In 2024, the consumer price index (CPI) was 315.61. Data represents U.S. city averages. The monthly inflation rate for the United States can be found here. United States urban Consumer Price Index (CPI) The U.S. Consumer Price Index is a measure of change in the price of consumer goods and services purchased by households. The CPI is defined by the United States Bureau of Labor Statistics as "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." To calculate the CPI, the Bureau of Labor Statistics considers the price of goods and services from various categories: housing, transportation, apparel, food & beverage, medical care, recreation, education and other/uncategorized. The CPI is a useful measure, as it indicates how the cost of urban living in the United States has changed over time, compared to a base period. CPI is also used to calculate inflation, or change in the purchasing power of money. According to the U.S. Bureau of Labor Statistics, the U.S. urban CPI has been rising steadily since 1992. As of 2023, the CPI was 304.7, up from 233 ten years earlier and up from 184 twenty years earlier. This indicates the extent to which, compared to a base period 1982-1984 = 100, the price of various goods and services has risen.

Key information about Egypt Core CPI Change

• Egypt Core CPI Change was reported at 12.090 % in Oct 2025.
• This records an increase from the previous number of 11.300 % for Sep 2025.
• Egypt Core CPI Change data is updated monthly, averaging 8.140 % from Jan 2005 to Oct 2025, with 250 observations.
• The data reached an all-time high of 41.000 % in Jun 2023 and a record low of 0.720 % in Jul 2020.
• Egypt Core CPI Change data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Core CPI: Y-o-Y Growth: Monthly.

The Central Bank of Egypt provides monthly Core Consumer Price Index Growth with base 2018-2019=100. Core Consumer Price Index covers Urban area only as it is the official index used for inflation calculation in Egypt. Core Consumer Price Index excludes regulated items and the most volatile food items, namely fruits and vegetables.

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

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

Explore the latest data on Saudi Arabia's Consumer Price Index (CPI) and Inflation Rate. Access comprehensive information and analysis on economic trends in Saudi Arabia.

Saudi Arabia CPI, Saudi Arabia Inflation Rate

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Note:- Data found here from January 2018 till January 2020 was 2013 base year CPI calculation- Data found here from October 2009 till December 2017 was 2007 base year CPI calculation.You can find Saudi Arabia Inflation Rate with the latest 2018 base year on KAPSARC Dataportal.

Overview with Chart & Report: Common Consumer Price Index y/y reflects common changes in prices of 55 components included in CPI calculation. The change as compared to the previous month is calculated in percentage. The index

PCE: PI: saar: Less Formula Effect (LFE) data was reported at -0.100 % Point in Mar 2025. This records a decrease from the previous number of -0.060 % Point for Dec 2024. PCE: PI: saar: Less Formula Effect (LFE) data is updated quarterly, averaging -0.160 % Point from Mar 2002 (Median) to Mar 2025, with 93 observations. The data reached an all-time high of 0.550 % Point in Jun 2020 and a record low of -0.590 % Point in Sep 2005. PCE: PI: saar: Less Formula Effect (LFE) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.I048: PCE Price Index and CPI Reconciliation: NIPA 2023: Quarterly.

🇱🇹 리투아니아 English The purpose of the calculation of the Service Prices Index (CPI) is to determine the general change in prices of services provided to economic operators over a period of time. The CPI is needed to calculate macroeconomic indicators at constant prices, analyse inflationary processes in the services sector, prepare forecasts. Geographical coverage - The entire economic territory of the country. Reporting period - Quarter. The classifications used are: Classification of economic activities (EERC Rev. 2); Classification of products by type of activity (CPA 2.1). For more information: https://osp.stat.gov.lt/documents/10180/5118910/%C5%AAkio+subjektams+suteikt%C5%B3+paslaug%C5%B3+kain%C5%B3+indeksai+%28PKI%29+ir+kain%C5%B3+poky%C4%8Diai+%5BLT%5D+604.html

AT: CPI: Local Source Base Year: All Items data was reported at 120.267 2020=100 in 2023. This records an increase from the previous number of 111.550 2020=100 for 2022. AT: CPI: Local Source Base Year: All Items data is updated yearly, averaging 55.823 2020=100 from Dec 1958 (Median) to 2023, with 66 observations. The data reached an all-time high of 120.267 2020=100 in 2023 and a record low of 14.452 2020=100 in 1958. AT: CPI: Local Source Base Year: All Items 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 Austria – Table AT.OECD.MEI: Consumer Price Index: COICOP 1999: OECD Member: Annual. The Austrian CPI measure price changes in a fixed basket of goods and services bought in Austria for the purpose of consumption by all Austrian households, foreign visitors and residents in institutional households. The prices used in the CPI calculation are the transaction prices actually paid by consumers including taxes less any general discounts, rebates or subsidies. Method of collection: Personal visits and mail questionnaire, paper collection forms, centrally collected prices by mail and telephone. Treatment of rentals for housing: Apartments rent abroad are included. Treatment of Owner-Occupied Housing: Regular payments for Owner occupied flats are included (payment approach), initial payments are excluded. House construction goods and services and major repairs are included. The purchase of a house and other real estate (land prices, housing agents) are not included. Treatment of missing prices: Prices are adjusted by the rate of change of the other price observations of the same product. If product offers are not available any more a new product offer is selected as replacement immediately after three months at latest. Treatment of quality changes: Quantity adjustment for food, Expert Judgment adjustment method e.g. for clothing, Option pricing method for durables and cars, Hedonic method for notebooks. Introduction of new products: New products are selected with respect to demand (turnover) and availability and they are introduced every December. New models and varieties are implemented by replacement as soon as they become relevant. Treatment of seasonal items: When a product offer disappears for seasonal non-availability, it is not replaced but its price relative is excluded from calculation. The index is then calculated with the rest of available prices. If the seasonal variety becomes available again the respective price relative is included in the calculation again (after potential quality adjustment). For a minority of products which would not at all be available in whole Austria the last prices are carried forward (e.g. schools and theatres in summer or public baths in winter).; Index series starts in January 1958

Overview with Chart & Report: Sweden Consumer Price Index with a Fixed Interest Rate (CPIF) y/y is calculated based on the same data that is used for the main CPI calculation. However, the direct impact of changes in the monetary

Inflation Rate in Iceland decreased to 3.70 percent in November from 4.30 percent in October of 2025. This dataset provides - Iceland Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, the average annual inflation rate in China ranged at around 0.2 percent compared to the previous year. For 2025, projections by the IMF expect nearly no inflation. The monthly inflation rate in China dropped to negative values in the first quarter of 2025. Calculation of inflation The inflation rate is calculated based on the Consumer Price Index (CPI) for China. The CPI is computed using a product basket that contains a predefined range of products and services on which the average consumer spends money throughout the year. Included are expenses for groceries, clothes, rent, power, telecommunications, recreational activities, and raw materials (e.g. gas, oil), as well as federal fees and taxes. The product basked is adjusted every five years to reflect changes in consumer preference and has been updated in 2020 for the last time. The inflation rate is then calculated using changes in the CPI. As the inflation of a country is seen as a key economic indicator, it is frequently used for international comparison. China's inflation in comparison Among the main industrialized and emerging economies worldwide, China displayed comparatively low inflation in 2023 and 2024. In previous years, China's inflation ranged marginally above the inflation rates of established industrialized powerhouses such as the United States or the European Union. However, this changed in 2021, as inflation rates in developed countries rose quickly, while prices in China only increased moderately. According to IMF estimates for 2024, Zimbabwe was expected to be the country with the highest inflation rate, with a consumer price increase of about 561 percent compared to 2023. In 2023, Turkmenistan had the lowest price increase worldwide with prices actually decreasing by about 1.7 percent.

Overview with Chart & Report: Sweden Consumer Price Index with a Fixed Interest Rate (CPIF) m/m is calculated based on the same data that is used for the main CPI calculation. However, the direct impact of changes in the monetary

United States PCE: PI: Less Formula Effect (LSE) data was reported at -0.120 Point in Mar 2018. This records an increase from the previous number of -0.210 Point for Dec 2017. United States PCE: PI: Less Formula Effect (LSE) data is updated quarterly, averaging -0.160 Point from Mar 2002 (Median) to Mar 2018, with 65 observations. The data reached an all-time high of 0.470 Point in Dec 2008 and a record low of -0.600 Point in Sep 2005. United States PCE: PI: Less Formula Effect (LSE) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A187: NIPA 2013: PCE Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Qtr: Less Formula Eff: Housing data was reported at -0.010 Point in Mar 2013. This stayed constant from the previous number of -0.010 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Housing data is updated quarterly, averaging -0.010 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.020 Point in Dec 2008 and a record low of -0.020 Point in Sep 2004. United States PCE: PI: Qtr: Less Formula Eff: Housing data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A274: NIPA 2009: PCE Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Less Formula Effect (LFE) data was reported at -0.020 Point in May 2018. This records a decrease from the previous number of -0.010 Point for Apr 2018. United States PCE: PI: Less Formula Effect (LFE) data is updated monthly, averaging -0.010 Point from Jan 2002 (Median) to May 2018, with 197 observations. The data reached an all-time high of 0.080 Point in Nov 2008 and a record low of -0.120 Point in Sep 2005. United States PCE: PI: Less Formula Effect (LFE) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A060: NIPA 2013: Personal Consumption Expenditure Price Index and CPI Reconciliation: Monthly.

United States PCE: PI: Less Formula Effect: Other data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Other data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.020 Point in Nov 2008 and a record low of -0.030 Point in Jan 2009. United States PCE: PI: Less Formula Effect: Other data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.

Abstract

The fourth expenditure and consumption survey (LECS 4) in Lao PDR is a survey in terms of socio-economy at the household echelon. This survey is conducted in every 5 years. The present round of surveys started from 1992 and the main statistical collection unit is the household. This survey is a sample survey which is carried out in every province and district over the whole country. The survey was undertaken from April 2007 to March 2008 (for a period of 12 months), in order to be able to provide data on expenditure and consumption covering all seasons and relating to aspects of every area and region in the Lao PDR.

The purpose of the expenditure and consumption survey (LECS) is to estimate the expenditure and consumption of household as well as production, investment, accumulation and other socio-economic aspects of the households in the formal and informal sector of the economy.

The results of expenditure and consumption survey in Lao PDR will provide necessary data to be used for calculation of various indicators and are intended for socio-economic planning. It will also provide data for calculation of GDP, definition of poverty line, data on nutrition and other important information. The LECS surveys are the most important surveys in the statistical data collection system of Lao PDR.

The main objectives of this survey are: - Estimation at macro level for national accounts, including private consumption, household investment, production and income from agriculture and household business; - Structure of household consumption (weight system) for consumption price index calculation (CPI); - Estimation on labor force; - Nutrition statistic; - Poverty statistics and statistics of income distribution.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Villages/ Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Selection

First Step: Description of Sample Village

The survey design for the LECS 4 uses the same methodology and sampling technique as used in the LECS 3. The sample selection is conducted in two steps. The first step is selection of sample villages using the zoom selection methodology according to the proportion of the population (PPS). Village unit is distributed according to the following echelon: village classified by province, district, rural area with access to road and rural area without access to road. The number of sample villages in each province is in between 17 to 48 villages depending on the number of villages, and the number of households in every survey area.

Comparing the last two surveys, LECS 3 and LECS 4, the number of sample villages is decreased from 540 to 518 villages. This is due to the situation of allocation and unification of small villages into larger villages, which in past years has appeared in every province in the whole country. In order to assure normal rule of distribution of sample, the number of sample households has been from 15 to 16 per village.

Each month the number of sample villages is almost the same, because the sample has been selected as zoom for every month.

Second Step: Selection of Sample Household

In the present expenditure and consumption survey half of the number of households are the same as households that were surveyed in the LECS 3, and the other half are new households that previously were not surveyed. The selection of households in the sample uses the zoom methodology on arbitrary and systematic basis. Selection of the 8 sample households from the survey of LECS 3 uses the zoom methodology on arbitrary basis by taking part in a lottery among LECS 3 households. New 8 sample household are selected among the other households in the village using the same methodology. Together the number of sample households in one village is 16. The selection of sample household is based on the number of existing households in the village at the time of the conduction of the survey. If the village has 16 or less households all households are covered by the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used to collect the 2007-2008 LECS: - Household Questionnaire - Dairy Sheet - Time USe Questionnaire - Village Questionnaire - Price Questionnaire

Cleaning operations

The survey data has been edited and data editing process include: - Structure checking and completeness - Checking and coding

Sampling error estimates

Sampling errors have been calculated for some important variables based on the confidence of 95% ("margin of errors").

United States PCE: PI: Less Formula Effect: Personal Computers & Peripheral Eqp data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Personal Computers & Peripheral Eqp data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.000 Point in May 2013 and a record low of -0.010 Point in Jul 2009. United States PCE: PI: Less Formula Effect: Personal Computers & Peripheral Eqp data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.

United States PCE: PI: Less Formula Effect: Health Care data was reported at 0.000 Point in May 2013. This stayed constant from the previous number of 0.000 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Health Care data is updated monthly, averaging 0.000 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.010 Point in Oct 2009 and a record low of 0.000 Point in May 2013. United States PCE: PI: Less Formula Effect: Health Care data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.A273: NIPA 2009: PCE Price Index and CPI Reconciliation.