Provide statistics on the number of second-class demobilized officers and soldiers

United States US: Military Expenditure data was reported at 609.758 USD bn in 2017. This records an increase from the previous number of 600.106 USD bn for 2016. United States US: Military Expenditure data is updated yearly, averaging 277.591 USD bn from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 711.338 USD bn in 2011 and a record low of 45.380 USD bn in 1960. United States US: Military Expenditure 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. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; ; Data for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex

About this dataset

Context

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

Key observations

The largest age group in Soldier, KS was for the group of age 40 to 44 years years with a population of 14 (18.92%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Soldier, KS was the Under 5 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 Soldier is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Soldier 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 Soldier Population by Age. You can refer the same here

United States US: Military Expenditure: % of GDP data was reported at 3.149 % in 2017. This records a decrease from the previous number of 3.222 % for 2016. United States US: Military Expenditure: % of GDP data is updated yearly, averaging 4.864 % from Sep 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.063 % in 1967 and a record low of 2.908 % in 1999. United States US: Military Expenditure: % of GDP 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. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

Provide the number of retired and discharged military personnel assisted by the employment classification of the Veterans Affairs Council.

This dataset, released by DoD, contains geographic information for major installations, ranges, and training areas in the United States and its territories. This release integrates site information about DoD installations, training ranges, and land assets in a format which can be immediately put to work in commercial geospatial information systems. Homeland Security/Homeland Defense, law enforcement, and readiness planners will benefit from immediate access to DoD site location data during emergencies. Land use planning and renewable energy planning will also benefit from use of this data. Users are advised that the point and boundary location datasets are intended for planning purposes only, and do not represent the legal or surveyed land parcel boundaries.

As of 2021, there were over 200,000 living United States veterans who served in the Second World War. The Department of Veteran Affairs projects that the number of living veterans will decline rapidly in the fifteen years until 2036, at which point just a few hundred Americans who served in the war will be still alive. The passing of the "Greatest Generation" is seen as symbolic by some, as for many people they represented the era when the United States' power on the world stage was at its greatest. The Second World war is particularly remembered as a "just" war in the U.S., as the United States was seen as fighting for democracy and self-determination, and against the tyrannies of Fascism, Nazism, and Japanese Imperialism.

The United States' involvement in the Second World War

World War II marked the peak in military enlistments in U.S. history, with over 16 million service members serving worldwide during the conflict. The U.S. joined the war in 1941 due to Imperial Japan's attack on the U.S. naval base at Pearl Harbor, Hawaii, before joining the European theater of the war in 1944 during the Invasion of Normandy. The U.S. military played a vital role in the defeat of Nazi Germany on the Western Front in May 1945, while the Soviet Red Army defeated the Wehrmacht in the East. The U.S. was also vital in the defeat of Fascist Italy, as they had led an allied invasion force onto the Italian peninsula from Northern Africa in September 1943. The final action of the war took place in the Asian theater of war, as Imperial Japan was the last of the Axis powers to concede defeat to the Allies. The United States effectively ended the war with the dropping of two nuclear bombs on Hiroshima and Nagasaki in August 1945, leading to as many as a quarter of a million deaths. It remains to this day the sole use of atomic weapons in an active conflict.

United States US: Military Expenditure as % of General Government Expenditure data was reported at 8.807 % in 2017. This records a decrease from the previous number of 9.042 % for 2016. United States US: Military Expenditure as % of General Government Expenditure data is updated yearly, averaging 11.141 % from Sep 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 11.769 % in 2011 and a record low of 8.807 % in 2017. United States US: Military Expenditure as % of General Government Expenditure 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. Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans' benefits, demobilization, conversion, and destruction of weapons. This definition cannot be applied for all countries, however, since that would require much more detailed information than is available about what is included in military budgets and off-budget military expenditure items. (For example, military budgets might or might not cover civil defense, reserves and auxiliary forces, police and paramilitary forces, dual-purpose forces such as military and civilian police, military grants in kind, pensions for military personnel, and social security contributions paid by one part of government to another.); ; Stockholm International Peace Research Institute (SIPRI), Yearbook: Armaments, Disarmament and International Security.; Weighted average; Data for some countries are based on partial or uncertain data or rough estimates.

Provide the statistical information on the services of caring for veterans by the committee for the counseling of retired military personnel.

Provide the statistical information on the vocational guidance and training for veterans by the Committee for the Counseling and Assistance of Retired Servicemen and Veterans.

This dataset was updated April, 2024.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes: Clipping input datasets to the California boundary Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc) Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California. Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only. Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs) In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset.Data Sources: GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.htmlData Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

Reservoirs of the U.S. Army Corps of EngineersImportant Note: This item is in mature support as of May 2025 and will be retired in September 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This feature layer, utilizing data from the U.S. Army Corps of Engineers (USACE), displays reservoirs. They are responsible for operating and maintaining hundreds of lock and dam projects nationwide. Per USACE, “…Corps reservoirs fall into eight general categories: flood control, navigation, hydroelectric power, irrigation, municipal/industrial water supply, water quality, fish/wildlife, and recreation.”Charles Mill Lake & Mohicanville ReservoirData currency: This cached Esri service is checked monthly for updates from its federal source (USACE Reservoirs)Data modification: noneFor more information: Access to Water Resources DataFor feedback: ArcGIScomNationalMaps@esri.comU.S. Army Corp of EngineersPer USACE, "With environmental sustainability as a guiding principle, our disciplined Corps team is working diligently to strengthen our Nation’s security by building and maintaining America’s infrastructure and providing military facilities where our servicemembers train, work and live. We are also researching and developing technology for our war fighters while protecting America’s interests abroad by using our engineering expertise to promote stability and improve quality of life."

Provide a web link to the content of the regulations for the administration of the National Army Retired Servicemen's Counseling Committee.

Although lacking the same preeminent status of air assault planning, air movement operations comprise a majority of Army utility and cargo helicopter combat aviation operations in terms of volume of customers and the endless appetite for rapid movement of troops across the battlespace. The data provided enabled research and development of a US Army Aviation air movement mission planning model to assist the mission planner by rapidly providing courses of action based on the commander's priorities. Features of the problem and the data provided include priority demand, multi-node refueling, aircraft and passenger time windows, maximum passenger transportation time, and the minimization of unsupported demand, aircraft utilization, and total flight time. The mathematical model provided is an extension of the dial-a-ride problem (DARP) that will coordinate air mission requests (AMRs) at the aviation task force-level or lower to generate courses of action that optimize helicopter fleet resourc..., Instances generated according to Nelson et al. (2023). Nelson, R.J., King, R.E., McConnell, B.M., and Thoney-Barletta, K. 2023. US Army Aviation air movement operations assignment, utilization and routing, Journal of Defense Analytics and Logistics, Vol 7, No 1, 2–28, https://doi.org/10.1108/JDAL-11-2022-0013, Data files may be opened with any language. Example Julia script provided. , # US Army Aviation Air Movement Request Problem Instances

Dataset contains Air Movement Request (AMR) instances from Nelson et al. (2023) for reproducibility and to allow other researchers to develop methods that surpass the results by Nelson et al. (2023).

Description of the data and file structure

The files below provide 10 instances of 6,90, and 100 AMRs; the code used; and the results from the code for the 6 AMR instances.

Instance Files: As an example, the instance zip files contain multiple instances for that problem size. For the n = 6 AMR problem, we provide 10 instances:

Instances n_6.zip

Team 2, n = 6, Instance = 1.mat
Team 2, n = 6, Instance = 2.mat
Team 2, n = 6, Instance = 3.mat
Team 2, n = 6, Instance = 5.mat
Team 2, n = 6, Instance = 6.mat
Team 2, n = 6, Instance = 7.mat
Team 2, n = 6, Instance = 8.mat
Team 2, n = 6, Instance = 9.mat
Team 2, n = 6, Instance = 10.mat

Similarly, we provide 10 instances for the n = 90 and n = 100 problems ...

Provide statistics on the number of veterans participating in the educational assistance program for college and university studies.

This data collection constitutes a portion of the historical data collected by the project "Early Indicators of Later Work Levels, Disease, and Death." With the goal of constructing datasets suitable for longitudinal analyses of factors affecting the aging process, the project is collecting military, medical, and socioeconomical data on a sample of white males mustered into the Union Army during the Civil War. The project seeks to examine the influence of environmental and host factors prior to recruitment on the health performance and survival of recruits during military service, to identify and show relationships between socioeconomic and biomedical conditions (including nutritional status) of veterans at early ages and mortality rates from diseases at middle and late ages, and to study the effects of health and pensions on labor force participation rates of veterans at ages 65 and over. This installment of the collection, Version C-3, supersedes all previous collections (Versions C-1 and C-2), and contains data from the censuses of 1850, 1860, 1900, and 1910 on veterans who were originally mustered into the Union Army in Connecticut, Delaware, District of Columbia, Illinois, Iowa, Kansas, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Vermont, and West Virginia. This version of the collection also contains observations from Wisconsin, Indiana, California, and New Mexico. Census Data, Part 1, includes place of residence, relationship to head of household, date and place of birth, number of children, education, disability status, employment status, number of years in the United States, literacy, marital status, occupation, parents' birthplace, and property/home ownership. The variables in Part 2, Linkage Data, indicate which document sources were located for each recruit.

Veterans are disproportionately represented among political elites, and the question of whether military experience shapes their behavior is a central puzzle in the study of international relations. Existing theories link military experience with hawkish or dovish foreign policy preferences. Rather than determining their positions on the use of force ex ante, we argue that domain-specific knowledge and their elevated social status will make veterans less likely to change their expressed positions, especially in response to wartime casualties. We test our argument by analyzing Congressional speeches referencing the American wars in Iraq and Afghanistan, finding strong support for our expectations. Our core insight is that veteran politicians are partisans first and veterans second, and that military experience may say more about whether they update, rather than establish, their political positions.

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 Military Service Personnel Photograph Collection Index, (circa 1938-1953) was created by Connecticut State Library staff to highlight and better utilize these unique archival photographs and honor those who served in the military.  Great effort was made to identify the individuals depicted using information provided with the photograph. Please keep in mind however that names, geographic locations, or other information may be misspelled or in error as a result. Branch of service, rank, military unit, residence, and other notations were included in the index to assist the researcher or family member to determine if there is an image for a specific individual. Please be aware that prior to 1947 the United Sates Air Force was a branch of the United States Army and as a result, images may be listed as Army Air Corps. Please keep in mind that names and locations may be misspelled as a result.   You may conduct a search in any of the columns, or any combination of columns to limit your search.  If a name of an individual of interest is found in the below Connecticut Military Service Personnel Photograph Collection Index, and a reproduction of the original record is desired, you may submit a request via E-mail or by contacting the History & Genealogy Unit of the Connecticut State Library at (860) 757-6580.  Reproduction formats and fees available, are as follows: Photocopy: black & white copy, 8 1/2 X 11″ or 11 X 14″ sized paper, 25 cents; 11 X 17″, 50 cents per photocopied page, plus a $3.00 handling fee and first-class postage charges. Photocopy: color copy 8 1/2 X 11″ or 11 X 14″ sized paper, $1.00 per photocopied page, 11 X 17″, $1.25 per photocopied page plus a $3.00 handling fee and first-class postage charges. Digital images (low or high resolution): PDF, JEG, TIFF, or DNG images, 25 cents per image, plus a $3.00 handling fee.  Digital file may be delivered via internet for no additional cost. Pre-payment is not needed as a bill will accompany the finished product, either in the mail with photocopies or with the digital images. Please include the military service person’s name and the box number _location in requesting a copy of the image.

About this dataset

Context

The dataset tabulates the Soldier 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 Soldier 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 2023, the population of Soldier was 103, a 0% decrease year-by-year from 2022. Previously, in 2022, Soldier population was 103, an increase of 0.98% compared to a population of 102 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Soldier decreased by 17. In this period, the peak population was 140 in the year 2012. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Soldier is shown in this column.
• Year on Year Change: This column displays the change in Soldier 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 Soldier Population by Year. You can refer the same here