- International Monetary Fund
- Published Date:
- September 2004
INTERNATIONAL MONETARY FUND
Producer Price Index Manual
Theory and Practice
International Labour Organization
International Monetary Fund
Organisation for Economic Co-operation and Development
United Nations Economic Commission for Europe
The World Bank
Copyright © 2004
International Bank for Reconstruction and Development / The World Bank
International Labour Organization
International Monetary Fund
Organisation for Economic Co-operation and Development
All rights reserved
Manufactured in the United States of America
Producer price index manual : theory and practice—[Washington, D.C.] : International
Monetary Fund, 2004.
Includes bibliographic references.
1. Wholesale price indexes—Handbooks, manuals, etc. I. International Monetary Fund.
Please send orders to:
International Monetary Fund, Publication Services
700 19th Street, N.W., Washington, D.C. 20431, U.S.A.
Tel.: (202) 623–7430 Telefax: (202) 623–7201
This Producer Price Index Manual replaces the United Nations’ Manual on Producers’ Price Indices for Industrial Goods issued in 1979 (Series M, No. 66). The development of the PPI Manual has been undertaken under the joint responsibility of five organizations—the International Labour Organization (ILO), International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), United Nations Economic Commission for Europe (UNECE), and World Bank—through the mechanism of an Inter-Secretariat Working Group on Price Statistics (IWGPS). It is published jointly by these organizations.
The Manuals contains detailed, comprehensive information and explanations for compiling a PPI. It provides an overview of the conceptual and theoretical issues that statistical offices should consider when making decisions on how to deal with the various problems in the daily compilation of a PPI, and it is intended for use by both developed and developing countries. The chapters cover many topics; they elaborate on the different practices currently in use, propose alternatives whenever possible, and discuss the advantages and disadvantages of each alternative. Given the comprehensive nature of the Manual, we expect it to satisfy the needs of many users.
The main purpose of the Manual is to assist producers of the PPI, particularly countries that are revising or setting up their PPI. The Manual draws on a wide range of experience and expertise in an attempt to describe practical and suitable measurement methods. It should also help countries to produce their PPIs in a comparable way, so that statistical offices and international organizations can make meaningful international comparisons. Because it brings together a large body of knowledge on the subject, the Manual may be used for self-learning or as a teaching tool for training courses on the PPI.
Other PPI users, such as businesses, policymakers, and researchers, make up another targeted audience of the Manual. The Manual will inform them not only about the different methods that are employed in collecting data and compiling such indices, but also about the limitations, so that the results may be interpreted correctly.
The drafting and revision process has required many meetings over a five-year period, in which PPI experts from national and international statistical offices, universities, and research organizations have participated. The Manual owes much to their collective advice and wisdom.
The electronic version of the Manual is available on the Internet at www.imf.org. The IWGPS views the Manual as a “living document” that it will amend and update to address particular points in more detail. This is especially true for emerging discussions and recommendations made by international groups reviewing the PPI, such as the International Working Group on Service Sector Statistics (the Voorburg Group) and the International Working Group on Price Indices (the Ottawa Group).
The IWGPS welcomes users’ comments on the Manual, which should be sent to the IMF Statistics Department (e-mail: TEGPPI@imf.org). They will be taken into account in any future revisions.
A. Sylvester Young
International Labour Organization
Rodrigo de Rato
International Monetary Fund
Organisation for Economic Co-operation and Development
United Nations Economic Commission for Europe
Development Data Group
The ILO, IMF, OECD, UNECE, and World Bank, together with experts from a number of national statistical offices and universities, have collaborated since 1998 in developing this Producer Price Index Manual. In addition, these organizations have consulted with a large number of potential users of the PPI Manual to get practical input. The developing organizations endorse the principles and recommendations contained in this Manual as good practice for statistical agencies in conducting a PPI program. Because of practical constraints, however, some of the current recommendations may not be immediately attainable by all statistical offices and, therefore, should serve as guideposts for agencies as they revise and improve their PPI programs. In some instances, there are no clear-cut answers to specific index number problems such as specific sample designs, the appropriate index estimation formula to use with given data inputs, making adjustments for quality changes, and handling the appearance of new products. Statistical offices will have to rely on the underlying principles laid out in this Manual and economic and statistical theory to derive practical solutions.
A. Producer Price Indices
PPIs measure the rate of change in the prices of goods and services bought and sold by producers. An output PPI measures the rate of change in the prices of products sold as they leave the producer. An input PPI measures the rate of change in the prices of the inputs of goods and services purchased by the producer. A value-added PPI is a weighted average of the two.
The PPI Manual serves the needs of different audiences. On the one hand are the compilers of PPIs. This Manual and other manuals, guides, and handbooks are important to compilers for several reasons. First, there is a need for countries to compile statistics in comparable ways so they can make reliable international comparisons of economic performance and behavior using the best international practices. Second, statisticians in each country should not have to decide on methodological issues alone. The Manual draws on a wide range of experience and expertise in an attempt to outline practical and suitable measurement methods and issues. Such measurement methods and issues are not always straightforward, and the Manual benefits from recent theoretical and practical work in the area. Third, much of the written material in some areas of PPI measurement covers a range of publications. This Manual brings together a large amount of what is known on the subject. It may therefore be useful for reference and training. Fourth, the Manual provides an independent reference on methods against which a statistical agency’s current methods, and the case for change, can be assessed. The Manual should serve the needs of users. Users should be aware not only of the methods employed by statistical offices in collecting data and compiling the indices, but also of the potential such indices have for errors and biases, so that users can properly interpret the results. For example, index number theory presents many issues on formula bias, and the Manual deals extensively with the subject.
Collecting data for PPIs is not a trivial matter. In practical terms, PPIs require sampling, from a representative sample of establishments, a set of well-defined products whose overall price changes are representative of those of the millions of transactions taking place. Statistical offices then monitor the prices of these same products on a periodic basis (usually monthly) and weight their price changes according to their net revenue. However, the quality of the commodities produced may be changing, with new establishments and commodities appearing and old ones disappearing on both a seasonal and permanent basis. Statistical offices need to closely monitor potential changes in quality. Yet the index compilers must complete the task of producing a representative index monthly, in a timely manner.
It is also important to have a well-developed theoretical basis for compiling such indices that is readily accessible for practitioners and users alike. There should be a firm understanding of user needs and how the index delivered fits both. Fortunately, there is a great body of research in this area, much of which is fairly recent. This Manual covers the theoretical basis of index numbers to help support some of the practical considerations.
This Manual provides guidelines for statistical offices or other agencies responsible for compiling a PPI, bearing in mind the limited resources available. Calculating a PPI cannot be reduced to a simple set of rules or a standard set of procedures that can be mechanically followed in all circumstances. Although there are certain general principles that may be universally applicable, the procedures followed in practice have to take account of particular circumstances. Statistical offices have to make choices. These include procedures for the collection or processing of the price data and the methods of aggregation. Other important factors governing methodology are the main use of the index, the nature of the markets and pricing practices within the country, and the resources available to the statistical office. The Manual explains the underlying economic and statistical concepts and principles needed to enable statistical offices to make their choices in efficient and cost-effective ways and to recognize the full implications of their choices.
The Manual draws on the experience of many statistical offices throughout the world. The procedures they use are not static but continue to evolve and improve, for a variety of reasons. First, research continually refines the economic and statistical theory underpinning PPIs and strengthens it. For example, recent research has provided clearer insights about the relative strengths and weaknesses of the various formulas and methods used to process the basic price data collected for PPI purposes. Second, recent advances in information and communications technology have affected PPI methods. Both theoretical and data developments can impinge on all the stages of compiling a PPI. New technology can affect the methods used to collect prices and relay them to the central statistical office. It can also improve the processing and checking, including the methods used to adjust prices for changes in the quality of the goods and services covered. Finally, improved formulas help in calculating more accurate higher-level indices.
B. Background to the Present Revision
Some international standards for economic statistics have evolved mainly to compile internationally comparable statistics. However, standards may also be developed to help individual countries benefit from the experience and expertise accumulated in other countries. All countries stand to gain by exchanging information about index methods. The UN published the existing Manual on Producers’ Price Indices for Industrial Goods (United Nations, 1979) over 25 years ago. The methods and procedures presented then are now outdated. Index number theory and practice and improvements in technology have advanced greatly over the past two decades.
B.1 Concerns with current index methods
The PPI Manual takes advantage of the wealth of recent research on index number theory. It recommends many new practices instead of just codifying existing statistical agency practices. There are a number of reasons for this.
First, the standard methodology for a typical PPI is based on a Laspeyres price index with fixed quantities from an earlier base period. The construction of this index can be thought of in terms of selecting a basket of goods and services representative of base-period revenues, valuing this at base-period prices, and then repricing the same basket at current-period prices. The target PPI in this case is defined to be the ratio of these two revenues. Practicing statisticians use this methodology because it has at least three practical advantages. It is easily explained to the public, it can use often expensive and untimely weighting information from the date of the last (or an even earlier) survey or administrative source (rather than requiring sources of data for the current month), and it need not be revised if users accept the Laspeyres premise. One notable advantage of the Laspeyres approach under the ease of explanation heading is its consistency in aggregation. It produces various breakdowns or subaggregates related to one another in a particularly simple way.
Statistical agencies implement the Laspeyres index by putting it into price-relative (price change from the base period) and revenue-share (from the base period) format. In this form, the Laspeyres index can be written as the sum of base-period revenue shares of the items in the index times their corresponding price relatives. Unfortunately, simple as it may appear, there still are a number of practical problems with producing the Laspeyres index exactly. Consequently, statistical agency practice has introduced some approximations to the theoretical Laspeyres target.
Until recently it has been impossible to get accurate revenue shares for the base period down to the finest level of commodity aggregation, so statistical agencies settle for getting base-period revenue weights at the level of 100 to 1,000 products.
For each of the chosen product aggregates, agencies collect a sample of representative prices for specific transactions from establishments rather than attempting to enumerate every possible transaction. They use equally weighted (rather than revenue-weighted) index formulas to aggregate these elementary product prices into an elementary aggregate index, which will be used as the price relative for each of the 100 to 1,000 product groups in the final Laspeyres formula. Practitioners recognize that this two-stage procedure is not exactly consistent with the Laspeyres methodology (which requires weighting at each stage of aggregation). However, for a number of theoretical and practical reasons, practitioners judge that the resulting elementary index price relatives will be sufficiently accurate to insert into the Laspeyres formula at the final stage of aggregation.
The above standard index methodology dates back to the work of Mitchell (1927) and Knibbs (1924) and other pioneers who introduced it about 80 years ago, and it is still used today.
Although most statistical agencies have traditionally used the Laspeyres index as their target index, both economic and index number theory suggest that some other types of indices may be more appropriate target indices to aim for: namely, the Fisher, Walsh, or Törnqvist-Theil indices. As is well known, the Laspeyres index has an upward bias compared with these target indices. Of course, these target indices may not be achievable by a statistical agency, but it is necessary to have some sort of theoretical target to aim for. Having a target concept is also necessary, so that the index that is actually produced by a statistical agency can be evaluated to see how close it comes to the theoretical ideal. In the theoretical chapters of this Manual, it is noted that there are four main approaches to index number theory:
(1) Fixed-basket approaches and symmetric averages of fixed baskets (Chapter 15);
(2) The stochastic (statistical estimator) approach to index number theory (Chapter 16);
(3) Test (axiomatic) approaches (Chapter 16); and
(4) The economic approach (Chapter 17).
Approaches 3 and 4 will be familiar to many price statisticians and expert users of the PPI, but perhaps a few words about approaches 1 and 2 are in order.
The Laspeyres index is an example of a fixed-basket index. The concern from a theoretical point of view is that it has an equally valid “twin” for the two periods under consideration—the Paasche index, which uses quantity weights from the current period. If there are two equally valid estimators for the same concept, then statistical theory tells us to take the average of the two estimators in order to obtain a more accurate estimator. There is more than one way of taking an average, however, so the question of the “best” average to take is not trivial. The Manual suggests that the “best” averages that emerge for fixed-base indices are the geometric mean of the Laspeyres and Paasche indices (Fisher ideal index) or the geometric average of the quantity weights in both periods (Walsh index). From the perspective of a statistical estimator, the “best” index number is the geometric average of the price relatives weighted by the average revenue share over the two periods (Törnqvist-Theil index).
There is one additional result from index number theory that should be mentioned here—the problem of defining the price and quantity of a product that should be used for each period in the index number formula. The problem is that the establishment may have sales for a particular product specification in the period under consideration at a number of different prices. So the question arises, what price would be most representative of the sales of this transaction for the period? The answer to this question is obviously the unit value for the transaction for the period, since this price will match up with the quantity sold during the period to give a product that is equal to the value of sales.1
Now consider concerns about the standard PPI methodology. There are six main areas of concern with the standard methodology:2
(1) At the final stage of aggregation, the standard PPI index is not a true Laspeyres index, since the revenue weights pertain to a reference base year that is different from the base month (or quarter) for prices. Thus the expenditure weights are chosen at an annual frequency, whereas the prices are collected at a monthly frequency. To be a true Laspeyres index, the base-period revenues should coincide with the reference period for the base prices. In practice, the actual index used by many statistical agencies at the last stage of aggregation has a weight reference period that precedes the base-price period. Indices of this type are likely to have some upward bias compared with a true Laspeyres index, especially if the expenditure weights are priceupdated from the weight reference period to the Laspeyres base period. It follows that they must have definite upward biases compared with theoretical target indices such as the Fisher, Walsh, or Törnqvist-Theil indices.
(2) At the early stages of aggregation, unweighted averages of prices or price relatives are used. Until relatively recently, when enterprise data in electronic form have become more readily available, it was thought that the biases that might result from the use of unweighted indices were not particularly significant. However, recent evidence suggests that there is potential for significant upward bias at lower levels of aggregation compared with results that are generated by the preferred target indices mentioned above.
(3) The third major concern with the standard PPI methodology is that, although statistical agencies generally recognize that there is a problem with the treatment of quality change and new goods, it is difficult to work out a coherent methodology for these problems in the context of a fixed-base Laspeyres index. The most widely received good practice in quality-adjusting price indices is “hedonic regression,” which characterizes the price of a product at any given time as a function of the characteristics it possesses relative to its near substitutes. In fact, there is a considerable amount of controversy on how to integrate hedonic regression methodology into the PPI’s theoretical framework. The theoretical and practical chapters in the Manual devote a lot of attention to these methodological problems. The problems created by the disappearance of old goods and the appearance of new models are now much more severe than they were when the traditional PPI methodology was developed some 80 years ago (then, the problem was mostly ignored). For many categories of products, those priced at the beginning of the year are simply no longer available by the end of the year. Thus, there is a tremendous concern with sample attrition, which impacts on the overall methodology; that is, at lower levels of aggregation, it becomes necessary (at least in many product categories) to switch to chained indices rather than use fixed-base indices. Certain unweighted indices have substantial bias when chained.
(4) A fourth major area of concern is related to the first concern: the treatment of seasonal commodities. The use of an annual set of products or the use of annual revenue shares is justified to a certain extent if one is interested in the longer-run trend of price changes. If the focus, however, is on short-term, month-to-month changes (as is the focus of central banks), then it is obvious that the use of annual weights can lead to misleading signals from a short-run perspective, since monthly price changes for products that are out of season (i.e., the seasonal weights for the product class are small for the two months being considered) can be greatly magnified by the use of annual weights. The problem of seasonal weights is a big one when the products are not available at all at certain months of the year. There are solutions to these seasonality problems, but the solutions do not appeal to traditional PPI statisticians because they involve the construction of two indices: one for the short-term measurement of price changes and another (more accurate) longer-term index that is adjusted for seasonal influences.
(5) A fifth concern with standard PPI methodology is the general exclusion of services from the PPI framework. A typical PPI will include mining, manufacturing, electricity, gas supply, and water supply activities, normally referred to as an industrial PPI. Many countries may also include agricultural prices. Thus, PPI coverage includes many more goods-producing activities than services. In a way, this just reflects the historical origins of existing PPI theory. National PPIs have essentially been concerned with coverage of goods for 80 years, but 80 years ago goods were much more significant than services. Hence, there was not much focus on the problems involved in measuring services. It is only over the past 30 or so years that the shift to services has caused services output to exceed output of goods. In addition to inertia, there are some serious conceptual problems involved in measuring the prices of many services. Some examples of difficult-to-measure services are insurance, gambling, financial services, advertising services, telecommunication services (with complex plans), entertainment services, and trade. In many cases, statistical agencies simply do not have appropriate methodologies to deal with these difficult conceptual measurement problems. Thus, output prices for these service sector PPIs are not widely measured.3
(6) A final concern with existing PPI methodology is that it tends not to recognize that more than one PPI may be required to meet the needs of different users. There are three basic types of PPIs that users might want: gross output price indices, intermediate input price indices, and value-added price indices. Most countries concentrate on producing output price indices by product and industry, with little attention given to input price indices. Another example for multiple indices is gross output indices versus net sector indices. Aggregating industry or product gross output indices includes double-counting the effects of input price changes—the input price change effects are included in both the originating sector and the using sector indices. Net sector indices exclude interindustry price effects and are, therefore, a better gauge of the evolution of inflation through the production chain. In addition, some users may require information on the month-to-month movement of prices in a very timely fashion. This requirement leads to a fixed-weight PPI along the lines of existing PPIs, where current information on weights is not necessarily available. However, other users may be more interested in a more accurate or representative measure of price change and may be willing to sacrifice timeliness for increased accuracy. Thus, statistical agencies might produce one of the theoretical target indices (e.g., Fisher, Walsh, or Törnqvist-Theil) that uses current- and base-period weight data with a delay of a year or two. These are entirely reasonable developments, recognizing that different users have different needs. Since all three approaches have strong support, it would be reasonable for a statistical agency to pick one approach for its flagship index but make available the other two treatments as “analytical series” for interested users. Another example where multiple indices would be useful occurs in the context of seasonal products. The usual PPI is a month-to-month index, and it is implicitly assumed that all products are available in each month. As noted in item (4) above, this assumption is not warranted. In this context, a month-to-month PPI will not be as “accurate” as a year-over-year PPI that compares the prices of products in this month with the corresponding products in the same month a year ago. Again, the need emerges for multiple indices to cater to the needs of different users.
Many of the above areas of concern are addressed in this PPI Manual. Frank discussions of these concerns should stimulate the interest of academic economists and statisticians to address these measurement problems and to provide new solutions that can be used by statistical agencies. Public awareness of these areas of concern should lead to a willingness on the part of governments to allocate additional resources to statistical agencies so that economic measurement will be improved. In particular, there is an urgent need to fill in some of the gaps that exist in the measurement of service sector outputs.
B.2 Efforts to address the concerns in index number methods
Several years ago it became clear that the outstanding and controversial methodological concerns related to price indices needed further investigation and analysis. An expert group consisting of specialists on price indices from national and international statistical offices and universities from around the world formed to discuss these concerns. It met for the first time in Ottawa in 1994. During six meetings between 1994 and 2001, the Ottawa Group presented and discussed over a hundred research papers on the theory and practice of price indices. While much of the research related to consumer price indices (CPIs), many of the issues carried across to PPIs. It became obvious there were ways to improve and strengthen existing PPI and CPI methods.
In addition, the Voorburg Group on Service Sector Statistics, with members from many national statistical offices, has held annual meetings for over a decade. Many agenda topics of the Voorburg Group related to expanding country PPIs to cover service industries and products. The Group has provided many technical papers on concepts and methods for compiling service PPIs. These papers serve as documentation that other countries can follow.
At the same time, the control of inflation had become a high-priority policy objective in most countries. Policymakers use both the CPI and PPI widely to measure and monitor inflation. The slowing down of inflation in many parts of the world in the 1990s, compared with the 1970s and 1980s, increased interest in PPI and CPI methods rather than reduce it. There was a heightened demand for more accurate, precise, and reliable measures of inflation. When the rate of inflation slows to only 2 or 3 percent each year, even a small error or bias becomes significant.
Recent concern over the accuracy of price indices led governments and research institutes in a few countries to commission experts to examine and evaluate the methods used, particularly for the CPI. The methods used to calculate CPIs and PPIs have been subject to public interest and scrutiny of a kind and level that were unknown in the past. One conclusion reached is that existing methods might lead to some upward bias in both the CPI and PPI. One reason for this was that many goods and services had inadequate allowance for improvements in their quality. The direction and extent of such bias will, of course, vary between commodity groups, and its total effect on the economy will vary among countries. However, the upward bias has the potential to be large, so this Manual addresses adjusting prices for changes in quality in some detail, drawing on the most recent research in this area. There are other sources of bias including that arising from no allowance, or an inappropriate one, made for changes in the bundle of items produced, when production switches between commodities with different rates of price change. Further, different forms of bias might arise from the sampling and price collection systems. Several chapters deal with these subjects, with an overall summary of possible errors and biases given in Chapter 11.
CPIs are widely used for the index linking of social benefits such as pensions, unemployment benefits, and other government payments. The cumulative effects of even a small bias could have notable longterm financial outcomes for government budgets. Similarly, a major use of PPIs is as an escalator for price adjustments to long-term contracts. Agencies of government, especially ministries of finance, and private businesses have taken a renewed interest in price indices, examining their accuracy and reliability more closely and carefully than in the past.
In response to the various developments outlined above, the need to revise, update, and expand the UN manual was gradually recognized and accepted during the late 1990s. The joint UNECE/ILO meeting of national and international experts on CPIs held at the end of 1997 in Geneva made a formal recommendation to revise Consumer Price Indices: An ILO Manual (ILO, 1989). The main international organizations interested in measuring inflation have taken responsibility for the revision. The United Nations Statistical Commission in 1998 approved this strategy and agreed to set up the Intersecretariat Working Group on Price Statistics (IWGPS).
C. Organization of the Revision
C.1 Agencies responsible for the revision
The following international organizations—concerned with measuring inflation, with policies designed to control inflation, and with measurement of deflators for national accounts—have collaborated on revising the CPI and PPI Manual:
The International Labour Organization (ILO);
The International Monetary Fund (IMF);
The Organisation for Economic Co-operation and Development (OECD);
The Statistical Office of the European Communities (Eurostat);
The UN Economic Commission for Europe (UNECE); and
The World Bank.
These organizations have provided, and continue to provide, technical assistance on CPIs and PPIs both to developing countries and to countries in transition from planned to market economies. They joined forces for the present revision of the CPI and PPI Manuals, setting up the IWGPS for this purpose. The group’s role was to organize and manage the process rather than act as an expert group.
The responsibilities of the IWGPS were as follows:
To appoint the various experts on price indices who shared in the revision either as members of the Technical Expert Group (who provided substantive advice on the contents of the Manual) or as authors of the various chapters;
To provide the financial and other resources needed;
To arrange meetings of the Technical Expert Group, prepare the agendas, and write the reports on the meetings; and
To arrange for the publishing and disseminating of the two Manuals
Members of the IWGPS were also members of the Technical Expert Groups. The experts taking part in the Technical Expert Groups were invited in their personal capacity as experts and not as representatives, or delegates, of the national statistical offices or other agencies that employed them. Participants were able to give their expert opinions without in any way committing the offices from which they might have come.
C.2 Links with the new Consumer Price Index Manual
One of the first decisions of the IWGPS was to produce a new international PPI Manual at the same time as the CPI Manual. There have been international standards for CPIs for over 70 years, but the UN’s 1979 PPI manual was the first international manual on producer prices. Despite the importance of PPIs for measuring and analyzing inflation, the methods used for compiling them have been comparatively neglected, at both national and international levels.
The IWGPS set up two Technical Expert Groups, one for each Manual, whose membership overlapped. The two manuals have similar contents and are fully consistent with each other conceptually, sharing common text when suitable. The two groups worked in close liaison with each other. The PPI and CPI methods have a lot in common. Both use essentially the same underlying economic and statistical theory, except that the CPI draws on the economic theory of consumer behavior, whereas the PPI draws on the economic theory of production. However, the two economic theories are isomorphic and lead to the same kinds of conclusions about index number compilation. The Manuals have practical and operational applications (Chapters 1–13 and the Glossary) that are supported by their theoretical underpinnings (Chapters 14–22).
Most members of the Technical Expert Groups on CPIs and PPIs also engaged as active members of the Ottawa Group. The two Manual were able to draw on the contents and conclusions of all the numerous papers presented at meetings of the Ottawa and Voorburg Groups.
The PPI Manual is the result of a five-year process that involved multiple activities. The first activities were the development of the Manual outline and the recruitment of individuals to draft the various chapters. Next, members of the Technical Expert Group on the PPI (TEG-PPI), the IWGPS, and others refereed the draft chapters. Then came the posting of the draft chapters on a PPI Manual website for comment by interested individuals and organizations. The final steps were consultation with a focus group of selected users from national statistical offices. Final copyediting of the Manual was coordinated in the IMF External Relations Department by James McEuen. The editor wishes to thank Mbaye Gueye for assistance in the final review of the Manual and all of those involved in the process, with special recognition for the following:
The author, or authors, of the chapters (with their affiliations).
Paul Armknecht (PPI Manual editor, IMF), W. Erwin Diewert (University of British Columbia), Peter Hill (CPI Manual editor, expert)Reader’s Guide
Paul Armknecht (IMF), Peter Hill (expert)Chapter 1
Paul Armknecht (IMF), David Collins (Australian Bureau of Statistics), Peter Hill (expert)Chapter 2
Andrew Allen (U.K. Office of National Statistics), Paul Armknecht (IMF), David Collins (Australian Bureau of Statistics)Chapter 3
Paul Armknecht (IMF), Irwin Gerduk (U.S. Bureau of Labor Statistics)Chapter 4
Paul Armknecht (IMF)Chapter 5
Paul Armknecht (IMF), Fenella Maitland-Smith (OECD)Chapter 6
Andrew Allen (U.K. Office of National Statistics), David Collins and Matthew Berger (Australian Bureau of Statistics)Chapter 7
Mick Silver (Cardiff University)Chapter 8
Mick Silver (Cardiff University)Chapter 9
Carsten B. Hansen (Denmark Central Bureau of Statistics), Peter Hill (expert), Robin Lowe (Statistics Canada), Mick Silver (Cardiff University)Chapter 10
Dennis Fixler (editor, U.S. Bureau of Economic Analysis); contributions from Australian Bureau of Statistics, Statistics Canada, Statistics Singapore, and U.S. Bureau of Labor StatisticsChapter 11
Mick Silver (Cardiff University)Chapter 12
David Fenwick (U.K. Office of National Statistics), Yoel Finkel (Israel Central Bureau of Statistics)Chapter 13
Paul Armknecht (IMF), Tom Griffin (expert)Chapter 14
Kimberly Zieschang (IMF)Chapter 15
W. Erwin Diewert (University of British Columbia), Paul Armknecht (IMF)Chapter 16
W. Erwin Diewert (University of British Columbia)Chapter 17
W. Erwin Diewert (University of British Columbia)Chapter 18
W. Erwin Diewert (University of British Columbia)Chapter 19
W. Erwin Diewert (University of British Columbia)Chapter 20
W. Erwin Diewert (University of British Columbia), Mick Silver (Cardiff University)Chapter 21
Mick Silver (Cardiff University), W. Erwin Diewert (University of British Columbia)Chapter 22
W. Erwin Diewert (University of British Columbia), Paul Armknecht (IMF)Glossary
David Roberts (OECD), Paul Schreyer (OECD)Glossary Appendix
Bert Balk (Statistics Netherlands, Appendix).
The individual members of the IWGPS and the TEG-PPI.
IWGPS: Organizational membership is as follows: Eurostat, ILO, IMF, OECD, UNECE, and World Bank. During the revision of the Manual, the CPI Manual editor (Peter Hill), TEG-CPI chairperson (David Fenwick), and PPI Manual editor and TEG-PPI chairperson (Paul Armknecht) were observers. The ILO was the Secretariat for the Group, and Sylvester Young the chairperson of the IWGPS.
The IWGPS met formally four times: September 24, 1998 (Paris), February 11, 1999 (Geneva), November 2, 1999 (Geneva), and March 21–22, 2002 (London). Informal meetings were held on several occasions.
TEG-PPI: Andrew Allen (U.K. Office of National Statistics), Paul Armknecht (chair, IMF), Bert Balk (Statistics Netherlands), Matthew Berger* (Australian Bureau of Statistics), David Collins* (Australian Bureau of Statistics), W. Erwin Diewert (University of British Columbia), Yoel Finkel (Israel Central Bureau of Statistics), Dennis Fixler (U.S. Bureau of Economic Analysis), Irwin Gerduk (U.S. Bureau of Labor Statistics), Jan Karlsson (UNECE), Robin Lowe (Statistics Canada), Richard McKenzie* (Australian Bureau of Statistics), David Roberts (OECD), Paul Schreyer (OECD), Mick Silver (Cardiff University), and Kimberly Zieschang (IMF). The IMF was the Secretariat for the Group.
The TEG-PPI met five times: November 2–3, 1999 (Geneva), September 20–22, 2000 (Madrid), October 29–30, 2001 (Geneva), March 19–21, 2002 (London), and February 25–27, 2003 (Washington, D.C.).4
The participants of a focus group seminar on the PPI Manual in Pretoria, South Africa.
The IMF Statistics Department and Statistics South Africa, supported by funding from the government of Japan through the Administered Account for Selected Fund Activities—Japan and the OECD Centre for Co-operation with Non-Member Countries, held a seminar with selected user agencies during June 23–27, 2003. Participants provided excellent feedback on the usefulness of the new Manual and made many good suggestions for improvements. The participants in the seminar and their affiliated agencies were Adnan Badran (Jordan Department of Statistics), Langa Benson (Statistics South Africa), Gustavo Javier Biedermann (Central Bank of Paraguay), Bikash Bista (Nepal Central Bureau of Statistics), Juleeemun Dhananjay (Mauritius Central Bureau of Statistics), Istvan Kölber (Hungarian Central Statistics Office), Inga Kunstvere (Latvia Central Bureau of Statistics), Phaladi Labobedi (Botswana Central Bureau of Statistics), Guergana Maeva (Bulgarian National Institute of Statistics), Moffat Malepa (Botswana Central Bureau of Statistics), Gopal Singh Negi (Indian Ministry of Commerce and Industry), Ali Rosidi (Statistics Indonesia), Matti Särngren (Statistics Sweden), Joy Sawe (Tanzanian National Bureau of Statistics), Soon Teck Wong (Statistics Singapore), Harry Thema (Statistics South Africa), and Bouchaib Thich (Morocco Direction de la Statistique).
Note that the Manual does not endorse taking unit values over heterogeneous items at this first stage of aggregation; it endorses only taking unit values over identical items in each period.
These problems are not ranked in order of importance; they all seem equally important.
The Voorburg Group, which meets annually, has included the expansion of PPIs to services as part of its work program. The OECD, as part of its contribution to this program, conducts periodic surveys on the extension of PPIs in services activities. The latest survey results along with developments in services statistics are available at http://www.oecd.org/document/43/0,2340, en_2649_34355_2727403_1_1_1_1,00.html.
Individuals with an asterisk (*) after their name served for only part of the period.
International Manuals in economic statistics have traditionally provided guidance about concepts, definitions, classifications, coverage, valuation, recording data, aggregation procedures, formulas, andso on. They have mainly aided compilers of the relevant statistics in individual countries. This Manuals hares this same principal objective.
The Manual will benefit users of PPIs, such as government and academic economists, financial experts, and other informed users. The PPI is a key statistic for policy purposes. It attracts much attention from the media, governments, and the public in most countries. The PPI is a sophisticated concept that draws on a great deal of economic and statistical theory and requires complex data manipulation. This Manual is therefore also intended to promote greater understanding of the properties of PPIs.
In general, compilers and users of economic statistics must have a clear view of what the statistics measure, in principle. Measurement without theory is unacceptable in economics, as in other disciplines. This Manual therefore contains a thorough, comprehensive, and up-to-date survey of relevant economic and statistical theory. This makes the Manual self-contained in both the theory and practice of PPI measurement.
The Manual, consequently, is large. Because different readers may have different interests and priorities, it is not possible to devise a sequence of chapters that suits all. Indeed, users do not read international Manuals from cover to cover in that order. Manuals also serve as reference works. Many readers may have interest in only a selection of chapters. The purpose of this Reader’s Guide is to provide a map of the contents of the Manual that will aid readers with different interests and priorities.
A. An Overview of the Sequence of Chapters
As mentioned in the preface, the chapters of this Manual are arranged so that practical and operational issues (Chapters 1–13 and the Glossary) are supported by theoretical underpinnings (Chapters 14–22). Specifically, the Manual is divided into four parts:
The remaining paragraphs in this section give synopses of the individual chapters.
A.1 Part I: Methodology, uses, and coverage
Chapters 1 is a general introduction to the theory and practice of PPIs. It is intended for all readers. It provides the basic information needed to understand the later chapters and a summary of index number theory, as explained in much more detail in Chapters 15–20. It then provides a summary of the main steps involved in compiling a PPI, drawing on material in Chapters 3–9. It does not provide a summary of the Manual as whole nor does it cover specific topics or special cases that are not of general relevance.
Chapters 2 outlines the history of price indices and how PPIs have changed in response to the demand for broader measures of price change. Chapters 3 presents a few basic concepts, principles, classifications, and the scope or coverage of an index. The scope of a PPI can vary significantly from country to country.
A.2 Part II: Compilation issues
Chapters 4–9 form an interrelated sequence of chapters describing the various steps involved in compiling a PPI, from collecting and processing the price data through calculating the final index. Chapters 4 discusses deriving the value weights attached to the price changes for different goods and services. Establishment censuses or surveys supplemented by data from other sources typically provide the weight data.
Chapters 5 deals with sampling issues. A PPI is essentially an estimate based on a sample of the prices of products produced by a sample of establishments. Chapters 5 considers sampling design and the pros and cons of random versus purposive sampling. Chapters 6 describes the procedures used to collect the prices from a selection of establishments and products. It deals with topics such as questionnaire design, specifying the transactions selected, and methods for collecting data, including the use of electronic media.
Chapters 7 addresses the difficult question of how to adjust prices for changes over time in the quality of the goods or services selected. Changes in value due to changes in quality count as changes in quantity not price. Disentangling the effects of quality change poses serious theoretical and practical problems for compilers. Chapters 8 addresses two closely related questions: first, how to deal with goods and services that disappear from the sample; second, how new goods or services not previously produced can enter the sample.
Chapters 9 gives a step-by-step description of editing procedures, calculating elementary price indices from the raw prices collected for small groups of products, and the resulting averaging of the elementary indices to obtain indices at various levels of aggregation up to the overall PPI itself. The chapter also provides a description of the process for the periodic update of the value weights.
Chapters 10 deals with a few cases that need special treatment. For example, it presents methods for handling seasonal agricultural and clothing products, petroleum refining, steel mills, electronic computers, motor vehicles, shipbuilding, construction, retail trade, telecommunication services, some financial services, legal services, and medical hospitals. Chapters 11 provides an overview of the errors and biases to which PPIs may be subject.
A.3 Part III: Operational issues
Chapters 12 deals with issues of organization and management. Conducting the price surveys and processing the results make for a massive operation that needs careful planning, organization, and efficient management. Chapters 13 addresses publication and dissemination standards for the PPI results.
A.4 Part IV: Conceptual and theoretical issues
Chapters 14 marks a break in the sequence of chapters because it is not concerned with compiling a PPI. Its purpose is to examine the place of the PPI in the general system of price statistics. The PPI is not a set of independent, isolated statistics. The flow of producer goods and services to which it relates is only one of a larger set of interdependent flows within the economy as a whole. The analysis of inflation requires more than one index, and it is essential to know exactly how the PPI relates to the CPI and to other price indices, such as indices of export and import prices. The supply and use matrix of the System of National Accounts 1993 (Commission of the European Communities and others, 1993) provides the proper conceptual framework for examining these interrelationships.
Chapters 15–18 provide a systematic and detailed exposition of the index number theory underlying PPIs. These chapters examine different approaches to index number theory. Collectively, they provide a comprehensive and up-to-date survey of index number theory, including recent methodological developments as reported in journals and conference proceedings.
Chapters 15 provides an introduction to index number theory, focusing on breaking up value changes into their price and quantity components. Chapters 16 examines the axiomatic and stochastic approaches to PPIs. The axiomatic, or test, approach lists many properties that are desirable for index numbers to have and tests specific formulas to see whether they have them.
Chapters 17 explains the economic approach, using the economic theory of producer behavior. In this approach, an output PPI is defined as a “fixed-input” economic price index that assumes fixed technology. Changes in the index arise solely from changes in the output prices between two periods. An input PPI is defined as a “fixed-output” economic price index that also assumes fixed technology. Changes in the index arise solely from changes in the input prices between two periods. Although these economic indices cannot be calculated directly, a certain class of index numbers, known as “superlative” indices, can be expected to approximate them in practice. From an economic perspective, the ideal index for PPI purposes should be a superlative index, such as the Fisher index. The Fisher index also is a very desirable index on axiomatic grounds.
Chapters 18 deals with aggregation issues. Chapters 19 presents a constructed data set to explain the numerical outcomes of using different index number formulas. It shows that, in general, the choice of index number formula can make a notable difference, but that different superlative indices all approximate one another.
Chapters 20 addresses the important question of what is the theoretically most appropriate elementary price index formula to use at the first stage of PPI compilation if no information is available on quantities or values. This has been a comparatively neglected topic until recently, even though the choice of formula for an elementary index can have a significant impact on the overall PPI. The elementary indices are the basic building blocks used to construct higher-level PPIs.
Chapters 21 and 22 conclude the Manual. They address two conceptually difficult issues. Chapters 21 considers the theoretical issues of adjusting for quality change on the basis of the hedonic approach. Chapters 22 examines the treatment of seasonal products.
A glossary of terms and a bibliography appear at the end of the sequence of chapters.
B. Alternative Reading Plans
Different readers may have different needs and priorities. Readers interested mainly in compiling PPIs may not wish to pursue all the finer points of the underlying economic and statistical theory. Conversely, readers more interested in the use of PPIs for analytic or policy purposes may not be interested in the details of the conduct and management of price surveys. Not all readers will want to read the entire Manual, or even want to follow the same reading plan.
However, all readers, whether users or compilers, will find it useful to read the first three chapters. Chapters 1 provides a general introduction to the whole subject by providing a review of the PPI theory and practice appearing in the Manual. It provides the basic knowledge needed for understanding later chapters. Chapters 2 explains the need for PPIs and their uses. Chapters 3 examines many basic conceptual issues and the scope of a PPI.
B.1 A compiler-oriented reading plan
Chapters 4–13 are mainly for compilers. They follow a logical sequence that roughly matches the various stages of compiling a PPI. They start with deriving the value weights and collecting the price data and finish with publishing the final index. Chapters 12, on organization and management, is intended for both managers and compilers. It discusses many important issues on the structure and mechanisms that statistical offices need to monitor, control, and ensure the quality of the PPI and to be efficient in the use of resources.
Chapters 14 is for both compilers and users of PPIs. It places PPIs in perspective within the overall system of price indices.
The remaining chapters, Chapters 15–22, are mainly theoretical. Compilers may find it necessary to follow certain theoretical topics in greater depth, in which case they have immediate access to the relevant material. It would be desirable for compilers to acquaint themselves with at least the basic index number theory set out in Chapters 15 and the numerical example developed in Chapters 19. The material in Chapters 20 on elementary price indices is also important for compilers.
B.2 A user-oriented reading plan
Chapters 7 and 8 discuss the treatment of quality change, item substitution, and new products. Users may also find Chapters 9 helpful because it provides a concise description of the various stages of compiling a PPI.
C. A Note on the Bibliography
In the past, international Manuals on economic statistics have not usually provided references to the associated literature. It was not helpful to cite references when the literature was confined mostly to printed volumes, including academic journals or proceedings of conferences, found only in university or major libraries. Compilers working in many statistical offices were unlikely to have ready access to such literature. However, this has changed with the Internet and the World Wide Web, which make all such literature readily accessible. Therefore, this Manual breaks with past tradition by including a comprehensive bibliography to the large literature that exists on index number theory and practice that many readers are likely to find useful. In addition, websites are referenced that contain specialist papers on index number theory and practice, including those of the Ottawa Group and the Voorburg Group.
Antilock brake system; Australian Bureau of StatisticsAF
Australian and New Zealand Standard Industrial ClassificationATM
Automated teller machineBPM5
Balance of Payments Manual, Fifth EditionBEA
Bureau of Economic AnalysisBLS
U.S. Bureau of Labor StatisticsCAPI
Computer-assisted personal interviewsCATI
Computer-assisted telephone interviewsCD
Compact disk-read-only memoryCD-RW
Current Industrial ReportCOFOG
Classification of the Functions of GovernmentCOICOP
Classification of Individual Consumption by PurposeCOL
Cost of livingCOPNI
Classification of the Purposes of Nonprofit Institutions Serving HouseholdsCOPP
Classification of the Purposes of ProducersCPA
Classification of Products by Activity, also known as PRODCOM (Eurostat)CAB
Cyclically adjusted fiscal balancesCPC
Central Product ClassificationCPI
Consumer price indexCSWD
Carruthers, Sellwood, Ward, Dalén price indexDRAM
Dynamic random-access memoryDRG
Disaster Recovery Plane-
Electronic (e-business, e-commerce, e-mail, etc.)EC
European Central BankECI
Employment cost indexEDI
Electronic data interchangeEFQM
European Foundation for Quality ManagementESMR
Enhanced specialized mobile radioEU
Statistical Office of the European CommunitiesFEPI
Final expenditure price indexFIOPI
Fixed-input output price indexFISIM
Financial Intermediation Services Implicitly Measuredf.o.b.
Free on boardFOIPI
Fixed-output input price indexFPI
Final uses price indexFPPI
Farm product price indexGB
General Data Dissemination System (IMF)GDP
Gross domestic productGPI
Global price index; government price indexGPS
Global positioning systemHBS
Household Budget SurveyHICPs
Harmonized Indices of Consumer Prices (Eurostat)HP
Hodrick- Prescott; horsepowerHPI
Household consumption price indexHS
Harmonized Commodity Description and Coding SystemICP
Implicit characteristic priceICPI
Intermediate consumption price indexIDI
Implicit deflator indexILO
International Labour Office /International Labour OrganizationIMF
International Monetary FundI/O
International Price ProgramISIC
International Standard Industrial Classification of All Economic ActivitiesISO
International Standards OrganizationIT
Inter-Secretariat Working Group on Price StatisticsKPI
Fixed capital formation price indexLIFO
Last in, first outLKAU
Local kind of activity unitLPG
Liquefied propane gasMHz
Import price indexMSA
Metropolitan Statistical AreaNACE
General Industrial Classification of Economic Activities within the European CommunitiesNAFTA
North American Free Trade AssociationNAICS
North American Industrial Classification System1993 SNA
Commission of the European Communities (Eurostat), International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, and World Bank, 1993, System of National Accounts 1993 (Brussels/Luxembourg, New York, Paris, and Washington)NPI
Inventory price indexNPISH
Nonprofit institution serving householdsOECD
Organisation for Economic Co-operation and DevelopmentOLS
Ordinary least squaresOttawa Group
International Working Group on Price IndicesPC
Carli price indexPCSWD
Carruthers, Sellwood, Ward, and Dalén price indexPD
Dutot price indexPDR
Fisher price indexPGL
Geometric Laspeyres price indexPGP
Geometric Paasche price indexPH
Harmonic average of price relativesPIT
Implicit Törnqvist price indexPJ
Jevons price indexPJW
Geometric Laspeyres price index (weighted Jevons index)PKB
Konüs and Byushgens price indexPL
Laspeyres price indexPLM
Lloyd-Moulton price indexPLo
Lowe price indexPME
Marshall-Edgeworth price indexPP
Paasche price indexPRH
Ratio of harmonic mean pricesPT
Törnqvist price indexPW
Walsh price indexPY
Young price indexPC
Personal consumption expendituresPCS
Personal communications servicePDA
Personal digital assistantPMC
Producer price indexPPP
Purchasing power parityPPS
Probability proportional to sizePR
Product/commodity classification system for the European CommunityRAM
Ratio of harmonic average pricesRMSE
Root mean square errorROSC
Reports on the Observance of Standards and Codesrpm
Revolutions per minuteRSA
Residential Service AreaSAF
Seasonal adjustment factorsSDDS
Special Data Dissemination Standard (IMF)SEHI
Superlative and exact hedonic indicesSIC
Standard Industrial ClassificationSITC
Standard International Trade ClassificationSMI
Supply markup indexSNA
System of National AccountsSPI
Supply price indexSSR
Structured Schedule ReviewSUT
Supply and use tableTEG-PPI
Technical Expert Group for the Producer Price IndexUN
UN Economic Commission for EuropeVAT
Value-added taxVoorburg Group
International Working Group on Service Sector StatisticsVPI
Valuables price indexWD
Weighted least squaresWPI
Wholesale price indexXPI
Export price indexYPI
Output price index