SUBSTITUTION COMPARABILITY RATIOS

APPAREL COMMODITIES

COMPARABILITY RATIOS

100%
95%
90%
85% |:
80%
75%
70% |
65%
60% |
55%
50%
45% y
40%

FIGURE 1



Notes

. The US CPI is a weighted average of 9,108 basic item-area indexes covering 44 geographic areas and
207 groupings of goods and services called item strata. BLS occasionally uses prices from more than
one area to get an adequate sample size for purposes of imputation.

. Valliant and Miller (1989) simulate the deleterious long run effects of chaining on the index’s variance.
They also provide a formula that minimizes the mean square error of a long run index. This formula
comes close to simply treating all substitutions as comparable.

. Descriptions of the hedonic index research for apparel appear in Armknecht (1984), Liegey, (1993) and
Liegey (1994).

. The procedures adopted in 1987 required waiting until the second month of the selling season to
substitute to a new version of a seasonal item. The purpose was to allow time for a complete selection
of new fashions to arrive in the stores. In 1992 BLS began permitting substitutions during the first
month of selling seasons because new fashions now arrive more quickly once selling seasons start.

. See Armknecht and Weyback's (1989) Table 1 and Callahan (1984).
. Kalton and Kasprzyk (1986) discuss class mean imputation. For additional information about BLS’s use

of class mean imputation see, “Improvements in CPI Procedures,” CP/ Detailed Report (Bureau of

Labor Statistics, August 1992, pp.3-4).

. For example, Reinsdorf (1993) discusses the example of entry by lower-priced outlets, while Griliches

and Cockburn (1994) discuss the linking in of lower-priced generic pharmaceuticals.
. Popkin (1993, p. 256) suggests that BLS should undertake such research.

. See Hsiao (1986) or Baltagi (1995). Moulton (1986) shows that error components can also arise in
cross-sectional regressions involving grouped data and that they can easily cause large biases in OLS

standard errors.


Page 3

price change between the two periods. BLS imputes the price change effect either from an index
containing either all similar items or from a specially selected set of similar items from the same
geographic area.’ These procedures are equivalent to chaining an index for the market basket containing
the replacement item with an index for the market basket containing the original item, with an intermediate
link based on a market basket containing neither item.

Noncomparable substitutions reduce the effective sample size for the CPI. In addition, Reinsdorf
(1994) points out that repeated chaining of a price index can make the index take on the character of a
geometric random walk. This type of time series has undesirable long run properties, including an
exploding variance, mean and coefficient of variation.? However, the most serious problem with
noncomparable substitutions is that they can cause bias when items undergoing design changes have
different price behavior from items that can be repriced.

To avoid this problem, BLS has reduced the frequency of noncomparable substitutions and improved
its price change imputation procedures. Procedures that diminish the need for noncomparable
substitutions have been most important in the apparel indexes, while the improved imputation method was
originally developed for the new car indexes. We will therefore discuss how BLS decreased
noncomparable substitutions in the context of the apparel indexes, and then discuss the new method of

price change imputation in the auto indexes.

ll. Reducing Noncomparable Substitution Rates: the Case of Apparel Indexes
A. Difficulties in Measuring Price Change for Apparel
Table 1 shows that noncomparable substitutions in the CPI fell from around 2 percent of all collected
prices in 1983 to about 1.1 percent in 1995. The primary cause of this fall was a decline from 10.2 percent
to 2.6 percent in noncomparable substitution rates in the apparel and upkeep components of the CPI.
Many types of apparel are subject to seasonal cycles that make price change measurement difficult.
Apparel retailers typically begin a selling season with a broad selection of items at high “regular” prices
because they cannot predict which fashions will be popular; see Pashigian (1988) and Lazear (1986).
Inventories of the most popular items may sell out early in the selling season. Other items turn out to be

less marketable and must be marked down in order to sell. Consequently, as the selling season


Table 2

Growth Rates of Annual Average Indexes for
Apparel Commodities, All Commodities, and All Items

1980-1995°

Years Compared Apparel Commodities All Commodities Allitems
1980-81 3.9 8.4 10.3
1981-82 1.9 41 6.2
1982-83 1.9 2.9 3.2
1983-84 1.3 3.4 4.3
1984-85 25 21 3.6
1985-86 : 0.2 -0.9 1.9
1986-87 45 3.2 3.6
1987-88 44 3.5 41
1988-89 26 47 4.8
1989-90 45 5.2 5.4
1990-91 3.6 3.1 4.2
1991-92 24 2.0 3.0
1992-93 1.2 1.9 3.0
1993-94 -0.5 1.7 2.6
10/94-10/95 . -0.7 1.7 2.8

a. Growth rate for 1994-5 is based on index changes from October, 1994 to October, 1995.


Page 8

are likely to be unaltered, and prices of unaltered products often behave differently from prices of
redesigned products.

In the motor vehicle industry, regular product redesigns are an institution as each year manufacturers
introduce new car and truck models. When comparing prices from different model years, BLS must
account for design changes that affect vehicle performance and efficiency. In addition, BLS treats
changes in mandated safety and pollution requirements as changes in quality.

Since the early 1960s, BLS has estimated the value of vehicle quality changes by marking up
manufacturers’ estimates of the production costs of those changes to the retail level. Although BLS
adjusts more than half of all new model year car and truck prices for quality changes in this way,
manufacturers are often unable to provide production cost data for extensive changes between model
years. For example, when a rear-wheel drive car becomes front-wheel drive, assessing the production
cost of the design changes with any degree of precision may be impossible. When BLS cannot directly
estimate a “pure” price change for a new model year vehicle that excludes the effects of quality changes, it
must impute a price change based on the behavior of other vehicles’ prices.

Before October 1989, when BLS had to impute a price change for a new vehicle model, it used an
index that included all other vehicles in the sample from the same geographic area. This meant that the
price change between a discontinued model and a dissimilar new model was imputed from a sample
consisting predominately of vehicles from a single model year. Using the entire sample for price change
imputation is appropriate when introductions of new versions of the product are unrelated to price change,
and BLS continues to use this method for most food items and all services. For automobiles, however, a
significant relationship exists between price change and the introduction of the new models. Within a
model year prices tend to be constant—or even to decline near the end of the year—while between model
years prices typically increase. Imputing price change for new model years based largely on price
changes for continuing models is, therefore, inappropriate.

Table 3 illustrates the effect of restricting the sample used for price change imputation to directly
comparable and quality-adjusted substitutions. When the model year does not change, prices of new cars
in Chicago decline, on average, by 0.2 to 0.7 percent per month. On the other hand, prices for new model

years average 0.2 to 5.8 percent higher than prices of the previous model year in the previous month,


Page 5

label category (local store or national), size range, type of lining (bodice or full), and type of outlet
(full service or discount).

Estimating hedonic adjustment coefficients is a challenging and demanding process. First, commodity
analysts must select a sample and review the data for completeness, consistency and accuracy. Next, the
commodity analysts must resolve econometric difficulties associated with model specification, adequacy

and performance. For example, Shepler (1994) notes that,

“Applying hedonic regression to women's apparel [is] especially problematic since perceived
fashionability can be as much a determinant of price as measurable characteristics such as
clothing fibers. Since difficulty also exists in separating out the effects of the [price determining]
variables, multicollinearity frequently occurs. Misspecification resulting in biased estimates is
another potential problem. There is also uncertainty as to the stability of the parameter estimates
due to the dynamics of the apparel marketplace.”

To date, CPI apparel analysts have developed more than 30 hedonic regression models for women's,
men’s, girls’, and boys’ apparel, as well as footwear, and jewelry. This research has improved the apparel
indexes in three major ways. First, as commodity analysts have learned more about how quality ©
characteristics affect apparel pricing, they have been able to make substitution comparability decisions
more accurately and more confidently. This is quite important because most apparel commodities display
some seasonal marketing pattern, and changes in styles often make finding the same item that was priced
in the previous fall/winter or spring/summer selling season impossible.

Second, BLS has revised the apparel data collection forms (known as “checklists’) to help its field
economists find substitute items that match the major price-determining characteristics of original items
that have become unavailable. This increases the chance of the BLS field economist choosing a
comparable substitute. Hedonic coefficient estimates and statistical significance measures have played
an important role in identifying the major price-determining characteristics for each type of apparel.

Lastly, since January 1991, BLS has used hedonic regression coefficients for direct quality
adjustments of apparel item prices. When major price-determining characteristics do not match,
commodity analysts attempt to use parameter estimates from hedonic models to adjust for quality
differences between original and substitute items. Such adjustments have further reduced reliance upon

noncomparable substitutions. The appendix provides an example of a hedonic quality adjustment.


Page 4

progresses, mark downs grow larger and more common. Eventually a new selling season begins and
heavily discounted "clearance-sale" items give way to new fashions with high "regular" prices. Many of
these items are also destined to sell for less than their initial asking price.

Rising price levels for clothing tend to take the form of higher prices at the start of selling seasons.
Once an item's selling season has begun, its price tends to be constant or falling, even in times of high
inflation. Thus, imputing price changes for new fashions from price changes for continuously available
apparel items is likely to cause downward bias. Unfortunately, however, changes in styles and fashions
often make matching garments that appear at the start of a new selling season with identical garments
from the corresponding selling season of the previous year impossible. Consequently, until a few years
ago, new fashions frequently entered the CPI as noncomparable substitutions whose price changes were
imputed from samples consisting primarily of repriced items.

During the early Eighties, efforts to avoid comparing prices of different quality items resulted in price
change imputation for about 60 percent of apparel substitutions. Armknecht and Weyback (1989, p. 113-
114) observe that these imputations reduced the growth rate of the apparel commodities index from 12.4
percent to 1.4 percent in 1984, and that over the nine year period ending in December 1986 the index for
apparel commodities increased by only 25.5% while the all-items CPI increased by 77.9%. Since industry
sources indicated that apparel inflation rates were higher than the CP! implied, BLS began work on
improving the CPI apparel indexes. Research on the use of hedonic regressions for apparel indexation

was a major component of this effort.3

B. Hedonic Regressions for Apparel
Hedonic regressions for apparel commodities estimate the average effect on price of garment

characteristics such as fiber content, style, type of designer and type of outlet. Their functional form is:

N

log P = By + >) BX, +e

Here B, is the expected effect of the n” characteristic on the natural logarithm of the item's price. In most
cases X, is dichotomous, but in some cases it measures a continuous quantity such as percentage wool.
For example, the hedonic regression model for women's dresses provides estimates of the effect on price

of 19 characteristics such as style (daytime or formal), number of pieces (one or two), fiber content, brand


Table 3

Effects of Class Mean imputation for New Cars in Chicago

September 1994 - January 1995°*

Sept94 Oct94 Nov94 Dec94 Jan95

Price change w/in the same model year -0.3% 06% -0.6% -0.7% -0.2%
(“Class A”) .
Price change between model years 0.8% 2.4% 5.8% 1.2% 0.2%
(“Class B”)

Weighted average price change for : 0.1%  -0.1% 1.1% -0.5% -0.2%
Classes A and B

Price change for noncomparable model
changeovers imputed by old method 0.1%  -0.1% 1.1% -0.5% -0.2%
(ie., imputed from Classes A and B)

Price change for noncomparable model

changeovers using class mean imputation +08% +24%  +5.8% 1.2% 0.2%
(i.e., imputed from Class B alone)

Index, old imputation method,
August 1994=100.0 99.858 99.712 100.789 100.335 100.180

Index, class mean imputation method,
August 1994=100.0 100.291 100.220 101.366 100.947 100.854

a. Most new car substitutions in the CPI occur between September and January.


Page 7
D. Results of the Apparel Index Improvements

In the early 1980's BLS classified only about 40 percent of the substitutions in the CPI apparel
samples as comparable.> Figure 1 shows that this pattern persisted until 1987. Then, between 1988 and
1990 the proportion of substitutions deemed comparable rose to around 65 percent due, in part, to help
from hedonic regression results in designing data collection forms and in making comparability decisions.
In 1991, BLS began to make quality adjustments based on hedonic regression results and apparel!
comparability ratios climbed again, reaching 85 percent.

Table 2 displays the annual rates of changes of the all-items, all-commodities and apparel
commodities indexes from 1980 to 1994. The gaps between the growth rates of the all-items and all-
commodities indexes on the one hand and the growth rate of the apparel commodities index on the other
hand seem implausibly large prior to 1987. Since then, however, the apparel commodities adex has
diverged much less from the broader indexes.

The effect of hedonic quality adjustments can be gauged by comparing indexes calculated with
hedonically adjusted prices to indexes that do not incorporate such adjustments. Liegey (1994) compares
price indexes calculated wi:nout hedonic quality adjustments to published indexes for seven types of
apparel from June to December, 1991. Three of the indexes omitting the hedonic quality adjustments are
higher than their official counterpart, three are lower, and in one case hedonic adjustments have no net
effect. Yet, when all seven indexes are aggregated into an index for apparel as a whole, hedonic
adjustments raise apparel's measured inflation rate by 0.2 percent. This occurs because the hedonically
adjusted indexes for the items with the lowest comparability ratios grow much more quickly than their
unadjusted counterparts. In particular, women's dresses rise 0.8 percent more with hedonic quality

adjustment, and women's suits rise by an additional 3.2 percent over the six month period.

lll. Using a Representative Sample to Impute a Price Change: The Case of Autos
Despite BLS's progress in reducing the number of noncomparable substitutions, product replacements
and modifications sometimes bring prices into CPI samples that cannot be compared with an earlier price.
Simply omitting such prices from CPI calculations is the solution that comes immediately to mind, but
doing this may cause bias. Dropping a redesigned product from the index is equivalent to imputing its

price change by an average price change of the remaining products in the index. Most of those products


BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR
Bureau of Labor Statistics

OFFICE OF PRICES AND
LIVING CONDITIONS

New Ways of Handling Quality Change in the
U.S. Consumer Price Index

Marshall B. Reinsdorf,
Paul Liegey, and

Kenneth Stewart

Working Paper 276
April 1996

Theviews expressed ara those of the authors and do not reflect the policies of the U.S. Bureau of Labor Statistics or the views of other BLS
staff members.


Page 10

advances this bias will be upward,’ but downward biases may also be common. In particular,
manufacturers often time real price increases to coincide with product redesigns or the introduction of new
models. Such behavior is particularly evident in industries that have regular cycles of product line
tenewal, such as apparel and autos.

In recent years the BLS has implemented new approaches for handling changes in item characteristics
that make the CPI much less prone to miss price increases obscured by simultaneous changes in product
attributes. First, noncomparable substitutions occur less often than they once did. This is true for many
types of items, but apparel, where the problems were most serious, has experienced the greatest
improvement. Improved substitute selection procedures and changes in the timing of apparel substitutions
have both helped to increase success rates in finding comparable substitutes. In addition, BLS now uses
hedonic regression coefficients to adjust prices of substitute apparel items to correct for differences in
quality. In many cases, such adjustments make price comparisons possible in situations that would once
have required price change imputation.

Second, when noncomparable substitutions involve a type of item whose price change may be related
to the introduction of a new product version, BLS now imputes the price change for a new or redesigned
item using a more representative subsample containing items that have also undergone changes. This
improvement provides an interesting illustration of the importance of considering sample selection effects
in designing statistical estimators.

BLS is continuing research on improved procedures for bringing new items and outlets into the apparel
indexes. One area of investigation is the role of outlet quality differences in outlet price differentials, and
the extent to which these price differentials should be reflected in the CPI when one outlet replaces
another during sample rotation.® BLS is also exploring the use of panel data for some apparel
commodities whose cross-sectional samples are too small to yield usable results. Ordinary least squares
(OLS) generally cannot be used for estimation with such data because the assumption of independent
errors is violated. Instead, an estimation method that allows for the presence of error components must be
used to avoid misleading statistical inference.? Some success in obtaining quality adjustment factors for

product characteristics has already been achieved.


Page 9

even after factoring out quality differences. Consequently, using a subsample containing only model
changeovers to impute the price change of any noncomparable new model raises the January 1995 index
from 100.18 to 100.85.

Stewart (1988, p. 12) reports similar patterns at the national level. He finds that for monthly collection
periods ending between March 1987and February 1988, car models undergoing repricing had, on average,
no price change. In contrast, after adjusting for quality differences, models that entered the index as
comparable substitutions cost an average of 3 percent more than their counterparts had cost in the
previous month. In cities that have bi-monthly collection periods for car prices, Stewart finds that the
average price change between visits was -0.2 percent for repriced models and 2.8 percent for directly
comparable or quality-adjusted substitutions. Over the twelve month period ending February 1988, a new
car index that just used comparable substitutions to impute price change for noncomparable substitutions
rose 2.76%, compared to 2.42% for the published index.

In October 1989 these results led BLS to adopt class mean imputation for estimating price changes for
new automotive models of dissimilar quality.© Now, when BLS lacks information needed for direct quality
adjustment of a new automobile model, it uses the average price change of directly comparable and
quality-adjusted model changeovers to impute a price change. Price changes for cars and trucks within a
model year are no longer used to impute price changes between model years.

Unusual price change behavior when a product is replaced by a new version occurs in many industries
besides automobiles. Section II notes, for example, that the apparel industry tends to time price increases
to coincide with the shipment of garments for a new selling season. BLS therefore began phasing in class
mean imputation for most commodities other than food in December 1992. Price changes for most
noncomparable commodity substitutions are now imputed from the average price change of directly

comparable and quality-adjusted substitutions in the same geographic area.

IV. Conclusion
Flux in the set of products found in the marketplace poses a challenging problem in the production of
price indexes. Simply linking new products into the index entails risks of bias (and elevated variance) that

are easy to overlook. When new products represent superior values or embody important technological


Page 6
C. Other Improvements to the Apparel Indexes

Procedural changes unrelated to hedonic regression have also played crucial roles in improving the
apparel indexes. The first of these was a change in the timing of substitutions for seasonal apparel items
that made more of these substitutions comparable and improved the representativeness of the apparel
samples (Sullivan, 1988, pp.10-14.) In particular, in 1987 BLS designated many classes of apparel items
as seasonal and prohibited substitution away from items in those classes when they are out of season.
Now, when a Seasonal item disappears after its selling season has ended, BLS waits until it is back in
season before substituting to a different item.*

The aim of the remaining changes in the apparel indexes was not avoiding noncomparable
substitutions but rather improving their execution. The first of these changes was the introduction in 1990
of an eight month c -erlap pricing period when a new sample of apparel outlets replaces an obsolete
sample. ‘BLS instituted this extended overlap pricing period because after the 1987 CPI revision a pattem
emerged of flat or falling apparel indexes in cities with new outlet samples... This pattern occurred even at
the start of selling seasons—when cities without new samples had large jumps in apparel prices—because
changes from sale to regular prices were under-represented in the rotation cities (Armknecht, 1989, p. 2).
The eight month lag permits the new sample to begin reflecting such changes before it enters the CPI.

Second, in January 1991 BLS modified its treatment of sale prices in noncomparable substitutions.
Since 1978, BLS had returned the price of an apparel item that was on sale before disappearing to its
pre-sale level before linking in a noncomparable substitute (which was also required to be at a non-sale
price.) Returning the sale price to its regular level before linking prevented downward bias from falsely
attributing the difference between the old item's sale price and new item's regular price to the higher
quality of the new item. In January 1991 BLS began returning sale prices to regular prices that were
adjusted for trend and seasonal time series components.

Finally, in December 1992 BLS began phasing out this treatment of sale prices in noncomparable
substitutions. Rather than returning sale prices to adjusted regular prices before a noncomparable

substitution, BLS now uses the price change imputation method that is discussed in Section III below.


Page 2

attention. Equally noteworthy is BLS’s success in developing improved procedures that should
enable the CPI to reflect most’price change coinciding with the appearance of new product models or

designs.

|. CPI Procedures for Repricing and Item Substitution

After the Bureau of Labor Statistics (BLS) selects a sample of outlets (stores), it selects a sample of
unique items in the sampled outlets and records detailed specification information for each of these unique
items. Then each period BLS field economists locate those unique items based on their specification
descriptions and record their.current prices. Collecting prices for exactly the same item as in the previous
period is known as repricing. Repricing ensures that comparisons of current to earlier prices hold quality
constant.

Sometimes repricing is impossible because the outlet no longer sells the sampled item. In this case,
BLS selects a new item for pricing—ideally, the most similar item in the same outlet. BLS refers to the
replacement of discontinued items with currently available items as substitutions. How BLS measures
price change for a substitution depends on whether the replacement item is of comparable quality to the
original item.

When a substitution occurs, a BLS commodity analyst decides whether the replacement item is
equivalent in quality to the discontinued item by comparing the items' specifications. If the commodity
analyst finds that the quality differences are negligible, BLS simply compares the price of the replacement
item to the discontinued item's price from the previous period. This is known as a comparable substitution.
Alternatively, the commodity analyst may have information (such as production cost data) that can serve
as a basis for estimating the value of the difference in quality between the discontinued and replacement
items. By netting out the portion of the price difference between the two items that is attributable to quality
variation, the analyst creates a comparable substitution whose price change is useable in the CPI.

Noncomparable substitutions occur when the commodity analyst determines that a replacement item
differs in quality from its predecessor but cannot directly estimate the value of the quality difference. In
these cases, the difference between the old item's price in last period and the replacement item's price in

the current period serves as an estimate of the value of the quality difference after netting out the effect of


Page 13

References

Armknecht, Paul A. 1989. "Apparel Rotation Methodology," Attachment to BLS Memorandum to Steve
Wright and Pat Jack, p. 2.

Armknecht, Paul A. 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 A., Moulton, Brent R. and Stewart, Kenneth J. 1994. "Improvements to the Food at
Home, Shelter and Prescription Drug Indexes in the U.S. Consumer Price Index," in International
Conference on Price Indices: Papers and Final Report, First Meeting of the Intemational Working
Group on Price Indices, Ottawa, Canada, November 1994, Ottawa: Statistics Canada.

Armknecht, Paul A. and Weyback, Donald. 1989. “Adjustments for Quality Change in the U.S.
Consumer Price Index," Journal of Official Statistics. Vol. 5, No. 2, pp. 107-123.

Armknecht, Paul A., Lane, Walter F. and Stewart, Kenneth J. 1994. “New Products and the U.S.
Consumer Price Index," Conference on Research in Income and Wealth - The Economics of New
Products, Robert J. Gordon and Timothy Bresnahan, eds. National Bureau of Economic Research,
forthcoming.

Baltagi, Badi H. Econometric Analysis of Panel Data, John Wiley & Sons Ltd, 1995.

Griliches, Zvi and Cockburm, lain. 1994. “Generics and New Goods in Pharmaceutical Price Indexes."
American Economic Review 84, no. 5, December, pp. 1213-32.

Hsiao, Cheng. Analysis of Panel Data, Cambridge University Press, 1986.

Kalton, Graham and Kasprzyk, Daniel. 1986. “The Treatment of Missing Survey Data," Survey
Methodology, (June), pp. 1-16, Statistics Canada, Vol. 12, No. 1.

Lazear, Edward P. 1986. “Retail Pricing and Clearance Sales," American Economic Review 76, (March),
pp. 14-32.

Liegey, Paul R. 1993. “Adjusting Apparel Indexes in the CPI for Quality Differences.” In Price
Measurements and Their Uses, Murray F. Foss, Marilyn E, Manser and Allan H. Young, eds. NBER
Studies in Income and Wealth Vol. 57. Univ. of Chicago Press, pp. 209-226.

Liegey, Paul R. 1994. “Apparel Price Indexes: Effects of Hedonic Adjustment,” Monthly Labor Review,
(May), pp. 38-45

Moulton, Brent R. 1986. “Random Group Effects and the Precision of Regression Estimates,” Journal of
Econometrics 32, pp. 385-397.

Pashigian, B. Peter. 1988. "Demand Uncertainty and Sales: A Study of Fashion and Markdown Pricing,"
American Economic Review 78, (December), pp. 936-53.


Table 1

Comparable, Noncomparable, and Quality-Adjusted Subtitutions

As a Percentage of Prices Collected*

Direct quality
Category Year Comparable _ Noncomparable adjustment Total
All 1995 2.01 1.09 0.43 3.53
1983° 1.56 1.97 0.32 3.85
Food & 1995 0.79 0.82 0.03 1.64
beverages 1983 0.52 1.29 0.00 1.81
Housing 1995 2.02 1.27 0.12 3.41
1983 2.21 1.89 0.15 4.25
Apparel & 1995 9.03 2.57 1.73 13.32
upkeep 1983 7.15 10.15 0.03 17.34
Transportation 1995 3.69 1.10 1.87 6.66
1983 3.13 1.41 2.18 6.72
Medical Care 1995 0.62 0.94 0.73 2.29
1983 0.65 0.94 0.64 2.22
Entertainment 1995 2.96 1.61 0.37 4.94
1983 1.92 2.51 0.18 4.61
Other goods 1995 1.25 0.86 0.27 2.38
& services 1983 1.44 1.69 0.17 3.30

a. Residential rent, homeowners’ equivalent rent, used cars, health insurance, and
magazines, periodicals, and books are excluded.

b. The 1983 substitution percentages come from Armknect & Weyback (1989), Journal
of Official Statistics 5 (2), Table 1 p.110.


New Ways of Handling Quality Change in the U.S. Consumer Price Index

by Marshall Reinsdorf, Paul Liegey, and Kenneth Stewart

Presented at the 1995 WEA meetings. Revised April 3, 1996.

One of the most difficult problems in measuring price change is that the set of items available for
purchase varies over time. Economists widely regard new products as a net source of upward bias in the
US Consumer Price Index (CPI) relative to a true cost of living index. This view is based upon the logic
that new products must gain acceptance in the marketplace by offering better values to consumers than
the products they displace.

In the CPI, the problem of evolving sets of items available for purchase is particularly pronounced in
the areas of apparel and automotive vehicles. In these important cases, however, failing to account for
price changes when new products replace old ones leads to a downward bias. Real price rises, rather than
price declines, often accompany turnover in populations of clothing items and automobiles because
manufacturers tend to time price increases to coincide with the introduction of updated styles or models.
Manufacturers do this for many reasons, Higher prices for the new line or model, together with discounted
prices for the discontinued product, help to clear out inventories of the old items. Also, in an inflationary
environment, manufacturers may delay price increases until their product line tums over to avoid menu
costs. Consumers may have less resistance to price increases that accompany product introductions
because they have difficulty assessing the value of the quality changes. Finally, novelty itself may be
valued by consumers. That is, the mere fact that a garment is the latest style, or an automobile is the
latest model year, may increase its desirability. When consumers’ preferences are unstable in away that
systematically favors new products, the logic that those products which succeed are revealed preferred, is
misleading. For example, no-iron polyester shirts were viewed as an improvement on cotton when they
first appeared, but a few years later many consumers regarded the substitution back to the more
comfortable cotton as another quality improvement (Triplett, 1988, p. 68).

The recent debate on the accuracy of the CPI has focused attention on the upward bias from failing to
credit consumers’ gains from better or cheaper new products. This is undeniably an important problem, but

downward biases from overlooking price increases accompanying new or changing product lines also merit


New Ways of Handling Quality Change in the U.S. Consumer Price Index

by Marshall Reinsdorf, Paul Liegey, and Kenneth Stewart

Presented at the 1995 WEA meetings. Revised April 3, 1996.
J.E.L. Nos.: C43, E31. Keywords: CPI; linking; apparel prices; automobile prices.

Abstract:

Economists widely regard new products as a net source of upward bias in the US Consumer Price
Index (CPI) relative to a true cost of living index. This view is based upon the logic that new products must
gain acceptance in the marketplace by offering better values to consumers than the products they
displace. In the CPI, however, the problem of evolving sets of items available for purchase is particularly
pronounced in the areas of apparel and automotive vehicles. In these important cases failing to account
for price changes when new products replace old ones leads to a downward bias. Real price rises, rather
than price declines, often accompany turnover in populations of clothing items and automobiles because
manufacturers tend to time price increases to coincide with the introduction of updated styles or models.

The recent debate on the accuracy of the CPI has focused attention on the upward bias from failing to
credit consumers’ gains from better or cheaper new products. This is undeniably an important problem, but
downward biases from overlooking price increases accompanying new or changing product lines also merit
attention. Equally noteworthy are BLS's procedural improvements that should enable the CPI to reflect

most price change coinciding with the appearance of new product models or designs.

te


Page 11
APPENDIX
Quality Adjustment for an Apparel Item Substitution Using Hedonic Regression
The price for an apparel item leaving the sample can be quality-adjusted based on the difference in
item characteristics for both old and new items. Parameter estimates developed in an hedonic regression
model for women’s coats and jackets are used to adjust price. The following example illustrates this

procedure under the assumption that only the fiber content for the new item is different.

Item Old Item Description New Item Description
Design Trenchcoat Trenchcoat
Fiber All Wool All Cotton
Length Mid-Calf Mid-Calf
Lining Full Lining Full Lining

ice ati

Old Item's Price = $63.00
New Item's Price = $40.00
Proportional Price Effect for All Wool Characteristic = 0.47896929

Proportional Price Effect for All Cotton Characteristic = 0.10039884

Hedonic Quality Adjustment Procedure:

1. Determine All Wool Value to Price (0.47896929)*(63.00) = 30.18
2. Determine All Cotton Value to Price (0.10039884)*(63.00) = 6.33
3. Determine the Quality Adjustment Amount (-30.18) + (6.33) = -23.85

4. Determine Quality Adjusted Price (-23.85) + (63.00) = 39.15

e New Price Change With Quality Ad ent:

New Item Price
Quality Adjusted Price Change = ( ----------—----=----——-—— -1)*100= 22%
Quality Adjusted Price
of Old Item


Page 14

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