This dataset provides values for HOUSE PRICE INDEX MOM reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Annual indexes of price differences between 15 cities in all provinces and territories, as of October of the previous year, for a selection of products (goods and services) from the Consumer Price Index (CPI) purchased by consumers in each of the 15 cities. The combined city average index is 100.

As of 2022, Israel had the highest price level index among listed countries, amounting to 138, with 100 being the average of OECD countries. Switzerland and Iceland followed on the places behind. On the other hand, Turkey and India had the lowest price levels compared to the OECD average. This price index shows differences in price levels in different countries. Another very popular index indicating the value of money is the Big Mac index, showing how much a Big Mac costs in different countries. This list was also topped by Switzerland in 2023.

Micro-price data collected from Germany's consumer price index are used to compile a highly disaggregated regional price index for the 402 counties and cities of Germany. A multistage version of the weighted country-product-dummy (CPD) method is introduced. The unique quality of the price data allows one to depart from previous spatial price comparisons and to compare only exactly identical products. It is found that the price levels are spatially autocorrelated and largely driven by the cost of housing. The price level in the most expensive region is about 27% higher than in the cheapest region.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

This dataset provides values for CONSUMER PRICE INDEX WITH FIXED INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In February 2025, the seasonally adjusted consumer price index (CPI) for food and beverages in the United States increased by *** percent compared to the same period in 2024. The highest change was registered in August 2022, when the consumer price index for food and beverages increased by **** percent compared to August 2021.

Context:

The Bureau of Labor Statistics defines the Consumer Price Index (CPI) as “a statistical measure of change, over time, of the prices of goods and services in major expenditure groups--such as food, housing, apparel, transportation, and medical care--typically purchased by urban consumers. Essentially, it compares the cost of a sample of goods and services in a specific month relative to the cost of the same "market basket" in an earlier reference period.

Make sure to read the cu.txt for more descriptive summaries on each data file and how to use the unique identifiers.

Content:

This dataset was collected June 27th, 2017 and may not be up-to-date.

The revised CPI introduced by the BLS in 1998 includes indexes for two populations; urban wage earners and clerical workers (CW), and all urban consumers (CU). This dataset covers all urban consumers (CU).

The Consumer Price Index (CPI) is a statistical measure of change, over time, of the prices of goods and services in major expenditure groups--such as food, housing, apparel, transportation, and medical care--typically purchased by urban consumers. Essentially, it compares the cost of a sample "market basket" of goods and services in a specific month relative to the cost of the same "market basket" in an earlier reference period. This reference period is designated as the base period.

As a result of the 1998 revision, both the CW and the CU utilize updated expenditure weights based upon data tabulated from three years (1982, 1983, and 1984) of the Consumer Expenditure Survey and incorporate a number of technical improvements, including an updated and revised item structure.

To construct the two indexes, prices for about 100,000 items and data on about 8,300 housing units are collected in a sample of 91 urban places. Comparison of indexes for individual CMSA's or cities show only the relative change over time in prices between locations. These indexes cannot be used to measure interarea differences in price levels or living costs.

Summary Data Available: U.S. average indexes for both populations are available for about 305 consumer items and groups of items. In addition, over 100 of the indexes have been adjusted for seasonality. The indexes are monthly with some beginning in 1913. Semi-annual indexes have been calculated for about 100 items for comparison with semi-annual areas mentioned below. Semi-annual indexes are available from 1984 forward.

Area indexes for both populations are available for 26 urban places. For each area, indexes are published for about 42 items and groups. The indexes are published monthly for three areas, bimonthly for eleven areas, and semi-annually for 12 urban areas.

Regional indexes for both populations are available for four regions with about 55 items and groups per region. Beginning with January 1987, indexes are monthly, with some beginning as early as 1966. Semi-annual indexes have been calculated for about 42 items for comparison with semi-annual areas mentioned above. Semi-annual indexes have been calculated for about 42 items in the 27 urban places for comparison with semi-annual areas.

City-size indexes for both populations are available for three size classes with about 55 items and groups per class. Beginning with January 1987, indexes are monthly and most begin in 1977. Semi-annual indexes have been calculated for about 42 items for comparison with semi-annual areas mentioned below.

Region/city-size indexes for both populations are available cross classified by region and city-size class. For each of 13 cross calculations, about 42 items and groups are available. Beginning with January 1987, indexes are monthly and most begin in 1977. Semi-annual indexes have been calculated for about 42 items in the 26 urban places for comparison with semi-annual areas.

Frequency of Observations: U.S. city average indexes, some area indexes, and regional indexes, city-size indexes, and region/city-size indexes for both populations are monthly. Other area indexes for both populations are bimonthly or semi-annual.

Annual Averages: Annual averages are available for all unadjusted series in the CW and CU.

Base Periods: Most indexes have a base period of 1982-1984 = 100. Other indexes, mainly those which have been added to the CPI program with the 1998 revision, are based more recently. The base period value is 100.0, except for the "Purchasing Power" values (AAOR and SAOR) where the base period value is 1.000.

Data Characteristics: Indexes are stored to one decimal place, except for the "Purchasing Power" values which are stored to three decimal places.

References: BLS Handbook of Methods, Chapter 17, "Consumer Price Index", BLS Bulletin 2285, April 1988.

Acknowledgements:

This dataset was taken directly from the U.S. Bureau of Labor Statistics web...

Time series of a domestic price index on a quarterly basis from 2010 onwards. The time series shall include the following: (a) Housing Price Index per quarter (basis: 2015) (b) Quarterly change (comparison with previous quarter) (c) Annual change (comparison with the corresponding quarter of previous year)

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Key information about Serbia Producer Price Index Growth

• Serbia Producer Price Index (PPI) grew 1.2 % YoY in Jun 2023, compared with a growth of 3.0 % YoY in the previous month.
• Serbia Producer Price Index data is updated monthly, available from Feb 2004 to Jun 2023, with an average change of 5.4 % YoY.
• The data reached an all-time high of 19.8 % YoY in Jul 2022 and a record low of -4.1 % YoY in May 2020.

CEIC calculates Producer Price Index Growth from monthly Producer Price Index. The Statistical Office of the Republic of Serbia provides Producer Price Index with base Same Month Previous Year=100. Producer Price Index Growth covers Industrial sector only.

***** Prefecture had the highest housing-related consumer prices compared to the national average in Japan in 2024. The retail prices of housing in Tokyo reached ***** index points compared to the national average of 100 points in Japan.

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 2019, Meat Price Index in Switzerland grew 4.2% from a year earlier. Need to compare country statistics and get a global overview? Find all data easily.

This table presents the Consumer price index (CPI) with reference year 1900 = 100. This index series is an estimation and has been constructed by multiplying the year-on-year mutations of several index series from different reference periods with the overlapping index from the previous reference period. The index shows the price change of the goods and services purchased by an average Dutch household in one year. Annual rate of change is measured as the year on-year change of the CPI, expressed as a percentage. The annual rate of change in this series may differ from the officially published annual rate of change as a result of rounding differences. Data available from: 1900 Status of the figures: The yearly figures are provisional when first published. Definitive figures are provided in the second version. Disparities between provisional and definitive figures must be attributed to new or updated source material that has become available. Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented. When will new figures be published? New figures are available at the beginning of the year.

Detailed yearly changes in consumer price index compared with the previous year published according to the COICOP classification divisions and groups. Base: April 2019 (=100).*Imputed rental for housing: the actual & imputed rent has been separated starting from May 2019**Accommodation services and social protection: Newley added in May 2019

Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data was reported at 90.467 2020=100 in 2022. This records a decrease from the previous number of 95.092 2020=100 for 2021. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data is updated yearly, averaging 119.871 2020=100 from Dec 1985 (Median) to 2022, with 38 observations. The data reached an all-time high of 167.358 2020=100 in 1997 and a record low of 53.850 2020=100 in 1985. Israel IL: Consumer Price Index (CPI): Local Source Base Year: Clothing and Footwear data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Israel – Table IL.OECD.MEI: Consumer Price Index: COICOP 1999: OECD Member: Annual. The CPI measures the change in prices which consumer pay for fixed market basket of consumption goods and services. Price coverage: Prices include applicable taxes (VAT) and fees on the products at the time of sale. Cash payments are the basis for the price survey. Monthly installment payment and credit card interest are excluded. Price collection procedure: The data collection methods are adapted according to the specific characteristics of the CPI classes. The main price surveys are: Computer Assisted Telephone Interviews (CATI), conducted by the CBS staff at the central office; Computer Assisted Personal Interviews (CAPI) by field collectors with handheld personal computers (HPC) and Direct Data Entry (DDE) into the database. Also for some special items Internet is used either in parallel with CAPI or as a part of DDE collection. The CPI includes a measure of rented housing Owner Occupied Housing (OOH) is included in the CPI and is calculated using rental equivalent method. The method for imputation of OOH is based on stratified average prices of contracts that are subject to renewal. In order to reduce variance in the monthly series, two month moving averages are compared each month. However, the method for OOH still leaves room for quality differences to play role in month-to-month average price changes. The method relies on successful stratification of apartments to groups whose relative price changes are as similar as possible. While the stratification is based on apartment location and number of rooms, some quality characteristics may experience month-to-month variation. Treatment of own account production is not included Goods and services sold illegally, second hand goods, goods and services partially or totally subsidized by the government and financial transactions are not included. Insurance: Insurance of personal transport and Health insurance (private and provided by the Government) are included. Treatment of missing items: Price changes for missing observations are imputed based on the price movements of other observations of the same item. Selection of replacement items: Products that become permantely unavailable are replaced in the sample and enumerators select a replacement possessing as many of the same quality characteristics as possible. Prices from previous period are sought for the replacement item for linking purpose. Treatment of quality change: There are two types of replacement approach: comparable and non-comparable. If a new product possesses the previously defined important characteristics of the old product, the new product is defined as comparable and a minor quality change is regarded as price change. Otherwise, if a significant quality change is introduced, the new product is defined as not comparable. The breakage in price series is treated by the linking method. Explicit quality adjustments are usually not performed. Hedonic methods are being considered but not yet implemented. In some cases, where the product cycle is short and new versions with improved quality characteristics are frequently introduced, the overlap method may give biased estimates. Introduction of new products: New items are introduced when the market basket is updated. New products are introduced into the sample as they gain significant market share. Business and professional periodicles are closely followed to gain information on new products that are gaining consumer demand. Seasonal items: Missing prices for seasonal products are imputed. Certain procedures are in place to avoid too early reintroduction of seasonal products back to the index. For price changes a bridge method is used when the items are reintroduced to the collection. Index series are also calculated and released in seasonally adjusted form.; Index series starts in November 1985

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to Aug 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

Between 2019 and June 2024 the U.S. share market has outperformed global precious metal prices, as measured by the S&P 500 and S&P GSCI precious metals indices respectively. While the difference between the two index values was around *** points in January 2019, by June 2024 this had ballooned to roughly ***** index points. However, it is notable that the S&P GSCI precious metals index did not suffer the same sharp decline in March 2020 due to the global coronavirus (COVID-19) pandemic, suggesting that the precious metals market may be more stable the equities during periods of economic turmoil. The S&P 500 tracks the stock price of *** of the publicly listed largest U.S. companies, while the S&P GSCI precious metals index tracks the price of precious metals futures contracts worldwide.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes