- 1. Navigate toMain facts and figures section
- 2. Navigate toThings you need to know section
- 3. Navigate to By ethnicity section
- 4. Navigate to By ethnicity over time (white and other ethnic groups) section
- 5. Navigate to By ethnicity over time section
- 6. Navigate to By ethnicity and gender section
- 7. Navigate to By ethnicity and age section
- 8. Navigate to By ethnicity over time (16 to 24 year olds only) section
- 9. Navigate to By ethnicity and area section
- 10. Navigate toData sources section
- 11. Navigate toDownload the data section
1. Main facts and figures
- in 2022, 22% of working age people in England, Scotland and Wales were economically inactive – this means they were out of work and not looking for a job
- 21% of white people were economically inactive, compared with 26% of people from all other ethnic groups combined
- the combined Pakistani and Bangladeshi ethnic group had the highest rate of economic inactivity (33%), and the white ‘other’ group had the lowest (15%)
- in every region, white people had a lower rate of economic inactivity than people from all other ethnic groups combined
- women were more likely to be economically inactive than men in every ethnic group except for the mixed ethnic group
- the biggest gap between men and women was in the combined Pakistani and Bangladeshi ethnic group, where 48% of women and 19% of men were economically inactive
Further research:
Recent research by the Equality Hub shows that migration and integration are factors that may affect the disparities that ethnic minorities face in the labour market. The study found generational differences in labour market outcomes within ethnic minority groups. Among certain ethnic groups, second generation ethnic minorities performed better than the first generation for the labour market outcomes of economic inactivity.
Some ethnic minority groups still face disparities in the labour market, such as a higher likelihood of being economically inactive than white British people. For Pakistani women, there is no evidence of a generational effect – first and second generation Pakistani women were more likely to be economically inactive compared to white British women, even after controlling for level of education and health in the fully adjusted regression model.
The reasons for poor labour market outcomes for certain ethnic groups are complex. According to research by the Department for Work and Pensions, some of the disparities might be due to the effects of segregation and cultural attitudes, where women are expected to stay at home and care for younger and older members of the household.
2. Things you need to know
What the data measures
The data measures the percentage of working age people (16 to 64 year olds) who were economically inactive.
A person is economically inactive if they are:
- out of work
- not actively looking for work
- not waiting to start a job
- not in full-time education
- caring for their family
- retired
Percentages are rounded to whole numbers. Population numbers are rounded to the nearest 100 people, but economic inactivity rates have been calculated using unrounded data.
Not included in the data
The data does not include estimates based on fewer than:
- 30 survey respondents for data covering all ethnic groups together
- 100 survey respondents for data by ethnicity
This is to protect people’s confidentiality and because the numbers involved are too small to make reliable generalisations.
The ethnic groups used in the data
The data uses the ethnic groups from the 2011 Census.
Data is aggregated for the black, mixed and other ethnic groups, which means estimates are shown for these groups as a whole.
Data is shown separately for white British people and all other white people (the white ‘other’ ethnic group). Separate figures are also shown for 3 different Asian ethnic groups (Indian, Pakistani and Bangladeshi combined, and Asian ‘other’).
Data by ethnicity and area is shown for 2 ethnic groups (white, and all other ethnic groups combined).
People whose ethnicity is not known are included in the figures for ‘All’.
Methodology
Read the detailed methodology document for this data.
The Annual Population Survey updated its ethnicity questions in 2011. This means estimates from before and after 2011 may not be consistent, and data for individual ethnic groups in 2011 is not available.
Local authority names and boundaries change over time. The data for local authorities in the data file does not use the most recent local authority boundaries for England, Scotland and Wales.
There are separate employment and economic inactivity figures in the ethnicity pay gap data published by the Office for National Statistics (ONS) in November 2023. The rates by ethnicity may be different to those shown on this page, because:
- the ONS data excludes extreme values that differ from most other data points in a dataset (‘outliers’)
- the datasets use different weighting rules
The figures on this page are based on survey data. Find out more about:
- interpreting survey data, including how reliability is affected by the number of people surveyed
- how weighting is used to make survey data more representative of the whole group being studied
In the data file
See Download the data for:
- estimates by region, age group and sex over time for detailed ethnic groups
- estimates by local authority for white and all other ethnic groups combined
- confidence intervals for each ethnic group – read about how we use confidence intervals
- sample sizes
3. By ethnicity
Ethnicity | % | Number of people economically inactive |
---|---|---|
All | 22 | 8,693,500 |
Asian | 26 | 874,700 |
Indian | 20 | 259,500 |
Pakistani, Bangladeshi | 33 | 390,500 |
Asian other | 24 | 224,800 |
Black | 26 | 397,200 |
Mixed | 26 | 183,500 |
White | 21 | 6,967,200 |
White British | 21 | 6,509,600 |
White other | 15 | 457,600 |
Other | 28 | 258,700 |
Unknown | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity’ (CSV) Source data for ‘By ethnicity’ (CSV)
Summary of Economic inactivity By ethnicity Summary
The data shows that:
- 22% of working age people in England, Scotland and Wales were economically inactive (out of work and not looking for a job) in 2022
- 33% of people from the combined Pakistani and Bangladeshi ethnic group were economically inactive – the highest percentage out of all ethnic groups, followed by people from ‘other’ ethnic groups, 28%
- 15% of people from the white ‘other’ ethnic group were economically inactive – the lowest percentage out of all ethnic groups
4. By ethnicity over time (white and other ethnic groups)
All | White | Other ethnic groups | Unknown | |||||
---|---|---|---|---|---|---|---|---|
time | All % | All Number of people economically inactive | White % | White Number of people economically inactive | Other ethnic groups % | Other ethnic groups Number of people economically inactive | Unknown % | Unknown Number of people economically inactive |
2004 | 24 | 8,823,700 | 23 | 7,593,100 | 35 | 1,220,900 | 28 | 9,600 |
2005 | 24 | 8,853,900 | 22 | 7,573,000 | 34 | 1,270,500 | 31 | 10,400 |
2006 | 23 | 8,843,900 | 22 | 7,520,100 | 33 | 1,314,300 | 33 | 9,600 |
2007 | 23 | 8,999,600 | 22 | 7,609,800 | 33 | 1,378,800 | 39 | 11,000 |
2008 | 23 | 9,017,300 | 22 | 7,570,400 | 33 | 1,437,600 | 35 | 9,300 |
2009 | 23 | 9,067,100 | 22 | 7,581,400 | 33 | 1,475,100 | 31 | 10,500 |
2010 | 24 | 9,339,000 | 23 | 7,800,000 | 33 | 1,527,800 | 29 | 11,200 |
2011 | 24 | 9,399,300 | not collected | not collected | not collected | not collected | not collected | not collected |
2012 | 23 | 9,143,700 | 22 | 7,526,600 | 32 | 1,603,800 | 45 | 13,300 |
2013 | 23 | 8,965,000 | 22 | 7,332,400 | 31 | 1,621,900 | 37 | 10,700 |
2014 | 23 | 8,957,700 | 21 | 7,273,100 | 31 | 1,664,900 | 27 | 19,700 |
2015 | 22 | 8,824,100 | 21 | 7,131,300 | 31 | 1,673,600 | 34 | 19,200 |
2016 | 22 | 8,849,900 | 21 | 7,103,700 | 30 | 1,734,500 | 32 | 11,700 |
2017 | 22 | 8,644,300 | 20 | 6,906,300 | 30 | 1,725,300 | 38 | 12,700 |
2018 | 22 | 8,624,100 | 20 | 6,849,300 | 30 | 1,763,200 | 31 | 11,600 |
2019 | 21 | 8,468,400 | 20 | 6,699,800 | 29 | 1,759,900 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2020 | 21 | 8,439,300 | 20 | 6,861,000 | 27 | 1,565,100 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2021 | 22 | 8,666,400 | 21 | 7,039,100 | 27 | 1,614,900 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2022 | 22 | 8,693,500 | 21 | 6,967,200 | 26 | 1,714,100 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity over time (white and other ethnic groups)’ (CSV) Source data for ‘By ethnicity over time (white and other ethnic groups)’ (CSV)
Summary of Economic inactivity By ethnicity over time (white and other ethnic groups) Summary
The data shows that:
- 21% of white people were economically inactive in 2022, compared with 26% of people from all other ethnic groups combined
- from 2004 to 2022, the economic activity rate went down from 23% to 21% for white people, and from 35% to 26% for people from all other ethnic groups combined
5. By ethnicity over time
All | Asian | Indian | Pakistani, Bangladeshi | Asian other | Black | Mixed | White | White British | White other | Other | Unknown | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
time | All % | All Number of people economically inactive | Asian % | Asian Number of people economically inactive | Indian % | Indian Number of people economically inactive | Pakistani, Bangladeshi % | Pakistani, Bangladeshi Number of people economically inactive | Asian other % | Asian other Number of people economically inactive | Black % | Black Number of people economically inactive | Mixed % | Mixed Number of people economically inactive | White % | White Number of people economically inactive | White British % | White British Number of people economically inactive | White other % | White other Number of people economically inactive | Other % | Other Number of people economically inactive | Unknown % | Unknown Number of people economically inactive |
2004 | 24 | 8,823,700 | 37 | 711,700 | 27 | 212,000 | 49 | 348,600 | 36 | 151,200 | 31 | 256,300 | 29 | 78,900 | 23 | 7,593,100 | 22 | 7,150,200 | 25 | 442,900 | 38 | 174,000 | 28 | 9,600 |
2005 | 24 | 8,853,900 | 37 | 740,500 | 26 | 209,600 | 49 | 364,700 | 36 | 166,200 | 30 | 257,900 | 30 | 80,500 | 22 | 7,573,000 | 22 | 7,134,800 | 23 | 438,100 | 36 | 191,600 | 31 | 10,400 |
2006 | 23 | 8,843,900 | 36 | 747,000 | 25 | 216,000 | 48 | 370,400 | 34 | 160,500 | 28 | 264,100 | 27 | 76,900 | 22 | 7,520,100 | 22 | 7,051,100 | 21 | 469,000 | 36 | 226,400 | 33 | 9,600 |
2007 | 23 | 8,999,600 | 36 | 789,900 | 26 | 222,600 | 48 | 390,100 | 33 | 177,100 | 28 | 267,400 | 28 | 84,500 | 22 | 7,609,800 | 22 | 7,118,300 | 21 | 491,500 | 36 | 237,000 | 39 | 11,000 |
2008 | 23 | 9,017,300 | 35 | 810,600 | 26 | 237,800 | 46 | 392,600 | 31 | 180,300 | 28 | 288,400 | 31 | 100,400 | 22 | 7,570,400 | 22 | 7,080,700 | 21 | 489,600 | 35 | 238,200 | 35 | 9,300 |
2009 | 23 | 9,067,100 | 34 | 829,600 | 25 | 246,300 | 44 | 398,500 | 32 | 184,800 | 29 | 300,400 | 30 | 106,700 | 22 | 7,581,400 | 22 | 7,070,000 | 21 | 511,400 | 36 | 238,500 | 31 | 10,500 |
2010 | 24 | 9,339,000 | 34 | 878,000 | 24 | 235,500 | 44 | 417,400 | 36 | 225,000 | 28 | 306,900 | 28 | 98,100 | 23 | 7,800,000 | 23 | 7,270,500 | 21 | 529,400 | 35 | 244,900 | 29 | 11,200 |
2011 | 24 | 9,399,300 | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected |
2012 | 23 | 9,143,700 | 33 | 928,700 | 23 | 247,300 | 42 | 436,400 | 34 | 245,000 | 27 | 314,200 | 29 | 121,500 | 22 | 7,526,600 | 22 | 7,061,200 | 20 | 465,400 | 35 | 239,400 | 45 | 13,300 |
2013 | 23 | 8,965,000 | 33 | 953,400 | 24 | 257,600 | 41 | 444,500 | 36 | 251,300 | 26 | 306,200 | 26 | 116,400 | 22 | 7,332,400 | 22 | 6,873,200 | 19 | 459,200 | 35 | 246,000 | 37 | 10,700 |
2014 | 23 | 8,957,700 | 32 | 943,200 | 24 | 267,900 | 40 | 442,500 | 33 | 232,800 | 27 | 331,200 | 28 | 126,100 | 21 | 7,273,100 | 22 | 6,797,100 | 19 | 476,000 | 37 | 264,400 | 27 | 19,700 |
2015 | 22 | 8,824,100 | 32 | 942,100 | 24 | 265,300 | 40 | 439,100 | 32 | 237,700 | 26 | 346,700 | 28 | 127,700 | 21 | 7,131,300 | 21 | 6,669,000 | 17 | 462,400 | 35 | 257,100 | 34 | 19,200 |
2016 | 22 | 8,849,900 | 32 | 995,400 | 23 | 260,800 | 39 | 459,700 | 34 | 275,000 | 25 | 336,800 | 28 | 142,400 | 21 | 7,103,700 | 21 | 6,607,900 | 17 | 495,800 | 34 | 259,800 | 32 | 11,700 |
2017 | 22 | 8,644,300 | 31 | 979,800 | 22 | 246,800 | 39 | 472,600 | 32 | 260,500 | 26 | 338,300 | 27 | 147,200 | 20 | 6,906,300 | 21 | 6,402,800 | 16 | 503,500 | 33 | 260,000 | 38 | 12,700 |
2018 | 22 | 8,624,100 | 30 | 960,700 | 21 | 233,100 | 38 | 469,900 | 32 | 257,700 | 27 | 374,800 | 28 | 152,200 | 20 | 6,849,300 | 21 | 6,383,500 | 15 | 465,700 | 34 | 275,600 | 31 | 11,600 |
2019 | 21 | 8,468,400 | 31 | 999,100 | 21 | 253,000 | 39 | 492,900 | 31 | 253,200 | 25 | 365,300 | 26 | 136,900 | 20 | 6,699,800 | 20 | 6,255,300 | 14 | 444,500 | 32 | 258,600 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2020 | 21 | 8,439,300 | 27 | 836,600 | 19 | 231,900 | 37 | 393,100 | 26 | 211,700 | 25 | 357,100 | 25 | 159,000 | 20 | 6,861,000 | 21 | 6,445,000 | 14 | 416,000 | 30 | 212,400 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2021 | 22 | 8,666,400 | 26 | 833,300 | 19 | 224,700 | 35 | 392,900 | 26 | 215,700 | 26 | 372,300 | 29 | 204,500 | 21 | 7,039,100 | 21 | 6,577,300 | 15 | 461,800 | 28 | 204,700 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
2022 | 22 | 8,693,500 | 26 | 874,700 | 20 | 259,500 | 33 | 390,500 | 24 | 224,800 | 26 | 397,200 | 26 | 183,500 | 21 | 6,967,200 | 21 | 6,509,600 | 15 | 457,600 | 28 | 258,700 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity over time’ (CSV) Source data for ‘By ethnicity over time’ (CSV)
Summary of Economic inactivity By ethnicity over time Summary
The data shows that:
- in all ethnic groups, the rate of economic inactivity was lower in 2022 than in 2004
- the biggest decreases were in the combined Pakistani and Bangladeshi ethnic group (down from 49% to 33%) and the Asian ‘other’ ethnic group (down from 36% to 24%)
- in every year from 2004 to 2022, the combined Pakistani and Bangladeshi ethnic group had the highest rate of economic inactivity out of all ethnic groups
6. By ethnicity and gender
All | Men | Women | ||||
---|---|---|---|---|---|---|
Ethnicity | All % | All Number of people economically inactive | Men % | Men Number of people economically inactive | Women % | Women Number of people economically inactive |
All | 22 | 8,693,500 | 18 | 3,614,300 | 25 | 5,079,200 |
Asian | 26 | 874,700 | 17 | 281,600 | 34 | 593,100 |
Indian | 20 | 259,500 | 13 | 87,400 | 27 | 172,000 |
Pakistani, Bangladeshi | 33 | 390,500 | 19 | 113,600 | 48 | 276,800 |
Asian other | 24 | 224,800 | 19 | 80,500 | 28 | 144,300 |
Black | 26 | 397,200 | 21 | 134,600 | 29 | 262,600 |
Mixed | 26 | 183,500 | 28 | 91,500 | 25 | 92,000 |
White | 21 | 6,967,200 | 18 | 2,999,100 | 24 | 3,968,100 |
White British | 21 | 6,509,600 | 18 | 2,837,800 | 24 | 3,671,800 |
White other | 15 | 457,600 | 11 | 161,300 | 18 | 296,300 |
Other | 28 | 258,700 | 22 | 101,500 | 35 | 157,200 |
Unknown | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity and gender’ (CSV) Source data for ‘By ethnicity and gender’ (CSV)
Summary of Economic inactivity By ethnicity and gender Summary
The data shows that:
- 25% of women and 18% of men were economically inactive in 2022
- women were more likely to be economically inactive than men in every ethnic group except for the mixed ethnic group
- the highest rate of economic inactivity for women was in the combined Pakistani and Bangladeshi ethnic group (48%) – the lowest rate was in the white ‘other’ group (18%)
- the highest rate of economic inactivity among men was in the mixed ethnic group (28%) – the lowest rate was in the white ‘other’ group (11%)
- the gap between men and women was biggest in the combined Pakistani and Bangladeshi ethnic group, where 48% of women and 19% of men were economically inactive – the gap was smallest in the mixed ethnic group, where 25% of women and 28% of men were economically inactive
7. By ethnicity and age
16-24 | 25-49 | 50-64 | All | |||||
---|---|---|---|---|---|---|---|---|
Ethnicity | 16-24 % | 16-24 Number of people economically inactive | 25-49 % | 25-49 Number of people economically inactive | 50-64 % | 50-64 Number of people economically inactive | All % | All Number of people economically inactive |
All | 40 | 2,645,600 | 12 | 2,556,700 | 27 | 3,491,200 | 22 | 8,693,500 |
Asian | 54 | 326,400 | 17 | 387,700 | 27 | 160,500 | 26 | 874,700 |
Indian | 52 | 93,700 | 12 | 107,500 | 25 | 58,300 | 20 | 259,500 |
Pakistani, Bangladeshi | 56 | 145,300 | 24 | 179,500 | 38 | 65,700 | 33 | 390,500 |
Asian other | 53 | 87,400 | 17 | 100,800 | 20 | 36,500 | 24 | 224,800 |
Black | 51 | 171,900 | 17 | 143,900 | 21 | 81,400 | 26 | 397,200 |
Mixed | 43 | 104,900 | 15 | 53,000 | 24 | 25,600 | 26 | 183,500 |
White | 37 | 1,943,800 | 11 | 1,860,100 | 28 | 3,163,300 | 21 | 6,967,200 |
White British | 36 | 1,797,800 | 11 | 1,671,600 | 28 | 3,040,200 | 21 | 6,509,600 |
White other | 44 | 146,000 | 9 | 188,400 | 20 | 123,100 | 15 | 457,600 |
Other | 62 | 96,600 | 18 | 106,300 | 32 | 55,800 | 28 | 258,700 |
Unknown | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity and age’ (CSV) Source data for ‘By ethnicity and age’ (CSV)
Summary of Economic inactivity By ethnicity and age Summary
The data shows that:
- 40% of all 16 to 24 year olds were economically inactive, compared with 27% of 50 to 64 year olds, and 12% of 25 to 49 year olds
- in every ethnic group, 16 to 24 year olds had the highest rate of economic inactivity – this is partly because people in this age group were more likely to be in education
- for 16 to 24 year olds, people from the ‘other’ ethnic group had the highest rate of economic inactivity (62%) – the lowest rate was in the white British group (36%)
- for 25 to 49 year olds, people from the combined Pakistani and Bangladeshi ethnic group had the highest rate of economic inactivity (24%) – the lowest rate was in the white ‘other’ group (9%)
- for 50 to 64 year olds, people from the combined Pakistani and Bangladeshi ethnic group had the highest rate of economic inactivity (38%) – the lowest rate was in the Asian ‘other’ and white ‘other’ groups (20%)
8. By ethnicity over time (16 to 24 year olds only)
All | Asian | Indian | Pakistani, Bangladeshi | Asian other | Black | Mixed | White | White British | White other | Other | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Year | All % | All Number of people economically inactive | Asian % | Asian Number of people economically inactive | Indian % | Indian Number of people economically inactive | Pakistani, Bangladeshi % | Pakistani, Bangladeshi Number of people economically inactive | Asian other % | Asian other Number of people economically inactive | Black % | Black Number of people economically inactive | Mixed % | Mixed Number of people economically inactive | White % | White Number of people economically inactive | White British % | White British Number of people economically inactive | White other % | White other Number of people economically inactive | Other % | Other Number of people economically inactive |
2004 | 32 | 2,099,700 | 53 | 252,800 | 46 | 75,400 | 56 | 118,000 | 57 | 59,300 | 47 | 83,500 | 39 | 43,500 | 29 | 1,668,500 | 29 | 1,574,400 | 36 | 94,100 | 55 | 49,000 |
2005 | 32 | 2,169,300 | 54 | 269,600 | 49 | 79,400 | 55 | 125,800 | 60 | 64,400 | 50 | 91,400 | 41 | 45,400 | 29 | 1,705,400 | 29 | 1,616,900 | 30 | 88,500 | 53 | 54,800 |
2006 | 33 | 2,223,800 | 51 | 248,900 | 44 | 70,900 | 52 | 119,000 | 60 | 59,000 | 48 | 94,300 | 38 | 41,100 | 30 | 1,769,400 | 30 | 1,671,600 | 27 | 97,800 | 53 | 65,200 |
2007 | 33 | 2,314,000 | 54 | 271,300 | 45 | 74,300 | 56 | 127,100 | 62 | 69,900 | 49 | 96,700 | 39 | 43,400 | 31 | 1,826,000 | 31 | 1,705,600 | 31 | 120,400 | 55 | 73,300 |
2008 | 34 | 2,374,700 | 52 | 274,700 | 48 | 84,400 | 52 | 116,600 | 59 | 73,700 | 53 | 116,400 | 45 | 57,700 | 31 | 1,847,600 | 31 | 1,728,000 | 30 | 119,600 | 50 | 73,800 |
2009 | 35 | 2,474,500 | 55 | 288,100 | 51 | 83,600 | 54 | 125,500 | 65 | 78,900 | 52 | 114,300 | 47 | 62,000 | 32 | 1,928,600 | 32 | 1,792,000 | 35 | 136,600 | 60 | 77,600 |
2010 | 38 | 2,665,700 | 58 | 322,900 | 51 | 86,900 | 56 | 134,800 | 70 | 101,300 | 55 | 125,400 | 45 | 56,100 | 34 | 2,079,500 | 34 | 1,947,000 | 37 | 132,500 | 61 | 76,900 |
2011 | 37 | 2,654,700 | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected | not collected |
2012 | 37 | 2,652,700 | 56 | 328,900 | 46 | 82,700 | 56 | 139,100 | 68 | 107,200 | 53 | 125,300 | 42 | 65,400 | 34 | 2,057,300 | 34 | 1,921,800 | 44 | 135,500 | 58 | 70,600 |
2013 | 38 | 2,670,500 | 57 | 332,500 | 54 | 89,000 | 52 | 134,400 | 69 | 109,100 | 53 | 133,900 | 38 | 63,400 | 35 | 2,062,700 | 34 | 1,936,600 | 42 | 126,100 | 63 | 76,200 |
2014 | 39 | 2,739,800 | 60 | 344,700 | 58 | 99,000 | 55 | 137,700 | 69 | 108,100 | 54 | 142,600 | 43 | 75,100 | 35 | 2,081,300 | 35 | 1,941,300 | 41 | 140,000 | 65 | 85,700 |
2015 | 37 | 2,629,100 | 57 | 330,700 | 54 | 92,300 | 53 | 136,600 | 67 | 101,800 | 56 | 159,600 | 46 | 78,400 | 34 | 1,985,200 | 34 | 1,862,400 | 36 | 122,700 | 59 | 70,500 |
2016 | 38 | 2,675,700 | 60 | 363,800 | 59 | 93,500 | 56 | 149,700 | 68 | 120,600 | 50 | 139,400 | 48 | 82,100 | 35 | 2,006,400 | 35 | 1,871,000 | 36 | 135,400 | 61 | 79,000 |
2017 | 38 | 2,639,100 | 58 | 370,600 | 49 | 85,500 | 58 | 169,000 | 65 | 116,100 | 56 | 141,300 | 45 | 83,100 | 35 | 1,954,900 | 34 | 1,789,500 | 40 | 165,400 | 63 | 83,100 |
2018 | 39 | 2,647,500 | 56 | 352,600 | 52 | 81,600 | 53 | 164,900 | 65 | 106,100 | 57 | 165,000 | 46 | 85,300 | 35 | 1,945,900 | 35 | 1,798,000 | 40 | 147,900 | 64 | 95,100 |
2019 | 39 | 2,607,100 | 58 | 374,000 | 56 | 95,200 | 56 | 176,800 | 64 | 101,900 | 53 | 154,100 | 48 | 79,300 | 35 | 1,905,600 | 35 | 1,772,700 | 35 | 132,900 | 63 | 92,400 |
2020 | 39 | 2,622,000 | 56 | 315,700 | 54 | 88,200 | 59 | 144,500 | 55 | 83,000 | 55 | 150,200 | 46 | 95,400 | 36 | 1,994,500 | 36 | 1,873,800 | 39 | 120,700 | 58 | 63,500 |
2021 | 41 | 2,709,400 | 55 | 321,900 | 59 | 92,900 | 53 | 146,400 | 53 | 82,500 | 56 | 164,100 | 53 | 129,200 | 38 | 2,031,400 | 37 | 1,892,300 | 46 | 139,100 | 56 | 61,300 |
2022 | 40 | 2,645,600 | 54 | 326,400 | 52 | 93,700 | 56 | 145,300 | 53 | 87,400 | 51 | 171,900 | 43 | 104,900 | 37 | 1,943,800 | 36 | 1,797,800 | 44 | 146,000 | 62 | 96,600 |
Download table data for ‘By ethnicity over time (16 to 24 year olds only)’ (CSV) Source data for ‘By ethnicity over time (16 to 24 year olds only)’ (CSV)
Summary of Economic inactivity By ethnicity over time (16 to 24 year olds only) Summary
16 to 24 year olds were more likely to be economically inactive than older people. This is partly because people in this age group were more likely to be students.
The data shows that:
- between 2004 and 2022, the overall rate of economic inactivity for 16 to 24 year olds went up from 32% to 40%
- the biggest increase was in the white ‘other’ ethnic group (up from 26% to 44%)
- for the Asian ‘other’ ethnic group, the rate of economic inactivity for 16 to 24 year olds went down from 57% to 53%
9. By ethnicity and area
All | Asian | Indian | Pakistani, Bangladeshi | Asian other | Black | Mixed | White | White British | White other | Other | Unknown | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Region | All % | All Number of people economically inactive | Asian % | Asian Number of people economically inactive | Indian % | Indian Number of people economically inactive | Pakistani, Bangladeshi % | Pakistani, Bangladeshi Number of people economically inactive | Asian other % | Asian other Number of people economically inactive | Black % | Black Number of people economically inactive | Mixed % | Mixed Number of people economically inactive | White % | White Number of people economically inactive | White British % | White British Number of people economically inactive | White other % | White other Number of people economically inactive | Other % | Other Number of people economically inactive | Unknown % | Unknown Number of people economically inactive |
All | 22 | 8,693,500 | 26 | 874,700 | 20 | 259,500 | 33 | 390,500 | 24 | 224,800 | 26 | 397,200 | 26 | 183,500 | 21 | 6,967,200 | 21 | 6,509,600 | 15 | 457,600 | 28 | 258,700 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
East Midlands | 23 | 667,400 | 27 | 63,900 | 25 | 36,000 | 35 | 14,000 | 28 | 13,800 | 24 | 21,100 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 22 | 557,900 | 23 | 529,400 | 14 | 28,400 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
East of England | 19 | 737,800 | 24 | 55,700 | 16 | 13,800 | 35 | 26,400 | 22 | 15,500 | 19 | 22,900 | 19 | 14,800 | 19 | 633,400 | 20 | 584,600 | 15 | 48,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
London | 20 | 1,244,100 | 24 | 281,300 | 18 | 81,600 | 31 | 121,100 | 23 | 78,600 | 25 | 190,700 | 27 | 54,800 | 17 | 609,500 | 18 | 445,900 | 15 | 163,600 | 27 | 105,700 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
North East | 26 | 423,200 | 29 | 13,600 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 26 | 392,300 | 26 | 382,700 | 22 | 9,600 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
North West | 23 | 1,046,000 | 33 | 108,800 | 23 | 22,300 | 41 | 68,700 | 26 | 17,800 | 29 | 32,400 | 33 | 21,900 | 22 | 855,200 | 22 | 828,500 | 15 | 26,700 | 39 | 27,300 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Scotland | 23 | 788,500 | 28 | 36,900 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 26 | 13,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 23 | 718,000 | 23 | 685,300 | 13 | 32,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
South East | 19 | 1,083,800 | 19 | 77,200 | 19 | 32,800 | 22 | 17,400 | 19 | 27,100 | 28 | 33,000 | 22 | 17,600 | 19 | 934,100 | 20 | 877,800 | 13 | 56,300 | 21 | 19,500 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
South West | 20 | 652,800 | 25 | 25,800 | 18 | 6,400 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 29 | 14,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 15 | 7,000 | 19 | 607,600 | 20 | 580,500 | 15 | 27,100 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Wales | 24 | 466,200 | 24 | 10,300 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 24 | 439,900 | 24 | 429,100 | 19 | 10,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
West Midlands | 23 | 818,800 | 28 | 130,300 | 22 | 40,800 | 37 | 75,000 | 19 | 14,500 | 30 | 53,600 | 39 | 25,900 | 21 | 575,400 | 21 | 547,200 | 16 | 28,200 | 31 | 31,400 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Yorkshire and The Humber | 23 | 765,000 | 31 | 70,800 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 30 | 39,400 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | 26 | 20,600 | 26 | 16,600 | 22 | 643,800 | 22 | 618,600 | 16 | 25,300 | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable | withheld because a small sample size makes it unreliable |
Download table data for ‘By ethnicity and area’ (CSV) Source data for ‘By ethnicity and area’ (CSV)
Summary of Economic inactivity By ethnicity and area Summary
Where data is available and not withheld due to small sample sizes, the data shows that:
- people from the white ‘other’ ethnic group had a lower rate of economic inactivity among 16 to 64 year olds in every region
- in particular, people from the white ‘other’ ethnic group had the lowest rate of economic inactivity in Scotland and the South East (both 13%)
- the combined Pakistani and Bangladeshi ethnic group had the highest rate of economic inactivity among 16 to 64 year olds in the East Midlands, East of England, London, and the North West
- in particular, the highest rate of economic inactivity was for people from the combined Pakistani and Bangladeshi ethnic group in the North West (41%)
10. Data sources
Source
Type of data
Survey data
Type of statistic
National Statistics
Publisher
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.
Publication frequency
Yearly
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
- education
- health
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.
11. Download the data
This file contains the following: Measure, Ethnicity, Ethnicity_type, Time, Time_type, Geography, Geography_type, Age, Value, Confidence_interval, Numerator, Denominator, Sample_size
This file contains the following: Measure, Ethnicity, Ethnicity_type, Time, Time_type, Geography, Geography_type, Age, Age_type, Sex, Value, Confidence_interval, Numerator, Denominator, Sample_size