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Welsh Government
Data Provider: Welsh Government Experimental Statistics Ethnicity by Region
None
Year[Filtered]
Measure[Filtered]
Measure2
[Collapse]Ethnicity(Ascending)[Filter]
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Ethnicity 1
[Collapse]Region[Filter]
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Region 1
[Collapse]TotalTotal column includes a small number where Ethnicity is unknown.Click here to sortTotalTotal column includes a small number where Ethnicity is unknown.
Click here to sortWhiteClick here to sortMixed / Multiple ethnic groupsClick here to sortAsianClick here to sortBlack / African / Caribbean / Black BritishClick here to sortOther ethnic groups
[Collapse]Total2,953,800(!) The data item has a coefficient of variation (CV) of between 5% and 10% and is therefore categorised as only \'reasonably precise.\’ Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 10% and 20% are considered ‘acceptable.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.29,500(!) The data item has a coefficient of variation (CV) of between 5% and 10% and is therefore categorised as only \'reasonably precise.\’ Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 10% and 20% are considered ‘acceptable.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.71,500(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.27,400(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.25,2003,110,000
TotalNorth Wales677,800(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.4,800(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.6,4002,2004,000695,500
Mid and South West Wales862,200(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.7,100(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.16,6004,700(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.6,500898,300
South East Wales1,413,900(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.17,600(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.48,600(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.20,500(!!) The data item has a coefficient of variation (CV) of between 10% and 20% and is therefore categorised as only \'acceptable.\' Only estimates with a CV of less than 5% are considered \'precise\', whilst estimates with a CV of between 5% and 10% are considered ‘reasonably precise.\’ Estimates with a CV of above 20% are considered unacceptable and suppressed. Note that CVs for this purpose are calculated using the standard algorithms in SAS. Typically these CVs are slightly lower than those calculated by ONS when they publish the data.14,7001,516,200

Metadata

Title

Annual Population Survey: Ethnicity

Last update

January 2022 January 2022

Next update

November 2022

Publishing organisation

Welsh Government

Source 1

Annual Population Survey, Office for National Statistics

Contact email

stats.inclusion@gov.wales

Designation

Experimental statistics

Lowest level of geographical disaggregation

Welsh Government economic regions

Geographical coverage

Wales

Languages covered

English and Welsh

Data licensing

You may use and re-use this data free of charge in any format or medium, under the terms of the Open Government License - see http://www.nationalarchives.gov.uk/doc/open-government-licence

General description

This table presents data on all people in Wales.

Data collection and calculation

The data is based on Welsh Government analysis of Annual Population Survey datasets provided by the Office for National Statistics.

Frequency of publication

Annual

Data reference periods

Figures shown relate to multi-year averages, as indicated

Rounding applied

Figures are rounded to the nearest 100 and so there may be some apparent slight discrepancies between the sum of constituent items and the totals as shown.

Keywords

Ethnicity; Equality & Diversity

Statistical quality

Annual Population Survey (APS) responses are weighted to official population projections. The projections for 2020 were 2018-based, and, therefore, were based on demographic trends that pre-dated the COVID-19 pandemic. To allow for different trends during the pandemic the responses for the APS have been reweighted on the 9 September 2021 to new populations derived using growth rates from HM Revenue and Customs (HMRC) Real Time Information (RTI). The reweighting has been applied from year ending March 2020 data onwards and gives improved estimates of both rates and levels. The changes ONS have made to the weighting should reduce the bias of estimates at high levels of aggregation. Some smaller breakdowns may be impacted negatively and more extreme changes could be seen given the reduced size of the underlying sample since the start of the pandemic.

As the data come from a survey, the results are sample-based estimates and therefore subject to differing degrees of sampling variability, i.e. the true value for any measure lies in a differing range about the estimated value. This range or sampling variability increases as the detail in the data increases, for example local authority data are subject to higher variability than regional data.

Name

EQU1031