Mexico is the country with the largest number of native Spanish speakers in the world. As of 2024, 132.5 million people in Mexico spoke Spanish with a native command of the language. Colombia was the nation with the second-highest number of native Spanish speakers, at around 52.7 million. Spain came in third, with 48 million, and Argentina fourth, with 46 million. Spanish, a world language As of 2023, Spanish ranked as the fourth most spoken language in the world, only behind English, Chinese, and Hindi, with over half a billion speakers. Spanish is the official language of over 20 countries, the majority on the American continent, nonetheless, it's also one of the official languages of Equatorial Guinea in Africa. Other countries have a strong influence, like the United States, Morocco, or Brazil, countries included in the list of non-Hispanic countries with the highest number of Spanish speakers. The second most spoken language in the U.S. In the most recent data, Spanish ranked as the language, other than English, with the highest number of speakers, with 12 times more speakers as the second place. Which comes to no surprise following the long history of migrations from Latin American countries to the Northern country. Moreover, only during the fiscal year 2022. 5 out of the top 10 countries of origin of naturalized people in the U.S. came from Spanish-speaking countries.

The United States is the non-hispanic country with the largest number of native Spanish speakers in the world, with approximately 41.89 million people with a native command of the language in 2024. However, the European Union had the largest group of non-native speakers with limited proficiency of Spanish, at around 28 million people. Furthermore, Mexico is the country with the largest number of native Spanish speakers in the world as of 2024.

As of 2023, around 37.99 million people of Mexican descent were living in the United States - the largest of any Hispanic group. Puerto Ricans, Salvadorans, Cubans, and Dominicans rounded out the top five Hispanic groups living in the U.S. in that year.

ACS DEMOGRAPHIC AND HOUSING ESTIMATES HISPANIC OR LATINO AND RACE - DP05 Universe - Total population Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 The terms “Hispanic,” “Latino,” and “Spanish” are used interchangeably. Some respondents identify with all three terms while others may identify with only one of these three specific terms. People who identify with the terms “Hispanic,” “Latino,” or “Spanish” are those who classify themselves in one of the specific Hispanic, Latino, or Spanish categories listed on the questionnaire (“Mexican, Mexican Am., or Chicano,” “Puerto Rican,” or “Cuban”) as well as those who indicate that they are “another Hispanic, Latino, or Spanish origin.” People who do not identify with one of the specific origins listed on the questionnaire but indicate that they are “another Hispanic, Latino, or Spanish origin” are those whose origins are from Spain, the Spanish-speaking countries of Central or South America, or another Spanish culture or origin. Origin can be viewed as the heritage, nationality group, lineage, or country of birth of the person or the person’s parents or ancestors before their arrival in the UnitedStates. People who identify their origin as Hispanic, Latino, or Spanish may be of any race.

In 2023, California had the highest Hispanic population in the United States, with over 15.76 million people claiming Hispanic heritage. Texas, Florida, New York, and Illinois rounded out the top five states for Hispanic residents in that year. History of Hispanic people Hispanic people are those whose heritage stems from a former Spanish colony. The Spanish Empire colonized most of Central and Latin America in the 15th century, which began when Christopher Columbus arrived in the Americas in 1492. The Spanish Empire expanded its territory throughout Central America and South America, but the colonization of the United States did not include the Northeastern part of the United States. Despite the number of Hispanic people living in the United States having increased, the median income of Hispanic households has fluctuated slightly since 1990. Hispanic population in the United States Hispanic people are the second-largest ethnic group in the United States, making Spanish the second most common language spoken in the country. In 2021, about one-fifth of Hispanic households in the United States made between 50,000 to 74,999 U.S. dollars. The unemployment rate of Hispanic Americans has fluctuated significantly since 1990, but has been on the decline since 2010, with the exception of 2020 and 2021, due to the impact of the coronavirus (COVID-19) pandemic.

About this dataset

Context

The dataset tabulates the Norway Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Norway, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Norway.

Key observations

Among the Hispanic population in Norway, regardless of the race, the largest group is of Mexican origin, with a population of 14 (100% of the total Hispanic population).

https://i.neilsberg.com/ch/norway-mi-population-by-race-and-ethnicity.jpeg" alt="Norway Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Norway
• Population: The population of the specific origin for Hispanic or Latino population in the Norway is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Norway total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Norway Population by Race & Ethnicity. You can refer the same here

In 2025, there were around 1.53 billion people worldwide who spoke English either natively or as a second language, slightly more than the 1.18 billion Mandarin Chinese speakers at the time of survey. Hindi and Spanish accounted for the third and fourth most widespread languages that year. Languages in the United States The United States does not have an official language, but the country uses English, specifically American English, for legislation, regulation, and other official pronouncements. The United States is a land of immigration, and the languages spoken in the United States vary as a result of the multicultural population. The second most common language spoken in the United States is Spanish or Spanish Creole, which over than 43 million people spoke at home in 2023. There were also 3.5 million Chinese speakers (including both Mandarin and Cantonese),1.8 million Tagalog speakers, and 1.57 million Vietnamese speakers counted in the United States that year. Different languages at home The percentage of people in the United States speaking a language other than English at home varies from state to state. The state with the highest percentage of population speaking a language other than English is California. About 45 percent of its population was speaking a language other than English at home in 2023.

People born in the ten most common non-English speaking background countries by LGA 2011, for the 2011.

Sample characteristics of the participants (N = 180).

In 2022, around 48.59 percent of New Mexico's population was of Hispanic origin, compared to the national percentage of 19.45. California, Texas, and Arizona also registered shares over 30 percent. The distribution of the U.S. population by ethnicity can be accessed here.

The study analyzes the forces leading to or impeding the assimilation of 18- to 32-year-olds from immigrant backgrounds that vary in terms of race, language, and the mix of skills and liabilities their parents brought to the United States. To make sure that what we find derives specifically from growing up in an immigrant family, rather than simply being a young person in New York, a comparison group of people from native born White, Black, and Puerto Rican backgrounds was also studied. The sample was drawn from New York City (except for Staten Island) and the surrounding counties in the inner part of the New York-New Jersey metropolitan region where the vast majority of immigrants and native born minority group members live and grow up. The study groups make possible a number of interesting comparisons. Unlike many other immigrant groups, the West Indian first generation speaks English, but the dominant society racially classifies them as Black. The study explored how their experiences resemble or differ from native born African Americans. Dominicans and the Colombian-Peruvian-Ecuadoran population both speak Spanish, but live in different parts of New York, have different class backgrounds prior to immigration, and, quite often, different skin tones. The study compared them to Puerto Rican young people, who, along with their parents, have the benefit of citizenship. Chinese immigrants from the mainland tend to have little education, while young people with overseas Chinese parents come from families with higher incomes, more education, and more English fluency. Respondents were divided into eight groups depending on their parents' origin. Those of immigrant ancestry include: Jewish immigrants from the former Soviet Union; Chinese immigrants from the mainland, Taiwan, Hong Kong, and the Chinese Diaspora; immigrants from the Dominican Republic; immigrants from the English-speaking countries of the West Indies (including Guyana but excluding Haiti and those of Indian origin); and immigrants from Colombia, Ecuador, and Peru. These groups composed 44 percent of the 2000 second-generation population in the defined sample area. For comparative purposes, Whites, Blacks, and Puerto Ricans who were born in the United States and whose parents were born in the United States or Puerto Rico were also interviewed. To be eligible, a respondent had to have a parent from one of these groups. If the respondent was eligible for two groups, he or she was asked which designation he or she preferred. The ability to compare these groups with native born Whites, Blacks, and Puerto Ricans permits researchers to investigate the effects of nativity while controlling for race and language background. About two-thirds of second-generation respondents were born in the United States, mostly in New York City, while one-third were born abroad but arrived in the United States by age 12 and had lived in the country for at least 10 years, except for those from the former Soviet Union, some of whom arrived past the age of 12. The project began with a pilot study in July 1996. Survey data collection took place between November 1999 and December 1999. The study includes demographic variables such as race, ethnicity, language, age, education, income, family size, country of origin, and citizenship status.

In 2023, Brazil ranked first by total population among the 24 territories presented in the ranking. Brazil's total population amounted to 211.14 million people, while Mexico and Colombia, the second and third territories, had records amounting to 129.74 million people and 52.32 million people, respectively.

This dataset, released in August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following countries are designated as ES: Canada, Ireland, New Zealand, South Africa, United Kingdom and the United States of America; the remaining countries are designated as NES. The dataset also includes the population of people born overseas and report poor proficiency in English. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Wages earned by men are often used as an indicator of the material standard of living (MSoL). However, this indicator relies on several assumptions when used for comparisons across time and space. Considering these assumptions will improve estimates of the MSoL from wages. One necessary assumption is that households in the compared populations relied on the primary income of the male head of household to a comparable degree. I demonstrate that the degree of reliance on the male income was closely associated with the complexity of households within the population. Nuclear households—typical of English-speaking countries—were more reliant on the male income than more complex households found elsewhere. Consequently, estimates based on male wages are less accurate for populations with complex households, likely underestimating their MSoL. While the complexity of households in historical populations is seldom known, it can be predicted using demographic and economic indicators. I conclude that populations at similar stages of industrialization and the demographic transition are the most comparable when using male wages to estimate their MSoL. Further, I use a reductive model to show that a household’s MSoL is determined by three factors: time spent on productive work, the market wage for men, and the female/male wage ratio. My analysis shows that including the female/male wage ratio does not change the ranking of the MSoL based on male wages. Nonetheless, I argue that there are compelling reasons to expect the wage ratio to be a useful addition when comparing the MSoL of historical populations.(Abstract of the associated article.)

Multivariable logistic regression modeling: Ever vaccinated for COVID-19.

Local ecological evidence is key to informing conservation. However, many global biodiversity indicators often neglect local ecological evidence published in languages other than English, potentially biassing our understanding of biodiversity trends in areas where English is not the dominant language. Brazil is a megadiverse country with a thriving national scientific publishing landscape. Here, using Brazil and a species abundance indicator as examples, we assess how well bilingual literature searches can both improve data coverage for a country where English is not the primary language and help tackle biases in biodiversity datasets. We conducted a comprehensive screening of articles containing abundance data for vertebrates published in 59 Brazilian journals (articles in Portuguese or English) and 79 international English-only journals. These were grouped into three datasets according to journal origin and article language (Brazilian-Portuguese, Brazilian-English and International). We analysed the taxonomic, spatial and temporal coverage of the datasets, compared their average abundance trends and investigated predictors of such trends with a modelling approach. Our results showed that including data published in Brazilian journals, especially those in Portuguese, strongly increased representation of Brazilian vertebrate species (by 10.1 times) and populations (by 7.6 times) in the dataset. Meanwhile, international journals featured a higher proportion of threatened species. There were no marked differences in spatial or temporal coverage between datasets, in spite of different bias towards infrastructures. Overall, while country-level trends in relative abundance did not substantially change with the addition of data from Brazilian journals, uncertainty considerably decreased. We found that population trends in international journals showed stronger and more frequent decreases in average abundance than those in national journals, regardless of whether the latter were published in Portuguese or English. Policy implications. Collecting data from local sources markedly further strengthens global biodiversity databases by adding species not previously included in international datasets. Furthermore, the addition of these data helps to understand spatial and temporal biases that potentially influence abundance trends at both national and global level. We show how incorporating non-English-language studies in global databases and indicators could provide a more complete understanding of biodiversity trends and therefore better inform global conservation policy. Methods Data collection We collected time-series of vertebrate population abundance suitable for entry into the LPD (livingplanetindex.org), which provides the repository for one of the indicators in the GBF, the Living Planet Index (LPI, Ledger et al., 2023). Despite the continuous addition of new data, LPI coverage remains incomplete for some regions (Living Planet Report 2024 – A System in Peril, 2024). We collected data from three sets of sources: a) Portuguese-language articles from Brazilian journals (hereafter “Brazilian-Portuguese” dataset), b) English-language articles from Brazilian journals (“Brazilian-English” dataset) and c) English-language articles from non-Brazilian journals (“International” dataset). For a) and b), we first compiled a list of Brazilian biodiversity-related journals using the list of non-English-language journals in ecology and conservation published by the translatE project (www.translatesciences.com) as a starting point. The International dataset was obtained from the LPD team and sourced from the 78 journals they routinely monitor as part of their ongoing data searches. We excluded journals whose scope was not relevant to our work (e.g. those focusing on agroforestry or crop science), and taxon-specific journals (e.g. South American Journal of Herpetology) since they could introduce taxonomic bias to the data collection process. We considered only articles published between 1990 and 2015, and thus further excluded journals that published articles exclusively outside of this timeframe. We chose this period because of higher data availability (Deinet et al., 2024), since less monitoring took place in earlier decades, and data availability for the last decade is also not as high as there is a lag between data being collected and trends becoming available in the literature. Finally, we excluded any journals that had inactive links or that were no longer available online. While we acknowledge that biodiversity data are available from a wider range of sources (grey literature, online databases, university theses etc.), here we limited our searches to peer-reviewed journals and articles published within a specific timeframe to standardise data collection and allow for comparison between datasets. We screened a total of 59 Brazilian journals; of these, nine accept articles only in English, 13 only in Portuguese and 37 in both languages. We systematically checked all articles of all issues published between 1990 and 2015. Articles that appeared to contain abundance data for vertebrate species based on title and/or abstract were further evaluated by reading the material and methods section. For an article to be included in our dataset, we followed the criteria applied for inclusion into the LPD (livingplanetindex.org/about_index#data): a) data must have been collected using comparable methods for at least two years for the same population, and b) units must be of population size, either a direct measure such as population counts or densities, or indices, or a reliable proxy such as breeding pairs, capture per unit effort or measures of biomass for a single species (e.g. fish data are often available in one of the latter two formats). Assessing search effectiveness and dataset representation We calculated the encounter rate of relevant articles (i.e. those that satisfied the criteria for inclusion in our datasets) for each journal as the proportion of such articles relative to the total number of articles screened for that journal. We assessed the taxonomic representation of each dataset by calculating the percentage of species of each vertebrate group (all fishes combined, amphibians, reptiles, birds and mammals) with relevant abundance data in relation to the number of species of these groups known to occur in Brazil. The total number of known species for each taxon was compiled from national-level sources (amphibians, Segalla et al. 2021; birds, (Pacheco et al., 2021); mammals, Abreu et al. 2022; reptiles, Costa, Guedes and Bérnils, 2022) or through online databases (Fishbase, Froese and Pauly, 2024). We calculated accumulation curves using 1,000 permutations and applying the rarefaction method, using the vegan package (Jari Oksanen et al., 2024). These represent the cumulative number of new species added with each article containing relevant data, allowing us to assess how additional data collection could increase coverage of abundance data across datasets. To compare species threat status among datasets, we used the category for each species available in the Brazilian (‘Sistema de Avaliação do Risco de Extinção da Biodiversidade – SALVE’, 2024) and IUCN Red List (IUCN, 2024), and calculated the percentage of species in each category per dataset. To assess and compare the temporal coverage of the different datasets, we calculated the number of populations and species across time. To assess geographic gaps, we mapped the locations of each population using QGIS version 3.6 (QGIS Development Team, 2019). We then quantified the bias of terrestrial records towards proximity to infrastructures (airports, cities, roads and waterbodies) at a 0.5º resolution (circa 55.5 km x 55.5 km at the equator) and a 2º buffer using posterior weights from the R package sampbias (Zizka, Antonelli and Silvestro, 2021). Higher posterior weights indicate stronger bias effect. Generalised linear mixed models and population abundance trends We used the rlpi R package (Freeman et al., 2017) to calculate trends in relative abundance. We calculated the average lambda (logged annual rate of change) for each time-series by averaging the lambda values across all years between the start and the end year of the time-series. We then built generalised linear mixed models (GLMM) to test how average lambdas changed across language (Portuguese vs English), journal origin (national vs international), and taxonomic group, using location, journal name, and species as random intercepts (Table 1). We offset these by the number of sampled years to adjust summed lambda to a standardised measure, to allow comparison across different observations with different length of time series and plotted the beta coefficients (effect sizes) of all factors. Finally, we performed a post-hoc test to check pairwise differences between taxonomic groups (Table S2). To assess the influence of national-level data on global trends in relative abundance, we calculated the trends for both the International dataset and the two combined Brazilian datasets (Brazilian-Portuguese and Brazilian-English), using only years for which data were available for more than one species, to be able to estimate trend variation. We also plotted the trends for the Brazilian datasets separately. All analyses were performed in R 4.4.1 (R Core Team, 2024).

In 2024, the total population of Spain was around 48.38 million people. By 2029, it was forecast to grow up to 50.76 million inhabitants.

Population of Spain While Spain’s fertility rate has been relatively decreasing over the past decade, its year-over-year population growth has been increasing continuously since 2016. The collapse of the job and real estate markets may have led the Spanish to postpone having (more) kids or to migrate to other countries in search of a more stable economy, while inflow of migrates has increased . This theory is supported by data on the average age of Spain’s inhabitants; a look at the median age of Spain’s population from 1950 up until today shows that the Spanish get older on average – perhaps due to the aforementioned factors.

Economic recovery Speaking of Spain’s economy, economic key factors suggest that the country is still recovering from the crisis. Its gross domestic product (GDP) was in admirable shape prior to the collapse, but it still has not returned to its former glory. Only recently has Spain reported actual GDP growth since 2008. Nevertheless, during 2020 and the COVID-19 pandemic, Spain's GDP had a decrease of more than 11 percent. This in turn, led to an increase of the country’s unemployment rate after years of slowly but surely decreasing following an alarming peak of 26 percent in 2013. Future perspectives are, however, somewhat brighter, as GDP is forecast to maintain a positive growth rate at least until 2029, even exceeding two percentage points in 2025.

An emerging CALD community refers to a place with a significant increase in the number of Culturally and Linguistically Diverse (CALD) populations according to ABS census counts. These communities may experience social barriers that adversely affect the quality of life. Emerging CALD Communities are an ongoing feature of the Australian cultural landscape. Further research has been required into the status of Emerging CALD Communities. This project concerns how social and environmental inequalities have been distributed in Australia's CALD populations over the last two decades. It aims to measure changes in the CALD populations and exposure to urban heat and greening due to social inequities and climate change. Two layers of CALD total populations at the SA1 level were generated for five consecutive Australian Census years (2001, 2006, 2011, 2016, 2021) using historic ABS Census datasets. The first layer represents individuals who speak a non-English language at home, while the second layer includes those born in a country where the main language is non-English. Both layers were transformed and aggregated to ensure consistency across Census years, providing a detailed analysis of CALD population trends over two decades. This project expands AURINʼs infrastructure of data and tools, in particular the integrated Heat Vulnerability Index toolkit developed by CI Sun that has provided cloud computing tools for deriving environmental indicators. The outcome of this project is a new nationwide longitudinal database with the quantification of CALD populations and social-environmental inequalities, which will fill a critical gap for AURINʼs data catalogue. The database supports and facilitates multidisciplinary research to perform spatial and statistical analyses to reveal the disproportionate exposure to urban heat and greening across CALD communities in Australia. Spatially explicit information can be generated from the database for planners to make intervention strategies for vulnerable CALD populations, to diminish the inequality for CALD. This significantly advances AURINʼs capability to support CALD research across social science, public health, and the environment, and achieve SDG goals.

Using data from reports such as the "English Proficiency Index" (EDU) from Education First, one can see the significant impact of culture, education and globalization on the ability of citizens of different countries to speak English.

The data collected aim to test whether English proficiency levels in a country are positively associated with higher democratic values in that country. English proficiency is sourced from statistics by Education First’s "EF English Proficiency Index" which covers countries' scores for the calendar year 2022 and 2021. The EF English Proficiency Index ranks 111 countries in five different categories based on their English proficiency scores that were calculated from the test results of 2.1 million adults. While democratic values are operationalized through the liberal democracy index from the V-Dem Institute annual report for 2022 and 2021. Additionally, the data is utilized to test whether English language media consumption acts as a mediating variable between English proficiency and democracy levels in a country, while also looking at other possible regression variables. In order to conduct the linear regression analyses for the dats, the software that was utilized for this research was Microsoft Excel.The raw data set consists of 90 nation states in two years from 2022 and 2021. The raw data is utilized for two separate data sets the first of which is democracy indicators which has the regression variables of EPI, HDI, and GDP. For this table set there is a total of 360 data entries. HDI scores are a statistical summary measure that is developed by the United Nations Development Programme (UNDP) which measures the levels of human development in 190 countries. The data for nominal gross domestic product scores (GDP) are sourced from the World Bank. Having strong regression variables that have been proven to have a positive link with democracy in the data analysis such as GDP and HDI, would allow the regression analysis to identify whether there is a true relationship between English proficiency and democracy levels in a country. While the second data set has a total of 720 data entries and aims to identify English proficiency indicators the data set has 7 various regression variables which include, LDI scores, Years of Mandatory English Education, Heads of States Publicly speaking English, GDP PPP (2021USD), Common Wealth, BBC web traffic and CNN web traffic. The data for years of mandatory English education is sourced from research at the University of Winnipeg and is coded in the data set based on the number of years a country has English as a mandatory subject. The range of this data is from 0 to 13 years of English being mandatory. It is important to note that this data only concerns public schools and does not extend to the private school systems in each country. The data for heads of state publicly speaking English was done through a video data analysis of all heads of state. The data was only used for heads of state who had been in their position for at least a year to ensure the accuracy of the data collected; with a year in power, for heads of state that had not been in their position for a year, data was taken from the previous head of state. This data only takes into account speeches and interviews that were conducted during their incumbency. The data for each country’s GDP PPP scores are sourced from the World Bank, which was last updated for a majority of the countries in 2021 and is tied to the US dollar. Data for the commonwealth will only include members of the commonwealth that have been historically colonized by the United Kingdom. Any country that falls under that category will be coded as 1 and any country that does not will be coded as 0. For BBC and CNN web traffic that data is sourced by using tools in Semrush which provide a rough estimate of how much web traffic each news site generates in each country. Which will be utilized to identify the average number of web traffic for BBC News and CNN World News for both the 2021 and 2022 calendar. The traffic for each country will also be measured per capita, per 10 thousand people to ensure that the population density of a country does not influence the results. The population of each country for both 2021 and 2022 is sourced from the United Nations revision of World Population Prospects of both 2021 and 2022 respectively.