Quality Adjustment

The hedonic method also provides opportuni-
ties to extend the scope of price index measures. It
can, for example, be used to estimate differences
in prices for goods and services across geo-
graphic areas at a point in time.'” By incorporat-
ing dummy variables for geographic area and
using a semilogarithmic model (as in equation
(2)), it is possible to interpret the coefficients of
these area variables as bilateral interarea price
indexes. It is also possible to include information
on outlets in the hedonic model. At present, the cP1
database contains categorical information on the
type of outlet from which a price quote is col-
lected, based on the principal business of that out-
let (for example, chain grocery store, bakery, or
beauty salon). The coefficients of outlet type
variables would provide some measure of the dis-
tribution of prices across outlets, Under a pilot
project recently begun at BLs, additional informa-
tion will be collected on the quality attributes of
the outlets as well. This will enable an analysis of
those quality attributes that are associated with
goods and services purchases, but are not physi-
cally attached to them, such as delivery services,
ease of the purchase transaction, and choices of
payment method.

Analysis of food items

The following analysis demonstrates the hedonic
approach for pricing selected food-at-home item
categories. These item groups were chosen to in-
clude those that are relatively homogeneous (such
as ground beef) as well as those that are heteroge-
neous (canned and packaged soups), and both
fresh items (such as apples) and prepared foods
(such as cookies). The item categories correspond
to the cri program’s definitions of entry level
items,'* with the list comprising: prepared flour
mixes (01012), white bread (02011), cookies
(02042), ground beef (03011), sirloin steak
(03061), fresh apples (11011), candy and chewing
gum (15011), cola beverages (17011), roasted
coffee (17031), and canned and packaged soups
(18011). Variables in the hedonic regression in-
clude those quality attributes that could be identi-
fied from the crt data, a set of dummy variables for
geographic area, a set of dummy variables for the
type of outlet from which the price was collected,
the month of the price quote, and a variable to in-
dicate whether the price quote came from a
sample newly rotated into the cri database. The
importance of geographic area for price differ-
ences has been shown by Diane Primont and Mary
Kokoski, and a decrease in the average level of
prices for food and energy at sample rotation has
been observed by Marshall Reinsdorf."”
Information on quality characteristics is pro-
vided when a specific product is chosen for inclu-

38 Monthly Labor Review December 1993

sion in the ci sample. This information is supplied
on a “checklist” form and accompanies the price
data for that particular item in the database, Be-
cause the CPi uses a probability sampling approach
to select the specific items to be priced, a wide
variety of products may represent a given ELI in
various outlets in the geographic areas in which
prices are collected. For example, cookies may be
represented by an array of specific products rang-
ing from bakery-fresh chocolate chip cookies sold
by the dozen to factory-produced 12-ounce pack-
ages of lemon cream sandwich cookies.

Although more complicated than a specifica-
tion-pricing approach (wherein a single specific
product represents a category of items in all outlets
in all areas), the probability sample better repre-
sents the consumption patterns of households and,
incidentally, provides a wealth of information for
analysis of the relationship between quality char-
acteristics and item prices. For some ELI’s, the
number of quality attributes identifiable from the
item checklists may exceed 200. Most of the
quality information from the checklists can be
used to construct binary variables for inclusion in
the regression. In the previous example, a variable
for “chocolate chip” can be created, which is equal
to | if the product is, indeed, this type of cookie,
and 0 otherwise; similar variables can be con-
structed for other cookie types, type of package,
size of package, and whether the cookies are sold
by the package or dozen, and so forth.

In addition to the quality characteristics that
physically describe the item, variables for the type
of outlet are included, These are based on the cP!
definitions of “type of business”—a set of possible
descriptions for the principal commercial activity
of the establishment from which the price quote is
collected. For food-at-home, the majority of the
individual prices are collected from chain grocery
stores, but some ELI's are represented by a wide
variety of outlet types. These include, for example,
convenience stores, membership warehouse out-
lets, beverage bottling companies, and variety
stores. The inclusion of these variables captures
any price differences across outlets, but may also
be interpreted as capturing some of the quality at-
tributes associated with the purchase of the item.
For example, while prices may be generally higher
in convenience stores than in chain grocery stores,
itis quicker and easier to find and purchase a given
item in the (aptly named) convenience store.

Because prices are collected monthly, a set of
dummy variables for month is included to control
for seasonal effects and trends over time in the
Prices of the selected food items. The ce! sample
used for this research includes 11 months of data
for 1991, covering January through November.
(The data for December were incomplete at the
time this research was begun.) In addition to tem-



Table 1.

items, based on a hedonic regression technique

Continued—Price parameter estimates of quality attributes for selected food

Commodity and Perameter ‘Commodity and Parameter
model variable estimate | ‘statistic model variable estimate |tstatlatic
March -.02 98 Canned and
April =103 68 packaged soup
may 73 Intercept -147 | -23.47
July =10 Type:
August .... 10 Bean 01 ~.28
September. . = 10 Beef... a5 5.53
October . . -07 Bee on 74 358
November a) icken 3
Minestrone... =27 | -10.77
Adjusted A? . 6243 - Onion . 26 11.69
‘Number of ‘observations 5,120 - Tomato . - 56 -25.90
coffee Vegetable with beet . =03 | -1.35
Roasted Vegetarian = 28 | 11.95
Intercept... . 1.51 -17.47
Type: renee 08 93
: septic. een -!
Bean type unspecified Box (Ret. = dried multipack) . 82 36.81
(Ref. = specified) . . ~ 06 6.24 Canned (Ref. = dried multipack) | -.66 | -24.02
100-percent coffee (Ret. Dried single
with additives) m 19 (Ref. = multipack) J) 55) | 20.86
Packaging Frozen (Ret. = dried multipack)..| ~ 68 | -7.53
Bag ve eeeceerrentensens ~ 38 Location:
ee ~ 25 Dall _a7_ | 800
Eterna wo ~ 23 Freezer 144 | -23.05
facuum-packsd bricks . “a Gourmet section -08 | -1.04
Form: Salad bar . -68 | -9.20
Ground = Shelf : ='52. | -1031
High extraction/extended ‘yield =.03 Other ingredients
oo ble bean : Sad With bacon ~a1 | 1.90
ontent: With ham... ~ 00 ~.08
Rogular 2 5.19 With noodles -.08 | -4.87
ee cateinated oo 7 13.78 With ric o.oo soca 36 8.63
- Brand
0 to 4.999 ounces 1.02 | 15.63 Regional brand (Ref. = national) .| -.22 | -7.96
14 to 20.999 ounces 00 05 Generic (Ret. = national) 49 9.79
21 10 32.999 ounces . =.08 | 1.27 [Pee oe a ’
Store brand (Ref. = national) -27 | -15.09
33 to 50 ounces... —26 | 628 |) costes
5 to 8.999 ounces . wea | 12.74 Loweslorie . ea | 1420
9 to 13.999 ounces ‘02 ‘61 Low soaiusa s ‘te
Type of outlet: sie strength (Ref. = condensed) 19 14.13
Chain drug stores 07 1.49 wth (Ret. = condensed) | :
Commodity oriented outlets, Type of outlet:
not elsewhere classified . 42 13.10 Chain drug stores...» 1.49 8.54
Convenience stores . . . 02 24 Commodity oriented outlet:
Discount department stores 09 55 not eisewhere classified... 28 377
Independent grocary stores... 07 6.69 Convenience stores 60 | 13.13
Liquor stores 54 8.32 Discount department stores. 41 3.45
Meat markets. . 07 24 Independent grocery stores . 3.22
Refreshment places 19 2.07 Produce markets : 131
Warehouse clubs, membership..| — .26 4.91 Warehouse clubs, membership ~ 32 3.45
Newsample ... 2.0. ....0.. 08 03 1.33 New sample ..............0005 08 3.28
Month: Month: ...
February 00 February ot 42
March -01 March... ‘01 ‘38
April = 01 April... 01 39
May = 02 May ooo ‘02 78
June. | 102 June. 01 @1
July ~ 04 day ‘04 1.74
‘August . | — 06 August - 04 177
September . —07 September . "04 197
October . | -.07 October 04 1.65
November ~ 08 November. 22.0.2... ‘03 1.43
Adjusted R? 6274 - Adjusted A? 8419 _-
Number of observations 4,457 — || Number of observations . 6.689 -

Note: “Ref.”
indicated in parentheses,

indicates use of a reference characteristic other than that cited in the text. The alternative characteristic is

Monthly Labor Review December 1993 43



Quality Adjustment

Table 1. Price parameter estimates of quality attributes for selected food items, based
ona hedonic regression technique
Commodity and [Parameter ‘Commodity and Parameter
model variate estimate |*atatistic model variable estimate |*statlstic
Prepared Notavailable ...... veee[ 2.81 102.28
flour mixes Not labeled—weighed one...) =.11 ~3.77

Intercept .........0cecceeeeees | 2.22 | -30.95 Not labeled—weighed two loose 10 -2.55

Additives required: Type of outlet:

Milk... becca 15 | 6.07 Bakeries . 02 79
Eggs . -12 | 5.89 Chain drug stores ......... 34 2.38
Water. : bese} =0t - 48 Commodity oriented outlets,

Packaging: not elsewhere classified .. . -49 | -952
Bag....... -42 | -777 Convenience stores. . . 43 6.22
BOX eee viene 30 | 618 Cost plus stores tees “04 22

Type: Dairy products stores .........| -.11 1.32
Bread mix ... 29 6.52 Full service department stores 82 5.90
Brownie mix... . 45 12.34 Gasoline service stations ...... 12 1.49

09 2.65 Independent grocery stores 05 3.32
66 15.74 Liquor stores . : 3.04
Mutfin mix 34 9.67 Meat markets . 1.31
Pancake mix . ~15 | 4.69 Outlet types not sisewhere

Piecrust mix . 23 7.46 classified 07 97
Roll mix . 24 3.84 Produce markets : 00 04
Features: Restaurants and lunchrooms ... | -.29 | -1.51
With icing packet (Ret. = Retail bakeries : 06 1.27
Frutttopping) . 58 1848 Warehouse clubs, member 4t 1.69
With nuts (Ref. = Fruit topping) .- 49 10.20 Newsample ............. 20005 06 2.51

T of outlet: .

‘Bonvenionce stores... 61 13.82 || Month:

Commodity oriented outlets, | == | — |l March...
not elsewhere classified 02 16
Independent grocery stores.....| 02 1.28
Produce markets . 21 39
Warehouse clubs, membership...) —.07 -.39
New sample... secre | 2412 | 4.04 August .
Month: ‘September .
February . vel =.02 ripuarte
March... 02 jovember
April 1 Adjusted R? ....
May . -.90 Number of observations -
dune. 00
July. 00 Cookies
August “00
September. ... 00 Intercept 1.78 | -26.72
October . . -.01
November -.02 Type:
Assortment ..... sees ~ 08
Adjusted A? . . 5149 = Chocolate chip... ~ 03
‘Number of observations ........| 3,883 _ Oatmeal . ~ 22
Plain sugar. = 10
White bread Sandi - 26
_ Packaging:

Intercept . . betes -254 | 48.00 Bag (Rel. = tin) 40

Features: Loose (Ref. = tin)... . 46
Buttertop ...... seep 19 10.10 Box (Ref. = tin) _ 27
Dietetic . vteteeeee 71 16.47 Plastic tray (Ref. = tin)... AT
Heat and serve . 07 4.97 || Feature:

Salt free - teceeeeee| 45 10.64 load 2.0... eae : v[ -20 | 461

Type: Without brand ‘abel
Franch ... : . 8.37 (Ret. =brand)...............{ -.24 | 7.26
tralian... -1.25 || Weight:

Raisin... 19.21 Not available ........ 00.6.2 26 7.47
Regular . 18.40 Not labeled—weighed one 34 3.23
Vienna . . 2.29 Not labelad—weighed two. -07 | -1.7

Condition:

Day old . -.65 |-10.90 || Type of outlet:

Frosh 00-02 cee eee 05 1.20 Bakeries i A 12.35
Frozen : see] 8? 4.82 Chain drug stores 64 277
Refrigorated. ss. ss sss 85 5.12 Commodity oriented outlets,

Weight: not elsewhere classified ......| —.96 -1.43
Not prepackaged Convenience stores... . ‘ 07 1.75
(Ref. = packaged) cee] -04 | -1.78 Cost plus stores. . —11 =119

See footnote at end of table.

40 Monthly Labor Review December 1993



poral effects, it is possible that prices vary system-
atically across geographic areas at any point in
time. To account for any interarea price differen-
tials, a set of dummy variables is included for the
areas defined by the crt in its aggregation of prices
into indexes. These geographic areas include 32
“self-representing” primary sampling units,” and
12 region/city-size class aggregates. A self-repre-
senting area is one with a population large enough
that only data from that area are used to calculate
its price index. Such areas typically correspond to
the larger urban metropolitan areas (such as Chi-
cago and Baltimore), but in some cases, such as
the New York City area, there are several primary
sampling units within a geographic designation.
The region/city-size class aggregates are devel-
oped from price quotes from a sample of cities of a
given size class in the specified region.”

In any year, certain primary sampling units are
scheduled for sample rotation. This sample rota-
tion process serves to update the sample of outlets
and items, based on the Continuing Point-of-Pur-
chase Survey,” to better represent the outlets and
products actually purchased by consumers in a
primarily sampling unit. Since the 1978 revision
of the cri, each area’s sample is replaced by a new
one every 5 years. In 1991, the areas for which
sample rotation was conducted included Pitts-
burgh, New York City, and Washington, pc, and
several smaller urban areas. To account for any
effect on the price of an ELI due to a change in the
sample (and unexplained by the other variables
in the regression), a binary variable was in-
cluded in the hedonic regression to indicate
whether the individual price quote belonged to
the new sample.

Thus, the hedonic regression equation for the
selected food ELI’s can be written as:

k r t
Q) InP,=6,,+ Ld,x,+ Lbm,+ LbBSNS,
me eS

A
+b AREA,,+ b, NEWSAMP, + €,
a2 "
where P. is the price of individual product i; x, is a
variable for characteristic k of item i; m, indicates
whether the price quote for product i is from
month f, BSNS,, indicates whether the price quote
is from outlet type /; AREA, is a variable indicat-
ing whether the price quote is from area a; and
NEWSAMP is a variable that identifies whether
the price quote for product i is from the newly in-
troduced sample. The error term, €,, embodies the
residual variation in the log of the ELI’s price that
cannot be explained by the variables in the regres-
sion equation.
Three sets of variables—months, outlet types,
and areas—are mutually exclusive and exhaus-
tive. Therefore, one member from each must be

dropped from the equation to permit estimation of
parameters for other members of the variable set.
The choice of the variable to be excluded from
each set is arbitrary, and, in this case, was made to
facilitate the interpretation of the results. January
was the excluded month, chain grocery stores was
the excluded outlet type, and Philadelphia was the
excluded area. The values of the excluded vari-
ables then serve as references to which the model-
derived coefficients of the remaining variables can
be compared. The characteristics variables are bi-
nary, or “dummy,” variables that could be con-
structed from the information on the cri checklists.
If acharacteristic is present, the respective dummy
variable is given a value of 1; if the characteristic
is absent, the variable has a value of 0.

The empirical results from this equation for the
selected food-at-home EL1’s are presented in table
1. Although the coefficient values for most vari-
ables are presented, results for the geographic area
variables are not presented or discussed here be-
cause a more detailed treatment of interarea differ-
ences is available elsewhere.”

The month in which the price was collected
does not seem to have a consistent pattern in terms
of the signs of the coefficient estimates, nor are
these estimated values statistically significant in
general. The variable NEWSAMP, indicating the
incorporation of a new sample of price quotes
(which, although independently selected, may in-
clude the very same outlets and products as in the
old sample), is statistically significant for most of
the ELI’s in this study. It is, however, as often
negative in sign as positive, which indicates the
absence of a systematic effect on prices of intro-
ducing a newly rotated sample. Because new
samples were rotated into the overall cri produc-
tion database in different months in those primary
sampling units scheduled for rotation in 1991, this
variable is not capturing a seasonal effect. Other
studies, which compared prices for rotating
samples only, and covered 2 years of cri data (but
did not include characteristics or outlet type vari-
ables), have indicated a downward effect on prices
resulting from sample rotation.” It is also true that
most of the primary sampling units for which
samples were rotated in 1991 were small urban
areas with relatively few price quotes. Therefore,
an explanation of the effect of this variable awaits
a more comprehensive analysis, incorporating all
food-at-home ELI’s, and possibly encompassing a
period during which more sample rotations can be
observed.

The characteristics variables are defined by the
specifications on the checklist for each ELI. To fa-
cilitate the empirical analysis, the reference (ex-
cluded) variable for an exhaustive set of character-
istics variables is, unless otherwise indicated, a
catch-all category, “other.” The exceptions are in-

Text continues on page 44

Monthly Labor Review December 1993 39



Quality Adjustment

Table 1. | Continued—Price parameter estimates of quality attributes for selected food
items, based on a hedonic regression technique
Commodity and Paramet ‘Commodity and Parameter
model varlabl estimate |*Statistic model variable estimate |*statlstic
Nougats....... 34 2.99 August 05 2.08
Other candy . -17) | 3.51 September 08 1.92
Powdered candy ~42 | -294 October . 06 2.22
Solid chocolate... 23 561 November 06 2.53
Solid hard candy . .. ~14 | 321 TT ngicted Ae. 7691 _
Stick, rock, ribbon candy At 1,92 ius we TTT 4 _
Suckers, lollipops . "24 3.97 Number of observations 4,606
Tally .. 22 69 Cola beverages
Packaging: Intercept... 6... cs eee e cee eee 4.64 40.35
Bag ..... -.04 -2.07
Box -.01 —.37 || Type:
Bulk | 04 95 Regular cola (Ref. = flavored) ...| .03 1.99
Metal tin 39 285 Diet cola (Ref. = flavored) 05 2.82
Roll 71 14.19 Packaging:
Single piece or bar’ | ~'23 | -1065 Case of 24 .. -34 | 916
Gum: Individual 09 443
Bubble gum ...... -.06 -1.78 12-pack ~ 18 7.66
Liquid-center tablet 05 69 6-pack 03 1.80
Stick. 63 16.66 B-pack ~ 26 ~7.92
Sugar: Container style:
Dietetic or sugarless Aluminum bottle 13 1.81
(Ref. = regular) 42 17.05 Glass bottle 25 2.87
Origin: Metal can. . a7 4.02
Domestic (Ret. = imported) .....} — 42 | 0.9 |} Praste (ret) bottle . 07 70
: ra
WE acunty wrapped Nationally advertised brand 62 10.42
(Ref. = not individually wrapped) | -.22 | -11.82 Regional or store brand . . 12 2.00
Size: Deposit legislation:
0 to 2.999 ounces . 04 | -138 Regional or local (Ret. 40 1.35
11 to 16.999 ounces ~'sa | 1731 Statewide deposit (Ref. ‘01 31
3 to 6.999 ounces, - 28 -8.06 |} Caffeine:
7 to 10.999 ounces AB -13.87 Reduced (Ref. = regular) ~ 02 113
Container:
Type of outlet: Returnable (Ref. =nonreturnable)| 01 34
Candy, nut, and confection Size:
stores . 65 20.63 Viiter eee eee 34 8.21
Chain drug stores _ 21 7.84 1/2 liter or 500 mi 38 5.63
Colleges, universities, and 10 ounces 70 11.51
professional schools -30 | -349 12ounces .... 48 491
‘Commodity oriented 16 ounces 28 7.38
outlets, not elsewhere classified 24 431 2 liters -.05 | ~255
Convenience stores . 02 56 32 ounces: 14 1.19
Cost plus stores. - 56 4.60
Discount department stores -.08 2.02 || Type of outlet:
Gasoline service stations 12 4.28 Chain drug stores 07 3.39
Hardware stores 13 1.03 Colleges, universities,
Health clubs or spas... - 31 1.24 and professional schools . AT 2.16
Independent drug stores . .. ~.36 | -5.85 Commodity oriented outlets,
Independent grocery stores . 10 4.88 not elsewhere classified -13 | 1.93
Liquor stores. . ef 14 57 Convenience stores : 25 14.92
Mail order houses 87 9.25 Dairy products stores... 23 3.40
Merchandising machine Discount department stores . 03 54
operations ...... : 05 19 Gasoline service stations 20 6.47
Outlet types not elsewhere Independent drug stores . 13 | -267
classified | -419 | -1.64 Independent grocery stores. 04 2.96
Primary and secondary schools |} 85 9.79 Liquor stores... . : 15 552
Produce markets . } ot -76 Outlet types not elsewhere
Refreshment places vf -.36 | -1.65 classified vee 08 1.88
Restaurants and lunchrooms ~34 | 617 Pet stores : ~ 24 | 1.21
Variety storas . -.41 | -10.21 Refreshment places : 64 5.20
Warehouse clubs, membership..| - 87 7.76 Restaurants and lunchrooms 54 7.76
Service oriented outlets, not
New sample 02 49 elsewhere classified. .........| 25 93
Month: Soft drink bottling companies -48 | -5.07
February 00 4 Variety stores... .| 25 | -325
March 02 ‘a1 Warehouse ciubs, membership . 03 42
ay : ee 1123 || New sample 08 2.07
June. 03 1.36 || Month:
July 05 2.18 February 1 38
‘See footnote at end of table.

42 Monthly Labor Review December 1993



Mary F. Kokoski

Mary F, Kokoski is an
economist in the Division
of Price and Index Number
Research, Bureau of

Labor Statisties.

Anatomy of price change

Quality adjustment

of price indexes

Hedonic analysis may provide a viable alternative

to other methods currently used

to net out the effects of quality change

in items in the cpt market basket,
so that the desired measure
of pure price change is obtained

s an intended measure of pure price

change over time, the Consumer Price In-

dex (cpl) should not incorporate as price
changes those differences in prices that may re-
flect differences in the quality attributes of the
goods, as perceived by the consumer. If, for ex-
ample, a microwave oven is replaced by the manu-
facturer with a new model that has a larger capac-
ity and a higher price, the value to the consumer of
the increase in oven capacity should be subtracted
from the observed price difference for purposes of
the cpl.

Unfortunately, the assessment of quality
change often is difficult. This article addresses the
problem of quality change in the cri, with par-
ticular emphasis on the empirical technique of he-
donic regression as a method of measuring the
value of quality differences in goods and services.
The hedonic methodology provides quantitative
information on the importance of measurable
quality differences. Given certain assumptions, it
offers statistical estimates of the value of differ-
ences in quality among heterogeneous goods and
services. Throughout the following discussion,
the valuation of a physical difference in product
characteristics will be referred to as a “real quality
difference.”

The sections below briefly discuss methods of
quality adjustment in the cpi, present a general

34 Monthly Labor Review December 1993

theoretical model for the hedonic methodology,
and provide empirical results for a sample of food
items typically purchased for home consumption.
‘The data relate to the sample of items used in the
cpt for the period January through November
1991.

Concepts of quality adjustment

The Consumer Price Index, as a measure of price
change, is defined within the context of a fixed
market basket of goods and services purchased by
consumers. Under ideal circumstances, a refer-
ence period market basket of specific items would
be chosen and, over time, the prices of these exact
items would be collected and compared to their
reference period prices. Because there is no quality
change, such an index would easily provide a
measure of pure price change.

In the real world, however, goods and services
change over time. These changes may be frequent,
as in the case of apparel items, or occasional, such
as the replacement of a popular beverage with a
new formulation. They may be minor, stylistic
changes, such as the neckline of a blouse, or major
innovations, such as the introduction of anti-lock-
ing brake systems on automobiles. Some of these
changes, in some proportion, represent “real qual-
ity change”; in other cases, it may be true that the



cations. If one assumes market equilibrium and
well-informed consumers, and that the outlet type
variable is a reasonable proxy for average outlet
quality characteristics, one may attribute some of
the outlet coefficient values to outlet quality dif-
ferences, However, if consumers adjust slowly to
changes in the market, or are not fully informed,”
one may attribute some of these coefficient values
to systematic pure price differences across outlets.

‘THE FOREGOING ANALYSIS Of the issue of quality ad-
justment and the contribution of hedonic methods
to this price index problem is based on a set of
hedonic regressions for selected categories of
food-at-home commodities. Although the regres-
sion results are not used here to specify quality
adjustment procedures for these food and bever-

Footnotes

age items, they do provide some insight into the
effects on price, or “implicit price,” of item char-
acteristics, including the types of outlets where
items are purchased.

Empirical analysis of apparel, conducted by
Armknecht and Weyback,* has shown that the
stability of the hedonic regression coefficients
over time should not be presumed. Further re-
search will examine the stability of the hedonic
relationship for other types of commodities and
services, and the importance of the specification
of the regression—that is, the importance of vari-
ables that may have been omitted or unobserved.
In addition, as experimental data become avail-
able for a sample of cri outlets, the analysis will be
extended to include quality characteristics of the
outlets themselves. O

ACKNOWLEDGMENT: The author acknowledges helpful com-
ments by Paul Arminecht, Marshall Reinsdorf, and Marilyn
Manser. The views expressed are those of the author and do
not necessarily represent an official position of the Bureau of
Labor Statistics.

‘Paul Armknecht and Donald Weyback, “Adjustments for
Quality Change in the U.S. Consumer Price Index,” Journal
of Official Statistics, vol. 5, 1989, pp. 107-23.

? Mokoto Ohta and Zvi Griliches, “Automobile Prices Re-
visited: Extensions of the Hedonic Hypothesis,” in Nestor
Terleckyj, ed., Household Production and Consumption,
National Bureau of Economic Research Studies in Income
and Wealth, no. 40, Conference on Research in Income and
Wealth (New York, Columbia University Press, 1975), pp.
325-90,

* See Armknecht and Weyback, “Adjustments for Quality
Change.”

Ibid.

* See Paul Liegey, “Adjusting Apparel Indexes in the cPt
for Quality Differences,” in M. Foss, M. Manser, and A.
Young, eds., Price Measurements and Their Uses, National
Bureau of Economic Research Studies in Income and
Wealth, no. 57 (Chicago, University of Chicago Press, 1993),
pp. 209-26.

5 Andrew Court, “Hedonic Price Indexes with Automobile
Examples,” in General Motors Corp., The Dynamics of Auto-
mobile Demand (New York, General Motors Corp., 1939),
pp 99-17.

7 Sherwin Rosen, “Hedonic Prices and Hedonic Markets:
Product Differentiation in Pure Competition,” Journal of
Political Economy, April 1974, pp. 34-55.

* Robert Mendelsohn, “Estimating the Structural Equa-
tions of Implicit Markets and Household Production Func-
tions,” Review of Economics and Statistics, November 1984,
pp. 673-77.

* Robert Halvorsen and Henry Pollakowski, “Choice of
Functional Form for Hedonic Price Equations,” Journal of
Urban Economics, July 1981, pp. 37-49.

°° See Ohta and Griliches, “Automobile Prices.”

“' Kelvin J. Lancaster, “A New Approach to Consumer
Theory," Journat of Political Economy, April 1966, pp. (32-57.

"? Jack Triplett, “Automobiles and Hedonic Quality Meas-
urement,” Journal of Potiticat Economy, vol. 77, 1969, pp.
408-17.

© Jack Triplett, “Price and Technological Change in a
Capital Good: A Survey of Research on Computers,” in D.
Jorgenson and R. Landau, eds., Technology and Capital
Formation (Cambridge, MA, MIT Press, 1990), pp. 127-213.

“ Robert Pollak, "The Treatment of ‘Quality’ in the Cost-
of-Living Index,” Journal of Public Economics, February
1983, pp. 25-53.

' Pollak, “The Treatment of ‘Quality’.”

Ibid.

Diane Primont and Mary Kokoski, “Comparing Prices
Across Cities: An Hedonic Approach,” Working paper no.
204 (Bureau of Labor Statistics, 1990); and Mary Kokoski,
“New research on interarea consumer price differences,”
Monthly Labor Review, July 1991, pp. 31-34.

"* The cP1 item structure has four levels of classification.
Seven major groups are made up of 69 expenditure classes,
which in tum are divided into 207 item strata composed of
generally similar items. Within each item stratum are one or
more substrata, called entry level items (ELI's), of which there
currently are 364, The EL's are the ultimate sampling units
for items as selected by the BLs national office. They are the
level of item definition at which data collectors begin it
sampling within each sample outlet. For more information,
see ats Handbook of Methods, Bulletin 2414 (Bureau of La-
bor Statistics, 1992), pp. 176~225.

9 See Primont and Kokoski, “Comparing Prices”; and
Marshall Reinsdorf, “The Effect of Outlet Price Differentials
in the U.S. Consumer Price Index,” in M, Foss, M. Manser,
and A, Young, eds., Price Measurements and Their Uses,
pp. 227-54.

* A primary sampling unit is a county or group of contigu-
ous counties, except in New England, where the primary
sampling unit is commonly a group of cities or towns.

1 See Primont and Kokoski, “Comparing Prices,” for fur-
ther discussion of area definitions and an analysis of interarea
price differences.

® The Bureau of the Census conducts the Continuing
Point-of-Purchase Survey for nts. The survey furnishes cur-
rent data on retail outlets from which urban households make
purchases of defined groups of commodities and services.

Monthly Labor Review December 1993 45



Quality Adjustment

dicated in table 1 by the parenthetically expressed
definition of “Ref.” The choice of a reference
characteristic is arbitrary, and was made in this
study on the basis of the way information is pre-
sented on the crt checklists.

To compare the effects of any two characteris-
tics within a set on the price of the item, one can
simply compare the values of the coefficients. For
example, in the regression results for prepared
flour mixes, a brownie mix is, other things equal,
more expensive than a muffin mix, and a cookie
mix is more expensive than any other type of mix.
Pancake mix is the only mix type less expensive,
all else equal, than the reference type “other,” be-
cause its parameter estimate is negative. In making
a quantitative comparison across characteristics,
the coefficient estimates are interpreted as the
percentage contribution of the presence of a char-
acteristic to the price of the overall item, as defined
by the ext. For example, the inclusion of an icing
packet in the prepared flour mix in table 1 adds 0.7
percent to the price of the mix, other things equal.
These comparisons are considered valid only if the
coefficient estimates are statistically significant—
that is, if the t-statistic on the coefficient estimate is
greater than or equal to 1.95.

A general examination of the empirical esti-
mates of the regression coefficients for the charac-
teristics variables seems to concur with a priori
expectations in most cases. For example, in table
1, the coefficient on “day old condition” for white
bread is negative and significant, and regular type
bread is relatively less expensive than the more
specialized types such as Vienna or French. For
ground beef, regular fat content is cheaper relative
to lean fat content, and extra lean commands a
price premium over all other categories of fat con-
tent. For cola beverages, the price per ounce is
lower if purchased by the case of 24 rather than by
the individual container, Colas of a nationally ad-
vertised brand command a price premium over ge-
neric colas, other things being equal. (Unfortu-

nately, information on brand name is difficult to
extract from the checklist for most items.) For
other groups of characteristics within some ELI’s,
such as the type of soup, or its ingredients, it is
difficult to form any expectations and to then inter-
pret the coefficients on this basis.

The set of variables that identify the type of
business were specified with the reference (or ex-
cluded) variable “‘chain grocery store,” which is
the most common outlet type in the sample for all
food-at-home ELI’s. Generally, the coefficient esti-
mates on these variables are consistent with prior
expectations and most are statistically significant.
To summarize these results, the coefficients on the
most common types of outlets that sell food and
beverage items are presented separately in table 2,
where an asterisk indicates that the estimated
value is statistically significant at the S-percent
level.

Purchasing from independent grocery estab-
lishments tends to have a significant effect on the
price for the selected EL1’s, but the direction of the
effect varies across ELI’s. Convenience stores have
higher prices, other things equal, than do chain
grocery stores, and purchasing at drug stores gen-
erally entails a price premium as well. Of the sta-
tistically significant coefficients for membership
warehouse clubs, the results indicate that these
types of outlets are cheaper than grocery stores.
The price effect of purchases at gasoline service
stations is a discount for cookies and a premium
for cola beverages. (These outlets overlap the con-
venience store category.) Because the regression
specification includes variables for the character-
istics of the goods, these outlet effects are not
due to differences among outlets in the attributes
of the products available that can be measured
from the item checklists. Some item attributes,
such as nationally recognized brand name for pro-
cessed foods or beverages, may vary systemati-
cally across outlet types, but this information is
more difficult to extract from the checklist specifi-

Table 2. __ Relationship of outlet type to prices, selected entry level items and outlet
types
item Independent | Convenience | Chain drug | independent | Warehouse | Gas
grocery store store drug store club station

Flour mixes ...... - 0.58 - _ *=0.10 _
White bread .. . 0.05 “13 *0.34 _ a4 0.12
Cookies ........... ~.00 07 * 64 - 18 * 66
Ground beet... +=06 08 = ~ 00
Fresh apples. *-.07 068 - - = -
Candy, chewing gum *=10 02 "24 *40.37 *-.87 12
Cola beverages . "25 "07 “13 *.20
Roasted coffee . . 07 02 08 = _
Canned, prepared soups * 05 * 60 1.49 - -

Note: Asterisk indicates that the estimated vaiue is statistically significant at the 5-percent level.

Dash indicates that the outlet type was not included in the sample for the commodity.

44 Monthly Labor Review December 1993



Table 1.

items, based on a hedonic regression technique

Continued—Price parameter estimates of quality attributes for selected food

Commodity and

Parameter

Commodity and

Parameter

model variable estimate {Statistic model variable estimate |*statistic
Gasoline service stations . - 66 4.17 || Adjusted A 5481 =
Independent grocery stores... .. = 00 = 111. || Number of observations 6,707 -
Liquor stores ~ 69 3.20
Produce markets... . 2.00.2... ~ 20 1.56 Fresh apples
Restaurants and lunchrooms -14 | -1.33
Rotail bakeries.............-. = 26 | -5.25 || Intercept . 2.83 |-131.62
Warehouse clubs, membership 18 82 | variety:
Golden Delicious . 03
New sample 03 87 Red Delicious ~.04
Granny Smith... 03
Me gbwuary . =.00 -.09 Gravensiein 78
March . =01 =16 nanan re
April 00 08 icIntosh -.0
May 00 40 Rome Beauty (Fed Rome) . =.03
bittiveeteeeeeetenes 4 Stayman 210
June 02 53
July 00 01 Winesap 06
Wy ores 90 ot York (York imperial va
Sentembar 00 ‘00 || Weight:
Roe er oe eo nto 6 E10 pounds) oO
‘ovember - Grade:
Other grade or grade not
Adjusted A? .... . A291 = sh _ 4
Number of observations .. 3,723 = Pace. (Ref. = extra fancy) or 2.29
Multipack (Ret. = loose). -25 | -29.41
Ground beet Single orn (Ret. = eo 36 6.86
Intercept .. -225 |-1638 || Size:
Weighed one multipack . -.06 3.24
Type: Weighed two apples -.01 - 68
Boot with vapattie protein = 05 ~ 98
100-percent beet . 08 1.80 || Type of outlet:
Form: Commodity oriented outlets,
Loose, not prepackaged . 31 2.36 not elsewhere classified .. - 38
Loose, prepackaged . 25 1.92 Convenience stores 06
Patties ... 31 2.37 Full service department stores ..| -.12
Tube .. 28 2.01 Independent grocery stores +07
Fat: Outlet types not elsewhere
Extra lean 07 9.16 classified — 46
Lean -05 | 562 Produce markets... =... -.16
Regular = 10 | 13.18
Source: New sample . -.04 | -3.08
‘Chuck (Ref. = round)........ =.10 | -15.27
Combination (Ref. = round) = 23 32.50 Month:
Sirloin (Ref. = round) ........ aM 12.11 February 03 2.64
State: March . “04 3.77
Frozen (Ref. = fresh) . -.07 J April 05 474
rozen (Ret. = fresh) 0 -2.33 na od a7
Type of outlet: June 15 13.68
‘Commodity oriented outlets, duly 18 16.55
not elsewhere classified .... ~30 | -5.75 ‘August... 5... 20 18.02
Convenience stores . . . 08 96 September AW 15.11
Discount department stores -...| -.42 | -4.19 October... eee ‘01 1.01
Freezer and locker meat November 03 2.79
providers ..... | =37 |-10.34
Independent grocery stores -.06 -9.90 Adjusted A? . 3328 -
Meat markets.............. . | =.09 “e'e4__ || Number of observations’ 9,423 _
Warehouse clubs, membership... | —.00 = 01 Candy and
New sample -.01 -.99 chewing gum
Month: Intercept... . - 84 714
February -.01 Candy:
March... 0... Assorted chocolate candies . 3t 5.99
April ‘Candy-coated chocolate . -10 | 228
May Caramels andtotfees ...... 1 20
dune... Chocolate-covered candy . .. -06 | -137
duly. ‘Cough drops : 96 15.37
August Filled hard candy -.25 115
September... ~.02 delies, gum drops rut 1.07 | -23.77
October . -.02 Licorice. -47 | -3.70
November = 01 Mints... -26 | 5.40

See footnote at end of table.

Monthly Labor Review December 1993 41



explicit assumptions to facilitate the given analyti-
cal purpose.’ In most empirical applications, a
linear or log-linear form is used because it is rela-
tively easy to estimate. In linear form, the hedonic
equation would appear as follows:

k

1 P= 5 ;

where P. is the price of item, a, is a constant, 5, is
the coefficient on characteristic k of item i, and z,
is the quantity (or presence or absence) of charac-
teristic & in item i. If characteristic k is quantifi-
able, such as the length or weight of an item, then
z,, is a continuous variable. If characteristic k is a
categorical variable, then z, is a dummy variable
(that is, it has the value 0 or 1, depending on
whether the attribute is present), an example being
whether or not an automobile has air conditioning.

The semilogarithmic specification is given by
equation (2) below:

kK

Q) InP, =a,+ 0, 2,»
Pa

where In P, is the natural logarithm of the item’s
price and the other variables are defined as for
equation (1). A log-log relationship would be the
same as equation (2), except that the characteris-
tics variables would also be expressed in logarith-
mic form, and these variables would have to be
continuous.

Under the hypothesis of the hedonic model, the
coefficients of the characteristics variables may be
interpreted as “implicit prices” or “shadow prices”
of the characteristics.'° This follows from the as-
sumption that consumers purchase goods, which
are bundles of various characteristics, in order to
consume these characteristics.'' Because char-
acteristics cannot be purchased independently,
the “prices” of the characteristics cannot be di-
rectly observed. They are instead estimated “im-
plicitly” by observing the prices of a spectrum of
goods that embody various levels of the relevant
characteristics.

{n theory, a logical extension of the hedonic
approach is to use these implicit prices to con-
struct a price index in the context of characteris-
tics, not goods. The resulting “hedonic index”
would seem to obviate the problem of quality ad-
justment, because quality change is defined as a
change in the quantities of characteristics. This
approach has been empirically investigated for
several categories of commodities such as auto-
mobiles” and computers,'’ but is more problem-
atic for most other items. Without unrealistically
restrictive assumptions, it is not possible to con-
struct an exact, or true cost-of-living, index or its
Laspeyres index approximation in terms of char-
acteristics.'* These assumptions include, first, that
the spectrum of varieties of goods on the market

be continuous; that is, that enough varieties exist
in every period to cover the range of possible com-
binations of characteristics. Although this may be
reasonably assumed for some goods (such as
housing units), it is less defensible for others, such
as major appliances and automobiles. If the spec-
trum of varieties is discrete, the hedonic relation-
ship is not the same over all regions of the spec-
trum of characteristics. In effect, a separate
hedonic model would need to be estimated for
each variety spectrum of goods on the market at
any given time, making the construction of a
hedonic index excessively complex. Second, it
must be assumed that, in any given period, the
consumer consumes more than one of the vari-
eties available for purchase. In the case of du-
table goods, and even certain nondurables for
which “brand loyalty” exists, however, it is
likely that only one variety will be consumed.'*
Finally, this approach also assumes that markets
are in economic equilibrium, and that consumers
have the same preferences, so that a single index
is representative of their valuation of quality
differences.

The difficulties inherent in the hedonic index
approach, however, do not eliminate the useful-
ness of the methodology for quality adjustment
and other issues in the context of market goods
and services. 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. A
quantitative quality adjustment, based on the mag-
nitudes of the regression coefficients, could be
used for such groups of items as apparel, food at
home, and other heterogeneous nondurables. For
goods that have a more discrete range of varieties,
and for which quality changes may be more pro-
nounced (such as major appliances or electronics),
the general hedonic concept may be combined
with the household production framework. In this
framework, the household is assumed to purchase
goods and services and to combine these pur-
chases with its own time and effort to produce
household “commodities,” analogous to the com-
bination of inputs by manufacturers to produce
goods and services. These “commodities” are
what yield satisfaction (or utility) to the house-
hold, and are produced according to a household
technology. An example would be the combina-
tion of grocery item purchases, energy for cook-
ing, and time to produce a home-cooked dinner. If
a specific model for this process could be de-
scribed, then it might be possible to construct a
“commodity” price index, which is more ame-
nable to the problem of disappearing varieties of
items.'® (Models that can accommodate new
goods, an extreme form of quality change, have
not yet been fully explored.)

Monthly Labor Review December 1993

37



Quality Adjustment

When neither overlapping prices nor indepen-
dent information on the cost of quality change are
available, the link technique is employed. Under
this approach, the price change between the old and
new products initially is ignored. The (weighted)
mean price change for other items in the same item
stratum (category of similar goods or services) and
geographic area at the time of substitution repre-
sents the price change for that stratum. This mean
price change between the two periods becomes the
imputed value of the difference between the ob-
served current month’s price for the new item and
the (unobserved) current month's price for the old
item. This approach is based on the assumption that
pure price movements are likely to pervade an
entire stratum of similar goods or services. It al-
lows for some allocation of the observed price
difference to both pure price change and real
quality change, which may be closer to the em-
pirical truth than attributing the difference entirely
to one factor or the other.

Ideally, if market data permitted pure price
and quality changes to be distinguished, the
consumer's valuation of the quality differences
would be used to compare old and new products
when substitutions must be made. Although this
information is not directly available, the data
collected for construction of the cri do permit
some estimation of the value of quality differ-
ences among individual products or items to be
made. This is possible because price quotes for
the cri are collected for a heterogeneous sample
of goods, and information on the quality at-
tributes of the individual products that represent
an item category is provided when items are in-
troduced to the sample. Given observations of
both price and each item’s physical attributes, it
is possible to apply the empirical technique of
hedonic regression to estimate the relationship
between a product's price and each of its identi-
fiable quality characteristics.

Under certain theoretical assumptions, the re-
gression coefficients from this technique may be
interpreted as estimates of the “implicit prices” of
the item quality characteristics.’ The Bureau of
Labor Statistics has applied the hedonic approach
in the case of apparel commodities, initially to de-
termine which quality characteristics are most
important when selecting substitutes for disap-
pearing products.’ If, for example, the regression
results indicated that consumers pay a substantial,
and statistically significant, price premium for
wool versus acrylic as the principal fabric in a
woman’s suit, other things equal, then a major cri-
terion for selection of a substitute product would
be that it have the same fabric content as the spe-
cific suit being replaced in the index sample.

If the coefficients on the characteristics are
stable over time, and over a wide array of product

36 Monthly Labor Review December 1993

varieties, it might be possible to use these esti-
mates to make direct quality adjustments when-
ever market basket substitutions are required, Paul
Armknecht and Donald Weyback have tested this
possibility for women’s coats and jackets, and for
women’s suits. Since 1991, this approach has
been in use for these item categories of the cri.

The hedonic approach

The hedonic, or characteristics, approach offers
promise for the problem of quality adjustment in
the cpt because it is based on empirical, market in-
formation. It thus is almost certainly an improve-
ment over procedures that, at worst, implicitly at-
tribute all of an observed price difference to either
pure price change or real quality change or, at best,
make imputations based on subjective judgement
or averages of price movements for other goods or
services. However, the methodology is not easy to
define or interpret in the context of microeco-
nomic theory, and requires a large sample of data
with detailed information on product characteris-
tics that can be quantified or categorized for statis-
tical analysis.

The hedonic approach relies on the general idea
that any good in the marketplace can be interpreted
as a bundle, or package, of characteristics. Indi-
vidual products vary according to presence or ab-
sence of characteristics, and also by the amount of
each characteristic they embody. The hedonic
function, which is the regression equation to be
estimated, was introduced to the economics lit-
erature in the 1930's as a method of relating an
item’s price to its characteristics.’ Several decades
later, it was given a theoretical foundation in the
context of demand and supply relationships by
Sherwin Rosen.’ His approach assumes that mar-
kets are in economic equilibrium and that consum-
ers are fully informed about the prices of goods
and their quality attributes. It also assumes that
there is a continuous spectrum of varieties of
goods—that is, there are many different specific
products—offering a large set of choices of char-
acteristics combinations. Under these conditions,
the hedonic function defines “the minimum price
of any package of characteristics.” It does not de-
scribe a demand relationship, but rather the budget
constraint imposed on the consumer in the context
of characteristics. (To construct a demand rela-
tionship from the information derived from a
hedonic equation,* one must have consumer-spe-
cific information that usually is lacking.)

Because the relationship between item at-
tributes and prices is a function of both the demand
and production sides of the market, economic
theory does not provide much guidance as to the
functional specification of the hedonic equation.
Therefore, the specification usually is made using



consumer is, other things equal, indifferent be-
tween the old and new products. An extreme case
of quality change in the market for consumer
goods and services is represented by the introduc-
tion of new goods—commodities that appear to
have few close substitutes and are not simply an-
other step in the gradual evolution of existing
products, Examples of such products have in-
cluded microcomputers, videocassette recorders,
home videorecorders, and the like.

In the construction of the cri, qualitative
changes in the market basket for U.S. consumers
are subject to methodological rules. When the
quality attributes of a good or service change—
that is, a specific product or service disappears
from the market—a substitution must be made in
the market basket. If that substitute has different
characteristics, a decision must be made as to its
treatment in the cri. This decision will implicitly,
or explicitly, accomplish one of the following: (a)
attribute any observed price difference to a pure
price difference; (b) attribute any observed price
difference to a real quality change; or (c) allocate
any price difference in some proportion to both
pure price change and real quality change. For
practical purposes, the first two alternatives are the
easiest to implement because they do not require
any additional market information or empirical
judgement.

For many items, the quality differences be-
tween a disappearing product and others of its
kind still on the market are negligible. Examples
would be different brands of bran flake cereal or
regular grade gasoline. In these cases, a new
item—one judged to be the most similar to its pre-
decessor—is directly substituted for the disap-
pearing product. This is called a comparable sub-
stitution, and it implicitly attributes any price
difference between the two varieties to a pure
price difference. In these cases, price differences
typically are small or nonexistent, so that an indi-
vidual substitution has a negligible effect on the
cri. (If a pure price change is implicitly imposed
by product “downsizing,” this effect is captured as
a pure price change. In the case of food items, for
which prices are converted to a per-ounce basis, a
newly substituted 15-ounce container of an other-
wise identical product at the same price as that of
the old 16-ounce size will be reflected in the index
as a pure price change.)

If the qualitative differences are perceived to
be important, then a noncomparable substitution
must be made. This can be done implicitly, by
overlap pricing, or by one of several methods of
more explicit quality adjustment, including direct
quality adjustment, the link method, or application
of the hedonic technique.'

Overlap pricing, which usually is applied
when a new variety or revised product is replacing

one being phased out by the manufacturer, re-
quires that there be a period during which both
products coexist in the market. In a given month,
the prices of both products are observed and the
difference between the two prices is implicitly as-
cribed to the differences in quality between prod-
ucts. In that month, say period ¢, the price change
for the old variety with respect to the previous
month, period 1-1, is the cri price change. In the
succeeding month, period ¢+1, the price change
for the new variety with respect to the month ¢ is
used to represent the price change in the cri.

The overlap method links the old and new
products without any explicit attempt to measure
either the quality or price differences between
them at any point in time. It thus implicitly as-
cribes all of the observed price difference to real
quality difference. The use of overlap pricing is
limited to situations in which old and new prod-
ucts both are available on the market in at least one
period for pricing, and may have a downward ef-
fect on the index when the price of the old product
is discounted in anticipation of introduction of the
new product, or when the new product is intro-
duced to the market at a premium price, or both.
Items such as apparel and new motor vehicles are
subject to this marketing pattern.

Direct quality adjustment involves making an
explicit comparison of the old and new items. If
the new item has features or additional equipment
that were not part of the old item, as is often the
case for new model automobiles, data on the
manufacturer’s costs of these innovations may be
used to impute the value of the quality changes,
This method assumes that the consumer perceives
and values the quality differences by the same
amount as the manufacturer’s reported cost to pro-
duce them. If the manufacturer’s reported cost
does, in fact, contain some amount of pure price
change, this approach imposes a downward effect
on the cpr.

This type of quality adjustment has been applied
in situations in which product innovations are
mandated by government legislation, as in the case
of antipollution devices (catalytic converters) on
new vehicles. Unfortunately, economic theory
does not provide unequivocal guidance for such
decisions. If it is assumed that the consumer per-
ceives the additional cost as commensurate with a
benefit (such as improved air quality and health),
then a quality improvement exists. However, pub-
lic goods and environmental amenities are beyond
the scope of the cpt, which is an index of changes in
the cost of private consumption only. Also, in these
situations, producers may exercise an incentive to
exaggerate the true production cost of the tech-
nological amendment, and it is difficult to make
an empirical judgement as to the consumer’s
valuation of such changes to the product.

Monthly Labor Review December 1993 35



Quality Adjustment

Data from the survey provide the sampling frame of outlets
for food and most services and commodities to be priced in
the cri. The Continuing Point-of-Purchase Survey is the
source of the outlet sampling frame for about 90 percent of
the commodity and service items by expenditure weight. See
sis Handbook of Methods, pp. 176-225, for more informa-
tion,

2 See Primont and Kokoski, “Comparing Prices”; and

Kokoski, “New research on interarea price differences.”

® See Reinsdorf, “The Effect of Outlet Price Differen-
tials.”

TT. Van Hoomissen, “Price Dispersion and Inflation: Evi-
dence from Israel,” Journal of Political Economy, vol. 96,
1988, pp. 1303-14.

25 See Armknecht and Weyback, “Adjustments for Quality
Change.”

Barbara Bailar receives Shiskin Award

Barbara Bailar, Executive Director of the American Statistical Association,
received the 14th annual Julius Shiskin Award for Economic Statistics.

Dr. Bailar was cited for her contributions in modernizing, improving, and
extending the Census Bureau's statistical programs. In the Census Bureau’s
Statistical Research Division, then later in the Research Center for Measure-
ment Methods, Dr. Bailar’s statistical research and analysis of the Decennial :
Census of Population and Housing and the Current Population Survey led to
major improvements in both of these programs. Later, as Associate Director
for Statistical Standards and Methodology, her work in undercount and
nonsampling error in the decennial census resulted in major improvements in
the quality of U.S. census data.

The presentation was made along with an honorarium at the Washington
Statistical Society’s annual dinner in June.

The award is named in honor of the ninth U.S. Commissioner of Labor i
Statistics. It is designed to honor unusually original and important contribu-
tions in the development of economic statistics or in the use of economic sta- H
tistics in interpreting the economy. Participating organizations in the program
are the Bureau of Labor Statistics, Bureau of the Census, Bureau of Economic
Analysis, Office of Management and Budget, National Bureau of Economic
Research, National Association of Business Economists, and the Washington
Statistical Society. The late Commissioner Shiskin was associated with all of
these organizations in his distinguished career.

46 Monthly Labor Review December 1993