View data of PCE, an index that measures monthly changes in the price of consumer goods and services as a means of analyzing inflation.

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. 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. 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.

PCE: PI: Less Formula Effect: Elec, Gas, Fuel Oil & Oth HH Fuels data was reported at 0.000 Point in May 2013. This records a decrease from the previous number of 0.010 Point for Apr 2013. PCE: PI: Less Formula Effect: Elec, Gas, Fuel Oil & Oth HH Fuels 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 Apr 2013 and a record low of -0.030 Point in Sep 2005. PCE: PI: Less Formula Effect: Elec, Gas, Fuel Oil & Oth HH Fuels 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.

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. 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. 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.

PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment data was reported at -0.030 Point in Mar 2013. This stayed constant from the previous number of -0.030 Point for Dec 2012. PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment data is updated quarterly, averaging -0.040 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of -0.010 Point in Mar 2002 and a record low of -0.110 Point in Dec 2009. PCE: PI: Qtr: Less Formula Eff: Video & Audio Equipment 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.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption data was reported at 0.000 Point in Mar 2013. This records an increase from the previous number of -0.010 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption data is updated quarterly, averaging 0.000 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.010 Point in Jun 2010 and a record low of -0.060 Point in Dec 2003. United States PCE: PI: Qtr: Less Formula Eff: Fd & Bev Off-Premises Consumption 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: Qtr: Less Formula Effect data was reported at -0.050 Point in Mar 2013. This records an increase from the previous number of -0.160 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Effect data is updated quarterly, averaging -0.160 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.710 Point in Dec 2008 and a record low of -0.450 Point in Sep 2005. United States PCE: PI: Qtr: Less Formula Effect 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.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

United States PCE: PI: Less Formula Effect data was reported at 0.000 Point in May 2013. This records a decrease from the previous number of 0.030 Point for Apr 2013. United States PCE: PI: Less Formula Effect data is updated monthly, averaging -0.010 Point from Jan 2002 (Median) to May 2013, with 137 observations. The data reached an all-time high of 0.110 Point in Nov 2008 and a record low of -0.080 Point in Sep 2005. United States PCE: PI: Less Formula Effect 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.

This dataset presents a comprehensive overview of household and per-capita income and expenditure patterns in various demographic, geographic, and socioeconomic contexts. It encompasses three main categories:Disposable IncomeConsumption ExpenditureFinal Monetary Consumption ExpenditureWithin each category, indicators detail averages, medians, and percentages across dimensions such as administrative region, nationality of the household head, age group, educational level, marital status, type of dwelling, type of ownership, household size, and income sources. The dataset thus enables in-depth analysis of how different factors influence income and expenditure.esearchers, policymakers, and analysts can employ these indicators to:Understand how household and per-capita incomes vary by social and economic factors.Examine consumption patterns and their drivers, including demographic variables.Analyze the final monetary consumption expenditure in more detail using COICOP divisions for targeted economic and social policy insights.In doing so, users can identify disparities, assess living standards, and formulate data-driven strategies to address economic and social challenges at both the household and regional levels.Notes:For the first time the methodology for calculating household disposable income and consumption expenditure is used in Household Income and Consumption Expenditure Survey of 2023

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.

United States PCE: PI: Qtr: Less Formula Eff: Personal Comp & Peripheral Eqp data was reported at -0.020 Point in Mar 2013. This stayed constant from the previous number of -0.020 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Personal Comp & Peripheral Eqp data is updated quarterly, averaging -0.020 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.000 Point in Sep 2010 and a record low of -0.080 Point in Sep 2003. United States PCE: PI: Qtr: Less Formula Eff: Personal Comp & 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.A274: NIPA 2009: PCE Price Index and CPI Reconciliation: Quarterly.

Abstract

SUSENAS (National Socio-economic Survey) was held for the first time in year 1963. In the last two decades, up to year 2010, SUSENAS was conducted every year. SUSENAS was designed to have 3 modules (Module of Household Consumption/Expenditure, Module of Education and Socio-culture, and also Module of Health and Housing) and each module should be conducted every 3 years. Household Consumption/ Expenditure Module of SUSENAS shall be conducted in year 2011.

To improve the accuracy of data result and in line with the increased frequency of household consumption/expenditure data request for quarterly GDP/GRDP and poverty calculation, data collection of household consumption/expenditure, it is planned that starting in 2011 it should be held quarterly. Each year, collecting data shall be conducted in March, June, September, and December.

In accordance with the 5-year cycle, in year 2012, BPS (Central Statistical Agency) shall have planned Survei Biaya Hidup-SBH (Cost of Living Survey) with the aim to generate a commodity package and a weigh diagram in the calculation of Consumer Price Index (CPI). Data of food and non-food consumption expenditures as well as household characteristics collected in SBH and SUSENAS has the same concept/definition, but different implementation time. In order to be more efficient in the utilization of resources of the two surveys and to have a better quality of results achieved, in year 2011 a trial of SUSENAS and SBH integration shall be conducted in 7 cities (Medan, Sampit, Denpasar, Kudus, Bulukumba, Tual, and South Jakarta).

Poverty data, CPI/Inflation data, GDP/GRDP are BPS strategic data that have to be released on time. Therefore, planning, field preparation, processing, and presentation of data SUSENAS 2011 activities and trial of integrating SUSENAS and SBH must be in accordance with the set schedule.

Activities of SUSENAS 2011 preparation shall be conducted in year 2010, covering activities of workshop/training of chief instructor with the aim to synchronize the perception toward the concept/definition as well as procedure and protocol of survey implementation. National instructor training will also be conducted in year 2010.

Geographic coverage

National coverage, representative to the district level

Analysis unit

Household Members (Individual) and Household

Universe

Susenas 2011 cover 300,000 household sample spread all over Indonesia where each quarter distribute about 75,000 household sample (including 500 households additional sample for Survey in Maluku Province). The result from each quarter can produce national and provincial level estimates. Meanwhile from the cummulative four quarter, the data can be presented until the district/municipality level.

Sampling procedure

Sampling method is the structured three phase sampling with the following method:

a. First phase, selection of nh census area from Nh with pps (Probability Proportional to Size)with sizeas the total households of SP2010 (M i ).The census area is then randomly allocated into four quarters. Total sampling will be nh= 30,000 census areas thus there will be 7,500 census areas for each quarter. From 7,500 census areas of the First Quarter of the National Socio-Economic Survey (Susenas), some 5,000 census areas are systemically selected for the First Quarter of the 2011 National Labor Force Survey (Sakernas) and will be used again for the second, third and fourth quarter

b. Second phase, to select: - two BS from each selected census area of the second and third quarter of Susenas, and the first quarter which is also selected for the first quarter of Sakernas, which then from the selected census blocks, is randomly allocated one for Susenas/SBH, and one [for] Sakernas, or - one BS from each selected census area of the fourth quarter and first quarter only for Susenas with pps with a household size of SP2010-RBL1.

c. Third phase, from each selected census block for Susenas, a number of regular households are systemically selected (m=10) based on the updated SP2010-C1 household listing by using the VSEN11-P List. Names of household head (KRT) are extracted from SP2010-C1 for name, address and education level variables, followed by field updates.

Mode of data collection

Face-to-face

This data selection represents a thematic extract from the comprehensive study “The Growth of the German Economy since the mid-19th Century“ (“Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts”) from 1965 by Walter G. Hoffmann. The main objective of Hoffmann’s study is to work out statistical figures concerning the long-term development of the German national economy, as well as the individual fields of this subject area. In doing so, the time series shall enable the verification of various hypotheses concerning economic growth. This aim, however, can only be reached if such time series are based on comparable statistical, methodical, and content-related concepts, and if they are collected for a period with maximum length. Consequently, this data selection comprises more than 800 pages with 250 tables, featuring almost every time series between 1850 and 1960 that can be considered relevant for the economic development. Whenever necessary, these materials were completed by estimates. Moreover, the above-named analyses of long-term tendencies aim at creating a reference system for the numerous short-term changes occuring within most national economies in the course of a century. Here the special focus of Hoffman’s work lies on the visualisation of the gained materials as regards the raise, distribution, and use of the national income. The respective calculation is based on the two production factors of labour and capital and culminates in an overview of production. The calculation of the distribution, on the other hand, deals with the functional and individual, i.e. personal distribution of (earned and capital) income. In its turn, the calculation of use is divided into the sectors of private and public consumption, investment, and the national trade balance.

Topics

Timeseries data available via the downloadsystem HISTAT

Data excerpt: Private and public Consumption (from the final expenditure compilation, the following factors have been taken into consideration):

(A) Total Consumption: - Consumption of vegetable foodstuffs - Consumption of foodstuffs obtained from livestock farming - Consumption of vegetable foodstuffs in prices of 1913 - Consumption of foodstuffs obtained from livestock farming and the total consumption of foodstuffs in prices of 1913 - Consumption of vegetable foodstuffs in current prices - Consumption of foodstuffs obtained from livestock farming and the total consumption of foodstuffs in current prices - Consumption of luxury foodstuffs - Consumption of luxury foodstuffs in prices of 1913 - Consumption of luxury foodstuffs in current prices - Consumption of ohter goods and services in prices of 1913 - Consumption of ohter goods and services in current prices

(B) Public Consumption: - Administration expenses in current prices - Public consumption in current prices - Public consumption in prices of 1913 - Public expenses for education in current prices

(C) Private Consumption - Private expenditures and private consumption for health care, personal hygiene and cleaning in prices of 1913 - Hispitals - Private expenditures and private consumption for health care, personal hygiene and cleaning in current prices - Onther consumption of education and recreation in prices of 1913 - Expenditures for education and recreation in current prices - Use of automobiles for private consumption - Purchase of automobiles for private consumption - Private consumption of traffic services in prices of 1913 - Private consumption of traffic services in current prices - Total private consumption in prices of 1913 - Total private consumption in current prices

United States PCE: PI: Qtr: Less Formula Eff: Tobacco data was reported at 0.000 Point in Mar 2013. This stayed constant from the previous number of 0.000 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Tobacco data is updated quarterly, averaging 0.000 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.010 Point in Jun 2003 and a record low of -0.090 Point in Jun 2009. United States PCE: PI: Qtr: Less Formula Eff: Tobacco 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.A139: NIPA 2009: Personal Consumption Expenditure Price Index and CPI Reconciliation: Quarterly.

Abstract

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

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) .

Analysis unit

Household, individual

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 target population in this sample survey 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 the 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

Stratification Four levels of stratification have been made: Stratification by District. Stratification by place of residence which comprises: (a) Municipalities (b) Villages (c) Refugee Camps
Stratification by locality size. Stratification by cell identification in that order.

Sample Size The sample size is about 3,591 households allowing for non-response and related losses .

Target cluster size

The next important issue in the sample design is the target cluster size or “sample-take” which is the average number of households to be selected per PSU. In this survey, the sample take is around 10 households.

Self-weighting design:

At the first stage, clusters or “cells” have been selected with PPS probability proportional to estimated measure of size (Mi) for unit (I):

Where the summation covers all clusters in the population; a-300 is the total number of selected clusters. It is highly desirable for the PECS to have a constant overall sampling rate (f), i.e. to have a self- weighting sample. This requires the second stage probability for the selection of households and persons within any sample cluster i to be as follows:

Where b is a constant (independent of i) to be determined to obtain the required sample size, n =3,591 households. Since the measure of size are likely to differ from the actual number of households listed in any cluster i, the actual number of households selected with the above shall vary from one cluster to another and are presented as:

Adding all clusters in the sample results in the required constant b, to achieve the target sample size n as:

Hence to control the overall sample size, b is determined after completing the listing in all sample areas.

The above procedure allows for variation in sample sizes bi at the level of individual clusters, in order to provide a self-weighting sample. Households within each sample cluster shall be selected systematically from the lists prepared for that purpose, using the sampling interval,

Where:

a  Number of cells in the sample (equals 360) 
Number of housing units in cell I    
Number of listed of households in cell I    
n  Proposed sample size (n= 3,591 HHs) 
b  Average sample take 
Sample take in cell I    
f  Sample rate 
First-stage sampling rate    
Second-stage sampling rate   

Which is fixed for each cluster but varies between clusters depending on the measure of size () with which the area was selected at the first stage.

The sample-take must be allowed to vary depending on the actual number of households found after listing. However, provision must be made to avoid extreme variation in cluster sample size. This could be done by using the above procedure to compute the ratio for each cluster in the sample. If this ratio lies outside the range say 0.5 - 4.0, adjust , i.e. the interval to be applied for the selection of households in the cluster, so as to keep the ratio within the above range.

Sample Rotation

The total number of (480) cells have been divided into (24) groups (subsided sample), each one constituted of (20) cells. A sub-sample of (360) cells is used year round by a monthly sample constituted of two minor samples (30 cells). The survey includes independent cells and not cross section ones, each of these is formed from (300) households for each month (round).

(Replication) L K J I H G F E D C B A Month
× 1
× 2
× 3
× 4
× 5
× 6
× 7
× 8
× 9
× 10
× 11
× 12

Estimations Procedure

The sample is self-weighting by design. To estimate a given total Y for a given sub-population A, we introduce the following formula:

But since W is constant for all j within i, then: the estimating formula becomes:

Where, U YA = Estimated total for variable Y in sub-population A h = The sub-stratum within the estimation domain i = The sample PSU (cell) j = The unit of analysis or element A = Subset of elements possessing a given attribute, that is, belonging to a given sub- population A = Observed value of variable “y” for j-the element of i-the sample PSU in stratum h = Final (adjusted) sampling weight for the element is the unweighted PSU total within h for sub-population A

The estimator for a given ratio for sub-population A is the following: (2)

Where: U RA =Estimate for the ratio of two variables, Y/X, in sub-population A U XA = Estimated total for variable X in sub-population A, given by formula (1)B U YA = Estimated total for variable Y in sub-population A, also given by formula (1)B

Means and proportions are special types of ratios. In the case of the mean, the variable X, in the denominator of the ratio, is defined to equal 1 for each element, so that denominator is the sum of weights in the sub-population. In the case of the proportion, the variable X in the denominator is also defined to equal 1 for all elements. In addition, the variable Y in the numerator is binomial and is defined to equal either 0 or 1, depending on the absence or presence of a specified attribute in the element observed.

Calculation of Variance

It is very important to calculate standard errors for the main survey estimates so that the user can have an idea of their reliability or precision.

The variance calculation will use the method of ultimate clusters. Within any domain of estimation, for a sub-population A, and for a characteristic Y, the formulas are: (a) The variance of an estimator of a total is estimated by:

(3)
Where:

(4)

and: (5)

The expression in (3) is an unbiased estimator of the variance. (b) The variance of an estimator of a ratio is estimated by:

(6)

Where:

U           U

V (YA) and V(XA) are calculated according to formula (3); U XA is calculated according to formula (1); and U RA according to formula (2).

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 house, 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

Abstract

The objective of the Household Budget Survey is to obtain data on the level and structure of household consumption expenditures. Data obtained from the Survey is used for updating and constructing of weights for national consumer price index. Furthermore, the data on the structure of household consumption expenditure is used for the needs of national accounts, i.e. for calculating of final household consumption, for calculating of imputed housing rents and for estimating figures on grey economy.

As the Household Budget Survey provides a number of information for monitoring economic and social conditions of life in households, the range of data users is very wide. Survey data are used for analyses and studies on living standards in population, measuring poverty, analyses of consumer habits and so on. Besides data on household expenditure, the Survey also collects other important data such as demographic data on household members, data on income and earnings by household members, data on housing characteristics and conditions, and so on.

Geographic coverage

Croatia

Universe

All household members. Exclusions: people who live in collective households or students in dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey was carried out on a random sample of private households. The sample frame used for the selection of dwellings occupied by private households was based on the Census 2011 data.

In 2014, the Household Budget Survey sample was selected in two stages. In the first one, 416 segments were selected (segments are territorial units consisting of one or several neighbouring enumeration areas). In the second stage, 10 occupied dwellings were randomly selected out of each of the selected segments. Thus, 4 160 dwellings occupied by private households were selected in the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Instruments used for data collection in the Household Budget Survey are survey questionnaires. There are four questionnaires:

• Questionnaire for Household Members, HBS-1
• Questionnaire for Households, HBS-2
• Diary, HBS-3.1
• Questionnaire on Food, Beverages, Tobacco and Consumer Products, HBS-3.2

Cleaning operations

Based on the defined deadlines, the collected survey data from regional units is submitted to the Statistics Department at the Croatian Bureau of Statistics, which is responsible for further actions relating to data processing procedure.

Data processing includes data entry, data checking, weighting, tabulating, analysis of results and final preparation of data for publishing. Data entry and checking is performed using the Blaise software program while data weighting and tabulation is done using the SAS software (Statistical Analysis System).

Response rate

There were 2029 private households that were successfully interviewed. The response rate at the household level was 54%.

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

United States PCE: PI: Qtr: Less Formula Eff: Other data was reported at -0.020 Point in Mar 2013. This stayed constant from the previous number of -0.020 Point for Dec 2012. United States PCE: PI: Qtr: Less Formula Eff: Other data is updated quarterly, averaging -0.050 Point from Mar 2002 (Median) to Mar 2013, with 45 observations. The data reached an all-time high of 0.160 Point in Jun 2008 and a record low of -0.190 Point in Mar 2008. United States PCE: PI: Qtr: Less Formula Eff: 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.A274: NIPA 2009: PCE Price Index and CPI Reconciliation: Quarterly.

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: Gasoline & Other Motor Fuel data was reported at 0.000 Point in May 2013. This records a decrease from the previous number of 0.030 Point for Apr 2013. United States PCE: PI: Less Formula Effect: Gasoline & Other Motor Fuel 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 0.090 Point in Nov 2008 and a record low of -0.040 Point in Nov 2007. United States PCE: PI: Less Formula Effect: Gasoline & Other Motor Fuel 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.