BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRICES AND LIVING

CONDITIONS 

 

A Comparison of Income, Expenditures, and Home Market Value Distributions Using 

Luxembourg Income Study Data from the 1990’s 

Eva Sierminska, Georgetown University 

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

 Working Paper 380

 March 2005 

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. 

Bureau of Labor Statistics.
A COMPARISON OF INCOME, EXPENDITURES, AND HOME MARKET 

VALUE DISTRIBUTIONS USING LUXEMBOURG INCOME STUDY DATA 

FROM THE 1990’S 

(Augmented with select data from the U.S. Consumer Expenditure Survey) 

by 

Eva Sierminska1

 and Thesia I. Garner2

August 2002 (revised December 2002) 

This version November 2003 

 

1 Corresponding author: Georgetown University, Center for Population and Health, 309 Healy Hall, 

Washington, DC 20057-1197, tel. 202-687-3234, fax. 202-687-2932, e-mail: 

esiermin@cph.georgetown.edu 

2 U.S. Department of Labor, DPINR, Postal Square Building, Room 3105, 2 Mass. Ave., N.E., Washington, D. 

C.20212, tel.202- 691-6576, fax. 202- 691-6583, e-mail: Garner_T@bls.gov

An earlier version of this paper was presented during the 27th General Conference of the International 

Association for Research in Income and Wealth, Stockholm, Sweden, August 2002, and is Luxembourg 

Income Study Working Paper No. 338, December 2002. 
Abstract 

The purpose of this paper is to review recent data made available through the Luxembourg 

Income Study (LIS) that include expenditures and asset valuations. The LIS data are 

augmented with comparable data from the U.S. Consumer Expenditure Survey. The surveys 

with expenditure data are reviewed in terms of collection units and variable definitions. 

Inequality statistics are produced and compared using income, expenditures, and market 

value of owned home. Rankings of countries by income and expenditure inequality are 

similar but not the same across the countries studied. Suggestions are made for the LIS to 

improve the expenditure data available following the COICOPS framework. 

JEL Classifications: D12 Consumer Economics: Empirical Analysis 

 D3 Distribution 

 D6 Welfare Economics 

Key Words: Distributional analysis, household expenditure surveys, Luxembourg Income 

Study 

Acknowledgement: 

The authors thank Paul Alkemade, David Jesuit, Teresa Munzi and Timothy Smeeding for 

answering questions regarding the Luxembourg Income Study and expenditure data therein. 

Information about other multi-country expenditure databases was provided by Duncan 

Ironmonger, Branko Milanovic, and John Schmitt. Thanks are also extended to Melinda 

Stevenson of the European Commission Delegation in the U.S. and Antonio Puente-Rodero 

of Eurostat, along with Klaas de Vos, about the European Union Household Budget Survey 

Program. Thanks also go to Jonathan Fisher for help with STATA, and Mike Hoke for other 

technical assistance. 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 views of other staff 

therein. The authors accept responsibility for all errors. 
I. Introduction 

 A. Motivation 

In the 2001 Canberra Group report on household income statistics, a consumptionbased approach is followed in defining income. The Group noted, “A positive resource flow 

(in money, goods or services) is considered as contributing towards economic well-being if it 

increases the recipient’s potential to consume or save, and a negative flow reduces well-being 

if it reduces the capacity to consume or save” (p. 4). Income is most often considered to be 

the best (or least bad) measure of this resource flow, proxing individual welfare or utility. 

After stating this, the Group emphasized that both consumption and wealth are important 

complementary measures of economic well-being. The theory is that the more one consumes 

the greater the economic well-being. Consumption reflects how individuals live as opposed 

their potential to consume as better measured by income. However, greater wealth can also 

be viewed as reflecting greater economic well-being. Wealth or accumulation can provide for 

future consumption as well as make it possible for an individual to gain access to credit to 

finance current consumption. 

A guide describing how income, expenditure, and wealth concepts can be brought 

together in an integrated way is presented in the Canberra Report (2001). Much of this work 

was based on discussions during the last Canberra Group meeting in Luxembourg in May 

2000. These discussions revolved around interest in the creation of a study group similar to 

the Canberra Group that would focus on expenditures and net worth, not only for national 

statistics, but also for cross-national comparisons and to serve as guidelines for data 

collection. The importance of data comparability and access were highlighted. However at 

that time, no new study group was formed. Yet, discussions continued in various venues. For 

example, in the autumn of 2001, a meeting of experts convened at the International Labour 

Organization (ILO) to review the current ILO recommendations concerning income and 

consumer expenditures for official statistical office purposes and the collection of these data 

(Young (2001) & ILO (2003)). The final product of this group is to be a guide on household 

budget surveys; however, accessibility to neither micro level data nor comparability is aims 

of this study. From these and related meetings and discussions, it appears that the wish, 

hope, and dream of many researchers are that multi-country comparable data are available for 
expenditures and wealth, as data are currently available for income. This idea has also been 

supported for the U.S. in particular. Nordhaus (2002) recommended that the U.S. Bureau of 

Economic Analysis “should develop a full set of linked national economic accounts that 

include production, income, consumption, accumulation, and wealth” (p. 17). The Australia 

Bureau of Statistics (McLennan (1995)) has also published a report which provides a 

framework of household, income, consumption, saving and wealth. 

At the present time, there exist several projects that include cross-national data from 

household expenditure surveys we focus on three of these. Data access is a goal of each. 

Data comparability is a goal, achieved or aimed for, for two. The three projects include the 

HEIDE data base created by researchers working with the World Bank (created by Ackland 

et al. 1997 and under the direction of Milanovic 2002), the Multinational Household 

Expenditure Study (MHES) project out of Australia (Ironmonger (2002)), and the 

Luxembourg Income Study (LIS). 

The HEIDE data base includes data from several transition countries for only one 

year in each country’s transition period, and has been made as comparable as possible using a 

format created by the World Bank team. Data are available for Rural Armenia, Urban 

Armenia (Yerevan), Bulgaria, Estonia, Hungary, Kyrgyz Republic, Poland, Russia, and 

Slovakia in the 1997 data file. Latvia has since been added using the same structure. Data 

files are accessible through the internet (http://worldbank.org/research/inequality/data.htm). 

The MHES was founded to serve the needs of researchers world-wide interested in 

money expenditures by households. To meet this goal, the project provides available micro 

level household survey data from four developed countries (i.e., Australia, Canada, the U.K. 

and the U.S.). Several years of data can be accessed for each country. An aim of the project is 

to include such data from many countries. The data are those that country statistical offices 

make available for use by researchers and are quite detailed in nature. The principal research 

center is located at the University of Melbourne, Australia, under the direction of Duncan 

Ironmonger. Future subsidiary researcher centers of the MHES will be located in Colchester 

and Halifax, and will be managed by co-directors Jonathan Gershuny and Andy Harvey, 

respectively. To gain access to the data, researchers must apply for associate status Once 

researchers have become research associates of the MHES project, micro level data files are 
sent to the researchers on CD ROM. 

The Luxembourg Income Study (LIS) includes micro level household expenditure 

data for 23 countries and some for several years. Detailed expenditure data are not available, 

only total aggregate expenditures for each household and/or for specific commodity 

aggregates like housing, transportation, or child care. Not all countries provide the same level 

of detail. Like for the MHES, researchers must apply to use the LIS data. Once the 

researcher has been granted access to LIS, computer statistical programs can be sent by email 

to the LIS and run using the data in the LIS files. Results are returned by email. Direct data 

access is not provided. 

Evaluating each of these projects regarding data access and comparability across 

countries and across time for expenditure and wealth data, none of the three projects meets 

all of these objectives. The HEIDE project is the only once of the three that includes 

comparable household expenditure survey micro level data, but for transition countries only, 

and for only one year. Variables indicating the ownership of durable goods are in the HEIDE 

data files as well as information on whether the household owns a store, etc. or is selfemployed. Although the MHES data in current form are not comparable, some preliminary 

work has been done to make them as comparable as possible using the Classification of 

Individual Consumption by Purpose (COICOP) system3 (Ironmonger (2002)), however an 

ultimate goal of the project is that the data will be comparable across the countries. The 

variables indicating ownership of durable goods are included in the individual country data 

as is information about self-employment. The value of one’s owned home is also included in 

most files. A drawback of the MHES is that only four countries are currently included and 

again the data are not comparable across countries. 

The LIS includes data for the largest number of countries when compared to the other 

two projects and the data are easily accessible once contact with LIS has been made. 

Expenditures are available for more countries than are wealth data. For now, the only wealth 

data available are the value of owned home for the most part. Another advantage of the LIS 

 

3 COICOP is part of the 1993 System of National Accounts. Eurostat uses this system in their production 

of harmonized expenditure tabulations. Smith and Schmidt (2001, 2002) are applying COICOP to U.S. 

Consumer Price Index categories and the U.S. Consumer Expenditure Survey. 
is that many researchers around the world are familiar with the LIS through their use of the 

income files. Although LIS does not require expenditure or asset valuations be submitted as 

part of the income data file, country representatives to the LIS are encouraged to provide 

these as part of the data file if they are available. The LIS has done a fine job making income 

data fairly comparable cross-nationally. However, a major drawback of the LIS for 

expenditure analysis is that no attempt has been made to make these data comparable across 

country data sets. The wealth variable, value of owned home, appears to be quite comparable 

across the country data sets. 

The foci of this study are the LIS data. These data were selected for examination and 

analysis due to their general accessibility and the large number of countries represented with 

household expenditure and wealth data. However, as we show in this paper, the information 

about the LIS expenditure data are lacking in several important ways. First and foremost 

they are lacking in terms of comparability, but also in terms of the information about the data 

included on the LIS web site. In this study, we examine recent household expenditure and 

home value data in the LIS. Then we use these data in inequality analysis. Finally we make 

recommendations for improving the LIS expenditure and wealth data if they continue to be a 

part of the LIS “family” of products. The approach taken in this study is strictly empirical. 

Included in the discussion and implications for future work are issues regarding the use of 

expenditure data for economic well-being analysis with a review of concepts and definitions. 

For this study, no attempt is made to explain differences across the countries in other than 

variable definitional terms. As far as we know, this is the first examination of the household 

expenditure data in the LIS. 

B. Outline of Paper 

 The remainder of the paper in divided into several sections. In section two we 

describe the methods including the data and analysis measures. Next, we present the results 

for inequality and welfare. In section four, a review of issues to consider when creating a set 

of comparable expenditure data files are outlined with recommendations for LIS with regard 

to expenditure and wealth data. 
C. Other Related Work 

While the Canberra Group was focusing its attention primarily on income in their 

meetings from 1996 to 2000, other researchers had been and still are focusing on 

expenditures as a measure of economic well-being cross-nationally. For a list of projects 

using expenditure data see Appendix A.Their focus has been comparability across countries 

and data access. In another World Bank project, the Living Standards Measurement Survey 

(LSMS), a fairly comparable survey instrument has been sued to collect expenditure, as well 

as other, data from households in many countries, mostly in developing countries and some 

transition countries. Although many of these data are comparable, not all are available to 

outside researchers (World Bank (2002)). Some problems that have been outlined recently as 

well as a review of past critiques can be found in Pyatt (2003). 

The World Bank and Inter-American Development Bank are coordinating a project 

known as the household survey initiative. The project is named Improvement of Surveys and 

the Measurement of Living Conditions in Latin America and the Caribbean (MECOVI). 

Within this project is an initiative “to develop an organized, documented, and standardized 

data bank composed by household surveys executed within the ISLC/MDCOVI Program and 

by household surveys executed independently from this program.” The MECOVI web site, 

and the Household Surveys Data Bank Initiative can be found at 

http://www.iadb.org/sds/POV/site_19_e.htm. 

Eurostat has made a major contribution to the issue of data comparability. That 

organization provides guidelines and technical support to European Union (EU) member 

countries to collect harmonized household expenditure data, but does not conduct its own 

survey across all countries. Rather, individual countries continue to conduct their own 

household surveys, as before the harmonization effort began. The harmonized data are made 

available by Eurostat in tabulated form (Wirtz (2002)). For micro-level data, researchers need 

to obtain data from the individual member countries’ statistical offices or by working with 

others that have access to these data. 

Household expenditures are also being used by an international team of researchers 
participating in a broader project, the Demand Patterns and Employment Growth 

(DEMPATEM) project funded by the European Commission Directorate General for 

Employment and Social Affairs (Schmitt (2002a, 2002b)). Two primary goals of the study 

are: to produce basic aggregated tables of household consumption patterns across a range of 

consumption categories and types of households, and to use the data for detailed, 

internationally comparable, modeling of household-level consumption. The DEMPATEM 

project design is to be consistent as possible across the countries. Categories of consumption 

have been identified. A challenge for the team is that some of the household survey data do 

not include information on consumption, only expenditures. Household survey data for 1997 

from five European countries (i.e., France, Germany, the Netherlands, Spain, and the United 

Kingdom) plus the U.S. are being used (Schmitt (2002b)). Whether the resulting comparable 

data will be made available to researchers at large is not known. 

II. Methodology and Data 

 Income, expenditure, and market value of owned home is analyzed. Data are from 

the Luxembourg Income study (LIS) with additional data from the U.S. Consumer 

Expenditure Survey. Basic statistical analyses are conducted, along with inequality and 

welfare analyses. The primary objective is to compare income and expenditure well-being 

across countries. Total expenditures are first considered followed by an examination of 

housing and food expenditures as these represent two of the primary commodities in the 

budgets of most households and for living in general. Total expenditures net of those for 

housing are also examined. 

A. Methodology and Measurement 

A common approach used in the analysis of the economic well-being of a household is 

measuring their standard of living. The level of consumption or income, and their 

distribution over households is an indication of households’ well-being and their standard of 

living to a certain extent. Measuring living standards can be done in a straightforward 

manner by providing single measures. The theoretical approach behind the use of summary 

measures comes from the use of social welfare functions outlined in Atkinson (1970) and 

summarized in Deaton (2000). Following their approach, we describe the basic framework in 

the analysis of social welfare that leads us to the interpretation of summary measures used in 
this paper. 

Assume x is our measure of living standards, then 

 ) ( , .... 1 2 n W = F x x x -- 

will be the value of social welfare, where 0 ' F > and n is the population size. W can be 

thought of as a summary measures that translates a distribution into a single number that 

provides information regarding welfare and its distribution. Social welfare functions can be 

transferred into a measure of inequality. For example, 

W = μ *(1− In), where μ is the mean of x and In is the measure of inequality. In this 

case several properties will hold. Besides satisfying the Pigou-Dalton condition (transfer 

from poor to rich should increase the inequality measure), they are invariant to population 

replication and permutations—social welfare depends on welfare levels in the society, not on 

who has which welfare level--(symmetry or anonymity), and are mean independent (do not 

change due to scalar multiplication). The coefficient of variation, the Gini coefficient and the 

Theil measure are among some of the measures that belong to this family. The social welfare 

approach to inequality measurement is important, because it precludes us from making a 

judgment regarding welfare based on inequality measures alone. 

For the statistical analysis, means and medians are produced (results not shown). The 

distributional/inequality analyses include the production of Lorenz curves (not shown), decile 

ratios (not shown), and summary measures of inequality including the Gini and three Theil 

measures, and the decile ratio. The decile ratio is used to place less emphasis on the extremes 

of the distributions. The Gini and Theil measures are used as checks on each other as the 

measures differ in their sensitivity to variations in income, expenditure, or market value in 

different parts of the distributions. The Gini is most sensitive around the mode. The Theil 

mean log-deviation is most sensitive to variations at the lower end of the distribution. The 

Theil entropy and half the coefficient of variation squared are more sensitive to variations in 

the upper end with the latter measure the more sensitive of the two. Welfare is examined 

using Generalized Lorenz Curves (results not shown) and Sen’s Welfare Index 

(SWI=mean*(1-Gini)) for the countries for which the PPP conversion is made.
B. Data Sources 

 The Luxembourg Income Study (LIS) is described in terms of coverage and data 

access. Specific LIS data used for this study are described in detail along with the criteria for 

selection. The U.S. Consumer Expenditure Survey (CE) Interview is used for the expenditure 

and home market value analyses. A brief description of this survey is included for 

comparison to the surveys covered in the LIS. 

 Multi-country income, expenditures, and home market values are currently available 

through the Luxembourg Income Study (LIS). Currently household micro-data from 29 

countries are included in the LIS, many with several years of data, all with income data but 

not all with expenditure or market value data. For the most part, national statistical office 

surveys or research institute data bases serve as the original sources of the data. Country 

representative and LIS staff work together to make the income and demographic data as 

comparable across countries as possible. To use the LIS data base, a researcher must sign a 

pledge of confidentiality and promise to submit any papers resulting from using these data to 

the LIS working paper series. The raw data cannot be directly accessed. Computer code to 

access the data is sent to Luxembourg via the internet and statistical output is sent back to the 

user. 

 As noted earlier, unlike for income, LIS does not provide guidelines regarding 

the definition of expenditures or market value. LIS takes whatever the countries provide and 

includes value for these variables in the country files. Expenditures and market value of 

one’s owned residence are included in many of the LIS data files. Twenty-three countries 

have thus far provided data for at least one expenditure category with 58 data files in all, and 

19 have provided market value data with 32 data files. The availability of expenditure and 

market value data for the LIS based countries is noted in Table 1. As noted in the table some 

data became available since the analysis was completed/ The U.S. data file in the LIS is from 

the Current Population Survey (CPS) and only includes income. To examine expenditures for 

the U.S., the basic LIS data file is augmented with expenditure and market value data from 

the U.S. Consumer Expenditure Survey (CE). With regard to data selection for this study, the 

criteria were that the data are from the 1990s and that expenditure data are included. 
Up to eight categories of expenditures are included in a single LIS data file. The 

market value of property may also be included. Expenditure categories include total, food, 

housing, apparel/clothing, transportation, medical out-of-pocket, education, and child care 

expenditures. The market value for owned residences and all property are asset variables. 

Table 2 lists the countries and years for which specific expenditure and/or market value data 

are available. As seen in Table 2 and summarized in Table 3, not all countries provide total 

expenditure or market value data. Switzerland is the only country that provides the market 

value of all owned property. All the others with market value data refer strictly to owned 

residence. Five countries, Hungary, Israel, Italy, Spain and R.O.C. Taiwan include data for 

total expenditures and market value of owned residence over several years represented by 10 

data sets. Recently, (November, 2003) Russia joined this group and additional datasets 

became available bringing the total to 13. 

 In the LIS, Mexico has provided the most extensive expenditure data with 

expenditures for all the categories in the last three of six waves. (Since country data is 

available for different years across countries, throughout the paper we will refer to them as 

waves). Childcare expenditures are included in only the last three survey years. The R.O.C. 

Taiwan includes expenditure data for four waves. France, Germany, Poland and the United 

Kingdom have provided expenditure data for three waves. The other countries have 

provided expenditure data for one or two waves only. 

 For this study, as noted earlier, we selected countries with data primarily from 

the mid- to later 1990’s in order to have the most recent expenditure data available. The most 

recent data are for Mexico (1998) and the oldest data are for Spain (1990). The analytical 

investigation is restricted to countries in the LIS with a minimum of disposal personal 

income (referred to as income from henceforth), total expenditure, and housing expenditure 

data (this excluded Italy from the analysis). Housing expenditures are primary to this analysis 

as owner-occupied housing is treated differently in the country data files. We wanted to be 

able to control for this difference in the analysis. Wealth data are not available in the LIS (but 

may become available in a few years with the launch of a new project-the Luxembourg 

Wealth Study), but the market value of owned home is. While the market value provides us 

with no information concerning home equity (defined as the market value of the primary 
residence less any outstanding mortgage debt of the property), we use market value as an 

indication of potential wealth. Wealth can be used to finance consumption as noted earlier.

For a discussion of the role and wealth and consumption, see for example Carroll and 

Samwick (1997), Attanzsio (1998), and Wolff (2002). For a discussion on measuring wealth, 

see Juster, Smith, and Stafford (1999), Juster, Lupton, and Cao (2002) and Kennickell, StarrMcCluer, and Suden (1997). Most of the data with expenditure information are from 

household budget or expenditure surveys. As a result, we applied the housing expenditure 

restriction since the definition of housing is sufficiently different across surveys. In some 

cases owner-occupied housing is valued in terms of the value of the flow of services using 

some type of imputed rents, while in other actual spending outlays are used to define 

housing. (Based on the definitions in the LIS, it appears that food is also defined rather 

differently but the differences are likely minor relative to those for housing.) Total 

expenditures are examined with and without housing expenditures. 

 Table 4 lists the data sources for this study, including those from LIS and the augment 

U.S. expenditure file. The U.S. CE data are collected from April 1997 through March 1998 

and refer primarily to expenditures made in 1997 (three month prior to the interview 

reference periods are used for most expenditure items). Data from 2001 are available from 

the CE but 1997 data were selected for the study to compare expenditure and income results. 

As noted earlier, the more recent U.S. income data in the LIS are from the 1997 Current 

Population Survey. 

 The CE Quarterly Interview is sponsored by the Bureau of Labor Statistics (BLS), 

U.S. Department of Labor and is used to collect data over a 12-month period with a personal 

interview being conducted once every three months. The Interview is designed to obtain data 

on the types of expenditures consumer units are expected to recall for a period for three 

months or longer. These include relatively large expenditures and those that occur on a fairly 

regular basis. The Interview survey also obtains data on expenditures incurred on trips. The 

survey collects detailed data on an estimated 60 to 70 percent of total household 

expenditures. Global estimates are obtained for food and other selected items, accounting for 

an additional 20-25 percent of total expenditures. Along with expenditure data, demographic, 

income and net worth information about the consumer unit is collected. For this analysis, 
expenditures are annualized by multiplying the quarterly expenditure data by four. Income 

data collected in the survey are collected to refer to the previous 12 months from the 

beginning of the month of the survey. Market values of the owned home are as of the time of 

the interview. When data are missing, values are imputed for expenditures and sometimes for 

demographics but no imputation is applied to the income data. The value of owned residence 

is imputed when missing. Flags are included in the data file indicating the type of missing 

value and the imputation procedure used. For this study, internal BLS data are used although 

the data are available in public use form with restrictions for confidentiality imposed (e.g., 

top coding). 

C. The LIS “Position” with Regard to Expenditure Data 

An important point to be made is that, unlike income data, the expenditure data are 

not comparable across the countries in the LIS. According to the web site (Variable 

Definition file), 

These [expenditure] variables have taken a backseat to income. If they are 

present on the file [submitted for comparable income data] or if there are 

components available for making them, LIS includes them. If the original file 

doesn’t have a “total expenditures” variable or doesn’t have clear schemata of 

the component variables, then LIS doesn’t provide “total expenditure”. 

This means that different definitions of expenditures can result for the LIS country files. In 

some cases consumption expenditures are provided and in others expenditures as outlays of 

spending are provided. This is important to note as research has found that seemingly similar, 

but in fact varying definitions of expenditures may give rise to differences in poverty 

measures when there are no real differences in well-being (See Lanjouw and Lanjouw (2001) 

for an example and suggestions for alleviating or minimizing this problem, also see Gibson, 

et al (2003)).The market value of the household’s primary residence is in the LIS and appears 

to be comparable across the countries. For this study, an attempt is made to make U.S. CE 

household expenditures comparable using one of the LIS expenditure definitions, outlays. 

 The most recent country expenditure data are available for the mid-1990’s with one 
exception. The most recent Spanish data are for 1990. The expenditure and income data that 

we use from the LIS are for the following nine countries: France, Hungary, Israel, Mexico, 

Poland, R.O.C. Taiwan, Russia, Spain, and the United Kingdom (UK). Among these, the 

market value of owned home is available for Hungary, Israel, and Spain. As noted earlier, for 

the U.S. the income data are from the LIS and expenditure and market value data are from 

the CE. 

D. Analysis Variables 

 The definitions given in the LIS data files are used as a starting point for the analyses. 

For the analysis of income, we use disposable person income (DPI) as defined in the LIS. 

This variable is assumed consistently defined by LIS across all the countries. DPI is defined 

as gross income minus taxes and mandatory contributions. Near cash benefits are included. 

These include all forms of transfers that are, in a strict sense, in-kind payments (i.e., they are 

tied to a specific requirement such as school attendance) but have a cash equivalent value 

equal or nearly equal to the market value, including near cash housing benefits. In-kind 

earnings are not included in DPI. These would include home production or in-kind income 

as a substitute for income, and would only be food commodities, homegrown food, board, or 

housing received as pay. Also not included in DPI are employer luncheon vouchers, 

education vouchers, medical benefits, etc. as these are counted as voluntary supplements to 

cash wages. 

 For the U.S., an additional income analysis is conducted using the CE data. For this 

analysis, disposable personal income is used, defined as FINCATAX in the CE data file. The 

only near cash benefit in CE income is the cash value of food stamps. The CE income 

analysis is restricted to what the BLS refers to as complete income reporters. The additional 

analysis is conducted since researchers using the CE data often restrict their samples to only 

include complete income reporters when examining expenditures for relative to incomes and 

when including income as an explanatory variable in expenditure models. 

 According to the BLS, the distinction between complete and incomplete income 

reporters is based, in general, on whether the respondent provides values for major sources of 

income, such as wages and salaries, self-employment income, and Social Security income. 

Even complete income reporters may not provide a full accounting of all income from all 
sources. The current practice within the BLS is to designate as invalid, across the board zero 

income reporting, and count this consumer unit as an incomplete income reporter. About 81 

percent of sampled consumer units in the 1997 data file are considered complete income 

reporters. (We further restrict the complete income reporters for the inequality analysis to 

consumer units with positive values of income. About 80 percent of all households are 

represented.) Using data from an earlier year, Garner and Blanciforti (1994) examined CE 

income data to determine how well the BLS definition of complete income reporter reflects 

income completeness in the survey. Using 1987 CE Interview data, approximately 87 

percent of all consumer units were complete income reporters using the BLS definition but 

only 69 percent were complete income reporters when a stricter definition of completeness 

was applied. For the stricter definition, a consumer unit was a complete reporter only when 

incomes across all consumer unit members were coded with valid responses only, and at least 

one non-zero amount was included among the set of valid responses. Another requirement 

was that for income that is not member specific, (e.g., unemployment compensation, public 

assistance or welfare, interest income) consumer unit level information also had to have valid 

responses for the consumer unit to be considered a complete income reporter. It is expected 

that income report completeness is even more of a problem with the more recent data than 

the earlier data and one should be cautious in restricting any CE sample to complete income 

reporters only and then make statements about the total population. Some researchers have 

attempted to deal with this through population ratio adjustment. 

 For some countries, the market value of owned residence definition was provided in 

the LIS information on the web site but in most cases it was not. The variable is defined in 

the CE data file with the following question: “About how much do you think this property 

would sell for on today’s market?” When not specified in the LIS, we assume that the market 

value of owned home is self-explanatory and that market value of owned residence means 

just that. Flags are included in the data file to indicate if imputation is used. 

 The definitions of the expenditure variables differ across surveys in the LIS and for 

the CE. Table 5 includes for each country in our study, the year of the survey and the variable 

definitions or other information available in the LIS. At this time we have made no attempt 

to contact the individuals who provided the LIS expenditure data for more specific variable 
definitions. As seen from the table, the most complete definitions are for total expenditures 

with less complete for housing and food. Although the variables are present in the LIS file, 

virtually no definitions are given for apparel/clothing, transportation, medical out-of-pocket, 

education, or childcare. Summary variables available in the U.S. CE data file are identified 

for most of the categories. When summary variables are not available, they can be created 

from detailed data such as for childcare. Flags do not exist in the LIS data file so we are not 

sure if the data are as reported or if some of the data were imputed. For the CE, hot deck 

imputation was used when expenditure data were missing. 

 When defined in the LIS, expenditures are identified as ‘spending’ or ‘consumption 

expenditures’. Spending appears to refer to outlays or out-of-pocket expenditures. 

Consumption expenditures include imputed rents for at least owned housing. It is not clear 

from the data documentation if the costs associated with owning a home are included in total 

or housing expenditures. To avoid double counting, these should not be counted when a 

consumption approach is used. For Israel, imputed values are included for owned vehicles. 

Based on the LIS documentation, ‘total consumption expenditures’ are available for Israel, 

R.O.C. Taiwan, and with some modifications, for Spain. For all other countries, spending or 

outlays-defined-expenditures are used. With regard to Spain, the total expenditure definition 

indicates that the following are excluded: self-provision, self-sufficiency, in-kind salaries, 

and imputed rents. However, the housing expenditure definition includes imputed rents for 

owner-occupants but it is not clear how housing expenditures for owners would be subtracted 

from the total to create a total consumption expenditure definition. As detailed data are 

available from the U.S. CE, different definitions of expenditures are possible. However, for 

this study, we use an outlays definition. A consumption-based approach could have been 

applied by replacing the expenditures associated with owning a home with the reported rental 

equivalence in the data file. 

Table 6 includes a list of the countries and years for which we conduct our analysis. 

LIS or CE variables names are noted. For most of the data sources, a zero value could 

represent actual zero income, expenditures, or market values, or a zero could represent a 

missing value. Zero and missing values are distinguishable in the Hungarian file only. In the 

U.S. CE data, all cases have positive values for total expenditures. Zeros are possible for the 
sub-components given that these were constructed as sums of monthly valuations in the CE 

data file. Missing values and zeroes can be distinguished in the underlying data by the flags. 

 The footnote to the table denotes which data sources and variables include zeros, missing, 

and negative values. 

Original LIS samples sizes and sample sizes for the income, expenditure, and market 

value analyses are presented in Table 7 for each of the data sets. Since we were unable to 

distinguish zeroes from missing values for most data sets, we restricted the data such that all 

cases with zero values were dropped. This is the same procedure followed by LIS. The 

procedure was done separately for income, expenditures, and market value using the LIS 

data. For the CE, the same sample is used for all the expenditure analyses. For the income 

analysis, no cases were dropped from the original sample for Israel or R.O.C. Taiwan. All 

other countries experience slight drops in sample size with 96 percent or more of all cases 

with non-zero income values. Cases were dropped for the expenditure analysis due to zero or 

missing values for total or housing expenditures, and for food expenditures when the data 

were available. From 97 to almost a 100 percent of the original sample cases have usable 

expenditure data. The exceptions are France and Russia. About 85 percent of the original 

sample has non-zero or non-missing values for expenditures for France, while only 68 

percent for Russia. The largest reductions for these countries are because zero or missing 

values are present for housing expenditures. Since these two countries appear to use an 

outlays approach to define expenditures, it is possible that these households do not have 

housing expenditures and own their homes outright without additional related housing 

expenditures. This issue requires further investigation. 

 For several of the LIS countries, the sum of the expenditure component values is 

greater than the total expenditure (TOTEXP) value. This occurs for Hungary, Mexico, 

Poland, Spain, and the United Kingdom. When this happened we replaced the total in the 

data file with the sum of the components for the total before conducting the statistical 

analysis. All other values remained the same. The countries for which this resulted are listed 

in Table 8 with the number of cases affected. The Spanish data file is most affected with 

18.3 percent of the total expenditure values replaced by the sums. As noted earlier, the 

difference in the total and sum for Spain appears to be related to the treatment of owner-
occupied housing in the total versus housing expenditure variables. The housing expenditure 

includes an imputed value for service flows from owner-occupied housing. This is excluded 

in the total variable. The documentation for the other countries is not sufficient to 

hypothesize the reason for the difference between the sum and total expenditures in the file. 

 Market values were available for four of the data sets: Hungary, Israel, Spain and the 

U.S.-CE. For Spain and the U.S. market values were only reported for owners and the values 

were greater than zero. Values were also only reported for homeowners in the Hungarian data 

file, however, a small percentage of cases were dropped as market values were recorded as 

zero. For Israel, market values were recorded for both renters and owners; a large percentage 

of the market values are zeroes. These could refer to renters, but again; zero values were 

dropped for the analysis. 

E. Time Period 

The time period for which the data refer are noted in the tables for each country. For 

the U.S. CE Interview, expenditure data are collected quarterly but are annualized for this 

study, as noted earlier. Each quarter of data is assumed to be independent, as is done for 

official BLS publications. For the other countries, expenditure as well as income data may 

be collected weekly and then annualized or collected for two weeks but with reference 

periods of a year, or the data may be collected with longer or shorter reference periods. LIS 

has made the best effort so the wave years refer as closely as possible to the reference period 

and not the collection period. For the analysis, all variable values have been converted to 

annual values by the country representatives, LIS staff, or us. 

F. Analysis Unit 

The unit of analysis is important since the primary concern of welfare analysis is the 

individual. However, since individuals do not function in a vacuum, household or family data 

are most often used in assessing the well-being of individuals. For this study, survey units 

are defined as households or consuming units for the countries in this study. For all but the 

U.S. income file from the CPS, the household is basically defined as a group of people who 

share expenditures in some way. Specific definitions are provided in the LIS data base for all 

of the countries expect for France, Russia, and Mexico. The U.S. CPS household is based on 
whether household members live and eat together. The household definitions are presented in 

the Appendix. A consumer unit rather than a household is the survey unit for the U.S. CE 

definition. The definition of a consumer unit for the U.S. is quite similar to a household as 

defined for the LIS surveys other than the CPS. For the U.S. CE, members must share some 

expenditures. Persons living away from home at the time of the interview are treated 

differently in the different surveys. Whether domestics are counted among household 

members also differs by country. 

G. Population Coverage 

For all but Israel, the total country is covered by the survey. Only urban localities are 

sampled in Israel. The national non-institutionalized population is covered for each country. 

H. Adjustment for Household Size 

For the analysis, we make a adjustments to household income, expenditures, and 

market values to account for the differences in adults and children and household size using 

an equivalence scale. The household welfare measures are obtained by dividing household 

values by the square root of family size (Ruggles (1990)). This is the scale often used with 

the LIS data (see, for example, Atkinson, Rainwater, and Smeeding (1995); Buhmann et al. 

(1988)). For a discussion on equivalence scales, see Citro and Michael, ed. (1995). 

I. Population Weighting 

For the analysis, population distributions are analyzed. Person weights are used to 

produce the population distributions, the focus of the analysis. 

J. Currency Unit Conversion 

For the welfare analysis, final consumption expenditure price indexes and Purchasing 

Power Parities (PPPs) for consumption are applied to income, expenditures, and market 

value to convert the national currencies into U.S. dollars for 1996. Specifically, country year 

data was divided by the country PPP to eliminate the differences in price levels between the 

country and the United States (U.S. PPP=1). Then a price index was used to obtain U.S. 1996 

dollars (PI(96)). The data sources for these are OECD publications (1999, 2001). Price 

index data are not available in these publications for Israel, R.O.C. Taiwan, or Russia. In 

addition, PPPs are not available for R.O.C. Taiwan. PPP information for the three countries 
is available from the Penn World Tables 6.1 (Heston et al (2002)). 

K. Treatment of Outliers for Income Analysis 

Researchers using the LIS often use a procedure to adjust for outliers in the income 

distributions when the summary measures are used. The procedure is described in Gottschalk 

and Smeeding (1997). Only for the income analysis are outliers recoded. This is done so that 

the results can be compared to those of other researchers who have used the LIS data for 

income inequality analyses. Values below the one percentile value of the income 

distributions are recoded to equal one percent of the equivalized mean. Values above the 99th

percentile value are recoded to equal 10 times the unequivalized median. 

III. Empirical Results 

 A. Inequality Results 

In Figures 1 and 2, we present rankings of the countries in our study by income and 

expenditure distributions. Table 9 summaries results from Tables 10 through 12 with 

rankings of countries using the Gini coefficient. Other statistics are shown in Tables 10 

through 12 and are discussed later in this section. The summary table includes the countries 

ranked by inequality in disposable personal income, total expenditures, total net of housing 

expenditures, housing and food expenditures, and for those countries with data, by market 

value of owned residence. For the U.S., results are presented for income using the CPS LIS 

data and data for complete income reporters as defined in the CE and for the full CE 

weighted sample. In general, countries with more equal distributions along one dimension 

also have more equal distributions along the other dimensions. However, there are 

exceptions and these are noted below. Income and total expenditures and income and total 

expenditures net of housing are discussed first followed by housing and food expenditures, 

and then market values. 

A.1 Income, Total Expenditures, and Total Expenditures Net of Housing

The Gini coefficient is used for ranking the countries in Figures 1a and 1b for DPI 

and total expenditures and Figures 2a and 2b for DPI and total expenditures net of housing. 

Summary rankings are presented for these three measures in Table 9. Details for income are 
presented in Tables 10a and 10b. Details for total expenditures and total expenditures net of 

housing are presented in Tables 11a and 11b. As seen in Figures 1a and 1b and the tables, 

R.O.C. Taiwan exhibits the greatest equality and Mexico the least when DPI and total 

expenditures are examined. Russia and the U.S. follow as the countries with the next most 

unequal distributions of income. Russia also follows as having the next most unequal 

distribution of total expenditures. The U.S. ranking relative to the other countries is the same 

regardless if the CPS data from the LIS are used or if after tax income for complete income 

reporters or for the full CE sample is used. However, the U.S. CPS income distribution from 

the LIS is more equal than the income distribution using CE data. The United Kingdom 

remains in the sixth most equal position with both income and expenditures. All the other 

countries’ rankings change as one moves from using income to total expenditures (also see 

Table 9). All of the remaining countries did not move by more than two places with the 

exception of Poland. Poland has the seventh most equal income distribution but is third in 

the total expenditure ranking. 

In every case but Spain, income inequality is greater than total expenditure inequality; 

however, the difference is marginal (income Gini=0.302 and expenditure Gini=0.303) but 

statistically significant. The difference between income and expenditure inequality for France 

is also only marginal but again the difference is statistically significant. 

The finding that income inequality is greater than expenditure (and in some cases 

consumption) inequality has been reported by researchers examining data from several 

countries. For example, Johnson and Shipp (1997) reported this relationship for the U.S., 

Pendakur (1998) for Canada, and Barret, Crossley, and Worswich (2000) for Australia. 

Wodon (1999) reported the relationship for Bangladesh and Deaton and Paxton (1994) for 

Taiwan. In contrast, researchers have reported income inequality to be less than expenditure 

inequality for Portugal (Gouveia and Tavares (1995)), Spain (Sastre (1999)), and the United 

Kingdom (Deaton and Paxson (1994), Goodman and Webb (1995)). Unlike earlier studies 

using United Kingdom data (e.g., Deaton and Paxson (1994), Goodman and Webb (1996)), 

the results of this study show income inequality to be greater than expenditure inequality. 

Differences across the studies and their rankings relative to each other are likely related to 

differences in the definition of expenditures but could also be related to the equivalence scale 
and inequality measure (see Garner, Sastre, and Ruiz-Castillo (2002)). 

When expenditures do not include those for housing, the expenditure rankings mostly 

remain the same as for total expenditures. The main exceptions are Poland, Hungary, and 

Israel (see Figures 2a and 2b, and Tables 9, 11a, and 11b). Poland’s expenditure distribution 

of these expenditures is more equal than that of Hungary. The reverse is the case with total 

expenditures. Israeli total expenditures net of housing are more unequal than those of the 

U.S. 

A.2 Disposable Personal Income 

 Table 10a and 10b include more detailed income inequality statistics for the 

countries. Results in Table 10a are for the full income sample. Table 10b includes the results 

based on adjusting the data for outliers using the procedure of Gottschalk and Smeeding 

(1997). The rankings across countries using the different measures remain essentially the 

same with and without top and bottom coding with one notable exception. The exception is 

that now Poland has now become fourth among the countries with the most equal income 

distribution from its earlier rank of seventh. 

 For all the countries, recoding the income outliers makes income more equal when 

measured by all the other summary inequality statistics with the exception of the mean logdeviation. Recoding results in a 66 percent increase in the mean log deviation index for 

Poland and 8.6 percent increase for the United Kingdom. The US CPS based mean logdeviation measure increased by less than one percent. These results might suggest that 

relatively more observations in the lower end of the countries’ distributions are being 

recoded and thus are becoming more concentrated than those in the lower ends of the 

distributions of the other countries. 

A.3 Housing Expenditures

 Housing expenditure inequality statistics are presented in Table 11c and summarized 

in Table 9. As seen in these tables, it is clear that when the well-being of a country is based 

on housing expenditures alone, ranking can vary dramatically. For example, using the 

ranking based on the Gini coefficient, these results reveal that the most equal distribution of 

housing expenditures are for Israel, following by Hungary and R.O.C. Taiwan, countries with 
equal Gini indexes (G=0.306). When disposable income, total expenditures, and total 

expenditures net of housing are used as the well-being measures, Israel ranked among the 

countries with greater inequality. Taiwan almost holds it position as having the most equal 

distribution of expenditures regardless of the measure used. The most unequal housing 

expenditure distributions are found for Mexico followed by Poland and Russia. The 

differences across countries with regard to housing expenditure inequality are likely related 

not only to differences across countries in their owned versus rental markets, but also how 

housing expenditures are defined. When imputed rents are included for owner occupied 

housing as opposed to out-of-pocket expenditures, differences in inequality would be 

expected within a country and across countries in terms of their relative rankings. 

A4. Food Expenditures 

 Again, Taiwan exhibits the most equal distribution when based on food expenditures 

(see Table 11d for specifics and Table 9 for a summary). Next come Poland and Spain. 

When it comes to food expenditures, Russia has the most unequal distribution followed by 

Mexico The remaining country results are similar to those for total expenditures with the 

exception of Hungary with fairly unequal food expenditures. Differences across countries 

may result depending on whether the value of home consumption for own consumption is 

included in the food expenditure. When these are included, food expenditure inequality is 

expected to be less than then they are not included. 

A5. Market Value of Residence 

Market value of owned residences is available for only four of the countries in our 

study: Spain, Hungary, Israel, and for the United States using the CE (Table 12). For the CE, 

two sets of results are presented, first for the full sample and then for the complete income 

reporters. The complete income reporter results are presented so that they can be compared to 

the complete income report results for income and expenditures. For the market value 

inequality analysis, only those observations with positive values are included. As noted 

earlier, market value is used here to assess potential housing wealth. With greater wealth, 

households can often borrow for consumption. Home equity data would be needed to 

determine the actual housing wealth holdings of a population. Such data are not available in 

the LIS. However, outstanding mortgage debt is available in the CE data file, although we do 
not use it for this comparison.4

 It is likely that the surveys similar to the CE from other 

countries collect this information as well. For countries with large outstanding housing 

mortgages, the market value of one’s primary residence is not a good proxy for potential 

wealth. 

 Table 12 includes the details of the market value of owned residence inequality 

analysis and Table 9 includes a summary of the ranking across countries using the Gini 

coefficient. Of the four countries in this part of the study, Israel exhibits the greatest equality 

and Hungary the least. Spanish market values are more equal than those in the United States. 

For Israel and Spain, the sample percentage of households reporting market values is the 

same as the percentage that are homeowners, over 70 percent. For Hungary and the U.S. 

some homeowners did not report market values. Based on the sample sizes presented in 

Table 12, home ownership is much more likely in Hungary, Israel, and Spain than it is in the 

U.S. About 61 percent of the sample units in the CE had market values, although about 63 

percent of the total population of consumer units owns their homes. 

B. Welfare Results 

 The Generalized Lorenz Curves (plots not shown but available from the authors) 

reveal that income welfare is greater than total expenditure welfare for Hungary. For the 

other countries included in the welfare analysis, total expenditure welfare is greater. In 

contrast, income welfare is greater than expenditure welfare when housing expenditures are 

deducted from the total for all countries except for Mexico and marginally for Poland. 

 The Sen Index (SI) results are consistent with those from the Generalized Lorenz 

Curves and are presented in Table 13. All index values are presented in 1996 U.S. dollars 

and are converted using PPPs and the relevant price indexes [adjusted SI=country 

SI/(PPP*PI96), where PI96=(xxyr/96yr)*100]. Data are weighted and the unit of analysis is 

welfare per equivalent adult. Again, Hungary exhibits greater income welfare than total 

expenditure welfare within country while France, Poland, Mexico, Spain, and the United 

 

4 Based on published data (Bureau of Labor Statistics (1999)), in 1997, 59 percent of all owners in the U.S. 

have mortgages and 57 percent of the average expenditures of owners are for mortgage interest payments 

and other charges. This does not include the expenditures for reductions in mortgage payments, unlike the 

definition used for this study. Also not included in mortgage and other charge expenditures but included in 

total owner housing expenditures are property taxes, expenditures for maintenance and repairs, property 
Kingdom exhibit greater welfare when total expenditures are used for the LIS countries with 

both income and expenditures. When income welfare based on the CPS is compared to total 

expenditure welfare based on the CE, income welfare is greater than expenditure welfare for 

the U.S. When income welfare is from the CE, expenditure welfare is greater. 

Only Mexico has greater welfare based on total expenditures net of housing as 

compared to welfare based on income, the same result found with the Generalized Lorenz 

Curve analysis. The results are again marginal for Poland but when the Sen Index is used, 

income welfare is slightly greater than total expenditure net of housing welfare. 

 When ranking the countries by welfare, we find that the United States has the greatest 

income welfare, followed by France, the United Kingdom, Spain, Hungary, Poland and 

Mexico. Expenditure welfare is greatest in the United States, followed by France, the United 

Kingdom, Spain, Hungary, Poland, and Mexico. 

 Welfare can also be compared based on the distribution and level of expenditures for 

certain commodities (also see Table 13). For this study, we examine food and housing as 

additional proxies for welfare. A comparison of welfare indexes based on food and housing 

expenditures reveals greater welfare using food expenditure as opposed to housing 

expenditures for all the countries except the United States. For Poland the magnitude of the 

difference in welfare is almost four times, while for other countries like the United Kingdom, 

the difference is marginal. For the United States, housing welfare is almost four times that of 

food welfare. 

 Although the analysis is much more limited for this study, the market values of the 

primary residence in the countries is also compared. Welfare based on market values is more 

meaningful for a country when a large proportion of the country owns homes without 

mortgages or other property liens. Among these, the U.S. has the higher welfare value, 

followed by Spain and Hungary. 

IV. Discussion and Recommendations for LIS 

Desirable characteristics of multi-country data bases are that the data are 

 

insurance and other charges. 
representative of the entire country, variables are comparably defined, and data are readily 

accessible. Although various researchers have made their comparable data available to 

others, the most often cited data are from the LIS. However, as noted above, the LIS has 

limited expenditure data and there is no attempt to use a comparable expenditure definition 

across the surveys. In reviewing the LIS data and using them in combination with detailed 

expenditure data from the U.S. Consumer Expenditure Survey, particular issues are 

highlighted. Although the data are quite usable, for example for inequality analysis, the 

interpretation of one’s results is dependent on the definition of expenditure used and whether 

all the people in a country are presented. Recommendations for future entries of expenditure 

data in the LIS and for other researchers creating multi-country data files with aims of data 

comparability are made. 

Basic among desirable characteristics of a multi-country data set include data 

comparability, in terms of variable definitions and data collection. Since most household 

surveys with expenditure and wealth data are conducted for specific reasons for country 

statistical offices, it is unlikely that the exact same rules will be used to collect the data. 

However, providing specific information regarding data collection, population coverage, and 

collection units would be most useful. For example, are people living in both urban and rural 

areas included in the sample? Does the population include some people living in institutional 

settings? How is the household, family, or consuming unit defined? Is this different when 

income, expenditures, and wealth data are collected? How are these units determined? Are 

college students living away from home included in the parents’ household or are they 

counted as separate consuming units? How are others living away from home for lengthy 

periods of time (e.g., military) treated? Are foreign nationals in the sample? How are 

expenditures treated when made outside of the country of residence by analysis/collection 

unit? 

In terms of variable definitions, much progress has been made. The COICOPS 

provides a structure for comparability, as do the National Accounts. There has been much 

discussion in Europe and some in the U.S. regarding structure and there appears to be a 

preference for COICOPs. The LIS could request that countries provide expenditure data in a 

format that is consistent with COICOPs. The forthcoming ILO guidelines (Young 2001), the 
Canberra Report (2001), the Eurostat (1997) recommendations, and the Australian Bureau of 

Statistics (McLennan 1995) could prove useful as examples to follow. Each of these 

documents focuses on definitions and the collection of expenditure data in particular. 

When focusing on expenditures, what is to be measured? Outlays, something like 

outlays, or consumption? What is consumption and what are expenditures? How are 

insurance, gambling, gifts given/received, cash transfer as an expenditure but not 

consumption for this household treated? Would installment debt or interest paid be an 

expenditure or consumption? What is the expenditure? Is it a transaction or a payment 

(installment and all)? Particular attention would be required for the presentation of 

expenditures and related information with regard to vehicles and other durables. For 

example, would the expenditures for vehicles be net of trade-in values (sales)? Would the 

full purchase price or the out-of-pocket amount be used when it is financed? For owner 

occupied housing, a preference for a flow of services definition would be consistent with 

COICOPs however a country may not provide such measures. In lieu of this, information 

could be made available concerning the characteristics of the rental and owned housing units 

in order that the researcher could impute an implicit rental value for owner occupied housing. 

Issue of reimbursements for health insurance or overpayments for utilities also need to be 

considered. The variable descriptions need to specify whether food, housing, and other 

commodities are subsidized, how are they subsidized and whether they are valued. If the 

focus is consumption, then researchers need to be able to distinguish between goods and 

services consumed by the household from those purchased to be given to others. The receipt 

of goods and services into the household would also count towards a household’s 

consumption. The value of home production for own consumption would be included as 

well. Again if one is actually interested in consumption rather than expenditures, is national 

spending for government provided benefits included in the data file? 

For wealth data, information is needed not only in terms of the value of a property but 

also the debt or liability associated with that property. 

Flags would be useful to identify whether data are missing or whether a zero value is 

actually zero. Information about bottom and top coding, truncation, outliers, imputations, and 

allocations would also provide researchers with useful information to conduct their analysis. 
If LIS decides to provide guidelines for its data providers, more detailed 

specifications would of course be needed. The goal here has been to point out issues to 

consider. If the expenditure data remain a part of LIS, it would be most desirable to have 

consistency across the survey definitions as much as possible, as with income. 
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Table 1. Country Surveys in the Luxembourg Income Study with Expenditures and/or Market Value of Property Data

At Least One

Country Year Survey Name Expenditure Market Value**

Australia 1989 Australian Income and Housing Survey X X

1994 X

1994* Austrian European Community Household Panel (ECHP) X

Austria 1995 Austrian Microcensus X

1997* Austrian European Community Household Panel (ECHP) X

Belgium 1985 Panel Survey of the Centre for Social Policy X

1988 X

1992 X

1997 X X

Canada 1991 Survey of Consumer Finances (1991) X

Denmark 1987 Income Tax Survey X

1992 X

1995 X

1997 X

Estonia 2000 Household Budget Survey X

Finland 1991 Income Distribution Survey X

France 1984 Family Budget Survey X

1989 X

1994 X

Germany 1973 Income and Consumer Survey (EVS) X

1978 X

1983 X

1981 The German Transfer Survey (Transferumfrage) X

2000* Socio-Economic Panel (GSOEP) X

Hungary 1991 Hungarian Household Panel X X

1994 X X

1999* Tarki Household Monitor Survey X X

Israel 1979 Family Expenditure Survey X

1992 X X

1997 X X

Italy 1991 Survey on Household Income and Wealth (SHIW) X X

1995 L'Indagine Campionaria sui Bilanci delle Famiglie Italiane X X

2000* X X

Luxembourg 1997* Panel Socio-Economique X

2000* na X X

Mexico 1984 National Household Survey on Income and Expenditure X

1989 (Encuesta Nacional de Ingresos y Gastos de los Hogares) X

1992 X

1994 X

1996 X

1998 X

Netherlands 1983 Additional Enquiry on the Use of (Public) Services (AVO) X

1991 Socio-Economic Panel (SEP) X

1994 X

Norway 1979 Income and Property Distribution Survey X

Poland 1986 Household Budget Survey X

1995 X

1999 X

R.O.C. 1981 Survey of Personal Income Distribution (Taiwan Area) X X

Taiwan 1986 X X

1991 X X

1995 X

Romania 1995* Romania Integrated Household Survey (RIHS) X

1997* X

Russia 1992 Russian Longitudinal Monitoring Survey X

1995 X

2000* X X

Slovenia 1997* Household Budget Survey (HBS) X

1999* X

Spain 1980 Expenditure and Income Survey X

1990 X X

Sweden 1975 Income Distribution Survey (Inkomstfördelningsundersokningen) X

1981 X

2000* X

Switzerland 1992 Swiss Poverty Survey X X

United 1986 The Family Expenditure Survey X

Kingdom 1991 na X

1994* Family Resources Survey (FRS) X X

1995 The Family Expenditure Survey X

1999* Family Resources Survey (FRS) X

United 1979 March Current Population Survey X

States 1997 X

* Data became available since the analysis was completed.

**NOTE: Market value for owned residence for all countries but Switzerland. For Switzerland, refers to all owned property.

 na- Not available

***Data recently available but not included in our study includes: Estonia (00), Hungary (99), Italy(00), Romania (95,97), 

 Russia (00), Slovenia (97,99)
Table 2. Country Data in the Luxembourg Income Study with Expenditures and/or Market Value of Property

Medical 

Apparel/ Trans- Out-of- Child Own All Owned

Country Year Total Food Housing Clothing port Pocket Education Care Residence Property

Australia 1989 X X

1994 X

Austria 1994* X

1995 X X

1997* X

Belgium 1985 X XX

1988 X X

1992 X

1997 X X

Canada 1991 X

Denmark 1987 X

1992 X

1995 X

1997 X

Estonia 2000* XX X X X X X

Finland 1991 X

France 1984 XX X X X

1989 XX X X X

1994 XX X X X X X X

Germany 1973 XX X X X

1978 XX X X X

1981 X

1983 XX X X X

2000* X

Hungary 1991 XX X X

1994 XX X X

1999* XX X X X X X

Israel 1979 X

1992 XX X X X X

1997 XX X X X X

Italy 1991 X X X

1995 X X X

2000* XX X X

Luxembourg 1997* XX X

2000* X X

Mexico 1984 XX X X X X X

1989 XX X X X X X

1992 XX X X X X X

1994 XX X X X X X X

1996 XX X X X X X X

1998 XX X X X X X X

Netherlands 1983 X

1991 X

1994 X

Norway 1979 X

Poland 1986 XX X X X

1995 XX X X X X X

1999 XX X X X X X

R.O.C. 1981 XX X X X X

Taiwan 1986 XX X X X X

1991 XX X X X X

1995 XX X X X X X

Romania 1995* XX X X X X X

1997* XX X X X X X

Russia 1992 XX X X X

1995 XX X X X X

2000* XX X X X X X X

Slovenia 1997* XX X X X X X

1999* XX X X X X X

Spain 1980 XX X X X

1990 XX X X X X

Sweden 1975 X

1981 X

2000* X X

Switzerland 1992 XX X

United 1986 XX X X X

Kingdon 1991 XX X X X

1994* X X X XX X

1995 XX X X X X X

1999* X XX

United 1979 X

States 1997 X

LIS datasets with no expenditure or wealth data for these years: Australia(81,85), Austria (87), Canada (71,75,81,87,94,97,98),

 Finland (87,95,00), France(79,81), Germany(84,89,94), Israel (86), Italy (86), Netherlands (87), Norway(86,91,95) Poland (92,00), 

 Sweden (67, 87, 92, 95), Switzerland (82), United Kingdom (69, 74,79), United States (69, 74, 86, 91,92,94,96).

Countries with no data for these variables: Czech Republic (92,96), Ireland (87), Luxembourg (85, 91, 94), Slovak Republic(92,96).

* Data became available since the analysis was completed.

Expenditures Market Value
Table 3. Data sets with Expenditure and Market Value of Residence Data 

Category Number of Data Sets 

Total Expenditures 41 

Expenditures for: 

 Food 45 

 Housing 50 

 Apparel/clothing 38 

 Transportation 37 

 Medical out-of-pocket 21 

 Education 17 

 Child Care 11 

Market Value 

 Owned residence 31 

 All property owned 1 

Total expenditures and Market Value of Owned Residence 13 

Table 4. Data Sources for Study

Luxembourg Income Study Year Survey NameFrance 1994 Family Budget Survey

Hungary 1994 Hungarian Household PanelIsrael 1997 Family Expenditure SurveyMexico 1998 National Household Survey on Income and Expen

Nacional de Ingresos y Gastos de los Ho

Poland 1995 Household Budget SurveyR.O.C. Taiwan 1995 Survey of Personal Income Distribution, TaRussia 1995 Russian Longitudinal Monitoring SuSpain 1990 Expenditure and Income SurveyUnited Kingdom 1995 The Family Expenditure SurveyUnited States 1997 March Current Population SurveyOther

United States 1997 Consumer Expenditure Interview Su
Luxembourg Income Study

YEAR TOTEXP HOUSEXP FOODEXP APPEXP TRANEXP MEDEX

France 1994 totexp housexp foodexpHungary 1994 *12 monthly amount spending all in

all

monthly portion ofhousehold utilities +monthly rent for flat orhouse+monthlyinstallment of credit usedto buy flat or home

how muchspending on food

Israel 1997 total consumption

expenditure=total outlays of thehousehold on the purchase ofgoods or services as well asimputed consumption onhousing and vehicles *(thepurchase of which is defined asinvestment and not asconsumption). Paymentssometimes include also interest,transportation or installationpayments. The full amount ofpurchase of a commodity isrecorded on the day of itsreceipt, even though only part ofits cost has been paid; as aconsequence, advance paymentson account of goods or servicesnot yet supplied or payments ofdebts on account of acommodity already inpossession of the household, arenot considered as consumptionexpenditure but as an increase insavings.

rent+housing consumptionof owned

foodexp includes imputed

rents for vehicles

Mexico 1998 no documentation no documentation no

documentation

Table 5. Expenditure Variable Definitions by Country
Poland 1995 *12 comment includes all major 

categories of expenditure except taxes (v11). Includes food, spirits and tobacco, clothing and shoes, dwelling costs, home furnishings, health care, personal hygiene, education, culture sport and recreation, transport and communications, other spending and expenditures 

cost of dwelling maintenance

food

R.O.C. Taiwan

1995 total consumption expenditure compute housexp = (rent 

and water charges) +(fuel and lighting) comment here we diverge from Taiwan formula, which includes furniture and family and facilities and household operations; our formula includes rent (including imputed rent of self-owned which is present as non-property income in v9 ) and 

utilities. 

compute foodexp = food+ beverage + tobacco; comment standard Taiwan formula includes tobacco 

Russia 1995 totexpr6 rent and utilities expense compute foodexp 

= dairyr6+meatr6+fishr6+potator6+breadr6+eggsr6+fatr6+fruitsr6+sugarr6+vegetr6+ofoodr6+alcohlr6+eatoutr6 

Spain 1990 Gastos' file CEPF total family 

monetary expenditure excludes self-provision, self- sufficiency, in-kind salaries, and imputed rent.

Includes gross rent, rent attributed in case of ownership, electricity and 

heating bills. 

(excludes alcoholic drinks and tobacco) 

United Kingdom

1995 p550tp rent+mortgage and interest 

payment+water +regular housing payments as for repairs and maintenance

p518t
Consumer Expenditure Interview Survey (definitions for this study)

total expenditure outlays housing expenditure 

outlays

food at home apparel transport 

outlays

medical oo

United States

1998 transaction costs, including 

excise and sales taxes, and outlays for mortgaged homes and financed vehicles for goods and services acquired during the interview reference period. Includes expenditures for gifts given but excludes purchases or portions of purchases directly 

assignable to business purposes. Excludes periodic credit on installment payments on goods or services already acquired except for vehicles. For owned home, includes reduction in principal payment, mortgage interest, property taxes. Meals and rent as pay included. (possible to replace owner costs with rental equivalence.)

expenditures for owned and rented dwellings and lodging away from home. For owned homes, mortgage principal reduction, interest, and property taxes. Other 

expenditures include rents, homeowner and rental insurance, fire and extended coverage insurance, maintenance and repairs, utilities, fuels and public services. 

for this study, food at home

all apparel, including accessories, and footwear; also other apparel produces and services 

including repairs, jewelry and watches, making clothing

vehicle purchases (net outlays) plus financing charges, gasoline and motor oil, maintenance and repairs, vehicle insurance, public transport, vehicle rental, leases, licenses, and other charges

health insurance, medical services and supplies, dru
Table 6. Variables across Surveys: Identification of positives, zeroes and missing values.

Year Income Total Food House Clothing Transportation

Out of Pocket 

Medical Educat

Luxembourg Income Study

DPI TOTEXP FOODEXP HOUSEXP APPEXP TRANEXP MEDEXP EDUCE

France 1994 0 (1) miss 0 and miss 0 and miss 0 and miss 0 and miss 0 and miss 0 and mHungary 1994 0 and miss >0 and miss 0 and miss 0 na na na na Israel 1997 >0 > 0 0 > 0 0 0 na na Mexico 1998 0 >0 0 0 0 0 0 0 Poland 1995 0(1) > 0 >0 0 0 0 0 0 ROC-Taiwan 1995 >0 > 0 0 > 0 0 0 0 0 Russia 1995 0(1) 0 0 0 0 0 and miss >0 and miss na Spain 1990 0(1) 0 0 0 0 0 na na UK 1995 0(1) >0 0 0 and miss(1) 0 0(1) 0 na US 1997 0(1) na na na na na na na 

Consumer Expenditure Interview Survey (Internal BLS data)(2)

FINCATAX outlays ZFOODHOM outlays ZAPPAREL ETRANPRT ZHEALTH ZEDUC

US 1997 CIR >0 0 0 0 0 0 0 Note: For countries with nonmissing values zeroes may indicate missing.(na) - data not available 0 - includes zeroesmiss - includes missing> 0 - includes values greater than zero onlyCIR-Complete Income Reporters: Provide values for major sources of income:wages and salaries, self-employment income  and Social Security Income. Across-the-board zero income reporting is treated as incomplete reporting.(1)- includes negative values(2)-available in the public use file(3)-can be created from microdata(4)-IF OWNYB ='100'*-zeroes are renter**-all missing are renters
Table 7. Sample Size

France Hungary Israel Mexico Poland ROC-Taiwan Russia Spain United Kingdom

Sample Size 1994 1994 1997 1998 1995 1995 1995 1990 1995 1997

Overall 11294 1992 5230 10952 32009 14706 3518 21153 6797 50

Income Analysis* 11289 1929 5230 10889 31985 14706 3373 21102 6794 50

Negative cases 3 na na na 423 na 1 2 44 6

Expenditure Analysis 9573 1942 5225 10869 31948 14705 2402 21041 6741 n

Cases dropped due to zero or 

missing (in this order)

HOUSEXP 1714 38 na 18 61 na 1093 2 48 n

TOTEXP na 10 na na na na na na na n

FOODEXP 7 2 5 65 na 1 23 110 8 n

NETHOUS 

Market Value gt 0 na 1435 3689 na na na na 16622 na n

Cases dropped due to zeroes na 43 1541 na na na na na na n

9573 1942 5225 10869 31948 2402 21041 6741

*It is impossible to distinguish between actual zero incomes and missing values in all LIS datasets we use, except for Hungary. As a result we exclude all zero incomes.

Note: na-not applicable

 CE-CIR-Complete Income Reporters and restriction that DPI>0**Market Value for CE-CIR restricted to DPI >0 sample
Table 8. Country Data Sets with Total Expenditure Edit 

Country Number of Cases with Sum of 

Expenditures > TOTEXP 

% of expenditure analysis cases 

Hungary 259 13.3 Mexico 8 0.1 Poland 22 0.1 Spain 3861 18.3 United Kingdom 641 9.5 
Table 9. Ranking of Countries by Inequality Using the Gini Coefficient and Income, Expenditures, and M

(most equal=1)  Ranking by Country Disposable 

Personal Income 

Total Expenditures 

Net of Housing Expenditures 

Housing Expenditures 

Food Expenditures 

R.O.C. Taiwan 1 1 1 2-3 1 France 2 4 4 5-6 5 Spain 3 5 5 8 3 Hungary 4 2 3 2-3 9 Israel 5 7 9 1 7-8 United Kingdom 6 6 6 4 4 Poland 7 3 2 10 2 United States - LIS 8 NA NA NA NA United States - CE CIR 9 8 7 5-6 7-8 United States - CE 11 9 8 7 6 Russia 10 10 10 9 11 Mexico 12 11 11 11 10 NA: data not available 
Table 10a. Income Inequality measures for chosen countries without top and bottom coding.

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) CPS 

Gini 0.290 0.003 0.325 0.002 0.337 0.004 0.515 0.007 0.348 (0.311) 0.003 0.267 0.002 0.451 0.006 0.302 0.003 0.347 0.005 0.374 I(0):Mean logdeviation 0.142 0.003 0.186 0.000 0.201 0.006 0.493 0.015 0.142 (0.176) 0.004 0.116 0.002 0.386 0.011 0.161 0.004 0.209 0.007 0.259 I(1):Theil entropy 0.153 0.005 0.193 0.001 0.197 0.009 0.530 0.028 0.234 (0.191) 0.008 0.123 0.003 0.363 0.013 0.162 0.008 0.215 0.009 0.254 I(2): 1/2 SCV 0.221 0.020 0.286 0.004 0.269 0.035 1.300 0.288 0.458 (0.337) 0.040 0.156 0.008 0.525 0.032 0.233 0.040 0.300 0.026 0.362 Decile ratio/10 0.354 0.040 0.419 0.003 0.486 0.013 1.155 0.038 0.404 (0.380) 0.003 0.338 0.003 0.939 0.039 0.396 0.005 0.457 0.007 0.557 Sample Size 11289 1929 5230 10889 31985 (31562) 14706 3373 21102 6794 50069 France 1994-data on taxes are incompletePoland 1995-data on taxes are incomplete*It is impossible to distinguish between actual zero incomes and missing values in all LIS datasets we use, except for Hungary. As a result we exclude all zero incomes. (1)-Standard Deviation of 200 BootstrapsTable 10b. Income Inequality measures for chosen countries with top and bottom coding.

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) CPS 

Gini 0.288 0.003 0.323 0.008 0.336 0.004 0.494 0.004 0.320 (0.306) 0.002 0.266 0.002 0.447 0.006 0.300 0.003 0.344 0.004 0.372 I(0):Mean logdeviation 0.142 0.003 0.183 0.010 0.198 0.005 0.452 0.008 0.235 (0.169) 0.003 0.115 0.002 0.380 0.010 0.158 0.003 0.227 0.006 0.260 I(1):Theil entropy 0.148 0.004 0.185 0.011 0.192 0.006 0.437 0.009 0.193 (0.173) 0.003 0.121 0.003 0.352 0.011 0.154 0.004 0.207 0.007 0.251 I(2): 1/2 SCV 0.198 0.012 0.240 0.023 0.243 0.016 0.647 0.018 0.256 (0.240) 0.008 0.149 0.005 0.483 0.022 0.187 0.009 0.267 0.015 0.352 Decile ratio/10 0.354 0.004 0.419 0.023 0.486 0.013 1.155 0.038 0.404 (0.380) 0.003 0.338 0.003 0.939 0.039 0.396 0.005 0.457 0.007 0.557 Sample Size 11289 1929 5230 10889 31985 (31562) 14706 3373 21102 6794 50069 France 1994-data on taxes are incompletePoland 1995-data on taxes are incomplete*It is impossible to distinguish between actual zero incomes and missing values in all LIS datasets we use, except for Hungary. As a result we exclude all zero incomes. (1)-Standard Deviation of 200 Bootstraps

Mexico Poland Taiwan Russia Spain United Kingdom 

FranceFrance Hungary Israel

Hungary Israel Mexico Poland Taiwan Russia Spain 

United Kingdom
Table 11. Expenditure inequality measures for chosen countries (no zeroes) *.

11a. Total Expenditures

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) 

Gini 0.288 0.003 0.242 0.005 0.336 0.004 0.452 0.006 0.283 0.002 0.236 0.002 0.418 0.011 0.303 0.003 0.312 0.004 

0.139 0.003 0.111 0.008 0.186 0.005 0.354 0.009 0.133 0.003 0.089 0.002 0.302 0.016 0.154 0.003 0.163 0.004 

I(1):Theil entropy 0.140 0.003 0.100 0.004 0.194 0.007 0.384 0.013 0.154 0.006 0.097 0.019 0.345 0.027 0.157 0.004 0.164 0.004 I(2): 1/2 SCV 0.170 0.008 0.112 0.007 0.255 0.021 0.657 0.046 0.263 0.030 0.126 0.003 0.657 0.092 0.201 0.011 0.202 0.008 Decile ratio/10 0.381 0.005 0.300 0.007 0.467 0.009 0.770 0.026 0.328 0.002 0.290 0.003 0.622 0.021 0.413 0.005 0.434 0.007 Sample Size 9573 1942 5225 10869 31948 14705 2402 21041 6741 11b. Total net of housing expenditures

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) 

Gini 0.315 0.003 0.302 0.007 0.391 0.005 0.455 0.007 0.291 0.002 0.247 0.002 0.430 0.012 0.336 0.003 0.361 0.004 

0.170 0.003 0.192 0.263 0.007 0.362 0.011 0.141 0.003 0.098 0.002 0.326 0.018 0.198 0.003 0.247 0.005 

I(1):Theil entropy 0.167 0.004 0.154 0.007 0.262 0.010 0.389 0.015 0.162 0.005 0.108 0.002 0.365 0.029 0.191 0.005 0.222 0.006 I(2): 1/2 SCV 0.202 0.009 0.175 0.356 0.033 0.668 0.054 0.275 0.028 0.147 0.004 0.705 0.100 0.248 0.015 0.270 0.011 Decile ratio/10 0.437 0.008 0.398 0.017 0.681 0.018 0.798 0.025 0.346 0.003 0.304 0.003 0.659 0.027 0.541 0.007 0.653 0.016 Sample Size 9573 1941 5225 10869 31948 14705 2402 21041 6741 11c. House Expenditures

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) 

Gini 0.396 0.004 0.306 0.007 0.252 0.004 0.545 0.008 0.488 0.006 0.306 0.002 0.446 0.015 0.430 0.006 0.324 0.004 

0.293 0.006 0.258 0.020 0.120 0.004 0.543 0.016 0.461 0.011 0.157 0.002 0.388 0.025 0.323 0.008 0.184 

I(1):Theil entropy 0.278 0.008 0.166 0.009 0.116 0.006 0.619 0.032 0.503 0.040 0.161 0.003 0.405 0.053 0.352 0.015 0.181 0.007 I(2): 1/2 SCV 0.396 0.032 0.190 0.018 0.145 0.018 1.618 0.284 2.029 0.920 0.211 0.006 0.924 0.339 0.588 0.056 0.232 Decile ratio/10 0.768 0.019 0.447 0.022 0.296 0.006 1.222 0.046 1.055 0.013 0.415 0.005 0.824 0.042 0.672 0.014 0.472 0.008 Sample Size 9573 1942 5225 10869 31948 14705 2402 21041 6739 11d. Food Expenditures

1994 (1) 1994 (1) 1997 (1) 1998 (1) 1995 (1) 1995 (1) 1995 (1) 1990 (1) 1995 (1) 

Gini 0.260 0.003 0.321 0.006 0.276 0.003 0.348 0.004 0.191 0.001 0.166 0.001 0.398 0.007 0.253 0.002 0.256 0.003 I(0):Mean log-deviation 0.122 0.003 0.325 0.136 0.003 0.221 0.005 0.061 0.001 0.045 0.001 0.300 0.010 0.119 0.003 0.117 0.002 I(1):Theil entropy 0.115 0.003 0.176 0.008 0.125 0.003 0.206 0.005 0.061 0.001 0.046 0.001 0.269 0.010 0.108 0.002 0.107 0.002 I(2): 1/2 SCV 0.130 0.005 0.184 0.133 0.004 0.254 0.012 0.069 0.004 0.056 0.001 0.329 0.018 0.122 0.003 0.115 0.003 Decile ratio/10 0.339 0.005 0.500 0.015 0.384 0.008 0.528 0.012 0.240 0.001 0.220 0.001 0.745 0.028 0.334 0.004 0.345 0.006 Sample Size 9573 1939 5225 10869 31948 14705 2402 21041 6741 * If sum of expenditures > total expenditures then total expenditures=sum of expenditures.The following number of changes were made: Spain-3861; UK-641; Hungary-259; Poland-22; Russia-86; Mexico-8. Additional cases excluded from sample: UK-2 (negative House Expenditures); Hungary-1(=0 Total Net of House);  Hungary-3(missing Food Expenditures).Note: SCV- Squared Coefficient of Variation (1)-Standard Deviation of 200 Bootstraps

France Hungary Israel Mexico Russia Spain

Spain

United Kingdom United KingdomUnited Kingdom

Russia Spain 

I(0):Mean log-deviation

France Hungary Israel Poland

I(0):Mean log-deviation

France Hungary Israel

Mexico Poland Taiwan

ROC-Taiwan

Taiwan

Poland

Mexico Poland TaiwanMexico

I(0):Mean log-deviation

France Hungary Israel Russia

Russia

Spain United Kingdom
Table 12. Market Value of Home Inequality Measures with standard deviation of 200 bootstraps 

 for chosen countries (no zeroes)*.Home Market Value

1994

SD of 200 

Bootstraps 1997

SD of 200 

Bootstraps 1990

SD of 200 

Bootstraps 1997

SD of 200 

Bootstraps 

Gini 0.429 0.011 0.343 0.005 0.390 0.004 0.410 0.003 

I(0):Mean log-deviation 0.345 0.019 0.205 0.006 0.280 0.006 0.340 0.006 I(1):Theil entropy 0.338 0.024 0.205 0.007 0.271 0.008 0.300 0.006 I(2): 1/2SCV 0.520 0.074 0.262 0.012 0.387 0.027 0.419 0.014 Decile ratio/10 0.760 0.057 0.484 0.021 0.650 0.011 0.778 0.017 Sample Size (Mkt val) 1435 3689 16622 13641 Percent of Sample Size 72.04% 70.54% 78.58% 61.41% Homeowners:Sample Size 1583 3689 16622 14008 Percent of Sample Size 79.50% 70.54% 78.58% 63.06%  SCV-Squared Coefficient of Variation; SD-standard deviationNote:* Zero values are excluded because v10-market value of owned home variable in Israel-includes both  renters and homeowners, hence lots of zeroes; in Hungary- includes only homeowners, but with zeroes;  in Spain- includes only homeowners with v10 gt 0  CIR-Complete Income ReportersCIR Sample for U.S. restricted to DPI>0 and CIR=1

Hungary Israel

LIS Data United S

Spain full sample
Table 13. Sen's Welfare Index per equivalent adult for chosen countries by region in 1996 USD - PPP adjusted.*

France Spain

United 

Kingdom Hungary Poland Mexico

1994 1990 1995 1994 1995 1998 1997 1997 (CE) 1997 (

Income $11,229 $7,740 $10,437 $4,626 $3,368 $2,199 $15,473 $12,408 $1Expenditures:Total $12,507 $9,639 $10,828 $4,130 $3,952 $2,875 $13,732 $1Net of Housing $9,280 $6,967 $8,141 $2,625 $3,301 $2,273 $8,574 $9Housing $2,427 $1,902 $2,027 $1,171 $438 $490 $4,404 $4Food $2,768 $2,517 $2,188 $1,603 $1,714 $1,073 $1,692 $Home Value $26,704 $19,014 $41,299 $4Note: All zeroes are dropped since unable to distinguish between missing and actual zeroes. CIR-Complete Income Reporters Market home value only for those with positive values.* For Israel, Russia, and Taiwan PPPs from the Penn World Tables were used in a given year.  Due to doubts regarding the comparability of adjustments we have omitted them from the welfare analysis.  Results are available from the authors upon request.

United States
*It is impossible to distinguish between actual zero incomes and missing values in all LIS datasets we use, except for Hung

 As a result we exclude all zero incomes.

Figure 1a. Income-DPI Gini's

(no missing or zeroes)*

0.267

0.290

0.302

0.325

0.337

0.347

0.348

0.374

0.407

0.451

0.515

0 0.2 0.4 0.6

Taiwan

France

Spain

Hungary

Israel

UK

Poland 

US (LIS)

US (CE-CIR)

Russia 

Mexico 

Figure 1b. Total Expenditu

(all values>0)

0.236

0.242

0.283

0.288

0.303

0.312

0.336

0.349

0.354

0.418

0.452

0 0.2 

Taiwan

Hungary

Poland 

France

Spain

UK

Israel

US (CE-CIR)

US (CE)

Russia 

Mexico 
*It is impossible to distinguish between actual zero incomes and missing values in all LIS datasets we use, except for Hu

 As a result we exclude all zero incomes.

Figure 2a. Income-DPI Gini's 

(no zeroes or missing)*

0.267

0.29

0.302

0.325

0.337

0.347

0.348

0.374

0.407

0.451

0.515

0 0.2 0.4 0.6

Taiwan

France

Spain

Hungary

Israel

UK

Poland 

US (LIS)

US (CE-CIR)

Russia 

Mexico 

Figure 2b. Total Expend

Housing Gini's (

0.247

0.291

0.302

0.315

0.336

0.361

0.375

0.385

0.391

0.430

0.455

0 0.2 

Taiwan

Poland 

Hungary

France

Spain

UK

US (CE-CIR)

US (CE)

Israel

Russia 

Mexico 
APPENDICES 

Appendix A 

Much work using expenditures as a measure of economic well-being has been conducted, 

though most studies have focused on individual countries. Examples of these studies follow. 

 For Canada see Pendakur (1988). For Australia, see Barrett, Crossley, and Worswick (2000). 

For Portugal, see Goodman and Webb (1995). For Spain, see Del Rio and Ruiz-Castillo 

(2001), Sastre (1999) and Gouveia and Tavares (1995). For the UK, see Deaton and Paxson 

(1994). For the U.S., see Cutler and Katz (1992), Garner (1993), and Johnson and Shipp 

(1997). For the Czech and Slovak Republics, see Garner (1998) and Garner et al (1995). For 

developing countries, see Woden (1999) concerning Bangladesh, and Deaton and Paxon 

(1994) for Taiwan, De Vos and Zaidi (1997) examined household budget data in their study 

of poverty in member states of the European Community. Garner, Ruiz-Castillo, and Sastres 

(2002) use expenditure data to compare inequality in Spain and the U.S. 

Appendix B 

Definition of Household 

Hungary. A household is composed of all persons living under the same 

roof, sharing income and expenditures. 

Israel. A group of person sharing the same dwelling most days of the week 

and having a common budget of expenditures on food. A household includes 

members who are soldiers or children studying in boarding schools. 

Poland. A household includes a single or unrelated person, or two or more 

persons that are related to each other and also unrelated people living together 

and (at least partly) sharing their incomes. Two or more families living 

together but not sharing their incomes are treated as different households. 

Persons absent due to work-related reasons are considered members of a 

household as far as they contribute to the household budget. Children 

attending school and living in dormitories or boarding schools, persons in 

military service/installations, in prison, nursing homes, etc., are not 
considered as members of the household. Persons sharing a house/apartment 

are considered as members of separate households. Servants/domestic 

personnel are neither considered as members of the household they are 

working for, nor as a separate household. Also students temporarily living in 

the household are not considered as members. 

Spain. A household is considered to be a group of persons sharing a dwelling 

(or part of it) and sharing a common budget. The definition does not include 

independent households living in institutions, even if they have expenditure 

autonomy. A person is considered a member of the household if he or she 

does not live in another dwelling and: (1) is present in the dwelling at least 

the day of the interview; (2) is economically dependent of the household 

budget; (3) is present at the dwelling at least three months within the six 

months before the interview took place (three of the 12 previous months the 

person is considered to be the head of the household). Persons fulfilling the 

first and second condition, although usually staying at another dwelling, are 

considered to be members of the household. Exceptions are guests 

(contributing or not to the household budget) and domestic workers. 

United Kingdom. A household comprises one person living alone or a group 

of people living at the same address having meals prepared together and with 

common housekeeping. Resident domestic servants are included. Members 

of the household are not necessarily related by blood or marriage. 

United States (CE). A consumer unit (not a household) comprises either: all 

members of a particular household who are related by blood, marriage, 

adoption, or other legal arrangements; a person living alone or sharing a 

household with others or living as a roomer in a private home or lodging 

house or in permanent living quarters in a hotel or lodging house or in 

permanent living quarters in a hotel or motel, but who is financially 

independent; or two or more persons living together who use their incomes to 

make joint expenditure decisions. The three major expense categories, 

housing, food, and other living expenses determine financial independence. 
To be considered financially independent, at least two of the three major 

expense categories have to be provided entirely, or in part, by the respondent. 

United States (CPS). A household consists of all the persons who occupy a house, an 

apartment, or other group of rooms, or a room, which constitutes a housing unit. A group of 

rooms or a single room is regarded as a housing unit when it is occupied as a separate living 

quarters that is, when the occupants do not live and eat with any other person in the structure, 

and when there is direct access from the outside or through a common hall. The count of 

households excludes persons living in group quarters, such as rooming houses, military 

barracks, and institutions. Inmates of institutions (mental hospitals, rest homes, correctional 

institutions, etc.) are not included in the survey. 

Appendix Table 1. Total Expenditures

France Hungary Israel Mexico Poland ROC-Taiwan Russia Spain

United

Kingdom United S

1994 1994 1997 1998 1995 1995 1995 1990 1995 1997 (CE-

Income and Total Expenditures 0.642 0.538 0.839 0.688 0.504 0.659 0.453 0.446 0.557 0.65

Income and Market Value >0 0.247 0.451 0.333 0.47Income and Market Value (1) 0.262 0.419 0.261 0.48Total Expenditures and Market Value >0 0.306 0.500 0.398 0.53Total Expenditures and Market Value (1) 0.290 0.471 0.314 0.53

(1)-includes homeowners with positve market value and renters with zeroes.CIR- Complete Income Reporters