Producer Price Inflation MoM in the United States increased to 0.10 percent in May from -0.20 percent in April of 2025. This dataset includes a chart with historical data for the United States Producer Price Inflation MoM.

In November 2024, the producer price index (PPI) in the United States was the highest in the four countries/areas under consideration. That month, its index score stood at above 146, compared to roughly 127 in the Euro Area, which was the second highest in the four areas. Contrarily, China is struggling with a decreasing PPI. The producer price index (PPI) measures the average change over time in the selling prices received by domestic producers for their output.

This table contains figures on the average price development of the selling prices, the import prices and the domestic consumption of industrial products with a base year of 2015=100. This data is available for both domestic and foreign sales. The products are classified based on the goods classification PRODCOM (PRODuction COMmunautaire). Data available from January 2012 up to and including December 2023. Status of the figures: The data for August 2023 up to and including December 2023 and the 2023 annual rate are provisional. Since this table has been stopped, the data is no longer made definitive. Changes as of March 6th 2024 None, this table is stopped. When will new figures be published? The results in this series are based on 2015=100. Due to the base shift this table is stopped. Figures based on 2021=100 are published in table Producer Price Index (PPI), output and importprices by product, 2021=100. Further information, see Base Year Revision Industrial Producer Price Index, 2021=100 in paragraph 3.

Producer Prices in the United States increased 2.60 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Producer Prices Change - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This statistic shows the monthly output Producer Price Index (PPI) of paper and paper products in the United Kingdom, from January 2016 to December 2024. After a period of fluctuation, the output price for paper and products in the UK has risen substantially between March 2017 and February 2019, declined throughout the rest of 2019 and 2020, and rapidly started to increase again from March 2021 to February 2023. Most recently, the PPI began to increase again, measuring at 131.6 in December 2024. Paper production in the United Kingdom The paper production industry is an important contributor to the United Kingdom's economy. In 2023, the UK produced over 3.2 metric tons' worth of paper products. The Year prior, the most produced type of paper was corrugated case materials, widely used in the form of cardboard boxes. Paper consumption around the world The United Kingdom is not anymore among some of the world's largest consumers of paper and paperboard by volume. China consumes more paper and paperboard than any other country, consuming over 137 million metric tons of paper and paperboard, compared with the 58 million metric tons consumed by the United States, the next highest nation by consumption of paper and paperboard.

This table contains figures on the average price development of the selling prices of Dutch industrial products with base year 2015=100. The monthly price developments are shown for both domestic and foreign sales. The data is further subdivided into a number of branches by economic activity SIC2008 of Statistics Netherlands. Data available from January 2012 up to and including December 2023 Status of the figures: The data for August 2023 up to and including December 2023 and the 2023 annual rate are provisional. Since this table has been stopped the data is no longer made definitive. Changes as of March 6th 2024 None, this table is stopped. When will new figures be published? The results in this series are based on 2015=100. Due to the base shift this table is stopped. Figures based on 2021=100 are published in table Producer Price Index; output prices by economic activity SIC 2008, 2021=100. Further information, see Base Year Revision Industrial Producer Price Index, 2021=100 in paragraph 3.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Wood Pulp (WPU0911) from Jan 1926 to May 2025 about wood, paper, commodities, PPI, inflation, price index, indexes, price, and USA.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April 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.

This table contains price index numbers and mutations for sales, imports and inland consumption of industrial products. The products are classified according to the European classification PRODCOM. Monthly and yearly figures. Data available from January 2005 up to and including December 2017 Status of the figures; The data for August 2017 up to and including December 2017 and the 2017 annual rate are provisional. Since this table has been stopped, the data is no longer made definitive. Change as of March 19th 2018 None, this table is stopped. When will new figures be published? The results in this series are based on 2010=100. Due to the base shift this table is stopped. Figures based on 2015=100 are published in table Producer Price Index (PPI), output and import prices by product, 2015=100. Further information, see Base Year Revision Industrial Producer Price Index, 2015=100 in paragraph 3.

Abstract

The National Statistics Office generates various establishment based price indices and one of these is the Producers Price Index. The 2008 PPI (2000=100) is generated from the results of the Producer Price Survey conducted monthly by the NSO. This is done through the collection of actual producer prices from sample establishments nationwide. The PPS uses a shuttle type questionnaire which provides the respondent establishments with a running account of all monthly responses for the year. For 2008, the survey covered 595 sample products produced by 309 manufacturing establishments.

The Producer Price Index (PPI) for Manufacturing is a composite figure of producers prices of representative commodities included in the market basket. The PPI serves various purposes, the most important of which are the following: a. measures monthly or yearly changes in the producers prices of key commodities in the manufacturing sector b. serves as deflator to Value of Production Index (VAPI) in the estimation of Volume of Production Index (VOPI) c. serves as deflator in the estimation of manufacturing production in real terms (at constant prices) in the system of national accounts.

The PPI is computed using the Paasche-type method of index computation . As such, the weights are continously revised upon availability of the latest data from the annual survey or census. In the case of 2008 PPI, the weights are taken from the 2005 Annual Survey of Philippine Business and Industry (ASPBI). The revision of weights , are however instituted at the beginning of each year and are used for the entire year.

Geographic coverage

The geographic domain is the whole country.

Analysis unit

The unit of analysis for this survey is the establishment. An establishment is defined as an economic unit under a single ownership or control, i.e., under a single legal entity, engaged in one or predominantly one kind of economic activity at a single fixed location.

Universe

Manufacturing establishments with total employment of 20 and over

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 PPS is a non-probability sampling survey of the manufacturing sector. Sample establishments and commodities were selected using the following criteria: a. the commodity has a relatively high market share b. the commodity was available in the market in 2000, this being the base year c. the commodity is being produced currently, and d. the market share of the commodity has been stable for the last 3 years

In the same manner, criteria were also set for the selection of establishments, as follows: a. establishment has an ATE of 50 and over b. establishment has relatively high concentration ratio c. establishment is good respondent in past and current surveys of NSO; that is, it submits prompt reports and provides quality data d. preferably, the establishment is a sample of the Monthly Integrated Survey of Selected Industries (MISSI).

The 2008 PPS utilizes the 3-digit and selected 4-digit amended Philippine Standard Industrial Classification (PSIC) as its industry domain which is patterned after ISIC version 3.

Thus, there are 20 major sectors with 10 further categorized into sub-sectors or a total of 37 sub-sectors for the 2008 MISSI. These are:

1. Food Manufacturing
   1.1. Processed meat and fish 1.2. Processed fruits and vegetables 1.3. Vegetable and animal oils and fats 1.4. Milk and dairy products 1.5. Grain mills products 1.6. Animal feeds 1.7. Bakery products 1.8. Milled and refined sugar 1.9. Coconut products 1.10. Miscellaneous foods
2. Beverages
3. Tobacco products
4. Textiles 4.1. Textile products 4.2. Cordage, rope and twine
   1. Footwear and wearing apparel
   2. Leather products
   3. Wood and wood products 7.1. Planning and sawmill 7.2. Veneer and plywood 7.3. Other wood products
   4. Paper and paper products
   5. Publishing and printing
   6. Petroleum products 10.1. Refined petroleum products 10.2. Coke and other fuel products
   7. Chemical products 11.1. Basic Chemicals and industrial gases 11.2. Fertilizers 11.3. Paints 11.4. Drugs and medicines 11.5. Cosmetics and toilet preparations 11.6. Miscellaneous Chemicals
   8. Rubber and plastic products 12.1. Rubber products 12.2. Plastic products
   9. Non-metallic mineral products 13.1. Glass and glass products 13.2. Cement 13.3. Miscellaneous non-metallic mineral products
   10. basic metals 14.1. Iron and steel 14.2. Non-ferrous metals
   11. Fabricated metal products
   12. Machinery except electrical 16.1. Office,accounting and computing machinery 16.2. Machinery and equipment n.e.c.
   13. Electrical machinery 17.1. Electrical appliances 17.2. Wires and wirings 17.3. Batteries 17.4. Lamps and fixtures 17.5. Microcircuits
   14. Transport equipment
   15. Furnitures and fixtures
   16. Miscellaneous Manufactures

Indicators generated from 2008 PPS (2000=100) are the following: 1. Producer Price Index (PPI), yearly and monthly growth rates

Imputation methods used for unit and item non-response are as follows: 1. Historical imputation without trend adjustment, or the use of the latest available data of the establishment
2. Imputed values are revised upon receipt of actual data for inclusion in the revised indices

Mode of data collection

Self Administered Questionnaire and/or Face-to-face interview

Research instrument

The Producer Price Survey utilizes a shuttle type questionnaire. This approach reduces cost and enhances consistency and accuracy in reporting since the respondent establishment is provided with a running account of all monthly responses for the year.

Cleaning operations

It is important to verify the reasonableness and reliability of the prices of products included in the market basket for a given month. Data editing consisted of three stages: field editing, office verification and machine validation.

· Field editing of data was done by the provincial staff upon collection of the accomplished questionnaires from the establishments. The objective is to check for completeness and consistency of entries in the questionnaires. Any inconsistent or missing data was corrected at this stage as this can be immediately verified from the respondents.

· Office verification was done by provincial office staff upon receipt of the accomplished questionnaires from the field men. In some instances, callback to the establishments in the form of phone call or email to verify some inconsistent or missing data is done.

· Desk verification was done by the ISD staff to check the consistency and reasonableness of entries in the accomplished questionnaires. This process also validates the status of establishments such as non-responding and reported closed, cannot be located, transferred, and out of scope. The telephone was extensively utilized to verify information from the establishment's contact person. The Internet was also used to obtain information on the contact address and to research for information on the status of the establishment.

.For unit or item non-response, the following are undertaken: 1. Establishments that stopped operation, temporary out of business (TOB), strike, etc., during the year, historical imputation without trend adjustment or the use of the latest available data of the establishment. This method is appropriate for the reason that the prices of a number of products/commodities do not change very much over a short period of time. 2. Imputed values are revised upon receipt of actual data for inclusion in the revised indices.

Response rate

The average monthly response rate is 88.84%, 35 days (preliminary tabulation) after the reference month and 95 % for the final table.

Sampling error estimates

Not applicable.

Data appraisal

The quality of the PPI indicators are measured in terms of the following:

Representativeness of the samples as measured in the CONCENTRATION RATIO- the combined production value of the samples as a percentage to the total industry production value

Response rate of the survey

Imputation method used for non-responses

Producer Prices in Australia increased 3.70 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Australia Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Core Producer Prices YoY in the United States decreased to 3 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States Core Producer Prices YoY.

Graph and download economic data for Producer Price Index by Industry: Plastics Material and Resin Manufacturing (PCU325211325211) from Jun 1976 to May 2025 about resin, plastics, materials, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

Activation peaks of PPI results.

Results of various approaches using HPRD and BioGrid PPI data.

This table shows the price indices, quarterly and yearly changes in prices of services that companies provide. The figures are broken down by type of services according to the Classification of Products by Activity (CPA 2015 version 2.1). For some services, a further breakdown has been made on the basis of market data that differ from the CPA. This breakdown is indicated with a letter after the CPA-code.

The base year for all Services producer price indices is 2021. The year average, quarterly and yearly changes are calculated with unrounded figures.

Data available from: 4th quarter 2002.

Status of the figures: The figures for the most recent quarter are revised provisional. These figures are made definite in the publication for the subsequent quarter.

Changes as of June 6 2025: The revised provisional figures of the 1st quarter 2025 have been added.

Changes as of August 15 2024: The price indices of 743, 7430 and 74301 Translation and interpretation; and 743011 Translation services and 743012 Interpretation services have also become visible for the 1st quarter of 2024. In the table update of June 14, 2024, no visible figures were yet included for these codes.

Changes as of June 14 2024: The price index of 631119A Datacenter colocation has become visible from the 4th quarter of 2022. In the table update of May 15, 2024, no visible figures were yet included for code 631119A Datacenter colocation.

For code 50401 Inland freight water transport services, more figures have become visible and the history of index figures now goes back to the 4th quarter of 2014. The same visibility of figures of code 504 Inland freight water transport services has been applied to the underlying market segments 5040 Inland freight water transport services and 50401 Inland freight water transport services.

For code 68201 Rental and operating services of own or leased real estate, more figures have become visible and the history of index figures now goes back to the 1st quarter of 2017. The same visibility of figures of coding 6820 Rental and operating services of own or leased real estate has been applied to the underlying market segment 68201 Rental and operating services of own or leased real estate.

For code 68311 Real estate agency services on a fee or contract basis, more figures have become visible and the history of index figures now goes back to the 1st quarter of 2006. The same visibility of figures of code 6831 Real estate agency services on a fee or contract basis has been applied to the underlying market segment 68311 Real estate agency services on a fee or contract basis.

When will new figures be published? New figures are available twice per quarter. Halfway each quarter, the results of the pricing method Model pricing (around half of the branches) are published and the other branches with the Unit value method follow at the end of the quarter. This concerns the price development of the previous quarter. The Services producer price index of the total commercial services is also calculated and published at the end of each quarter.

The Services producer price indices publication schedule can be downloaded as an Excel file under section: 3 Relevant articles. More information about the pricing method can be found in the video under section: 3 Relevant articles.

Background One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein-protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms. Results Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ? 1 × 10(-40)), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes. Conclusions Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Producer Prices in South Africa decreased to 102.40 points in May from 102.70 points in April of 2025. This dataset provides the latest reported value for - South Africa Producer Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

An extensive dataset of binary physical protein-protein interaction extracted from STRING 12.0 (>12,000 organisms) with artificially generated negatives. The dataset includes 72M positive pairs with STRING confidence scores> 0.9 and 720M negative pairs. The corresponding protein sequences are located in the .fasta files. The generation of the negatives was derived from https://doi.org/10.1016/j.isci.2024.110371