In 2024, 90 of the United Kingdom's 650 Members of Parliament were non-white, 66 of which were members of the Labour Party, while 15 were in the Conservative party, and five non-white MPs were members of the Liberal Democrats.

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

White people made up 83.4% of civil servants in March 2024 – they made up 80.7% of the working age population (16 to 64 year olds) in the 2021 Census.

In 2024, approximately 8.4 percent of police officers in England and Wales were from ethnic minority backgrounds, compared with 8.1 percent in 2022, and just 3.5 percent in 2005.

GENERAL INFORMATIONThe dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ SHARING/ACCESS INFORMATIONLinks to publications that cite or use the data: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.Was data derived from another source? Yes - the data was derived from the internal OU data Recommended citation for this dataset: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.DATA & FILE OVERVIEWThe dataset contains coefficients of a logistic and linear regression that was used to model 3 student outcomes in 3 STEM courses - 1) completion, 2) passing and 3) overall score. The results are split into four tabs1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_B comp_SE - passing: pass_B pass_SE - overall score: score_B score_SE 2. LogReg Marginal Effectsthe marginal effect coefficients for the two dichotomous outcomes from the previous tab (completion and passing) More information about the marginal effects: https://www.statisticshowto.com/marginal-effects/3. Reg_BAME - These are the regression coefficients reported in the in the first tab, for the same outcomes (i.e. completion/passing/overall score), but disaggregated by whether the student is identified as BAME or not. Note that the analysis does not contain the 'BAME' coefficients, because it would be constant4. Red_IMDSimilarly as for BAME (point 3), these are regression coefficients disaggregated by IMD quintiles. IMD_Missing is a special category capturing the students without any IMD, i.e. international students.Regression coefficient variablesThe variables entering the regressions can be split into three categories and the intercept(1) Student level - age - banded into age_60, age_MISSING (reference category: age_[21-24]) - gender - gender_F (reference category Gender_M) - an indicator of linked qualification - linked_qual (reference category: linked_qual =False) - declared disability - disability (reference category: disability=False) - caring responsibility carer_NO, carer_YES (reference category: carer_MISSING) - flag whether the student is new at the OU - is_new (reference category: is_new=False) - highest previous education - ed_NoFormal, ed_HE_Qual, ed_PostGrad (reference category: ed_A Level/Equivalent) - average previous score - discretised into prev_score_LOW, prev_score_MOD, prev_score_VERY_HIGH (avg.prev.score=MISSING, i.e. the student did not study any previous course) these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have values of the average score. - number of other credits studied - banded as credits_other_[1-60], credits_other_>=61 (reference category: credits_other=0) - number of previous attempts of the course - prev_attempt_=1, prev_attempt >1 (reference category: prev_attempt_0) - IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_=81 imd_MISSING (reference category: imd_[41-60]) - whether the student is identified as BAME - BAME_YES (reference category: BAME_NO) - Membership in the intervention group - group_INT (reference category: group_INT=0) (2) Teacher level - no. of students the teacher is responsible for - stud_in_group - avg. student pass rate in the previous years they were teaching - tut_pr_pass_LOW, tut_pr_pass_HIGH, tut_pr_pass_VERY_HIGH, tut_pr_pass_MISSING (reference category: tut_pr_pass_MOD) - these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have different pass rates (3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)(4) intercept

According to the 2021 Census, 62.9% (37.5 million) of the overall population of England and Wales was of ‘working age’ (between 16 and 64 years old).

Chosen names reflect changes in societal values, personal tastes and cultural diversity. Vogues in name usage can be easily shown on a case by case basis, by plotting the rise and fall in their popularity over time. However, individual name choices are not made in isolation and trends in naming are better understood as group-level phenomena. Here we use network analysis to examine onomastic (name) datasets in order to explore the influences on name choices within the UK over the last 170 years. Using a large representative sample of approximately 22 million forenames from England and Wales given between 1838 and 2014, along with a complete population sample of births registered between 1996 and 2016, we demonstrate how trends in name usage can be visualised as network graphs. By exploring the structure of these graphs various patterns of name use become apparent, a consequence of external social forces, such as migration, operating in concert with internal mechanisms of change. In general, we show that the topology of network graphs can reveal naming vogues, and that naming vogues in part reflect social and demographic changes. Many name choices are consistent with a self-correcting feedback loop, whereby rarer names become common because there are virtues perceived in their rarity, yet with these perceived virtues lost upon increasing commonality. Towards the present day, we can speculate that the comparatively greater range of media, freedom of movement, and ability to maintain globally-distributed social networks increases the number of possible names, but also ensures they may more quickly be perceived as commonplace. Consequently, contemporary naming vogues are relatively short-lived with many name choices appearing a balance struck between recognisability and rarity. The data are available in multiple forms including via an easy-to-use web interface at http://demos.flourish.studio/namehistory.

Between 2018 and 2022, people in households in the ‘other’, Asian and black ethnic groups were the most likely to be in persistent low income, both before and after housing costs, out of all ethnic groups.

In the 10 years to July 2024, the percentage of further education students who were from Asian, Black, Mixed and Other ethnic backgrounds went up from 19.7% to 27.9%.

In 2023, there were approximately ***** million millennials in the United Kingdom, making it the largest generational cohort at that time. Millennials surpassed the Baby Boomer generation as the largest generation for the first time in 2019. The two youngest generations, Gen Z and Gen Alpha, numbered approximately **** million, and *** million respectively. Gen X are, as of the most recent year, the second-largest generation in the UK at ***** million people, with their parent's generation, the Silent Generation, numbering around *** million people in the same year. There were estimated to be ****** people who belonged to the Greatest Generation, the parents of the Baby Boomer generation, who lived through major events such as the Great Depression and World War Two. Post-War Baby Boom The baby boomer generation was the largest generation for much of this period due to the spike in births that happened after the Second World War. In 1947, for example, there were over *** million live births in the United Kingdom, compared with just ******* live births just thirty years later in 1977. Members of this generation are typically the parents of millennials, and were the driving force behind the countercultural movement of the 1960s, due to their large numbers relative to older generations at the time. The next generational cohort after Boomers are Generation X, born between 1965 and 1980. This generation had fewer members than the Boomer generation for most of its existence, and only became larger than it in 2021. Millennials and Gen Z As of 2022, the most common single year of age in the United Kingdom in 2020 was 34, with approximately ******* people this age. Furthermore, people aged between 30 and 34 were the most numerous age group in this year, at approximately 4.67 million people. As of 2022, people in this age group were Millennials, the large generation who came of age in the late 1990s and early 2000s. Many members of this generation entered the workforce following the 2008 financial crash, and suffered through high levels of unemployment during the early 2010s. The generation that followed Millennials, Generation Z, have also experienced tough socio-economic conditions recently, with key formative years dominated by the COVID-19 pandemic, climate change, and an increasingly unstable geopolitical situation.

Black people were 3.5 times more likely to be detained than white people under the Mental Health Act in the year to March 2023.

As of February 2022, only 4.4 percent of professional football managers in England were Black, as opposed to 43 percent of Premier League players. Less than two percent of executive and ownership roles were held by Black people.