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

Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

This dataset shows Qatar’s score and ranking in the Quality of Life Index. The index uses an empirical formula that incorporates the following factors: purchasing power (higher is better), pollution (lower is better), house price-to-income ratio (lower is better), cost of living (lower is better), safety (higher is better), healthcare (higher is better), traffic commute time (lower is better), and climate (higher is better).

In 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

The analysis of real wages has a long tradition in Germany. The focus of the acquisition is on company wages, on wages of certain branches or for categories of workers as well as on the investigation of long term aggregated nominal and real wages. The study of Ashok V. Desai on the development of real wages in the German Reich between 1871 and 1913 is an important contribution to historical research on wages. The study is innovative and methodically on an exemplary level. But mainly responsible for the upswing in the historical research on wages in the 50s and 60s is an extraordinary publication by Jürgen Kuczynski. “The new historical research on wages in Germany is insolubly connected with Jürgen Kuczynski. In his broad researches the history of wages is only one section among many other themes but it is a very important one can be seen as the core piece of his work.” (Kaufhold, K.H., 1987: Forschungen zur deutschen Preis- und Lohngeschichte (seit 1930). In: Historia Socialis et Oeconomica. Festschrift für Wolfgang Zorn zum 65. Geburtstag. Stuttgart: Franz Steiner Verlag, S, 83). In his first study on long series on nominal and real wages in Germany he used a broad empirical basis and encouraged more research in this area. His weaknesses are methodological inconsistencies and a restricted representativeness. For example he includes tariff wages but also actually paid wages. Some important industries like food or textile industry are not taken into account. Wages in agriculture were often estimated but without enough material necessary for a good estimation. Wages for work at home are not regraded in the calculation of the index. The weight of cities in the calculation of the index is relatively too high compared to rural regions and therefor it leaks regional representativeness.In his study Desai uses the reports of trade associations for the Reich´s insurance office on the persons who are insured in the accident insurance and their wages as a basis for the calculation of annual nominal average wages. Desais focusses on industrial wages because only for them long term series are available. As the insurance premiums are calculated according to the income level the documents of the trade associations can be used for the calculation of an index for wages development. Desais study is also very useful regarding the calculation of a new index for costs of living based the model of a typical worker family. „F. Grumbach and H. König have used the same sources to derive indices of industrial earnings. The main differences between their series and ours are: (a) we have adopted the industrial classification followed by the Reichsversicherungsamt, while Grumbach and König have made larger industrial groups, (b) we have calculated average annual earnings, while they claim to have calculated average daily earnings (i.e. to have adjusted the annual figures for the average number of days worked per year per worker), and (c) they have failed to correct distortions in the original data” (Desai, A.V., 1968: Real Wages in Germany 1871–1913. Oxford. Clarendon Press, S. 4). Register of tables in HISTAT:A. OverviewsA.1 Overview: Different estimations of the real and nominal gross wages in the German Reich, index 1913 = 100 (1871-1913)A.2 Overview: Development of costs of living, index 1913 = 100 (1871-1913)A.3 Overview: Development of nominal and real wages, index 1913=100 (1844-1937) D. Study by Ashok V. DesaiD.01 Different estimations of real wages in the German Reich, index 1895 = 100 (1871-1913)D.02 Annual average wage (1871-1886)D.03 Annual gross wages in chosen production segments (1887-1913)D.04 Annual average wage in industry, transportation and trade (1871-1913)D.05 Construction of an index for costs of living, 1895 = 100 (1871-1913)D.06 Real wages, in constant prices from 1895 (1871-1913)D.07 Wheat prices and prices for wheat bread (1872-1913)D.08 Rye prices and prices for rye bread (1872-1913)D.09 Average export prices by product groups, index 1895 = 100 (1872-1913)D.10 Average import prices by product groups, index 1895 = 100 (1872-1913)D.11 Average export prices, import prices and terms of trade, index 1895 = 100 (1872-1913) O. Study by Thomas J. OrsaghO. Adjusted indices for costs of living and real wages after Orsgah, index 1913 = 100 (1871-1913)

In 2025, Luxembourg reached the highest score in the quality of life index in Europe, with 220 points. In second place, The Netherlands registered 211 points. On the opposite side of the spectrum, Albania and Ukraine registered the lowest quality of life across Europe with 104 and 115 points respectively. The Quality of Life Index (where a higher score indicates a higher quality of life) is an estimation of overall quality of life, calculated using an empirical formula. This formula considers various factors, including the purchasing power index, pollution index, house price-to-income ratio, cost of living index, safety index, health care index, traffic commute time index, and climate index.

Abstract

The goal of the Household Budget Survey (HBS) is to gather data about the revenues, expenditures and consumption of the households. Except that, the Survey gathers data about certain important data of the living standards (housing conditions, heating of the apartment-house, supply with permanent consumption goods), as well as basic data about the demographic, economic and sociological characteristics of the households.

The gathered data, using the relevant methods, give the opportunity to realize the level and structure of the personal consumption in the households in general and especially by certain socio-economic categories. The distribution of the households in socio-economic categories enables to realize the existing differences in the level and structure of the personal consumption of the households as a significant material component of the living standards of the population.

Additionally, the data of the survey shall be used for drawing up the weights for calculation of the cost of living index, calculation of the personal consumption balance etc. The data of the survey shall also enable to calculate the poverty lines by household types which are bases for the cash benefits and social assistance.

Geographic coverage

The Household Budget Survey is implemented on the entire territory of the Republic of Macedonia.

Analysis unit

Households and Individuals

Universe

All household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection framework has been divided in 16 stratums. The stratification is territorial according to the eight NUTSZ regions and according to types (urban and other). The census circles and households were randomly selected within these stratums. Using this methodology, 5040 households have been randomly selected and they are distributed throughout the entire territory of the Republic of Macedonia.

Mode of data collection

Face-to-face [f2f]

Abstract

A household budget survey or Household Income and Expenditure survey (HES) as it is commonly called, is one of the most important economic surveys carried out by the Management and Information Systems Division (MISD). The survey is household-based and serves to provide up-to-date and comprehensive information on the components of the average household budget.

Household expenditure surveys are normally carried out every five to seven years so that updated information can be obtained on spending patterns and most importantly, on the composition of the 'basket of goods'.

In a HES, information on both income and expenditure is collected. Background variables such as household composition, age and sex structure and economic activity are also included to help classify the households in various demographic and socio-economic groups and to provide updated estimates on previous household surveys.

The primary purpose of the HES was to collect up-to-date detailed information on the expenditure of households to provide new weights for the calculation of the Cost of Living Index estimated here by the Retail Price Index (RPI).

A second important use of this survey is to provide data on aggregate consumers' expenditure and income to be used in the compilation of the Gross Domestic Product (GDP) and National Income accounts. The 'expenditure approach' of the GDP calculation usually estimates the consumer expenditure component. Results from this survey will thus provide data to crosscheck those estimates.

Another key purpose of the HES survey is that it makes available information on the level and distribution of household incomes. Such information is useful in the assessment of the social and economic planning systems. The distribution of household income provides an approximate measure of poverty in society.

In general, the survey provides the public with useful and interesting information on current spending patterns of the households in Seychelles. These patterns are expected to have changed considerably over the last decade.

Geographic coverage

The survey covered households on Mahe, Praslin and La Digue (the three mainly inhabited islands), and for practical consideration, excluded those on the outer islands.

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities / items

Universe

Persons living in hospitals, military barracks, prisons etc. were excluded. Households headed by expatriates were also excluded, because the income and spending patterns of such households are expected to be different from those of the average Seychellois household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design The most appropriate sampling frame available was the list of households obtained from the 1997 Population and Housing Census. Although not updated over the two years prior to the survey, the database provided the ideal frame for direct sampling given that the sampling units would be the households themselves.

The frame listed 17,878 households enumerated during the 1997 census covering all the islands. In consideration of logistic and administrative problems, the geographical coverage was restricted to the three main islands (Mahe, Praslin and La Digue), which account for 99% of all households.

The sampling was done in two stages. An overall sample of 10% (around 1788 households) was desired. In the first stage the households were stratified by district. The sample size was distributed among the districts representative of their size (number of households), to determine the number of households to be drawn from each district (i.e. proportional allocation). From each district, the allocated number of households was then drawn using systematic sampling method whereby households are selected at equal intervals starting from a chosen random number. With each household having the same probability of being selected, the sample becomes self-weighting.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The data were captured on personal computers using a programme written in DELPHI. The software for data capturing made provisions to enter all details collected. For the account book (Form HES3) items purchased or acquired (although it would not be possible to analyse all the descriptive details because of the variety of specifications, units, packaging etc, description and units of items) were captured to help identify commonly purchased items for future pricing.

The data files were then merged into one database and processed in SPSS and MS EXCEL for tabulation .

Response rate

The original sample drawn included 1696 households representing around 9.5 percent of households on Mahe, Praslin and La Digue. The enumeration covered 1219 households but after post-enumeration checks, data from just over 800 or 67% of these households were used in the final analysis.

This collection of wage data was published in „Die Geschichte der Lage der Arbeiter in Deutschland von 1789 bis in die Gegenwart“ by Jürgen Kuczynski (volume I and volume II, here quoted after 6th edition, Berlin 1953, 1954). The data contains wage indices of a certain base year and the corresponding wage raw data (hourly wages, weekly wages, annual wages in marks and pfennigs). The wage data is regionally widely spread until the year 1914; it contains single cities as well as bigger regional units. Since 1924 Kuczynski’s surveys rely on the publications of the statistical office. The wage data is ordered by professional groups, industry and agriculture and by certain industrial sectors. Kuczynski’s wage index is mainly based on publications of trade unions and on reports of different chambers of commerce. The weaknesses of the indices are due to the methodological inconsequence and the limited representative status concerning the election of geographical units. Union wages and also actually paid wages are considered in the calculations, like for example daily, weekly and annual wages or layer wages for miners. On the other side important industrial sectors such as the food or the textile sector are not taken into account. Wage data for agriculture relies often on estimations or is calculated with insufficient material. Wages for work at home are not taken into account in the index calculation. There are also problems with the representative status of the index regarding regional units because cities are weighted too important compared with rural regions. Another topic of the survey is the construction of an index of costs of living. For a long time Kuczynski’s index for costs of living was without any concurrence. It was used by different authors without any changes or modifications. The substantial weakness of the index is that for the calculation of the development of the costs of living, it only takes costs of food and rent into account. Prices of food and rent were weighted in the ratio 3 to 1. Kuczynski does not give an explanation for this weighting. Further the certain price indices for food and rent were calculated by the aggregation of incomplete regional price developments.

Data tables in HistatA – Tables for the period from 1800 to1870:A.1 Wage Data (in Mark and Pfennig)A.2 Wage indices, base 1900 = 100A.3 Costs of living and real wages 1900 = 100 B - Tables for the period from 1870 to 1932:B.1 Wage Data (in Mark and Pfennig)B.2 Wage indices, base 1900 = 100B.3 Costs of living and real wages 1900 = 100 C - Tables for the period from 1932 to 1945:C.1 Wage Data (in Mark and Pfennig)C.2 Wage indices, base 1900 = 100C.3 Costs of living and real wages 1932 = 100

Consumer Price Index CPI in India increased to 194.20 points in June from 193 points in May of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The CCI is intended for use in the survey of living conditions of households, calculation of the consumer price index, calculation of final consumption expenditures of the institutional household sector in accordance with the methodology of the system of national accounts (SNA), in particular international comparisons of gross domestic product (GDP) by categories of costs. The CCI provides a comparison of national statistics, in particular data on the final consumption expenditures of the household sector, with relevant data from the European Union countries and statistical services of other countries. Order of the State Statistics Committee dated December 29, 2007 Ü 480.

In 2024, the average annual inflation rate in China ranged at around 0.2 percent compared to the previous year. For 2025, projections by the IMF expect slightly negative inflation. The monthly inflation rate in China dropped to negative values in the first quarter of 2025. Calculation of inflation The inflation rate is calculated based on the Consumer Price Index (CPI) for China. The CPI is computed using a product basket that contains a predefined range of products and services on which the average consumer spends money throughout the year. Included are expenses for groceries, clothes, rent, power, telecommunications, recreational activities, and raw materials (e.g. gas, oil), as well as federal fees and taxes. The product basked is adjusted every five years to reflect changes in consumer preference and has been updated in 2020 for the last time. The inflation rate is then calculated using changes in the CPI. As the inflation of a country is seen as a key economic indicator, it is frequently used for international comparison. China's inflation in comparison Among the main industrialized and emerging economies worldwide, China displayed comparatively low inflation in 2023 and 2024. In previous years, China's inflation ranged marginally above the inflation rates of established industrialized powerhouses such as the United States or the European Union. However, this changed in 2021, as inflation rates in developed countries rose quickly, while prices in China only increased moderately. According to IMF estimates for 2024, Zimbabwe was expected to be the country with the highest inflation rate, with a consumer price increase of about 561 percent compared to 2023. In 2023, Turkmenistan had the lowest price increase worldwide with prices actually decreasing by about 1.7 percent.

Abstract

The goal of the Household Budget Survey (HBS) is to gather data about the revenues, expenditures and consumption of the households. Except that, the Survey gathers data about certain important data of the living standards (housing conditions, heating of the apartment-house, supply with permanent consumption goods), as well as basic data about the demographic, economic and sociological characteristics of the households.

The gathered data, using the relevant methods, give the opportunity to realize the level and structure of the personal consumption in the households in general and especially by certain socio-economic categories. The distribution of the households in socio-economic categories enables to realize the existing differences in the level and structure of the personal consumption of the households as a significant material component of the living standards of the population.

Additionally, the data of the survey shall be used for drawing up the weights for calculation of the cost of living index, calculation of the personal consumption balance etc. The data of the survey shall also enable to calculate the poverty lines by household types which are bases for the cash benefits and social assistance.

Geographic coverage

The Household Budget Survey is implemented on the entire territory of the Republic of Macedonia.

Analysis unit

Households and Individuals

Universe

All household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey is conducted on sample of 5040 households on the whole territory of the country, which is about 1% of the total number of households in the country. The frame of the selection are all enumeration districts that contain above 20 households, from Census 2002. The sample is two-stage stratified. Primary sampling units are the enumeration districts and secondary sampling units are the adresses of households.

For the needs of the sample stratification on two levels with the total 16 strata was made: - Eight geographical regions, NUTS 3; - Two contingents- urban and rural areas.

In the sample were 210 enumeration districts. The allocation of the sample in the stratas for the first stage is proportionally to the number of households in the region, and in the second stage for each selected enumeration district each quarter 6 households were selected randomly with equal probability. Each quarter were interviewed 1260 households, or 5040 different households on annual level.

Mode of data collection

Face-to-face [f2f]

Response rate

The non-response rate for HBS-2009 was 17.0% and the refusal rate was 9.8%

Abstract

The goal of the Household Budget Survey (HBS) is to gather data about the revenues, expenditures and consumption of the households. Except that, the Survey gathers data about certain important data of the living standards (housing conditions, heating of the apartment-house, supply with permanent consumption goods), as well as basic data about the demographic, economic and sociological characteristics of the households.

The gathered data, using the relevant methods, give the opportunity to realize the level and structure of the personal consumption in the households in general and especially by certain socio-economic categories. The distribution of the households in socio-economic categories enables to realize the existing differences in the level and structure of the personal consumption of the households as a significant material component of the living standards of the population.

Additionally, the data of the survey shall be used for drawing up the weights for calculation of the cost of living index, calculation of the personal consumption balance etc. The data of the survey shall also enable to calculate the poverty lines by household types which are bases for the cash benefits and social assistance.

Geographic coverage

The household consumption survey is conducted on the entire territory of the Republic of Macedonia FYR.

Analysis unit

Households and Individuals

Universe

All household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey is conducted on sample of 5040 households on the whole territory of the country, which is about 1% of the total number of households in the country. The frame of the selection are all enumeration districts that contain above 20 households, from Census 2002. The sample is two-stage stratified. Primary sampling units are the enumeration districts and secondary sampling units are the adresses of households.

For the needs of the sample stratification on two levels with the total 16 strata was made: - Eight geographical regions, NUTS 3; - Two contingents- urban and rural areas.

In the sample were 210 enumeration districts. The allocation of the sample in the stratas for the first stage is proportionally to the number of households in the region, and in the second stage for each selected enumeration district each quarter 6 households were selected randomly with equal probability. Each quarter were interviewed 1260 households, or 5040 different households on annual level.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey is conducted through 4 basic forms: 1. Form APD-1 (15-day Diary) 2. Form APD-2 (Questionnaire-replacement for a Diary) 3. Form APD-3 (Household Questionnaire) 4. Form APD-4 (Non-response Questionnaire)