sou · GNB3124 · 2000-01-01 00:00:00 — Med ovan redovisade siffror skulle summan av KPI-effekterna bli -0,05+0,025- 0,015=-0,04, alltså en underskattning med . Vi betraktar inte detta som någon initierad bedömning men dock som mittpunkten i ett ganska brett osäkerhetsintervall som också innehåller positiva värden. Fel i övriga delindex KPI:s elundersökning har fram haft betydande brister. Framöver kommer stora förändringar att ske på denna marknad och metoden är f.n. under utredning vid SCB. Innan den framtida metoden är fastställd är det svårt att diskutera dess felrisker, men klart är att om man väljer en Laspeyresartad teknik kan de överskattande substitutions- effekterna p.g.a. leverantörsbyten bli betydande. (har byten varit nästan omöjliga varför den eventuella biasens riktning varit obestämd.) Det går dock inte att säga något bestämt om dessa effekter. Detsamma gäller de relativt sett mindre brister som föreligger i flera andra av de mindre underökningarna på bostadsområdet, avseende energi, va-avgifter, reparationer m.m. Vi betraktar därför denna biaskomponent som omöjlig att bedöma f.n., dock med en större risk för över- än underskattning. Vi redovisar i tabell 9 i sammanfattande form de kvantitativa biasbedömningar som gjorts ovan. Tabell 9: Sammanställning av biasbedömningar. Produktsubstitution, hög nivå LTIX: -0,04, KTIX: +0,08 D:o, mellan- och låg nivå Nya produkter, varianter samt kvalitetsförändringar Undertäckning, offentliga tjänster (-0,04) -0,30* Hyror, summa av flera fel -0,04 LTIX: 0,01 (-0,03) KTIX: 0,13 (0,09) En delpost i tabellen avser fel vars storleksestimat inte är relevant för framtiden. Den har satts inom parentes liksom den summa där denna ingår. Vad gäller HIKP som bygger på korttidsindex ingår inte egnahemsposten. Om denna post läggs till erhålls värdet 0,43, som emellertid måste räknas upp med hänsyn till den mindre viktsumma som HIKP omfattar. Med hänsyn till detta blir vår biasbedömning för det svenska HIKP i relation till ett levnadskostnadsindex (som dock ej erkänns som måttstock för detta index) 0,5, betingat av att vi accepterar den av HIKP tillämpade principen att utesluta egnahemskostnaderna. Dessa siffror står för en så initierad bedömning som är möjlig att göra på nuvarande kunskapsnivå av den bias som kan bedömas erhållas om KPIs nuvarande metodlösning bibehålls. De avser en genomsnittlig biaseffekt . Under ett enskilt år kan effekterna bli väsentligt annorlunda såsom framgår av detaljanalysen. Den betydande osäkerheten i bedömningen till följd av de underliggande mätkonceptens oprecisa formulering måste klart poängteras. Vi avstår dock från att ge uttryck för denna osäkerhet i ett intervall. BLS (1997): Measurement Issues in the Consumer Price Index. Response to the U.S. Congress, , 1997. Boskin, M.J., Dulberger, , och (1996): Toward a of the Cost of Living. Final Report to , 4, 1996 Boskin, M.J., Dulberger, , och (1998): Consumer Prices, the Consumer Price Index, and the Cost of Living. 12, 1, 3–26. (1993): Measurement Biases in the Canadian CPI: A Technical Note. Bank of , Summer, 21–36. (1998): Measurement Biases in the Canadian CPI: An Update. Bank of , Spring, 38–56. (1996): Measurement Biases in Price . An Application to the UKs RPI. (1992): Computing Elementary Aggregates in the Swedish Consumer Price Index. 8, No.2, 129– 147. Diewert, W.E. (1995): Prepared Statement. In: Consumer Price Index. Senate, 104th Congress, 115–118. Government Printing Office. Fri Köpenskap (1998): Få handlare tror på Internet. Artikel, vecka 48. , R.J. (1990): The Measurement of Durable Goods Prices. , R.J. (1999): The Boskin Commission Report and its Aftermath. Presented at Conference on the Measurement of Inflation, , , – 1 sptember, 1999. Griliches, Z. och Cockburn, I. (1994): Generics and the Producer Price Index for Pharmaceuticals, i Competitive Strategies in the , edited by , , s. 19–34. Hausman, J. (1996): Valuation of New Goods under Perfect and . I Bresnahan och , eds. The Economics of . (1998): in . Diskussionspapier 1/98, . , L., , B. och Reichel, J. (1993): Handeln . Klumpner, J. (1996): Fact and : CPI Biases and the Federal Budget. Business Economics, April, 22–29. Lequiller, F. (1997): Lindice des prix à la consommation, surestime-t- il linflation. Économie et Statistique No 303, 3–32. Moulton, B. och Smedley, K. (1995): A Comparison of Estimators for Elementary Aggregates. (1995): Measuring Inflation in a High-Tech . , /, 13–25. (1998): Ny princip för varubyten i KPIs lokalprissystem . SCB, , PM . Nordhaus, W. (1998): Quality Change in Price Indexes. 12, 1, 59–68. Oulton, N. (1993): Do UK Price Indexes Overstate Inflation? , 152, 60–75. Popkin, J. (1997): CPI Commissions Findings are not Convincing. and Company, , D.C., January 6. , W. (1988): Housing Depreciation and Aging Bias in the Consumer Price Index. Statistics, 6, 359–371. Reinsdorf, M. (1993): The Effect of Outlet Price Differentials on the U.S. Consumer Price Index. In , , (eds.) Price Measurements and Their Uses. NBER Studies in Income and Wealth 57, 227–254. SCB (1997a): Nationalräkenskaper 1980-1996. SCB (1997b): Bostads- och hyresundersökningen 1997. Del 1: Lägenhetsdata för hyres- och bostadsrättslägenheter. & Wilcox, D. (1996): Mismeasurement in the Consumer Price Index: An Evaluation. NBER Macroeconomics Annual, 94–142. & Wilcox, D. (1997): Alternative Strategies for Aggregating Prices in the CPI. of , 79, no.3, 113–125. Shiratsuka, S. (1999): Measurement Errors in Japanese Consumer Price Index. Svensk Dagligvaruhandel (, 1996–1998): Marknadsanalys, tabell. Measures to reduce KPI-bias due to quality change by , Ph.D. In the report on KPI bias (Bilaga 6) it was estimated that the present treatment of quality change and new products may contribute to an overestimating effect of about 0.3 percentage points per year compared with a true cost-of living index. This is the largest single bias factor in the KPI along with the model error for owner-occupied housing (for which the error has the opposite sign). As could be seen in the international review in Bilaga 6 there is largely agreement on this judgement among CPI bias investigators although the exact size of the bias is uncertain and under debate. The Swedish bias estimate is a net of over- and underestimating effects, where, however, the over-estimating factors dominate. The aim of this report is to discuss measures to reduce this bias. After stating our fundamental starting points, we present and discuss available methods one by one. Thereafter we look at the major product areas of the KPI and discuss how they should or should not be applied. Before discussing actions against bias, we state some matters of principle which follow from earlier reports. A)The ideal index is a plutocratically weighted, cost-of-living index, conditional on the decided expenditure bound and other external factors which are to be held . (See , 1998a and 1998b for more discussion of this issue.) B)The valuation of quality should always be based on the perception of the average consumer of a certain item. Where possible we should look for market manifestations of this consumer valuation. The economic notion of revealed preference can provide guidance here. C)We propose that the Boskin paradigm (, 1998b) be critically accepted. That is, as a general rule, not without exceptions, it can be assumed that a new product, a new model of a high-tech product or a new outlet which gains market shares in competition with other close substitutes does so because it gives the consumer more value for money and thus creates a consumer surplus. Somewhat loosely this follows from the notion of revealed preference. This added value is a result of either a lower price which is not accompanied by a correspondingly lower quality or a higher quality which is not accompanied by a correspondingly higher price. D)Changes in products required by legislation should only count as quality improvements to the extent that the consumer in her market behaviour has shown her preparedness to pay more for the characteristic/optional extra. Available methods their potentials and shortcomings In this section we present a number of methods which are generally available and used or discussed today. We discuss their motivation and potential as well as the bias risks associated with each one of them. Simple methods: direct price comparison, link- to-show-no-change, imputation and deletion The usual procedure in a CPI, used in most product groups, is to somehow select a well specified item in a particular outlet in the base period and continue to look for the price of exactly that item in that outlet as long as it is still sold there in significant quantites. When this is no longer the case a replacement is chosen which is priced thereafter. The quality adjustment issue then is how price change is estimated between the last month of the replaced item and the first month of the replacing item. Direct price comparison means that we just continue to compare the prices as if no replacement has taken place. In the individual case this is clearly appropriate only if the quality difference between the items is negligible. But on the aggregate scale, which is what matters in statistics, the method is only biased to the extent that there is an average quality change in a certain direction, else the individual biases cancel. In many products areas, such as high-tech goods, we believe that there is quality improvement and this method will then have an upward bias. The method is, however, clearly a candidate for product groups with zero or very small aggregate quality change. Such product groups might be clothing and shoes, furniture and other low-tech goods and services. Link-to-show-no-price-change means that we treat the whole of the price between the last month of the replaced item and the first month of the replacment item as a quality difference, thus assuming that there has been no price change. (Usually, the reference price is adjusted by the amount or ratio of the difference.) This method is in many countries used as a default method under the excuse that the two items are non- comparable. It is easy to demonstrate that this method has a downward bias if there is positive inflation going on in the product group to which the item belongs. For products where price increases are accompanied by quality improvements of the same order this method would have a smaller bias than direct comparison. Still, there is no way to estimate the extent of the bias in this case. Imputation means that, between the last month of the replaced item and the first month of the replacing item, price change is imputed from matched items in the same elementary or higher aggregate to which the item belongs. This method is unbiased unless there is a tendency in the market to introduce price changes precisely when new varieties are introduced. (A variant of this method is to use, for the imputation, only replacements for which some other approved method has been used. This method is used by the BLS under the name class mean imputa- tion). Deletion means that a disappearing item is deleted from the computation altogether, without replacement, after its last month of so that price change is based only on matching items. It differs from imputation as described above by deleting also observed price change for the deleted item from the reference period to the last period before the deletion. Although simpler, this method is thus inferior to imputation in that some observed price change is arbitrarily thrown out of the estimate. All of the simple methods are biased. Under special circumstances these biases could be assumed to be small and the methods would then be acceptable. At present, the deletion method is used for food and daily necessities where its bias is thought to be negligible. Direct price comparison is used for restaurant meals and in other places, often without explicitly recognising the quality changes. Other methods are not much used. There is, by now, a large body of literature on the theory and practice of hedonic regression for estimating price change in dynamic markets, where products and their characteristics undergo continual change. The method consists in estimating the value of the characteristics through multiple regression with price as the dependent variable and by using the estimated coefficients for estimating the price index in one way or another. The application of hedonic regression in current CPI calculations has so far meant that multiplicative coefficients are estimated intermittently (often once a year) by regressing the log of the price against a number of characteristics based on sample of varieties and their prices that existed in the reference period or earlier. When there are replacements in the index during the year these coefficients are then used for adjusting the reference price. (It would be possible to use other methods based on continual reestimation of the coefficients but such methods are as yet untested.) The advantages and problems connected with the hedonic regression method have been extensively discussed by many authors and are impossible to even summarise here. It seems important to make a distinction between its use in retrospective studies of price change on one hand (which is what the scientific literature has almost exclusively been occupied with) and its use in an ongoing price index program on the other hand. Kokoski (1993) at BLS has the latter perspective and makes a number of interesting observations on what is required for a true cost- of-living index to be to be constructed in terms of characteristics . The bottom line of her argument is that where the variety spectrum is wide and quality changes are not too large, the implicit prices of the characteristics of goods are probably useful measures of real quality differences. Moulton and (1997), also at BLS, make the following assessment of the hedonic method from an applied point of view: Well-specified hedonic models probably represent the most robust method currently used by the BLS, but it is well known that errors in hedonic model specification can lead to errors of quality adjustment in either direction. 329–330) One such troublesome error is when a new characteristic turns up in the current period which did not exist in any model in the reference period (e.g. a DVD player in a PC). This method is so far used in only for clothing items. In the U.S. the BLS uses it for clothing and for rents and plans to extend it to many other product groups. The experience is largely positive, the main reason probably being the wide spectrum of varieties and stable set of characteristics over time. We should be aware, however, that we are not able to cover new types of garments in the regressions, so the new product problem is not resolved. For cars, attempts to use hedonics in CPIs have so far not been successful. For example, (1990) notes that hedonics cannot cope with fuel economy which is a key quality charac-teristic. The cofficient tends to get the wrong sign, partly because it is collinear with weight whose coefficient has the opposite sign. In CSO (1993) a paper to an conference on CPIs the UK CSO describes negative results with an experimental index: coefficients were not significant and unstable over time and the resulting sometimes behaved erratically. Other similar but undocumented negative experiences are part of the oral tradition among price statisticians. For PCs, on the other hand, hedonics has been used with some success in Producer Price , see e.g. and Catron (1990) or (1992) but not yet CPIs. There is no a prioiri reason why hedonics could not work for CPIs also. A factor to bear in mind is the cost of the method. to i) the need to carefully specify detailed regression models for many narrow product categories (for clothing there are at present about 25 different models used for different garments) and ii) the need for a largescale collection of quality characteristics for a fairly large sample of prices, the method will, at the present state of the art, be very expensive to implement on a large scale. experiments with and applications of hedonics are clearly desirable. The most promising areas appear to be household appliances (refrigerators, washing , TVs etc.) and PCs, where several studies with positive results have been done internationally. Much of the work is preferably done in international cooperation and due to the cost restrictions this is likely to be the best way forward. But hedonics will not become the panacea for all quality change problems in the foreseeable future. The subjective assessment of the value of quality adjustment is the most common method used today in the KPI. In the form of expert judgement it is used for new cars and in the form of price collector adjustment it is used for a large number of commodities, e.g. shoes, furniture, TV/stereo/video, household appliances and equipment and more. The idea behind judgemental assessment is that the expert or price collector should act as a representative consumer and reflect her willingness to pay for a certain kind of quality change. For cars this method is thought to have an annual upward bias of about and is thus in a need for change. For clothing it was abandoned in 1990 due to a severe underestimating bias for some womens garments. For other products no exhaustive evaluation has been done. As a whole our feeling is that this method is vulnerable to uncontroled bias unless it is carefully monitored and assessed. Although no evidence of large biases besides those for cars exist at the present time, it would be desirable to move successively towards more objective procedures. Under certain conditions overlap pricing is known to be an unbiased procedure. The method consists in pricing both a disappearing and a new item together in at least one period so that price change could be estimated for the disappearing item up to that period and on the new item thereafter. This method is presently applied on a large scale in the base period () each year when a new link is started up. In we thus have a planned overlap procedure. We consider this method to be unbiased if consumers are well informed and have a real choice between the two products. It is sometimes said that it is downward biased, where the disappearing item is sold at reduced prices in clearance sales in the overlap period at the same time as the new item is coming in at a regular level. We are not sure that this is always the case. The fact that an old item is sold at reduced prices may just as well be a reflection of its relative inferiority compared with the new item. The price reduction of the old item is in this interpretation what it needed to continue to market it alongside the new one and thus a true reflection of the quality difference. Which of these views is correct? We would like to suggest here that it depends on whether there is an obsolescence element involved. By this term we understand the phenomenon that the quality of new items (in their unused form) of products decreases over time? ladies and other garments is one example1 another is newly published novels which end up in sales and pocket editions after a few years. For products where there is an element of obsolescence the overlap method will cause an underestimating bias since the outgoing item is in its late, low quality phase, but the new one is in its early, high quality, phase. Where there is no such element the overlap method is ap- proximately unbiased. The problem then reduces to that of dividing the products into two categories according to this criterion. It is not obvious how to do it. Take one example, PCs. New PCs are clearly better by objective standards – they have a faster processor, larger hard disk and memory etc. You can store more advanced software on them and perform new tasks like internet surfing and cooler video games etc. But there is also an obsolescence element to the extent that performing the old task on the old model becomes more difficult. For example, if you do word processing today on a 286 PC you will have no service and you cannot buy any new software for it. This an element of obsolescence in the PC market which may cause a certain underestimating bias with the overlap method. In our judgement this element is small, however, so the bias will also be small. Besides obsolescence, there are two other bias risks associated with the overlap method in todays consumer markets. The first one could be called the discontinuity of inflation – prices are changed only intermittently due to menu costs and often in connection with the introduction of a new variety. If then the overlap period is very short and just reflects a short period of clearance sales and the inflation is high, then price increases may be lost in this way leading to an underestimating bias. The second bias risk goes in the opposite direction. Where the new variety gives much better value for money than its predecessor it will quickly gain market shares. The price differential existing during the overlap period will then be smaller than the quality difference, resulting in an overestimating bias. In the present dynamic, low inflation economy we would judge the second bias risk to be greater than the first one. Under conditions of high inflation this judgement may well be reversed, however. We are thus identified three possible risks for bias associated with overlap pricing. Two of them are underestimating and one is 1 Another way to look at obsolescence in womens clothing is to note that the assortment is larger in the early season. Later the more popular items get sold out whereas the less popular ones remain to the late season, when they are sold at reduced prices. Also under this interpretation, the average quality of the product mix is lower in the late season, so we have obsolescence. We consider obsolescence to be the greatest problem of these and where this effect is large (such as for womens fashion clothing), we will have to avoid (or correct for) overlap pricing. But large obsolescence effects are exceptions in consumer markets. This statement would obviously need more research. But if it is accepted for large sections of products, notably durables and particularly appliances and electronic goods, the overlap method would be a robust and fairly inexpensive way to reduce KPI biases. The net risk associated with the overlap method is probably more under- than overestimating. When inflation is high this tendency would be stronger, whereas under deflation it may even be reversed. Since we have an inevitable upward bias resulting from the failure to take the consumer surplus created by new products into full account, we might be prepared to accept a risk for a small underestimating bias due to quality change. We believe this to be the case if any variant of the overlap method is used today in product areas with no or small obsoles- cence effects. A particular kind of overlap – monthly chaining and resampling (MCR) One practical difficulty with the overlap method is that it is often not known in advance how long a certain variety will stay in the market. Some kind of planned overlap is then needed, whereby a new variety is selected before the old one disappears. With the usual one-to-one matching this is difficult to achieve, since it is difficult for price collectors to predict when replacements should be made. An option, suggested by , is to use what he calls a multiperiod overlap and which we choose here to call MCR (Monthly Chaining and Resampling). The definition is as follows: Monthly Chaining and Resampling, Definition 1.A fairly loose definition of a representative item is chosen. (E.g. PC systems, 26–34 TV sets).