The Producer Price Index (PPI) is a family of indexes that measures the average change over time in selling prices received by domestic producers of goods and services. PPIs measure price change from the perspective of the seller. This contrasts with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's perspective. Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution costs. There are three main PPI classification structures which draw from the same pool of price information provided to the BLS by cooperating company reporters: Industry classification. A Producer Price Index for an industry is a measure of changes in prices received for the industry's output sold outside the industry (that is, its net output). The PPI publishes approximately 535 industry price indexes in combination with over 4,000 specific product line and product category sub-indexes, as well as, roughly 500 indexes for groupings of industries. North American Industry Classification System (NAICS) index codes provide comparability with a wide assortment of industry-based data for other economic programs, including productivity, production, employment, wages, and earnings. Commodity classification. The commodity classification structure of the PPI organizes products and services by similarity or material composition, regardless of the industry classification of the producing establishment. This system is unique to the PPI and does not match any other standard coding structure. In all, PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services (seasonally adjusted and not seasonally adjusted), organized by product, service, and end use. Commodity-based Final Demand-Intermediate Demand (FD-ID) System. Commodity-based FD-ID price indexes regroup commodity indexes for goods, services, and construction at the subproduct class (six-digit) level, according to the type of buyer and the amount of physical processing or assembling the products have undergone. The PPI publishes over 600 FD-ID indexes (seasonally adjusted and not seasonally adjusted) measuring price change for goods, services, and construction sold to final demand and to intermediate demand. The FD-ID system replaced the PPI stage-of-processing (SOP) system as PPI's primary aggregation model with the release of data for January 2014. The FD-ID system expands coverage in its aggregate measures beyond that of the SOP system by incorporating indexes for services, construction, exports, and government purchases. For more information, visit: https://www.bls.gov/ppi

Graph and download economic data for Producer Price Index by Commodity: Real Estate Services (Partial): Nonresidential Real Estate Services (WPS431) from Jan 2020 to Aug 2025 about nonresidential, real estate, commodities, services, real, PPI, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to Sep 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Final Demand (PPIFIS) from Nov 2009 to Aug 2025 about final demand, headline figure, PPI, inflation, price index, indexes, price, and USA.

The production price index (PPI) for construction materials and components in the United States decreased slightly in 2024. Up until 2020, construction prices had been rising fairly steadily. However, in the years after that construction producer prices have been very unstable. Production price index A PPI of *** in 2022, indicates that the real-world price has risen by *** percent in comparison to the base year - 1982 in this case. Similarly, under the same baseline, the PPI for construction machinery and equipment has also risen steadily until 2018. Like all prices, there are regional differences within the United States. The PPI acts as a measurement for the average changes in prices that domestic producers receive for their output. In the United States, the PPI is one of the oldest continuous statistical datasets published by the government. Common construction materials Some building materials are essential to construction work, and the decision on which to use is important for the life and the endurance of the building. Materials such as cement, steel, and sand are essential to many construction projects. The production of cement is tightly linked to the demand that comes from the construction industry. The durability and potency of steel gives it an advantage over wood and concrete, providing buildings with a higher resistance but a cheaper price tag. Sand is commonly used in buildings, but it is especially common in roads that require stones of various grades and granulation.

Turkey Real Effective Exchange Rate Index: PPI Based data was reported at 77.220 2003=100 in Oct 2018. This records an increase from the previous number of 70.590 2003=100 for Sep 2018. Turkey Real Effective Exchange Rate Index: PPI Based data is updated monthly, averaging 102.945 2003=100 from Jan 2003 (Median) to Oct 2018, with 190 observations. The data reached an all-time high of 117.560 2003=100 in Aug 2008 and a record low of 70.530 2003=100 in Aug 2018. Turkey Real Effective Exchange Rate Index: PPI Based data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.M008: Real Effective Exchange Rate Index.

Context

The US Bureau of Labor Statistics monitors and collects day-to-day information about the market price of raw inputs and finished goods, and publishes regularized statistical assays of this data. The Consumer Price Index and the Producer Price Index are its two most famous products. The former tracks the aggregate dollar price of consumer goods in the United States (things like onions, shovels, and smartphones); the latter (this dataset) tracks the cost of raw inputs to the industries producing those goods (things like raw steel, bulk leather, and processed chemicals).

The US federal government uses this dataset to track inflation. While in the short term the raw dollar value of producer inputs may be volatile, in the long term it will always go up due to inflation --- the slowly decreasing buying power of the US dollar.

Content

This dataset consists of a packet of files, each one tracking regularized cost of inputs for certain industries. The data is tracked-month to month with an index out of 100.

Acknowledgements

This data is published online by the US Bureau of Labor Statistics.

Inspiration

• How does the Producer Price Index compare against the Consumer Price Index?
• What have the largest spikes in input costs been, historically? Can you determine why they occurred?
• What is the overall price index trend amongst US producers?

This dataset provides values for PRODUCER PRICE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Germany PPI: 2005=100: Mfg: Other: Imitation Jewellery & Rel. Articles data was reported at 107.900 2005=100 in Jul 2013. This stayed constant from the previous number of 107.900 2005=100 for Jun 2013. Germany PPI: 2005=100: Mfg: Other: Imitation Jewellery & Rel. Articles data is updated monthly, averaging 101.900 2005=100 from Jan 2000 (Median) to Jul 2013, with 163 observations. The data reached an all-time high of 107.900 2005=100 in Jul 2013 and a record low of 93.300 2005=100 in Jan 2001. Germany PPI: 2005=100: Mfg: Other: Imitation Jewellery & Rel. Articles data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.I038: Producer Price Index: 2005=100.

Producer Prices in the United States increased to 149.78 points in September from 149.32 points in August of 2025. This dataset provides the latest reported value for - United States Producer Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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

United Kingdom PPI: Service: Real Estates Activities (RE) data was reported at 113.700 2010=100 in Sep 2018. This records an increase from the previous number of 113.600 2010=100 for Jun 2018. United Kingdom PPI: Service: Real Estates Activities (RE) data is updated quarterly, averaging 101.050 2010=100 from Dec 2008 (Median) to Sep 2018, with 40 observations. The data reached an all-time high of 113.700 2010=100 in Sep 2018 and a record low of 98.900 2010=100 in Dec 2009. United Kingdom PPI: Service: Real Estates Activities (RE) data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.I020: Producer Price Index: 2010=100: Service.

Croatia Real Effective Exchange Rate Index: PPI Deflated: 2001=100 data was reported at 85.635 2001=100 in May 2009. This records a decrease from the previous number of 87.453 2001=100 for Apr 2009. Croatia Real Effective Exchange Rate Index: PPI Deflated: 2001=100 data is updated monthly, averaging 93.774 2001=100 from Jan 1996 (Median) to May 2009, with 161 observations. The data reached an all-time high of 104.786 2001=100 in May 2000 and a record low of 85.282 2001=100 in Dec 2008. Croatia Real Effective Exchange Rate Index: PPI Deflated: 2001=100 data remains active status in CEIC and is reported by Croatian National Bank. The data is categorized under Global Database’s Croatia – Table HR.M015: Effective Foreign Exchange Rate Index. Rebased from 2001=100 to 2005=100 Replacement series ID: 240008603

Producer Price Inflation MoM in the United States increased to 0.30 percent in September from -0.10 percent in August of 2025. This dataset includes a chart with historical data for the United States Producer Price Inflation MoM.

Graph and download economic data for Producer Price Index by Industry: Information (PCUAINFOAINFO) from Dec 2006 to Aug 2025 about information, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Telecommunication, Cable, and Internet User Services: Cellular Phone and Other Wireless Telecommunication Services (WPU3721) from Mar 2009 to Sep 2025 about wireless, phone, telecom, internet, services, commodities, PPI, inflation, price index, indexes, price, and USA.

This dataset provides values for PRODUCER PRICE INDEX. reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Paraguay Real Exchange Rate Index: Business Partner: Producer Price Index data was reported at 345.844 1995=100 in Sep 2018. This records an increase from the previous number of 343.556 1995=100 for Aug 2018. Paraguay Real Exchange Rate Index: Business Partner: Producer Price Index data is updated monthly, averaging 209.690 1995=100 from Jan 1995 (Median) to Sep 2018, with 285 observations. The data reached an all-time high of 345.844 1995=100 in Sep 2018 and a record low of 100.000 1995=100 in Jan 1995. Paraguay Real Exchange Rate Index: Business Partner: Producer Price Index data remains active status in CEIC and is reported by Central Bank of Paraguay. The data is categorized under Global Database’s Paraguay – Table PY.M007: Real Exchange Rate Index: 1995=100.

The price of coarse grains tracks the prices of barley, oats and sorghum. Other varieties of coarse grains, such as rye and rough rice, are excluded from the report, as they only account for about 2% of production. Producer price indexes for these three grains are combined using a weighted average based on yearly disappearance levels. Annual figures presented in this report are the equally weighted averages of monthly means. Data is in real terms with a base of 1982 and sourced from the Bureau of Labor Statistics.

The dynamics of the PPi release during the transcription elongation of bacterial RNA polymerase and its effects on the Trigger Loop (TL) opening motion are still elusive. Here, we built a Markov State Model (MSM) from extensive all-atom molecular dynamics (MD) simulations to investigate the mechanism of the PPi release. Our MSM has identified a simple two-state mechanism for the PPi release instead of a more complex four-state mechanism observed in RNA polymerase II (Pol II). We observed that the PPi release in bacterial RNA polymerase occurs at sub-microsecond timescale, which is ∼3-fold faster than that in Pol II. After escaping from the active site, the (Mg-PPi)2− group passes through a single elongated metastable region where several positively charged residues on the secondary channel provide favorable interactions. Surprisingly, we found that the PPi release is not coupled with the TL unfolding but correlates tightly with the side-chain rotation of the TL residue R1239. Our work sheds light on the dynamics underlying the transcription elongation of the bacterial RNA polymerase.