Fiscal rules are laws aimed at reducing the incentive to accumulate debt, and many countries adopt them to discipline local governments. Yet, their effectiveness is disputed because of commitment and enforcement problems. We study their impact applying a quasi-experimental design in Italy. In 1999, the central government imposed fiscal rules on municipal governments, and in 2001 relaxed them below 5,000 inhabitants. We exploit the before/after and discontinuous policy variation, and show that relaxing fiscal rules increases deficits and lowers taxes. The effect is larger if the mayor can be reelected, the number of parties is higher, and voters are older.

Financial Times Interactive Data LLC offers a vast repository of economic and financial data, providing valuable insights into global markets and trading. With a focus on delivering timely and accurate information, the company has established itself as a go-to source for financial institutions, investors, and researchers seeking to stay ahead of the curve.

our vast database is comprised of historic financial statements, economic indicators, and proprietary data from leading sources, including government agencies, regulatory bodies, and industry associations. By providing access to this trove of information, Financial Times Interactive Data LLC enables its clients to make informed decisions, identify trends, and uncover new opportunities in the rapidly evolving world of finance.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Al Baydaa, Al Jawf, Mareb, Sadah, the Island of Socotra, and several districts in other governorates were excluded due to their small size, remoteness or security issues. The excluded areas represent approximately 23% of the population. In addition, due to the ongoing security situation, during field over one-fourth of the PSUs were replaced with a similar PSU in the same province.

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Yemen, Rep. is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021. Data on the Municipal Fiscal Indicators is included in the following datasets: Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8 Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657 Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing. Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics. The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details. Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year. Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP. In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending. The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

The CBT database builds on an existing database which has been created in 2006 as a multi-country database and developed over the years by various Research Fellows at the Centre, and earlier at the Institute for Fiscal Studies. The original version uses various sources such as OECD Tax Database, IBFD (International Bureau of Fiscal Documentation), World Tax Database from the University of Michigan, KPMG and E&Y and covered mainly OECD countries. The data currently in the database comes from various sources, mainly from: • The Worldwide Corporate Tax Guide published by E&Y; years available: 2002-2017 • data for 2011 - 2017 comes mainly from the online IBFD Tax Research Platform where they provide very detailed Country Surveys • G20 countries data has been updated to be consistent with IBFD "Global corporate tax handbook" (years 2007 - 2010) and "European tax handbook" (years 1990 - 2010) • ZEW Intermediate Report 2011, “Effective Tax levels using Devereux/Griffith methodology” • Deloitte Tax Highlights and International Tax and Business Guide; years available: 2009, 2010 • KPMG Tax Rate Survey; years available: 1998 - 2009 • PKF Worldwide Tax Guide; years available: 2007 - 2009

Key information about Ecuador Consolidated Fiscal Balance: % of GDP

• Ecuador Consolidated Fiscal Balance recorded a deficit equal to 1.3 % of its Nominal GDP in Sep 2024, compared with a deficit equal to 1.8 % in the previous quarter
• Ecuador Consolidated Fiscal Balance to GDP data is updated quarterly, available from Dec 2000 to Sep 2024, with an average ratio of -1.4 %
• The data reached an all-time high of 3.8 % in Jun 2008 and a record low of -9.7 % in Sep 2016

CEIC calculates quarterly Consolidated Fiscal Balance as % of Nominal GDP from quarterly Consolidated Fiscal Balance and quarterly Nominal GDP. The Central Bank of Ecuador provides quarterly Consolidated Fiscal Balance in USD and quarterly Nominal GDP in USD. Consolidated Fiscal Balance prior to Q4 2012 is based on old methodology.


Further information about Ecuador Consolidated Fiscal Balance: % of GDP

• In the latest reports, Ecuador National Government Debt reached 82.8 USD bn in Sep 2024
• Ecuador Nominal GDP reached 28.9 USD bn in Mar 2023

Cash Network is a financial services company that specializes in providing secure online platforms for users to manage their finances. Founded with a mission to empower users to take control of their financial futures, Cash Network has established itself as a reputable name in the industry, boasting a comprehensive platform that caters to a wide range of financial needs.

Throughout its platform, users can expect to find a vast array of financial data, from market trends to personal financial records. With its focus on security and user experience, Cash Network's online presence provides a reliable and efficient way for users to access and manage their financial information, making it an essential resource for anyone looking to stay on top of their financial game.

Francis Financial is a reputable financial services company that provides a range of products and services to its clients. The company's data holdings are vast and varied, encompassing financial market data, economic trends, and industry insights. With a strong focus on serving its clients' needs, Francis Financial's data repository is a treasure trove of valuable information for anyone looking to gain a deeper understanding of the financial world.

From company reports and financial statements to market analysis and industry news, Francis Financial's data collection is a comprehensive archive of important financial information. By leveraging this data, users can gain valuable insights into market trends, spot emerging patterns, and make informed decisions. With its extensive data holdings and commitment to providing high-quality information, Francis Financial is an important player in the financial data landscape.

The Comprehensive Annual Financial Reports are presented in three main sections; the Introductory Section, the Financial Section, and the Statistical Section. The Introductory Section includes a financial overview, discussion of Iowa's economy and an organizational chart for State government. The Financial Section includes the state auditor's report, management's discussion and analysis, audited basic financial statements and notes thereto, and the underlying combining and individual fund financial statements and supporting schedules. The Statistical Section sets forth selected unaudited economic, financial trend and demographic information for the state on a multi-year basis. Reports for multiple fiscal years are available.

27 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Financial Transactions Report.

43 sheets of data, each sheet representing a table from the database that stores the information. The order of these sheets is based on the sequence of reporting forms in the Financial Transactions Report. Humboldt County data is not included because the county failed to file their Financial Transactions Report.

Thomson Financial Software, a leading provider of financial research and analysis, has been a trusted source for business and market intelligence since its establishment in 1993. As a respected name in the industry, Thomson Financial Software offers a vast repository of financial data, covering a range of topics including economic trends, company profiles, and market research.

With years of expertise in the financial sector, Thomson Financial Software has built a reputation for delivering accurate and reliable data, making it a go-to destination for professionals seeking to stay informed about the financial markets. By leveraging its extensive network of financial institutions and industry experts, Thomson Financial Software provides in-depth insights into the global financial landscape, making it an invaluable resource for anyone seeking to stay ahead of the curve in the rapidly changing financial world.

Belgium Federal Government Revenue: Current: Non Fiscal data was reported at 558.664 EUR mn in Apr 2024. This records an increase from the previous number of 289.079 EUR mn for Mar 2024. Belgium Federal Government Revenue: Current: Non Fiscal data is updated monthly, averaging 3.449 EUR mn from Jan 2005 (Median) to Apr 2024, with 232 observations. The data reached an all-time high of 11,448.851 EUR mn in Dec 2013 and a record low of -10.081 EUR mn in Jun 2020. Belgium Federal Government Revenue: Current: Non Fiscal data remains active status in CEIC and is reported by Federal Public Service Finance. The data is categorized under Global Database’s Belgium – Table BE.F012: Federal Government Revenue.

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 the Change in Excess of Liabilities. 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.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Northwest Territories, Yukon, and Nunavut (representing approximately 0.3 percent of the Canadian population) were excluded.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Canada is 1007.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

The regions of Gao, Kidal, Mopti, and Tombouctou were excluded for security reasons. Quartiers and villages with less than 50 inhabitants were also excluded from the sample. The excluded areas represent 23 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Mali is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Key information about US Tax revenue: % of GDP

• United States Tax revenue: % of GDP was reported at 16.6 % in Dec 2024.
• This records an increase from the previous number of 16.2 % for Dec 2023.
• US Tax revenue: % of GDP data is updated yearly, averaging 17.0 % from Dec 1968 to 2024, with 57 observations.
• The data reached an all-time high of 19.5 % in 2000 and a record low of 13.7 % in 2009.
• US Tax revenue: % of GDP data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Tax Revenue: % of Nominal GDP: Annual.

CEIC calculates annual Tax Revenue as % of Nominal GDP from monthly Tax Revenue and annual Nominal GDP. Tax Revenue is calculated as the sum of Individual Income Taxes, Corporation Income Taxes, Social Insurance Taxes, Excise Tax, Estate and Gift Taxes and Customs Duties. The Bureau of the Fiscal Service provides Tax Revenue in USD. The Bureau of Economic Analysis provides Nominal GDP in USD.

Ciclo Italian Investment Co., a trusted financial services provider, offers unique market insights and research to its clients. With a focus on Italy, the company provides in-depth analysis of the country's economic trends, making it an valuable resource for investors and business professionals.

Through their platform, Ciclo Italian Investment Co. provides access to a wide range of financial data, including market reports, economic indicators, and company profiles. By understanding the Italian market, businesses can make informed decisions and capitalize on new opportunities.

This dataset reports summary state-by-state total expenditures by program for the Medicaid Program, Medicaid Administration and CHIP programs. These state expenditures are tracked through the automated Medicaid Budget and Expenditure System/State Children's Health Insurance Program Budget and Expenditure System (MBES/CBES). For more information, visit https://medicaid.gov/medicaid/finance/state-expenditure-reporting/expenditure-reports/index.html.