Chair: Johannes Hoffmann (Deutsche Bundesbank)
Background note and key issues for discussion Papers: Adjustments for quality change and new goods
Mick Silver (IMF)
The US experience with quality adjustment Jeremy Rudd (Federal Reserve Board) Summary of discussion
Background note and key issues for discussion
The treatment of quality changes constitutes one of the greatest challenges for CPI compilers. It is sometimes called the “house-to-house combat of price measurement” (Shapiro and Wilcox, 1996). The issue is not new, of course, but it has become more important in recent years. On the one hand, it has gained in importance as a result of the high pace of innovation observed for many goods and services. Innovation is now a widespread phenomenon and generates improve-ments in product characteristics on an ongoing basis. On the other hand, the opening of markets and increased competition have widened the range of product quality offered on the market, especially for many services. For example, low-cost airlines set on-flight services to a minimum, and competition has forced the incumbent carriers to forego their traditional level of on-flight services. Similar observations can be made for apparel. Measuring the constant-quality-price trend for a particular (broadly defined) product can be particularly demanding if characteristics are changing rapidly or are difficult to measure, as in the case of services.
The problem of quality adjustment arises if a specific item sampled for price observation is no longer available on the market or has lost importance for consumer purchases. Then a replacement has to be chosen for price observation, and the difference in characteristics between the old and the new item has to be valued and accounted for in order to obtain the “pure” price change of the product. Quality adjustment is thus “the procedure of making an allowance for a quality change by increasing or decreasing the observed current or reference prices by a factor or an amount equivalent to the value of that quality change” (Eurostat, 2001). Sometimes the choice of a replacement is straightforward, if, e.g. a car manufacturer introduces a new car model with better safety features. Then the value to the consumer of these new features should be subtracted by CPI compilers from the observed price for the new car model. Sometimes the choice of a replacement item is less straightforward, if, e.g. a product is withdrawn from the market and no new product variant replaces it directly. Finally, additional new products may be introduced. The question arises whether the prices of these new products can be linked in any meaningful way to the prices of existing products (consider, e.g. the case of microwave ovens and traditional ovens, or of mobile phones and fixed-line phones). Therefore, truly new goods, which are not simply improvements of existing goods, are brought into a CPI often only when the index is rebased. Then, however, the welfare gain to consumers arising from the introduction of new goods is lost for inflation measurement. This issue refers to the new product bias in CPIs and will be discussed in Sessions 5 and 7.
There exist a number of procedures to handle quality changes in CPI compilation. The CPI Manual classifies them under explicit or implicit methods. Implicit methods rely heavily on the assumption of perfectly competitive markets, whereas explicit methods try to value the differ-ence in product attributes directly.
Implicit methods account for quality changes by implicitly attempting to value the new char-acteristics of products. When old and new versions of a particular good exist in an overlapping period, it may seem to be straightforward to infer the monetary value of the difference in qual-ity between the two product variants from the price difference in that particular period. The so-called overlap method scales the price in the current period for the ratio of the price of the previous version of the product and the one for the new version (like treating a break in series or linking an old and new time series for the same variable). It implicitly assumes that price dif-ferences are caused by quality changes. When no overlapping prices exist for old and new ver-sions of a particular good, the so-called class mean imputation might be used. This method presumes that the true quality adjusted price change between two product variants can be read from the price development of similar products but without changes in characteristics. The major weaknesses of the overlap and the mean imputation methods are the following. Firstly, there is no reason to expect that the prices of disappearing and newly emerging products are on the equilibrium price-quality locus. Secondly, with respect to the class mean imputation method, it has to be kept in mind that with menu costs it pays to link product replacements to true changes in prices. Empirical evidence of this phenomenon has been summarised by Moulton/Moses (1997). Then, however, the class mean imputation method will lead to distorted estimates of quality-adjusted price change.
A generalisation of the overlap-method, which has become increasingly feasible with elec-tronic data processing, is the so-called chaining and re-sampling method, which may also take into account the changing relative importance of the various price observations in terms of turnover. In a traditional (fixed base) index, only prices for a small number of items are observed for each product category, and no weights are applied at the lowest level of aggregation. The chaining and re-sampling method considers the prices of all product variants of a specific product category and refreshes the sample and the weights each month. Thus, this method avoids the arbitrariness of choosing one particular product variant for price observation and one particular period for refresh-ing the sample. The product-specific price index is obtained by linkrefresh-ing the prices of each specific item in each month with those of the corresponding items from the preceding month. The main weakness of this method is that it may give rise to serious index drift, especially with strong sea-sonality in the data. The same is true with respect to products with a pronounced shape of prices over the life cycle. With apparel, e.g. only the price rebates of the end-of-season sales would be captured, but never the price increases related to the introduction of a new fashion.
Two less sophisticated indirect methods are the direct price comparison method, which pre-sumes that there is no change in quality, and the “link-to-show-no-price-change” method, which attributes the entire price change to quality differences. For the European HICP, the latter method is not admissible, unless it can be justified (EC Regulation 1749/96).
This leads to the explicit methods of quality adjustment. A traditional method of quality adjustment relies on expert knowledge, or on the valuation of quality changes by the price col-lector. He/she has to make a decision on the monetary value of the difference in characteristics between the old and the new product variant. Often, either the full change in price is attributed to changes in characteristics, or the prices of the different product variants are compared directly.
In these polar cases, direct quality adjustment is identical to the simple implicit quality adjust-ment methods adjust-mentioned above. But the price collectors or the expert may also decide to neu-tralise only a part of the price difference for inflation measurement. The problem with this widely applied method is that it cannot cope with quality changes accompanied by invariant prices, which may be not untypical in a low inflation environment (Hoffmann, 1999). One explicit method is the so-called option cost adjustment, which consists of estimating the value of a new product feature on the basis of the market value of that feature observed as a separately priced option in earlier periods. This method is frequently applied for cars, and also for personal computers (PCs). For example, the value of a standard inclusion of airbags in cars might be gauged from the price of airbags in the list of separately priced accessories for the same car in the previous period. This requires extensive and detailed data from manufacturers and vendors on the market value of different options in the reference period. The major weakness of this approach is that options made standard are not valued by all customers alike. Hence, the mone-tary value of the difference in characteristics may be overstated.
An increasingly popular approach for explicit quality adjustment is the hedonic method, which can be understood as a more advanced method of the quality adjustment methods based on expert knowledge and option costs. Hedonics consists of regressing the prices of the various product variants on the relevant attributes. For instance, the price of a “car” might be regressed on its size, power, gear-box, safety features (such as airbags or ABS), emission controls and other items used in marketing the product (e.g. audio system, seat heating, electric window con-trol, rain or distance sensors). From these estimates, which may be performed separately for each period or may be pooled across two or more periods, the quality-adjusted price change can be inferred. With pooled data, it can be read directly from a time dummy added to the charac-teristics. With period-specific regression, either a quality-adjusted price can be imputed either for the reference period or for the observation period, or a price index over characteristics can be computed (Feenstra, 1995). The proper application of the hedonic method requires an exten-sive set of detailed, quantifiable and market-based information on product qualities, an excellent knowledge of the working of the specific markets as well as econometric expertise.
Many price statisticians have embraced hedonics as a proper method to deal with quality adjustment, in particular because it seems to avoid subjective judgment and is based on actual market data on prices and the quality characteristics of the items priced. However, economic theory, even under perfect competition, does not give a clear interpretation of hedonic coeffi-cients. In practice, it is also uncertain whether the use of hedonic method can eliminate entirely the quality bias, as it might be rather difficult to account for all the attributes that affect a
prod-BACKGROUND NOTE AND KEY ISSUES
be even more restrictive than those required by the hedonic approach. The hedonic approach is more objective and transparent than others, at least if it is applied following established and comparable criteria. As highlighted by Kenny and Ahnert (2003), the transparency and credibil-ity of hedonically adjusted CPIs could be further enhanced if the statistical compiler would pro-vide detailed and documented information on the hedonic functions they use when adjusting the prices for particular products for quality adjustment. They also suggest that international or supranational statistical organisations, such as Eurostat, could play a role in the harmonisation of hedonic methods in order to improve their comparability across countries.
Unlike the new products and substitution biases in CPI statistics, which tend to systemat-ically overstate consumer price inflation, the quality adjustment bias might be either positive or negative. Firstly, product quality might either improve or deteriorate. Secondly, there might be an over-adjustment or an under-adjustment to quality changes. Significant upward bias is typic-ally found for durable goods, where technical innovation has been particularly pronounced.
Evidence of negative quality bias has been relatively limited, mostly to clothing and rental val-ues.1As will be discussed in session 7, most empirical studies on CPI measurement bias have found that quality bias accounts for the large part of the overall upward CPI bias.2
Whilst there is a clear recognition of the need for dealing adequately with quality changes for inflation measurement, no consensus has so far emerged concerning the most appropriate methods to account for it. Moreover, it is also unclear for which products there is a need for an explicit adjustment for quality changes. Where explicit quality adjustments are attempted, com-pilers focus mostly on goods and, in particular, on consumer durables. Nevertheless, quality changes in services might also be substantial and have a sizeable impact on the CPI, as services account for a growing proportion of household expenditures.
A large number of countries use expert judgement to adjust prices for quality change, some-times for very important items in the CPI (see Table 1). For instance, in Australia the price of processed food, clothing, rent, household appliances and motor vehicles are quality-adjusted by an expert. In the Netherlands, judgement-based adjustments are made for household appliances, telephone and internet services, while in Hong Kong it is used for clothing. Option cost adjust-ment is used by various national statistical institutes, is in the case of the US (for cars and PCs), Belgium (for cars and PCs) and the Netherlands (cars, audio-visual equipment, photographic and information processing material). In Germany, the Federal Statistical Agency estimates hedonic coefficients, which help price collectors performing explicit quality adjustments.
Different national CPI compilers, such as in Australia, France, Germany, Italy, Japan, Spain and the US, have started to make use of hedonics, in general but not only for high-tech goods subject to rapid innovative progress. National practices and views regarding the choice of prod-uct categories to which hedonics are, or should be, applied differ significantly. In the US, for instance, the BLS has moved aggressively to expand the use of hedonic methods for a large range of products, but, in the case of PCs, returned to the option cost method. Many economists have encouraged such enhancements to CPI methodologies. Others, however, have argued that the BLS might have implemented those techniques too extensively in recent years.
In recent years, many countries have opted for chained Laspeyres indices either at the ele-mentary level, (for instance, Brazil, China, South Africa, Canada), or at the higher-levels (for instance, US, Europe, Spain, Mexico, Thailand, Korea, Sweden and Russia).
Issues for discussion