By the year 2030, it is projected that China will eclipse the United States and have the largest gross domestic product (GDP) in the world, at 31.7 trillion U.S. dollars. The United States is projected to have the second largest GDP, at 22.9 trillion U.S. dollars.

What is gross domestic product?

Gross domestic product, or GDP, is an economic measure of a country’s production in time. It includes all goods and services produced by a country and is used by economists to determine the health of a country’s economy. However, since GDP just shows the size of an economy and is not adjusted for the country’s size, this can make direct country comparisons complicated.

The growth of the global economy

Currently, the United States has the largest GDP in the world, at 20.5 trillion U.S. dollars. China has the second largest GDP, at 13.4 trillion U.S. dollars. In the coming years, production will become faster and more global, which will help to grow the global economy.

The average for 2025 based on 184 countries was 3.13 percent. The highest value was in Libya: 17.3 percent and the lowest value was in Equatorial Guinea: -4.2 percent. The indicator is available from 1980 to 2030. Below is a chart for all countries where data are available.

The statistic shows global gross domestic product (GDP) from 1985 to 2024, with projections up until 2030. In 2020, global GDP amounted to about 85.76 trillion U.S. dollars, two and a half trillion lower than in 2019. Gross domestic product Gross domestic product, also known as GDP, is the accumulated value of all finished goods and services produced in a country, often measured annually. GDP is significant in determining the economic health, growth and productivity in the country, and is a stat often used when comparing several countries at a time, most likely in order to determine which country has seen the most progress. Until 2020, Global GDP had experienced a growth every year since 2010. However, a strong growth rate does not necessarily lead to all positive outcomes and often has a negative effect on inflation rates. A severe growth in GDP leads to lower unemployment, however lower unemployment often leads to higher inflation rates due to demand increasing at a much higher rate than supply and as a result prices rise accordingly. In terms of unemployment, growth had been fairly stagnant since the economic downturn of 2007-2009, but it remains to be seen what the total impact of the coronavirus pandemic will be on total employment.

This statistic shows the projected top ten largest national economies in 2050. By 2050, China is forecasted to have a gross domestic product of over ** trillion U.S. dollars.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

The Kenya CEM has five main messages. First, Kenya has performed well in the past decade in terms of economic growth, and modern services are behind the acceleration of growth. Expansion in these services, such as financial intermediation and mobile communications have stimulated demand for other services such as trade. The CEM discusses how to maximize the potential of services, especially given that most formal, high quality jobs are created in this sector. Second, agriculture, which still contributes to over a quarter of the economy, and manufacturing have stagnated. The CEM discusses the reasons behind this stagnation, noting that agriculture and manufacturing have not been able to create enough jobs for Kenya’s growing working age population. Most of the jobs are created by the informal economy and are concentrated in low productivity segments of trade, hospitality, and jua kali. Improving the ease of doing business is one way towards job creation and higher productivity. However there is still a need for creating job opportunities for the rural poor, for poverty reduction and achieving shared prosperity. Reviving agriculture, in particular, remains the pathway for poverty reduction. Third, accelerating growth to meet Kenya’s development goals requires technological advances and innovation that raise firms’ productivity. Fourth, achieving rapid growth will require macroeconomic stability to boost investment and savings. And as the government strives to build Kenya’s energy and transport infrastructure, this needs to be complemented with improvements in the public investment management process and better execution. Fifth, the discovery of oil opens a possibility for raising Kenya’s growth. Kenya’s recent oil discoveries, if used prudently, can contribute to achieving the Vision 2030 goals. The World Bank Group is proud of its long-standing relationship with Kenya, and looks forward to continuous collaboration with both National and County Governments and other partners. Working together, Kenya can realize its potential to lift millions of families out of poverty and achieve shared prosperity.

United States US: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 88.394 NA in 2023. This records an increase from the previous number of 80.619 NA for 2022. United States US: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 49.669 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 88.394 NA in 2023 and a record low of 45.419 NA in 2005. United States US: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Since the beginning of the 21st century, the BRICS countries have been considered the five foremost developing economies in the world. Originally, the term BRIC was used by economists when talking about the emerging economies of Brazil, Russia, India, and China, however these countries have held annual summits since 2009, and the group has expanded to include South Africa since 2010. China has the largest GDP of the BRICS country, at 16.86 trillion U.S. dollars in 2021, while the others are all below three trillion. Combined, the BRICS bloc has a GDP over 25.85 trillion U.S. dollars in 2022, which is slightly more than the United States. BRICS economic development China has consistently been the largest economy of this bloc, and its rapid growth has seen it become the second largest economy in the world, behind the U.S.. China's growth has also been much faster than the other BRICS countries; for example, when compared with the second largest BRICS economy, its GDP was less than double the size of Brazil's in 2000, but is almost six times larger than India's in 2021. Since 2000, the country with the second largest GDP has fluctuated between Brazil, Russia, and India, due to a variety of factors, although India has held this position since 2015 (when the other two experienced recession), and it's growth rate is on track to surpass China's in the coming decade. South Africa has consistently had the smallest economy of the BRICS bloc, and it has just the third largest economy in Africa; its inclusion in this group is due to the fact that it is the most advanced and stable major economy in Africa, and it holds strategic importance due to the financial potential of the continent in the coming decades. Future developments It is predicted that China's GDP will overtake that of the U.S. by the end of the 2020s, to become the largest economy in the world, while some also estimate that India will also overtake the U.S. around the middle of the century. Additionally, the BRICS group is more than just an economic or trading bloc, and its New Development Bank was established in 2014 to invest in sustainable infrastructure and renewable energy across the globe. While relations between its members were often strained or of less significance in the 20th century, their current initiatives have given them a much greater international influence. The traditional great powers represented in the Group of Seven (G7) have seen their international power wane in recent decades, while BRICS countries have seen theirs grow, especially on a regional level. Today, the original BRIC countries combine with the Group of Seven (G7), to make up 11 of the world's 12 largest economies, but it is predicted that they will move further up on this list in the coming decades.

Although the hydrocarbon-fueled growth model has delivered substantial gains, Azerbaijan today acknowledges both its constraints and the opportunities arising from the clean energy transition. Azerbaijan’s economy remains heavily dependent on fossil fuels. While the 2000-2010 surge in oil and gas production tripled per capita gross domestic product (GDP) and led to gains in poverty and human development (Figure ES.1), the 2014 collapse in oil prices triggered an economic contraction with GDP per capita falling back roughly 30 percent by 2021 compared to the 2014 peak. Oil and gas today still account for over 90 percent of exports and one-third of GDP. Although the surge in energy prices driven by Russia’s invasion of Ukraine is yielding short- to medium-term windfalls and an uptick in growth, exposure to global energy price downswings remains a fundamental feature of Azerbaijan’s economy. The non-oil private sector is held back by several constraints, including access to skilled labor and finance, and bottlenecks to market competition, with state-owned enterprises (SOEs) retaining a large presence in the economy. Recent policy statements, such as the Azerbaijan 2030: National Priorities for Socio-Economic Development’ (Azerbaijan 2030), and the ‘Republic of Azerbaijan Socio-Economic Strategy for 2022-2026’ (SEDS), acknowledge the limits of the hydrocarbon-fueled growth model. The 2022-2026 SEDS recognizes the country’s limited progress in finding other sources of comparative advantage and lays out an ambitious program of economic diversification focused on digital technologies, human capital, and new areas of industrial exports. It also commits Azerbaijan to substantial investments toward the clean energy transition, including renewable energy, electric mobility, and energy efficiency, as well as enabling reforms such as cost-reflective energy pricing.

Trinidad and Tobago TT: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 68.194 NA in 2023. This records an increase from the previous number of 64.019 NA for 2022. Trinidad and Tobago TT: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 57.462 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 68.194 NA in 2023 and a record low of 40.225 NA in 2019. Trinidad and Tobago TT: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Despite various economic setbacks, Zimbabwe regained lower middle-income country (LMIC) status in 2018 and aspires to become an upper middle-income country (UMIC) by 2030. The focus of this Country Economic Memorandum (CEM) is to identify options for structural reforms to help Zimbabwe accelerate economic growth and to achieve UMIC status. This is the first CEM for Zimbabwe since 1985 and it comes at a critical juncture along Zimbabwe’s development path. The objective of the report is to support and inform policy makers and stakeholders on policies to accelerate economic growth, boost productivity, and create high-quality jobs. In this regard, the CEM first establishes macroeconomic stability as a necessary condition for high and sustained growth. It then uses productivity as an overall framing to identify key structural bottlenecks, before providing deep-dives on informality and trade as priority areas to address in order to unleash productivity growth. Importantly, the report also aims to present data about Zimbabwe’s economic performance in a systematic fashion, focusing on the previous two decades and comparing Zimbabwe with its peers in the region, as well as aspirational peers globally.

Palau PW: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 61.844 NA in 2023. This records an increase from the previous number of 54.975 NA for 2022. Palau PW: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 37.044 NA from Sep 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 61.844 NA in 2023 and a record low of 31.975 NA in 2005. Palau PW: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Palau – Table PW.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Jordan JO: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 71.294 NA in 2019. This records an increase from the previous number of 69.825 NA for 2018. Jordan JO: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 65.350 NA from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 71.294 NA in 2019 and a record low of 60.569 NA in 2017. Jordan JO: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Vietnam’s real gross domestic product (GDP) has been experiencing positive growth for the past five years since 2019, and is projected to continue to do so through 2030. In 2023, Vietnam’s real GDP increased by around five percent compared to the previous year. Learning from real GDP Real gross domestic product (GDP) is a measure that reflects the value of all goods and services an economy produces within a given year. It is expressed in base-year prices, and is thus an inflation-adjusted way to compare a country’s economic output through the years. The GDP growth rate is a significant indicator of a country’s economic health, as it reacts to the economy’s expansions and contractions. Vietnam’s optimistic future As indicated by the positive growth rate of its real GDP, Vietnam’s economy is expanding due to growth in exports, domestic demand, and the manufacturing sector. As the economy expands, so does the total expenditure of Vietnamese consumers. The average monthly income per capita in Vietnam increased to almost 3.8 percent in 2018, and is spent on fast moving consumer goods from popular brands like Vinamilk and P/S.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.

Virgin Islands (British) VG: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 26.962 NA in 2023. This records an increase from the previous number of 26.194 NA for 2022. Virgin Islands (British) VG: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 18.169 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 27.194 NA in 2021 and a record low of 14.119 NA in 2015. Virgin Islands (British) VG: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Virgin Islands (British) – Table VG.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Peru has many opportunities to develop and implement comprehensive climate policies that also increase productivity and reduce poverty. It can achieve low-carbon and resilient development if it can implement the right reforms and fund critical investments for water security and decarbonization. While public investments must cover critical investments, the right regulations, information systems, social services and fiscal incentives can also ensure households and the private sector plays a large role. The resulting productivity and efficiency gains could increase GDP by 2 percent by 2030 and potentially much more by 2050, and create many jobs. While the overall impacts of climate reforms and investments are positive on growth and job creation, policies need to be carefully designed to be politically acceptable. Fiscal reforms to reduce emissions can be designed to ensure increased support to the poorest and reduction of inequality. Technical support to the agriculture sector can target smallholder farmers and ensure informal subsistence farmers can be either integrated in more formal value chains or to the forestry sector. Social protection can be scaled up to support the poorest households after climate shocks and to support job transitions away from the most vulnerable or emitting sectors. Finally, the mining sector can be transformed to ensure that local populations benefit from revenues and participate in decision making.

China SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 78.744 NA in 2023. This records an increase from the previous number of 73.862 NA for 2022. China SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 53.056 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 78.744 NA in 2023 and a record low of 47.306 NA in 2005. China SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

IL: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 70.894 NA in 2023. This records an increase from the previous number of 66.350 NA for 2022. IL: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 41.513 NA from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 70.894 NA in 2023 and a record low of 39.550 NA in 2010. IL: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Pakistan PK: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 86.825 NA in 2022. This stayed constant from the previous number of 86.825 NA for 2021. Pakistan PK: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 67.106 NA from Jun 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 86.825 NA in 2022 and a record low of 63.263 NA in 2015. Pakistan PK: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;