The International Monetary Fund's (IMF) data portal publishes global financial data, as well as economic and trade indicators.

The International Financial Statistics database covers about 200 countries and areas, with some aggregates calculated for selected regions, plus some world totals. Topics covered include balance of payments, commodity prices, exchange rates, fund position, government finance, industrial production, interest rates, international investment position, international liquidity, international transactions, labor statistics, money and banking, national accounts, population, prices, and real effective exchange rates.

The International Financial Statistics is based on various IMF data collections. It includes exchange rates series for all Fund member countries plus Anguilla, Aruba, China, P.R.: Hong Kong, China, P.R.: Macao, Montserrat, and the Netherlands Antilles. It also includes major Fund accounts series, real effective exchange rates, and other world, area, and country series. Data are available for most IMF member countries with some aggregates calculated for select regions, plus some world totals.

International Monetary Fund global crop prices provided to Federal Reserve Economic Data.

The average consumer price index (CPI) is a measure of a country's average level of prices based on the cost of a typical basket of consumer goods and services in a given period. The rate of inflation is the percent change in the average CPI. Source: World Economic Outlook (April 2021)

The Portfolio Investment Positions by Counterpart Economy dataset (formerly Coordinated Portfolio Investment Survey, or CPIS) is a voluntary data collection exercise conducted under the auspices of the IMF. To participate, an economy provides data on its holdings of portfolio investment securities (data are separately requested for equity and investment fund shares, long-term debt instruments, and short-term debt instruments). The survey covers end-December holdings from 2001 to date and end-June holdings beginning with data for end-June 2013. All economies are welcome to participate. The IMF augments the data that are reported in the dataset with aggregated data from two other surveys, i.e., Securities Held as Foreign Exchange Reserves (SEFER), and Securities Held by International Organizations (SSIO). SEFER provides geographic and instrument detail on securities that are held as reserve assets, and SSIO provides the geographic and instrument detail on securities that are held by international organizations. Similar to the Portfolio Investment Positions by Counterpart Economy, SEFER is conducted semi-annually starting with data for end-June 2013, whereas SSIO is conducted annually. Data from the portfolio investment positions by counterpart economy (formerly CPIS) and SSIO surveys provide comprehensive information on holdings of portfolio investment securities and, together with aggregated data from the SEFER survey, the geographic detail captured in these three surveys can be used to derive estimates of portfolio investment liabilities. In response to requests from data users, a number of enhancements to the Portfolio Investment Positions by Counterpart Economy (formerly CPIS) were implemented starting with data for end-June 2013. These enhancements include increased frequency (as noted above, semi-annual - data collections were implemented), improved timeliness (acceleration in both the collection and re- dissemination of data), and expanded scope (collection of data on an encouraged basis on the institutional sector of the nonresident issuer of securities; on short or negative positions; and on the institutional sector of the resident holder cross-classified by the institutional sector of selected nonresident issuers).

This dataset FDI main aggregates, BMD4 is updated every quarter and includes quarterly and annual aggregate inward and outward Foreign Direct Investment (FDI) flows, positions and income for OECD reporting economies and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa).

It is a simplified dataset with fewer breakdowns compared to the other separate datasets specifically dedicated to FDI flows, FDI positions or FDI income aggregates. In this dataset, FDI statistics are presented on directional basis only (unless otherwise specified, see metadata attached at the reporting country level) and resident Special Purpose Entities (SPEs), when they exist, are excluded (unless otherwise stated, see metadata attached at the reporting country level).

FDI aggregates are measured in USD millions, in millions of national currency and as a share of GDP.

This dataset supports FDI aggregates indicators available from the FDI in Figures.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates

​The International Monetary Fund (IMF) provides comprehensive frameworks for analyzing a country's economic transactions with the rest of the world through two key statistical statements:​

Balance of Payments (BOP): The BOP is a statistical statement that summarizes transactions between residents and non-residents during a specific period. It includes accounts for goods and services, primary income, secondary income, capital, and financial transactions. ​

International Investment Position (IIP): The IIP is a statistical statement that shows, at a point in time, the value of financial assets of residents of an economy that are claims on non-residents, and the liabilities of residents to non-residents.

Both balance of payments and IIP data are presented in accordance with the standard components of the sixth edition of the Balance of Payments and International Investment Position Manual, BPM6.

Details on the methodology are available at Sixth edition of the IMF's Balance of Payments and International investment Position Manual (http://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm)

Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

In an effort to further coordinate and harmonize the collection of CPI data, the international organizations agreed that the International Monetary Fund (IMF) and the Organisation for Economic Cooperation and Development (OECD) would assume responsibility for the international collection and dissemination of national CPI data. Under this data collection initiative, countries are reporting the aggregate all items index; more detailed indexes and weights for 12 subgroups of consumption expenditure (according to the so-called COICOP-classification), and detailed metadata. These detailed data represent a valuable resource for data users throughout the world and this portal would not be possible without the ongoing cooperation of all reporting countries. In this effort, the OECD collects and validates the data for their member countries, including accession and key partner countries, whereas the IMF takes care of the collection of data for all other countries.

The Data Template on International Reserves and Foreign Currency Liquidity is an innovative single framework that integrates the concept of international reserves and foreign currency liquidity by covering data on on-balance-sheet and off-balance-sheet international financial activities of country authorities as well as supplementary information. It aims to provide a comprehensive account of official foreign currency assets and drains on such resources arising from various foreign/domestic currency liabilities and commitments of the authorities.

Non-IMF Public & Private Institutions (this includes Non-IMF Non Public Schools, Unconventional Schools, and Institutions for the Delinquent).

The international investment position (IIP) is a statistical statement that shows at a point in time the value and composition of: -financial assets of residents of an economy that are claims on non-residents and gold bullion held as reserve assets, and -liabilities of residents of an economy to non-residents. The difference between an economy’s external financial assets and liabilities is the economy’s net IIP, which may be positive or negative. Respectively the net international investment position (NIIP) provides an aggregate view of the net financial position (assets minus liabilities) of a country vis-à-vis the rest of the world. It allows for a stock-flow analysis of external position of the country. The indicator is expressed in percent of GDP. The indicator is based on the Eurostat data from the Balance of payment statistics, i.e. the same data source used for the current account balance. Definitions are based on the IMF Sixth Balance of Payments Manual (BPM6).

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

ISEE-1 Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.

The selection of key indicators from the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) climate scenarios comprises Primary Energy Mix, Fossil Fuel Prices, Final Energy Mix, Emissions and CCS, Shadow Carbon Price, and Mean Surface Temperature. Sources: NGFS (2023), Scenarios Portal; and IIASA (2023), NGFS Phase 4 Scenario Explorer.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. For details please see the NGFS climate scenarios presentation the Climate scenarios technical documentation, and the User guide for data access.MethodologyThe database of key indicators is curated by the IMF in collaboration with the NGFS. The full data set can be found at the original sources. The license of the NGFS applies. The transition pathways for the NGFS scenarios have been generated with three well-established integrated assessment models, namely GCAM, MESSAGEix-GLOBIOM and REMIND-MAgPIE. These models combine macro-economic, agriculture and land-use, energy, water and climate systems into a common numerical framework that enables the analysis of the complex and non-linear dynamics in and between these components. The IAM results have been downscaled to the level of 140 countries. For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access. The table that follows give the correspondence of the variables in the IMF Climate Change Indicators Dashboard and the NGFS climate scenarios database.

Series

IMF Variable

NGFS Variable

Primary Energy

Coal

Primary Energy|Coal

Oil

Primary Energy|Oil

Gas

Primary Energy|Gas

Biomass

Primary Energy|Biomass

Hydro

Primary Energy|Hydro

Wind

Primary Energy|Wind

Geothermal

Primary Energy|Geothermal

Solar

Primary Energy|Solar

Nuclear

Primary Energy|Nuclear

Energy Prices

Price: Coal

Price|Primary Energy|Coal

Price: Oil

Price|Primary Energy|Oil

Price: Gas

Price|Primary Energy|Gas

Final Energy

Electricity

Final Energy|Electricity

Gases

Final Energy|Gases

Heat

Final Energy|Heat

Hydrogen

Final Energy|Hydrogen

Liquids

Final Energy|Liquids

Solids

Final Energy|Solids

Emissions and CCS1)

Energy and industrial processes CO2 emissions

= Emissions|CO2 – Emissions|CO2|LULUCF Direct+Indirect

LULUCF CO2 emissions

Emissions|CO2|LULUCF Direct+Indirect

Total non-CO2 emissions

Emissions|Total Non-CO2

Fossil energy CCS

Carbon Sequestration|CCS|Fossil

Bioenergy with CCS

Carbon Sequestration|CCS|Biomass

Industrial processes CCS

Carbon Sequestration|CCS|Industrial Processes

Carbon Price

Price: Carbon

Price|Carbon

Surface Temperature2)

5th percentile

Country Temperature|Downscaling|5.0th Percentile

50th percentile

Country Temperature|Downscaling|50.0th Percentile

95th percentile

Country Temperature|Downscaling|95.0th Percentile

1)World emissions are IMF staff calculations. 2)Postprocessed results. World mean surface temperature relative to pre-industrial levels; variable names “AR6 climate diagnostics|Surface Temperature (GSAT)|MAGICCv7.5.3|5.0th Percentile”, “50.0th Percentile”, and “95.0th Percentile”, respectively.Data series: Primary Energy MixFossil Fuel PricesFinal Energy MixEmissions and CCSShadow Carbon PriceMean Surface Temperature

ISEE-3 Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.

We use real-time data on vessel movements—Automatic Identification System (AIS) signals of vessels—as our primary data source. Source: Arslanalp, S., Koepke, R., & Verschuur, J. Tracking Trade from Space: An Application to Pacific Island Countries. IMF Working Paper No. 2021/225. https://www.imf.org/en/Publications/WP/Issues/2021/08/20/Tracking-Trade-from-Space-An-Application-to-Pacific-Island-Countries-464345 Concepts:Chokepoint: the full list of chokepoints we cover and associated additional information can be found here. Transit Calls: a transit call is defined when a ship transits through the chokepoint boundary. Transits that span across several days are counted only once and a threshold of 48 hours is applied before counting the same ship again.Trade Volume Estimates: as described in the paper, we use the vessel information (length, width, draft, capacity, block coefficient) to estimate the payload (or utilization rate) of the vessel when transiting through the chokepoint. The vessel payload (in percentage points) multiplied by the vessel’s deadweight tonnage (the maximum carrying capacity) in metric tons is the resulting trade volume estimate in metric tons carried by the ship. Ship Categories: all the indicators are available by 5 main ship categories: container, dry bulk, general cargo, ro-ro and tanker. Variables: date: all transit call dates are expressed in Coordinated Universal Time (UTC), a standard used to set all time zones around the world.year: as extracted from date.month: month 1-12 extracted from date. day: day 1-31 extracted from date. portid: chokepoint id. Full list of chokpoints and associated additional information can be found here. portname: chokepoint name. n_container: number of container ships transiting through the chokepoint at this date. n_dry_bulk: number of dry bulk carriers transiting through the chokepoint at this date. n_general_cargo: number of general cargo ships transiting through the chokepoint at this date. n_roro: number of ro-ro ships transiting through the chokepoint at this date. n_tanker: number of tankers transiting through the chokepoint at this date. n_cargo: total number of ships (excluding tankers) transiting through the chokepoint at this date. This is the sum of n_container, n_dry_bulk, n_general_cargo and n_roro.n_total: total number of ships transiting through the chokepoint at this date. This is the sum of n_container, n_dry_bulk, n_general_cargo, n_roro and n_tanker.capacity_container: total trade volume (in metric tons) of all container ships transiting through the chokepoint at this date.capacity_dry_bulk: total trade volume (in metric tons) of all dry bulk carriers transiting through the chokepoint at this date. capacity_general_cargo: total trade volume (in metric tons) of all general cargo ships transiting through the chokepoint at this date. capacity_roro: total trade volume (in metric tons) of all ro-ro ships transiting through the chokepoint at this date. capacity_tanker: total trade volume (in metric tons) of all tankers transiting through the chokepoint at this date. capacity_cargo: total trade volume (in metric tons) of all ships (excluding tankers) transiting through the chokepoint at this date. This is the sum of capacity_container, capacity_dry_bulk, capacity_general_cargo and capacity_roro.capacity: total trade volume (in metric tons) of all ships transiting through the chokepoint at this date. This is the sum of capacity_container, capacity_dry_bulk, capacity_general_cargo, capacity_roro and capacity_tanker. How to Cite?These datasets are based on raw AIS data from the United National Global Platform and estimates by the PortWatch team based on the methodology described in the paper. The recommended citation is: “Sources: UN Global Platform; IMF PortWatch (portwatch.imf.org).”About AIS DataThe UN has made available satellite-based AIS data through the UN Global Platform (UNGP) to national and international agencies that are members to the UN-CEBD (UN, 2021). The platform contains live data and global archive data from December 1, 2018. AIS data at the UNGP are provided by Spire, which collects AIS messages from two different satellite constellations, with more than 65 AIS equipped satellites. Spire complements this information with data collected by FleetMon through terrestrial receivers. There are several challenges with using AIS data. The AIS was originally developed by the International Maritime Organization (IMO) in 2004 as an outcome of amendments to the International Convention SOLAS (Safety of Life at Sea) in 2002. It is a self-reporting system, which allows vessels to periodically broadcast their identity, navigation, position data and other characteristics. The AIS has been made compulsory for all international commercial ships with gross tonnage of 300 or more tons (i.e., virtually all commercial ships) and all passenger ships regardless of size. There are three main types of information in AIS messages. AIS broadcasts voyage-related information (including ship location, speed, course, heading, rate of turn, destination, draft, and estimated arrival time), static information (including ship ID, ship type, ship size and dimensions), and dynamic information. Dynamic information such as the positional aspects (latitude and longitude) is automatically transmitted, depending on the vessels’ speed and course. The signals can be picked up by satellite or terrestrial receivers. For ships in open seas, however, the signals can only be picked up by satellite receivers as terrestrial receivers typically cover only about 15–20 nautical miles from the coast. For island states, satellite data tend to be much more reliable as the coverage of terrestrial receivers can be low (or nonexistent) for these smaller countries. Terrestrial receivers are useful for congested ports where congestion may make it difficult for satellites to capture all emitted messages. Additional information on AIS data can be found in Arslanalp et al. (2019), Verschuur et al. (2020), and the UN’s AIS Handbook.References: Arslanalp, S., Koepke, R., & Verschuur, J. Tracking Trade from Space: An Application to Pacific Island Countries. IMF Working Paper No. 2021/225. https://www.imf.org/en/Publications/WP/Issues/2021/08/20/Tracking-Trade-from-Space-An-Application-to-Pacific-Island-Countries-464345 AIS Handbook https://unstats.un.org/wiki/display/AIS/AIS+Handbook

Graph and download economic data for Breakeven Fiscal Oil Price for Iran, Islamic Republic of (IRNPZPIOILBEGUSD) from 2000 to 2025 about Iran, REO, oil, government, and price.

ACE Weimer propagated solar wind data and linearly interpolated time delay, cosine angle, and goodness information of propagated data at 1 min Resolution. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.

The export market share is calculated by dividing the exports of the country by the total exports of the region/world. The indicator measures the degree of importance of a country within the total exports of the region/world. For the calculation at current prices, the market share refers to the world trade (world export market share). Data on the values of exports of goods and services are compiled as part of the Balance of Payments of each country. To capture the structural losses in competitiveness that can accumulate over longer time periods, the indicator is calculated as 5 years % change - comparing year Y with year Y-5. A country might lose shares of export market not only if exports decline but most importantly if its exports do not grow at the same rate of world exports and its relative position at the global level deteriorates. The MIP scoreboard indicator is the percentage change of export market shares (of goods and services) over five years, with a lower indicative threshold of -6%. The formula is: [[(EXPc,t/EXPworld,t)-(EXPc,t-5/EXPworld,t-5)]/(EXPc,t-5/EXPworld,t-5)]*100 Source of total world data used as denominator: International Monetary Fund (IMF).

The external debt (or the foreign debt), at any given time, is the outstanding amount of the actual current (and not contingent) liabilities that require payment(s) of principal and/or interest by the debtor at some point(s) in the future and that are owed to non-residents by residents of an economy. The external debt is the portion of a country's debt that was borrowed from creditors outside the country, including commercial banks, other governments or international financial institutions (such as the International Monetary Fund (IMF) and the World Bank). The assets/liabilities include debt securities, such as bonds, notes and money market instruments, as well as loans, deposits, currency, trade credits and advances due to non-residents. The loans must usually be paid in the currency in which they was made. In order to earn the needed currency, the borowing country may sell and export goods to the lender's country. The data are expressed in % of GDP.