In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

This statistic looks at which socio-economic demographics retailers target in the United Kingdom in 2016. According to the survey, ** percent of retailers focus on the AB social-economic group (upper middle and middle classes) while only one percent focus on groups DE (working and non-working classes).

Legacy unique identifier: P00032

Economically active and non-active residents of households and those aged 16-64 who are economically active by National Statistics Socio-Economic classification as defined by own occupation. To provide 2001 Census based information about the National Statistics Socio-Economic (NS-SEC) Group of the population within each area as defined by own occupation. Legacy unique identifier: P00032

Over the period from 2010 to 2020, profile creation increased among all socio-economic group. During the survey in 2020, it was revealed that 83 percent of responding individuals from social grade C1 reported setting up their personal profile on a social networking platform.

This dataset presents the percentage of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) Index of Relative Socio-economic Disadvantage (IRSD) group. The data has been aggregated to the 2006 Census Collection Districts (CD). This datasets presents the IRSD groups to 10 categories, where group 1 is the 10% most disadvantaged 15-64 year old population and group 10 presents the most advantaged 10% of 15-64 year old population. For more information please visit the ACT Government Data Portal. Please note: AURIN has spatially enabled the original data.

A 2024 survey among internet users in the United Kingdom (UK) found that users belonging to the socio-economic group C1 (lower middle class) accounted for ** percent of medium online users, who used the internet for **** to ***** types of online activities.

This spread sheet shows ABS geographic standards from 2006 across Australia and the % of the 15-64 year old population within each Socio-Economic Indexes for Individuals (SEIFI) IRSAD group. The data used to create this information was the same as used in the research paper “Socio-Economic Indexes for Areas: Getting a handle on individual diversity within areas” by Phillip Wise and Rosalynn Mathews. It is advised that this paper is read to further develop an understanding of the concepts and caveats associated with the analytical output contained in the spreadsheet.] Roughly, the most disadvantaged 10% of the 15–64 year old population falls into group 1, whilst group 10 contains the most advantaged 10%. The smallest group in terms of 15–64 year old population proportion is group 6 with 7.78%, compared to group 7 with the largest percentage at 12% due to clustering at this point in the distribution of scores. Group 1 – Approx. 9.6% of the 15-64 year old population Group 2 – Approx. 10.0% of the 15-64 year old population Group 3 – Approx. 11.5% of the 15-64 year old population Group 4 – Approx. 8.6% of the 15-64 year old population Group 5 – Approx. 11.4% of the 15-64 year old population Group 6 – Approx. 7.8% of the 15-64 year old population Group 7 – Approx. 12.0% of the 15-64 year old population Group 8 – Approx. 9.1% of the 15-64 year old population Group 9 – Approx. 9.5% of the 15-64 year old population Group 10 – Approx. 10.5% of the 15-64 year old population

age-class altersklasse classe-d_a_ge entite_-ge_opolitique-_de_clarante_ europa_ische-sozioo_konomische-gruppen european-socio-economic-groups fre_quence-_relative-au-temps_ geopolitical-entity-_reporting_ geopolitische-meldeeinheit groupes-socioe_conomiques-europe_ens maßeinheit nomenclature-statistique-des-activite_s-e_conomiques-dans-la-communaute_-europe_enne-_nace-re_v_-2_ statistical-classification-of-economic-activities-in-the-european-community-_nace-rev_-2_ statistische-systematik-der-wirtschaftszweige-in-der-europa_ischen-gemeinschaft-_nace-rev_-2_ time-frequency unit-of-measure unite_-de-mesure zeitliche-frequenz

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.

SAP2022T9T2LPT - Number of Private Households by Socio-economic Group of Reference Person. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Number of Private Households by Socio-economic Group of Reference Person...

COVID-19 has had worse health, education and labor market effects on groups with low socio-economic status (SES) than on those with high SES. Little is known, however, about whether COVID-19 has also had differential effects on non-cognitive skills that are important for life outcomes. Using panel data from before and during the pandemic, we show that COVID-19 affects one key non-cognitive skill, i.e., prosociality. While prosociality is already lower for low-SES students prior to the pandemic, we show that COVID-19 infections within families amplify the prosociality gap between French high-school students of high- and low-SES by almost tripling its size in comparison to pre-COVID-19 levels.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The first census report to tabulate social class was 1951, but this collection also includes a table from the Registrar-General's 1931 Decennial Supplement which drew on census occupational data to tabulate social class by region. In 1961 and 1971 the census used a more detailed classification of Socio-Economic Groups, from which the five Social Classes are a simplification.

This is a new edition. Data from the Census of Scotland have been added for 1951, 1961 and 1971. Wherever possible, ID numbers have been added for counties and districts which match those used in the digital boundary data created by the GBH GIS, greatly simplifying mapping.

Population by employment and socio-economic status, sex, age group and county, 31 december 2011.

Population (Number) by Disability Type, Socio Economic Group, CensusYear, Sex and Age Group

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This study examines whether or not political representation in the Netherlands is biased toward the rich and higher educated by comparing the political orientations of members of parliament to those of the electorate. The analyses reveal stark differences in the representation of different socio-economic groups. The political views of elected national representatives are far more similar to those of rich, higher educated citizens than to those with less income and education. Moreover, a longitudinal analysis reveals that inequalities in political representation have actually grown in recent years. We also show that the use of measures of ideological self-identification might to lead to highly misleading results regarding the nature of political representation as opposed to the use of issue items. We conclude that, despite a highly proportional electoral system, the views which are represented in the Dutch lower house of parliament contain major distortions of the views of the broader electorate.

During the observed period, the level of extreme poverty has increased in the social groups mentioned. However, the group most at risk of extreme poverty in Poland were households living mainly from so-called unearned sources and farmers. As of 2024, farmers have a poverty rate of **** percent, while those living on unearned sources have a rate of **** percent.

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Licensed under: Creative Commons Attribution 4.0

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.