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.

The American Civil War is the conflict with the largest number of American military fatalities in history. In fact, the Civil War's death toll is comparable to all other major wars combined, the deadliest of which were the World Wars, which have a combined death toll of more than 520,000 American fatalities. The ongoing series of conflicts and interventions in the Middle East and North Africa, collectively referred to as the War on Terror in the west, has a combined death toll of more than 7,000 for the U.S. military since 2001. Other records In terms of the number of deaths per day, the American Civil War is still at the top, with an average of 425 deaths per day, while the First and Second World Wars have averages of roughly 100 and 200 fatalities per day respectively. Technically, the costliest battle in U.S. military history was the Battle of Elsenborn Ridge, which was a part of the Battle of the Bulge in the Second World War, and saw upwards of 5,000 deaths over 10 days. However, the Battle of Gettysburg had more military fatalities of American soldiers, with almost 3,200 Union deaths and over 3,900 Confederate deaths, giving a combined total of more than 7,000. The Battle of Antietam is viewed as the bloodiest day in American military history, with over 3,600 combined fatalities and almost 23,000 total casualties on September 17, 1862. Revised Civil War figures For more than a century, the total death toll of the American Civil War was generally accepted to be around 620,000, a number which was first proposed by Union historians William F. Fox and Thomas L. Livermore in 1888. This number was calculated by using enlistment figures, battle reports, and census data, however many prominent historians since then have thought the number should be higher. In 2011, historian J. David Hacker conducted further investigations and claimed that the number was closer to 750,000 (and possibly as high as 850,000). While many Civil War historians agree that this is possible, and even likely, obtaining consistently accurate figures has proven to be impossible until now; both sides were poor at keeping detailed records throughout the war, and much of the Confederacy's records were lost by the war's end. Many Confederate widows also did not register their husbands death with the authorities, as they would have then been ineligible for benefits.

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 TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Census Bureau includes landmarks such as military installations in the MTDB for locating special features and to help enumerators during field operations. In 2012, the Census Bureau obtained the inventory and boundaries of most military installations from the U.S. Department of Defense (DOD) for Air Force, Army, Marine, and Navy installations and from the U.S. Department of Homeland Security (DHS) for Coast Guard installations. The military installation boundaries in this release represent the updates the Census Bureau made in 2012 in collaboration with DoD.

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.

This comprehensive report chronicles the history of women in the military and as Veterans, profiles the characteristics of women Veterans in 2009, illustrates how women Veterans in 2009 utilized some of the major benefits and services offered by the Department of Veterans Affairs (VA), and discusses the future of women Veterans in relation to VA. The goal of this report is to gain an understanding of who our women Veterans are, how their military service affects their post-military lives, and how they can be better served based on these insights.

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.

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

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 U.S. military’s nonpartisan norms are important for healthy civil-military relations. Some research, however, suggests these norms are weakening. This study examines the evidence for eroding nonpartisan norms by analyzing U.S. military servicemembers’ partisan affiliations and political activism levels from 2008 to 2018. It finds that since 2008, military servicemembers have become more likely to identify as partisans. Servicemembers have also become more politically active than civilians, although this is due to decreasing activism among the American public. It also finds that longer-serving service members have stronger nonpartisan norms, but that newer servicemembers are more politically active than both longer-serving servicemembers and civilians. These findings provide a firmer empirical foundation for previous claims of eroding norms and suggest more research is needed to understand how increased partisanship and political activism impact military readiness and civil-military relations.

In 2023, the ratio of military expenditure to gross domestic product (GDP) in the United States remained nearly unchanged at around 3.36 percent. Military expenditure figures refer to the amount of money spent on a country's armed forces, including peacekeeping and defense operations, among others. When comparing international figures, there may be some inconsistencies depending on what respective countries consider as military spending.Find more key insights for the ratio of military expenditure to gross domestic product (GDP) in countries like Mexico and Canada.

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 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.

Asian Americans and Pacific Islanders (AAPI) make up approximately 4-5% of the total U.S. Armed Forces (U.S. Department of Defense, 2017); however, little is known about their experiences and general well-being. Recent studies that have explored the well-being of service members suggest a lack of adequate representation of AAPIs in research and also identify AAPIs as having the highest rates of suicide within the military (Eisen et al., 2012; Foynes et al., 2015; Pietrzak et al., 2015; Schoenbaum et al., 2014). Social interactions and teamwork are essential to efficient operations and functioning in the military. Positive social experiences, such as strong unit cohesion, may serve as a protective factor against negative life events, whereas instances of discrimination may place chronic strain on an individual’s well-being (Adams et al., 2017; Adler & Castro, 2013; Williams et al., 2016). To address a significant gap in the current literature and develop a better understanding of AAPIs in the military, this study examined how AAPIs’ social experiences in the U.S. military influence their health and well-being. Significant relationships were identified between perceived workplace discrimination and unit cohesion, perceived discrimination and health outcomes, and unit cohesion and health outcomes. Implications for policy, practice, and theory, limitations of the current study, and future directions for research are discussed.

Services and Support Programs for Military Service Members and Veterans, 2012-13 (PEQIS 19), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997-98 at https://nces.ed.gov/pubsearch/getpubcats.asp?sid=016. PEQIS 19 (https://nces.ed.gov/peqis/) is a cross-sectional survey that collected information on the services and support programs available to students who are military service members and veterans at the institution. The study was conducted using self-administered paper-and-pencil questionnaires of a person at the postsecondary institution that is familiar with the institution’s programs for military service members and veterans. Key statistics produced from PEQIS 19 were services and support programs for military members and veterans.

Description

The Correlates of War project hosts a variety of datasets related to the study of inter-state conflict.

Details

As of 2007-09-22 the following datasets were listed:

State System Membership (v2004.1): This data set records the fluctuating composition of the state system since 1816. It also identifies countries corresponding to the standard Correlates of War country codes. Access the system membership data here.

Inter-, Extra- and Intra-State War (v3.0): War takes many forms in the contemporary era, including serious military conflicts between states (inter-state war), between states and non-state actors (extra-state war), and within states (intra-state war). This data set records such events over the 1816-1997 period. Access the Interstate War data here. Access the Extrastate War data here. Access the Intrastate War data here.

Militarized Interstate Disputes (v3.02): This data set records all instances of when one state threatened, displayed, or used force against another. Version 3.0 covers the 1816-2001 period, and can be downloaded from this page.

National Material Capabilities (v3.02): Power is considered by many to be a central concept in explaining conflict, and six indicators—military expenditure, military personnel, energy consumption, iron and steel production, urban population, and total population—are included in this data set. It serves as the basis for the most widely used indicator of national capability, CINC (Composite Indicator of National Capability) and covers the period 1816-2001. Access the capabilities data here.

Formal Alliances (v3.03): Alliances have been credited with preventing wars and provoking wars, and they have been important instruments of statecraft for centuries. This data set records all formal alliances among states between 1816 and 2000, including mutual defense pacts, non-aggression treaties, and ententes. This data set is hosted by Douglas Gibler, University of Kentucky. It may be downloaded here.

Territorial Change (v3.0): Territory has played an important role in interstate conflict, and this data set records all peaceful and violent changes of territory from 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the territorial change data here.

Direct Contiguity (v3.0): Geographic factors are known to play an important role in conflict. The Direct Contiguity data set registers the land and sea borders of all states since the Congress of Vienna, and covers 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the direct contiguity data here.

Colonial/Dependency Contiguity (v3.0): The Colonial/Dependency Contiguity data set registers contiguity relationships between the colonies/dependencies of states (by land and by sea up to 400 miles) from 1816-2002. Access the colonial/dependency contiguity data here.

Intergovernmental Organizations (v2.1): Although the number of intergovernmental organizations (IGOs) grew dramatically during the late 20th century, they have been part of the world scene for much longer. This data set tracks the status and membership of such organizations from 1815-2000. Access information about this data here. This data set is hosted by Timothy Nordstrom, University of Mississippi, and John Pevehouse, University of Wisconsin.

Diplomatic Exchange (v2006.1): The Diplomatic Exchange data set tracks diplomatic representation at the level of chargé d'affaires, minister, and ambassador between states from 1817-2005. Access information about this data here. This data set is hosted by Reşat Bayer, Koç University.

Bilateral Trade: Trade is considered by many to have a pacifying effect on the relations of states. This collection of bilateral trade data begins in 1870 and covers most members of the interstate system. Access trade data here.

Openness: Open (?)

• Access: all datasets available for direct download in convenient formats (e.g. zipped csv)
• License: no license specified but does state on the datasets page: "The data sets listed on this page are all available for download." Some datasets require citation as a condition of use (with the exact form stated on the website).

The U.S. Defense Department oversees the USA"s armed forces and manages over 30 million acres of land. With over 2.8 million service members and civilian employees the department is the world"s largest employer.Dataset SummaryPhenomenon Mapped: Lands managed by the U.S. Department of DefenseGeographic Extent: United States, Guam, Puerto RicoData Coordinate System: WGS 1984Visible Scale: The data is visible at all scalesSource: DOD Military Installations Ranges and Training Areas layer. Publication Date: May 2025This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Department of Defense lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "department of defense" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "department of defense" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

This dataset contains variables describing congressional service and selected biographical characteristics for each person who has served in the United States Congress. This release of the data includes members of the 104th Congress. Approximately 11,455 individuals are represented in this file, each identified by a unique five-digit identification number. A data record exists for every Congress in which an individual served, as well as for each chamber in which a person may have served in a given Congress. To illustrate, a member of the House of Representatives who is appointed to fill a vacancy in the Senate during a term of service will have two data records for that Congress. The congressional service variables include political party affiliation, district, state and region represented, and exact and cumulative dates of service in each Congress and each chamber, as well as total congressional service. The biographical variables cover state and region of birth, education, military service, occupation, other political offices held, relatives who also have held congressional office, reason for leaving each Congress, and occupation and political offices held subsequent to service in Congress. Many of these specific variables are summarized in a collapsed variable.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.