Monthly Labor Review February 1999 35

P

 roductivity growth for the U.S. business sector has been slow since 1973, compared with

that of earlier periods. From 1959 to 1973,

for example, estimates prepared by the Bureau of

Labor Statistics showed that labor productivity

(output per hour) in the business sector increased

by about 3 percent per year. Since 1973, by contrast, the growth rate has been just slightly more

than 1 percent per year. Interestingly, however,

during this recent period of relatively slow productivity growth, industrial technology advanced

considerably, and the financial markets were

healthy as well, phenomena often associated with

increases in the rate of productivity growth.

As a result of this apparent contradiction, many

economists and government officials have begun

to question whether the slower growth was real or

the result of measurement problems in the official

government productivity statistics. In particular,

recent discussions have focused on the issue of

possible upward bias in the Consumer Price Index

(CPI), leading some economists to assert that productivity growth has been understated as a result.

This article attempts to add to these discussions

by examining the relationship between price indexes and productivity statistics, gauging the relative importance of each of the various indexes used.

The Office of Productivity and Technology

publishes productivity statistics for major sectors

and subsectors of the U.S. economy, as well as for

many domestic industries and foreign countries.1 In

this article, the focus is on labor productivity statistics for the business sector of the U.S. economy.

These statistics relate the real output of an aggregate sector of the U.S. economy to the labor resources used to produce that output. Hence, data

series on labor productivity, or output per hour, capture changes in output that cannot be attributed to

changes in labor inputs. Growth in labor productivity can be a result of many influences, including

changes in technology, capital investment, purchased

inputs from outside the sector, capacity utilization,

returns to scale, and workforce skill and effort.

To calculate labor productivity for the U.S. business sector, BLS combines indexes of real output

from the national income and product accounts (national accounts), produced by the Bureau of Economic Analysis (BEA) of the U.S. Department of

Commerce, with hours measures constructed primarily using data from the BLS Current Employment

Statistics program, Current Population Survey (CPS),

and the Hours-at-Work Survey.2 Thus, the article

begins with a discussion of three methods used by

BEA to construct time series for real components of

gross domestic product (GDP) and for the business

sector output used to construct BLS productivity measures: deflation, extrapolation and direct-valuation

techniques. (The deflation and extrapolation techniques make use of various price indexes, including

the CPI.)

 Prices and Productivity Measures

How price indexes affect BLS

productivity measures

Price indexes play a significant role in measuring real

output and productivity; thus, potential bias in price

indexes, as well as a lack of price indexes,

can impact the accuracy of productivity measures

Lucy P. Eldridge

Lucy P. Eldridge is

an economist in

the Office of

Productivity and

Technology.
36 Monthly Labor Review February 1999

Next, using BEA data from 1997, the relative importance

of various price indexes in measuring business sector output

is examined. These calculations indicate that components of

the CPI are used to construct approximately 57 percent of the

business sector output measure used for BLS productivity statistics. Due to its relative importance in measuring real output, the CPI receives more attention here than do the other

indexes. In addition, some aspects of the recent controversy

surrounding the CPI and its methodology are reviewed, including a discussion of the Advisory Commission to Study

the Consumer Price Index (Boskin Commission) December

1996 report to the Senate Finance Committee and the official

BLS response to the Boskin Commission’s findings.

Finally, the article discusses an area that has not been highlighted in the recent evaluation of productivity measures: the

use of input-based methods to construct components of real

output. Input-based methods include the use of input data to

construct deflators and to construct output trends using extrapolation techniques. Although the major portion of real

business sector output is constructed using the CPI, input-based

methods have important implications for measuring productivity as well. Because productivity statistics relate the growth

in output to the growth in inputs, an output measure that grows

at a rate dominated by the growth in inputs may result in a

measure of productivity that is biased toward zero. Thus, the

role that these techniques play in measuring output and the

implications for measuring productivity are examined.

Business sector output

The most widely known measure of aggregate output for the

U.S. economy is GDP. GDP is the sum of (1) personal consumption expenditures, (2) gross private domestic investment,

(3) net exports of goods and services, and (4) government

consumption expenditures and gross investment. BEA constructs nominal output for detailed components of GDP from

various data sources. These components of nominal GDP are

converted to real measures and then aggregated to the four

expenditure categories, which are further aggregated to calculate GDP.

As a fundamental part of the national accounts, BEA also

distinguishes homogeneous aggregate groups, or sectors, of

GDP, the members of which are engaged in similar types of

transactions.3 The national accounts identify three primary

sectors: business, household, and government. The household

sector includes the services rendered by paid household workers and the services rendered by nonprofit institutions serving households. The government sector includes the services

rendered by government employees and by government fixed

capital. The household and government sectors are relatively

small, compared with the business sector. The business sector accounts for the bulk of national output. BEA calculates

the measure of business sector output by removing from GDP

the gross product of general government, private households

and nonprofit institutions.4

Although it would be preferable to measure productivity

for the U.S. economy at the most aggregate level of domestic

output, GDP, it is not possible to construct reliable estimates at

such an aggregate level. Productivity measurement requires

independently produced, well-defined, data on inputs and outputs. As a result, BLS excludes several activities from aggregate output in order to remove potential sources of bias specific to productivity measurement. The real gross products

of general government, private households, and nonprofit institutions are estimated primarily using data on hours worked.

The trends in such output measures will, by definition, move

with measures of input data and will tend to imply little or no

labor productivity growth. Although these measures are the

best available estimates of nonmarket components of GDP,

including them in measures of aggregate productivity for the

economy would bias labor productivity trends toward zero.

Therefore, the business sector is the most comprehensive sector for which BLS publishes an output-per-hour measure.

The BLS measure of business sector output is slightly narrower than the BEA measure, excluding the gross product of

owner-occupied housing and the rental value of buildings and

equipment owned and used by nonprofit institutions serving

individuals.5 (See table 1.) These components are excluded

because no adequate corresponding labor input measures can

be developed. In 1997, the BLS measure of business sector

output accounted for approximately 77 percent of the value

of GDP, while the corresponding BEA measure accounted for

84 percent of GDP. For the remainder of this article, the term,

“business sector,” will refer to the BLS measure, not the BEA

measure.

Calculating real output. BEA effectively separates changes

in current-dollar GDP into price-change elements and quantity-change elements. The quantity-change elements are often referred to as real components of GDP.

6 BEA constructs

quantity-change measures, or real output, at fine levels of detail

and then aggregates these components to arrive at real GDP. In

early 1996, BEA began featuring national accounts data using

Table 1. GDP and business sector output, 1997

[Billions of current 1997 dollars]

Gross domestic product ....................................................... $8,110.9

Less:

Private households ........................................................... 12.0

General government ......................................................... 912.9

Nonprofit institutions ......................................................... 349.4

BEA business sector ............................................................ 6,836.6

Less:

Gross housing product, owner-occupied ........................... 518.7

Rental value of buildings and equipment owned

and operated by nonprofit institutions

serving individuals ......................................................... 48.6

BLS business sector ............................................................. 6,269.3

Prices and productivity measures
 Monthly Labor Review February 1999 37

chain-type annually-weighted indexes to avoid a systematic

bias that was embodied in the earlier constant-dollar indexes

of real output.7 The chain Fisher index is used to combine

aggregate growth rates between pairs of adjacent years to create an aggregate index-number time series.

BEA uses three different methods to calculate real output

for detailed product-side components of GDP: deflation, quantity extrapolation, and direct base-year valuation.8 To calculate real output for the business sector, the real gross products

of general government, private households, nonprofit institutions, and owner-occupied housing are removed using the

annual-weighting technique.9 Using information published

by BEA in 1998,10 and with the help of the BEA staff, the relative importance of each of these methods in computing GDP

and business sector output were estimated. The estimates use

1997 current-dollar data, as revised in July 1998. Table 2

shows the distribution of methods used to measure real GDP

and real output for the business sector.

Deflation. The deflation method is applied to the majority

of both GDP and business sector output. Using the deflation

method, the quantity index is obtained by dividing current

dollars by an appropriate index. BEA makes use of several

different indexes, including BLS consumer price indexes, producer price indexes, export and import price indexes, as well

as BEA price indexes, Census price indexes, composite indexes of input prices and costs, and other indexes.11 BEA’s

composite indexes of input prices and costs are based primarily on input data, and will be referred to as input-based indexes. BEA uses composite indexes based on input data as

deflators for those categories of expenditures for which specific price indexes do not exist, such as for nonresidential

buildings. In GDP, real trends for nonmarket outputs are also

constructed using cost data; there are no nonmarket outputs

in the business sector for which real trends are calculated by

deflation.

Based on the estimates of this study, using 1997 currentdollar data, the following tabulation shows the relative importance of the various indexes used to construct real output

with the deflation method.

 GDP Business

sector

Total ..................................................... 83.6 93.3

BLS consumer price index ........................... 49.7 55.9

BLS producer price index............................. 11.8 15.3

BEA input-based indexes ............................. 13.2 10.6

BLS export price indexes ............................. 8.5 11.0

BLS import price indexes ............................. –9.9 –12.8

Other price indexes ..................................... 10.3 13.3

As can be seen, BLS consumer price indexes are used as

deflators to construct real values for the majority of both GDP

and business sector output. BEA primarily uses consumer price

indexes as deflators for components of personal consumption

expenditures.12 Input-based indexes also play a significant

role in the construction of real GDP and output for the business sector. Roughly 11 percent of business sector output is

constructed using BEA input-based indexes as deflators. This

is a reduction from the 13 percent of GDP that uses these indexes, resulting mainly from the exclusion of the gross product of nonprofit institutions from the business sector. BLS

producer price indexes are primarily used by BEA to construct

real components of gross private domestic investment, while

BLS export and import price indexes are primarily used as

deflators for net exports of goods and services.

A relatively small portion of both GDP and business sector

output use other price indexes as deflators. This category is

dominated by the use of the Bureau of the Census price index

for single family homes and the BEA price index for multifamily homes. These two indexes account for about 22 percent of the “other price indexes” used to construct real business sector output and real GDP. Other price indexes also include the BEA price index for computers, Department of Defense prices, the BEA price index for airline transportation,

implicit price deflators, and other composite indexes.13

Extrapolation and direct valuation. BEA uses extrapolation

methods to calculate real trends for approximately 6 percent

of output for the business sector, and for about 14 percent of

GDP. The remaining components are constructed using direct

base-year valuation methods. The quantity-extrapolation and

direct-valuation techniques are similar in that they both use

explicit quantity data. For quantity extrapolation, real output

is calculated using various quantity indicators to extrapolate

the base-year nominal value in both directions. For example,

real output for services furnished without payment by financial intermediaries is calculated by using paid employee hours

as the quantity indicator. Quantity indicators that are made

up of input data, such as employee hours, will be referred to

here as input-based quantity indicators.

For some expenditure categories, a deflated value measure is used as the quantity indicator to extrapolate real output. For example, the quantity indicator for auto insurance

expenditures is the deflated value of premiums; the quantity

indicator for health insurance expenditures is the deflated value

of benefits; and the quantity indicator for parimutuel net receipts is the deflated value of gross winnings. For the insurance measures, a component index of the CPI is used to create

the deflated value quantity indicator; for gross winnings, the

all-item CPI is used to create the deflated value quantity indiTable 2. Methods used to calculate real output

[Percentages based on annual 1997 current dollars]

Deflation Extrapolation Direct valuation

Gross domestic product ........ 83.6 14.3 2.1

Business sector ..................... 93.3 6.1 .7

Output measure
38 Monthly Labor Review February 1999

cator. For the remaining components of output that are extrapolated, physical quantity data are used as the quantity indicators. For example, the quantity indicator for brokerage

charges is based primarily on BEA estimates of orders derived

from volume data from the Security and Exchange Commission and trade sources.

Based on an analysis of 1997 current-dollar data, the following tabulation shows the relative importance of the various

quantity indicators used to extrapolate real output.

GDP Business

sector

Total .................................................. 14.3 6.1

Input-based indicators .......................... 12.3 3.5

Deflated value indicators (CPI) ............. 1.2 1.5

Other indicators .................................... .8 1.1

The use of input-based data to extrapolate series of real output is more significant for GDP than for business sector output. This is due to the fact that most of the activities of general government, which are removed from business sector

output, are extrapolated using hours-worked data.14 The single

category of services furnished without payment by financial

intermediaries accounts for almost the entire portion of business sector output for which BEA uses input-based indicators

to extrapolate real output.

Finally, the direct-valuation method is used to calculate

quantity indexes by multiplying the base-year price by the

actual quantity data for the index period; the resulting series

is then expressed as an index with the base year equal to 100.

This method is used only for a small portion of output, including used automobiles; inventories of utilities; purchases

of stocks of coal, petroleum, and natural gas, and some government expenditures.15

The Consumer Price Index

BEA constructs approximately 50 percent of GDP and 56 percent of business sector output using component indexes of

the CPI as deflators. In addition, BEA uses components of the

CPI to construct deflated-value quantity indicators to extrapolate output for about 1 percent of GDP and 2 percent of business sector output. When the quantity indicator that is used

for extrapolation is itself a deflated value based on a component index of the CPI, the CPI will directly influence the real

output series. Thus, based on the estimates of this study, component indexes of the CPI influence approximately 51 percent

of GDP and 57 percent of business sector output. For this reason, the recent attention focusing on possible upward bias in

the CPI is of interest to those who analyze trends in productivity. In general, if there is a bias in price measures used to

construct real output trends, then there will be some resulting

bias in output and productivity growth.

Background. The CPI, produced and published by the BLS

Office of Prices and Living Conditions, measures the change

in the prices paid by consumers for a fixed market basket of

goods and services. The components of the fixed market basket are established using information collected from the BLS

Consumer Expenditure Survey, and the Point-of-Purchase Survey conducted by the Bureau of the Census.

The CPI, a Laspeyres-type index, is constructed in two

stages, or levels, of aggregation. In the first stage, price data

are collected for specific items and aggregated to form subindexes for categories of items, a process referred to as lowerlevel aggregation. The CPI classifies all expenditure items into

206 categories, or strata, of items and 44 geographical strata,

creating 9,064 item-area strata indexes. To construct the individual strata indexes, BLS uses price data collected from a

sample of outlets and items. The Point-of-Purchase Survey

identifies the places where households purchase various types

of goods and services and is used to select the sample of outlets. From each outlet, a BLS field economist selects one or

more varieties of items for which prices will be collected,

with probability of selection proportional to sales.

New samples for most item categories are routinely introduced

to keep the CPI sample representative of consumer spending patterns. Historically, the samples for approximately 20 percent of

the indexes were rotated each year, with full rotation completed

every 5 years.16 Strata indexes are constructed using base-period

weights generated from either the Consumer Expenditure Survey or the Point-of-Purchase Survey.

In the second stage of aggregation, known as upper-level

aggregation, the item-area strata indexes are aggregated using base-period weights from theConsumer Expenditure Survey to derive the all-item CPI. The base-period weights determine the importance of each item in the index structure, reflecting consumer spending for the base period. The current

weights are based on expenditures during 1993–95 and were

introduced in January 1998. These weights historically have

been updated at roughly 10-year intervals.17

The CPI and business sector productivity. Because BEA uses

component indexes of the CPI to develop about 57 percent of

the components of real output for the business sector, a bias

in the CPI—of any size, in either direction—potentially also

will bias the growth rates of real output and productivity measures. To evaluate the impact of any possible bias in component indexes of the CPI on business sector output and productivity, it is necessary to focus on only those components that

are constructed using the CPI.

It is important to be aware that goods and services enter

business sector output in different proportions than they enter

the all-item CPI market basket. Table 3 shows the shares of all

components of business sector output that are constructed using component indexes of the CPI, using 1997 data and presented according to 1997 CPI major-item groups. The table

also shows the importance of the same CPI major-item groups

Prices and productivity measures
 Monthly Labor Review February 1999 39

in the all-item CPI.

18

The portion of business sector output that is constructed

using components of the CPI consists predominantly of components of personal consumer expenditures. However, there

are a few components of government, private investment, and

net exports that are also constructed using consumer price

indexes.

For several of the CPI major-item groups, the groups constitute a much smaller share of business sector output than of

the all-item CPI. Note that the housing group’s share in the

all-item CPI is more than double its share in business sector

output. The primary components of housing in the business

sector are renters’ costs and household furnishings. In the allitem CPI, the largest component of housing is shelter, which

includes renters’ costs, homeowners’ costs, and owners’

equivalent rents. Recall that in the construction of the business sector data, the gross product of owner-occupied housing is removed from GDP. Also note that transportation CPI

indexes play a smaller role in business sector output than in

the all-item CPI. The primary components of the transportation major-item group in the CPI include new vehicles, motor

fuels, and auto insurance. These components have smaller

weights in the national accounts. In addition, real expenditures for several transportation components are constructed

without using the CPI—for example, net purchases of used

vehicles and air transportation.

Also of interest is the relatively small role that the consumer price indexes for medical care play in measuring business sector output. A significant portion of medical care services, such as physicians’ services, medical laboratories, home

health care, and for-profit and government hospitals and nursing homes, are constructed by deflation with producer price

indexes. Thus, only about 13 percent of personal consumption expenditures on medical care are constructed using the

CPI.

In December 1996, the Boskin Commission’s report to the

Senate Finance Committee was released. This report questioned the methodology used to construct the CPI, claiming

that the index is biased. The report stated that the CPI, when

judged as a cost-of-living index, is biased upward and that

the best estimate of the bias, looking forward, is 1.1 percentage points per year (a range of 0.8 to 1.6 percent per year).19

Because the distribution of the components of the all-item CPI

is different from the distribution in business sector output,

adjustments must be made when considering the influence of

possible bias in the CPI on business sector output and productivity. That is, if bias estimates are presented for component

indexes of the CPI to generate an estimate of bias for the allitem CPI, these component biases must be re-weighted to reflect their impact on the business sector.

It should be noted that BLS disagreed with the findings of

the Boskin Commission, especially concerning the level of

bias in the CPI. Conducting their own research on the issue,

BLS price economists found fault with many aspects of the

Boskin Commission’s report.20 The following sections present

some of the specific BLS criticisms of the report.

Sources of CPI bias

The price measurement literature separates potential sources

of bias in the CPI into four categories: upper-level substitution bias, lower-level substitution bias, new-outlet substitution bias, and quality-change and new-product bias. In this

section, each of these categories are discussed, presenting both

the Boskin Commission’s conclusions and the BLS response.

The existence of upper- and lower-level substitution bias in

the CPI has been studied extensively by BLS. Research on the

other sources of bias, by contrast, has been much more limited. And while outlet-substitution, quality-change, and newproduct bias are often discussed when estimating sources of

bias in the CPI, very little empirical evidence to support such

claims can be found in the literature on price indexes.

Substitution bias in the CPI. Substitution bias refers to the

inability of a fixed-weight index number to account for consumers’ tendencies to substitute among items or categories of

items as relative prices change over time. The CPI has the

potential for substitution bias when judged from the perspective of a cost-of-living index. A true cost-of-living index provides a means for assessing changes in consumer welfare.

Hence, such an index would capture substitutions made by

consumers responding to price changes, as they attempt to

maintain a fixed level of satisfaction. The CPI is constructed

using fixed base-period weights that preclude such substitutions. Substitution bias can cause an overstatement of growth

in an index for periods after the base year. The CPI is a 2-

tiered index that uses some fixed weights for both lower and

upper levels of aggregation. Thus, there is the potential for

substitution bias at both stages of index construction.

The Boskin Commission’s findings claimed that substitution bias exists both at upper and lower levels of aggregation.21 BLS acknowledges that the formulas currently used to

construct the CPI do not fully account for substitution by conTable 3. Relative importance of CPI component indexes

[Percentages based on 1997 data]

Share of business Share of all-

 sector output item CPI

Total ................................... 57.4 100.0

1. Food and beverage ................ 12.1 17.5

2. Housing ................................. 17.3 41.5

3. Apparel and upkeep ............... 5.6 5.3

4. Transportation ........................ 9.4 16.6

5. Medical care .......................... 3.4 7.4

6. Entertainment ........................ 5.1 4.3

7. Other goods and services ...... 4.4 7.4

CPI major item groups
40 Monthly Labor Review February 1999

sumers but disagrees with the Boskin Commission on the estimated degree of bias.22 By implementing the 1998 CPI revision, and by adopting geometric mean indexes for a portion

of the CPI in January 1999, BLS has taken steps to reduce substitution bias in the CPI.

23

Upper-level substitution. BEA primarily uses component indexes of the CPI, not the all-item CPI, to construct real output

for detailed components of GDP. Thus, upper-level aggregation in the CPI should have no influence on GDP. To compute

indexes of aggregate output, BEA recently moved away from

a fixed-weight measure of constant-dollar GDP to a changingweight Fisher index. The Fisher index, a superlative index

number formula, changes weights annually to reflect shifts in

the relative importance of the various expenditure categories

that occur over time. In particular, these changing weights

will reflect consumers’ substitution resulting from relative

price changes. Therefore, any potential bias associated with

upper-level substitution does not significantly affect BEA’s GDP

data or the BLS major-sector productivity statistics.

Lower-level substitution. As noted earlier, BEA uses component indexes of the CPI to construct trends in real output for a

significant portion of the business sector. Therefore, the possibility of lower-level substitution bias is a concern in constructing business sector output and productivity measures.

Over the past several years, BLS has been investigating alternative ways to aggregate price quotes to address lower-level

substitution bias. The geometric mean formula, a superlative

index formula, has been suggested as a possible alternative

index formula.24

Several studies conducted by BLS have shown that Laspeyres

price indexes grow faster than superlative price indexes.25 A

1995 BLS study by Brent Moulton and Karin Smedley conducted a detailed comparison of a weighted geometric mean

index formula and the current CPI formula for basic component indexes over the period from June 1992 to December

1994.26 This study showed that the Laspeyres-type formula

tends to grow at a faster rate than the geometric mean index

for most items. Also, the study showed that the magnitude of

the variation between the two formulas differed significantly

between classes of items. The largest difference between the

two indexes was for fresh fruits and vegetables. Other items,

such as housekeeping services and motor fuels, had a much

smaller difference between the two formulas. This finding was

consistent with the fact that the different item-strata of goods

and services have varying price elasticities of substitution, and

different degrees of heterogeneity in price trends.

The preference for the Laspeyres formula versus the geometric mean formula hinges on the within-strata price elasticity of substitution. Research results show that if items have an

elasticity of substitution equal to one, the geometric mean formula is an exact cost-of-living formula.27 Alternatively, if the

elasticity of substitution is equal to zero, the fixed market basket formula is the correct formula. It is probable that the actual

elasticities of substitution for many subindexes of the CPI are

somewhere along the continuum between zero and one. Thus,

the geometric mean index will approximate the preferred formula for some components of the CPI where it is anticipated

that consumers will make substitutions to offset relative price

changes; possible examples are apples, men’s footwear, and

major appliances. However, the Laspeyres index will approximate the preferred formula for other item stratas where customers are less likely to make substitutions; possible examples

are electricity, physicians’ services, and rent of primary residence.28

The Boskin Commission’s estimate of a 0.25-percent per

year lower-level substitution bias in the CPI was based primarily on the assumption that the geometric mean is the preferred

formula for all components of the CPI. The Boskin Commission calculated this estimate by combining components of various BLS research efforts. The 1995 study by Moulton and

Smedley showed that the difference between the Laspeyrestype index used for lower-level aggregation by BLS and a

weighted geometric mean formula for all nonshelter items is

approximately 0.49 percentage points per year.

Since that study was released, BLS has introduced several

new procedures into the CPI. These procedures effectively reduced several sources of bias in the components of food-athome, shelter, and prescription drugs.29 BLS estimated that the

implementation of these changes would reduce bias by 0.14

percent per year.30 Additional changes for nonshelter items

made in 1996 would further reduce the bias by 0.1 percent

per year.31 The Boskin Commission removed these BLS estimates from the Moulton-Smedley estimate to arrive at the 0.25-

percent per year lower-level substitution bias estimate.

BLS did not agree with the 0.25-percent per year bias estimate because it was based exclusively on using the geometric

mean index formula to calculate all component indexes of the

CPI. The geometric mean index formula assumes that the price

elasticity of substitution is equal to one, and thus consumers

make substitutions as relative prices change in order to maintain constant expenditure shares for each item. This assumption probably does not hold for all items in the CPI, and thus

BLS viewed the 0.25-percent per year upward bias in the CPI

as an upper bound for estimating lower-level substitution bias.

Since the Boskin Commission’s report was released, BLS

economists have worked to determine which CPI basic indexes

are best calculated using the geometric mean formula.

Early in 1997, BLS introduced experimental consumer price

indexes, or CPI-U-XG indexes, that use geometric means to combine price quotations at the lower level of aggregation. Because BLS has adopted methodological changes for the CPI since

1990, experimental test-Laspeyres indexes, or CPI-U-XL indexes, were released for historical comparisons with the CPIU-XG. The CPI-U-XL differs from the CPI-U-XG only in the use

Prices and productivity measures
 Monthly Labor Review February 1999 41

of the Laspeyres formula rather than the geometric mean formula for aggregation of price quotations.32 Both the CPI-U-XG

and the CPI-U-XL incorporate the methodological improvements

adopted by the CPI in 1995 and 1996.

Using the experimental geometric mean series, BLS conducted research to identify, for each basic index category, the

extent to which consumers can be expected to alter their spending when relative prices change. This research examined the

following evidence: (1) highly detailed supermarket scanner

data on prices charged for, and quantities sold of, a limited

number of individual item categories; (2) measures of the extent of substitution at index calculation levels above the basic

level, which can be viewed as providing evidence concerning

the likelihood of substitution behavior within item categories;

and (3) estimates of the magnitude and prevalence of the substitution effect derived from a survey of the relevant empirical

literature. From the results of this research effort, BLS concluded that “this evidence did not provide definitive support

concerning the existence and magnitude of the substitution

effect in each of the basic index categories. . . . Taken in its

entirety, however, the evidence unambiguously supported the

proposition that consumers can, and do, alter their purchasing

behavior in response to changes in the array of prices confronted

in the market place.”33 In early 1998, BLS announced that, beginning in January 1999, it would adopt the geometric mean formula to construct index categories for approximately 61 percent

of total consumer spending represented by the CPI-U.

34

Using the experimental geometric mean indexes for the

period from May 1996 to December 1997, this study estimates

the influence on business sector output of replacing components of the current consumer price indexes with geometric

mean indexes. Table 4 shows the impact on business sector

output of adopting the CPI-U-XG formula, by CPI major-item

groups.35

Comparing the CPI-U-XL and the CPI-U-XG for the period

from May 1996 to December 1997 reveals that the all-item

CPI containing only geometric mean basic indexes grows at a

rate that is approximately 0.26 percent slower than the existing all-item CPI. This estimate corresponds to the Boskin

Commission’s bias estimates. Table 4 shows that replacing

all of the CPI item indexes with the geometric mean formula

will increase the measure of business sector output by approximately 0.19 percent per year. The two major-item groups

that contribute most significantly to the total increase in business sector output are housing and apparel. Within the housing group, household furniture and audio equipment contribute the major portion of the increase in output. Within the

apparel major-item group, women’s and girls’ clothing accounts for approximately 40 percent of the estimated bias.

Recent BLS research results suggesting that the geometric

mean formula is preferred for 61 percent of the CPI, lead to a

slightly smaller estimate of bias in business sector output. A

consumer price index containing geometric mean indexes for

only 61 percent of its components grows at an annual rate that

is approximately 0.21 percent slower than the existing allitem CPI.

36 Table 4 shows that replacing these CPI item indexes with the geometric mean formula also would increase

measured business sector output growth by approximately 0.19

percent per year. This increase in business sector output has

the potential to increase business sector productivity measures.

In July 1998, BEA revised the 1995–97 GDP data using the CPI

geometric mean indexes, thus eliminating this source of lowerlevel substitution bias in GDP and the BLS business sector output and productivity data.

Outlet substitution. Just as consumers respond to changes

in prices by substitution in favor of relatively lower-priced

items, consumers also respond to price changes by shopping

at different retail outlets. When consumers switch to new this

move is detected in the Point-of-Purchase Survey that is conducted for 20 percent of the CPI sample on an annual basis.

The new outlets are linked into the survey, and prices at the

old and new outlets are not compared directly. Therefore,

any savings that consumers potentially receive from shopping

at the new outlets are not reflected by the CPI, understating

consumer gains. However, if the outlets were compared directly, and the new outlet provides a lower level of services, a

direct comparison could lead to an overstatement of consumer

gains.37 Because it is difficult to measure the quality of services provided by outlets, it is difficult to provide a precise

measure of outlet-substitution bias.

In a 1993 study, Marshall B. Reinsdorf evaluated the treatment of new discount outlets in BLS sampling and estimation

procedures for food and motor fuel items. The study compared prices of items during periods of sample rotation. Assuming that prices are not offset by a decline in quality,

Reinsdorf found that a possible new-outlet-substitution bias

of 0.25 percent per year may exist for certain food and motor

fuel items.38

At this point there is little empirical evidence on the impact

Table 4. Impact on business sector output of adopting the

geometric mean index formula

[Weights based on 1997 current dollars]

Total ................................................ .192 .188

1. Food and beverage ..................... .030 .030

2. Housing ...................................... .037 .036

3. Apparel ....................................... .055 .055

4. Transportation ............................. .013 .013

5. Medical care ............................... .017 .014

6. Entertainment ............................. .031 .031

7. Other goods and services ........... .008 .008

Adopting the

geometric

mean formula

for all CPI

component

indexes

Adopting the

geometric mean

formula for 61

percent of CPI

component

indexes

CPI major item groups
42 Monthly Labor Review February 1999

of discount outlets on the components of the all-item CPI. In a

1994 study, David E. Lebow and others identified 11 categories of the CPI in which they assume outlet substitution bias is

present.39 These categories account for 40 percent of the allitem CPI, and include food and beverages, household maintenance and repair commodities, household fuels, household

furnishings, housekeeping supplies, apparel commodities,

motor fuel, medical care commodities, entertainment commodities, tobacco and smoking products, and personal care. The

authors apply the Reinsdorf estimate to these categories and

estimate a 0.1-percent per year outlet-substitution bias in the

all-item CPI (0.25 times 40 percent). The Boskin Commission

refers to both of these studies to suggest that the current methodology used to construct the CPI does not account sufficiently

for shifts of consumers to discount outlets, adopting the estimate of 0.1 percent per year upward bias in the CPI due to

outlet substitution.

The categories identified by Lebow and his co-authors account for approximately 32 percent of business sector output.

If the 0.25-percent Reinsdorf estimate of upward bias is applied to these components, the suggested 0.1-percent per year

upward bias in the CPI could lead to a 0.08-percent per year

downward bias in business sector output. However, there are

serious concerns associated with using the Reinsdorf estimate

to project bias for all items in the CPI. As Brent R. Moulton

noted in 1996, the results of the Reinsdorf study are barely

statistically significant.40 In addition, Reinsdorf looked at only

two item categories—food and motor fuels—over a 2-year

period. There is no reason to believe that these items are representative of other CPI item categories, and it is unlikely that

the 2 years are representative of long-term trends. Therefore,

Reinsdorf’s bias estimates should be viewed with some skepticism. In the absence of research on additional item categories and additional years, it is not possible to determine if the

estimates of outlet-substitution bias are too high or too low.41

Quality-change and new-product bias in the CPI. The quality of goods and services changes over time, reflecting changes

in technology and consumer demand. Such quality changes

often are reflected in changes in market prices and quantities

demanded. Therefore, the accuracy of output measures is contingent upon price measures that control for quality variations

over time.

BLS makes a conscious effort to account for quality change

as it collects price data. Using checklists of detailed commodity descriptions, price-data collectors are able to detect when

commodity characteristics change between collection periods.

To assist in adjusting for quality changes, commodities in the

market basket are replaced using a procedure called item substitution. BLS has various methods to make adjustments for

quality when item substitutions occur, including direct comparisons between old and new products, overlap pricing of old

and new products, linking of old and new products, class-mean

price imputation, and direct quality adjustments, such as hedonic regression techniques.42

Adjustments made by BLS for quality changes in goods and

services amounted to approximately 1.76 percentage points

during 1995.43 BLS also has procedures in place to introduce

new products into the market basket. New expenditure shares

are introduced every 10 years, and, to keep pace with the constantly shifting market, 20 percent of the component indexes

of the CPI are re-sampled each year. This periodic rotation of

the sample of items and outlets allows new goods and services

to enter the market basket. In 1999, BLS will redesign the

sample rotation procedures in order to accelerate the introduction of new items and outlets.

Although significant steps are taken, BLS acknowledges that

the CPI does not capture all quality changes, especially in the

difficult-to-measure services industries. The inability to account precisely for rapid introduction of new products and

quality changes will lead to some level of bias in price measures, which will inevitably affect the real output and productivity measures for the business sector. In preparing real trends

in output components of the national accounts, BEA uses alternative price indexes for output components where it anticipates that quality measurement problems exist in the CPI.

44

The Boskin Commission report evaluates the CPI, examining each of the 27 major-item categories, in an attempt to quantify bias that may be a result of mismeasured quality change.

In this exercise, the Boskin Commission treats new-product

introduction as a component of quality-change bias and evaluates them jointly. Because of a lack of hard evidence regarding the magnitude of biases, the Boskin Commission uses research on bias in one category to estimate bias for other categories that seem related. In areas where no research exists,

the Boskin Commission estimates the level of bias by employing its best judgment based on how various developments in

the marketplace may have influenced consumer value. In this

exercise, the Boskin Commission concludes that the CPI is biased upward by 0.6 percentage points per year.45

A 1977 study by Brent R. Moulton and Karin E. Moses

also evaluates the 27 major-item categories and questions the

Boskin Commission’s estimates of upward bias in the CPI due

to quality change and new-product introduction.46 The study

concludes that the Boskin Commission’s bias estimates are too

high for many item categories, including rent, fresh fruits and

vegetables, apparel, new vehicles, and motor fuels.47 The authors state that

there are two major components, medical care and appliances, where we agree with the Advisory [Boskin]

Commission that the evidence of upward bias due to

quality and new goods is convincing. We also agree

that new products contribute an upward bias across many

components of the index, though we think the Commission and others have overstated the magnitude of this

bias. There are many important components of the in-

 Prices and productivity measures
 Monthly Labor Review February 1999 43

dex, though, including shelter, apparel, and new vehicles, where we have not seen convincing evidence of

upward bias, and indeed think there is the possibility

that the methods used by the BLS may in some circumstances over-adjust for quality change. 48

The task of adjusting price fluctuations to reflect changes

in quality requires an evaluation of product characteristics, as

well as consumers’ perceptions of value. Because consumers

are heterogeneous and preferences change over time, such

adjustments are difficult. Due to the difficulties involved in

collecting data to analyze quality issues, research in this area

tends to be focused on specific products or narrow data sets

for tractability. It is unclear that results of this sort can be

considered representative for broad categories of the CPI.

Therefore, specific conclusions based on the limited body of

research in the area of quality change should be viewed skeptically.

Having said that, the following “what if” exercise is conducted in response to public inquiry. Assuming the Boskin

Commission’s estimates of bias in components of the CPI are

correct, when the category weights in table 3 are combined

with the Boskin Commission’s bias figures, the impact of the

suggested quality-related bias on the measure of business sector output can be estimated. Thus, if the Boskin Commission’s

estimates are accurate, the upward bias of 0.6 percent per year

in the CPI generates a downward bias in real output growth for

the business sector of approximately 0.32 percent per year.

The Boskin Commission study suggests that, in 1996, consumer electronics and medical services were the largest sources

of quality-change bias in the CPI. Note that within the business sector components of the national accounts, BEA uses

alternative price indexes for portions of consumer electronics

and medical services. In medical services, BEA uses producer

price indexes to construct real trends for the output of physicians’ services, medical laboratories, home health care, and

for-profit and government hospitals and nursing homes. In

consumer electronics, BEA uses a quality-adjusted index to

construct real trends for computers.

Using the Boskin Commission’s bias estimates for detailed

components, the major-item groups of housing and apparel

contribute the largest shares to the bias estimates for total

quality change in the business sector. Apparel and upkeep

accounts for approximately 17 percent of the bias associated

with unmeasured quality change, while housing accounts for

35 percent. The major group, housing, includes expenditures

on audio and video products and the subcomponent, personal

computers. Note that the Boskin Commission’s bias estimate

for audio and video products is 4 percent per year. By contrast, the Boskin Commission’s bias estimate for personal

computers is 15 percent per year. Because the CPI is not used

to deflate the output measure for personal computers, the audio and video category only accounts for about 14 percent of

the business sector bias estimate. The major-item group, medical care, contributes 15 percent of the estimated bias in business sector output, with the majority of the bias accounted for

by dental services.50 (Note that the Boskin Commission’s

bias estimate for professional medical services is primarily

based on research concerning services of physicians.)

The study by Moulton and Moses agreed that evidence of

upward bias due to quality and new goods is convincing in

medical care and consumer electronics. The magnitude of

the bias estimates, however, remains controversial. For other

item categories, such as apparel and new vehicles, BLS flatly

disagrees with the Boskin Commission on the presence of any

upward bias in the CPI indexes due to quality change. It should

be emphasized that, due to the lack of strong data-based analysis of quality-related bias, it is uncertain whether the bias estimate of 0.6 percent per year is accurate. Thus, its impact

on business sector output and productivity also is uncertain.

Because about 57 percent of business sector output is constructed using components of the CPI, any measurement bias

in the CPI will play a significant role in trends in business

sector output and productivity. This article has discussed some

of the aspects of the recent controversy concerning the construction of the CPI and its impact on business sector output

and productivity measures. If the Boskin Commission’s estimate of upward bias of 1.1 percent per year is accepted, a

downward bias in business sector output of approximately

0.6 percent per year could result. But the recent adoption of

the geometric mean index formula for components of business sector output based on personal consumption expenditures however, reduces this bias to 0.4 percent per year.

Due to weaknesses in the Boskin Commission’s analysis

and a lack of strong evidence to support many of its claims,

the report’s findings should be viewed skeptically. Although

evidence exists to suggest that, through 1998, a 0.2-percent

lower-level substitution bias was present in the CPI, there is

insufficient evidence to determine whether the estimates of

outlet-substitution and quality-related bias are, in fact, too high

or too low.51

BLS price economists often have been in the forefront of research to discover sources of bias in the CPI and have developed

innovative techniques for reducing known bias. In addition,

changes resulting from the 1998 revision of the CPI have further

reduced sources of bias.52 Specifically, the market basket expenditure weights were updated, item categories were redefined,

and a new geographic sample was introduced. Further, in January 1999, experimental geometric mean indexes were adopted

for 61 percent of component indexes of the CPI.

In constructing real trends in output for the national accounts, BEA makes an effort to reduce measurement problems

identified in the CPI. In some instances, this requires that they

use alternative price indexes. However, in areas where the CPI

is used, as improvements are made in the CPI and its subindexes, and as BEA adopts these improvements, the BLS pro-
44 Monthly Labor Review February 1999

ductivity measures for major sectors of the economy also will

improve.

Input-based methods

Several recent studies have asserted that the CPI is a possible

source of bias in BLS productivity measures.53 A potential

source of bias that has received much less attention, however,

is the use of input data to construct trends in output. As discussed earlier, for productivity measurement, it is important

to have well-defined measures of inputs and outputs that are

measured independently. For several nonmarket components

of GDP, BEA calculates output using cost data and thus uses

input-based indicators to generate real values. Although these

techniques provide a means to measure components of output

that are difficult to measure, they also inherently bias productivity measures. For some market outputs in GDP and the business sector, BEA uses input-based methods primarily because

accurate price indexes do not exist.

BEA calculates approximately 11 percent of business sector output using composite indexes of data on input prices

and costs as deflators. In addition, BEA constructs real output

using input-based quantity indicators to extrapolate real output for about 3 percent of business sector output. Thus, based

on the 1997 estimates in this study, input-based methods are

used in computing approximately 14 percent of business sector output. Composite indexes of input data are used to deflate life insurance, the bulk of investment in nonresidential

structures, and some components of government expenditures.

Input-based extrapolation techniques are used to construct the

real output of services furnished without payment by financial intermediaries, using employee hours as the quantity indicator. Services furnished without payment by financial intermediaries are the largest component of final demand expenditures on banking.

The use of input-based methods to construct real output

has been a concern for BLS. The development of real output

using input data implicitly assumes that the rate of growth in

output will be correlated with the rate of growth in inputs.

Because productivity statistics relate the growth in output to

the growth in inputs, an output measure that grows at a rate

dominated by the growth in inputs may result in a measure of

productivity that is biased toward zero. Specifically, if output

actually rises more rapidly than inputs due to gains in productivity, then the use of an input measure to create real output

will understate output and productivity growth. If productivity growth occurs, input prices (or costs) can rise more rapidly than the prices of the output precisely because productivity has grown. Hence, deflation with input prices will lead to

an underestimation of productivity.

BEA is well aware of the shortcomings of input-based techniques for generating real output for productivity measurement. However, there are no price indexes currently available for these components of the national accounts. It should

be noted that the majority of the components that are constructed using input-based methods are expenditures on services.54 The output of services is characterized by measurement problems that make accurate price measurement difficult.55 BLS has been working to expand price index coverage

in service industries, which has led to a decline in BEA’s use

of input-based deflators. In addition, BLS and BEA are working jointly to develop price indexes for construction, a category that is currently dominated by input-based methods. As

more price indexes become available to replace input-based

methods, and as they are integrated into the national accounts,

productivity measures will improve.56

METHODS USED BY BEA TO CALCULATE REAL OUTPUT directly affect BLS measures of productivity for the business sector and

other major sectors of the U.S. economy. Price indexes play a

significant role in measuring real output and productivity; thus

potential bias in price indexes, as well as a lack of price indexes,

can impact the accuracy of productivity measures.Whenever

posible, BEA takes steps to reduce the impact of bias in the CPI on

the real trends in GDP and business sector output.

The adoption by BLS of the geometric mean formula in

January 1999 eliminated a downward substitution bias of approximately 0.2 percent in the BLS business sector output and

productivity data. Because the CPI is used in calculating about

57 percent of measured output, however, potential bias in the

CPI—whether due to outlet substitution effects or mismeasured

quality changes—will affect the accuracy of measured growth

in output and productivity.

In addition to possible bias in the CPI, the use of inputbased methods to calculate real output series has important

implications for measuring productivity as well. Real output

trends that are developed using input-based indexes will tend

to grow, by construction, with input data. Because productivity statistics relate the growth in output to the growth in inputs, an output measure that grows at a rate dominated by the

growth in inputs may result in a measure of productivity that

is biased toward zero. The use of input-based methods to construct output measures could lead to measurement error in

about 14 percent of business sector output and productivity.

Footnotes

ACKNOWLEDGMENT: This article benefited from comments from Katharine

G. Abraham, Edwin R. Dean, Michael J. Harper, Phyllis Otto, William

Gullickson, Dennis Fixler, John Greenlees, Robert Parker, Clint McCully,

Brent Moulton, and others at the Bureau of Labor Statistics and the Bureau

of Economic Analysis. However, the author alone is responsible for the

content of this article.

1

 The major-sector and industry-level measures are developed indepen-

 Prices and productivity measures
 Monthly Labor Review February 1999 45

dently. Measures of productivity for industries are based on the production of an industry and thus rely heavily on industry producer price indexes. Relatively few industry measures, approximately 14 percent of industries published by BLS, make use of consumer price indexes. In addition, BLS industry productivity statistics do not use input data to measure

output and they do not use input data to construct output deflators. For

more information, see BLS Handbook of Methods, Bulletin 2490 (Bureau of

Labor Statistics, 1997), pp. 89–121. 2 BLS publishes labor productivity measures for six major sectors. Business sector, nonfarm business sector and nonfinancial corporations make

use of BEA data; manufacturing, durable goods manufacturing, and nondurable goods manufacturing do not. 3

 Allan H. Young and Helen Stone Tice, “An Introduction to National Economic Accounting,” Survey of Current Business, March 1985, pp. 59–76. 4

 The gross product of general government is the sum of government

expenditures on compensation of general government employees and the

general government consumption of fixed capital, which measures the services of general government fixed assets. Government expenditures on

goods and services purchased from the private sector are not excluded from

business sector output. The gross product of private households is the

compensation of paid employees of private households. The gross product

of nonprofit institutions serving individuals is the compensation paid to

employees of these institutions. 5

 The rental value of buildings and equipment owned and used by nonprofit institutions serving individuals is measured as the sum of consumption of fixed capital, indirect business taxes, and interest paid. 6

 It should be noted that this article discusses components of total GDP

and business sector GDP, which are derived by aggregating final expenditures on goods and services. The output of individual industries, such as

BEA’s data on gross product originating (GPO) by industry or the gross output of industries is not discussed. For the latest BEA estimates of gross

product by industry and a discussion of the relationship between the estimates of GDP by expenditures and by industry, see BEA’s Survey of Current

Business, pp. 20–40. 7

 For a detailed discussion of BEA’s new measures of output, see Steven

J. Landefeld and Robert P. Parker, “Preview of the Comprehensive Revision of the National Income and Product Accounts: BEA’s New Featured

Measures of Output and Prices,” Survey of Current Business, July 1995,

pp. 31–38. 8

 A listing of the product-side components of the national accounts, the

methods used to calculate real output, and the source data is published

annually by BEA in the Survey of Current Business. The most recent listing

appears in the September 1998 issue, pp. 14–35.

9

 The real gross product of general government, private households,

and owner-occupied housing are included in total GDP as components of

expenditures, and removed by BEA to construct business sector output measures for BLS. However, the output of nonprofit institutions enters GDP

through a variety of product-side expenditure categories and then is removed by BEA as two income-side components. The first component is an

independent aggregate based on compensation of employees of nonprofit

institutions, referred to as gross product of nonprofit institutions. This

series is constructed as the nonprofits’ share of industry employee compensation, as defined by legal form of organization. The compensation of

employees of nonprofit institutions is deflated by BEA using a BEA index of

compensation per hour for those industries in which nonprofits are concentrated. This deflator is constructed using primarily BLS wages and hours

data. The second component is the annual series of rental value of buildings and equipment owned and operated by nonprofit institutions. This

component is deflated using an implicit price deflator constructed from the

BEA nonresidential building series, which is calculated using a BEA composite cost index. Hence, both of these real components for nonprofits are

constructed using input-based deflators. In order to prepare data on the

methods used to calculate the various components of real output, this article subtracts the value of these components from the total amount of GDP

that is constructed using input-based deflators. This technique is reasonable because the product-side components that reflect nonprofit activities

(for example, nonprofit hospitals, clubs and fraternal organizations, religious and welfare activities, and education and research) are also constructed

using input-based deflators.

10 “Updated Summary Methodologies,” Survey of Current Business,

September 1998, pp. 14–35.

11 “Updated Summary Methodologies.”

12 Based on 1997 current-dollar data, personal consumption expenditures account for about 68 percent of GDP and 73 percent of business sector

output.

13 Other composite indexes refer to BEA deflators that are composites

of input data and price indexes. In instances when output data is not available at a lower level of detail, this study groups these components in the

category of deflation by other indexes. Approximately 11 percent of all of

the “other price indexes” used to construct both GDP and business sector

output are composites of input data and price indexes.

14 For military compensation, hours worked are adjusted for changes

in employment by rank and length of service. For civilian defense employee compensation, hours worked are adjusted for changes in employment by grades. For State and local government education compensation,

hours worked are adjusted for changes in education and experience. For

additional details, see “Government Transactions,” Methodology Article

Series MP-5, November 1998.

15 “Updated Summary Methodologies.”

16 As part of the 1998 revision, the sample was updated and item categories were redesigned to allow for the introduction of new items. In

1999, the sample rotation is being redesigned to accelerate the introduction of new items and outlets. For an overview of the 1998 revision, see

John S. Greenlees and Charles C. Mason, “Overview of the 1998 revision

of the Consumer Price Index, Monthly Labor Review, December 1996, pp.

3–9; see also, Brent R. Moulton, “Bias in the Consumer Price Index, What

is the evidence?” The Journal of Economic Perspectives, Fall 1996, pp.

159–77.

17 Moulton, “Bias in the Consumer Price Index.”

18 The components are presented in the seven major-item groups of the

CPI. The expenditure components of the business sector have been regrouped

into these CPI groups. In January 1998, the CPI redesigned its major groups

to include an eighth major group, education and communications. This

new group contains components previously in housing and other goods

and services.

19 Toward a More Accurate Measure of the Cost of Living, final report

to the Senate Finance Committee from the Advisory Commission to Study

the Consumer Price Index, S.Prt. 104–72 (U.S. Senate, Committee on Finance, December 1996.)

20 “Measurement Issues in the Consumer Price Index” (Bureau of Labor

Statistics, June 1997). Prepared in response to a letter from Congressman Jim

Saxton to Katharine G. Abraham, Commissioner of Labor Statistics.

21 Upper-level substitution bias refers to a measurement error that occurs as the strata indexes are aggregated to form the all-item CPI, using

fixed (Computer Expenditure Survey) base-period weights; this can occur

if consumers substitute across categories, or strata, of goods and services

as relative prices change. Lower-level substitution bias refers to a measurement error that occurs as price data are aggregated to calculate the

component, or strata, indexes, using fixed (Point-of-Purchase Survey and

Computer Expenditure Survey) base-period weights; this can occur as consumers substitute among items within a specific category, or item strata, as

relative prices change.

22 “Measurement Issues in the Consumer Price Index.”

23 As part of the 1998 CPI revision, BLS introduced new expenditure

weights used for upper-level aggregation. Beginning with January 1998

data, expenditure weights are based on 1993–95 spending patterns in order

to reflect more accurately what consumers are buying.

24 The geometric mean index formula requires expenditure and price

data for each period and makes use of these data to allow for substitution.

This index assumes that customers will substitute to offset relative price

changes in order to maintain constant expenditure shares for each item.

25 For a review of studies on upper-level substitution bias, see Ana M.

Aizcorbe and Patrick C. Jackman, “The commodity substitution effect in

CPI data, 1982–91,” Monthly Labor Review, December 1993, pp. 24–33.
46 Monthly Labor Review February 1999

For a review of studies on lower-level substitution bias, see Brent R.

Moulton, “Basic components of the CPI: estimation of price changes,”

Monthly Labor Review, December 1993, pp. 13–24; and Brent R. Moulton

and Karin E. Smedley, “A Comparison of Estimators for Elementary Aggregates of the CPI,” paper presented at the Western Economic Association

International Conference, San Diego, CA, July 7, 1995. 26 Moulton and Smedley, “A Comparison of Estimators,” July 1995.

27 The seasoning method used to estimate the CPI component indexes is

also unbiased if the following assumptions hold: stationarity, small correlation of relative prices over time, and utility functions characterized by

constant elasticity of substitution. See Matthew D. Shapiro and David W.

Wilcox, “Mismeasurement in the Consumer Price Index: An Evaluation,”

in Ben S. Bernanke and Julio J. Rotemberg, eds. NBER Macroeconomic

Annual 1996 (Cambridge, MA, MIT Press, 1996). 28 Katharine G. Abraham, Commissioner of Labor Statistics, “Research

issues related to the geometric mean formula for elementary indexes,” attachment C of materials presented by the author in testimony given to the

Senate Budget Committee, January 30, 1997. 29 In the January 1996 comprehensive revision of the national income

and product accounts, BEA incorporated the adjustments made to the CPI’s

for food and housing. BEA incorporated these changes in revised estimates

of output beginning with 1978 data. 30 Improvements in CPI methodologies after 1993 are discussed in Paul

A. Armknecht, Brent R. Moulton, and Kenneth J. Stewart, “Improvements

to the Food at Home, Shelter, and Prescription Drug Indexes in the U.S.

Consumer Price Index,” BLS Working Paper 263, February 1995. 31 See “Extending the improvements in the CPI sample rotation procedures and improving the procedures for substitute items,” CPI Detailed Report: Data for March 1996, April 1996, pp. 4–5. 32 See “The experimental CPI using geometric means,” CPI Detailed

Report: Data for March 1997, May 1997, pp. 5–18. 33 See “Planned change in the Consumer Price Index formula,” CPI Detailed Report: Data for April 1998, June 1998, pp. 6–8. 34 There are 15 categories that will continue to be calculated as they

are currently. These categories are: rent of primary residence; owners’

equivalent rent of primary residence; housing at school, excluding board;

electricity; utility natural gas service; residential water and sewerage maintenance; State and local registration, license, and motor vehicle property

tax; telephone services, local charges; cable television; physicians’ services;

dental services; eyeglasses and eye care; services by other medical professionals; hospital services; and nursing homes and adult daycare. 35 The business sector estimates presented in table 4 are based on the

difference between the CPI-U-XG and the CPI-U-XL at a finer level of detail

than the seven categories presented. The detailed differences are weighted

using business sector shares and aggregated to the seven major-item groups.

The seven major-item groups reflect the categories used by BLS prior to

January of 1998. Beginning in January 1998, BLS introduced a new structure of eight major-item groups. Paul Liegey, of the BLS Division of Consumer Prices and Price Indexes, provided CPI-U-XG and CPI-U-XL measures

for sub-indexes of the CPI for May 1996 and December 1997. 36 This value corresponds with the 0.2 bias estimate discussed in Kenneth Stewart, “The experimental CPI using geometric means,” BLS working

article, April 1997. The 0.2 level of bias is based on data from the middle

of 1996 through December of 1997. 37 Moulton, “Bias in the Consumer Price Index.”

38 Marshall B. Reinsdorf, “The Effect of Output Price Differentials on the

U.S. Consumer Price Index,” in Murray F. Foss, Marilyn Manser, and Allan H.

Young, eds., Price Measurements and Their Uses, Studies in Income and

Wealth, vol. 57 (Chicago, University of Chicago Press for NBER, 1993).

39 David E. Lebow, John M. Roberts, and David J. Stockton, “Monetary Policy and ‘The Price Level’” (Board of Governors of the Federal

Reserve System, August 1994). 40 Moulton, “Bias in the Consumer Price Index.”

41 In a more recent study, “Price dispersion, seller substitution and the

U.S. CPI,” BLS Working Paper 252 (Bureau of Labor Statistics, 1994),

Reinsdorf shows that, for CPI food indexes, over the period from 1948 to

1963 and from 1967 to 1976, price decline due to new outlets was about

0.1 percent per year. Although this estimate is smaller than the 0.25 percent estimate of his earlier study, there is insufficient evidence to generate

estimates of bias for other CPI component indexes. 42 For a detailed discussion of quality change in the CPI, see Brent R. Moulton

and Karin E. Moses, “Addressing the Quality Change Issue in the Consumer

Price Index,” Brookings Papers on Economic Activity, no. 1, 1997. 43 This quality effect is based on an arithmetic method of aggregation.

If outliers—which may be a result of sample turnover—are eliminated, the

estimate drops to 1.1 percent. Alternatively, a logarithmic method of aggregation indicates a range of quality change of 0.28 to 0.44 percent. Ibid. 44 For all periods through 1997, BEA uses a quality-adjusted index for

computers, and beginning in 1995, uses an alternative index for cellular

phone service. Beginning with 1998 data, BEA will begin using the new CPI

for computers, which uses a hedonic model to adjust prices for changes in

quality. BEA uses PPI’s for for-profit and government hospitals, medical

laboratories, physicians’ services and home health care, beginning with

1993, 1994, 1995, and 1997, respectively. 45 Toward a More Accurate Measure of the Cost of Living. 46 Moulton and Moses, “Addressing the Quality Change Issue.”

47 Due to the lack of available data, Moulton and Moses do not attempt

to make specific quantity estimates of the potential bias that might result

from quality change and new product introduction. 48 Moulton and Moses, “Addressing the Quality Change Issue.”

49 Toward a More Accurate Measure of the Cost of Living. 50 In the national accounts, there is a category of other medical professionals for which real trends are calculated using the PPI and the CPI. Detailed data is not available to disaggregate this component and therefore it

is not counted as using the CPI. Thus the estimate above should be viewed

as a lower bound. 51 See Kenneth Stewart, “The experimental CPI.”

52 For an overview of the 1998 CPI revision, see Greenlees and Mason,

“Overview of the 1998 revision of the Consumer Price Index.” 53 L. Slifman and C. Corrado (“Decomposition of Productivity and

Costs,” Occasional Staff Studies, OSS no. 1, Federal Reserve Board, November 1996) present service industry measures of productivity, displaying negative productivity trends that seem inconsistent with anecdotal evidence of technological advances in service industries. Note that the study

primarily discusses data for gross product originating for industries, which

are not used to construct business sector GDP. 54 Expenditures on services constitute about 41 percent of final demand in the business sector, and approximately 54 percent of GDP, based

on 1997 current dollars. Of the 14 percent of GDP that is constructed using

input-based methods, 78 percent is expenditures on services; of the 11 percent of business sector output constructed with input-based methods, 50

percent is expenditures on services. 55 For a detailed discussion of the difficulties in measuring service output, see Mark K. Sherwood, “Difficulties in the Measurement of Service

Output,” Monthly Labor Review, March 1994, pp. 11–19.

56 For a brief discussion of BLS and BEA efforts to improve the national accounts

data, see the article in this issue of the Review by Edwin R. Dean, pp. 24–34.

Prices and productivity measures