MEASURE DHS+ assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Funded by the U.S. Agency for International Development 

(USAID), MEASURE DHS+ is implemented by ORC Macro in Calverton, Maryland. 

 

The main objectives of the MEASURE DHS+ project are: 

1) to provide decisionmakers in survey countries with information useful for informed policy 

 choices, 

2) to expand the international population and health database, 

3) to advance survey methodology, and 

4) to develop in participating countries the skills and resources necessary to conduct high- 

 quality demographic and health surveys. 

 

Information about the MEASURE DHS+ project or the status of MEASURE DHS+ surveys is available on 

the Internet at http://www.measuredhs.com or by contacting: 

ORC Macro 

11785 Beltsville Drive, 

Suite 300 

Calverton, MD 20705 USA 

Telephone: 301-572-0200 

Fax: 301-572-0999 

E-mail: reports@orcmacro.com
DHS Comparative Reports No. 6 

The DHS Wealth Index

Shea Oscar Rutstein 

Kiersten Johnson 

August 2004 

ORC Macro 

Calverton, Maryland USA
This publication was made possible through support provided by the U.S. Agency for International 

Development under the terms of Contract No. HRN-C-00-97-00019-00. The opinions expressed herein 

are those of the author and do not necessarily reflect the views of the U.S. Agency for International 

Development. 

Recommended citation: 

Rutstein, Shea O. and Kiersten Johnson. 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. 

Calverton, Maryland: ORC Macro.
iii

Contents

Foreword............................................................................................................................. v 

Preface ..............................................................................................................................vii 

Acknowledgments............................................................................................................viii

Executive Summary ...........................................................................................................ix 

1 Introduction........................................................................................................... 1 

 1.1 Background............................................................................................... 1 

 1.2 Household Income .................................................................................... 2 

 1.3 Household Consumption Expenditures..................................................... 3 

 1.4 Household Wealth..................................................................................... 4 

 1.5 Economic Status versus Socioeconomic Status........................................ 5 

 1.5.1 Establishing a Poverty Line ............................................................ 5 

 1.6 Issues Regarding an Index of Economic Status........................................ 6 

 1.6.1 Public Services................................................................................ 6 

 1.6.2 Individual Effects............................................................................ 6 

 1.6.3 Equivalization—Adjusting for Household Needs (Size and Age).. 7 

 1.6.4 What Are We Trying to Measure? .................................................. 7 

2 Construction of the DHS Wealth Index ................................................................. 8 

 2.1 Indicator Variables.................................................................................... 8 

 2.2 Construction of the Index ......................................................................... 9 

 2.3 Construction of Quintiles.......................................................................... 9 

 2.4 Variations................................................................................................ 10 

 2.5 Alternative Measures of Economic Status.............................................. 10 

 2.6 Wealth versus Expediture: Guatemala.................................................... 10 

3 Who Has What? ................................................................................................... 15 

4 Who Are the Poor? .............................................................................................. 24 

 4.1 Area of Residence and Region................................................................ 24 

 4.2 Household Head...................................................................................... 24 

 4.3 Household Size ....................................................................................... 26 

 4.4 Percentage of Children in Poverty .......................................................... 26 

 4.5 Characteristics of Household Members .................................................. 27 

5 Importance of Wealth .......................................................................................... 29 

 5.1 Reproductive and Maternal Health ......................................................... 29 

 5.2 Child Mortality and Health ..................................................................... 32 

 5.3 Environmental Health Conditions........................................................... 35 

 5.4 Education ................................................................................................ 36 

 5.5 Use of Public Services ............................................................................ 39 
iv

6 Targeting Programs and Services by Wealth and Mapping Poverty ................... 42 

 6.1 Where Do the Poor Live? ....................................................................... 42 

 6.2 Which States Are the Poorest?................................................................ 46 

 6.3 Nongeographic Targeting ....................................................................... 47 

7 Joining with Other Poverty Measures.................................................................. 48 

8 Further Work........................................................................................................ 50 

References......................................................................................................................... 51 

Appendix A....................................................................................................................... 53 

Appendix B ....................................................................................................................... 57 

Appendix C ....................................................................................................................... 59 
v

Foreword 

This comparative report on the Demographic and Health Survey (DHS) wealth index documents the 

background, decisions taken, and procedures used in constructing the index and provides examples of its 

use. Because of the timing of the report and contractual obligations, no results from the most recently 

conducted surveys can be provided at this time. A subsequent revision of this report will include these 

results.
vii

Preface 

One of the most significant contributions of the MEASURE DHS+ program is the creation of an 

internationally comparable body of data on the demographic and health characteristics of populations in 

developing countries. The DHS Comparative Reports series examines these data across countries in a 

comparative framework. The DHS Analytical Studies series focuses on specific topics. The principal objectives of both series are to provide information for policy formulation at the international level and to 

examine individual country results in an international context. Whereas Comparative Reports are primarily descriptive, Analytical Studies take a more analytical approach. 

The Comparative Reports series covers a variable number of countries, depending on the availability of data sets. Where possible, data from previous DHS surveys are used to evaluate trends over 

time. Each report provides detailed tables and graphs organized by region. Survey-related issues such as 

questionnaire comparability, survey procedures, data quality, and methodological approaches are addressed as needed. 

The topics covered in Comparative Reports are selected by MEASURE DHS+ staff in conjunction with the MEASURE DHS+ Scientific Advisory Committee and USAID. Some reports are updates 

and expansions of reports published previously. 

It is anticipated that the availability of comparable information for a large number of developing 

countries will enhance the understanding of important issues in the fields of international population and 

health by analysts and policymakers. 

Martin Vaessen 

Project Director 
viii

Acknowledgments 

The authors benefited enormously from discussions with World Bank staff, particularly Davidson 

Gwatkin, Adam Wagstaff, Deon Filmer, Lant Pritchet, and others at the Bank, and from comments from 

USAID, UNICEF, DFID, and WHO staff, and In-Depth Network members during the many presentations 

and from individual discussions. 

The authors would also like to thank Fred Arnold, Sidney Moore, and Katherine Senzee for their 

work in reviewing, editing, and publishing this report, and Kaye Mitchell for document production. 
ix

Executive Summary 

 Typically, fertility surveys and demographic and health surveys have included little information 

on economic status. In the past, socioeconomic status has been determined using the education level of 

the respondent and/or spouse, sometimes in combination with their own or their spouse’s occupation. A 

few studies have used household construction, mostly type of flooring, as an economic indicator, and 

some others have combined several housing characteristics into ad hoc indexes. The DHS wealth index is 

an attempt to make better use of existing data in the Demographic and Health Surveys in a systematic 

fashion to determine a household’s relative economic status. 

 This report documents the philosophy, history, and background of the DHS wealth index and 

describes the methodology employed in its construction and the decisions made about possible variations 

in the methodology. After discussion of the advantages and disadvantages of using a wealth index as 

opposed to income and expenditure measures of economic status, a comparison is made between the 

wealth index and the expenditure index in a particular setting. This comparison shows that the wealth 

index explains the same or a greater amount of the differences between households on a set of health 

indicators, even though the wealth index requires far less effort from respondents, interviewers, data 

processors, and analysts. 

 Comparisons are made for five selected countries in the distribution of wealth among households 

and for some key demographic and social indicators. Additionally, as examples, key health, education, 

and use of public services indicators are tabulated according to quintile of the population distribution of 

household wealth, and comparative results for health indicators in the poorest quintile are presented for 44 

countries. 

 The use of the wealth index for addressing the needs of the poor is discussed and illustrated 

through poverty maps and nongeographic analysis. Also discussed is the joining of the wealth index to 

more traditional measures of poverty. Finally, suggestions are offered for extension of the DHS wealth 

index, both by inclusion of additional items and by refinement of the methodology of calculation. 
1

I Introduction 

The purpose of this report is to give the background, philosophy, and construction of the wealth index 

based on the Demographic and Health Surveys (DHS); to compare the wealth index with other measures 

of economic status; and to give examples of how such an index has been and can be used. 

1.1 Background 

History 

Socioeconomic status has been long thought to be associated with health status. Current interest in using 

the DHS survey for measuring health outcomes by socioeconomic status dates back to preparations for 

the World Health Organization’s 1997 conference, “Health Equity for All in the New Millennium.” Shea 

Rutstein, a member of the DHS staff at ORC Macro, was contacted by an organizer of the conference, 

Paula Braveman at the University of California, San Francisco, to discuss how DHS data could be used to 

measure and monitor health equity. Discussions between Rutstein and Braveman led to a host of equity 

differentials that were considered for the conference. 

Prior to this, Rutstein had produced a rough indicator of economic status, based on assets and services 

(wealth index), for internal use by ORC Macro. Rutstein used this indicator as part of his presentation of 

measures of health equity in the DHS survey, using the then recently completed 1996 Zambia DHS. The 

health equity conference was also attended by Davidson Gwatkin of the World Bank, another 

organization that was becoming increasingly interested in poverty indicators. 

Shortly after the conference, Rutstein was asked to make a presentation to World Bank staff. One of the 

issues discussed during the presentation was the weighting of the specific variables used to produce the 

index. This weighting had been done on an ad hoc basis by Rutstein. Two attendees at the presentation, 

Lant Pritchett and Deon Filmer, suggested that factor analysis (or principal components analysis) could be 

used to determine the variable weightings. Pritchett and Filmer proceeded to undertake an analysis of 

education based on the wealth index for India, using the 1992-93 National Family Health Survey (NFHS), 

a DHS-type survey. To validate the wealth index as a measure of economic status, they compared results 

from neighboring countries, using the World Bank’s Living Standard Measurement Surveys (LSMS), for 

the wealth index and consumption expenditures. They concluded that the wealth index actually performed 

better than the traditional consumption expenditure index in explaining differences in educational 

attainment and attendance (Filmer and Pritchett, 2001). 

Soon after, ORC Macro was awarded a contract with the World Bank to develop wealth indexes for 

recent surveys and to produce a set of “poverty health indicators.” Reports for 44 countries were produced 

by Shea Rutstein and Kiersten Johnson (also at ORC Macro), together with Davidson Gwatkin, Rohini 

Pande, and Adam Wagstaff of the World Bank. These reports included 33 poverty health indicators for 

the entire country, urban and rural areas, and males and females, by quintiles of households according to 

wealth. 

A second contract between the World Bank and ORC Macro provided for the formulation of wealth 

indexes for an additional 37 countries and a total of 162 health and education indicators for all 81 

countries. These indexes and indicators are currently being produced. Additionally, sampling errors for 

each quintile and a concentration index and its standard error are also being calculated. 

Equity 

Fairness in health is related to several concepts: equality in health status, equality in health services, and 

equity in health services. A further distinction can be made for fairness at the individual and societal 
2

levels. Our work has concerned fairness at the societal level, that is, among groups of people rather than 

between individuals. Equality in health status is probably impossible to achieve, even at the societal level, 

because of the differing environmental, cultural, and genetic factors involved; it would not be desirable if 

it meant reducing the health status of those who are relatively healthy. Equality in health services is a 

theoretical possibility, but given inequalities in health status, it is not desirable. The third concept is 

equity in health services, which means access to services according to need. This is obvious on an 

individual basis, since healthier people generally need to use health services less. On a societal basis, 

there can also be differing needs for services. An obvious example is equity between women and men: 

men do not need access to maternal health services. 

On the societal level, equity in health services can be measured for several important groupings. Among 

those usually considered are groupings by gender, area of residence, occupation, education, ethnic and 

language groups, migration status, and economic status. 

Thus, a main reason for constructing a measure of economic status is to ascertain the equity of health 

programs and other publicly or privately provided services. There are three principal indicators of 

economic status: household income, household consumption expenditures, and household wealth. 

1.2 Household Income 

For many economists, household income is the theoretical indicator of choice. However, it is extremely 

difficult to measure accurately for a number of reasons: 

1) Many, if not most, people do not know their income or only know it in broad ranges. This 

lack of knowledge is especially true in less developed countries where a) there are no income 

taxes for most families, so that an annual accounting of income is not made, and b) many, if 

not most, families have self-employed earners and/or home production, and therefore costs of 

goods sold or produced are not recorded, no depreciation is calculated, and in the case of 

retail commerce, some of the goods bought wholesale are used for consumption. 

2) Most people try to hide their income from interviewers, especially if the interviewers are 

from a government agency. Those hiding income include both poor people (to appear poorer 

and therefore get assistance or additional assistance) and rich people (fearful of the 

possibility of taxation, political repercussions, and robbery). 

3) Many different members may be earners and a) do not share all of their income with the rest 

of the household and b) do not inform other household members of their income. 

4) An earner may have several sources of income at one time or during a given period of time: 

a) more than one place of employment, b) sales on the side, c) illicit income, and d) obtaining 

goods and services through theft (such as connecting to the electrical system and bypassing 

the meter). 

5) In many households and for many if not most earners, income is variable daily, weekly, or 

seasonally. 

6) There is a problem of how to value home production and unpaid production of goods and 

services: For example, when does a garden become a principal source of livelihood? Should 

time taken off for personal benefit, such as building one’s own dwelling, be valued as income 

at the going wage rate for laborers? 
3

7) The reporting of unearned income is problematic, such as that gained through interest on 

loans, property rents, or gambling winnings. 

For these reasons, obtaining valid information on household income requires a long and detailed 

interview with each member of the household over about age 12 (and sometimes younger). In the DHS 

setting, this process would be so time-consuming as to preclude asking questions on other topics. 

1.3 Household Consumption Expenditures 

One proposed alternative is using consumption expenditures as a proxy for income. This is based on the 

basic economic division of income by use: Y = C + S + T (where Y is income, C is consumption, S is 

savings, and T is taxes). It presumes that savings and taxes are almost nil or are proportional to income so 

that the distribution of income does not change with the level of income and that savings do not vary 

among households at the same level of income. These presumptions are clearly not true, but household 

consumption expenditures are often used as a proxy for household income so that measures have a 

monetary value. 

Measuring consumption expenditures has many of the drawbacks of measuring income. 

1) Expenditures are made by the different members of the household. Alcoholic beverages may 

be bought by the adult males, and foods and cosmetics may be purchased by the adult 

females. Children may also buy food and snacks. Adolescents may spend a large amount of 

their own earnings on CDs, music equipment, and clothing. However, household 

consumption expenditures typically are obtained from one adult household member who is at 

home when the interviewer arrives, and expenditures by other members may be omitted or 

misstated. 

2) Most expenditure surveys have been conducted to ascertain a market basket of goods and 

services in order to calculate a consumption price index. This approach uses a set number of 

items that are usually consumed daily, such as foods. However, for proper assessment of 

economic status, a much more extensive list of items needs to be included, many of which are 

large and irregular or with few periodic payments. Examples are purchases of vehicles and 

household appliances, holiday and birthday gifts, and school uniforms and textbooks, as well 

as payment of school fees. 

3) Even with a long list of consumption items, there are questions as to what period of time 

should be covered (e.g., the past 24 hours or past seven days for foods, the past 30 days for 

other items such as payment for electricity and phone service, or purchases of clothing). 

4) Whether to include other expenditures is still being debated: should all health expenditures, 

only routine health expenditures, or no health expenditures be included in overall 

expenditures? Should loan payments be included? Should large irregular expenditures, such 

as those for festivals, weddings, and funerals, be included? What about purchases of 

construction materials for one’s own dwelling? 

A common problem with both household income and consumption expenditures is their volatility. Income 

is very changeable in less developed countries, on both a seasonal and random basis. Households try to 

maintain core and nondiscretionary consumption expenditures in periods of declining income, but not 

discretionary expenditures. However, the economic status of households is better measured by 

discretionary expenditures, which may be more volatile than income itself. Since health outcomes and 

behaviors are probably more related to “permanent income” than current income, both measures of 

current income and current expenditures will not properly represent underlying differentials in health 

(Friedman, 1957). 
4

1.4 Household Wealth 

As a measure of economic status, wealth has several advantages. It represents a more permanent status 

than does either income or consumption. In the form that it is used, wealth is more easily measured (with 

only a single respondent needed in most cases) and requires far fewer questions than either consumption 

expenditures or income. 

Philosophy of the Wealth Index 

Wealth or its equivalent, net assets, is a theoretically measurable quantity. One can imagine making a list 

of all assets (including both physical and monetary assets), assigning them a value based on the market, 

depreciating them, and summing the values. The same can be done for debts, and then the debts can be 

subtracted from the assets to determine net assets. However, this procedure has the same problems as 

income and expenditures. Fortunately, there is another way to measure relative wealth that can be used to 

ascertain a household’s relative economic status. 

Wealth can be considered as an underlying unobserved variable. One then needs to have indicator 

variables that are associated with a household’s relative position in the distribution of the underlying 

wealth factor. DHS surveys have collected a number of such indicator variables, usually for purposes 

other than ascertaining economic status, but which are thought to be correlated with a household’s 

economic status. Figure 1.1 shows how certain goods and services may be associated with an underlying 

wealth scale. 

Increasing wealth

Surface water source

TV

Fridge

Motorcycle

Figure 1.1 Assumed distribution of assets and services

Proportion of households

In this figure, the proportion of households having a TV and a refrigerator (fridge) increases with 

increasing household wealth, while the proportion of households with a surface source (pond, lake, 

stream) of drinking water declines. The relationships are not linear, however, as indicated in the figure. 

Some goods or services, such as motorcycles, may have an intermediate relationship, at first increasing 

and then decreasing in prevalence, as wealth increases. 

Table 1.1 shows the usual assets and services collected in DHS surveys. 
5

Information on each of these items was collected for purposes other than determining wealth. Flooring 

type is associated with diarrhea in young children as are water supply and sanitation facilities. Television 

and radio were included to ascertain who was able to receive mass media health messages. Vehicle 

possession and type are related to emergency medical transportation possibilities. Having a nonelectric 

source of lighting and having several persons per sleeping room are thought to be related to increased 

transmission of respiratory illness. Two other indicators of wealth are generated from other variables: 1) 

household ownership of agricultural land (from type of land worked by respondent and her spouse), and 

2) presence of a domestic servant (from type of occupation of respondent and her spouse and their 

relationship to the head of the household, i.e., being unrelated). 

1.5 Economic Status versus Socioeconomic Status 

There are two other principal types of variables that are normally associated with socioeconomic status: 

type of occupation and level of education. These two types of variables were deliberately left out of the 

set of indicator variables for the wealth index so that a pure economic variable could be determined. Also, 

education and occupation each have their own effects on health status and use of health services, which 

may offset low economic status. Certain occupations provide health insurance, and higher levels of 

education allow for increased capacity of home care through knowledge gained from books and other 

materials. 

1.5.1 Establishing a Poverty Line 

To determine who is poor, a poverty line needs to be established. There are many ways in which poverty 

lines have been determined. A consideration that needs to be made is whether poverty is relative or 

absolute. In reality, both concepts are valid and useful. A person who would not be considered poor in 

one country may still be economically (and politically) disadvantaged in another because other people 

may have a higher economic position. Another consideration is whether national or international 

standards should be used. These decisions depend on how the poverty indicators are to be used. Table 1.2 

shows the different combinations and associated criteria. 

Table 1.2 Bases of poverty lines 

Poverty line National criteria International criteria 

Absolute poverty Minimum wage $1.00 per person per 

day of purchasing 

power 

 National minimum 

calorie consumption 

 Internationally 

determined minimum 

calorie consumption 

Relative poverty National percent 

distribution 

 International percent 

distribution 

Table 1.1 Assets and services usually asked about in DHS surveys 

 Type of flooring Refrigerator 

 Water supply Type of vehicle 

 Sanitation facilities Persons per sleeping room 

 Electricity Ownership of agricultural land 

 Radio Domestic servant 

 Television Country-specific items 

 Telephone 
6

Most countries have a national poverty line based on household income. In the United States, for 

example, the poverty line is based on a baseline food market basket providing a minimum number of 

calories multiplied by the inverse of the recommended proportion of expenditures on food to total income 

for varying family sizes. This poverty line was established many years ago and is adjusted by the 

consumer price index. One of its drawbacks is that the calorie needs and the market basket to supply them 

have not changed since their inception. (See Appendix A for a summary of the development of the 

poverty line in the U.S. by Gordon Fisher.) 

Other countries use the minimum wage as the basis for an absolute poverty line. However, in most 

countries the minimum wage is politically determined and adjusted infrequently for the effects of 

inflation. The World Bank’s “dollar-a-day” criterion is also arbitrary. In many countries, almost no one 

would be considered poor under this criterion: a family of four would have to have income of less than 

$1,460 per year. Another problem is that there is no adjustment for differences in publicly provided and 

subsidized goods and services and taxation, climate differences requiring heating and additional clothing, 

and so forth. 

A poverty line based on a national percentile distribution of households by economic status, such as 

wealth quintiles, is useful in assessing the reach of public health programs for both the poorer and richer 

sections of society. Often the poverty line is drawn at the 20th, 33rd, or 40th percentile. A set poverty line 

based on a national distribution is useful for comparison across countries and often shows similar results 

for health measures in different countries. The national quintile distribution can be made compatible with 

a national absolute poverty line if data are available on the percentage of the population below the 

absolute poverty line. This same percentage can then be used in a distribution of households on a relative 

index basis, such as the wealth index. 

1.6 Issues Regarding An Index of Economic Status 

There are several issues that can be raised regarding an index of economic status, particularly, a wealth 

index. These concern the handling of publicly provided goods and services, the direct effects of the 

indicator variables that make up indexes, adjustment for differing household needs based on size and age 

distribution, and the use to which the index will be put. Some of these issues also pertain to household 

income and consumption indexes. 

1.6.1 Public Services 

Certain services that could be included among the indicator variables are usually publicly provided, such 

as electricity and piped water. The question is whether they also reflect the economic status of a 

household or whether only privately acquired assets and services should be included. In the DHS wealth 

index, publicly provided services are included with the following reasoning: 1) Wealthy households will 

tend to reside in areas that provide such services, both through their decision of where to live and because 

of political pressure to provide these services, and 2) the provision of publicly provided services increases 

economic position by lowering the costs that would otherwise be incurred (such as candles and kerosene 

for lighting) and allowing greater productivity (such as better use of the time taken to get water). 

1.6.2 Individual Effects 

Indicator variables can have their own direct effects over health and the use of health services; for 

example, poor sanitation is associated with an increased prevalence of diarrheal disease. Almost every 

indicator variable in the existing DHS surveys was included for its direct effect, rather than for measuring 

economic status. Thus, the question is raised as to whether the effect is due to overall economic status or 

to direct effects of the component indicators. This would be a problem with the use of very few indicator 
7

variables, and the correlation of the indicator variables with the index is not very high when more than a 

few are used. Moreover, correlation of the indicator variables with the health outcome variables is not 

high, and many of the outcomes and services to be analyzed, such as family planning services, fertility 

rates, and vaccination rates, are not directly related to indicator variables. Upcoming DHS surveys may 

include variables that specifically measure economic status and are not directly related to health status 

and services (see below). 

1.6.3 Equivalization—Adjusting for Household Needs (Size and Age) 

The size and age structure of households affect their needs. Total household income and consumption 

expenditures need to be adjusted for size and age structure to properly represent the household’s 

economic position. A household with twice the income but with twice the number of members is not 

twice as well off. However, the relationship between size and income or expenditure is not lineal. This is 

because many goods and services can be shared among the members (e.g., appliances, heating, lighting) 

and because children, depending on their ages, place smaller demands on many goods and services (e.g., 

food, space, transportation). 

It is less clear that a wealth index needs to be equivalized (adjusted for size and age distribution of the 

household through the calculation of the number of adult equivalent members). Most of the assets and 

services included as indicator variables are shared between household members, and most are just 

indicators of possession of at least one or none, rather than quantities. Examples are type of flooring, type 

of water supply, type of sanitation, and possession of a vehicle. A few component variables, such as 

number of rooms or number of sleeping rooms, need to be adjusted for household size but most do not. 

An unpublished investigation, conducted by Rutstein and Johnson of ORC Macro and Wagstaff of the 

World Bank, showed that dividing the wealth index score for each household by its number of adult 

equivalent members distorted the economic status distribution and its associations with health status and 

services, resulting in unreasonable results. Therefore, the index or the majority of its component 

indicators were not equivalized. 

1.6.4 What Are We Trying to Measure? 

The employment of a relative index of economic status such as the DHS wealth index depends on the 

intended use of the index. There are two principal uses for a measure of economic status with regard to 

health programs: the ability to pay for health services and the distribution of services among the poor. 

The ability to pay for health services has been a prime concern of health economists who desire to 

rationalize services through the charging of user fees. The measures utilized for this purpose are the 

proportions of income or expenditures that health expenditures make up and the income elasticity of 

health expenditures. For these purposes an absolute monetary measure of economic status is appropriate. 

However, information obtained by estimating mean amounts of health expenditure according to a relative 

index, such as according to wealth quintile, can provide much useful information to policymakers on how 

to allocate fees. 

The distribution of health services to the poor can be determined by a wealth index as well as or better 

than an income or expenditure index. This is because of the lower volatility of wealth as compared with 

that of income and expenditures. In analyzing the distribution of health services (and publicly provided 

health services), only the relative aspect of economic status is used. 
8

2 Construction of the DHS Wealth Index 

There are several steps to the construction of the DHS wealth index: determination of indicator variables, 

dichotomization, calculation of indicator weights and the index value, and calculation of distribution cut 

points. 

2.1 Indicator Variables 

The selection of indicator variables is relatively straightforward. Almost all household assets and utility 

services are to be included, including country-specific items. The reason for using a broad criterion rather 

than selected items is that the greater the number of indicator variables, the better the distribution of 

households with fewer households being concentrated on certain index scores. Generally, any item that 

will reflect economic status is used. 

Two additional items are constructed for most surveys: whether there is a domestic servant and whether 

the household owns agricultural land. The first is constructed by examining the occupation of interviewed 

members who are not related to the head of the household. If the respondent or spouse works as a 

domestic servant and is not related to the head, then the household is considered to have a domestic 

servant. The second is also based on interviewed members. If any interviewed member (related to the 

head or not) or interviewed member’s spouse works his or her own or his or her family’s land, then the 

household is considered to own agricultural land. 

Many indicator variables are categorizations. To determine the weights and apply them to form the index, 

it is necessary to break these variables into sets of dichotomous variables. Figure 2.1 shows an example of 

the presumed relationship between type of toilet facility and type of flooring with the underlying wealth 

scale. 

Figure 2.1 Underlying Unmeasured Wealth Scale 

Bush Flush

Dirt Cement Parquet

Wealth ei r

Poorer

Type of toilet

Type of flooring

Many times there is no obvious ordering of the categories. For example, are wealthier people more likely 

to use carpeting or ceramic tiling than parquet? A possibility would be to collapse these categories into a 

single one, but doing so would decrease the distinctions that could be made between households on the 

index. Some categories are routinely collapsed in constructing the DHS wealth index. The category 

“surface water” includes supplies of drinking water from “river,” “pond,” and “stream,” since differences 

between these categories have more to do with location of source than wealth. Sometimes an indicator 
9

variable is combined with another to form combination categories, which are then dichotomized. This is 

the case for the variable “shared toilet.” The categories of flush toilet and latrine are split by whether they 

are shared with other households, doubling the number of dichotomies used. However, the category 

“bush, field” is not split by whether it is used by other households. 

As indicated above, the number of sleeping rooms (or total rooms if there is no variable for sleeping 

rooms) is divided into the number of household members as an equivalization. 

It can be seen that the determination of specific indicator variables is somewhat of an art, depending on 

knowledge of conditions in each country. Sometimes variables need to be removed from the set of 

indicators in order for the resulting wealth index to make sense. Such is the case for “having a dacha” in 

the Central Asian Republics. While the term “dacha” is used for the country house of rich Russian 

families, it can also represent a small cottage or even just a rural garden plot with a small shed that many 

poor families have as a means of extending their income. When “dacha” was included in the set of 

indicator variables for the Central Asian Republics, the resulting index changed sign, with wealthier 

people having lower (negative) index scores than poor people (positive). The anomalous relationship was 

investigated by consulting with country natives, who recommended excluding this variable. With “dacha” 

removed, the index righted itself. 

2.2 Construction of the Index 

There are various ways to assign weighting values to the indicator variables. Ad hoc weights, such as 

assigning “1” for a bicycle, “3” for a motorcycle, and “5” for a car or truck, work to a certain extent, but 

they are arbitrary with regard to researcher and are difficult to assign when the wealth ordering is not 

readily apparent. For this reason, Filmer and Pritchett recommended using principal components analysis 

(PCA) to assign the indicator weights, the procedure that is used for the DHS wealth index. DHS uses the 

SPSS factor analysis procedure. This procedure first standardizes the indicator variables (calculating zscores); then the factor coefficient scores (factor loadings) are calculated; and finally, for each household, 

the indicator values are multiplied by the loadings and summed to produce the household’s index value. 

In this process, only the first of the factors produced is used to represent the wealth index. The resulting 

sum is itself a standardized score with a mean of zero and a standard deviation of one. 

2.3 Construction of Quintiles 

For tabular analysis with the DHS wealth index, quintiles are used. These quintiles are based on the 

distribution of the household population rather than on the distribution of households. The distribution is 

population based because it is thought that most analyses are concerned with poor people rather than poor 

households. Quintiles are used instead of other percentiles as a compromise between limiting the number 

of categories to be tabulated and adequately representing the relationship between wealth and the 

phenomenon of interest. Other percentiles can be just as easily determined as quintiles. 

The cut points in the wealth index at which to form the quintiles are calculated by obtaining a weighted 

frequency distribution of households, the weight being the product of the number of de jure members of 

the household and the sampling weight of the household. Thus, the distribution represents the national 

household population, where each member is given the wealth index score of his or her household. The 

persons are then ordered by the score, and the distribution is divided at the points that form the five 20- 

percent sections. Then the household score is recoded into the quintile variable so that each member of a 

household also receives that household’s quintile category. 

One distribution is used for all tabular analyses, rather than separate ones for different analyses, such as 

quintiles of births for infant mortality or quintiles of currently married women of reproductive age for 
10

contraceptive prevalence rates. A single distribution eliminates confusion that multiple distributions 

would entail, with having, say, a poor child living with his or her not poor mother. A consequence, 

however, is that terms such as “the poorest 20 percent of children” or the “richest 40 percent of women” 

should not be used because they are inaccurate. Rather, “children from the poorest quintile of the 

household population” is preferred. 

For nontabular analyses, such as correlations and multivariate analyses, the individual household score 

can be used directly, as well as the quintile value. 

 

2.4 Variations 

Other procedures have been suggested instead of PCA. One is to use the inverse of the proportion of 

households with an asset or service as the weight for the indicator. The thinking behind this procedure is 

that the costlier an item, the wealthier a household needs to be to possess one, giving the highest weights 

to the least possessed assets. Presumably, “negative assets,” such as “having a dirt floor,” would be used 

as inverses (i.e., “not having a dirt floor”). One of the problems with this weighting scheme is that certain 

assets, such as motorcycles, may be rare since better substitutes, such as a car or truck, are possessed by 

wealthier households. Additionally, certain items, such as drinking water from a spring, are rarely used, 

and when they are used, it is usually by poorer people. 

An alternative that may be promising is hierarchical ordered probit (HOPIT) analysis (Ferguson et al., 

2002). This procedure also assumes that there is an underlying unmeasured scale. Depending on its 

position on this scale, a household will possess an asset or use a service. For example, on a scale from 0 

to 1, households with a position of 0.8 or more would have a refrigerator, and those below 0.8 would not; 

similarly, households at or above 0.3 would have electricity, and those below would not. Thus, each 

indicator has its position on the scale, which determines the weight of the indicator when calculating a 

household’s score. “Negative assets” are inverted in this procedure. 

2.5 Alternative Measures of Economic Status 

Although theoretically and practically superior, the wealth index does not produce results that are similar 

to either an income- or expenditure-based index. Such a comparison has been done by both Filmer and 

Pritchett, and Montgomery and others (Montgomery et al., 2000). Montgomery and others concluded that 

the wealth index was not a good proxy for income. However, the wealth index was never meant to predict 

household income, so its utility in producing differentials by economic status was not properly evaluated. 

As indicated above, Filmer and Pritchett concluded that a wealth index produced a better analysis of 

education differentials by economic status than did an expenditure index. 

As part of the 1997 Guatemala Health Demand and Expenditure Survey (linked to the 1997 Guatemala 

DHS), household consumption expenditures were collected in an investigation of health expenditures in 

the four altiplano departments of Guatemala (Instituto Nacional de Estadística, 1999a and 1999b.) Since 

this survey also had questions on assets and services, the two measures could be compared. This 

comparison is described in section 2.6. 

2.6 Wealth versus Expenditure: Guatemala 

The expenditure index is based on household expenditures for goods and services with varying reference 

periods. The individual items were converted into average monthly expenditures for each, and then they 

were summed. There are two types of expenditure indexes: those based on the household total and those 

based on per-member expenditures. The total index was used in the published analysis of household 

health expenditures in Guatemala. For comparisons with the wealth index, quintiles of households were 
11

formed from 1) the distribution of total expenditure per household and 2) the total divided by the number 

of household members (de jure household population), then formed into quintiles of the distribution of the 

household population by per member expenditure. Table 2.1 shows the distribution of households when 

grouped into population quintiles by total monthly household expenditures (in quetzales—about 6 

quetzales per US$). 

Table 2.1 Monthly household expenditure and number of households by quintile of 

household total expenditure, Guatemala Health Demand and Expenditure Survey, 1997 

Quintile of household 

total expenditure 

Mean 

expenditure 

(in quetzales) 

Number 

 of 

households 

Standard 

deviation 

Mean number 

of household 

members 

Q1: 0-530.69 384.58 523 105.79 4.1 

Q2: 530.70-734.09 634.34 516 59.05 5.5 

Q3: 734.10-971.39 843.85 517 66.30 6.0 

Q4: 971.40-1350.79 1,140.68 509 106.04 6.4 

Q5: 1,350.80 or more 2,040.73 497 696.81 6.6 

 

Total 999.10 2,562 650.79 5.7 

6 quetzales = US$1 

This table shows that larger households are concentrated in the higher quintiles, so that the quintiles 

represent a combination of increased economic status and more members, rather than just economic 

status. 

Table 2.2 shows the expenditures divided by the number of household members and then divided in fifths 

of the household population. This table shows that the number of members is greater in the poorer 

households when taken on a per member basis. 

Table 2.2 Monthly per-member household expenditure and number of household 

members by quintile of per-member household total expenditure, Guatemala Health 

Demand and Expenditure Survey, 1997 

Quintile of 

household total 

expenditure 

Mean permember 

expenditure 

(in quetzales) 

Number 

 of household

members 

Standard 

deviation 

Mean number 

of household 

members 

Lowest 69.07 2,939 14.31 7.5 

Second 104.68 2,937 9.42 6.7 

Middle 139.16 2,937 10.84 6.1 

Fourth 190.28 2,938 2,0.39 5.3 

Highest 368.56 2,937 165.63 4.3 

 

Total 174.34 14,688 129.5 5.7 

6 quetzales = US$1 

The wealth index for this survey was calculated with the items in Table 2.3. 
12

Table 2.4 shows the mean and standard deviation of per-member expenditures classified according to the 

wealth index. The number of households is almost equal in each quintile even though the quintiles are 

based on population rather than households. The mean number of members per household is fairly 

constant across the wealth quintiles, except for the lowest, which was not the case for either total or permember household expenditure quintiles. 

Table 2.4 Monthly per-member household expenditure and number of household 

members by quintile of per-member household total expenditure, Guatemala Health 

Demand and Expenditure Survey, 1997 

Wealth index 

quintile 

Mean permember 

expenditure 

Number 

 of household

members 

Standard 

deviation 

Mean number 

of household 

members 

Lowest 115.19 2,969 63.77 6.5 

Second 132.35 2,979 71.56 5.7 

Middle 143.70 2,916 77.79 5.5 

Fourth 176.75 2,945 93.19 5.5 

Highest 306.73 2,888 191.14 5.5 

 

Total 174.34 14,688 129.15 5.7 

Table 2.5 shows the households cross-classified by quintiles based on per member expenditures and based 

on wealth. If all households were classified in the same quintiles for each measure, only the diagonal cells 

would be filled. According to this tabulation, however, only 36 percent of the households are classified in 

the same quintile by both measures, and 28 percent of households are classified differently by more than 

one quintile. Therefore, wealth is not a straight proxy for per-member expenditures. 

Table 2.5 Wealth index quintiles by quintiles for per-member expenditure 

Wealth index Quintiles for per-member expenditure 

quintile 1.00 2.00 3.00 4.00 5.00 Total 

1.00 159 104 84 73 39 459 

2.00 109 130 110 103 64 515 

3.00 81 115 128 126 82 532 

4.00 39 73 112 158 148 530 

5.00 5 18 50 98 354 525 

 

Total 393 440 484 558 687 2,582 

Table 2.3 Items in Guatemala Health Demand and 

Expenditure Survey wealth index 

Assets Services 

 Radio Electricity 

 Television Water supply 

 Telephone Toilet facility 

 Refrigerator 

 

Vehicle Flooring 

 Bicycle 

 Motorcycle Ownership of dwelling 

 Automobile 

 Tractor 
13

In determining which performs better, two types of comparisons evaluated performance of the indexes. 

The first is in regard to characteristics of the households, and the second is in regard to outcomes. 

Tables 2.6 through 2.8 show how the quintiles perform with respect to three characteristics of households: 

percentage with a dirt floor, percentage with a television, and percentage with piped drinking water. As 

seen in Table 2.6, for quintiles based on the expenditure measures, one out of four households in the 

highest quintile have dirt floors. This is not the case for the quintiles based on the wealth index, which 

produces a greater distinction between quintiles. Similarly, Tables 2.7 and 2.8 show that the distribution 

of households with regard to television and piped water, respectively, is much more believable for 

quintiles based on wealth than those based on total or per-member expenditures, reinforcing the better 

distinction of economic status by the wealth index.1

 These altiplano departments are considered among 

the poorest in Guatemala; therefore, it is surprising to see almost half of the poorest households in the 

poorest region have piped water and more than one in ten have television sets when classified according 

to expenditures. 

Table 2.6 Percentage of households with dirt as the principal floor 

 Quintile 

Index Lowest Second Middle Fourth Highest 

Total expenditures 78 71 62 49 24 

Per-member expenditures 82 75 64 57 27 

Wealth 97 85 70 35 4 

Table 2.7 Percentage of households with a television 

 Quintile 

Index Lowest Second Middle Fourth Highest 

Total expenditures 11 24 33 50 74 

Per-member expenditures 11 20 31 41 67 

Wealth 1 4 23 64 93 

Table 2.8 Percentage of households using piped water in dwelling for 

drinking 

 Quintile 

Index Lowest Second Middle Fourth Highest 

Total expenditures 47 53 56 58 73 

Per-member expenditures 48 55 53 58 66 

Wealth 12 50 56 75 86 

 

1

 It must also be considered that these assets and services are in part used to form the wealth index so we would 

expect a better performance of the wealth index when judged against these assets. However, the lack of distinction 

by the expenditure-based indexes is surprising and indicates that they are not adequately representing different 

underlying economic statuses. 
14

Figure 2.2 compares the contraceptive prevalence rate according to the expenditure indexes and the 

wealth index. Figure 2.3 compares the proportion of births attended by a physician according to the 

indexes. In both cases, the wealth index gives a distinction in outcome that is as good as or greater than 

that of the expenditure-based indicators, and the results are similar for the per-member expenditure index. 

Figure 2.2

Percentage of Women Using a Contraceptive Method

by Type of Measure of Poverty

*

*

*

*

*

,

,

,

,

,

&

&

&

&

&

Lowest Second Middle Fourth Highest .

Quintile

0

5

10

15

20

25

Percent

& Total expendiitures Per-member expenditures Wealth index , *

Figure 2.3

Percentage of Births Attended by a Physician by Type of 

Measure of Poverty

*

*

* *

*

,

,

,

,

,

&

&

& &

&

Lowest Second Middle Fourth Highest .

Quintile

0

10

20

30

40

50

60

Percent

& Total expendiitures Per-member expenditures Wealth index , *

Thus, it can be seen that compared with expenditure measures, the wealth index is the easiest measure of 

economic status to collect and produces superior, more believable results and equal or greater distinctions 

in health outcomes. 
15

3 Who Has What? 

Figures 3.1 through 3.5 show the distribution of households by the value of the wealth index for five 

selected country surveys (one for each world region): Egypt 1995, India 1992-1993, Kenya 1998, Peru 

1996, and Uzbekistan 1996. The differences in distribution between countries are quite clear. In Kenya 

1998 and India 1992-93, the indexes are skewed to the right, with the majority of households below the 

mean value and a long tail above. In Egypt 1995, the opposite is true: the distribution is somewhat 

skewed to the left, with a long tail at the lower end of the distribution. In Peru 1996 and Uzbekistan 1996, 

the wealth distribution is not skewed but appears to be bimodal with the number of households with 

middle values less than the number with higher or lower values on the index. 

Figure 3.1 

HH wealth index score

2.50

11.502.00

.00

-1-.500.00.50

.00

-1.50-2-2.00

.50

-3.00-3.50-4.00-4.50

-5.00

-6-5.50

.00

-6.50-7.00

Distribution of household wealth index scores

Egypt 1995

Cases weighted by HHWEIGHT

3000

2000

1000

0

Std. Dev = 1.01 

Mean = -.02

N = 10759.28

Figure 3.2 

HH Wealth index score

3.25

2.75

2.25

1.751.25

-1-.75-.25

.25

.75

.25

-1.75

Distribution of household wealth index scores

India 1992-93

Cases weighted by HHWGT

20000

10000

0

Std. Dev = .95 

Mean = -.18

N = 88544.43
16

Figure 3.3 

WLTHSCOR

5.755.25

34.254.75

.75

12.252.753.25

.75

.71.25

5

-.

.25

25

-1.2

-.75

5

Distribution of household wealth index scores

Kenya 1998

Cases weighted by HHWGT

2000

1000

0

Std. Dev = 1.08 

Mean = .06

N = 8379.88

Figure 3.4 

WLTHSCOR

2.75

2.50

2.25

2.00

1.75

1.50

1.25

1.00

.75

.50

.25

0.00

-.25

-.50

-.75

-1.00

-1.25

-1.50

-1.75

Distribution of household wealth index scores

Peru 1996

Cases weighted by HHWGT

3000

2000

1000

0

Std. Dev = 1.01 

Mean = .19

N = 28122.41
17

Figure 3.5 

WLTHSCOR

2.00-1-.500.00.501.001.50

.00

-1.50-2-2.00

.50

-3.00-4-3.50

.00

Distribution of household wealth index scores

Uzbekistan 1996

Cases weighted by HHWGT

500

400

300

200

100

0

Std. Dev = .95 

Mean = -.23

N = 3703.06

Table 3.1 shows key summary statistics for the distribution of households and household populations by 

the DHS wealth index for the five selected country surveys. Five summary statistics are given in this 

table: mean, median, mode, skewness, and kurtosis for households. The mean of the wealth index scores 

is close to zero since the index is standardized for households to produce z-scores. The median and mode, 

when compared to the mean and to each other, indicate the amount of skewing in the distribution (also 

measured by the skewness statistic). In Kenya and India, both the median and modal values are below the 

respective means; in Egypt, both are above the mean. In Peru, the median is close to the mean, but the 

mode is much higher; in Uzbekistan, the median is above the mean, but the mode is below. 

Table 3.1 Distribution statistics and quintile cutoff values for the wealth index, selected 

DHS surveys, 1992-1996 

Egypt 

1995 

India 

1992-93 

Kenya 

1998 

Peru 

1996 

Uzbekistan 

1996 

Mean -0.020 -0.178 0.065 0.188 0.234 

Median 0.153 -0.496 -0.344 0.223 0.375 

Mode 0.815 -0.957 -0.395 1.120 0.162 

Skewness -1.135 0.972 1.965 0.017 -0.517 

Kurtosis 1.911 0.004 4.563 -1.154 -0.463 

Quintile cutoff values 

Lowest-second -0.983 -0.988 -0.773 -0.927 -0.146 

Second-middle -0.313 -0.709 -0.518 -0.151 0.278 

Middle-fourth 0.176 -0.213 -0.223 0.598 0.704 

Fourth-highest 0.694 0.653 0.526 1.192 1.105 
18

The skewness statistic measures the symmetry of the wealth distribution around its mean. Its values 

indicate the following: India and Kenya have high positive skewness (i.e., skewed to the right), Egypt has 

high negative skewness (skewed to the left), Uzbekistan has some negative skewness, and Peru has no 

skewness. Kurtosis is the measure of concentration (pointedness) of the distribution compared with that of 

the normal distribution. Kenya shows the highest positive value, indicating that the distribution is much 

more concentrated than the normal distribution. Peru has the highest negative value of kurtosis, indicating 

much less concentration than a normal curve. In contrast, the wealth distribution of Indian households is 

just as concentrated as the normal distribution. 

Table 3.1 also shows the values of the quintile cutoffs, which are based on the household population, not 

the households themselves. The cutoff values between the lowest and second quintiles are most negative 

in Egypt, India, and Peru, indicating that poor people in these countries have much less wealth than others 

in the country, and least negative in Uzbekistan, indicating that the poor are not so relatively poor. At the 

other end of the distribution, the cutoff values between the fourth and highest quintiles are highest in Peru 

and Uzbekistan, indicating that the people in the fourth quintile are fairly well off. Visual representations 

of these values are shown in Figures 3.1 through 3.5. Table B.1 gives the values for all 44 countries. 

For a comparison of what people have in the selected countries, the assets and services used to construct 

the wealth index were tabulated according to quintile of the wealth distribution. The percentage of 

households that have these assets and services by quintile are shown in Tables 3.2 through 3.6. 
19

Table 3.2 Percentage of households with specific wealth indicators by wealth quintile, Egypt 1995 

Quintile (percent or number) 

Indicator Lowest Second Middle Fourth Highest Average 

Has electricity 80.7 99.0 99.6 99.9 100.0 95.8 

Has radio 30.1 56.6 64.2 77.8 94.5 64.6 

Has television 43.9 79.3 87.4 94.2 98.9 80.7 

Has refrigerator 2.9 18.8 54.5 91.7 99.1 53.4 

Has bicycle 6.6 15.4 21.5 20.8 21.2 17.1 

Room for cooking 20.5 49.6 72.0 90.4 98.9 66.3 

Household goods: B&W television 42.4 73.0 67.6 49.3 22.4 50.9 

Household goods: video 0.2 0.2 0.7 2.6 29.4 6.7 

Household goods: electric fan 9.9 34.4 53.6 76.5 94.4 53.8 

Household goods: gas/elec.stove 2.6 25.9 72.0 96.8 99.7 59.4 

Household goods: water heater 0.1 0.4 2.4 16.0 89.2 21.7 

Household goods: sewing machine 1.3 6.1 12.1 21.6 40.8 16.4 

Household goods: auto washer 0.0 0.3 0.9 0.8 31.1 6.6 

Household goods: other washer 20.9 66.5 89.3 97.3 85.3 71.8 

Has car/motorcycle 0.8 2.9 4.1 6.2 26.1 8.0 

Has farm/other land 43.4 38.3 29.2 14.0 11.3 27.2 

Has livestock 56.9 45.5 28.0 9.8 2.8 28.6 

If HH has a domestic worker 

 not related to head 0.0 0.0 0.0 0.0 0.1 0.0 

If household works own 

 or family’s agricultural land 32.6 23.0 11.3 3.3 0.9 14.2 

If piped drinking water in residence 16.2 57.2 82.1 96.0 99.3 70.2 

If has a well in residence 19.7 13.9 7.6 1.9 0.4 8.7 

If uses river, canal or surface water 

 for drinking 0.5 0.2 0.0 0.0 0.0 0.2 

Other source of drinking water 8.4 5.3 2.9 1.2 0.3 3.6 

If uses modern flush toilet 0.2 0.7 3.8 21.2 89.6 23.2 

If uses a trad. flush toilet with 

 a tank flush 0.5 0.6 1.7 3.4 1.5 1.5 

If uses bush, field as latrine 21.4 3.7 0.9 0.6 0.0 5.3 

If other type of latrine 4.0 0.8 0.3 0.0 0.0 1.0 

If has dirt, sand, dung as principal 

 floor in dwelling 90.1 58.6 20.6 1.7 0.2 34.2 

If uses a trad. flush toilet with 

 bucket flush 44.6 76.6 87.8 73.2 8.8 58.1 

If has cement principal floor 9.2 32.6 37.5 11.2 1.1 18.3 

If has other type of flooring 0.0 0.0 0.2 0.5 1.9 0.5 

If uses a public faucet (piped) 31.2 16.5 5.4 0.7 0.0 10.8 

If uses a traditional public well 24.0 6.9 1.9 0.1 0.0 6.6 

If uses a traditional pit toilet 29.2 17.5 5.7 1.6 0.1 10.8 

If has parquet or polished 

 wood floors 0.0 0.1 0.1 0.3 4.4 1.0 

If has tiles for main flooring material 0.7 8.5 41.2 85.4 83.6 43.9 

If has carpeted flooring 0.0 0.0 0.4 0.8 8.7 2.0 

Number of members per 

 sleeping room1

 3.8 3.5 3.0 2.6 2.0 3.0 

1 Mean 
20

Table 3.3 Percentage of households with specific wealth indicators by wealth quintile, Kenya 1998 

Quintile (percent or number) 

Indicator Lowest Second Middle Fourth Highest Average 

Has electricity 0.0 0.2 0.1 1.8 56.7 11.7 

Has radio 27.4 55.3 75.7 79.0 93.9 66.4 

Has television 0.0 0.0 1.2 7.5 60.3 13.8 

Has refrigerator 0.0 0.0 0.0 0.0 18.7 3.7 

Has bicycle 15.6 26.5 34.7 35.1 29.1 28.3 

Has motorcycle 0.0 0.0 0.1 0.4 4.1 0.9 

Has car 0.0 0.0 0.1 1.9 22.9 5.0 

Has telephone 0.0 0.0 0.0 0.1 13.4 2.7 

If HH has a domestic worker 

 not related to head 0.0 0.0 0.0 0.5 9.4 2.0 

If household works own or 

family’s agricultural land 60.0 48.1 28.7 26.3 8.2 34.1 

If piped drinking water in residence 0.0 0.8 3.4 27.6 65.9 19.5 

If piped drinking water in public tap 1.1 8.0 12.2 15.6 10.0 9.4 

If inside well drinking water 2.9 8.6 12.5 8.5 7.3 8.0 

If uses river, canal, or surface water 

 for drinking 71.5 61.5 46.6 26.7 6.9 42.5 

Other source of drinking water 0.2 1.4 1.7 3.5 2.4 1.8 

If uses shared flush toilet 0.0 0.0 0.0 1.0 14.8 3.2 

If has pit latrine 56.6 77.2 87.2 81.8 34.9 67.6 

If uses ventilated improved pit latrine 0.0 0.7 3.1 10.3 16.9 6.2 

If uses bush, field as latrine 42.8 21.7 9.0 5.2 0.8 15.9 

If other type of latrine 0.2 0.1 0.2 0.2 0.1 0.2 

If has dirt, earth principal floor 

 in dwelling 100.0 99.9 98.5 36.8 1.4 67.3 

If has wood, plank principal floor 

 in dwelling 0.0 0.0 0.2 3.4 0.8 0.9 

If has cement principal floor 0.0 0.1 0.8 58.4 90.5 30.0 

If has tile flooring 0.0 0.0 0.0 0.3 7.1 1.5 

If has other type of flooring 0.0 0.0 0.0 0.0 0.2 0.0 

If has natural material roofing 98.5 40.8 5.2 4.0 0.3 29.6 

If has corrugated iron roofing 1.5 59.2 93.1 94.2 83.1 66.4 

If has roofing tiles 0.0 0.0 0.0 0.1 14.3 2.9 

If has other roofing 0.0 0.0 1.3 0.8 1.9 0.8 

If uses rain for drinking water 0.1 0.3 1.1 2.0 2.9 1.3 

If uses a public well 19.3 13.2 15.5 11.8 3.8 12.7 

If has own flush toilet 0.0 0.0 0.0 0.6 32.4 6.6 

Number of members per 

 sleeping room1 4.6 3.6 2.7 2.6 2.5 3.2 

1 Mean 
21

Table 3.4 Percentage of households with specific wealth indicators by wealth quintile, India 1992-93 

Quintile (percent or number) 

Indicator Lowest Second Middle Fourth Highest Average 

Has electricity 0.4 10.1 54.8 92.2 99.5 51.4 

Has radio 9.4 23.6 38.8 57.6 80.1 41.9 

Has television 0.0 0.2 2.6 25.5 81.9 22.1 

Has refrigerator 0.0 0.1 0.2 1.0 32.8 6.8 

Has bicycle 31.7 40.1 46.0 54.5 63.4 47.1 

Has motorcycle 0.1 0.4 2.1 7.3 35.3 9.0 

Has car 0.0 0.0 0.1 0.4 6.0 1.3 

Rooms in household 2.1 2.5 2.9 3.4 3.9 3.0 

Separate room used as kitchen 29.8 42.1 57.4 66.7 83.2 55.8 

HH owns agricultural land 76.9 60.0 62.1 53.2 27.6 56.0 

Size of nonirrigated agricultural land 

(acres)1

 3.1 2.1 3.4 2.9 3.9 3.1 

Size of irrigated land (acres)1 3.2 2.8 2.8 3.4 3.6 3.2 

Own any livestock 95.2 62.4 63.9 53.6 18.5 58.7 

Own bullock 48.0 28.8 30.1 19.6 4.4 26.2 

Own cow 51.2 32.3 34.5 28.8 10.9 31.6 

Own buffalo 34.1 25.5 31.1 29.6 8.7 25.8 

Own goat 34.0 17.5 13.9 9.6 2.6 15.5 

Own sheep 3.2 1.6 2.2 1.3 0.2 1.7 

Own camel 0.9 0.5 0.6 0.5 0.0 0.5 

Own other animal 4.4 2.9 2.5 2.0 0.8 2.5 

Sewing machine 1.3 4.5 10.8 27.8 56.8 20.2 

Clock/watch 15.3 36.9 53.5 80.5 97.5 56.8 

Sofa set 0.0 0.1 1.0 6.2 41.1 9.7 

Fan 0.2 0.9 10.2 59.8 96.1 33.4 

VCR/VCP 0.1 0.5 1.0 1.3 10.7 2.7 

Tractor 0.2 0.4 1.5 3.6 3.0 1.7 

If household works own or family’s 

 agricultural land 89.8 88.5 88.7 88.5 87.4 88.6 

If piped drinking water in residence 0.4 1.8 5.5 19.5 62.6 18.0 

If piped drinking water in public tap 6.0 11.2 18.9 21.7 9.8 13.5 

If well drinking water in residence 5.0 6.9 8.8 10.1 7.2 7.6 

If public well for drinking water 33.9 24.9 20.9 11.7 1.9 18.7 

If uses spring for drinking water 0.8 0.8 1.1 0.6 0.1 0.7 

If uses river, canal, or surface water 

 for drinking 5.6 4.3 3.3 1.8 0.3 3.1 

If uses rainwater for drinking 0.0 0.0 0.0 0.1 0.0 0.0 

If uses tanker truck for drinking water 0.1 0.1 0.2 0.6 0.6 0.3 

Other source of drinking water 1.0 1.8 2.2 1.7 0.5 1.5 

If has private flush toilet 0.0 0.2 2.2 13.3 68.2 16.8 

If has public flush toilet 0.1 0.2 1.2 3.6 3.4 1.7 

If uses bush, field as latrine 99.0 95.2 86.3 63.5 6.6 70.1 

If other type of latrine 0.0 0.0 0.1 0.1 0.1 0.1 

Residential handpump 11.0 18.2 16.9 19.7 12.8 15.7 

Public handpump 36.2 29.7 22.2 12.5 4.0 20.9 

Private latrine 0.7 3.5 7.1 11.6 11.1 6.8 

Public latrine 0.1 0.3 1.1 1.9 1.1 0.9 

Shared latrine 0.1 0.4 1.2 2.3 2.3 1.2 

Electricity for lighting 0.4 10.1 54.8 92.2 99.5 51.4 

Kerosene for lighting 98.9 89.3 44.5 7.6 0.4 48.2 

Gas for lighting 0.1 0.1 0.1 0.0 0.0 0.1 

Oil for lighting 0.3 0.2 0.2 0.0 0.0 0.2 

Other lighting 0.3 0.2 0.3 0.1 0.0 0.2 

Wood cooking fuel 79.5 76.0 80.5 69.8 17.6 64.7 

Dung cooking fuel 14.2 15.3 11.8 10.3 2.5 10.8 

Coal cooking fuel 0.7 2.3 3.5 5.3 6.3 3.6 

Charcoal cooking fuel 0.1 0.2 0.4 0.5 0.7 0.4 

Kerosene cooking fuel 0.0 0.2 1.5 10.3 19.6 6.3 

LPG cooking fuel 0.0 0.0 0.1 1.1 49.6 10.2 

Biogas cooking fuel 0.0 0.0 0.3 1.0 1.9 0.6 

Other cooking fuel 5.0 5.4 1.2 0.6 0.1 2.5 

House from high-quality materials 0.3 2.6 9.2 27.9 77.6 23.5 

House from low-quality materials 88.8 75.5 51.9 22.9 2.9 48.4 

House from mixed quality materials 10.8 21.7 38.7 49.0 19.2 27.9 

If animals sleep inside house 29.4 19.4 17.8 14.1 4.1 17.0 

If animals sleep outside house 65.5 42.7 45.8 39.3 14.3 41.5 

Electricity for cooking 0.0 0.0 0.0 0.3 1.2 0.3 

If has shared flush toilet 0.0 0.1 1.0 3.7 7.1 2.4 

Number of members per 

 sleeping room1

 3.9 3.2 2.9 2.6 2.1 2.9 

1

 Mean 
22

Table 3.5 Percentage of households with specific wealth indicators by wealth quintile, Peru 1996 

Quintile (percent or number) 

Indicator Lowest Second Middle Fourth Highest Average 

Has electricity 2.6 43.7 90.3 99.1 99.9 67.1 

Has radio 63.7 82.3 89.0 96.2 99.4 86.1 

Has television 4.3 49.6 86.4 97.0 99.8 67.4 

Has refrigerator 0.0 2.3 21.5 56.0 96.1 35.2 

Has bicycle 8.1 21.8 28.8 24.7 42.7 25.2 

Has motorcycle 0.2 1.5 3.2 3.5 8.5 3.4 

Has car 0.2 2.0 4.2 8.1 38.0 10.5 

Has telephone 0.0 0.1 1.9 12.2 78.9 18.7 

Has computer 0.0 0.0 0.2 0.7 14.5 3.1 

If HH has a domestic worker not 

 related to head 0.0 0.3 0.8 1.4 11.0 2.7 

If household works own or family’s 

 agricultural land 60.4 25.1 6.9 2.5 0.9 19.2 

Number of members per 

 sleeping room1

 5.0 3.9 3.5 2.8 1.8 3.4 

If piped drinking water in residence 3.6 34.3 57.6 89.9 98.0 56.7 

If has a well in residence 4.2 7.1 5.2 1.1 0.2 3.5 

If uses river, canal or surface water 

 for drinking 65.6 14.5 2.1 0.1 0.0 16.5 

Other source of drinking water 8.1 10.2 4.5 0.8 0.1 4.7 

If uses a flush toilet in residence/ 

 private 0.0 2.2 28.3 75.9 96.5 40.7 

If uses bush, field as latrine 76.6 40.4 11.1 0.7 0.0 25.8 

If other type of latrine 0.4 2.2 3.3 0.6 0.1 1.3 

If has dirt, sand, dung as principal 

 floor in dwelling 92.0 84.4 44.3 6.3 0.1 45.4 

If has wood, plank principal floor 

 in dwelling 3.4 5.5 7.8 4.5 2.0 4.6 

If has cement principal floor 0.3 8.7 44.5 81.8 52.5 37.6 

If has other type of flooring 4.4 1.0 1.1 1.1 0.8 1.7 

If rain for drinking water 0.3 0.0 0.0 0.0 0.0 0.1 

If uses a public faucet (piped) 8.4 14.0 12.7 2.5 0.3 7.6 

If uses a traditional public well 7.9 7.6 2.4 0.5 0.2 3.7 

If uses a private latrine 19.5 44.6 36.6 10.3 0.5 22.2 

If uses a public latrine 2.9 5.8 4.4 0.8 0.0 2.8 

If has parquet or polished wood floors 0.0 0.1 0.5 0.9 21.0 4.5 

If has tiles for main flooring material 0.0 0.0 1.0 4.4 17.8 4.7 

If has vinyl or asphalt strips as 

 flooring material 0.0 0.0 0.6 0.9 6.0 1.5 

If uses water that is piped into 

 the building 1.5 3.7 6.9 2.0 0.4 2.9 

If uses bottled water 0.5 8.2 8.5 3.2 0.9 4.2 

If uses a flush toilet in residence/public 0.1 2.0 10.7 10.5 2.5 5.1 

If uses a flush toilet outside residence/ 

 private 0.1 0.8 1.5 0.4 0.0 0.5 

If uses a flush toilet outside of 

 residence/public 0.3 1.6 4.1 0.9 0.2 1.4 

1 Mean 
23

Table 3.6 Percentage of households with specific wealth indicators by wealth quintile, Uzbekistan 1996 

Quintile (percent or number) 

Indicator Lowest Second Middle Fourth Highest Average 

Has electricity 97.6 100.0 100.0 100.0 100.0 99.5 

Has radio 37.9 61.7 57.6 75.4 79.0 62.3 

Has television 71.3 91.4 98.0 99.4 98.5 91.7 

Has refrigerator 7.3 65.3 70.0 96.6 97.3 67.2 

Has bicycle 18.2 24.6 26.0 29.8 17.1 23.1 

Has motorcycle 12.8 17.5 18.2 11.4 1.9 12.4 

Has car 6.3 18.2 25.5 39.9 35.2 25.0 

Has telephone 2.4 6.7 26.7 33.8 72.0 28.2 

If household works own or family’s 

 agricultural land 0.8 1.0 0.9 0.5 0.1 0.6 

If piped drinking water in residence 3.6 19.2 63.1 95.1 98.9 55.9 

If has a well in residence 26.4 22.7 13.8 3.1 0.8 13.4 

If uses river, canal or surface water 

 for drinking 10.4 7.6 1.4 0.0 0.0 3.9 

If uses own flush toilet 0.0 0.0 0.2 1.7 68.8 13.9 

If uses a shared flush toilet 0.0 1.1 2.4 2.6 3.5 1.9 

If uses bush, field as latrine 0.1 0.0 0.0 0.0 0.0 0.0 

If has dirt, sand, dung as principal floor 

 in dwelling 53.2 19.6 9.3 0.7 0.2 16.6 

If has wood, plank principal floor 

 in dwelling 44.8 78.0 86.6 95.3 66.3 74.2 

If has cement principal floor 0.9 0.6 0.3 0.1 0.1 0.4 

If has other type of flooring 0.0 0.0 0.0 0.0 0.1 0.0 

If rain for drinking water 0.0 0.3 0.8 0.2 0.0 0.3 

If uses a public faucet (piped) 43.0 38.7 14.0 0.6 0.3 19.4 

If uses a traditional public well 10.4 6.6 3.4 0.4 0.0 4.2 

If uses a traditional pit toilet 99.9 98.9 97.4 95.4 27.5 84.0 

If uses a VIP latrine 0.0 0.0 0.0 0.3 0.2 0.1 

If has parquet or polished wood floors 0.0 0.0 0.3 0.5 8.2 1.8 

If has tiles for main flooring material 0.3 0.0 0.0 0.0 0.0 0.1 

If has straw or sawdust flooring 0.6 1.1 0.8 2.2 1.7 1.3 

If has vinyl or asphalt strip flooring 0.2 0.5 2.7 1.1 23.3 5.5 

If has carpeted flooring 0.0 0.2 0.0 0.0 0.0 0.0 

If uses water from a tanker truck 2.7 4.2 3.0 0.3 0.0 2.1 

If uses bottled water 0.1 0.7 0.5 0.3 0.0 0.3 

If gets drinking water from a spring 3.3 0.0 0.0 0.0 0.0 0.7 

Number of members per sleeping room1 3.1 2.5 2.3 2.0 1.8 2.3 

1 Mean 
24

4 Who Are the Poor? 

To show some of the different aspects of poverty, a few key background characteristics are analyzed in 

this section. Basic information on the wealth index by country is included in Appendix C. The following 

is a summary of the illustrative information. 

4.1 Area of Residence and Region 

Table 4.1 shows the percentage of households that are in urban areas, distributed by wealth quintile. 

Table 4.1 Percentage of urban households in each wealth quintile, by region 

Quintile (percent) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 4 7 14 38 81 31 

Near East and North Africa 23 36 54 80 94 60 

Europe and Central Asia 11 17 39 63 92 48 

South and Southeast Asia 3 9 16 31 66 26 

Latin America and Caribbean 12 38 65 85 95 64 

 

Total 8 17 30 53 84 41 

The totals indicate that rural areas are mostly inhabited by poorer households and that the richest 

households live mostly in urban areas. However, in the Near East and North Africa region, almost onefourth of the poorest households are urban, and in the South and Southeast Asia region, about one-third of 

the richest households are in rural areas. 

4.2 Household Head 

The characteristics of the head of the household are important to the living conditions of all household 

members. Tables 4.2 through 4.5 examine the sex, age, education, and marital status of the head. 

Table 4.2 Percentage of female household heads in each wealth quintile, by region 

Quintile (percent) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 22 22 23 26 24 24 

Near East and North Africa 8 8 9 11 9 9 

Europe and Central Asia 14 14 16 20 29 19 

South and Southeast Asia 8 10 10 11 14 11 

Latin America and Caribbean 17 22 25 27 26 24 

Total 17 18 20 22 22 20 

A common premise is that many poor households are headed by women, usually single mothers, widows, 

or women who have been abandoned. Table 4.2 shows that, overall, only one in six households in the 

lowest quintile are headed by women and that women-headed households tend to be somewhat wealthier. 

Indeed, even in sub-Saharan Africa, where more than a fifth of poor households are headed by women, 

the percentage of female-headed households is higher in the richer households. 
25

The marital status of the household head is determined by whether a spouse is a member of the 

household. Table 4.3 shows that there are small differences in marital status by wealth. In four of the five 

regions, the richest households have fewer married heads than do the poorest households. Thus, there is 

no evidence to support the notion that women with no spouse in the household disproportionately head 

poor households in less developed countries as they do in the more developed countries. 

Table 4.3 Percentage of currently married household heads in each wealth quintile, by region 

Quintile (percent) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 76 76 74 70 72 73 

Near East and North Africa 89 90 89 88 89 89 

Europe and Central Asia 81 83 80 81 74 80 

South and Southeast Asia 88 87 87 86 85 87 

Latin America and Caribbean 82 78 75 72 72 75 

Total 81 80 78 76 76 78 

Overall, there is little difference by wealth quintile in the age of the head of the household (Table 4.4). By 

region, the heads of the poorest households are 3.4 years younger than those of the richest in South and 

Southeast Asia, and they are 2.4 years older in Europe and Central Asia. 

Table 4.4 Mean age of household heads in each wealth quintile, by region 

Quintile (mean age in years) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 43.7 44.3 44.4 43.6 43.3 43.8 

Near East and North Africa 45.3 45.2 44.8 45.3 46.1 45.3 

Europe and Central Asia 45.3 46.1 45.9 44.8 42.9 44.8 

South and Southeast Asia 42.1 42.8 43.7 44.8 45.6 43.8 

Latin America and Caribbean 43.3 43.1 43.1 43.5 46.1 43.9 

Total 43.6 44.0 44.2 44.0 44.5 44.0 

The number of years of education of the head of the household varies substantially according to the 

household’s economic status (Table 4.5). Overall, there is a difference of 5.5 years between the lowest 

and highest quintiles. The Latin America and Caribbean and Near East and North Africa regions show the 

greatest difference in the education of the household head (7.1 and 6.9 years, respectively). The Europe 

and Central Asia region, where education is in general much higher, shows the least difference by wealth 

(3.2 years). 

Table 4.5 Mean number of years of education for household heads in each wealth quintile, by region 

Quintile (mean number of years of education) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 1.9 2.2 2.9 3.9 7.0 3.7 

Near East and North Africa 2.8 3.9 4.8 6.3 9.7 5.7 

Europe and Central Asia 7.6 8.2 8.7 9.2 10.8 9.1 

South and Southeast Asia 2.8 3.7 4.4 5.7 8.5 5.1 

Latin America and Caribbean 2.4 3.6 4.9 6.4 9.5 5.7 

Total 2.8 3.5 4.3 5.4 8.3 5.0 
26

4.3 Household Size 

One of the reasons for equivalization is the idea that larger households may have more income recipients 

and therefore may be able to afford more assets used in common. Are wealthy households, as determined 

by the wealth index, larger than poorer households? Table 4.6 shows that, overall, this is not the case, and 

in the regions where it is (sub-Saharan Africa and South and Southeast Asia), the differences by 

household wealth are minor. 

Table 4.6 Mean number of household members in each wealth quintile, by region 

Quintile (mean number of persons) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 5.4 5.2 5.1 5.1 5.5 5.2 

Near East and North Africa 5.9 6.2 6.0 5.6 5.3 5.8 

Europe and Central Asia 5.2 5.1 4.9 4.4 3.5 4.5 

South and Southeast Asia 5.3 5.0 5.2 5.5 5.6 5.3 

Latin America and Caribbean 5.3 4.7 4.7 4.6 4.6 4.8 

Total 5.4 5.1 5.1 5.0 5.1 5.1 

4.4 Percentage of Children in Poverty 

Of the age groups making up the poor population, children are thought to be the most numerous. This 

observation is usually based on experience from the developed countries. Tables 4.7 and 4.8 evaluate this 

notion for less developed countries by examining the mean number of children under five years of age in 

households, by wealth quintile, and the distribution of children under age 15 years, by wealth quintile, 

respectively. 

Table 4.7 indicates that, overall, the poorest households have 1.5 times the number of young children in 

rich households. The difference between the poorest and richest households is least in South and 

Southeast Asia and greatest in Latin America and the Caribbean. 

Table 4.7 Mean number of children under age five in each wealth quintile, by region 

Quintile (mean number of children under five) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 1.6 1.5 1.5 1.4 1.3 1.5 

Near East and North Africa 1.5 1.5 1.2 1.0 0.8 1.2 

Europe and Central Asia 1.0 0.9 0.8 0.7 0.4 0.7 

South and Southeast Asia 1.1 1.1 1.0 1.0 0.9 1.0 

Latin America and Caribbean 1.4 1.1 1.0 0.8 0.6 0.9 

Total 1.4 1.3 1.2 1.1 0.9 1.2 

Table 4.8 shows that all children under 15 years of age are fairly well distributed across the quintiles. 

Sub-Saharan Africa and South and Southeast Asia have distributions that are mostly level across the 

quintiles. The attenuation of the differences between quintiles shown for children under five years of age 

may be due to increased child mortality experienced by the poorer households. 
27

Table 4.8 Percent distribution of children under age 15 by wealth quintile, according to region 

Quintile (percent) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 20 19 19 21 21 100 

Near East and North Africa 22 23 21 18 16 100 

Europe and Central Asia 23 21 21 19 16 100 

South and Southeast Asia 21 20 20 20 18 100 

Latin America and Caribbean 22 21 21 19 17 100 

Total 21 20 20 20 19 100 

4.5 Characteristics of Household Members 

The background characteristics of all household members according to wealth quintile are given in Tables 

4.9 through 4.11; sex, age, and education are presented, respectively. Because most country surveys did 

not ask about the marital status of household members and relationship to head cannot be used to indicate 

marital status, there is no table showing marital status by wealth for all members. 

Table 4.9 shows the percentage of members who are female, by wealth. 

Table 4.9 Percentage of female household members in each wealth quintile, by region 

Quintile (percent) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 53 54 54 54 54 54 

Near East and North Africa 51 50 50 50 51 50 

Europe and Central Asia 50 51 52 53 54 52 

South and Southeast Asia 50 50 51 51 52 51 

Latin America and Caribbean 52 52 54 56 58 54 

Total 52 52 53 54 54 53 

Overall, women make up more of the household population than do men, probably for two reasons: 

greater mortality among men and greater likelihood of men to live in institutional and common housing 

(e.g., the armed forces, mining and other camps, prisons). There is little difference overall and in most of 

the regions by wealth quintile. The two regions where quintile makes minor differences are Latin America 

and the Caribbean and Europe and Central Asia, where richer households tend to have more female 

members. 

The average age of household members is about half of that of the household head (Table 4.10). With 

increasing wealth, there is a small increase in the age of members. The largest increases are in Latin 

America and Caribbean countries, with 6.1 years between the poorest and richest households. 
28

Table 4.10 Mean age of household members in each wealth quintile, by region 

Quintile (mean age in years) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 19.4 20.1 20.2 20.3 20.7 20.2 

Near East and North Africa 20.6 20.9 21.7 22.9 24.2 22.0 

Europe and Central Asia 22.4 23.8 24.5 25.0 26.3 24.4 

South and Southeast Asia 21.6 22.5 23.2 23.8 25.1 23.3 

Latin America and Caribbean 20.1 21.3 22.2 23.7 26.2 22.9 

Total 20.3 21.2 21.7 22.2 23.4 21.8 

As with the household head, there is a strong relationship between wealth and education of all members, 

as shown by the mean number of years of education (Table 4.11). The association between education of 

all household members and economic status is weaker than that between education of the household head 

and economic status. 

Table 4.11 Mean number of years of education for household members in ech wealth quintile, by region 

Quintile (mean number of years of education) 

Region Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 1.8 2.2 2.7 3.7 6.4 3.6 

Near East and North Africa 3.0 4.1 5.1 6.7 9.4 5.7 

Europe and Central Asia 7.7 8.2 8.5 9.1 10.5 8.9 

South and Southeast Asia 2.5 3.4 4.2 5.5 7.9 4.9 

Latin America and Caribbean 2.6 3.9 5.4 7.0 9.6 6.3 

 

Total 2.7 3.5 4.3 5.4 8.0 5.1 
29

5 Importance of Wealth 

To demonstrate the value of using economic status measures in general and the wealth index in particular 

to explain equity differences in health outcomes and services, this section discusses how reproductive and 

maternal health, child mortality and health, environmental health conditions, and education vary by 

economic status.2

 

5.1 Reproductive and Maternal Health 

Fertility levels and contraceptive use vary substantially by wealth, as does use of health services and 

knowledge of the sexual transmission of HIV/AIDS (Table 5.1). Figures 5.1 and 5.2 show the relationship 

between wealth and the total fertility rate and the contraceptive prevalence rate, respectively, in India. 

Table 5.1 Reproductive and maternal health indicators by wealth quintile, DHS surveys 1992-1998 

 Quintile Low/high 

Survey Lowest Second Middle Fourth Highest 

Population 

average ratio 

Total fertility rate 

Egypt 1995 4.4 3.8 3.4 3.1 2.7 3.6 1.6 

India 1992-93 4.1 3.6 3.2 2.8 2.1 3.4 2.0 

Kenya 1998 6.5 5.6 4.7 4.2 3.0 4.7 2.2 

Peru 1996 6.6 4.6 3.4 2.6 1.7 3.5 3.9 

Uzbekistan 1996 4.4 3.7 3.2 3.2 2.2 3.3 2.0 

 

Contraceptive prevalence rate 

Egypt 1995 28.2 39.0 47.1 52.0 57.4 45.5 0.5 

India 1992-93 24.9 27.5 36.1 42.0 50.6 36.5 0.5 

Kenya 1998 12.6 24.1 30.7 39.7 50.1 31.5 0.3 

Peru 1996 24.0 37.5 45.2 48.9 50.3 41.3 0.5 

Uzbekistan 1996 46.0 55.1 55.5 47.7 52.2 51.3 0.9 

 

Medical prenatal care, 3+ visits 

Egypt 1995 11.1 15.6 31.7 45.6 75.3 34.9 0.1 

India 1992-93 21.6 30.4 42.8 56.9 81.4 44.1 0.3 

Kenya 1998 77.4 78.5 82.4 84.3 86.5 81.4 0.9 

Peru 1996 28.7 54.9 71.5 81.8 93.7 62.3 0.3 

Uzbekistan 1996 82.4 82.8 79.3 84.5 84.4 82.5 1.0 

 

Knowledge of sexual 

transmission of HIV/AIDS 

Egypt 1995 u u u u u u u 

India 1992-93 u u u u u u u 

Kenya 1998 94.5 96.3 97.0 97.9 98.6 97.0 1.0 

Peru 1996 67.1 83.7 93.1 96.0 97.8 89.2 0.7 

Uzbekistan 1996 u u u u u u u 

u = Unknown (not available) 

 

2

 In the next revision of this document, relationships between economic status and women’s status and domestic 

violence will be included, which have to be left out of the present document due to contractual obligations. 
30

Figure 5.1

Total Fertility Rate by Wealth Quintile, India 1992-93

4.1

3.6

3.2

2.8

2.1

Lowest Second Middle Fourth Highest

Wealth quintile

0

1

2

3

4

5

Births per woman

Figure 5.2

Contraceptive Prevalence by Wealth Quintile, India 1992-93

25

28

36

42

51

Lowest Second Middle Fourth Highest

Wealth quintile

0

10

20

30

40

50

60

Percent of married women
31

Figures 5.3 and 5.4 show TFR values and contraceptive prevalence for the poorest quintiles of various 

countries. 

Figure 5.3

Total Fertility Rates for the Poorest Wealth Quintile, 

by Region, DHS Surveys 1990-1998

Niger 1998

Madagascar 1997

Tanzania 1996

Uganda 1995

Senegal 1997

Zambia 1996

Benin 1996

Togo 1998

Malawi 1992

Chad 1996/1997

Mali 1995/1996

Namibia 1992

Ghana 1993

Kenya 1998

Nigeria 1990

Comoros 1996

Côte d'Ivoire 1994

Zimbabwe 1994

Mozambique 1997

CAR 1994/1995

Morocco 1993

Philippines 1998

Nepal 1996

Pakistan 1990/1991

Egypt 1995

Uzbekistan 1996 India 1992/1993

Bangladesh 1996/1997

Turkey 1993 Indonesia 1997

Kazakstan 1995

Vietnam 1997

Guatemala 1995

Paraguay 1990

Bolivia 1998

Haiti 1994/1995

Nicaragua 1997/1998

Peru 1996

Colombia 1995

Dominican Republic 1996

Brazil 1996

0

2

4

6

8

10

Births p

er w

o

m

an
32

5.2 Child Mortality and Health 

Figure 5.5 shows the level of under-five mortality by wealth quintile in India. There is almost a three to 

one ratio between the lowest and highest quintiles. As seen in Table 5.2, two other countries have greater 

differentials in the under-five mortality rate. The differences in child mortality by economic status are 

larger than the differences for most other variables, including mother’s education. 

Figure 5.4

Contraceptive Prevalence Rates for the Lowest Wealth Quintile, 

by Region, DHS Surveys 1990-1998

Chad 1996/1997

Mali 1995/1996

Nigeria 1990

CAR 1994/1995

Mozambique 1997

Senegal 1997

Côte d'Ivoire 1994

Benin 1996

Madagascar 1997

Togo 1998

Niger 1998

Malawi 1992

Tanzania 1996

Ghana 1993

Namibia 1992

Zambia 1996

Comoros 1996

Kenya 1998

Uganda 1995

Zimbabwe 1994

Pakistan 1990/1991

Nepal 1996

Morocco 1993

Philippines 1998

Turkey 1993 India 1992/1993

Egypt 1995

Bangladesh 1996/1997

Kazakstan 1995 Indonesia 1997

Vietnam 1997

Uzbekistan 1996

Haiti 1994/1995

Guatemala 1995

Bolivia 1998

Paraguay 1990

Peru 1996

Nicaragua 1997/1998

Colombia 1995

Dominican Republic 1996

Brazil 1996

0

10

20

30

40

50

60

P

ercent us ni g

a contrace

ptiv

e

m

e ht o

d
33

Figure 5.5

Under-Five Mortality Rates by Wealth Quintile, India 1992-93

155 153

120

87

54

Lowest Second Middle Fourth Highest

Wealth quintile

0

20

40

60

80

100

120

140

160

180 Deaths per 1,000 births

Table 5.2 Child mortality and other health indicators by wealth quintile, DHS surveys 1992-1998 

Quintiles 

Indicator Lowest Second Middle Fourth Highest 

Population 

average 

Low/high 

ratio 

Under-five mortality rate 

Egypt 1995 147.2 118.7 85.4 62.0 39.1 95.9 3.8 

India 1992-93 154.7 152.9 119.5 86.9 54.3 118.8 2.8 

Kenya 1998 136.2 130.4 92.3 84.9 60.7 105.2 2.2 

Peru 1996 110.0 76.2 48.0 44.1 22.1 68.4 5.0 

Uzbekistan 1996 70.3 43.8 55.4 51.7 50.4 55.2 1.4 

Full basic vaccination rate 

Egypt 1995 65.1 72.8 81.0 86.6 92.5 79.1 0.7 

India 1992-93 17.1 21.7 34.7 48.2 65.0 35.4 0.3 

Kenya 1998 48.1 57.6 71.0 64.6 59.9 59.5 0.8 

Peru 1996 55.3 63.8 63.5 71.7 66.0 63.0 0.8 

Uzbekistan 1996 80.9 76.8 79.4 77.2 77.5 78.7 1.0 

Percent stunted 

 (< 2 SD below mean) 

Egypt 1995 38.4 33.7 28.6 25.1 20.2 29.8 1.9 

India 1992-93 60.2 58.8 54.4 47.8 34.3 51.9 1.8 

Kenya 1998 44.1 37.5 30.2 30.5 17.1 33.0 2.6 

Peru 1996 45.6 30.8 18.8 10.0 5.2 25.8 8.8 

Uzbekistan 1996 39.6 29.5 29.5 24.5 30.5 31.3 1.3 

Private medical 

 treatment of ARI 

Egypt 1995 28.0 35.3 44.1 53.4 62.7 43.5 0.4 

India 1992-93 44.3 41.3 45.2 56.8 68.8 48.6 0.6 

Kenya 1998 16.0 22.4 17.7 17.7 35.2 20.6 0.5 

Peru 1996 0.9 3.7 6.7 11.6 21.7 5.9 0.0 

Uzbekistan 1996 * * * * * (3.2) na 

Note: An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 

Figures in parentheses are based on 25-49 unweighted cases. 

SD = Standard deviation 

na = Not applicable 
34

Under-five mortality for the lowest wealth quintile in each country is shown in Figure 5.6. In general, 

sub-Saharan Africa has the highest levels, reaching almost 300 deaths per 1,000 births in Mali. In no 

country except Kazakhstan does under-five mortality in the lowest wealth quintile fall below 50 per 1,000 

deaths. 

As seen in Table 5.2, chronic child malnutrition as measured by the percent stunted among children under 

five years is also highly variable by wealth, with the level of stunting for the lowest quintile being almost 

nine times the level for the highest quintile in Peru. 

Use of health services is also unequally distributed, as indicated by full basic vaccination rates and the 

percentage of children ill with acute respiratory infection (ARI) who have been treated in private medical 

facilities (Table 5.2). 

The relationship between economic status and child health is not because wealthier households tend to be 

more educated, as noted above; rather, both have a strong, independent effect. Table 5.3 shows both 

unadjusted and adjusted odds ratios for child mortality and stunting by mother’s education, residence, and 

wealth quintile for selected countries. (There was no stunting information in the Egypt or Indonesia data 

used for this analysis.) The table, based on logistic regression, indicates that both mother’s education and 

economic status as measured by the wealth index are important both before and after adjustment for each 

other and a number of other important variables. Table 5.3 also shows that after the adjustment for the 

various factors, residence is not significant in and of itself. 

Figure 5.6

Under-Five Mortality for the Lowest Wealth Quintile, 

by Region, DHS Surveys 1990-1998

Mali 1995/1996

Niger 1998

Mozambique 1997

Malawi 1992

Nigeria 1990

Zambia 1996

Benin 1996

Madagascar 1997

CAR 1994/1995

Uganda 1995

Côte d'Ivoire 1994

Senegal 1997

Chad 1996/97

Togo 1998

Ghana 1993

Kenya 1998

Tanzania 1996

Comoros 1996

Namibia 1992

Zimbabwe 1994

Nepal 1996 India 1992/1993

Egypt 1995

Bangladesh 1996/97

Turkey 1993

Pakistan 1990/1991

Morocco 1993 Indonesia 1997

Philippines 1998

Uzbekistan 1996

Vietnam 1997

Kazakstan 1995

Haiti 1994/1995

Bolivia 1998

Peru 1996

Brazil 1996

Dominican Republic 1996

Guatemala 1995

Nicaragua 1997/1998

Paraguay 1990

Colombia 1995

0

50

100

150

200

250

300

350

D

e

aths p

er 1 0, 0

0

bir ht s
35

5.3 Environmental Health Conditions 

For the selected countries, Table 5.4 shows two environmental health conditions: safe water supply and 

safe sanitation procedures. Safe water supply is defined as drinking water supplied through a piped 

system, either into the household or available at a public tap, or from a covered well. Among the poorest 

people in Egypt and Uzbekistan, almost half have access to a safe supply. However, in India and Kenya, 

even one out of four people in the highest quintile do not have access to safe drinking water. 

Regarding safe sanitation, defined as access to private or shared flush toilets (including bucket flush) or a 

ventilated improved pit (VIP) latrine, only in Egypt do any of the people in the lowest quintile have 

access (46 percent). Among people in the highest quintile, only in Kenya and Uzbekistan do less than 80 

percent have access to safe sanitation. 

Table 5.3 Unadjusted odds ratios and adjusted (adj.) odds ratios for under-five mortality (U5MR) and stunting by education, residence, and wealth 

quintile, DHS surveys in Peru, Egypt, Côte d’Ivoire, India, and Indonesia 

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– 

 Peru Egypt Côte d’Ivoire India Indonesia 

 ––––––––––––––––––––––– –––––––––– ––––––––––––––––––––––– ––––––––––––––––––––––– –––––––––– 

 U5MR Stunting U5MR U5MR Stunting U5MR Stunting U5MR 

 –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– 

 Adj. Adj. Adj. Adj. Adj. Adj. Adj. Adj. 

 Odds odds Odds odds Odds odds Odds odds Odds odds Odds odds Odds odds Odds odds 

 ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio ratio 

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– 

Education 

 No education 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

 Primary 0.7849 0.9027 0.8567 0.8098 0.7557 0.9330 0.7558 0.7626 1.0379 0.8188 0.6279 0.7787 0.7566 0.7678 0.6867 0.7527 

 Secondary 0.3697 0.6508 0.4693 0.5801 0.3661 0.6095 0.5316 0.5788 0.5459 0.5360 0.4070 0.6622 0.8007 0.6103 0.3445 0.5377 

 Higher 0.2417 0.4529 0.1597 0.3452 0.1732 0.3232 * ns * ns 0.1678 0.3106 0.3934 0.3154 0.2259 0.5807 

 

Residence 

 Urban 0.4998 ns 0.3953 ns 0.5626 ns 0.7175 ns 0.5540 0.6114 0.5532 ns 0.8054 ns 0.5572 ns 

 Rural 1.0000 ns 1.0000 ns 1.0000 ns 1.0000 ns 1.0000 1.0000 1.0000 ns 1.0000 ns 1.0000 ns 

Wealth quintile 

 Lowest 1.000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

 Second 0.9548 0.9127 0.8724 0.8736 0.8058 0.8899 0.9456 0.9666 1.0000 1.0221 1.0144 1.0584 1.0000 1.0950 0.8869 1.0395 

 Middle 0.6462 0.8321 0.6062 0.7159 0.6701 0.7594 0.8328 0.8852 0.9381 0.9386 0.7821 0.8993 0.8938 0.9210 0.8079 0.9588 

 Fourth 0.4622 0.7193 0.3611 0.5790 0.4515 0.6109 0.7599 0.7701 0.5522 0.5796 0.5420 0.7305 0.8514 0.8280 0.5900 0.8032 

 Highest 0.3271 0.5795 0.1427 0.3757 0.2798 0.4653 0.5167 0.5501 0.4753 0.5890 0.3422 0.6721 0.5872 0.7233 0.3464 0.7328 

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– 

Note: Odds ratios for under-five mortality are adjusted through logistic regression for birth spacing, birth order, mother’s age at birth, and death of 

preceding child (if any). Births occurring 60 to 179 months prior to the survey are used. Odds ratios for stunting are adjusted for the preceding variables 

as well as prenatal care, delivery attendance, tetanus toxoid vaccinations during the pregnancy, and the wantedness of the birth. Living children under 

five years of age are included. The surveys in Egypt and Indonesia did not measure children for nutritional status. 

* Not shown due to small number of births (53) 

ns = Not statistically significant 
36

Table 5.4 Environmental health indicators by wealth quintile, DHS surveys 1992-1998 

Quintile (percent) 

Indicator Lowest Second Middle Fourth Highest 

Population

average 

Low/high 

ratio 

Safe water supply (piped and 

 covered well) 

 

Egypt 1995 47 73 87 97 99 81 0.5 

India 1992-93 6 15 27 44 74 33 0.1 

Kenya 1998 1 9 16 43 76 29 0.0 

Peru 1996 14 60 87 97 100 72 0.1 

Uzbekistan 1996 47 59 78 96 99 75 0.5 

 

Safe sanitation disposal 

 (flush toilet and VIP latrine) 

Egypt 1995 46 78 94 97 100 83 0.5 

India 1992-93 0 0 4 22 80 22 0.0 

Kenya 1998 0 1 3 12 64 16 0.0 

Peru 1996 0 7 44 87 100 48 0.0 

Uzbekistan 1996 0 1 2 5 70 16 0.0 

VIP = Ventilated improved pit (latrine) 

5.4 Education 

Section 4 describes the characteristics of household heads and household members by wealth quintile and 

level of education. Figure 5.7 shows the level of literacy among poor women by country, and Figure 5.8 

shows the proportion of poor children age 6 to 14 years who have never attended school or who have 

dropped out. 
37

Figure 5.7

Percentage of Poor Women Who Are Literate,

by Region, DHS Surveys 1990-1999

Mali 1995/96

Niger 1998

Benin 1996

Chad 1996/97

CAR 1994/95

Senegal 1997

Mozambique 1997

Nigeria 1990

Togo 1998

Côte d'Ivoire 1994

Uganda 1995

Comoros 1996

Ghana 1993

Zambia 1996

Tanzania 1996

Kenya 1998

Zimbabwe 1994

Namibia 1992

Pakistan 1990/1991

Nepal 1996

Egypt 1995

India 1992/1993

Bangladesh 1996/97

Indonesia 1997

Philippines 1998

Kazakhstan 1995

Uzbekistan 1996

Haiti 1994/1995

Bolivia 1998

Nicaragua 1997/1998

Peru 1996

Dominican Republic 1996

Brazil 1996

Colombia 1995

Paraguay 1990

0

20

40

60

80

100

P

ercent of p

o

or w

o

m

en 15-4

9
38

Both figures indicate that there is substantial variation in literacy and education among the poor across 

countries, much more so than across world regions. Another use of this information is shown in Figure 

5.9, which compares drop-out rates with the percentage of children with no education among children age 

6-14 years in the lowest quintiles. Except for Brazil and the Dominican Republic, there is a strong linear 

relationship between the two. Thus, poor children in these countries are doubly burdened: even those who 

are fortunate enough to start school are likely to drop out before completing their education. 

Figure 5.8

Among Poor Children Age 6-14 Who Are Not Attending School,

Percentage Who Never Attended School and Percentage Who Attended 

But Dropped Out, by Region, DHS Surveys 1990-1999

Mali 1995/1996

Niger 1998

Chad 1996/1997

Benin 1996

Côte d'Ivoire 1994

Tanzania 1996

Comoros 1996

CAR 1994/1995

Nigeria 1990

Uganda 1995

Togo 1998

Mozambique 1997

Zambia 1996

Ghana 1993

Kenya 1998

Zimbabwe 1994

Namibia 1992

Pakistan 1990/1991

India 1992/1993

Nepal 1996

Egypt 1995

Bangladesh 1996/1997

Philippines 1998

Uzbekistan 1996

Indonesia 1997

Kazakstan 1995

Haiti 1994/1995

Nicaragua 1997/1998

Dominican Republic 1996

Brazil 1996

Colombia 1995

Peru 1996

Paraguay 1990

Bolivia 1998

0

20

40

60

80

100

Percent of po

or chi dl ren 6-14

Never attended Dropped out
39

 

5.5 Use of Public Services 

Another way in which the wealth index can be used is in evaluating the reach of public health services. 

Figure 5.10 shows the percentage of children under five with acute respiratory infection (ARI) treated for 

the illness in a public facility, by wealth quintile. The figure compares Bolivia and Turkey. In both 

countries, the greatest use of public facilities for treating ARI is in the fourth wealth quintile. The poorest 

make the least use of services. Figure 5.11 shows the use of public facilities for childbirth in the two 

countries. Again, the lowest quintile has the least use, and the fourth and highest quintiles have the most 

use. Figure 5.12 shows that the relationship is reflected by more countries than just Bolivia and Turkey. 

The use of publicly provided childbirth delivery, ARI, and diarrhea services increases with increasing 

wealth quintile in 11 countries. These results, which need further investigation, may be related to public 

facilities being located in or near more affluent urban areas. 

Figure 5.9

Relationship Between No Education and the Drop-Out Rate, 

among Poor Children Age 6-14, DHS Surveys 1990-1999 

0

20

40

60

80

100

0 20 40 60 80 100

Percentage with No Education

Drop-Out Rate

Dominican Republic

Brazil
40

Figure 5.10

Use of Public Health Services for Acute Respiratory

Infection (ARI), by Wealth Quintile, DHS Surveys

in Bolivia and Turkey

Bolivia Turkey

0

5

10

15

20

25 Percentage of children with ARI

Lowest Second Middle Fourth Highest

Note: Children under five with ARI in the two weeks preceding the survey

Figure 5.11

Use of Public Health Services for Assistance at Childbirth, 

by Wealth Quintile, DHS Surveys in Bolivia and Turkey

Bolivia Turkey

0

20

40

60

80

100 Percentage of births

Lowest Second Middle Fourth Highest
41

Figure 5.12 

Use of Public Health Services for Delivery, ARI Treatment, 

and Diarrhea Treatment, by Wealth Quintile (11 countries)

0

10

20

30

40

50

60

Lowest Second Middle Fourth Highest

Wealth quintile

Percent of births

0

2

4

6

8

10

12

14

16

18

Percentage of ill children

Delivery

ARI Treatment

Diarrhea Treatment
42

6 Targeting Programs and Services by Wealth and Mapping Poverty 

An important use of the wealth index can be for targeting services, programs, and projects that alleviate 

poverty. Many programs use geographical criteria for targeting their activities, and so mapping poverty or 

wealth is an important component. Several illustrations are presented below as to how the DHS wealth 

index can be used for geographic poverty targeting with both tables and maps. 

6.1 Where Do the Poor Live? 

Figure 6.1 presents the distribution of the household population in the lowest quintile by state from the 

India 1992-93 NFHS survey. This figure shows that three states, Uttar Pradesh, Bihar, and West Bengal, 

account for more than half of the poorest people in India. Figure 6.2 shows a map of India illustrating the 

geographic distribution of people in the lowest wealth quintile. Table 6.1 shows the distribution of the 

population by state according to wealth quintile. 

Figure 6.1 Distribution of the Population in the 

Lowest Wealth Quintile by State, India 1992-93 

0

5

10

15

20

25

30

Uttar Pradesh

Bihar 

West Bengal 

Madhya Pradesh

Rajasthan 

Maharashtra 

Orissa 

Andhra Pradesh 

Assam 

Karnataka 

Gujarat 

Tamil Nadu 

Haryana

Tripura

Jammu

Kerala

Meghalaya 

Himachal Pradesh

Punjab 

Manipur 

ArunachalPradesh 

Nagaland 

Mizoram 

Goa 

New Delhi

Percent of People in Poorest Quintile
43

Figure 6.2 

Map of India Showing Distribution of the Population in the 

Lowest Quintile by State, India 1992-93 
44

Table 6.1 Percent distribution of the household population by state, according to wealth quintile, 

India 1992-93 

Quintile 

State Lowest Second Middle Fourth Highest Total 

Uttar Pradesh 25.1 23.8 16.3 13.2 13.6 18.4 

Bihar 18.3 18.0 9.4 5.1 5.9 11.3 

West Bengal 10.6 9.9 5.3 5.8 7.2 7.8 

Madhya Pradesh 9.2 7.4 10.8 6.6 6.8 8.2 

Rajasthan 8.3 4.5 5.7 5.6 4.2 5.7 

Maharashtra 5.1 5.4 9.6 11.2 14.2 9.1 

Orissa 5.1 5.4 2.9 2.7 1.5 3.5 

Andhra Pradesh 4.7 6.8 8.6 7.8 7.4 7.0 

Assam 3.8 4.3 2.6 1.4 1.4 2.7 

Karnataka 3.3 4.1 7.7 6.4 5.4 5.4 

Gujarat 2.8 2.4 4.8 6.0 7.3 4.7 

Tamil Nadu 2.2 4.4 6.5 7.3 6.2 5.3 

Haryana 0.3 0.7 1.9 4.9 2.9 2.1 

Tripura 0.3 0.4 0.4 0.4 0.2 0.3 

Jammu 0.2 0.2 0.4 0.8 0.6 0.4 

Kerala 0.2 1.3 3.7 5.5 4.7 3.1 

Meghalaya 0.1 0.3 0.3 0.3 0.2 0.2 

Himachal Pradesh 0.1 0.2 1.0 1.2 0.4 0.6 

Punjab 0.1 0.3 1.2 5.5 4.9 2.4 

Manipur 0.1 0.1 0.3 0.4 0.2 0.2 

Arunachal Pradesh 0.1 0.1 0.2 0.1 0.1 0.1 

Nagaland 0.0 0.1 0.2 0.3 0.1 0.1 

Mizoram 0.0 0.0 0.1 0.2 0.1 0.1 

Goa 0.0 0.0 0.1 0.2 0.3 0.1 

New Delhi 0.0 0.0 0.1 0.9 4.2 1.0 

 

India 100.0 100.0 100.0 100.0 100.0 100.0 

Note: Quintiles are based on distribution of national population. 

Another way to look at the same data, i.e., percentage of people in the lowest quintile by state, is shown in 

Figure 6.3. In this figure, six states, Bihar, Rajasthan, Orissa, Assam, Uttar Pradesh, and West Bengal, 

have more than one in four people in the lowest wealth quintile. Under either criterion, Uttar Pradesh, 

Bihar, and West Bengal are the states with the highest levels of poverty. The full distribution of poverty 

by state is shown in Table 6.2. 
45

Figure 6.3 Percentage of the Population in the Lowest Wealth Quintile, 

by State, India 1992-93 

Table 6.2 Percentage of household population in each wealth quintile, according to state, India 

1992-93 

Quintile 

State Lowest Second Middle Fourth Highest Total 

Bihar 32.3 31.7 16.6 9.0 10.4 100.0 

Rajasthan 29.4 15.9 20.1 19.8 14.9 100.0 

Orissa 28.8 30.8 16.2 15.5 8.7 100.0 

Assam 28.0 31.8 19.2 10.5 10.5 100.0 

Uttar Pradesh 27.3 25.8 17.8 14.3 14.8 100.0 

West Bengal 27.2 25.5 13.7 15.0 18.5 100.0 

Madhya Pradesh 22.5 18.1 26.4 16.2 16.6 100.0 

Tripura 16.3 23.7 24.6 25.6 9.8 100.0 

Andhra Pradesh 13.3 19.3 24.4 22.1 20.9 100.0 

Karnataka 12.3 15.3 28.8 23.7 20.0 100.0 

Gujarat 11.9 10.2 20.7 25.8 31.4 100.0 

Arunachal Pradesh 11.9 17.3 31.2 27.9 11.6 100.0 

Meghalaya 11.3 23.5 22.5 26.1 16.6 100.0 

Maharashtra 11.3 11.9 21.0 24.7 31.2 100.0 

Jammu 9.0 7.9 19.9 36.4 26.7 100.0 

Tamil Nadu 8.4 16.4 24.5 27.4 23.4 100.0 

Manipur 5.5 12.7 24.8 36.1 20.9 100.0 

Himachal Pradesh 4.1 5.4 36.0 41.5 13.0 100.0 

Nagaland 3.0 7.1 28.4 45.3 16.1 100.0 

Haryana 2.9 6.7 17.3 45.9 27.2 100.0 

Mizoram 1.3 7.8 18.0 50.3 22.7 100.0 

Kerala 1.0 8.6 23.8 35.8 30.8 100.0 

Punjab 0.9 2.5 9.7 46.1 40.7 100.0 

Goa 0.6 3.3 11.8 33.5 50.9 100.0 

New Delhi 0.1 0.4 2.3 17.5 79.7 100.0 

 

India 20.0 20.0 20.0 20.0 20.0 100.0 

Note: Quintiles are based on distribution of national population. 

0

5

10

15

20

25

30

35

Bihar 

Rajasthan 

Orissa 

Assam 

Uttar Pradesh

West Bengal 

Madhya Pradesh 

Tripura 

Andhra Pradesh 

Karnataka 

Gujarat 

ArunachalPradesh 

Meghalaya 

Maharashtra 

Jammu 

Tamil Nadu 

Manipur 

Himachal Pradesh 

Nagaland 

Haryana 

Mizoram 

Kerala 

Punjab 

Goa 

New Delhi 

Percent of State Population
46

6.2 Which States Are the Poorest? 

Using Peru as an example, Table 6.3 compares two measures of overall economic status of the household 

population according to state (called “departments” in Peru). Both the average quintile value for the state 

and the average wealth index score are presented. There is little difference in the rankings of the 

departments by either indicator, and where there is a reversal, it is very small (noted by italics). In Peru, 

the poorest departments tend to cluster geographically, as seen in Figure 6.4. 

Table 6.3 Mean wealth quintile and mean wealth 

index score by department, Peru 1996 

Region 

Quintile 

mean 

Mean 

index 

score 

Huancavelica 1.538 -0.892 

Cajamarca 1.838 -0.681 

Amazonas 1.918 -0.610 

Apurimac 1.939 -0.626 

Ayacucho 2.117 -0.475 

Huanuco 2.215 -0.429 

Pasco 2.324 -0.307 

Puno 2.325 -0.304 

Ucayali 2.357 -0.275 

San Martin 2.385 -0.264 

Cusco 2.406 -0.235 

Loreto 2.519 -0.185 

Junin 2.754 0.029 

Madre de Dios 2.787 0.033 

La Libertad 2.810 0.049 

Piura 2.881 0.085 

Ancash 2.889 0.083 

Lambayeque 3.169 0.299 

Tumbes 3.327 0.416 

Moquegua 3.353 0.435 

Ica 3.474 0.540 

Arequipa 3.549 0.597 

Tacna 3.852 0.819 

Lima 3.861 0.837 

 

Peru 3.003 0.191 

Note: Quintile and index scores are based on 

household population. Italics indicates a reversal in 

ranking. 
47

Figure 6.4 

Map of Peru Showing Mean Wealth Index Scores, by Department, Peru 1996 

6.3 Nongeographic Targeting 

Many times, geographic targeting is too broad to achieve program and project goals. Not all poor people 

live in poor areas, and not all who live in poor areas are poor themselves. The wealth index score 

coefficients can be useful for targeting families if they are used judiciously. If reliable information can be 

gathered on assets and services for families attending health facilities, then the resultant score could be 

used for, say, assessment of user fees. The basic problem is obtaining information—perhaps through a 

visit to the household—that is not underestimated by the patient in order to lower the user fees he or she 

is charged. It appears that the wealth index could have significant advantages over asking about 

expenditures or income for targeting, since much information can be gathered by observation. However, 

once patients know that user fees are tied to the responses they give, simple questions in facilities with no 

confirming observations could influence them to hide or misstate their assets and services. 
48

7 Joining with Other Poverty Measures 

As mentioned previously, there may be official poverty lines based on non-DHS data and criteria other 

than quintiles. Usually, a consumption survey is combined with a minimum wage criterion to specify the 

percentage of the population (or households) below the poverty line. Drawbacks in using consumption 

expenditure data and politically determined criteria were mentioned above. However, for administrative 

needs, it is often necessary to use official figures. The percentage of the population below the official 

poverty line for selected countries is shown in Table 7.1. Uzbekistan does not have official figures, so 

those of Kazakhstan are used instead. 

Table 7.1 Percentage of population below official 

poverty line in selected countries 

Country 

Percentage

of 

population 

Corresponding 

cutoff value of 

the wealth 

index 

Egypt 23 -0.855812 

India 25 -0.923202 

Kenya 50 -0.028722 

Kazakhstan 26 -1.060894 

Peru 50 0.245489 

Selected health and asset indicators are shown according to the official poverty line in Table 7.2. In Egypt 

1995 and Peru 1996, under-five mortality rates for those below the poverty line (124 and 74, respectively) 

are at least twice as high as under-five rates for those above the line (63 and 34, respectively). There is 

little difference in Kazakhstan (48 and 45), however. From a broader perspective, the under-five mortality 

rates for children below the poverty line in Kazakhstan and Peru (48 and 74) are below those for children 

above the line in India (96). Part of the difference may be the setting of the line. In Peru, 50 percent of the 

population is below the line, while in India, only 25 percent is below. If, in Peru, only the poorest 25 

percent were classified as below the line, the above result would probably not hold since the lowest 

quintile has a rate of 110 deaths per 1,000 births. 

Other notable differences between the poor and the nonpoor in Table 7.2 are seen in India 1992-93 and 

Kazakhstan 1995. A high proportion of the nonpoor in India probably would be classified as poor in most 

other countries since several of the indicator values are low: only 28 percent of births are delivered with 

the assistance of a physician, only 42 percent of children age 12-23 months have had all basic 

vaccinations, almost half of the children are stunted, and 61 percent of the population have no toilet or 

latrine facilities. At the other extreme is Kazakhstan, which shows little difference between the poor and 

nonpoor populations. Mortality rates, contraceptive use, and vaccination rates are almost the same in the 

two groups, and even among the poor, 79 percent of the population live in households with a television. 

While the simple division into poor and nonpoor can be useful for getting a quick idea of health and other 

differences, it can hide large differentials within the groups and is hard to compare across countries 

because of political considerations in establishing official poverty lines. 
49

Table 7.2 Health and asset indicators for household populations above and below the official poverty line in selected 

countries 

Egypt 1995 India 1992-93 

Kazakhstan 

1996 Kenya 1998 Peru 1996 

Indicator Poor 

Not 

poor Poor 

Not 

poor Poor 

Not 

poor Poor 

Not 

poor Poor 

Not 

poor 

Under-five mortality rate 124.0 63.0 142.4 96.2 47.9 44.5 127.3 74.8 74.2 34.2 

Use of modern contraception 29.9 49.9 25.1 40.1 44.1 46.6 23.5 45.4 33.8 48.7 

Delivery assistance by 

 a physician 15.3 48.3 5.5 27.8 70.7 81.2 8.2 21.6 18.2 55.2 

Full basic vaccination 65.2 83.6 17.3 42.3 20.3 24.7 58.2 62.2 60.8 66.3 

Percent of children stunted 37.8 26.8 60.3 48.7 26.1 12.2 37.4 23.1 35.6 10.2 

Knowledge of sexual 

 transmission of HIV/AIDS u u u u u u 51.3 66.0 46.8 78.8 

Piped water into residence 41.2 73.1 0.7 23.7 0.3 78.4 3.9 48.1 27.6 92.1 

Has no latrine or toilet 22.5 5.5 98.7 60.6 1.4 0.3 23.2 2.7 45.2 0.8 

Has television 58.6 85.1 0.0 29.4 78.6 94.0 0.8 37.5 44.0 95.8 

Indicators: 

Under-five mortality rate: for the five-year period preceding the survey 

Use of modern contraception: by currently married women age 15-49 

Delivery assistance by a physician: births in the last five years 

Full basic vaccination: children age 12-23 months getting BCG, three doses of DPT and polio, and measles vaccine 

Percent of children stunted: children under age five years who are below -2 SD on the WHO/CDC/NCHS height-for-age 

standard 

Knowledge of sexual transmission of HIV/AIDS: women age 15-49 

Assets: Percentage of household population with asset 

u = Unknown (not available) 
50

8 Further Work 

There are several ways in which the DHS wealth index can be improved. 

One is by extending the list of assets and services beyond those in the 

current core set. In several countries, more assets have been asked about, 

such as possession of a computer, cell phone, and sofa set. Table 8.1 gives 

potential areas for extension of the asset and service list. Individual new 

items are shown in Appendix Tables C.1 to C.11. The idea is have 

questions that are easy to answer (i.e., people will be willing to respond) or 

items that can be observed. An example would be whether there is glass in 

the windows of the dwelling, which is easily observable. It is also easier to 

ask whether someone in the household has a bank account of any kind 

(e.g., checking, savings, loan) rather than how much is in the account. 

Possession of jewelry and clothing (including shoes) presumably should 

not be asked but can be observed by the interviewer. While some items 

will be country specific, others should be considered for any new DHS core questionnaires. It is important 

to include items that distinguish households throughout the economic status scale and not just at one end 

or the other. However, individual items can be effective at only the low end, at only the high end, or in the 

middle, and an appropriate mix of such items should be asked. At the low end, items such as dining tables 

and chairs can be asked about or observed. At the higher end, computers and DVD players can be asked 

about. Household possession of business and farm equipment, machinery, livestock, and inventory can be 

asked about. 

Currently, the DHS survey covers the household population. Not all people in a country are covered, 

especially some of the poor who may not be living in households or in dwellings. Special samples have 

been suggested to include the homeless, those living in group quarters, and people living in refugee 

camps. Since the homeless and people living in noninstitutional group quarters are part of the general 

population, ways of including them should be examined, such as special samples with interviews 

occurring during a given night in the locations where they sleep. Ascertaining their economic status is 

problematic, especially for the homeless, but they can probably be included in the lowest quintile. On the 

other hand, institutional populations, such as people in the army, hospitals, asylums, prisons, and 

orphanages, generally do not have access to health services available for the general population, but they 

may have access to specialized health services. These people should not be included in analyses that 

cover the general population of the country but rather should be the subject of special studies. 

Table 8.1 New wealth index 

areas (presence of item) 

________________________ 

Farm equipment 

Livestock 

Business equipment 

Financial assets, loans 

Land holdings and real estate 

 - Agricultural 

 - Nonagricultural 

Furnishings 

Clothing and jewelry 

Communications 
51

References 

Ferguson, B., A. Tandon, E. Gakidou, and C.J.L. Murray. 2002. Estimating permanent income using 

indicator variables. (Evidence and Information for Policy Cluster). Geneva, Switzerland: World Health 

Organization. 

Filmer, D. and L. Pritchett. 2001. Estimating wealth effects without expenditure data—or tears: An 

application to educational enrollments in states of India. Demography 38(1):115-132. 

Friedman, M. 1957. A theory of the consumption function. Princeton, New Jersey: Princeton University 

Press. 

Gwatkin, D.R., S. Rutstein, K. Johnson, R.P. Pande, and A. Wagstaff. 2000. Socio-economic differences 

in health, nutrition, and population. HNP/Poverty Thematic Group. Washington, D.C.: World Bank. 

Instituto Nacional de Estadística. 1999a. Encuesta de Demanda y Gastos en Salud 1997: Informe 

descriptivo. In Salud Materno Infantil en los Departamentos del Altiplano. Guatemala City, Guatemala: 

Instituto Nacional de Estadística. 

Instituto Nacional de Estadística. 1999b. Encuesta de Demanda y Gastos en Salud 1997: Informe 

econométrico. In Salud Materno Infantil en los Departamentos del Altiplano. Guatemala City, 

Guatemala: Instituto Nacional de Estadística. 

Montgomery, M,, M. Gragnolati, K. Burke, and E. Paredes. 2000. Measuring living standards with proxy 

variables. Demography 37(2): 155-174 
53

Appendix A 

The Development and History of the U.S. Poverty Thresholds — 

A Brief Overview 

by 

Gordon M. Fisher 

Department of Health and Human Services 

[GSS/SSS Newsletter [Newsletter of the Government Statistics Section and the Social Statistics Section of 

the American Statistical Association], Winter 1997, pp. 6-7] 

In view of the recent major proposal to revise the way in which the United States measures poverty, it 

may be useful to review the development and subsequent history of the current official poverty 

thresholds. 

The poverty thresholds were originally developed in 1963-1964 by Mollie Orshansky of the Social 

Security Administration. She published an analysis of the poverty population using these thresholds in a 

January 1965 Social Security Bulletin article. Orshansky based her poverty thresholds on the economy 

food plan — the cheapest of four food plans developed by the Department of Agriculture. The actual 

combinations of foods in the food plans, devised by Agriculture Department dietitians using complex 

procedures, constituted nutritionally adequate diets; the Agriculture Department described the economy 

food plan as being “designed for temporary or emergency use when funds are low.” (Orshansky also 

developed a second set of poverty thresholds based on the Agriculture Department’s somewhat less 

stringent low-cost food plan, but relatively little use was ever made of these higher thresholds.) 

Orshansky knew from the Department of Agriculture’s 1955 Household Food Consumption Survey (the 

latest available such survey at the time) that families of three or more persons spent about one third of 

their after-tax money income on food in 1955. Accordingly, she calculated poverty thresholds for families 

of three or more persons by taking the dollar costs of the economy food plan for families of those sizes 

and multiplying the costs by a factor of three — the “multiplier.” In effect, she took a hypothetical 

average family spending one third of its income on food, and assumed that it had to cut back on its 

expenditures sharply. She assumed that expenditures for food and non-food would be cut back at the same 

rate. When the food expenditures of the hypothetical family reached the cost of the economy food plan, 

she assumed that the amount the family would then be spending on non-food items would also be 

minimal but adequate. (Her procedure did not assume specific dollar amounts for any budget category 

besides food.) She derived poverty thresholds for two-person families by multiplying the dollar cost of 

the food plan for that family size by a somewhat higher multiplier (3.7) also derived from the 1955 

survey. She derived poverty thresholds for one-person units directly from the thresholds for two-person 

units, without using a multiplier. The base year for the original thresholds was calendar year 1963. 

Orshansky differentiated her thresholds not only by family size but also by farm/nonfarm status, by the 

sex of the family head, by the number of family members who were children, and (for one- and twoperson units only) by aged/non-aged status. The result was a detailed matrix of 124 poverty thresholds, 

although the figures generally cited were weighted average thresholds for each family size. 

In her January 1965 article, Orshansky presented the poverty thresholds as a measure of income 

inadequacy, not of income adequacy — “if it is not possible to state unequivocally ‘how much is enough,’ 

it should be possible to assert with confidence how much, on an average, is too little.” 
54

While the poverty thresholds had been calculated on the basis of after-tax money income, they were 

applied to income data — the Census Bureau’s Current Population Survey — that used a before-tax 

definition of money income; this was done because when the thresholds were being developed, the 

Current Population Survey was the only good source of nationally representative income data. Orshansky 

was aware of the inconsistency involved, but there was no other alternative; she reasoned that the result 

would yield “a conservative underestimate” of poverty. 

In April-May 1965, it was decided to set farm poverty thresholds at 70 percent of the corresponding 

nonfarm thresholds, and to update the thresholds for annual price changes by the yearly change in the per 

capita cost of the economy food plan. In May 1965 — just over a year after the Johnson Administration 

had initiated the War on Poverty — the Office of Economic Opportunity adopted Orshansky’s poverty 

thresholds as a working or quasi-official definition of poverty. 

As early as November 1965, Social Security Administration policymakers and analysts began to express 

concern about how to adjust the poverty thresholds for increases in the general standard of living. (There 

is extensive historical evidence from the U.S. and other countries that successive poverty lines developed 

as absolute poverty lines show a pattern of getting higher in real terms as the real income of the general 

population rises; this evidence is discussed in the author’s “Relative or Absolute — New Light on the 

Behavior of Poverty Lines Over Time,” in the Summer 1996 issue of this newsletter.) In 1968, the Social 

Security Administration tried to take a very modest step towards raising the poverty thresholds to reflect 

increases in the general standard of living. The Bureau of the Budget (the predecessor of the Office of 

Management and Budget) prohibited the modest increase in the poverty thresholds, but initiated an 

interagency Poverty Level Review Committee to re-evaluate the poverty thresholds. This Committee 

decided to adjust the thresholds only for price changes, and not for changes in the general standard of 

living. In 1969, the Committee decided that the thresholds would be indexed by the Consumer Price 

Index instead of by the per capita cost of the economy food plan, and that farm poverty thresholds would 

be set at 85 percent rather than 70 percent of corresponding nonfarm thresholds. In August 1969, the 

Bureau of the Budget designated the poverty thresholds with these revisions as the federal government’s 

official statistical definition of poverty. 

In 1973, three interagency subcommittees were formed to conduct a thorough review of federal income 

and poverty statistics. The Subcommittee on Updating the Poverty Threshold recommended that the 

poverty thresholds be updated every ten years using a revised food plan and a multiplier derived from the 

latest available food consumption survey; this would generally have resulted in higher poverty thresholds 

at each decennial revision. The Subcommittee also recommended that the definition of income used to 

measure overall income should also be the income definition used to calculate the multiplier for revised 

poverty thresholds. No changes were made in the poverty definition as a result of the 1973 review of 

poverty and income statistics. 

In 1974, in response to a Congressional requirement, an interagency Poverty Studies Task Force was 

established to undertake an intensive review of the current poverty measure and alternative measurement 

schemes. A final report, The Measure of Poverty, was submitted to Congress in 1976 along with 

seventeen Technical Papers. This report thoroughly explored the issues involved in developing and 

revising poverty measures, but did not recommend specific changes in the current poverty measure. 

In 1981, several minor changes were made in the poverty thresholds in accordance with recommendations 

of an interagency committee. The farm/nonfarm differential was eliminated by applying nonfarm poverty 

thresholds to all families. The distinction between thresholds for “female-headed” and “male-headed” 

families was eliminated by averaging. The poverty threshold matrix was extended to make the largest 

family size category “nine persons or more” rather than “seven or more persons.” These changes reduced 

the number of thresholds in the detailed matrix of poverty thresholds from 124 to 48. 
55

During the 1980s, there were extensive debates about poverty measurement — particularly about 

proposals to count government noncash benefits as income for measuring poverty without making 

corresponding changes in the poverty thresholds. (For comments on these proposals, see pp. 9, 65-66, 

205, and 227-231 of the report cited in the next paragraph.) However, no changes were made in the 

official poverty definition during the 1980s. 

In 1990, a Congressional committee requested a study of the official U.S. poverty measure by the 

National Academy of Sciences/National Research Council to provide a basis for a possible revision of the 

poverty measure. In 1992, the NRC’s Committee on National Statistics appointed a Panel on Poverty and 

Family Assistance to conduct this study. In May 1995, the Panel published its report of the study 

(Constance F. Citro and Robert T. Michael (editors), Measuring Poverty: A New Approach, Washington, 

D.C., National Academy Press, 1995). In the report, the Panel proposed a new approach for developing an 

official poverty measure for the U.S. — although it did not propose a specific set of dollar figures. The 

Panel’s proposal has been summarized and discussed in a number of sources, including earlier issues of 

this newsletter. 

(This article is a summary of Gordon M. Fisher, “The Development and History of the Poverty 

Thresholds,” Social Security Bulletin, Vol. 55, No. 4, Winter 1992, pp. 3-14; the article was condensed 

from a 75-page unpublished paper. [This unpublished paper — subsequently revised to 88 pages — is 

available on the Census Bureau’s Poverty Measurement Web site at http://www.census.gov/hhes/poverty/ 

povmeas/papers/orshansky.html.] For additional information, you may contact Gordon Fisher by 

telephone (202-690-6143) or e-mail gordon.fisher@hhs.gov. The views expressed here are those of the 

author, and do not represent the position of the Department of Health and Human Services.) 
57

Appendix B 

Table B.1 Distribution statistics and quintile cutoff values for the wealth index 

Quintile cutoff values 

 Country and survey Mean Median Mode Skewness Kurtosis 1st-2nd 2nd-3rd 3rd-4th 4th-5th 

 Sub-Saharan Africa 

Benin 1996 0.084 -0.190 -1.065 0.971 0.165 -0.8631 -0.4743 -0.0134 0.9419 

CAR 1994-95 0.016 -0.329 -0.587 3.205 13.784 -0.5727 -0.3463 -0.0572 0.7773 

Chad 1996-97 -0.226 -0.489 -0.639 4.264 24.511 -0.6390 -0.5212 -0.4114 -0.0295 

Comoros 1996 0.065 -0.344 -0.395 1.965 4.563 -0.7726 -0.5178 -0.2232 0.5259 

Côte d'Ivoire 1994 -0.042 -0.257 -1.067 0.891 0.230 -0.9234 -0.5166 0.0270 0.9414 

Ghana 1998 0.086 -0.236 -0.622 1.251 1.089 -0.7886 -0.5417 -0.0990 0.8452 

Kenya 1998 0.065 -0.344 -0.395 1.965 4.563 -0.7726 -0.5178 -0.2232 0.5259 

Madagascar 1997 -0.124 -0.432 -0.836 1.899 4.234 -0.8109 -0.6007 -0.2656 0.3706 

Malawi 1992 -0.194 -0.534 -0.589 2.390 5.773 -0.6210 -0.5509 -0.3857 0.1798 

Mali 1995-96 -0.018 -0.293 -0.819 3.065 12.457 -0.5592 -0.3276 -0.1019 0.4635 

Mozambique 1997 -0.123 -0.401 -0.438 3.993 18.626 -0.5501 -0.4177 -0.2766 0.2252 

Namibia 1992 0.093 -0.463 -0.719 0.818 -0.884 -0.7776 -0.6952 -0.4022 0.8945 

Niger 1998 -0.080 0.028 0.284 -0.579 1.505 -0.9018 -0.1680 0.2878 0.8157 

Nigeria 1990 -0.204 -0.423 -1.122 1.001 0.156 -0.9615 -0.5931 -0.3144 0.5026 

Senegal 1997 0.000 -0.461 -0.699 1.575 1.996 -0.7105 -0.5839 -0.1851 0.8381 

Tanzania 1996 -0.050 -0.398 -0.559 2.846 10.268 -0.5854 -0.5043 -0.3329 0.3761 

Togo 1998 0.070 -0.089 -1.142 0.861 0.540 -0.9054 -0.4137 0.0564 0.8034 

Uganda 1995 -0.220 -0.434 -0.635 3.992 21.706 -0.5968 -0.4944 -0.3713 -0.1366 

Zambia 1996 0.145 -0.443 2.224 1.317 0.583 -0.6823 -0.5233 0.1042 1.3364 

Zimbabwe 1994 0.061 -0.252 -0.904 0.851 -0.365 -0.9035 -0.6553 -0.2161 0.9787 

 

 Near East and North Africa 

Egypt 1995 -0.020 0.153 0.815 -1.135 1.911 -0.9829 -0.3133 0.1757 0.6939 

Morocco 1992 0.000 -0.068 1.239 0.041 -1.545 -1.1056 -0.5611 0.3662 1.1747 

 

 Europe and Central Asia 

Kazakhstan 1995 -0.133 -0.385 1.446 0.402 -1.083 -1.1238 -0.7824 -0.3434 0.5266 

Turkey 1993 0.020 0.004 0.586 0.169 -0.796 -1.0347 -0.4791 0.1940 0.8551 

Uzbekistan 1996 -0.231 -0.280 -0.015 0.241 -0.350 -1.1141 -0.6670 -0.1712 0.2792 

 

South and Southeast Asia 

Bangladesh 1996-97 -0.035 -0.399 -0.694 2.376 4.967 -0.5609 -0.4719 -0.2428 0.2297 

India 1992-93 -0.178 -0.496 -0.957 0.972 0.004 -0.9879 -0.7092 -0.2127 0.6529 

Indonesia 1997 0.136 0.075 1.285 0.305 -0.494 -0.7247 -0.1616 0.3874 1.0176 

Nepal 1996 -0.046 -0.389 -0.749 2.277 5.397 -0.6315 -0.4727 -0.2318 0.2720 

Pakistan 1990-91 -0.273 -0.685 -1.132 1.110 0.103 -1.0109 -0.7899 -0.3146 0.7124 

Philippines 1998 0.183 0.193 1.123 0.045 -1.009 -0.8669 -0.0992 0.5531 1.1699 

Viet Nam 1997 -0.053 -0.175 0.275 0.633 -0.079 -0.9389 -0.4388 0.0634 0.7040 

 

Latin America and Caribbean 

Bolivia 1998 0.145 0.299 1.492 -0.251 -0.977 -0.9396 0.0169 0.5226 1.0711 

Brazil 1996 -0.123 -0.401 -0.438 3.993 18.626 -0.5501 -0.4177 -0.2766 0.2252 

Colombia 1995 0.020 0.372 0.893 -0.951 -0.139 -0.9344 -0.0061 0.5317 0.8931 

Dominican Rep. 1996 0.221 0.024 2.164 0.299 -0.764 -0.6806 -0.2350 0.4063 1.2968 

Guatemala 1995 0.297 0.017 2.411 0.527 -0.933 -0.8408 -0.4327 0.3192 1.3283 

Haiti 1994-95 0.188 0.223 1.120 0.017 -1.154 -0.9274 -0.1510 0.5978 1.1919 

Nicaragua 1997-98 0.168 0.122 2.210 0.205 -0.996 -0.9832 -0.3154 0.3346 1.1027 

Paraguay 1990 0.128 -0.120 -0.446 0.357 -1.240 -0.9417 -0.4462 0.1653 1.1870 

Peru 1996 0.188 0.223 1.120 0.017 -1.154 -0.9274 -0.1510 0.5978 1.1919 
59

Appendix C 

Table C.1 Percent urban by wealth quintile 

 Quintile 

 Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 14 15 22 49 87 40 

Cameroon 1991 3 10 28 43 94 35 

CAR 1994-95 9 13 39 69 86 43 

Chad 1996-97 2 2 6 28 83 23 

Comoros 1996 12 18 18 29 62 30 

Côte d'Ivoire 1994 2 6 32 69 88 42 

Eritrea 1995 1 3 5 31 98 33 

Ghana 1993 4 7 11 56 91 38 

Kenya 1998 1 4 10 21 67 23 

Madagascar 1997 11 11 9 21 77 28 

Malawi 1992 1 2 4 10 45 12 

Mali 1995-96 5 9 14 36 84 32 

Mozambique 1997 0 7 9 31 72 24 

Namibia 1997 5 3 10 49 94 38 

Niger 1998 0 1 3 12 83 20 

Togo 1998 2 5 14 51 88 38 

Uganda 1995 2 3 4 7 54 15 

Zambia 1996 2 7 23 78 97 45 

Zimbabwe 1994 0 1 2 37 95 32 

 

Near East and North Africa 

Egypt 1995 6 20 38 66 90 46 

Jordan 1997 61 82 87 91 94 84 

Morocco 1992 1 7 36 84 97 49 

 

Europe and Central Asia 

Kazakhstan 1995 13 19 42 80 97 57 

Kyrgyzstan 1997 6 8 20 36 83 34 

Turkey 1993 16 34 67 87 97 64 

Uzbekistan 1996 8 8 28 49 91 38 

 

South and Southeast Asia 

Bangladesh 1996-97 1 3 4 9 43 12 

India 1992-93 2 7 12 30 78 26 

Indonesia 1997 2 9 22 39 70 28 

Nepal 1996 1 2 3 4 34 8 

Pakistan 1990-91 2 4 12 49 86 31 

Philippines 1998 14 35 57 70 85 57 

Vietnam 1997 2 3 5 15 66 19 

 

Latin American and Caribbean 

Bolivia 1998 6 42 82 96 99 71 

Brazil 1996 40 76 89 95 99 82 

Colombia 1995 14 48 88 98 98 75 

Dominican Republic 1996 21 47 65 86 95 67 

Guatemala 1995 7 14 33 61 81 43 

Haiti 1994-95 1 8 20 64 95 44 

Nicaragua 1997-98 10 37 69 88 95 65 

Paraguay 1990 6 14 52 82 96 56 

Peru 1996 8 51 86 97 99 73 

 

Average 8 17 30 53 84 41 
60

Table C.2 Mean number of children under age five by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 2.2 2.1 2.3 1.8 1.3 1.9 

Cameroon 1991 1.7 1.8 1.6 1.5 1.2 1.6 

CAR 1994-95 1.2 1.3 1.4 1.7 2.1 1.5 

Chad 1996-97 1.8 1.5 1.6 1.9 1.8 1.7 

Comoros 1996 1.5 1.2 1.1 1.1 0.9 1.1 

Côte d'Ivoire 1994 2.1 2.4 2.4 2.1 1.7 2.1 

Eritrea 1995 1.5 1.3 1.0 1.0 0.7 1.1 

Ghana 1993 1.5 1.3 1.2 1.1 0.8 1.2 

Kenya 1998 1.3 1.1 1.0 1.0 0.8 1.0 

Madagascar 1997 1.7 1.3 1.3 1.2 0.8 1.3 

Malawi 1992 1.2 1.1 1.1 1.0 1.2 1.1 

Mali 1995-96 1.4 1.6 1.8 2.0 1.6 1.7 

Mozambique 1997 1.1 1.0 1.4 1.4 1.4 1.3 

Namibia 1997 1.9 2.4 2.1 1.6 1.2 1.8 

Niger 1998 2.1 1.7 1.9 2.1 1.8 1.9 

Togo 1998 2.0 2.0 1.7 1.2 1.0 1.5 

Uganda 1995 1.6 1.6 1.6 1.5 1.4 1.5 

Zambia 1996 1.5 1.4 1.4 1.3 1.5 1.4 

Zimbabwe 1994 1.5 1.2 1.2 0.9 0.9 1.1 

 

Near East and North Africa 

Egypt 1995 1.4 1.5 1.1 0.9 0.7 1.1 

Jordan 1997 1.7 1.6 1.4 1.2 1.0 1.4 

Morocco 1992 1.5 1.4 1.2 0.8 0.7 1.1 

 

Europe and Central Asia 

Kazakhstan 1995 0.8 0.7 0.7 0.5 0.3 0.6 

Kyrgyzstan 1997 1.1 0.9 0.9 0.8 0.5 0.8 

Turkey 1993 1.1 0.9 0.7 0.6 0.4 0.7 

Uzbekistan 1996 1.2 1.0 0.9 0.9 0.6 0.9 

 

South and Southeast Asia 

Bangladesh 1996-97 0.9 0.9 0.9 0.9 0.9 0.9 

India 1992-93 1.2 1.1 1.1 1.1 0.9 1.1 

Indonesia 1997 0.8 0.7 0.7 0.7 0.7 0.7 

Nepal 1996 1.4 1.3 1.3 1.4 1.0 1.3 

Pakistan 1990-91 1.4 1.4 1.5 1.7 1.6 1.5 

Philippines 1998 1.3 1.0 0.8 0.8 0.6 0.9 

Vietnam 1997 1.0 0.9 0.7 0.6 0.6 0.8 

 

Latin America and Caribbean 

Bolivia 1998 1.3 1.1 0.9 0.7 0.5 0.9 

Brazil 1996 0.9 0.7 0.6 0.5 0.4 0.6 

Colombia 1995 1.3 0.9 0.8 0.7 0.4 0.8 

Dominican Republic 1996 1.1 0.9 0.9 0.7 0.6 0.8 

Guatemala 1995 1.7 1.4 1.2 1.0 0.7 1.2 

Haiti 1994-95 1.5 1.3 1.2 1.0 0.7 1.1 

Nicaragua 1997-98 1.8 1.4 1.2 0.9 0.7 1.1 

Paraguay 1990 1.6 1.3 1.2 1.0 0.7 1.1 

Peru 1996 1.3 1.1 0.9 0.8 0.5 0.9 

Average 1.4 1.3 1.2 1.1 0.9 1.2 
61

Table C.3 Mean number of household members by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 6.6 5.7 6.3 5.7 5.8 6.0 

Cameroon 1991 6.0 6.5 5.9 5.7 7.3 6.2 

CAR 1994-95 5.5 5.6 5.8 5.5 5.0 5.5 

Chad 1996-97 4.3 4.0 4.3 5.8 7.7 4.9 

Comoros 1996 6.2 5.5 4.3 5.5 5.6 5.3 

Côte d'Ivoire 1994 6.5 5.8 6.2 6.5 6.6 6.3 

Eritrea 1995 5.7 5.3 3.5 3.9 4.5 4.4 

Ghana 1993 3.7 4.0 4.1 3.6 3.6 3.8 

Kenya 1998 4.9 4.8 4.3 4.1 3.6 4.3 

Madagascar 1997 5.9 4.8 4.5 4.7 4.8 4.9 

Malawi 1992 4.2 4.2 4.4 4.6 5.1 4.5 

Mali 1995-96 4.5 5.2 5.9 6.3 6.7 5.6 

Mozambique 1997 4.6 3.5 4.4 4.9 6.3 4.6 

Namibia 1997 6.0 8.0 6.7 5.5 4.8 6.0 

Niger 1998 6.6 6.0 5.5 5.6 6.3 5.9 

Togo 1998 6.0 6.0 5.6 4.5 5.2 5.4 

Uganda 1995 4.7 4.7 4.9 4.7 4.8 4.8 

Zambia 1996 5.6 4.7 5.0 5.4 6.4 5.4 

Zimbabwe 1994 6.1 4.9 5.0 3.9 4.0 4.7 

 

 Near East and North Africa 

Egypt 1995 5.9 6.1 5.6 5.1 4.3 5.3 

Jordan 1997 5.8 5.9 6.1 6.1 5.9 6.0 

Morocco 1992 6.0 6.5 6.3 5.7 5.7 6.0 

 

Europe and Central Asia 

Kazakhstan 1995 4.5 4.3 4.2 3.2 3.0 3.7 

Kyrgyzstan 1997 5.7 5.3 5.3 4.7 3.3 4.7 

Turkey 1993 5.1 4.9 4.6 4.2 4.0 4.5 

Uzbekistan 1996 5.7 5.9 5.6 5.5 3.9 5.2 

 

 South and Southeast Asia 

Bangladesh 1996-97 4.7 5.0 5.3 5.7 5.9 5.3 

India 1992-93 5.8 5.3 5.7 5.7 5.6 5.6 

Indonesia 1997 4.3 4.1 4.1 4.3 4.7 4.3 

Nepal 1996 5.9 5.1 5.2 5.8 5.6 5.5 

Pakistan 1990-91 6.2 6.4 6.7 7.1 7.6 6.8 

Philippines 1998 5.2 4.8 4.9 5.1 5.4 5.1 

Vietnam 1997 5.0 4.6 4.7 4.6 4.6 4.7 

 

 Latin America and Caribbean 

Bolivia 1998 4.3 4.3 4.4 4.2 4.4 4.3 

Brazil 1996 4.5 4.3 4.0 3.9 3.8 4.1 

Colombia 1995 4.9 4.5 4.4 4.3 4.2 4.4 

Dominican Republic 1996 4.2 4.2 4.3 4.3 4.4 4.3 

Guatemala 1995 6.2 5.2 5.4 5.0 4.6 5.2 

Haiti 1994-95 6.0 4.7 4.9 4.5 5.3 5.0 

Nicaragua 1997-98 6.7 5.5 5.4 5.3 5.1 5.5 

Paraguay 1990 5.6 5.4 5.2 4.7 4.5 5.1 

Peru 1996 5.0 4.6 4.7 4.9 5.0 4.8 

 

Average 5.4 5.1 5.1 5.0 5.1 5.1 
62

Table C.4 Percent female of household heads by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 7 10 12 23 22 15 

Cameroon 1991 12 12 23 28 31 23 

CAR 1994-95 22 15 16 23 20 19 

Chad 1996-97 22 19 14 16 18 18 

Comoros 1996 32 30 30 33 34 32 

Côte d'Ivoire 1994 11 14 19 21 16 16 

Eritrea 1995 12 19 28 36 41 28 

Ghana 1993 36 41 35 54 41 42 

Kenya 1998 31 35 38 37 30 34 

Madagascar 1997 17 24 21 19 21 21 

Malawi 1992 33 28 26 20 14 24 

Mali 1995-96 7 5 4 6 8 6 

Mozambique 1997 29 29 18 23 20 24 

Namibia 1997 36 27 32 32 33 32 

Niger 1998 9 11 9 9 12 10 

Togo 1998 10 15 24 37 26 23 

Uganda 1995 23 24 21 23 27 24 

Zambia 1996 25 25 24 22 19 23 

Zimbabwe 1994 40 37 50 37 22 36 

 

Near East and North Africa 

Egypt 1995 11 9 9 8 6 8 

Jordan 1997 6 5 5 6 7 6 

Morocco 1992 8 9 14 19 14 13 

 

Europe and Central Asia 

Kazakhstan 1995 24 20 23 30 39 28 

Kyrgyzstan 1997 14 20 19 26 35 24 

Turkey 1993 6 6 7 6 5 6 

Uzbekistan 1996 13 10 17 17 37 19 

 

South and South East Asia 

Bangladesh 1996-97 9 8 7 9 9 8 

India 1992-93 5 9 9 9 8 8 

Indonesia 1997 9 8 7 8 6 8 

Nepal 1996 10 13 10 9 12 11 

Pakistan 1990-91 4 6 7 6 8 6 

Philippines 1998 7 10 16 16 17 14 

Vietnam 1997 15 16 15 20 36 21 

 

Latin America and Caribbean 

Bolivia 1998 13 16 18 21 21 18 

Brazil 1996 17 19 21 19 18 19 

Colombia 1995 17 22 26 27 24 24 

Dominican Republic 1996 19 24 28 34 32 28 

Guatemala 1995 15 18 19 22 21 19 

Haiti 1994-95 26 35 44 47 53 43 

Nicaragua 1997-98 19 31 35 41 33 33 

Paraguay 1990 11 11 17 16 15 14 

Peru 1996 13 17 20 20 16 17 

Average 17 18 20 22 22 20 
63

Table C.5 Mean age of household head by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 44.8 46.1 46.3 45.0 45.6 45.5 

Cameroon 1991 45.5 45.6 47.8 45.1 43.2 45.3 

CAR 1994-95 39.5 39.5 40.7 43.8 45.0 41.6 

Chad 1996-97 41.9 43.7 42.4 42.3 42.8 42.8 

Comoros 1996 44.9 43.8 44.8 46.1 46.0 45.2 

Côte d'Ivoire 1994 47.7 50.0 47.8 45.7 44.8 47.0 

Eritrea 1995 46.2 46.0 43.8 45.5 48.1 46.1 

Ghana 1993 42.2 41.5 42.0 39.9 40.4 41.1 

Kenya 1998 42.6 43.7 44.7 42.9 39.3 42.4 

Madagascar 1997 41.0 40.6 41.4 43.5 43.3 42.0 

Malawi 1992 41.6 41.0 41.2 41.5 41.5 41.4 

Mali 1995-96 43.4 44.8 45.8 46.3 47.4 45.6 

Mozambique 1997 39.5 38.9 40.5 41.9 43.7 41.0 

Namibia 1997 53.2 56.0 54.7 46.5 42.3 49.7 

Niger 1998 43.4 44.1 44.9 46.4 45.8 44.9 

Togo 1998 45.8 45.5 46.0 44.8 44.0 45.1 

Uganda 1995 39.4 39.5 40.3 39.9 38.6 39.5 

Zambia 1996 42.0 43.5 43.7 40.5 40.6 41.9 

Zimbabwe 1994 45.2 47.2 45.0 41.0 40.6 43.4 

 

Near East and North Africa 

Egypt 1995 45.6 45.8 44.8 43.8 43.4 44.6 

Jordan 1997 40.6 39.1 40.1 43.7 45.4 41.8 

Morocco 1992 49.9 50.7 49.6 48.3 49.4 49.5 

 

Europe and Central Asia 

Kazakhstan 1995 44.6 46.4 45.5 42.8 42.1 44.0 

Kyrgyzstan 1997 46.6 47.0 47.8 46.8 42.3 45.9 

Turkey 1993 45.5 43.8 41.7 40.6 41.1 42.3 

Uzbekistan 1996 44.6 47.2 48.6 48.8 46.2 47.1 

 

South and Southeast Asia 

Bangladesh 1996-97 40.4 42.5 42.8 44.5 45.4 43.1 

India 1992-93 44.8 44.0 45.3 45.9 46.7 45.3 

Indonesia 1997 40.9 41.3 41.8 42.2 43.0 41.8 

Nepal 1996 41.9 42.2 44.1 43.9 44.5 43.3 

Pakistan 1990-91 45.8 46.6 46.3 47.7 47.2 46.7 

Philippines 1998 40.6 41.9 43.8 45.3 46.7 44.1 

Vietnam 1997 40.0 41.0 42.1 44.0 45.6 42.6 

 

Latin America and Caribbean 

Bolivia 1998 42.0 41.3 40.1 41.6 47.0 42.6 

Brazil 1996 42.6 43.2 43.1 43.6 44.4 43.4 

Colombia 1995 44.5 43.8 42.9 43.9 47.1 44.5 

Dominican Republic 1996 40.8 41.7 42.5 44.1 47.2 43.6 

Guatemala 1995 42.4 42.3 42.3 42.2 44.0 42.7 

Haiti 1994-95 47.0 46.4 45.4 41.3 43.3 44.4 

Nicaragua 1997-98 43.9 43.6 43.3 45.3 46.5 44.7 

Paraguay 1990 43.2 43.9 46.5 44.4 46.3 45.0 

Peru 1996 43.0 41.4 41.8 45.6 49.7 44.7 

Average 43.6 44.0 44.2 44.0 44.5 44.0 
64

Table C.6 Mean number of years of education of household heads by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 0.5 0.9 1.0 2.5 6.4 2.4 

Cameroon 1991 3.0 2.6 4.8 6.1 8.1 5.1 

CAR 1994-95 1.7 2.6 3.3 4.0 6.0 3.4 

Chad 1996-97 0.5 0.8 1.0 1.8 4.3 1.6 

Comoros 1996 0.8 1.2 1.7 1.7 4.6 2.1 

Côte d'Ivoire 1994 0.9 1.3 1.9 3.2 7.4 3.1 

Eritrea 1995 0.2 0.3 0.5 1.5 4.4 1.6 

Ghana 1993 3.5 3.5 4.7 6.4 9.7 5.8 

Kenya 1998 4.5 5.1 5.7 7.0 10.0 6.7 

Madagascar 1997 1.9 2.3 2.9 4.0 7.6 3.9 

Malawi 1992 2.5 3.0 3.5 4.2 7.3 4.1 

Mali 1995-96 0.3 0.4 0.5 1.1 4.4 1.5 

Mozambique 1997 1.4 2.0 2.1 2.9 5.2 2.7 

Namibia 1997 1.9 2.3 3.1 4.2 8.5 4.4 

Niger 1998 0.2 0.2 0.4 0.6 3.4 1.0 

Togo 1998 1.5 1.8 2.6 3.6 7.5 3.7 

Uganda 1995 3.3 3.9 4.2 4.9 8.2 4.9 

Zambia 1996 4.1 4.4 5.6 7.7 10.8 6.8 

Zimbabwe 1994 3.9 3.9 5.1 6.9 8.9 6.0 

 

Near East and North Africa 

Egypt 1995 1.8 2.9 4.6 7.1 11.9 5.9 

Jordan 1997 6.3 8.3 8.6 9.7 12.1 9.0 

Morocco 1992 0.2 0.6 1.1 2.2 5.2 2.1 

 

Europe and Central Asia 

Kazakhstan 1995 8.6 9.4 9.9 10.6 11.6 10.2 

Kyrgyzstan 1997 9.3 9.3 9.9 9.9 11.7 10.1 

Turkey 1993 3.0 4.0 5.1 6.3 8.7 5.7 

Uzbekistan 1996 9.6 10.1 10.1 10.1 11.3 10.3 

 

South and Southeast Asia 

Bangladesh 1996-97 1.3 1.7 2.8 4.3 7.4 3.5 

India 1992-93 1.9 2.5 3.5 5.1 9.2 4.5 

Indonesia 1997 4.2 4.8 5.4 6.9 10.1 6.2 

Nepal 1996 1.3 1.6 1.8 2.7 5.0 2.4 

Pakistan 1990-91 1.0 1.7 2.2 3.8 7.0 3.1 

Philippines 1998 5.0 6.7 7.9 9.4 11.5 8.5 

Vietnam 1997 4.8 6.6 7.3 8.0 8.9 7.1 

 

Latin America and Caribbean 

Bolivia 1998 3.8 5.2 6.9 8.4 12.0 7.8 

Brazil 1996 2.0 3.7 4.9 6.6 8.6 5.4 

Colombia 1995 2.5 4.0 5.8 7.4 9.8 6.3 

Dominican Republic 1996 3.4 5.0 5.9 7.4 10.2 6.7 

Guatemala 1995 1.0 1.3 2.4 4.4 9.0 4.0 

Haiti 1994-95 0.6 1.0 1.7 3.2 6.8 3.0 

Nicaragua 1997-98 1.6 2.6 3.9 5.6 8.7 4.9 

Paraguay 1990 3.6 4.0 5.1 6.3 9.6 6.1 

Peru 1996 3.7 5.5 7.3 8.3 10.5 7.4 

Average 2.8 3.5 4.3 5.4 8.3 5.0 
65

Table C.7 Percent of household heads who are currently married by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 79 75 73 62 57 68 

Cameroon 1991 82 81 68 65 63 71 

CAR 1994-95 75 81 79 70 74 76 

Chad 1996-97 79 82 86 84 81 82 

Comoros 1996 78 78 75 70 72 74 

Côte d'Ivoire 1994 78 70 66 67 73 71 

Eritrea 1995 87 80 70 63 57 70 

Ghana 1993 63 58 65 46 57 57 

Kenya 1998 69 64 60 62 67 64 

Madagascar 1997 80 74 76 77 77 77 

Malawi 1992 66 71 75 80 85 76 

Mali 1995-96 92 95 95 93 90 93 

Mozambique 1997 80 78 84 68 75 77 

Namibia 1997 57 67 62 56 60 60 

Niger 1998 90 88 90 90 87 89 

Togo 1998 83 79 70 58 68 71 

Uganda 1995 74 75 77 75 70 74 

Zambia 1996 79 78 78 76 79 78 

Zimbabwe 1994 60 62 49 61 73 62 

 

Near East and North Africa 

Egypt 1995 86 88 89 91 92 89 

Jordan 1997 93 94 94 94 92 93 

Morocco 1992 87 88 83 78 84 84 

 

Europe and Central Asia 

Kazakhstan 1995 71 78 73 75 67 72 

Kyrgyzstan 1997 79 77 76 74 66 74 

Turkey 1993 90 89 90 92 93 91 

Uzbekistan 1996 84 87 83 81 70 81 

 

South and Southeast Asia 

Bangladesh 1996-97 89 88 89 86 88 88 

India 1992-93 88 85 86 85 87 86 

Indonesia 1997 90 91 92 91 93 91 

Nepal 1996 85 83 86 87 85 85 

Pakistan 1990-91 90 88 87 88 84 87 

Philippines 1998 90 87 83 83 80 84 

Vietnam 1997 86 85 87 85 80 84 

 

Latin America and Caribbean 

Bolivia 1998 84 81 79 76 74 78 

Brazil 1996 82 79 78 81 81 80 

Colombia 1995 81 76 72 72 75 75 

Dominican Republic 1996 82 76 72 67 70 73 

Guatemala 1995 84 81 82 77 77 80 

Haiti 1994-95 75 66 59 52 41 57 

Nicaragua 1997-98 82 72 71 63 69 71 

Paraguay 1990 86 87 83 83 81 84 

Peru 1996 84 81 77 76 79 79 

Average 81 80 78 75 76 78 
66

Table C.8 Percent female of household members by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 51 53 53 55 55 54 

Cameroon 1991 56 52 56 55 54 55 

CAR 1994-95 54 54 55 54 52 54 

Chad 1996-97 55 56 53 54 52 54 

Comoros 1996 52 53 54 55 54 54 

Côte d'Ivoire 1994 53 51 55 54 55 54 

Eritrea 1995 52 53 54 54 57 54 

Ghana 1993 55 55 55 58 58 56 

Kenya 1998 53 55 55 55 56 55 

Madagascar 1997 51 52 52 51 53 52 

Malawi 1992 55 55 53 52 50 53 

Mali 1995-96 54 53 53 53 53 53 

Mozambique 1997 52 55 53 55 54 54 

Namibia 1997 56 55 54 56 56 55 

Niger 1998 52 53 52 54 53 53 

Togo 1998 52 52 53 56 58 54 

Uganda 1995 54 54 55 53 55 54 

Zambia 1996 53 54 53 53 54 53 

Zimbabwe 1994 53 54 56 54 56 55 

 

Near East and North Africa 

Egypt 1995 49 48 49 49 50 49 

Jordan 1997 50 49 49 49 50 49 

Morocco 1992 54 52 53 53 55 53 

 

Europe and Central Asia 

Kazakhstan 1995 52 52 54 54 57 54 

Kyrgyzstan 1997 50 53 52 54 55 53 

Turkey 1993 50 50 50 50 50 50 

Uzbekistan 1996 50 51 53 52 54 52 

 

South and Southeast Asia 

Bangladesh 1996-97 51 50 51 50 50 50 

India 1992-93 48 50 49 49 49 49 

Indonesia 1997 50 50 50 50 52 51 

Nepal 1996 52 51 51 51 51 51 

Pakistan 1990-91 50 48 49 49 50 49 

Philippines 1998 50 51 52 54 58 54 

Vietnam 1997 51 51 52 51 52 51 

 

Latin America and Caribbean 

Bolivia 1998 52 52 54 55 57 54 

Brazil 1996 52 53 56 54 55 54 

Colombia 1995 50 52 56 58 59 55 

Dominican Republic 1996 50 52 54 56 59 55 

Guatemala 1995 53 52 53 54 57 54 

Haiti 1994-95 54 53 54 58 60 56 

Nicaragua 1997-98 51 52 53 56 57 54 

Paraguay 1990 50 51 52 53 57 53 

Peru 1996 51 52 53 54 56 54 

Average 52 52 53 54 54 53 
67

Table C.9 Mean age of household members by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 19.7 19.8 19.4 19.5 20.0 19.7 

Cameroon 1991 20.3 20.8 21.6 21.1 20.6 20.9 

CAR 1994-95 20.1 19.9 19.6 19.5 19.0 19.5 

Chad 1996-97 17.6 20.0 19.2 18.7 18.9 19.0 

Comoros 1996 20.5 22.3 23.1 23.5 24.2 22.8 

Côte d'Ivoire 1994 20.1 20.8 19.9 20.0 20.1 20.2 

Eritrea 1995 19.8 20.7 21.7 22.5 23.7 21.8 

Ghana 1993 19.0 19.3 20.0 19.3 20.8 19.7 

Kenya 1998 18.6 19.4 20.4 20.3 21.2 20.0 

Madagascar 1997 18.0 19.7 19.8 20.5 22.7 20.2 

Malawi 1992 19.9 19.6 20.3 20.6 19.5 20.0 

Mali 1995-96 20.0 19.7 19.7 19.4 21.0 20.0 

Mozambique 1997 20.7 20.8 19.7 20.1 20.4 20.3 

Namibia 1997 21.0 20.8 21.6 22.2 22.5 21.6 

Niger 1998 18.1 20.3 20.1 20.0 19.6 19.7 

Togo 1998 19.6 19.4 20.1 20.8 21.6 20.4 

Uganda 1995 18.2 18.1 17.9 18.4 18.2 18.2 

Zambia 1996 19.2 19.9 19.7 19.0 18.8 19.3 

Zimbabwe 1994 18.9 21.1 20.1 20.6 21.0 20.3 

 

Near East and North Africa 

Egypt 1995 21.2 21.6 22.3 23.0 24.2 22.3 

Jordan 1997 18.5 18.3 19.4 21.3 22.4 20.0 

Morocco 1992 22.1 22.8 23.3 24.6 26.0 23.8 

 

Europe and Central Asia 

Kazakhstan 1995 23.1 25.2 25.9 26.7 28.0 25.8 

Kyrgyzstan 1997 22.5 23.4 24.2 24.5 25.7 24.0 

Turkey 1993 22.3 23.9 23.8 24.6 25.9 24.0 

Uzbekistan 1996 21.6 22.9 24.2 24.2 25.5 23.7 

 

South and Southeast Asia 

Bangladesh 1996-97 21.6 22.2 22.9 23.5 24.5 23.0 

India 1992-93 22.9 23.3 23.7 24.2 25.7 24.0 

Indonesia 1997 23.0 24.1 24.6 24.6 25.6 24.4 

Nepal 1996 20.5 21.7 22.4 21.9 24.0 22.1 

Pakistan 1990-91 21.7 21.7 21.5 21.4 22.1 21.7 

Philippines 1998 19.9 21.8 23.7 25.2 26.7 23.9 

Vietnam 1997 21.4 23.1 24.0 25.6 26.7 24.1 

 

Latin America and Caribbean 

Bolivia 1998 20.0 20.4 20.9 23.0 26.2 22.5 

Brazil 1996 21.4 23.5 24.9 26.3 27.5 24.8 

Colombia 1995 21.3 22.9 23.7 25.4 28.4 24.6 

Dominican Republic 1996 20.5 22.0 22.7 24.0 26.4 23.4 

Guatemala 1995 18.6 20.0 20.8 21.9 25.4 21.5 

Haiti 1994-95 21.0 21.8 21.6 21.5 22.8 21.8 

Nicaragua 1997-98 18.8 20.0 20.9 22.8 25.5 21.8 

Paraguay 1990 19.0 20.4 22.4 23.6 25.7 22.4 

Peru 1996 20.3 21.2 22.4 24.6 28.3 23.8 

Average 20.3 21.2 21.7 22.2 23.4 21.8 
68

Table C.10 Mean number of years of education of household members by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 0.3 0.5 0.8 1.9 5.0 1.8 

Cameroon 1991 2.8 2.9 4.8 5.9 7.7 5.0 

CAR 1994-95 1.1 1.7 2.4 3.6 5.3 3.1 

Chad 1996-97 0.3 0.5 0.6 1.3 3.7 1.4 

Comoros 1996 1.0 1.6 2.2 2.6 5.2 2.8 

Côte d'Ivoire 1994 0.9 1.4 1.7 2.5 5.6 2.7 

Eritrea 1995 0.2 0.4 0.5 1.8 5.3 2.0 

Ghana 1993 2.8 2.8 4.2 6.2 9.2 5.3 

Kenya 1998 4.6 5.5 6.1 7.0 9.7 6.8 

Madagascar 1997 1.9 2.2 2.8 3.9 7.5 4.0 

Malawi 1992 1.8 2.5 2.9 3.5 6.2 3.5 

Mali 1995-96 0.2 0.3 0.5 0.9 3.9 1.5 

Mozambique 1997 1.1 1.5 1.7 2.5 4.7 2.5 

Namibia 1997 3.1 3.6 4.3 5.1 8.0 5.0 

Niger 1998 0.3 0.3 0.4 0.7 3.5 1.1 

Togo 1998 1.1 1.2 2.0 3.1 5.9 3.0 

Uganda 1995 2.8 3.1 3.7 4.2 7.4 4.3 

Zambia 1996 3.9 4.3 5.3 7.0 9.6 6.4 

Zimbabwe 1994 4.4 4.6 5.6 7.1 9.0 6.4 

 

Near East and North Africa 

Egypt 1995 2.1 3.3 5.0 7.2 11.3 5.6 

Jordan 1997 6.5 8.2 8.6 9.6 11.3 8.9 

Morocco 1992 0.3 0.7 1.7 3.3 5.8 2.7 

 

Europe and Central Asia 

Kazakhstan 1995 9.1 9.6 9.9 10.7 11.4 10.2 

Kyrgyzstan 1997 9.4 9.7 9.8 10.1 11.5 10.1 

Turkey 1993 2.6 3.6 4.3 5.5 7.9 4.9 

Uzbekistan 1996 9.7 10.0 10.1 10.3 11.3 10.3 

 

South and Southeast Asia 

Bangladesh 1996-97 1.0 1.5 2.5 3.9 6.7 3.4 

India 1992-93 1.5 2.2 3.0 4.7 8.4 4.2 

Indonesia 1997 4.0 4.6 5.3 6.7 9.8 6.2 

Nepal 1996 0.9 1.2 1.5 2.2 4.4 2.1 

Pakistan 1990-91 0.7 1.3 1.7 3.1 6.1 2.8 

Philippines 1998 5.3 7.1 8.5 9.8 11.2 9.1 

Vietnam 1997 4.1 6.0 6.8 7.7 9.0 6.8 

 

Latin America and Caribbean 

Bolivia 1998 3.2 4.9 6.9 8.7 11.8 8.0 

Brazil 1996 2.4 4.4 5.6 7.1 8.8 6.0 

Colombia 1995 2.9 4.7 6.4 8.0 10.0 6.9 

Dominican Republic 1996 3.5 5.4 6.6 8.1 10.6 7.5 

Guatemala 1995 1.0 1.4 2.4 4.7 8.6 4.2 

Haiti 1994-95 0.9 1.3 2.6 4.1 7.5 3.8 

Nicaragua 1997-98 1.9 3.3 4.8 6.6 9.0 5.7 

Paraguay 1990 3.9 4.3 5.7 6.7 9.7 6.5 

Peru 1996 3.5 5.4 7.4 8.7 10.5 7.8 

Average 2.6 3.9 5.4 7.0 9.6 6.3 
69

Table C.11 Distribution of children under age fifteen by wealth quintile 

Quintile 

Country Lowest Second Middle Fourth Highest Total 

Sub-Saharan Africa 

Benin 1996 20 19 21 20 20 100 

Cameroon 1991 23 13 11 43 10 100 

CAR 1994-95 16 16 18 22 29 100 

Chad 1996-97 18 25 16 21 21 100 

Comoros 1996 22 20 19 20 19 100 

Côte d'Ivoire 1994 17 19 21 22 22 100 

Eritrea 1995 24 21 16 17 22 100 

Ghana 1993 20 20 20 20 19 100 

Kenya 1998 22 21 19 19 18 100 

Madagascar 1997 26 19 17 20 18 100 

Malawi 1992 19 18 21 20 22 100 

Mali 1995-96 15 18 20 22 25 100 

Mozambique 1997 24 10 20 20 27 100 

Namibia 1997 18 24 21 19 18 100 

Niger 1998 17 24 17 19 23 100 

Togo 1998 20 21 20 18 21 100 

Uganda 1995 20 20 20 18 22 100 

Zambia 1996 22 15 18 20 25 100 

Zimbabwe 1994 24 18 21 18 19 100 

 

Near East and North Africa 

Egypt 1995 24 24 20 17 15 100 

Jordan 1997 22 22 21 19 17 100 

Morocco 1992 21 23 21 18 17 100 

 

Europe and Central Asia 

Kazakhstan 1995 23 21 21 19 16 100 

Kyrgyzstan 1997 23 20 21 20 15 100 

Turkey 1993 25 21 21 18 15 100 

Uzbekistan 1996 22 22 21 20 16 100 

 

South and Southeast Asia 

Bangladesh 1996-97 19 21 19 21 19 100 

India 1992-93 21 20 21 21 17 100 

Indonesia 1997 23 21 20 19 18 100 

Nepal 1996 23 18 21 21 17 100 

Pakistan 1990-91 19 19 21 21 20 100 

Philippines 1998 22 20 19 19 20 100 

Vietnam 1997 23 21 20 18 17 100 

 

Latin America and Caribbean 

Bolivia 1998 21 21 21 20 17 100 

Brazil 1996 24 23 20 17 16 100 

Colombia 1995 22 21 22 19 16 100 

Dominican Republic 1996 20 20 20 21 19 100 

Guatemala 1995 24 20 21 20 15 100 

Haiti 1994-95 20 20 20 20 21 100 

Nicaragua 1997-98 24 20 20 19 16 100 

Paraguay 1990 23 20 19 19 18 100 

Peru 1996 22 21 21 20 16 100 

Average 21 20 20 20 19 100 
71

Appendix D 

Possible New Additions to Wealth Index Indicators 

Services 

Trash collection 

Mail service (to dwelling or PO box) 

Email account 

Transport 

Boat 

Horse, donkey, mule 

Cart 

Household construction (by observation 

 of main component) 

Wall construction 

Roof construction 

Glass windows 

Appliances 

Type of cooking appliance 

 • Clock 

 • Blender 

 • Grinder 

 • Water heater 

 • Fan 

 • Air conditioner 

 • Washing machine 

 • Water pump for dwelling 

 • Electronic generator for dwelling 

Electronics and communications 

Sound equipment (cassette, CD, HI-FI, stereo) 

Video equipment (VCR, DVD, video camera) 

Photographic camera (film or digital) 

Telephone 

 • Landline 

 • Mobile 

TV cable, satellite dish 

Computer 

Land holdings and real estate ownership 

Agricultural land by type (ask size) 

 • Irrigated agricultural land 

 • Non-irrigated wet crop land 

 • Non-irrigated dry crop land 

 • Grazing land 

Non-agricultural land and buildings 

 • Main residence 

 • Other residences 

 • Rental residential property 

 • Commercial and industrial property 

 • Vacant land 

Farm capital and livestock 

Motorized agricultural implements, machines 

 • Tractor, combines, harvesters, threshers 

Non-motorized agricultural implements, machines 

 • Plows 

Irrigation pumps 

Electric generators for agricultural machinery 

Livestock and herds 

Business capital and inventory 

Business machinery 

 • Water pump for business 

 • Electric generator for business 

Business inventory 

Financial assets 

Bank account 

 • Checking account 

 • Savings account 

Membership in self-help organizations 

Stocks and bonds 

Loan, mortgage from an institution 

Debts to others 

Non-institutional loans made to others 

Life insurance 

Furnishings 

Furniture 

 • Tables, chairs, benches, sofas, beds, 

 armoires, shelves 

Curtains, tablecloth 

Coffee pot, tea pot 

Clothing and jewelry 

Shoes 

Watch 

Gold rings, bracelets, necklaces