In 2024, the average age for personnel in the UK Armed Forces overall was 31 years old. The average age of those serving in the British Army was also 31, with the average age rising to 32 for the Royal Navy and Marines, and was 33 for the Royal Air Force (RAF). The average age for officers in the Armed Forces was 37, with this rising to 38 for officers in the Royal Air Force. Personnel falls to historical lows in 2024 Overall, there has been a long-term decline in the size of the armed forces, with the number of personnel declining from over 200,000 in 2000, to around 138,120 by 2024. In fact, the size of the armed forces has been in almost constant decline since the end of World War Two. Britain's retreat from its colonial empire throughout this period removed many of the UK's overseas commitments, with the end of the Cold War also leading to further cuts to personnel. Operational deaths since the Second World War Since 1945 there have been 7,193 operational deaths in the United Kingdom’s armed forces, with the deadliest year occurring in 1951 when there were 851 deaths. This was due to three separate conflicts: the Malayan Emergency, the 1951 Anglo-Egyptian War and the Korean War. Since 1959, there were only three years that had more than 100 operational deaths: 1972, 1973 and 1982. By comparison, between 2016 and 2024, there have been only nine operational deaths, with just one death reported throughout the whole of 2023.

In the United States, active duty Armed Forces personnel tended to be young, with the majority under the age of 30 years old. In 2023, there were 546,361 U.S. Defense Armed personnel aged 25 and under. In the age group 26 to 30, there were 275,756 Armed Forces personnel.

This scatter chart displays median age (year) against military expenditure (% of GDP) and is filtered where the country is Trinidad and Tobago. The data is about countries per year.

This dataset is about countries in the Americas per year, featuring 4 columns: country, date, median age, and military expenditure. The preview is ordered by date (descending).

This scatter chart displays military expenditure (% of GDP) against median age (year) and is filtered where the country is Burkina Faso. The data is about countries per year.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Soldier. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Soldier. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Soldier, where there exist only two delineated age groups, the median household income is $71,250 for householders within the 25 to 44 years age group, compared to $35,625 for the 65 years and over age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

This statistic shows the distribution of military personnel in France in 2013, by age group and gender. According to the French Ministry of Defense, more than 15 per cent of the male personnel were aged 36 to 40, while most of the female personnel were aged 26 to 30.

This scatter chart displays military expenditure (% of GDP) against median age (year) and is filtered where the country is Georgia. The data is about countries per year.

In 2022, 41.73 percent of the male Americans aged 75 years and over were veterans - the most out of any age group or gender. In comparison, less than one percent of the female Americans aged 75 and over were veterans in that year.

In 2022, about 3.23 million veterans in the United States were male and between 35 and 54 years of age. In the same age group, about 627,148 females were veterans in that year.

The Average Age by State dataset provides the average age of Government life insurance policyholders in the administered programs in each state. The data is reported at the state level, plus insureds in Military America, Military Europe, Military Pacific, American Samoa, Federated States of Micronesia, Guam, Marshall Islands, Northern Mariana Islands, Puerto Rico, and the Virgin Islands.

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

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

This scatter chart displays median age (year) against military expenditure (% of GDP) and is filtered where the country is Botswana. The data is about countries per year.

Cube of aggregated data containing the new direct-law military pensions of the armed military, liquidated by the State Retirement Service. It provides data on the following topics: Effective, Average Age, Average Monthly Amount and Average Durations.

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

About this dataset

Context

The dataset tabulates the Soldier household income by age. The dataset can be utilized to understand the age-based income distribution of Soldier income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Soldier, IA annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Soldier, IA household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Soldier income distribution by age. You can refer the same here

The Second World War had a profound impact on gender ratios within the Soviet Union's population, and its effect on different age groups varied greatly. The Soviet population structure had already been shaped heavily by the First World War, the Russian Revolution, and the famines of the early 1920s and early 1930s. The impact of these events on mortality and fertility meant that, in 1941, the generations whose births corresponded with these events had a lower population than would be expected on a typical population model. For example, in 1941, those aged between 5 and 9 had a significantly lower population than those aged 10 to 14, due to the effects of the Soviet famine of 1932-1933. Additionally, women outnumbered men in all age groups except the very youngest, due to the disproportionate effect of conflict and infant mortality on male populations. Impact of WWII In order to observe the impact of the war, one must compare populations of specific age groups in 1941 with the following age group in 1946. For men of "fighting age" in 1941, i.e. those aged between 15 and 44, these populations experience the most substantial decrease over the course of the war. For example, there are 5.6 million men aged 15-19 in 1941, but just 3.5 million aged 20-24 in 1946, giving a decrease of 38 percent. This decrease of almost forty percent can be observed until the 45-49 group, where the difference is 25 percent. Additionally, women aged between 15 and 34 saw a disproportionate decrease in their populations over this period, as many enlisted in the army and took an active part in the conflict, most notably as medics, snipers, and pilots.

The war's impact on fertility and child mortality meant that, in 1946, the total population under four years old was less than half its size in 1941. Generally, variations between age groups then fluctuated in line with pre-war patterns, however the overall ratio of women to men increased further after the war. For all age groups over 20 years, the number of men decreased between these years, whereas all women's age groups over 30 years saw an increase; this meant that, despite the war, women over 30 had a higher life expectancy in 1946.

Number and crude rates per 100,000 person years and age–specific rate ratios for suicide in male regulars who left the UKAF and male regulars serving in the UKAF at the time of death, 1996–2018*.

The age structure of the personnel of the Swedish Armed forces varies between the ranks. Whereas 55 to 59 years was the most common age of commissioned officers in 2021, the most common age of non-commissioned officers was 30 to 34 years. The highest number of personnel of other ranks was between 16 and 24 years.

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, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for 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 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2009-2013 American Community Survey (ACS) data generally reflect the February 2013 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, 2009-2013 5-Year American Community Survey