Japan Population Census: Male: Age 25 to 29 Years data was reported at 3,255,717.000 Person in 2015. This records a decrease from the previous number of 3,691,723.000 Person for 2010. Japan Population Census: Male: Age 25 to 29 Years data is updated yearly, averaging 3,875,527.000 Person from Dec 1920 (Median) to 2015, with 20 observations. The data reached an all-time high of 5,426,289.000 Person in 1975 and a record low of 1,603,664.000 Person in 1945. Japan Population Census: Male: Age 25 to 29 Years data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G002: Population: Annual.

The Holocaust was the systematic extermination of Europe's Jewish population in the Second World War, during which time, up to six million Jews were murdered as part of Nazi Germany's "Final Solution to the Jewish Question". In the context of the Second World War, the term "Holocaust" is traditionally used to reference the genocide of Europe's Jews, although this coincided with the Nazi regime's genocide and ethnic cleansing of an additional eleven million people deemed "undesirable" due to their ethnicity, beliefs, disability or sexuality (among others). During the Holocaust, Poland's Jewish population suffered the largest number of fatalities, with approximately three million deaths. Additionally, at least one million Jews were murdered in the Soviet Union, while Hungary, Latvia, Lithuania, the Netherlands and Yugoslavia also lost the majority of their respective pre-war Jewish populations. The Holocaust in Poland In the interwar period, Europe's Jewish population was concentrated in the east, with roughly one third living in Poland; this can be traced back to the Middle Ages, when thousands of Jews flocked to Eastern Europe to escape persecution. At the outbreak of the Second World War, it is estimated that there were 3.4 million Jews living in Poland, which was approximately ten percent of the total population. Following the German invasion of Poland, Nazi authorities then segregated Jews in ghettos across most large towns and cities, and expanded their network of concentration camps throughout the country. In the ghettos, civilians were deprived of food, and hundreds of thousands died due to disease and starvation; while prison labor was implemented under extreme conditions in concentration camps to fuel the German war effort. In Poland, six extermination camps were also operational between December 1941 and January 1945, which saw the mass extermination of approximately 2.7 million people over the next three years (including many non-Poles, imported from other regions of Europe). While concentration camps housed prisoners of all backgrounds, extermination camps were purpose-built for the elimination of the Jewish race, and over 90% of their victims were Jewish. The majority of the victims in these extermination camps were executed by poison gas, although disease, starvation and overworking were also common causes of death. In addition to the camps and ghettos, SS death squads (Einsatzgruppen) and local collaborators also committed widespread atrocities across Eastern Europe. While the majority of these atrocities took place in the Balkan, Baltic and Soviet regions, they were still prevalent in Poland (particularly during the liquidation of the ghettos), and the Einsatzgruppen alone are estimated to have killed up to 1.3 million Jews throughout the Holocaust. By early 1945, Soviet forces had largely expelled the German armies from Poland and liberated the concentration and extermination camps; by this time, Poland had lost roughly ninety percent of its pre-war Jewish population, and suffered approximately three million further civilian and military deaths. By 1991, Poland's Jewish population was estimated to be just 15 thousand people, while there were fewer than two thousand Jews recorded as living in Poland in 2018.

The indicator counts, on 1 January 2011 and 1 January 2021, conventional dwellings according to the year of construction of the building. Collective housing (e.g. a nursing home) and non-conventional housing (e.g. a mill) are not included in the indicator. On the other hand, empty dwellings (housing under construction, second residence, etc.) are taken into account. The definition of housing in the Census is also a broader one than that in the cadastre because some dwellings not recognized in the cadastre were identified as such in the Census, often in situations where it was assumed that a single-family house (so recorded in the cadastre) was actually divided into apartments. Note that the few dwellings whose building age is indeterminate are well included in the denominator but not in the numerator. Some indicators of the theme are also available by statistical sectors on the website of the "\2". Infra-municipal data are not yet available for 2021. Note: The housing indicators derived from the Census are constructed from the crossing of several databases (the national register, the previous census carried out by survey of the entire population in 2001, the cadastre, etc.).

The 6th Population Census, this is an extraordinary census conducted just after the end of the war. In order to clarify the state of Japan’s population and households, the population census has been conducted in Japan almost every five years.More details on the "Population Census of Japan" overall including other years can be found here: https://d-infra.ier.hit-u.ac.jp/Japanese/statistical-yb/b001.html. 　　　　　Following the enactment of the Statistics Act, the Population Census was implemented as the "Designated Statistics No. 1" based on the Statistics Act. The census examined, for example, whether people had returned to Japan after the war. Due to the war, no Population Census was conducted in 1945.

Japan Population Census: Male: Age 30 to 34 Years data was reported at 3,684,747.000 Person in 2015. This records a decrease from the previous number of 4,221,011.000 Person for 2010. Japan Population Census: Male: Age 30 to 34 Years data is updated yearly, averaging 3,849,696.500 Person from Dec 1920 (Median) to 2015, with 20 observations. The data reached an all-time high of 5,421,545.000 Person in 1980 and a record low of 1,805,587.000 Person in 1945. Japan Population Census: Male: Age 30 to 34 Years data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G002: Population: Annual.

The indicator counts, on 1 January 2011 and 1 January 2021, conventional dwellings according to the year of construction of the building. Collective housing (e.g. a nursing home) and non-conventional housing (e.g. a mill) are not included in the indicator. On the other hand, empty dwellings (housing under construction, second residence, etc.) are taken into account. The definition of housing in the Census is also a broader one than that in the cadastre because some dwellings not recognized in the cadastre were identified as such in the Census, often in situations where it was assumed that a single-family house (so recorded in the cadastre) was actually divided into apartments. Note that the few dwellings whose building age is indeterminate are well included in the denominator but not in the numerator. Some indicators of the theme are also available by statistical sectors on the website of the "\2". Note: The housing indicators derived from the Census are constructed from the crossing of several databases (the national register, the previous census carried out by survey of the entire population in 2001, the cadastre, etc.).

Japan Population Census: Male: Age 20 to 24 Years data was reported at 3,046,392.000 Person in 2015. This records a decrease from the previous number of 3,266,240.000 Person for 2010. Japan Population Census: Male: Age 20 to 24 Years data is updated yearly, averaging 3,915,965.500 Person from Dec 1920 (Median) to 2015, with 20 observations. The data reached an all-time high of 5,344,885.000 Person in 1970 and a record low of 2,024,120.000 Person in 1945. Japan Population Census: Male: Age 20 to 24 Years data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G002: Population: Annual.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Jefferson County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 10(0.20%) households where the householder is under 25 years old, 1,214(24.83%) households with a householder aged between 25 and 44 years, 1,945(39.78%) households with a householder aged between 45 and 64 years, and 1,721(35.19%) households where the householder is over 65 years old.
• In Jefferson County, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Jefferson County median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Jefferson County, WV, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 19,634, while the mean income for the highest quintile (20% of households with the highest income) is 252,194. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 381,881, which is 151.42% higher compared to the highest quintile, and 1945% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Jefferson County median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Perinton town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Perinton town. The dataset can be utilized to understand the population distribution of Perinton town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Perinton town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Perinton town.

Key observations

Largest age group (population): Male # 55-59 years (1,817) | Female # 55-59 years (1,945). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Perinton town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Perinton town is shown in the following column.
• Population (Female): The female population in the Perinton town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Perinton town for each age group.

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 Perinton town Population by Gender. You can refer the same here

The dataset shows the population of the Canton of Basel-Stadt by home, 1-year age classes at the end of the year. The data is updated annually. Methodological notes: In category CH, the cantonal citizens (category BS) are also included. If the two categories are added together, the cantonal citizens are counted twice.- In the years 1964 to 1990, the population is based on continuation of census; In the years 1990 to 2011 the annual updates were based on the inventory of the cantonal population register on 31.12.1990.- Since 2012, the population figures are based directly on evaluations from the cantonal population register.- In 1989 and 1990: From the 94th. The data were aligned with the population control of Basel-Stadt. In 2019: As a result of a system change without frontier worker with weekly stay.

In 1938, the year before the outbreak of the Second world War, the countries with the largest populations were China, the Soviet Union, and the United States, although the United Kingdom had the largest overall population when it's colonies, dominions, and metropole are combined. Alongside France, these were the five Allied "Great Powers" that emerged victorious from the Second World War. The Axis Powers in the war were led by Germany and Japan in their respective theaters, and their smaller populations were decisive factors in their defeat. Manpower as a resource In the context of the Second World War, a country or territory's population played a vital role in its ability to wage war on such a large scale. Not only were armies able to call upon their people to fight in the war and replenish their forces, but war economies were also dependent on their workforce being able to meet the agricultural, manufacturing, and logistical demands of the war. For the Axis powers, invasions and the annexation of territories were often motivated by the fact that it granted access to valuable resources that would further their own war effort - millions of people living in occupied territories were then forced to gather these resources, or forcibly transported to work in manufacturing in other Axis territories. Similarly, colonial powers were able to use resources taken from their territories to supply their armies, however this often had devastating consequences for the regions from which food was redirected, contributing to numerous food shortages and famines across Africa, Asia, and Europe. Men from annexed or colonized territories were also used in the armies of the war's Great Powers, and in the Axis armies especially. This meant that soldiers often fought alongside their former-enemies. Aftermath The Second World War was the costliest in human history, resulting in the deaths of between 70 and 85 million people. Due to the turmoil and destruction of the war, accurate records for death tolls generally do not exist, therefore pre-war populations (in combination with other statistics), are used to estimate death tolls. The Soviet Union is believed to have lost the largest amount of people during the war, suffering approximately 24 million fatalities by 1945, followed by China at around 20 million people. The Soviet death toll is equal to approximately 14 percent of its pre-war population - the countries with the highest relative death tolls in the war are found in Eastern Europe, due to the intensity of the conflict and the systematic genocide committed in the region during the war.

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Artesia: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 127(2.90%) households where the householder is under 25 years old, 1,201(27.43%) households with a householder aged between 25 and 44 years, 1,945(44.42%) households with a householder aged between 45 and 64 years, and 1,106(25.26%) households where the householder is over 65 years old.
• In Artesia, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Artesia median household income by age. You can refer the same here

Sources: Official Statistics: Population-Census-data, police registration, information of the civil registry office, civil status registration of the finance office, medical profession’s statistics of the medical association, administration statistics of magistrate departement 15 (departement of tuberculosis abatement), annual report of Vienna’s hospitals, containment measurement of magistrate departement 22, information about household refuse and potential recyclable of magistrate departement 48. Additional: Microcensus.

The total population in Slovakia was estimated at 5.4 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Slovakia Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Chester town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 1,336(30.61%) households with a householder aged between 25 and 44 years, 1,945(44.57%) households with a householder aged between 45 and 64 years, and 1,083(24.82%) households where the householder is over 65 years old.
• In Chester town, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Chester town median household income by age. You can refer the same here

Russian estimates suggest that the total population of the Soviet Union in 1941 was 195.4 million people, before it fell to 170.5 million in 1946 due to the devastation of the Second World War. Not only did the USSR's population fall as a consequence of the war, but fertility and birth rates also dropped due to the disruption. Hypothetical estimates suggest that, had the war not happened and had fertility rates remained on their pre-war trajectory, then the USSR's population in 1946 would have been 39 million higher than in reality. Gender differences When it comes to gender differences, the Soviet male population fell from 94 million in 1941, to 74 million in 1946, and the female population fell from 102 to 96 million. While the male and female population fell by 19 and 5.5 million respectively, hypothetical estimates suggest that both populations would have grown by seven million each had there been no war. In actual figures, adult males saw the largest change in population due to the war, as a drop of 18 to 21 percent was observed across the three age groups. In contrast, the adult female population actually grew between 1941 and 1946, although the population under 16 years fell by a number similar to that observed in the male population due to the war's impact on fertility.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Pennington County: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 413(6.69%) households where the householder is under 25 years old, 1,945(31.52%) households with a householder aged between 25 and 44 years, 2,103(34.08%) households with a householder aged between 45 and 64 years, and 1,709(27.70%) households where the householder is over 65 years old.
• In Pennington County, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Pennington County median household income by age. You can refer the same here