Graph and download economic data for Nominal Gross Domestic Product for United States (NGDPNSAXDCUSQ) from Q1 1950 to Q2 2025 about GDP and USA.

Switzerland IMF Forecast: General Government: Revenue: % of GDP data was reported at 33.329 % in 2023. This stayed constant from the previous number of 33.329 % for 2022. Switzerland IMF Forecast: General Government: Revenue: % of GDP data is updated yearly, averaging 32.416 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 33.497 % in 2015 and a record low of 28.201 % in 1991. Switzerland IMF Forecast: General Government: Revenue: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Switzerland – Table CH.IMF.FM: Government Finance Statistics.

Based on IMF forecasts from October 2023, the real GDP growth in industrial countries will slow in 2023, only growing by *** percent. This is because of the impact of the high global inflation rates. On the other hand, the GDP of emerging and developing countries is expected to grow by around **** percent both in 2022, 2023, and 2024.

Thailand TH: IMF Forecast: General Government: Expenditure: % of GDP data was reported at 22.810 % in 2023. This records an increase from the previous number of 22.735 % for 2022. Thailand TH: IMF Forecast: General Government: Expenditure: % of GDP data is updated yearly, averaging 21.713 % from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 26.472 % in 1999 and a record low of 17.176 % in 1995. Thailand TH: IMF Forecast: General Government: Expenditure: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Thailand – Table TH.IMF.FM: Government Finance Statistics.

International Monetary Fund, World Economic Outlook GDP per Capita projections 2000-2016

Graph and download economic data for Gross Domestic Product Deflator for Poland (NGDPDSAIXPLQ) from Q1 1995 to Q1 2025 about Poland and GDP.

Vietnam VN: IMF Forecast: General Government: Gross Debt: % of GDP data was reported at 58.120 % in 2023. This records an increase from the previous number of 57.648 % for 2022. Vietnam VN: IMF Forecast: General Government: Gross Debt: % of GDP data is updated yearly, averaging 48.237 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 59.942 % in 2016 and a record low of 31.432 % in 2000. Vietnam VN: IMF Forecast: General Government: Gross Debt: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Vietnam – Table VN.IMF.FM: Government Finance Statistics.

United States US: Imports: % of GDP data was reported at 14.600 % in 2016. This records a decrease from the previous number of 15.300 % for 2015. United States US: Imports: % of GDP data is updated yearly, averaging 15.750 % from Dec 2001 (Median) to 2016, with 16 observations. The data reached an all-time high of 17.300 % in 2008 and a record low of 13.400 % in 2002. United States US: Imports: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF: Contribution to GDP.

Graph and download economic data for Real Gross Domestic Product for United States (NGDPRSAXDCUSQ) from Q1 1950 to Q2 2025 about real, GDP, and USA.

Switzerland IMF Forecast: General Government: Gross Debt: % of GDP data was reported at 33.694 % in 2023. This records a decrease from the previous number of 34.880 % for 2022. Switzerland IMF Forecast: General Government: Gross Debt: % of GDP data is updated yearly, averaging 43.870 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 59.161 % in 2004 and a record low of 33.694 % in 2023. Switzerland IMF Forecast: General Government: Gross Debt: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Switzerland – Table CH.IMF.FM: Government Finance Statistics.

Imports of environmental goods comprise all environmental goods entering the national territory. A relatively high share of environmental goods imports indicates that an economy purchases a significant share of environmental goods from other economies. Exports of environmental goods comprise all environmental goods leaving the national territory. A relatively high share of environmental goods exports indicates that an economy produces and sells a significant share of environmental goods to other economies. An economy’s environmental goods trade balance is the difference between its exports and imports of environmental goods.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. Accessed on 2023-06-28. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. Accessed on 2023-06-28; IMF staff calculations.Category: Cross-Border IndicatorsData series: Comparative advantage in environmental goodsEnvironmental goods exportsEnvironmental goods exports as percent of GDPEnvironmental goods exports as share of total exportsEnvironmental goods importsEnvironmental goods imports as percent of GDPEnvironmental goods imports as share of total importsEnvironmental goods trade balanceEnvironmental goods trade balance as percent of GDPTotal trade in environmental goodsTotal trade in environmental goods as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Environmental goods imports and exports are estimated by aggregating HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports are estimated by aggregating all commodities. Environmental goods trade balance is calculated as environmental goods exports less environmental goods imports. A positive trade balance means an economy has a surplus in environmental goods, while a negative trade balance means an economy has a deficit in environmental goods.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are environmental goods to the proportion of global exports that are environmental goods. Total trade in environmental goods is calculated as the sum of environmental goods exports and environmental goods imports. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.Methodology Attachment Environmental Goods Harmonized System Codes

Potential national income loss from climate risks can be computed using simple damage functions that estimate damages based on the temperature outcomes inferred from the emissions trajectories projected by the transition scenarios. Potential national income benefit from avoided climate damages can be computed by contrasting the damages estimates based on the temperature outcomes from the transition scenarios with the policy, or mitigation, costs from climate action needed to meet a particular temperature outcome.Sources: Network for Greening the Financial System (2023), Scenarios Portal; and International Institute for Applied Systems Analysis (2023), NGFS Phase 4 Scenario Explorer; IMF Staff Calculations.Category: Transition to a Low-Carbon EconomyMetadataThe framework of the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) allows to simulate, in a forward-looking fashion, the dynamics within and between the energy, land-use, economy, and climate systems. Consistent with that framework, the NGFS explores a set of seven climate scenarios, which are characterized by their overall level of physical and transition risk. The scenarios in the current Phase IV (NGFS climate scenarios data set) are Low Demand, Net Zero 2050, Below 2°C, Delayed Transition, Nationally Determined Contributions (NDCs), Current Policies, and Fragmented World. Each NGFS scenario explores a different set of assumptions for how climate policy, emissions, and temperatures evolve. To reflect the uncertainty inherent to modeling climate-related macroeconomic and financial risks, the NGFS scenarios use different models, over and above the range of scenarios. These integrated assessment models (IAMs) are, by their acronyms: GCAM, MESSAGEix-GLOBIOM, and REMIND-MAgPIE. GDP losses and benefits are derived based on the National Institute Global Econometric Model (NiGEM). NiGEM consists of individual country models for the major economies, which are linked together through trade in goods and services and integrated capital markets. Country level data (or country aggregates, whenever country level disaggregation is not present) for GDP, population, primary energy consumption by fuel type, useful energy and carbon taxes from the IAM output is used as an input into the NiGEM scenarios. Climate scenarios within NiGEM can be broadly categorized into physical and transition events. While the effects of physical and transition shocks alongside policy decisions are contemporaneous, the scenarios in NiGEM can be run in a “stacked” manner, where each scenario uses the information provided by the previous scenario as its starting point. This allows for decomposition of shocks and their effects. Results are available for three scenarios: Net Zero 2050, Delayed Transition, and Current Policies. For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access.MethodologyThe NGFS climate scenarios database contains information on mitigation policy costs, business confidence losses, chronic climate damages, and acute climate damages. Mitigation policy costs reflect transition risk in a narrow sense and is measured against the Current Policies scenario (for which it is zero). Business confidence losses result from unanticipated policy changes, and only in the Delayed Transition scenario. GDP losses from chronic risks arise from an increase in global mean temperature. Estimates of the macroeconomic impact of acute risks are based on physical risk modelling covering different hazards. Acute risks are modeled independent of the input IAM. Results are available at the original sources for four hazards: droughts impacting on crop yields, tropical cyclones directly damaging assets, heatwaves affecting productivity and demand, and riverine floods directly damaging assets too. Apart from floods acute risks are the result of randomized stochastic output, yielding 60th to 99th percentile GDP impacts. In accordance with the presentation of the scenario results by the NGFS, the 90th percentile has been chosen as the representative confidence bound. That way, the results are focusing on tail risk. While the choice of the percentile will lead to marked differences for the GDP losses indicator, its influence on the GDP benefits indicator is muted due to comparing like-with-like. Further, the sum of the impacts from the four hazards is taken as the acute physical risk measure; see what follows for the methodology in deriving the net benefits. Net benefits can be calculated by comparing the impact of stronger climate action to the reference scenario, the Current Policies scenario: Net Benefit = 100 * (GDP[Policy scenario] / GDP[Current Policies] – 1). GDP in either scenario can be inferred from the hypothetical baseline with no transition nor physical risk and the percentage losses due to mitigation policy (MP), business confidence (BC), chronic climate (CC), and acute climate (AC): GDP = Baseline * (1 + (MP + BC + CC + AC) / 100). Plugging this into the above equation one finds after some algebra: Net Benefit = (MP[Policy scenario] – MP[Current Policies] + BC[Policy scenario] – BC[Current Policies] + CC[Policy scenario] – CC[Current Policies] + AC[Policy scenario] – AC[Current Policies]) / (1 + (MP + BC + CC + AC)[Current Policies] / 100). Obviously, MP[Current Policies] = BC[Current Policies] = BC[Net Zero 2050] = 0. In order to achieve consistency in aggregation of the four components to the total benefit, the denominator is kept fixed, while for the individual contributions only one component at a time, MP, BC, CC, or AC, is used in the numerator. Results are presented for the 49 countries, five geographic regions covering the remainder of countries, and a global and European total. The coverage of the five remainder regions refers to the country classification of emerging market and developing economies in the IMF’s World Economic Outlook.Data series: Potential National Income Loss From Climate RisksPotential National Income Benefit From Avoided Climate Damages

Ukraine UA: IMF Forecast: General Government: Overall Balance: % of GDP data was reported at -2.008 % in 2023. This records an increase from the previous number of -2.111 % for 2022. Ukraine UA: IMF Forecast: General Government: Overall Balance: % of GDP data is updated yearly, averaging -2.621 % from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 4.954 % in 1999 and a record low of -6.041 % in 2009. Ukraine UA: IMF Forecast: General Government: Overall Balance: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Ukraine – Table UA.IMF.FM: Government Finance Statistics.

United Kingdom UK: IMF Forecast: General Government: Primary Balance: % of GDP data was reported at 0.630 % in 2023. This records an increase from the previous number of 0.560 % for 2022. United Kingdom UK: IMF Forecast: General Government: Primary Balance: % of GDP data is updated yearly, averaging -1.382 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 3.043 % in 2000 and a record low of -8.701 % in 2009. United Kingdom UK: IMF Forecast: General Government: Primary Balance: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United Kingdom – Table UK.IMF.FM: Government Finance Statistics.

Graph and download economic data for Gross Domestic Product for United States (USANGDPRPCH) from 1980 to 2030 about GDP, rate, and USA.

Jordan JO: GDP: Gross Fixed Capital Formation data was reported at 5,175.792 JOD mn in 2016. This records an increase from the previous number of 5,125.704 JOD mn for 2015. Jordan JO: GDP: Gross Fixed Capital Formation data is updated yearly, averaging 590.700 JOD mn from Dec 1959 (Median) to 2016, with 58 observations. The data reached an all-time high of 5,402.197 JOD mn in 2014 and a record low of 17.000 JOD mn in 1961. Jordan JO: GDP: Gross Fixed Capital Formation data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Jordan – Table JO.IMF.IFS: Gross Domestic Product: by Expenditure: Annual.

Tanzania TZ: IMF Forecast: General Government: Gross Debt: % of GDP data was reported at 35.234 % in 2023. This records a decrease from the previous number of 36.704 % for 2022. Tanzania TZ: IMF Forecast: General Government: Gross Debt: % of GDP data is updated yearly, averaging 36.972 % from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 50.239 % in 2001 and a record low of 21.515 % in 2008. Tanzania TZ: IMF Forecast: General Government: Gross Debt: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Tanzania – Table TZ.IMF.FM: Government Finance Statistics.

Graph and download economic data for Real Gross Domestic Product for Argentina (NGDPRNSAXDCARQ) from Q1 2004 to Q1 2025 about Argentina, real, and GDP.

Graph and download economic data for Gross Domestic Product Deflator for United States (NGDPDIXUSA) from 1950 to 2024 about GDP and USA.

Uruguay UY: IMF Forecast: General Government: Primary Balance: % of GDP data was reported at 0.484 % in 2023. This records a decrease from the previous number of 0.574 % for 2022. Uruguay UY: IMF Forecast: General Government: Primary Balance: % of GDP data is updated yearly, averaging 0.387 % from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 3.958 % in 2005 and a record low of -1.466 % in 1999. Uruguay UY: IMF Forecast: General Government: Primary Balance: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Uruguay – Table UY.IMF.FM: Government Finance Statistics.