Monthly food price inflation estimates by country
25 countries, 2008/01/01-2022/06/01, version 2022/06/30
Afghanistan, Burkina Faso, Burundi...and 22 more, 2008 - 2022
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Reference ID
WLD_2021_RTFP-CTRY_v02_M
Producer(s)
Bo Pieter Johannes Andrée
Collection(s)
Metadata
Created on
Jun 30, 2022
Last modified
Jun 30, 2022
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Identification
Survey ID Number
WLD_2021_RTFP-CTRY_v02_M
Title
Monthly food price inflation estimates by country
Subtitle
25 countries, 2008/01/01-2022/06/01, version 2022/06/30
Country/Economy
| Name | Country code |
|---|---|
| Afghanistan | AFG |
| Burkina Faso | BFA |
| Burundi | BDI |
| Cameroon | CMR |
| Central African Republic | CAF |
| Chad | TCD |
| Congo, Dem. Rep. | COD |
| Congo, Rep. | COG |
| Gambia, The | GMB |
| Guinea-Bissau | GNB |
| Haiti | HTI |
| Iraq | IRQ |
| Lao PDR | LAO |
| Lebanon | LBN |
| Liberia | LBR |
| Mali | MLI |
| Mozambique | MOZ |
| Myanmar | MMR |
| Niger | NER |
| Nigeria | NGA |
| Somalia | SOM |
| South Sudan | SSD |
| Sudan | SDN |
| Syrian Arab Republic | SYR |
| Yemen, Rep. | YEM |
Study type
Monthly food price estimates in fragile countries
HIGH-LOW CHART, AND INFLATION
Series Information
This dataset is part of a series of frequently-updated data files providing monthly food prices and inflation estimates for a series of fragile countries.
The following datasets are part of this series:
Country-level inflation:
- All countries: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP-CTRY_v02_M
Market-level estimates:
- All countries: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP_v02_M
- Afghanistan: https://microdata.worldbank.org/index.php/catalog/study/AFG_2021_RTFP_v02_M
- Burkina Faso: https://microdata.worldbank.org/index.php/catalog/study/BFA_2021_RTFP_v02_M
- Burundi: https://microdata.worldbank.org/index.php/catalog/study/BDI_2021_RTFP_v02_M
- Cameroon: https://microdata.worldbank.org/index.php/catalog/study/CMR_2021_RTFP_v02_M
- Central African Republic: https://microdata.worldbank.org/index.php/catalog/study/CAF_2021_RTFP_v02_M
- Chad: https://microdata.worldbank.org/index.php/catalog/study/TCD_2021_RTFP_v02_M
- Congo, Dem. Rep.: https://microdata.worldbank.org/index.php/catalog/study/COD_2021_RTFP_v02_M
- Congo, Rep.: https://microdata.worldbank.org/index.php/catalog/study/COG_2021_RTFP_v02_M
- Gambia, The: https://microdata.worldbank.org/index.php/catalog/study/GMB_2021_RTFP_v02_M
- Guinea-Bissau: https://microdata.worldbank.org/index.php/catalog/study/GNB_2021_RTFP_v02_M
- Haiti: https://microdata.worldbank.org/index.php/catalog/study/HTI_2021_RTFP_v02_M
- Iraq: https://microdata.worldbank.org/index.php/catalog/study/IRQ_2021_RTFP_v02_M
- Lao PDR: https://microdata.worldbank.org/index.php/catalog/study/LAO_2021_RTFP_v02_M
- Lebanon: https://microdata.worldbank.org/index.php/catalog/study/LBN_2021_RTFP_v02_M
- Liberia: https://microdata.worldbank.org/index.php/catalog/study/LBR_2021_RTFP_v02_M
- Mali: https://microdata.worldbank.org/index.php/catalog/study/MLI_2021_RTFP_v02_M
- Mozambique: https://microdata.worldbank.org/index.php/catalog/study/MOZ_2021_RTFP_v02_M
- Myanmar: https://microdata.worldbank.org/index.php/catalog/study/MMR_2021_RTFP_v02_M
- Niger: https://microdata.worldbank.org/index.php/catalog/study/NER_2021_RTFP_v02_M
- Nigeria: https://microdata.worldbank.org/index.php/catalog/study/NGA_2021_RTFP_v02_M
- Somalia: https://microdata.worldbank.org/index.php/catalog/study/SOM_2021_RTFP_v02_M
- South Sudan: https://microdata.worldbank.org/index.php/catalog/study/SSD_2021_RTFP_v02_M
- Sudan: https://microdata.worldbank.org/index.php/catalog/study/SDN_2021_RTFP_v02_M
- Syrian Arab Republic: https://microdata.worldbank.org/index.php/catalog/study/SYR_2021_RTFP_v02_M
- Yemen, Rep.: https://microdata.worldbank.org/index.php/catalog/study/YEM_2021_RTFP_v02_M
The following datasets are part of this series:
Country-level inflation:
- All countries: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP-CTRY_v02_M
Market-level estimates:
- All countries: https://microdata.worldbank.org/index.php/catalog/study/WLD_2021_RTFP_v02_M
- Afghanistan: https://microdata.worldbank.org/index.php/catalog/study/AFG_2021_RTFP_v02_M
- Burkina Faso: https://microdata.worldbank.org/index.php/catalog/study/BFA_2021_RTFP_v02_M
- Burundi: https://microdata.worldbank.org/index.php/catalog/study/BDI_2021_RTFP_v02_M
- Cameroon: https://microdata.worldbank.org/index.php/catalog/study/CMR_2021_RTFP_v02_M
- Central African Republic: https://microdata.worldbank.org/index.php/catalog/study/CAF_2021_RTFP_v02_M
- Chad: https://microdata.worldbank.org/index.php/catalog/study/TCD_2021_RTFP_v02_M
- Congo, Dem. Rep.: https://microdata.worldbank.org/index.php/catalog/study/COD_2021_RTFP_v02_M
- Congo, Rep.: https://microdata.worldbank.org/index.php/catalog/study/COG_2021_RTFP_v02_M
- Gambia, The: https://microdata.worldbank.org/index.php/catalog/study/GMB_2021_RTFP_v02_M
- Guinea-Bissau: https://microdata.worldbank.org/index.php/catalog/study/GNB_2021_RTFP_v02_M
- Haiti: https://microdata.worldbank.org/index.php/catalog/study/HTI_2021_RTFP_v02_M
- Iraq: https://microdata.worldbank.org/index.php/catalog/study/IRQ_2021_RTFP_v02_M
- Lao PDR: https://microdata.worldbank.org/index.php/catalog/study/LAO_2021_RTFP_v02_M
- Lebanon: https://microdata.worldbank.org/index.php/catalog/study/LBN_2021_RTFP_v02_M
- Liberia: https://microdata.worldbank.org/index.php/catalog/study/LBR_2021_RTFP_v02_M
- Mali: https://microdata.worldbank.org/index.php/catalog/study/MLI_2021_RTFP_v02_M
- Mozambique: https://microdata.worldbank.org/index.php/catalog/study/MOZ_2021_RTFP_v02_M
- Myanmar: https://microdata.worldbank.org/index.php/catalog/study/MMR_2021_RTFP_v02_M
- Niger: https://microdata.worldbank.org/index.php/catalog/study/NER_2021_RTFP_v02_M
- Nigeria: https://microdata.worldbank.org/index.php/catalog/study/NGA_2021_RTFP_v02_M
- Somalia: https://microdata.worldbank.org/index.php/catalog/study/SOM_2021_RTFP_v02_M
- South Sudan: https://microdata.worldbank.org/index.php/catalog/study/SSD_2021_RTFP_v02_M
- Sudan: https://microdata.worldbank.org/index.php/catalog/study/SDN_2021_RTFP_v02_M
- Syrian Arab Republic: https://microdata.worldbank.org/index.php/catalog/study/SYR_2021_RTFP_v02_M
- Yemen, Rep.: https://microdata.worldbank.org/index.php/catalog/study/YEM_2021_RTFP_v02_M
Abstract
Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations.
This data set includes food price 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.
This data set includes food price 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.
Version
Version Date
2022/06/30 (generated on 2022-06-28)
Scope
Notes
List of food products included in estimates (not all products are included in country-level estimates): apples, bananas, beans, bread, bulgur, cabbage, carrots, cassava, cassava flour, cassava meal, cheese, chickpeas, cocoyam, cowpeas, cucumbers, dates, eggplants, gari, garlic, groundnuts, lentils, maize, maize flour, maize meal, milk, millet, oil, onions, oranges, parsley, pasta, plantains, potatoes, pulses, rice, salt, salt iodised, sesame, sorghum, spinach, sugar, tea, tomatoes, tomatoes paste, watermelons, wheat, wheat flour, yam, yogurt
Keywords
| Keyword |
|---|
| inflation |
| food security |
| famine |
| fragility |
| fragile country |
| price imputation |
| food price crisis |
| food price monitor |
| fpm |
| Commodity prices |
| Food Crises |
| Maize |
| Sorghum |
| Wheat |
| Rice |
| Flour |
| Food Insecurity |
| Agricultural prices |
Coverage
Geographic Unit
Country
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Bo Pieter Johannes Andrée | World Bank, Development Data Group (DECDG), Data Analytics and Tools unit (DECAT) |
Funding Agency/Sponsor
| Name | Abbreviation | Grant number | Role |
|---|---|---|---|
| Foreign, Commonwealth & Development Office | FCDO (formerly DFID) | Support to data analytics | |
| Foreign, Commonwealth & Development Office | FCDO (formerly DFID) | KP-P174529-KMCE-TF0B4149 | Data documentation and dissemination (FCV Data Platform) |
Other Identifications/Acknowledgments
| Name | Role | Affiliation |
|---|---|---|
| World Food Programme (WFP) | Source of market price data | United Nations |
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2008/01 | 2022/06 |
Time periods
| Start date | End date |
|---|---|
| 2008/01 | 2022/06 |
Data Processing
Methodology notes
Information on the model used for Afghanistan (see working paper for more information)
Components: Bread (1 KG, Index Weight = 1), Rice (Low Quality) (1 KG, Index Weight = 1), Wheat (1 KG, Index Weight = 1)
Currency: AFN
Number of markets used: 9
Number of markets covered: 40
Number of food items: 3
Number of observations: bread: 1192, rice: 1715, wheat: 1727
Data coverage: 74.48%
Data coverage previous 12 months: 40.43%
Average annualized inflation: 6.27%
Maximum drawdown: -42.27%
Average annualized volatility: 7.96%
Average monthly price correlation between markets: 0.57
Average annual price correlation between markets: 0.86
R squared individual food items: bread: 0.91, rice: 0.87, wheat: 0.91
F confidence score: 0.9
Imputation model: bread: nonlinear, rice: nonlinear, wheat: nonlinear
Information on the model used for Burkina Faso (see working paper for more information)
Components: Beans (Niebe) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 63
Number of markets covered: 64
Number of food items: 3
Number of observations: beans: 4507, maize: 5331, millet: 6190
Data coverage: 45.59%
Data coverage previous 12 months: 86.14%
Average annualized inflation: 7.73%
Maximum drawdown: -37.49%
Average annualized volatility: 14.53%
Average monthly price correlation between markets: 0.66
Average annual price correlation between markets: 0.87
R squared individual food items: maize: 0.8, millet: 0.79, sorghum: 0.78
F confidence score: 0.79
Imputation model: beans: linear, maize: nonlinear, millet: nonlinear
Information on the model used for Burundi (see working paper for more information)
Components: Rice (Low Quality, Local) (1 KG, Index Weight = 1), Beans (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Cassava Flour (1 KG, Index Weight = 1), Maize Flour (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Sweet Potatoes (1 KG, Index Weight = 1)
Currency: BIF
Number of markets used: 63
Number of markets covered: 72
Number of food items: 8
Number of observations: rice: 3872, beans: 3361, maize: 3550, bananas: 3364, cassava_flour: 3954, maize_flour: 3176, onions: 3495, potatoes: 3979
Data coverage: 30.39%
Data coverage previous 12 months: 52.78%
Average annualized inflation: 5.27%
Maximum drawdown: -28.59%
Average annualized volatility: 10.9%
Average monthly price correlation between markets: 0.51
Average annual price correlation between markets: 0.76
R squared individual food items: beans: 0.89, cassava_flour: 0.84, potatoes: 0.77, rice: 0.83, maize: 0.82, bananas: 0.7, maize_flour: 0.82, onions: 0.54
F confidence score: 0.79
Imputation model: rice: linear, beans: nonlinear, maize: linear, bananas: linear, cassava_flour: nonlinear, maize_flour: linear, onions: linear, potatoes: nonlinear
Information on the model used for Cameroon (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Long Grain, Imported) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Maize (90 KG, Index Weight = 0.01), Sorghum (Red) (90 KG, Index Weight = 0.01), Bananas (12 KG, Index Weight = 0.08), Potatoes (1 KG, Index Weight = 1), Cassava (Fresh) (5 KG, Index Weight = 0.2), CocoYam (Macabo) (20 KG, Index Weight = 0.05), Plantains (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 12
Number of markets covered: 64
Number of food items: 10
Number of observations: oil: 632, rice: 631, wheat_flour: 632, maize: 293, sorghum: 299, bananas: 416, potatoes: 740, cassava: 405, cocoyam: 431, plantains: 726
Data coverage: 21.61%
Data coverage previous 12 months: 17.43%
Average annualized inflation: 2.11%
Maximum drawdown: -15.93%
Average annualized volatility: 6.38%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.42
R squared individual food items: potatoes: 0.64, plantains: 0.69, oil: 0.97, rice: 0.98, wheat_flour: 0.98, maize: 0.98, sorghum: 0.99, bananas: 0.97, cassava: 0.96, cocoyam: 0.97
F confidence score: 0.94
Imputation model: oil: linear, rice: linear, wheat_flour: linear, maize: linear, sorghum: linear, bananas: linear, potatoes: nonlinear, cassava: linear, cocoyam: linear, plantains: nonlinear
Information on the model used for Central African Republic (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 20
Number of markets covered: 42
Number of food items: 3
Number of observations: oil: 983, rice: 891, maize: 843
Data coverage: 25.26%
Data coverage previous 12 months: 75.62%
Average annualized inflation: 1.28%
Maximum drawdown: -17.28%
Average annualized volatility: 8.85%
Average monthly price correlation between markets: 0.21
Average annual price correlation between markets: 0.08
R squared individual food items: maize: 0.6, cassava: 0.54, oil: 0.4
F confidence score: 0.52
Imputation model: oil: linear, rice: linear, maize: nonlinear
Information on the model used for Chad (see working paper for more information)
Components: Maize (White) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (Red) (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 37
Number of markets covered: 61
Number of food items: 3
Number of observations: maize: 1661, millet: 3191, sorghum: 2679
Data coverage: 35.06%
Data coverage previous 12 months: 54.05%
Average annualized inflation: 4.2%
Maximum drawdown: -42.43%
Average annualized volatility: 16.8%
Average monthly price correlation between markets: 0.54
Average annual price correlation between markets: 0.84
R squared individual food items: maize: 0.67, millet: 0.75, sorghum: 0.73
F confidence score: 0.72
Imputation model: maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Congo, Dem. Rep. (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Local) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Beans (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Cassava Flour (1 KG, Index Weight = 1), Cassava (Cossette) (1 KG, Index Weight = 1), Plantains (1 KG, Index Weight = 1), Maize Meal (1 KG, Index Weight = 1)
Currency: CDF
Number of markets used: 15
Number of markets covered: 83
Number of food items: 10
Number of observations: oil: 1647, rice: 1498, salt: 1383, sugar: 1396, beans: 1353, maize: 1486, cassava_flour: 1653, cassava: 1195, plantains: 1485, maize_meal: 1304
Data coverage: 48.45%
Data coverage previous 12 months: 18.06%
Average annualized inflation: 7.59%
Maximum drawdown: -18.02%
Average annualized volatility: 7.78%
Average monthly price correlation between markets: 0.3
Average annual price correlation between markets: 0.7
R squared individual food items: oil: 0.85, rice: 0.87, salt: 0.85, sugar: 0.92, beans: 0.87, maize: 0.83, cassava_flour: 0.84, cassava: 0.86, plantains: 0.84, maize_meal: 0.81
F confidence score: 0.86
Imputation model: oil: nonlinear, rice: nonlinear, salt: nonlinear, sugar: nonlinear, beans: nonlinear, maize: nonlinear, cassava_flour: nonlinear, cassava: nonlinear, plantains: nonlinear, maize_meal: nonlinear
Information on the model used for Congo, Rep. (see working paper for more information)
Components: Bread (1 KG, Index Weight = 1), Oil (Palm) (1 L, Index Weight = 1), Rice (Mixed, Low Quality) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Cassava (Fresh) (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 5
Number of markets covered: 11
Number of food items: 6
Number of observations: bread: 300, oil: 349, rice: 470, wheat_flour: 223, groundnuts: 330, cassava: 430
Data coverage: 24.28%
Data coverage previous 12 months: 0%
Average annualized inflation: 1.88%
Maximum drawdown: -17.24%
Average annualized volatility: 9.02%
Average monthly price correlation between markets: 0.43
Average annual price correlation between markets: 0.33
R squared individual food items: bread: 0.84, oil: 0.67, rice: 0.75, wheat_flour: 0.93, groundnuts: 0.67, cassava: 0.79
F confidence score: 0.8
Imputation model: bread: linear, oil: linear, rice: linear, wheat_flour: linear, groundnuts: linear, cassava: linear
Information on the model used for Gambia, The (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Rice (Small Grain, Imported) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Beans (Dry) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Potatoes (Irish) (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Milk (1 KG, Index Weight = 1), Cabbage (1 KG, Index Weight = 1), Carrots (1 KG, Index Weight = 1), Garlic (1 KG, Index Weight = 1), Tea (1 Unit, Index Weight = 1)
Currency: GMD
Number of markets used: 13
Number of markets covered: 28
Number of food items: 16
Number of observations: oil: 1112, rice: 2274, salt: 1140, sugar: 1145, beans: 1122, groundnuts: 2149, millet: 2190, bananas: 1135, onions: 1151, potatoes: 1093, tomatoes: 1054, milk: 1124, cabbage: 1065, carrots: 1096, garlic: 1041, tea: 1117
Data coverage: 43.4%
Data coverage previous 12 months: 91.67%
Average annualized inflation: 4.59%
Maximum drawdown: -14.81%
Average annualized volatility: 6.59%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.73
R squared individual food items: rice: 0.88, groundnuts: 0.83, millet: 0.79, oil: 0.81, salt: 0.92, sugar: 0.87, beans: 0.71, bananas: 0.72, onions: 0.79, potatoes: 0.83, tomatoes: 0.64, milk: 0.74, cabbage: 0.61, carrots: 0.65, garlic: 0.75, tea: 0.79
F confidence score: 0.79
Imputation model: oil: linear, rice: nonlinear, salt: linear, sugar: linear, beans: linear, groundnuts: nonlinear, millet: nonlinear, bananas: linear, onions: linear, potatoes: linear, tomatoes: linear, milk: linear, cabbage: linear, carrots: linear, garlic: linear, tea: linear
Information on the model used for Guinea-Bissau (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 41
Number of markets covered: 45
Number of food items: 3
Number of observations: oil: 746, rice: 714, sugar: 776
Data coverage: 18.4%
Data coverage previous 12 months: 88.96%
Average annualized inflation: 3.86%
Maximum drawdown: -8.93%
Average annualized volatility: 4.05%
Average monthly price correlation between markets: 0.41
Average annual price correlation between markets: 0.87
R squared individual food items: oil: 0.86, rice: 0.89, sugar: 0.82
F confidence score: 0.86
Imputation model: oil: linear, rice: linear, sugar: linear
Information on the model used for Haiti (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 Gallon, Index Weight = 0.26), Sugar (White) (1 Marmite, Index Weight = 0.37), Wheat Flour (Imported) (1 Marmite, Index Weight = 0.37), Beans (Black) (1 Marmite, Index Weight = 0.37), Pasta (350 G, Index Weight = 2.86), Maize Meal (Local) (1 Marmite, Index Weight = 0.37)
Currency: HTG
Number of markets used: 9
Number of markets covered: 9
Number of food items: 6
Number of observations: oil: 864, sugar: 512, wheat_flour: 1557, beans: 989, pasta: 387, maize_meal: 1557
Data coverage: 58.4%
Data coverage previous 12 months: 69.6%
Average annualized inflation: 9.27%
Maximum drawdown: -30.75%
Average annualized volatility: 10.73%
Average monthly price correlation between markets: 0.61
Average annual price correlation between markets: 0.87
R squared individual food items: wheat_flour: 0.83, maize_meal: 0.83, oil: 0.93, sugar: 0.89, beans: 0.89, pasta: 0.95
F confidence score: 0.89
Imputation model: oil: linear, sugar: linear, wheat_flour: nonlinear, beans: linear, pasta: linear, maize_meal: nonlinear
Information on the model used for Iraq (see working paper for more information)
Components: Bread (Khoboz) (1 Unit, Index Weight = 1), Oil (Vegetable) (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Milk (Powder) (1 KG, Index Weight = 1), Dates (1 KG, Index Weight = 1), Tea (1 KG, Index Weight = 1), Cheese (Local) (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1), Salt (Iodised) (1 KG, Index Weight = 1)
Currency: IQD
Number of markets used: 18
Number of markets covered: 18
Number of food items: 14
Number of observations: bread: 1607, oil: 1431, rice: 851, sugar: 1520, wheat_flour: 1513, beans: 817, potatoes: 847, tomatoes: 887, milk: 838, dates: 828, tea: 834, cheese: 842, lentils: 815, salt_iodised: 845
Data coverage: 42.55%
Data coverage previous 12 months: 88.1%
Average annualized inflation: 0.74%
Maximum drawdown: -13.89%
Average annualized volatility: 3.45%
Average monthly price correlation between markets: 0.14
Average annual price correlation between markets: 0.36
R squared individual food items: bread: 0.97, oil: 0.95, rice: 0.95, sugar: 0.94, wheat_flour: 0.93, beans: 0.9, potatoes: 0.89, tomatoes: 0.88, milk: 0.88, dates: 0.75, tea: 0.89, cheese: 0.87, lentils: 0.85, salt_iodised: 0.85
F confidence score: 0.91
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, beans: linear, potatoes: linear, tomatoes: linear, milk: linear, dates: linear, tea: linear, cheese: linear, lentils: linear, salt_iodised: linear
Information on the model used for Lao PDR (see working paper for more information)
Components: Oil (Soybean) (1 L, Index Weight = 1), Rice (Glutinous, Second Quality) (1 KG, Index Weight = 1), Sugar (Brown) (1 KG, Index Weight = 1), Garlic (Small) (1 KG, Index Weight = 1)
Currency: LAK
Number of markets used: 17
Number of markets covered: 17
Number of food items: 4
Number of observations: oil: 1357, rice: 1671, sugar: 1330, garlic: 1295
Data coverage: 52.62%
Data coverage previous 12 months: 69.49%
Average annualized inflation: 1.85%
Maximum drawdown: -3.71%
Average annualized volatility: 2.06%
Average monthly price correlation between markets: 0.1
Average annual price correlation between markets: 0.3
R squared individual food items: oil: 0.88, rice: 0.87, sugar: 0.88, garlic: 0.73
F confidence score: 0.85
Imputation model: oil: linear, rice: linear, sugar: linear, garlic: linear
Information on the model used for Lebanon (see working paper for more information)
Components: Bread (Pita) (1 KG, Index Weight = 1), Oil (Sunflower) (5 L, Index Weight = 0.2), Rice (Imported, Egyptian) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (White) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Milk (Powder) (900 G, Index Weight = 1.11), Pasta (Spaghetti) (1 KG, Index Weight = 1), Cabbage (1 KG, Index Weight = 1), Cucumbers (Greenhouse) (1 KG, Index Weight = 1), Tomatoes (Paste) (1.3 KG, Index Weight = 0.77), Bulgur (Brown) (1 KG, Index Weight = 1), Cheese (Picon) (160 G, Index Weight = 6.25), Chickpeas (1 KG, Index Weight = 1), Lentils (Red) (1 KG, Index Weight = 1)
Currency: LBP
Number of markets used: 26
Number of markets covered: 26
Number of food items: 16
Number of observations: bread: 2149, oil: 1667, rice: 2295, salt: 2036, sugar: 2285, wheat_flour: 876, beans: 2269, milk: 2231, pasta: 2316, cabbage: 820, cucumbers: 757, tomatoes_paste: 1771, bulgur: 2350, cheese: 2236, chickpeas: 1603, lentils: 1159
Data coverage: 55.87%
Data coverage previous 12 months: 37.7%
Average annualized inflation: 36.84%
Maximum drawdown: -18.08%
Average annualized volatility: 18.98%
Average monthly price correlation between markets: 0.84
Average annual price correlation between markets: 1
R squared individual food items: bread: 0.97, oil: 0.97, rice: 0.91, salt: 0.93, sugar: 0.92, beans: 0.91, milk: 0.98, pasta: 0.87, cabbage: 0.9, tomatoes_paste: 0.92, bulgur: 0.92, cheese: 0.96, wheat_flour: 0.77, cucumbers: 0.75, chickpeas: 0.72, lentils: 0.8
F confidence score: 0.88
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, salt: nonlinear, sugar: nonlinear, wheat_flour: linear, beans: nonlinear, milk: nonlinear, pasta: nonlinear, cabbage: nonlinear, cucumbers: linear, tomatoes_paste: nonlinear, bulgur: nonlinear, cheese: nonlinear, chickpeas: linear, lentils: linear
Information on the model used for Liberia (see working paper for more information)
Components: Oil (Palm) (1 Gallon, Index Weight = 0.26), Rice (Imported) (50 KG, Index Weight = 0.02), Cassava (Fresh) (50 KG, Index Weight = 0.02), Cowpeas (1 KG, Index Weight = 1)
Currency: LRD
Number of markets used: 17
Number of markets covered: 24
Number of food items: 4
Number of observations: oil: 987, rice: 1365, cassava: 1052, cowpeas: 1076
Data coverage: 25.36%
Data coverage previous 12 months: 0%
Average annualized inflation: 8.03%
Maximum drawdown: -9.69%
Average annualized volatility: 6.34%
Average monthly price correlation between markets: 0.24
Average annual price correlation between markets: 0.39
R squared individual food items: oil: 0.89, rice: 0.95, cassava: 0.85, cowpeas: 0.89
F confidence score: 0.89
Imputation model: oil: linear, rice: linear, cassava: linear, cowpeas: linear
Information on the model used for Mali (see working paper for more information)
Components: Rice (Local) (1 KG, Index Weight = 1), Beans (Niebe) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 80
Number of markets covered: 127
Number of food items: 6
Number of observations: rice: 9835, beans: 5015, groundnuts: 4545, maize: 7529, millet: 10558, sorghum: 10051
Data coverage: 52.88%
Data coverage previous 12 months: 76.7%
Average annualized inflation: 4.57%
Maximum drawdown: -25.56%
Average annualized volatility: 7.89%
Average monthly price correlation between markets: 0.6
Average annual price correlation between markets: 0.9
R squared individual food items: rice: 0.95, maize: 0.86, millet: 0.88, sorghum: 0.89, beans: 0.63, groundnuts: 0.59
F confidence score: 0.84
Imputation model: rice: nonlinear, beans: linear, groundnuts: linear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Mozambique (see working paper for more information)
Components: Oil (Vegetable, Local) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Sugar (Brown, Local) (1 KG, Index Weight = 1), Wheat Flour (Local) (1 KG, Index Weight = 1), Groundnuts (Small, Shelled) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Cowpeas (1 KG, Index Weight = 1), Maize Meal (White, First Grade) (1 KG, Index Weight = 1)
Currency: MZN
Number of markets used: 24
Number of markets covered: 95
Number of food items: 8
Number of observations: oil: 3236, rice: 3235, sugar: 3267, wheat_flour: 2310, groundnuts: 2096, maize: 3524, cowpeas: 2280, maize_meal: 2246
Data coverage: 59.8%
Data coverage previous 12 months: 34.51%
Average annualized inflation: 7.33%
Maximum drawdown: -29.44%
Average annualized volatility: 7.42%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.88
R squared individual food items: oil: 0.93, rice: 0.91, sugar: 0.94, wheat_flour: 0.89, groundnuts: 0.84, maize: 0.91, cowpeas: 0.75, maize_meal: 0.9
F confidence score: 0.89
Imputation model: oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, groundnuts: nonlinear, maize: nonlinear, cowpeas: nonlinear, maize_meal: nonlinear
Information on the model used for Myanmar (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Pulses (1 KG, Index Weight = 1), Rice (Low Quality) (1 KG, Index Weight = 1)
Currency: MMK
Number of markets used: 33
Number of markets covered: 195
Number of food items: 3
Number of observations: oil: 2660, pulses: 2878, rice: 4372
Data coverage: 42.46%
Data coverage previous 12 months: 36.17%
Average annualized inflation: 4.63%
Maximum drawdown: -31.82%
Average annualized volatility: 9.14%
Average monthly price correlation between markets: 0.27
Average annual price correlation between markets: 0.64
R squared individual food items: oil: 0.93, rice: 0.88, salt: 0.85
F confidence score: 0.89
Imputation model: oil: nonlinear, pulses: linear, rice: nonlinear
Information on the model used for Niger (see working paper for more information)
Components: Rice (Imported) (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 68
Number of markets covered: 79
Number of food items: 4
Number of observations: rice: 9212, maize: 8418, millet: 10488, sorghum: 9241
Data coverage: 73.61%
Data coverage previous 12 months: 64.89%
Average annualized inflation: 3.18%
Maximum drawdown: -23.59%
Average annualized volatility: 8.93%
Average monthly price correlation between markets: 0.49
Average annual price correlation between markets: 0.76
R squared individual food items: rice: 0.94, maize: 0.83, millet: 0.85, sorghum: 0.82
F confidence score: 0.87
Imputation model: rice: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Nigeria (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Imported) (50 KG, Index Weight = 0.02), Salt (250 G, Index Weight = 4), Sugar (1.3 KG, Index Weight = 0.77), Groundnuts (Shelled) (100 KG, Index Weight = 0.01), Maize (White) (100 KG, Index Weight = 0.01), Millet (100 KG, Index Weight = 0.01), Sorghum (White) (100 KG, Index Weight = 0.01), Bananas (1.3 KG, Index Weight = 0.77), Maize Flour (1.3 KG, Index Weight = 0.77), Tomatoes (0.5 KG, Index Weight = 2), Cassava Meal (Gari, Yellow) (100 KG, Index Weight = 0.01), Cowpeas (White) (100 KG, Index Weight = 0.01), Milk (20 G, Index Weight = 50), Yam (1 KG, Index Weight = 1), Oranges (400 G, Index Weight = 2.5), Watermelons (2.1 KG, Index Weight = 0.48), Spinach (300 G, Index Weight = 3.33), Gari (White) (100 KG, Index Weight = 0.01)
Currency: NGN
Number of markets used: 33
Number of markets covered: 35
Number of food items: 19
Number of observations: oil: 1136, rice: 1268, salt: 864, sugar: 886, groundnuts: 1304, maize: 1516, millet: 1384, sorghum: 1466, bananas: 1012, maize_flour: 1087, tomatoes: 957, cassava_meal: 1067, cowpeas: 1381, milk: 1041, yam: 968, oranges: 1036, watermelons: 1074, spinach: 778, gari: 1248
Data coverage: 27.93%
Data coverage previous 12 months: 43.33%
Average annualized inflation: 2.03%
Maximum drawdown: -51.01%
Average annualized volatility: 12.32%
Average monthly price correlation between markets: 0.29
Average annual price correlation between markets: 0.66
R squared individual food items: oil: 0.95, rice: 0.95, groundnuts: 0.94, maize: 0.94, millet: 0.94, sorghum: 0.93, cassava_meal: 0.95, cowpeas: 0.92, gari: 0.94, salt: 0.89, sugar: 0.99, bananas: 0.95, maize_flour: 0.98, tomatoes: 0.82, milk: 0.96, yam: 0.98, oranges: 0.95, watermelons: 0.75, spinach: 0.83
F confidence score: 0.87
Imputation model: oil: nonlinear, rice: nonlinear, salt: linear, sugar: linear, groundnuts: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear, bananas: linear, maize_flour: linear, tomatoes: linear, cassava_meal: nonlinear, cowpeas: nonlinear, milk: linear, yam: linear, oranges: linear, watermelons: linear, spinach: linear, gari: nonlinear
Information on the model used for Somalia (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Milk (Camel) (1 L, Index Weight = 1)
Currency: SOS
Number of markets used: 21
Number of markets covered: 29
Number of food items: 4
Number of observations: oil: 699, rice: 1687, maize: 2185, milk: 541
Data coverage: 31.59%
Data coverage previous 12 months: 33.33%
Average annualized inflation: 6.03%
Maximum drawdown: -42.16%
Average annualized volatility: 10.71%
Average monthly price correlation between markets: 0.42
Average annual price correlation between markets: 0.75
R squared individual food items: rice: 0.9, maize: 0.88, sorghum: 0.87, milk: 0.43
F confidence score: 0.82
Imputation model: oil: linear, rice: nonlinear, maize: nonlinear, milk: linear
Information on the model used for South Sudan (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (Red) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Maize (White) (3.5 KG, Index Weight = 0.29), Millet (White) (3.5 KG, Index Weight = 0.29), Sorghum (White, Imported) (3.5 KG, Index Weight = 0.29), Sesame (3.5 KG, Index Weight = 0.29)
Currency: SSP
Number of markets used: 12
Number of markets covered: 24
Number of food items: 8
Number of observations: oil: 960, wheat_flour: 475, beans: 1222, groundnuts: 951, maize: 890, millet: 481, sorghum: 1182, sesame: 707
Data coverage: 39.49%
Data coverage previous 12 months: 62.59%
Average annualized inflation: 41.97%
Maximum drawdown: -55.05%
Average annualized volatility: 36.19%
Average monthly price correlation between markets: 0.71
Average annual price correlation between markets: 0.96
R squared individual food items: wheat_flour: 0.89, beans: 0.88, groundnuts: 0.88, maize: 0.85, millet: 0.85, sorghum: 0.83, sesame: 0.87, oil: 0.83
F confidence score: 0.87
Imputation model: oil: linear, wheat_flour: nonlinear, beans: nonlinear, groundnuts: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear, sesame: nonlinear
Information on the model used for Sudan (see working paper for more information)
Components: Wheat (90 KG, Index Weight = 0.01), Millet (3.5 KG, Index Weight = 0.29), Sorghum (White) (90 KG, Index Weight = 0.01)
Currency: SDG
Number of markets used: 15
Number of markets covered: 15
Number of food items: 3
Number of observations: wheat: 680, millet: 1920, sorghum: 1425
Data coverage: 48.09%
Data coverage previous 12 months: 45.56%
Average annualized inflation: 51.29%
Maximum drawdown: -28.31%
Average annualized volatility: 28.79%
Average monthly price correlation between markets: 0.67
Average annual price correlation between markets: 0.94
R squared individual food items: millet: 0.93, sorghum: 0.95, wheat: 0.81
F confidence score: 0.95
Imputation model: wheat: linear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Syrian Arab Republic (see working paper for more information)
Components: Bread (Bakery) (1.1 KG, Index Weight = 0.91), Oil (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Apples (1 KG, Index Weight = 1), Dates (1 KG, Index Weight = 1), Eggplants (1 KG, Index Weight = 1), Yogurt (1 KG, Index Weight = 1), Bulgur (1 KG, Index Weight = 1), Cheese (1 KG, Index Weight = 1), Chickpeas (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1), Salt (Iodised) (1 KG, Index Weight = 1), Parsley (1 Packet, Index Weight = 2)
Currency: SYP
Number of markets used: 56
Number of markets covered: 97
Number of food items: 19
Number of observations: bread: 3758, oil: 4024, rice: 3235, sugar: 4021, wheat_flour: 3944, beans: 2609, bananas: 1967, potatoes: 2173, tomatoes: 3013, apples: 2032, dates: 2820, eggplants: 2061, yogurt: 2873, bulgur: 2639, cheese: 2821, chickpeas: 2825, lentils: 3944, salt_iodised: 2594, parsley: 2889
Data coverage: 38.67%
Data coverage previous 12 months: 82.17%
Average annualized inflation: 32.42%
Maximum drawdown: -22.25%
Average annualized volatility: 15.62%
Average monthly price correlation between markets: 0.63
Average annual price correlation between markets: 0.96
R squared individual food items: bread: 0.95, oil: 0.95, rice: 0.92, sugar: 0.93, wheat_flour: 0.93, lentils: 0.91, beans: 0.85, bananas: 0.83, potatoes: 0.78, tomatoes: 0.85, apples: 0.82, dates: 0.85, eggplants: 0.83, yogurt: 0.83, bulgur: 0.86, cheese: 0.89, chickpeas: 0.79, salt_iodised: 0.82, parsley: 0.88
F confidence score: 0.88
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, beans: linear, bananas: linear, potatoes: linear, tomatoes: linear, apples: linear, dates: linear, eggplants: linear, yogurt: linear, bulgur: linear, cheese: linear, chickpeas: linear, lentils: nonlinear, salt_iodised: linear, parsley: linear
Information on the model used for Yemen, Rep. (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (Kidney Red) (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1)
Currency: YER
Number of markets used: 23
Number of markets covered: 23
Number of food items: 11
Number of observations: oil: 1591, rice: 1498, salt: 1521, sugar: 1621, wheat: 1878, wheat_flour: 1190, beans: 1650, onions: 1811, potatoes: 1775, tomatoes: 1800, lentils: 1525
Data coverage: 43.04%
Data coverage previous 12 months: 73.85%
Average annualized inflation: 12.03%
Maximum drawdown: -27.67%
Average annualized volatility: 15.01%
Average monthly price correlation between markets: 0.68
Average annual price correlation between markets: 0.84
R squared individual food items: wheat: 0.77, wheat_flour: 0.8, oil: 0.73, rice: 0.79, salt: 0.77, sugar: 0.82, beans: 0.81, onions: 0.69, potatoes: 0.73, tomatoes: 0.59, lentils: 0.75
F confidence score: 0.76
Imputation model: oil: linear, rice: linear, salt: linear, sugar: linear, wheat: nonlinear, wheat_flour: nonlinear, beans: linear, onions: linear, potatoes: linear, tomatoes: linear, lentils: linear
Components: Bread (1 KG, Index Weight = 1), Rice (Low Quality) (1 KG, Index Weight = 1), Wheat (1 KG, Index Weight = 1)
Currency: AFN
Number of markets used: 9
Number of markets covered: 40
Number of food items: 3
Number of observations: bread: 1192, rice: 1715, wheat: 1727
Data coverage: 74.48%
Data coverage previous 12 months: 40.43%
Average annualized inflation: 6.27%
Maximum drawdown: -42.27%
Average annualized volatility: 7.96%
Average monthly price correlation between markets: 0.57
Average annual price correlation between markets: 0.86
R squared individual food items: bread: 0.91, rice: 0.87, wheat: 0.91
F confidence score: 0.9
Imputation model: bread: nonlinear, rice: nonlinear, wheat: nonlinear
Information on the model used for Burkina Faso (see working paper for more information)
Components: Beans (Niebe) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 63
Number of markets covered: 64
Number of food items: 3
Number of observations: beans: 4507, maize: 5331, millet: 6190
Data coverage: 45.59%
Data coverage previous 12 months: 86.14%
Average annualized inflation: 7.73%
Maximum drawdown: -37.49%
Average annualized volatility: 14.53%
Average monthly price correlation between markets: 0.66
Average annual price correlation between markets: 0.87
R squared individual food items: maize: 0.8, millet: 0.79, sorghum: 0.78
F confidence score: 0.79
Imputation model: beans: linear, maize: nonlinear, millet: nonlinear
Information on the model used for Burundi (see working paper for more information)
Components: Rice (Low Quality, Local) (1 KG, Index Weight = 1), Beans (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Cassava Flour (1 KG, Index Weight = 1), Maize Flour (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Sweet Potatoes (1 KG, Index Weight = 1)
Currency: BIF
Number of markets used: 63
Number of markets covered: 72
Number of food items: 8
Number of observations: rice: 3872, beans: 3361, maize: 3550, bananas: 3364, cassava_flour: 3954, maize_flour: 3176, onions: 3495, potatoes: 3979
Data coverage: 30.39%
Data coverage previous 12 months: 52.78%
Average annualized inflation: 5.27%
Maximum drawdown: -28.59%
Average annualized volatility: 10.9%
Average monthly price correlation between markets: 0.51
Average annual price correlation between markets: 0.76
R squared individual food items: beans: 0.89, cassava_flour: 0.84, potatoes: 0.77, rice: 0.83, maize: 0.82, bananas: 0.7, maize_flour: 0.82, onions: 0.54
F confidence score: 0.79
Imputation model: rice: linear, beans: nonlinear, maize: linear, bananas: linear, cassava_flour: nonlinear, maize_flour: linear, onions: linear, potatoes: nonlinear
Information on the model used for Cameroon (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Long Grain, Imported) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Maize (90 KG, Index Weight = 0.01), Sorghum (Red) (90 KG, Index Weight = 0.01), Bananas (12 KG, Index Weight = 0.08), Potatoes (1 KG, Index Weight = 1), Cassava (Fresh) (5 KG, Index Weight = 0.2), CocoYam (Macabo) (20 KG, Index Weight = 0.05), Plantains (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 12
Number of markets covered: 64
Number of food items: 10
Number of observations: oil: 632, rice: 631, wheat_flour: 632, maize: 293, sorghum: 299, bananas: 416, potatoes: 740, cassava: 405, cocoyam: 431, plantains: 726
Data coverage: 21.61%
Data coverage previous 12 months: 17.43%
Average annualized inflation: 2.11%
Maximum drawdown: -15.93%
Average annualized volatility: 6.38%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.42
R squared individual food items: potatoes: 0.64, plantains: 0.69, oil: 0.97, rice: 0.98, wheat_flour: 0.98, maize: 0.98, sorghum: 0.99, bananas: 0.97, cassava: 0.96, cocoyam: 0.97
F confidence score: 0.94
Imputation model: oil: linear, rice: linear, wheat_flour: linear, maize: linear, sorghum: linear, bananas: linear, potatoes: nonlinear, cassava: linear, cocoyam: linear, plantains: nonlinear
Information on the model used for Central African Republic (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 20
Number of markets covered: 42
Number of food items: 3
Number of observations: oil: 983, rice: 891, maize: 843
Data coverage: 25.26%
Data coverage previous 12 months: 75.62%
Average annualized inflation: 1.28%
Maximum drawdown: -17.28%
Average annualized volatility: 8.85%
Average monthly price correlation between markets: 0.21
Average annual price correlation between markets: 0.08
R squared individual food items: maize: 0.6, cassava: 0.54, oil: 0.4
F confidence score: 0.52
Imputation model: oil: linear, rice: linear, maize: nonlinear
Information on the model used for Chad (see working paper for more information)
Components: Maize (White) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (Red) (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 37
Number of markets covered: 61
Number of food items: 3
Number of observations: maize: 1661, millet: 3191, sorghum: 2679
Data coverage: 35.06%
Data coverage previous 12 months: 54.05%
Average annualized inflation: 4.2%
Maximum drawdown: -42.43%
Average annualized volatility: 16.8%
Average monthly price correlation between markets: 0.54
Average annual price correlation between markets: 0.84
R squared individual food items: maize: 0.67, millet: 0.75, sorghum: 0.73
F confidence score: 0.72
Imputation model: maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Congo, Dem. Rep. (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Local) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Beans (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Cassava Flour (1 KG, Index Weight = 1), Cassava (Cossette) (1 KG, Index Weight = 1), Plantains (1 KG, Index Weight = 1), Maize Meal (1 KG, Index Weight = 1)
Currency: CDF
Number of markets used: 15
Number of markets covered: 83
Number of food items: 10
Number of observations: oil: 1647, rice: 1498, salt: 1383, sugar: 1396, beans: 1353, maize: 1486, cassava_flour: 1653, cassava: 1195, plantains: 1485, maize_meal: 1304
Data coverage: 48.45%
Data coverage previous 12 months: 18.06%
Average annualized inflation: 7.59%
Maximum drawdown: -18.02%
Average annualized volatility: 7.78%
Average monthly price correlation between markets: 0.3
Average annual price correlation between markets: 0.7
R squared individual food items: oil: 0.85, rice: 0.87, salt: 0.85, sugar: 0.92, beans: 0.87, maize: 0.83, cassava_flour: 0.84, cassava: 0.86, plantains: 0.84, maize_meal: 0.81
F confidence score: 0.86
Imputation model: oil: nonlinear, rice: nonlinear, salt: nonlinear, sugar: nonlinear, beans: nonlinear, maize: nonlinear, cassava_flour: nonlinear, cassava: nonlinear, plantains: nonlinear, maize_meal: nonlinear
Information on the model used for Congo, Rep. (see working paper for more information)
Components: Bread (1 KG, Index Weight = 1), Oil (Palm) (1 L, Index Weight = 1), Rice (Mixed, Low Quality) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Cassava (Fresh) (1 KG, Index Weight = 1)
Currency: XAF
Number of markets used: 5
Number of markets covered: 11
Number of food items: 6
Number of observations: bread: 300, oil: 349, rice: 470, wheat_flour: 223, groundnuts: 330, cassava: 430
Data coverage: 24.28%
Data coverage previous 12 months: 0%
Average annualized inflation: 1.88%
Maximum drawdown: -17.24%
Average annualized volatility: 9.02%
Average monthly price correlation between markets: 0.43
Average annual price correlation between markets: 0.33
R squared individual food items: bread: 0.84, oil: 0.67, rice: 0.75, wheat_flour: 0.93, groundnuts: 0.67, cassava: 0.79
F confidence score: 0.8
Imputation model: bread: linear, oil: linear, rice: linear, wheat_flour: linear, groundnuts: linear, cassava: linear
Information on the model used for Gambia, The (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Rice (Small Grain, Imported) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Beans (Dry) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Potatoes (Irish) (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Milk (1 KG, Index Weight = 1), Cabbage (1 KG, Index Weight = 1), Carrots (1 KG, Index Weight = 1), Garlic (1 KG, Index Weight = 1), Tea (1 Unit, Index Weight = 1)
Currency: GMD
Number of markets used: 13
Number of markets covered: 28
Number of food items: 16
Number of observations: oil: 1112, rice: 2274, salt: 1140, sugar: 1145, beans: 1122, groundnuts: 2149, millet: 2190, bananas: 1135, onions: 1151, potatoes: 1093, tomatoes: 1054, milk: 1124, cabbage: 1065, carrots: 1096, garlic: 1041, tea: 1117
Data coverage: 43.4%
Data coverage previous 12 months: 91.67%
Average annualized inflation: 4.59%
Maximum drawdown: -14.81%
Average annualized volatility: 6.59%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.73
R squared individual food items: rice: 0.88, groundnuts: 0.83, millet: 0.79, oil: 0.81, salt: 0.92, sugar: 0.87, beans: 0.71, bananas: 0.72, onions: 0.79, potatoes: 0.83, tomatoes: 0.64, milk: 0.74, cabbage: 0.61, carrots: 0.65, garlic: 0.75, tea: 0.79
F confidence score: 0.79
Imputation model: oil: linear, rice: nonlinear, salt: linear, sugar: linear, beans: linear, groundnuts: nonlinear, millet: nonlinear, bananas: linear, onions: linear, potatoes: linear, tomatoes: linear, milk: linear, cabbage: linear, carrots: linear, garlic: linear, tea: linear
Information on the model used for Guinea-Bissau (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 41
Number of markets covered: 45
Number of food items: 3
Number of observations: oil: 746, rice: 714, sugar: 776
Data coverage: 18.4%
Data coverage previous 12 months: 88.96%
Average annualized inflation: 3.86%
Maximum drawdown: -8.93%
Average annualized volatility: 4.05%
Average monthly price correlation between markets: 0.41
Average annual price correlation between markets: 0.87
R squared individual food items: oil: 0.86, rice: 0.89, sugar: 0.82
F confidence score: 0.86
Imputation model: oil: linear, rice: linear, sugar: linear
Information on the model used for Haiti (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 Gallon, Index Weight = 0.26), Sugar (White) (1 Marmite, Index Weight = 0.37), Wheat Flour (Imported) (1 Marmite, Index Weight = 0.37), Beans (Black) (1 Marmite, Index Weight = 0.37), Pasta (350 G, Index Weight = 2.86), Maize Meal (Local) (1 Marmite, Index Weight = 0.37)
Currency: HTG
Number of markets used: 9
Number of markets covered: 9
Number of food items: 6
Number of observations: oil: 864, sugar: 512, wheat_flour: 1557, beans: 989, pasta: 387, maize_meal: 1557
Data coverage: 58.4%
Data coverage previous 12 months: 69.6%
Average annualized inflation: 9.27%
Maximum drawdown: -30.75%
Average annualized volatility: 10.73%
Average monthly price correlation between markets: 0.61
Average annual price correlation between markets: 0.87
R squared individual food items: wheat_flour: 0.83, maize_meal: 0.83, oil: 0.93, sugar: 0.89, beans: 0.89, pasta: 0.95
F confidence score: 0.89
Imputation model: oil: linear, sugar: linear, wheat_flour: nonlinear, beans: linear, pasta: linear, maize_meal: nonlinear
Information on the model used for Iraq (see working paper for more information)
Components: Bread (Khoboz) (1 Unit, Index Weight = 1), Oil (Vegetable) (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Milk (Powder) (1 KG, Index Weight = 1), Dates (1 KG, Index Weight = 1), Tea (1 KG, Index Weight = 1), Cheese (Local) (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1), Salt (Iodised) (1 KG, Index Weight = 1)
Currency: IQD
Number of markets used: 18
Number of markets covered: 18
Number of food items: 14
Number of observations: bread: 1607, oil: 1431, rice: 851, sugar: 1520, wheat_flour: 1513, beans: 817, potatoes: 847, tomatoes: 887, milk: 838, dates: 828, tea: 834, cheese: 842, lentils: 815, salt_iodised: 845
Data coverage: 42.55%
Data coverage previous 12 months: 88.1%
Average annualized inflation: 0.74%
Maximum drawdown: -13.89%
Average annualized volatility: 3.45%
Average monthly price correlation between markets: 0.14
Average annual price correlation between markets: 0.36
R squared individual food items: bread: 0.97, oil: 0.95, rice: 0.95, sugar: 0.94, wheat_flour: 0.93, beans: 0.9, potatoes: 0.89, tomatoes: 0.88, milk: 0.88, dates: 0.75, tea: 0.89, cheese: 0.87, lentils: 0.85, salt_iodised: 0.85
F confidence score: 0.91
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, beans: linear, potatoes: linear, tomatoes: linear, milk: linear, dates: linear, tea: linear, cheese: linear, lentils: linear, salt_iodised: linear
Information on the model used for Lao PDR (see working paper for more information)
Components: Oil (Soybean) (1 L, Index Weight = 1), Rice (Glutinous, Second Quality) (1 KG, Index Weight = 1), Sugar (Brown) (1 KG, Index Weight = 1), Garlic (Small) (1 KG, Index Weight = 1)
Currency: LAK
Number of markets used: 17
Number of markets covered: 17
Number of food items: 4
Number of observations: oil: 1357, rice: 1671, sugar: 1330, garlic: 1295
Data coverage: 52.62%
Data coverage previous 12 months: 69.49%
Average annualized inflation: 1.85%
Maximum drawdown: -3.71%
Average annualized volatility: 2.06%
Average monthly price correlation between markets: 0.1
Average annual price correlation between markets: 0.3
R squared individual food items: oil: 0.88, rice: 0.87, sugar: 0.88, garlic: 0.73
F confidence score: 0.85
Imputation model: oil: linear, rice: linear, sugar: linear, garlic: linear
Information on the model used for Lebanon (see working paper for more information)
Components: Bread (Pita) (1 KG, Index Weight = 1), Oil (Sunflower) (5 L, Index Weight = 0.2), Rice (Imported, Egyptian) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (White) (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Milk (Powder) (900 G, Index Weight = 1.11), Pasta (Spaghetti) (1 KG, Index Weight = 1), Cabbage (1 KG, Index Weight = 1), Cucumbers (Greenhouse) (1 KG, Index Weight = 1), Tomatoes (Paste) (1.3 KG, Index Weight = 0.77), Bulgur (Brown) (1 KG, Index Weight = 1), Cheese (Picon) (160 G, Index Weight = 6.25), Chickpeas (1 KG, Index Weight = 1), Lentils (Red) (1 KG, Index Weight = 1)
Currency: LBP
Number of markets used: 26
Number of markets covered: 26
Number of food items: 16
Number of observations: bread: 2149, oil: 1667, rice: 2295, salt: 2036, sugar: 2285, wheat_flour: 876, beans: 2269, milk: 2231, pasta: 2316, cabbage: 820, cucumbers: 757, tomatoes_paste: 1771, bulgur: 2350, cheese: 2236, chickpeas: 1603, lentils: 1159
Data coverage: 55.87%
Data coverage previous 12 months: 37.7%
Average annualized inflation: 36.84%
Maximum drawdown: -18.08%
Average annualized volatility: 18.98%
Average monthly price correlation between markets: 0.84
Average annual price correlation between markets: 1
R squared individual food items: bread: 0.97, oil: 0.97, rice: 0.91, salt: 0.93, sugar: 0.92, beans: 0.91, milk: 0.98, pasta: 0.87, cabbage: 0.9, tomatoes_paste: 0.92, bulgur: 0.92, cheese: 0.96, wheat_flour: 0.77, cucumbers: 0.75, chickpeas: 0.72, lentils: 0.8
F confidence score: 0.88
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, salt: nonlinear, sugar: nonlinear, wheat_flour: linear, beans: nonlinear, milk: nonlinear, pasta: nonlinear, cabbage: nonlinear, cucumbers: linear, tomatoes_paste: nonlinear, bulgur: nonlinear, cheese: nonlinear, chickpeas: linear, lentils: linear
Information on the model used for Liberia (see working paper for more information)
Components: Oil (Palm) (1 Gallon, Index Weight = 0.26), Rice (Imported) (50 KG, Index Weight = 0.02), Cassava (Fresh) (50 KG, Index Weight = 0.02), Cowpeas (1 KG, Index Weight = 1)
Currency: LRD
Number of markets used: 17
Number of markets covered: 24
Number of food items: 4
Number of observations: oil: 987, rice: 1365, cassava: 1052, cowpeas: 1076
Data coverage: 25.36%
Data coverage previous 12 months: 0%
Average annualized inflation: 8.03%
Maximum drawdown: -9.69%
Average annualized volatility: 6.34%
Average monthly price correlation between markets: 0.24
Average annual price correlation between markets: 0.39
R squared individual food items: oil: 0.89, rice: 0.95, cassava: 0.85, cowpeas: 0.89
F confidence score: 0.89
Imputation model: oil: linear, rice: linear, cassava: linear, cowpeas: linear
Information on the model used for Mali (see working paper for more information)
Components: Rice (Local) (1 KG, Index Weight = 1), Beans (Niebe) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 80
Number of markets covered: 127
Number of food items: 6
Number of observations: rice: 9835, beans: 5015, groundnuts: 4545, maize: 7529, millet: 10558, sorghum: 10051
Data coverage: 52.88%
Data coverage previous 12 months: 76.7%
Average annualized inflation: 4.57%
Maximum drawdown: -25.56%
Average annualized volatility: 7.89%
Average monthly price correlation between markets: 0.6
Average annual price correlation between markets: 0.9
R squared individual food items: rice: 0.95, maize: 0.86, millet: 0.88, sorghum: 0.89, beans: 0.63, groundnuts: 0.59
F confidence score: 0.84
Imputation model: rice: nonlinear, beans: linear, groundnuts: linear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Mozambique (see working paper for more information)
Components: Oil (Vegetable, Local) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Sugar (Brown, Local) (1 KG, Index Weight = 1), Wheat Flour (Local) (1 KG, Index Weight = 1), Groundnuts (Small, Shelled) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Cowpeas (1 KG, Index Weight = 1), Maize Meal (White, First Grade) (1 KG, Index Weight = 1)
Currency: MZN
Number of markets used: 24
Number of markets covered: 95
Number of food items: 8
Number of observations: oil: 3236, rice: 3235, sugar: 3267, wheat_flour: 2310, groundnuts: 2096, maize: 3524, cowpeas: 2280, maize_meal: 2246
Data coverage: 59.8%
Data coverage previous 12 months: 34.51%
Average annualized inflation: 7.33%
Maximum drawdown: -29.44%
Average annualized volatility: 7.42%
Average monthly price correlation between markets: 0.35
Average annual price correlation between markets: 0.88
R squared individual food items: oil: 0.93, rice: 0.91, sugar: 0.94, wheat_flour: 0.89, groundnuts: 0.84, maize: 0.91, cowpeas: 0.75, maize_meal: 0.9
F confidence score: 0.89
Imputation model: oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, groundnuts: nonlinear, maize: nonlinear, cowpeas: nonlinear, maize_meal: nonlinear
Information on the model used for Myanmar (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Pulses (1 KG, Index Weight = 1), Rice (Low Quality) (1 KG, Index Weight = 1)
Currency: MMK
Number of markets used: 33
Number of markets covered: 195
Number of food items: 3
Number of observations: oil: 2660, pulses: 2878, rice: 4372
Data coverage: 42.46%
Data coverage previous 12 months: 36.17%
Average annualized inflation: 4.63%
Maximum drawdown: -31.82%
Average annualized volatility: 9.14%
Average monthly price correlation between markets: 0.27
Average annual price correlation between markets: 0.64
R squared individual food items: oil: 0.93, rice: 0.88, salt: 0.85
F confidence score: 0.89
Imputation model: oil: nonlinear, pulses: linear, rice: nonlinear
Information on the model used for Niger (see working paper for more information)
Components: Rice (Imported) (1 KG, Index Weight = 1), Maize (1 KG, Index Weight = 1), Millet (1 KG, Index Weight = 1), Sorghum (1 KG, Index Weight = 1)
Currency: XOF
Number of markets used: 68
Number of markets covered: 79
Number of food items: 4
Number of observations: rice: 9212, maize: 8418, millet: 10488, sorghum: 9241
Data coverage: 73.61%
Data coverage previous 12 months: 64.89%
Average annualized inflation: 3.18%
Maximum drawdown: -23.59%
Average annualized volatility: 8.93%
Average monthly price correlation between markets: 0.49
Average annual price correlation between markets: 0.76
R squared individual food items: rice: 0.94, maize: 0.83, millet: 0.85, sorghum: 0.82
F confidence score: 0.87
Imputation model: rice: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Nigeria (see working paper for more information)
Components: Oil (Palm) (1 L, Index Weight = 1), Rice (Imported) (50 KG, Index Weight = 0.02), Salt (250 G, Index Weight = 4), Sugar (1.3 KG, Index Weight = 0.77), Groundnuts (Shelled) (100 KG, Index Weight = 0.01), Maize (White) (100 KG, Index Weight = 0.01), Millet (100 KG, Index Weight = 0.01), Sorghum (White) (100 KG, Index Weight = 0.01), Bananas (1.3 KG, Index Weight = 0.77), Maize Flour (1.3 KG, Index Weight = 0.77), Tomatoes (0.5 KG, Index Weight = 2), Cassava Meal (Gari, Yellow) (100 KG, Index Weight = 0.01), Cowpeas (White) (100 KG, Index Weight = 0.01), Milk (20 G, Index Weight = 50), Yam (1 KG, Index Weight = 1), Oranges (400 G, Index Weight = 2.5), Watermelons (2.1 KG, Index Weight = 0.48), Spinach (300 G, Index Weight = 3.33), Gari (White) (100 KG, Index Weight = 0.01)
Currency: NGN
Number of markets used: 33
Number of markets covered: 35
Number of food items: 19
Number of observations: oil: 1136, rice: 1268, salt: 864, sugar: 886, groundnuts: 1304, maize: 1516, millet: 1384, sorghum: 1466, bananas: 1012, maize_flour: 1087, tomatoes: 957, cassava_meal: 1067, cowpeas: 1381, milk: 1041, yam: 968, oranges: 1036, watermelons: 1074, spinach: 778, gari: 1248
Data coverage: 27.93%
Data coverage previous 12 months: 43.33%
Average annualized inflation: 2.03%
Maximum drawdown: -51.01%
Average annualized volatility: 12.32%
Average monthly price correlation between markets: 0.29
Average annual price correlation between markets: 0.66
R squared individual food items: oil: 0.95, rice: 0.95, groundnuts: 0.94, maize: 0.94, millet: 0.94, sorghum: 0.93, cassava_meal: 0.95, cowpeas: 0.92, gari: 0.94, salt: 0.89, sugar: 0.99, bananas: 0.95, maize_flour: 0.98, tomatoes: 0.82, milk: 0.96, yam: 0.98, oranges: 0.95, watermelons: 0.75, spinach: 0.83
F confidence score: 0.87
Imputation model: oil: nonlinear, rice: nonlinear, salt: linear, sugar: linear, groundnuts: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear, bananas: linear, maize_flour: linear, tomatoes: linear, cassava_meal: nonlinear, cowpeas: nonlinear, milk: linear, yam: linear, oranges: linear, watermelons: linear, spinach: linear, gari: nonlinear
Information on the model used for Somalia (see working paper for more information)
Components: Oil (Vegetable, Imported) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Maize (White) (1 KG, Index Weight = 1), Milk (Camel) (1 L, Index Weight = 1)
Currency: SOS
Number of markets used: 21
Number of markets covered: 29
Number of food items: 4
Number of observations: oil: 699, rice: 1687, maize: 2185, milk: 541
Data coverage: 31.59%
Data coverage previous 12 months: 33.33%
Average annualized inflation: 6.03%
Maximum drawdown: -42.16%
Average annualized volatility: 10.71%
Average monthly price correlation between markets: 0.42
Average annual price correlation between markets: 0.75
R squared individual food items: rice: 0.9, maize: 0.88, sorghum: 0.87, milk: 0.43
F confidence score: 0.82
Imputation model: oil: linear, rice: nonlinear, maize: nonlinear, milk: linear
Information on the model used for South Sudan (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (Red) (1 KG, Index Weight = 1), Groundnuts (Shelled) (1 KG, Index Weight = 1), Maize (White) (3.5 KG, Index Weight = 0.29), Millet (White) (3.5 KG, Index Weight = 0.29), Sorghum (White, Imported) (3.5 KG, Index Weight = 0.29), Sesame (3.5 KG, Index Weight = 0.29)
Currency: SSP
Number of markets used: 12
Number of markets covered: 24
Number of food items: 8
Number of observations: oil: 960, wheat_flour: 475, beans: 1222, groundnuts: 951, maize: 890, millet: 481, sorghum: 1182, sesame: 707
Data coverage: 39.49%
Data coverage previous 12 months: 62.59%
Average annualized inflation: 41.97%
Maximum drawdown: -55.05%
Average annualized volatility: 36.19%
Average monthly price correlation between markets: 0.71
Average annual price correlation between markets: 0.96
R squared individual food items: wheat_flour: 0.89, beans: 0.88, groundnuts: 0.88, maize: 0.85, millet: 0.85, sorghum: 0.83, sesame: 0.87, oil: 0.83
F confidence score: 0.87
Imputation model: oil: linear, wheat_flour: nonlinear, beans: nonlinear, groundnuts: nonlinear, maize: nonlinear, millet: nonlinear, sorghum: nonlinear, sesame: nonlinear
Information on the model used for Sudan (see working paper for more information)
Components: Wheat (90 KG, Index Weight = 0.01), Millet (3.5 KG, Index Weight = 0.29), Sorghum (White) (90 KG, Index Weight = 0.01)
Currency: SDG
Number of markets used: 15
Number of markets covered: 15
Number of food items: 3
Number of observations: wheat: 680, millet: 1920, sorghum: 1425
Data coverage: 48.09%
Data coverage previous 12 months: 45.56%
Average annualized inflation: 51.29%
Maximum drawdown: -28.31%
Average annualized volatility: 28.79%
Average monthly price correlation between markets: 0.67
Average annual price correlation between markets: 0.94
R squared individual food items: millet: 0.93, sorghum: 0.95, wheat: 0.81
F confidence score: 0.95
Imputation model: wheat: linear, millet: nonlinear, sorghum: nonlinear
Information on the model used for Syrian Arab Republic (see working paper for more information)
Components: Bread (Bakery) (1.1 KG, Index Weight = 0.91), Oil (1 L, Index Weight = 1), Rice (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (White) (1 KG, Index Weight = 1), Bananas (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Apples (1 KG, Index Weight = 1), Dates (1 KG, Index Weight = 1), Eggplants (1 KG, Index Weight = 1), Yogurt (1 KG, Index Weight = 1), Bulgur (1 KG, Index Weight = 1), Cheese (1 KG, Index Weight = 1), Chickpeas (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1), Salt (Iodised) (1 KG, Index Weight = 1), Parsley (1 Packet, Index Weight = 2)
Currency: SYP
Number of markets used: 56
Number of markets covered: 97
Number of food items: 19
Number of observations: bread: 3758, oil: 4024, rice: 3235, sugar: 4021, wheat_flour: 3944, beans: 2609, bananas: 1967, potatoes: 2173, tomatoes: 3013, apples: 2032, dates: 2820, eggplants: 2061, yogurt: 2873, bulgur: 2639, cheese: 2821, chickpeas: 2825, lentils: 3944, salt_iodised: 2594, parsley: 2889
Data coverage: 38.67%
Data coverage previous 12 months: 82.17%
Average annualized inflation: 32.42%
Maximum drawdown: -22.25%
Average annualized volatility: 15.62%
Average monthly price correlation between markets: 0.63
Average annual price correlation between markets: 0.96
R squared individual food items: bread: 0.95, oil: 0.95, rice: 0.92, sugar: 0.93, wheat_flour: 0.93, lentils: 0.91, beans: 0.85, bananas: 0.83, potatoes: 0.78, tomatoes: 0.85, apples: 0.82, dates: 0.85, eggplants: 0.83, yogurt: 0.83, bulgur: 0.86, cheese: 0.89, chickpeas: 0.79, salt_iodised: 0.82, parsley: 0.88
F confidence score: 0.88
Imputation model: bread: nonlinear, oil: nonlinear, rice: nonlinear, sugar: nonlinear, wheat_flour: nonlinear, beans: linear, bananas: linear, potatoes: linear, tomatoes: linear, apples: linear, dates: linear, eggplants: linear, yogurt: linear, bulgur: linear, cheese: linear, chickpeas: linear, lentils: nonlinear, salt_iodised: linear, parsley: linear
Information on the model used for Yemen, Rep. (see working paper for more information)
Components: Oil (Vegetable) (1 L, Index Weight = 1), Rice (Imported) (1 KG, Index Weight = 1), Salt (1 KG, Index Weight = 1), Sugar (1 KG, Index Weight = 1), Wheat (1 KG, Index Weight = 1), Wheat Flour (1 KG, Index Weight = 1), Beans (Kidney Red) (1 KG, Index Weight = 1), Onions (1 KG, Index Weight = 1), Potatoes (1 KG, Index Weight = 1), Tomatoes (1 KG, Index Weight = 1), Lentils (1 KG, Index Weight = 1)
Currency: YER
Number of markets used: 23
Number of markets covered: 23
Number of food items: 11
Number of observations: oil: 1591, rice: 1498, salt: 1521, sugar: 1621, wheat: 1878, wheat_flour: 1190, beans: 1650, onions: 1811, potatoes: 1775, tomatoes: 1800, lentils: 1525
Data coverage: 43.04%
Data coverage previous 12 months: 73.85%
Average annualized inflation: 12.03%
Maximum drawdown: -27.67%
Average annualized volatility: 15.01%
Average monthly price correlation between markets: 0.68
Average annual price correlation between markets: 0.84
R squared individual food items: wheat: 0.77, wheat_flour: 0.8, oil: 0.73, rice: 0.79, salt: 0.77, sugar: 0.82, beans: 0.81, onions: 0.69, potatoes: 0.73, tomatoes: 0.59, lentils: 0.75
F confidence score: 0.76
Imputation model: oil: linear, rice: linear, salt: linear, sugar: linear, wheat: nonlinear, wheat_flour: nonlinear, beans: linear, onions: linear, potatoes: linear, tomatoes: linear, lentils: linear
Access policy
Restrictions
The estimates presented in this dataset are all based on publicly-available data from the World Food Programme.
The dataset of price estimates is published as open data.
The dataset of price estimates is published as open data.
Citation requirements
Please cite this dataset as follows: Andrée, B. P. J. (2021). Monthly food price inflation estimates by country (Version 2022-06-28). WLD_2021_RTFP-CTRY_v02_M. Washington, DC: World Bank Microdata Library.
Access authority
| Name | Affiliation | URL |
|---|---|---|
| Data Help Desk | World Bank, Development Data Group | Link |
Location of Data Collection
World Bank Microdata Library, FCV