Elderly Dependency Ratio(Sample Survey): Heilongjiang data was reported at 25.990 % in 2023. This records an increase from the previous number of 24.440 % for 2022. Elderly Dependency Ratio(Sample Survey): Heilongjiang data is updated yearly, averaging 11.600 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 25.990 % in 2023 and a record low of 8.300 % in 2002. Elderly Dependency Ratio(Sample Survey): Heilongjiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

The model contains a fixed effect for period and a random effect for district (n = 15).

Abstract

The Zimbabwe Demographic and Health Survey (ZDHS) is one of a series of surveys carried out by the Central Statistical Office (CSO) as part of the Zimbabwe National Household Survey Capability Programme. Conducted immediately following the second round of the Intercensal Demographic survey in 1988, the objective of the ZDHS was to make available to policy-makers and planners current information on fertility and child mortality levels and trends, contraceptive knowledge, approval and use and basic indicators of maternal and child health. To obtain these data, a nationally representative sample of 4201 women 15-49 was interviewed in the survey between September 1988 and January 1989.

The ZDHS is one of a series of surveys undertaken by the Central Statistical Office (CSO) as part of the Zimbabwe National Household Survey Capability Programme (ZNHSCP). The ZDHS was conducted immediately after the second round of the Intercensal Demographic Survey (ICDS) in 1988. The main objective of the ZDHS was to provide information on: - fertility levels, trends and preferences; - family planning awareness, approval and use; - maternal and child health, including infant and child mortality; - and other topics relating to family health.

The survey was designed to obtain information on family planning use similar to that provided by the 1984 Zimbabwe Reproductive Health Survey (ZRHS) and data on fertility and mortality which would complement information collected in the two rounds of the Intercensal Demographic Survey (ICDS). In addition, participation in the worldwide Demographic and Health Survey project offered an opportunity to strengthen survey capability in Zimbabwe, as well as further comparative research by contributing to the international demographic and health database.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49
• Children under five years

Universe

The population covered by the 1988 ZDHS is defined as the universe of all women age 15-49 in Zimbabwe. Eligibility for the individual interview was determined on a de facto basis, i.e., a woman was eligible if she was 15 to 49 years of age and had spent the night prior to the household interview in the household, irrespective of whether she was a usual member of the household or not.

Kind of data

Sample survey data

Sampling procedure

To achieve this objective, a nationally representative, self-weighting sample of women 15- 49 was selected and interviewed in the survey. The ZDHS sample was drawn from the Zimbabwe Revised Master Sample (ZRMS). The ZRMS was based on the master sample constructed at the initiation of the Zimbabwe National Household Survey Capability Programme (ZNHSCP) and revised for the first round of the Intercensal Demographic Survey in 1987.

The ZRMS can be considered as a two-stage sample, which is self-weighting at the household level. The sample is stratified by eight provinces and six sectors. The sectors, which are determined by land use include: (1) communal lands, (2) large-scale commercial farming areas, (3) small-scale commercial farming areas, (4) urban and semi-urban areas, (5) resettlement schemes, and (6) national parks, forest and other areas.

A subsample of 167 enumeration areas (EAs) from the 273 EAs in the ZRMS was selected for the ZDHS, including 114 in rural areas and 53 in urban areas. The EAs were selected systematically with probability proportional to the number of households in the 1982 census. Household listings prepared prior to the 1987 ICDS were used in selecting the households to be included in the ZDHS from the selected EAs. All women 15-49 present in the households drawn for the ZDHS sample on the night before the interview were eligible for the survey.

Mode of data collection

Face-to-face

Research instrument

Two questionnaires were used for the ZDHS, a household and an individual woman's questionnaire. The questionnaires were adapted from the DHS Model "B" Questionnaire, intended for use in countries with low contraceptive prevalence. A pretest was conducted, and the questionnaires were modified, taking into account the pretest results. The household and individual questionnaires were administered in Shona, Ndebele, or English, with these major languages appearing on the same questionnaire.

Information on the age and sex of all usual members and visitors in the selected households was recorded on the household questionnaire and used to identify women eligible for the individual questionnaire. Eligibility for the individual interview was determined on a de facto basis, i.e., a woman was eligible if she was 15 to 49 years of age and had spent the night prior to the household interview in the household, irrespective of whether she was a usual member of the household or not.

The individual questionnaire was used to collect information on the following topics: - Respondent's background; - Reproduction; - Contraception; - Health and breastfeeding; - Marriage; - Fertility preferences; - Husband's background and women's work; - Height and weight of children 3-60 months.

Cleaning operations

Data entry and editing began in October 1988 and was completed in February 1989, two weeks after fieldwork ended. The initiation of data processing during the fieldwork allowed the errors that were detected to be communicated immediately to the field teams for corrective measures, thus improving the quality of the data. All data processing activities were carried out in Harare, by a team of five data capture operators under a data processing coordinator. The operators were responsible for office editing and coding, as well as for the entry of the questionnaires. The computer hardware consisted of three IBM-compatible micro-computers. The Integrated System for Survey Analysis (ISSA) software package, developed by IRD for the DHS programme, was used for all phases of the data entry, editing and tabulation. Range, skip and most consistency checks were performed during the data capture itself; only the more sophisticated consistency checks were done during secondary editing.

Response rate

Of the 4789 households selected for the ZDHS, 4337 were located in the field; of these, 4107 households were successfully interviewed. Within the households successfully interviewed, 4467 women were identified as eligible, and, among these eligible women, 4201 women were interviewed. The overall response rate, which is the product of the household (95 percent) and individual (94 percent) response rates was 89 percent.

The overall response rate, which is the product of the household and individual response rate, was 89 percent for the whole sample. It was 90 percent or higher, except in Manicaland (89 percent), Mashonaland East (88 percent) and Harare/Chitungwiza (74 percent).

Sampling error estimates

Sampling error is a measure of the variability between all possible samples that could have been selected from the same population using the same design and size. For the entire population and for large subgroups, the ZDHS sample is sufficiently large so that the sampling error for most estimates is small. However, for small subgroups, sampling errors are larger and, thus, affect the reliability of the data. Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, ratio, etc.), i.e., the square root of the variance. The standard error can be used also to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic as measured in 95 percent of all possible samples with the same design will fall within a range of plus or minus two times the standard error for that statistic.

The computations required to provide sampling errors for survey estimates which are based on complex sample designs like those used for the ZDHS survey are more complicated than those based on simple random samples. The software package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology. The CLUSTERS program treats any percentage or average as a ratio estimate, r=y/x, where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration.

In addition to the standard errors, CLUSTERS computes the design effect (DEFT) for each estimate, which is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1,0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1,0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. CLUSTERS also computes the relative error and confidence limits for estimates.

Sampling errors are presented below for selected variables considered to be of major interest. Results are presented in the Final Report for the whole country, urban and rural areas, three broad age groups and three educationaI levels. For each variable, the type of statistic (mean, proportion) and the base population are given in B.1 of the Final Report. For each variable, Tables B.2-B.5 present the value of the statistic, its standard error, the number of unweighted and weighted cases, the design effect, the relative standard errors, and the 95 percent confidence limits.

The relative standard error for most

Elderly Dependency Ratio(Sample Survey): Gansu data was reported at 19.240 % in 2021. This records an increase from the previous number of 18.500 % for 2020. Elderly Dependency Ratio(Sample Survey): Gansu data is updated yearly, averaging 12.000 % from Dec 2002 (Median) to 2021, with 20 observations. The data reached an all-time high of 19.240 % in 2021 and a record low of 9.100 % in 2002. Elderly Dependency Ratio(Sample Survey): Gansu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

This is a sample dataset for the Biodemography Workshop. Within this dataset, input files related to demographic statistics will be considered, specifically population by gender and by Nuts2 in Italy, as well as shapefiles for map creation. The variables to be analyzed include the ratio between male and female, and vice versa. The final output consists of two maps. The data source is Istat, which provides these with a CC BY license: 1-https://demo.istat.it/app/?i=POS&l=it 2-https://www.istat.it/it/archivio/222527 To conduct the analysis, the open-source software R-Studio was used. The data management methodology will also be outlined in a Data Management Plan, written using Overleaf, in which we will provide more detailed information.

Data Statistics of example 2.

The model contains a fixed effect for treatment and a random effect for pens (n = 24) nested within rooms (n = 2).

In the last quarter of 2023, the debt to equity ratio in the United States amounted to 84.24 percent. Debt to equity ratio explained The debt to equity financial ratio indicates the relationship between shareholders' equity and debt used to finance the assets of a company. In order to make the calculation the data of the two required components are taken from the firm’s balance sheet. If the company is a publicly traded company then it is possible to make the calculation by taking the market value for both.The composition of debt and equity of an enterprise is much debated as is the influence that it is able to exert on the value of the firm. Nevertheless, it is important in helping investors such as banks to identify companies that are highly leveraged and therefore pose a higher risk. It is best explained by taking the example of an entrepreneur wishing to expand their operation and going to the bank for a loan. If this small business owner had total assets amounting to 120,000 U.S. dollars and liabilities (mostly loans) amounting to 100,000 U.S. dollars the bank to which the request is being made would first have to deduce the business owner’s equity; 20,000 dollars (total assets minus liabilities). With this figure the bank would proceed to divide total liabilities by equity, which gives the ratio of 500 percent. In other terms, this means that for every one dollar of equity the small business owner has 5 dollars of debt. He is highly leveraged and therefore represents high risk to the bank.

About this dataset

Context

The dataset tabulates the population of Lexington by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Lexington. The dataset can be utilized to understand the population distribution of Lexington by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Lexington. 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 Lexington.

Key observations

Largest age group (population): Male # 0-4 years (16) | Female # 0-4 years (11). 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 Lexington population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Lexington is shown in the following column.
• Population (Female): The female population in the Lexington 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 Lexington 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 Lexington Population by Gender. You can refer the same here

This is the raw data for the method paper submitted in Limnology and Oceanography: Methods titled "Sample preservation methods for nitrous oxide concentration and isotope ratio measurements in aquatic environments".

This dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm. The data span a wide range of latitudes poleward of 40 degrees in both the Northern and Southern Hemispheres and cover the period 2009-01-10 to 2018-05-21.

Elderly Dependency Ratio(Sample Survey): Hubei data was reported at 24.850 % in 2023. This records an increase from the previous number of 23.850 % for 2022. Elderly Dependency Ratio(Sample Survey): Hubei data is updated yearly, averaging 13.700 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 24.850 % in 2023 and a record low of 11.100 % in 2003. Elderly Dependency Ratio(Sample Survey): Hubei data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

About this dataset

Context

The dataset tabulates the population of Happy Valley by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Happy Valley. The dataset can be utilized to understand the population distribution of Happy Valley by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Happy Valley. 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 Happy Valley.

Key observations

Largest age group (population): Male # 45-49 years (1,195) | Female # 5-9 years (1,424). 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 Happy Valley population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Happy Valley is shown in the following column.
• Population (Female): The female population in the Happy Valley 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 Happy Valley 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 Happy Valley Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Nashville by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Nashville. The dataset can be utilized to understand the population distribution of Nashville by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Nashville. 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 Nashville.

Key observations

Largest age group (population): Male # 50-54 years (128) | Female # 60-64 years (102). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Nashville population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Nashville is shown in the following column.
• Population (Female): The female population in the Nashville 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 Nashville 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 Nashville Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Bunker Hill by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Bunker Hill. The dataset can be utilized to understand the population distribution of Bunker Hill by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Bunker Hill. 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 Bunker Hill.

Key observations

Largest age group (population): Male # 45-49 years (62) | Female # 30-34 years (71). 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 Bunker Hill population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bunker Hill is shown in the following column.
• Population (Female): The female population in the Bunker Hill 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 Bunker Hill 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 Bunker Hill Population by Gender. You can refer the same here

For those of "fighting age" during the Second World War, gender ratios changed significantly as a result of the conflict. In nature, gender ratios at birth are generally between 103 and 107 boys per 100 girls, with these numbers balancing in early adulthood due to the disproportionate impact of conflict and childhood diseases on male populations. However, the scale of conflicts in the early twentieth century meant that gender ratios became even more imbalanced than typically expected, with countries most-heavily involved in the World Wars feeling these effects the most.

Additionally, of the listed European countries involved in the First World War and other European conflicts of the early-twentieth century, another large decline can be observed among those aged over 50 (for example, those aged 50-54 would have been in their late teens during the First World War).

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

About this dataset

Context

The dataset tabulates the population of Long Beach by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Long Beach. The dataset can be utilized to understand the population distribution of Long Beach by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Long Beach. 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 Long Beach.

Key observations

Largest age group (population): Male # 55-59 years (98) | Female # 60-64 years (125). 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 Long Beach population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Long Beach is shown in the following column.
• Population (Female): The female population in the Long Beach 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 Long Beach 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 Long Beach Population by Gender. You can refer the same here

This dataset contains a common standard template for representing the metadata of stable isotope results environmental samples (e.g., soils, rocks, water, gases) and a CSIRO-specific vocabulary for use across CSIRO research activities. The templates includes core properties of stable isotope results, analytical methods, and uncertainty of analyses, as well as associated metadata such as such as their name, identifier, type, and location. The templates enables users with disparate data to find common ground regardless of differences within the data itself i.e. sample types, collections. The standardized templates can prevent duplicate sample metadata entry and lower metadata redundancy, thereby improving the stable isotope data curation and discovery. They have been developed iteratively, revised, and improved based on feedback from researchers and lab technicians. Use of this template and vocabularies will facilitate interoperable and machine-readable platform-ready data collections.

Lineage: CSIRO, in partnership with the Australian Nuclear Science and Technology Organisation (ANSTO), Geoscience Australia, and the National Measurement Institute, has developed a common metadata template for reporting stable isotope results. The common template was designed to provide a shared language for stable isotope data so that the data can be unified for reuse. Using a simplified data structure, the common template allows for the supply of data from different organisations with different corporate goals, data infrastructure, operating models and different specialist skills. The common ontology describes the different concepts present in the data, giving meaning to the stable isotope observations or measurements of (isotopic) properties of physical samples of the environment. It coordinates this description of samples with standardised metadata and vocabularies, which facilitate machine-readability and semantic cross-linking of resources for interoperability between multiple domains and systems. This is to assist in reducing the need for human data manipulation which can be prone to errors, to provide a machine-readable format for new and emerging technology use-cases, and to also help stable isotope data align with Australia public data FAIR. In addition to the common template, the partners have developed a platform for making unified stable isotope data available for reuse, co- funded by the Australian Research Data Commons (ARDC). The aim of IsotopesAU is to repurpose existing publicly available environmental stable isotope data into a federated data platform, allowing single point access to the data collections. The IsotopesAU platform currently harmonises and federates stable isotopes data from the partner agencies' existing public collections, translating metadata templates to the common template.

The templates have been developed iteratively, revised, and improved based on feedback from project participants, researchers, and lab technicians.

Gross Dependency Ratio(Sample Survey): Hebei data was reported at 51.630 % in 2023. This records a decrease from the previous number of 51.990 % for 2022. Gross Dependency Ratio(Sample Survey): Hebei data is updated yearly, averaging 37.950 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 51.990 % in 2022 and a record low of 33.000 % in 2008. Gross Dependency Ratio(Sample Survey): Hebei data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Gross Dependency Ratio: By Region.