A guide to unemployment data

During 2020 shocking unemployment statistics hit the headlines. This includes the largest single monthly increase in recorded unemployment – 850,000 new claimants across the UK  (equal to an increase of 66%) between March and April. This resource takes a look at the data behind the headlines. We explore the origins of unemployment data and what it can and can’t be used for. 

Neighbourhood level unemployment data is available in our tool, Local Insight (alongside more than 1,000 other indicators). If you would like to find out more about unemployment in your service patch, sign up for a free demo. 

The definition of unemployment

The definition of unemployment is  those people without a job who have been actively seeking work within the last four weeks and are available to start work within the next two weeks. 

When we look at the unemployment rate, this is not based upon the proportion of the total population. Instead, it is the proportion of the economically active population.

The economically active population – sometimes referred to as the labour force, refers to those currently in work plus those seeking and available to work.  

This means those that are economically inactive are excluded from these figures entirely. For example, people who are neither in employment nor unemployed, such as those looking after a home and family, long- or short-term sick, injured or disabled people, or those who are retired.

National unemployment data

National unemployment figures in the headlines.

National unemployment figures are based on a self-reported, national survey called the ‘Labour Force Survey’ that the Office for National Statistics publishes. These are the official, national unemployment statistics.

The ONS collects and publishes data on a monthly basis. There is a two-month time lag between when the publication date and the timepoint it refers to. For example, a January release will cover unemployment up to the previous November. 

The data also refers to the unemployment rate over a three month period. So in the example above, the figure will be for unemployment from September to November.

National unemployment data is great for… 

• Comparing across different countries:  The national unemployment figures allow us to compare data across different countries, as the definition of unemployment used is synonymous to that used by the International Labour Organisation. 
• Capturing individuals that don’t claim benefits: As this is a self-reported survey, it will include individuals that meet the definition of unemployment but that do not claim unemployment benefits for whatever reason. This may be out of choice or due to sanctions or comprehension barriers.

National unemployment data isn’t suitable for… 

• Providing local detail: While these statistics are useful for giving us a high-level view of the nation, they don’t tell us what is happening at a local level. There is a lot of variation between (and within) local authorities that these figures mask. The sample size is far too small to analyse for local authorities.  Model-based estimates are available, but the resulting figures do not look like they reflect the known reality. In fact, some local authorities have seen a reduction in unemployment since March 2020 according to these figures.
• Looking at subsets of population: Survey data gathers information from a sample rather than a whole population. The sample aims to be as accurate as possible. However, there is some degree of uncertainty, so they should be considered estimates. This is particularly true if you were to look at these figures for a subset of the population (say a certain age bracket). The pool of participants will be significantly smaller and so will be less reliable. 

Local unemployment data

Local unemployment figures in the headlines

The Department of Work and Pensions publishes more granular data on unemployment.

The Jobseeker’s Allowance (JSA) & Universal Credit indicator gives the most comprehensive figures at a local level on those that are currently unemployed. You may also see this referred to simply as the ‘claimant count’.  This figure is a combination of JSA claimants and a subset of Universal Credit claimants, which covers those that are required to seek work and be available for work.

Whereas the national figures use a self-reported survey, this data is administrative data. Administrative data is data that has been collected by government for other non-statistical reasons. In this case, the JSA & Universal Credit indicator is a record of who is currently in receipt of unemployment benefits.

As well as the overall ‘claimant count’ – this data is also available for different subsets of the population.  For example, youth unemployment and other age and gender breakdowns. 

Local unemployment data is great for…

• Seeing changes over a relatively short period of time: One of the most useful things about this dataset is how often it is published. The DWP publishes unemployment data on a monthly basis, with only a one month lag. This was particularly useful during the COVID pandemic. It  meant there wasn’t a huge waiting time to see the initial economic impacts of the pandemic and lockdown. This allowed researchers and policy makers to identify areas that were in need of most additional support.
• Providing local detail: The JSA and Universal Credit indicator is also published at neighbourhood level. In this case the data is published at LSOA level (see our guide for more information on understanding statistical geographies). This means we can get a good insight into how unemployment differs across smaller neighbourhoods and see local nuance.
• A robust data sample: As this dataset is collected as administrative data, it is not reliant on a survey sample. This makes the dataset robust and reliable to use at small area level. It provides a 100% sample of the number of people in a neighbourhood claiming a benefit

Local unemployment data isn’t suitable for… 

• Measuring those that don’t claim benefits:  The JSA & Universal Credit indicator excludes those that fit the definition of unemployment but that do not claim unemployment benefits. This could be individuals that are in the process of navigating the application process, individuals that are not aware of what they are entitled to, or those currently unable to claim benefits due to sanctions. For these reasons, it is likely the true unemployment figure is higher than reported figures. It also means those being excluded are potentially among the most vulnerable – as they may face limited access to funds or support to get back into employment.

Universal credit

The Universal Credit rollout has also caused some additional challenges in interpreting unemployment figures.

Universal Credit is a combined benefit payment for those in and out of work. It replaces some of the benefits that until recently were individual benefits. For example, housing benefit, child tax credit, income support, jobseekers allowance and working tax credits.

The full-service roll out was completed in 2018. However, the managed migration of existing benefit claimants was delayed by the pandemic and is not yet complete. It is largely the long-term unemployed that still receive JSA payments. 

The total number of people still in receipt of Jobseekers Allowance is in fact quite low. Currently 264,000 of just under 2.25 million unemployment benefit claimants are in receipt of JSA – around 11% of claimants.

Challenges of interpreting Universal Credit data

Alignment of Universal Credit and  JSA requirements

The different aspects of Universal Credit are broken down into different ‘conditionality regimes’. The ‘Searching for Work’ group relates to unemployment. Unfortunately, this does not overlap perfectly with JSA.

The ‘Searching for Work’ group also includes those that have not previously been eligible for JSA, such as:

• Those with a work-limiting illness awaiting a Work Capability Assessment
• Individuals currently on low incomes that work less than 16 hours per week. This replaces working tax credits.

This has led to some challenges in accurately being able to measure change over time.  This now includes an extra group of people.  There also isn’t any way of distinguishing between the different groups.

Universal Credit rollout is happening in different places at different times

New claimants are automatically assigned to Universal Credit. However,  different places have a different approach to migrating those on ‘legacy benefits’ (in this case JSA) to the new Universal Credit system. So, areas with a higher rollout may show falsely higher figures.

Turn2Us have an excellent guide on the timetable for benefit changes over the years.

Incorrect classifications in the wake of COVID-19

The pandemic has thrown up some additional challenges for interpretation.

There was (and continues to be) a huge surge in the number of Universal Credit applications in the wake of mass redundancies. Applying to Universal Credit involves the individual making their claim, somebody assessing the claim and then assigning the applicant their ‘conditions.’

The condition relating to unemployment is the ‘Searching for Work’ group. However, the huge surge in numbers has meant that assessments haven’t been as thorough. There is evidence that this has led to some incorrect classifications – for example, individuals being added to the ‘Searching for Work’ group, rather than the ‘No Work Requirements’  group. This group covers those people that are not expected to enter the labour market, due to health reasons, for example.

Some furloughed groups are also getting rolled into the Universal Credit figures as well as the figures for the Coronavirus Job Retention Scheme. Lack of contact with work coaches during the lockdown and a backlog of cases has meant that the conditionality statuses have not been updated as quickly as prior to the pandemic. Some individuals who made an application for UC before the Furloughing scheme was in place have been double-counted.

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