5 datasets found
  1. t

    Guaranteed and Direct Loan Financing, Net Activity

    • fiscaldata.treasury.gov
    • wayback.stanford.edu
    • +1more
    Updated Jul 13, 2020
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    (2020). Guaranteed and Direct Loan Financing, Net Activity [Dataset]. https://fiscaldata.treasury.gov/datasets/monthly-treasury-statement/
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    Dataset updated
    Jul 13, 2020
    Description

    This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of all direct and guaranteed loan financing for federal credit programs under the Credit Reform Act of 1990. Guaranteed loan financing is issuing any debt obligation with a guarantee, insurance, or other pledge that payment of all or a part of the principal or interest will be made to the lender. This table applies to lending to non-federal borrowers by non-federal lenders that carries some form of guarantee by the federal government. Exceptions include the insurance of deposits, shares, or other withdrawable accounts in financial institutions. 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.

  2. t

    Federal Agency Borrowing Financed Through the Issue of Treasury Securities

    • fiscaldata.treasury.gov
    • wayback.stanford.edu
    • +1more
    Updated Jul 13, 2020
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    (2020). Federal Agency Borrowing Financed Through the Issue of Treasury Securities [Dataset]. https://fiscaldata.treasury.gov/datasets/monthly-treasury-statement/
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    Dataset updated
    Jul 13, 2020
    Description

    This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government, providing a detailed view of transactions and account balances for agency programs that borrow from the United States Treasury or from the Federal Financing Bank. 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.

  3. g

    IBO Federal Stimulus Budget and Spending Tracker (ARPA and CRRSAA)

    • gimi9.com
    • catalog.data.gov
    Updated Mar 8, 2023
    + more versions
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    (2023). IBO Federal Stimulus Budget and Spending Tracker (ARPA and CRRSAA) [Dataset]. https://gimi9.com/dataset/data-gov_ibo-federal-stimulus-budget-and-spending-tracker-arpa-and-crrsaa
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    Dataset updated
    Mar 8, 2023
    Description

    This dataset contains budget and spending data for City Initiatives that use American Rescue Plan Act of 2021 (ARPA) or Coronavirus Response and Relief Supplemental Appropriations Act of 2021 (CRRSAA) federal funds. Each row is a different "Initiative Detailed", which is IBO's understanding of the purpose of the funding. IBO developed seven categories of initiative to standardize the comparison of budgeted and spent amounts across agencies: "Initiative Category". The IBO definitions of these categories are below, in order of prioritization (e.g. if a budget code fits the definition of 2. COVID Response – Public Programs and 4. Programmatic Support, it is listed under 2). If readers require more detailed information on spending, the underlying data with the previous initiative names is available for download. Covid Response – City Operations: Spending to keep city agencies operating during the Covid pandemic, such as city employee leave for quarantining and vaccinations, air purifiers, personal protective equipment (PPE) for city employees, etc. Covid Response – Public Programs: Programs created to protect people in New York City from Covid-19. Direct Human Services: Public services provided to meet the financial, physical, or mental needs of New York City residents. This includes ongoing services for housing, food, addiction treatment, childcare, education, anti-poverty, etc. These services are either provided by the government or a nonprofit. Programmatic Support: Funds used on temporary governmental programs. Note: this includes youth training and summer work programs because they are optional and extra-curricular, while public education and adult job training programs are direct human services. Government Operations: Federal funding supplemented lost revenue during the pandemic-related recession. These funds are for Other than Personal Spending, those administrative costs or public services provided in perpetuity (as opposed to services defined as Programmatic Support). Personnel/Staffing: Federal funding supplemented lost revenue during the pandemic-related recession. These funds are for salaries and wages paid to city employees, often called “Personal Services.” Salaries and wages related to Covid response, temporary programs, and direct human services are excluded from this category. Hiring & Attrition Management: Administrative costs related to managing the inflow and outflow of city employees.

  4. Data from: Global Tax Expenditures Database (GTED)

    • zenodo.org
    bin
    Updated Apr 14, 2023
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    Agustin Redonda; Christian von Haldenwang; Flurim Aliu; Agustin Redonda; Christian von Haldenwang; Flurim Aliu (2023). Global Tax Expenditures Database (GTED) [Dataset]. http://doi.org/10.5281/zenodo.5940166
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    binAvailable download formats
    Dataset updated
    Apr 14, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Agustin Redonda; Christian von Haldenwang; Flurim Aliu; Agustin Redonda; Christian von Haldenwang; Flurim Aliu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The GTED collects all publicly available data on tax expenditures (TEs) published by national governments worldwide from 1990 onwards, covering a total of 218 jurisdictions. Based on a step-by-step search process, 121 jurisdictions are currently classified as Non-reporting Jurisdictions. The remaining 97 ones do provide some type of TE data, which was gathered by the GTED team.

    Wherever available, the GTED gathers revenue forgone estimates and number of beneficiaries of individual TE provisions. It also gathers metadata including the definition of the TE provision, its legal basis and duration.

    Each record in the GTED is classified in four main categories: Tax Base, Policy Objective, Beneficiaries and Type of TE used. In some cases, second- or third-level categories have been introduced. For instance, Fuel Tax data is categorised at the third level within Tax Base: Taxes on Good and Services Excise Taxes Fuel Tax. If the information for a record is not available or unclear, the respective category is classified as Not stated/unclear.

    When governments do not publish provision-level data but rather some kind of aggregated information, the GTED gathers this aggregate data. Likewise, if governments report on specific areas of TE only (such as tax incentives for investments, or TEs on income taxes) the GTED presents data on these areas alone. The terms TE reporting or TE report are used broadly, and refer to a large variety of public documents, ranging from annual, comprehensive reports on TEs that are part of governmental budget documentation to individual documents issued by a public body and providing some aggregate information on some specific TE mechanisms. As a minimum requirement, reports must contain some kind of information on the actual use of TE provisions. For instance, a list of available tax deductions for investments, provided by a governmental investment promotion agency, would not be considered a TE report unless they provide revenue forgone estimates or any other data that would allow users of the GTED to obtain information about the actual use of the respective TEs.

    The GTED distinguishes regular and irregular reporting. A sequence of reports from 1995 to 2005 would not be considered regular reporting in the GTED, since the country had reported on a yearly basis, but not anymore. Likewise, regular is not necessarily related to annual reporting. Germany, for instance, publishes federal subsidy reports including TE data every two years since 1967. A total of 15 such reports have been issued since 1990, containing data on 29 budget years (until 2018). The GTED counts this as 29 years reported, because data is provided on a year-by-year basis and can be consulted and analysed as such.

    The data is processed in a consistent format seeking to increase the level of longitudinal and cross-country comparability. Whereas revenue forgone estimates are provided as reported by governments (in local currency units, current prices), the GTED also provides figures converted into US dollars as well as indicators providing the revenue forgone through TE provisions as shares both of GDP and Tax Revenue – to compute these two indicators, data from the UNU-WIDER Government Revenue Dataset is used as input. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries’ central governments. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries’ central governments.

    Besides all the effort put into ensuring comparability, cross-country analysis of TE data needs to be done cautiously. The main issue, which is inherent to TE data, regards benchmarking. TEs are defined as departures from – usually country-specific – normal tax structures or benchmarks. On this note, the GTED uses the data published by official governmental institutions, sticking to their own definitions of benchmarks, without trying to complement official figures or challenge what different countries consider as the standard tax system or the benchmark.

    When it comes to the methodology used by governments to compute the fiscal cost of TE provisions, the vast majority of countries report on TEs based on the revenue forgone approach that estimates the amount by which taxpayers have their tax liabilities reduced as a result of a TE based on their actual current economic behaviour. Since the revenue forgone methodology is static, the potential interconnections between different TE provisions are not taken into account when computing the fiscal cost of TEs based on it. Hence, aggregating revenue forgone estimates of the individual provisions computed separately and without taking behavioural changes into account would not result in a figure that represents the total cost of all TEs.

    While providing users of the database with the opportunity to draw comparisons across countries or country groups, we want to be clear that any such comparison should be mindful of different levels of reporting, differences in national benchmark systems and methodological shortcomings of revenue forgone estimations.

    Country Income Groups and Regional Classifications are based on the latest World Bank classifications.

  5. Z

    Data from: Global Tax Expenditures Database (GTED)

    • data.niaid.nih.gov
    Updated Mar 8, 2024
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    Aliu, Flurim (2024). Global Tax Expenditures Database (GTED) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5940165
    Explore at:
    Dataset updated
    Mar 8, 2024
    Dataset provided by
    von Haldenwang, Christian
    Aliu, Flurim
    Redonda, Agustin
    Description

    The GTED collects all publicly available data on tax expenditures (TEs) published by national governments worldwide from 1990 onwards, covering a total of 218 jurisdictions. Based on a step-by-step search process, 113 jurisdictions are currently classified as Non-reporting Jurisdictions. The remaining 105 ones do provide some type of TE data, which was gathered by the GTED team. Wherever available, the GTED gathers revenue forgone estimates and number of beneficiaries of individual TE provisions. It also gathers metadata including the definition of the TE provision, its legal basis and duration. Each record in the GTED is classified in four main categories: Tax Type, Policy Objective, Beneficiaries and Type of TE used. In some cases, second- or third-level categories have been introduced. For instance, Fuel Tax data is categorised at the third level within Tax Type: Taxes on Good and Services Excise Taxes Fuel Tax. If the information for a record is not available or unclear, the respective category is classified as Not stated/unclear. When governments do not publish provision-level data but rather some kind of aggregated information, the GTED gathers this aggregate data. Likewise, if governments report on specific areas of TE only (such as tax incentives for investments, or TEs on income taxes) the GTED presents data on these areas alone. The terms TE reporting or TE report are used broadly, and refer to a large variety of public documents, ranging from annual, comprehensive reports on TEs that are part of governmental budget documentation to individual documents issued by a public body and providing some aggregate information on some specific TE mechanisms. As a minimum requirement, reports must contain some kind of information on the actual use of TE provisions. For instance, a list of available tax deductions for investments, provided by a governmental investment promotion agency, would not be considered a TE report unless they provide revenue forgone estimates or any other data that would allow users of the GTED to obtain information about the actual use of the respective TEs. The GTED distinguishes regular and irregular reporting. A sequence of reports from 1995 to 2005 would not be considered regular reporting in the GTED, since the country had reported on a yearly basis, but not anymore. Likewise, regular is not necessarily related to annual reporting. Germany, for instance, publishes federal subsidy reports including TE data every two years since 1967. A total of 16 such reports have been issued since 1990, containing data on 29 budget years (until 2021). The GTED counts this as 31 years reported, because data is provided on a year-by-year basis and can be consulted and analysed as such. The data is processed in a consistent format seeking to increase the level of longitudinal and cross-country comparability. Whereas revenue forgone estimates are provided as reported by governments (in local currency units, current prices), the GTED also provides figures converted into US dollars as well as indicators providing the revenue forgone through TE provisions as shares both of GDP and Tax Revenue – to compute these two indicators, data from the UNU-WIDER Government Revenue Dataset is used as input. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments. Besides all the effort put into ensuring comparability, cross-country analysis of TE data needs to be done cautiously. The main issue, which is inherent to TE data, regards benchmarking. TEs are defined as departures from – usually country-specific – normal tax structures or benchmarks. On this note, the GTED uses the data published by official governmental institutions, sticking to their own definitions of benchmarks, without trying to complement official figures or challenge what different countries consider as the standard tax system or the benchmark. When it comes to the methodology used by governments to compute the fiscal cost of TE provisions, the vast majority of countries report on TEs based on the revenue forgone approach that estimates the amount by which taxpayers have their tax liabilities reduced as a result of a TE based on their actual current economic behaviour. Since the revenue forgone methodology is static, the potential interconnections between different TE provisions are not taken into account when computing the fiscal cost of TEs based on it. Hence, aggregating revenue forgone estimates of the individual provisions computed separately and without taking behavioural changes into account would not result in a figure that represents the total cost of all TEs. While providing users of the database with the opportunity to draw comparisons across countries or country groups, we want to be clear that any such comparison should be mindful of different levels of reporting, differences in national benchmark systems and methodological shortcomings of revenue forgone estimations. Country Income Groups and Regional Classifications are based on the latest World Bank classifications.

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(2020). Guaranteed and Direct Loan Financing, Net Activity [Dataset]. https://fiscaldata.treasury.gov/datasets/monthly-treasury-statement/

Guaranteed and Direct Loan Financing, Net Activity

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 13, 2020
Description

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of all direct and guaranteed loan financing for federal credit programs under the Credit Reform Act of 1990. Guaranteed loan financing is issuing any debt obligation with a guarantee, insurance, or other pledge that payment of all or a part of the principal or interest will be made to the lender. This table applies to lending to non-federal borrowers by non-federal lenders that carries some form of guarantee by the federal government. Exceptions include the insurance of deposits, shares, or other withdrawable accounts in financial institutions. 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.

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