1. Main points

  • Between 2019 and 2022, the economic inactivity rate among the population aged 16 to 64 years rose by just under half (0.45) a percentage point, from 21.23% to 21.68% (these figures are based on the Annual Population Survey, which smooths quarterly trends and therefore differs from headline labour market data).

  • The percentage of people who report a long-lasting health condition that limits either the kind or amount of work they can do rose from 16.4% to 18.1% over the same period.

  • A rise in the prevalence of work-limiting health conditions was the largest contributing factor to the rise in the economic inactivity rate over the period 2019 to 2022, as found by our decomposition analysis.

  • The rise in work-limiting health conditions would have raised economic inactivity by an estimated 0.63 percentage points (138% of the actual rise) if the probability of being economically inactive by age and health status had remained at 2019 values, with the health category "other problems or disabilities" accounting for the majority of this effect.

  • Changes in age structure are estimated to have contributed 0.29 percentage points (63% of the actual rise) to the rise in economic inactivity.

  • Structural and behavioural changes in the labour market (for example, rising cost of living and plentiful job vacancies) are estimated to have brought inactivity down by 0.46 percentage points (negative 101% of the actual rise), leading to a lower rise than expected from changes in health and age alone.

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These are Experimental Statistics. The analysis has been produced by the Office for National Statistics (ONS) for the first time and remains subject to testing of quality, volatility and ability to meet user needs. We advise caution when using the data.

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3. Separating the contributions of health and ageing to the rise in economic inactivity, 2019 to 2022

In this section, we use statistical modelling techniques (decomposition analysis), to explore the relationship between changing population age structures, the rise in work-limiting health conditions, and rising economic inactivity among the working age population between 2019 and 2022.

In the Annual Population Survey, economic inactivity rose by just under half (0.45) a percentage point between 2019 and 2022, from 21.23% to 21.68% of the population aged 16 to 64 years. We decompose this rise according to the contributions from the rise in work-limiting health problems and changes to the age structure of the population. The decomposition analysis aims to understand the factors associated with the change in economic inactivity between 2019 and 2022 and does not provide information on the factors associated with the level of inactivity at a point in time. For more information on the modelling techniques used, please see Section 6: Data sources and quality.

The relative contribution of health, age and other factors in rising inactivity

This analysis estimates that, if the probability of inactivity by age and health status had remained at 2019 values, the rise in the prevalence of work-limiting health problems would have raised economic inactivity by 0.63 percentage points (138% of the actual rise). Additionally, changes to the age structure of the population would have raised inactivity by 0.29 percentage points (63% of the actual rise). Alongside these influences, there are unobserved structural and behavioural changes which are estimated to have brought inactivity down by 0.46 percentage points (negative 101% of the rise). These figures sum to the observed rise of 0.45 percentage points (subject to rounding error), which is lower than predicted by changes in health and age alone because of this downward effect from unobserved structural and behavioural changes.

The downward effect of structural and behavioural changes is consistent with the pre-coronavirus (COVID-19) pandemic downward trend in inactivity continuing, which may have been enhanced since the pandemic by labour shortages and cost of living pressures acting to keep people in the workforce. This underlying downward trend, however, has been more than offset by the larger effects of rising work-limiting health conditions and changes to the age structure of the population, as shown in Figure 3.

The contribution of specific health conditions in rising inactivity

The rise in work-limiting health conditions between 2019 and 2022 is not equal among all health conditions. The largest rise was among people who reported their main condition to be "other health problems or disabilities", meaning their main condition was not covered by the specific categories available in the survey. In 2019, 2.0% of the population reported that they had a work-limiting health problem and chose "other health problems or disabilities" as their main condition. In 2022, this had risen to 2.8%. Conversely, some conditions showed small decreases. For example, there was a decrease in people reporting work-limiting health problems and choosing "problems with arms and hands" as their main condition. This fell from 1.1% to 0.9% between 2019 and 2022.

The decomposition analysis was expanded to understand the contribution of specific health conditions to the rise in economic inactivity. For the purpose of the decomposition analysis, health conditions were grouped into four categories, more details of which can be found in Section 5: Glossary. The four categories included:

  • cardiovascular and digestive problems

  • mental health problems

  • musculoskeletal problems

  • other problems and disabilities (including various small categories)

In line with the rise in its prevalence, "other health problems or disabilities" accounts for the majority of the health effect on economic inactivity. The rise in this category is estimated to produce a rise in economic inactivity of 0.49 percentage points (107% of the total rise in inactivity). Within the decomposition analysis, this category was grouped with some other categories, including progressive illness not elsewhere classified, and learning difficulties and autism (see Section 5: Glossary for more information), meaning the size of the effect is also influenced by these categories.

Smaller portions of the rise in inactivity were attributed to mental health problems (0.14 percentage points, representing 31% of the total rise in inactivity) and cardiovascular and digestive problems (0.06 percentage points, representing 13% of the total rise in inactivity). Changes in the prevalence of musculoskeletal problems were estimated to have brought inactivity down slightly (by 0.06 percentage points, or negative 13% of the total rise in inactivity).

This article has shown a strong link between the observed deterioration in health among the population aged 16 to 64 years and a rise in economic inactivity since the coronavirus pandemic between 2019 and 2022. The impact of health on economic inactivity has not been driven by a marked increase in the inactivity rate of people with work-limiting health problems, confirming the findings of our Worker movements and economic inactivity in the UK: 2018 to 2022 article. Rather, there are simply many more people with work-limiting health problems, a group that already had a much higher economic inactivity rate than those without a work-limiting health problem.

Changes in age structure between 2019 and 2022 have also increased the economic inactivity rate, even after taking account of the effect of worsening health among older people. Finally, structural and behavioural changes in the labour market have brought inactivity down (for example, the rising cost of living and plentiful job vacancies), leading to a lower rise than expected from changes in health and age alone.

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4. Health, demographic and labour market influences on economic activity data

Health, demographic and labour market influences on recent rises in economic activity, UK: 2019 to 2022
Dataset | Released 19 May 2023 Estimates of the links between work-limiting ill health, demographic and labour market changes, and recent rises in economic inactivity, using Annual Population Survey data. Experimental Statistics.

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5. Glossary

Economic inactivity

People not in employment who have not been seeking work within the last four weeks, or are unable to start work within the next two weeks, or both.

Working age population

People aged 16 to 64 years.

Work-limiting health condition

People who self-reported to have a health condition that has lasted or is expected to last 12 months or more, and which limits either the kind or amount of work they can carry out. These questions are asked of all persons aged 16 to 64 years, including those who are economically inactive.

Musculoskeletal health problems

Includes problems or disabilities with arms, hands, legs, feet, back or neck.

Cardiovascular and digestive health problems

This includes:

  • problems with chest or breathing

  • heart problems, blood pressure, asthma, or bronchitis

  • stomach, liver, kidney or digestive problems

  • diabetes

Mental health problems

This includes:

  • depression, bad nerves or anxiety

  • mental illness, phobias, panics

Other problems and disabilities

This includes:

  • progressive illness not elsewhere classified, such as some cancers and Parkinson's disease

  • epilepsy

  • severe or specific learning difficulties

  • autism

  • speech impediment

  • severe disfigurement, skin condition

  • difficulty in seeing

  • difficulty in hearing

  • other problems or disabilities

  • people who did not disclose their health problem

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6. Data sources and quality

Our analysis is based on Annual Population Survey (APS) data for 2019 and 2022. The year 2019 represents the year before the start of the coronavirus (COVID-19) pandemic, while 2022 is the most recently available APS data at the time of analysis. The purpose of the analysis reported here is to separate the underlying effects of health, age structure and behaviour change on inactivity over the coronavirus pandemic period, rather than to report on the most recent quarterly trends. For this reason, we chose annual data as opposed to more recent quarterly Labour Force Survey (LFS) data. Using annual data smooths some quite large changes between quarters and provides a larger sample for analysis by age bands and work-limiting health problems. Published headline labour market indicators are based on quarterly data and therefore differ from annual data.

We used statistical modelling techniques to explore the relationship between the age and health of the population and rates of economic inactivity. Specifically, we performed decomposition analysis of the rise in economic inactivity between 2019 and 2022. We used a form of decomposition analysis based on logistic regression. Decomposition analysis can be used with many types of regression. Logistic regression was used, as it is suitable when looking at categorical outcomes (such as whether someone is economically inactive or not).

Decomposition analysis

Decomposition analysis is a statistical modelling technique used to understand the difference between two groups (for example, men and women, as in gender pay gap studies). In this case, the "groups" are 2019 and 2022, and the technique decomposes the influences on the difference in the inactivity rate in 2022 compared with 2019. The analysis estimates how much of the difference in inactivity rate can be explained by changes in the population's health and age structure between the two years.

Decomposition analysis in this context works by applying the probability of being inactive in 2019 by health status and age group to the health and age structure of the 2022 population aged 16 to 64 years. This identifies the relative contribution of deteriorating health and of changes to the age structure to the rise in economic inactivity. The remaining "unexplained" component of change in economic inactivity over this period is assumed to be attributable to:

  • unobserved structural changes (for example, employment and educational opportunities)

  • behavioural changes arising from the coronavirus (COVID-19) pandemic and changing economic and labour market conditions (for example, cost of living, plentiful job vacancies and changes to working practices).

The decomposition analysis was based on the mvdcmp command in Stata, using logistic regression models for each year with a binary dependent variable capturing economic inactivity (yes equals 1, no equals 0). The code used for this analysis is available on github. Independent variables were:

  • a series of binary (1 or 0) dummy variables capturing work-limiting health status (cardiovascular and digestive problems; mental health problems; musculoskeletal problems; other problems and disabilities; no work-limiting health condition)

  • a series of age range or single-year binary (1 or 0) age dummy variables (reflecting people in the age bands 16, 17, 18 to 19, 20 to 24, 25 to 29, 30 to 34, 35 to 39, 40 to 44, 45 to 49, 50 to 54, 55 to 57, 58 to 59, 60, 61, 62, 63, 64 years)

The reference categories (against which changes are compared) included not having a work-limiting health problem and being aged 30 to 34 years. This age range was used as the reference category because it has the lowest economic inactivity rate. Further information on how these variables were derived is outlined below. The sex composition of the population has changed very little over the three-year period of interest, so it is not included in the analysis.

Logistic regression

Logistic regression is a type of statistical modelling that estimates the probability of an event occurring (such as being economically inactive), based on the observations in a dataset. In this context, logistic regression models were used to predict the probability of being economically inactive based on age and work-limiting health status, based on the Annual Population Survey (APS) 2019 and 2022. These probabilities were used as the 2019 and 2022 inactivity rates in the decomposition analysis described above.

Measuring inactivity

The decision to withdraw from the labour market (or, for younger people, not to enter it) has many contributing factors, including employment and education opportunities, caring responsibilities, and health and financial situation. Therefore, there are limits in what can be inferred from the stated main reason for economic inactivity. For example, health could be an important factor for some who nevertheless report their main reason as retired. To provide a comprehensive picture, we base our analysis on inactivity for any reason.

Measuring health

Not all health problems affect ability to work (or only marginally so). Since our focus is on economic inactivity, we base our analysis on whether or not people say they have a health problem that limits either the kind or amount (or both) of work they can do. The work-limiting questions are asked of all respondents aged 16 to 64 years who say they have a health condition that has or is expected to last over 12 months, including the economically inactive.

In 2020, the Annual Population Survey added "autism" as an additional option when asking respondents about their health conditions. This means people who reported autism in 2022 may have listed another condition prior to this option being available. This means that the effect of "other health problems or disabilities" on economic inactivity between 2019 and 2022 may be overstated, and the effect of the other health categories may be understated. However, the net effect is zero, that is, the overall number of people reporting a work-limiting health condition is not affected. In order to mitigate this issue, we have kept the "other health problems and disabilities" category as broad as possible, by including learning difficulties, for example (for more information see Section 5: Glossary).

Measuring age

Economic inactivity changes rapidly with age for young and older adults. Economic inactivity diminishes rapidly through the age range 16 to 19 years as young adults move from education to economic activity, and again over the age of 55 years as health, financial and family circumstances change. We therefore use single-year ages for 16, 17, and 60 to 64 years when inactivity varies significantly between single ages, and slightly larger bands for 18 to 19 years, 55 to 57 years, and 58 to 59 years. Other age ranges are in five-year bands from 20 to 54 years. We have chosen these age categories to capture age-variation in inactivity, while avoiding comparing single years at age ranges where there is little difference in inactivity between single-year ages.

More quality and methodology information on strengths, limitations, appropriate uses, and how the data were created is available in our Annual Population Survey Quality and Methodology Information (QMI) report.

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8. Cite this article

Office for National Statistics (ONS), released 19 May 2023, ONS website, article, Health, demographic and labour market influences on economic inactivity, UK: 2019 to 2022

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Manylion cyswllt ar gyfer y Erthygl

Donald Houston, Jane Evans and Vahé Nafilyan
health.data@ons.gov.uk
Ffôn: +44 1633 455046