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.

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 ...

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.

The inflation rate for the Retail Price Index (RPI) in the United Kingdom was 3.4 percent in February 2025, down from 3.6 percent in the previous month. From 2021 onwards, prices in the UK rose rapidly, with the RPI inflation rate peaking at 14.2 percent in October 2022. Although inflation fell in subsequent months, it wasn't until July 2023 that inflation fell below double digits, and as of late 2024, the RPI rate was still above three percent. The CPI and CPIH While the retail price index is still a popular method of calculating inflation, the consumer price index (CPI) is the current main measurement of inflation in the UK. There is also an additional price index, which includes some extra housing costs, known as the Consumer Price Index including homer occupiers' costs (CPIH) index, which is seen by the UK's Office of National Statistics as the official inflation rate. As of December 2024, the CPI inflation rate stood at 2.5 percent, while the CPIH rate was 3.5 percent. Core inflation down in 2024 Another way of measuring inflation is to strip out the volatility of energy and food prices and look at the underlying core inflation rate. As of December 2024, this was 3.2 percent, slightly higher than the overall CPI rate, but more aligned with the overall figure than it was in 2022 and 2023. When inflation peaked at 11.2 percent in October 2022, for example, core inflation stood at just 6.5 percent. After energy prices in 2023 fell relative to 2022, the overall inflation rate in the UK declined quite rapidly, with core inflation overtaking the overall rate in July 2023. During the most recent period of high inflation, core inflation peaked at 7.1 percent in May 2023, and while taking longer to fall than the overall figure, has generally been declining since then.

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index.

This table contains figures on the price movements of a package of goods and services purchased by an average Dutch household. This is called the Consumer Price Index (CPI). The table also shows the derived consumer price index: this is the CPI that removes the impact of changes in the rates of taxes on products (e.g. VAT and excise duties on alcohol and tobacco) and subsidies and of taxes on consumption (e.g. motor vehicle taxes).

In addition, the table shows the monthly and annual changes in the CPI. The annual rate of change in total expenditure is an important indicator of inflation. These figures can be viewed across 337 product groups. There are also 34 aggregations of product groups (special aggregates) in the table. For each product group, you can also find out how much the Dutch consumer spends on it in relation to his total expenditure. This is called the weighting coefficient. The total weighting is 100,000.

Figures of the CPI are published every month. In addition, an annual figure will be published at the end of the year. The CPI of a calendar year is calculated as the average of the 12-month indices of that year.

Data available from: January 1996.

Status of figures: Rapid estimate figures are published immediately at the end of a reporting month or shortly thereafter. The rapid estimate provides figures for the year-on-year and month-on-month changes in the main level of the CPI and a number of special aggregates. These figures are calculated on the basis of incomplete source data. These numbers are not suitable for indexing. Therefore, the rapid estimate does not publish indices. The changes in the rapid estimate are characterised as preliminary.

The rapid estimate is followed by the first publication of all indices and changes for the month under review. These figures are also provisional. A month later, the figures for the same reporting month become final. Differences between provisional and final indices are due to met source data.

Changes compared to the previous version: Data for a new period has been added and/or adjustments have been made.

Changes as of 8 February 2024: From the reporting month of January 2024, a price development will be published for spending category 063000 Hospital services. This new index series is based on December 2023. This means that the index level of 100 corresponds to the price level as measured on December 2023. Previously, an index for the same spending category was also published between 2006 and 2009. That index series had as base year 2006=100, and stopped after December 2009. The current series will start again at 100 as of December 2023.

Changes as of 1 June 2016: Data for the period 1996 to January 2015 have been added for all series. In order to get an overall picture, the existing series have been extended to include the spending categories that have been discontinued for the period 2015.

These are the categories of expenditure: 2006=100: - 011320 Frozen fish - 031100 Clothing fabrics - 031420 Repair and rental services of clothing - 032200 Repair and rental services of shoes - 043210 Plumber services - 043230 Heating maintenance - 043250 Carpentry services - 043290 Ov. maintenance services home - 051300 Repair of furniture and the like - 053190 Other large household appliances - 063000 Hospital services - 091420 Unrecorded data carriers - 094240 Hire of equipment for culture - 096010 Package holidays inland 2000=100: - 134000 Property tax

Because these series do not have a base year 2015=100, the base year 2006=100 or 2000=100 has been used for this purpose. Where there is a different base year, this is explicitly included in the explanatory memorandum.

When will there be new figures? The figures for the rapid estimate shall be published on the last working day of the month to which the figures refer, or shortly thereafter.

The new indices are usually published between the first and second Thursday of the month following the month under review. The indices of the previous reporting month will then become definitive.

All publication times of the CPI are published on the publicatieplanning.

Abstract

This dataset corresponds to the baseline survey for the ProActing project (Global Network for Food Crisis Prevention) in the Grand Sud of Madagascar. The objective is to find out the situation of households before the project in terms of various indicators of food consumption, sources of income, access to basic services, ownership of assets (productive and non-productive), agricultural and fisheries production, adoption of innovative techniques, social safety nets, exposure to shocks and survival strategies. The questionnaire is based on the Short RIMA questionnaire, which collects the minimum amount of information necessary to calculate the resilience capacity index using the RIMA methodology.

Geographic coverage

Great South of Madagascar, Androy, Anosy and Atsimo-Atsinanana regions.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

As the intervention area consists of 21 communes from 7 districts in the Atsimo-Atsinanana, Androy and Anosy regions, the study was conducted on a representative sample at district level. The control group was drawn from the same communes but in fokontany not covered by the project. A two-stage sampling plan was adopted for a sample of 1,260 households, divided into 840 beneficiary households and 420 control group households. At the primary level, the sample is made up of 84 fokontany drawn at random with probability proportional to their size. In each fokontany sample, the secondary sample consists of 15 households drawn with equal probability. This sample allows for an accuracy of results of around 10% at the district level.

Mode of data collection

Computer Assisted Personal Interview [capi]

Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

In an effort to further coordinate and harmonize the collection of CPI data, the international organizations agreed that the International Monetary Fund (IMF) and the Organisation for Economic Cooperation and Development (OECD) would assume responsibility for the international collection and dissemination of national CPI data. Under this data collection initiative, countries are reporting the aggregate all items index; more detailed indexes and weights for 12 subgroups of consumption expenditure (according to the so-called COICOP-classification), and detailed metadata. These detailed data represent a valuable resource for data users throughout the world and this portal would not be possible without the ongoing cooperation of all reporting countries. In this effort, the OECD collects and validates the data for their member countries, including accession and key partner countries, whereas the IMF takes care of the collection of data for all other countries.

From January 2018 to January 2025, the health index in Belgium ranged from 106 to 135.5. The value of the health index in January 2025 was 135.52. Such an index is calculated by removing certain products, such as alcoholic beverages, tobacco, and fuel, out of the list to calculate the consumer price index.

Survey for the calculation of the Active Ageing Index of the City of Madrid. OBJECTIVE OF THE STUDY: Know the result of certain indicators that allow to calculate the Active Ageing Index. The Active Ageing Index is an ideal tool both for comparison with other areas or cities and for its value in long-term longitudinal monitoring. Compare the potential of older people to have active and healthy aging. The index measures the independent standard of living of older people, their participation in paid work and social activities, as well as their ability to age actively. It is carried out for citizens over 55 years of age residing in the municipality of Madrid. It is part of the evaluation of the Madrid Plan Friendly with the Elderly, as an impact indicator. It consists of 22 indicators that are grouped into four dimensions: employment, social participation, independent and safe living, and capacity for healthy ageing.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

Singapore led the Index of Economic Freedom in 2024, with an index score of 83.5 out of 100. Switzerland, Ireland, Taiwan, and Luxembourg rounded out the top five. Economic Freedom Index In order to calculate the Economic Freedom Index, the source takes 12 different factors into account, including the rule of law, government size, regulatory efficiency, and open markets. All 12 factors are rated on a scale of zero to 100 and are weighted equally. Every country is rated within the Index in order to provide insight into the health and freedom of the global economy. Singapore's economy Singapore is one of the four so-called Asian Tigers, a term used to describe four countries in Asia that saw a booming economic development from the 1950s to the early 1990. Today, the City-State is known for its many skyscrapers, and its economy continue to boom. It has one of the lowest tax-rates in the Asia-Pacific region, and continues to be open towards foreign direct investment (FDI). Moreover, Singapore has one of the highest trade-to-GDP ratios worldwide, underlining its export-oriented economy. Finally, its geographic location has given it a strategic position as a center connecting other countries in the region with the outside world. However, the economic boom has come at a cost, with the city now ranked among the world's most expensive.

These files include a derived 50 meter spatial resolution Compound Topographic (or Wetness) Index (CTI or TWI) and Flow Accumulation (as represented by specific catchment area, SCA) calculated from a continuous, transboundary DEM developed across the Alaska perhumid coastal temperate rainforest (AKPCTR). The extent of this dataset includes all of the Alaska and Canada watersheds that discharge into southeast Alaska coastal waters, which covers essentially the northern half of the full PCTR.The transboundary DEM used to calculate the CTI can be here: linkFlow accumulation is represented by a grid of specific catchment area (SCA), which is the contributing area per unit contour length using the multiple flow direction D-infinity approach. Unit contour length is equal to the DEM resolution of 50 meters.There are two versions of both the CTI and SCA provided:CTI_AKPCTR_NoFlowMask.tif and SCA_AKPCTR_NoFlowMask.tif This is the resulting CTI file when no masks are applied to the study area before the CTI procedure is run. After the CTI procedure is run, all glacial areas are masked out, as CTI is not meaningful over glaciers.CTI_AKPCTR_FlowMask.tif and SCA_AKPCTR_FlowMask.tif This is the resulting CTI file where prior to running the CTI procedure, we apply a mask across all active glaciers and all downslope cells receiving flow from glacially affected cells. These masked cells are excluded from the CTI procedure. This results in fewer CTI values on the landscape. This dataset is provided to identify cells in the direct downslope path of glaciers because CTI values for cells receiving upslope accumulation from glaciers may not be reliable due to uncertainties in surface water flowpaths in glaciated areas.Processing Steps: 1. Using the pitremove function from TAUDEM, filled sinks in the DEM following the method of Planchon and Darboux (2001). 2. (For *_FlowMask versions only) Mask out glacial areas and associated downslope cells. Prior to calculating flow direction and flow accumulation, change cells in the filled DEM to NoData where glaciers exist, using the Randolph Glacier Inventory version 5.0 raster dataset to identify the presence of glaciers. Cells downslope from the masked glaciers will then also be identified as NoData in subsequent processing and the final CTI raster. Compute D-infinity slopes and flow direction and flow accumulation (as specific catchment area) rasters from the filled DEM using TAUDEM D-infinity method. As part of the D-infinity routine, TauDEM uses the method of Garbrecht & Martz (1997) to resolve flats.3. The D-infinity slope raster was modified in the following way: if slope = 0, then change slope value to 0.0001; otherwise leave slope value unchanged. This was done to avoid dividing by zero when calculating CTI.4. Compute Compound Topographic Index (CTI) using these steps: Dsca = D-infinity specific catchment area raster Dslp = D-infinity slope raster Then, CTI = Ln(Dsca/Dslp)5. Clip the CTI raster to the Alaska perhumid coastal temperate rainforest (AKPCTR) watershed boundary.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

The IMF-adapted ND-GAIN index is an adaptation of the original index, adjusted by IMF staff to replace the Doing Business (DB) Index, used as source data in the original ND-GAIN, because the DB database has been discontinued by the World Bank in 2020 and it is no longer allowed in IMF work. The IMF-adapted ND-GAIN is an interim solution offered by IMF staff until the ND-GAIN compilers will review the methodology and replace the DB index.Sources: ND-GAIN; Findex - The Global Findex Database 2021; Worldwide Governance Indicators; IMF staff calculations. Category: AdaptationData series: IMF-Adapted ND-GAIN IndexIMF-Adapted Readiness scoreReadiness score, GovernanceReadiness score, IMF-Adapted EconomicReadiness score, SocialVulnerability scoreVulnerability score, CapacityVulnerability score, EcosystemsVulnerability score, ExposureVulnerability score, FoodVulnerability score, HabitatVulnerability score, HeathVulnerability score, SensitivityVulnerability score, WaterVulnerability score, InfrastructureMetadata:The IMF-adapted ND-GAIN Country Index uses 75 data sources to form 45 core indicators that reflect the vulnerability and readiness of 192 countries from 2015 to 2021. As the original indicator, a country's IMF-adapted ND-GAIN score is composed of a Readiness score and a Vulnerability score. The Readiness score is measured using three sub-components – Economic, Governance and Social. In the original ND-GAIN database, the Economic score is built on the DB index, while in the IMF-adapted ND-GAIN, the DB Index is replaced with a composite index built using the arithmetic mean of “Borrowed from a financial institution (% age 15+)” from The Global Financial Index database (FINDEX_BFI) and “Government effectiveness” from the Worldwide Governance Indicators database (WGI_GE). The Vulnerability, Social and Governance scores do not contain any DB inputs and, hence, have been sourced from the original ND-GAIN database. Methodology:The procedure for data conversion to index is the same as the original ND-GAIN and follows three steps: Step 1. Select and collect data from the sources (called “raw” data), or compute indicators from underlying data. Some data errors (i.e., tabulation errors coming from the source) are identified and corrected at this stage. If some form of transformation is needed (e.g., expressing the measure in appropriate units, log transformation to better represent the real sensitivity of the measure etc.) it happens also at this stage. Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linear interpolation is adopted to make up for the missing data. In the second instance, the indicator is labeled as "missing" for that country, which means the indicator will not be considered in the averaging process. Step 3. This step can be carried out after of before Step 2 above. Select baseline minimum and maximum values for the raw data. These encompass all or most of the observed range of values across countries, but in some cases the distribution of the observed raw data is highly skewed. In this case, ND-GAIN selects the 90-percentile value if the distribution is right skewed, or 10-percentile value if the distribution is left skewed, as the baseline maximum or minimum. Based on this procedure, the IMF–Adapted ND-GAIN Index is derived as follows: i. Replace the original Economic score with a composite index based on the average of WGI_GE and cubic root of FINDEX_BFI1, as follows:IMF-Adapted Economic = ½ · (WGI_GE) + ½ · (FINDEX_BFI)1/3 (1) The IMF-adapted Readiness and overall IMF-adapted ND-GAIN scores are then derived as: IMF-Adapted ND-GAIN Readiness = 1/3 · ( IMF-Adapted Economic + Governance + Social) IMF-Adapted ND-GAIN = ½·( IMF-Adapted ND-GAIN Readiness+ND-GAIN Vulnerability) ii. In case of missing data for one of the indicators in (1), IMF-Adapted ND-GAIN Economic would be based on the value of the available indicator. In case none of the two indicators is available, the IMF-Adapted Economic score would not be produced but the IMF-Adapted ND-GAIN Readiness would be computed as average of the Governance and Social scores. This approach, that replicates the approach used to derive the original ND-GAIN indexes in case of missing data, ensures that the proposed indicator has the same coverage as the original ND-GAIN database.
1 Given that the FINDEX_BFI data are positively skewed, a cubic root transformation has been implemented to induce symmetry.

Index to the reports of work carried out by the BGS and its precursors. The index was set up in 1988 and has worldwide coverage. These reports cover a wide range of scientific and technical disciplines and were produced for a variety of purposes. The reports are not published but copies can be provided on demand subject to any restrictions. All registered Technical Reports held in collection are indexed. Start date of digital index circa 1988. Technical reports date from circa 1950 onwards.

In 2024, Ireland was the country in Europe with the highest score in the work-life balance index, with 78.7 points out of 100. Following were Iceland and Denmark registering 76.8 and 74 respectively. The work-life balance index assigns a score to each country, evaluating the balance between work and well-being. It considers various factors and policies that influence this relationship, including statutory annual leave, minimum statutory sick pay, statutory maternity leave, minimum wage, healthcare quality, happiness index scores, LGBTQ+ inclusivity, and safety standards.

Mauritius Construction Cost Index: Single Storey House: Work: Setting Out data was reported at 130.300 2Q2009=100 in Jun 2018. This stayed constant from the previous number of 130.300 2Q2009=100 for May 2018. Mauritius Construction Cost Index: Single Storey House: Work: Setting Out data is updated monthly, averaging 122.500 2Q2009=100 from Apr 2009 (Median) to Jun 2018, with 111 observations. The data reached an all-time high of 130.300 2Q2009=100 in Jun 2018 and a record low of 100.000 2Q2009=100 in Jun 2009. Mauritius Construction Cost Index: Single Storey House: Work: Setting Out data remains active status in CEIC and is reported by Statistics Mauritius. The data is categorized under Global Database’s Mauritius – Table MU.EA002: Construction Cost Index: 2nd Quarter2009=100.