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.

Chile Consumer Price Index (CPI): Housing: Hardware: Calculator data was reported at 24,832.770 1998=100 in Dec 2008. This records an increase from the previous number of 24,608.830 1998=100 for Nov 2008. Chile Consumer Price Index (CPI): Housing: Hardware: Calculator data is updated monthly, averaging 24,642.810 1998=100 from Dec 1998 (Median) to Dec 2008, with 121 observations. The data reached an all-time high of 26,104.340 1998=100 in Mar 2003 and a record low of 23,585.500 1998=100 in Apr 2000. Chile Consumer Price Index (CPI): Housing: Hardware: Calculator data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.I014: Consumer Price Index: Greater Santiago: Dec1998=100.

The Program Access Index (PAI) is one of the measures FNS uses to reward states for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). Performance awards were authorized by the Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill). The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits. The purpose of this step-by-step guide is to describe the calculation of the Program Access Index (PAI) in detail. It includes all of the data, adjustments, and calculations used in determining the PAI for every state.

Since 1981, the ISPF has been responsible for the calculation of the Consumer Price Index (CPI), which is the instrument for measuring inflation in French Polynesia. This official statistical indicator reflects the increase, fall or stability of prices from one month to another, or over 12 slippery months. It is used as a reference index when reviewing contracts (rents, maintenance, etc.) and to fix the amount of the SMIG. Its calculation is standardised and comes from the collection of numerous prices of products and services, of constant quality, in the territory. Hybrid indexes are linear compositions of elementary indices derived from the consumer price index and/or building and public works indexes. In some cases, the basic indices are complemented by very specific collections to monitor significant budget items not otherwise represented. Since 1981, the ISPF has been responsible for calculating the construction indexes, which reflect the evolution of the burden on construction companies and are used in the revision and updating of public procurement prices. Their calculation is based on the combination of the prices of different types of goods and services according to the division of burdens on an activity. These charges are divided into four major sub-assemblies: — wage and social charges; — energy-related loads; — loads related to the building materials used; — charges related to miscellaneous operating products and services.

This table shows the evolution of the house price index since 2016. The index makes it possible to calculate the evolution of the prices of dwellings, subdivided into apartments, houses and villas. Historically, the General Administration of Heritage Documentation (AGDP) performed its calculations based on the first quarter of 2003 as the reference period. Since then, AGDP has had complete electronic data for these calculations. Price indices could not be calculated for 2015. Indeed, the mathematical model of the AGDP was based on the output of the Cadnet/Loco application. Cadnet/Loco was replaced by STIPAD in 2015. Therefore, the sales of which data were available were insufficient to calculate an index for the whole year. In view of the improvements allowed by the new STIPAD application, we took over the publication of the index from 2016 as the reference year (=100 %). Since 2022, we have also calculated the evolution of this index by Region.

Excel files using output from the LandSHIFT model to calculate changes in BII in India for the four scenarios and the base year 2010.

Bangladesh BD: Net Barter Terms of Trade Index data was reported at 68.332 2000=100 in 2020. This records an increase from the previous number of 65.803 2000=100 for 2019. Bangladesh BD: Net Barter Terms of Trade Index data is updated yearly, averaging 103.596 2000=100 from Dec 1980 (Median) to 2020, with 41 observations. The data reached an all-time high of 162.264 2000=100 in 1985 and a record low of 57.575 2000=100 in 2011. Bangladesh BD: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources’ impact over time. Our dataset differentiates the context in which citations occur to distinguish between ‘awareness’ and ‘usage’, and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.

Topographic Wetness Index (TWI) is calculated as log_e(specific catchment area / slope) and estimates the relative wetness within a catchment.

The TWI product was derived from the partial contributing area product (CA_MFD_PARTIAL), which was computed from the Hydrologically enforced Digital Elevation Model (DEM-H; ANZCW0703014615), and from the percent slope product, which was computed from the Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). Both DEM-S and DEM-H are based on the 1 arcsecond resolution SRTM data acquired by NASA in February 2000.

Note that the partial contributing area product does not always represent contributing areas larger than about 25 km2 because it was processed on overlapping tiles, not complete catchments. This only impacts TWI values in river channels and does not affect values on the land around the river channels. Since the index is not intended for use in river channels this limitation has no impact on the utility of TWI for spatial modelling.

The TWI data are available in gridded format at 1 arcsecond and 3 arcsecond resolutions.

The 3 arcsecond resolution TWI product was generated from the 1 arcsecond TWI product and masked by the 3” water and ocean mask datasets. Lineage: Source data 1. 1 arcsecond resolution partial contributing area derived from the DEM-H (ANZCW0703014615). 2. 1 arcsecond resolution slope percent derived from DEM-S (ANZCW0703014016) 3. 3 arcsecond resolution SRTM water body and ocean mask datasets

TWI calculation TWI was calculated from DEM-H following the methods described in Gallant and Wilson (2000). The program uses a slope-weighted multiple flow algorithm for flow accumulation, but uses the flow directions derived from the interpolation (ANUDEM) where they exist. In this case, they are the ANUDEM-derived flow directions only on the enforced stream lines, so the flow accumulation will follow the streams. The different spacing in the E-W and N-S directions due to the geographic projection of the data was accounted for by using the actual spacing in metres of the grid points calculated from the latitude.

Contributing area was converted to specific catchment area using the square root of cell area as the best estimate of cell width on the approximately rectangular cells. The contributing area value was also reduced by half of one grid cell to provide better estimates at tops of hills.

Slope was converted from percent to ratio, as required by the TWI calculation, by dividing by 100. A minimum slope of 0.1% was imposed to prevent division by zero.

The TWI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.

The 3 arcsecond resolution version was generated from the 1 arcsecond TWI product. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The 3” TWI data were then masked using the SRTM 3” ocean and water body datasets.

Note that the limitation of partial contributing area due to tiled processing, so that catchment areas extending beyond about 5 km from a tile edge are not captured, has little impact on topographic wetness index. TWI is useful as a measure of position in the landscape on hillslopes (not river channels) and all hillslope areas will be accurately represented by the partial contributing area calculations.

Some typical values for TWI in different positions on the landscape are:

Position Specific catch. Slope (%) TWI area (m)
Upper slope 50 20 5.5 Mid slope 150 10 7.3 Convergent lower 3000 3 11.5 slope

In channels, some typical values would be (using flow width of 30 m):

Contributing Specific catch. Slope (%) TWI area (km2) area (103 m) 1 33 1 15.0 25 833 0.5 18.9 1000 33,333 0.1 24.2

Values of TWI larger than about 12 are most likely in channels or extremely flat areas where the physical concepts behind TWI are invalid and probably are not useful for measuring relative wetness, topographic position or any other geomorphic property. Contributing area (for channels) and MrVBF are more likely to be useful indicators of geomorphic properties in these areas. See, for example, McKenzie, Gallant and Gregory (2003) where soil depth is estimated using TWI on hillslopes and MrVBF in flat valley floors: the range of validity for TWI in that example was approximately 4.8 to somewhat beyond 8.5.

Hence the omission of contributing areas larger than about 25 km2 has no effect on the practical applications of TWI.

Gallant, J.C. and Wilson, J.P. (2000) Primary topographic attributes, chapter 3 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

McKenzie, N.J., Gallant, J.C. and Gregory, L. (2003) Estimating water storage capacities in soil at catchment scales. Cooperative Research Centre for Catchment Hydrology Technical Report 03/3.

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.

The package includes the weights of the house price index. “Weight” means the percentage or promil of the relative share of household monetary expenditure for the purchase of land-based housing belonging to the basic population of the CCI. The higher the weight, the greater the impact of a change in the price level of a land-based housing classification on the price development of a higher level of land-based housing classification. “Weight reference period” means the period during which the weight of the index is calculated. The following procedures for checking and editing the statistics received are carried out: rejecting transactions in which the purchased dwellings are unfit for life due to a lack of completion (< 80%), analysing the purchase-sale transaction data of the dwellings attributed to each basic whole compared to the previous quarters. The editing and validation of data shall be carried out using a computer program for checking price statistics. The resulting price trends are compared to the trends in house prices recorded by real estate agencies. Information on factors influencing changes in house prices is regularly monitored in the press, surveys and reports published by other companies and institutions. The main source of statistical data for the calculation of the CCI is the data of the Real Property Register of the Centre of Registers of the SE and the databases of transactions. Source data is obtained quarterly. The BKI base period is 2015 (2015: 100). Another change to the CCI base period is foreseen for 2026, the former time line will be converted into a new index base period and published after calculation in QI 2026. CCI

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.

Brochure Theme: A0 – Analysis and studies – General Under Theme: A000.01 – Statistical studies

Vietnam Industrial Production Index: MoM: GSO Calculation data was reported at 2.860 % in Nov 2018. This records a decrease from the previous number of 5.340 % for Oct 2018. Vietnam Industrial Production Index: MoM: GSO Calculation data is updated monthly, averaging 2.220 % from Jan 2018 (Median) to Nov 2018, with 11 observations. The data reached an all-time high of 22.770 % in Mar 2018 and a record low of -17.080 % in Feb 2018. Vietnam Industrial Production Index: MoM: GSO Calculation data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.B005: Industrial Production Index: VSIC 2007: 2015=100: MoM Growth: GSO Calculation.

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.

Calculation of K-index.

Vietnam Industrial Production Index: YoY: GSO Calculation data was reported at 9.560 % in Nov 2018. This records an increase from the previous number of 7.730 % for Oct 2018. Vietnam Industrial Production Index: YoY: GSO Calculation data is updated monthly, averaging 9.560 % from Jan 2018 (Median) to Nov 2018, with 11 observations. The data reached an all-time high of 20.160 % in Jan 2018 and a record low of 6.070 % in Feb 2018. Vietnam Industrial Production Index: YoY: GSO Calculation data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.B009: Industrial Production Index: VSIC 2007: 2015=100: YoY Growth: GSO Calculation.

The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), conducted a study from June 2014 through November 2014 to identify the hydrologic, chemical and microbiologic processes affecting declining pump performance and frequent pump failure at a confined disposal facility (CDF) in East Chicago, Indiana. A decline in groundwater pump performance through time is not uncommon and is generally attributed to biofouling. To better understand the causes behind declining pump performance, data were collected to describe the geochemistry and microbiology of groundwater and solids collected from extraction and monitoring wells at the CDF. Mineral-saturation indices were computed using PHREEQC software (Parkhurst and Appelo, 2013) for groundwater samples collected from extraction wells ( EW-4B, EW-22B, and EW-14A) and monitoring wells (MW-4A, MW-11A, and MW14A) during four sampling regimes between September 9th and November 6th, 2014. In addition, miner ...

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).