Tax revenue (% of GDP) in World was reported at 14.34 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Tax revenue (% of GDP) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Pakistan, the registration can be from any of the following: SECP Securities and Exchange Commission of Pakistan, Federal Board of Revenue, Board of Revenue Punjab, Sindh Revenue Board, Board of Revenue Balochistan, Board of Revenue Khyber-Pakhtunkhwa, and Drugs Regulatory Authority of Pakistan.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Pakistan 2022 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire and the follow-up questionnaire cover several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Pakistan 2022 WBES included additional questions covering innovation. These questions were selected in collaboration with the members of the WB local country team.

Response rate

Overall survey response rate was 68.4%.

Ukraine BOOST offers one of the most granular micro fiscal database available to date with expenditure and revenue data available from 2003 onward by administrative, economic, functional, programmatic and geographic classification. Starting in 2018, the ministry of Finance has also began presenting such data in user friendly formats in its fiscal transparency portal.

Abstract

In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage, including Ndjamena (Capital city), other urban and rural

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12. Gender based Violence (for the fourth wave) 13. Vaccine (for the fourth wave)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of Gambia, registration was with the Gambia Revenue Authority.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Gambia 2023 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Gambia 2023 WBES included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 67.8%.

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

National coverage

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 Zimbabwe 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.

Taxes on international trade (% of revenue) in World was reported at 3.8736 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Taxes on international trade (% of revenue) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Laos LA: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 3.500 NA in 2017. This stayed constant from the previous number of 3.500 NA for 2016. Laos LA: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 3.500 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 3.500 NA in 2017 and a record low of 2.500 NA in 2007. Laos LA: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Mali ML: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 3.500 NA in 2017. This stayed constant from the previous number of 3.500 NA for 2016. Mali ML: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 3.500 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 4.000 NA in 2007 and a record low of 3.500 NA in 2017. Mali ML: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Abstract

The overall objective envisaged by the survey is to collect information to assess the food security of households, their level of vulnerability and to define the criteria for targeting beneficiaries for intervention in favor of food insecure people. Specifically, the survey will collect new field data for: - Analyze the current food situation prevailing in the regions and the different food economy zones; - Estimate the number of food insecure people by indicating their geographic location and their socio-economic profile; - Analyze the market situation through the review of secondary data (food availability, prices, supply, functioning of markets and terms of trade); - Determine the causes of food insecurity and analyze the current constraints facing households according to their socioeconomic status in relation to their means of access to food and income; - Determine the severity of food insecurity and analyze the cyclical / chronic risks and shocks to which households are most exposed and their adaptive capacity or survival strategies; - Develop scenarios over the next six months to forecast the evolution of the food and nutrition security situation; and - Define the types of food and non-food assistance to save lives and to strengthen the livelihoods and resilience capacity to shocks of vulnerable households and communities.

Geographic coverage

Regional coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The overall objective envisaged by the survey is to collect information to assess the food security of households, their level of vulnerability and to identify the beneficiaries targeting criteria for interventions. The full ENSA sample is composed of 8.921 households living in 61 departments. The present work is based on a sample of 6946 households located in rural areas only. Specifically, the sample is composed of those households interviewed just after the rainy season in October 2014. The sample is representative at the regional level. For the ENSA three different types of data collection have been applied: - A focus group with opinion leaders, traditional leaders, local officials, resource persons, NGOs to discuss the main priority to add into the questionnaire; - Household-level interviews with heads of households or their representatives getting all the possible information about household life; and food security. - Community levels interviews to assess price developments and market supply systems.

Mode of data collection

Face-to-face [f2f]

Venezuela Central Government: Cash Revenue: Current: External: World Bank data was reported at 0.000 VEF th in Sep 2010. This stayed constant from the previous number of 0.000 VEF th for Jun 2010. Venezuela Central Government: Cash Revenue: Current: External: World Bank data is updated quarterly, averaging 0.000 VEF th from Mar 1998 (Median) to Sep 2010, with 51 observations. The data reached an all-time high of 15,565.393 VEF th in Dec 1998 and a record low of 0.000 VEF th in Sep 2010. Venezuela Central Government: Cash Revenue: Current: External: World Bank data remains active status in CEIC and is reported by Ministry of Economy, Finance and Public Banking. The data is categorized under Global Database’s Venezuela – Table VE.F003: Central Government: Cash Revenue: Quarterly.

Mauritania MR: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 4.000 NA in 2017. This stayed constant from the previous number of 4.000 NA for 2016. Mauritania MR: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 4.000 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 4.000 NA in 2017 and a record low of 3.500 NA in 2011. Mauritania MR: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mauritania – Table MR.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Yemen YE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 2.500 NA in 2017. This records a decrease from the previous number of 3.000 NA for 2016. Yemen YE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 3.000 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 3.000 NA in 2016 and a record low of 2.500 NA in 2017. Yemen YE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Taxes on income, profits and capital gains (% of revenue) in Ethiopia was reported at 22.83 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ethiopia - Taxes on income, profits and capital gains (% of revenue) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Marshall Islands MH: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 2.500 NA in 2017. This stayed constant from the previous number of 2.500 NA for 2016. Marshall Islands MH: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 2.500 NA from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 2.500 NA in 2017 and a record low of 2.500 NA in 2017. Marshall Islands MH: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Marshall Islands – Table MH.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Kenya KE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 4.000 NA in 2017. This stayed constant from the previous number of 4.000 NA for 2016. Kenya KE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 4.000 NA from Jun 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 4.000 NA in 2017 and a record low of 4.000 NA in 2017. Kenya KE: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Jamaica JM: Revenue and Grants: Revenue: Tax Revenue: % of GDP data was reported at 26.064 % in 2016. This records an increase from the previous number of 24.834 % for 2015. Jamaica JM: Revenue and Grants: Revenue: Tax Revenue: % of GDP data is updated yearly, averaging 22.495 % from Dec 1988 (Median) to 2016, with 29 observations. The data reached an all-time high of 26.064 % in 2016 and a record low of 8.804 % in 1993. Jamaica JM: Revenue and Grants: Revenue: Tax Revenue: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jamaica – Table JM.World Bank: Government Revenue, Expenditure and Finance. Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue.; ; International Monetary Fund, Government Finance Statistics Yearbook and data files, and World Bank and OECD GDP estimates.; Weighted average;

Taxes on goods and services (% of revenue) in World was reported at 32.14 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Taxes on goods and services (% of revenue) - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Tuvalu TV: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 3.000 NA in 2017. This stayed constant from the previous number of 3.000 NA for 2016. Tuvalu TV: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 3.000 NA from Dec 2012 (Median) to 2017, with 6 observations. The data reached an all-time high of 3.000 NA in 2017 and a record low of 3.000 NA in 2017. Tuvalu TV: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tuvalu – Table TV.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Nigeria NG: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data was reported at 3.000 NA in 2017. This stayed constant from the previous number of 3.000 NA for 2016. Nigeria NG: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data is updated yearly, averaging 3.000 NA from Dec 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 3.000 NA in 2017 and a record low of 3.000 NA in 2017. Nigeria NG: CPIA: Efficiency of Revenue Mobilization Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Policy and Institutions. Efficiency of revenue mobilization assesses the overall pattern of revenue mobilization--not only the de facto tax structure, but also revenue from all sources as actually collected.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;