The dataset contains the historical series of the census population for the years from 1861 to 2011, broken down by marital status and gender. The 1891 and 1941 censuses were not carried out, the former for organizational and financial reasons and the latter for war reasons. The censuses of 1861 and 1871 reveal the de facto population while from 1881 the resident population is considered, the data refer to the borders of the time. For further information, it is possible to consult the Istat website http://seriestoriche.istat.it/ This dataset was released by the municipality of Milan.

About this dataset

Context

The dataset tabulates the Norfolk 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 Norfolk. The dataset can be utilized to understand the population distribution of Norfolk by age. For example, using this dataset, we can identify the largest age group in Norfolk.

Key observations

The largest age group in Norfolk, NE was for the group of age 10-14 years with a population of 1,886 (7.55%), according to the 2021 American Community Survey. At the same time, the smallest age group in Norfolk, NE was the 80-84 years with a population of 414 (1.66%). 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

Variables / Data Columns

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

Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 14 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th 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. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

About this dataset

Context

The dataset tabulates the Papillion 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 Papillion. The dataset can be utilized to understand the population distribution of Papillion by age. For example, using this dataset, we can identify the largest age group in Papillion.

Key observations

The largest age group in Papillion, NE was for the group of age 15-19 years with a population of 1,886 (7.90%), according to the 2021 American Community Survey. At the same time, the smallest age group in Papillion, NE was the 80-84 years with a population of 338 (1.42%). 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

Variables / Data Columns

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

Die Studie ist ein Beitrag zur regionalen Erwerbsstruktur in Deutschland im 19. Jahrhundert. Den Schwerpunkt bilden die sektoralen Erwerbsstrukturunterschiede zwischen preußischen Provinzen (teils auch Regierungsbezirken) und außerpreußischen deutschen Bundesstaaten für die Zeit von 1861 bis 1907. Zielsetzung der Untersuchung ist es, Hypothesen über die die regionale Verteilung der deutschen Industrialisierung im 19. Jahrhundert zu überprüfen. Tipton verwendet als Maß den Grad der Spezialisierung der Beschäftigten auf industrielle Berufe und unterscheidet insgesamt 32 Regionen (preußische Provinzen, deutsche Einzelstaaten) im Deutschen Reich. Er analysiert die konkreten Veränderungen der regionalen Entwicklungsmuster in einem breiten Rahmen erklärender Variablen, wobei die regionale Spezialisierung eine zentrale Erklärungsgröße darstellt. Als Ursache der von Tipton ab 1860 verstärkt beobachteten Differenzen sieht er die Verteilung des Gewerbes im Raum an. Tipton kommt im ganzen zu dem Schluss, dass sich die Unterschiede der Erwerbsstruktur in dieser Zeit der Industrialisierung Deutschlands kontinuierlich verschärften und sich zwischen den industrialisierten Regionen des Ruhrgebiets, Sachsens, Berlins, Oberschlesiens, Elsaß-Lothringen einerseits und den östlichen preußischen Provinzen andererseits eine immer größere Kluft auftrat. Diesen Pr0zeß will er nicht zu sehr vereinfachen: Auch unter den Industrieregionen verstärkten sich die Unterschiede; auch tertiäre Regionen wie Hamburg und Bremen spezialisierten sich immer mehr; auch im Westen gab es zurückgebliebene Agrarregionen. Verzeichnis der Tabellen in HISTAT: Beschäftigungsstruktur in Deutschland (1882-1907)Beschäftigungsstruktur in Ost- und West-Preussen (1861-1882)Beschäftigungsstruktur in Ost-Preussen (1882-1907)Beschäftigungsstruktur in West-Preussen (1882-1907)Beschäftigungsstruktur in Posen (1861-1907)Beschäftigungsstruktur in Pommern (1861-1907)Beschäftigungsstruktur in Oppeln, Oberschlesien (1861-1907)Beschäftigungsstruktur in Breslau, Liegnitz (1861-1907)Beschäftigungsstruktur in Frankfurt/Oder (1861-1907)Beschäftigungsstruktur in Potsdam (1861-1907)Beschäftigungsstruktur in Berlin (1861-1907)Beschäftigungsstruktur in Mecklenburg-Schwerin, Mecklenburg-Strelitz (1882-1907)Beschäftigungsstruktur in Schleswig-Holstein (1861-1907)Beschäftigungsstruktur in Hannover (1867-1882)Beschäftigungsstruktur in Hannover, Oldenburg, Braunschweig,Schaumburg-Lippe (1882-1907)Beschäftigungsstruktur in Lübeck, Bremen, Hamburg (Hansestädte) (1882-1907)Beschäftigungsstruktur im Königreich Sachsen (1849-1907)Beschäftigungsstruktur in Sachsen (Preussen) (1861-1882)Beschäftigungsstruktur in Magdeburg, Anhalt (1882-1907)Beschäftigungsstruktur in Merseburg, Erfurt, Thüringen (1882-1907)Beschäftigungsstruktur in Münster, Minden, nördl. Westfalen, ohne Lippe, Waldeck (1861-1875)Beschäftigungsstruktur in Münster, Minden, nördl. Westfalen, mit Lippe, Waldeck (1882-1907)Beschäftigungsstruktur in Düsseldorf, Arnsberg (Ruhr) (1861-1907)Beschäftigungsstruktur in Aachen (1861-1907)Beschäftigungsstruktur in Köln (1861-1907)Beschäftigungsstruktur in Trier, Koblenz (1861-1907)Beschäftigungsstruktur in Hessen-Nassau, Oberhessen Posen (1867-1907)Beschäftigungsstruktur in Bayern (1847-1907)Beschäftigungsstruktur in Württemberg, Hohenzollern (1861-1907)Beschäftigungsstruktur in Baden (1847-1907)Beschäftigungsstruktur in Hessen ohne Oberhessen (1867-1882)Beschäftigungsstruktur in Hessen mit Oberhessen (1882-1907)Beschäftigungsstruktur in Rheinpfalz (1847-1907)Beschäftigungsstruktur in Lothringen (1882-1907)Beschäftigungsstruktur im Elsass (1882-1907)

About this dataset

Context

The dataset tabulates the Columbus 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 Columbus. The dataset can be utilized to understand the population distribution of Columbus by age. For example, using this dataset, we can identify the largest age group in Columbus.

Key observations

The largest age group in Columbus, NE was for the group of age 10 to 14 years years with a population of 1,886 (7.80%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Columbus, NE was the 80 to 84 years years with a population of 472 (1.95%). 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 Columbus is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Columbus 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 Columbus Population by Age. You can refer the same here

The authors construct a unique panel of income and Protestant church attendance for six waves of up to 175 Prussian counties spanning 1886-1911; to study the interplay between religion and the economy. In particular income levels and religious participation. Their unique database on historical church attendance stems from the practice of the Protestant Church in Germany to count the number of participations in Holy Communion every year, which Hölscher (2001) gathered at the church-district (Kirchenkreis) level from regional archives covering modern Germany. The Sacrament Statistics (Abendmahlsstatistik) stem from a uniform annual survey organized by the Statistical Central Office at the Protestant Higher Church Council in Berlin from 1880 (with precursors) to World War II. Data collection was done by the parish priests on a preprinted form following uniform surveying directives. Regional Consistories combined these parish data into registers at the level of church districts, which usually comprised 10-20 adjacent parishes. Their main indicator of church attendance is the number of participations in Holy Communion divided by the number of Protestants in a district. Our income data refer to the average annual income of male elementary-school teachers, available every five years from 1886 to 1911 for all Prussian counties (Kreise) from Education Censuses (Galloway (2007)). Their dataset covers an unbalanced panel of 175 territorial entities (“counties”) in 1886-1911. This sample of Prussian counties constitutes the intersection between end-of-19 -century Prussia (for which income data are available) and modern Germany (for which church attendance data are available) and is thus not necessarily representative of Prussia or of Germany. To this dataset, we merge cross-sectional data for Prussian counties used in Becker and Woessmann (2009). The data source for church attendance is Hölscher (2001) based on Sacrament Statistics. The Protestant Regional Churches of Germany conducted annual surveys of “Expressions of Churchly Life” between 1880 (with precursors) and World War II. Their main indicator of church attendance is the “sacrament participation” (Hölscher (2001)), measured as the number of participations in Holy Communion divided by the number of Protestants in a church district. Hölscher kindly provided the authors with digital versions of the data as published in the Data Atlas. After assigning IDs to every church district (Kirchenkreis) and cross-checking the data, they combined the data into one panel dataset. The data source for teacher income: Galloway (2007) based on Education Censuses. The data are drawn from the Galloway (2007) Prussia Database and are based on the following volumes of the Preussische Statistik: Volume 101, pp. 2-391 (for 1886); Volume 120, part II, pp. 2-313 (for 1891); Volume 151, part II, pp. 2-315 (for 1896); Volume 176, part III, pp. 2-485 (for 1901); Volume 209, part III, pp. 2- 513 (for 1906); and Volume 231, part II, pp. 2-599 (for 1911). The data were collected by the Prussian Statistical Office and reported at the level of administrative counties (Kreise). Teacher income data are available for all Prussian counties in all the years 1886, 1891, 1896, 1901, 1906, and 1911. There are two changes in how teacher income is reported over time. First, in 1886 and 1891, teacher income covers only direct wage payments, but not extras such as housing allowances and any other allowances. From 1896 onwards, data include all components of income. To make data consistent over time, we pre-multiply direct wage payments in 1886 and 1891 by the county-specific ratio of total income over (only) wage payments observed in 1896. In 1911, income is only reported as total income of male and female elementary- school teachers combined, whereas for all other years both genders are reported separately. In 1911, we impute income of male elementary-school teachers by pre-multiplying total income of elementary-school teachers by the county-specific share of male teachers in total income observed in 1906. The control variables used in Table A3 are taken from the Prussian Population Census in 1871. First used in Becker and Woessmann (2009), who provide variable definitions and detailed documentation (see also iPEHD), they are based on Königliches Statistisches Bureau, Die Gemeinden und Gutsbezirke des Preussischen Staates und ihre Bevölkerung: Nach den Urmaterialien der allgemeinen Volkszählung vom 1. December 1871 (Berlin: Verlag des Königlichen Statistischen Bureaus, 1874). They merge the church attendance and income data by assigning the income data, available at the level of the administrative county, to that church district (for which they have church attendance data) which contains the capital of the administrative county (same for the 1871 control variables available for administrative counties). In cases where several county capitals are located in the same district, they aggregated the county data up to the church district level (taking population- weighted averages of income data). To make regional entities comparable over time in face of territorial changes during our period of observation, they aggregated church-district and county data up to the highest level at which consistency over time is given.

About this dataset

Context

The dataset tabulates the Russell town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Russell town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Russell town was 1,886, a 0.16% increase year-by-year from 2021. Previously, in 2021, Russell town population was 1,883, an increase of 0.53% compared to a population of 1,873 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Russell town increased by 93. In this period, the peak population was 1,886 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Russell town is shown in this column.
• Year on Year Change: This column displays the change in Russell town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Russell town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Martin 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 Martin County. The dataset can be utilized to understand the population distribution of Martin County by age. For example, using this dataset, we can identify the largest age group in Martin County.

Key observations

The largest age group in Martin County, NC was for the group of age 55 to 59 years years with a population of 1,886 (8.58%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Martin County, NC was the 85 years and over years with a population of 438 (1.99%). 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 Martin County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Martin 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 Martin County Population by Age. You can refer the same here

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 t...

About this dataset

Context

The dataset tabulates the Coalinga 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 Coalinga. The dataset can be utilized to understand the population distribution of Coalinga by age. For example, using this dataset, we can identify the largest age group in Coalinga.

Key observations

The largest age group in Coalinga, CA was for the group of age 30-34 years with a population of 1,886 (10.74%), according to the 2021 American Community Survey. At the same time, the smallest age group in Coalinga, CA was the 70-74 years with a population of 173 (0.99%). 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

Variables / Data Columns

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

About this dataset

Context

The dataset tabulates the Plaquemines Parish 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 Plaquemines Parish. The dataset can be utilized to understand the population distribution of Plaquemines Parish by age. For example, using this dataset, we can identify the largest age group in Plaquemines Parish.

Key observations

The largest age group in Plaquemines Parish, LA was for the group of age 10 to 14 years years with a population of 1,886 (8.09%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Plaquemines Parish, LA was the 85 years and over years with a population of 243 (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 Plaquemines Parish is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Plaquemines Parish 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 Plaquemines Parish Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Pelham 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 9(0.21%) households where the householder is under 25 years old, 1,234(29.14%) households with a householder aged between 25 and 44 years, 1,886(44.53%) households with a householder aged between 45 and 64 years, and 1,106(26.12%) households where the householder is over 65 years old.
• In Pelham 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 Pelham town median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Pittsfield by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pittsfield. The dataset can be utilized to understand the population distribution of Pittsfield by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pittsfield. 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 Pittsfield.

Key observations

Largest age group (population): Male # 60-64 years (1,719) | Female # 60-64 years (1,886). 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

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 Pittsfield population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pittsfield is shown in the following column.
• Population (Female): The female population in the Pittsfield 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 Pittsfield 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 Pittsfield Population by Gender. You can refer the same here

長生郡長柄町(千葉県)の労働力人口（男）の統計データです。最新の2020年の数値「1,886人」を含む2000～2020年までの推移グラフや人口が近い不破郡関ケ原町(岐阜県)と長生郡睦沢町(千葉県)との比較表などの情報を無料で公開しています。csv形式でのダウンロードも可能でEXCELでも開けますので、研究や分析レポートにお役立て下さい。

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Bridgeport, the largest racial group is White alone with a population of 1,886 (83.86% 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 Bridgeport
• Population: The population of the racial category (for Non-Hispanic) in the Bridgeport is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Bridgeport 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 Bridgeport Population by Race & Ethnicity. You can refer the same here

石川郡平田村(福島県)の就業者（男）の統計データです。最新の2020年の数値「1,886人」を含む2000～2020年までの推移グラフや人口が近い十勝総合振興局士幌町(北海道)と小県郡長和町(長野県)との比較表などの情報を無料で公開しています。csv形式でのダウンロードも可能でEXCELでも開けますので、研究や分析レポートにお役立て下さい。

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Superior charter township: 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 78(1.42%) households where the householder is under 25 years old, 1,886(34.34%) households with a householder aged between 25 and 44 years, 2,134(38.86%) households with a householder aged between 45 and 64 years, and 1,394(25.38%) households where the householder is over 65 years old.
• In Superior charter township, 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 Superior charter township median household income by age. You can refer the same here

板野郡藍住町(徳島県)の就業者・家事のほか仕事の統計データです。最新の2015年の数値「1,886人」を含む2000～2015年までの推移グラフや人口が近い中頭郡西原町(沖縄県)と久慈市(岩手県)との比較表などの情報を無料で公開しています。csv形式でのダウンロードも可能でEXCELでも開けますので、研究や分析レポートにお役立て下さい。