Economic inactivity by qualification level
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- 1. Navigate to Main facts and figures section
- 2. Navigate toEconomic inactivity by ethnicity and qualification level section
- 3. Navigate toEconomically inactive men by ethnicity and qualification level section
- 4. Navigate toEconomically inactive women by ethnicity and qualification level section
- 5. Navigate to Methodology section
- 6. Navigate to Data sources section
- 7. Navigate to Download the data section
1. Main facts and figures
at most qualification levels, White people were less likely to be economically inactive than other ethnic groups
for all ethnic groups, people with lower qualification levels were more likely to be economically inactive than people with higher qualification levels
the differences in economic inactivity between White and other ethnic groups were larger for women than for men
The ethnic categories used in this data
For this data, the number of people surveyed (the ‘sample size’) was too small to draw any firm conclusions about specific ethnic categories. Therefore, the data is broken down into the following 5 broad ethnic groups used in the 2011 Census:
- White (including White ethnic minorities)
2. Economic inactivity by ethnicity and qualification level
|Level 4 and above||11||14||10||9||11||19|
|Below Level 2||26||33||30||35||25||44|
|Other qualifications||21||31||27||withheld because a small sample size makes it unreliable||18||27|
Summary of Economic inactivity by qualification level Economic inactivity by ethnicity and qualification level Summary
3. Economically inactive men by ethnicity and qualification level
|Level 4 and above||7%||5%||5%||7%||8%||9%|
|Below level 2||16%||13%||21%||withheld because a small sample size makes it unreliable||16%||withheld because a small sample size makes it unreliable|
|Other qualifications||13%||11%||16%||withheld because a small sample size makes it unreliable||13%||17%|
|No qualifications||40%||35%||53%||withheld because a small sample size makes it unreliable||40%||34%|
Summary of Economic inactivity by qualification level Economically inactive men by ethnicity and qualification level Summary
4. Economically inactive women by ethnicity and qualification level
|Highest qualification held||All||Asian||Black||Mixed||White||Other|
|Level 4 and above||15%||24%||13%||10%||14%||28%|
|Below level 2||35%||53%||39%||withheld because a small sample size makes it unreliable||34%||55%|
|Other qualifications||32%||54%||39%||withheld because a small sample size makes it unreliable||26%||39%|
|No qualifications||61%||78%||49%||withheld because a small sample size makes it unreliable||59%||70%|
Summary of Economic inactivity by qualification level Economically inactive women by ethnicity and qualification level Summary
The APS is a continuous household survey. Most cases are first interviewed face to face, then later by telephone.
The sample is formed from:
- waves 1 and 5 of the Labour Force Survey, in which selected addresses are contacted every 3 months
- ‘boost’ cases, which are in the sample for 4 waves, spread one year apart
The main source for contacting participants is the Royal Mail postcode address file. However, the NHS communal accommodation list and (in the case of remote parts of Scotland) telephone directories are also used.
All eligible individuals found at a selected address may be interviewed. This analysis only uses data based on responses from the individual concerned or a response on their behalf from a family member.
The complex survey design has been taken into account when calculating confidence intervals.
Confidence intervals for each ethnic group are available in the ‘download the data’ section.
Based on the APS results, it’s estimated that 50% of of White 16 to 64 year olds with no qualifications who are not in full-time education were economically inactive in 2016.
The APS is based on a sample of 16 to 64 year olds, rather than all 16 to 64 year olds in Great Britain. This measure makes reliable estimates of the percentage of people in this age bracket in Great Britain who were economically inactive, but it’s impossible to be 100% certain of the true percentage.
It’s 95% certain, however, that somewhere between 48.7% and 50.4% of all White 16 to 64 year olds with no educational qualifications in Great Britain who were not in full-time education were economically inactive in 2016. In statistical terms, this is a 95% confidence interval. This means that if 100 random samples were taken, then 95 times out of 100 the estimate would fall in this range (that is, between the upper and lower confidence interval). But 5 times out of 100 it would fall outside this range.
The smaller the survey sample, the more uncertain the estimate and the wider the confidence interval. For example, fewer 16 to 64 year olds from the Asian ethnic group responded to the survey than their White counterparts, so we can be less certain about the estimate for the smaller group. This greater uncertainty is expressed by a wider confidence interval, for example of between 53.9% and 62.3% for Asians aged 16 to 64 in 2016.
Statistically significant findings have been determined where the 95% confidence intervals of an ethnic group do not overlap with the value for all ethnicities in Great Britain.
Surveys seek information about a particular group of people – we call this the target population.
Every target population will have a particular age and gender profile – for example, teachers are predominantly female and under 50. Some target populations will also have a regional profile – for example, they might be clustered in a particular part of the country.
Surveys collect information from a random sample of the target population to make generalisations (reach ‘findings’) about everyone within that population.
For those findings to be reliable, the sample of people should ideally contain the same mix of age, gender and regional location as the target population.
Where this isn’t the case (because some people haven’t responded, for example) analysts use statistical tools to ‘weight’ the data. Weighting rebalances the survey responses so they represent the target population more accurately. They can then be used to reach meaningful conclusions.
For the data presented here, the Office for National Statistics population estimates and projections are used as the basis for this weighting process.
The sample of approximately 275,000 is weighted to make it more representative of the spread of ages and the male/female mix in the whole population of 16 to 64 year olds.
Suppression rules and disclosure control
In data covering all ethnic groups combined, estimates based on sample sizes of fewer than 30 people have been suppressed.
‘Suppression’ means these figures have not been included in the data, because the numbers involved are too small to draw any reliable conclusions.
For data broken down by ethnic groups, estimates based on sample sizes under 100 have been suppressed (shown by a question mark instead of data in the tables and graphs on this page).
Breaking the figures down by gender and the relatively smaller overall population of those who are unemployed reduces sample sizes further and makes the figures less reliable.
Figures have been suppressed at some qualification levels for the Mixed and Other groups because of the very small numbers. N/A
Estimates in the charts and tables are rounded to whole percentages. Estimates in the download file are rounded to 1dp.
Further technical information
6. Data sources
Type of data
Type of statistic
Office for National Statistics
Note on corrections or updates
Higher-level figures may differ from those published by the Department for Work and Pensions and the Office for National Statistics that use the Labour Force Survey.
Purpose of data source
The Annual Population Survey (APS) is the largest ongoing household survey in the UK and covers a range of topics, including:
- personal characteristics
- labour market status
- work characteristics
The purpose of the APS is to provide information on important social and socio-economic variables at local levels, such as labour market estimates.
The published statistics also allow the government to monitor estimates on a range of issues between censuses.