Elementary indices for each products sub-class in each region and in each outlet
Higher-level indices for each products sub-class for the whole geographic coverage
Expenditure weights for products sub-classes (e.g.
apples), classes (e.g. fruit) and groups (e.g. food)
Outlet weights Higher-level indices for each products sub-class in each
region
Regional weights
Box 1 – Price Compilation: an overview
The higher-level indices are obtained by applying a Young (or Laspeyres) formula, defined as a weighted average of the elementary indices using weights derived expenditures in some earlier period.7While this formula presents the advantage of being relatively simple, it fails to account for substitution (“substitution bias” at the upper level). Indeed, it does not reflect the fact that consumers tend, on the one hand, to reduce their quantity consumed of products, which have become relatively more expensive and, on the other hand, to increase the quantity of items that have been relatively cheaper.
Different solutions have been proposed to reduce this bias. First, the use of chained-Laspeyres index, which updates frequently the base period weights, should reduce significantly this bias, in comparison with a direct Laspeyres formula, since it takes into account the under-lying information in terms of changes in quantity.8Nevertheless, it is has been argued that this method is not entirely satisfactory, as updated weights still refer to an earlier period and there-fore does not allow for complete substitution. Superlative indices (e.g. Walsh, Fisher, Tornqvist – also called ideal indices) use expenditure figures both for the base and current period as weights, are considered to account properly for this type of bias.9
Specific challenges related to price measurement
Apart from the measurement issues mentioned above, there might be other aspects of particular importance. In their conduct of monetary policy, policymakers assess regularly inflationary
7 If weight expenditure refers to the reference period (i.e. period where the index is set to 100), the index is defined as a Laspeyres index.
8 In this context, the European Commission (Eurostat) requires that HICP weights are frequently up-to-date, “Each month Member States shall produce HICPs using weightings which reflect consumers’ expenditure patterns in a weighting reference period ending no more than seven years before the preceding December” . . . Where reliable evi-dence shows that a weighting change that would affect the change in the HICP by more than 0.1 percentage point on average over one year against the previous year, Member States shall adjust the weightings of the HICP appro-priately” (Council Regulation, EC, n 2454/97, 1997). See for more details the issues note on session 7.
9 In this context, the BLS has started in 2002 to compute and to publish officially a superlative index, the C-CPI-U, in add-ition to the tradadd-itional US CPI. This index employs a Tornqvist formula and expenditure data in adjacent time period.
However, the superlative indices have a drawback in terms of availability of data, which might be serious for the conduct of monetary policy. Weights for the current period are not available on a timely basis; therefore, revisions might be important until the definitive publication of these expenditure data. See for more details the issues note on session 7.
developments. In this context, they need to determine the factors behind the inflationary pres-sures. For that reason, it might be relevant to determine the impact of administered/regulated prices, as they usually do not reflect macroeconomic factors. While there is no agreed defini-tion, this term tends to refer to prices that are not determined by market forces (demand versus supply) and that are heavily influence by the government (price or quantity). This might involve direct price-setting, significant subsidisation, regulation related to production of products by the government or some regulatory body. These prices are in general fixed for a relatively long time period, they do not respond to business cycle and tend to show important relative price changes, when a change occurs. The proportion of administered prices varies greatly among countries.
For instance, they account for more than 33% in Brazil, while in the US they represent less than 7% of the CPI basket.10In recent years, this issue has been intensively discussed in particular in the euro area, as administered prices had an important upward effect on headline inflation and are considered to be an important factor behind the relative price stickiness of the HICP over the last years. However, it has proved to be rather difficult to assess with precision the impact of administered prices on the euro area HICP inflation, as for instance, in some countries the price of a specific product might be market-determined, while in other, it might be strongly influenced by the government. In addition, certain products are considered borderline, and it might be dif-ficult to distinguish whether price changes are related to market forces or to government actions (e.g. food prices subject to the Common Agricultural Policy in the EU). Further work needs to be accomplished on this issue, in order to capture or isolate accurately the impact of adminis-tered prices on headline inflation.
The CPI includes all taxes, such as sales taxes, excise duties and value added taxes, as they are part of the price paid by consumers. Another potential issue of concern for central bankers might therefore arise from changes in indirect taxation, which cause a one period change in the price index. These changes tend to be particularly important for beverages, tobacco, and fuels, as these products usually contain a relatively higher level of value added taxes and excise duties.
For the purpose of monetary policy, policy makers might be interested to determine the impact of these effects on headline inflation, since it does not reflect a change in the underlying infla-tion pressures but only to changes in an economic/fiscal tool. In this context, they might be interested by a constant tax index, also called net price index, in which taxes on consumer goods and services are deducted from the purchased prices. In practice, it might be difficult to assess the impact of change in indirect taxation, as it is hard to determine to which extent these changes are passed on to consumers. A typical example concerns a change in taxes on fuel. This will directly affect fuel prices, which might in turn affect the price of transport services (e.g. bus). It might also affect the prices of transported goods and the price paid for these goods by con-sumers. A given change in indirect taxation might have secondary effects, which might be diffi-cult to distinguish.
The existence of seasonal products pose serious challenges for CPI compilers and also for central bankers in their regular assessment of price stability. Seasonal products refer either to items that are not available during certain seasons of the year or, to items available throughout the entire year but for which quantities fluctuate widely with the different seasons. These aspects are particularly important for food and clothes. It creates important problems for price statisti-cians as, for instance, the disappearance of a seasonal commodity in a particular month makes it impossible to compute month-on-month price changes. This issue might also be important for central bankers, as it complicates the understanding of the underlying trend in short-term price changes created by some noises in the headline inflation measure. Especially, it is rather diffi-cult, based on non-seasonally adjusted CPI data, to determine whether developments between two months do reflect a normal seasonal pattern or a modification of the economic environment.
Central bankers usually cope with this issue by comparing a monthly index in particular month with the monthly index in the corresponding month of the previous year, instead of focusing on month-on-month movement in the price index. However, this might not be entirely satisfactory in case the seasonal patterns change over time.11Hence, some central banks prefer to remove completely the different seasonal patterns using specific econometric techniques. Besides pro-viding a better understanding of inflationary developments, seasonally adjusted data might also be relevant in the context of international comparisons, since CPIs in different countries might present different seasonal patterns.12Finally, seasonally adjusted CPI data are very useful in economic modelling or for forecasting.
BACKGROUND NOTE AND KEY ISSUES
However, seasonally adjusted CPI data are not free of controversy. In particular, different techniques exist, and it is unclear which technique performs better, to account for changing seasonality pattern.13Additional aspects that need to be resolved include, the best time span to consider, and of the treatment of outliers.
All these different measurement problems are closely related to core inflation measure.
Indeed, removing administered prices, taxes and seasonal factors is somehow similar to excluding volatile components (see the background note for session 6).
Issues for discussion
• Is the so-called formula bias, for instance related to the choice of formula to construct elem-entary price indices or to the choice of index for calculating the weighted aggregate index, significant in national CPI’s? Is best practice to address this potential bias evolving in this respect (e.g. the move to geometric means and superlative indices)?
• How often should the CPI basket be revised? Is it useful to set out rules or best practices such as those currently applied for the HICP in the EU (ie products are included as soon as the expenditure for them is higher than 0.1% of the total household spending)? How useful are the so-called chaining indices and which periodicity would be useful when these are applied (e.g. yearly or monthly chaining)?
• How significant are the difficulties related to administered prices, changes in indirect taxation and seasonality from the perspective of central banks? Are there appropriate methods to deal with these issues?
• Are there other measurement issues that CPI compilers should be paying more attention to as they update the CPI Manual?
References
Diewert E. (2005), “Identifying Important Areas for Future Price Work at the International level”, University of Columbia, paper presented at the OECD Conference, Inflation Measures: Too High – Too Low – Internationally Comparable? Paris, 21–22 June 2005.
Eurostat (2001), “Compendium of HICP reference documents”, Luxembourg.
Hausman J. (2002), “Sources of Bias and solutions to bias in the CPI”, NBER, WP 9298.
ILO, IMF, OECD, UNECE, Eurostat and the World Bank (2004), “Consumer price index manual: theory and practice”, Geneva.
OECD (1999), “A Review of bias in the CPI”, paper presented at the ECE/ILO meeting on Consumer Prices, October 1999.
Wu T. (2003), “Improving the way we measure Consumer Prices”, FRBSF Economic Letter, August 2003.
PROCEEDINGS IFC WORKSHOP – SESSION 5
13 Two main approaches might be distinguished: the Census, the X-12 ARIMA seasonal adjustment method, which is commonly used by many institutions. This method is based on moving average filters and it has been developed by the U.S. Bureau. The other commonly used seasonal adjustment method is the TRAMO/SEATS, which is a model-based seasonal adjustment method.