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

This table provides information on price developments in the construction industry. Data were calculated by Statistic Netherlands (CBS) and are based on building permits with a value of 50 thousand euros or more issued by municipal authorities, and the reported construction costs as stated in the permits. On the basis of these building permits and the construction time, construction output is calculated by means of average waiting times prior to the start of the construction activities. Price indices listed in the table are used to eliminate the effect of price changes on the construction output. Therefore, the price index can be used to as a deflator to calculate volume developments in the building sector. Price indices are calculated for two sections (Construction of new buildings and Other buildings) and three sectors (dwellings, buildings for the private sector and buildings for the (semi-)public or non-commercial sector). Data available from: 1st quarter 2015 Status of the figures: Price index figures up to and including the 4th quarter 2023 and the annual figure 2023 are final. Changes since 30 January 2025: The figures of the 4th quarter and the year 2024 have been added to the table. Due to a method improvement the indices for the subseries New dwellings in the period 2019 quarter 3 and the subseries Existing buildings private sector in the period 2021 quarter 3 have been corrected by 0.1 index point. The improvement relates to the underlying price indices used to eliminate the effect of price changes on the construction output. When will new figures become available? Provisional figures for the 1st quarter of 2025 will be released in April 2025.

Consumer Price Index: Shadow data was reported at 419.080 1980=100 in Feb 2025. This records an increase from the previous number of 416.570 1980=100 for Jan 2025. Consumer Price Index: Shadow data is updated monthly, averaging 273.440 1980=100 from Jan 1980 (Median) to Feb 2025, with 542 observations. The data reached an all-time high of 419.080 1980=100 in Feb 2025 and a record low of 95.300 1980=100 in Jan 1980. Consumer Price Index: Shadow data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.I005: Consumer Price Index: Fixed and Shadow: 1980=100. Shadow Index is being used to calculate CPI. The shadow index numbers are adjusted to take into account certain inadequacies in the basic data on prices or in the methods used for calculating fixed index numbers. [COVID-19-IMPACT]

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.

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.

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.

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.

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.

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 Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description

This table shows the consumer price index for all households (CPI), split up into an index for frequent "out-of-pocket" purchases (FROOPP) and less frequent or "non-out-of-pocket" purchased items (non-FROOPP). Frequent purchased items are purchases that are typically done at least monthly. Out-of-pocket purchases are those that are considered to be typically paid for by the consumer directly and actively. This table also includes the monthly and yearly price developments.

The FROOPP and non-FROOPP are special extracts of the CPI. The corresponding CPI weights and prices are used to calculate both indices. The segmentation used is derived from the FROOPP-classification of Eurostat.

Data available from: January 2006 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 18 May 2016 None, this table is stopped.

Changes as of 10 December 2015 On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

When will new figures be published? Not applicable.

This table includes all price index numbers calculated according to the Harmonised consumer price index (HICP) for the Netherlands, the Euro area and the European Union (EU). In all member states of the EU, these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

The table also includes the harmonised consumer price index for the Euro area. This index figure reflects the average price increase/decrease in the countries which have adopted the euro as their currency. The table also includes the European consumer price index, i.e. the harmonised consumer price index for the member states of the European Union.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: The HICP results for the Netherlands in this table are in most cases final immediately upon publication. At that time, the results for the euro area are still based on the flash estimate and are characterized as provisional. A month later, these figures become final.

The results of the HICP are only marked as provisional if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

Abstract

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

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

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

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

United States PPI: Weights: PO: Formula Feeds data was reported at 0.413 % in 2024. This records a decrease from the previous number of 0.432 % for 2023. United States PPI: Weights: PO: Formula Feeds data is updated yearly, averaging 0.381 % from Dec 2007 (Median) to 2024, with 18 observations. The data reached an all-time high of 0.469 % in 2022 and a record low of 0.266 % in 2007. United States PPI: Weights: PO: Formula Feeds data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I062: Producer Price Index: by Commodities: Weights.

United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds was 260.89400 Index 1982=100 in February of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds reached a record high of 304.54900 in September of 2022 and a record low of 83.30000 in March of 1975. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Processed Foods and Feeds: Formula Feeds - last updated from the United States Federal Reserve on March of 2025.

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive).

Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.

All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').

Version 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.

Additionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset.

Consumer Price Index CPI in India decreased to 192.50 points in February from 193.40 points in January of 2025. This dataset provides - India Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly indexes and percentage changes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

This table includes figures on the price developments of a package of goods and services purchased by consumers in the Netherlands. The figures are consistent with European directives also known as the harmonised consumer price index (HICP). In all member states of the European Union (EU), these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

This table also contains the HICP at constant taxes: this price index excludes the effect of changes in the rates of product-related taxes (e.g. VAT and excise duty on alcohol and tobacco).

The table also includes the month-on-month and year-on-year changes of the HICP. The year-on-year change of total consumer expenditure is known as inflation. The figures are shown for 327 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed. The weighting coefficient shows how much consumers in the Netherlands spend on each product group in relation to their total expenditure. The total weighting is 100,000.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: Figures of the flash estimate are published at the end of a reporting month, or shortly thereafter. At the flash estimate, figures are made available for the all items category and for a selection of special aggregates. These figures are calculated on the basis of still incomplete source data. The results of the flash estimate are characterized as provisional.

In most cases, the figures are final in the second publication of the same reporting month. Differences between the provisional and final indices are caused by source material that has become available after the flash estimate. The results of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis. In that case, the figures become final one month later.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, price changes will be published for expenditure categories 053290 Other small electric household appliances and 103000 Post-secondary non-tertiary education. The base period for this new index series is December 2024. This means that the index level of 100 is the price level measured in December 2024.

Changes as of 8 February 2024: Starting in the reporting month of January 2024, a price change will be published for expenditure category 063000 Hospital Services. The base period for this new index series is December 2023. This means that the index level of 100 is the price level measured in December 2023. Previously, between 2000 and 2009, an index was published for the same expenditure category. The base year for that index series was 2005=100. It was discontinued after December 2009. The current series starts again from 100 in December 2023.

When will new figures be published? The figures of the flash estimate are published on the last working day of the month to which the figures relate, or shortly thereafter.

Final figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) Hydrometric Areas (Kral et al., 2015; https://doi.org/10.5285/3a4e94fc-4c68-47eb-a217-adee2a6b02b3). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [1]. SPI is calculated for different accumulation periods: 1, 3, 6, 9, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Standardised Precipitation Index time series for IHU hydrometric areas (1961-2012)' SPI_IHU_HA, apart from the temporal extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting documentation for more details). Within Historic Droughts project (grant number: NE/L01016X/1), the Met Office has digitised historic rainfall and temperature data to produce high quality historic rainfall and temperature grids, which motivated the change in the underlying data to calculate SPI. The methodology to calculate SPI is the same in the two datasets. This release supersedes the previous version, https://doi.org/10.5285/d8655cc9-b275-4e77-9e6c-1b16eee5c7d5, as it addresses localised issues with the source data (Met Office monthly rainfall grids) for the period 1960 to 2000. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California.