Little research has been conducted on how internal migration of different ethnic groups, in and out of areas, contributes to population patterns. Research that has explored migration by ethnicity has compared 'white' with 'non-white' people to discuss patterns of segregation, ethnic concentration and majority population 'ghettos' or 'enclaves'. However, it is likely that there will be variations within the minority ethnic group that will offer important insights to these debates. This research will explore patterns of residential migration within different ethnic groups within England and Wales. Specifically, whether there is a tendency for people from different ethnic groups to move into or out of areas where their own ethnic group has a high or low density. These research questions will be explored through secondary analysis of the 2001 census using a specially commissioned table containing information about migration and ethnicity. Inflows and outflows of individual ethnic groups (as defined by the 2001 census) will be separately measured at the ward level (to assess local level migration). Patterns will be mapped using GIS software. This detailed analysis will help to establish if patterns of internal migration for different ethnic groups are related to the densities of their own and other groups.
According to a survey on racial and religious harmony in Singapore, 70.7 percent of Malay respondents said that they knew of someone from the Chinese community with whom they could speak with to clarify issues regarding practices from said community. Singapore is a multi-ethnic and multi-religious society, with citizens categorized into four main ethnic groups, known as CMIO: Chinese, Malay, Indian, and Others. Those categorized under the "Others" include Eurasians, Caucasians, Arabs, and Filipinos, among others. Those from an ethnic Chinese background make up the majority of the population in Singapore.
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To assess differences in psychological outcomes as well as risk and protective factors for these outcomes among several USA ethnic groups and identify correlates of these psychological outcomes among adults with diabetes in the second Diabetes Attitudes, Wishes and Needs (DAWN2) study. The core USA DAWN2 sample was supplemented by independent samples of specific ethnic minority groups, yielding a total of 447 White non-Hispanics, 241 African Americans, 194 Hispanics, and 173 Chinese Americans (n = 1055). Multivariate analysis examined ethnic differences in psychological outcomes and risk/protective factors (disease, demographic and socioeconomic factors, health status and healthcare access/utilization, subjective burden of diabetes and social support/burden). Separate analyses were performed on each group to determine whether risk/protective factors differed across ethnic groups. Psychological outcomes include well-being, quality of life, impact of diabetes on life domains, diabetes distress, and diabetes empowerment. NCT01507116. Ethnic minorities tended to have better psychological outcomes than White non-Hispanics, although their diabetes distress was higher. Levels of most risk and protective factors differed significantly across ethnic groups; adjustment for these factors reduced ethnic group differences in psychological outcomes. Health status and modifiable diabetes-specific risk/protective factors (healthcare access/utilization, subjective diabetes burden, social support/burden) had strong associations with psychological outcomes, especially diabetes distress and empowerment. Numerous interactions between ethnicity and other correlates of psychological outcomes suggest that ethnic groups are differentially sensitive to various risk/protective factors. Potential limitations are the sample sizes and representativeness. Ethnic groups differ in their psychological outcomes. The risk/protective factors for psychological outcomes differ across ethnic groups and different ethnic groups are more/less sensitive to their influence. These findings can aid the development of strategies to overcome the most prominent and influential psychosocial barriers to optimal diabetes care within each ethnic group.
This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.
The diversity index measures the likelihood of two randomly selected people belonging to different racial and ethnic groups. A score of 0 represents that everyone in that area shares the same racial and ethnic background. A score nearing 100 represents that almost everyone of the population in that area has different racial and ethnic backgrounds.1The following racial and ethnic groups were used to calculate the diversity index:2American Indian and Alaska Native (AIAN)Asian/Asian AmericanBlack/African AmericanHispanic/Latina/o/xNative Hawaiian and Other Pacific Islander (NHPI)WhiteMultiracialOf Another Race
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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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.
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The coronavirus disease (COVID-19) has revealed existing health inequalities in racial and ethnic minority groups in the US. This work investigates and quantifies the non-uniform effects of geographical location and other known risk factors on various ethnic groups during the COVID-19 pandemic at a national level. To quantify the geographical impact on various ethnic groups, we grouped all the states of the US. into four different regions (Northeast, Midwest, South, and West) and considered Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Hispanic, Non-Hispanic Asian (NHA) as ethnic groups of our interest. Our analysis showed that infection and mortality among NHB and Hispanics are considerably higher than NHW. In particular, the COVID-19 infection rate in the Hispanic community was significantly higher than their population share, a phenomenon we observed across all regions in the US but is most prominent in the West. To gauge the differential impact of comorbidities on different ethnicities, we performed cross-sectional regression analyses of statewide data for COVID-19 infection and mortality for each ethnic group using advanced age, poverty, obesity, hypertension, cardiovascular disease, and diabetes as risk factors. After removing the risk factors causing multicollinearity, poverty emerged as one of the independent risk factors in explaining mortality rates in NHW, NHB, and Hispanic communities. Moreover, for NHW and NHB groups, we found that obesity encapsulated the effect of several other comorbidities such as advanced age, hypertension, and cardiovascular disease. At the same time, advanced age was the most robust predictor of mortality in the Hispanic group. Our study quantifies the unique impact of various risk factors on different ethnic groups, explaining the ethnicity-specific differences observed in the COVID-19 pandemic. The findings could provide insight into focused public health strategies and interventions.
According to a survey on racial and religious harmony in Singapore, **** percent of respondents whose ethnicity was categorized under 'Others' agreed with the statement that they liked meeting and getting to know people from other ethnic groups than their own. Singapore is a multi-ethnic and multi-religious society, with citizens categorized into four main ethnic groups, known as CMIO: Chinese, Malay, Indian, and Others. Those categorized under the "Others" include Eurasians, Caucasians, Arabs, and Filipinos, among others. Those from an ethnic Chinese background make up the majority of the population in Singapore.
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Context
The dataset tabulates the population of Carbon by race. It includes the population of Carbon across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Carbon across relevant racial categories.
Key observations
The percent distribution of Carbon population by race (across all racial categories recognized by the U.S. Census Bureau): 100% are white.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Carbon Population by Race & Ethnicity. You can refer the same here
Recent research shows important racial-ethnic differences in how individuals spend time in housework. Yet, our understanding of how the racial-ethnic makeup of couples shapes gender equality in the division of housework remains limited. We use couple-level data from the 2017-2019 waves of the Panel Study of Income Dynamics to visually illustrate how each partner’s race-ethnicity and their combination are associated with the gender division of housework in Black, Hispanic, and white individuals. Results show significant heterogeneity in the share of housework and total housework hours between racial-ethnic groups, underscoring the need for a couple-level understanding of how the racial-ethnic makeup of couples may shape the gender division of housework.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Decatur County by race. It includes the population of Decatur County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Decatur County across relevant racial categories.
Key observations
The percent distribution of Decatur County population by race (across all racial categories recognized by the U.S. Census Bureau): 92.93% are white, 1.64% are Black or African American, 0.29% are American Indian and Alaska Native, 0.52% are Asian, 0.01% are Native Hawaiian and other Pacific Islander, 1.04% are some other race and 3.56% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Decatur County Population by Race & Ethnicity. You can refer the same here
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License information was derived automatically
We examine the link between ethnic diversity and food insecurity. Using data from the Ghana Living Standards Survey, we measure food insecurity using the Food Insecurity Experience Scale and measure ethnic diversity at the district level using information from the Ghana Population and Housing Census. After addressing endogeneity, we find that an increase in ethnic diversity is associated with an increase in food insecurity. This result is robust to different approaches to addressing endogeneity, different measures of ethnic diversity and alternative ways of conceptualising food insecurity. Our findings further show that trust, threat of criminal violence and threat of war/conflict are important channels through which ethnic diversity influences food insecurity.
Ethnic Foods Market Size 2025-2029
The ethnic foods market size is forecast to increase by USD 32.82 billion at a CAGR of 10.6% between 2024 and 2029.
The market is experiencing significant growth, driven by the increasing popularity of diverse culinary traditions, particularly Italian cuisine. This trend is influenced by the globalization of food culture and the expanding palates of consumers. Moreover, leading companies in the market are innovating and implementing sustainable packaging solutions to cater to evolving consumer preferences and reduce environmental impact. However, the market is not without challenges, including the fluctuating prices of raw materials, which can impact profitability and operational planning. Companies seeking to capitalize on market opportunities must stay agile and adapt to these dynamics, while also navigating the complexities of international trade regulations and cultural sensitivities.
To succeed, strategic partnerships, product diversification, and a strong commitment to sustainability are essential. Companies that can effectively manage these challenges and leverage market trends will be well-positioned for growth and long-term success. Food festivals, demonstrations, cooking classes, ethnic restaurants, food trucks, and grocery stores cater to the diverse needs and preferences of consumers. Food service software and inventory management systems streamline operations for businesses in this market.
What will be the Size of the Ethnic Foods Market during the forecast period?
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The market encompasses a diverse range of cuisines that reflect the rich cultural heritage and traditions of various communities worldwide. This market is characterized by its dynamic growth and innovation, driven by increasing consumer interest in cross-cultural food experiences, dietary preferences, and food allergies. The demand for gluten-free products, halal and kosher certified items, organic foods, and sustainably sourced ingredients continues to rise, shaping market trends. Ethical business practices, such as fair trade and food waste reduction, are also gaining traction. Food accessibility, equity, and community initiatives are essential aspects of the market, ensuring that diverse culinary traditions are preserved and shared.
Ethnic foods are celebrated through various channels, including food education, cultural heritage preservation, food history, food anthropology, and food media. The market is further enriched by the fusion of culinary arts, food photography, food styling, recipe development, food writing, food blogging, and food vlogging. Overall, the market is a vibrant and evolving landscape that reflects the richness and diversity of global culinary traditions.
How is this Ethnic Foods Industry segmented?
The ethnic foods industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Distribution Channel
Offline
Online
Type
Non-vegetarian
Vegetarian
Product Type
Ready-to-eat meals
Frozen meals
Packaged meals
Dried meals
Canned meals
Variant
Asian
Italian
Mexican
Others
Geography
Europe
France
Germany
Italy
UK
APAC
China
India
Japan
South Korea
North America
US
Canada
Middle East and Africa
South America
By Distribution Channel Insights
The offline segment is estimated to witness significant growth during the forecast period. Ethnic cuisine continues to captivate consumers with its unique and authentic flavors, driving demand for specialty foods and international cuisine. Cultural food preferences are at the forefront of global food trends, with regional specialties and traditional recipes gaining popularity. Food import and ingredient sourcing are crucial aspects of the ethnic food market, with ethnic grocery stores, restaurants, and food delivery services playing significant roles in the industry. Food retail, manufacturing, and processing industries cater to the diverse needs of consumers, offering a wide range of ethnic ingredients, spices and herbs, sauces and marinades, frozen foods, canned foods, dry goods, fresh produce, meat and poultry, dairy products, bakery goods, ethnic snacks, and beverages.
Food preservation techniques, food innovation, and new product development are essential for maintaining the freshness and authenticity of ethnic food products. Food safety, labeling, and dietary preferences are critical considerations in the ethnic food market, with cultural influences shaping consumer demand. Food festivals, cultural events, and food tourism further fuel the growth of the ethnic food industry. Food distribution, supply chain management, and logistics are essential co
This statistic shows the share of ethnic groups in Australia in the total population. 33 percent of the total population of Australia are english.
Australia’s population
Australia’s ethnic diversity can be attributed to their history and location. The country’s colonization from Europeans is a significant reason for the majority of its population being Caucasian. Additionally, being that Australia is one of the most developed countries closest to Eastern Asia; its Asian population comes as no surprise.
Australia is one of the world’s most developed countries, often earning recognition as one of the world’s economical leaders. With a more recent economic boom, Australia has become an attractive country for students and workers alike, who seek an opportunity to improve their lifestyle. Over the past decade, Australia’s population has slowly increased and is expected to continue to do so over the next several years. A beautiful landscape, many work opportunities and a high quality of life helped play a role in the country’s development. In 2011, Australia was considered to have one of the highest life expectancies in the world, with the average Australian living to approximately 82 years of age.
From an employment standpoint, Australia has maintained a rather low employment rate compared to many other developed countries. After experiencing a significant jump in unemployment in 2009, primarily due to the world economic crisis, Australia has been able to remain stable and slightly increase employment year-over-year.
Biennial statistics on the representation of Black, Asian and Minority Ethnic groups as victims, suspects, offenders and employees in the Criminal Justice System.
These reports are released by the Ministry of Justice and produced in accordance with arrangements approved by the UK Statistics Authority.
This report provides information about how members of Black, Asian and Minority Ethnic (BME) Groups in England and Wales were represented in the Criminal Justice System (CJS) in the most recent year for which data were available, and, wherever possible, across the last five years. Section 95 of the Criminal Justice Act 1991 requires the Government to publish statistical data to assess whether any discrimination exists in how the CJS treats people based on their race.
These statistics are used by policy makers, the agencies who comprise the CJS and others to monitor differences between ethnic groups and where practitioners and others may wish to undertake more in-depth analysis. The identification of differences should not be equated with discrimination as there are many reasons why apparent disparities may exist.
The most recent data on victims showed differences in the risks of crime between ethnic groups and, for homicides, in the relationship between victims and offenders. Overall, the number of racist incidents and racially or religiously aggravated offences recorded by the police had decreased over the last five years. Key Points:
Per 1,000 population, higher rates of s1 Stop and Searches were recorded for all BME groups (except for Chinese or Other) than for the White group. While there were decreases across the last five years in the overall number of arrests and in arrests of White people, arrests of those in the Black and Asian group increased.
Data on out of court disposals and court proceedings show some differences in the sanctions issued to people of differing ethnicity and also in sentence lengths. These differences are likely to relate to a range of factors including variations in the types of offences committed and the plea entered, and should therefore be treated with caution. Key points:
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Musculoskeletal performance is a complex trait influenced by environmental and genetic factors, and it has different manifestations in different populations. Heilongjiang province, located in northern China, is a multi-ethnic region with human cultures dating back to the Paleolithic Age. The Daur, Hezhen, Ewenki, Mongolian and Manchu ethnic groups in Heilongjiang province may have strong physical fitness to a certain extent. Based on the genetic characteristics of significant correlation between some important genes and skeletal muscle function, this study selected 23 SNPs of skeletal muscle strength-related genes and analyzed the distribution of these loci and genetic diversity in the five ethnic groups. Use Haploview (version 4.1) software to calculate the chi-square and the Hardy-Weinberg equilibrium to assess the difference between the two ethnic groups. Use R (version 4.0.2) software to perform principal component analysis of different ethnic groups. Use MEGA (version 7.0) software to construct the phylogenetic tree of different ethnic groups. Use POPGENE (version 1.32) software to calculate the heterozygosity and the FST values of 23 SNPs. Use Arlequin (version 3.5.2.2) software to analyze molecular variance (AMOVA) among 31 populations. The results showed that there was haplotype diversity of VDR, angiotensin-converting enzyme, ACTN3, EPO and IGF1 genes in the five ethnic groups, and there were genetic differences in the distribution of these genes in the five ethnic groups. Among them, the average gene heterozygosity (AVE_HET) of the 23 SNPs in the five populations was 0.398. The FST values of the 23 SNPs among the five ethnic groups varied from 0.0011 to 0.0137. According to the principal component analysis, the genetic distance of Daur, Mongolian and Ewenki is relatively close. According to the phylogenetic tree, the five ethnic groups are clustered together with the Asian population. These data will enrich existing genetic information of ethnic minorities.
We present a field experiment that uses geo-referenced smartphone advertisements to measure ethnic preferences at a highly disaggregated level. Different types of banners advertising a vote matching tool are randomly displayed to mobile Internet users in Bosnia and Herzegovina, while recording their spatial coordinates. Differences in the response (click) rate to different ethnic cues on these banners are used to measure temporal and spatial variation in ethnic preferences among the population of Bosnia and Herzegovina. Our study lays out the theoretical and practical underpinnings of this technology and discusses its potential for future applications, but also highlights limitations of this approach.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Naples town by race. It includes the population of Naples town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Naples town across relevant racial categories.
Key observations
The percent distribution of Naples town population by race (across all racial categories recognized by the U.S. Census Bureau): 96.96% are white, 1.04% are Asian and 2% are multiracial.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Naples town Population by Race & Ethnicity. You can refer the same here
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
75% of households from the Bangladeshi ethnic group were in the 2 lowest income quintiles (after housing costs were deducted) between April 2021 and March 2024.
Little research has been conducted on how internal migration of different ethnic groups, in and out of areas, contributes to population patterns. Research that has explored migration by ethnicity has compared 'white' with 'non-white' people to discuss patterns of segregation, ethnic concentration and majority population 'ghettos' or 'enclaves'. However, it is likely that there will be variations within the minority ethnic group that will offer important insights to these debates. This research will explore patterns of residential migration within different ethnic groups within England and Wales. Specifically, whether there is a tendency for people from different ethnic groups to move into or out of areas where their own ethnic group has a high or low density. These research questions will be explored through secondary analysis of the 2001 census using a specially commissioned table containing information about migration and ethnicity. Inflows and outflows of individual ethnic groups (as defined by the 2001 census) will be separately measured at the ward level (to assess local level migration). Patterns will be mapped using GIS software. This detailed analysis will help to establish if patterns of internal migration for different ethnic groups are related to the densities of their own and other groups.