This dataset includes worldwide data on a long timespan: - War: HCED Data v2.csv gather data about conflicts since 1468 BC to August 15, 2022. The data includes battle locations and years. This dataset has been created with the intention of producing a worldwide exhaustive catalogue of wars. - Population: population.csv holds records and estimates of world population, by location, since 10000 BCE.

Personally, I intend to use these two in conjunction with the popular Kaggle dataset Countries of the World, since I might need countries areas to estimate population densities.

Check out the output of my Cleaning War and Population Data notebook for a cleaner version of the dataset.

world_battles_and_demographics_master_table is my final version of the dataset, it holds a selected subset of the original information in a single place. Check out the output of my Wrangling War and Population Data if you're interestd in how I combined the tables.

Comprehensive demographic dataset for War, WV, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

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

Key observations

The largest age group in War, WV was for the group of age 15 to 19 years years with a population of 83 (14%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in War, WV was the 25 to 29 years years with a population of 0 (0%). 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 War is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of War 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 War Population by Age. You can refer the same here

One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred. Here we develop a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. By analogy with the interactions between single-celled and multicellular organisms, we build a theory of group lifecycles as an emergent property of single cell demographic and expansion behaviours. We find that once the transition from small-scale to large-scale groups occurs, a few large-scale groups continue expanding while small-scale groups gradually become scarcer, and large-scale groups become larger in size and fewer in number over time. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. Our results conform to a pattern of human political change in which religions and nation states come to be represented by a few large units and many smaller ones. Future enhancements of the model should include decision-making rules and probabilities of fragmentation for large-scale societies. We suggest that the synthesis of population ecology and social evolution will generate increasingly plausible models of human group dynamics.

About this dataset

Context

The dataset tabulates the data for the War, WV population pyramid, which represents the War population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for War, WV, is 30.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for War, WV, is 48.0.
• Total dependency ratio for War, WV is 78.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for War, WV is 2.1.

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 for the War population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the War for the selected age group is shown in the following column.
• Population (Female): The female population in the War for the selected age group is shown in the following column.
• Total Population: The total population of the War for the selected age group is shown in the following column.

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 War Population by Age. You can refer the same here

Research on conflict traditionally focuses on its initiation, duration, and severity, but seldom on its consequences. Yet, demographic and economic recovery from the consequences of war lasts far longer and may be more devastating than the waging war. Our concern is with war losses and post-war recovery leading to convergence with pre-war performance. To test this proposition, we choose the most severe international and civil wars after 1920. We find that all belligerents recover or overtake demographic losses incurred in war. Economic assessments differ. The most-developed belligerents recover like a “phoenix” from immense destruction in one generation. For less-developed societies, the outcomes are mixed. The less-developed belligerents recover only a portion of their pre-war performance. The least-developed societies suffer the most and fall into lasting poverty traps. The overlapping generation growth model accounts for such differences in recovery rates based on pre-war performance challenging arguments from Solow's neoclassical growth perspective. Our results imply that foreign aid is incidental to the post-war convergence for the most-developed societies, can prompt recovery for the less-developed societies, and is not effective—unless it is massive and sustained—for the least-developed societies. World War II may provide a poor guide to current post-war challenges in Iraq and in Afghanistan.

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.

During the Second World War, the three Axis powers of Germany, Italy, and Finland mobilized the largest share of their male population. For the Allies, the Soviet Union mobilized the largest share of men, as well as the largest total army of any country, but it was restricted in its ability to mobilize more due to the impact this would have on its economy. Other notable statistics come from the British Empire, where a larger share of men were drafted from Dominions than from the metropole, and there is also a discrepancy between the share of the black and white populations from South Africa.

However, it should be noted that there were many external factors from the war that influenced these figures. For example, gender ratios among the adult populations of many European countries was already skewed due to previous conflicts of the 20th century (namely WWI and the Russian Revolution), whereas the share of the male population eligible to fight in many Asian and African countries was lower than more demographically developed societies, as high child mortality rates meant that the average age of the population was much lower.

Replication Data for: "Demographic Consequences of Major Wars"

During the American Civil War, not only was the Confederacy made up of fewer states than the Union, but these states were also much less populous than many in the North. For example, in the final census before the war in 1860, the five largest states in the South had around one million inhabitants each, while the largest states in the North had three to four million. In addition to the Union's larger population, the fact that European immigration into urban and industrial centers in the North was much higher also gave the Union a steady supply of recruits that were drafted as the war progressed, which was vital to the Union's victory in 1865.

The current population of Afghanistan is 41,422,675 as of Tuesday, May 9, 2023, based on Worldometer elaboration of the latest United Nations data. Afghanistan 2020 population is estimated at 38,928,346 people at mid year according to UN data. Afghanistan population is equivalent to 0.5% of the total world population. Afghanistan ranks number 37 in the list of countries (and dependencies) by population. The population density in Afghanistan is 60 per Km2 (154 people per mi2). The total land area is 652,860 Km2 (252,071 sq. miles) 25.4 % of the population is urban (9,904,337 people in 2020) The median age in Afghanistan is 18.4 years.

Graph and download economic data for Population Level - Men, Veterans, Gulf War Era II, 18 Years and over (LNU00066413) from Jan 2006 to Sep 2025 about gulf war, 18 years +, veterans, males, civilian, population, and USA.

Note: None of the participants identified four other kinds of trauma, including combat or exposure to a war zone (in the military or as a civilian), captivity (being kidnapped, abducted, held hostage, prisoner of war), severe human suffering, and sudden, violent death (homicide, suicide).Demographic and Sample Characteristics (N = 95).

Graph and download economic data for Employment-Population Ratio - Men, Veterans, Gulf War Era II, 18 Years and over (LNU02366413) from Jan 2006 to Aug 2025 about gulf war, 18 years +, veterans, employment-population ratio, males, household survey, population, employment, and USA.

The Second World War severely altered the demographic composition of many countries, particularly in terms of gender ratios across certain age groups. For age groups below 14 years, there is little observable impact of the war on gender ratios, however, some countries see a drastic change across older generations, particularly in the Soviet Union. For men in their twenties (i.e. those in their late-teens or early-twenties when the war began), the ratio drops from 98 men per 100 women in the 15-19 age group, to 68 men per 100 women in the 25-29 group.

In addition to the Second World War, these figures are affected by trends in nature and other historical events. For example, women tend to have higher overall life expectancies than men, which typically sees gender ratios widen among older generations. The impact of the First World War is also most-observable in France's gender ratios for those aged in their fifties. Additionally, the gap in ratios remains high for the Soviet Union across older age groups due to the impact of the First World War and the famine of the early 1930s, however the figures for Russia itself are even lower as it was disproportionately affected by the Russian Revolution and famine of the 1920s.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

United States - Population Level - Men, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over was 4621.00000 Thous. of Persons in September of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Men, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over reached a record high of 11434.00000 in September of 2008 and a record low of 4621.00000 in September of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Men, Veterans, World War II or Korean War or Vietnam Era, 18 Years and over - last updated from the United States Federal Reserve on December of 2025.

Dataset

This dataset was created by Suraj Subedi

Released under MIT

Contents

The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023.

A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities).

The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022.

The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war.

COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions.

This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files.

In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.

Russia launched its armed aggression against Ukraine in February 2014, seizing Crimea and subsequently occupying parts of the Donetsk and Luhansk oblasts of Ukraine. On February 24, 2022, Russia started a large-scale invasion of Ukraine on multiple fronts, deploying troops and shelling Ukrainian cities and infrastructure. As of the end of 2023 the war against Ukraine is still ongoing and its outcome is unknown. At different stages of the war, KIIS has studied the public opinion of the Ukrainian population regarding Russian aggression. It included surveys on people's attitudes towards the annexation of Crimea, and Ukraine's countermeasures in Eastern Ukraine (Anti-Terrorist Operation, ATO) covering the period from 2014 to 2018. Since 2022, public opinion polls have asked questions regarding people's feelings and opinions about the ongoing war between Ukraine and Russia, perceptions of the government's actions, readiness for concessions / compromises to end the war, etc. Data from individual surveys for the period 2014-2023 (14 in total) were combined into a merged dataset. Each of these polls is representative of the Ukraine's adult population (aged 18 and older), and typically includes about 2,000 respondents. The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement). These data provide a snapshot of public opinion of the Ukrainian population on some aspects of the Russian-Ukrainian war. Some questions are repeated, which makes it possible to track changes in opinions over time.