Quick guide to published tables and results for financial year ending 2022
Updated 26 January 2024
Applies to England, Scotland and Wales
There are two types of tables presented in this publication:
- contains statistics related to the caseload measure of take-up.
- contains statistics related to the expenditure measure of take-up.
The following illustrations are intended as a guide to interpreting the tables for each benefit. Data for financial year ending (FYE) 2010 to FYE 2016 data does not appear here but it is in the main tables. Some FYE 2021 figures are missing as data was not published for this year due to data issues following the coronavirus (COVID-19) pandemic. Missing data is represented by [x] within the guide.
Methodological refinements have been applied to the data from FYE 2017. Though not shown in this guide, there is a dashed grey line in the tables to indicate this.
The examples below are for Pension Credit but the same principles apply for Housing Benefit (for pensioners) tables.
1. Understanding tables presenting caseload take-up statistics
Key parts of a Take-up caseload table
Pension Credit Overall, Guarantee Credit and Savings Credit only columns – these different columns are to be used to compare statistics for different demographic groupings or benefit components. They will be different on other Pension Credit tabs and the Housing Benefit tables.
Number of recipients - this shows the average number of recipients across the year (in private households) based on the Department for Work and Pensions (DWP) administrative sources (data on numbers of recipients are published monthly for some benefits, quarterly for others).
Estimated number of entitled non-recipients (with range) – this shows the estimate of people who did not claim the Pension Credit benefit, that were entitled to, based on Policy Simulation Model (PSM) data
Estimated caseload take-up (with range) – this shows estimated take-up percentages. The first number shows the central estimate, with the data in brackets showing the lower bound and upper bound estimates.
An annotated table to help with understanding caseload take-up statistics
2. Understanding tables presenting expenditure take-up statistics
Key parts of a Take-Up expenditure table
Pension Credit Overall, Guarantee Credit and Savings Credit only columns - these different columns are to be used to compare statistics for different demographic groupings or benefit components. They will be different on other Pension Credit tabs and the Housing Benefit (for pensioners) tables.
Mean weekly amount claimed - this shows the average weekly amount of benefit actually received (by those in private households) based on DWP administrative sources (data on numbers of recipients and amounts received are published monthly for some benefits, quarterly for others).
Mean weekly amount unclaimed and Median weekly amount unclaimed - these averages are used to present a picture of what the ‘typical’ unclaimed amounts are. Mean (average) amounts unclaimed alone may present a distorted picture of the typical amount where they are affected by small or very large values. Presenting the median alongside the mean in this way helps present a more balanced picture of the typical amounts unclaimed. These values are based on PSM data.
The top half of an annotated expenditure table to help with understanding expenditure take-up statistics
Total amount claimed - This shows the total amount of Pension Credit received (by those in private households) over the course of the year based on DWP administrative sources.
Estimated amount unclaimed (with range) – this shows the total amount of Pension Credit estimated to have been left unclaimed, based on PSM data.
Estimated expenditure take-up (with range) – this shows estimated take-up percentages. The first number shows the central estimate, with the data in brackets showing the lower bound and upper bound estimates.
The bottom half of an annotated expenditure table to help with understanding expenditure take-up statistics
Note, our statistical practice is regulated by the Office for Statistics Regulation (OSR).