Throughout the 19th century, the share of military personnel employed by the United States government was below 0.2 percent of the total population in most years. There were noticeable spikes in enlistments and conscriptions during the American Civil War (1861-65), the First World War (1917-18*), and Second World War (1941-45*), as well as smaller increases during the Mexican-American War (1946-48) and the Spanish-American War (1898), but figures were generally much lower than the post-WWII era.

Following the Second World War, the United States abandoned many of its isolationist positions as it sought to become the world's leading superpower. This involved stationing millions of troops in overseas bases during the Cold War, in strategically important locations such as West Germany, Japan, and Taiwan. Additionally, involvement in conflicts such as the Korean War (1950-1953) and Vietnam War (1964-1973*) kept military employment high, usually between 1-2 percent until the 1970s. Figures remained just below the one percent mark until the 1990s, when the end of the Cold War and the growing influence of technology in conventional warfare saw a decrease in demand for many traditional combat roles. Despite U.S. involvement in a number of overseas conflicts in the 21st century, military personnel represented less than 0.5 percent of the total population in most years between 2000 and 2016.

The 2012 US Army Anthropometric Survey (ANSUR II) was executed by the Natick Soldier Research, Development and Engineering Center (NSRDEC) from October 2010 to April 2012 and is comprised of personnel representing the total US Army force to include the US Army Active Duty, Reserves, and National Guard. The data was made publicly available in 2017. In addition to the anthropometric and demographic data described below, the ANSUR II database also consists of 3D whole body, foot, and head scans of Soldier participants. These 3D data are not publicly available out of respect for the privacy of ANSUR II participants. The data from this survey are used for a wide range of equipment design, sizing, and tariffing applications within the military and has many potential commercial, industrial, and academic applications.These data have replaced ANSUR I as the most comprehensive publicly accessible dataset on body size and shape. The ANSUR II dataset includes 93 measurements from over 6,000 adult US military personnel, comprising 4,082 men (ANSUR_II_MALE_Public.csv) and 1,986 women (ANSUR_II_FEMALE_Public.csv).

The ANSUR II working databases contain 93 anthropometric measurements which were directly measured, and 15 demographic/administrative variables.

Much more information about the data collection methodology and content of the ANSUR II Working Databases may be found in the following Technical Reports, available from theDefense Technical Information Center (www.dtic.mil) through:

a. 2010-2012 Anthropometric Survey of U.S. Army Personnel: Methods and Summary
Statistics. (NATICK/TR-15/007)
b. Measurer’s Handbook: US Army and Marine Corps Anthropometric Surveys,
2010-2011 (NATICK/TR-11/017)

The graph illustrates the number of personnel in each branch of the U.S. Military for the year 2025. The x-axis lists the military branches: Army, Navy, Marine Corps, Air Force, Space Force, and Coast Guard. The y-axis represents the number of personnel, ranging from 41,477 to 449,265. Among the branches, the Army has the highest number of personnel with 449,265, followed by the Navy with 333,794 and the Air Force with 317,675. The Marine Corps and Coast Guard have 168,628 and 41,477 personnel, respectively. The data is displayed in a bar graph format, effectively highlighting the distribution of military personnel across the different branches.

In 2024, 61 percent of survey respondents in the United States said they had either a great deal or quite a lot of confidence in the military. This is a slight increase from the previous year, when 640percent of respondents had confidence in the U.S. military. Additionally, this is an increase of six points from 1975 levels, when only 58 percent of Americans had confidence in the military.

This graph shows the total number of soldiers who were enlisted in the Union and Confederate armies during the American Civil War, between 1861 and 1865. The total population of the Union states was 18.9 million in 1860, and the Confederate states in the south had a population of 8.6 million. The Border States, who primarily supported the Union but sent troops to both sides, had a population of 3.5 million. From the graph we can see that over the course of the war a total of 2.1 million men enlisted for the Union Army, and 1.1 million enlisted for the Confederate Army. The Union Army had roughly double the number of soldiers of the Confederacy, and although the Confederacy won more major battles than the Union in the early stages of the war, the strength of numbers in the Union forces was a decisive factor in their overall victory as the war progressed.

United States - Total Population: All Ages including Armed Forces Overseas was 343157.45000 Thous. in December of 2025, according to the United States Federal Reserve. Historically, United States - Total Population: All Ages including Armed Forces Overseas reached a record high of 343157.45000 in December of 2025 and a record low of 156309.00000 in January of 1952. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Population: All Ages including Armed Forces Overseas - last updated from the United States Federal Reserve on June of 2025.

Content

Lists the military spending, GDP, and population estimate for the US each year from 1960 to 2020.

Acknowledgements

Banner image source: https://unsplash.com/photos/BQgAYwERXhs

United States US: Armed Forces Personnel: % of Total Labour Force data was reported at 0.828 % in 2016. This records a decrease from the previous number of 0.838 % for 2015. United States US: Armed Forces Personnel: % of Total Labour Force data is updated yearly, averaging 0.995 % from Sep 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 1.704 % in 1990 and a record low of 0.828 % in 2016. United States US: Armed Forces Personnel: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Defense and Official Development Assistance. Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces. Labor force comprises all people who meet the International Labour Organization's definition of the economically active population.; ; International Institute for Strategic Studies, The Military Balance.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

About this dataset

Context

The dataset presents the median household income across different racial categories in Soldier. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Soldier population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95% of the total residents in Soldier. Notably, the median household income for White households is $43,236. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $43,236.

https://i.neilsberg.com/ch/soldier-ia-median-household-income-by-race.jpeg" alt="Soldier median household income diversity across racial categories">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Soldier.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Soldier median household income by race. You can refer the same here

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES VETERAN STATUS - DP02 Universe - Civilian population 18 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.

Analysis of ‘US Military Spending by Year (1960 - 2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brandonconrady/us-military-spending-by-year-1960-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Lists the military spending, GDP, and population estimate for the US each year from 1960 to 2020.

Acknowledgements

Banner image source: https://unsplash.com/photos/BQgAYwERXhs

--- Original source retains full ownership of the source dataset ---

This dataset is about countries, has 1 rows. and is filtered where the country includes United States. It features 4 columns: country, region, continent, and military expenditure. The preview is ordered by population (descending).

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2010, the 2010 Census provides the official counts of the population and housing units for the nation, states, counties, cities and towns. For 2006 to 2009, the Population Estimates Program provides intercensal estimates of the population for the nation, states, and counties..Explanation of Symbols:.An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2006-2010 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2006-2010 American Community Survey

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.

This data collection was designed to analyze the relationships among height, morbidity, and mortality among individuals recruited into the Union Army. Information about each recruit includes date, place, and term of enlistment, place of birth, military ID number, random number assigned to each company, occupation before enlistment, age at enlistment, and height. Population figures for 1850 to 1860 by race, sex, and county of birth also are included by county and town of both recruit's birth and enlistment places. In addition, the latitude and longitude of the population centroids of each civil division were also computed.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

National Center for Health Statistics (NCHS) population health survey data have been linked to VA administrative data containing information on military service history and VA benefit program utilization. The linked data can provide information on the health status and access to health care for VA program beneficiaries. In addition, researchers can compare the health of Veterans within and outside the VA health care system and compare Veterans to non-Veterans in the civilian non-institutionalized U.S. population. Due to confidentiality requirements, the Restricted-use NCHS-VA Linked Data Files are accessible only through the NCHS Research Data Center (RDC) Network. All interested researchers must submit a research proposal to the RDC. Please see the NCHS RDC website (https://www.cdc.gov/rdc/index.htm) for instructions on submitting a proposal.

This map shows schools, school districts, and population density throughout the US. Click on the map to learn more about the school districts and schools within an area. A few things you can learn within this map:How many public/private schools fall within the district?What type of population density lives within this district? Socioeconomic factors about the Census Tracts which fall within the district:School enrollment of under 19 by grade Children living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of the population within the districtFor more information about the data sources:Socioeconomic factors:The American Community Survey (ACS) helps us understand the population in the US. This app uses the 5-year estimates, and the data is updated annually when the U.S. Census Bureau releases the newest estimates. For detailed metadata, visit the links in the bullet points above. Current School Districts layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Private Schools layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Public Schools layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.WorldPop Populated Foorprint layer:This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building.

Annual Population Estimates for the United States; States; Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Related Statistical Areas; Counties; and Subcounty Places; and for Puerto Rico and Its Municipios: April 1, 2010 to July 1, 2016 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through May. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in July 2015. // The 2010 Census did not ascertain the military status of the household population. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on this file. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., Vintage 2016) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

The ratio of military expenditure to gross domestic product (GDP) in the United States saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 3.45 percent. Still, 2022 marked the second consecutive decline of the ratio. These figures refer to the total amount of money spent on a country's military, as a share of its gross domestic product (GDP). These figures apply to current expenditure on a country's armed forces, including peacekeeping forces and defense ministries, among others.Find more key insights for the ratio of military expenditure to gross domestic product (GDP) in countries like Mexico and Canada.