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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 ob-
jectives 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 primar-
ily descriptive, Analytical Studies take a more analytical approach.
The Comparative Reports series covers a variable number of countries, depending on the avail-
ability 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 ad-
dressed as needed.
The topics covered in Comparative Reports are selected by MEASURE DHS+ staff in conjunc-
tion 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
W
e
a
l
t
h
i
e
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 z-
scores); 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 per-
member
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 per-
member 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 per-
member
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
Quintiles for per-member expenditure
Wealth index
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
.
5
0
2.00
1.50
1
.
00
.
50
0.00
-.50
-
1
.
0
0
-1
.
5
0
-2.00
-
2
.
5
0
-3
.
0
0
-3.5
0
-
4
.00
-4
.
5
0
-5
.
0
0
-5.50
-
6
.
0
0
-6
.
5
0
-7.0
0
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
.7
5
1
.2
5
.75
.
25
-.25
-.75
-1
.
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
.
75
5
.
2
5
4.75
4.25
3
.
75
3.25
2.75
2.25
1
.
7
5
1.25
.
7
5
.25
-
.
2
5
-.75
-1.2
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
.
0
0
1.50
1.00
.50
0.00
-.50
-1
.
0
0
-
1
.
5
0
-2.00
-2
.
5
0
-
3
.
0
0
-3.50
-4
.
0
0
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 room
1
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 room
1
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 room
1
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 room
1
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 room
1
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 one-
fourth 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
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a
n
1
9
9
6
I
n
d
i
a
1
9
9
2
/
1
9
9
3
B
a
n
g
l
a
d
e
s
h
1
9
9
6
/
1
9
9
7
T
u
r
k
e
y
1
9
9
3
I
n
d
o
n
e
s
i
a
1
9
9
7
K
a
z
a
k
s
t
a
n
1
9
9
5
V
i
e
t
n
a
m
1
9
9
7
G
u
a
t
e
m
a
l
a
1
9
9
5
P
a
r
a
g
u
a
y
1
9
9
0
B
o
l
i
v
i
a
1
9
9
8
H
a
i
t
i
1
9
9
4
/
1
9
9
5
N
i
c
a
r
a
g
u
a
1
9
9
7
/
1
9
9
8
P
e
r
u
1
9
9
6
C
o
l
o
m
b
i
a
1
9
9
5
D
o
m
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n
i
c
a
n
R
e
p
u
b
l
i
c
1
9
9
6
B
r
a
z
i
l
1
9
9
6
0
2
4
6
8
10
B
i
r
t
h
s
p
e
r
w
o
m
a
n
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
C
h
a
d
1
9
9
6
/
1
9
9
7
M
a
l
i
1
9
9
5
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1
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9
6
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g
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r
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a
1
9
9
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C
A
R
1
9
9
4
/
1
9
9
5
M
o
z
a
m
b
i
q
u
e
1
9
9
7
S
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n
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g
a
l
1
9
9
7
C
ô
t
e
d
'
I
v
o
i
r
e
1
9
9
4
B
e
n
i
n
1
9
9
6
M
a
d
a
g
a
s
c
a
r
1
9
9
7
T
o
g
o
1
9
9
8
N
i
g
e
r
1
9
9
8
M
a
l
a
w
i
1
9
9
2
T
a
n
z
a
n
i
a
1
9
9
6
G
h
a
n
a
1
9
9
3
N
a
m
i
b
i
a
1
9
9
2
Z
a
m
b
i
a
1
9
9
6
C
o
m
o
r
o
s
1
9
9
6
K
e
n
y
a
1
9
9
8
U
g
a
n
d
a
1
9
9
5
Z
i
m