The United States Census Bureau has conducted surveys of manufacturing activity since 1810 with fluctuating frequency. Between 1919 and 1939 the Census of Manufactures (CM) was conducted biennially. This data collection consists of individual-plant data from the Census of Manufactures for 1929, 1931, 1933, and 1935, the only years in this span for which original returns are available. The records of the Motor Vehicle Industry have been coded to produce an electronic data set to provide the basis for microeconomic evidence for the study of the Great Depression. The data set contains observations on: basic information about the plants (e.g. name, location, owner, etc.), products made and materials used, operation and working hours, employment, wages and salaries, costs and amount of materials used, value and quantity of products by type, and power used.

In Soviet Russia (RSFSR) in 1939 and 1959, ethnic Russians made up the largest share of the total population, with a share of approximately 83 percent. Tatars were the second largest ethnic group, followed by Ukrainians. Russians were consistently the largest ethnic group in the Soviet Union as a whole, with an overall share of 53 percent in 1979.

Between 1944–1950, almost eight million expellees arrived in West Germany. We introduce a rich county-level database on the expellees’ socio-economic situation in post-war Germany. The database contains regionally disaggregated information on the number, origin, age, gender, religious denomination and labour force status of expellees. It also records corresponding information on the West German population as a whole, on the pre-war economic and religious structure of host and origin regions, and on war destructions in West Germany. The main data sources are the West German censuses of 1939, 1946, 1950 and 1961. Altogether, the database consists of 18 data tables (in xsls format). We have digitized the data as printed in the statistical sources, adding only an English translation of the table head (along with the original table head in German). Each data table has two tabs: The first tab (named “source”) lists the reference(s) of the printed source, the second (“data”) contains the actual data. Please consult the readme file for an overview of each data table’s content and the paper for additional information.

PERIOD: Population census on Oct. 1, 1930. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet].

The Second World War did not only cause many deaths but also leaded to broad changes in the population and settlement structure. This data compilation shows selected consequences of population movements in the context of the displacement of persons on the population structure in the Federal Republic of Germany and partly also in the German Democratic Republic. Under the command of the first federal minister for matters concerning displaced persons Hans Lukaschek the term ‘displaced persons’ was defined nationwide in the federal expellee law (find the legislative text attached).
The data compilation is passed on data published by the Federal Statistical Office and on data from selected scientific publications. The study in hand is subdivided in section A which is based on publications from the Federal Statistical Office and section B which is based on different individual scientific publications.

Subsection A1 contains selected data from censuses and extrapolations from censuses from sources of the Federal Statistical Office. Subsection A2 contains selected data from the micro census from sources of the Federal Statistical Office. Subsection B1 contains selected data from a publication by Heinz Günter Steinberg. Subsection B2 contains selected data from a publication by Gerhard Reichling. Subsection B2 contains selected data from a publication by Friedrich Edding and Eugen Lemberg.

Data tables in HISTAT:

A: Federal Statistical Office A1: Results and extrapolations from the censuses
A1.01 Resident population and displaced persons in 1000 by federal states, end-of-year values (1945-1966) A1.02 Displaced persons in 1000 by federal states, half-year values (1946-1956) A1.03 Influx of displaced persons by sex and federal state (1952-1960) A1.04a Displaced persons altogether in the federal territory by age in 1000 (1950-1953) A1.04b Male displaced persons in the federal territory by age in 1000 (1950-1953) A1.04c Female displaced persons in the federal territory by age in 1000 (1950-1953) A1.05 Displaced persons in the federal territory by age groups in 1000 (1950-1966) A1.06 Resettlement of displaced persons (1949-1962) A1.07 Marriages of displaced persons and the rest of the population in the FRG (1950-1960) A1.08 Marriages of displaced persons and the rest of the population in the FRG in absolute numbers in the different federal states (1950-1960)

A2: Results from the micro census A2.01 Displaced persons among the resident population by sex and federal state in 1000 (1958-1973) A2.02a Displaced persons among the resident population by sex and age group in the FRG in 1000 (1958-1973) A2.02b Displaced persons among the resident population by sex and age group in Schleswig-Holstein in 1000 (1958-1973) A2.02c Displaced persons among the resident population by sex and age group in Hamburg in 1000 (1958-1973) A2.02d Displaced persons among the resident population by sex and age group in Niedersachsen in 1000 (1958-1973) A2.02e Displaced persons among the resident population by sex and age group in Bremen in 1000 (1958-1973) A2.02f Displaced persons among the resident population by sex and age group in Nordrhein-Westfalen in 1000 (1958-1973) A2.02g Displaced persons among the resident population by sex and age group in Hessen in 1000 (1958-1973) A2.02h Displaced persons among the resident population by sex and age group in Rheinland-Pfalz in 1000 (1958-1973) A2.02i Displaced persons among the resident population by sex and age group in Baden-Württemberg in 1000 (1958-1973) A2.02j Displaced persons among the resident population by sex and age group in Bayern in 1000 (1958-1973) A2.02k Displaced persons among the resident population by sex and age group in West-Berlin in 1000 (1958-1973) A2.02l Displaced persons among the resident population by sex and age group in Saarland in 1000 (1958-1973) A2.03 Displaced persons among the resident population by federal sate and civil status in 1000 (1958-1973)

B: Scientific publications B1: Steinberg: Population development in Germany in the Second World War B1.01 Changes in population in German states (1939-1946) B1.02 Regional development of the civilian population in Germany (1939-1945) B1.03 Displaced persons in Germany by territory and date of displacement (1944-1955) B1.04 Arrival of displaced persons in Germany by territory of displacement (1944-1955) B1.05 Selected data on socio-economic development in Germany (1946-1987) B1.06 Regional development of population in the Federal Republic of Germany and the German Democratic Republic by states (1939-1990)

B2: Reichling: German displaces persons in numbers, part 2 B2.01a Germans from the eastern territories and from foreign countries in the federal territory in 1000 (1946-1970) B2.01b Germans from the eastern territories and from foreign countries in Schleswig-Holstein in 1000 (1946-1970) B2.01c Germans from the eastern territories and from foreign countries in Hamburg in 1000 (1946-1970) B2.01d Germans from...

Abstract copyright UK Data Service and data collection copyright owner.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

Main Topics:

The Great Britain Historical Database is a large database of British nineteenth and twentieth-century statistics. Where practical the referencing of spatial units has been integrated, data for different dates have been assembled into single tables.

The Great Britain Historical Database currently contains :

• Statistics from the 1861 Census and the Registrar General's reports, 1851-1861
  
• Employment statistics from the census, 1841-1931
  
• Demographic statistics from the census, 1841-1931
  
• Mortality statistics from the Registrar General's reports, 1861-1920
  
• Marriage statistics from the Registrar General's reports, 1841-1870
  
• Trade union statistics for the Amalgamated Society of Engineers (ASE), 1851-1918
  
• Trade union statistics for the Amalgamated Society of Carpenters and Joiners (ASCJ), 1863-1912
  
• Official poor law statistics, 1859-1915 and 1919-1939
  
• Wage statistics, 1845-1906
  
• Hours of work statistics, 1900-1913
  
• Small debt statistics from county courts, 1847-1913 and 1938
  

There are six tables in this part of the Great Britain Historical Database :

Lee_emp holds simplified and standardised versions of the country-level employment statistics for Great Britain given in the printed census reports from 1841 to 1931, using the employment categories defined by the Standard Industrial Classification.

Emp1901m holds male employment statistics from the 1901 census for county boroughs, municipal boroughs and urban districts over 10,000 population in England and Wales.

Emp1901f holds female employment statistics from the 1901 census for county boroughs, municipal boroughs and urban districts over 10,000 population in England and Wales.

Emp1911m holds male employment statistics from the 1911 census for counties, county boroughs, municipal boroughs and urban districts over 10,000 population in England and Wales.

Emp1911f holds female employment statistics from the 1911 census for counties, county boroughs, municipal boroughs and urban districts over 10,000 population in England and Wales.

Emp1921 holds male and female employment statistics from the 1921 census for county boroughs, municipal boroughs, urban districts and rural districts, plus certain civil parishes in England and Wales.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

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.

About this dataset

Context

The dataset tabulates the Montgomery town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Montgomery town. The dataset can be utilized to understand the population distribution of Montgomery town by age. For example, using this dataset, we can identify the largest age group in Montgomery town.

Key observations

The largest age group in Montgomery Town, New York was for the group of age 55 to 59 years years with a population of 1,939 (8.35%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Montgomery Town, New York was the 85 years and over years with a population of 423 (1.82%). 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

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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Montgomery town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Montgomery town 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 Montgomery town Population by Age. You can refer the same here

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.

It is estimated that the Second World War was responsible for the deaths of approximately 3.76 percent of the world's population between 1939 and 1945. In 2022, where the world's population reached eight billion, this would be equal to the death of around 300 million people.

The region that experienced the largest loss of life relative to its population was the South Seas Mandate - these were former-German territories given to the Empire of Japan through the Treaty of Versailles following WWI, and they make up much of the present-day countries of the Marshall Islands, Micronesia, the Northern Mariana Islands (U.S. territory), and Palau. Due to the location and strategic importance of these islands, they were used by the Japanese as launching pads for their attacks on Pearl Harbor and in the South Pacific, while they were also taken as part of the Allies' island-hopping strategy in their counteroffensive against Japan. This came at a heavy cost for the local populations, a large share of whom were Japanese settlers who had moved there in the 1920s and 1930s. Exact figures for both pre-war populations and wartime losses fluctuate by source, however civilian losses in these islands were extremely high as the Japanese defenses resorted to more extreme measures in the war's final phase.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Reading: 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 118(2.39%) households where the householder is under 25 years old, 1,556(31.49%) households with a householder aged between 25 and 44 years, 1,939(39.24%) households with a householder aged between 45 and 64 years, and 1,328(26.88%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Reading, showcasing varying income levels among different age demographics.

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

Estimates of London's population between 1801 and 2001 (persons present 1801 to 1991 and residents for 2001 onwards) derived from historic Census data.

Sources: years to 1971 - Greater London Council Research Memorandum 413, The Changing Population of the London Boroughs; 1981 Census Small Area Statistics, Table 1; 1991 Census Small Area Statistics, Table 1. Figure for Year-1939 is a mid-year estimate for the year 1939. Figure for Year-2001 onwards is the number of residents because the number of persons present is not available from 2001. Note that totals for Greater London may not match due to rounding errors. Figures are estimates to the nearest thousand.

About this dataset

Context

The dataset tabulates the DeSoto County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for DeSoto County. The dataset can be utilized to understand the population distribution of DeSoto County by age. For example, using this dataset, we can identify the largest age group in DeSoto County.

Key observations

The largest age group in DeSoto County, MS was for the group of age 10 to 14 years years with a population of 14,441 (7.76%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in DeSoto County, MS was the 85 years and over years with a population of 1,939 (1.04%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the DeSoto County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of DeSoto 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 DeSoto County Population by Age. You can refer the same here

During the Holocaust, approximately six million Jews were killed. In France, the Jewish population had decreased by 140,000 individuals between 1939 and 1945. It then increased between the end of World War II and the 1970s, reaching 530,000 individuals in 1970. However, according to the source, the number of Jews in France has declined by more than 15 percent between that period and 2020, and is now estimated to be 442,000.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Muscle Shoals: 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 213(3.43%) households where the householder is under 25 years old, 1,939(31.27%) households with a householder aged between 25 and 44 years, 2,157(34.78%) households with a householder aged between 45 and 64 years, and 1,892(30.51%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Muscle Shoals, showcasing varying income levels among different age demographics.

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

PERIOD: Population census on Oct. 1, 1935. SOURCE: [Survey by the Statistics Bureau, Imperial Cabinet].

Aggregate data from documents from the state bureaus of the census

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Glynn 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 1,939(5.36%) households where the householder is under 25 years old, 8,577(23.73%) households with a householder aged between 25 and 44 years, 12,004(33.21%) households with a householder aged between 45 and 64 years, and 13,627(37.70%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the county of Glynn County, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-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 Glynn County median household income by age. You can refer the same here

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.

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...