Local authority quality of population estimates for England and Wales

Mid-year population estimates (MYEs) are some of the most widely used and valued statistics produced by the Office for National Statistics (ONS). They measure the usually resident population: people who live in a specific country continuously for 12 months or more. MYEs provide a denominator to enable the production of other important statistics produced by the ONS, and at a national and local level, they support decisions about public services, funding, planning and policy.

Producing estimates for local authorities is more challenging than national level estimates. Feedback from users, reinforced by the Office for Statistics Regulation’s review of Mid-year population estimates for England and Wales, has highlighted the need for the ONS to improve the prominence and clarity of how it communicates the uncertainty that this challenge can create. This includes describing uncertainty to users, as well as helping them to interpret the uncertainty, while using the estimates in decision making. This article focuses on uncertainty as a measure of quality, which forms part of accuracy and reliability in the ONS definition of quality.

This article explores where statistical uncertainty can be introduced, and quantified, in the population estimates process. This is primarily through migration, with higher rates of international and internal migration leading to higher levels of uncertainty around an area’s population estimates. Some user uncertainty cannot be quantified. This can come from potential future revisions, measuring specific definitions of the population, and comparisons between the official estimates and other data sources.

We have published this supporting methodology article alongside our Population estimates for England and Wales bulletin. To accompany the bulletin, we have also published:

• our Mid-year population estimates for England and Wales quality and methods guide

• our Population statistics and sources guide

• our Improving population estimates for England and Wales article

• our Subnational long-term international migration methodology article

• our Effect of the coronavirus pandemic on the quality of mid-year population estimates for England and Wales article

This section describes the methods used to produce population estimates, which helps to understand where uncertainty can arise and how we measure it.

The cohort component method

We produce population estimates for England and Wales, and the local authorities within them, using the cohort component method. This method is widely used internationally to estimate the size of the population. It starts with the estimate of the population from the census as its base and builds up more recent estimates using components of population change (for example, births, deaths and migration) over the period from 1 July to 30 June.

A major strength of this method is that it is consistent over time and across geographical areas, and the published statistics for components of population change are coherent with the population estimates. It uses the highest quality data available to inform the components of population change.

The cohort component method starts with the previous year’s population estimate and then follows six processes:

1. Add one year to the age of everyone in the resident population from the previous year.

2. Add children born between 1 July and 30 June as the population aged zero years.

3. Remove people who have died between 1 July and 30 June.

4. Add or remove people who have entered or left England and Wales from outside the UK between 1 July and 30 June.

5. Adjust areas' populations to account for those that have moved within the UK between 1 July and 30 June.

6. Update the population for changes in special populations (prisoners and armed forces) not captured in points 1 to 5.

Measuring uncertainty in population estimates

While every effort is made to produce the most accurate population estimates possible, there is uncertainty associated with estimates. This can come from the base (census estimate for England and Wales) and from the measurement of each component of population change.

We produce a number of uncertainty estimates. Our preferred approach is bias-adjusted confidence intervals, which are used throughout this article. In simple terms, bias-adjusted confidence intervals represent a range of values within which we think the true value sits, reflecting the potential under or over estimation of the true value. You can find out more about what they are and how to interpret them in our methodology for interpreting statistical measures of uncertainty in local authority mid-year population estimates (MYEs).

To calculate the confidence intervals, we run the cohort component method 1,000 times, using different plausible values for the main sources of uncertainty – the census base and internal and international migration. This produces a range of possible population estimates. The uncertainty estimates are rolled forward each year so that uncertainty carried forward, and uncertainty introduced in the year, are reflected. Read more on how we calculate uncertainty in our Methodology for measuring uncertainty in Office for National Statistics local authority MYEs.

In Figure 1, our latest uncertainty measures show the increasing levels of uncertainty around population estimates as we move away from the previous census. This reflects the decreasing influence of the census base and the increasing influence of measures of migration on the population estimates.

In 2021, over 90% of local authorities had population estimates with uncertainty ranges of less than 5% of the mid-year estimates; by 2025 only around 50% of local authorities have population estimates with uncertainty ranges of less than 5%.

Figure 1: The level of uncertainty in population estimates increased through the decade

Bias-adjusted 95% uncertainty interval range, as a percentage of the mid-year population estimates, England and Wales, 2021 to 2025

This section outlines where quantifiable uncertainty arises in the cohort component method and what this means for interpretation of the estimates. Data on births and deaths come from registration data and contribute very little uncertainty. International and internal migration are harder to measure precisely at a local level and are therefore the greatest contributors to uncertainty.

Local authority estimates are more uncertain than national estimates because they need to estimate not only how many people enter and leave the UK, but also where those people live and how people move between local authorities.

Ageing on from the census

The first step is to move everyone in the previous year’s estimate on by one year of age. This is a straightforward step and does not add much uncertainty by itself. However, because each estimate builds on the previous year, any uncertainty in the starting population can be carried forward into later estimates.

The current estimates use Census 2021 as their starting point. Census 2021 provides high-quality information about the population at that time, but it took place during the coronavirus (COVID-19) pandemic. We have taken steps to account for changes since then, including students returning to study, higher international migration, and increased movement within the UK. Some effects and uncertainty arising from the COVID-19 pandemic will remain. Read more on this in our Effect of the coronavirus pandemic on the quality of mid-year estimates for England and Wales article.

Births and deaths

Natural change is the difference between the number of births and deaths in an area. In the population estimates, children born between 1 July and 30 June are added to the population aged zero years, and people who died during the same period are removed.

Births and deaths are measured using civil registration data, which are high quality and provide reliable information on where and when these events took place. For this reason, the uncertainty introduced to local authority population estimates by natural change is negligible.

International migration

International migration adds people who have moved to England and Wales from outside the UK and removes people who have left to live abroad. These estimates count long-term migrants: people who move for 12 months or more (in line with the United Nations definition of a long-term migrant).

Estimating international migration at national level is challenging but estimating it for local authorities is more difficult. National estimates need to estimate how many people enter and leave the UK. Local authority estimates also need to estimate where those people live within England and Wales.

The data sources used for international migration do not include information on where people go within the UK. To produce local authority estimates, we use other administrative data sources to estimate where people go, which includes making adjustments and assumptions to reduce the impact of data limitations. Read more in our methodology for producing subnational long-term international migration estimates.

This approach helps produce the best possible local estimates, but it also adds uncertainty. Therefore, local authorities where international migration makes up a larger share of population change have a greater level of uncertainty around their estimates.

International migration estimates are initially published using the best information available at the time. These provisional estimates may later be revised when more complete data show whether people stayed in, or left, the UK for at least 12 months. These revisions improve the estimates but can create uncertainty for users in how they should use the provisional estimates if they are likely to be changed a year later.

Figure 2 shows the percentage of uncertainty for local authorities in England and Wales, and the amount of international migration as a percentage of the population estimate. It shows that local authorities with higher levels of international migration generally have more uncertainty associated with the estimate. This does not mean the estimates are wrong, but users should be aware of, and consider, the uncertainty around the estimates when using them to inform any local decision making.

Figure 2: Higher levels of international migration, into and out of local authorities, is associated with higher levels of statistical uncertainty in population estimates

Statistical uncertainty as a percentage of population estimate, percentage of local authority population consisting of international migration, local authorities in England and Wales, 2025

Notes:

1.The local authority City of London is an outlier because of its small population size.

Internal migration

Internal migration measures movement of people between areas within the UK between 1 July and 30 June.

Internal migration is primarily estimated using the NHS Personal Demographics Service (PDS) to compare people's area of residence in consecutive years. Any changes of location are known as “transitions”. However, not all transitions will be captured by administrative data, or will be captured in later years, introducing uncertainty into the estimates.

For students, we also use Jisc’s Higher Education Statistics Agency (HESA) data and make further adjustments through the Higher Education Leavers Methodology (HELM) to better account for changes in location of higher education leavers. Estimating internal migration of students and higher education leavers is explored further in Section 4: Case studies of quantifiable uncertainty.

The PDS data used for transitions cannot capture “within-year moves” that happen between the two mid-year time points, for example:

• if people move multiple times in a year

• if babies move within the year they are born

• if people die or emigrate before the end of a year

Different weekly data can capture within-year moves, as these data are more timely compared with the annual stock file.

The relationship between moves and transitions is used to adjust our initial transitions to better account for all moves within the reference period. This scaling process can introduce uncertainty, as it might not reflect all population groups. For example, young populations are likely to be healthy and therefore interact with healthcare services less regularly. They are also likely to be more mobile and move multiple times. This could mean that they do not appear on PDS data as accurately, and subsequently their moves may not be captured in our data.

Cross-border flows between England and Wales and Scotland and Northern Ireland are agreed between the ONS, National Records of Scotland (NRS) and Northern Ireland Statistics and Research Agency (NISRA). Differences in data sources and methodology for estimating migration between countries can contribute additional uncertainty when producing coherent UK-wide estimates.

Figure 3 shows the percentage of uncertainty for local authorities in England and Wales and the amount of internal migration as a percentage of the population estimate. It shows that local authorities with higher levels of internal migration generally have more uncertainty associated with the estimate. This does not mean the estimates are wrong, but users should be aware of, and consider, the uncertainty around the estimates when using them to inform any local decision making.

Figure 3: Higher levels of internal migration, into and out of local authorities, is associated with higher levels of statistical uncertainty in population estimates

Statistical uncertainty as a percentage of population estimate, percentage of local authority population consisting of internal migration, local authorities in England and Wales, 2025

Armed forces and prisoners

We adjust the population estimates for some population groups that are not captured by standard migration estimates: members of the armed forces and prisoners. These special populations have specific, relatively stable age structures, so are not aged-on with the rest of the population.

We assume that movements of prisoners, UK and foreign armed forces (and their dependants) are not captured by the data sources used to estimate international and internal migration.

We receive specific data for military and prison populations, so these groups introduce limited uncertainty into our population estimates, but they do affect the demographics of the areas where prisons, or military installations, are located. More information on how we handle these populations can be found in our accompanying quality and methods guide.

This section includes three case studies that demonstrate how different local authority (LA) characteristics can make estimation more challenging and therefore introduce uncertainty.

Students and higher education leavers

Populations of students and recent graduates are challenging to estimate accurately because they are often young and highly mobile. These groups might be slow to update their GP registration, meaning that we may not identify all the moves into or out of student or graduate areas.

To improve estimates, we use additional administrative data (Higher Education Statistics Agency) and methods (Higher Education leavers Methodology) to identify where students are living while studying and where graduates are likely to move after leaving higher education.

Population estimates for areas with large student populations are typically more uncertain. This is because measuring how students and graduates move between areas requires additional data and assumptions, creating more opportunity for uncertainty to be introduced.

Ceredigion is a useful example because its population is more affected by moves within England and Wales than by international migration. It also has a large student population, with students making up 10% to 20% of the local authority population, according to our population estimates quality indicators. This means estimates for Ceredigion rely more heavily on HESA data and the Higher Education (HE) Leavers Methodology, which adds uncertainty because they involve extra data sources, adjustments and assumptions.

Figure 4 shows the composition of internal migration flows for Ceredigion in mid-2025 by age. Although most moves are captured by changes in GP registrations, in Ceredigion, a high proportion of moves is captured using HESA data and the HE Leavers Methodology to account for flows of students that we estimate are missed in GP registrations. This is an assumption, rather than an observation, introducing uncertainty, particularly around student and graduate ages. Read more on the use of HESA data and the HE Leavers Methodology in our accompanying quality and methods guide.

Figure 4: Areas with proportionately larger student populations are subject to more uncertainty

Contributions of the three elements of internal migration to Ceredigion’s internal migration estimates, by single year of age, 2021 to 2025

For local authorities, this highlights the importance of understanding the characteristics of their population, how they are measured and the uncertainty this can introduce, all of which should be considered when using population estimates to inform decision making. Local authorities should recognise that areas with large student populations are inherently more challenging to measure and may be more affected by future revisions as new information becomes available.

London

London presents specific challenges for estimating the population because it experiences high levels of both international and internal migration.

As the UK's capital city and largest economic centre, it attracts large numbers of international migrants for employment, higher education and other opportunities. This means international migration contributes more to population change in London than in most other areas of England and Wales. The larger volume of people arriving, departing and moving within the region creates more opportunities for migration to be missed or for delays in the updates to administrative data to occur or be estimated using assumptions. This increases overall uncertainty in population estimates.

High levels of connectivity between boroughs means that people frequently move internally across LA boundaries. Some uncertainty arises because of the increased number of internal moves over short distances. Moves over short distances are less likely to be reflected in administrative data as individuals may move without updating records, such as GP registrations, as it seems less important or necessary. This is compounded by London's relatively young and mobile population, who may be less likely to update administrative records after moving. As internal migration estimates rely on PDS data, some moves may not be captured, introducing more uncertainty compared with moves across larger distances.

London also has higher numbers of short-term residents and transient populations compared to other regions in England and Wales, as described in our article on the changing picture of short-term international migration. These populations still require services but do not meet the usual residence definition. Therefore, their presence can make it more difficult to interpret how well the estimates reflect local population dynamics. Read more about populations not measured and how they introduce uncertainty in Section 5: Unquantifiable uncertainty.

Figure 5 shows the combined migration (international and internal) as a percentage of the population for local authorities in England and Wales. The London region has higher combined migration, particularly inner London. This is why there may be increased uncertainty in estimates for London local authorities.

Figure 5: London, particularly inner London, has high levels of migration, leading to greater uncertainty around population estimates

Combined migration (international and internal) as a percentage of population, local authorities of England and Wales

Population churn and uncertainty

Many local authorities within England and Wales are subject to higher levels of churn from both international and internal migration. Population churn refers to the total number of moves into and out of an area. These areas have higher levels of uncertainty than areas where there is just one, or no particular, cause for change. Read more in Section 7: Definitions.

This can be illustrated by comparing two local authorities with populations of similar sizes, but different amounts of migration and, as a result, uncertainty around their estimates.

York and Calderdale each have a population of around 210,000. In York, however, internal migration makes up 10 to 20% of the population and international migration makes up 2 to 5%. In Calderdale, a local authority with a similar sized population, internal migration makes up 5 to 10% of its population, and international migration makes up 0 to 2%.

Figure 6 shows the populations of these two areas, and the upper and lower limits of their bias-adjusted confidence intervals, from 2021 to 2025. The widening of York’s confidence interval relative to Calderdale’s highlights greater uncertainty introduced over time by the higher churn.

Figure 6: Different rates of migration can lead to different levels of uncertainty in areas with similarly sized populations

Population estimates with 95% confidence intervals for Calderdale and York, 2021 to 2025

Alongside differing levels of quantifiable, statistical uncertainty, there can be additional uncertainty that cannot be quantified, such as uncertainty from:

• planned revisions

• populations we are measuring

• comparisons with other data sources

Revisions

The methods used to produce population and migration statistics make the best use of available data to produce the best possible statistics at a point in time. Population estimates are first published and then revised to:

• incorporate more complete, corrected or new source data that were not available at the time of the initial publication

• capture routine recalculations, such as revising provisional international migration estimates and/or replacing them with final estimates

• replace assumptions about expected behaviour (such as whether an international migrant is expected to stay or leave the country for more than 12 months) with actual data

• reflect improved methodology, such as changes in statistical methods or systems and changes in concepts, definitions and classifications

Planned revisions should not be confused with corrections to errors in released statistics, which are genuine mistakes corrected in a timely manner, announced and clearly explained to users. More information can be found in our Revisions policy.

In the latest published estimates, we have revised estimates for mid-2022, mid-2023 and mid-2024 to use updated data for international migration. Revisions to mid-2024 local authority population estimates are larger for children than for adults.

Figure 7 shows population estimates for Cambridge back to 2011. From 2011, the original, rolled-forward estimate is low. Between censuses, estimates can "drift" away, in either direction, from the number that is produced at the next census. This is expected and we investigate the size and causes of the drift, and rebase our population estimates accordingly. Rebasing occurred for our 2021 estimates, as is standard practice following a census, and shows the estimate for Cambridge should have been higher throughout the decade.

Since 2021, the estimates have been changed in the 2024, 2025 and 2026 releases, as revisions occur and are backdated, and as improved, higher-quality data become available. Although all the revised estimates are within the bias-adjusted confidence intervals of previous estimates, this can create uncertainty for users, as they must use estimates to make important decisions, which they know might be revised in future.

Figure 7: Revisions to local population estimates can introduce uncertainty that we are unable to quantify

Revisions to mid-year population estimates for Cambridge, 2011 to 2025

Short-term populations

The mid-year population estimates are consistent with the standard UN definition for population estimates, which is based upon the concept of usual residence and includes people who reside, or intend to reside, in the country for at least 12 months, whatever their nationality. Visitors and short-term migrants (who enter or leave the UK for less than 12 months) are not included.

Engagement with local authorities has highlighted that having an additional estimate of the total population (including those present at a particular time) would better meet their needs. In many cases, users of estimates are interested in the population that is likely to use local services. This includes short-term residents and visitors, as well as a usually resident population. This means that the estimates we produce may not align with how population is experienced locally.

On an annual basis, the presence of short-term populations in an area introduces unquantifiable uncertainty for users. We only record short-term residents through a decennial census that provides a unique opportunity to research this population and how they engage in society in more detail.

Their presence can make it more difficult to interpret how well the estimates reflect local population dynamics. This is particularly the case in areas with large numbers of temporary residents or people who spend considerable time both within and outside England and Wales, where it may be less clear how many usual residents there are.

We published a working paper on estimating population by time of day, which used an experimental method to produce daytime population statistics for 14 local authorities. We are building on previously published work in our Population and migration estimates – exploring alternative definitions: May 2023 article, to continue to develop methods for measuring international temporary mobility.

Comparisons with other data sources

Our population estimates can be compared with other data sources. Sometimes this can also reduce user confidence. For example, users have raised concerns around the estimates of populations of children because of the relationship between mid-year estimates (MYEs) and the Department for Education (DfE)’s Schools, pupils and their characteristics data.

Following Census 2021, we rebased the MYEs. Some of the estimates for school-aged children, particularly those aged 5 to 11 years, appeared to be lower than the DfE’s data nationally, and in some local authorities, at points between 2016 and 2026. At the national level this is unusual, as the MYEs should be slightly higher than the DfE data. When this is not the case, it understandably leads to questions about our estimates.

It can be difficult to directly compare the numbers in the schools, pupils and their characteristics data to the MYEs, as we would expect there to be some differences between them. This is because they have different reference points, have different residency requirements, and aim to measure different populations. The DfE data seek to count all pupils in school in January, whereas the MYEs estimate the number of usually resident children in the country on 30 June each year. The DfE data will also include some short-term residents that the MYEs would exclude, increasing counts compared with the MYEs. In contrast, the MYEs will include children in elective home education or missing education, increasing numbers relative to the DfE data.

At a local level, the data for children in independent schools can create additional differences. The schools, pupils and their characteristics data reflect the location of the school, as opposed to the MYE which should estimate the population based on a child’s area of usual residence. This can complicate comparisons as some children will attend independent schools in different local authorities to where they live. These inconsistencies in what is being measured, as well as the differences in the numbers, can create uncertainty for users as they try to determine which are the best estimates for them to use.

Local authorities use data on children to inform local planning activities, such as decisions about provision of school places or forecasting school closures. When available data sources are not aligned, local authorities may find it difficult to have confidence in using population estimates to assess future need and make evidence-based planning decisions. Understanding where and why differences remain is therefore important for supporting confidence in population estimates and their use in local decision making.

We are continuing to work with the DfE to better understand the source of the differences, especially where our population estimates appear lower than their published data. We are working on the best ways to address these differences in the programme to continuously improve our population estimates.

We have published an improvement plan that describes how we will continuously improve population estimates over the next two years. The plan focuses on reducing the accumulation of uncertainty and error while improving transparency about the quality of the estimates.

One of our main priorities in the improvement plan for population estimates is improving estimates for local authorities, where uncertainty is higher than at the national level. Research is focused on the two components that contribute most to this uncertainty: international and internal migration. Improvements will be supported by greater use of administrative data, stronger quality assurance and testing new methods.

Alongside improvements to population estimates themselves, we will develop new approaches to validating estimates in between censuses. This includes a wider range of data sources to compare against population estimates and to identify where estimates diverge from other indicators of population change. This will help us better understand and communicate uncertainty, particularly for population groups and local authorities where estimating the population is most challenging. We intend to share these findings to provide greater transparency and to support the appropriate use of the statistics in decision making.

We will continue to work with local authorities and others to test improvements and understand their impact on the quality of population estimates. This will help ensure future developments address user needs and improve confidence in the estimates.

Administrative data

Collections of data maintained for administrative reasons, for example, registrations, transactions, or record-keeping. They are used for operational purposes, and their statistical use is secondary. These sources are typically managed by other government bodies.

Population churn

Population churn refers to the total number of moves into and out of an area. More rural areas tend to be more accurate than urban areas, as the populations experience much lower levels of churn.

Cohort component method

A standard demographic approach that estimates the size of the population using the components of population change to update a population base.

Components of change

Components of change are the factors that contribute to population change. This includes births and deaths (referred to as natural change) and net migration. Migration includes movements of people between the UK and the various countries of the world (international migration) and between local authority areas within the UK (internal migration).

Internal migration

Internal migration describes moves made between local authorities, regions or countries within the UK. Unlike international migration, there is no internationally agreed definition.

International migration

International migration (also called long-term international migration) statistics estimate the flow of people to and from the UK. This article uses the United Nations-recommended definition of a long-term international migrant, which is: "A person who moves to a country other than that of his or her usual residence for a period of at least a year (12 months), so that the country of destination effectively becomes his or her new country of usual residence".

Mid-year population estimates

Population estimates are produced using the cohort component method. This is a standard demographic method that estimates the size of the population using the components of population change to update a population base. These are accredited official statistics.

Short-term migrant

A person who moves to a country other than that of his or her usual residence for a period of at least three months but less than a year (12 months), except in cases where the movement to that country is for purposes of recreation, holiday, visits to friends or relatives, business, medical treatment or religious pilgrimage.

Natural change

The difference between births and deaths. If natural change is greater than zero, then there have been more births than deaths. If natural change is negative, then there have been more deaths than births.

Rolled-forward estimates

The practice of using the population estimate from the previous reference date as the starting point for estimating the population at the current reference date. The previous population estimate is aged on, and data on births, deaths and migration are used to reflect population change during the reference period.

Usually resident population

These data estimate the usually resident population. The standard United Nations definition is used, including only people who reside in a country for 12 months or more, making them usually resident in that country. As such, visitors and short-term migrants are excluded.