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Chapter 14

Producer Prices

IN THIS CHAPTER

Background................................................................ 1

Description of Survey................................................. 2

Universe ................................................................. 2

Prices...................................................................... 2

Product change and quality adjustment ................. 3

  Classification.......................................................... 4

Data sources and collection methods......................... 7

Data processing.......................................................... 8

Estimating Procedures ............................................... 9

Index calculation.................................................... 9

Weights................................................................... 9

Missing prices........................................................ 10

Rounding policy..................................................... 10

Seasonal adjustment............................................... 10

Analysis and Presentation.......................................... 11

Analysis.................................................................. 11

Presentation............................................................ 12

Uses and Limitations.................................................. 14

Economic indicator................................................ 14

  Deflator .................................................................. 15

Private business uses.............................................. 15

Discontinued data................................................... 15

Technical References................................................. 16

The Producer Price Index (PPI) measures average 

changes in prices received by domestic producers for 

their output. Most of the information used in calculating producer price indexes is obtained through the systematic sampling of industries. In the mining and manufacturing 

sectors, price information from virtually every industry is 

captured. By contrast, although PPI coverage of the service 

sector of the economy is substantial (more than 70 percent), it 

remains incomplete. The PPI program also includes data that 

track other sectors of the economy: agriculture, fishing, forestry, utilities (natural gas and electricity), and construction.

As of January 2014, the PPI program included the following indexes:

• Price indexes for approximately 535 mining, forestry,

utility, construction, manufacturing, and services industries; 

over 500 indexes for groupings of industries; and more than 

4,000 indexes for specific within-industry product and service categories; 

• 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; 

• Over 600 indexes for aggregate measures of price

change, including the aggregation system for final demand–

intermediate demand (FD–ID).

Together, these elements constitute a system of price measures designed to meet the need for both aggregate information and detailed applications, such as following price trends 

for specific industries and products, as well as the need for

tracking price movements at a more aggregated level relative 

to the overall economy.

Background

Known until 1978 as the Wholesale Price Index, or WPI, the 

PPI is one of the oldest continuous systems of statistical data 

published by the Bureau of Labor Statistics (BLS), as well 

as one of the oldest economic time series compiled by the 

federal government. When it was first published in 1902, the

index covered the years from 1890 through 1901. The origins 

of the index can be found in an 1891 U.S. Senate resolution 

authorizing the Senate Committee on Finance to investigate 

the effects of tariff laws “upon the imports and exports, the 

growth, development, production, and prices of agricultural 

and manufactured articles at home and abroad.”1

The first index published, with its base period 1890–99,

was an unweighted average of price relatives for about 250 

commodities. Since that time, many changes have been made 

1Senate Committee on Finance, Wholesale Prices, Wages, and Transportation, Senate Report No. 1394, “The Aldrich Report,” Part I, 52nd Congress, 2d sess., March 3, 1893; and U.S. Department of Labor, Course of 

Wholesale Prices, 1890–1901, Bulletin No. 39, March 1902, pp. 205–09.
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in the sample of commodities, the base period, and the method of calculating the index. For example, a system of weighting was first used in 1914, and major expansions of the sample and reclassifications were implemented in 1952 and 1967.

The PPI program’s original intent was to measure changes 

in prices received for goods sold in primary markets of this 

country. The conceptual framework and economic theory 

guiding the program’s evolution, though more implicit than 

explicit, concentrated on obtaining the price received by either a domestic producer or an importer for the first commercial transaction. 

A major limitation of the original methodology was its reliance on judgmental sampling of commodities and producers; that is, commodities and producers were selected without 

the use of probability-based statistical methods. This practice resulted in a system that was too heavily composed of 

volume-selling products made by larger firms. As a result,

the PPI did not adequately reflect the behavior of the multitude of products whose individual transaction values might 

have been small, but that collectively accounted for a sizable portion of the economy. Another result of judgmentbased sampling was that the output of many industries was 

completely overlooked. Before the transition to the current 

probability-based statistical method, which began in the late 

1970s, products covered by the PPI program accounted for 

only about half of the total value of output from the mining 

and manufacturing sectors. The practice of assigning equal 

weight to price reports from each producer of a given commodity, regardless of any disparity in size among these firms,

may have caused additional distortions. 

Another limitation of the earlier methodology was its 

commodity orientation, which, although important, was not 

compatible with the industry orientation of most other federal 

economic time series. The PPI’s unique commodity classification scheme made it difficult to compare producer price

movements with data for most other economic variables, 

which at that time were expressed in terms of the Standard 

Industrial Classification (SIC).

These and other weaknesses in the PPI program, combined 

with increased development of the theory of price indexes 

in preretail markets, spurred several changes in terminology 

and operations during the 1970s. The 1978 change in the program name from Wholesale Price Index to Producer Price 

Index, for example, was intended to reemphasize the fact that 

the PPI program was based on prices received by producers 

of goods and services from whoever made the purchase. Also 

in 1978, the new nomenclature was accompanied by a shift 

in the analytical focus from the All Commodities Price Index 

(which was popularly called the Wholesale Price Index) to 

the Finished Goods Price Index and the other commoditybased stage-of-processing (SOP) price indexes.

Beginning in the mid-to-late 1970s, the PPI transitioned 

from judgmental sampling by commodity to probabilitybased sampling by industry, organized in accordance with the 

SIC. This overhaul was phased in gradually, until the transition to the current methodology was essentially completed in 

January 1986. Over the two decades that followed, the PPI

allocated substantial resources toward expanding its coverage of the services and construction sectors. With the release 

of PPI data for January 2004, the PPI program replaced the 

SIC with the North American Industry Classification System

(NAICS).

Culminating the longstanding PPI objective to expand 

coverage, in January 2014 the PPI transitioned to the FD–ID

aggregation system from the SOP model. By incorporating

indexes for services, construction, government purchases, 

and exports, and by more than doubling PPI coverage of the 

U.S. economy to over 75 percent of in-scope domestic production in its primary aggregation system, the FD–ID model

improves upon the SOP system that preceded it.

Description of Survey

Universe

The PPI universe consists of the output of all industries in 

the goods-producing sectors of the U.S. economy—mining, 

manufacturing, agriculture, fishing, and forestry—as well as

the output of the natural gas, electricity, and construction industries. Recycled goods that compete with those made in the 

goods-producing sectors, such as waste and scrap materials, 

also are outputs that are part of the survey. Imports no longer are included within the PPI universe; however, the BLS 

International Price Program publishes price indexes for both 

imports and exports. (See chapter 15.) Goods shipped between establishments owned by the same company (termed 

interplant or intracompany transfers) are included, as is a 

substantial percentage of the domestic production of goods 

specifically made for the military.

The output of the services sector and other sectors that 

do not produce physical products also is conceptually within 

the PPI universe. As of January 2014, the PPI program covered more than 70 percent of the service sector’s output, publishing data for selected industries in the following sectors: 

wholesale and retail trade; transportation and warehousing; 

information; finance and insurance; real estate brokering,

rental, and leasing; professional, scientific, and technical services; administrative, support, and waste management services; health care and social assistance; and accommodation. 

For the construction sector, selected indexes for nonresidential construction industries, maintenance and repair construction, and nonresidential construction contractor services also 

are published.

Prices

One crucial task in designing a price index is defining what

constitutes the price whose changes are to be measured. A 

seemingly simple question such as “What is the price of 

steel?” is unanswerable until the definition of price is made 

more specific.

For industries in sectors other than wholesale and retail 

trade, the PPI price is defined as the net revenue accruing to

a specified producing establishment from a specified kind of

buyer for a specified product shipped, or service provided,

under specified transaction terms on a specified day of the
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month. This definition points out several price-determining

variables that must be clarified before a cooperating business

establishment can report a meaningful product or service 

price to the BLS. For example, if a company charges more 

for a red widget than a white one, color is one of the pricedetermining variables; if all widgets sell for the same price, 

regardless of color, color is not a price-determining variable. 

The type of buyer also can be a price-determining variable. 

For instance, if a car rental company offers different rental 

rates to business travelers, leisure travelers, and customers 

who are replacing their vehicle under an insurance agreement, then buyer type is a price-determining characteristic. 

Under this circumstance, the PPI sample of prices representing each type of transaction would be appropriately weighted.

Because the PPI is meant to measure changes in net revenues received by producers, changes in excise taxes—revenues collected by producers on behalf of the federal, state, 

and local government—are not reflected in the index. But

changes in rebate programs, in low-interest financing plans,

and in other sales promotion techniques are reflected to the

extent that these policies affect the net proceeds ultimately 

realized by the producer for a unit sale of a good or the provision of a service. Thus, if an auto manufacturer offers retail 

customers a rebate of $500, the manufacturer’s net proceeds 

are reduced by $500 and the PPI for manufactured new cars 

would reflect a lower price. However, if an automobile dealer

offers its customers an additional rebate whose cost is absorbed by the dealer rather than the manufacturer, the rebate 

would not affect the PPI for auto manufacturing but would be 

included in the PPI for automobile dealers. (The Consumer 

Price Index (CPI), of course, would reflect a customer rebate,

regardless of whether it was sponsored by the manufacturer 

or the dealer.) 

In contrast to all goods-producing industries and most 

service-providing industries, establishments engaged in 

wholesale and retail trade purchase goods primarily for direct resale to other businesses and consumers. The PPI views 

wholesalers and retailers as suppliers of distributive services 

(rather than goods), because little, if any, transformation of 

these goods takes place. This approach implies that the output of a wholesale or retail trade establishment is represented 

by the difference between its selling price of a good and the 

acquisition price for that same item. Gross margin prices reflect the value added by the establishment for services such as

marketing, storing, and displaying goods in convenient locations and making the goods easily available for customers to 

purchase.

The statistical accuracy of producer price indexes depends 

heavily on the quality of the information voluntarily provided 

by respondents. The BLS emphasizes to cooperating businesses the need for reports of realistic transaction prices, including all discounts, premiums, rebates, allowances, and so 

forth, rather than list or book prices. The use of list prices in 

the PPI program has been, and continues to be, the exception 

rather than the rule. Even before the conversion to the current 

methodology for sampling a representative set of net transaction prices, a BLS survey showed that only about 20 percent 

of commodity indexes were based on list prices. Inasmuch 

as the current methodology is more systematic than the older 

methodology in concentrating on actual transaction prices, 

the use of list prices is even less frequent now.

Neither order prices nor “futures” prices are collected by 

the survey, because the PPI tries to capture the price for output being shipped in that same month, not some other time. 

Changes in transportation costs are reflected in PPIs only

when the producing company delivers the product itself without hiring a third-party shipper.

Most prices refer to one particular day of the month, 

namely, the Tuesday of the week containing the 13th of the 

month; this pricing date can range between the 9th and the 

15th. There are exceptions for some products and services, 

however. For example, a number of farm products are priced 

on a day of the week other than Tuesday, and some service 

industries report prices that reflect average changes for some

portion of the month or for the entire month. Although most 

prices for goods reported to the PPI program are free-onboard (f.o.b.) point-of-production prices, some prices are 

those quoted on organized commodity exchanges or at central 

markets. This practice is used most often for farm products.

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. 

An adjustment is especially important when a product is 

replaced by a new one. Even when companies report their 

prices on the basis of altered transaction selling terms (e.g., 

price per 1,000 sold instead of price per 100) or when there is 

a change in the number or identity of companies reporting to 

the BLS, routine steps are taken to ensure that only true price 

changes influence the index.

When a company respondent reports a price that reflects a

physical change in a product or a change in the characteristics 

of a service, the BLS uses one of several quality adjustment 

methods. The direct comparison method is used when the 

change in the specification is so minor that no product cost

differences result. In this instance, the new price is compared 

directly with the last reported price under the former specifications and the affected index reflects any price difference.

However, when changes in physical characteristics of a 

product cause product cost differences, the BLS attempts to 

make an accurate assessment of real price change by systematically taking account of differences in quality. The explicit 

quality adjustment method is especially important with automobiles, machinery, and other types of goods that undergo 

periodic changes in their model. The usual method of quality 

adjustment involves the collection of data from companies in 

the PPI sample reporting on the costs they incurred in connection with the quality change. For example, if the manufacturer’s price of a new-model car is $500 more than the price 

for the previous model year’s version, and if $200 of that 

increase is due to the extra product cost and normal margin 

associated with the addition of government-mandated safety 
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equipment, then the real price has risen by only $300. In that 

case, the change in the passenger car index will reflect only

that amount, not the nominal price rise of $500. 

Unfortunately, it is not always possible to obtain a value 

for a quality adjustment. If, for instance, the respondent is 

unable to estimate the difference in production cost between 

an old item and a new one, or if an explicit comparison 

between an entirely new product and a previous product 

is not feasible, then no quality adjustment value will be 

forthcoming. In such cases, the BLS may have to assume 

that any difference in price between the old and the new 

items is due entirely to differences in quality. If possible, the 

BLS then employs the overlap method of quality adjustment, 

under which prices are collected for both the old and the 

new item over a designated period and a particular month 

is chosen as the overlap month. The difference between the 

prices of the two items in the overlap month is assumed to 

represent the value of the difference in quality between the 

items. For purposes of calculating the official price index,

the BLS uses price changes for the old item through the 

overlap month but thereafter follows price changes only for 

the new item. 

In some instances, when the 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-tech” 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 in the area of quality adjustment.2 An alternative 

quality adjustment technique using hedonic regressions has 

been incorporated into PPI adjustment processes.3 Hedonic 

regressions estimate the functional relationship between the 

characteristics embodied in the products in a market and the 

products’ prices. 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. 

Classification 

The PPI family of indexes is divided into several major classification systems, each with its own structure, history, and

uses. However, indexes in all classification systems draw

from the same pool of price information provided to the BLS 

by cooperating company reporters. The three most important 

classification structures are (1) industry, (2) commodity, and

(3) FD–ID.

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 (i.e., its net output). Measures—or indexes—of price change classified by industry

form the basis of the program. These indexes reflect the price

trends of a constant set of goods and services that together 

represent the total output of an industry. Standardized industry-based index codes provide comparability with a wide assortment of industry-based data on other economic phenomena, including productivity, production, employment, wages, 

and earnings. 

For more than 20 years, the PPI program used the SIC 

system as the structure for the collection and presentation of 

industry-based price data. However, that system received increasing criticism about its inability to handle rapid changes 

in the U.S. economy. Developments in information services, 

new forms of health care, an expansion in the service sector, 

and the advent of high-tech manufacturing are examples of 

industrial changes that could not be studied under the SIC 

system.

The PPI program began publishing industry-based price 

data organized in accordance with NAICS with the release 

of data for January 2004. Developed in cooperation with 

Canada and Mexico, NAICS represents one of the most profound changes in statistical programs focusing on emerging 

economic activities. It uses a production-oriented conceptual 

framework to group establishments into industries on the basis of the primary activity in which they are engaged. Establishments using similar raw-material inputs, similar capital 

equipment, and similar labor are classified under the same

industry. 

In general, there may be as many as three kinds of product 

price indexes for a given industry. Every industry has primary product indexes that show changes in prices received by 

establishments in the industry for products made primarily, 

but not necessarily exclusively, by that industry. The industry within which an establishment is classified is determined

by those products which account for the largest share of the 

establishment’s total value of shipments. In addition, most 

industries have secondary product indexes that show changes in prices received by establishments in the industry for 

products made chiefly in some other industry. Finally, some

industries may have miscellaneous receipts indexes to show 

price changes in other sources of revenue received by establishments within the industry. 

Commodity classification. The commodity classification

structure of the PPI organizes products by similarity of 

end use or similarity of material composition, regardless of 

2See James Sinclair and Brian Catron, “An experimental price index for 

the computer industry,” Monthly Labor Review, October 1990, pp. 16–24,

http://www.bls.gov/opub/mlr/1990/10/art2full.pdf. 3Since January 1991, the Bureau has published a computer price index incorporating these quality adjustment procedures. In addition, series 

for other high-tech industries related to computers may incorporate these 

techniques of adjusting for technological changes in the characteristics of 

a product.
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whether the products are classified as primary or secondary

in their industry of origin. This system is unique to the PPI 

and does not match any other standard coding structure, such 

as NAICS or the U.N. Standard International Trade Classification. The historical continuity of index series, the needs of 

index users, and a variety of ad hoc factors were important in 

developing the PPI commodity classification. Prior to January 2009, the commodity classification system included only

goods-based price indexes. With the release of data for January 2009, PPI expanded the commodity classification structure to include services and construction products. 

The commodity classification system is organized as a hierarchical structure that starts with major commodity groupings (at the 2-digit level of aggregation). Major groupings 01 

through 15 encompass commodity-based goods indexes. Major groupings 30 though 61 include services-based commodity indexes, and major grouping 80 encompasses constructionbased commodity indexes. Each major commodity grouping 

includes (in descending order of aggregation) subgroups (at 

the 3-digit level), product classes (4-digit level), subproduct 

classes (5- and 6-digit level), item groupings (7-digit level), 

and individual items (8-, 9-, and 10-digit levels). 

Commodity-based FD–ID classification. The FD–ID system

replaced the PPI stage-of-processing system as PPI’s primary 

aggregation model with the release of data for January 2014. 

The FD–ID model expands coverage beyond that of the SOP

system through the addition of services, construction, exports, and government purchases.4

Commodity-based FD–ID price indexes regroup commodities at the subproduct class (six-digit) level, according 

to the class of buyer and type of commodity. The two primary classes of buyers included in the FD–ID system are final-demand (personal consumption, capital investment, government, export) buyers and intermediate-demand (business 

purchases, excluding capital investment) buyers. The main 

source of data used to determine the class of buyer is the table 

titled “Use of commodities by industries, before redefinition”

from the Benchmark Input–Output Data Tables of the United

States, produced by the U.S. Bureau of Economic Analysis 

(BEA).5 In many cases, the same commodity is purchased 

by different types of buyers. As a result, commodities are often included in several FD–ID indexes. For example, regular

gasoline is purchased for personal consumption, export, government use, and business use. The PPI program publishes 

only one commodity index for regular gasoline (wpu057104), 

reflecting sales to all types of buyers, and this index is used in

all FD–ID aggregations, regardless of whether the gasoline is

sold for personal consumption, as an export, to government, 

or to businesses. Proportions based on BEA “Use of commodities by industries” data are used to allocate the correct 

portion of the total weight of gasoline to each use category. 

In cases when buyer type is an important price-determining 

characteristic, indexes are often created on the basis of the 

specific type of buyer. For example, within the PPI category

for loan services, separate indexes for consumer loans and 

business loans were constructed. 

Final demand. The final-demand portion of the FD–ID structure measures price change for commodities sold as personal 

consumption, as capital investment, to government, and as 

exports. The system is composed of six main final-demand

price indexes: goods; trade services; transportation and warehousing services; services excluding trade, transportation, 

and warehousing; construction; and overall final demand.

The final-demand goods price index measures price

change for both unprocessed and processed goods sold to 

final demand. Fresh fruits sold to consumers and computers sold as capital investment are examples of transactions 

included in this index. The final-demand trade services index measures price change for the retailing and wholesaling of merchandise sold to final demand, generally without

transformation. (Trade indexes measure changes in margins 

received by wholesalers and retailers.) The final-demand

transportation and warehousing services index tracks price 

change for transportation of passengers, as well as transportation of cargo sold to final demand, and includes prices for

warehousing and storage of goods sold to final demand. The

price index for final-demand services less trade, transportation, and warehousing measures price change for all services 

other than trade and transportation sold to final demand. Publishing, banking, lodging, and health care are examples of 

these services. The final-demand construction index tracks

price change for new construction and maintenance and repair construction sold to final demand. Construction of office

buildings is an example of a commodity that is included in 

the final-demand construction index. Lastly, the overall finaldemand index combines the other five final-demand component indexes in order to track price change for all types of 

commodities sold to final demand.

Intermediate demand. The intermediate-demand portion of 

the FD–ID system tracks price change for goods, services,

and construction products sold to businesses as inputs to 

production, excluding capital investment. The system includes two parallel treatments of intermediate demand. The 

first treatment organizes intermediate-demand commodities

by type. The second organizes intermediate demand commodities into production stages, with the explicit goal of 

developing a forward-flow model of production and price

change. 

The treatment having to do with intermediate demand by 

type of commodity organizes commodities by similarity of 

product. This treatment is composed of six main intermediate-demand price indexes: unprocessed goods; processed 

goods; trade services; transportation and warehousing services; services less trade, transportation, and warehousing; 

and construction. 

4For more information relating to the FD-ID structure, see Jonathan C. 

Weinhagen, “A new, experimental system of indexes from the PPI program,” 

Monthly Labor Review, February 2011, pp. 3–24, http://www.bls.gov/opub/

mlr/2011/02/art1full.pdf . 5See “Benchmark Input–Output Accounts” (U.S. Bureau of Economic

Analysis), http://www.bea.gov/industry/index.htm#benchmark_io.
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The price index for unprocessed goods for intermediate demand measures price change for goods that have

undergone no fabrication and that are sold to businesses as 

inputs to production. Crude petroleum sold to refineries is

an example of an unprocessed good sold to intermediate 

demand. The index for processed goods for intermediate 

demand tracks price change for fabricated goods sold as 

business inputs. Examples are car parts sold to car manufacturers and gasoline sold to trucking companies. The index for trade services for intermediate demand measures 

price change for the services of retailing and wholesaling goods purchased by businesses as inputs to production. The price index for transportation and warehousing 

services for intermediate demand measures price change 

for business travel, as well as for transportation and warehousing of cargo sold to intermediate demand. The index 

for intermediate-demand services less trade, transportation, and warehousing tracks price change for nontrade and 

nontransportation services purchased by firms as inputs to

production. Legal and accounting services purchased by 

businesses are examples of the services measured by this 

index. Finally, the price index for construction for intermediate demand measures price change for construction 

purchased by firms as inputs to production. Given that new

construction is categorized in the final-demand portion of

the economy as capital investment, this index tracks only 

price change for maintenance and repair construction purchased by firms.

The production flow treatment of intermediate demand

is a stage-based system of price indexes. These indexes can 

be used to study price transmission across stages of production and on into final demand. The system is constructed in

a manner that maximizes the forward flow of production

between stages, while minimizing backflow of production.

The production flow treatment contains four main indexes:

intermediate-demand stages 1, 2, 3, and 4. The main sources 

of data used to develop these indexes were the BEA tables 

titled “Use of commodities by industries, before redefinition” and “Make of commodities by industries, before redefinition.”6

Indexes for the four stages were developed by first assigning each industry in the economy to one of four stages of 

production. Industries were assigned to stages with the goal 

of maximizing net forward flow between stages. Net forward

flow is defined as

(forward shipments of the industry stage + inputs received 

from previous stages of processing) – (backward shipments

of the industry stage + inputs received from forward stages 

of processing). 

The PPI program implemented a two-step procedure to attempt to maximize net forward flow. In the first step, a set of

rules was used to assign industries to stages and select the 

appropriate number of stages for the system. The set of rules 

used to assign industries to the four stages is summarized as 

follows:

1. Assign an industry to stage 4 if shipments sold to final

demand ≥ 75 percent of industry production.

2. Assign an industry to stage 3 if shipments sold to final

demand and to stage 4 ≥ 65 percent of industry production

and shipments sold to final demand < 75 percent of production.

3. Assign an industry to stage 2 if shipments sold to final

demand, to stage 4, and to stage 3 ≥ 65 percent of industry

production; and shipments sold to final demand and to stage

4 < 65 percent of production; and shipments sold to final

demand < 75 percent of production.

4. Assign an industry to stage 1 if the industry does not 

meet any of conditions 1, 2, and 3. 

The PPI program examined many different numbers of 

stages and sets of rules before selecting the ones just described. The program eventually chose the aforementioned 

system because it performed very well in terms of maximizing net forward flow and minimizing internal flow.

After the assignment of industries to stages by sale of output, the second step in the procedure to maximize net forward flow was to examine the effects on net forward flow of

moving individual industries to stages to which they were not 

originally assigned. In cases in which there were substantial 

gains to net forward flow, industries were moved to the new

stage. The PPI production-flow-based system exhibits strong

forward flow and little backflow. After weighting, 83.6 percent of transactions in the system are forward flowing, 5.7

percent are back flowing, and 10.7 percent are internally

flowing.

The final step in constructing stages for the productionflow-based intermediate-demand indexes was to determine

the commodities to be included and weights to be used in 

the indexes. The production flow indexes track prices for

inputs consumed by industries, as opposed to prices for the 

output produced by industries, in each of the four stages of 

production. The BEA use table was consulted to determine 

the commodities consumed by industries in each of the four 

stages. The production flow indexes exclude the weight for

inputs both produced and consumed within an industry production stage, thereby eliminating any multiple counting of 

price change. The fourth-stage intermediate-demand index, 

for example, tracks price change for inputs consumed, not 

produced, by industries included in the fourth stage of production. Recall that industries classified in the fourth stage of

production produce mostly goods and services sold to final

demand. The intermediate-demand index for stage 4 therefore measures price change for the inputs to production of industries that produce primarily final-demand goods, services,

and construction (stage-4 producers). 

Examples of heavily weighted goods-producing industries 

in stage 4 are the manufacture of light trucks and utility vehicles, the manufacture of automobiles, and the manufacture 

of pharmaceuticals. Retail trade, food service and drinking 

places, and hospitals are examples of heavily weighted ser- 6 Ibid.
7

vice industries included in stage 4, which also includes all 

new construction industries. Examples of goods consumed 

by stage-4 industries are motor vehicle parts, commercial 

electric power, plastic construction products, biological products, and beef and veal. Engineering services, machinery and 

equipment wholesaling, long-distance motor carrying, and 

legal services are examples of services consumed by stage-4 

industries.

Examples of highly weighted goods-producing industries 

included in stage 3 are motor vehicle parts manufacturing, 

animal (except poultry) slaughtering and processing, and 

semiconductor manufacturing. Services industries classified

into stage 3 include wholesale trade; insurance carriers; architecture, engineering, and related services; and hotels and 

motels. Examples of goods consumed by stage-3 industries 

are slaughter steers and heifers, industrial electric power, and 

hot rolled steel bars, plates, and structural shapes. Commissions from sales of property and casualty insurance, business 

loans, temporary help services, and administrative and general management consulting services are examples of services 

commonly consumed by stage-3 industries.

Petroleum refineries; electricity generation, transmission,

and distribution; natural gas distribution; cattle ranching and 

farming; and plastic materials and resin manufacturing are 

among the goods-based industries assigned to stage 2. Examples of service industries that are heavily weighted in 

stage 2 are management of companies and enterprises; nondepository credit intermediation and related activities; insurance agencies, brokerages, and related activities; and services 

to buildings and dwellings. Goods commonly purchased by 

stage-2 industries include crude petroleum, natural gas, formula feeds, and primary basic organic chemicals. Examples 

of services that are heavily weighted in the intermediate-demand stage-2 index are legal services, business loans, and 

cellular phone and other wireless telecommunication.

Goods-producing industries in stage 1 include oil and gas 

extraction, paper mills, and grain farming. Real estate, legal 

services, and advertising services are examples of highly 

weighted service industries included in stage 1. Examples 

of goods consumed by stage-1 industries include gasoline 

and commercial and industrial electric power. Examples of 

services commonly consumed by stage-1 industries are solid 

waste collection, chemicals and allied products wholesaling, 

and guestroom or unit rental. All inputs purchased by stage-1 

industries are, by definition, produced either within stage 1 or

by later stages of processing, leaving stage 1 less useful for 

price transmission analysis.

Other. There are several additional classification structures

within the PPI family of indexes. For example, PPIs for goods 

are available by durability of product. The allocation of individual commodities to durability-of-product categories (such 

as durable manufactured goods and total nondurable goods) 

is based on the U.S. Census Bureau definition: products with

an expected lifetime of less than 3 years are classified as

nondurable, and products with a longer life expectancy are 

considered durable goods. Special commodity grouping indexes (such as indexes for fabricated metal products, selected 

textile mill products, prescription pharmaceuticals, and overthe-counter pharmaceuticals) rearrange PPI commodity data 

into different combinations of price series. In 1986, the BLS 

began publishing indexes that measure changes in prices of 

two kinds of inputs into construction industries: material inputs and supply inputs. 

Most PPIs, whether commodity oriented or industry oriented, are national, rather than regional, in scope. However, 

regional price indexes are published for a few selected items, 

such as electric power distribution, ready-mix concrete, and 

construction sand and gravel, for which price-determining

regional markets are the rule rather than the exception.

Data Sources and Collection Methods

An industry as a whole is the basic starting point for 

sampling, and each industry has an individually designed 

and tailored sample. The first step in selecting a sample is

to construct a frame that includes all the establishments 

classified within that industry. The primary source for

compiling this list of the universe of establishments is the 

Unemployment Insurance system, because most employers 

are legally required to participate in it. Supplementary 

information from other publicly available lists is used 

to refine the industry’s frame of establishments. For

example, for service-sector industries in particular, it is 

sometimes necessary to use frames other than the list from 

the Unemployment Insurance system so that additional 

establishment data can be analyzed.

The next step in constructing an industry sample consists 

of clustering companies’ establishments into price-forming 

units. Each member of a price-forming unit must belong to 

the same industry; establishments in a profit center that belong to another industry are excluded in this step. An establishment is defined as a production entity in a single location.

Two establishments may occupy the same or adjacent space 

if they are separable by physical identification, recordkeeping, or both. Establishments are the units for which production and employment data usually are collected; however, in 

many cases establishments are not the appropriate unit for the 

collection of producer price data. For example, several establishments owned by a single firm may be operated as a cluster

and constitute a profit-maximizing center. In such cases, the

business maximizes profits for the cluster as a whole, rather

than for any one establishment. 

Once a list of price-forming units in an industry has been

compiled, the list may be stratified by variables appropriate

for that industry. The criterion for identifying the sampling 

strata is whether price trends may be different for different 

values of a variable. For example, the size of the production 

unit may cause differences in production technologies and, 

as a result, different responses to changes in demand or input 

costs. Some industries may be characterized by geographically independent markets, which may become strata. Within 

each stratum, units usually are ordered by size in order to 

ensure a proportionate distribution of the sample. 
8

The next step is to assign the number of units to be selected in each stratum. This number may be in direct proportion 

to the value of shipments by units in each stratum, but if there 

is evidence that some strata have more heterogeneity in price 

change, those strata will be assigned a greater proportion of 

the total sample than their simple shipment values would 

require. Finally, a sample of price-forming units is selected 

systematically, with a probability of selection proportional to 

its size. Ideally, the proper measure of size would be the total 

revenue of the unit. In practice, however, employment is used 

as a proxy, because employment information is more readily 

available. 

Once an establishment or cluster of establishments is selected for pricing, a BLS field economist visits the unit to

solicit its cooperation. The management of the unit is assured 

that its assistance is completely voluntary, that any information it agrees to provide to the BLS will be used for statistical 

purposes only, and that the BLS will hold that information in 

confidence to the full extent permitted by law.

If the establishment agrees to participate in the PPI program, the BLS field economist proceeds to select those transactions which are to be priced through time from among all 

of the unit’s revenue-producing transactions. A probability 

sampling technique called disaggregation is used to select 

the transactions. The disaggregation procedure assigns, to 

each category of items shipped and to each category of other 

types of receipts, a probability of selection proportional to the 

value of the category within the reporting unit. The categories selected are broken into additional detail in subsequent 

stages, until unique items or unique types of other receipts 

are identified.

Even after a physically unique item has been selected, it 

is usually necessary to disaggregate further. If the same item 

is sold at more than one price, then the conditions which determine that price—such as the size of the order, the type 

of customer, and so forth—also must be selected on the basis of probability. This method for identifying the terms of 

sale (or transaction terms) both ensures that the same type of 

transaction is priced over time and eliminates any bias in the 

selection of the terms of sale. (To view a sample PPI program 

initiation questionnaire, link to http://www.bls.gov/ppi/bls_

form_1810e.pdf.) 

To minimize the reporting burden on cooperating establishments, the initiation interview process usually is completed in less than 2 hours. Subsequently, reporting establishments agree to supply prices for those items selected on 

an agreed-upon schedule—usually monthly, but sometimes 

less often. The PPI program has developed a secure Webbased reporting system so that respondents can provide price 

updates via the Internet. This system replaces mail and fax 

procedures, which were employed previously. The PPI program continues to encourage the conversion to the secure 

Web-based price-reporting method. It is expected that the 

transition to Internet-based reporting of price information 

will result in program efficiencies, reduced respondent burden, and improved index quality. For those respondents who 

prefer it, BLS Form 473P (http://www.bls.gov/ppi/bls_

form_473p.pdf) is used to report prices through the U.S. 

mail and by fax. Overall, respondent cooperation generally

remains high, although some companies decline to participate from the beginning and others drop out of the program 

over the course of the sample’s timeframe. 

The publication of company-specific data in identifiable

form is prohibited in the statistical and research work of the 

BLS. Data from firms participating in the PPI program are

protected to ensure respondent confidentiality even within

the BLS, so that only those few staff members with an absolute need to know can identify a respondent. Furthermore, 

the BLS has publication criteria that prevent the inadvertent 

revelation of a respondent’s identity to the public through 

movements in a published index. 

The BLS sample of each industry’s producers and output 

must be updated periodically to account for changing market 

conditions. This procedure, called resampling, takes place 

relatively often for industries marked by dynamic changes 

in production methods, by technological transformation, or 

by substantial producer entry or exit. More stable industries 

need to undergo resampling less frequently. In practice, many 

of the reporting establishments and products included in the 

sample may be the same both before and after resampling. 

Data Processing

Producer price indexes are the output of a series of computer 

subsystems that automate most operations. Although previously limited to relying upon mainframe computers, PPI data 

processing now relies on microcomputer and local area network (LAN) technologies.

After BLS field representatives secure the cooperation of

a reporting establishment, the product descriptions, terms 

of transaction, prices, and company contact information 

are entered into a data collection system. The BLS regional 

and national office staffs are then able to review the data

electronically to ensure their consistency and completeness. 

At that point, paper or electronic survey forms that are 

tailored specifically to each establishment can be prepared.

Paper or faxed forms are sent to reporting establishments 

on an agreed-upon schedule, while respondents reporting 

price information through the secure Web-based Internet 

site receive email notices when scheduled price updates are 

requested. 

In the BLS price-update system, paper or faxed survey 

forms returned by respondents are scanned by an optical 

character reader, which logs in each form and captures the 

essential data elements. When Web-based respondents provide price update information, BLS computer systems capture these changes directly. BLS economists then verify the 

reported information, checking for changes that might have 

been missed by the character reader, reviewing Web-based 

respondent entries, and double-checking large price movements. This price-update system makes possible the collection and processing of the current prices of more than 100,000 

items, as well as any changes in the price-determining characteristics of those items. 
9

Product and commodity aggregation weights. A price index 

for even the most finely detailed commodity or product (usually termed a cell index) cannot be calculated without applying a policy for weighting the individual prices reported to 

the BLS for each item. Reports from some establishments 

are given more weight than those from others, in accordance 

with value-of-shipments data provided to BLS field representatives during the initial interviews with reporting establishments. The data are adjusted by BLS probability selection 

techniques. 

To calculate product and commodity indexes for levels of 

aggregation above the cell index, the BLS compiles weights 

on the basis of values of shipments derived from information 

provided by the Census Bureau and a few other sources.7

Industry index weights, however, are based only on values of 

shipments for those aggregations of products made within the 

same industry; shipment values for the same products made 

in other industries are not counted.

Industry net output weights. In compiling price indexes for 

six-digit NAICS industries, as well as for more highly aggregated industry groups, the BLS employs net output values of shipments as weights. Net output values of shipments 

include only shipments from establishments in one industry 

to establishments in other industries or to final demand. By

definition, then, net output values of shipments differ from

gross output values of shipments by excluding shipments 

among establishments within the same industry, even if those 

establishments are owned by separate, independent firms.

The meaning of net output depends on the index grouping. 

The net output for total manufacturing, for example, would 

be the value of manufactured output shipped outside the entire manufacturing sector—for example, to the construction 

sector or to consumers. The BLS constructs net output price 

indexes through the use of data on detailed industry flows

from input–output tables compiled by the BEA and from other detailed industry data. 

Weights for commodity groupings. Weights for individual 

commodity price indexes and, in turn, for commodity grouping price indexes are based on gross values-of-shipments or 

revenue data, as compiled by the Census Bureau and a few 

other sources. These weights, which are 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, f.o.b. production 

point, exclusive of any excise taxes. Since January 1987, 

values of shipments between establishments owned by the 

7 Information currently used to calculate weights throughout the PPI 

family of indexes is taken largely from the following censuses conducted 

by the Census Bureau of the U.S. Department of Commerce: (1) Census of 

Manufactures, (2) Census of Mineral Industries (which includes oil and gas 

production), (3) Census of Agriculture, and (4) Census of Service Industries. 

Other current sources of weighting include the Energy Information Administration of the U.S. Department of Energy and the National Marine Fisheries Service of the U.S. Department of Commerce. Weights are updated at

approximately 5-year intervals.

Using data from the price-update system, the estimation 

system calculates the indexes and generates a variety of 

outputs for the BLS Internet site and for printed statistical 

tables. 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 the respondents.

Estimating Procedures

Index calculation

In concept, the Producer Price Index is calculated according 

to the modified Laspeyres formula

= (∑ / ∑ ) ×100, t QaPt QaPo I

where

It is the price index in the current period;

Qa represents the quantity shipped during the base period;

Pt is the current price of the commodity; and

Po is the price of a commodity in the comparison period.

An alternative formula more closely approximates the 

actual computation procedure:

. –1

–1

t

o

t a o

o

t a o

t I

P

P Q P

P

P Q P

I ×





























 



 



 









 



 



 





=

∑

∑

In this form, the index is the weighted average of price relatives—that is, price ratios for each item .The e

Qa Po represents the weights in value form, and the elements 

P and Q (both of which originally relate to period a, but are 

adjusted for price change from period a to period o) are not 

derived separately. When specifications or samples change,

the item relatives must be computed by linking (multiplying) 

the relatives for the separate periods for which the data are 

exactly comparable.

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 weight structure. However, given the desire for numerous indexes for 

groupings of individual products, there is a need for a weight 

system that will let 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 index structure than a 10-percent 

rise in greeting card prices. 

.



 





o

t

P

P
10

same company (termed interplant or intracompany transfers) 

have been included in commodity and commodity grouping 

weights; interplant transfers had been excluded from the 

weight structure before then.

Commodity and commodity grouping weights are updated 

periodically to take into account changing production patterns. Since January 2012, these weights have been derived 

from the total value of commodities reported in the 2007 

economic censuses. From January 2007 to December 2011, 

the weights were derived from the 2002 economic censuses. 

From January 2002 to December 2006, the 1997 economic 

censuses were used. Between January 1996 and December 

2001, the 1992 economic censuses were the basis for commodity grouping weights. From January 1992 through December 1995, 1987 values of shipments formed the foundation for commodity and commodity grouping weights. From 

January 1987 through December 1991, 1982 weights were 

used. Between January 1976 and December 1986, 1972 

weights were used. Updated weights are incorporated into the 

PPI system in a manner that does not require the recalculation 

of indexes for earlier periods.

The BLS does not publish the actual values used as 

weights, but does publish what is called a relative importance 

for each commodity and commodity grouping. For goods, 

the relative importance of an item represents its basic value 

weight, including any imputations, multiplied by the relative 

price change from the weight date to the date of the relative 

importance calculation, expressed as a percentage of the total 

value weight of the All Commodities Index. For whereverprovided services, individual commodity relative importance 

figures are presented as a percentage of their two-digit aggregate group. Data showing the relative importance of commodities with respect to the FD–ID aggregation system also

are available. The BLS calculates relative importance data for 

December of each year. Except when entirely new weights 

are introduced from the latest industrial censuses or when a 

sample change affects a given grouping, relative importance 

data usually change from one December to another solely because of relative price movements. The relative importance 

of a commodity will rise if its price rises faster than the All 

Commodities Index. Conversely, a commodity whose price 

falls or rises more slowly than the All Commodities Index 

will show a smaller relative importance relative to the All 

Commodities Index. The BLS does not, however, use published relative importance data as fixed inputs to the calculation of monthly price indexes. Rather, each commodity’s 

actual weight value fluctuates each month in accordance with

that commodity’s previous price movements. Theoretically, 

the BLS could calculate and publish a new set of relative importance data every month. Relative importance data for any 

given commodity grouping also change when the grouping’s 

components are subjected to a change in sample.

FD–ID indexes. For detailed FD–ID indexes, weights are

allocated to detailed indexes at the subproduct class (i.e., sixdigit) level of the commodity code series. Detailed FD–ID indexes are in turn aggregated to broader FD–ID indexes, such

as the index for final demand, and also to FD–ID indexes for

special groupings, such as the index for final demand less

foods and energy. 

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. For example, a portion of the value weight of the citrus fruits index has been assigned to the index for unprocessed goods 

for intermediate demand in order to represent the proportion 

of citrus fruit sold to food processors; most of the rest of the 

value weight for this grouping has been assigned to the index 

for finished consumer foods. The allocations of these value

weights to various FD–ID categories currently are based on

input–output studies for 2002 conducted by the BEA. The

total relative value weight for a subproduct class within the 

FD–ID system is equal to the total relative value weight for

that subproduct class within the All Commodities Index classification scheme.

Current and archived relative importance figures for

goods indexes as a percentage of the All Commodities Index, 

wherever-provided services indexes as a percentage of their 

respective two-digit groupings, and allocations of subproduct 

class indexes relative to their FD–ID aggregate indexes appear in tables of relative importance data published on the 

PPI home page of the BLS website (http://download.bls.

gov/pub/special.requests/ppi/).

Missing prices 

If no price report from a participating company has been received in a particular month, the change in the price of the 

associated item will, in general, be estimated by averaging 

the price changes for the other items within the same cell 

(i.e., for the same kind of products) for which price reports 

have been received. 

Rounding policy

Whenever rounding is performed to prepare PPI data for publication, indexes are rounded to the tenths decimal place. To 

derive monthly or annual average indexes, the BLS bases its 

calculations on unrounded data; index figures are rounded

during the final step only. Before 1991, annual averages for

index series based on commodity code data were calculated 

with the use of the rounded published indexes for the individual months. This is no longer the case. Annual averages 

for industry indexes always have been based on unrounded 

monthly indexes. When the BLS displays percent changes in 

association with any index data, the changes are calculated 

on the basis of the published rounded indexes. 

Seasonal adjustment

The PPI program publishes seasonally adjusted time-series 

data on a monthly basis. 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 series such that directly adjusted component indexes 

are combined into a higher level time series. 
11

Direct adjustment. The PPI program tests series that are 

eligible for direct seasonal adjustment, and if seasonality is 

found, the series are seasonally adjusted. Currently, six-digit, 

four-digit, and three-digit commodities are eligible for direct 

adjustment. Both seasonality testing and direct seasonal adjustment are accomplished with the use of X-13 ARIMA, a 

software package published by the U.S. Census Bureau for 

seasonal adjustment applications.8 Within X-13 ARIMA, PPI 

seasonal adjustments are based on the X-11 variant of the 

Census II seasonal adjustment method. The X-11 variant is a 

filter-based approach that employs moving averages to estimate trend and seasonal components in turn. Components are 

refined through several iterations of weighted moving averages. By default, X-13 ARIMA uses the multiplicative timeseries decomposition model 

Yt = Tt St It. 

In this model, Yt is the value of the observed series, Tt

represents the trend-cycle component, St denotes the seasonal 

component, and It is the irregular component. To enable the 

use of symmetric moving-average filters on a series, X-13

ARIMA uses an autoregressive integrated moving-average 

(ARIMA) modeling facility to forecast and backcast observations at the endpoints of the data.

The PPI program utilizes three primary measures—F(s), 

M7, and Q—to determine whether a particular PPI should be 

seasonally adjusted. F(s) is a measure of stable seasonality, 

M7 determines the amount of moving seasonality relative to 

the amount of stable seasonality, and Q is a weighted average 

of several diagnostic statistics. Indexes that are found to exhibit a level of seasonality warranting adjustment are directly 

adjusted by applying a seasonal factor to the unadjusted index according to the formula 

Is = Iu / SF × 100,

where 

Is is the seasonal index value, 

Iu is the unadjusted index value, and 

SF is the seasonal factor. 

Seasonal factors indicate the seasonal pattern of a time series and are derived from historical unadjusted data. PPI typically uses 8 years of unadjusted monthly data in developing 

factors and testing for seasonality. 

Intervention analysis. Nonseasonal events such as natural disasters or wars can distort the underlying seasonal pattern of 

an index. Intervention analysis entails estimating and removing the effects of these events from indexes prior to testing 

them for seasonality and developing seasonal factors. The 

goals of intervention analysis are to determine whether a seasonal pattern exists and to estimate seasonal factors correctly 

despite distortions that might arise in the pattern. The PPI 

program applies intervention analysis to selected directly adjusted indexes. Indexes are eligible for intervention analysis 

if they make up at least 1 percent of any of the following FD–

ID indexes: final-demand goods, final-demand services, unprocessed goods for intermediate demand, processed goods 

for intermediate demand, and services for intermediate demand. All PPIs that meet the relative importance criteria are 

examined for the possibility of intervention analysis. X-13 

automatic outlier detection, a regression-based program that 

searches for and identifies statistically significant intervention variables, is the primary tool used to select candidates. 

Each year, the program is run on all series that are eligible 

for intervention analysis. Indexes for which automatic outlier detection finds significant interventions are then analyzed

further as potential intervention candidates.

Indirect adjustment. The FD–ID indexes are indirectly seasonally adjusted by aggregating lower level indexes that are 

their components. Seasonally adjusted components are used 

when available (i.e., when the lower level index was shown 

to be seasonal and a seasonal index was calculated); otherwise, unadjusted indexes are used. In addition to the FD–ID

indexes, four- and three-digit commodity indexes that are aggregates of six-digit intervention indexes are indirectly adjusted. In this manner, interventions estimated for lower level 

indexes are indirectly included in their aggregate indexes. 

Yearly revisions and projected factors. Each year, with the 

release of the January data, PPI seasonal factors are recalculated to reflect price movements that occurred during the

just-completed calendar year. Seasonal factors are recalculated for the previous 5 years, and all seasonally adjusted 

data are updated on the basis of these new factors. After the 

yearly revision, directly adjusted PPIs for the upcoming year 

are calculated with the previous year’s set of seasonal factors.

Analysis and Presentation

Analysis

In 1978, with the transition to the SOP system, the PPI saw

its first major shift in analytical focus since the beginning

of the program. Prior to 1978, economic analysis of the PPI 

was focused on the All Commodities Index and the Industrial 

Commodities Index, as well as other indexes for highly aggregated major two- and three- digit commodity groupings. 

During the 1970s, when price changes were particularly volatile, it became clear that these indexes were subject to multiple-counting bias. Briefly, multiple-counting bias means that

price changes for components that go through many stages of 

processing have an excessive influence on aggregate index

series. The problem is common among highly aggregated PPI 

commodity groupings because their price changes are calculated from price changes of commodities at several stages 

of the production process, in which each individual price 

8

The reference manual for the ARIMA X-13 software package is available from the U.S. Census Bureau at http://www.census.gov/ts/x13as/

docX13AS.pdf. Full documentation, including access to the software package, is available at http://www.census.gov/srd/www/x13as/.
change is weighted by its total gross value of shipments in the 

weight-base year. The introduction of the SOP system partly

corrected this problem by creating the categories of finished

goods (completely processed goods destined for household 

consumption or capital investment) and crude goods (unprocessed goods to be used for further processing). The SOP index for intermediate goods, however, still included multiple 

counting, because many processed goods for intermediate 

(business) demand go though several levels of fabrication as 

materials, supplies, and components prior to their transformation to final-demand goods.

To illustrate the multiple-counting problem, suppose that 

the price of raw cotton rises sharply. If the price increase is 

passed through by spinners of cotton yarn and thread, then 

by weavers of gray cotton fabric, then by producers of finished cotton fabric, and, finally, by shirt manufacturers, the

single price increase for the raw cotton would have been 

included five times in the All Commodities Index and four

times in both the Industrial Commodities Index and the major commodity grouping index for textile products and apparel. Inasmuch as prices throughout the economy are always 

changing at different rates, multiple counting can result in 

rates of change for aggregated price indexes that are highly 

misleading, both because prices of raw materials tend to be 

more volatile than prices of final-demand goods and because

gross output values are used as weights for major commodity 

groups. Specific, detailed commodity indexes, such as sixand 8-digit commodity-based PPIs, are effectively free of this 

multiple-counting defect. 

The January 2014 transition of the PPI from the SOP system to the FD–ID system is arguably of greater significance

than the earlier shift to the SOP system. The FD–ID system

provides for a roughly 50-percent expansion of coverage 

related to final-demand goods, through the introduction of

indexes for government purchases of goods and exports of 

goods. The final-demand portion of this system also includes,

for the first time, indexes tracking changes in prices for services to final demand—trade services, transportation and

warehousing services, and other services for final demand—

as well as an index for final-demand construction.

Within the intermediate-demand portion of the FD–ID

system, two parallel treatments of intermediate demand are 

now provided. Under the commodity-type treatment of intermediate demand, the indexes for unprocessed goods for 

intermediate demand and processed goods for intermediate 

demand (formerly titled crude goods for further processing and intermediate materials, supplies, and components, 

respectively) are augmented with a set of indexes tracking 

services sold as intermediate demand: business purchases of 

trade, transportation and warehousing, and other services. An 

index tracking prices for maintenance and repair construction 

for intermediate demand also was introduced. 

The second treatment of intermediate demand is a production flow treatment based on a four-stage model. The production flow treatment of intermediate demand can be used

to study price transmission across four stages of production 

leading to final demand. The resulting system is constructed

in a manner that maximizes the forward flow of production

between stages while minimizing backflow.

Stage-4 producers produce primarily goods and services 

for final demand, and the stage-4 intermediate-demand index

measures price changes for products and services purchased 

by stage-4 producers. Stage-3 producers produce primarily 

goods and services for stage 4, and the stage-3 intermediate-demand index measures price changes for products and 

services purchased by stage-3 producers. Stage-2 producers 

produce primarily goods and services for stage 3, and the 

stage-2 intermediate-demand index measures price changes 

for products and services purchased by stage-2 producers. 

Finally, stage-1 producers produce primarily goods and services for stage 2, and the stage-1 intermediate-demand index 

measures price changes for products and services purchased 

by stage-1 producers. All inputs purchased by stage-1 industries are, by definition, produced either within stage 1 or by

later stages of processing, leaving stage 1 less useful for price 

transmission analysis.

The FD–ID system improves upon the SOP system in

several areas. First, the FD–ID system provides more complete coverage than the SOP system through the addition of

services, construction, exports, and government purchases. 

Providing more complete coverage allows for more accurate inflation analysis. Second, through stage creation and

net weighting, the production flow treatment of intermediate

demand eliminates the multiple-counting problems found in 

the processed-goods portions of intermediate demand within 

both the SOP and the FD–ID systems, again leading to more

accurate inflation analysis. Finally, the production flow treatment of intermediate demand facilitates potential price transmission analysis. 

Presentation

PPIs usually are issued in the second or third week of the 

month following the reference month. The specific monthly

release dates for a given year are posted to the BLS website prior to the beginning of that calendar year. All PPIs are 

available at the time of release, 8:30 a.m., and are considered 

officially published at that time. Data may be obtained over

the Internet or by contacting the PPI program staff.

In 1995, the BLS began posting PPI time-series data, news 

releases, and technical materials to its website (http://www.

bls.gov/ppi). The PPI home page provides mechanisms that 

permit users to download, in HTML, spreadsheet, or text format, nearly all current and discontinued PPI time-series data. 

The site also provides access to PPI news releases, which focus on major FD–ID categories and the commodity indexes

that lead changes in the FD–ID indexes. Over time, many PPI

reference files, as well as explanatory documents, also have

been added to the site.

The monthly PPI Detailed Report is available on the PPI 

website on the day PPI data are issued. This report includes 

most indexes within the PPI family of indexes that are not 

seasonally adjusted. The report also shows yearly percent 

changes, unadjusted monthly percent changes, and a few 

seasonally adjusted indexes and percent changes. In addi12
tion, the publication contains a narrative section explaining 

the most important price movements within the major FD–ID

categories for that month. When appropriate, special technical articles discuss the latest changes in the PPI sample (usually effective in January and July of each year), updates in 

seasonal adjustment factors or weights, or other changes in 

methodology or presentation. Occasionally, longer articles

provide more in-depth explanation of the economic background underlying recently observed price movements. The 

PPI Detailed Report does not include information on actual 

dollar prices for any item.

Seasonally adjusted data. Because price data are used for 

different purposes by different groups, the BLS publishes 

seasonally adjusted, as well as unadjusted, data each month. 

For economic analysis of short- to medium-term price trends, 

seasonally adjusted data usually are preferred because they 

are designed to eliminate the effect of changes that normally 

occur at about the same time and are about the same magnitude each year. Among such changes are price movements 

resulting from a host of factors, including normal weather 

patterns, regular production and marketing cycles, new-model-year motor vehicle introductions, seasonal discounts, and 

holidays. Data that are seasonally adjusted can therefore reveal long-term trends more clearly. 

Normally, over-the-month and quarterly analyses of PPI 

price movements are based on seasonally adjusted data. Unadjusted data are used for analysis when a series has not been 

selected for seasonal adjustment, as well as for the analysis of 

year-over-year trends. Because seasonal adjustment is a tool 

for enhancing economic analysis, indexes that BLS deemphasizes for the purpose of economic analysis are deliberately not seasonally adjusted. In particular, those PPIs which are 

subject to the multiple-counting problem described earlier, 

such as the All Commodities Index and the indexes for the 

major commodity groupings, are not available on a seasonally adjusted basis.

The unadjusted versions of PPI data are of primary interest to those who need information that can be more readily 

related to the dollar values of transactions. For example, unadjusted data typically are used in price escalation clauses of 

long-term sales or purchase contracts.

Revised data. All PPIs are routinely subject to revision 4 

months after their original publication, to reflect late reports

and corrections reported by company respondents. Once

revised, unadjusted indexes are considered final. (However,

seasonally adjusted indexes also are revised on an annual 

basis, as described in the next paragraph.) When PPIs are 

first released, they typically are based on a substantial

portion of the total number of returns that eventually will 

be received from respondents. Hence, subsequent revisions 

normally are minor, especially at the more highly aggregated 

grouping levels. Changes in previously published data 

caused by a processing error are indicated by a notice on the 

PPI website or in the PPI Detailed Report; such occurrences 

are rare. 

At the beginning of each year, the previous 5 years of 

seasonally adjusted data are recalculated to take into account more recent seasonal patterns from the just-completed 

calendar year. This revision is carried out in addition to the 

regular monthly revision to indexes 4 months after their 

original publication. As a result, seasonally adjusted indexes are recalculated five times, on an annual basis, until the

index is outside of the 5-year recalculation period. The revised 5-year histories for seasonally adjusted data are made 

available each year with the release of January data in midFebruary. 

Calculating index changes. Movements of price indexes 

from one month to another should be expressed as percent 

changes, rather than as changes in index points, because the 

latter are affected by the level of the index in relation to its 

base period while the former are not. Each index measures 

price changes from a reference period defined to equal 100.0.

Many commodity-oriented PPI series have an index base of 

1982 = 100, but many other indexes, including most FD–ID

indexes, began after 1982 and have a base date indicating the 

month and year of their introduction. The following example 

of the computation of index point change and 12-month percent change is based on the unadjusted PPI for final demand

for December 2010 and December 2011:

Index point change

December 2011 Final-Demand Price Index 106.0

Minus December 2010 Final-Demand Price Index 102.7

Equals index point change 3.3

Index percent change

Index point change 3.3

Divided by December 2010 Final-Demand Price Index 102.7

Equals 0.032

Multiplied by 100 0.032 × 100

Equals percent change 3.2

An increase of 10 percent from the base period (November 2009) for the Final-Demand Index would be shown as 

an index level of 110.0 and would be expressed in dollars 

as “Prices received by domestic producers of a systematic 

sample of final-demand products have risen from $100 in

November 2009 to $110 today.” Likewise, a current index 

of 90.0 would indicate that prices received by producers of 

final-demand products have fallen 10 percent from what they

were in the base period. 

From time to time, the BLS has updated the standard 

base period for PPIs. The last major index rebasing occurred in January 1988, when index base dates were recalculated to 1982 = 100; before that, 1967 was used as 

the standard base year. For reasons explained earlier, any 

change in the index base period leaves calculations of the 

percent changes for any index virtually unaffected. However, care must be taken to ensure that indexes referring 

to one base period are not incorrectly compared against 

indexes for the same series expressed with reference to a 

different base period.

13
14

Because prices for food and energy have tended to be erratic, over time some economists have come to focus attention on indexes that measure price changes for intermediatedemand and final-demand goods to the exclusion of food and

energy as clearer measures of what is sometimes referred to 

as the underlying rate of inflation. Among the goods indexes 

that exclude food and energy are the PPIs for final demand

less foods and energy, finished goods less foods and energy, processed goods less foods and energy, and unprocessed 

goods less foods and energy.

Uses and Limitations

Producer price indexes are used for many purposes by government, business, labor, universities, and other kinds of organizations, as well as by members of the general public. 

Economic indicator

Since 1978, the finished-goods index and other indexes 

that constitute the SOP system have been some of the

nation’s most closely watched indicators of economic 

health. Movements in SOP indexes have been used to

study the transmission of inflation though the economy, 

including the stages of production, and as a potential 

leading indicator of retail inflation as measured by the 

BLS Consumer Price Index. With its transition to the 

FD–ID system in January 2014, the PPI has expanded

coverage of the final-demand portion of the U.S. economy 

by roughly 300 percent by including indexes for goods 

sold to government and goods sold for export, as well as 

measuring the changes in prices for many services sold 

to households, to capital investment, to government, and 

for export. From an intermediate-demand perspective, the 

commodity-type treatment now tracks price movements 

for services purchased by businesses, in addition to 

price movements for processed and unprocessed goods, 

roughly doubling PPI coverage of the intermediatedemand portion of the economy. The production flow 

model of intermediate demand tracks price changes as 

they course through the various stages of production. 

This treatment provides an opportunity to systematically 

monitor and assess to what degree changes in rates of 

inflation faced by producers at earlier stages of production 

are transmitted to subsequent stages, including final 

demand. The commodity-type treatment also provides an 

opportunity to track inflation passthrough; however, this 

treatment, while less complicated, is also less systematic 

and rigorous in its construction. In sum, taken in its 

entirety, the FD–ID system is well suited for analyzing

the inflation transmission process.9

Although some users of price index data attempt to employ PPI data as a potential leading indicator of the CPI, there 

are many reasons that price movements in the PPI and the 

CPI can diverge. The differences can be classified into three

main areas: scope and coverage, categorization, and other 

technical differences.10

Scope and coverage differences. The scope of the personal 

consumption portion of the PPI includes all marketable output sold by domestic producers to the personal consumption 

sector of the economy. The scope of the CPI includes goods 

and services provided by business or government, paid for by 

consumers, and for which explicit user charges are assessed. 

The most heavily weighted item in the All Items CPI—owners’ equivalent rent—accounts for approximately 24 percent 

of the overall index. Owners’ equivalent rent is the implicit

rent that owner occupants would have to pay if they were 

renting their homes and is included in the CPI to capture the 

cost of shelter for owner-occupied housing units.11 The PPI 

does not include owners’ equivalent rent, which is out of 

scope because it is not a domestically produced, marketable 

output.

The CPI comprises goods and services purchased by 

domestic consumers and therefore includes imports. The PPI, 

in contrast, does not include imports, because imports are, 

by definition, not produced by domestic firms. Also, the CPI

includes only components of personal consumption that are 

directly paid for by the consumer, whereas the PPI includes 

components of personal consumption that are not paid for by 

the consumer. For example, the PPI includes medical services 

paid for by third parties, such as employers or the federal 

government. The CPI, in contrast, includes only payments 

made directly by consumers for medical care. The final

difference in scope between the PPI and CPI occurs in regard 

to services whose prices contain an interest rate component. 

The CPI excludes changes in interest rates or interest costs.12

The CPI includes some services, such as ATM banking 

services, whose prices have an interest rate component, but 

does not include the interest rate component of these services. 

The scope of the PPI, by contrast, encompasses services, such 

as banking services and insurance services, whose prices 

include an interest rate component. Thus, within the PPI, 

changes in interest rates will affect price indexes for banking 

and insurance, whereas changes in interest rates do not affect 

the CPI. 

In contrast to the CPI, the PPI does not currently have complete coverage of services. The PPI program began expanding coverage beyond mining, manufacturing, agriculture, and 

10The CPI program publishes three official CPIs: the CPI for All Urban

Consumers (CPI-U), the CPI for Urban Wage Earners and Clerical Workers 

(CPI-W), and the Chained CPI for All Urban Consumers (C-CPI-U). For a 

more detailed comparison of PPI with CPI and other government statistics, 

see Jonathan C. Weinhagen, “Comparing new final-demand producer price

indexes with other government price indexes,” Monthly Labor Review, January 2014. 11Spending to purchase and improve houses is considered investment 

and thus is not in the scope of the CPI. Investment in residential housing 

is in the scope of the PPI, but would be classified in the investment portion

of final demand, as opposed to the personal consumption component. As

of 2013, the PPI’s coverage of the construction sector of the economy was 

incomplete and did not include residential construction. 12See BLS Handbook of Methods, chapter 17, “The Consumer Price Index,” p. 3, http://www.bls.gov/opub/hom/pdf/homch17.pdf. 

9See Jonathan Weinhagen, “An empirical analysis of price transmission 

by stage of processing,” Monthly Labor Review, November 2002, pp. 3–11,

http://www.bls.gov/opub/mlr/2002/11/art1full.pdf.
15

utilities in the mid-1980s, introducing its first services price

index in 1985, and the program’s effort to expand coverage 

into the services sector of the economy is ongoing. In January 

2014, the PPI covered approximately 72 percent of services, 

as measured by revenue reported in the 2007 census. Because 

the PPI does not have complete coverage of services, a number of services included in the CPI are absent from the PPI for 

personal consumption. Among the most important of these 

services are education services, composing slightly over 3 

percent of the CPI, and residential rent, accounting for approximately 6.5 percent of the CPI. 

Categorization differences. The PPI and the CPI categorize a number of goods and services differently within their 

structures. Categorization differences for goods and services 

are mitigated at high levels of aggregation, but can create discrepancies at lower levels. The PPI, for example, considers 

utilities, such as electric power and natural gas, to be goods, 

while the CPI categorizes utilities as services. The two indexes also differ in their categorization and treatment of trade 

and transportation. The PPI generally separates the costs of 

transporting, retailing, and wholesaling of goods from the 

goods themselves and classifies trade and transportation as

services. In contrast, prices for goods, as measured by the 

CPI, typically include the value of the goods, the value of 

transporting the goods, and the trade margins associated with 

the sale of the goods. 

Other technical differences. Several technical differences 

also exist between the PPI and the CPI. The PPI and the CPIU are both constructed with the use of a modified Laspeyres

index formula, but the CPI updates weights every 2 years and 

the PPI updates weights every 5 years. In addition, the CPI 

implements a geometric mean formula at the item level that 

the PPI does not. The geometric calculation reduces substitution bias, leading to lower measures of inflation in periods of

price increases. The PPI attempts to collect prices for a specific day of the month (the Tuesday of the week containing

the 13th), while the CPI collects prices throughout the month. 

Finally, prices measured by the CPI include sales and excise 

taxes, while prices measured by the PPI exclude these taxes.13

Deflator

PPI data for capital equipment are used by the U.S. Department of Commerce to calculate the gross domestic product 

(GDP) deflator and many of its components. PPI data at all

levels of industry and commodity aggregation can be used 

to deflate dollar values expressed in current dollars to constant-dollar values for a variety of economic time series, such 

as inventories, sales, shipments, and capital equipment replacement costs. To illustrate the deflation concept, suppose

that nominal values of shipments for a given industry have 

doubled over a 10-year span. If the PPI for that industry has 

tripled over the same span, then the “real” (that is, inflationadjusted) value of shipments for the said industry actually has 

declined; in that case, higher prices would more than account 

for the doubling of dollar shipment values, and physical volume would implicitly have fallen. 

Private business uses

Private firms use PPI data to assist their operations in a variety of ways, in addition to using the data for general economic analysis or as a deflator of some other quantity. PPIs frequently are cited for price escalation purposes in long-term 

sales or purchase contracts as a means of protecting both the 

buyer and the seller from unanticipated surges or drops in 

prices. For example, an escalation clause might specify that 

the price for x number of widgets being sold by company 

A to company B each year will go up or down by a specified fraction of the percentage of change in material costs, as

measured by one or more specified PPIs (often in conjunction

with the change in a measure of labor costs, such as the Employment Cost Index). A PPI data user survey done in 2012 

suggests that trillions of dollars in contract values are tied to 

PPIs through these price escalation clauses, which are common in both government and private sector contracts.14

Companies also use PPI data to compare changes in material costs they incur against changes in the PPI for the material in question. By the same token, they can compare changes 

in the prices they charge for their own output with changes in 

the PPI for the same kind of product. PPI data are employed 

as well in econometric models, forecasting, market analysis, 

and academic research. PPIs are frequently used in last-in, 

first-out (LIFO) inventory accounting systems by firms wishing to avoid the kind of “phantom profits” that might appear

on their books with a first-in, first-out (FIFO) inventory accounting system.

Discontinued data

Those wishing to follow PPI data for a particular series over 

a prolonged timespan should be aware that the BLS is more 

likely to discontinue highly detailed indexes than aggregated 

indexes. During the industry resampling process, for example, an industry-level index commonly maintains continuity, 

but indexes for detailed products within that industry may 

be discontinued and replaced by indexes for items that are 

new or that previously had not been selected for tracking. 

Finely detailed indexes also may be vulnerable to temporary 

suspension of publication, due to low response rates. When a 

detailed index disappears, either temporarily or permanently, 

the BLS routinely recommends that users who had been following that index either choose another detailed index within 

the same product grouping or switch their attention to a more 

highly aggregated grouping index.

13 Ibid. 

14See Producer Price Indexes: Escalation Guide for Contracting Parties

(U.S. Bureau of Labor Statistics, May 14, 2012), http://www.bls.gov/ppi/

ppiescalation.htm.
Archibald, Robert B., “On the Theory of Industrial Price

Measurement: Output Price Indexes,” Annals of 

Economic and Social Measurement, winter 1977. 

Buszuwski, J. A., and S. Scott, “On the Use of Intervention

Analysis in Seasonal Adjustment,” in Proceedings of 

the Business and Economics Section (Alexandria, VA, 

American Statistical Association, 1988), pp. 337–342.

Doherty, Maureen P., “The Behavior of the Producer Price 

Index in a global economy,” Monthly Labor Review, 

September 2012, pp. 14–27, http://www.bls.gov/opub/

mlr/2012/09/art2full.pdf.

Early, John F., “Improving the measurement of producer price 

change,” Monthly Labor Review, April 1978, pp. 7–15.

Gousen, Sarah, Kathy Monk, and Irwin Gerduk, Producer 

Price Measurement: Concepts and Methods (U.S. 

Bureau of Labor Statistics, June 1986). 

Popkin, Joel, “Integration of a System of Price and Quantity Statistics with Data on Related Variables,” Review 

of Income and Wealth, March 1978, series 24, no. 1, pp. 

25–40.

Producer Price Indexes: Escalation Guide for Contracting 

Parties (U.S. Bureau of Labor Statistics, February 19, 

2014), http://www.bls.gov/ppi/ppiescalation.htm.

Producer Price Indexes: Hedonic Models in the Producer 

Price Indexes (PPI) (U.S. Bureau of Labor Statistics, 

June 2011), http://www.bls.gov/ppi/ppicomqa.htm.

Sager, Scott, “Effect of 1992 weights on Producer Price 

Indexes,” Monthly Labor Review, July 1996, pp. 13–23,

http://www.bls.gov/opub/mlr/1996/07/art2full.pdf. 

Technical References

Sinclair, James, and Brian Catron, “An experimental 

price index for the computer industry,” Monthly Labor 

Review, October 1990, pp. 16–24, http://www.bls.gov/

opub/mlr/1990/10/art2full.pdf. 

Tibbetts, Thomas R., “An Industrial Price Measurement 

Structure: The Universe Matrix of Producers and 

Products,” in 1978 Proceedings of the Section on 

Survey Research Methods (Washington, DC, American 

Statistical Association, 1979), pp. 511–516.

Weinhagen, Jonathan, “An empirical analysis of price 

transmission by stage of processing,” Monthly Labor 

Review, November 2002, pp. 3–11, http://www.bls.gov/

opub/mlr/2002/11/art1full.pdf.

Weinhagen, Jonathan C., “A new, experimental system of 

indexes from the PPI program,” Monthly Labor Review, 

February 2011, pp. 3–24, http://www.bls.gov/opub/

mlr/2011/02/art1full.pdf.

Weinhagen, Jonathan C., “Comparing new final-demand

producer price indexes with other government price 

indexes,” Monthly Labor Review, January 2014, http://

www.bls.gov/opub/mlr/2014/article/pdf/comparingnew-final-demand-producer-price-indexes-withother-government-price-indexes.pdf. 

Weinhagen, Jonathan C., and Bonnie H. Murphy, “New 

wherever-provided services and construction indexes for 

PPI,” Monthly Labor Review, August 2009, pp. 19–24,

http://www.bls.gov/opub/mlr/2009/08/art2full.pdf.

Weinhagen, Jonathan C., Jeffrey S. Wilson, and Steven M. 

Muri, “PPI and CPI seasonal adjustment: an update,” 

Monthly Labor Review, July 2010, pp. 10–20, http://

www.bls.gov/opub/mlr/2010/07/art2full.pdf.

16