This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

Employment by socio-economic classification and sex, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

This dataset provides Census 2021 estimates that classify Household Reference Persons aged 16 years and over in England and Wales by NS-SEC of Household Reference Person and by household composition. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Data about household relationships might not always look consistent with legal partnership status. This is because of complexity of living arrangements and the way people interpreted these questions. Take care when using these two variables together. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Household composition

Households according to the relationships between members.

One-family households are classified by:

• the number of dependent children
• family type (married, civil partnership or cohabiting couple family, or lone parent family)

Other households are classified by:

• the number of people
• the number of dependent children
• whether the household consists only of students or only of people aged 66 and over

As of 2023, ** percent of people in the UK classed as the highest social grade, AB, cycled every week, with this group consisting of people in upper managerial positions. Meanwhile, the cycling participation rate among people classed as DE was ** percent.

Presents data on birth registrations in England and Wales by National Statistics Socio-economic Classification (NS-SEC) of father as defined by occupation. This publication has been discontinued as a result of a Consultation with users. Releases of this data will be included within Further Parental Characteristics. The last edition to be published was 2011. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: Live births by NS-SEC of father, England and Wales

Legacy unique identifier: P00032

According to a study conducted in 2023, the majority of men who identified as incels (involuntary celibates) were of a middle class socio-economic status in both the United Kingdom and the United States.

Additional data to complement the Childbearing by socio-economic status and country of birth of mother, England and Wales, 2014 publication

Legacy unique identifier: P00031

Additional file 1: Table S1. Definitions of socioeconomic status, environment pollution, healthy lifestyle, environment pollution and chronic comorbidity factors, and infectious diseases from UK biobank. Table S2. Definitions of socioeconomic status, healthy lifestyle, environment pollution and chronic comorbidity factors, and infectious diseases from and US NHANES. Table S3. Mean posterior probabilities, prevalence of latent classes, and item-response probabilities in models with three to five latent classes in the UK Biobank and US NHANES. Table S4. Associations of SES, lifestyle, environment pollution and chronic comorbidity factors with respiratory, digestive and blood or sexually transmitted infectious diseases in UK Biobank participants under multivariate linear regression. Table S5. Associations of SES, lifestyle, environment pollution and chronic comorbidity factors with respiratory infectious diseases in UK Biobank PSM matching subgroup under univariate linear regression. Table S6. Associations of SES, lifestyle, environment pollution and chronic comorbidity factors with digestive infectious diseases in UK Biobank PSM matching subgroup under univariate linear regression. Table S7. Associations of SES, lifestyle, environment pollution and chronic comorbidity factors with blood or sexually transmitted infectious diseases in UK Biobank PSM matching subgroup under univariate linear regression. Table S8. Associations of SES, lifestyle and environment pollution factors with infectious diseases in UK Biobank participants in 2010 under univariate linear regression. Table S9. Adjusted odds ratios for environmental pollution on infection in each quantile in the UK Biobank study. Table S10. Mediation effects of socioeconomic factors on infectious diseases by individual lifestyle, environment pollution or chronic comorbidity factors in UK Biobank. Table S11. Interaction between SES and individual lifestyle, environment pollution or chronic comorbidity factors on infectious diseases in UK Biobank. Table S12. Lifestyle scores on infectious diseases in different SES subgroups from UK biobank. Table S13. Individual lifestyle factors, and environment pollution and chronic comorbidity factors on infectious diseases in different SES subgroups from UK biobank. Table S14. EPS on infectious diseases in different SES subgroups from UK biobank. Table S15. APS on infectious diseases in different SES subgroups from UK biobank. Table S16. SES on infection in different gender and ethnic/race subgroups in UK Biobank. Table S17. Sex and race on infectious diseases in different SES subgroups from UK Biobank. Table S18. Baseline characteristics and infection status of all participants from US NHANES. Table S19. Associations of SES, lifestyle, environment pollution and chronic comorbidity factors with infectious diseases in US NHANES participants under multivariate linear regression. Table S20. Mediation effects of socioeconomic factors on infectious diseases by individual lifestyle, environment pollution or chronic comorbidity factors in US NHANES. Table S21. Interaction between SES and lifestyle or chronic comorbidity factors on infectious diseases in US NHANES. Table S22. Lifestyle scores on infectious diseases in different SES subgroups from US NHANES. Table S23. Individual lifestyle factors, and chronic comorbidity factors on infectious diseases in different SES subgroups from US NHANES. Table S24. SES on infection in different gender and ethnic subgroups in US NHANES. Table S25. Sex and race on infectious diseases in different SES subgroups from US NHANES. Table S26. Distribution of lifestyle factors across ethnic/race subgroups in UK Biobank.

BackgroundSocio-economic position (SEP) and ethnicity influence type 2 diabetes mellitus (T2DM) risk in adults. However, the influence of SEP on emerging T2DM risks in different ethnic groups and the contribution of SEP to ethnic differences in T2DM risk in young people have been little studied. We examined the relationships between SEP and T2DM risk factors in UK children of South Asian, black African-Caribbean and white European origin, using the official UK National Statistics Socio-economic Classification (NS-SEC) and assessed the extent to which NS-SEC explained ethnic differences in T2DM risk factors. Methods and FindingsCross-sectional school-based study of 4,804 UK children aged 9–10 years, including anthropometry and fasting blood analytes (response rates 70%, 68% and 58% for schools, individuals and blood measurements). Assessment of SEP was based on parental occupation defined using NS-SEC and ethnicity on parental self-report. Associations between NS-SEC and adiposity, insulin resistance (IR) and triglyceride differed between ethnic groups. In white Europeans, lower NS-SEC status was related to higher ponderal index (PI), fat mass index, IR and triglyceride (increases per NS-SEC decrement [95%CI] were 1.71% [0.75, 2.68], 4.32% [1.24, 7.48], 5.69% [2.01, 9.51] and 3.17% [0.96, 5.42], respectively). In black African-Caribbeans, lower NS-SEC was associated with lower PI (−1.12%; [−2.01, −0.21]), IR and triglyceride, while in South Asians there were no consistent associations between NS-SEC and T2DM risk factors. Adjustment for NS-SEC did not appear to explain ethnic differences in T2DM risk factors, which were particularly marked in high NS-SEC groups. ConclusionsSEP is associated with T2DM risk factors in children but patterns of association differ by ethnic groups. Consequently, ethnic differences (which tend to be largest in affluent socio-economic groups) are not explained by NS-SEC. This suggests that strategies aimed at reducing social inequalities in T2DM risk are unlikely to reduce emerging ethnic differences in T2DM risk.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

Coronavirus affects some members of the population more than others. Emerging evidence suggests that older people, men, people with health conditions such as respiratory and pulmonary conditions, and people of a Black, Asian Minority Ethnic (BAME) background are at particular risk. There are also a number of other wider public health risk factors that have been found to increase the likelihood of an individual contracting coronavirus. This briefing presents descriptive evidence on a range of these factors, seeking to understand at a London-wide level the proportion of the population affected by each.

This cohort investigated the distribution of particulate matter exposure across socioeconomic status and its influence on long-term mortality in South Korea. The cohort included a nationally representative sample of over 1.4 million participants aged at least 30 years old from all economic strata in South Korea. Participants were followed up from 2007 until they lost health care eligibility, died, or the study concluded in 2019.

BackgroundThe environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. Several studies have found that socioeconomic status (SES) modifies the heritability of children's intelligence. Among low-SES families, genetic factors have been reported to explain less of the variance in intelligence; the reverse is found for high-SES families. The evidence however is inconsistent. Other studies have reported an effect in the opposite direction (higher heritability in lower SES), or no moderation of the genetic effect on intelligence. MethodsUsing 8716 twin pairs from the Twins Early Development Study (TEDS), we attempted to replicate the reported moderating effect of SES on children's intelligence at ages 2, 3, 4, 7, 9, 10, 12 and 14: i.e., lower heritability in lower-SES families. We used a twin model that allowed for a main effect of SES on intelligence, as well as a moderating effect of SES on the genetic and environmental components of intelligence. ResultsWe found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power - a difference of 11% to 53% in heritability, in low- (−2 standard deviations, SD) and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an environment-environment interaction. ConclusionsIn a UK-representative sample, the genetic effect on intelligence is similar in low- and high-SES families. Children's shared experiences appear to explain the greater variation in intelligence in lower SES.

UK Social Contact Network

Age, Gender, and Household Characteristics

By [source]

About this dataset

This dataset contains a detailed overview of the 2013 Van Hoek Social Contact Network Study in the UK. With this dataset, we have a unique opportunity to see how age, gender, and household size impact our social contact networks. From looking at the day of week contact was made and how often family contacted each other, to understanding the socio-economic backgrounds of participants and ethnicities represented - this data provides us with an interesting look into how our social connections are shaped. By diving deeper into these variables, we can gain valuable insight into our current culture's trends regarding who we interact with on a daily basis

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How to use the dataset

In order to use this dataset effectively, it is important to familiarize oneself with all of the columns included: dayofweek (the day of week on which contact was made), Contact.Day.if.different.from.allocated.day (the day of week on which contact was made if different from allocated day), day (day of month when contact was made), month (month when Contact was made), FTM (frequency of contact with family members), Socia_economic (social economic status participant belongs to), Noe_of _siblings (number of siblings participant has) ,Ethnicity(ethnicity details) part_age_detail(age detail).

Once you become familiar with all columns included in this dataset you can begin to identify relationships between these demographic factors and how different social contacts were enacted among them by examining how frequently households interacted as well as what age/gender/ethnic composition within each household look like at different times during a given month or year period; or even see what variables have an influence over who is contacted more often than not within a household setting or across multiple households- all depending on your need for specific insights from your research perspective!

Research Ideas

• Analyzing seasonal Social Contact trends: Using the month and dayofweek features, we could analyse how contact tends to vary across different seasons (e.g., more contact during summer months).

• Predicting Participants' Age Group: With the age detail, gender, and soci_economic features, predictive models can be built in order to estimate the age range of participants from given socio-demographic information.

• Evaluating Cyber Bullying and Online Social Networking Trends: Studying the self-reported frequency of contact between family members (FTM), researchers can evaluate cyber bullying trends in various communities as well as measure changes in social network size over time with respect to a given demographic group such as gender or ethnicity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2013_VanHoek_UK_sday.csv | Column name | Description | |:------------------------------------------------|:-------------------------------------------------------------| | dayofweek | The primary day contact was made. (String) | | Contact.Day.if.different.from.allocated.day | Any days when contact deviated from allocated days. (String) | | day | The day of the month contact was made. (Integer) | | year | The year contact was made. (Integer) |

File: 2013_VanHoek_UK_participant_extra.csv | Column name | Description | |:--------------------|:--------------------------------------------------------| | FTM | Frequency of contact with family members. (Numeric) | | Socia_economic | Socioeconomic status of each participant. (Categorical) | | No.of.siblings | Number of siblings for each participant. (Numeric) | | Ethnicity | Ethnicity for each participant. (Categorica...

The aim of this study was to examine trends to upward social mobility among a specially selected group of people with above average intelligence. This is a continuous survey, but this dataset is the only wave held at the Data Archive.

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.