UNDERSTANDING SOCIETY
Putting the Place into Public Services
Please note: For optimal experience, we recommend browsing maps on desktop.
This year’s Understanding Society draws on 8 MRP models, to take a granular view of public satisfaction with public services and shed light on how the government can best channel its efforts to deliver meaningful improvements and keep the public's trust.
Each MRP map in this publication presents estimates of net satisfaction ( the % satisfied minus the % dissatisfied) with a public service or aspect of local areas in each lower tier local authority in the UK. A guide on how to interpret the maps is given below.
How to interpret MRP estimates
The quality of MRP estimates will depend on the quality of:
The survey data used to measure public opinion. The starting point for our models is a survey of over 20,000 UK adults aged 16+ interviewed in September 2024 via Ipsos UK’s online random probability KnowledgePanel.
The population statistics used to create a post-stratification frame. We have used the 2021 Census in England, Wales and Northern Ireland, and the 2022 Census in Scotland.
The MRP models, including which variables are and are not included. The models are based on differences by demographics such as age, gender, ethnicity, working status, housing tenure, children in the household, education, and local and regional characteristics such as ONS output area classification. Other factors which might also have an impact on local perceptions are not taken into account (e.g., the models do not include ‘real world’ service outcome inputs or political variables and are only intended to estimate local perceptions of public services based on demographics and area).
As the modelling makes use of a national survey, caution should be taken when looking at estimates for individual local authorities. While MRP is good at taking into account the different demographic profiles of each local authority, with relatively few respondents per local authority, it is unlikely to capture the full local context, e.g. where a public service is over or underperforming substantially due to specific local factors, the model will not be able to take all of these into account.
Furthermore, as with any survey approach there will be margins of error in each estimate. These will be wider than an equivalent survey as there is uncertainty from the modelling as well as the normal survey-based margins of error. The full data, including margins of error, can be downloaded here. Further details are given in the technical note but an average margin of error is 12 points. We would encourage readers to not place too much certainty into specific point estimates.
