Index figures on production prices of dwellings and other buildings reflect the relation between the output value and the output volume and can be used to convert the value of construction output from current prices to fixed prices. The output price index is derived from the series "New dwellings; output indices 2000=100". From the 2nd quarter 2009 on, the figures of the series 2005 = 100 are used and linked to the series 2000 = 100. Statistics Netherlands publishes data on the value of construction output. The volume of construction output, however, cannot be deduced from the value, which is subject to price changes. The price index on the building costs of new dwellings eliminates the effect of price changes. The price index on construction output is calculated by distributing the value of the output (current prices) over the quarters essential to the price setting of the building project. Subsequently, the quarterly output is calculated in fixed prices by using the price index on the building costs of new dwellings. The index figure of the output price is the sum of the current prices divided by the sum of the fixed prices (*100).

Possibilities for selection: - Total construction - Total construction of new dwellings/buildings - New dwellings - New buildings in the private sector - New buildings in the non-commercial sector - Total other buildings - Other dwellings - Other buildings in the private sector - Other buildings in the non-commercial sector

Data available from 1st quarter 2000 till 4th quarter 2016 Frequency: discontinued

Status of the figures: The figures of 2016 are provisional. Since this table has been discontinued, the data will not become definitive.

Changes as of January 29 2018 None, this table is discontinued.

When will new figures become available? This table is succeeded by Production on buildings; price index 2015 = 100. See paragraph 3.

Linking recommendation If you want to compile long-term series with linked price indices on production of buildings, you can link the figures on price level 1995 with the figures on price level 2000. For that, the percentage change from the 2nd quarter 2005 with the 1st quarter 2005 must be calculated, as the price index for the 1st quarter 2005 is the last figure published on price level 1995. This change must then be adjusted to the figures for the 1st quarter 2005 of the series 1995. The 2nd quarter index of the linked series is calculated by calculating the difference between the 1st quarter 2005 and the 2nd quarter 2005 according to the series on price level 2000 and multiplying this by the index for the 1st quarter 2005 according to the series on price level 1995.

In the example: (119/120) x 148=147 (rounded). For the 3rd quarter 2005 the index is calculated analogously, where because of rounding problems the first quarter figures must be used for the link.

Data in table tells us about the year-wise Consumer price index number for industrial workers. Parameters used to classify data in the table are: Clothing,bedding,footwear, Food and General. The specific weightage of these parameters is also calculated as percentage of whole. Data is gathered from 2007-2016 for Indian States and UTs and also for All India.

Note: The total of the respective weights may not tally as the original weight are calculated to six places of decimals whereas the weights given here are rounded to two places of decimals.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

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.

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

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

Russia Consumer Price Index (CPI): Weekly Estimate: Prev Week=100: Millet data was reported at 101.000 Prev Week=100 in 13 Aug 2018. This records an increase from the previous number of 100.900 Prev Week=100 for 06 Aug 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Week=100: Millet data is updated weekly, averaging 100.800 Prev Week=100 from Feb 2018 (Median) to 13 Aug 2018, with 26 observations. The data reached an all-time high of 101.800 Prev Week=100 in 23 Jul 2018 and a record low of 100.300 Prev Week=100 in 07 May 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Week=100: Millet data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA007: Consumer Price Index: Weekly Estimate.

Abstract

Transition to free economic structure and, as a consequence, processes of privatization of large agricultural and industrial organizations and birth of numerous new economic entities led to significant changes in quantitative and qualitative characteristics of industrial organizations and peasant farms in RA. During the last decade and especially the last 4-5 years, the structural changes, in their turn, caused also certain complications in the mentioned fields in terms of ensuring collection, comprehensiveness and reliability of statistical data on prices and pricing.

In particular, in case of radical structural changes, international recommendations require the weights upon which price indexes are based to be periodically updated. In order to have a real picture and dynamics of the present situation on creation of indicators for new base year, i.e. collection of information on set of goods-representatives, their weights, average annual prices, prices and price changes, it would be necessary to periodically conduct sample surveys for further improvement of the methodology for price index calculation.

The objectives of the survey were: • to improve the sample, develop a new sample, • to revise the base year and weights, • to receive additional information on prices of sales of industrial, agricultural product and purchase (acquisition of production means) in RA, • to improve methodology for price observation and calculation of price indexes (survey technology, price and other necessary data collection, processing, analyzing), • to revise the base year for producer price indexes, components structure, shares, calculation mechanism, etc., • to derive price indexes that would be in line with the international definitions, standards and classifications, • to complement the NSS RA price indexes database and create preconditions for its regular updating, • to update the information on economic units covered by price indexes calculation, • to ensure use of international standards and classifications in statistics, • to form preconditions for extension of sample observation mechanisms in the state statistics.

Besides the above mentioned, the need of the given survey was also stipulated by the following reasons: - a great mobility of micro-sized, small and medium-sized organizations mainly caused by increased speed of their births, activity and produced commodity changes or deaths that decreases the opportunity to create long-term fixed-base time series of prices and price indexes, - According to the CPA classification coding and recoding activities related to the introduction of Armenian classification of economic activities - NACE (based on the European Communities’ NACE classification).

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

Agriculture The sample of the survey was desighned in the conditions of lack of farm register. The number of peasant farms was calculated and derived by database analysis. The number of villages (quotas) selected from each marz was determined taking into account the percent of rural population of marzes. The villages from marz were selected randomly. The peasant farms covered by the survey were selected based on number of privatized plots. The survey was carried out in 200 rural communities selected from 10 marzes, in 5-20 households from each community. Pilot survey was conducted with 1 901 farms in the sample.

Industry The sample frame for the survey was designed as follows: 1. The industrial organizations with share 5 and more percent have been selected by reduction method from fifth level (each subsection) of NACE for whole RA industry. 476 out of 2231 industrial organizations covered by statistical observation were selected for pilot survey.

1. 70 organizations suggested by Industry statistics division of NSS RA and 70 organizations included in state observations on prices conducted previously by the NSS RA (in all 140 organizations), which are considered important and representative for price observation and excluded from the above-mentioned sample, were separated from the general population. These organizations have also been included in sample population of the pilot survey. As it became obvious from further work, the sample covered both the large and medium-sized and the small and micro-sized organizations, which ensured the representativeness of separate branches of industry and organizations by size. As a result, given by the objective of the survey, as well as available financial constraints, the sample population of the pilot survey comprised 616 industrial organizations, the volumes of produced production of whichaccording to the data for January-October of 2006 comprised more than 86% of total volume of RA industrial production. 165 (92.7%) out of 178 classes of NACE were covered by the sample.

Mode of data collection

Face-to-face [f2f]

The Program Access Index (PAI) is one of the measures the USDA Food and Nutrition Service (FNS) uses to reward States for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). The Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill) directed USDA to establish a number of indicators of effective program performance and to award bonus payments to States with the best and most improved performance. The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits.

Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Russia Consumer Price Index (CPI): Weekly Estimate: Prev Month=100: Cabbage data was reported at 94.500 Prev Mth=100 in 13 Aug 2018. This records a decrease from the previous number of 95.800 Prev Mth=100 for 06 Aug 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Month=100: Cabbage data is updated weekly, averaging 100.450 Prev Mth=100 from Jul 2017 (Median) to 13 Aug 2018, with 58 observations. The data reached an all-time high of 118.200 Prev Mth=100 in 18 Jun 2018 and a record low of 55.000 Prev Mth=100 in 31 Jul 2017. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Month=100: Cabbage data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA007: Consumer Price Index: Weekly Estimate.

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

This study offers a broad methodical basis for the evaluation of the price development in Germany after the Napoleonic Wars. Hereby historical sources are considered for the calculation of a total index number, which represent the changes in several commodity prices in a single column of numbers. This process is targeted on an insight in the changes of the entire price level, reflecting the purchasing power of the respective currency as well. Tables in HISTAT:A.1 Average exchange rate of the mark courant of Hamburg as to the cark banco (1792-1872).A.2 Average agio for 100 taler courant against coin at 42 Groschen (the German groat) per taler (1816-1823)A.3 Value of 100 taler ‘Münze’ (‘coin’) in the cities of Berlin, Breslau and Königsberg (1808-1823)B.1 Weighing, partial index numbers – ratio in percent (1820-1913)B.2 Proportion of collective groups with alternatingly weighed total index number in percent (1792-1933)B.3 Proportion of single groups with alternatingly weighed total index number in percent (1792-19330)C.1 Price index numbers of commodities, 1913 = 100 (1792-1934)C.2 Group and total index numbers weighed on a constantly equal basis, 1913=100 (1792-1934)C.3.1 Group and total index numbers for periods with temporarily different weighing bases, 1820=100 (1792-1830)C.3.2 Group and total index numbers for periods with temporarily different weighing bases, 1840=100 (1820-1850)C.3.3 Group and total index numbers for periods with temporarily different weighing bases, 1860=100 (1840-1873)C.3.4 Group and total index numbers for periods with temporarily different weighing bases, 1880=100 (1860-1895)C.3.5 Group and total index numbers for periods with temporarily different weighing bases, 1913=100 (1885-1934)C.4 Group and total index numbers with alternating weighing, 1913=100 (1792-1934)D.1 Index numbers of wholesale prices in Great Britain, France, and the United States, 1913=100 (1791-1934)D.2 Wholesale price index numbers of raw materials for industrial and consumer goods (industrial raw materials), 1913=100 (1791-1934)D.3 Index numbers of prices for fatstock and products of animal origin, 1913=100 (1792-1934)E.1 Operational length of the German railway system (1840-1930)E.2 Telegraph and phone lines; phone facilities (1885-1932)E.3 Number of German seagoing vessels in 1,000 NRT (1871-1934)

Price index of consumer goods and services is calculated on the basis of the results of: - surveys on prices of consumer goods and services on the retail market, - surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system. Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP). The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

ABSTRACT The objective of this study was to develop a feedlot beef cattle calculation model and production cost analysis and, from the results obtained, devise a production cost index. A case study was conducted to understand the characteristics of the productive processes of a commercial feedlot. Then, based on the Economic Theory, cost items of the farm under analysis were identified and transferred to a spreadsheet. The survey included ten feedlot farmers from the state of São Paulo and other nine from the state of Goiás and was carried out to determine representative properties, and prices of items used were monitored. Production costs of each farm were calculated, and theoretical concepts of index numbers were used to devise the feedlot cattle production cost index. The cost allocation scheme was divided into four cost groups: variable, semi-fixed, fixed, and production remuneration factors. The developed model allowed a cost prognosis of the analyzed systems. Highest total costs for São Paulo State feedlots were R$ 9.17 kg−1 and R$ 9.08 kg−1 for average-sized and large farms, respectively, as contrasted to that of Goiás, of R$ 8.29 kg−1. Between the months April and June, the cost of production for feedlot beef cattle showed reductions of 1.48 and 1.40% for the average and large feedlots in the State of São Paulo and 9.13% for the Goiás feedlot by the Konüs Exact Index, respectively. Studies available in literature were compared and it was concluded that the model can help feedlot cattle farmers take production decisions. The Konüs Index allows for a methodological advancement in relation to other studies carried out on the Brazilian livestock industry; besides, it can contribute to the sector organization.

Consumer Price Index CPI in India increased to 197.30 points in October from 197 points in September of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description: From an economic point of view the production encompasses manufacturing, including related ‘industrial services’ as long as they are provided in the production industry. After the guidelines of the official statistics on the measurement of production, all products produced to be sold including repair works, montages and contract processing should be captured. Own consumption and wage work is included. For the calculation of the production indices the primary used data are the monthly production surveys. For this surveys reports of chosen local units of enterprises in the production, in the mining sector and extraction of stones and earth with 50 or more employees are used. Until 2006 the reporting threshold was fixed for 20 or more employees. The manufacturing trade is always included. The production index should demonstrate the development of the quantitative production of the production industry and its sub-areas in Germany, adjusted for chances in prices and structures to provide continuous data. Differences in size and changes in structures can be avoided, by presenting the production output not in total numbers, but in from of index number series orientated towards a basis year. For the calculation of production index numbers, current monthly production values (quantity of sales or sale values) are presented as a ratio of the monthly averages of the base year. Until 1993 the Federal Statistical Office calculated two types of production indices: gross-production indices and net-production indices. From the index system 1991=100 on there is only one production index, defined as e net production index. Both index types differ from one another among other things by the definition of the performance dimensions (value added or value of gross production) and by the way it is structured (net production index by economic sectors, gross production index by types of commodities). Indices of net production in the Federal Republic of Germany exist since 1950. During the past decades the base year changed several times and also the content wise classification economic sectors changed repeatedly trough the introduction of new classification systems. The series with different base years overlap, which gives the opportunity to calculate a continuous series with one single base, if the classification of economic sectors did not change in the entire period. Content-related interlinking of indices with different bases is controversial and the results can only be interpreted with care and under certain assumptions. The net production indices are also used to measure productivity in the production industry. Labor productivity (of a local unit, an enterprise, an economic sector or of the entire national economy) can be defined as the ratio of quantity of production and labor input in a certain period. Interpreting this coefficient, it is important to note that labor productivity also depends on the use of other production factors. The index for labor productivity is defined as the “production results per input component of the working volume”. Two different manifestations of the working volume are used for the calculation of the index: (1) hours of work by employees and (2) number of hours worked. Until 1994 in addition a distinction between “number of workers” and “number of employees” was made. The total national working productivity serves as an indicator for economic performance and competitiveness of an economic sector or of the entire national economy with regard to the entire labor input. Labor productivity (after the results of the national accounts) is apparently the most used productivity notion for the entire economy. It shows how effective the input labor is used in the production process. Anyway, it is important to note that the partial productivity indicator not only depends on the factor work but also on the endowment of a certain sector or the entire economy with machines and their degree of modernity and on the infrastructure, which also has an impact on the production result. Productivity can be measured regarding the following two aspects: production result per worker (per capita productivity) and production result per working hour (hourly productivity). For the entire national economy the labor productivity is measured as the ratio of the gross national product (in constant prices) and the average number of employees. To look at the development of labor productivity of an entire national economy, usua...

Learn the differences between the consumer price index (CPI) and the personal consumption expenditures (PCE) price index. Find out what measures are used to gauge underlying inflation, or the long-term trend in prices, such as median and trimmed-mean inflation rates and core inflation.

Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Fresh Tomatoes data was reported at 51.600 Prev Dec=100 in 13 Aug 2018. This records a decrease from the previous number of 53.200 Prev Dec=100 for 06 Aug 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Fresh Tomatoes data is updated weekly, averaging 85.350 Prev Dec=100 from Jul 2017 (Median) to 13 Aug 2018, with 58 observations. The data reached an all-time high of 112.500 Prev Dec=100 in 25 Dec 2017 and a record low of 51.600 Prev Dec=100 in 13 Aug 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Fresh Tomatoes data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA007: Consumer Price Index: Weekly Estimate.

Price index of consumer goods and services is calculated on the basis of the results of:
- surveys on prices of consumer goods and services on the retail market,
- surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system.

Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP).

The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.