Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Mai-Ndombe, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, North Eastern, Rift Valley, Coast, Eastern, Nairobi, , Central, Nyanza, Attapeu, Louangnamtha, Champasack, Bokeo, Bolikhamxai, Khammouan, Oudomxai, Phongsaly, Vientiane, Xiengkhouang, Louangphabang, Salavan, Savannakhet, Sekong, Vientiane Capital, Houaphan, Xaignabouly, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Sagaing, Kayah, Shan (East), Chin, Magway, Bago (East), Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Adamawa, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Market Average

Indonesia Official Exchange Rate: Period Average: Local Currency to USD data was reported at 16,466.999 USD/IDR in 2025. This records an increase from the previous number of 15,856.567 USD/IDR for 2024. Indonesia Official Exchange Rate: Period Average: Local Currency to USD data is updated yearly, averaging 9,319.032 USD/IDR from Dec 1987 (Median) to 2025, with 39 observations. The data reached an all-time high of 16,466.999 USD/IDR in 2025 and a record low of 1,640.844 USD/IDR in 1987. Indonesia Official Exchange Rate: Period Average: Local Currency to USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.GEM: Foreign Exchange Rates: Annual. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

Japan JP: Official Exchange Rate: Average: per USD data was reported at 112.166 JPY/USD in 2017. This records an increase from the previous number of 108.793 JPY/USD for 2016. Japan JP: Official Exchange Rate: Average: per USD data is updated yearly, averaging 141.301 JPY/USD from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 360.000 JPY/USD in 1970 and a record low of 79.790 JPY/USD in 2012. Japan JP: Official Exchange Rate: Average: per USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Exchange Rates and Real Effective Exchange Rates. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).; ; International Monetary Fund, International Financial Statistics.; ;

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Market Average

India Official Exchange Rate: Period Average: Local Currency to USD data was reported at 85.987 USD/INR in 2025. This records an increase from the previous number of 83.669 USD/INR for 2024. India Official Exchange Rate: Period Average: Local Currency to USD data is updated yearly, averaging 45.732 USD/INR from Dec 1987 (Median) to 2025, with 39 observations. The data reached an all-time high of 85.987 USD/INR in 2025 and a record low of 12.949 USD/INR in 1987. India Official Exchange Rate: Period Average: Local Currency to USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.GEM: Foreign Exchange Rates: Annual. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

Hong Kong SAR HK: Real Effective Exchange Rate Index data was reported at 124.430 2010=100 in 2018. This records a decrease from the previous number of 125.184 2010=100 for 2017. Hong Kong SAR HK: Real Effective Exchange Rate Index data is updated yearly, averaging 116.693 2010=100 from Dec 1995 (Median) to 2018, with 24 observations. The data reached an all-time high of 152.442 2010=100 in 1998 and a record low of 98.962 2010=100 in 2011. Hong Kong SAR HK: Real Effective Exchange Rate Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Exchange Rates and Real Effective Exchange Rates. Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs.; ; International Monetary Fund, International Financial Statistics.; ;

Egypt EG: Official Exchange Rate: Average: per USD data was reported at 17.783 EGP/USD in 2017. This records an increase from the previous number of 10.025 EGP/USD for 2016. Egypt EG: Official Exchange Rate: Average: per USD data is updated yearly, averaging 0.783 EGP/USD from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 17.783 EGP/USD in 2017 and a record low of 0.348 EGP/USD in 1961. Egypt EG: Official Exchange Rate: Average: per USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Exchange Rates and Real Effective Exchange Rates. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).; ; International Monetary Fund, International Financial Statistics.; ;

Real effective exchange rate index (2010 = 100) in Venezuela was reported at 742 % in 2016, according to the World Bank collection of development indicators, compiled from officially recognized sources. Venezuela - Real effective exchange rate index (2000 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: North, South, Artibonite, Centre, South-East, Grande'Anse, North-East, West, North-West, Market Average

The dataset is from world data bank and it is from 2020 to 2024 The dataset uses columns as : "country": country which data belong "iso3":short form of country "components":products "currency":currency "start_date_observations" start of observation date "end_date_observations": end of observation date "number_of_markets_modeled":number of market modeled "number_of_markets_covered":number of market covered "number_of_food_items":num of food item in components "number_of_observations_food":num of observation food "number_of_observations_other":observations of others "data_coverage_food"::data coverage of food "data_coverage_previous_12_months_food":for 12 months previous price "total_food_price_increase_since_start_date":total food price "average_annualized_food_inflation":average annualized inflation "maximum_food_drawdown":maximum food drawdown "average_annualized_food_volatility":avg food volatility "average_monthly_food_price_correlation_between_markets":avg monthly food price correlation "average_annual_food_price_correlation_between_markets":annulaly food price correlation "Rsquared_individual_food_items":food item error "Rsquared_individual_other_items":individual item error "index_confidence_score":confidence score "imputation_model":principle used

data source:https://microdata.worldbank.org/index.php/catalog/6160

STUDY TYPE Monthly currency exchange rate estimates in fragile countries

SERIES INFORMATION Real Time Prices (RTP) is a live dataset compiled and updated weekly by the World Bank Development Economics Data Group (DECDG) using a combination of direct price measurement and Machine Learning estimation of missing price data. The historical and current estimates are based on price information gathered from the World Food Program (WFP), UN-Food and Agricultural Organization (FAO), select National Statistical Offices, and are continually updated and revised as more price information becomes available. Real-time exchange rate data used in this process are from official and public sources.

RTP consists of three sub-series, Real Time Food Prices (RTFP) includes prices on a variety of food items that primarily include country-specific staple foods, Real Time Energy Prices (RTEP) includes fuel prices, and Real Time Exchange Rates (RTFX) and includes unofficial exchange rate estimates as well as possible other unofficial deflators.

RTFP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP_v02_M RTEP: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTEP_v01_M RTFX: https://microdata.worldbank.org/index.php/catalog/study/WLD_2023_RTFX_v01_M To produce smooth price series, outliers in the data are often adjusted using non-parametric density estimation and other techniques. Generalized Auto-Regressive Conditional Heteroskedasticity models are used to estimate intra-month price ranges. These models allow for excess kurtosis using a Generalized Error Distribution (GED). Open, High, Low, and Close price estimates are provided based on the modeled time-varying price distributions.

Data are produced from 2007 to the present and estimates are given for individual commodity items at geo-referenced market locations. Predicted data for missing entries are based on exchange rates, and price data available either at other market locations or from related price items.

RTP estimates of historical and current prices may serve as proxies for sub-national price inflation series or substitute national-level Consumer Price Inflation (CPI) indicators when complete information is unavailable. Therefore, RTP data may differ from other sources with official data, including the World Bank’s International Comparison Program (ICP) or inflation series reported in the World Development Indicators.

Comoros Official Exchange Rate: Period Average: Local Currency to USD data was reported at 434.474 USD/KMF in May 2025. This records a decrease from the previous number of 438.476 USD/KMF for Apr 2025. Comoros Official Exchange Rate: Period Average: Local Currency to USD data is updated monthly, averaging 401.029 USD/KMF from Jan 1987 (Median) to May 2025, with 461 observations. The data reached an all-time high of 1,060.522 USD/KMF in Dec 2020 and a record low of 246.026 USD/KMF in Sep 1992. Comoros Official Exchange Rate: Period Average: Local Currency to USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.GEM: Foreign Exchange Rates. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).

• © 2016 The World Bank Group, All Rights Reserved.
• (2010 = 100)
• Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs.
• Based on data from the Saudi Central Department of Statistics and Information under the authority of the Ministry of Economy and Planning.

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: North Eastern, Rift Valley, Coast, Eastern, Nairobi, , Central, Nyanza, Market Average

Nominal Effecive Exchange Rate in Uruguay was reported at 0 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Uruguay - Nominal Effecive Exchange Rate - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Hungary

Long Name: Hungary
Income Group: High income
Region: Europe & Central Asia
2-AlphaCode: HU
Balance Of Payments Manual In Use: BPM6
Capital: Budapest
Currency Unit: Hungarian forint
Government Accounting Concept: Consolidated central government
IMF Data Dissemination Standard: Special Data Dissemination Standard (SDDS)
Id: HUN
Latest Household Survey: World Health Survey, 2003
Latest Population Census: 2011
Latest Trade Data: 2018
Latestagriculturalcensus: 2010
Latestindustrialdata: 2013
National Accounts Base Year: Original chained constant price data are rescaled.
Nationalaccountsreferenceyear: 2015
SNA Price Valuation: Value added at basic prices (VAB)
Short Name: Hungary
Source Of Most Recent Income And Expenditure Data: Income survey (IS), 2014
System Of Trade: Special trade system
System of National Accounts: Country uses the 2008 System of National Accounts methodology
Table Name: Hungary
Vitalregistrationcomplete: Yes
WB2-code: HU

Europe & Central Asia

Long Name: Europe & Central Asia
2-AlphaCode: Z7
Id: ECS
Short Name: Europe & Central Asia
Special Notes: Europe and Central Asia regional aggregate (includes all income levels).
Table Name: Europe & Central Asia
WB2-code: Z7

Romania

Long Name: Romania
Income Group: High income
Lending Category: IBRD
Region: Europe & Central Asia
2-AlphaCode: RO
Balance Of Payments Manual In Use: BPM6
Capital: Bucharest
Currency Unit: New Romanian leu
Government Accounting Concept: Consolidated central government
IMF Data Dissemination Standard: Special Data Dissemination Standard Plus (SDDS Plus)
Id: ROU
Latest Household Survey: Romania Reproductive Health Survey, 2004
Latest Population Census: 2011
Latest Trade Data: 2018
Latestagriculturalcensus: 2010
Latestindustrialdata: 2013
National Accounts Base Year: Original chained constant price data are rescaled.
Nationalaccountsreferenceyear: 2005
SNA Price Valuation: Value added at basic prices (VAB)
Short Name: Romania
Source Of Most Recent Income And Expenditure Data: Income survey (IS), 2016
Special Notes: The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In this country, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. An alternative estimate (“alternative conversion factor” - PA.NUS.ATLS) is thus calculated as a weighted average of the different exchange rates in use in the country. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. For this country, this applies to the period 1987-1992. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.
System Of Trade: Special trade system
System of National Accounts: Country uses the 2008 System of National Accounts methodology
Table Name: Romania
Vitalregistrationcomplete: Yes
WB2-code: RO

European Union

Long Name: European Union
2-AlphaCode: EU
Id: EUU
Short Name: European Union
Special Notes: European Union aggregate.
Table Name: European Union
WB2-code: EU

This dataset contains country level economic and social measures for 183 countries. Part 1, World Tables (1980 File), contains, where available, measures of (1)population, (2)national accounts and price data for 1950, 1955, 1960 through 1977, (3)data on external trade for 1962, 1965, 1970, and 1977, (4)data on balance of payments, debt, central government finance and trade indices for 1970-1977, and (5)social data for 1960, 1970, and (estimated) 1977. More specifically, the groupings include population, GDP by industrial origin and expenditures in constant local prices and current local prices, exchange rates and indices, balance of payments and external debt ($US), central government finance in local currency, social indicators, and external trade. Part 2, World Tables (1982 File), contains data on national accounts, prices, exchange rates and population for 1960-1981. The groupings include GDP by industrial origin as well as expenditure in current local prices and constant local prices, area, population, exchange rates, and indices and savings.

Kenya KE: Official Exchange Rate: Average: per USD data was reported at 103.374 KES/USD in 2017. This records an increase from the previous number of 101.504 KES/USD for 2016. Kenya KE: Official Exchange Rate: Average: per USD data is updated yearly, averaging 19.160 KES/USD from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 103.374 KES/USD in 2017 and a record low of 7.001 KES/USD in 1973. Kenya KE: Official Exchange Rate: Average: per USD 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: Exchange Rates and Real Effective Exchange Rates. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).; ; International Monetary Fund, International Financial Statistics.; ;

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

Nigeria NG: Official Exchange Rate: Average: per USD data was reported at 305.790 NGN/USD in 2017. This records an increase from the previous number of 253.492 NGN/USD for 2016. Nigeria NG: Official Exchange Rate: Average: per USD data is updated yearly, averaging 5.951 NGN/USD from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 305.790 NGN/USD in 2017 and a record low of 0.547 NGN/USD in 1980. Nigeria NG: Official Exchange Rate: Average: per USD 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: Exchange Rates and Real Effective Exchange Rates. Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).; ; International Monetary Fund, International Financial Statistics.; ;