Explore a comprehensive dataset detailing the list of products exported by GCC countries . Find information on commodities such as cocoa, apparel, pearls, vehicles, plastics, and more. Discover valuable insights into the export activities of Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

Cocoa and cocoa preparations, Articles of apparel and clothing accessories, knitted or crocheted, Natural or cultured pearls, precious or semi-precious stones, precious metals, metals clad ..., Vehicles other than railway or tramway rolling stock, and parts and accessories thereof, Plastics and articles thereof, Raw hides and skins (other than furskins) and leather, Prepared feathers and down and articles made of feathers or of down; artificial flowers; articles ..., All products, Railway or tramway locomotives, rolling stock and parts thereof; railway or tramway track fixtures ..., Special woven fabrics; tufted textile fabrics; lace; tapestries; trimmings; embroidery, Animal or vegetable fats and oils and their cleavage products; prepared edible fats; animal ..., Paper and paperboard; articles of paper pulp, of paper or of paperboard, Wadding, felt and nonwovens; special yarns; twine, cordage, ropes and cables and articles thereof, Furniture; bedding, mattresses, mattress supports, cushions and similar stuffed furnishings; ..., Other base metals; cermets; articles thereof, Lead and articles thereof, Toys, games and sports requisites; parts and accessories thereof, Wool, fine or coarse animal hair; horsehair yarn and woven fabric, Cork and articles of cork, Essential oils and resinoids; perfumery, cosmetic or toilet preparations, Tobacco and manufactured tobacco substitutes, Preparations of vegetables, fruit, nuts or other parts of plants, Live animals, Works of art, collectors' pieces and antiques, Beverages, spirits and vinegar, Ores, slag and ash, Miscellaneous edible preparations, Clocks and watches and parts thereof, Glass and glassware, Zinc and articles thereof, Headgear and parts thereof, Live trees and other plants; bulbs, roots and the like; cut flowers and ornamental foliage, Man-made staple fibres, 'TOTAL, Silk, Nickel and articles thereof, Inorganic chemicals; organic or inorganic compounds of precious metals, of rare-earth metals, ..., Edible vegetables and certain roots and tubers, Tools, implements, cutlery, spoons and forks, of base metal; parts thereof of base metal, Lac; gums, resins and other vegetable saps and extracts, Products of the milling industry; malt; starches; inulin; wheat gluten, Fish and crustaceans, molluscs and other aquatic invertebrates, Albuminoidal substances; modified starches; glues; enzymes, Aircraft, spacecraft, and parts thereof, Optical, photographic, cinematographic, measuring, checking, precision, medical or surgical ..., Umbrellas, sun umbrellas, walking sticks, seat-sticks, whips, riding-crops and parts thereof, Articles of iron or steel, Wood and articles of wood; wood charcoal, Rubber and articles thereof, Cotton, Explosives; pyrotechnic products; matches; pyrophoric alloys; certain combustible preparations, Iron and steel, Mineral fuels, mineral oils and products of their distillation; bituminous substances; mineral ..., Copper and articles thereof, Soap, organic surface-active agents, washing preparations, lubricating preparations, artificial ..., Commodities not elsewhere specified, Ships, boats and floating structures, Other made-up textile articles; sets; worn clothing and worn textile articles; rags, Tin and articles thereof, Organic chemicals, Articles of leather; saddlery and harness; travel goods, handbags and similar containers; articles ..., Carpets and other textile floor coverings, Fertilisers, Sugars and sugar confectionery, Edible fruit and nuts; peel of citrus fruit or melons, Miscellaneous chemical products, Manufactures of straw, of esparto or of other plaiting materials; basketware and wickerwork, Furskins and artificial fur; manufactures thereof, Printed books, newspapers, pictures and other products of the printing industry; manuscripts, ..., Meat and edible meat offal, Aluminium and articles thereof, Products of animal origin, not elsewhere specified or included, Pharmaceutical products, Electrical machinery and equipment and parts thereof; sound recorders and reproducers, television, Preparations of meat, of fish or of crustaceans, molluscs or other aquatic invertebrates, Preparations of cereals, flour, starch or milk; pastrycooks' products, Photographic or cinematographic goods, Coffee, tea, spices, Impregnated, coated, covered or laminated textile fabrics; textile articles of a kind suitable, Knitted or crocheted fabrics, Miscellaneous manufactured articles, Dairy produce; birds' eggs; natural honey; edible products of animal origin, not elsewhere, Footwear, gaiters and the like; parts of such articles, Arms and ammunition; parts and accessories thereof, Articles of stone, plaster, cement, asbestos, mica or similar materials, Cereals, Man-made filaments; strip and the like of man-made textile materials, Musical instruments; parts and accessories of such articles, China, Salt; sulphur; earths and stone; plastering materials, lime and cement, Pulp of wood or of other fibrous cellulosic material; recovered (waste and scrap) paper, Other vegetable textile fibres; paper yarn and woven fabrics of paper yarn, Residues and waste from the food industries; prepared animal fodder, Ceramic products, Tanning or dyeing extracts; tannins and their derivatives; dyes, pigments and other colouring, Machinery, mechanical appliances, nuclear reactors, boilers; parts thereof, Oil seeds and oleaginous fruits; miscellaneous grains, seeds and fruit; industrial or medicinal, Vegetable plaiting materials; vegetable products not elsewhere specified or included, Miscellaneous articles of base metal, Articles of apparel and clothing accessories, not knitted or crocheted, Exports, Exporters, Commodity

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia, United Arab EmiratesFollow data.kapsarc.org for timely data to advance energy economics research..

Bahrain Sources: ITC calculations based on UN COMTRADE statistics since January, 2020.

       ITC

calculations based on Central Informatics Organisation (CIO) statistics since January, 2015 and until January, 2020.

       ITC

calculations based on UN COMTRADE statistics until January, 2015.

China Sources: ITC calculations based on General Customs Administration of China statistics since January, 2015.

       ITC

calculations based on UN COMTRADE statistics until January, 2015.

India Sources: ITC calculations based on UN COMTRADE statistics since January, 2019.

       ITC

calculations based on Directorate General of Commercial Intelligence & Statistics statistics since January, 2015 and until January, 2019.

       ITC

calculations based on UN COMTRADE statistics until January, 2015.

Qatar Sources: ITC calculations based on UN COMTRADE statistics since January, 2016.

       ITC

calculations based on Ministry of Development Planning and Statistics statistics since January, 2009 and until January, 2016.

       ITC

calculations based on UN COMTRADE statistics until January, 2009.

Saudi Arabia Sources: ITC calculations based on Central Department Of Statistics & Information statistics since January, 2019.

       ITC

calculations based on UN COMTRADE statistics since January, 2018 and until January, 2019.

       ITC

calculations based on Central Department Of Statistics & Information statistics since January, 2015 and until January, 2018.

       ITC

calculations based on UN COMTRADE statistics until January, 2015.

Kuwait Sources: ITC calculations based on UN COMTRADE statistics since January, 2018.

       ITC

calculations based on Central Statistical Bureau statistics since January, 2012 and until January, 2018.

       ITC

calculations based on UN COMTRADE statistics since January, 2010 and until January, 2012.

       ITC

calculations based on Central Statistical Bureau statistics since January, 2009 and until January, 2010.

       ITC

calculations based on UN COMTRADE statistics until January, 2009.

United Arab Emirates Sources: ITC calculations based on Federal Competitiveness and Statistics Authority statistics since January, 2017.

       ITC

calculations based on UN COMTRADE statistics until January, 2017.

Oman Sources: ITC calculations based on UN COMTRADE statistics since January, 2016.

       ITC

calculations based on Public Authority for Investment Promotion and Export Development (Ithraa) statistics since January, 2015 and until January, 2016.

       ITC

calculations based on UN COMTRADE statistics until January, 2015.

Crude petroleum was the most imported commodity into India, at about 21 percent in fiscal year 2024. Gold came in second at around six percent, an increase of over 30 percent compared to the previous year.

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.

The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on GitHub. For more information please refer to https://fabio.fineprint.global.

The database consists of the following main components, in compressed .rds format:

Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.

Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.

X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.

L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).

E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).

mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).

mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).

A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS

Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see Bruckner et al. 2019). Product, process and country classifications can be found in the file fabio_classifications.xlsx.

Footprint results are not contained in the database but can be calculated, e.g. by using this script: https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R

How to cite:

To cite FABIO work please refer to this paper:

Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: 10.1021/acs.est.9b03554

License:

This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at martin.bruckner@wu.ac.at.

Known issues:

The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

The price index of natural gas dropped sharply in October 2022 after having reached around 893 points in August 2022 relative to the base year of 2016. By August 2024, coal had the highest consumer price index of the selected commodities at 196.6. In other words, coal prices worldwide were nearly two times higher in that month than in 2016. The cost of several commodities, especially energy resources, rose at the end of February 2022 after the Russian invasion of Ukraine.

In 2023, Nicaragua’s number one most exported product was gold. This raw, semi-manufactured or powdered commodity contributed approximately 1.1 billion U.S. dollars to the Nicaraguan total export value. At around 609 million dollars, coffee was the second most exported product in Nicaragua. It was followed by frozen, fresh, and refrigerated bovine meats that, combined, made up roughly 690 million dollars of the exported value for this Central American country. In addition to being the most important export products for Nicaragua, both gold and coffee, were among the top ten most exported goods in Central America.

Nicaragua’s leading trade partners

Whereas several Central American countries were among the lead trading partners of Nicaragua, it was the United States that led the list. The United States was by far the main trade partner of Nicaragua, both in terms of import and export, exceeding a total trade value of 6.8 billion U.S. dollars in 2022. In the case of China, however, the trade balance is rather uneven, as Nicaragua imported significantly more than it exported. This means that China is among the top ten main destinations of Nicaraguan exports, but not on the list of origin countries regarding imports.

Decline in Nicaraguan trade balance

The above-mentioned gap between import and export value in the case of China becomes even more relevant when looking at the trade balance of goods in Nicaragua over the past 10 years. Despite experiencing small fluctuations, Nicaragua has not registered a single trade surplus (positive trade balance) since 2012. The trade deficit in 2022 has, however, only slightly increased compared to the previous year.

The United States' total Imports in 2024 were valued at US$3.36 Trillion, according to the United Nations COMTRADE database on international trade. The United States' main import partners were: Mexico, China and Canada. The top three import commodities were: Machinery, nuclear reactors, boilers; Electrical, electronic equipment and Vehicles other than railway, tramway. Total Exports were valued at US$2.06 Trillion. In 2024, The United States had a trade deficit of US$1.29 Trillion.

For the supply-chain layer, the output of the country trade layer is coupled to a multi-regional input-output table (EORA MRIO). A MRIO captures how industrial production and consumption is dependent on sector input from within the domestic economy and from other economies (the MRIO covers 170 economies globally). By coupling this to the country trade network, we can embed the maritime transport of flows within the tables, thereby capturing how these trade flows are used in the economy. This therefore does not only capture how a supply-chain is exposed to downtime because of trade flowing through a certain port (direct links), but also indirectly how you are exposed as a firm because you rely on firms upstream in your supply-chain that trade through a port prone to downtime (the firms you depend on, 1st order suppliers, or firms that the firms that you depend on depend on, 2nd order suppliers, etc). In other words, as a supply-chain, you may be indirectly exposed to port disruptions without your direct input or consumption goods flowing through an at-risk port (e.g. you are exposed to closure of a raw materials exporting port without you directly using raw materials in your production process).To understand the supply-chain dependencies between global supply-chains and a port, the Hypothetical Extraction Method (HEM) is used. The HEM hypothetically removes the share of trade flows between countries that go through a certain port from the MRIO and re-evaluates the industrial production globally without these flows, without any adaptation in the economic system. The difference between the original production and the HEM production is the dependency of various supply-chains on a specific port. The base year considered in 2022, which is the latest available MRIO table available.Within the portal, the absolute amount of industry production and final consumption at-risk can be visualized (all in value terms), as well as the relative amount of production and final consumption (as a fraction of an economy’s total production and consumption). This can be done in aggregate terms (across all commodity sectors) or per 13 commodity sector. Similar as with the country trade layer, the absolute numbers are indicative numbers only, and represents the relative exposure of specific supply-chains to a port disruption, which allows identifying at-risk supply chains across countries and sectors. The absolute values should however be treated with care. The 13 commodity sectors are based on the International Convention on the Harmonized Commodity Description and Coding System (HS Convention) and align to the 21 HS Sections as per the table below.NameHS SectionAnimal & Animal Products1Vegetable Products2Prepared Foodstuffs & Beverages3+4Mineral Products5Chemical & Allied Industries6Plastics, Rubber, Leather7+8Wood & Wood Products9+10Textiles & Footwear11+12Stone & Glass13+14Metals15Machinery & Electrical Equipment16+18Vehicles & Equipment17Miscellaneous19+20+21Source: Verschuur, J., Koks, E.E. & Hall, J.W. Ports’ criticality in international trade and global supply-chains. Nat Commun 13, 4351 (2022). https://doi.org/10.1038/s41467-022-32070-0How to cite? These dataset combine data from the journal article published by researchers affiliated with Oxford University and calculations by the PortWatch team. The recommended citation is: “Sources: University of Oxford; IMF PortWatch (portwatch.imf.org).”Variables:from_portid = port id. Full list of ports can be found here.from_portname = port name. from_country = country the port resides in.from_iso3 = ISO 3-letter country code of the port.to_country = country for which we compute industry production and final consumption that are at-risk of being affected because of a disruption at from_portname.to_iso3 = ISO 3-letter country code of the country for which we compute industry production and final consumption that are at-risk of being affected because of a disruption at from_portname.industry = one of the following: - Animal & Animal Products- Vegetable Products- Prepared Foodstuffs & Beverages- Mineral Products- Chemical & Allied Industries- Plastics, Rubber, Leather- Wood & Wood Products- Textiles & Footwear- Stone & Glass - Metals- Machinery & Electrical Equipment- Vehicles & Equipment- Miscellaneous- Total (sum of all of the above)hs_section = corresponding HS section(s)daily_consumption_at_risk = daily to_country’s consumption that is at-risk of being affected because of a disruption at from_portname.daily_industryoutput_at_risk = daily to_country’s industry output that is at-risk of being affected because of a disruption at from_portname.unit = all values are expressed in US Dollars.scale = all values are expressed in units.

Graph and download economic data for Producer Price Index by Commodity: Metals and Metal Products: Cold Rolled Steel Sheet and Strip (WPU101707) from Jun 1982 to Apr 2025 about steel, metals, commodities, PPI, inflation, price index, indexes, price, and USA.

Sugar rose to 16.69 USd/Lbs on June 9, 2025, up 1.11% from the previous day. Over the past month, Sugar's price has fallen 5.46%, and is down 10.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Sugar - values, historical data, forecasts and news - updated on June of 2025.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Wood Pulp (WPU0911) from Jan 1926 to May 2025 about wood, paper, commodities, PPI, inflation, price index, indexes, price, and USA.

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which enumerated in 1997 and the numeration area consists of buildings and housing units and has in average about 150 households in it. We use the enumeration areas as primary sampling units PSUs in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is stratified cluster systematic random sample with two stages: First stage: selection a systematic random sample of 120 enumeration areas. Second stage: selection a systematic random sample of 12-18 households from each enumeration area selected in the first stage.

Sample strata:

The population is divided by: 1-Region (North West Bank, Middle West Bank, South West Bank, Gaza Strip) 2-Type of Locality (urban, rural, refugee camps)

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Sample Size:

The calculated sample size is 1,714 households, the completed households were 1,231 (812 in the west bank and 419 in the Gaza strip).

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month, and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e., Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-55 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

Cleaning operations

Raw Data

Data editing took place through a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 1,714 households interviewed over a twelve months period between (January 2007-January 2008).1,231 households completed the interview, of which 812 were from the West Bank and 419 households in Gaza Strip; the response rate was 71.8% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimates enable the user to identify the accuracy of estimates and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting of all the various related activities. The work team spared no effort at the different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the "programming package" CENVAR

Data appraisal

The impact of errors on the data quality was reduced to the minimal due to the high efficiency and outstanding selection, training, and performance of the fieldworkers. Procedures adopted during the fieldwork of the survey were considered a necessity to ensure the collection of accurate data, notably: 1) Develop schedules to conduct field visits to households during survey fieldwork. The objectives of the visits and the data that is collected on each visit were predetermined. 2) Fieldwork editing rules were applied during the data collection to ensure corrections were implemented before the end of fieldwork activities 3) Fieldworker were instructed to provide details in case of extreme expenditure or consumption of the household. 4) Postpone the questions on income to the last visit at the end of the month 5) Validation rules were embedded in the data processing systems along with procedures to verify data entry and data editing.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The basic goal of the Household and Consumption Survey is to provide a necessary database for formulating national policies at various levels. This survey provides the contribution of the household sector to the Gross National Product (GNP). It determines the incidence of poverty, and provides weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Furthermore, this survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The target population in the sample survey comprises all private household living in the West Bank and Gaza Srip, excluding nomads and students.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all permanently residing individuals in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

Sample and Frame:

The target population in the survey sample comprises all households living in the West Bank and Gaza Strip, excluding nomads and students. The sample design is a stratified two-stage design for households selected to be interviewed. At the first stage a sample of cells (PSUs) was selected from PCBS master sample frame. At the second stage, a sample of households was selected after a complete household listing of the sampled cells.

Sample design:

Four levels of stratification have been made: 1. Stratification by District. 2. Stratification by place of residence, which comprises: (a) Municipalities (b) Villages (C) refugees camps 3. Stratification by locality size 4. Stratification by cell identification in that order

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 10 households.

Sample size:

The total sample size collected, after excluding non-response and related losses, is 2851 households.

Detailed information/formulas on the sampling design are available in the user manual.

Sampling deviation

The standard errors for the main survey estimates were calculated to give the user an idea of their reliability or precision. Whereas, the variance was calculated using the method of ultimate clusters within any domain of estimation.

Detailed information on the sampling design deviation and calculation of the variance is available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month,and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e, Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 707 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The basic goal of the Household and Consumption Survey is to provide a necessary database for formulating national policies at various levels. This survey provides the contribution of the household sector to the Gross National Product (GNP). It determines the incidence of poverty, and provides weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Furthermore, this survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 1997; the enumeration area consists of buildings and housing units and is composed of an average of 120 households. The enumeration areas were used as Primary Sampling Units (PSUs) in the first stage of the sampling selection. The enumeration areas of the master sample were updated in 2003.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 299 enumeration areas. Second stage: selection of a systematic random sample of 12-18 households from each enumeration area selected in the first stage. A person (18 years and more) was selected from each household in the second stage.

Sample strata:

The population was divided by: 1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Sample Size:

The calculated sample size is 3,781 households.

Target cluster size:

The target cluster size or "sample-take" is the average number of households to be selected per PSU. In this survey, the sample take is around 12 households.

Detailed information/formulas on the sampling design are available in the user manual.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month,and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e, Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of shelter, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Second section: The second section of the questionnaire includes a list of 54 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-54 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year.

Cleaning operations

Raw Data

Both data entry and tabulation were performed using the ACCESS and SPSS software programs. The data entry process was organized in 6 files, corresponding to the main parts of the questionnaire. A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consistency checks and cross-validation. Complete manual inspection was made of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consists of about 3,781 households interviewed over a twelve-month period between January 2004 and January 2005. There were 3,098 households that completed the interview, of which 2,060 were in the West Bank and 1,038 households were in GazaStrip. The response rate was 82% in the Palestinian Territory.

Sampling error estimates

The calculations of standard errors for the main survey estimations enable the user to identify the accuracy of estimations and the survey reliability. Total errors of the survey can be divided into two kinds: statistical errors, and non-statistical errors. Non-statistical errors are related to the procedures of statistical work at different stages, such as the failure to explain questions in the questionnaire, unwillingness or inability to provide correct responses, bad statistical coverage, etc. These errors depend on the nature of the work, training, supervision, and conducting all various related activities. The work team spared no effort at different stages to minimize non-statistical errors; however, it is difficult to estimate numerically such errors due to absence of technical computation methods based on theoretical principles to tackle them. On the other hand, statistical errors can be measured. Frequently they are measured by the standard error, which is the positive square root of the variance. The variance of this survey has been computed by using the “programming package” CENVAR.

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