The Armenian Household Budget Survey (HBS) 1996 was designed to be a nationally representative survey capable of measuring the standard of living in the Republic of Armenia (ROA) through the collection of data on the family, demographic, socio-economic and financial status of households. The survey was conducted in November - December 1996, on the whole territory of the republic by the State Department of Statistics (SDS) of ROA with technical and financial assistance from the World Bank.The data collected included information on household composition, housing conditions, education level of household members, employment and income, savings, borrowing, as well as details on levels of expenditure including those on food, non-food, health, tourism and business. The survey covered about 100 villages and 28 towns. The size of the sample was 5,040 households of which 4,920 responded which makes the survey the largest carried out in Armenia to date and one with a very high response rate for a transition economy. The expenditure part of the data was collected using two different methods administered for different households. The methods are: recall method in which households were asked, during the interview, about their expenditures made during the last 30 days preceding the date of the interview; and a diary method where households were given a diary they used to record details about their income and expenditure on a daily basis for 30 days during the interview period. About 25% of the total sample of interviewed households used diaries and 75% used the recall method. The unit of study in the survey was the household, defined as a group of co-resident individuals with a common living budget. As will be explained in detail, the AHBS 96 was generally designed as a two stage stratified sampling, but for large urban areas with an almost definite probability of being selected, a one stage sampling was adopted.The Armenian HBS 1996 is not a standard Living Standards Measurement Study (LSMS) survey - the questionnaire used is more limited in scope and much different in format from a typical LSMS. This survey used no community or price questionnaires; it did not use most of LSMS’ prototypical fieldwork and data quality procedures, and the technical assistance did not come from the LSMS group in the World Bank. Nonetheless, the goals are some what LSMS-like and the data is certainly worth archiving. They are therefore being entered into the LSMS archives to guarantee their future accessibility to World Bank and other users.

Since 1960, the U.S. Department of Agriculture has provided estimates of expenditures on children from birth through age 17. This technical report presents the most recent estimates for married- couple and single-parent families using data from the 2011-15 Consumer Expenditure Survey (all data presented in 2015 dollars). Data and methods used in calculating annual child-rearing expenses are described. Estimates are provided for married-couple and single-parent families with two children for major components of the budget by age of child, family income, and region of residence. For the overall United States, annual child-rearing expense estimates ranged between $12,350 and $13,900 for a child in a two-child, married-couple family in the middle-income group. Adjustment factors for households with less than or greater than two children are also provided. Expenses vary considerably by household income level, region, and composition, emphasizing that a single estimate may not be applicable to all families. Results of this study may be of use in developing State child support and foster care guidelines, as well as public health and family-centered educational programs. i

The household budget survey (HBS) focuses on consumption expenditure behaviors of households residing in Italy. Such survey has replaced the old one. As deep changes have been introduced in every stage of the process, no comparison can be made using data prior to 2014. The survey deals with all expenditures incurred by the families to purchase goods and services for family consumption or to make gifts to people outside the family. That definition also includes goods coming from their own vegetable garden or farm directly consumed by the family (self-consumption) or donated, the goods and services provided by the employer to employees for wages or services, imputed rent of owner-occupied housings or dwellings provided without charge. Any other expenditure for purposes other than consumption is excluded from the survey. The collection of the expenditures is accompanied by the collection of the main socio-economic characteristics of the individuals within the family. The survey is conducted with two different techniques used in the three phases of data collection: an initial CAPI (Computer-Assisted Personal Interviewing) interview, through which the characteristics of the household and the dwelling, as well as some periodic housing expenditures, are recorded; the self-completion daily expenditure records, on which the family takes note of food, goods and services expenses for a period of 14 days; a final CAPI interview to collect other less frequent or exceptional family expenses. The observed phenomena include: - Socio-demographic information - Characteristics of the dwelling in which the family lives and other family owned dwellings - Means of transport and communication - Spending habits - Housing expenditures - Expenses over the past 12 months - Expenses over the last 3 months - Expenses over the last month - Personal or business travels The survey records expenditures with very different reference periods. The calculation of total expenditure therefore requires a time standardization of the collected data, i.e. a common reference period (the month), so that all the costs can be added up together. The survey also represents the basis for the official estimates of absolute and relative poverty in Italy. 15.013 families. Two-stage stratified random sample self-administered questionnaire Computer-Assisted Personal Interviewing (CAPI)

The household budget survey (HBS) focuses on consumption expenditure behaviors of households residing in Italy. Such survey has replaced the old one. As deep changes have been introduced in every stage of the process, no comparison can be made using data prior to 2014. The survey deals with all expenditures incurred by the families to purchase goods and services for family consumption or to make gifts to people outside the family. That definition also includes goods coming from their own vegetable garden or farm directly consumed by the family (self-consumption) or donated, the goods and services provided by the employer to employees for wages or services, imputed rent of owner-occupied housings or dwellings provided without charge. Any other expenditure for purposes other than consumption is excluded from the survey. The collection of the expenditures is accompanied by the collection of the main socio-economic characteristics of the individuals within the family. The survey is conducted with two different techniques used in the three phases of data collection: an initial CAPI (Computer-Assisted Personal Interviewing) interview, through which the characteristics of the household and the dwelling, as well as some periodic housing expenditures, are recorded; the self-completion daily expenditure records, on which the family takes note of food, goods and services expenses for a period of 14 days; a final CAPI interview to collect other less frequent or exceptional family expenses. The observed phenomena include: - Socio-demographic information - Characteristics of the dwelling in which the family lives and other family owned dwellings - Means of transport and communication - Spending habits - Housing expenditures - Expenses over the past 12 months - Expenses over the last 3 months - Expenses over the last month - Personal or business travels The survey records expenditures with very different reference periods. The calculation of total expenditure therefore requires a time standardization of the collected data, i.e. a common reference period (the month), so that all the costs can be added up together. The survey also represents the basis for the official estimates of absolute and relative poverty in Italy. 15.409 families. Two-stage stratified random sample self-administered questionnaire Computer-Assisted Personal Interviewing (CAPI)

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Household/families
• Individuals

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

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

The Household Budget Survey records all residing household expenditures to purchase goods and services to meet their needs; it is the main data source to describe, analyze and explain household spending behaviours. The survey allows to analyze the evolution of the household expenditure level and composition, according to household socio-demographic characteristics. The survey is conducted through two different technique: a self-filled diary, in which the household records purchases over a seven-day period, and a final face-to-face interview. According to sample design, approximately 28,000 households are interviewed each year. The survey refers to the total cost of the good or service purchased, apart from the moment of consumption or use and the payment instrument (instalments or credit card). Every item of expenditure reported in the dataset refers to a period of one month. The survey is used by Istat for the determination of the relative poverty. about 28.000 families. Two-stage stratified random sample face to face interview self-administered questionnaire

The file "dataset_regression_income.csv" contains a dataset developed in the analysis of inflation heterogeneity for Italian Households in the period 2015-2023.The dataset is the outcome of merging the yearly Household Budget Surveys (HBS) conducted by the Italian National Institute of Statistics (Istat), the Harmonised Index of Consumer Prices (HICP) which is calculated monthly by Istat, according to EU regulations, and the Survey on Households Income and Wealth (SHIW) conducted by Bank of Italy.Mapping price information into consumption decisions and aggregating an individual price index for each household according to a Laspeyres Formula leads to the computation of household-level inflation rates.Furthermore, we compute non-durable equivalent expenditure for each household as a proxy of living standards. The variable is obtained by subtracting durable expenditure from total aggregate expenditure and scaling down by an household equivalent scale (in the benchmark specification, the square root of the household size). The decile distribution of the variable is also computed.Finally, we apply a statistical matching procedure to integrate income information from SHIW data sources. The output is a synthetic dataset containing both expenditure and income information that preserves the joint distribution and correlation structures of the original datasets.The file "ISTAT_MFR_HBS_EUR.csv" is a conversion table that maps ECOICOP items to HBS expenditure voices.

This dataset provides detailed, household-level records of income and expenses, including transaction categories, payment methods, recurrence patterns, and basic household demographics. It enables comprehensive budgeting analysis, supports financial literacy initiatives, and can power personalized financial recommendations and research into household spending habits.

Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable. Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

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

Abstract: This article seeks to analyze the demand’s sensibility for organic food and their conventional counterparts to variations in prices and expenditure in Brazilian households. We estimated an aggregate demand system for 14 categories (seven organics and seven conventional) using the QUAIDS model with correction for the Zero Consumption Problem by the Shonkwiller and Yen two-step estimation method. The database was from the microdata from the Brazilian Household Budget Survey (POF/IBGE) 2008/2009. Results showed that Brazilian consumers are more sensitive to variations in prices and expenditure of organic than conventional food. Moreover, there is asymmetry in substitution/complementarity relations between the two types of food. These results suggest that it is relatively difficult to induce consumers that are used to purchase organic products to “revert” their spending habits changing organic products to conventional ones. Furthermore, results showed that consumers do not view organic food as a substitute for conventional food in most cases. Examining the expenditure elasticities, we conclude that organic foods can be classified as luxury goods.

The Household Budget Survey records all residing household expenditures to purchase goods and services to meet their needs; it is the main data source to describe, analyze and explain household spending behaviours. The survey allows to analyze the evolution of the household expenditure level and composition, according to household socio-demographic characteristics. The survey is conducted through two different technique: a self-filled diary, in which the household records purchases over a seven-day period, and a final face-to-face interview. According to sample design, approximately 28,000 households are interviewed each year. The survey refers to the total cost of the good or service purchased, apart from the moment of consumption or use and the payment instrument (instalments or credit card). In order to compare expenditure levels of households with different socio-economic features and expenditure behaviours, and to provide poverty estimates, imputed expenditures are also considered, such as those for self-consumptions (household self-produced goods from vegetables garden or farms, consumed by the household itself) or the imputed rents for owner-occupied or free of charge houses. The imputed rent is the return household would have if it rented at market prices the house it is living in and it has to be taken into account because the householders have housing service at their disposal. about 28.000 families. Two-stage stratified random sample face to face interview self-administered questionnaire

The Household Budget Survey records all residing household expenditures to purchase goods and services to meet their needs; it is the main data source to describe, analyze and explain household spending behaviors. The survey allows analyzing the evolution of the household expenditure level and composition, according to household socio-demographic characteristics. Households are selected at the beginning of yearly cycle by registry list in the sample district. The survey is conducted through two different techniques: a self-consumption diary, in which the household records purchases over a seven-day period, and a final face-to-face interview. In the reference week, households daily take note about purchase of large consumption goods using a questionnaire named “Purchase Diary”. If households produce goods by themselves, they take note in another diary called “Self-consumption Diary”. At the beginning of the next month, a final face-to-face interview is arranged by ISTAT. According to the sample design, each year approximately 28,000 households are interviewed. The survey refers to the total cost of the goods or services purchased, apart from the moment of consumption or the payment instruments. The main topic of this survey are as follows: - socio-demographic characteristics - dwelling characteristics - main dwelling (characteristics, services, tenure status, expenditures, maintenance) - secondary dwelling (expenditures, maintenance) - durable goods - furniture and equipments purchase - indoor and garden furniture - small equipments and home accessories - clothing and footwear - health - public transportation and communication - means of transport - communication devices - leisure time, culture and education - leisure time - culture - education - holidays - other services - personal belongings, not classified otherwise - recurring and extraordinary expenditures - spending habits - salary and savings - purchase diary - self-consumption diary There’s a cross-section “Salary and savings” where information about components income, wage earners, medium income of households and yearly savings are collected. Every item of expenditure reported in the dataset refers to a period of one month. Istat uses the survey for the determination of the relative poverty. 20.930 families. Two-stage stratified random sample face to face interview self-administered questionnaire

This data supports the publication titled 'Discrepancies between two long-term dietary datasets in the United Kingdom (UK)'. Longitudinal dietary data for the United Kingdom (UK) on food supply, provided by FAO food balance sheets (1961-2018) (FAO-FBS), and food purchases, provided Defra household budget surveys (National Food Survey [1942-2000] and Family Food Module [2001-2018]) (Defra-HBS). Studying dietary trends can shed light on progress towards healthier and more sustainable diets but longitudinal data are often confounded by lack of standardized methods. Two main data sources are often used for longitudinal analysis of diets: food balance sheets (per capita food supply estimated from production and trade data) and household budget surveys (household surveys on food purchased). The impact of these different collection methods has not been quantified for the UK. The data provided here were used to assess how trends in dietary change compared between the two collection methods for calories, meat and fish, nuts and pulses, and dairy, and how disparities between FAO-FBS and Defra-HBS have changed over time. These food types are comparable between FAO-FBS and Defra-HBS and can be used to monitor consumption and protein intake. The primary differences in quantities estimated by FAO-FBS and Defra-HBS occur in part due to inclusion of retail waste in FAO data and likely under-reporting of consumption in Defra data. \( \ \) “DataFrom_Figures_2_3_4_Tables_2_3” contains data used in the figures and tables of this publication. In version 2 of this dataset “DataFrom_Figures_2_3_4_Tables_2_3” has been updated to reflect revisions made to the publication. The methodology of FAO data was updated in 2014. Previously we handled data between 1961 and 2018 as a continuous time series, without adjusting for this change in methodology. However, it is necessary to adjust for the methodology change to provide accurate estimates of changes in food supply over time. The 2014 to 2018 values have been adjusted to give values consistent with the older methodology. The FAO has also published updated data for 2018 since the time of publishing, so we have updated the publication to include the updated data values. These updates affect the reported change in food supply between 1961 and 2018 (and between 2008 and 2018) for most food groups, and trends in the differences between Defra-HBS and FAO-FBS values, but do not affect the conclusions of the publication. \( \ \) UK National Food Survey data for 1942-1973 were downloaded on 08/01/2020 from https://webarchive.nationalarchives.gov.uk/ukgwa/20130103024837/http://www.defra.gov.uk/statistics/foodfarm/food/familyfood/nationalfoodsurvey/ National Food Survey data are named: “Household nutrient data from 1940 to 2000 – 1940-2000” and “Household consumption of selected foods from 1942 to 2000 – 1942-2000”. UK Family Food data for 1974 onwards were first downloaded on 19/02/2020 from https://www.gov.uk/government/statistical-data-sets/family-food-datasets. Datasets are named “UK - household purchases”, “UK - eating out purchases” and “UK - household and eating out nutrient intakes”.

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. The CE program is comprised of two separate components (each with its own survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week. The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files. The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. "Processing Files" of the Diary Survey Users' Guide. A second documentation guide, the "Users' Guide to Income Imputation," includes information on how to appropriately use the imputed income data. Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over. The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on "Other" in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

In light of the rapid socio-economic development in this era, it is necessary to make data on household expenditure and income available, as well as the relationship between those statistics and various variables with direct or indirect impact. Therefore, most of the countries are nowadays keen to periodically carry-out Household Expenditure and Income surveys. Given the continuous changes in spending patterns, income levels and prices, as well as in population both internal and external migration, it was now mandatory to update data for household income and expenditure over time. The main objective of the survey is to obtain detailed data on HH income and expenditure, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, it was well considered that the sample should be representative on the sub-district level. Hence, the data collected through the survey would also enable to achieve the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 2- Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns. 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators. 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it. 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps.. 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty.

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

Geographic coverage

This survey was carried-out for a sample of 12678 households distributed on urban and rural areas in all the Kingdom governorates.

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size has been uniformly selected, and in the second stage, a systematic approach guaranteing a representative sample of all sub-districts (Qada) has been applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires:

(1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data

The design and implementation of this survey procedures are: 1. Sample design and selection. 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals. 3. Design the tables template to be used for the dissemination of the survey results. 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks. 5. Selection and training of survey staff to collect data and run required data checkings. 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results. 7. Data collection. 8. Data checking and coding. 9. Data entry. 10. Data cleaning using data validation programs. 11. Data accuracy and consistency checks. 12. Data tabulation and preliminary results. 13. Preparation of the final report and dissemination of final results.

Harmonized Data

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

Abstract

The 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

Cross Price, expenditure, and quality-expenditure elasticities matrix1.