The ratio of the combined population aged between 0 to 14 years old and the population aged of 65 years and older to the population aged between 15 to 64 years old. This ratio is presented as the number of dependents for every 100 people in the working age population.

Reinsurance: Premium Adequacy to Claim Paid Ratio data was reported at 0.000 % mn in Feb 2025. This records a decrease from the previous number of 0.000 % mn for Jan 2025. Reinsurance: Premium Adequacy to Claim Paid Ratio data is updated monthly, averaging 0.000 % mn from Jan 2016 (Median) to Feb 2025, with 110 observations. The data reached an all-time high of 0.001 % mn in Jan 2024 and a record low of 0.000 % mn in Dec 2020. Reinsurance: Premium Adequacy to Claim Paid Ratio data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA006: Insurance Statistics: Claim Ratio.

Recent three-year property insurance market accident insurance claims rate statistics - (annual system) (Insurance Bureau)

Graph and download economic data for Retailers: Inventories to Sales Ratio (RETAILIRNSA) from Jan 1992 to Jun 2025 about ratio, inventories, sales, retail, and USA.

Affordability ratios calculated by dividing house prices for existing dwellings, by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

United States Employment Population Ratio: Age 16 to 24 data was reported at 53.500 Unit in Jun 2018. This records an increase from the previous number of 49.900 Unit for May 2018. United States Employment Population Ratio: Age 16 to 24 data is updated monthly, averaging 54.100 Unit from Jan 1948 (Median) to Jun 2018, with 846 observations. The data reached an all-time high of 69.200 Unit in Jul 1989 and a record low of 42.600 Unit in Jan 2010. United States Employment Population Ratio: Age 16 to 24 data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G015: Current Population Survey: Employment Population Ratio.

Direct, indirect and total tax to GDP ratio, direct, indirect and total gross central taxes to GDP ratio, and direct, indirect and total states share in central taxes to GDP ratio

In 2023, the life insurance market in Germany was *************** than the non-life insurance market based on the expense ratio. Meanwhile, the opposite was true in the Netherlands. The expense ratio, which is the sum of expenses divided by premiums earned, is a measure of profitability used to compare insurance markets. It can be displayed as a measure of one or as a percentage of 100. A ratio of under one means that companies are earning more premiums than they are paying out in expenses.

The Second World War had a sever impact on gender ratios across European countries, particularly in the Soviet Union. While the United States had a balanced gender ratio of one man for every woman, in the Soviet Union the ratio was below 5:4 in favor of women, and in Soviet Russia this figure was closer to 4:3.

As young men were disproportionately killed during the war, this had long-term implications for demographic development, where the generation who would have typically started families in the 1940s was severely depleted in many countries.

Graph and download economic data for Employment-Population Ratio - Men (LNS12300001) from Jan 1948 to Jul 2025 about employment-population ratio, males, 16 years +, household survey, population, employment, and USA.

Relative “informativeness” of different anthropometric measures in relation to mortality; χ2 likelihood ratio statistics for each measure and, within brackets, percentage of χ2 for waist-to-hip ratio.

Abstract

This dataset was derived by the Bioregional Assessment Programme from source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset consisits of three shapefiles containing point features of Hunter subregion surface water nodes with summarised hydrological response variables (HRV) data for zero flow days (ZFD), high flow days (HFD) and annual flow (AF). Maps use the ratio data for mapping with classes.The changes in the number of ZFD, HFD and AF due to additional coal resource development relative to the interannual variability flow days under the baseline has been adopted to put some context around the modelled changes. This ratio of absolute maximum change and variability range (5th, 50th and 95th percentiles) has been calculated qualitatively for each surface water model node .

Dataset History

These shape files bring model node spatial location and HRV ratios together.

The input surface water HRV shape file (HUN_SW_Modelling_Reaches_and_HRV_lookup_20171121_v05 (GUID: 8c330d59-2ecc-4c35-8f9e-68b91a4ae98a) has been augmented with data from the spreadsheet (HUN_HRVs_scatterplots_version3.xlsx, GUID:1c0a19f9-98c2-4d92-956d-dd764aaa10f9), by linking to the node number.

For each dataset, a new shapefile was produced - zero flow days (ZFD), high flow days (HFD) and annual flow (AF).

The data taken from the spreadsheet was:

ZFD. ZFD_corrected worksheet for ratio data with p_max data coming from ZFD worksheet.

HFD. FD_corrected worksheet for both ratio and amax data

AF. AF_corrected worksheet for both ratio and amax data

Maps use the ratio data for mapping with classes shown on maps except where the following was true, the ratio was replaced with 'no significant change'

amax for LFD <3 (i.e. 3 is a blue dot, not an open circle)

amax for FD <3 (i.e. 3 is a blue dot not an open circle)

pmax for AF <1 percent (note this is pmax NOT amax)

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN Surface Water Model Nodes 20170110 HRV Ratios v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/155fdbdc-3cb1-4fb9-bb76-5380463e955c.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From SYD ALL climate data statistics summary

• Derived From HUN AWRA-L simulation nodes_v01

• Derived From Hunter River Salinity Scheme Discharge NSW EPA 2006-2012

• Derived From Geofabric Surface Network - V2.1

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Bioregional Assessment areas v06

• Derived From Hunter AWRA Hydrological Response Variables (HRV)

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Bioregional Assessment areas v04

• Derived From Geofabric Surface Network - V2.1.1

• Derived From HUN AWRA-R Gauge Station Cross Sections v01

• Derived From Gippsland Project boundary

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From BA All Regions BILO cells in subregions shapefile

• Derived From Hunter Surface Water data v2 20140724

• Derived From HUN AWRA-R River Reaches Simulation v01

• Derived From HUN SW Model nodes 20170110

• Derived From HUN AWRA-L simulation nodes v02

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From BA SYD selected GA TOPO 250K data plus added map features

• Derived From HUN gridded daily PET from 1973-2102 v01

• Derived From HUN AWRA-R Irrigation Area Extents and Crop Types v01

• Derived From Bioregional Assessment areas v03

• Derived From IQQM Model Simulation Regulated Rivers NSW DPI HUN 20150615

• Derived From HUN AWRA-R calibration catchments v01

• Derived From Bioregional Assessment areas v05

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From National Surface Water sites Hydstra

• Derived From Selected streamflow gauges within and near the Hunter subregion

• Derived From HUN SW Modelling Reaches and HRV lookup 20171121 v05

• Derived From ASRIS Continental-scale soil property predictions 2001

• Derived From HUN Comparison of model variability and interannual variability

• Derived From HUN River Perenniality v01

• Derived From Hunter Surface Water data extracted 20140718

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From HUN AWRA-R calibration nodes v01

• Derived From HUN AWRA-R Observed storage volumes Glenbawn Dam and Glennies Creek Dam

• Derived From HUN future climate rainfall v01

• Derived From HUN AWRA-LR Model v01

• Derived From HUN AWRA-L ASRIS soil properties v01

• Derived From HUN AWRAR restricted input 01

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Victoria - Seamless Geology 2014

• Derived From HUN AWRA-L Site Station Cross Sections v01

• Derived From HUN AWRA-R simulation catchments v01

• Derived From HUN AWRA-R Simulation Node Cross Sections v01

• Derived From Climate model 0.05x0.05 cells and cell centroids

Statistical Table of the Total Number of National Pension Payments, Total Amount of Payments, and Benefit Ratios for Indigenous Peoples from 2009 to 2019

About this dataset

Context

The dataset tabulates the population of United States by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for United States. The dataset can be utilized to understand the population distribution of United States by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in United States. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for United States.

Key observations

Largest age group (population): Male # 30-34 years (11.65 million) | Female # 30-34 years (11.41 million). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the United States population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the United States is shown in the following column.
• Population (Female): The female population in the United States is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in United States for each 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 United States Population by Gender. You can refer the same here

This data release includes biomarker ratio values calculated from measurements made at the USGS for the reference oil NSO-1 that were reported in a journal article entitled Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS.

The average home in the U.S. sold for several percent below its asking price in December 2022, as a result of the housing market slowing. Just a few months before that, In the second quarter of 2022, the so-called sale-to-list price ratio went above ***. This reflected the high housing demand and the need of prospective home buyers to bid above the asking price. Housing demand - as measured in pending home sales - went up, as mortgage rates were historically low and plummeted once rates were increased.

Graph and download economic data for Infra-Annual Labor Statistics: Employment Rate Total: From 55 to 64 Years for United States (LREM55TTUSM156S) from Jan 1977 to Jun 2025 about 55 to 64 years, employment-population ratio, employment, population, rate, and USA.

Market Statistics - G6b Retention Ratio - Direct Business

Ireland: Poverty, percent of population: The latest value from 2021 is 14 percent, an increase from 12.9 percent in 2020. In comparison, the world average is 22.31 percent, based on data from 66 countries. Historically, the average for Ireland from 2019 to 2021 is 13.57 percent. The minimum value, 12.9 percent, was reached in 2020 while the maximum of 14 percent was recorded in 2021.

Finland FI: Sex Ratio at Birth: Male Births per Female Births data was reported at 1.047 Ratio in 2017. This stayed constant from the previous number of 1.047 Ratio for 2016. Finland FI: Sex Ratio at Birth: Male Births per Female Births data is updated yearly, averaging 1.047 Ratio from Dec 1962 (Median) to 2017, with 21 observations. The data reached an all-time high of 1.052 Ratio in 1972 and a record low of 1.040 Ratio in 1992. Finland FI: Sex Ratio at Birth: Male Births per Female Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Finland – Table FI.World Bank.WDI: Population and Urbanization Statistics. Sex ratio at birth refers to male births per female births. The data are 5 year averages.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Weighted average;