About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Switzerland County, the largest racial group is White alone with a population of 9,381 (95.54% 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 Switzerland County
• Population: The population of the racial category (for Non-Hispanic) in the Switzerland County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Switzerland County 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 Switzerland County Population by Race & Ethnicity. You can refer the same here

In 1800, the region of present-day Switzerland had a population of approximately 1.8 million people. This figure would grow steadily throughout the 19th century, as political and religious grievances gave way to a united federation, whose economic policies saw Switzerland emerge as one of Europe's most prosperous and stable countries. Growth boomed between 1890 and 1910, as industrialization would see significant economic growth and migration to the country. While Switzerland’s neutrality in both World Wars would prevent the mass fatalities experienced across the rest of Europe during the early 20th century, Switzerland’s population would nevertheless stagnate in both the First and Second World War and in the Great Depression in the 1930s, as the economic turmoil and conflict abroad would halt the migration that had previously driven population growth.

Following the end of the Second World War, growth would resume and would rise steadily until the late 1970s, before an economic recession saw the population fall again as workers migrated in search of employment elsewhere. However, population growth has steadily risen since the 1980s, reaching seven million in the mid-1990s and eight million in 2012. Today, with a population of 8.7 million, Switzerland is ranked among the wealthiest and most developed nations in the world, with very high standards of living.

This statistic shows the growth of Switzerland's population from 2013 to 2023, in comparison to the previous year. In 2023, Switzerland's population grew by approximately 1.26 percent compared to the previous year. See Switzerland's population figures for comparison. The Swiss population The Swiss population has been growing at a steady rate for the past few years; in general the country has experienced around a one percent population growth rate since the 1970s. Between 2004 and 2007, population growth was slightly below one percent, but has rebounded since then. This growth is supported by immigration, as the fertility rate is well below the replacement rate. The country’s strong and stable economy and the free movement of people within the European Union has helped attract foreigners. In 2015, the population of Switzerland was around 8.25 million and its foreign-born population amounted to 2.26 million people that same year, meaning that around 1 out of every four people in Switzerland are of foreign origin. But even if you are born in Switzerland, you are not automatically granted Swiss nationality, and many people who are of “foreign” origin were actually born in Switzerland but keep the nationality of their parents or do not go through what can be a lengthy process to obtain Swiss nationality. Another characteristic of the Swiss population is that Swiss people are getting older. Due to its high standard of living, Switzerland has one of the highest life expectancies in the world, and the median age of the population is now estimated at 42.3 years.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Switzerland County, IN (B03002010E018155) from 2009 to 2023 about Switzerland County, IN; IN; non-hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset tabulates the Swiss town population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Swiss town.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Swiss, Wisconsin Population Breakdown by Race
• Swiss, Wisconsin Non-Hispanic Population Breakdown by Race
• Swiss, Wisconsin Hispanic or Latino Population Distribution by Their Ancestries

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/.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Residential building including single family home, mutiple family home, farm, and apartment building; other buildings (e.g. factory or commercial buildings) if they contain at least one unit for residential purposes; other accommodations (e.g., barracks, mountain farms, wagons) if they are occupied on the census day. - Group quarters: Collective households are groups of persons who live in hotels, boarding homes, care homes, boarding schools, hospitals, company dormitories. Other collective households include staff members and company workers who live in a common accommodation but do not keep house and are neither connected to another household.

Universe

All persons residing in Switzerland, except foreign diplomats stationed in Switzerland and their families.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Federal Statistical Office

SAMPLE DESIGN: Systematic sample of every 20th household, drawn by the Federal Statistical Office

SAMPLE UNIT: Household

SAMPLE FRACTION: 5%

SAMPLE SIZE (person records): 317,803

Mode of data collection

Face-to-face [f2f]

Research instrument

There are three sets of questionnaires: (i) person questionnaire, (ii) household questionnaire, and (iii) building questionnaire

About this dataset

Context

The dataset presents the median household income across different racial categories in Swiss town. 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 Swiss town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 73.14% of the total residents in Swiss town. Notably, the median household income for White households is $61,111. Interestingly, despite the White population being the most populous, it is worth noting that American Indian and Alaska Native households actually reports the highest median household income, with a median income of $83,295. This reveals that, while Whites may be the most numerous in Swiss town, American Indian and Alaska Native households experience greater economic prosperity in terms of median household income.

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 Swiss town.
• 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 Swiss town median household income by race. You can refer the same here

Some other race Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Switzerland County, Indiana by age, education, race, gender, work experience and more.

The youth unemployment rate in Switzerland increased by 0.5 percentage points (+6.67 percent) in 2023. In total, the youth unemployment rate amounted to eight percent in 2023. The youth unemployment rate refers to the share of the economically active population aged 15 to 24 currently without work but in search of employment. The youth unemployment rate does not include economically inactive persons such as the long-term unemployed or full-time students.Find more statistics on other topics about Switzerland with key insights such as labor participation rate among the total population aged between 15 and 64.

As of January 2024, 59 million people lived in Italy. About 28.9 million individuals were males and 30.1 million people were females. The most populated area of the country was the north-west, where 15.9 million people lived. Furthermore, the southern regions counted 13.4 million inhabitants, representing the second most populous area of the country. Regional census The northern region of Lombardy is the most populous region of Italy. One-sixth of all the Italian population is concentrated in this area. Lazio and Campania follow with approximately 5.7 million and 5.6 million individuals, respectively. On the other hand, Aosta Valley, a northern region bordering to France and Switzerland, counted 123,000 inhabitants, representing the smallest region of Italy in terms of residents as well as area. More and more Italians are moving abroad Between 2006 and 2022, the number of Italians reported living abroad increased. In 2022, 5.8 million people lived outside Italy, almost three million more than in 2006. The country hosting the largest Italian population is Argentina. In 2019, beyond one million Italian citizens lived in the South American nation. Other large Italian communities resided in Germany, Switzerland, and Brazil.

In 1800, the region of Germany was not a single, unified nation, but a collection of decentralized, independent states, bound together as part of the Holy Roman Empire. This empire was dissolved, however, in 1806, during the Revolutionary and Napoleonic eras in Europe, and the German Confederation was established in 1815. Napoleonic reforms led to the abolition of serfdom, extension of voting rights to property-owners, and an overall increase in living standards. The population grew throughout the remainder of the century, as improvements in sanitation and medicine (namely, mandatory vaccination policies) saw child mortality rates fall in later decades. As Germany industrialized and the economy grew, so too did the argument for nationhood; calls for pan-Germanism (the unification of all German-speaking lands) grew more popular among the lower classes in the mid-1800s, especially following the revolutions of 1948-49. In contrast, industrialization and poor harvests also saw high unemployment in rural regions, which led to waves of mass migration, particularly to the U.S.. In 1886, the Austro-Prussian War united northern Germany under a new Confederation, while the remaining German states (excluding Austria and Switzerland) joined following the Franco-Prussian War in 1871; this established the German Empire, under the Prussian leadership of Emperor Wilhelm I and Chancellor Otto von Bismarck. 1871 to 1945 - Unification to the Second World War The first decades of unification saw Germany rise to become one of Europe's strongest and most advanced nations, and challenge other world powers on an international scale, establishing colonies in Africa and the Pacific. These endeavors were cut short, however, when the Austro-Hungarian heir apparent was assassinated in Sarajevo; Germany promised a "blank check" of support for Austria's retaliation, who subsequently declared war on Serbia and set the First World War in motion. Viewed as the strongest of the Central Powers, Germany mobilized over 11 million men throughout the war, and its army fought in all theaters. As the war progressed, both the military and civilian populations grew increasingly weakened due to malnutrition, as Germany's resources became stretched. By the war's end in 1918, Germany suffered over 2 million civilian and military deaths due to conflict, and several hundred thousand more during the accompanying influenza pandemic. Mass displacement and the restructuring of Europe's borders through the Treaty of Versailles saw the population drop by several million more.

Reparations and economic mismanagement also financially crippled Germany and led to bitter indignation among many Germans in the interwar period; something that was exploited by Adolf Hitler on his rise to power. Reckless printing of money caused hyperinflation in 1923, when the currency became so worthless that basic items were priced at trillions of Marks; the introduction of the Rentenmark then stabilized the economy before the Great Depression of 1929 sent it back into dramatic decline. When Hitler became Chancellor of Germany in 1933, the Nazi government disregarded the Treaty of Versailles' restrictions and Germany rose once more to become an emerging superpower. Hitler's desire for territorial expansion into eastern Europe and the creation of an ethnically-homogenous German empire then led to the invasion of Poland in 1939, which is considered the beginning of the Second World War in Europe. Again, almost every aspect of German life contributed to the war effort, and more than 13 million men were mobilized. After six years of war, and over seven million German deaths, the Axis powers were defeated and Germany was divided into four zones administered by France, the Soviet Union, the UK, and the U.S.. Mass displacement, shifting borders, and the relocation of peoples based on ethnicity also greatly affected the population during this time. 1945 to 2020 - Partition and Reunification In the late 1940s, cold war tensions led to two distinct states emerging in Germany; the Soviet-controlled east became the communist German Democratic Republic (DDR), and the three western zones merged to form the democratic Federal Republic of Germany. Additionally, Berlin was split in a similar fashion, although its location deep inside DDR territory created series of problems and opportunities for the those on either side. Life quickly changed depending on which side of the border one lived. Within a decade, rapid economic recovery saw West Germany become western Europe's strongest economy and a key international player. In the east, living standards were much lower, although unemployment was almost non-existent; internationally, East Germany was the strongest economy in the Eastern Bloc (after the USSR), though it eventually fell behind the West by the 1970s. The restriction of movement between the two states also led to labor shortages in the West, and an influx of migrants from...

10985 Global export shipment records of Race with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

35 to 64 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Switzerland County, Indiana by age, education, race, gender, work experience and more.

Total household population Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Switzerland County, Indiana by age, education, race, gender, work experience and more.

Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are: - Alpha taxonomic community metrics: species richness and Shannon diversity - Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion. - Community weighted means of 8 functional traits - The local community contributions on the functional and taxonomic beta diversity (LCBD).

The dataset also includes the following: - The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity). -The three types of protected areas, defined according to the protective measures. - The model evaluation, variable importance and partial dependece data.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 50.82% 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 Switzerland County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Switzerland County 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 Switzerland County Population by Race & Ethnicity. You can refer the same here

The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete’s age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25–29 years, but the average speeds of groups 20–24 and 30–34 years are close. Participation, however, peaks for the age group 40–44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.

In 2023, the average life expectancy of the world was 70 years for men and 75 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest.

What is life expectancy?

Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future.

Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standard continuing.

Highest and lowest life expectancy worldwide

Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2021, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and South Korea, all at 84 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53 years.

18 to 24 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Switzerland County, Indiana by age, education, race, gender, work experience and more.

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 Swiss town. 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 Swiss town, the median household income for the households where the householder is White increased by $11,141(22.30%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $49,970 in 2013 and $61,111 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 Swiss town.
• 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 Swiss town median household income by race. You can refer the same here