There were approximately ** thousand Bangladeshi nationals residing in the United Kingdom in 2021, the same as the ** thousand Bangladeshi nationals residing in the United Kingdom in 2008. The highest number of Bangladeshi nationals residing in the United Kingdom was ** thousand in 2010 and 2012.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.

Estimated population density per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per square kilometer. The units are number of people per square kilometre based on country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2019 Revision of World Population Prospects). The mapping approach is Random Forest-based dasymetric redistribution.

According to the 2021 Census, 81.7% of the population of England and Wales was white, 9.3% Asian, 4.0% black, 2.9% mixed and 2.1% from other ethnic groups.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Bangladesh data available from WorldPop here.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

Cultural psychologists have shown that people from Western, Educated, Industrialised, Rich, Democratic (WEIRD) countries often exhibit different psychological processing to people from less-WEIRD countries. The former exhibit more individualistic and less collectivistic social orientation, and more analytic and less holistic cognition, than non-Westerners. Yet the mechanisms responsible for maintaining this cultural variation are unclear. Immigration is an ideal ‘natural experiment’ for uncovering such mechanisms. We used a battery of psychological measures previously shown to vary cross-culturally to compare the social orientation and cognitive style of 286 residents of East London from three cultural backgrounds: (i) 1st-generation British Bangladeshi immigrants; (ii) 2nd-generation British Bangladeshis raised in the UK to Bangladeshi-raised parents; and (iii) non-migrants whose parents were born and raised in the UK. Model comparison revealed that individualism and dispositional attribution, typical of Western societies, are driven primarily by horizontal cultural transmission (e.g. via mass media), with parents and other family members having little or no effect, while collectivism, social closeness and situational attribution were driven by a mix of vertical/oblique cultural transmission (e.g. via family contact) and horizontal cultural transmission. These individual-level transmission dynamics can explain hitherto puzzling population-level phenomena, such as the partial acculturation of 2nd-generation immigrants on measures such as collectivism (due to the mix of vertical and horizontal cultural transmission), or the observation in several countries of increasing individualism (which is transmitted horizontally and therefore rapidly) despite little corresponding change in collectivism (which is transmitted partly vertically and therefore more slowly). Further consideration of cultural transmission mechanisms, in conjunction with the study of migrant communities and model comparison statistics, can shed light on the persistence of, and changes in, culturally-variable psychological processes.

This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies:

1. 5236 British Pakistani/British Bangladeshi adults from East London Genes and Health (ELGH)
2. 2624 British South Asian mothers from Born in Bradford (mostly Pakistani) (BiB)
3. 1061 British South Asian adults from Birmingham (mostly Pakistani) (Birm)

All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866.

In the sample list file, the columns of interest to most people will be: vcf.id - sample ID from the vcf cohort - which cohort they're in sex.assigned - sex inferred from coverage on the X and Y chromosomes. Individuals for whom this did not match their reported sex have been discarded total, chrX and chrY - coverage within bait regions across all chromosomes, chrX and chrY respectively

Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

Early epidemiology indicated older members of Britain’s Bangladeshi communities were disproportionately affected by COVID-19 related morbidity and mortality. Bangladeshis were more likely to have comorbidities and live in poorer, overcrowded areas in the UK’s urban centres where viral contagion was more likely. This cross-section of socioeconomic, geographical and health related factors underlined the need for clear messaging about social distancing in a complex and shifting risk scenario – messages that this vulnerable group, who speak an oral language (Sylheti), may not have been able to access directly due to low literacy and English language proficiency.

This study identified the practices adopted by Bangladeshis in East London in response to the pandemic, the underlying attitudes and beliefs and whether and how these had been influenced by messages about social distancing. Drawing on our earlier work, it examined the role of social learning in how messages were accessed and interpreted and whether and how the health interactions of this older group were mediated by friends, family members and acquaintances. Remote interviews with older Bangladeshis and their social contacts who performed this mediating role provided insights into how linguistically and culturally appropriate messaging could build on existing beliefs and practices to promote compliance, and on social mediation as a dissemination strategy. We identified the role of choice of language (English or Sylheti), the differences between written and oral representations of COVID-19 risk, and the manifold ways in which linguistic choices give salience to aspects of a risk scenario.

Early epidemiology indicated older members of Britain’s Bangladeshi communities were disproportionately affected by COVID-19 related morbidity and mortality. Bangladeshis were more likely to have comorbidities and live in poorer, overcrowded areas in the UK’s urban centres where viral contagion was more likely. This cross-section of socioeconomic, geographical and health related factors underlined the need for clear messaging about social distancing in a complex and shifting risk scenario – messages that this vulnerable group, who speak an oral language (Sylheti), may not have been able to access directly due to low literacy and English language proficiency.

This study identified the practices adopted by Bangladeshis in East London in response to the pandemic, the underlying attitudes and beliefs and whether and how these had been influenced by messages about social distancing. Drawing on our earlier work, it examined the role of social learning in how messages were accessed and interpreted and whether and how the health interactions of this older group were mediated by friends, family members and acquaintances. Remote interviews with older Bangladeshis and their social contacts who performed this mediating role provided insights into how linguistically and culturally appropriate messaging could build on existing beliefs and practices to promote compliance, and on social mediation as a dissemination strategy. We identified the role of choice of language (English or Sylheti), the differences between written and oral representations of COVID-19 risk, and the manifold ways in which linguistic choices give salience to aspects of a risk scenario.

This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

Means and standard deviations are raw values before transformation. Unstandardised regression coefficients estimate the difference denoted in the column heading, with 95% confidence intervals in square brackets. Note that in the regressions several measures are logged, and some models are non-linear (see text for details), so coefficients should not be compared across models/measures. Differences comprising CIs that do not cross zero are shown in bold. 1st gen = 1st generation British Bangladeshi, 2nd gen = 2nd generation British BangladeshiFor categorisation, higher values indicate holistic cognition, lower indicate analytic.

This dataset represents ethnic group (19 tick-box level) by economic activity status and by occupation, for England and Wales combined. The census data are also broken down by age and by sex for each subtopic.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

This dataset shows population counts for usual residents aged between 16 to 64 years old only. This is to focus on ethnic groups differences among the working age. Population counts in these tables may be different from other publications which use different age breakdowns.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

For quality information in general, please read more from here.

For specific quality information about labour market, please read more from here

Ocupation counts classifiy people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation. (Occupation is classified using the Standard Occupation Classification 2020 version). Details of SOC code can be found here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

This dataset represents ethnic group (19 tick-box level) by general health, by disabled and non-disabled populations, and provision of unpaid care, for England and Wales combined. The data are also broken down by age and sex for each subtopic.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

Read more about this quality notice.

The population base for unpaid care is usual residents aged 5 and above. 5-year age bands have been used for the majority of analysis; however, age groups "5 to 17" and "18 to 24" have been used to allow commentary on young carers and young working age carers.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

_General health _

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

_Disability _

The definition of disability used in the 2021 Census is aligned with the definition of disability under the Equality Act (2010). A person is considered disabled if they self-report having a physical or mental health condition or illness that has lasted or is expected to last 12 months or more, and that this reduces their ability to carry out day-to-day activities.

Unpaid care

An unpaid carer may look after, give help or support to anyone who has long-term physical or mental ill-health conditions, illness or problems related to old age. This does not include any activities as part of paid employment. This help can be within or outside of the carer's household.

As of the third quarter of 2025, the unemployment rate for people of white ethnicity in the United Kingdom was 4.3 percent, the lowest of the provided ethnic groups in this quarter. By contrast, the unemployment rate for people in the Bangladeshi ethnic group was 18.1 percent.

The TSABC analysis assumes the 1KGP demographic model in each population.

Tasks used in the present study, and previously-found cultural differences.

This dataset represents ethnic group (19 tick-box level) by dwelling tenure and by occupancy rating, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

All housing data in these tables do not include commual establishments.

For quality information in general, please read more from here.

For specific quality information about housing, please read more from here

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

Occupancy rating of bedrooms: 0 or more

A household’s accommodation has an ideal number of bedrooms or more bedrooms than required (under-occupied)

Occupancy rating of bedrooms: -1 or less

A household’s accommodation has fewer bedrooms than required (overcrowded)

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.

In the combined Pakistani and Bangladeshi ethnic group in 2021, 16.2% of workers were self-employed, which is the highest percentage out of all ethnic groups.

Summary of predicted models used in model comparison.