Handbook of Methods Producer Price Indexes Calculation
Producer Price Indexes: Calculation
The Producer Price Index (PPI) tracks the average changes in prices received by domestic producers for the sale of their products. The PPI survey is constructed from prices received from a confidential, probability-based sample of producers, and price indexes are calculated using proprietary computer systems.
Data processing
After the U.S. Bureau of Labor Statistics (BLS) field representatives secure the cooperation of a reporting establishment, product descriptions, terms of transaction, prices for a set of statistically selected transactions, as well as company contact information, are entered into a data collection system. BLS regional and national office staff are then able to review the data to ensure their consistency and completeness. At that point, survey forms that are tailored specifically to each establishment are prepared. On a regular basis, typically monthly, PPI transmits notifications requesting price updates from respondents. Respondents report price information through the secure BLS Internet Data Collection Facility (IDCF).
The IDCF records essential data elements provided by survey respondents, such as changes to prices, product specifications, and terms of transactions. BLS economists then verify the reported information by reviewing respondent entries and confirming large price movements. This price-update system collects and processes current prices of about 50,000 items on a monthly basis, as well as any changes in the price-determining characteristics of those items.
Using data from the price-update system, the estimation system then calculates the various PPI data and generates a variety of outputs that are available from the BLS website. These automated data-processing systems for the PPI facilitate the accuracy and timeliness of published PPI data and protect the confidentiality of data supplied by respondents.
Producer Price Index calculation
In concept, the Producer Price Index is calculated according to the modified Laspeyres formula.
where
It is the price index in the current period;
Qb represents the quantity shipped during the base period;
Pt is the current price of the commodity during the current period; and
P0 is the price of a commodity in the comparison period.
An alternative formula more closely approximates the actual computation procedure:
In this form, the index is the weighted average of price relatives—that is, price ratios for each item (Pt / Po). The expression QbPo represents the weights in value form. The elements P and Q, both of which originally relate to period b but are adjusted for price change from period b to period o, are not derived separately.
Generally, with a Laspeyres formula, assigned weights are fixed quantities for both items and indexes, while this formula allows prices to vary monthly, driving index changes. By fixing quantities, the Laspeyres formula restricts substitution in response to relative price changes. In practice, PPI updates item weights when the program updates producer samples, and index weights are updated roughly every 5 years. For more information about the various formulae that can be used for index calculation, see the Monthly Labor Review article on measuring the substitution effect in PPI data.
Weights
Within PPI, individual item weights are used to assign importance to each price included in the calculation of lowest-level indexes. Index-level weights are used to assign importance to each lowest-level index and to each index in the aggregation structure. The subsections below describe both item-level and index-level weights within PPI.
Item weights
A price index for even the most detailed industry-based or commodity-based product index (referred to as a cell level price index) cannot be calculated without weighting the individual prices reported to BLS. PPI assigns an item weight to each transaction selected for price-tracking. Item weights are based on the relative importance of the producer within its industry and the importance of the selected product line at the establishment relative to other product lines that the establishment produces. Weights are calculated using value-of-shipments or revenue data provided to BLS field representatives during the initial interviews with reporting establishments and are adjusted to account for industry-specific sample-frame construction and probability of selection. The result is that items from some establishments are given more weight than those from other establishments.
Index weights
If the PPI system were composed merely of indexes for individual products, with no grouping or summarization, there would be no need to devise a comprehensive index-weight structure. However, given the need for aggregate indexes for groupings of individual products, there is a need for a weighting system that lets more important products have a greater effect on movements of groupings. Without such a weight system, a 10-percent rise in gasoline prices would have no more importance within the aggregated index structure than a 10-percent rise in greeting card prices.
To calculate weights for levels of aggregation above the cell index level, BLS compiles value of shipments or revenue data primarily derived from the U.S. Census Bureau’s economic census. PPI index weights are updated at approximately 5-year intervals following the release of economic census data. This update permits PPI to consider changing production patterns. Since January 2023, weights have been derived from the total value of commodities reported in the 2017 economic censuses.
Weights for industry net output indexes
Industry index weights are based primarily on economic census shipments or revenue values for products made by a specific industry; values for the same products made in other industries are not included. In compiling price indexes for 6-digit North American Industry Classification System (NAICS) industries, as well as for more highly aggregated industry groups, BLS employs net output values as weights, which eliminates the possibility of multiple counting of price changes (as discussed in the history section). Net output values include only sales from establishments in one industry or industry grouping to establishments in other industries, industry groupings, or to final demand. The meaning of net output depends on the index grouping. For example, the net output for total manufacturing industries would be the value of manufactured output shipped outside the manufacturing sector—such as to the construction sector, the trade sector, or to consumers. BLS constructs net output price indexes using data on detailed industry flows from input–output tables compiled by the Bureau of Economic Analysis (BEA) and from other detailed industry data.
Weights for commodity grouping indexes
Weights for commodity price indexes are based primarily on gross shipments or revenue values as compiled by the Census Bureau. These weights, in contrast to the net output weights used for industry indexes, represent the total selling value of goods, services, or construction products produced or processed in the United States, regardless of their industry of origin. These weights reflect total product revenue, free-on-board (f.o.b.) production point, exclusive of excise taxes. Since January 1987, values of shipments between establishments owned by the same company (termed interplant or intracompany transfers) have been included in commodity and commodity grouping weights.
Weights for inputs to industry indexes
BLS determines the products to include in an industry input index based on the BEA Use of Commodities by Industries table. BLS calculates weights for each commodity in an industry input index using this information in combination with shipments and revenue data from the U.S. Census Bureau. The gross weight of each commodity in an industry input index equals the share of the total commodity’s value consumed by the industry, multiplied by the Census revenue for that commodity. BLS then converts the gross weight to a net weight by removing the portion of the input commodity’s value that was produced by the industry. BLS uses the BEA Make of Commodities by Industries table to determine the portion of the commodity’s value that is produced by the industry. BLS publishes two sets of input indexes, one only including domestically produced inputs, and an experimental set of satellite indexes that include both domestic and imported inputs. For a summary of the aggregation system that only uses domestically-produced inputs, visit the Updated Inputs to Industry Producer Price Indexes notice. For information on the set of satellite indexes that include both domestic and imported inputs, view the BLS satellite series webpage and the Monthly Labor Review article on methodology and uses.
BLS does not publish the actual dollar-denominated values used as weights, but it does publish what is called a relative importance for each commodity and commodity grouping relative to its aggregate index. BLS does not publish relative importance figure tables for the industry net output aggregation structure. This is because of complexities relating to the application of net output weighting during index construction. At each successive level of aggregation within the industry model—from 5-digit industry groups through the industry sector aggregate indexes—a unique set of weights are calculated. For the industry aggregation model, as of 2023 there exist over 1,000 individual weight schemas employed for the various levels of aggregation within the industry index structure. However, industry index relative importance data are available upon request by email.
Commodity index relative importance figures
Within the PPI commodity index structure for goods, the relative importance of an index represents its value weight, including any imputations, multiplied by the relative price change from the weight reference date to the date of the relative importance calculation, expressed as a percentage of the total updated value weight of the All Commodities Index. For wherever-provided services, individual commodity relative importance figures are presented as percentages of their 2-digit aggregate groups.
FD–ID index relative importance figures
BLS also publishes relative importance figure tables for commodities with respect to the Final Demand–Intermediate Demand (FD–ID) aggregation system. For detailed FD–ID indexes, weights are allocated to detailed indexes at the 6-digit index level (subproduct class) of the commodity series. Detailed FD–ID indexes are in turn aggregated to broader FD–ID indexes, such as the index for final demand, and to FD–ID indexes for special groupings, such as the index for final demand less foods, energy, and trade services.
The value weight of a single subproduct class index may be allocated among several different commodity-based FD–ID categories to reflect different classes of buyers. The allocations of these value weights to various FD–ID categories currently are based on input–output data for 2012, collected and published by BEA. The total value weight for a subproduct class across the FD–ID system is equal to the total value weight for that subproduct class within the commodity index system.
BLS publishes updated relative importance figures for December of each year. Relative importance figure changes from one December to the next are solely because of relative price movements, except when entirely new weights are introduced from the latest economic censuses, or when a sample change affects the publication structure for a commodity grouping. For example, the relative importance of a commodity will rise if its price rises faster, or falls less, than the aggregate index to which it contributes. Conversely, a commodity whose price falls more, or rises less, than the aggregate index to which it contributes will have a smaller relative importance compared with its aggregate index.
Missing prices
If a producer who initially agreed to supply price information to the PPI fails to report a price for a particular month, the change in the price for that producer’s associated items will, in general, be estimated by the average price change for the other items within the same cell level index. This imputation procedure means that prices for similar products for which price reports have been received move the weight of items for which prices were not received. This procedure effectively eliminates any impact that non-reported prices might otherwise have on PPI index calculation. If item prices from non-reporting survey respondents were estimated using any relative price movement other than the relative movement of reported prices within the detailed cell to which it contributes, it would impact calculation results. For example, assume that a cell level index comprises 10 prices, and in a certain month 7 prices are reported and all 7 prices are reported as increases. The remaining three prices in the cell level index are not reported. Directly including the three non-reported prices in the index as unchanged prices would reduce the increase in the cell level index.
Index precision and rounding policies
In July 2021, PPI updated procedures to include index publication to three-decimal-place precision. Prior to this update, PPIs were published to a level of one-decimal-place precision. This modification allows users to calculate percentage changes between indexes more accurately. For example, using indexes posted at three-decimal-place precision permits users to accurately calculate a percentage change to the one-hundredth of a percent (two-decimal place precision); whereas, under the prior method percent changes calculated to the tenth of a percent still included substantial rounding issues. For presentation purposes, PPI tabular products publish index levels to three-decimal-place precision, but percentage changes still are published to one-decimal-place precision. When BLS displays percentage changes in association with index data, the percentage changes are calculated on the basis of the published rounded indexes. However, for the derivation of monthly or annual average indexes, BLS bases its calculations on unrounded data; index figures are rounded during the final step only.
Index revisions
With the release of data for November 2021, PPI index revisions include iterative updates for the 3 interim months after first issuance but prior to the posting of final indexes, 4 months after original publication. This change in procedure means that effective with data for November 2021, indexes undergo five iterative updates from first issuance through final posting. This modification in publication standards shifts index revisions and accompanying over-the-month percentage changes to the appropriate month on an immediate basis. Under the prior method, final PPIs could experience larger revisions because they had not been updated since first issuance. In addition, under the previous method, first-issued percent changes for the most recent month had embedded in them all revisions from the interim months.
Seasonal adjustment
The PPI program publishes seasonally adjusted time-series data monthly. The program utilizes both direct and indirect seasonal adjustment methods. Direct seasonal adjustment is accomplished by applying seasonal factors to unadjusted data to remove within-year seasonal patterns. Indirect adjustment is a method of seasonal adjustment used for aggregate indexes such that directly adjusted component indexes are combined into a higher-level time series. Additional information on seasonal adjustments to the PPI can be found on the Seasonal Adjustment webpage.
Each calendar year, BLS recalculates the previous five years of PPI seasonal factors based on the most recent eight years of price movements. These factors are used to recalculate seasonally adjusted index values for the previous 5 years. With the release of January data, the revised factors and seasonally adjusted index values are published. The most recent year’s factors are used to calculate the coming year’s seasonally adjusted index values.
Product change and quality adjustment
The same product usually is priced month after month; therefore, it is necessary to provide a means for bridging over changes in detailed specifications so that only actual price changes for comparable products and services are measured.
Quality adjustment is necessary when there is any substantive change to a product being priced or how it is sold. Routine steps are taken to monitor the product and to conduct quality adjustment if there are changes such as a shift to a replacement product, a change to the terms of transaction, or a change to the unit of measure.
Several varying circumstances can arise when producers report a change in product specification. In some cases, change in the specification is so minor that no product cost differences result. In these instances, the new price is compared directly with the last reported price under the former specifications, and the affected index reflects any price difference. Other times, when changes in characteristics of a product cause product cost differences, BLS attempts to make an accurate assessment of real price change by systematically taking account of differences in quality. Unfortunately, it is not always possible to obtain a value for a quality adjustment. In such cases, BLS may have to assume that any difference in price between the old and the new items is due entirely to differences in quality.
In some instances, when a reporter is unable to provide information about the resource costs of changes in product attributes, a different yardstick is employed to measure these missing values. For example, it is difficult to estimate the value of improvements or deteriorations in products such as computers and semiconductors manufactured by companies in high-technology industries. These industries frequently develop new products that are technologically superior yet cost less to produce than the products they replace. This situation contrasts sharply with those which call for conventional quality adjustment methods, which assume that increased resource costs for producing a product are necessary for improved performance. The inverse relationship between cost changes and quality changes in high-tech industries requires many different techniques for the construction of an index, especially for quality adjustment. An alternative quality adjustment technique using hedonic regressions has been incorporated into PPI quality adjustment processes. Hedonic regressions estimate the functional relationship between the characteristics embodied in a product and its price. Such regressions yield estimates of implicit prices for specified product characteristics that may be used to value the improvement in quality resulting from changes in the various characteristics embodied in a product. The value of the improvement in quality can then be removed from the reported price change, yielding a measure of the pure price change that is appropriate for the PPI.
Additional information about PPI quality adjustment methods can be found on the Quality Adjustment section of the PPI website. The document titled, Quality Adjustment in the Producer Price Index, provides detailed explanations of the various approaches to quality adjustment in the PPI, as well as, examples of the implementation of these approaches.
Variance estimates
The PPI program began publishing an annual variance estimates report in 2016, based on data for 2015. This annual report is produced after data for the prior calendar year are final. The variance estimates report includes measures of 1-month and 12-month median standard errors. To provide a frame of reference, corresponding 1-month and 12-month measures of median-absolute percent change also are provided. The PPI data included in this report include information pertaining to the final demand-intermediate demand structure, PPI commodity-based indexes, and PPI NAICS-based indexes. Standard error, a commonly used measure of sampling variability, may be used to calculate a confidence interval around a corresponding sample percentage change. Standard error measures estimate the sample distribution, or spread, of the sample estimates from the true population value. For a detailed description of PPI methods pertaining to the calculation of variance estimates, as well as, to access the archive of annual PPI variance reports, visit the section on variances of the PPI webpage.
Last Modified Date: July 31, 2025
United States Department of Labor
