BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRICES AND LIVING

CONDITIONS 

 

Constructing Household Specific Consumer Price Indexes: 

An Analysis of Different Techniques and Methods 

Robert A. Cage, U.S. Bureau of Labor Statistics 

Thesia I. Garner, U.S. Bureau of Labor Statistics 

Javier Ruiz-Castillo, Universidad Carlos III de Madrid 

 Working Paper 354

 March 2002 

Cage is an economist (email: Cage_R@BLS.GOV; telephone: (202) 691-6957) and Garner is a senior research 

economist (email: Garner_T@BLS.GOV; (202) 691-6579) with the Bureau of Labor Statistics. Ruiz-Castillo is a 

professor of economics (email: JRC@ECO.UC3M.ES; telephone: +34 1 624 95 88) at the Department of Economics, 

Universidad Carlos III de Madrid. All views expressed in this paper are those of the authors and do not reflect the views 

or policies of the Bureau of Labor Statistics or the Department of Economics, Universidad Carlos III de Madrid. 
Constructing Household Specific Consumer Price Indexes: 

An Analysis of Different Techniques and Methods 

Robert A. Cage

Division of Consumer Prices and Price Indexes 

and 

Thesia I. Garner

Division of Price and Index Number Research 

 Bureau of Labor Statistics 

Postal Square Building 

2 Massachusetts Avenue N.E., Washington, D. C. 20212 

UNITED STATES 

and 

Javier Ruiz-Castillo

Department of Economics

Universidad Carlos III de Madrid 

Madrid 126, Getafe 

Madrid 28903 

SPAIN 

July 6, 2001 

Cage is an economist (email: Cage_R@BLS.GOV; telephone: (202) 606-6957) and Garner is a senior research economist (email: 

Garner_T@BLS.GOV; (202) 606-6579) with the Bureau of Labor Statistics. Ruiz-Castillo is a professor of economics (email: 

JRC@ECO.UC3M.ES; telephone: +34 1 624 95 88) at the Department of Economics, Universidad Carlos III de Madrid. All views expressed in 

this paper are those of the authors and do not reflect the views or policies of the Bureau of Labor Statistics or the Department of Economics, 

Universidad Carlos III de Madrid. 

This research was begun in the summer of 1996 when Ruiz-Castillo was an ASA/NSF/BLS Research Fellow in the Office of Survey Methods 

Research at the Bureau of Labor Statistics and has been supported by the Bureau and by the Department of Economics, Universidad Carlos III de 

Madrid. An earlier version of this paper was presented at the 1997 National Bureau of Economic Research Summer Institute on Price Index 

Measurement. We thank Barbara Fraumeni for her comments as the discussant of the paper and others attending the summer institute for theirs 

as well. Special thanks are also extended to our colleagues in the Divisions of Consumer Prices and Price Indexes, and Price and Index Number 

Research for their comments and suggestions, especially Mary Kokoski, Brent Moulton and Patrick Jackman. Thanks are also extended to 

Anthony Yezer, George Washington University, for suggestions and discussions.
ABSTRACT 

The primary purpose of this study is to produce household specific price indexes for consumer 

units or households living in the United States in the early 1990s. This paper is a report on how 

these household specific indexes were created. With household specific indexes, households are 

assumed to have nonhomothetic preferences, so changes in prices involve relative price changes 

between different sets of commodities and the resulting indexes will differ systematically 

between different households. We examine several different approaches to construct these 

indexes. Our indexes are based on internal U.S. Consumer Expenditure Survey (CEX) data for 

1990-91 and Bureau of Labor Statistics Consumer Price Index (CPI) data from winter 1981, 

1987, and 1991. Our base period is 1990-91. Using these data we produce Paasche type 

household specific indexes. In addition we propose an alternative definition of total 

expenditures, based on the CPI market basket commodity space, to be used for welfare analysis. 

Our underlying motivation for conducting this study was to compare real welfare inequality in 

Spain and the U.S. in the 1980s for another study (Garner et al. forthcoming 1997). Because of 

this comparison, we were somewhat restricted in our approach. 

CEX data are used to calculate CPI market basket item budget shares for each interviewed 

household. Price indexes are merged with the household budget data at various levels of 

geographic and market basket item aggregation, and the variability in these indexes are compared 

in order to measure the value of using detailed consumption space over aggregated consumption 

space. In this study we introduce two novel approaches to producing household specific price 

indexes using BLS data. First, expenditure data from the Consumer Expenditure Diary survey, 

which is more detailed than the Interview, are used to impute missing consumption items for the 

Interview households. And second, a method to impute household indexes for the rural 

population is presented. Two different types of samples, horizontal and vertical (based on 

assumptions about the Interview households selected to define the base period), are used to 

provide the weights for the price indexes. Indexes are presented based on Interview only items 

and all items commodity spaces for the horizontal and vertical samples with additional indexes 

produced for consumer units living in urban and rural areas. 

From our study we conclude that indexes based on expenditures for the horizontal and vertical 

samples do not differ significantly for the time periods of our study. However, differences in the 

indexes do result for the urban versus rural samples, with consumer units living in urban areas 

facing greater changes in relative prices than are faced by consumer units living in rural areas. 

The all-item indexes produced slightly higher index values than did the Interview-only item 

indexes. Relative prices appear to be pro-poor during the 1980s. 
 1

I. Introduction 

Price changes affect households differently if households have varying patterns 

of expenditures, reflecting disparity in tastes and preferences. Most official measures of 

inflation, like the U.S. Consumer Price Index, are aggregate indexes which are based on 

data reflecting some representative or average household. However, different groups 

in the population are likely to have faster or slower growth in their real expenditures 

than is recorded by changes in the official CPI. Thus with nonhomothetic preferences, 

richer households will have larger budget shares for luxuries and smaller budget shares 

for necessities so that if changes in prices involve relative price changes between 

luxuries and necessities, the index will differ systematically between poor and rich 

households (Deaton and Muellbauer 1991). Because of this, it is desirable to use 

household specific price indexes to adjust expenditures when examining changes in real 

inequality over time. 

In this paper, we examine several different approaches to constructing 

household specific consumer price indexes. U. S. Consumer Expenditure Survey (CEX) 

data from 1990-91 are used to calculate Consumer Price Index (CPI) market basket item 

budget shares for each interviewed consumer unit (also referred to as “household” in 

this study). In general a consumer unit is a collection of people who share a budget and 

some living quarters (see Appendix 1 for more details). Price indexes from 1981, 1987 

and 1991 are merged with the household budget data at various levels of geographic 

and market basket item aggregation, and the variability in these indexes are compared 
 2

in order to measure the value of using detailed consumption space over aggregated 

consumption space. 

We use as our base sample of consumer units those participating in the CEX 

Interview survey in 1990-91. We produce two sets of indexes for the commodity space 

bundles: one which is entirely based on CEX Interview data (141 items) and another 

which is based on a combination of Interview and Diary data (207 items). The 

difference between the Interview-only consumption bundle (hereafter referred to as 

X141) and the full consumption bundle (hereafter referred to as X207) reflects the fact 

that the Diary collects more detailed expenditure information than does the Interview. 

The use of the Diary data to impute and allocate more specific commodities to the 

Interview sample is novel and has not been used in previous studies (as far as we 

know). Another novel approach followed in this study is to create household indexes 

for the rural population based on the relative prices faced by urban nonmetropolitan 

consumer units living in various geographic areas.1

 Thus, price indexes are produced 

for urban and rural consumer units as well as for the combined sample. We also 

produce indexes for samples based on two different sample structures which we refer to 

as horizontal and vertical. For the vertical sample, a quarterly Interview is treated as an 

independent and separate case (there are a maximum of four Interviews per consumer 

 

1

 The official CPI-U represents about 84 percent of the total U.S. noninstitutional population. The rural sector of the 

country is not included in the population universe of the CPI-U. Monthly price relatives are not available for rural 

areas. In order to include the rural portion of the CEX Interview sample in our study, we assigned the available price 

indexes for the urban nonmetropolitan areas (or “D-sized” areas) to rural households by geographic region 

(Northeast, Midwest, South, and West). 
 3

unit). In the horizontal sample, expenditures from every interview available for the 

consumer unit are aggregated together. 

Inflation experience between ‘the winter of 1981 and the winter 1991’ and ‘the 

winter of 1987 and the winter of 1991’ are measured for each consumer unit. Since 

current period weights (winter 1991) are used to produce the indexes, Paasche indexes 

result. The base period is winter (January, February, and March) 1991. This period was 

selected as the base in order to match our indexes with those produced for a joint 

Spanish-U.S. comparison of expenditure inequality (Garner et al., forthcoming 1997). 

Our study is different from others (e.g, Jorgenson and Slesnick (1983), and 

Kokoski (1987)) who have produced household specific price indexes for consumer 

units living in the U.S. First, we use a broader commodity space than has been used in 

the past to produce the indexes. Second, we impute price indexes to consumer units 

living in rural areas. Third, we use more recent data than have been used in the past. 

And fourth, we test the sensitivity of our results to assumptions made about the base 

sample upon which the indexes are based: whether the indexes differ when quarterly 

expenditures are assumed to be independent or not. The base samples are referred to as 

horizontal and vertical; quarterly independence is assumed for the vertical sample but 

not for the horizontal sample. 

From our study we conclude that indexes based on expenditures for the 

horizontal and vertical samples do not differ significantly for the time periods under 

investigation. However, differences in the indexes do result for the urban versus rural 
 4

samples, with consumer units living in urban areas facing greater changes in relative 

prices than are faced by consumer units living in rural areas. The X207 or all items 

indexes produce slightly higher index values than did the X141 or Interview only item 

indexes. Relative prices appear to be pro-poor during the 1980’s. All analyses are 

based on unweighted data. 

The remainder of the paper is divided into four sections. In section two, we 

present our motivation for this study. In section three we describe the data and index 

number construction methods that we used. Section four includes our results, while 

section five provides a summary and conclusion.

II. Motivation 

It has long been recognized in the empirical literature that different groups are 

not affected in the same manner by the evaluation of relative prices (for early studies on 

India, see Iyengar (1967) and Mahalanobis (1972); for U.K. studies, see Prais (1959), 

Nicholson (1975), Lesser (1976), and the references quoted in Muellbauer (1974b); for the 

U.S., see Amble and Stewart (1994), Garner et al. (1996), Michael (1970), Hollister and 

Palmer (1972), Hagemann (1982), Jorgenson and Slesnick (1983), Kokoski (1987), and 

Snyder (1961); and for Spain, see Abadia (1986)). However, it appears that the idea that 

price movements should be included in intertemporal income inequality comparisons 

was originally suggested by Iyengar and Battacharya (1965), some time before the pathbreaking work of Atkinson (1970), Kolm (1976a, b), and Sen (1973) on the axiomatic 

foundations of inequality measurement. Subsequently, Muellbauer (1974a) proved that 
 5

an inequality measure derived from a strictly quasi-concave social welfare function, 

defined over the distribution of individual indirect utility levels, is independent of price 

changes if and only if preferences are identical and homothetic for all consuming units. 

We do not accept such strong restrictions on individual preferences, thus our choice to 

use household specific price indexes for our comparison of real inequality. 

In the absence of such strong restrictions on individual preferences, the usual 

procedure of expressing two income distributions at common prices by using a single 

inflation rate for all individuals is not warranted. The reason, of course, is that changes 

in relative prices have redistributive consequences. Therefore, changes in money 

inequality need not coincide with changes in real inequality. 

To estimate the change in real inequality we need household specific price 

indexes. But once armed with a set of such price indexes, we may use them for other 

interesting purposes, like the estimation of the real change in poverty or welfare, or the 

construction of group price indexes for relevant partitions of the population. 

There are two approaches to construct household specific price indexes. For one, 

a complete commodity demand system could be estimated, which in turn would be 

used to construct a true cost of living index for every household (Muellbauer 1974a, 

1974b, Slesnick). Second, one could construct a set of household specific statistical price 

indexes. All that is neded for this second approach is a vector of price relatives at the 

maximum disaggregation level, provided by the statistical agency in charge of the 

official consumer price index, and a vector of household specific budget shares defined 
 6

on the corresponding commodity space. It is well known that at the individual level, 

statistical price indexes of the Laspeyres (Paasche) type provide an upper (lower) bound 

for the corresponding theoretical constructions (See, for example, Pollak, Diewert). 

Consequently, in this approach we can only hope to estimate convenient bounds for the 

change in real terms of the social concepts of interest, namely, the change in the mean, 

poverty, inequality, or welfare. 

Recently, the second approach has been applied in Spain on the basis of three 

large household budget surveys, the Encuestas de Presupuestos Familiares (or EPF for 

short), collected by the Spanish Instituto Nacional de Estadística (INE for short) with 

the main purpose of estimating the weights of the official consumer price index.2

Coming now to the evidence during the 80's in the U.S., most of the literature 

points to an increase in earnings, income and consumption inequality, however 

measured. In particular, using CEX data at the BLS, Johnson and Shipp (1996) find that 

"consumption inequality peaks in 1986 and then falls slightly, peaks again in 1990 and 

1991 and falls during 1993 and 1994." In their conclusions, these authors suggest that 

"...using Slottje (1987) and Slesnick (1994) as a starting point, the next stage of research 

[should] examine how inflation and prices affect inequality." 

Given the tightness of the empirical bounds obtained in the Spanish case for the 

relevant conceptual constructs, we have started a program to study the following two 

 

2

 Higueras and Ruiz-Castillo (1992) construct household specific price indexes using the EPFs of 1973-74 and 1980-

81. Ruiz-Castillo (1995, 1997) compares the changes in real inequality and welfare from 1973-74 to 1980-81. RuizCastillo and Sastre (1996) construct household specific price indexes for the 1990-91 EPF. Del Río and RuizCastilllo (1996, 1997a, 1997b, 1997c) compare real inequality and poverty from 1980-81 to 1990-91. Sastre (1997) 

compares in real terms the three available EPFs. 
 7

topics: (1) the evolution of household expenditures real inequality in the U.S. during the 

80 ́s, using household specific statistical price indexes to capture the distributional role 

of changes in relative prices; and (2) the comparison of household expenditure 

inequality and welfare between the U.S. and Spain. 

In the Spanish studies, the 1990-91 household expenditure distribution at current 

prices is expressed at constant prices of the 1991 winter (by means of Laspeyres price 

indexes) and the 1981 winter (by means of Paasche price indexes). Correspondingly, in 

this paper we use CEX data to construct several alternatives for the U.S. distribution of 

household expenditures at current prices for the 1990-91 reference period. Then we 

construct Laspeyres and Paasche household specific statistical price indexes to express 

these distributions at winter 1991 and winter 1981 constant prices. 

In addition, to test the Johnson and Shipp (1996) finding of a change in the trend 

in inequality during the mid 80 ́s, we express also the 1990-91 household expenditures 

distributions at winter 1987 constant prices. Applying the lessons learned in this paper, 

in order to study the change in real household expenditures inequality and welfare 

during the 80's we plan to extract from CEX data a 1980-81 household expenditures 

distribution, and to construct household specific price indexes to express it at winter 

1981, winter 1987 and winter 1991 constant prices. 

III. Construction of Household Specific Price Indexes 

 To evaluate the options we take in regard to these points, it is important to 

understand how the official aggregate consumer price index is constructed. In both 

Spain and the U.S., the CPI (Consumer Price Index) is a fixed weight price index 
 8

calculated according to a modified Laspeyres formula. In both countries the fixed 

weights are estimated from the microeconomic information provided by a household 

budget survey. There are two main differences. First, the Spanish EPF is a 

representative sample of consumer units interviewed during a given year, while the 

CEX is a continuous budget survey in which one fourth of of all households rotate 

every quarter and every household provide expenditure data during four consecutive 

quarters. Second, the EPF is a single instrument for the collection of expenditures on all 

types of goods and services, while the CEX consists of two separate instruments: a 

Diary during a period of two weeks, focused on the recording of expenditures on items 

with a high frequency of purchase, and an Interview which records all expenditures 

during a three month period. 

 To place in context our treatment of the U.S. case, we start this section with a 

brief review of the much simpler Spanish case. 

A. The Spanish Case 

 In the Spanish case, the EPF is a household budget survey in which interviews 

are spread out uniformly over a period of 52 weeks. All household members 14 years of 

age or older are supposed to record all expenditures taking place during a sample 

week. Then, in-depth interviews are conducted to register past expenditures over 

reference periods beyond a week and up to a year. From that information the INE 

estimates annual household total expenditures. For the details on the last two EPFs, 

collected from April 1980 to March 1981 and from April 1990 to March 1991, 

respectively, see INE (1983) and INE (1992). 

 Let there be H and H’ households in the two situations, and let x = (x1,..., xH) 

and y = (y1,..., yH’) be the 1980-81 and 1990-91 distributions of household expenditures, 

respectively. Let j = 1,...J denote the subset of items for which the INE constructs the 

Spanish Indice de Precios de Consumo (or IPC for short). For each h, we can write xh = Σj 
 9

xh;j, where xh;j is household h expenditure on commodity j. Let Xj be the aggregate 

expenditure on commodity j, i.e., Xj = Σhxh;j, and let X be the aggregate expenditure on 

all commodities, i.e., X = Σj Xj = ΣjΣh xh;j. The fixed weights for the official IPC are given 

by the J-dimensional vector W = (W1,..., WJ), where Wj = Xj /X. The IPC for period t, 

based in the period 1983, is then computed according to the modified Laspeyres 

formula 

 

 IPCt,0 = ML(pt, p0; W) = Σj Wj (ptj/p0j), 

where pt

 = price vector in period t, and p0 = price vector in the base year 1983(Note *). 

 It is well known that the overall IPC is a weighted average of the household 

specific IPC’s. Let wh = (wh;1,..., wh;J) be household h vector of budget shares, with wh;j

= xh;j/xh. The modified Laspeyres IPC for household h is given by 

 IPCh;t

0 = ML(pt

, p0; wh) = Σj wh;j (ptj/p0j). 

Let αh = xh /X, so that Σh αh = 1. It is easy to see that, for each j, 

 Wj = Σh αh wj. 

Therefore, 

 IPCt,0 = Σh αh IPCh;t

0 . 

In other words, the overall IPC is equal to the weighted average of the individual IPC’s 

with weights equal to household expenditures. This is the reason why the IPC is called 

a "plutocratic" price index in which the rich weight more than the poor(Note **). 

 Which is the connection between this individual IPC and the corresponding 

true cost-of-living index? It is often forgotten that the answer is: none. To understand 

this point, let us assume that household h has a utility function Uh defined on the Jdimensional vector of quantities qh = (qh;1,..., qh;J). Let us divide the Spanish collection 
 10

period into four quarters indexed by s = 1,..., 4, where 1 = Spring 1980, 2 = Summer 

1980, 3 = Fall 1980, and 4 = Winter 1981, and assign each household to the quarter in 

which it is interviewed. We assume that a household h interviewed in quarter s with 

annual total expenditures xh;s faces a vector of current prices ps = (ps1,..., psJ). Denote by 

qh;s= (qh;s1,..., qh;sJ) the solution to household h’s utility maximization problem 

restricted to Σj psjqh;sj ≤ xh;s. If we denote by φh the indirect utility function and by ch 

the expenditure or cost function, we have that uh;s = φh(xh;s, ps) and xh;s = ps qh ;s= 

ch(uh;s, ps). Then the cost-of-living index to compare price vectors pt and p0

maintaining constant the utility level uh;s is defined by 

 CL(pt, p0; uh;s) = ch(uh;s, pt)/ch(uh;s, p0). 

Clearly, between IPCh;t

0 and CL(pt

, p0; uh;s) there need not be any connection at all. 

 This negative result does not preclude an appropriate solution to the 

comparison of the money distributions x = (x1,..., xH) and y = (y1,..., yH’) at constant 

prices. We first choose the winter of 1991 as the period in which to express both 

distributions. For this purpose, we begin by computing a set of Laspeyres statistical 

price indices for the 1980-81 households. For each household h interviewed in quarter s 

= 1,..., 4, we have 

 L(pt

, ps; wh;s) = Σj wh;sj (ptj/psj) = pt qh;s/ps qh;s, 

where 

 (ptj/psj) = (ptj/p0j)/(psj/p0j) 

t = winter 1991, and 0 = 1983. Consider the Laspeyres cost-of-living index CL(pt, ps; uh;s

) to compare price vectors pt and ps mantaining constant the utility level uh;s: 

 CL(pt

, ps; uh;s) = ch(uh;s, pt)/ch(uh;s, ps) = ch(uh;s, pt)/ps qh;s. 
 11

Now, clearly we have: 

 CL(pt, ps; uh;s) ≤ L(pt, ps; wh;s). 

That is, the household specific Laspeyres statistical price index, L(pt

, ps; wh;s), is an 

upper bound of the corresponding Laspeyres cost-of-living index CL(pt, ps; uh;s). 

Denote by xh;t household expenditures xh;s at prices pt. Ideally we would like to 

estimate xh;t by the product xh;s times CL(pt, ps; uh;s). However, in the absence of a 

knowledge of household preferences, we must be content to estimate that magnitude as 

xh;t = xh;s L(pt, ps; wh;s) = pt qh;s, which provides an upper bound to the true construct. 

 For the 1990-91 distribution, let vh;sj, = (vh;s1,..., vh;sJ) be the vector of budget 

shares of household h interviewed in quarter s of the 1990-91 EPF, where vh;sj= yh;j/yh

and s = 1,..., 4 with 1 = Spring 1990, 2 = Summer 1990, 3 = Fall 1990, and 4 = Winter 

1991, Then, for every h and s we can estimate 

 L(pt, ps; vh;s) = Σj vh;sj (ptj/psj) = pt qh;s/ps qh;s. 

Similarly, yh;t = yh;s L(pt

, ps; vh;s) = pt qh;s provides also an upper bound to the true 

construct. Distributions xt = (x1;t,..., xH;t) and yt = (y1;t,..., yH’;t) are both expressed at 

constant winter 1991 prices, and can be compared for the purpose of inequality, poverty 

and welfare in real terms. 

 Since we are interested in expressing both money distributions at prices of 

situation 1, we now choose t’ = winter of 1981. We construct statistical Laspeyres price 

indices L(pt’, ps

; wh;s) for the 1980-81 household distribution, and Paasche price indices 

P(pt’, ps; vh;s) for the 1990-91 household distribution: 

 L(pt’, ps; wh;s) = Σj wh;sj (pt’j/psj) = pt’ qh;s/ps qh;s, 

 P(pt’, ps; vh;s) = 1/(Σj vh;sj (pt’j/psj)) = ps qh;s/pt’ qh;s, 
 12

where 

 (pt’j/psj

) = (pt’j/p0j)/(psj/p0j), 

t’ = winter 1981, 0 = 1983, and s = 1,..., 4 with 1 = Spring 1980 or 1990, 2 = Summer 1980 

or 1990, 3 = Fall 1980 or 1990, and 4 = Winter 1981 or 1991. Now we can compare in real 

terms xt’ = (x1;t’,..., xH;t’) and yt’ = (y1;t’,..., yH’;t’), where xh;t’ = xh;s L(pt’, ps; wh;s) and yh;t’

= yh;s/P(pt’, ps; vh;s). Since statistical Paasche price indices provide a lower bound for 

the corresponding Paasche cost-of-living indices, for every h our estimate yh;t’ is an 

upper bound for the true construct. 

 Finally, we can consider the rate of inflation from period t’ to period t measured 

according to 1980-81 and 1990-91 household tastes. First, for each h interviewed in 

quarter s of 1980-81, we can define a cost-of-living index 

 CL(pt

, pt’; uh;s) = ch(uh;s, pt)/ch(uh;s, pt’). 

Notice that xh;t = pt qh;s and xh;t’ = pt’ qh;s provide an upper bound for ch(uh;s, pt) and 

ch(uh;s, pt’), respectively. Since period t = winter 1991 is further apart from 1980-81 than 

period t’ = winter 1981, we expect that the rate of inflation approximated by 

 xh;t/xh;t’ = pt qh;s/pt’ qh;s 

provides an upper bound for the true construct CL(pt, pt’; uh;s). Second, for each h 

interviewed in quarter s of 1990-91, we can similarly define a cost-of-living index CL(pt

, 

pt’; uh;s) which can be approximated by 

 yh;t/yh;t’ = pt qh;s/pt’ qh;s. 

Now we expect that yh;t’ provides a worse upper bound approximation to pt’ qh;s than 

yh;t relative to pt qh;s. Therefore, our estimate of the rate of inflation from period t’ to 

period t, measured according to 1990-91 household tastes, provides a lower bound to 
 13

the true construct CL(pt, pt’; uh;s). Of course, there is no a priori reason to expect the 

distribution of CL(pt, pt’; uh;s) for the 1980-81 households to be, on average, greater or 

smaller than the distribution of CL(pt, pt’; uh;s) for the 1990-91 households. However, in 

practice we would prefer to find that our lower bound estimate according to 1990-91 

tastes is below our upper bound estimate according to 1980-81 tastes. 

B. The determination of the 1990-91 household expenditures distribution

 The continuous and rotating nature of the CEX in the US case, poses special 

problems for the determination of the 1990-91 household expenditures distribution at 

current prices, that is, the equivalent of the y distribution in the Spanish case. In this 

subsection, we limit ourselves to the Interview survey only (See Appendix 1). 

Households are interviewed from one to five times, with the first interview used for 

bounding purposes only. Expenditures recorded in quarters 2 to 5 refer to those made 

in the previous three months. In the US case there are two ways to estimate annual 

expenditures yh. On the one hand, we may assume that the quarters are independent so 

that each quarterly expenditure would be multiplied by four to obtain an annual value. 

On the other hand, we may recognize that quarters are not independent, in which case 

expenditures are the sum of four quarterly values recorded during the quarters in 

which the consumer unit participates in the survey. In the first case we have what we 

call a vertical sample, while in the second case we have a horizontal sample. 

 Let us begin with the horizontal sample. CEX files consist of all households who 

have provided one, two, three or four quarters of data. Restricting ourselves to so-called 

complete households with four quarters of data would be unnecessarily restrictive. 

Including some incomplete households allows us to increase the sample size. The 

obvious thing to do is to impute missing information with the help of the information 

actually recorded. Specifically, missing data are made equal to the average of non 

missing quarterly values. However, we do not think that it makes much sense imputing 
 14

as many as three quarters of data. Therefore, we restrict ourselves to all households 

with at least two quarters of data. 

 In the horizontal case, the annual household expenditures is: 

 yh = Σ

r yh;r 

where yh;r is household h’s total expenditures -recorded or imputed- for quarter r. The 

sum goes over four quarters of data for each household selected. Which households 

should be included? Consider all households whose expenditures refer to the 1990-91 

period, that is, those households whose second to fifth interviews are collected from the 

Summer 1990 (Q903) to the Spring 1991 (Q912). We say that these households belong to 

"Group 0". There are only 1,367 households in Group 0 with at least two quarters of 

data, a relatively small sample. We suggest to include also the following groups: 

- Households for whom three quarters of data belong to the 1990-91 period, that 

is, households whose second to fifth interviews are collected from the spring 

1990 (Q902) to the winter 1991 (Q911) [Group -1] and from the Fall 1990 (Q904) 

to the Summer 1991 (Q913) [Group +1]. 

-Households for whom two quarters of data belong to the 1990-91 period, that 

is, households whose second to fifth interviews are collected from the winter 

1990 (Q901) to the fall 1990 (Q904) [Group -2] and from the winter 1991 (Q914) 

to the fall 1991 (Q914) [Group +2]. 

For these four additional groups, we require that the minimum two quarters of 

recorded data must fall inside the 1990-91 period. There are 4,917 households from 

these four groups. Therefore, the horizontal sample consists of 6,284 consumer units. 

 Table 1 provides an illustration of the horizontal sample design. "Quarter #" 

refers to the number of quarters from which we drew the Interview data; eight 

interview periods were used. "Collection quarter" refers to the quarter in which the data 
 15

were collected (quarter one 1990 through quarter four 1991), while "expenditure 

reference quarter" refers to the reference period (quarter four 1989 through quarter 

three 1991) of the reported expenditures. The interview households eligible for the 

horizontal sample belong to the Groups -2 to +2 shaded in Table 1. 

Table 1. Illustration of Horizontal and Vertical Sample Design 

 Quarter # 1 2 3 4 5 6 7 8 

 Collection Quarter Q894 Q901 Q902 Q903 Q904 Q911 Q912 Q913 Q914 Q921

 Expenditure Reference Quarter Q893 Q894 Q901 Q902 Q903 Q904 Q911 Q912 Q913 Q914

-3 2 3 4 5 

G -2 2 3 4 5 

R -1 2 3 4 5 

O 0 2 3 4 5 

U 1 2 3 4 5 

P 2 2 3 4 5 

 3 2 3 4 5 

Horizontal sample, quarterly interviews summed 

Vertical sample, quarterly interviews independent 

 In the vertical sample, the annual household expenditure is: 

 yh = 4 yh;s 

where yh;s is the quarterly data, and s = Spring 90,...,Winter 1991 as in the Spanish 

subsection. Which households should be included? To begin with, those households 

from Groups -2 through +2 satisfying two conditions: having at least two quarters of 

recorded expenditure data, and having one of them in the 1990-91 period. As we can see 

in Table 1, under these conditions we draw data from four different Groups with 

expenditures recorded in the two central quarters (Summer and Fall 1990), but at most 
 16

from three different Groups for the remaining quarters (Spring 1990, Winter 1991). 

Therefore, to have a balanced vertical sample, provided they have two quarters of data 

we include also those households with the fifth interview in Spring 1990 -Group -3- and 

those households with their second interview in Winter 1991 -Group +3. These are also 

indicated in Table 1. 

C. Vector of budget shares

 In order to define the vector of budget shares, we first need to define the 

commodity space. For Spain, a single survey instrument is used to collect expenditure 

data from a sample of consumer units considered to be representative of the total 

Spanish population and all commodities are covered in each interview. In contrast, two 

independent samples are used to collect data in the U.S. Using only data from the CEX 

Interview, 141 items (based on selected detailed expenditures and three global 

questions) are covered, while if we use the official CPI market basket structure, 207 

items (based on a combination of Interview data and detailed expenditure reports 

available from the Diary and ) are included. 

 The first step in the construction of budget shares for each household in both 

the vertical and horizontal sample was to map their reported expenditures to the 207 

CPI market basket strata. Roughly 60 percent of the market basket (122 items) are 

defined in their entirety by expenditures reported in the CEX Interview. An additional 

16 items are defined in part by the domain of the CEX Interview. Furthermore, 

aggregate estimates (based on global questions) are also available for food at home, 

food away from home, and alcohol at home. Thus, expenditure reports available for 

each household were partitioned into 141 items (3 at an aggregate level). See Appendix 

2 for a listing of the item strata by source. 

 The remaining 68 items, consisting primarily of food, housekeeping supplies, 

and personal care expenditures, are available only from the CEX Diary. In order to 
 17

estimate expenditures at this level of detail for each household in the vertical and 

horizontal samples, CEX Diary data from 1990 and 1991 were used to allocate major 

expenses reported in the Interview to the item strata level. First, the average budget 

share of total food-at-home was calculated for each of the 52 food-at-home items, by 

index-area and consumer unit size in the Diary sample. These shares were then mapped 

to the CEX Interview sample by index-area and consumer unit size, and used to allocate 

the total food-at-home expenditure reported in the Interview to the detailed item. 

Similar allocations were performed for food-away-from home, alcohol at home, and the 

remaining Diary-specific items. This process also functioned to impute expenditures for 

roughly 40 specific goods and services not covered by the domain of the Interview (e.g., 

soaps, laundry and cleaning products, tolls, over-the-counter drugs, pet food, and 

personal care products). This was accomplished by calculating the expenditure for the 

Diary-unique item, as a percent of total food expense, and taking the product of this 

factor and the total food expense reported in the Interview. 

 Some expenditures were adjusted to meet CPI market basket definitions. These 

include (1) adjusting maintenance and repair goods and services and major appliance 

purchases made by homeowners to a ìrental equivalenceî definition; (2) adjusting 

homeowner insurance to reflect the portion covering personal property and to exclude 

the portion covering real property; (3) adjusting the expenditures for new vehicles to 

reflect gross purchase price and adjusting the expenditures for used vehicles to reflect 

ìdealer profitî; (4) adjusting the expenditures for health insurance to reflect ìretained 

earningsî and reallocating the remainder to other health care goods and services; (5) 

and other miscellaneous allocations. These adjustments were made using official CPI 

methodologies for consumer units included in the CPI-U (urban) population. The 

adjustments for rural consumer units were accomplished by using national averages to 

simulate the official methodologies (BLS 1997). 
 18

 Ultimately, both in the vertical and the horizontal sample, two separate 

estimates of total CPI market basket expenditures were calculated for each household: 

one based on 141 items and one based on all 207 items. Recall that annual household 

expenditures in the vertical sample was defined as 

 yh = 4 yh;s, 

where yh;s is the quarterly data, and s = Spring 90,...,Winter 1991. In the horizontal 

sample, annual household expenditures was estimated as 

 yh = S

r yh;r, 

where yh;r is household h’s total expenditures -recorded or imputed- for quarter r. In 

both cases, denote the household h’s budget shares vector by vh = (vh;1,...,vh;J), where J 

= 141, 207. Naturally, budget shares are defined differently for both samples. For a 

household h in quarter s of the vertical sample, we have: 

 vh;sj = yh;sj/yh;s. 

For a household h in the horizontal sample, we compute four sets of (quarterly) 

weights, one for each of the r quarters making up the annual household expenditures. 

The details are different for each Group (see Table 1): 

 vh;rj = yh;rj/yh;r, with: 

 r = Fall 1989,..., Summer 1990 if h belongs to Group -2 

 r = Winter 1990,..., Fall1990 if h belongs to Group -1 

 r = Spring 1990,..., Winter 1991 if h belongs to Group 0 

 r = Summer 1990,..., Spring 1991 if h belongs to Group +1 
 19

 r = Fall 1990,..., Summer 1991 if h belongs to Group +2. 

D. Quarterly Item-Area Price Relatives

 Monthly price indexes, ptj/p0j, based in period 0 = 1982-84, were first extracted 

for each item stratum-index area (item-area) combination for all months belonging to 

the quarters involved beyond the base period 1982-84, that is, Winter 1987 and from Fall 

1989 through Summer 1991. An item-area cell represents the elementary level of 

aggregation in the CPI; that is, the lowest level at which price quotes are pooled 

together to estimate monthly price change. There are 41 areas and 207 items. In 

addition, aggregate monthly indexes for (a) food at home, (b) food away from home, 

and (c) alcohol at home were extracted for use in processing Interview-only 

consumption (see Appendix 2 for a listing of the item strata, and Table 3 for a listing of 

the index areas). 

Obtaining price indexes for the 207 by 41 item-area matrix for the winter 81 

months was more problematic. The item-area structure of the CPI at this time was 

based on the 1978 Revision market basket definition and does not represent a one-toone correspondence with the 1987 Revision-based structure. A concordance between 

the 1978-structure and the 1987-structure was necessary to adjust the January 1987 price 

index Pi a

t

,

=8701 for each item-area to the winter 81 months. This was accomplished in two 

steps. First, Pi a

t

,

=8701 was adjusted to Pi a

t

,

=8301 using the concordance methodology first 

developed by Aizcorbe and Jackman (1993). Of note, sampling areas that did not exist 

prior to 1987 (i.e., Denver, New Orleans, and Tampa Bay) were moved by their 

corresponding region-city size area index pre-1987 (i.e., west-medium MSA, south-
 20

medium MSA, and south-medium MSA, respectively). The January 1983 index for each 

item-area was calculated as: 

(1) P P

P

P i a

t

i a

t I A

t

I A

, , t

,

,

= =

=

= = ∗ 8301 8701

8301

8701 

where i = item stratum, 1987 structure; a = index area, 1987 structure; I = 

Aizcorbe/Jackman item concordance; A= Aizcorbe/Jackman area concordance. This 

imputation works for roughly 90 percent of the item-area cells. If no corresponding 

item-area cell existed in Aizcorbe/Jackman, then Pi a

t

,

=8301 was imputed by the average 

price relative for its expenditure class 

P

P

EC A

t

EC A

t

,

,

=

=

8301

8701 .

3

 This occurred for roughly 40 items. If 

there was no corresponding expenditure class, then Pi a

t

,

=8301 was imputed by the average 

price relative 

P

P

MG A

t

MG A

t

,

,

=

=

8301

8701 for its major group.4

 This method was used for maintenance and 

repair items, major appliances, and personal computers - items that were omitted from 

the Aizcorbe/Jackman research. 

The second step in the estimation of Pi a

t m

, m to

=

=

81

1 3 was to deflate Pi a

t

,

=8301 by the 

price relative 

P

P

i a

t m

m to

i a

t

,

,

=

=

=

81

1 3

8301 . Since item-area indexes prior to December 1982 could be 

obtained in neither a cost-effective nor timely manner, aggregate indexes at the region-

 

3

 An expenditure class is a grouping of item strata which are expected to have similar price movement. It is used to 

impute price change when actual price change data are not available. 

4

 Major group for the purpose of this imputation was defined as food, housing, and all other items. 
 21

major expenditure group level were used.5 The January, February, and March 1981 

index for each item-area was calculated as: 

(2) P P

P

P i a

t m

m to i a

t MG R

t m

MG R

, , t

,

,

=

=

=

=

= ∗ =

81

1 3

8301

81

8301 

Finally, the arithmetic mean of the monthly indexes was calculated to represent the 

quarterly price index for each quarter in the study period: 

(3) P

P

i a

q

i a

t

t qia

,

,

; ,

= ∈

∑

3

Long-term price relatives were then calculated by dividing each quarterly index by the 

index value in each of the expenditure reference quarters included in the study, 

(b=Q894 through Q913): 

(4) P

P

P qbia

i a

q

i a

b (, ;, )

,

,

=

 All quarterly price relatives ptj/p0j were calculated as the simple average of the 

corresponding monthly data. 

E. Household specific price indexes

 We begin with urban households, the only ones for whom we have price 

relatives. Among them, let us start with the vertical sample. We want to express each 

 

5

 Region defined as northeast, midwest, south, and east. Major expenditure class defined as (1) food and beverages, 

(2) utilities, (3) housefurnishings and services, (4) apparel, (5) transportation, (6) health care, (7) entertainment, (8) 

shelter, and (9) all other goods and services. 
 22

household annual expendituures at prices of winter 1981, 1987 and 1991. Let (ptj/psj) = 

(ptj/p0j)/(psj/p0j). In the case of t = winter 1991, for each household h in quarter s = 

Spring 1990,..., Fall 1990 we construct the following Laspeyres indices: 

 L(pt, ps; vh;s) = Sj vh;sj (ptj/psj) = pt qh;s/ps qh;s. 

When t = winter 1987, winter 1981, we need Paasche indices: 

 P(pt, ps; vh;s) = 1/(Sj vh;sj (ptj/psj)) = ps qh;s/ pt qh;s. 

Annual expenditures for any household h interviewed in quarter s of the vertical 

sample at constant prices of period t is simply defined by: 

 yh;t = 4 yh;s L(pt

, ps; vh;s) = 4 pt qh;s, if t = winter 1991 

 4 yh;s/P(pt

, ps; vh;s) = 4 pt qh;s, if t = winter 1987, winter 1981. 

In the horizontal case and t = Winter 1991, for each household h we compute a set of 

four Laspeyres price indices, L(pt, pr; vh;r), one for each quarter r. Similarly, when t = 

Winter 1987, Winter 1981, we compute a set of four Paasche indices P(pt, pr; vh;r). 

Annual expenditures for any household h of the horizontal sample at constant prices of 

period t is simply defined by: 

 yh;t = Sr yh;r L(pt

, pr; vh;r) = Sr pt qh;r, if t = winter 1991 

 Sr yh;r/P(pt, pr; vh;r) = Sr pt qh;r, if t = winter 1987, winter 

1981. 

 Finally, as we saw in the first part of this Section, we can compute a set of 

household specific inflation rates from our three reference periods. For any household h 
 23

in either the vertical or the horizontal sample, and for any distinct t, t’ = Winter 1991, 

Winter 1987, Winter 1981, the ratios yh;t’/yh;t provide a lower bound for the true 

inflation rate between period t and t’ according to this household preferences. 

F. Comparison of the Different Samples 

The horizontal sample includes data from 6,284 consumer units participating in 

CEX Interviews during the January 1990-December 1991 period with a minimum of two 

interviews (see Table 2). Of these, 5,590 live in urban areas, as defined by the CPI, and 

694 live in rural areas. The vertical sample includes 18,986 consumer unit interviews, 

with 16,857 consumer units living in urban areas and 2,129 living in rural areas. 

CEX Diary and Interview Samples. Table 2, columns 1 and 2 include the means 

and the percentage distributions for selected consume unit characteristics of the CEX 

Diary and Interview. These are compared to determine if the Diary and Interview 

samples are similar enough to support our use of Diary reports to impute and allocate 

expenditures to the Interview sample. In general, we find that the Diary and Interview 

samples are quite similar. Small differences emerge between the two survey samples 

for the number of persons in the consumer unit, the race of the reference person, and the 

number of earners. Consumer units in the Diary are slightly more likely than Interview 

consumer units to have consumer units with two persons only. The percentage of 

consumer units with 2 earners in the Diary sample is marginally higher than the 

percentage of consumer units with 2 earners in the Interview sample. 
 24

CEX Samples versus Study Samples. Since we wanted to determine how well 

our study samples are like the samples from which the expenditure weights are drawn, 

we compare the characteristics of the CEX samples (columns 1 and 2) with the study 

horizontal Interview (column 3) and vertical Interview (column 6) samples. We see that 

the characteristics of all of these samples are quite similar. However, the study samples 

tend to have reference persons who are slightly older than those in the CEX samples, 

and tend to have slightly greater percentages of consumer units with 3 or more persons. 

The CEX samples and the vertical study sample are more alike in terms of regional 

distribution than is the horizontal sample. In terms of CPI index area, the CEX samples 

and the study samples have almost identical distributions. 

Horizontal versus Vertical Samples. When comparing the horizontal and 

vertical Interview samples (see columns 3 and 6 in Table 2), we see that consumer unit 

characteristics are fairly similar with a few notable exceptions. The average age of the 

reference person tends to be slightly lower in the horizontal sample than in the vertical 

sample. The horizontal sample is composed of slightly more consumer units with one 

person and those with three persons, more female reference persons, and a smaller 

percentage of consumer units with non-black reference persons. Consumer units with 

one earner are more common in the horizontal sample while those with no earners, with 

2 or more earners are more common in the vertical sample. The greatest difference 

between the horizontal and vertical samples appears to be the regional characteristics 

“Midwest”; approximately 27 percent of the horizontal sample lives in the Midwest 
 25

while only about 22 percent of the vertical sample consumer units live in the region. 

The horizontal sample also includes greater percentages of consumer units living in the 

South and West, but the percentage difference is quite small. In terms of distribution by 

CPI index area, the horizontal and vertical samples are again quite similar (see columns 

3 and 6 in Table 2). 

Urban versus Rural Samples. The percentage distributions of consumer units 

living in urban only and rural only areas are also presented in Table 2: columns 4 and 5 

for the horizontal sample and 7 and 8 for the vertical sample. The characteristics 

distribution of consumers units in the horizontal and vertical rural subsamples are the 

same with one exception, as are the horizontal and vertical urban subsamples. The 

exception is that consumer units of size three are more prevalent in the horizontal urban 

sample than in the rural horizontal sample. In contrast, in the vertical urban sample 

they are less prevalent than in the rural vertical sample. The differences between the 

subsamples appear to be more between consumer units living in urban versus rural 

areas, and not between horizontal and vertical samples. When comparing the urban 

and rural samples we find consumer units living in rural areas to have more members 

in the unit, an older reference person, more persons less than 18 years of age, and more 

persons greater than 64 years of age. A larger percentage of single person consumer 

units tend to live in urban areas relative to rural areas. There is a greater percentage of 

consumer units with male reference persons in rural areas and a greater percentage of 

units with female reference persons in urban areas. There is a greater percentage of 
 26

 Table 2. Characteristics of Consumer Units Participating in the CEX in 1990-91 

1 2 3 4 5 6 7 8 

 Horizontal Horizontal Horizontal Vertical Vertical Vertical

 Diary Interview Interview Urban Rural Interview Urban Rural 

 Survey1 Survey2 Sample3 Only4 Only5 Sample6 Only Only 

 

Number of observations 23,654 41,039 6,284 5,590 694 18,986 16,857 2,129 

MEANS OF: 

 Family size 2.5 2.5 2.6 2.6 2.7 2.6 2.6 2.8 

 Age of reference person 46.8 47.2 47.6 47.3 49.6 48.0 47.8 49.9 

 Persons less than 18 0.7 0.7 0.7 0.7 0.8 0.7 0.7 0.8 

 Persons over 64 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4 

PERCENT DISTRIBUTION OF:

Consumer-unit size 

1 26.9 27.4 26.6 27.4 20.3 25.8 26.6 19.3 

2 30.7 29.4 29.2 28.8 32.7 29.6 29.2 32.7 

3 16.9 17.0 17.6 17.6 17.4 17.5 17.4 18.2 

4 14.7 14.9 15.4 15.2 17.1 15.6 15.4 17.1 

5 or more 10.8 11.2 11.2 11.0 12.4 11.5 11.3 12.8 

Sex of reference person 

Male 64.6 64.9 64.0 62.4 77.1 64.5 63.0 77.0 

Female 35.4 35.1 36.0 37.6 22.9 35.5 37.0 23.0 

Race of reference person 

Non-Black 90.1 89.0 89.2 88.3 96.4 89.4 88.6 96.0 

Black 9.9 11.0 10.8 11.7 3.6 10.6 11.4 4.0 

Number of earners 

0 17.2 19.2 18.8 18.8 19.0 19.0 19.0 19.5 

1 35.7 36.3 35.2 35.7 32.0 34.5 34.9 30.9 

2 36.0 33.6 34.1 34.0 35.2 34.4 34.2 35.6 

3 or more 11.1 10.9 11.9 11.6 16.8 12.2 11.9 14.1 

Region 

Northeast 26.2 26.8 21.5 22.3 14.8 21.6 22.6 14.1 

Midwest 20.5 21.6 26.9 26.0 34.4 22.1 26.2 34.4 

South 30.9 29.9 29.9 29.1 36.7 29.8 28.9 37.2 

West 22.5 21.6 21.6 22.6 14.0 21.4 22.3 14.3 

Area type 

Large MSA 58.2 57.9 57.5 64.3 2.4 57.8 64.8 2.6 

Medium MSA 11.1 10.8 10.9 12.2 0.6 10.9 12.2 0.7 

Small MSA 13.0 13.2 13.6 15.1 1.3 13.1 14.6 1.2 

Urban non-metro 6.9 7.5 7.6 8.4 0.6 7.6 8.5 0.6 

Rural 10.8 10.6 10.5 Na 95.1 10.7 na 95.0 

CPI index area 

New York 6.2 6.3 6.0 6.8 0.0 6.2 7.0 0.0 

Philadelphia 1.7 1.8 1.7 1.9 0.0 1.7 1.9 0.0 

Boston 1.7 1.9 1.9 2.1 0.0 1.8 2.1 0.0 

Pittsburgh 1.5 1.7 1.7 1.9 0.0 1.7 1.9 0.0 

Buffalo 1.6 1.7 1.7 1.9 0.0 1.8 2.0 0.0 

Northeast medium-sized MSA 2.4 2.5 2.6 2.9 0.0 2.6 2.9 0.1 

Northeast small-sized MSA 2.0 2.3 2.3 2.6 0.0 2.3 2.6 0.0 

Northeast urban non-metro 1.7 1.9 1.9 2.1 0.1 1.9 2.1 0.1 

Northeast rural 1.6 1.6 1.6 0.0 14.7 1.6 0.0 13.9 

Chicago 2.7 2.6 2.6 2.9 0.0 2.7 3.0 0.0 
 27

Table 2. Characteristics of Consumer Units Participating in the CEX in 1990-91 (Cont’d.) 

 

1 2 3 4 5 6 7 8 

 Horizontal Horizontal Horizontal Vertical Vertical Vertical

 Diary Interview Interview Urban Rural Interview Urban Rural 

 Survey1 Survey2 Sample3 Only4 Only5 Sample6 Only Only 

Detroit 1.8 2.0 2.0 2.2 0.1 2.1 2.3 0.2 

St. Louis 1.7 1.7 1.8 2.0 0.0 1.8 2.0 0.0 

Cleveland 1.8 1.9 1.9 2.1 0.1 1.8 2.1 0.1 

Minneapolis-St. Paul 2.0 1.9 1.9 2.1 0.0 2.0 2.2 0.0 

Milwaukee, WI PMSA 1.6 1.7 1.8 2.0 0.0 1.8 2.0 0.0 

Cincinnati 1.8 1.9 1.8 2.0 0.3 1.8 2.0 0.2 

Kansas City 1.5 1.6 1.7 1.9 0.1 1.7 1.9 0.1 

Midwest medium-sized MSA 2.0 2.0 2.0 2.3 0.0 2.0 2.3 0.0 

Midwest small-sized MSA 3.6 3.5 3.7 4.1 0.6 3.6 4.0 0.0 

Midwest urban non-metro 2.0 2.7 2.2 2.4 0.0 2.1 2.4 0.5 

Midwest rural 3.7 3.7 3.7 0.0 33.1 3.7 0.0 33.2 

Washington, DC 1.8 1.8 1.8 1.9 0.3 1.8 2.0 0.4 

Dallas-Fort Worth 1.9 1.8 2.0 2.2 0.0 1.9 2.2 0.0 

Baltimore 1.6 1.5 1.5 1.7 0.1 1.5 1.7 0.1 

Houston 1.6 1.6 1.7 1.9 0.0 1.6 1.8 0.0 

Atlanta 2.1 1.9 1.8 2.0 0.1 1.8 2.0 0.1 

Miami 1.8 1.7 1.7 1.9 0.0 1.7 2.0 0.0 

Tampa-St. Petersburg 1.6 1.5 1.6 1.8 0.0 1.6 1.8 0.0 

New Orleans, LA MSA 1.4 1.4 1.4 1.5 0.1 1.4 1.5 0.1 

South medium-sized MSA 5.9 5.5 5.6 6.2 0.6 5.5 6.1 0.6 

South small-sized MSA 5.4 5.4 5.3 5.9 0.6 5.2 5.8 0.5 

South urban non-metro 1.6 1.8 1.8 2.0 0.0 1.9 2.1 0.0 

South rural 4.1 3.9 3.9 0.0 34.9 4.0 0.0 35.4 

Los Angeles 4.2 4.3 4.3 4.9 0.0 4.3 4.9 0.0 

San Francisco 2.1 2.0 1.9 2.1 0.1 1.9 2.1 0.1 

Seattle 1.7 1.7 1.7 1.9 0.0 1.7 1.9 0.0 

San Diego 2.0 1.7 1.7 1.9 0.3 1.6 1.8 0.4 

Portland 2.0 1.8 1.8 2.1 0.1 1.8 2.0 0.1 

Honolulu 1.5 1.4 1.3 1.4 0.3 1.3 1.4 0.3 

Anchorage 1.4 1.4 1.4 1.5 0.1 1.4 1.6 0.1 

Denver 1.8 1.6 1.5 1.7 0.0 1.5 1.7 0.0 

West medium-sized MSA 0.8 0.8 0.7 0.8 0.0 0.8 0.9 0.0 

West small-sized MSA 1.9 2.0 2.2 2.4 0.1 1.9 2.2 0.1 

West urban non-metro 1.6 1.6 1.7 1.8 0.4 1.7 1.8 0.5 

West rural 1.4 1.4 1.4 0.0 12.4 1.4 0.0 12.5 

 

NOTES 

1 Characteristics are for all consumer units participating in the 1990 and 1991 CEX Diary Survey. Each weekly Diary is 

treated as an independent interview, and statistics are unweighted. 

2 Characteristics are for all interviewed consumer units in the 1990 and 1991 CEX Interview Survey. Each quarterly 

interview is treated independently, and statistics are unweighted. 

3 The "horizontal" sample is comprised of all consumer units interviewed in 1990 and 1991, with at least 2 of their 4 

interviews occurring in or between Q903 through Q912. Data from each Interview were aggregated into one record per 

consumer unit. 

4 Urban is defined as the CPI-U urban population: all urban households in metropolitan statistical areas and in urban 

places of 2,500 inhabitants or more. Excluded are urban farmers, rural, Military, and institutional population. 5 Rural is defined as the non CPI-U population. This primarily includes households living in rural areas, but also includes 

urban farmers and the military population. 

6 The "vertical" sample is comprised of all consumer units interviewed in and between Q903 and Q912, as long as the 

consumer unit had a minimum of 2 interviews in and between Q902 and Q913. 
 28

non-black reference person consumer units living in rural than in urban areas. 

Consumer units living in rural areas are more represented in the Midwest and South 

than the other two regions. 

Expenditure values and budget shares using 1990-91 data are presented in Table 

3. As expected, the All Items or X207 mean expenditures are greater than those based on 

Interview items (X141) only. When comparing the mean expenditures for the total 

(urban and rural combined) horizontal and vertical samples, they are slightly larger for 

the vertical (both X141 and X207). Mean expenditures for the vertical sample-urban 

only are greater than those for the horizontal sample-urban only as well. In contrast, 

mean expenditures for the horizontal sample-rural only are greater than those for the 

vertical sample-rural only. We expect this difference between the horizontal and 

vertical sample means because the horizontal sample has the affect of mitigating 

outliers. For example, a huge vehicle purchase by a consumer unit will only count once 

in the horizontal sample estimate of annual expenditures, but it will count four times in 

the annualization of the vertical sample expenditures. So this finding is intuitive and 

expected. 

Horizontal versus Vertical. When comparing the budget shares for the 

horizontal and vertical samples, total sample only, we find some differences between 

the two sets of values. For example, for the vertical sample, the budget shares for food 

at home, shelter, and fuels and utilities are greater than the budget shares for the 

comparable horizontal sample. In contrast, the budget shares for housefurnishings and 
 29

equipment and transportation are greater in the horizontal-total sample, than in the 

vertical sample. 

Vertical versus Horizontal in Urban and Rural Areas. As for the total samples, 

the vertical-urban only sample has greater budget shares for food at home, and shelter 

than does the horizontal-urban sample. The horizontal-urban only sample has greater 

budget shares for transportation and education than does the vertical-urban only 

sample. When comparing the budget shares of consumer units living in rural areas we 

find more differences between the horizontal and vertical samples. For example, for the 

vertical-rural sample, budget shares are greater for food at home, food away from 

home, shelter, fuels and utilities, and housekeeping supplies and services than they are 

for the horizontal-rural sample. 

Urban versus Rural. When examining the shares for the horizontal and vertical 

samples (the pattern is the same for these two samples) we find prominent differences 

between the shares for consumer units in the urban only versus rural only subsamples. 

Rural consumer units spend a greater share of their consumption expenditures on food 

at home, housefurnishings and equipment, transportation, health care, entertainment, 

and other goods and services. Urban consumer units spend a larger share on food away 

from home, shelter, apparel, personal care products and services, and education. 

IV. Results 

 The means of annualized consumption expenditures and household specific 

Paasche indexes are presented in Table 4 for the horizontal and vertical samples. 

Results are further disaggregated by urban and rural. Descriptive statistics for the 
 30

Table 3. Budget Shares for Horizontal and Vertical Samples: 1990-91 

HORIZONTAL SAMPLE VERTICAL SAMPLE 

Total Urban Only Rural only Total Urban Only Rural only 

Interview All Interview All Interview All Interview All Interview All Interview All 

ITEMS only1 ITEMS2 ITEMS only ITEMS ITEMS only ITEMS ITEMS only ITEMS ITEMS only ITEMS ITEMS only ITEMS 

MEANS OF: CPI Market-basket expenditures $26,552 $27,251 $27,108 $27,793 $22,071 $22,893 $26762 $27471 $27,396 $28,089 $21,750 $22,579 Budget shares:  Food at home 14.4 13.9 14.3 13.8 15.3 14.6 14.9 14.3 14.7 14.2 16.4 15.6  Food away from home 6.9 6.6 7.2 7.0 4.2 4.0 6.9 6.7 7.3 7.0 4.3 4.1  Alcoholic beverages 1.0 1.0 1.0 1.0 0.9 0.9 1.0 1.0 1.0 1.0 0.9 0.8  Shelter 28.6 27.8 29.1 28.3 24.1 23.3 29.8 29.0 30.4 29.6 25.2 24.3  Fuels and utilities 8.8 8.5 8.8 8.5 8.9 8.6 9.3 9.0 9.2 9.0 9.5 9.1  Housefurnishings and equipment 3.7 3.7 3.7 3.6 4.1 4.4 3.5 3.5 3.5 3.4 4.0 4.4  Housekeeping supplies and services 1.1 2.9 1.1 2.9 1.1 2.7 1.1 3.0 1.1 3.0 1.2 2.9  Apparel 4.7 4.6 4.8 4.7 4.2 4.0 4.7 4.6 4.8 4.7 4.3 4.1  Transportation 16.4 16.0 16.0 15.7 19.6 18.9 14.4 14.1 14.2 13.8 16.6 16.1  Health care 6.1 6.0 5.7 5.6 9.0 9.2 6.2 6.1 5.8 5.7 9.2 9.3  Entertainment 3.5 3.5 3.5 3.4 3.9 4.3 3.5 3.4 3.4 3.4 3.6 4.0  Personal care products and services 1.0 1.8 1.0 1.9 0.9 1.5 1.1 1.9 1.1 1.9 0.9 1.6  Education 1.7 1.7 1.8 1.8 1.1 1.0 1.5 1.5 1.6 1.6 1.0 1.0  Other goods and services 2.1 2.1 2.0 2.0 2.7 2.6 2.0 2.0 2.0 1.9 2.6 2.6 

NOTES 1 Interview only items include those CPI market basket expenditures reported in the CEX Interview survey. There are 207 item strata in the CPI market basket. Of these, 122 are defined in whole by items reported in the CEX Interview, and another 16 are defined in part. In addition, aggregate estimates for (a) food at home, (b) food away from home, and (b) alcohol at home are available from the Interview, bringing the total number of items covered to 141. 2 All-items includes expenditure estimates for all 207 CPI market basket item strata. Of these, 122 are derived exclusively from the interview. An additional 16 are comprised of categories reported inthe Interview and categories allocated from the Diary. Food at home, food away from home, and alcohol at home expenditure estimates from the Interview were allocated into an additional 68 itemsusing Diary allocation factors.
 31

Table 4. Expenditure Values and Price Indexes for Horizontal and Vertical Samples + 

 

HORIZONTAL SAMPLE VERTICAL SAMPLE 

Total Urban Rural Total Urban Rural 

MEANS OF: 

Total annualized expenditures in 1990-91, 

CEX definition 

$28,354 $ 28,772 $ 24,982 $28,572 $ 29,038 $ 24,884 

 

Annualized CPI-market basket expenditures, 

all-items 

27,251 27,793 22,893 27,471 28,089 22,579 

 Market-basket cost at winter 81 prices 18,436 18,766 15,772 18,557 18,940 15,524 

 Market-basket cost at winter 87 prices 23,461 23,927 19,705 23,613 24,147 19,389 

 Market-basket cost at winter 91 prices 27,748 28,303 23,280 27,932 28,570 22,886 

 

Annualized CPI-market basket expenditures, 

Interview only items 

26,552 27,108 22,071 26,762 27,396 21,750 

 Market-basket cost at winter 81 prices 18,048 18,401 15,200 18,124 18,526 14,941 

 Market-basket cost at winter 87 prices 22,790 23,267 18,944 22,886 23,426 18,611 

 Market-basket cost at winter 91 prices 27,007 27,574 22,441 27,125 27,767 22,029 

 

Household Paasche Indexes 

 All-items, 81 to 91 1.5137 1.5173 1.4848 1.5192 1.5232 1.4870 

 Interview-only items, 81 to 91 1.5064 1.5089 1.4861 1.5124 1.5153 1.4889 

 

 All-items, 87 to 91 1.1848 1.1850 1.1839 1.1876 1.1878 1.1862 

 Interview-only items, 87 to 91 1.1876 1.1876 1.1874 1.1907 1.1908 1.1903 

households specific price indexes are presented in Table 5 for the horizontal and 

vertical samples and for the X141 and X207 commodity space based indexes. Statistical 

differences in the horizontal and vertical and X141 and X207 commodity space indexes 

are presented. For illustration purposes, in Table 6 we examine the distributional 

impact of changes in prices from 1981 to 1991 and from 1987 to 1991 using our 

household specific indexes for consumer units of different sizes. 

A. Consumption Expenditure Values and Household Specific Price Indexes 

 In Table 4, average consumption expenditures with and without price 

adjustment are presented along with the average household Paasche price indexes. For 

the horizontal and vertical total samples, the annualized CPI-market basket 
 32

expenditures are very similar when based on the all items commodity space. The same 

pattern emerges for the Interview only items when comparing the horizontal and 

vertical sample results. In all cases, expenditures for consumer units living in urban 

areas are higher than those for consumer units living in rural areas. Average 

expenditures of the vertical urban sample are always higher than those of the 

horizontal urban sample. In contrast, in all cases, average expenditures of the vertical 

rural sample are smaller than those of the horizontal rural sample. From this we 

conclude that using the horizontal or vertical samples produces the same estimate of 

mean expenditures; however differences are likely to result for different subgroups of 

the samples. 

 When comparing the household specific indexes we find greater differences, 

although within period and commodity space definition, the indexes are quite similar. 

For the total, urban, and rural sample based indexes, the vertical sample produces 

indexes which are slightly higher than those for the horizontal sample in all cases. Thus 

we can conclude, for our study, the vertical sample exhibits greater price inflation than 

does the horizontal sample. The indexes for consumer units living in urban areas are 

higher than those for consumer units living in rural areas. This is not surprising since 

shelter accounts for about 30 percent of the budget for consumer units living in urban 

areas but only about 25 percent of the budget for consumer units living in rural areas, 

and shelter prices increased somewhat faster than the prices of other commodities. 

Shelter prices increased roughly 7 percent more so than did all-items from 1983 to 1991 
 33

and the increase in urban areas (A,B, and C sizes) was roughly 3 percent greater than 

the urban nonmetropolitan areas (D size areas) which were applied to rural households. 

B. Descriptive Statistics for the Household Specific Price Indexes 

 In Table 5 we present basic descriptive statistics for the horizontal and vertical 

sample based household specific price indexes. Commodity specific food at home 

indexes are presented to help us understand the impact of the allocation and 

imputation of Diary information for certain commodities to Interview consumer units 

consumption expenditures. Note, this process allowed us to move from the X141 

commodity space to the X207 commodity space with the Interview as the basic survey 

upon which our indexes are based. 

 In Table 5, as noted for Table 4, the household specific indexes are always 

greater for the vertical sample when compared to those based on the horizontal sample 

on average. The same is true when we compare the medians. The ranges, standards 

errors, and coefficients of variation are also larger for the vertical sample based indexes 

when compared to those for the horizontal sample. Greater variation in the vertical 

indexes is expected due to the shorter time period upon which the index weights are 

based. 

 For both time periods, 1981 to 1991 and 1987 to 1991, the coefficients of 

variation for the indexes are greater for the Interview only item based indexes 

compared to the all items indexes. In contrast the coefficients of variation for the 

Interview only food-at-home indexes are smaller than those based on the Diary 

imputation for the X207 commodity bundle. We would expect the Diary imputation 
 34

coefficients of variations to be greater than those for the Interview only based indexes 

since we have more price variation within the food-at-home index. Why the opposite 

pattern results for the commodity space defined in terms of all items versus the 

Interview only items needs further examination. The Diary imputation based food-athome indexes, when compared to the food-at-home indexes based on the global 

questions in the Interview only, produce means and medians which are also smaller.

 In order to examine whether the household specific indexes are statistically 

significantly different from each other, indexes for group=0 are compared. This group 

is selected for the comparison since it is the only group for which the four quarterly 

(vertical) interviews are entirely contained in the horizontal sample as well. A statistical 

test for the difference between two means, based on data from non-independent 

samples, is used. We test whether differences in the indexes are statistically significant 

when comparing the horizontal and vertical sample based indexes, and the all items 

versus Interview only items indexes. Our analysis reveals that there is no statistically 

significant difference between the horizontal and vertical sample based indexes, using 

the all items or the Interview-only items, when evaluated at the 0.01 level of 

significance. However, at the 0.05 level, there is a statistically significant difference 

between the horizontal and vertical sample based indexes for the following: (1) 

Interview-only, 1981 to 1991; and (2) all items, 1987 to 1991. 

C. Application: Distributional Impact of Inflation 

Empirical research has long revealed that different groups are not affected in the same 

manner by the evaluation of relative prices. For example, Deaton and Muellbauer 

(1991) report that from 1965 to 1971 in Britain, inflation was relatively neutral across the 
 35

income (defined in terms of expenditures) groups, however from 1971 to 1975 there was 

a strong “anti-poor” bias to consumer price indexes. Hollister and Palmer (1972) report 

that in the U.S. during the 1950’s and 1960’s that inflation was relatively neutral. 

However, by the 1970’s, relative prices moved against the poor (Williamson 1977). A 

similar pattern emerged for Canada during these time periods (see Ariat 1977). (For 

early studies on India, see Iyengar (1967) and Mahalanobis (1972); Jorgenson and 

Slesnick 1983; for other U.K. studies, see Prais (1959), Nicholson (1975), Lesser (1976), 

and the references quoted in Muellbauer (1974b); for the U.S., see Michael (1970), 

Hagemann (1982); and for Spain, see Abadia (1986)). 

Table 5. Descriptive Statistics for Household Specific Price Indexes 

 

Minimum maximum mean median range Standard 

error 

coefficient 

of variation

 

All-items, 81 to 91 

 Horizontal 1.0380 2.2980 1.5137 1.5018 1.2600 0.112 7.372 

 Vertical 0.7492 2.5478 1.5192 1.5089 1.7987 0.125 8.233 

 

Interview-only, 81 to 91* 

 Horizontal 1.0274 2.2874 1.5063 1.4962 1.2600 0.113 7.498 

 Vertical 0.7465 2.5654 1.5124 1.5046 1.8189 0.127 8.401 

 

All-items, 87 to 91* 

 Horizontal 0.8546 1.4900 1.1848 1.1815 0.6355 0.047 3.993 

 Vertical 0.7346 1.5614 1.1876 1.1859 0.8268 0.053 4.494 

 

Interview-only, 87 to 91 

 Horizontal 0.8481 1.4844 1.1876 1.1846 0.6363 0.048 4.031 

 Vertical 0.7315 1.5710 1.1907 1.1897 0.8395 0.054 4.561 

 

Food at home, 81 to 91 

 Diary imputation 1.0033 1.7429 1.4340 1.4631 0.7396 0.149 10.418 

 Interview-only 1.1877 1.5846 1.4570 1.4677 0.3969 0.068 4.673 

 

Food at home, 87 to 91 

 Diary imputation 1.0014 1.3330 1.1862 1.2005 0.3316 0.064 5.369 

 Interview-only 1.1391 1.3745 1.2210 1.2198 0.2355 0.033 2.671 

*For group=0 only consumer units, statistically significantly different at theα = 0.05 level but not at α = 0.01. 
 36

For illustration purposes only, we present the all items indexes for the horizontal 

and vertical samples for winter 1981 (base period winter 1991) and winter 1987 (base 

period winter 1991) to examine whether our household specific price indexes differ 

systematically between poorer and richer consumer units (Table 6). For this analysis, 

consumer units are ranked from lowest to highest using their 1990-91 annualized CPI 

market basket consumption expenditures (X141 and X207). Three expenditure groups 

(low, medium, and high) are used to distinguish poorer versus richer consumer units. 

Results are presented for each of five consumer unit sizes in order avoid selecting an 

equivalence scale for the comparison. These results are based on unweighted data.

From 1981 to 1991 and from 1987 to 1991, on average, the price indexes are lower 

for poorer consumer units than they are for consumer units in the medium and upper 

expenditure groups. We can interpret this result to mean that the indexes are pro-poor. 

This pattern is shown for consumer units with 3, 4, or 5 persons in the horizontal sample 

using the 1981 to 1991 index and for consumer units with 1, 3, 4, or 5 persons for the 

1987 to 1991 index. For the vertical sample, this same pattern also exists for all but one 

of the consumer unit size groups. The exception is two person consumer units using the 

1981 to 1991 index. Thus, we conclude that between 1981 and 1991 and between 1987 

and 1991, relative price changes were pro-poor, and thus that the relative prices of 

luxuries rose faster than those for necessities. 
 37

Table 6. Distributional Impact of Inflation in the U.S. 1981-91: Paasche 

Household Specific Price Indexes, All Items 

Horizontal Sample

 Consumption Expenditures 

 Low Medium High 

Time Period Consumer Unit Size n mean Std n mean std n mean std 

1981 to 1991 

 1 557 1.5361 0.12 558 1.5317 0.11 557 1.5320 0.14 

 2 612 1.5226 0.10 613 1.5298 0.12 612 1.5197 0.12 

 3 368 1.4875 0.10 369 1.5096 0.10 369 1.5077 0.12 

 4 322 1.4807 0.08 323 1.4955 0.10 323 1.5086 0.11 

 5 233 1.4703 0.08 234 1.4828 0.09 234 1.5019 0.10 

1987 to 1991 

 1 557 1.1861 0.04 559 1.1858 0.04 557 1.1856 0.06 

 2 612 1.1879 0.04 613 1.1924 0.05 612 1.1850 0.05 

 3 368 1.1772 0.04 369 1.1888 0.05 369 1.1809 0.05 

 4 322 1.1798 0.04 323 1.1827 0.04 323 1.1855 0.05 

 5 233 1.1749 0.03 234 1.1819 0.04 234 1.1830 0.05 

Vertical Sample

 Consumption Expenditures 

 Low Medium High 

Time Period Consumer Unit Size n Mean Std n mean Std n Mean std 

1981 to 1991 

 1 1632 1.5309 0.13 1633 1.5344 0.12 1633 1.5467 0.16 

 2 1875 1.5246 0.11 1876 1.5329 0.12 1876 1.5356 0.14 

 3 1108 1.4926 0.10 1109 1.5139 0.11 1108 1.5116 0.14 

 4 986 1.4844 0.09 987 1.5056 0.11 986 1.5133 0.13 

 5 725 1.4761 0.10 726 1.4952 0.10 726 1.5056 0.13 

1987 to 1991 

 1 1632 1.1844 0.05 1633 1.1878 0.05 1633 1.1901 0.07 

 2 1875 1.1896 0.04 1876 1.1947 0.05 1876 1.1915 0.06 

 3 1108 1.1805 0.04 1109 1.1927 0.05 1108 1.1835 0.06 

 4 986 1.1820 0.04 987 1.1886 0.05 986 1.1864 0.06 

 5 725 1.1774 0.04 726 1.1878 0.05 726 1.1837 0.06 

V. Summary and Conclusions 

The primary purpose of this study was to produce household specific price 

indexes for consumer units living in the U.S. during the early 1990’s. Our motivation 

was to produce indexes for a comparison of real consumption inequality in Spain and 

the U.S. Because of this comparison we were forced to make certain decisions such that 

our measures of consumption and prices were comparable for the two countries. To 

produce the U.S. indexes, we used expenditure data from the CEX for 1990-91 and 
 38

monthly CPI-U price data from the winter of 1981, the winter of 1987, and 1989-91. The 

base period was winter 1991. 

The household budget survey designs used by Spain and the U.S. differ, so our 

first decision was to reconcile the differences. We used the Interview component of the 

U.S. CEX since it is used to collect expenditure data from consumer units over a 12 

month period using quarterly Interviews. However, to account for all commodities, we 

augmented the Interview data with information on the spending patterns of consumer 

units for certain items using the CEX Diary. By using data from both survey 

instruments we were best able to match the Spanish design. Second, we assigned to 

consumer units living in rural areas, as defined by the BLS, relative prices based on the 

price indexes available from the corresponding urban non-metropolitan areas. The 

official CPI-U only covers about 84 percent of the total population, while our indexes 

are designed to cover the total population. 

Household specific indexes were produced for what we refer to as two different 

samples: a horizontal sample and a vertical sample. For the vertical sample, quarterly 

expenditures are assumed to be independent while for the horizontal sample 

expenditures are aggregated over the quarters in which the consumer unit participates 

in the survey. We produced both sets of indexes because we were not sure, a priori, 

which sample would produce the smallest variance. 

From our study we conclude that indexes based on expenditures for the 

horizontal and vertical samples do not differ significantly for the time periods of our 

study. However, differences in the indexes do result for the urban versus rural samples, 
 39

with consumer units living in urban areas facing greater changes in relative prices than 

are faced by consumer units living in rural areas. The all items indexes produced 

slightly higher index values than did the Interview only item indexes. In our 

illustration of the distributional impact of relative prices on expenditures, we find 

relative prices to be pro-poor during the 1980’s. 

Household specific price indexes could be used to produce indexes for 

subgroups of the population. Other researchers have examined this issue and have 

found conflicting results (see references cited earlier supporting the proposition that 

different groups in the population are likely to have faster or slower growth in their cost 

of living than recorded by the changes in the CPI-U, and Boskin and Hurd (1985) for a 

reference not supporting this). However, in the Boskin Report (Boskin et al. 1996), 

commission members stated that “...work on this subject remains to be done. In 

particular, the prices actually paid, not just expenditure shares, may differ” (p. 71). 
 40

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 45

Appendix 1: Description of Basic Data 

1. Consumer Expenditure Survey (CEX) 

 The CEX has two components: a Diary or recordkeeping survey completed by 

participating consumer units for two consecutive one-week periods, and an Interview 

survey in which the expenditures of consumer units are obtained in five interviews 

conducted every three months. As noted, data from both are used for this study. A 

consumer unit is defined as a member of a household related by blood, marriage, 

adoption, or other legal arrangement; a single person living alone or sharing a 

household with others but who is financially independent; or two or more persons 

living together who share responsibility for at least two out of three major types of 

expenses-food, housing, and other expenses. Students living in university-sponsored 

housing are also included in the sample as separate consumer units. 

Survey participants record dollar amounts for goods and services purchased 

during the reporting period whether or not the payment is made at the time of 

purchase. The expenditure amounts include all sales and excise taxes for all items 

purchased by the consumer unit for itself or for others. Excluded for both surveys are 

all business-related expenditures and expenditures for which the consumer unit is 

reimbursed. 

The Diary and Interview queries independent samples of consumer units which 

are representative of the U.S. population. For the Diary, about 5,000 consumer units are 

sampled each year, yielding about 10,000 diaries a year. The Interview sample is 

selected on a rotating panel basis, targeted at 5,000 consumer units each quarter. About 
 46

twenty percent of the sample are interviewed for the first time each quarter while 

twenty percent are interviewed for the last time. Consumer units are interviewed up to 

five times, at three-month intervals. Data from the first interview are used to ‘bound’ 

expenditures for subsequent interviews and are not used in estimation. 

In general the Interview is used to collect data on the types of expenditures 

which respondents can be expected to recall for a period of three months or longer; 

these tend to be relatively large expenditures. These include items such as those for real 

property, automobiles, and major appliances, or expenditures which occur on a regular 

basis, such as rent, utilities, or insurance premiums. A global estimate for food 

spending is also collected. In contrast, the Diary is used to collect expenditures on 

small, frequently purchased items which are normally difficulty for respondents to 

recall. Items for which expenditures are collected in detail in the Diary include the 

following: food and beverages (both at home and in eating places), tobacco, 

housekeeping supplies, nonprescription drugs, and personal care products and services. 

Expenditures incurred by members of the consumer unit while away from home 

overnight or longer are not collected in the Diary survey. The Interview survey covers 

about 95 percent of total expenditures. When Interview data are used in combination 

with those from the Diary for BLS published tables of expenditures a complete 

accounting of consumer expenditures is provided (BLS (1995)). 

2. Consumer Price Index 

The official Consumer Price Index (CPI) in the U.S. is used as a summary 

measure of the effects of price changes on individual households or consumer units in 
 47

the population. The CPI is a modified (modified in the sense we use expenditures with 

prices and implicit quantities) Laspeyres fixed weight index whose weights are the 

households’ mean expenditure shares. This index however does not represent the effect 

of price changes for each individual household in the population. However, it is often 

used to adjust incomes and benefit levels for various groups of individuals in the 

population. 

The CPI is a measure of the average change in prices over a fixed market basket 

of goods and services. The CPI is based on prices of food, clothing, shelter, fuels, 

transportation fares, charges for doctors’ and dentists’ services, medicine, and other 

goods that people buy for day-to-day living. Prices are collected in 85 urban areas 

(including metropolitan areas and urban parts of nonmetropolitan areas) across the 

country from about 57,000 housing units and approximately 19,000 retail 

establishments. All taxes directly associated with the purchase and the use of items are 

included in the index (see BLS (1997)). 
Appendix 2: Item Strata 

Interview Only Interview & Diary Item Strata

X141 items X207 items Code Description of Item Strata

Global food at home D2 0101 Flour and prepared flour mixes 

Global food at home D 0102 Cereal 

Global food at home D 0103 Rice, pasta, cornmeal 

Global food at home D 0201 White bread 

Global food at home D 0202 Other breads, rolls, biscuits, and muffins 

Global food at home D 0204 Cakes, cupcakes, and cookies 

Global food at home D 0206 Other bakery products 

Global food at home D 0301 Ground beef 

Global food at home D 0302 Chuck roast 

Global food at home D 0303 Round roast 

Global food at home D 0304 Other steak, roast, and other beef 

Global food at home D 0305 Round steak 

Global food at home D 0306 Sirloin steak 

Global food at home D 0401 Bacon 

Global food at home D 0402 Pork chops 

Global food at home D 0403 Ham 

Global food at home D 0404 Other pork, including sausage 

Global food at home D 0501 Other meats 

Global food at home D 0601 Fresh whole chicken 

Global food at home D 0602 Fresh or frozen chicken parts 

Global food at home D 0603 Other poultry 

Global food at home D 0701 Canned fish and seafood 

Global food at home D 0702 Fresh or frozen fish and seafood 

Global food at home D 0801 Eggs 

Global food at home D 0901 Fresh whole milk 

Global food at home D 0902 Other fresh milk and cream 

Global food at home D 1001 Butter and other dairy products 

Global food at home D 1002 Cheese 

Global food at home D 1004 Ice cream and related products 

Global food at home D 1101 Apples 

Global food at home D 1102 Bananas 

Global food at home D 1103 Oranges 

Global food at home D 1104 Other fresh fruits 

Global food at home D 1201 Potatoes 

Global food at home D 1202 Lettuce 

Global food at home D 1203 Tomatoes 

Global food at home D 1204 Other fresh vegetables 

Global food at home D 1301 Fruit juices and frozen fruits 

Global food at home D 1303 Canned and dried fruits 

Global food at home D 1401 Frozen vegetables 

Global food at home D 1402 Canned and other processed vegetables 

Global food at home D 1501 Candy and other sweets 

Global food at home D 1502 Sugar and artificial sweeteners 

Global food at home D 1601 Fats and oils 

Global food at home D 1701 Carbonated drinks 

Global food at home D 1703 Coffee 

Global food at home D 1705 Other noncarbonated drinks 

Global food at home D 1801 Canned and packaged soup 
Appendix 2: Item Strata (Cont’d.)

Interview Only Interview & Diary Item Strata

X141 items X207 items Code Description of Item Strata

Global food at home D 1802 Frozen prepared foods 

Global food at home D 1803 Snacks 

Global food at home D 1804 Spices, seasonings, condiments, sauces 

Global food at home D 1806 Other prepared food 

Global food away D 1901 Lunch 

Global food away D 1902 Dinner 

Global food away D 1903 Other meals and snacks 

Global food away D 1909 Unpriced board and catered affairs 

Global alcoholic 

 beverages 

D 2001 Beer, ale, and alcoholic malt 

Global alcoholic 

 beverages 

D 2002 Distilled spirits at home 

Global alcoholic 

 beverages 

D 2003 Wine at home 

Global alcoholic 

 beverages 

D 2005 Alcoholic beverages away from home 

 I1 2101 Rent of dwelling 

I 2102 Lodging while out of town 

I 2103 Lodging while at school 

I 2201 Owners’ equivalent rent 

I 2202 Household insurance 

I 2301 Property maintenance and repair services 

I 2401 Materials, supplies, equipment for home repairs 

I 2404 Other property maintenance commodities 

I 2501 Fuel oil 

I 2502 Other fuels 

I 2601 Electricity 

I 2602 Utility natural gas service 

I 2701 Telephone services, local charges 

I 2702 Water and sewerage maintenance 

I 2703 Community antenna and cable television 

I 2704 Garbage and trash collection 

I 2705 Interstate telephone services 

I 2706 Intrastate telephone services 

I 2801 Linens, curtains, drapes, sewing materials 

I 2901 Bedroom furniture 

I 2902 Sofas 

I 2903 Living room chairs and tables 

I 2904 Other furniture 

I 3001 Refrigerators and home freezers 

I 3002 Laundry equipment 

I 3003 Stoves, ovens, portable dishwashers, window air 

conditioners 

I 3101 Television sets 

I 3102 Video cassette recorders, disc players, and tapes 

I 3103 Audio components, radios, recordings, and other 

 Diary only3 3109 Unpriced accessories for electronic equipment 

I 3201 Floor/window coverings,outdoor/infant/laundry 

equipment 
Appendix 2: Item Strata (Cont’d.)

Interview Only Interview & Diary Item Strata

X141 items X207 items Code Description of Item Strata

I 3202 Clocks, lamps, and decorator items 

I 3203 Tableware, serving pieces, nonelectric kitchenware 

I 3204 Lawn and garden equipment, tools, hardware 

I 3205 Small kitchen appliances, sewing machines, 

portable heating/cooling equip 

I 3206 Indoor plants and fresh cut flowers 

I 3209 Unpriced household equipment parts, small 

furnishings 

 Diary only 3301 Laundry and cleaning products 

 Diary only 3303 Household paper products, including stationery 

I 3305 Other household products, lawn and garden 

supplies 

 Diary only 3401 Postage 

I 3402 Baby-sitting 

I 3403 Domestic service 

I 3404 Other household services 

I 3406 Appliance and furniture repair 

I 3407 Care of invalids, elderly, and convalescents in the 

home 

I 3409 Unpriced rent/repair of household equipment, sound 

equipment 

I 3501 Tenants’ insurance 

I 3601 Men’s suits, coats, sportcoats, jackets 

I 3603 Men’s furnishings 

I 3604 Men’s shirts 

I 3605 Men’s pants and shorts 

I 3609 Unpriced uniforms and other clothing 

I 3701 Boys’ apparel 

I 3709 Unpriced boys’ uniforms and other clothing 

I 3801 Women’s coats and jackets 

I 3802 Women’s dresses 

I 3803 Women’s separates, sportswear 

I 3804 Women’s underwear, nightwear, accessories 

I 3805 Women’s suits 

I 3809 Unpriced uniforms and other clothing 

I 3901 Girls’ apparel 

I 3909 Unpriced uniforms and other clothing 

I 4001 Men’s footwear 

I 4002 Boys’ and girls’ footwear 

I 4003 Women’s footwear 

I 4101 Infants’ and toddlers’ apparel 

I 4109 Unpriced accessories and other clothing 

I 4201 Sewing materials, notions, luggage 

I 4301 Watches 

I 4302 Jewelry 

I 4401 Other apparel services 

I 4402 Apparel laundry and dry-cleaning, excluding coin 

operated 

I 4501 New cars 
Appendix 2: Item Strata (Cont’d.)

Interview Only Interview & Diary Item Strata

X141 items X207 items Code Description of Item Strata

I 4502 New trucks 

I 4503 New motorcycles 

I 4601 Used cars 

I 4609 Unpriced other used vehicles 

I 4701 Motor fuel 

I 4702 Motor oil, coolant, and other fluids 

I 4801 Tires 

I 4802 Vehicle parts and equipment other than tires 

I 4901 Automotive body work 

I 4902 Automotive drive-train, front-end repair 

I 4903 Automotive maintenance and servicing 

I 4904 Automotive power plant repair 

I 4909 Unpriced automotive repair service policy 

I 5001 Automobile insurance 

I 5101 Automobile finance charges 

I 5109 Unpriced other vehicle finance charges 

I 5201 State and local automobile registration, license, 

inspection 

I 5205 Other automobile-related fees 

I 5209 Unpriced docking and landing fees 

I 5301 Airline fare 

I 5302 Other intercity transportation 

I 5303 Intracity transportation 

I 5309 Unpriced school bus 

I 5401 Prescription drugs and medical supplies 

 Diary only 5502 Nonprescription drugs and medical supplies 

I 5503 Nonprescription medical equipment and supplies 

I 5601 Physicians’ services 

I 5602 Dental services 

I 5603 Eyeglasses and eye care 

I 5604 Services by other medical professionals 

I 5701 Hospital room, in patient 

I 5702 Other in-patient services 

I 5703 Hospital out-patient services 

I 5709 Unpriced rent or repair of medical equipment 

I 5801 Commercial health insurance 

I 5802 Blue Cross/Blue Shield 

I 5803 Health Maintenance Organizations 

I 5804 Other health insurance 

I 5901 Newspapers 

I 5902 Magazines 

 Diary only 5909 Unpriced newsletters 

I 6001 Sports vehicles, including bicycles 

I 6002 Sports equipment 

I 6101 Toys, hobbies, and other entertainment commodities

I 6102 Photographic supplies and equipment 

I 6103 Pets and pet products 

 Diary only 6109 Unpriced souvenirs, fireworks, optic goods 
Appendix 2: Item Strata (Cont’d.)

Interview Only Interview & Diary Item Strata

X141 items X207 items Code Description of Item Strata

I 6201 Club membership dues and fees 

I 6202 Fees for participant sports 

I 6203 Admissions 

I 6204 Fees for lessons or instructions 

I 6205 Photographers, film processing, pet services 

I 6209 Unpriced rental of recreational vehicles 

I 6301 Tobacco and smoking supplies 

I 6309 Unpriced smoking products and accessories 

I 6401 Hair, dental, shaving, miscellaneous personal care 

products 

 Diary only 6403 Cosmetics, bath/nail/make-up preparations and 

implements 

I 6501 Beauty parlor services for females 

I 6502 Haircuts and other barber shop services for males 

I 6509 Unpriced repair of personal care appliances 

I 6601 School books and supplies for college 

I 6602 Reference books and elementary and high school 

books 

I 6609 Unpriced miscellaneous school purchases 

I 6701 College tuition and fees 

I 6702 Elementary and high school tuition and fees 

I 6703 Child daycare, nursery school 

I 6704 Other tuition and fees 

I 6709 Unpriced miscellaneous school items, rentals, and 

other services 

I 6801 Legal fees 

I 6802 Banking and accounting expenses 

I 6803 Cemetery lots and funeral expenses 

 Diary only 6809 Unpriced miscellaneous personal services 

I 6901 Information processing equipment 1

 “I” denotes expenditure is from the Interview. 

2

 “D” denotes detailed expenditure allocated from the Diary to Interview. 3

 “Diary only” denotes that an expenditure amount is imputed for the Interview from the Diary.