Much of Argentina's modern society and culture is rooted in the Spanish Empire's colonization of the region in the 16th century, along with the influx of European migration to the country around the turn of the twentieth century. There are records of human presence in the region dating back to the paleolithic period (3.3 million to 9,650 BCE) and the Incan Empire is known to have extended into the region before Columbus' arrival in the Americas in 1492; however most of this culture and civilization was wiped out by Europeans in the 1500s. During Spanish colonization, the majority of Argentina was a part of the Viceroyalty of the Río de la Plata (which also included territories in modern-day Chile, Bolivia, Paraguay and Uruguay) and was still economically bound to the Spanish crown. With a population of just 0.5 million in 1800, a combination of mass migration (particularly from Southern Europe) and high birth rates have helped Argentina's population grow above 45 million over the past two centuries.

Independence, Immigration and the Gold rush The age of enlightenment and revolutions in Europe inspired a longing in the region for independence, and Napoleon's invasion of Spain in 1808 was the catalyst for the Spanish Empire's downfall in the Americas, with Argentinian independence declared in 1816. The Spanish military was then defeated in mid 1800s, but for the majority of the next century there was little political or economic stability in the region, with several small-scale civil wars between the different Argentinian states. Starting with the government of Julio Argentino Roca in 1880, ten consecutive federal governments actively pursued a liberal economic policy which led to a massive wave of state-promoted European immigration; so much so that the number of migrants received by Argentina in that period was second only to the United States worldwide. This immigration led to the rejuvenation and reinvention of Argentinian society and economy to such an extent, that by 1908 the country had the seventh largest economy in the world. This in turn led to further immigration and higher standards of living. It is also worth noting that the Tierra del Fuego gold rush that started around 1883 and lasted to around 1906 also contributed greatly to immigration. Unfortunately, Argentina was unable to retain it's acquired economic might; it failed to develop industrially at the same speed as the rest of the world, and the Great Depression of 1929 set in motion an economic decline that contributed to much civil and political unrest.

The impact of Perón, and modern Argentina
The election of Juan Perón in 1946 proved to be a defining point in Argentina's history; Perón was a demagogue who imprisoned (and reportedly tortured) his rivals and critics, and whose isolationist policies and radical spending contributed to severe inflation. With the death of Perón's extremely popular wife, Eva Duarte, in 1952, his popularity declined and he was eventually exiled following a coup in 1955. Despite this exile, Perón returned in 1973 and re-assumed the presidency, until his death in 1974, where he was the succeeded by his third wife. Peron's political philosophy, known as "Peronism", is a mixture of right wing nationalist and left wing populist theories; although Peronism has developed greatly over time, its core belief system is the foundation of Argentina's largest party, the Justicialist Party (although they have become increasingly left wing since the Kirchner administrations).

With the expulsion of Perón in 1955, Argentina's trend of military coups and failed governments continued, and the country faced further economic instability. Despite all of this, medical advancements and improvements to quality of life across the globe helped Argentina's mortality rate to decline, and the population grew at a faster rate than ever before. In April 1982, Argentine forces invaded the British territory of the Falkland Islands, leading to a ten week war between the nations, that ended with Argentina's surrender in June. The war had a relatively small death toll, but contributed to riots in Buenos Aires, which helped to topple the military dictatorship and established the current democratic system. Following a severe recession that began in 2001, President Néstor Kirchner assumed office in 2003, and his wife took over from 2007 to 2015; during the Kirchner administrations, more than eleven million people were lifted out of poverty, and Argentina's economy grew in stature to become one of the Group of Twenty.

About this dataset

Context

The dataset tabulates the population of Argentine township by race. It includes the population of Argentine township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Argentine township across relevant racial categories.

Key observations

The percent distribution of Argentine township population by race (across all racial categories recognized by the U.S. Census Bureau): 95.46% are white, 0.44% are Black or African American, 0.58% are Asian, 0.10% are Native Hawaiian and other Pacific Islander, 0.84% are some other race and 2.57% are multiracial.

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Argentine township
• Population: The population of the racial category (excluding ethnicity) in the Argentine township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Argentine township total 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 Argentine township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Argentine township by race. It includes the distribution of the Non-Hispanic population of Argentine township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Argentine township across relevant racial categories.

Key observations

Of the Non-Hispanic population in Argentine township, the largest racial group is White alone with a population of 6,551 (96.39% of the total Non-Hispanic population).

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Argentine township
• Population: The population of the racial category (for Non-Hispanic) in the Argentine township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Argentine township total Non-Hispanic 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 Argentine township Population by Race & Ethnicity. You can refer the same here

Argentina Ethnic Wear market to add USD 330 million by 2030, supported by handmade textiles and traditional pattern revival.

Argentina AR: Population: per 1 000 Inhabitants data was reported at 45,808.750 Person in 2021. This records an increase from the previous number of 45,376.760 Person for 2020. Argentina AR: Population: per 1 000 Inhabitants data is updated yearly, averaging 38,930.325 Person from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 45,808.750 Person in 2021 and a record low of 32,580.850 Person in 1990. Argentina AR: Population: per 1 000 Inhabitants data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Argentina AR: Population: Ages 15-64: % of Total Population data was reported at 68.540 % in 2021. This records an increase from the previous number of 68.330 % for 2020. Argentina AR: Population: Ages 15-64: % of Total Population data is updated yearly, averaging 66.435 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 68.540 % in 2021 and a record low of 64.030 % in 1990. Argentina AR: Population: Ages 15-64: % of Total Population data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Argentina AR: Women: % of Total Population data was reported at 50.380 % in 2023. This records a decrease from the previous number of 50.390 % for 2022. Argentina AR: Women: % of Total Population data is updated yearly, averaging 50.650 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 50.770 % in 1991 and a record low of 50.380 % in 2023. Argentina AR: Women: % of Total Population data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Argentina AR: Population Density: Inhabitants per sq km data was reported at 16.890 Person in 2022. This records an increase from the previous number of 16.740 Person for 2021. Argentina AR: Population Density: Inhabitants per sq km data is updated yearly, averaging 14.300 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 16.890 Person in 2022 and a record low of 11.910 Person in 1990. Argentina AR: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.GGI: Social: Demography: Non OECD Member: Annual.

This dataset tracks annual two or more races student percentage from 2011 to 2023 for Argentine Middle School vs. Kansas and Kansas City School District

Abstract Debates on the incorporation of migrants in receiving societies is a controversial issue in academic research. In this paper we focus our attention on the spatial dimension in the analysis of the patterns of incorporation of Peruvian migrants in the city of Cordoba in the period of 2001-2010. First, we analyze patterns of spatial segregation of the native population and the Peruvian population in the city. Second, considering the clustered/scattered cleavage, we analyze the sociodemographic characteristics of clustered and dispersed peruvians in the city. We suggest the interaction of two processes of spatial incorporation: a) there are specific patterns of location of Peruvian migrants in the city of Cordoba. The native population with higher education levels, in turn, tends to settle in the center and northwest part of the city. The central areas were the stable places where the Peruvians are located, however, in recent years there has been a changing pattern with new places of settlement; b) we identify structural differences between clustered and dispersed Peruvians.

About this dataset

Context

The dataset presents the median household income across different racial categories in Argentine township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Argentine township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95.46% of the total residents in Argentine township. Notably, the median household income for White households is $91,190. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $91,190.

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Argentine township.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Argentine township median household income by race. You can refer the same here

Racing Club 2025 Get comprehensive football data including match results, statistics, team information, player details, league standings, and real-time updates from professional football leagues worldwide.

Abstract The most used measure of the load of demographic dependence is the relation of demographic dependence. This index lacks theoretical support and is based on a purely biological criterion. The objective is to generate an alternative index to estimate the load of economic dependence from a multidimensional theoretical perspective that adjusts to spatial inequalities and, in the theoretical field, admits the existence of a diffuse link between demography and economy, as well as the lack of a consensus concerning economic growth determinants and the creation of employment. The application of the alternative index is constructed for a disaggregation level corresponding to the census fraction for the Metropolitan Region of Buenos Aires in 2010.

ABSTRACT: Objective: To assess the impact of the COVID-19 pandemic on mortality in Argentina, considering temporal trends in life expectancy at birth and premature mortality rate during 2010-2020. Methods: Based on demographic projections, this ecological time-series study compares a “normal” versus a “COVID-19” mortality scenario for 2020 over a set of 11 Argentine provinces. Annual life expectancy at birth and age-standardized rates of premature mortality were estimated from 2010 to 2020. Joinpoint regression and multilevel models were used. Results: A potential reduction in life expectancy at birth (a gap between scenarios >1 year) was observed. A significant (negative) point of inflection in temporal trends was identified for the country and most of the provinces, under the COVID-19 mortality scenario. However, our findings reveal disparities between provinces in the estimated life expectancy reduction toward 2020 (values range from -0.63 to -1.85 year in females and up to -2.55 years in males). While men showed more accentuated declines in life expectancy at birth in 2020 (a national gap between scenarios of -1.47 year in men vs. -1.35 year in women), women experienced more unfavorable temporal trends of premature mortality. In the absence of COVID-19, an improvement in both indicators was estimated toward 2020 in both sexes, while a return to levels reported in the past was observed under the COVID-19 scenario. Conclusion: The COVID-19 pandemic might seriously affect the trends of mortality and exacerbate health disadvantages in Argentina. A temporal and contextual perspective of health inequities merits special attention in the COVID-19 research.

About this dataset

Context

The dataset tabulates the population of Argentine township by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Argentine township across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 51.61% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Argentine township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Argentine township total 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 Argentine township Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Argentine township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Argentine township, the median household income for the households where the householder is White increased by $9,270(11.32%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $81,920 in 2013 and $91,190 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Argentine township.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Argentine township median household income by race. You can refer the same here

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

This study aims to investigate the factors regulating population size and colony distribution of Adelie penguins (Pygoscelis adeliae) at three colonies on Ross Island (Cape Royds, Bird and Crozier) and when logistics allowed Beaufort Island through studying the importance of key resources (nesting space and food) and the way they are allocated by behavioural traits (philopatry, immigration and emigrations). A range of techniques was used to collect data. A sub-colony was fenced off and an automated weighbridge and data logger was installed at each colony. Several pairs of breeding birds were implanted with a passively interrogated transponder. When a bird steps on the weighbridge, the data logger would record direction of travel, weight and tag number, if a tag was present. These data were used to determine foraging effort (feeding trip duration and food load size). The productivity and chick condition index was measured in birds in the enclosure and those in an undisturbed colony to check for negative effects of the weighbridge. The stomach contents of Adelie penguin adults and chicks were examined for food prey species over time and between colonies. The diet quality was compared by analysing stable isotope (C and N) ratios in samples collected from dead chicks (protein rich, lipid poor tissue). GIS and satellite images were used to determine the effects of sea ice conditions on colony success (availability of food, breeding success, etc). To measure the effect of sea ice extent and availability of food on breeding success, 50 chicks/week/colony were randomly caught, weighed and flipper length measured to calculate a chick condition index as an indication of food availability during chick rearing stage. These data were comparable between years and colonies. To measure the immigration/emigration and philopatry behaviour role in colony size and distribution, chicks were banded at each colony (up to 1000 chicks/colony/season). In following seasons, searches were made for banded penguins at all colonies to estimate age specific fecundity and survival rates, age of first breeding and to measure immigration/emigration rates for each of the study colonies. The role of natal philopatry in the structure of colony formation was assessed by measuring genetic homogeneity among and within the colonies on Ross Island using mitochondrial DNA analysis. Radio telemetry, satellite tracking and time depth recorders were used to track individual penguins at sea to determine their foraging behaviour. To track post breeding migration, winter migration and over wintering feeding grounds, archival tags that determine geo-location were deployed on birds from Cape Royds and Crozier. Birds were recaptured the following season and data downloaded.