This data represents all of the County’s residential real estate properties and all of the associated tax charges and credits with that property processed at the annual billing in July of each year, excluding any subsequent billing additions and/or revisions throughout the year. This dataset excludes the names of the property owners. The addresses in this database represent the address of the property. For more information about the individual taxes and credits, please go to http://www.montgomerycountymd.gov/finance/taxes/faqs.html#credit. Update Frequency: Updated Annually in July

This study developed a framework for quantifying the amount of risk sharing among states in the United States, and constructed data that allowed researchers to decompose the cross-sectional variance in gross state product into levels of smoothing capital markets, federal government, and credit market smoothing. The collection contains 67 Excel data files, that were grouped into 17 datasets based on the organizational ordering schematic provided by the principal investigator, including: Dataset 1 - State Personal Income: n=1,938, 51 variables Dataset 2 - Federal Taxes and Contributions: n=17,948, 424 variables Dataset 3 - State Population: n=1,887, 51 variables Dataset 4 - State and Local Personal Taxes: n=11,526, 306 variables Dataset 5 - Interests on State and Local Funds: n=7,609, 205 variables Dataset 6 - Transfers: n=5,814, 153 variables Dataset 7 - Non Federal State Income: n=1,887, 51 variables Dataset 8 - Federal Grants: n=1,938, 51 variables Dataset 9 - Federal Transfers to Individuals: n=27,415, 766 variables Dataset 10 - Federal Personal Taxes: n=1,938, 51 variables Dataset 11 - State Government Expenditure: n=1,887, 51 variables Dataset 12 - Disposable State Income: n=1,836, 51 variables Dataset 13 - State Consumption: n=5,508, 153 variables Dataset 14 - State and Local Transfers: n=1,836, 51 variables Dataset 15 - Gross State Product: n=1,910, 52 variables Dataset 16 - Retail Sales: n=3,774, 102 variables Dataset 17 - Personal Consumption Expenditures: n=38, 2 variables

These statistics come from more than three million data items reported on about 250,000 sales tax returns filed quarterly and on about 300,000 returns filed annually. The dataset categorizes quarterly sales and purchases data by industry group using the North American Industry Classification System. The status of data will change as preliminary data becomes final.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for functions of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Payments in Lieu of Taxes (PILT) and All Service Receipts (ASR) (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/41ef25ff-5472-4367-8f87-e76fa4f505e6 on 26 January 2022.

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

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. This data is intended for read-only use. Payments In Lieu of Taxes (PILT) and All Service Receipts (ASR) are combined into a base layer that is used in Forest Service business functions, as well as by other entities such as states and counties. This layer depicts Forest Service lands that qualify for PILT and/or ASR. Payments in Lieu of Taxes are Federal payments to local governments that help offset losses in property taxes due to the existence of nontaxable Federal lands within their boundaries. All Service Receipts data provides acreage inputs to the FS All Service Receipts program that tracks receipt data by unit and computes revenue sharing payments to states and counties. Please note, the publication of this dataset in EDW replaces the file geodatabase on the Public Lands and Realty Management website. Metadata and Downloads.

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

“Public Goods through Private Eyes” Project. General information“Public Goods through Private Eyes” is a full-scale comparative public opinion survey carried out in 2013 and 2014 as a result of the 2009 ERC funding of 1.73 million Euro awarded to dr hab. Natalia Letki (project PI, University of Warsaw). The aim of the project was to collect high-quality survey data on attitudes and behaviour towards public goods and the state in 14 post-communist countries:Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovakia, Slovenia and Ukraine.PGPE QuestionnaireThe questionnaire was developed by the PGPE team with support from the Project’s Advisory Board: Rene Bekkers (VU University Amsterdam), Klarita Gërxhani (EUI), Erich Kirchler (University of Vienna), Stephan Muehlbacher (University of Vienna), Kristina Murphy (Griffith University), Pamela Paxton (The University of Texas at Austin) and Michael Wenzel (Flinders University). It contained 7 main modules:A. Local communityB. Social trust and social cohesionC. Personality and commitmentD. Public goodsE. Institutional qualityF. Tax behaviour and law complianceG. Green behaviourH. Political participationI. Socio-economic background.The survey also contains 4 vignettes – fully randomized survey-embedded experiments.In each country respondents were clustered in 75 stratified sampling points (with the exception of Ukraine, where Russian annexation of Crimea interfered with fieldwork and only data in 74 sampling points were collected). The sampling points were designed to cover a spatially relatable area that respondents could refer to when contextualizing their survey responses. For details, see PGPE document on sampling procedure.ResultThe result of the project is a multi-level multi-component survey, comprising of:1. The main survey of 22039 respondents nested in 1049 SPs in 14 countries.2. The survey of interviewers (each interviewer had to complete the survey prior to commencing work for the project).3. Survey of ecological data for the PSU level in all countries.Surveys 2 and 3 can be linked to survey 1 to explore interviewer effects (survey 2) and contextual effects (survey 3). For confidentiality reasons, surveys 2 and 3 cannot be released.Sampling procedure was developed in cooperation with dr Matthias Ganninger, dr Sabine Haeder and dr Siegfried Gabler (GESIS Mannheim) (for details, see a document on PGPE sampling).Weights were prepared based on the ESS standards by Jan-Philipp Kolb (GESIS Mannheim). The following weight types are provided in the PGPE dataset: DWEIGHT (design weight), PSPWGHT (post-stratification weight), PWEIGHT (population weights) and PPSPWGHT (combination of post-stratication weights and population size weights).Main fieldwork was carried out by two companies and their subcontractors: in Poland it was carried out by CBOS, in all other countries - by IPSOS Strategic Marketing. PAPI and CAPI were used as data collection methods.QualityTo achieve as high a quality as possible the key following principles were applied:· Sampling was designed and carried out centrally, by the PGPE team in cooperation with sampling experts. · Pre-listing of address was verified centrally, by the PGPE team, on the basis of maps.· Questionnaire was translated following the ESS round 5 guidelines.· Questionnaire was tested qualitatively, and a quantitative pre-test on quota samples were carried in each country prior to the main survey.· Data was centrally checked and screened for inconsistencies and feedback was given to the fieldwork companies to be taken into account in the subsequent phases of fieldwork.· Post-survey control in most countries was carried out with the participation of the PGPE team members.Translation of the questionnaireTranslation guidelines were modeled after ESS round 5, with particular emphasis on keeping the equivalence of meaning and the symmetry and consistency of scales. Translation was carried out in three stages: i) questionnaire was translated by two independently working, experienced translators per language; ii) two versions were adjudicated by an experienced adjudicator; iii) language versions used as minority questionnaires in other countries were harmonized by country coordinatorsQuestionnaire testingThe PGPE Questionnaire was tested in two stages. First, a Qualitative pre-test was performed in Polish by GFK Polonia in 3 waves of question testing in a studio with a venetian mirror. Second, a Quantitative pilot was performed by fieldwork companies on a representative quota sample of N=80 in each country. The aims of both qualitative and quantitative pre-testing was to control the quality of translation and to shorten the questionnaire.

Tax Increment Financing (TIF) Districts is established by a municipality around an area that requires public infrastructure to encourage public and private real property development or redevelopment. The property values at the time the District is created are determined and the property taxes generated by that original value continue to go to the taxing entities (municipality and state). This dataset provides the TIF boundaries provided by the municipalities as part of the TIF process. Learn more about the Vermont Increment Financing Districts Program.

The Corporate Tax Rate in Japan stands at 30.62 percent. This dataset provides - Japan Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Download In State Plane Projection Here ** In addition to the Tax Parcel polygons feature class, the hyperlink download above also contains a parcel point data layer ** Parcel boundaries are developed from deeds, plats of subdivision and other legal documents going back to the mid 1800's, following generally accepted practices used in Public Land Survey System states, and following guidelines established by the Illinois Department of Revenue and the International Association of Assessment Officials. Lake County's parcel coverage is based on resolving the accumulated evidence of all of the legal documents surrounding a particular parcel or subdivision, and not the result of a countywide resurvey. These parcel boundaries are intended to be a visual inventory of property for tax and other administrative purposes; they are not intended to be used in place of an on-site survey or for the precise determination of property corners or PLSS features based on GIS coordinates. In Illinois, only a registered professional land surveyor is authorized to determine boundary locations. Included are the tax parcel boundaries, represented as polygons and centroids, for all changes resulting from legal records submitted to the Recorder of Deeds up to December 31st of the preceding year, as well as any court orders, municipal annexations and other transactions which impact the tax parcel boundaries. NOTE: The ONLY attribute included is the Property Index Number, or PARCEL_NUM. Additional assessment attribute data can be downloaded here This parcel layer is used for tax assessment purposes and for a variety of other local government functions. It changes often, both spatially and in its attribution, based on divisions or consolidations, the sale of property and other transactions. Example: PIN 08-17-304-014 can be interpreted as follows: Township 08, Section 17, Block 304, Parcel 014. Note that the first digit of block, "3" in this example, signifies that the parcel lies in quarter section 3. The quarter sections are labeled from 1 through 4, representing the northwest, northeast, southwest and southeast quarter sections, respectively. Update Frequency: This dataset is updated on a weekly basis.

Analysis of ‘Real Property Tax - 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/048c3929-9847-4bc5-83f6-d76a8a1281e4 on 12 February 2022.

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

This data represents all of the County’s residential real estate properties and all of the associated tax charges and credits with that property processed at the annual billing in July of each year, excluding any subsequent billing additions and/or revisions throughout the year. This dataset excludes the names of the property owners. The addresses in this database represent the address of the property. For more information about the individual taxes and credits, please go to http://www.montgomerycountymd.gov/finance/taxes/faqs.html#credit. Update Frequency: Updated Annually in July

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

On October 11, 2017, the Cook County Board repealed the Sweetened Beverage Tax Ordinance, effective December 1, 2017. This dataset is historical and no longer maintained.

Disclaimer: This list was last updated on 10/05/2017 and is updated monthly on the website. If up-to-the-minute accuracy is needed, contact us at 312-603-6328. This dataset contains registered Sweetened Beverage Distributors.

Residential Zoned Land Tax Annual Draft Map for 2025. Published by Department of Housing, Local Government, and Heritage. Available under the license cc-zero (cc-zero).The Government’s Housing For All – A New Housing Plan for Ireland proposed a new tax to activate vacant land for residential purposes as a part of the Pathway to Increasing New Housing Supply. The Residential Zoned Land Tax was introduced by the Finance Act 2021. The dataset contains the land identified as being covered by the tax from all of the local authorities in the state. The available datasets will comprise the draft annual map, published on 1 February 2024. The draft map dataset published 1 November 2022, the supplemental map dataset published 1 May 2023 and the final map published 1 December 2023 are also available, however the annual draft map represents the most recent dataset of land identified as either being in-scope for the tax, or proposed to be removed from the map due to not meeting the criteria. The dataset will identify serviced land in cities, towns and villages which is residentially zoned and ‘vacant or idle’ mixed use land. Unless specifically identified for removal, the lands identified on the maps are considered capable of increasing housing supply as they meet the criteria for inclusion in the tax. Certain settlements will not be identified due to lack of capacity or services or due to out of date zonings. The dataset will also identify the amount in hectares of zoned serviced land for each settlement....

Publication Date: November 2024. This data represents Federal properties in New York State derived from a combination of the USGS National Boundary Dataset (NBD) with NYS Publicly Available Parcel data: USGS GU_Reserve feature class "...include extents of forest, grassland, park, wilderness, wildlife, and other reserve areas useful for recreational activities, such as hiking and backpacking. Boundaries data are acquired from a variety of government sources. The data represents the source data with minimal editing or review by USGS." More information and detailed metadata is available here: https://data.usgs.gov/datacatalog/data/USGS:6dcde538-1684-48a0-a8d6-cb671ca0a43e. NYS ITS Geospatial Services publicly available parcel data selection of [OWNER_TYPE] field, where 1 = Federal. Classification is based solely on the parcel owner name indicating that the property is owned by the United States. Parcel data that is not publicly available is not included. More information and detailed metadata is available here: https://gis.ny.gov/parcels.These two datasets were combined with a minimum of available common attributes, indicating the Name, Owner, and Address of the property where applicable and/or available. Unique identifiers were retained to link records back to the original datasets. Work to improve and expand upon this Federal properties GIS dataset is on-going. Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions.

The Corporate Tax Rate in France stands at 25 percent. This dataset provides the latest reported value for - France Corporate Tax Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Corporate Tax Rate in Lebanon stands at 17 percent. This dataset provides - Lebanon Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains the data from the Council’s Annual Budget. The budget is comprised of Tables A to F and Appendix 1 & 2. Each table is represented by a separate data file.Table A is the Calculation of the Annual Rate on Valuation for the Financial Year. It is comprised of a number of sections and a series of calculations to determine the Annual Rate on Valuation.The data in this dataset is best interpreted by comparison with Table A in the published Annual Budget document which can be found at www.fingal.ieSection 1 of Table A contains the Budgeted ‘Expenditure’ and ‘Income’ per Council Division and the ‘Estimated Outturn’ per Council Division for the previous Financial Year.The ‘Gross Revenue Expenditure and Income’ is the total of section 1Section 2 of Table A contains ‘Provision of Debit Balance’The ‘Adjusted Gross Expenditure and Income’ is the total of Section 1 and Section 2Section 3 of Table A contains ‘Provision for Credit Balance’, ‘Local Property Tax’ and ‘Pension Related Deduction’The ‘Amount of Rates to be Levied’ is the ‘Adjusted Gross Expenditure and Income’ minus the total of Section 3Section 4 of Table A contains ‘Net Effective Valuation’The ‘General Annual Rate on Valuation’ is the ‘Amount of Rates to be Levied’ divided by the ‘Net Effective Valuation’Data fields for Table A are as follows –Doc : Table ReferenceHeading : Indicates sections in the Table - Table A is comprised of four sections; each section is represented by a sequential number in the heading field i.e. Heading = 1 for all records in the first section; etc.Ref : Item Reference (In section 1 = Division Reference; In other sections, DB = Provision for Debit Balance; CB = Provision for Credit Balance; LPT = Local Property Tax; PRD = Pension Related Deduction; NEV = Net Effective Valuation)Description : Item DescriptionExpenditure : Expenditure for this ItemIncome : Income for this ItemPY : Estimated Outturn for this Item for previous Financial YearABP-PUB-989

On May 3, 2011 Montgomery County passed legislation (Bill 8-11) that places a five-cent charge on each paper or plastic carryout bag provided by retail establishments in the County to customers at the point of sale, pickup or delivery. Retailers retain 1 cent of each 5 cents for the bags they sell a customer. This dataset represents information that has been captured since this law went into effect. Update Frequency - Monthly

Cook County 10-digit parcels with attached distances to various spatial features. When working with 10-digit Parcel Index Numbers (PINs) make sure to zero-pad them to 10 digits. Some datasets may lose leading zeros for PINs when downloaded. 10-digit PINs do not identify individual condominium units. Additional notes:Centroids are based on Cook County parcel shapefiles. Older properties may be missing coordinates and thus also missing attached spatial data (usually they are missing a parcel boundary in the shapefile). Attached spatial data does NOT all go back to 2000. It is only available for more recent years, primarily those after 2012. This dataset contains data for the current tax year, which may not yet be complete or final. Assessed values for any given year are subject to change until review and certification of values by the Cook County Board of Review, though there are a few rare circumstances where values may change for the current or past years after that. Rowcount for a given year is final once the Assessor has certified the assessment roll all townships. Data will be updated annually as new parcel shapefiles are made available.For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub. Read about the Assessor's 2025 Open Data Refresh.

The Medicare Secondary Payer project is an annual process which attempts to identify working Medicare beneficiaries and/or their spouses. The first stage of this process is to extract all of the Medicare beneficiaries from the MBR. Prior to 2015, CSPOTRUN performed this function. Beginning in 2015, CSRETAP accomplishes this. In this process two files are prepared. One file goes to the Internal Revenue Service (IRS) for a tax return search and the other file is used for the Master Earnings File (MEF) search. IRS searches their tax return database and identifies returns that have spouses identified and returns this information to SSA. This file is then run against the MEF to obtain any current employment information for the beneficiary or the spouse. This data is sent to CMS for their process to determine whether Medicare should be the secondary payer for hospital and doctors bills. They determine whether the beneficiary and/or spouse have current health insurance coverage from their employer.