This dataset includes the number of people enrolled in DSS services by town and by assistance type from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. 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. Notes by year 2021 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. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, 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. 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. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

This U.S. Census Bureau American Community Survey (ACS) five-year estimates data set contains household income estimates during the past 12 months and in inflation-adjusted dollars. The data is available for a number of geographies ranging from statewide to census tract level. The data is available for years 2009-2016. This includes the income of the householder and all other individuals 15 years old and over in the household, whether they are related to the householder or not. Income is based on “money” income–income received on a regular basis before payments for personal income taxes, social security, union dues, etc. Money income does not include noncash benefits that may be received.

This table shows actual key figures of benefits concerning labour disablement, unemployment, income support and national insurances.

Data available from: January 1998.

Status of the figures: The figures for the three most recent months are provisional, while the figures for the preceding months are definitive. The figures concerning income support of the three most recent months are based on an estimation and therefore provisional. After three month these figures will be replaced by definitive figures. The monthly and quarterly figures represent the situation at the end of a period; the annual figures are averages.

Changes as of 31th July 2025:

Added are: - The provisional figures from May 2025;

The figures mentioned below have become final: - The figures from February 2025.

When will new figures be published? New figures will be published in August 2025.

Find out about retirement trends in PBGC's data tables. The tables include statistics on the people and pensions that PBGC protects, including how many Americans are in PBGC-insured pension plans, how many get PBGC benefits, and where they live. This data set will be updated periodically. (Updated annually)

This table aims to show the distribution of welfare of persons in the Netherlands, measured by their income. The figures in this table are broken down to different person characteristics.

The population consists of all persons in private households with income on January 1st of the reporting year. In the population for the subject low-income persons, persons in both student households and households with income only for a part of the year have been excluded. The population for the subject economic independence consists of all persons aged from 15 to the OAP-age in private households with income on January 1st of the reporting year, except for students and pupils.

Data available from: 2011

Status of the figures: The figures for 2011 to 2022 are final. The figures for 2023 are preliminary.

Changes as of November 2024: The preliminary figures for 2023 have been added.

When will new figures be published? New figures will be published in the fall of 2025.

Analysis of ‘Average Weekly Benefits Amount’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3641c306-7d05-4e08-ac66-d5e3b3b859d9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Average Weekly Benefit Amount (AWBA) is the average dollar amount a claimant is qualified to receive in Unemployment benefits. These figures include only Regular UI, and exclude any Federal/Military claims and extensions. The AWBA is calculated using “Benefits Paid for Total Unemployment” divided by “Weeks Compensated for Total Unemployment”. As defined by the United States Department of Labor, total unemployment represents the number of individuals, 16 years of age or older, who do not have a job and are eligible for UI benefits. This amount includes individuals who are partially employed and receiving unemployment benefits.

--- Original source retains full ownership of the source dataset ---

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

Analysis of ‘Continued Claims for Unemployment Benefits by Town’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c05ba52e-cf40-4f5a-b457-82422b79ecd8 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

--- Original source retains full ownership of the source dataset ---

A Veteran user is defined as any Veteran who received or used at least one VA benefit or service during the fiscal year. Veteran spouses, Veteran dependents, and active military service members who used VA benefits and services were not included in the analysis. Each Veteran is only counted once in the overall total even if he/she used multiple programs.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,776 observations with 37 variables; Profiles Layer Field = 1,493 observations with 64 variables; Profiles Layer Lab= 1,386 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The BENEFIT-REALISE program implements its interventions in 60 PSNP weredas in four regions (Tigray, Amhara, Oromia, and SNNPR).Accordingly, in 2019, BENEFIT-REALISE along with the MoA initiated a wereda-wide soil resource characterization and mapping task at1:50,000 scale in 15 BENEFIT-REALISE intervention weredas: 3 of Tigray, 6 of Amhara, 3 of Oromia, and 3 of SNNPR. Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. 10.13140/RG.2.2.31759.41123. Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b.

TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Initial Jobless Claims in the United States decreased to 217 thousand in the week ending July 19 of 2025 from 221 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average UK household incomes taxes and benefits by household type, tenure status, household characteristics and long-term trends in income inequality.

Initial Claims for UI released by the CT Department of Labor. Initial Claims are applications for Unemployment Benefits. Initial Claims may not result in receiving UI benefits if the individual doesn't qualify. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

The initial claims reported in these tables are "processed" claims to the extent that duplicates and "reopened" claims have been eliminated. The claim counts in this dataset may not match claim counts from other sources.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on initial claims at the town level, see the dataset "Initial Claims for Unemployment Benefits by Town," here: https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

For data on continued claims see the following two datasets:

"Continued Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-in-Conn/f9e5-rn42

"Continued Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

Analysis of ‘📈 Pension Insurance Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pension-insurance-datae on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The tables include statistics on the people and pensions that PBGC protects, including how many Americans are in PBGC-insured pension plans, how many get PBGC benefits, and where they live.

Note: Links in the first sheet associated with each table following.

Source: https://catalog.data.gov/dataset/pension-insurance-data-tables

This dataset was created by Data Society and contains around 100 samples along with Data Book Listing, Table, technical information and other features such as: - Data Book Listing - Table - and more.

How to use this dataset

• Analyze Data Book Listing in relation to Table
• Study the influence of Data Book Listing on Table
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Data Society

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This submission includes publicly available data extracted in its original form. If you have questions about the underlying data stored here, please contact WINDExchange (windcommunitybenefits@nrel.gov). "This searchable database reflects agreements, funds, donations, and other forms of benefits offered to communities by land-based and offshore wind energy developments in the U.S. compiled by the National Renewable Energy Laboratory (NREL) from 2022 to 2024. What Forms of Community Benefits Does This Database Include? Benefits to communities for wind energy projects can be structured in many ways, but the following categories are the most common and are the focus of this database: Formal Agreements signed by Developers, Local Governments, Tribal Governments, and/or Community Organizations: Developers and representatives of a government or community may sign an agreement stating the benefits that will be provided from a project and detailing the mechanisms and timelines for delivering benefits. Terminology may vary, depending on factors like the type of infrastructure or who the signatories are. Common names or types include community benefit agreement, host community agreement, good neighbor agreement, and Tribal benefit agreement. Payments to Local Governments Outside of a Formal Agreement: Developers may provide payments, donations, or other financial benefits to a local or Tribal government outside of the bounds of a formal agreement; these are often one-time payments. Funds Established by Developers: Developers may establish funds that distribute funding to different causes or recipients in the community over time, often through the form of grants. Terminology and structure may vary, with common names or types including community benefit fund, community fund, or scholarship fund. Direct Contributions to Local Priorities or Programs: Developers may directly donate or contribute to local organizations, programs, or causes in the community (e.g., schools, fire departments, community service organizations). What Forms of Community Benefits Are Not Included in This Database? Agreements and related forms of benefits may be provided alongside other agreements or economic impacts that serve different purposes, such as: Land lease payments to landowners that host wind turbines. Project labor agreements for construction of wind energy projects. Taxes or tax agreements like payment in lieu of taxes (PILOTs). Direct compensation to impacted stakeholders, such as commercial fishermen. This database does not include these other types of wind energy benefits, as they differ from agreements and related benefit mechanisms in several key ways; namely, the data included in this database are unrelated to taxation, are intended to provide benefits to the community as a whole rather than a specific group of people, like landowners, and are separate from impact mitigation measures required by permitting agencies." Quote from https://windexchange.energy.gov/projects/community-benefit-agreements

This study investigates whether people consider elements beyond health when valuing Quality-Adjusted Life-Years (QALYs) monetarily and the influence of inclusion on this value. A Willingness to Pay (WTP) experiment was administered among the general public in which people were asked to assign monetary values to QALYs. Our results show that (stated) UoC increases with quality of life but that instructing people to consider UoC does not impact their monetary valuation of the QALY. Furthermore, many respondents consider elements beyond health when valuing QALYs but the impact on the monetary value of a QALY is limited.This dataset includes the documents related to the construction of the (sub-versions of the) questionnaire, the raw data from the (subversions of the) questionnaire collected by and received from the sampling agency, and the data after merging the individual datasets for the subversions into one dataset, and the code to analyze the data.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.