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Toward a Cost-of-Living Index:Progress and Prospects

Katharine G. Abraham

Almost anyone with an interest in consumer price trends wants to know how

those trends affect consumers, and, in principle, that effect is best captured by

an appropriate cost-of-living index. It therefore seems curious that so few of

the statistical offices around the world have adopted the cost-of-living index objective

for their consumer price indexes. Accurately tracking the cost of purchasing a fixed

market basket of goods and services no doubt offers a more comfortable standard for

a statistical agency to aspire to than accurately tracking the cost of living. But if thelatter is what one really would like to measure, it seems better just to say so.

For some years, the theory of the cost-of-living index has guided Bureau of

Labor Statistics (BLS) decisions about the Consumer Price Index (CPI), and in the

recent past, the agency has stated more explicitly that the goal for the CPI program

is to approximate a conditional cost-of-living indexthat is, an index that mea-

sures changes in the cost of living that are due to changes in prices of goods and

services, conditional on other outside influences that affect the standard of living

remaining unchanged. It may be that a true cost-of-living index is unattainable as

a practical matter. Still, the Consumer Price Index surely can be moved closer to

that ultimate objective, and the theory of the cost-of-living index has the consider-able advantage of offering a unified framework for the evaluation of the methods

used to produce the CPI.1

1 Triplett (2001) provides a nice discussion of issues related to adoption of the cost-of-living index as the

conceptual framework for a consumer price index. For some different perspectives, see Hill (1999) and

Turvey (1999). Greenlees (2001) discusses how the Bureau Labor Statistics has used the cost-of-living

index framework to guide its decisions about the Consumer Price Index.

yKatharine G. Abraham is Professor, Joint Program in Survey Methodology, University of Maryland, College Park, Maryland. From 1993 to 2001, she served as Commissioner, Bureau

of Labor Statistics, U.S. Department of Labor, Washington, D.C.

Journal of Economic PerspectivesVolume 17, Number 1Winter 2003Pages 4558


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In this paper, I consider the improvements the Bureau of Labor Statistics has

made to the Consumer Price Index since the mid-1990s. These include changes

designed to make the index more representative of current expenditure patterns;

to account for consumer substitution in response to relative price change; andto account for changes in the quality of the goods and services that con-

sumers purchase. Drawing on selected recommendations made by the National

Academy of Sciences Panel on Conceptual, Measurement, and Other Statistical

Issues in Developing Cost-of-Living Indexes recently chaired by Charles Schultze

(hereafter, the Schultze panel), I highlight those additional areas for exploration

that I believe have the greatest potential and discuss those areas I believe to be less

promising.

A Primer on the Consumer Price Index

The Bureau of Labor Statistics describes the CPI on its website at http://

www.bls.gov/cpi/cpifaq.htm as a measure of the average change over time in the

prices paid by urban consumers for a market basket of consumer goods and ser-

vices. Construction of the Consumer Price Index begins with the selection of the

individual items to be priced. Prices are collected in each of 87 geographic areas for

items in each of 186 item categories or strata.2 All of the nations 31 largest metropol-

itan areas are included in the survey sample; another 56 areas are selected to represent

the remainder of the countrys urban population. The item categories are designedto be as homogeneous as possible while still covering everything a consumer might

purchase for current consumption. There are, for example, separate item strata for

breakfast cereal; garbage and trash collection; bedroom furniture; household paper

products; womens dresses; dental services; and college tuition and fees. Subindexes

for each of the area/item strata cells form the building blocks for the aggregate CPI.

The selection of specific items for pricing is designed to ensure that the items

priced are representative of what consumers actually purchase. First, for each

geographic area, the outlets contacted by the Consumer Price Index data collectors

are selected randomly, with a probability of selection proportional to the outlets

share of sales in an item category based on the Point of Purchase Survey (POPS),which collects information on where consumers shop. The outlets selected may

include on-line vendors or catalog retailers in addition to bricks-and-mortar stores

and offices. Once the outlet sample has been picked, the specific items to be priced

also are selected randomly, again with a probability of selection proportional to

sales, as closely as that can be determined.3 Each item chosen for inclusion in the

2 There are another 25 strata for which no prices are collected. Price change in these strata, whichaccount for less than 1 percent of total consumer spending, is imputed from the movements in priced

strata.3 It is perhaps worth noting that no other country in the world uses a fully probabilistic process like thatemployed in the United States to select the items to be priced in the Consumer Price Index. The typical

46 Journal of Economic Perspectives


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CPI sample receives a weight that reflects, at least approximately, the aggregate

volume of base period sales it represents, and that weight is used in the calculation

of the relevant area/item stratum subindex. Prior to 1999, the change in a subindex

between the base period and the current period was calculated for all strata as theweighted arithmetic mean of the change in included items prices; as will be

described shortly, a different formula now is used in a majority of strata.

Once the subindexes have been computed, they must be aggregated to form

the overall CPI. This is done using a set of expenditure share weights that represent

the purchases of a typical consumer. The expenditure data used to form these weights

are taken from the Consumer Expenditure Survey, an ongoing survey that collects

detailed information on the spending patterns of a representative sample of house-

holds. The change in the overall index between the base period and the current

period equals the weighted arithmetic mean of the changes in the component indexes.

Faster Rotation of Items into the Consumer Price Index

Historically, the sample of outlets at which prices were collected for the Con-

sumer Price Index and the specific items priced in those outlets were updated on a

five-year schedule, so that each year the full sample of priced items was replaced in

20 percent of the urban areas covered by the Consumer Price Index. Beginning

in 1998, the Point of Purchase Survey (POPS) has been converted from an in-person

to a telephone survey, and the Bureau of Labor Statistics has shortened the timeframe for the scheduled rotation of all priced items from five years to four years.

Because drawing a new outlet sample and making first visits to begin data

collection in those outlets is labor intensive, further shortening the period over

which a full item sample rotation occurs would be very expensive. It is technically

possible to target selected item categories for more rapid sample rotation, but as

Lane (2001) explains, this would have to be done very selectively indeed. At

present, the BLS has the resources to carry out a full rotation of a quarter of the CPI

outlet and item sample each year. If one were to rotate 25 percent of the CPI

sample every two years rather than every four years, it would be possible to rotate

the remaining 75 percent of the price quotations only once every six years.Largely because of cost and resource considerations, the BLS has begun to

develop alternative approaches. New outlets and new items for the prescription drug

stratum were selected in February 2000. During 2002, a new set of prescription drugs

practice in other countries is to collect prices for a list of speci fic items designated by the statisticalagencys headquarters office to represent the different categories of consumer expenditure. In Canada,for example, prices are collected for something over 600 speci fic items, and the set of outlets in whichprice data are collected is determined judgmentally based on regional managers knowledge of which

outlets have the largest sales volume. Although there is no evidence that this process generatessystematic bias in the Canadian CPI, there is also no real guarantee that the set of items selected forpricing is representative of what consumers actually purchase.

Katharine G. Abraham 47


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was selected for pricing at the existing set of prescription drug outlets. A standard

sample rotation is scheduled for the prescription drug stratum in 2004, with an

item-only updating again following in 2006, and so on. In other words, even though the

outlets will be rotated at the same pace as previously (on a four-year cycle), the itemswill be rotated more rapidly (on a two-year cycle). The Bureau of Labor Statistics

received new funding in 2002 to support the sort of interim item-only sample updating

first attempted for prescription drugs across many of the commodities and services

tracked to produce the CPI. Where implemented, this change will effectively reduce

the planned sample life of any specific item from four years to two years. To the extent

that changes in consumer purchasing patterns are reflected in sales at particular

outlets, rather than only in shifts of sales across outlets, this change should go a great

distance toward ensuring the currency of the item sample.

In addition, other more narrowly targeted efforts have been made to ensurethe currency of the sample in particular item categories where rapid change is

recognized to be the norm. For example, since 1995, whenever a generic equivalent

for a prescription drug priced in the index becomes available, the generic drug has

been given the opportunity to replace brand name price quotes using a probabi-

listic process that should yield a share for the generic roughly equivalent to its

market share six months after introduction. Since 2000, new personal computer

samples have been selected in existing outlets once every six months.

One important question about bringing new items into the CPI sample is

whether and how their prices will be compared to those of the items they replace.

In each of the examples just cited, a different approach has been adopted. At leastfor now, when a two-year interim updating of the item sample is carried out at the

existing sample of outlets, the prices of the new items will not be directly compared

to the prices of the items they replace. Not comparing the new and old items prices

in essence attributes the entire price difference to a difference in the items quality.

In the generic drug case, the full difference in price between the branded and the

generic drug is captured as a price decline, thereby effectively treating the branded

and the generic drug as equivalent in quality. In the personal computer case, the

price differences between the new computers and those they replace are adjusted

based on hedonic models designed to capture the value of the quality differences

between the new and old machines, with any remaining price difference treated asa true price change. Identifying the best approach to handling these comparisons

is an important part of the ongoing effort at the BLS to improve the treatment of

quality change in the CPI, a subject I will take up shortly.

Keeping the Expenditure Weights Current

Prior to 1998, the Consumer Expenditure Survey weights used in aggregating

the CPI subindexes to form the overall CPI were updated only about once every tenyears. The weights were based on three years of survey data. Because of lags in the

collection and processing of those data, the weights were 312 years old when first



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introduced. By the time the weights were replaced a decade or so later, they

obviously were much older.

Effective in January 2002, the Bureau of Labor Statistics has shifted to updating

the expenditure share weights every two years. A 50 percent expansion of theConsumer Expenditure Survey sample from roughly 5,000 to roughly 7,500 house-

holds that took effect in 1999 allows the weights to be calculated based on two years,

rather than three years, of data. Moreover, the processing of the expenditure data

has been accelerated. Together, these changes mean that the CPI expenditure

share weights are now two years old (rather than 312 years old) at the time of their

introduction and will be four years old (rather than 13 to 15 years old) at the time

they are replaced. For example, the expenditure share weights introduced in

January 2002 used information from 1999 and 2000. In January 2004, they will be

replaced with newer weights based on 20012002 data.The age of the weights has a less straightforward relationship to the rate of

growth in the Consumer Price Index than might have been expected. The BLS

announcement that future weight updates would occur on a two-year cycle, avail-

able at http://www.bls.gov/cpi/cpiupdt.htm , notes that implementation of this

policy following the 1987 revision of the CPI would have slowed the rate of growth

in the index by 0.17 percentage point per year from December 1988 to December

1997. All of this slowing, however, is accounted for by the replacement of the

19821984 weights introduced in 1987 with 1986 1987 weights in 1989; subsequent

biennial weight updates would have had virtually no net effect. Still, relying on

out-of-date information clearly undermines the face validity of the price index, andthere are likely to be some years in which the use of updated expenditure weights

makes a substantive difference. For both of these reasons, having more current

expenditure weights is a change to be welcomed.

Better Accounting for Consumer Substitution

One long-standing criticism of the Consumer Price Index has been that it fails

to account for changes in consumers purchases in response to changes in relative

prices. If the price of an item a consumer is in the habit of purchasing rises, theconsumer will tend to buy relatively less of that item and relatively more of

something else. To sustain the persons level of well-being, it should not be

necessary to provide enough money to purchase the original consumption bundle;

some lesser compensation should suffice. But a price index based on a fixed market

basket simply tracks the cost of purchasing the original bundle, and thus will rise by

more than the true increase in the consumers cost of living.

In 1999, in an effort to move the CPI closer to its cost-of-living index objective,

a new formula was adopted for calculating the CPI subindexes in item strata

accounting for about two-thirds of the total index weight. Whereas the old Las-peyres formula for a price index assumed that the consumer would continue to

purchase the same bundle as in the base period, the new geometric mean formula

Toward a Cost-of-Living Index: Progress and Prospects 49


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assumes instead that the share of expenditures devoted to a particular item remains

constant. If the price of Wacky Wizmos doubles and other prices do not change, for

example, the geometric mean formula is consistent with the expectation that

purchases of Wacky Wizmos will fall by half. This is equivalent to assuming a unitaryelasticity of substitution among the items in any category in which the geometric

formula is adopted. It is unlikely that the elasticity of substitution is exactly equal to

one within any item category, but assuming a value of one almost certainly is

preferable to assuming no substitution at all. For certain kinds of items, including

residential rent, owners equivalent rent, utilities and professional and hospital

medical care services, the assumption that consumers will substitute across items

within a stratum when relative prices change seems less plausible; for these strata,

the historical CPI formula that assumes continued consumption of the base-period

market basket has been retained. At the time of its introduction, the BLS estimatedthat the planned adoption of the geometric mean formula for calculating the CPI

would slow the rate of growth of the index by approximately 0.2 percentage point

per year.4

The absence of direct evidence about substitution among items within partic-

ular item strata arguably makes it easier to take (approximate) corrective action: no

available formula will be exactly right, so one makes a sensible choice and moves

on. Correcting for substitution across item strata is, in some ways, more complex.

Given information on current and past expenditures together with information on

current and past prices, it is possible to infer the responsiveness of consumption to

changes in relative prices. Diewert (1976, 1981) has shown that, when there issubstitution in response to relative price change, the true cost-of-living index can be

approximated using any of the superlative family of price index formulas, all of

which have in common a requirement for information on how consumption

spending has changed over the index period. The Consumer Expenditure Survey

provides the needed information on expenditures at the item stratum level, but

only with a lag. Accounting for substitution across item strata in a real-time

Consumer Price Index thus can be accomplished only in one of two ways: One

could publish an approximate value for the substitution-corrected index and revise

the index later, or one could publish an approximate value but not revise the index,

even when data later become available that imply a different value.

The widespread use of the Consumer Price Index for indexation purposes

makes the first choice of revising the index later unattractive, at least in the eyes of

many data users. To take a relatively recent example, the fact that the CPI is not

revised retroactively was one of the principal reasons for its selection by the

Department of the Treasury as the index for its inflation-protected bonds, first

issued in January 1997. The comments received from individuals, academicians,

investment management firms, dealers and institutional investors in response to

4 Dalton, Greenlees and Stewart (1998) discusses the adoption of the geometric mean formula in theConsumer Price Index.



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Treasurys 1996 Advance Notice of Proposed Rulemaking indicated a clear con-

sensus that the selected index should be: widely recognized, published frequently,

accurate, easily obtainable, easily understood and not revised retroactively (Federal

Register, September 27, 1996, italics added).

But the alternative of maintaining an index value that is at odds with the most

recent data also seems unattractive. For now, the decision has been to leave the

existing Consumer Price Index unchanged, but to introduce a new revisable

indexthe Chained Consumer Price Index for All Urban Consumers, or C-CPI-U

that is similar to the CPI except that it is constructed using a Tornqvist index

formula, one of the superlative index number formulas designed to account for

consumer substitution. Starting with the release of July 2002 data, preliminary

values for the new index have been published each month along with the existing

CPI; current index values will be subject to two rounds of revision over thesubsequent two years before becoming final.5

Historical simulations based on data for 1990 through 1995 had suggested that

the C-CPI-U might grow at an annual rate 0.1 to 0.2 percentage point below the

growth rate of the CPI for All Urban Consumers (CPI-U). More recent data suggest

a larger difference, reflecting greater variability in growth rates across CPI subin-

dexes and correspondingly larger shifts in consumer spending in recent years. In

2000, the only year for which final C-CPI-U data are available, the growth rate of the

C-CPI-U was 0.8 percentage point below that of the CPI-U. Additional observations

will be required to determine whether recent experience or the experience of theearly 1990s is more typical.

Whatever the answer to that question, it will be interesting to see whether and

how the new index ends up being used. For example, following the Schultze panels

suggestion, Social Security beneficiaries could receive cost-of-living adjustments

each year based on the preliminary values of the C-CPI-U, plus or minus an

adjustment to reflect the difference between the final and the preliminary values of

the two-years-earlier index (Schultze and Mackie, 2002, pp. 194 195). If a sufficient

number of those currently using the Consumer Price Index for indexation pur-

poses switch over to the revisable C-CPI-U, it is possible (though perhaps not likely)

that this index eventually could replace the CPI-U.6

5 Information regarding the new C-CPI-U can be found on the Bureau of Labor Statistics website at http://www.bls.gov/cpi/superlink.htm .6 The history of congressional decisions regarding the use of the Consumer Price Index for indexationpurposes is not especially encouraging with regard to the C-CPI-U being adopted to replace the existingCPI. There are in fact two existing CPI measures: the CPI for Urban Wage Earners and Clerical Workers(CPI-W) and the CPI for All Urban Consumers (CPI-U). Only the CPI-W existed when it was decided in1972 to index Social Security benefits to the CPI. When the more inclusive CPI-U was introduced as part

of the 1978 CPI revision, many assumed that it would be adopted in place of the CPI-W for SocialSecurity indexation, but this has never happened, although the CPI-U typically is used for programindexation purposes legislated since 1978.



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Better Accounting for Quality Change

Better accounting for changes in the quality of the goods and services that

consumers purchase is perhaps the greatest remaining challenge in improving the

Consumer Price Index. In the past, quality change for the most part has been

addressed indirectly. When a priced item disappears from an outlet and must be

replaced, the CPI staff must decide whether the replacement item is similar enough

to the original item that their prices can be compared. If not, the standard practice

is to ignore the point-in-time difference in price between the replacement and the

original item in calculating the index. In effect, this approach attributes the full

point-in-time difference in the items prices to a difference in their quality.7 The

standard approach to the introduction of new item samples also has been to treat

any price differences between the items in the new and the old samples asattributable to differences in their quality.

To account better for changes in quality, the Bureau of Labor Statistics has

extended the use of hedonic models designed to estimate the value the market-

place attaches to different item characteristics. Fitting a hedonic model requires

data on the prices and characteristics of a sample of specific items. Price is

regressed on item characteristics, and the estimated coefficients on the character-

istics variables can be used to estimate the characteristics value in the market. Prior

to 1998, such hedonic models were used almost exclusively for apparel items. Over

the past few years, hedonic models have been introduced for personal computers,

washers and dryers, microwave ovens, refrigerators, televisions, VCRs, DVD players,camcorders, consumer audio product components and college textbooks.

It would be nice to know whether broadening the application of hedonic

techniques beyond the appliances and boys toys to which they predominantly

have been applied to date would change their impact. The Schultze panel suggests

that the Bureau of Labor Statistics pursue experiments to analyze quality change

affecting randomly selected items, increasing the probability that within-sample

quality change biases both upward and downwardwill be identified (Schultze

and Mackie, 2002, p. 140). The set of items for which hedonic models have been

estimated to date largely has been determined by data availability. Constraints on

data availability are likely to pose difficulties for the evaluation of quality change forrandomly selected items along the lines just described, though this is not to say that

the task should not be attempted.8

Once a hedonic model has been estimated, present practice generally calls for

the results of the model to be used to make quality-adjusted price comparisons only

7 See Moulton and Moses (1997) for an illuminating discussion of the historical approach to adjustingfor quality differences between replacement and disappearing items in the Consumer Price Index.8 The Schultze panels recommendation echoes a suggestion made by William Nordhaus, during a 1997meeting of the Brookings Panel on Economic Activity, that the Bureau of Labor Statistics conduct an

audit of the Consumer Price Index, picking a random sample of items for more careful evaluation ofquality change and then using the results to estimate the overall quality bias in the CPI (Gordon andBosworth, 1997, p. 363).



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when a replacement must be substituted for a specific item that no longer is

available in an outlet. This means, however, that quality-adjusted price differences

between the old and new item samples at the point of a sample rotation are not

captured explicitly. As already noted, in the future, the BLS will be updating thesamples of items priced in particular outlets two years after the introduction of the

original item sample for the outlet. In many cases, it should be possible to compare

the prices of the new and the old items, and to use hedonic models to apportion

the price difference into a quality piece and a true price change piece. Although

one would want to proceed carefully, this approach certainly merits exploration.

Making comparisons between the prices of items collected at different outlets, as

often would be required when a full sample rotation is conducted, would be more

difficult. Prices at different outlets may vary due to differences in the amenities

associated with shopping at the different outlets, and at present there is no goodbasis for valuing such amenity differences. The Schultze panel recommends that

research be directed toward devising such methods (Schultze and Mackie, 2002,

pp. 176 197), a recommendation with which I concur.

Capturing and Valuing Novel New Goods

Perhaps the single sharpest criticism of the Consumer Price Index has been

directed toward the unacceptably long lags that have on occasion occurred between

the appearance of novel new goods in the marketplace and their incorporation intothe index. Items first appearing in the index long after their market debut have

included the VCR, the home computer and the cell phone. These delays typically have

arisen as a consequence of the item in question not fitting neatly into an existing CPI

category and thus not being captured through routine rotations of the item sample. In

hindsight, the propensity of the Bureau of Labor Statistics to hold off on introducing

such items until the next scheduled item category overhaul was ill-advised. Failing to

introduce highly visible items into the pricing structure invited easy criticism, and the

cumulative effect of slow introductions on the index could have been significant.

Without a doubt, however, such instances will be handled differently in the

future, as the Bureau of Labor Statistics staff has become highly sensitized to thedesirability of bringing new goods into the index quickly. The treatment of the

recent introduction of Viagra is a case in point. In 1999, the BLS augmented the

prescription drug sample by adding Viagra price quotes. The weight for these

quotes was determined based on national market share data, and the weights for

price quotes already in the index were reduced commensurately. The motivation

for this decision was in part to avoid the sort of criticism directed toward the long

lags in introducing VCRs, home computers and cell phones; if asked whether

Viagra was represented in the index, all involved wanted to be able to answer yes.

Although that objective was realized, none of those involved was entirely comfort-able with the ad hoc way in which it was accomplished. For the future, it would be

desirable to develop clearer guidelines for deciding when a new product is impor-



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tant enough that there should be an intervention to bring it into the index. Once

that decision has been made, a reasonable approach might be to broaden the

boundaries of an existing item category to encompass the new product, if necessary,

and then to reselect the item sample for that category to give the new product theopportunity of being picked for pricing.

A suggestion that has emerged from the outside groups asked to review the

Consumer Price Index in recent years has been that the Bureau of Labor Statistics

arrange for regular consultations with panels of experts . . . persons who are likely to

know when important new consumer products have recently or soon will reach the

market (Conference Board, 1999, p. 25, cited approvingly in Schultze and Mackie,

2002, p. 167). Although it is hard to argue against receptivity to outside advice, this

particular recommendation seems to me to be based on a misdiagnosis of why the BLS

did not introduce new products as quickly as now seems desirable. The CPI staffincludes a group of commodity analysts who are charged with monitoring develop-

ments in the product areas for which they are responsible. I am confident that these

individuals were well aware of the growing sales of VCRs, home computers and cell

phones from a very early stage. These items were not introduced into the CPI more

quickly because of adherence to an overly rigid process, not because of a lack of

information about product market developments. Managing an outside advisory group

would consume a considerable amount of staff time and energy, and my judgment is

that, in this case, that time and energy could be more productively devoted elsewhere.

While there is near universal agreement that new goods should enter the Con-

sumer Price Index relatively quickly, there is disagreement, even among those who takethe position that the cost-of-living index should be the measurement objective for the

CPI, about whether the Bureau of Labor Statistics should attempt to incorporate into

the index the value to consumers of newly available goods. Especially when new goods

are not simply substitutes for existing products, they may generate substantial con-

sumer surplus. As a result, the consumer may be able to attain any given level of utility

with a lower income than would otherwise have been the case. Hausman (1997, 1999)

has developed techniques for estimating the magnitude of the consumer surplus

associated with new goods that involve estimating the virtual price that would set the

(compensated) demand for the new good to zero. While the Schultze panels conclu-

sion thatvirtual price reductions associated with the introduction of new goods shouldnot be imputed for use in the CPI (Schultze and Mackie, 2002, p. 160) may be too

categorical in its rejection of these methods, I would certainly concur that adopting

these methods any time soon would be a serious mistake.

In part, I am concerned that the complexity of these new methods would make

them difficult to explain to the public. Although it undoubtedly is true that there

is much about the methods used currently to construct the CPI that the public does

not understand, any proposed change nonetheless would require a clear explana-

tion. The continuing professional debate over Hausmans techniques also stands in

the way of their adoption. Hausmans methods require strong assumptions aboutthe shape of the demand curve and the nature of shocks to demand. Further,

different assumptions can lead to very different conclusions regarding the con-



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sumer surplus associated with new products (for example, Bresnahan, 1997). As a

general matter, adopting cutting-edge techniques without allowing time for a

coalescence of professional views about whether and how they should be used

would create a real risk of undermining public trust in the CPI. I am not confidentthat professional opinion ever will coalesce around techniques for valuing the

consumer surplus associated with new goods, given their heavy reliance on assump-

tion as opposed to observation, but it clearly has not done so at this point.9

In addition to the question of whether one might wish to adopt Hausman s

methods in the construction of the CPI, there are serious questions about whether

this would be feasible. Practical concerns include obtaining the necessary data, the

workload associated with extending Hausmans illustrative efforts into production

mode, and the necessity for continuing index revisions that after-the-fact calcula-

tions of consumer surplus could require. None of this, of course, is an argumentagainst actively pursuing research in this area. It is, however, an argument against

folding adjustments for the value of new products into the calculation of the official

CPI, at least given the present state of knowledge.

Should the Data Collection Process Be Rethought?

Perhaps the most frequently suggested change in the data used to construct

the CPI has been to substitute electronic scanner data generated at the checkout

register for data collected byfield economists. Scanner data include information onboth quantities and prices, so that their use also holds out the promise of improved

accounting for consumer substitution. Exploiting the considerable potential of

such data, however, will be far more difficult than most observers had initially

appreciated. Scanner data generally do not represent the full set of items pur-

chased by consumers, so that even if scanner data were widely employed in index

construction, they likely still would need to be augmented with data collected by

BLS field economists. Properly weighting data derived from multiple sources and

ensuring complete but nonoverlapping coverage of purchases from the full range

of outlets could be difficult. Furthermore, scanner data are collected for purposes

other than meeting CPI needs. Scanner data codes change frequently, and the BLScould be hard-pressed to keep up with these changes. Quality adjustment could be

more difficult if the index were being calculated using scanner data, as there would

be no field economist who could also provide the necessary information on product

characteristics. Given that scanner data are collected by private firms who seek to

make a profit from their sale, it seems unlikely that using scanner data would

reduce the costs of index construction. It could be risky to rely on data that a private

vendor collects mainly to serve other clients as a critical input to a major economic

9Although the specific issues are different, the Schultze panel and others have made similar argumentsfor caution in incorporating hedonic techniques more widely into the CPI. For an interesting discussionof the political economy of statistical measurement in this context, see Hulten (2002).



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indicator, although means could perhaps be devised to address this last concern.10

At least for the short run, my sense is that the BLS should set modest goals for its

work with scanner data, focusing on learning whether and how index calculations

based on scanner data might differ from those using traditional CPI data.Some have expressed concern about the Consumer Expenditure Survey data

that provide the CPI expenditure weights (Schultze and Mackie, 2002, pp. 253256;

Branch, 1994; Lebow and Rudd, 2001, pp. 9 13). The primary basis for this

concern is that the expenditure share weights derived from Consumer Expenditure

Survey seem inconsistent with the Personal Consumption Expenditure estimates

that are produced by the Bureau of Economic Analysis.

One possible source of problems is the enormous amount of information

requested from Consumer Expenditure Survey respondents. The survey includes

both an interview and a personal diary component. The interview instrument,printed on 11 17 inch paper, is 143 pages long, and the diary instrument, printed

in the same format, is 67 pages long.11 In each case, respondents are asked to

answer hundreds of questions. It would not be surprising if respondents who knew

that reporting a purchase would lead to further questions chose to leave some out.

Recall also may be a problem. Very large purchases (like car purchases) and

regularly recurring outlays such as monthly rent or utility payments likely are easier

to report with reasonable accuracy than are smaller and/or less regular expendi-

tures, particularly in cases where purchases have been made by more than one

member of a household. In addition, certain socially undesirable expenditures like

tobacco or alcohol purchases may be systematically underreported.Finding ways to reduce the respondent burden associated with the Consumer

Expenditure Survey has become a high priority for the Bureau of Labor Statistics.

One strategy for obtaining better data might be to divide the survey questionnaire

into parts, so that each household would be asked only for details about certain

categories of expenditure. Investigating whether such a change could increase

reporting accuracy seems worthwhile, though the research value of having com-

plete household-level expenditure data for a representative sample of households

suggests thinking hard about whether there is a way to produce useful estimates of

households spending in broader categories without collecting all of the underlying

detail.Exploring the use of Personal Consumption Expenditure estimates in the

construction of Consumer Price Index weights, as suggested by the Schultze panel

(Schultze and Mackie, 2002, p. 274), also seems worthwhile. It should be noted,

however, that the Personal Consumption Expenditure estimates have their own

problems. They are derived principally from business surveys and censuses that ask

10 Greenlees (forthcoming) discusses challenges to the incorporation of scanner data into indexconstruction, including some of those I have mentioned.11 Those who have never done so may find it illuminating to look at the Consumer ExpenditureSurvey interview and diary instruments, available on the Bureau of Labor Statistics website at http://

www.bls.gov/cex/home.htm#forms .



13/15

about sales of different products and services. The nature of the underlying source

data makes it difficult for the Bureau of Economic Analysis to disentangle fully

business and household expenditures. Still, the Personal Consumption Expendi-

ture estimates may be more reliable for certain kinds of household expendituresfor example, spending on alcohol and tobaccoso that some sort of hybrid

approach to the construction of CPI weights may merit study.

Conclusion

The Consumer Price Index is a work in progress. Although the initiative for

many if not most future improvements will come from the professional staff of the

Bureau of Labor Statistics, it is very much to be hoped that the heightened interestin price measurement issues so apparent in the academic community will translate

into research that also will be helpful. Although producing a true cost-of-living

index may be impossible, that concept remains the polestar offering direction to

our efforts to improve the Consumer Price Index.

y I am grateful to Kenneth Dalton, John Greenlees, Alan Krueger, Charles Schultze, Timothy

Taylor and Michael Waldman for helpful comments on an earlier draft of this paper.

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