

For State Pension:

  • ‘claims completed’ online means claims that were submitted via the State Pension online portal and created in the Customer Account Management system
  • ‘all claims’ means all claims to State Pension

16.1 Latest data (updated August 2015)

Month Proportion of State Pension claims completed online
July 2015 21.0%
June 2015 21.1%
May 2015 22.0%

Historical data on the indicator:

In order to offer a convenient and modern service for those approaching State Pension age, the department has made a number of changes.

From a customer perspective the best service of all is one where they do not need to provide information the department has already got. For those customers who are claiming one of the department’s working age benefits as they approach State Pension age, the department is now using existing information to help assess their entitlement to State Pension. In July 2015, 10,809 cases have been considered in this way and these are not included in the percentage take-up figures for State Pension online.

For other people approaching State Pension age, the next best thing is usually the convenience and control of an online service. 72% of State Pension claimants already have access to the internet and could therefore benefit from this service.

State Pension online is a relatively new service and the department has used its first year of operation to learn how customers use it. During this period the department has not been raising awareness of the service, except during December when the department tested including information about the service in the letter sent to people approaching State Pension age.

The department has recently introduced some improvements to the service and is raising awareness of it through:

  • a redesign of the customer experience by setting clear customer expectations at the initial stage of applying for State Pension online
  • making it more intuitive and easier to use: helping customers who begin a claim online to submit it online
  • improving the identity verification service to enable more customers to register for State Pension online
  • informing customers about the online service, including through the letter and booklet the department sends to everyone approaching State Pension age
  • ensuring that staff are confident in selling the benefits of transacting with the department online and know how to do so

The department closely monitors the service and has a continuous improvement plan to support the development of the State Pension online service.

17. Proportion of households that are workless

The indicator measures the number of workless households as a proportion of all households. Estimates focus on households in the UK that contain at least one person aged 16-64. Such households are defined as workless if none of the household members aged 16 or over are in employment. Data are not seasonally adjusted so only year on year comparisons are meaningful.

17.1 Latest data (updated March 2015)

The latest data shows that the estimated proportion of households that were workless was 15.9% in October to December 2014. This is 0.9 percentage points lower than the 16.8% estimated for October to December 2013.

The decrease over the latest year was statistically significant. The October to December 2014 estimate (15.9%) has a sampling variability of ± 0.4 percentage points. A change over time has estimated sampling variability of approximately ± 0.5 percentage points. The survey sample size is around 45,000 households.

Proportion of households that are workless in the UK (%). Year on year comparison only

Year Apr-Jun Oct-Dec
2014 15.9% 15.9%
2013 17.3% 16.8%
2012 18.0% 17.4%
2011 18.8% 18.8%
2010 19.2% 18.9%
2009 18.5% 18.9%

Further information is available from:

This indicator is available from Table A, in the following files:

Households by combined economic activity status of household members

CSV: Households by combined economic activity status of household members, October to December

CSV: Households by combined economic activity status of household members, April to June

17.2 Technical description

Technical definition

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

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

Rationale

DWP’s vision is to promote high levels of employment by helping those currently out of work into employment. The structural reform priority to tackle the causes of poverty recognises that work is the best route out of poverty. This indicator aligns with both of these objectives.

Formula

Number of workless households / total number of households = proportion of households that are workless

Worked Example:

Using latest October to December 2014 data: 3,283,000 / 20,623,000 = 15.9%

Start date

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

Indicator type

This indicator was previously labelled as an ‘Other Key Data Sets’ indicator, that is, it was neither an impact or input 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 October to December 2013 and October to December 2014, the proportion of households that are workless had to fall by at least 0.5 percentage points.

As the 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 (for example, they may have arisen from the different samples by chance).

The threshold of a year-on-year change greater than 0.5 percentage points between 2013 and 2014 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, two consecutive year-on-year changes, neither of which are statistically significant, may combine to show a significant change over the two-year period. Similarly, looking at a series of estimates over time will aid interpretation of trends.

The proportion of households that are workless is calculated from the number of workless households and the total number of households (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 workless households or a change in the total number of households (or both).

Changes to the total number of households, for example through population growth and changes in family formation, will affect this indicator. For example, it would be possible for there to be a fall in the proportion of households that are workless at the same time as an increase in the number of workless households, if the total number of households rose at a greater rate than the number of workless households. In the year to October to December 2014, the number of households increased slightly, but the number of workless households fell.

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

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

Behavioural impact

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

Comparability

There is not an internationally recognised indicator that can be used to make comparisons. Eurostat (the EU statistics agency) publishes data on the number of individuals aged 18-59 living in workless households in EU countries, but not the total number of workless households (in EU statistics these are referred to as ‘jobless households’). This indicator is linked to the ‘children in workless households’ impact indicator.

Collection frequency

Published approximately every 6 months for Quarter 2 (April-June) and Quarter 4 (October-December). April-June and October-December figures are not directly comparable as the data are not seasonally adjusted; only year on year comparisons are meaningful.

Time lag

Generally around 2-4 months after the end of the reference quarter.

Data source (which data collection it comes from)

Household Labour Force Survey (HLFS).

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

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 ONS provides a confidence interval of ± 0.4 percentage points around the October to December 2014 estimate (see Table A of the following quality measures published by the Office for National Statistics).

The analysis is based on a random sample of around 45,000 households each quarter, used for a wide range of National Statistics.

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 (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

Unit and format of measurement is: 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

The headline data can be broken down by whether all members of the household are unemployed, all are inactive, or a combination of the two. The data can be compared with households where all members are working, and with households containing both working and workless members.

The headline data can also be broken down by:

  • household type: ie single household, couple household or other type of household, all with or without dependent children
  • region or country of the UK
  • housing tenure

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

Further guidance

This indicator is published in Excel and CSV format.

18. Proportion of customers for whom providers have achieved a Job Outcome payment at 12 months on the Work Programme

This indicator measures the proportion of claimants for whom providers were paid a job outcome payment at 12 months following referral to the Work Programme, by monthly cohorts of referral.

For example, for those that were referred to the Work Programme in June 2011 the indicator will show the proportion of the June 2011 referral cohort that has achieved a job outcome by the end of June 2012.

From 27 June 2013 the indicator will be released quarterly on the same release schedule as the Official Statistics.

Statistics on an equivalent 24 month measure were published for the first time in the September 2013 release of Official Statistics for June 2011 referrals.

Each time the Business Plan Transparency Indicator is published the proportion of those that have achieved a job outcome by the 12 month point will be updated for each of the monthly cohorts that have already been published. Due to some payment information being received late or subsequently changed the indicator figures may change over time. More information on this can be found in the background information note which can be found on the Work Programme Official Statistics homepage.

18.1 Latest data (updated July 2015)

Comparison of those who achieved a job outcome payment during their first 12 months on the Work Programme:

Month Percentage Month Percentage
March 2014 17.1% March 2013 12.7%
February 2014 17.3% February 2013 12.1%
January 2014 17.8% January 2013 12.9%

The full historical statistical series is refreshed with each release. Please see Work Programme Official Statistics Background Information Note for more information.

Proportion of customers for whom providers have achieved a job outcome payment at 12 months on the Work Programme

Cohort Percentage Number of referrals Number of outcomes
March 2014 17.1% 21,700 3,720
February 2014 17.3% 21,800 3,770
January 2014 17.8% 27,410 4,880

Proportion of customers for whom providers have achieved a job outcome payment at 12 months on the Work Programme

Further monthly cohort information is available which tracks the proportion of each cohort achieving a job outcome in the months following referral to the Work Programme. This cohort analysis is available via the data visualisation tool which can be viewed on the Work Programme Official Statistics homepage.

The full suite of Work Programme Official Statistics can be viewed via the tabulation tool which can be accessed via the Work Programme Official Statistics homepage - link provided above.

Breakdown of the indicator by contract

Proportion of customers for whom providers have achieved a job outcome payment at 12 months on the Work Programme by contract

Breakdown of the indicator by payment group

Proportion of customers for whom providers have achieved a job outcome payment at 12 months on the Work Programme by payment group

More Work Programme statistics

18.2 Technical description

Proportion of customers for whom providers have achieved a job outcome payment at 12 months on the programme

Short title

Proportion of job outcomes achieved at 12 months following referral to the Work Programme.

Technical definition

The proportion of claimants for whom providers were paid a job outcome payment at 12 months after their referral to the Work Programme by monthly cohorts of referral.

For example: For those that were referred to the Work Programme in June 2011 the indicator will show the proportion of the June 2011 referral cohort that has achieved a job outcome by the end of June 2012.

The indicator is sourced from Work Programme Official Statistics.

Further information on all topics covered in this technical description can be found in the Work Programme background information note which can be accessed from the Work Programme Official Statistics homepage (linked above).

References are made throughout this document to the link provided above.

For information on the timescales when job outcomes can be achieved for each payment group see Work Programme background information note.

The indictor is calculated on a cohort basis looking at those referred each month and how many job outcomes have been paid for those referrals 12 months later.

Statistics on an equivalent 24 month measure were published for the first time in the September 2013 release of Official Statistics.

Each time the business plan transparency indicator is published the proportion of those that have achieved a job outcome by the 12 month point will be updated for each of the monthly cohorts that have already been published. Due to some payment information being received late or subsequently changed the indicator figures may change over time. For more information see Work Programme background information note.

For example: publication date - 27 November 2012

  • Release the proportion of the June 2011 and July 2011 referral cohorts achieving a job outcome by June 2012 and July 2012 respectively.

Next scheduled publication date – 27 June 2013

  • Release updated proportions for the June 2011 and July 2011 cohorts.
  • Release for the first time proportions for the monthly cohorts of August 2011, September 2011, October 2011, November 2011, December 2011, January 2012, February 2012, March 2012.

From 27 June 2013, the Work Programme Official Statistics will be more timely and frequent; within 3 rather than 4 months of the reporting period and on a quarterly basis aligned to the financial year, with publications in September, December, March and June – on the same release schedule as the Work Programme Official Statistics.

For more information on the improvements in the timeliness and full scope of the Work Programme Official Statistics see Work Programme background information note.

Rationale

The indicator looks at performance by cohort once claimants are referred to the Work Programme and have had time to receive a reasonable duration of support and allows comparison of performance between cohorts.

The focus of the Work Programme is on supporting participants to achieve sustained employment. Job outcome payments are paid to the provider after a customer has been in cumulative employment of 13 or 26 weeks, dependant on which payment group they are referred to.

The Work Programme will support a wide range of different claimants, in receipt of different benefits. For a full list of types of claimants who are referred to each payment group and for information on job outcome trigger points see Work Programme background information note.

Formula

The proportion is calculated by dividing the total job outcomes paid to providers at 12 months following referral by the total number of referrals in the cohort month.

For example: the June 2011 cohort indicator proportion is calculated by the following method:

  • number of job outcomes paid to providers by end of June 2012 for those referred to the Work Programme between 1 June 2011 and 30 June 2011 divided by total number of referrals to the Work Programme between 1 June and 30 June 2011.

Proportions are displayed as totals for monthly cohorts and are also available broken down by payment group and contract.

Start date

The indicator was released for the first time on 27 November 2012 on the same date that the ‘outcome’ official statistics were released.

The Work Programme commenced in June 2011 therefore the first monthly cohort of referrals is for June 2011.

Type of indicator

This indicator was previously labelled as an ‘Other Key Data Sets’ indicator, that is, it was neither an impact or input indicator.

Good performance (optional)

An increase in the indicator over time would demonstrate that the proportion of paid job outcomes for later cohorts is rising.

Behavioural impact

No.

Comparability

There is not an internationally recognised indicator that can be used to make comparisons.

Collection frequency

Data on referrals and job outcomes is collected from the administrative systems on a monthly basis.

Data is collected from the payment validation team every 3 months.

For further details on job outcome validation procedures see technical annex in the Work Programme background information note.

Time lag

From 27 June 2013, in response to user views and changes in the validation cycle, the Work Programme Official Statistics will be more timely and frequent.

Previously the indicator had a 4 month lag. However from 27 June 2013 it will be published approximately 3 months after the 12 month point.

Time is needed for the administrative systems to be updated, data to be extracted and for post-payment validation procedures to be performed.

For more information on the improvements in the time lag see Work Programme background information note found on the Work Programme Official Statistics homepage.

The official statistics data production team then merge the administrative data and perform cleansing and quality assurance to create an analytical dataset. The validation adjustment factors are finally merged onto the dataset to enable job outcomes to reflect final payments made to providers.

This dataset is then used to produce Official Statistics outputs in the form of the tabulation tool and data visualisation tool; both of which can be viewed via the Work Programme Official Statistics homepage.

For further details on the job outcome validation procedure/timings see technical annex in the Work Programme background information note.

Data source (which data collection it comes from)

Data on referrals are obtained from the Labour Market System (LMS). This is the administrative system JCP uses to administer customer claims and also refer claimants to the Work Programme.

Data on job outcomes are obtained from the Provider Referral and Payment data (PRaP). This is the system which underpins the Work Programme and which providers use to claim job outcome payments.

Data are obtained from the results of routine job outcome validation exercises. For more information on this see technical annex in the Work Programme background information note.

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

The indicator is sourced from Work Programme Official Statistics which are available on the Work Programme Official Statistics homepage.

Users can view the official statistics on this webpage using the following tools:

  • tabulation tool
  • data visualisation tool

The Official Statistics were submitted for formal accreditation to become National Statistics in June 2014. This application is currently being considered.

Robustness and data limitations

The indicator is sourced from Work Programme official statistics which have been developed and undergone quality assurance processes in accordance with UK Statistical Authority guidelines.

For details on data limitations see technical annex in the Work Programme background information note.

Collecting organisation

DWP.

Return format

Unit and format of measurement is: Percentage and caseload (displayed in thousands).

Geographical coverage

GB coverage.

How indicator can be broken down

The indicator can be broken down by payment group and contract.

Payment Groups: Claimants have different entry points to the Work Programme, and different participation requirements, depending on their circumstances, such as age and benefit type. There is a summary of the payment groups and participants’ entry points in the Work Programme background information note.

Contracts: 18 Prime providers have been selected to deliver 40 Work Programme contracts across the 18 contract areas throughout Great Britain. Some organisations are delivering across several contract areas, with seven being the largest number of contracts awarded to one provider. All Prime providers have assembled supply chains involving smaller specialist and local organisations with the expertise and experience to deliver services to participants with a wide range of different needs. Official Statistics published will report information on the number of referrals and attachments to the 40 Prime contracts. Providers remain responsible for the participant if they move out of the Contract Package Area during their time on the Work Programme.

Further geographical breakdowns are available for monthly cohort information via the data visualisation tool which can be viewed on the Work Programme Official Statistics homepage.

For further breakdowns of the Official Statistics see the tabulation tool also available on the Work Programme Official Statistics homepage.

Further guidance

A background information note with technical annex can be found on the Work Programme Official Statistics homepage.

The purpose of this background information note is to provide background and context to the Work Programme Official Statistics. A technical annex provides supplementary information on some of the processes involved in developing and releasing Official Statistics on the Work Programme.

18.3 Further information

DWP research

DWP statistics

DWP Tabulation Tool allows users to create their own detailed tables using the National Database that underpins DWP benefit caseload and client statistics

DWP small area geographical breakdowns

Other Labour Market National Statistics

NOMIS labour market statistics

Neighbourhood statistics

DWP publications and research

Health and Safety statistics

Previous DWP business plan transparency indicator measures on the National Archives