Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

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How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, States and Territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 states, the District of Columbia, and the United States territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the data files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Most variables have a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. Beginning with the 2020 files, the analytic data files are supplemented by four additional data files containing select policy information featured in the annual reports (prior to 2020, the full detail of the annual reports was reproduced as data files). The supplemental data files are available as 4 datasets (Datasets 33-36) and present key aspects of the differences in CCDF-funded programs across all states and territories as of October 1 of each year (2009-2022). The files include variables that are calculated using several variables from the analytic data files (Datasets 1-32) (such as copayment amounts for example family situations) and information that is part of the annual project reports (the annual Book of Tables) but not stored in the full database (such as summary market rate survey information from the CCDF plans). 2. The project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Data

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Oregon by race. It includes the distribution of the Non-Hispanic population of Oregon across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Oregon across relevant racial categories.

Key observations

Of the Non-Hispanic population in Oregon, the largest racial group is White alone with a population of 17,071 (93.71% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Oregon
• Population: The population of the racial category (for Non-Hispanic) in the Oregon is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Oregon total Non-Hispanic 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 Oregon Population by Race & Ethnicity. You can refer the same here

This record description is for the FEMA Applicant Case Tracker (Fac-trax - Grants Manager) portion of the unioned query required due to migration of Public Assistance (PA) Recovery records from the EMMIE database into the Fac-trax database. This dataset contains data on Public Assistance project awards (obligations), including the project obligation date(s); dollar amount of Federal Share Obligated for each project and its obligation date(s); FEMA region; state; disaster declaration number; descriptive cause of the declaration (incident type); entity requesting public assistance (applicant name); and distinct name for the repair, replacement or mitigation work listed for assistance (Project Title). The PA Grant Awards Activities dataset does not collect, maintain, use, or disseminate any Personally Identifiable Information (PII).rnrnAs part of Congressional bill HR 152 - the Sandy Recovery Improvement Act of 2013, FEMA is providing the following information for our stakeholders:rn• Regionrn• Disaster Declaration Numberrn• Disaster Typern• Statern• Applicantrn• Countyrn• Damage Category Codern• Federal Share Obligatedrn• Date ObligatedrnrnFEMA obligates funding for a project directly to the Recipient (State or Tribe). It is the Recipient's responsibility to ensure that the eligible subrecipient (listed in the dataset as Applicant Name) receives the award funding.rnThis dataset lists details about project versions. Versions occur when the scope/cost changes for a project. Versions adjust the cost of the project with positive additions called obligations and subtractions called deobligations. Combined, they reconcile to reflect the Total Federal Share Obligation, but reconciliation occurs over the life of the project, sometimes years after the declaration date. The dataset represents project obligations within a seven-day period prior to the listed date but does not include obligations uploaded on the same day as the publication. Open projects still under pre-obligation processing are not represented.rnFor more information on the Public Assistance process see: https://www.fema.gov/assistance/public/process.rnThis is raw, unedited data from FEMA's Fac-trax database and as such is subject to a small percentage of human error. The financial information is derived from Fac-trax and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. The data represents number of active recipients who received benefits from a type of assistance (TOA) in that calendar year and month. A recipient may have received benefits from multiple TOAs in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) For privacy considerations, a count of zero is used for counts less than five. The methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.

About this dataset

Context

The dataset tabulates the population of United States by race. It includes the population of United States across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of United States across relevant racial categories.

Key observations

The percent distribution of United States population by race (across all racial categories recognized by the U.S. Census Bureau): 65.88% are white, 12.47% are Black or African American, 0.84% are American Indian and Alaska Native, 5.77% are Asian, 0.19% are Native Hawaiian and other Pacific Islander, 6.05% are some other race and 8.80% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the United States
• Population: The population of the racial category (excluding ethnicity) in the United States is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of United States 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 United States Population by Race & Ethnicity. You can refer the same here

USER NOTE: This database no longer contains the most up-to-date information. Some errors and missing data from the previous years have been fixed in the most recent data release in the CCDF Policies Database Series. The most recent release is a cumulative file which includes the most accurate version of this and all past years' data. Please do not use this study's data unless you are attempting to replicate the analysis of someone who specifically used this version of the CCDF Policies Database. For any other type of analysis, please use the most recent release in the CCDF Policies Database Series.

The Child Care and Development Fund (CCDF) provides federal money to States, Territories, and Tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, to attend training, or receive education. Within the broad federal parameters, States and Territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every State and Territory.

The CCDF Policies Database project is a comprehensive, up-to-date database of inter-related sources of CCDF policy information that support the needs of a variety of audiences through (1) Analytic Data Files and (2) a Book of Tables. These are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of alternative child care subsidy policies and practices on the children and families served, specifically parental employment and self-sufficiency, the availability and quality of care, and children's development. A description of the Data Files and Book of Tables is provided below:

1. Detailed, longitudinal Analytic Data Files of CCDF policy information for all 50 States, the District of Columbia, and United States Territories that capture the policies actually in effect at a point in time, rather than proposals or legislation. They focus on the policies in place at the start of each fiscal year, but also capture changes during that fiscal year. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including:

• Eligibility Requirements for Families and Children (Datasets 1-5)

• Family Application, Terms of Authorization, and Redetermination (Datasets 6-13)

• Family Payments (Datasets 14-18)

• Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27)

• Overall Administrative and Quality Information Plans (Datasets 28-32)

The information in the Data Files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the documents submitted by States/Territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between submission dates.

Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Every variable has a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables.

2. The Book of Tables is available as a single dataset (Dataset 33) and it presents key aspects of the differences in CCDF funded programs across all states, territories, and tribes as of October 1, 2009. The Book of Tables include

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Ohio County. 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 Ohio County. 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 Ohio County, householders within the 45 to 64 years age group have the highest median household income at $77,801, followed by those in the 25 to 44 years age group with an income of $60,966. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $47,125. Notably, householders within the under 25 years age group, had the lowest median household income at $22,299.

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 Ohio County median household income by age. You can refer the same here

Public US Government data about financial assistance to restaurants during the pandemic. According to the Small Business Administration website

The American Rescue Plan Act established the Restaurant Revitalization Fund (RRF) to provide funding to help restaurants and other eligible businesses keep their doors open. This program will provide restaurants with funding equal to their pandemic-related revenue loss up to $10 million per business and no more than $5 million per physical location. Recipients are not required to repay the funding as long as funds are used for eligible uses no later than March 11, 2023.

The original data and data dictionary are published here. I downloaded and minimally processed the data using this notebook.

About this dataset

Context

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

Key observations

The largest age group in St. Louis city, MO was for the group of age 25-29 years with a population of 31,444 (10.38%), according to the 2021 American Community Survey. At the same time, the smallest age group in St. Louis city, MO was the 80-84 years with a population of 3,867 (1.28%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the St. Louis city is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of St. Louis city 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 St. Louis city Population by Age. You can refer the same here

Key Table Information.Table Title.Family Size by Cash Public Assistance Income or Households Receiving Food Stamps/SNAP Benefits in the Past 12 Months.Table ID.ACSDT1Y2024.B19123.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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.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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates...

The Food Assistance Program provides Electronic Benefit Transfer (EBT) cards that can be used to buy groceries at supermarkets, grocery stores and some Farmers Markets. This dataset provides data on the number of households, recipients and cash assistance provided through the Food Assistance Program participation in Iowa by month and county starting in January 2011 and updated monthly.

Beginning January 2017, the method used to identify households is based on the following: 1. If one or more individuals receiving Food Assistance also receives FIP, the household is categorized as FA/FIP. 2. If no one receives FIP, but at least one individual also receives Medical Assistance, the household is categorized as FA/Medical Assistance. 3. If no one receives FIP or Medical Assistance, but at least one individual receives Healthy and Well Kids in Iowa or hawk-i benefits, the household is categorized as FA/hawk-i. 4. If no one receives FIP, Medical Assistance or hawk-i , the household is categorized as FA Only.

Changes have also been made to reflect more accurate identification of individuals. The same categories from above are used in identifying an individual's circumstances. Previously, the household category was assigned to all individuals of the Food Assistance household, regardless of individual status. This change in how individuals are categorized provides a more accurate count of individual categories.

Timing of when the report is run also changed starting January 2017. Reports were previously ran on the 1st, but changed to the 17th to better capture Food Assistance households that received benefits for the prior month. This may give the impression that caseloads have increased when in reality, under the previous approach, cases were missed.

The Stephanie Tubbs Jones Child Welfare Services Program provides grants to States and Indian tribes for programs directed toward the goal of keeping families together. They include preventive intervention so that, if possible, children will not have to be removed from their homes. If this is not possible, children are placed in foster care and reunification services are available to encourage the return of children who have been removed from their families. Services are available to children and their families without regard to income.

These funds are a small but integral part of State social service systems for families who need assistance in order to stay together. These funds, often combined with State and local government, as well as private funds, are directed to accomplish the following purposes:

States can use a portion of their funds (no more than their 2005 expenditure level) for foster care maintenance payments, adoption assistance and day care related to employment or training for employment. States must limit expenditures for administrative costs 10 percent or less of their expenditures under this program.

Each state receives a base amount of $70,000. Additional funds are distributed in proportion to the state's population of children under age 21 multiplied by the complement of the state's average per capita income. The state match requirement is 25 percent. Funding is approximately $282,000,000 for FY 2008.

Metadata-only record linking to the original dataset. Open original dataset below.

Counts and rates of children who received an investigation or alternative response from child protective services agencies for the last five federal fiscal years for which data are available.

To view more National Child Abuse and Neglect Data System (NCANDS) findings, click link to summary page below: https://healthdata.gov/stories/s/kaeg-w7jc

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Mahoning County. 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 Mahoning County. 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 Mahoning County, householders within the 45 to 64 years age group have the highest median household income at $65,626, followed by those in the 25 to 44 years age group with an income of $62,650. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $46,935. Notably, householders within the under 25 years age group, had the lowest median household income at $30,859.

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 Mahoning County median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in California City. 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 California City. 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 California City, householders within the 45 to 64 years age group have the highest median household income at $74,145, followed by those in the 25 to 44 years age group with an income of $48,269. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $38,068. Notably, householders within the under 25 years age group, had the lowest median household income at $36,445.

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 California City median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Grayson County. 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 Grayson County. 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 Grayson County, householders within the 45 to 64 years age group have the highest median household income at $85,105, followed by those in the 25 to 44 years age group with an income of $81,593. Meanwhile householders within the under 25 years age group report the second lowest median household income of $52,573. Notably, householders within the 65 years and over age group, had the lowest median household income at $52,497.

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 Grayson County median household income by age. You can refer the same here

How we access information and use technology is rapidly changing. With so many ways to access an ever increasing amount of information, it is becoming increasingly difficult for information clearinghouses and technical assistance providers to be responsive to the needs and preferences of a diverse child welfare workforce and to get useful, trusted information into the hands of those who need it most. The Child Welfare Information Gateway, funded by the Children's Bureau, conducted a research study to better understand how professionals search for, access, and share information, including their use of social media and technology. The study gathered data about the behaviors and preferences of current and future members of the child welfare workforce, including child welfare agency professionals, child welfare professionals working with Tribes, legal professionals, and students in social work programs through an online survey, tailored to each respondent group, and telephone focus groups. To ensure the study design and instruments were informed by appropriate stakeholders, various experts were engaged through stakeholder groups to provide structured feedback on overall study design, target audiences, and instrument development. Stakeholder groups were composed of experts in child welfare systems, issues, policies, technology, communication, and research methodology. Study participants were invited to be a part of the study through a variety of channels, including the agencies for which they worked, through intermediary organizations such as professional associations, and through contacts at university social work programs. Because of the different contexts of each of the targeted audiences, recruitment approaches were tailored and multiple methods were used to maximize responses. Ultimately, 4,134 individuals responded to the survey, including 3,191 child welfare agency professionals, 122 child welfare professionals working with Tribes, 371 legal professionals, and 450 students in social work programs. Study findings are meant to support the enhanced design and reach of information, resources, and services for child welfare agency administrators, program managers, supervisors, caseworkers, judges and attorneys, and future members of the child welfare workforce so that they are more accessible, useful, and effective for improving child welfare practice. Investigators: Brian Deakins, U.S. Department of Health and Human Services, Administration for Children and Families, Children's Bureau Christine Leicht, Child Welfare Information Gateway Michael Long, Child Welfare Information Gateway Sharika Bhattacharya, Child Welfare Information Gateway Elizabeth Eaton, Child Welfare Information Gateway Dannele Ferreras, Child Welfare Information Gateway Katelyn Sedelmyer, Child Welfare Information Gateway Sarah Pfund, Child Welfare Information Gateway Christina Zdawczyk, Child Welfare Information Gateway

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Powell County. 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 Powell County. 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 Powell County, householders within the 45 to 64 years age group have the highest median household income at $72,689, followed by those in the 25 to 44 years age group with an income of $40,464. Meanwhile householders within the under 25 years age group report the second lowest median household income of $32,604. Notably, householders within the 65 years and over age group, had the lowest median household income at $28,750.

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 Powell County median household income by age. You can refer the same here