<_value> NADA Monthly food price estimates by product and market Madagascar, 465 markets, 2007/01/01-2026/03/01, version 2026-03-10 Real Time Food Prices MDG_2021_RTFP_v02_M Bo Pieter Johannes Andrée

World Food Programme (WFP)

Food and Agriculture Organization (FAO)

MetadataEditor Foreign, Commonwealth & Development Office of the United Kingdom Foreign, Commonwealth & Development Office of the United Kingdom Department of Foreign Affairs and Trade of Australia Department of Foreign Affairs and Trade of Australia Federal Ministry for Economic Cooperation and Development of Germany as part of the World Bank’s Food Systems 2030 Multi-Donor Trust Fund Federal Ministry for Economic Cooperation and Development of Germany as part of the World Bank’s Food Systems 2030 Multi-Donor Trust Fund KP-P174529-KMCE-TF0B4149 TF0B6892 TF0B6579 TF073570 TF0C0728 Monthly food price estimates in fragile countries FCV Data Platform Food Price Monitor FPM Real Time Food Prices RTFP Inflation Food Price Inflation Food security Food Insecurity Food Crisis Famine Fragility FCS FCV Food Price Crisis Commodity prices Maize Sorghum Wheat Rice Agricultural prices Food prices Real Time Food Prices Price measurement Real-time exchange rate data Commodity items Geo-referenced market locations Sub-national price inflation series Consumer Price Inflation (CPI) National-level CPI indicators International Comparison Program (ICP) Inflation series Market prices Price analysis Real-time data National prices International prices Price fluctuations Price trends Price Volatility Madagascar Madagascar The data cover the following sub-national areas: Vakinankaratra, Alaotra Mangoro, Vatovavy Fitovinany, Atsimo Andrefana, Androy, Anosy, Atsimo Atsinanana, Analamanga, Itasy, Amoron I Mania, Analanjirofo, Boeny, Menabe, Atsinanana, Betsiboka, Bongolava, Diana, Haute Matsiatra, Ihorombe, Melaky, Sava, Sofia, Market Average Sub-national level, Admin 2 (selected) World Food Programme (WFP) https://data.humdata.org/organization/wfp?vocab_Topics=prices World Food Programme (WFP); data published in the Humanitarian Data Exchange (HDX) data catalog at https://data.humdata.org/ Food and Agriculture Organization (FAO) https://fpma.fao.org/giews/fpmat4/#/dashboard/tool/domestic Local exchange rates and implied exchange rates data derived from commodity pairs published by FAO through the Food Price Monitoring and Analysis (FMPA) Tool World Bank Microdata Library, FCV Collection The estimates presented in this dataset are all based on publicly-available data. The dataset of price estimates is published as open data. Data Help Desk MDG_RTFP_mkt_2007_2026-03-10.csv Monthly price estimates at market/commodity level (all available countries) 107646 46 2026/03/10 Country code 107646 ISO3C codes, also known as ISO 3166-1 alpha-3 codes, are three-letter country or territory codes that are part of the ISO 3166 international standard. These codes are used to uniquely represent and identify countries and dependent territories in a standardized manner. Each ISO3C code corresponds to a specific country or territory and is often used in various applications, such as international trade, banking, internet domain names, and statistical analysis, to simplify and standardize country and territory references. Country 107646 Country names follow their appearance in the World Bank World Development Indicators. Area name (admin. level 1) 107646 Administrative names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names. Area name (admin. level 2) 107646 Administrative names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names. Market name 107646 The mkt_name variable represents the name of the market associated with each price data point in the dataset. This field provides a clear, textual identifier for each market location, offering a more intuitive and user-friendly way to reference and distinguish between different markets. The market name is crucial for qualitative analysis and for users familiar with regional market names, facilitating easy identification and comparison of market-specific trends and patterns. Market names follow their appearance in the underlying data bases from the World Food Program, FAO and HDX and may be further simplified for better machine readability. As such, these names may differ from official names. When analyzing market price data, geo_id helps in correlating price information with specific, named market locations, enhancing the contextual understanding of the data. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates, while the geo_id is unique for each location. Latitude 107415 Geographic positioning of the market location at which price data is tracked, expressed as a geographic coordinate that measure the east-west positioning on Earth relative to the Prime Meridian in Greenwich, England. Use the geo_id field to obtain a better understanding of unique market locations. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates. Longitude 107415 Geographic positioning of the market location at which price data is tracked, expressed as a geographic coordinate that measure the north-south positioning on Earth relative to the equator. Use the geo_id field to obtain a better understanding of unique market locations. Note that market names may change over time, market names may be shared between multiple geographic locations, and that multiple markets may share similar coordinates. Market location identifier 107646 The geo_id variable serves as a unique identifier for each market location in the RTP datasets, derived from geographic coordinates. This identifier is essential for accurately linking market price data to specific geographical locations. It ensures precise tracking and comparison of prices across different areas and is particularly useful for spatial analysis and mapping trends geographically. The uniqueness of each geo_id aids in the clear distinction and aggregation of data by location, making it a key element in any geographical or location-based analysis of market prices. The geo_id is shared across Real Time Food Prices (RTFP), Real Time Energy Prices (RTEP) and Real Time Exchange Rates (RTFP) that share the same timestamp (RTP data are generated weekly, they share the same timestamp when they are in the same week). The geo_id may be used to link data sets from different time periods, but caution is recommended. Use also the mkt_name field and Longitude and Latitude to obtain a better understanding of the market location, while noting that market names may change over time, market names may be shared between multiple locations, or multiple markets may share similar coordinates. Year 107646 The year variable represents the year associated with each market price data point, provided in numerical format (e.g., 2023). This field is allows segmenting and analyzing the price data on an annual basis. Month 107646 The month variable indicates the month number (1-12) corresponding to each market price data point, presented in a numerical format. This field facilitates more granular temporal insights and may be used to calculate seasonal adjustments to inflation estimates by the user. Currency 107646 The currency variable specifies the currency unit in which each market price data point is denominated. This field is essential for ensuring accurate financial interpretation and comparison of market prices across different regions or countries. Note that all the data is in Local Currency Unit (LCU). In countries with multiple competing currencies, the currency with the highest response rate in the underlying price survey data is used. Note that for both Real Time Food Prices (RTFP) and Real Time Energy Prices (RTEP) the USD/LCU variable in Real Time Exchange Rates (RTFP) can be used to dollarize the country data using area-specific estimates of prevailing unofficial retail exchange rates. Components 107646 Start dense data 107646 Last survey point 107646 Data coverage 107646 Data coverage recent 107646 Index confidence score 107646 Spatial interpolation (0/1) 107646 The spatially_interpolated variable is a binary indicator distinguishes between prices that are directly measured or estimated for a specific location at which prices are measured in other time periods, and those derived solely through spatial interpolation. When spatially_interpolated is set to true (or 1), it indicates that the price has been calculated using an inverse distance weighted interpolation method, based on data and estimates from nearby market locations. This is crucial for understanding the precision and context of each price point. The data is likely more accurate for the specific locations where data is or has been measured, while spatially interpolated prices provide estimations based only on surrounding data, useful for areas without direct measurements. This variable is key for assessing the reliability and geographical relevance of the price data. Rice 3760 Sugar 1168 Wheat flour 1159 Open estimate - Rice 89006 o_rice indicates the monthly opening price estimate for the commodity rice. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. High estimate - Rice 89006 h_rice denotes the highest price achieved by the commodity rice within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility. Low estimate - Rice 89006 l_rice represents the lowest price point for the commodity rice in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress. Close estimate - Rice 89006 c_rice is the closing price estimate for the commodity rice at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month. Inflation - Rice 83414 inflation_rice provides the 12-month inflation rate, or price change rate, for commodity rice. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment. Trust - Rice 89006 trust_rice offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for rice. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence. Open estimate - Sugar 89006 o_sugar indicates the monthly opening price estimate for the commodity sugar. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. High estimate - Sugar 89006 h_sugar denotes the highest price achieved by the commodity sugar within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility. Low estimate - Sugar 89006 l_sugar represents the lowest price point for the commodity sugar in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress. Close estimate - Sugar 89006 c_sugar is the closing price estimate for the commodity sugar at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month. Inflation - Sugar 83414 inflation_sugar provides the 12-month inflation rate, or price change rate, for commodity sugar. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment. Trust - Sugar 89006 trust_sugar offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for sugar. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence. Open estimate - Wheat flour 89006 o_wheat_flour indicates the monthly opening price estimate for the commodity wheat_flour. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. High estimate - Wheat flour 89006 h_wheat_flour denotes the highest price achieved by the commodity wheat_flour within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility. Low estimate - Wheat flour 89006 l_wheat_flour represents the lowest price point for the commodity wheat_flour in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress. Close estimate - Wheat flour 89006 c_wheat_flour is the closing price estimate for the commodity wheat_flour at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month. Inflation - Wheat flour 83414 inflation_wheat_flour provides the 12-month inflation rate, or price change rate, for commodity wheat_flour. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment. Trust - Wheat flour 89006 trust_wheat_flour offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for wheat_flour. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence. Open estimate - Food price index 89006 o_food_price_index indicates the monthly opening price estimate for the commodity food_price_index. It represents the initial market price at the start of each month, crucial for analyzing the opening market sentiment and baseline valuation. In financial analysis, especially in OHLC (Open, High, Low, Close) objects, the opening price is key to understanding the initial market conditions. Open price estimates are estimated as conditional means using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. High estimate - Food price index 89006 h_food_price_index denotes the highest price achieved by the commodity food_price_index within a month. This data point captures market peaks, reflecting the maximum demand or valuation during the period. High price estimates are estimated as the expected value of the upper half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. These data points are instrumental in plotting the price data in candlestick charts, which are pivotal for visual market analysis and identifying potential price trends, intra-month price volatility, or observe trend reversals that are significant when contrasted to natural monthly price spreads. In candlestick charting, the high price is indicated by the upper shadow or wick, marking the top end of the price range. Understanding the highest price point helps analyze the monthly price spread and market volatility. Low estimate - Food price index 89006 l_food_price_index represents the lowest price point for the commodity food_price_index in the given month. This variable is essential for understanding market dips, buyer interest at lower prices, and the floor value of the commodity. Low price estimates are estimated as the expected value of the lower half of the price distribution based on conditional variance estimated using a fractionally integrated GARCH (Generalized Autoregressive Heteroscedasticity) model estimated using a Generalized Error Distribution that allows for excess kurtosis. In candlestick charting, the low price forms the lower end of the candle or wick, showcasing the lowest market reach. Analyzing the low price is integral to understanding the full monthly price range and assessing market stability or distress. Close estimate - Food price index 89006 c_food_price_index is the closing price estimate for the commodity food_price_index at the end of each month. This figure indicates the final market price as recorded in the underlying surveys or estimated contemporaneously based on the other recorded price data, reflecting the closing market sentiment and valuation after a month's trading activity. In candlestick charts, the closing price helps form the main body of the candle, indicating the final standing of the market. It is vital for evaluating the closing market conditions, final demand, and forming comparative analysis with the opening price to understand market dynamics over the month. Inflation - Food price index 83414 inflation_food_price_index provides the 12-month inflation rate, or price change rate, for commodity food_price_index. This metric is calculated by comparing the current price against the price from 12 months prior, giving an annualized percentage change. Inflation rates are crucial economic indicators, reflecting the purchasing power and cost of living changes. For a more comprehensive understanding of overall inflation, analyzing a basket of food items rather than single commodities is recommended, as it offers a broader perspective of general price trends. This data is instrumental in economic planning, policy making, and understanding the macroeconomic environment. Trust - Food price index 89006 trust_food_price_index offers a trust score, ranging from 1-10, reflecting the reliability of the inflation calculation for food_price_index. These scores are specific to each market, time period, and commodity, considering the data availability and accuracy for the preceding 12 months. Higher scores indicate greater confidence and robustness in the inflation figures, based on the quality and quantity of data used and the cross-validated accuracy of imputed data. This score is key for users to assess the credibility and dependability of the inflation data, aiding in more informed economic and financial analysis. A score of 10 corresponds to an entry for which up to 12 months of preceding data has been fully observed. Values below 6 highlight observations generated with extremely low confidence. Date in yyyy-mm-dd format 107646 For comparing historical data, forecasting, or daily analysis, price_date provides a temporal reference. The field corresponds to the dates of each market price data point, formatted as yyyy-mm-dd. It denotes when prices were recorded or the time period for which price data are predicted, enabling chronological analysis and trend tracking of completed market and commodity price series.