The Hedonic Method
Issac J Williams
There has been a strong recommendation that the BLS explore the use of hedonic regression methods for quality adjustment in the Consumer Price Index (CPI). Until recently data limitations have made this goal difficult to implement for many categories of goods and services. This paper reports the preliminary results of employing data purchased by BLS from an outside source to produce hedonic regression-based quality-adjusted price indices for consumer audio electronics products. The effects of hedonic-based quality adjustment are examined. Hedonic indices are derived directly from the regression coefficients, and compared to the adjusted CPI values. Issues of regression specification and practical problems for CPI quality adjustment are also addressed.
There has been strong recommendation that the BLS explore the use of hedonic methods for quality adjustment in the Consumer Price Index (CPI) for decades. The Price Statistics Review Committee (the Stigler Commission Report) in 1961 expressed the view that hedonic analysis would provide a “more objective” approach to addressing quality change than the BLS standard methods of dealing with this issue (Triplett (1990)). More recently, the Advisory Commission to Study the Consumer Price Index (the Boskin Commission Report, 1996) reiterated this recommendation, recognizing that accurate measures of quality change will enable a more accurate measure of pure price, or “cost-of-living” change. Categories of goods and services where quality changes are frequent and relatively easy to identify are the best candidates for using hedonic methods, given that data can be acquired.
A price index, such as the CPI, intends to measure the effects of price changes while holding other economic factors, such as the physical attributes of the goods available, constant. In the real world, however, goods and services are always changing in their physical characteristics. This makes it necessary to find some method of subtracting out the value of quality change when the market basket and prices change. Traditionally, the BLS has used several methods of quality adjustment. These include overlap pricing, direct quality adjustment using information from producers, and linking methods. Basically, all of these methods rely upon the subjective assessment of BLS personnel (commodity analysts) in selecting newly appeared products that most closely match the disappearing ones. Hedonic methods have been recently introduced into the BLS toolkit for housing (to correct for age bias) and apparel commodities.
Research is underway which considers the use of hedonic methods for quality adjustment for personal computers, televisions, VCRs, and even college textbooks, using or expanding the CPI sample to estimate the hedonic regressions. In the CPI, the sample size for a category of good or service is a function of the relative importance of that category in the average consumer household’s total annual average expenditures. For many types of goods and services where hedonic methods are likely to be useful, the sample of CPI data is too small for such an empirical application. Possible solutions to this problem are to collect additional observations on these goods for this purpose or to use supplementary data sources to provide hedonic coefficient estimates that may be used for quality adjustment when substitutions occur in the CPI sample.
For consumer audio products, the BLS is investigating the use of hedonic-based quality adjustment methods from detailed and extensive market data acquired from NPD (Intelect Group, Inc.). In this paper we present the preliminary results of this effort, examining the effects of quality adjustment on this CPI component by comparing adjusted index values to a simulated unadjusted CPI audio component. We discuss issues of regression specification, practical problems encountered in integrating results from other data sources into the CPI item structure, and also compare quality-adjusted results to a direct hedonic index from the NPD regressions themselves.
The purpose of a consumer price index is to measure the effects of price changes on consumer households. In a true cost-of-living index, substitution behavior in response to price changes is incorporated, and the index compares two price regimes with respect to a fixed reference level of satisfaction. If a fixed weight formula, such as the Laspeyres, is used for the index, relative prices of items are compared with respect to a fixed market basket of goods and services. In either case, it is assumed that the spectrum of products, and the available attributes of the goods or services from which the consumer may choose are the same in both the reference and comparison periods.
In practice, however, the specific items on the market are often changing. Models disappear and new, different ones appear to take their shelf space. Sometimes the differences between old and new models are minor, or are regarded as such by the consumer. Sometimes qualitative changes can occur which make the new products difficult to compare to the old ones. At the extreme are goods which are sufficiently different from other items on the market as to be categorized “new goods”, since they embody attributes, or specific combinations of attributes, which existing goods lack (e.g. cellular telephones, and recordable portable minidiscs). These physical changes in consumer products and services can be observed, but their value to the consumer must be excluded from a consumer price index measure. Thus, they must be identified, categorized and/or quantified, and their implicit value to the consumer estimated.
The treatment of quality changes in the CPI has varied according to the nature and degree of the change, feasible methods for making an adjustment, and available data resources. Whether implicitly or explicitly, these adjustments attribute the observed price change between two goods as: (a) entirely to price change, (b) entirely to quality differences, or (c) partially to price change and the balance to quality difference (Kokoski (1993)). Where the observed differences between a new and disappearing product are negligible (e.g. brand of bran flake cereal), the price collector usually simply substitutes the new product for the old one. This is termed a comparable substitution and it implicitly attributes all of any observed price difference between the two products to pure price change. Product “downsizing”, as when 16-ounce cans of tomato sauce are replaced by otherwise identical and similarly priced 14.5-ounce cans, also attributes all of the difference in price-per-ounce to pure price change (Kokoski (1993)).
When qualitative attributes between two goods are judged to be more important, then one of several methods of non comparable substitution is employed. One such method, used when both the old and new product are present in at least one time period, is overlap pricing. In that overlap period, say period t, the price change for the item category represented by these products is given by the price change for the old product between period t-1 and period t. The price relative for this item category between periods t+1 and t is represented by the new product. Empirically seamless, this method does not require direct comparison of the prices or attributes of the two products. It implicitly attributes all of any difference in price between the old and new products to real quality difference. Where information is available on the additional cost to producers of making a specific change in the attributes of a product, then a direct quality adjustment may be made. This cost is then subtracted from any observed change in the price paid by the consumer for the new instead of the old product (Triplett (1988)). This direct method assumes that the perceived value of the quality change to the consumer is the same as the cost incurred by the manufacturer to provide it.
In the absence of either overlapping prices or independent information from producers on the costs of qualitative changes, a linking method is employed to make non comparable substitutions. Aside from sample rotations, when entirely new and independent product samples are drawn for the CPI, linking techniques are the most prevalently used in the CPI (Armknecht and Weyback (1989), Fixler (1993)). In this case the old product makes its final appearance in period t-1 and the new product, which effectively replaces it on the retail shelf, first appears in period t. Since the two products cannot be directly compared in the same time period, the price change between period t-1 and period t for this good is proxy by the observed price change between these two periods by other goods in the same goods category. The new product then represents the good in the price index for subsequent time periods. This method assumes that pure price changes are likely to be the same for all goods in a class (e.g. price changes for cotton Oxford shirts will be the same as for other types of shirt). By implicitly imputing a price to the new product in period t-1, had it existed then, this method attributes some of the price difference between the new and old products to pure price change and the rest to quality differences between the two products (Kokoski (1993)). All of the above methods would miss any pure price change imposed by the producer at the opportunity offered by model changeovers.
In all these cases, some degree of judgment by the BLS commodity analyst is required. For comparable substitutions, the analyst selects the new item which most closely resembles the old one and judges any differences between them to be negligible. For non comparable substitution methods, the new item is still chosen on the basis of this criterion, and then quantitative adjustments applied as the new item enters the index.
The currently preferred method of quality adjustment is the hedonic method. This method (or class of methods (Triplett (1990)), relies on statistical techniques to estimate the implicit prices of product characteristics from observed prices and quantities sold in the marketplace. These implicit prices may then be used as measures of the value of observable qualitative differences in products to consumers, and thus help disaggregate the observed price difference between two products into quality change and pure price change. The first application of hedonic methods to the CPI was in the apparel categories (Armknecht (1984), Armknecht and Weyback (1989)). Initially, hedonic regressions were estimated on the CPI sample, and the coefficient values for the attributes used to provide a structured set of criteria for selecting the most comparable substitute for a disappearing item. For example, if the fiber content of a jacket was statistically significant and a quantitatively substantial attribute in determining the jacket’s price, then the new jacket chosen for the CPI sample would have to have the same fiber content as the old one. This procedure then advanced to using the hedonic regressions to provide estimates of quality change directly into the index (Liegey (1993), Armknecht, Moulton, and Stewart (1995)). For example, when a new jacket was brought into the index to substitute for a disappeared one, its introductory price was quantitatively adjusted based on the coefficients from the hedonic regression on that apparel category. The use of hedonic regressions in apparel employed the data collected by BLS for the CPI, and was facilitated by a fairly large sample, and relatively easily identified and empirically manipulated characteristics information from the CPI checklists.
Hedonic methods are now being proposed for other categories of goods and services in the CPI. These include personal computers, televisions, VCRs, refrigerators and other major appliances, and even college textbooks. In some cases, a larger number of price quotes is being collected to expand the sample and thus provide a sufficiently large database for estimating hedonic regressions. Because expanding sample size is not a costless endeavor, in other cases the BLS is considering the acquisition and use of data sources outside the agency for this task. These include data purchased from A.C. Nielsen, collected from electronic scanners in retail outlets, data gleaned from published sources such as Consumer’s Digest (Liegey and Shepler (1998)), and data purchased from independent firms which collect and process retail transactions information. For consumer audio products, data are being purchased from NPD. While large and detailed, these other data resources do present some additional issues for quality adjustment of the CPI: (a) the samples are not collected under the same probability sampling procedures used for the CPI sample, so the relative degree of representation of specific models in the respective samples will differ, (b) the item definitions, categorization, and attributes identified will differ, and c) the representative outlets from which the BLS collects price quotes for the CPI differs from those sampled by other data sources, thus effecting the product mix and prices.
Hedonic analysis has long been recommended as a preferred method of quality adjustment of the CPI. For many CPI components a hedonic approach will likely be adopted before the next scheduled revision in 2002. This paper presents the preliminary results of employing average price and quantity data from a private source to this end for consumer audio electronics products. We have used the hedonic regression coefficients from these data to supply quantitative estimates of quality differences for those situations when substitutions were made in the CPI sample. Also, we have compared the resulting index values to direct hedonic indices calculated from the time dummy variables in the hedonic regressions.
Analysis of these results suggests several interesting empirical issues worthy of further investigation. The quality adjusted indices indicate price decreases over the time period under study, but less so than their unadjusted counterparts. The differences are small, however, so it would be useful to continue empirical investigation, especially during periods where physical changes to audio products are rapid and pronounced. The regression specification with respect to characteristics variables appears to be stable and consistent over time. Interestingly, among the direct hedonic formulas compared, we observed that for all but one product category, the Laspeyres index value is below that of the Paasche index. Altogether, these results support the proposition that new products may be entering the sample at higher quality adjusted prices than those of extant models, an issue that bears further investigation.
Future research will continue to focus on issues of regression specification. Recognizing that the theoretical premise of the hedonic hypothesis is a comparative static model, it is advisable to examine the behavior of characteristics implicit prices in the dynamic market context. The importance of currently unobserved quality attributes in the hedonic model merits more research, especially given that the vintage variable appeared to be important to the numerical results.
Arguea, Nestor, and Cheng Hsiao (1993) “Econometric issues of estimating hedonic price functions,” Journal of Econometrics, 56, pp.243-267.
Armknecht, Paul (1984) “Quality Adjustment in the CPI and Methods to Improve It,” Proceedings of the Business and Economic Statistics Section, American Statistical Association, pp. 57-63.
Armknecht, Paul, and Donald Weyback (1989) “Adjustments for Quality Change in the U.S. Consumer Price Index, Journal of Official Statistics, 5, pp. 107-123.
Armknecht, Paul, Brent Moulton, and Kenneth Stewart (1995) “Improvements to the Food at Home, Shelter, and Prescription Drug Indexes in the Consumer Price Index,”
BLS Working Paper No. 263.
Court, Andrew (1939) “Hedonic Price Indexes with Automobile Examples,” in General Motors Corp. The Dynamics of Automobile Demand, New York: General Motors Corp., pp. 99-117.
Edmonds, Radcliffe (1985) “Some Evidence on the Intertemporal Stability of Hedonic Price Functions,” Land Economics, 61, pp. 445-451.
Epple, Dennis (1987) “Hedonic Prices and Implicit Markets: Estimating Demand and Supply Functions for Differentiated Products,” Journal of Political Economy, 95, November, pp. 59-80.
Fisher, F., and K. Shell (1972) The Economic Theory of Price Indices: Two Essays on the Effects of Taste, Quality, and Technological Change. New York: Academic Press.
Fixler, Dennis (1993) “The Consumer Price Index: underlying concepts and caveats,” Monthly Labor Review, 116, December, pp. 3-12.
Griliches, Zvi (1971) Price Indexes and Quality Change: Studies in New Methods of Measurement. Cambridge: Harvard University Press.
Kokoski, Mary (1993) “Quality adjustment of price indexes,” Monthly Labor Review, 116, December, pp. 34-46.
Kokoski, Mary, and Keith Waehrer (1998) “Hedonics and Quality Adjustment for Price Indices for Consumer Electronics Products,” draft presented to NBER Summer Institute Conference on Price Indices, July.
Liegey, Paul (1993) “Adjusting Apparel Indexes in the CPI for Quality Differences, in Foss, M., M. Manser, and A. Young (eds.) Price Measurements and Their Uses. National Bureau of Economic Research Studies in Income and Wealth, 57, Chicago: University of Chicago Press, pp. 209-226.
Liegey, Paul, and Nicole Shepler (1999) “Using Hedonic Methods to Quality Adjust VCR Prices: Plucking a Piece of the US CPI’s ‘Low Hanging Fruit’?” Monthly Labor Review, forthcoming.
Moulton, Brent, Timothy Lafleur, and Karin Moses (1999) “Research on Improved Quality Adjustment in the CPI: The Case of Televisions,” Proceedings of the Fourth Meeting of the International Working Group on Price Indices, U.S. Dept. of Labor, January, pp. 77-79.
Parker, P. (1992) “Price Elasticity Dynamics Over the Adoption Life Cycle,” Journal of Marketing Research, August, pp. 358-367.
Rosen, Sherwin (1974) “Hedonic Prices and Hedonic Markets: Product Differentiation in Pure Competition,” Journal of Political Economy, April, pp. 34-55.
Silver, Mick (1998) “Bias in the Compilation of Consumer Price Indices when Different Models of an Item Coexist,” paper presented to the 1998 Ottawa Conference at the U.S. Bureau of Labor Statistics, Washington, D.C, April.
Stavins, J. (1997) “Estimating Demand Elasticities in a Differentiated Product Industry: The Personal Computer Market,” Journal of Economics and Business, 49, pp. 347-367.
Triplett, Jack (1986) “The Economic Interpretation of Hedonic Methods,” Survey of Current Business, January, pp. 36-40.
Triplett, Jack (1988) “Hedonic functions and hedonic indexes,” in The New Palgraves Dictionary of Economics, pp. 630-634.
Triplett, Jack (1990) “Hedonic methods in statistical agency environments: an intellectual biopsy,” in Berndt, E.R., and J.E. Triplett (eds.) Fifty years of economic measurement: the Jubilee Conference on Research in Income and Wealth, NBER Studies in Income and Wealth, Chicago: University of Chicago Press.