Food Price Index in World decreased to 126.40 Index Points in October from 128.50 Index Points in September of 2025. This dataset includes a chart with historical data for World Food Price Index.

The FAO Food Price Index (FFPI) averaged 128.8 points in September 2025. This represents an increase of 3.4 percent compared to the same month of the previous year. Food prices worldwide Some food commodities have been hit harder than others in the past years. Global dairy, meat, and vegetable oil prices were on an upward trajectory in the first half of 2025. Regionally, the European Union (EU) and the UK have experienced a particularly high increase in the annual consumer prices for food and non-alcoholic beverages, as compared to other selected countries worldwide. Inflation in Europe The inflation rate for food in the EU grew from 0.2 percent in May 2021 to 19.2 percent in March 2023, as compared to the same month in the previous year. In the following months, the food inflation started decreasing again, yet has picked up again in 2025 in line with the global trend. The overall inflation rate in the Euro area reached its peak in December 2022 at 9.2 percent. The rate has since fallen to 2.4 percent in December 2024. As measured by the Harmonized Index of Consumer Prices (HICP), inflation rates in Europe were highest in Turkey, Romania, and Estonia as of April 2025.

The annual FAO Food Price Index* (FFPI) averaged 127.8 points in 2025. This represents an increase of 4.7 percent compared to the previous year. That year, the highest price index was registered in the vegetable oils category, at 160.1 points.

The ****FAO Food Price Index**** consists of the average of 5 commodity group rice indices mentioned here, weighted with the average export shares of each group for 2014-2016: in total 95 price quotations considered by FAO commodity specialists as representing the international prices of the food commodities are included in the overall index. Each sub-index is a weighted average of the price relatives of the commodities included in the group, with the base period price consisting of the averages for the years 2014-2016.

• Unlike for other commodity groups, most prices utilized in the calculation of the FAO Meat Price Index are not available when the FAO Food Price Index is computed and published; therefore, the value of the Meat Price Index for the most recent months is derived from a mixture of projected and observed prices. This can, at times, require significant revisions in the final value of the FAO Meat Price Index which could in turn influence the value of the FAO Food Price Index.

Graph and download economic data for Global price of Food index (PFOODINDEXM) from Jan 1992 to Jun 2025 about World, food, indexes, and price.

The FAO vegetable oil Price Index* reached 178.32 index points in June of 2008 during the financial crisis. During the pandemic, the price index rose to 184.56 points in October of 2021. After the start of the war in Ukraine, the index jumped to over 251 points in March of 2022. As of September 2025, the index had slightly declined to 167.9 points. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page. For further information about the Russian invasion of Ukraine, please visit our dedicated page on the topic.

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, required safety net financing, and corresponding estimates expressed on the Integrated Phase Classification (IPC) scale. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage. This model has been extended to express outputs on the IPC scale by converting estimates using a nonlinear beta regression estimated on a normalized range, and distributionally adjusted using a smooth threshold transformation.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

Data Sources: https://www.fao.org/worldfoodsituation/foodpricesindex/en https://uk.finance.yahoo.com/quote/ABF.L/history?p=ABF.L

Data Headings: Date: The date of the record, in the format DD/MM/YYYY. Food Price Index: A measure of the monthly change in international prices of a basket of food commodities. Meat: The index value for meat prices. Dairy: The index value for dairy product prices. Cereals: The index value for cereal prices. Oils: The index value for oil prices. Sugar: The index value for sugar prices. Monthly High ABF.L Stock Price: The highest stock price of ABF.L (Associated British Foods plc) for each month.

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.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

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

In September 2025, the The FAO Food Price Index (FFPI) for cereals averaged 105 points. This represents a decrease of 7.6 percent compared to the same month of the previous year.

Cereals Price Index in World decreased to 103.60 Index Points in October from 104.90 Index Points in September of 2025. This dataset includes a chart with historical data for World Cereals Price Index.

This replication folder describes the Stata v17 “do file” (code file) for statistical analysis for "Food inflation and child undernutrition in low and middle income countries " by Derek Headey & Marie Ruel. This do file can be used to replicate the analysis in the study mentioned above, published in Nature Communications. The study uses a combination of Demographic Health Survey (DHS) data for child, maternal, household level variables and national level indicators on real food price changes drawn from FAOSTAT, as well as conflict and climate variables. In summary, this is a large multi-country DHS dataset merged with FAO food and total consumer price indices (CPIs) and various other national level control variables. These are DHS surveys from 2000 onwards only.

The authors cannot publicly share the DHS data but can share it upon request, provided we can obtain approval from the DHS implementers. To make a request to access the data for this paper, please email Derek Headey at d.headey@cgiar.org. Alternatively researchers can access the raw DHS data from: https://dhsprogram.com/data/available-datasets.cfm and the country level indicators from the Food and Agriculture Organisation Consumer Prices data portal (https://www.fao.org/faostat/en/#data/CP) as well as The World Bank World Development Indicators (https://databank.worldbank.org/source/world-development-indicators) for obtaining data on various control variables.

Определение: Индикатор аномалий цен на продовольствие (IFPA) определяет аномально высокие рыночные цены. IFPA основывается на взвешенном совокупном темпе роста, который учитывает как рост цен в течение года, так и рост цен за год. Индикатор непосредственно оценивает рост цен за конкретный месяц на протяжении многих лет с учетом сезонности на сельскохозяйственных рынках и инфляции, позволяя ответить на вопрос о том, является ли изменение цен ненормальным для какого-либо конкретного периода. [Переведено с en: английского языка] Тематическая область: Цели в области устойчивого развития [Переведено с en: английского языка] Область применения: ПОКАЗАТЕЛЬ 2.c.1 - Показатель аномалий цен на продовольствие [Переведено с en: английского языка] Единица измерения: Индекс [Переведено с en: английского языка] Источник данных: Онлайн-база данных по мониторингу и анализу цен на продовольствие (FPMA): https://fpma.apps.fao.org/giews/food-prices/tool/public/#/dataset/domestic. [Переведено с es: испанского языка] Последнее обновление: Jan 7 2024 11:19PM Организация-источник: Глобальная база данных Организации Объединенных Наций по ЦУР [Переведено с en: английского языка] Definition: The indicator of food price anomalies (IFPA) identifies markets prices that are abnormally high. The IFPA relies on a weighted compound growth rate that accounts for both within year and across year price growth. The indicator directly evaluates growth in prices over a particular month over many years, taking into account seasonality in agricultural markets and inflation, allowing to answer the question of whether or not a change in price is abnormal for any particular period. Thematic Area: Sustainable Development Goals Application Area: INDICATOR 2.c.1 Indicator of food price anomalies Unit of Measurement: Index Data Source: Food Price Monitoring and Analysis (FPMA) online database: https://fpma.apps.fao.org/giews/food-prices/tool/public/#/dataset/domestic. Last Update: Jan 7 2024 11:19PM Source Organization: United Nations Global SDG Database

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

Oils Price Index in World increased to 169.40 Index Points in October from 167.90 Index Points in September of 2025. This dataset includes a chart with historical data for World Oils Price Index.

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

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

STUDY TYPE Monthly currency exchange rate estimates in fragile countries

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

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

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

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

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

The Nitrogen Fertilizer Application data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Phosphorus Fertilizer Application data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of phosphorus fertilizer nutrients applied to croplands. The national-level phosphorus fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) "Fertilizer Use by Crop 2002" statistics database that is available by request from the Food and Agriculture Organization (FAO).The number of crop-specific fertilizer application rates reported for each country ranged from 2 crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Abstract

Starting June 1999, after the intervention of NATO in the conflict between Kosovo and Serbia (FRY), the United Nations provided interim administration for the province. The consequences of the conflict on the living standards of the population were severe, with the collapse of the industrial sector, the paralysis of agriculture, and extensive damage to private housing, education and health facilities and other infrastructure. In addition, the conflict brought massive population displacement both within Kosovo and abroad.

A year later, Kosovo was in a process of transition from emergency relief to long-term economic development. The purpose of the survey was to provide crucial information for policy and program design for use by the United Nations Interim Administration Mission in Kosovo (UNMIK), international donors, non-governmental organizations (NGOs), and the Kosovar community at large for poverty alleviation and inequality reduction.

During the same period, the Food and Agriculture Organization (FAO) was planning an agriculture and livestock survey. It was decided to join both surveys, in order to pool resources and provide better assistance to the newly re-formed Statistical Office of Kosovo (SOK) and to take into account the extensive Kosovar peasant household economy. Therefore the agriculture and food aid modules are more developed than those of a standard LSMS survey.

The International Organization for Migration (IOM) also was interested in information related to labor force and employment. They had run a socio-demographic and reproductive health survey with the United Nations Population Fund, covering approximately 10,000 households at the end of 1999. IOM provided the urban sampling frame for the present survey.

Geographic coverage

Kosovo. Domains: Urban/rural; Area of Responsibility (American, British, French, German, Italian); Serbian minority

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

The sample design used in the Kosovo LSMS 2000 had to contend with the fact that the last census, conducted in 1991, was rendered obsolete by the boycott of the Albanian population and by the massive displacements since March 1998. A Housing Damage Assessment Survey (HDAS) was conducted in February 1999 and updated in June 1999 by the International Management Group (IMG) and the United Nations High Commissioner for Refugees (UNHCR) in the rural areas. The survey covered 95 percent of the Albanian rural areas and provided the basis for the rural sampling frame, after updating. The updating and household listings in selected villages were conducted by FAO.

Since the HDAS did not cover Serbian villages, a quick counting4 of housing units was performed in these villages, following a procedure similar to the one in the urban areas. In urban areas, the original plan was to use the information from the on-going individual voters’ registration conducted by the Organization for Security and Cooperation in Europe (OSCE). Since the registration was limited to individuals above 16 years old, it was then decided to conduct a quick counting of households in the 22 urban areas. The quick counting and subsequent listing of households was performed by IOM, under the supervision of the sampling expert hired by the World Bank. . FRAMEWORK

UNMIK divided Kosovo into 5 areas of responsibility (AR), roughly equivalent to the former regions (American – Southeast, British – East including Pristina, French – North, German-South, Italian – West). The rural frame used the IMG/UNHCR Housing Damage Assessment Survey. It was updated with the collaboration of FAO and provided much better information on which to build the sample for the survey. Aerial pictures of the villages selected in the survey were used to help identifying housing units. Only one household was interviewed in each housing unit. For the Serbian villages, counting households and making listings had to be elaborated by the survey team.

In urban areas, IOM contracted the quick counting to SOK in the Albanian cities and to firms in the Serb areas. These firms updated existing lists, or performed some quick counting. Using the updated information IOM created enumeration areas of size 150-200 housing units. Based on this quick counting, a full listing took place in all the selected EAs and 12 households were randomly selected. Given safety issues and quality problems discovered at the enumeration stage, the Serb urban listings were revised after the end of the survey, by the Serb survey team, who had performed the rural listings.

The sample was preset at 2,880 households in order to allow analyses in the following breakdowns: (a) Kosovo as a whole; (b) by area of responsibility, (c) by urban/rural locations. In addition, the survey data can be used to derive separate estimates for the Serbian minority.

In the rural area, 30 Albanian villages were randomly selected in each AR and a listing of all households in the village was established.5 In each village, 12 households were then randomly selected (8 for interviewing and 4 reserve households). Similarly, 30 urban enumeration areas (between 150 and 200 households lie in each urban EA) were randomly selected in the Albanian part of each AR. Twelve households were then selected in each EA. In the rural area, 30 Serb villages were selected from the three municipalities in the northern part of Kosovo, the enclaves and the municipality of Strepce. Thirty urban EA were selected in the same region. In each village and urban area, 12 households were then randomly selected.

STRATIFICATION

In addition to the explicit stratification of the areas of responsibility and the ethnic composition in each rural and urban category, an implicit stratification of geographic ordering in a serpentine method in the villages and urban enumeration areas was followed. In order to be able to provide estimates for the separate domains described above, it was recommended that 240 households be interviewed in each domain. We had very little prior knowledge of response rates. In the rural villages, it was decided to select 12 households and identify 4 of them as “reserve households”. These reserve households were to be used only in specific cases, described at length to the logistics person/driver of the interviewing team. The final sample size was 1,200 rural and urban Albanian households and 240 rural and urban Serb households, for a total sample size of 2,880 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used to collect the information: a household questionnaire and a community questionnaire. No anthropometric information was collected as malnutrition problems, facing Kosovar children and women, would not be detected by these procedures.

Since FAO and SOK were conducting a price survey in 7 cities of Kosovo, on a monthly basis, it was decided to not include a separate price questionnaire but use the data from the FAO-SOK price survey. The Kosovo LSMS 2000 collected information using a household questionnaire, which was based in part on the standard LSMS questionnaire developed in Grosh and Glewwe (2000).

The standard questionnaire was adapted to the specifics of the Kosovar environment and special modules about displacement, food aid and social protection were added. Individual modules were administered as much as possible to most informed respondents. Box 1 contains a summary of the content of the questionnaire.

The community questionnaire was designed to collect information on community-level infrastructure, with a special emphasis on school and health facilities as well as displaced persons issues. Box 2 contains a summary of the content of the community questionnaire. [Note: Community is defined as the Primary Sampling Unit (PSU) of the survey. In rural areas, it generally encompasses villages unless these are less than 50 households (in which case, they were grouped with a neighboring village) or more than 200 households (in which case, they were broken-up in PSUs of 50-200 households). In urban areas, community is defined as the Enumeration Area but includes the larger city when referring to secondary school and university, hospitals and factories.]

Response rate

Households from the original sample selection which could not be interviewed were replaced by reserve households to reach the final sample size. The non-response rate among households originally selected for inclusion in the sample in rural Albanian areas was 11.8 percent and 20.8 percent in urban Albanian areas. These rates in the Serbian areas were 14.2 percent among rural households and 39.2 percent among urban households.

In the rural Albanian areas, non-response came mostly from households having moved outside of the village. A few refusals were due to the fact that households were in mourning or celebrating other religious occasions (wedding, baptisms, circumcisions, etc…), or the household head was a women alone. There were only 20 actual refusals of the originally selected households, only 2 percent of the 1,200 households originally contacted.

In the Serbian rural areas, half of the non-responses were due to households having traveled to Serbia for visits (holidays, health care issues, indefinite travel….). Other reasons included: interviewer’s safety (houses too isolated) and households refusing to respond in the absence of the head. There were only 5 such cases, again only 2 percent of the 240 households originally contacted. In the urban areas, 10 percent of the non-responses were linked to listings problems (non-existent addresses).

[Переведено с en: английского языка] Definition: This indicator corresponds to the intensity of water use for the agriculture sector (extraction of water for agricultural use / added value of agriculture at constant 2010 prices in million dollars). The indicator reflects the pressure on water resources due to the extraction of water for agricultural fines and their economic production. A low indicator value shows that less water is used to produce a unit of agricultural value added. When this value increases, it would detect an increase in the amount of water used to produce a unit of agricultural value added. Agricultural water withdrawal refers to annual quantity of self-supplied water withdrawn for irrigation, livestock and aquaculture purposes. Water for the dairy and meat industries and industrial processing of harvested agricultural products is included under industrial water withdrawal. Water withdraw for the agricultural sector can include water from primary renewable and secondary freshwater resources, as well as water from over-abstraction of renewable groundwater or withdrawal from fossil groundwater, direct use of agricultural drainage water, direct use of (treated) wastewater, and desalinated water. The added value of agriculture is calculated for agriculture, livestock, hunting, forestry and fishing in millions of dollars at constant 2010 prices. Thematic Area: Environmental Statistics and Indicators Application Area: Abstraction, use and returns of water Unit of Measurement: Agricultural water withdrawal (thousand m3) per million dollars of added value (at constant 2018 prices) Data Source: calculated on the basis of FAO, Global water information system (AQUASTAT) [online] Comments: For further information about these variables, see the following websites: -Global Information System on water use in agriculture and rural AQUASTAT of the United Nations for Food and Agriculture Organization (FAO): http://www.fao.org/nr/water/aquastat/main/indexesp.stm -CEPALSTAT. Annual Gross Domestic Product (GDP) by activity at constant prices. https://cepalstat-prod.cepal.org/cepalstat/tabulador/SisGen_MuestraFicha_puntual.asp?id_aplicacion=1&id_estudio=1&indicador=2216&idioma=i Last Update: Nov 20 2023 6:24PM Source Organization: Economic Commission for Latin America and the Carribbean