Chinese and Vietnamese students made up the largest share of foreign students in South Korea as of **********. Vietnamese students made up **** percent, while Chinese students made up **** percent of the foreign students. According to the source, the total number of foreign students in South Korea had dropped ** percent from **********.

Languages:Percent Korean Speakers: Basic demographics by census tracts in King County based on current American Community Survey 5 Year Average (ACS). Included demographics are: total population; foreign born; median household income; English language proficiency; languages spoken; race and ethnicity; sex; and age. Numbers and derived percentages are estimates based on the current year's ACS. GEO_ID_TRT is the key field and may be used to join to other demographic Census data tables.

In 2024, approximately 958,959 Chinese (including those of Korean descent) resided in South Korea, the largest group of foreign nationals. This was followed by citizens from Vietnam, with around 305,936 people.

In 2023, approximately ******* South Korean nationals lived in Japan, indicating a ******** from the previous year. The number of South Korean residents in Japan gradually ******** during the past decade.

This data harmonizes waves 2, 4, and 5 from the European Social Survey, waves 5 and 6 from the World Values Survey, and wave 4 from the European Values Study. The aim of the study was to analyze gender attitudes using the statement "Men should have more right to a job than women when jobs are scarce". For information on those people who stayed in the sending countries data from WVS6 for the following countries was chosen: Algeria, Argentina, Australia, Brazil, Chile, China, Colombia, Cyprus, Ecuador, Estonia, Ghana, Hong Kong, India, Iraq, Japan, Kazakhstan, Kyrgyzstan, Lebanon, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, Poland, Romania, Russia, Rwanda, Singapore, South Africa, South Korea, Thailand, Tunisia, Turkey, Ukraine, the United States, Uruguay, and Zimbabwe.

I also employ data for several countries from Wave 5 for those societies that were not covered during the last wave: Bulgaria, Canada, Egypt, Finland, Hungary, Indonesia, Italy, Iran, Moldova, Norway, Vietnam, Serbia and Montenegro, and Zambia.

I add European societies that have not been covered by the WVS by using the European Values Study 2008: Albania, Austria, Bosnia and Herzegovina, Croatia, Czech Republic, Denmark, Greece, Ireland, Lithuania, Luxembourg, Macedonia, Slovak Republic, and Slovenia. This gives 65 sending societies in total. As people could have migrated from the European countries of the main focus, namely, Belgium, Germany, France, the Netherlands, Portugal, Spain, Sweden, Switzerland, and the UK, I add those as well, with a final total of 73 sending countries.

Such variables as age, gender, migration status, religiosity measured by self-attribution (How religious are you?), Importance of God, and church attendance as well as denomination are added. Education is binarized for higher o higher. Employment is measured by 6 categories, marital status - by 5 categories. Those who refused to answer were coded into a separate category "refused".

Country-level variables: Human Development Index (HDI), GDP per capita, Polity IV, Freedom House Civil Liberties Index, Gender Inequality Index (by UNDP), unemployment ratio of women to men; percentage of women in the labor market, percentage of women in parliaments, percentage of Islamic population in the country, Islamic majority in the country (binary), level of religiosity in the country (country average for ``How important is God in your life?"), post-communism, Cultural zones from Inglehart's cultural map (8 groups).

ACS DEMOGRAPHIC AND HOUSING ESTIMATES RACE - DP05 Universe - Total population Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The concept “race alone” includes people who reported a single entry (e.g., Korean) and no other race, as well as people who reported two or more entries within the same major race group (e.g., Asian). For example, respondents who reported Korean and Vietnamese are part of the larger “Asian alone” race group.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in North Korea. Power Plant emissions from all power plants in North Korea were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information

As the first step to discover protein disease biomarkers from saliva, global analyses of the saliva proteome have been carried out since the early 2000s, and more than 3,000 proteins have been identified in human saliva. Recently, ethnic differences in the human plasma proteome have been reported, but such corresponding studies on human saliva in this aspect have not been previously reported. Thus, here, in order to determine ethnic differences in the human saliva proteome, a Korean whole saliva (WS) proteome catalogue indexing 480 proteins was built and characterized through nLC-Q-IMS-TOF analyses of WS samples collected from eleven healthy South Korean male adult volunteers for the first time. Identification of 226 distinct Korean WS proteins, not observed in the integrated human saliva protein dataset, and significant gene ontology distribution differences in the Korean WS proteome compared to the integrated human saliva proteome strongly support ethnic differences in the human saliva proteome. Additionally, the potential value of ethnicity-specific human saliva proteins as biomarkers for diseases highly prevalent in that ethnic group was confirmed by finding 35 distinct Korean WS proteins likely to be associated with the top 10 deadliest diseases in South Korea. Finally, the present Korean WS protein list can serve as the first level reference for future proteomic studies including disease biomarker studies on Korean saliva.

The aging of the brain is a well-investigated topic, but existing analyses have mainly focused on Caucasian samples. To investigate brain aging in East Asians, we measured cortical and subcortical volumes from magnetic resonance imaging (MRI) scans of 1,008 cognitively normal elderly Koreans from the Gwangju Alzheimer’s and Related Dementia cohort and 342 Caucasians from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. To determine whether the aging effect varies with ethnicity and sex, beta coefficients of age and confidence intervals (CIs) were estimated in each ethnicity–sex group using a bootstrap method and a regression analysis using the relative volume to intracranial volume as predicted. The betas or aging slopes largely were not significantly different between ethnicity and sex groups in most types of brain structures. However, ethnic differences between the two female groups were found in the brain, most cortical regions, and a few subcortical regions. Ethnic differences in brain aging are likely due in large part to genetic factors; thus, we compared carriers and non-carriers of a gene relevant to longevity and neurodegenerative diseases, such as apolipoprotein E (APOE) ε4. The regions with ethnic differences in women also showed significant differences between Korean APOE ε4 non-carriers and Caucasian APOE ε4 carriers. Furthermore, Caucasian women showed significant APOE ε4 effects in the largest number of regions. These results illustrate that much of the ethnic differences in females may be explained by synergistic effects of ethnic background and APOE ε4 carrier status. Our results suggest that sex-dependent differences of aging between ethnic backgrounds may be due to ethnicity-dependent effects of genetic risk factors, such as APOE ε4. We also presented the normative information on volume estimates of the brain structures of the elderly Korean people in the subdivided age groups. This normative information of the aging brain stratified by ethnicity provides the age-related reference ranges quantified to replace visual judgment and facilitate precise clinical decision-making.

Brain aging is becoming an increasingly important topic, and the norms of brain structures are essential for diagnosing neurodegenerative diseases. However, previous studies of the aging brain have mostly focused on Caucasians, not East Asians. The aim of this paper was to examine ethnic differences in the aging process of brain structures or to determine to what extent ethnicity affects the normative values of lobar and subcortical volumes in clinically normal elderly and the diagnosis in multi-racial patients with Alzheimer's disease (AD). Lobar and subcortical volumes were measured using FreeSurfer from MRI data of 1,686 normal Koreans (age range 59–89) and 851 Caucasian, non-Hispanic subjects in the ADNI and OASIS datasets. The regression models were designed to predict brain volumes, including ethnicity, age, sex, intracranial volume (ICV), magnetic field strength (MFS), and MRI scanner manufacturers as independent variables. Ethnicity had a significant effect for all lobar (|β| > 0.20, p < 0.001) and subcortical regions (|β| > 0.08, p < 0.001) except left pallidus and bilateral ventricles. To demonstrate the validity of the z-score for AD diagnosis, 420 patients and 420 normal controls were selected evenly from the Korean and Caucasian datasets. The four validation groups divided by race and diagnosis were matched on age and sex using a propensity score matching. We analyzed whether and to what extent the ethnicity adjustment improved the diagnostic power of the logistic regression model that was built using the only z-scores of six regions: bilateral temporal cortices, hippocampi, and amygdalae. The performance of the classifier after ethnicity adjustment was significantly improved compared with the classifier before ethnicity adjustment (ΔAUC = 0.10, D = 7.80, p < 0.001; AUC comparison test using bootstrap). Korean AD dementia patients may not be classified by Caucasian norms of brain volumes because the brain regions vulnerable to AD dementia are bigger in normal Korean elderly peoples. Therefore, ethnicity is an essential factor in establishing normative data for regional volumes in brain aging and applying it to the diagnosis of neurodegenerative diseases.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The numbers by detailed Asian groups do not add to the total population. This is because the detailed Asian groups are tallies of the number of Asian responses rather than the number of Asian respondents. Responses that include more than one race and/or Asian group are counted several times. For example, a respondent reporting "Korean, Filipino, and Black or African American" would be included in the Korean as well as the Filipino numbers. "Specified" includes the remaining Other Asian write-in responses that were not tallied into separate groups in the table. "Not specified" includes respondents who checked the Other Asian response category on the ACS questionnaire and did not write in a specific group or wrote in a generic term such as "Asian" or "Asiatic.".The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

From Source: Food budget shares and income and price elasticities are estimated, using 1996 data, for nine major consumption groups and eight food subgroups across 114 countries. The broad groups include food, beverage, and tobacco; clothing and footwear; education; gross rent, fuel, and power; house furnishings and operations; medical care; recreation; transport and communications; and other items. Food subgroups include bread and cereals, meat, fish, dairy products, fats and oils, fruit and vegetables, beverages and tobacco, and other food products. The depth and breath of these data provide an opportunity to incorporate the elasticities into research on changing food demand patterns. Albania Antigua & Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bolivia Botswana Brazil Bulgaria Cameroon Canada Chile Congo Cote d'Ivoire Czech Republic Denmark Dominica Ecuador Egypt Estonia Fiji Finland France Gabon Georgia Germany Greece Grenada Guinea Hong Kong Hungary Iceland Indonesia Iran Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea Kyrgyzstan Latvia Lebanon Lithuania Luxembourg Macedonia Madagascar Malawi Mali Mauritius Mexico Moldova Mongolia Morocco Nepal Netherlands New Zealand Nigeria Norway Oman Pakistan Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Senegal Sierra Leone Singapore Slovakia Slovenia Spain Sri Lanka St. Kitts & Nevis St. Lucia St.Vincent & Grenadines Swaziland Sweden Switzerland Syria Tajikistan Tanzania Thailand Trinidad & Tobago Tunisia Turkey Turkmenistan Ukraine United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam Yemen Zambia Zimbabwe

Korea Racing Authority provides period-by-period performance information of jockeys active at racecourses in Seoul, Busan-Gyeongnam, and Jeju. (Information provided includes racecourse (meet), jockey name (jkName), jockey number (jkNo), total number of appearances within the period (raceCnttsum), number of first-place finishes within the period (firstCnt), number of second-place finishes within the period (secondCnt), number of third-place finishes within the period (thirdCnt), winning rate (winRateTsum), and place rate (quRateTsum) data.) - You can search for data using the racecourse type (meet-1. Seoul, 2. Jeju, 3. Busan-Gyeongnam), search start date for race day (rc_date_fr), search end date for race day (rc_date_to), page number (pageNo), and the number of rows to be displayed per page (numOfRows) in the request message.

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Ethnic Foods Market Size 2025-2029

The ethnic foods market size is valued to increase USD 32.82 billion, at a CAGR of 10.6% from 2024 to 2029. Increasing popularity of Italian cuisine will drive the ethnic foods market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 36% growth during the forecast period.
By Distribution Channel - Offline segment was valued at USD 25.99 billion in 2023
By Type - Non-vegetarian segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 120.55 million
Market Future Opportunities: USD 32815.90 million
CAGR : 10.6%
APAC: Largest market in 2023

Market Summary

The market encompasses a diverse range of cuisines from around the world, with core technologies and applications continually shaping its evolution. Notably, the increasing popularity of Italian cuisine, driven by its rich flavors and cultural appeal, represents a significant market trend. Leading companies are also innovating in sustainable packaging solutions to cater to evolving consumer preferences. However, the market faces challenges such as the fluctuating price of raw materials, which can impact production costs.
According to recent market research, the global ethnic food market is projected to account for over 25% of the total food industry revenue by 2025. This underscores the market's significant influence and the potential for continued growth in this sector.

What will be the Size of the Ethnic Foods Market during the forecast period?

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How is the Ethnic Foods Market Segmented and what are the key trends of market segmentation?

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

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.

Ethnic food markets continue to evolve, with sensory evaluation methods playing a crucial role in product development. Compliance with dietary guidelines is essential for reaching diverse consumer segments. New product launches incorporate advanced food processing technologies, such as high-pressure processing and pulsed electric fields, to extend shelf life and enhance flavor profiles. Ingredient traceability systems are increasingly important for ensuring food authenticity and consumer trust. Consumer purchasing behavior is influenced by cultural food traditions and market segmentation strategies. Consumer preference mapping and flavor compound analysis guide product development and marketing efforts. Ethnic food distribution relies heavily on hygiene and temperature control to maintain product quality and food safety regulations.

Sustainable sourcing practices and nutrition labeling standards are becoming more critical as consumers demand transparency and healthier options. Food product formulation and e-commerce food delivery services are growing trends, with culinary ingredient sourcing and quality control procedures ensuring consistent product quality. The ethnic food market is experiencing significant growth, with sales in the offline distribution channel dominated by supermarkets and hypermarkets. These channels account for approximately 70% of ethnic food sales due to their vast shelf spaces, storage capacity, and consumer convenience. In the online distribution channel, e-commerce platforms are gaining popularity, with sales projected to increase by 15% year-over-year.

Additionally, food preservation techniques, such as spice blend optimization and authentic recipe replication, are essential for maintaining product authenticity and extending shelf life. Product lifecycle management, restaurant menu engineering, and food safety regulations are also key considerations for businesses operating in the ethnic food market. Food waste reduction is a growing concern, with ingredient cost optimization and sustainable sourcing practices becoming increasingly important for maintaining profitability and reducing environmental impact. The ethnic food market is expected to grow by 12% in the next five years, driven by changing consumer preferences and the increasing availability of diverse food options.

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The Offline segment was valu

Number of people belonging to a visible minority group as defined by the Employment Equity Act and, if so, the visible minority group to which the person belongs. The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.' The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Latin American, Arab, Southeast Asian, West Asian, Korean and Japanese.

The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas (SIGMA) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the Hong Kong cohort was part of the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) consortium, which is a NIDDK-funded international research consortium that seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. The T2D-GENES Project is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGaP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGaP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases. Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow-Up Study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1 and Follow-Up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow-Up Study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow-Up Study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 352 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmö-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow-Up Study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow-Up Study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 and SIGMA v2 Starr County, Texas US 1762 1738 Hispanic SIGMA v1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMA v1 and SIGMA v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMA v1 and SIGMA v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMA v1 and SIGMA v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multiethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multiethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 531 538 South Asian T2D-GENES Project 1 and Follow-Up Study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow-Up Study Pakistan Risk of Myocardial Infarction Study (PROMIS) Pakistan 914 932 The research studies in Hong Kong contributed 493 cases and 485 controls to T2D-GENES Follow-Up study.

Perilla Leaf Kimchi Market Outlook

According to our latest research, the global Perilla Leaf Kimchi market size reached USD 1.12 billion in 2024, demonstrating a robust expansion driven by rising international interest in Korean cuisine and fermented foods. The market is registering a CAGR of 7.8% and is projected to attain a value of USD 2.21 billion by 2033. This growth is primarily attributed to increasing consumer awareness about the health benefits of fermented foods, growing popularity of Korean food culture worldwide, and the rising demand for convenient, ready-to-eat ethnic food products.

The primary growth factor for the Perilla Leaf Kimchi market is the surge in global demand for fermented food products, recognized for their probiotic content and associated digestive health benefits. Consumers are increasingly seeking out natural, minimally processed foods that support gut health, immunity, and overall wellness. Perilla Leaf Kimchi, with its unique flavor profile and nutritional properties, has gained significant traction not only in traditional markets such as South Korea but also across North America, Europe, and parts of Asia Pacific. The proliferation of health-conscious consumers, coupled with a growing preference for plant-based and vegan food options, has further fueled the adoption of Perilla Leaf Kimchi as an integral part of modern diets.

Another pivotal factor propelling the market is the global rise of Korean pop culture, commonly referred to as the "K-wave" or Hallyu. The increasing popularity of Korean dramas, music, and culinary shows has introduced international audiences to a wide array of Korean dishes, with kimchi being at the forefront. As a result, Perilla Leaf Kimchi has seen heightened visibility in international food festivals, specialty stores, and digital platforms. This cultural phenomenon has not only expanded the consumer base but has also encouraged local manufacturers and food service providers in various regions to include Perilla Leaf Kimchi in their product offerings, thereby driving market expansion.

Additionally, the market is witnessing significant innovation in product development, packaging, and distribution channels. Manufacturers are introducing a variety of Perilla Leaf Kimchi products, including organic, flavored, and fusion variants, to cater to diverse consumer preferences. Advancements in packaging technology, such as vacuum-sealed pouches and resealable jars, have enhanced product shelf life and convenience, making Perilla Leaf Kimchi more accessible to global consumers. The rise of e-commerce and online grocery delivery platforms has also played a crucial role in expanding the reach of Perilla Leaf Kimchi, enabling producers to tap into previously underserved markets and demographics.

The growing interest in Korean cuisine has also opened doors for other varieties of kimchi, such as Arrowhead Cabbage Kimchi. This variant, known for its crisp texture and mild flavor, is gaining popularity among consumers who are exploring different types of kimchi beyond the traditional napa cabbage. Arrowhead Cabbage Kimchi offers a unique taste experience, appealing to those who prefer a less pungent and more subtle flavor profile. As consumers become more adventurous in their culinary pursuits, the demand for diverse kimchi varieties, including Arrowhead Cabbage Kimchi, is expected to rise. This trend is further supported by the increasing availability of specialty ingredients and the growing influence of Korean food culture worldwide.

Regionally, Asia Pacific continues to dominate the Perilla Leaf Kimchi market, accounting for over 51% of global revenue in 2024. However, North America and Europe are emerging as high-growth regions, driven by increasing immigrant populations, rising interest in ethnic foods, and growing awareness of the health benefits of fermented products. The market in these regions is characterized by a strong presence of specialty stores, ethnic supermarkets, and a burgeoning online retail sector. Meanwhile, the Middle East & Africa and Latin America are witnessing gradual adoption, with market players focusing on educating consumers and expanding distribution networks to unlock new growth opportunities.

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Ethnic Wear Market Size 2025-2029

The ethnic wear market size is forecast to increase by USD 45.9 billion, at a CAGR of 8.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the expansion of the fashion industry and the increasing popularity of traditional clothing. Ethnic wear, with its unique designs and cultural significance, has gained prominence in the global fashion landscape. This trend is further fueled by the increased online presence of ethnic wear brands through e-commerce, making it more accessible to consumers worldwide. Seasonal demand is another key driver for the market. Traditional clothing holds cultural significance and is often worn during festivals and special occasions. As a result, there is a consistent demand for ethnic wear throughout the year, providing a steady revenue stream for businesses in this sector.
However, challenges persist, including the need for authenticity and cultural sensitivity in design and production. Additionally, competition from mass-market fashion brands offering ethnic-inspired designs presents a significant challenge for ethnic wear brands. Navigating these challenges requires a deep understanding of consumer preferences and cultural nuances, as well as a commitment to authenticity and innovation. Companies that can effectively address these challenges and capitalize on the growing demand for ethnic wear will be well-positioned to succeed in this dynamic market.

What will be the Size of the Ethnic Wear Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market trends shaping the industry's landscape. Traditional techniques, such as block printing and weaving, coexist with modern designs, resulting in a fusion of cultural significance and contemporary fashion. Consumer preferences for lehenga cholis and salwar kameez remain strong, with price points and fabric weight influencing purchasing decisions. Brands position themselves in various sectors, catering to formal, casual, and daily wear markets. Modern designs incorporate traditional techniques, resulting in innovative creations. Quality control is paramount, ensuring garment durability and consumer satisfaction. Ethical sourcing and sustainable practices are gaining importance, with an increasing focus on fair trade and e-commerce platforms.

Consumers seek personal styling options, leading to seasonal collections and diverse target demographics. Textile dyes, thread count, and fiber content vary, with natural and synthetic options available. Digital printing and screen printing techniques add visual interest to garments. Garment care instructions are essential for maintaining the longevity of ethnic wear. Supply chain management and retail channels continue to evolve, with wholesale markets playing a crucial role in distribution. Price points and fabric weight influence consumer choices, with daily wear and casual options often more affordable than formal wear. Modern designs and traditional techniques blend seamlessly, creating a vibrant and ever-changing market landscape.

The market's continuous dynamism reflects the industry's ability to adapt to consumer preferences and cultural influences.

How is this Ethnic Wear Industry segmented?

The ethnic wear industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Women
  Men
  Kids


Distribution Channel

  Offline
  Online


Geography

  North America

    US
    Canada


  Europe

    Germany


  APAC

    China
    India
    Japan
    Singapore
    South Korea


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The women segment is estimated to witness significant growth during the forecast period.

Ethnic wear holds significant cultural significance and continues to be a popular choice for women worldwide. In 2024, the women's segment led the market, accounting for the largest revenue share. Factors such as cultural events, festivals, weddings, and other occasions drive demand for ethnic wear. Traditional techniques like block printing, weaving, and embroidery remain essential in creating authentic ethnic wear. However, modern designs, fusion styles, and consumer preferences for comfortable daily wear, formal occasion wear, and casual attire are influencing market trends. E-commerce platforms have emerged as a significant retail channel, enabling easy access to ethnic wear from various regions.

Seasonal collections and personal styling services cater to the evolving consumer preferences. Ethical sourcing and quality control are

In 2023, U.S. citizens accounted for approximately **** percent of foreign grooms who married South Korean women. This was followed by Chinese and Vietnamese grooms.

Final trimmed multivariate logistic regression with significant interaction effects by ethnicity (unweighted N = 3,710; weighted N = 1,710,233).