DHS METHODOLOGICAL 

REPORTS 15

Intertemporal Comparisons 

of Poverty and Wealth with 

DHS Data: A Harmonized Asset 

Index Approach

SEPTEMBER 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Sarah Staveteig and Lindsay Mallick of ICF International.

DHS Methodological Reports No. 15 

Intertemporal Comparisons of Poverty and Wealth with 

DHS Data: A Harmonized Asset Index Approach 

Sarah Staveteig 

Lindsay Mallick 

ICF International 

Rockville, Maryland, USA 

September 2014 

Corresponding author: Sarah Staveteig, ICF International, 530 Gaither Road, Rockville, Maryland, USA; 

Phone: 301-572-0577; Fax: 301-407-6501 Email: sarah.staveteig@icfi.com 
Acknowledgments: We are grateful to Tom Pullum for his guidance of the project and to Karen Foreit for 

her insightful review of an earlier draft. 

Editor: Bryant Robey 

Document Production: Natalie LaRoche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the author and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about the DHS Program contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500, fax: 301-407-6501, email: reports@dhsprogram.com, Internet: www.dhsprogram.com. 

Recommended citation: 

Staveteig, Sarah and Lindsay Mallick. 2014. Intertemporal Comparisons of Wealth with DHS Data: A 

Harmonized Asset Index Approach. DHS Methodological Reports No. 15. Rockville, Maryland, USA: ICF 

International. 
v 

Contents 

List of Tables ............................................................................................................................................. vii

List of Figures ............................................................................................................................................. ix

Preface ......................................................................................................................................................... xi

Abstract ..................................................................................................................................................... xiii

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

1.1. Purpose ............................................................................................................................. 1

1.2. The DHS Comparative Wealth Index .............................................................................. 2

1.3. The International Wealth Index ........................................................................................ 3

1.4. Assessing Metrics ............................................................................................................. 4

2. Methods .............................................................................................................................................. 7

2.1. Survey Selection ............................................................................................................... 7

2.2. Construction of a Harmonized Wealth Index ................................................................... 7

2.2.1. Harmonizing Asset Measures Across Surveys ............................................................. 7

2.2.2. Computing a Harmonized Wealth Index .................................................................... 10

3. Country Case Studies ...................................................................................................................... 13

3.1. Bangladesh ..................................................................................................................... 13

3.2. Bolivia ............................................................................................................................ 17

3.3. Cameroon ....................................................................................................................... 21

3.4. Egypt .............................................................................................................................. 24

3.5. Ghana ............................................................................................................................. 28

3.6. Indonesia ........................................................................................................................ 31

3.7. Nepal .............................................................................................................................. 34

3.8. Zimbabwe ....................................................................................................................... 37

4. Discussion and Conclusions ............................................................................................................ 41

Appendix .................................................................................................................................................... 43

References .................................................................................................................................................. 52
vi 
vii 

List of Tables 

Table 1. Illustrative Application of the Comparative Wealth Index (CWI), Ghana ..................................... 3

Table 2. Harmonized Assets by Country ...................................................................................................... 9

Table 3. Summary of CWI and HWI in Bangladesh, 1993-2011 ............................................................... 13

Table 4. Correspondence between pooled CWI and HWI quintiles in Bangladesh, 1993-2011 ................ 17

Table 5. Summary of CWI and HWI in Bolivia, 1994-2008 ...................................................................... 17

Table 6. Correspondence between pooled CWI and HWI quintiles in Bolivia, 1994-2008 ....................... 20

Table 7. Summary of CWI and HWI in Cameroon, 1991-2011 ................................................................. 21

Table 8. Correspondence between pooled CWI and HWI quintiles in Cameroon, 1991-2011 .................. 24

Table 9. Summary of CWI and HWI in Egypt, 1995-2008 ........................................................................ 25

Table 10. Correspondence between pooled CWI and HWI quintiles in Egypt, 1995-2008 ....................... 27

Table 11. Summary of CWI and HWI in Ghana, 1993-2008 ..................................................................... 28

Table 12. Correspondence between pooled CWI and HWI quintiles in Ghana, 1993-2008 ...................... 30

Table 13. Summary of CWI and HWI in Indonesia, 1997-2012 ................................................................ 31

Table 14. Correspondence between pooled CWI and HWI quintiles in Indonesia, 1997-2012 ................. 33

Table 15. Summary of CWI and HWI in Nepal, 1996-2011 ...................................................................... 34

Table 16. Correspondence between pooled CWI and HWI quintiles in Nepal, 1996-2011 ....................... 36

Table 17. Summary of CWI and HWI in Zimbabwe, 1994-2011 ............................................................... 37

Table 18. Correspondence between pooled CWI and HWI quintiles in Zimbabwe, 1994-2011 ................ 39

Table A1. Example of Asset Harmonization Table, Bangladesh ................................................................ 44
viii 
ix 

List of Figures 

Figure 1. Mean CWI and HWI versus GDP per capita (PPP), Bangladesh 1993-2011 .............................. 14

Figure 2. Distribution of comparative wealth index (CWI) in Bangladesh by year ................................... 15

Figure 3. Distribution of harmonized wealth index (HWI) in Bangladesh by year .................................... 15

Figure 4. Quantile-Quantile plots of HWI versus CWI, Bangladesh .......................................................... 16

Figure 5. Mean CWI and HWI versus GDP per capita (PPP), Bolivia 1994-2008 .................................... 18

Figure 6. Distribution of comparative wealth index (CWI) in Bolivia by year .......................................... 18

Figure 7. Distribution of harmonized wealth index (HWI) in Bolivia by year ........................................... 19

Figure 8. Quantile-Quantile plots of HWI versus CWI, Bolivia ................................................................. 20

Figure 9. Mean CWI and HWI versus GDP per capita (PPP), Cameroon .................................................. 21

Figure 10. Distribution of comparative wealth index (CWI) in Cameroon by year ................................... 22

Figure 11. Distribution of harmonized wealth index (HWI) in Cameroon by year .................................... 23

Figure 12. Quantile-Quantile plots of HWI versus CWI, Cameroon .......................................................... 24

Figure 13. Mean CWI and HWI versus GDP per capita (PPP), Egypt 1995-2008 ..................................... 25

Figure 14. Distribution of comparative wealth index (CWI) in Egypt by year .......................................... 26

Figure 15. Distribution of harmonized wealth index (HWI) in Egypt by year ........................................... 26

Figure 16. Quantile-Quantile plots of HWI versus CWI, Egypt ................................................................. 27

Figure 17. Mean CWI and HWI versus GDP per capita (PPP), Ghana 1993-2008 .................................... 28

Figure 18. Distribution of comparative wealth index (CWI) in Ghana by year .......................................... 29

Figure 19. Distribution of harmonized wealth index (HWI) in Ghana by year .......................................... 29

Figure 20. Quantile-Quantile plots of HWI versus CWI, Ghana ................................................................ 30

Figure 21. Mean CWI and HWI versus GDP per capita (PPP), Indonesia ................................................. 31

Figure 22. Distribution of comparative wealth index (CWI) in Indonesia by year .................................... 32

Figure 23. Distribution of harmonized wealth index (HWI) in Indonesia by year ..................................... 32

Figure 24. Quantile-Quantile plots of HWI versus CWI, Indonesia ........................................................... 33

Figure 25. Mean CWI and HWI versus GDP per capita (PPP), Nepal 1996-2011 ..................................... 34

Figure 26. Distribution of comparative wealth index (CWI) in Nepal by year .......................................... 35

Figure 27. Distribution of harmonized wealth index (HWI) in Nepal by year ........................................... 35

Figure 28. Quantile-Quantile plots of HWI versus CWI, Nepal ................................................................. 36

Figure 29. Mean CWI and HWI versus GDP per capita (PPP), Zimbabwe 1994-2011 ............................. 37

Figure 30. Distribution of comparative wealth index (CWI) in Zimbabwe by year ................................... 38

Figure 31. Distribution of harmonized wealth index (HWI) in Zimbabwe by year .................................... 38

Figure 32. Quantile-Quantile plots of HWI versus CWI, Zimbabwe ......................................................... 39
x 
xi 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to continually assess and improve the methodology and 

procedures used to carry out national-level surveys as well as to offer additional tools for analysis. 

Improvements in methods used will enhance the accuracy and depth of information collected by The DHS 

Program and relied on by policymakers and program managers in low- and middle-income countries. 

While data quality is a main topic of the DHS Methodological Reports series, the reports also examine 

issues of sampling, questionnaire comparability, survey procedures, and methodological approaches. The 

topics explored in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Methodological Reports will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries, and will be used to 

enhance the quality and analysis of survey data. 

Sunita Kishor 

Director, The DHS Program 
xii 
xiii 

Abstract 

Over the past 30 years, the Demographic and Health Surveys (DHS) Program has conducted more than 300 

household surveys in over 90 countries. As the number of countries with multiple surveys has risen, there 

is an increased opportunity for comparisons of health in relation to economic status over time. However, 

the DHS Wealth Index, which ranks relative economic standing among surveyed households, is computed 

separately for each survey. Existing approaches to make the DHS Wealth Index comparable across surveys 

appear to encounter problems when comparing early DHS surveys to later DHS surveys; the latter 

frequently contain triple the number of questions related to wealth than earlier surveys. 

This study focuses on the particular challenge of conducting intertemporal analysis using DHS data from 

the mid-1990s to the present. It demonstrates how to generate a Harmonized Wealth Index (HWI) based 

on common assets and services across surveys. In eight focal countries—Bangladesh, Bolivia, 

Cameroon, Egypt, Ghana, Indonesia, Nepal, and Zimbabwe—we use pooled household data to compute 

an HWI. Results show that the HWI is highly correlated with the existing DHS Wealth Index. Loss of 

information due to asset harmonization compresses the index, but this occurs primarily toward the top of 

the distribution in the most recent survey; except for Bolivia in 1998 and Ghana in 2008, the HWI appears 

to perform well at differentiating gradations of poverty. Overall, the HWI approach is a promising avenue 

for analysts and policymakers interested in intertemporal comparisons of health and poverty in specific 

countries. 
xiv 
1 

1. Introduction 

The Demographic and Health Survey (DHS) Wealth Index, which was developed a decade ago, is an asset 

index designed to compare relative economic standing of households in the absence of income and 

expenditure data (Rutstein and Johnson 2004). The index is computed among the household population 

within each particular survey based household ownership of assets, such as a radio, a television, a car, 

livestock, land; household services such as the source of drinking water; and housing construction materials, 

such as type of flooring. It uses a methodology developed by Filmer and Pritchett (1999; 2001) of principal 

component analysis to produce a positive or negative weight associated with each household asset.1

 The 

resulting item weights are applied to the assets and summed to a composite factor score for each household. 

The recoded dataset from almost every DHS survey from 1992 forward includes two wealth variables—the 

Wealth Index factor score, a continuous variable derived through principal component analysis, and the 

Wealth Index quintile, which ranks the de jure household population into five equal-sized groups based on 

their household wealth factor score.2

 The DHS Wealth Index is designed to measure economic well-being 

of households independently from education and health. Answers to questions on assets, services, and 

interviewer observations of construction materials in the context of a DHS survey are considered more 

reliable than self-reported income and expenditures both due to misreporting and because monetary income 

may be seasonal or transient in nature. Additionally, it is difficult to monetize in-kind income. Information 

on assets, services, and housing materials can provide a more stable picture of household economic status 

than monetary income alone; they reflect ‘permanent income’ (Friedman 1957). 

1.1. Purpose 

Over the past 30 years, the Demographic and Health Surveys Program has conducted more than 300 

nationally-representative household surveys in over 90 countries. As the number of countries with multiple 

surveys has risen, there is an increased interest in and opportunity for comparisons of health in relation to 

economic status over time. However, the DHS Wealth Index is designed to compare households within a 

specific survey. Scores are relative, not absolute; the mean wealth score is 0 in any given survey. As 

economic status of the population in a country improves and household services (like piped drinking water) 

become more widely available and household assets (like televisions) become more affordable, the 

combination of assets and services that ranks a household in the top quintile in an earlier survey could rank 

a household toward the bottom quintile in a later survey. 

An additional challenge of comparing household economic status over time is the changing number of asset 

questions available. Before the development of the DHS Wealth Index, most dimensions of household 

wealth were captured because of their relationship with other health indicators. For example, sanitation and 

flooring were important for analysis of prevalence of nutrition and diarrhea. Radios and televisions were 

important to help measure access to mass media for health messages such as family planning. The DHS 

Phase 3 Model Questionnaire (Macro International, Inc. 1995), which spans DHS surveys from 1992 to 

 

1

 Principal component analysis is a data reduction technique to identify underlying uncorrelated dimensions of the 

relationship between variables in a dataset (Dunteman 1989). It computes a set of item weights designed to maximize 

the explained variance. 

2

 In most DHS household datasets, the wealth index factor score variable is hv271 and the wealth index quintile is 

hv270. In 88 surveys from before 2003, the wealth index is contained in a separate dataset that must be merged into 

the household file. In those datasets the wealth index factor score is a variable called ‘wlthindf’ and the wealth quintile 

is called ‘wlthind5.’ 
2 

1997, contains 12 questions about assets, services, and construction materials.3 In later years, after the 

development of the DHS Wealth Index, the number of questions about household assets and services 

increased dramatically. The most recent DHS Phase 6 Core Questionnaire (2008-2013) contains 28 

questions that are used in the DHS Wealth Index, with the instruction to add at least 9 additional questions 

on asset ownership so that the final questionnaire “includes at least three items that even a poor household 

may have, at least three items that a middle income household may have, and at least three items that a high 

income household may have” (ICF International 2011). While individual surveys adapt the core 

questionnaire to local circumstances and survey objectives, over the course of the past twenty years, the 

number of questions in the DHS core questionnaire that can be included in the DHS Wealth Index has more 

than trebled, from 12 to 37. 

The Comparative Wealth Index (CWI) (Rutstein and Staveteig 2014) is intended to adjust the DHS Wealth 

Index factor score of any household from any survey relative to the baseline survey, Vietnam 2002. It 

produces a linear displacement of the wealth index factor score provided in DHS datasets. The mean values 

of CWI perform well compared with trends in GDP per capita and are generally robust to sensitivity testing. 

However, in some countries there are apparent distortions in the CWI between earlier surveys with 

relatively few assets and later surveys with many more assets. 

This study is intended to extend, test, and complement the CWI approach. It focuses on the particular 

challenge of doing trend analysis within a country using a small number of surveys. It compares a pooledgeneration approach of common assets across surveys within a given country (that is, a Harmonized Wealth 

Index, or HWI) with the CWI approach, in order to test a different method of adjusting wealth in relation 

to the approach in current use. It is important to note that the HWI, unlike CWI, is not a universal metric. 

It is a method of computing a pooled asset index for a small set of surveys. The HWI is intended as an 

analytical approach that is likely to be useful to researchers and policymakers conducting trend analysis in 

a small set of countries. 

1.2. The DHS Comparative Wealth Index 

The DHS Comparative Wealth Index (CWI) (Rutstein and Staveteig 2014) is a methodology to adjust the 

DHS Wealth Index factor scores from a given DHS standard recode file so that they are comparable to one 

another. It uses an anchoring-points approach originally developed for the World Health Organization’s 

World Health Survey (Murray et al. 2000; Murray et al. 2003). Eight anchoring points from across the 

economic distribution are regressed against those anchoring points on the baseline wealth index: four 

cutpoints based on the Unsatisfied Basic Needs framework4

 as well as the wealth index score at which half 

of the households had a television, refrigerator, a car/truck, and a fixed landline telephone. Regressing these 

anchoring points against a baseline survey (Vietnam 2002) produces an intercept (α) and coefficient (β) 

that are used to displace the original DHS Wealth Index factor score5

 such that: 

 

3

 Source of drinking water, type of toilet facility, type of flooring, number of sleeping rooms, electricity, telephone, 

television, radio, refrigerator, bicycle, motorcycle, and car/truck. The DHS model questionnaire is adapted for use in 

each survey; refer to individual survey documentation to determine the number of questions on household assets, 

services, and construction materials. 

4

 The version of the Unsatisfied Basic Needs index (UBN) developed by ECLAC for Peru (Llanos and Instituto 

Nacional de Estadistica e Informatica [Peru] 2000) was used to determine the appropriate criteria: inadequate dwelling 

construction, overcrowded housing, inadequate sanitation, and high economic dependency. Anchoring points were 

derived from the wealth scores of the proportion of households with each sum of unsatisfied basic needs (1-4). 

5

 The raw wealth factor score provided in hv271 (interchangeably v191, mv191) typically has five implicit decimal 

places and needs to be divided by 100,000 to equal the wealth index factor score. 
3 

× ߚ + ߙ = ܫܹܥ

271ݒh

100,000

The CWI was calculated for 172 surveys by regressing anchoring points designed to capture a range of 

economic status points against a baseline wealth index. An advantage of the CWI is that, because it is a 

linear displacement of the DHS Wealth Index factor score, it is able to include the full range of unique 

assets used in the original DHS Wealth Index for each survey. 

One concern about the CWI is the possible distortion introduced by linear displacement. The original DHS 

Wealth Index is normalized but unbounded: scores are centered at 0 with a standard deviation of 1, but the 

minimum and maximum scores vary widely, from around +/-1 to up to +/-4 or higher. The CWI 

computations provide an alpha and beta to displace the original raw score. Applying this linear displacement 

to such a wide range of scores may work well for the majority of cases, but toward either end of the 

distributions the displacement may cause some distortion. 

Table 1 shows an example from data for Ghana. In 1993 the DHS Wealth Index factor score for Ghana had 

a minimum of -.801, whereas in 2008 the score had a minimum of -2.338. After the scores for each year 

are displaced using the CWI alpha and beta provided, the resulting minimum and maximum CWI scores 

are quite variable over time. The nature of asset and service accumulation over time means that economic 

status, unlike income, should be somewhat robust to short-term shocks. In some cases, apparent 

discrepancies in minimums and maximums are not meaningful: the maximum CWI score in 1993 is higher 

than the maximum CWI score in 2008, but only 17 household members in 1993 actually score above the 

2008 maximum, a result that could either be ‘real’ or simply the effect of sampling variation. In other cases, 

however, the differences appear to be problematic. For example in 2008, 12.7 percent of Ghanaian 

household members scored below -1.27 on the CWI, meaning they were poorer than the poorest household 

members in 1993. It is difficult to imagine how this result could be plausible. In the case of a severe 

economic shock we would naturally expect the distribution of wealth to fluctuate, but not for one-eighth of 

the population to become poorer than the absolute poorest from an earlier survey. The CWI requires 

additional validation testing to ensure that a comparable set of assets from one survey produces a similar 

score as that set of assets in a different survey. 

Table 1. Illustrative Application of the Comparative Wealth Index (CWI), Ghana 

Household 

members 

DHS 

Wealth 

score min 

DHS 

Wealth 

score max CWI Alpha CWI Beta CWI min CWI max 

1993 21,900 -0.801 5.555 -0.573 0.868 -1.27 4.25 

1998 21,665 -1.193 3.223 -0.380 0.828 -1.37 2.29 

2003 25,154 -1.024 3.578 -0.439 0.927 -1.39 2.88 

2008 44,080 -2.338 3.878 -0.194 0.965 -2.45 3.55 

1.3. The International Wealth Index 

A parallel effort to harmonize measures of wealth and poverty across household surveys is the International 

Wealth Index (IWI), developed by Smits and Steendijk (2012). The basic methodology of the IWI was to 

pool households from 165 different household surveys, primarily DHS and Multiple Indicator Cluster 

Surveys (MICS), to develop a universal set of asset weights based on 12 common assets and asset 

categories. A major advantage of the IWI, at least from the point of analysts, is that it creates a stable set of 

asset weights that can be applied to successive surveys without additional computation. 
4 

However the IWI has some key drawbacks. First, as new surveys are conducted and the socioeconomic 

status of the population changes over time, the original weights become increasingly less applicable. Second 

and more important, the index suffers from the problem of missing and reduced number of asset questions 

in earlier surveys compared with later surveys. The IWI includes 12 components: water source, floor type, 

toilet type, television, refrigerator, phone, electricity, car/truck, bicycle, cheap utensils (such as a watch or 

radio), expensive utensils (such as a computer or a car/truck), and number of sleeping rooms. There are 298 

formulas available to enable analysts to adjust scores if a household has as many as three missing 

components. The wisdom of imputing important components of the wealth index is unclear. For example, 

earlier surveys frequently excluded number of sleeping rooms. The only type of toilet that counts as being 

of high quality is a private flush toilet, but information about toilet sharing is missing from many surveys. 

To impute an adjustment for a given average survey score might be reasonable, but to impute an adjustment 

to compare households across surveys with missing asset categories might be misleading. 

The IWI counts the number of sleeping rooms independent of the number of members in a household. The 

underlying measure of living standards intended to be captured by the number of sleeping rooms is 

household crowding (Feres and Mancero 2001; Llanos and Instituto Nacional de Estadistica e Informatica 

[Peru] 2000). But with two bedrooms, for example, a household of four members and a household of ten 

have very different levels of crowding. A simple linear correlation of pooled DHS data from Bolivia6

indicates only a moderate correlation (-.43) between number of sleeping rooms and household members 

per sleeping room. The failure to use data on number of household members may reduce the accuracy of 

the index. 

The IWI also introduces a subtle but important intertemporal distortion. It groups assets into ‘expensive 

utensils’ (such as a car or truck, refrigerator, television) and ‘cheap utensils’ (such as radios, chairs, and 

watches). A household need only have one of the possible assets in order to receive the score for expensive 

or cheap utensil. The problem this creates is that in later surveys, which ask about a much larger set of 

durable assets, the proportion of households with either category of utensil is biased upward. For example, 

if an earlier survey only asks about radios and a later survey asks about radios, watches, and chairs, a 

household in the later survey has a greater chance of being counted as having a cheap utensil, independent 

of the household’s actual economic status. This distortion is problematic because, as discussed previously, 

early DHS core surveys included 12 questions related to the wealth index, including seven questions on 

ownership of durable goods (Macro International, 1995). In the Phase 6 DHS Core Questionnaire, there are 

37 questions related to the wealth index, including 20 suggested questions on ownership of durable goods 

(ICF International, 2011). While some countries elected to add a few additional asset questions early on, 

these changes in the structure of the surveys themselves likely contribute to an apparent increase in 

household wealth over the past two decades. Hence, while the IWI is a valuable innovation, there are 

reasons for pursuing an alternative approach for intertemporal analysis. 

1.4. Assessing Metrics 

By definition, the measure of wealth from household assets is a latent construct; in the absence of alternate 

external measures it is difficult to determine whether one asset index fits the data better than another. In 

some cases monetary income or expenditures are used as a gold standard to evaluate the accuracy of a 

wealth index (Azzarri et al. 2006; Foreit and Schreiner 2011; Sahn and Stifel 2003; Ucar 2014). As 

conceived by Filmer and Pritchett (2001) and as implemented by Rutstein and Johnson (2004), however, 

the DHS Wealth Index is intended as an alternative to rather than a proxy for income and expenditures. The 

wealth index is intended to measure ‘permanent income’, or long-term well-being, rather than short-term 

monetary income. The two are related but are not equal. When the set of assets measured by a survey is a 

 

6

 Data were pooled from the 1994, 1998, 2003, and 2008 surveys. Households with non-numeric sleeping rooms 

(DK/NA) were excluded. 
5 

larger share of monetary expenditures, the resulting asset index will more closely approximate consumption 

(Filmer and Scott 2012; Montgomery et al. 2000). In the case of early DHS surveys, five of the twelve 

indicators of economic status are tied to housing construction (type of floor) and to public services (water, 

toilets) rather than to direct consumer durables. 

Several analysts have addressed the challenge of comparing asset data across surveys by computing a 

harmonized index in the course of their analysis. For example, Sahn and Stifel (2000) sought to compare 

poverty over time and across countries in Africa by using the DHS to calculate a wealth index based on 

household asset information collected in the surveys. They computed their index using factor analysis 

instead of principal component analysis to account for covariance of a small number of common factors. 

The index was computed using pooled DHS data. Variables included in the index were asset, construction, 

and service variables typically included in the DHS Wealth Index, as well as the number of years of 

education of the head of household. The authors set poverty lines at the 25th and 40th percentiles of the index 

and compare poverty headcount ratios and tests of welfare dominance. 

Booysen et al. (2008) assessed trends in poverty in sub-Saharan Africa using asset data from DHS surveys. 

Rather than pooling the data, the authors computed a baseline set of weights using multiple correspondence 

analysis, which they suggested was more appropriate than principal component analysis for creating an 

index of categorical variables. They applied these pre-computed weights to seven asset variables from 

multiple surveys in focal countries. The analysts noted the limitations of including earlier surveys with a 

limited number of assets. The study found that reductions in poverty tended to be driven by accumulation 

of private assets such as bicycles and televisions rather than by improvements in public services such as 

electricity and drinking water. 

With many possible approaches, including IWI, CWI, and additional methods, it is important to consider 

the desirable attributes of a metric. One characteristic of a good metric is internal consistency: households 

with the same assets at two points in time should receive the same relative score. The method of 

computation should be standard and include, for example, consistent treatment of separate rural and urban 

indices. An additional criterion is ease of use. From the point of view of analysts, it is appealing to have a 

metric that can be quickly computed for new data without the need to compare with earlier datasets. At the 

same time, the metric should also accurately reflect household welfare and living standards. Finally, it is 

desirable to have a metric that can explain a sizeable amount of variation in household assets, in order to 

appropriately differentiate households from one another. 
6 
7 

2. Methods 

2.1. Survey Selection 

All countries that had three or more Standard DHS surveys7

, at least one of which was in or before 1998 

and at least one of which was in or after 2008, were examined for inclusion in this study. Twenty-seven 

countries8

 met these criteria. Two countries with continuous DHS surveys (Peru and Senegal) were 

excluded from consideration as case studies because the asset questions were identical among recent 

surveys. 

The remaining 25 countries were narrowed down to eight focal countries for case studies. Within each 

region (South and Southeast Asia, Latin America and the Caribbean, sub-Saharan Africa, North 

Africa/West Asia)9

, focal countries were chosen to maximize diversity across GDP ranking, with slight 

preference given to countries with four or more surveys during the time period and to countries whose asset 

questions were more comparable over time.10 The final eight focal countries selected for analysis are: 

Bangladesh, Bolivia, Cameroon, Egypt, Ghana, Indonesia, Nepal, and Zimbabwe. 

2.2. Construction of a Harmonized Wealth Index 

2.2.1. Harmonizing Asset Measures Across Surveys 

Within each focal country, a listing of all household-level measures of assets, services, and construction 

materials was created from each survey. For each relevant variable, a table was produced to identify the 

category, label, and frequencies. The main requirement for a harmonized category was that it had to 

correspond with a category in all surveys covered. Appendix table A1 demonstrates the process of 

harmonization using the Bangladesh data from 1993 to 2011. In many cases, later surveys include many 

more detailed categories than were asked about in earlier surveys. In earlier surveys in Bangladesh, for 

example, jute/bamboo/mud walls were a single category whereas in later surveys these wall types were 

coded separately. For the purpose of harmonization, the disaggregated categories in later years naturally 

had to be collapsed back into a single group. Categories that appeared in later years only, such as wooden 

planks, were impossible to harmonize from earlier surveys and were necessarily collapsed into the ‘other’ 

category. 

The process of harmonization was challenging at times. In earlier DHS surveys that asked about source of 

drinking water there were typically two types of wells, a well in the compound and a public well. 

Intermediate DHS surveys might differentiate between open and protected wells and by three well locations 

 

7

 In addition to standard DHS surveys, the DHS Program also conducts the AIDS Indicator Surveys (AIS) and the 

Malaria Indicator Surveys (MIS), along with continuous DHS surveys and interim/special DHS surveys. These 

surveys generally ask about a smaller number of assets than a standard DHS survey. 

8

 Bangladesh, Benin, Bolivia, Burkina Faso, Cameroon, Colombia, Cote d'Ivoire, Egypt, Ghana, Haiti, Indonesia, 

Jordan, Kenya, Madagascar, Malawi, Mali, Mozambique, Namibia, Nepal, Niger, Nigeria, Peru, Philippines, Senegal, 

Tanzania, Uganda, and Zimbabwe. Note that some additional countries such as Madagascar would have qualified 

except that the earlier survey did not include a wealth index. 

9

 DHS also conducts surveys in Central Asia and Australasia, but no countries in these regions met the criteria for 

inclusion. 

10 The average number of harmonized assets in the focal countries was 11.4 compared to 10.4 in the non-focal 

countries; this is largely owing to Egypt as an outlier with 16 harmonized assets, versus the next highest sum of 12 

harmonized assets in other countries. 
8 

(in dwelling, in yard/plot, and public well), resulting in six possible types of wells. More recent DHS 

surveys distinguish only between protected and unprotected wells, not by location. In this case, to 

harmonize the categories across all surveys it would be necessary to keep ‘well’ as a single category of the 

source of drinking water. Special care was given to avoid distortions involving a large number of cases 

being classified into the ‘other’ category due to lack of backwards compatibility. 

Table 2 summarizes, by country, household assets and variables related to housing condition distilled from 

the harmonization procedure to compute a harmonized wealth index for each focal country. The factors are 

divided into household services and housing conditions (source of drinking water, type of toilet, type of 

walls, type of roof, type of cooking fuel, members per sleeping room, and whether the household has 

electricity) versus household assets such as a bicycle or a refrigerator. For dichotomous variables an “X” 

indicates the presence or absence of that variable; for categorical variables the number of common 

categories is shown. Members per sleeping room is a continuous truncated integer value that starts at 0 

(more than one sleeping room per member) and ends at the maximum for that country, often around 20. A 

sum of the total number of variables harmonized for each indicator is given the second-to-last column. 

Nepal had the smallest number of harmonized variables (8) while Egypt had the largest number (16). 
9 

Table 2. Harmonized Assets by Country 

Household services and 

housing conditionsa Household Assets 

Country Survey Years 

Source of drinking water 

Type of cookin

g fuel 

Type of toilet 

Type of floorin

g

Type of walls Type of roof 

Members 

per sleeping

Electricit

y

Animal-drawn cart 

Automatic washin

g

Bic

ycle 

Car/Truck 

Electric fan 

Landline tele

phone 

Motorcycle/Scooter 

Owns dwellin

g

Owns land 

Radio 

Refri

gerator 

Sewing machine 

Table/chair 

Television 

Video deck or DVD 

Wardrobe 

Water heater 

Total number of 

harmonized variables Proportion of total variance explainedb

Bangladesh 

1993-94, 1996-97, 1999-2000, 2004, 2007, 2011 5 6 4 5 4 X X X X X X X 12 0.192 

Bolivia 1994, 1998, 2003, 2008 6 6 5 5 X X X X X X 10 0.223 Cameroon 1991, 1998, 2004, 2011 7 5 5 X X X X X X X X 11 0.183 Egypt 1995, 2000, 2005, 2008 5 6 9 X X X X X X X X X X X X X 16 0.149 

Ghana 1993, 1998, 2003, 2008 8 7 9 X X X X X X X X X 12 0.141 Indonesia 

1997, 2002-03, 2007, 2012 9 6 7 4 6 X X X X X X X 12 0.120 

Nepal 1996, 2001, 2006, 2011 8 5 6 X X X X X 8 0.180 

Zimbabwe 

1994, 1999, 2005-06, 2010-11 8 6 8 X X X X X X X 10 0.181 

a. For variables that indicate a type of service or material the number of common categories is listed (‘missing’ is a residual/excluded category). For all other yes/no 

variable, an “X” indicates the presence of that variable. Members per sleeping room is a continuous integer variable that starts at 0 (less than 1 member per sleeping 

room) and goes to the maximum for that country, generally around 20. 

b. The proportion of total variance explained by the first component of the common factor principal components analysis of the pooled harmonized wealth index; see 

text for details 

9 
10 

2.2.2. Computing a Harmonized Wealth Index 

In the eight focal countries selected for this study (Bangladesh, Bolivia, Cameroon, Egypt, Ghana, 

Indonesia, Nepal, and Zimbabwe), we pooled household data within each country and computed a 

harmonized wealth index based on the common factors shown in Table 1. Elements of the wealth index 

that need to be merged from individual interviews (whether there is a domestic servant in the household, 

whether any members of the household work their own agricultural land, and whether any member owns a 

dwelling unit) were not included unless measured at the household level11; the harmonized wealth index is 

computed using variables from the household interview only. 

Using the harmonized variables, a pooled wealth index was computed in each country, following the 

standard DHS procedure outlined in Rutstein and Johnson (2004). Asset and service variables with yes/no 

answers (has electricity, has television) were recoded into dichotomous variables, with missing/DK/NA 

assumed to be 0, as is DHS standard procedure. For categorical variables, a variable for each harmonized 

category of a particular asset was created as a dichotomous variable. For example, in Bolivia, the six 

harmonized categories for floor material were: earthen, wooden, tile/ceramic/vinyl, cement, brick, or other, 

including carpet. Six mutually exclusive dichotomous variables were created representing each type of 

floor.12 Additionally, if the number of sleeping rooms was asked about in each survey, then the number of 

members per sleeping room was computed using standard DHS procedure and truncated to the nearest 

integer, with 0 representing more than one sleeping room per household member. 

Household member weights were normalized in the pooled country datasets so that each survey year would 

count equally. Using these normalized weights, a principal component analysis of the asset variables 

described above was computed using Stata version 12. As is the case in the DHS Wealth Index, a common 

factor index is computed first. Then, following the procedure outlined in Rutstein (2008) and currently used 

by DHS, the pooled HWI in each country was computed using a separate factor analysis for urban and rural 

areas. These rural and urban scores were regressed on the common factor scores to produce a constant term 

and a coefficient to displace those scores and recombine into a common wealth index. Quintiles are based 

on relative ranking within the pooled datasets. 

Table 2 shows the proportion of total variance explained by the initial common factor analysis in the pooled 

dataset. It ranged from 12 percent in Indonesia to 22 percent in Bolivia. It is not possible to directly compare 

the share of variance explained by the HWI with that of the standard DHS Wealth Index, as the latter is, by 

definition, computed within a given survey only, rather than for pooled data. However, initial investigation 

suggests that the percent of variance explained by the HWI compares favorably to that of the DHS Wealth 

Index.13 

 

11 One reason for not including these individual metrics of wealth is that they introduce nonstandardization due to the 

skip pattern of individual interviews within each country. For example, in many countries only half of the households 

are eligible for male interviews. In some countries only ever-married women are interviewed. These skip patterns or 

subsamples introduce inconsistency because not every household has the opportunity for adults to provide information 

on occupation or ownership of assets. 

12 The missing category is not a variable unto itself; it is the residual. 

13 For example, the total variance explained by the DHS Wealth Index in Ghana 2008 was 10.3 percent and in Egypt 

2008 was 10.4 percent compared with 14.1 percent and 14.9 percent, respectively, explained by the HWI. 
11 

It is important to note that, while the HWI is centered at 0 across the surveys in a given country, the CWI 

is not. The CWI was centered at 0 for Vietnam in 2002 and every other survey is centered in relation to 

Vietnam. This means that in almost every country the mean CWI score will be different from 0. Therefore, 

in the analysis that follows the mean score of HWI and CWI should not be directly compared; instead we 

will focus on patterns in distribution and ranking. 
12 
13 

3. Country Case Studies 

3.1. Bangladesh 

In Bangladesh, six different DHS surveys from 1993 to 2011 were used to compute a harmonized wealth 

index (HWI). The harmonization of assets shown in Appendix Table A1 resulted in 12 common factors, 

five of which were categorical and seven of which were dichotomous. As described in the methods section, 

the computations were done with normalized weights so that the household population in each survey would 

count equally. 

Table 3 shows the overall summary statistics by year for the CWI, as computed by Rutstein and Staveteig 

(2014), versus the results of the HWI for each year.14 The CWI and HWI are highly correlated in any given 

year, from 94.8 to 98.8 percent; because the CWI is a linear displacement of the DHS Wealth Index in each 

survey, then by extension the HWI and the DHS Wealth Index are highly correlated. As expected, in all six 

surveys the minimum value of HWI is almost perfectly constant, as is the maximum value (-1.25 and 3.19, 

respectively) compared with a range in the minimum value and an increasing maximum value computed 

by the CWI. The minimum of the CWI in 2011 (-2.23) is below that of earlier years; additional analysis 

indicates that, in 2011, 11.3 percent of the household population scored below the minimum wealth score 

in 2007 data, a finding that suggests problematic linear displacement of the CWI. 

Table 3. Summary of CWI and HWI in Bangladesh, 1993-2011 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1993-1994 49,895 -1.07 0.53 -1.59 0.82 -0.32 0.90 -1.25 3.19 0.988 

1996-1997 45,820 -0.93 0.55 -1.35 1.11 -0.22 0.94 -1.25 3.19 0.976 

1999-2000 50,965 -0.76 0.62 -1.55 1.37 -0.05 0.99 -1.25 3.19 0.980 

2004 52,129 -0.81 0.77 -1.75 2.44 0.02 0.93 -1.25 3.19 0.976 

2007 48,919 -0.77 0.78 -1.62 2.56 0.21 0.98 -1.17 3.19 0.948 

2011 78,909 -0.62 1.04 -2.23 3.17 0.36 1.01 -1.25 3.19 0.960 

While the CWI and the HWI are not intended to be comparable to GDP per capita, a comparison in overall 

trends of mean CWI and HWI is a useful check against some of the trends observed in each indicator. 

Figure 1 charts the mean CWI and mean HWI against the GDP per capita at purchasing power parity (World 

Bank 2014). Recall that HWI and CWI lines should not necessarily overlap; the HWI is intended to average 

out to 0 across these six surveys, whereas the CWI is based in relation to the baseline scores for Vietnam 

2002. Only the parallels between the two lines should be considered. Figure 1 shows that in Bangladesh the 

mean CWI and HWI almost always increase in tandem with GDP, but that mean CWI has an early peak 

around 1999 and then increases only slowly, while the mean HWI increases steadily. 

 

14 Results shown differ slightly from the Rutstein and Staveteig report, which used household weights; this report 

weights by household members. 
14 

Figure 1. Mean CWI and HWI versus GDP per capita (PPP), Bangladesh 1993-2011 

Figures 2 and 3 show the shape of the overall distribution of CWI and HWI in the household population in 

Bangladesh for all six surveys. In nearly every year there is a characteristic right-skewed distribution, with 

most relative wealth scores clumped toward the left end of the distribution with a relatively long rightward 

tail, indicating a small wealthy elite. The HWI data echo these trends, and also show a slight bimodal 

distribution in 2007 and 2011, suggestive of an emerging middle class in Bangladesh. 

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita

PPP

CWI (mean)

HWI (mean)
15 

Figure 2. Distribution of comparative wealth index (CWI) in Bangladesh by year 

Figure 3. Distribution of harmonized wealth index (HWI) in Bangladesh by year 
16 

To compare the compression of each metric, a Quantile-Quantile (Q-Q) plot was created for HWI versus 

CWI for each year (Figure 4). For the purposes of this analysis, the distance between the Q-Q line and the 

y=x line is unimportant; this is determined by the mean CWI relative to Vietnam in 2002. Bangladesh is 

consistently poorer than Vietnam; consequently, the mean CWI is less than 0 and the Q-Q line is always 

above the y=x line. Instead, what is important is the shape of the line. If the two indices are perfectly 

correlated then the Q-Q line will be straight. The slope may be more or less than 1 because the range of 

values of the two metrics differs. What deserves attention is the extent to which the Q-Q line curves in one 

direction or the other; this reflects compression in an indicator. 

Figure 4 shows that in most years the shape of the Q-Q plot is linear, but that in the 1999-2000 survey we 

start to see a compression of the Q-Q line toward the top of the range on the HWI metric. This indicates 

that a greater share of the household population has essentially ‘topped out’ at an upper bound in the HWI, 

but according to the CWI these households are still distinct. To the extent that most analysts use wealth 

quintiles rather than factor scores, the compression of values at a small extreme of the distribution should 

not be problematic. At the same time, the compression is less than desirable and indicates that, as expected, 

the HWI is missing assets that would have helped differentiate wealthier households from one another. 

Figure 4. Quantile-Quantile plots of HWI versus CWI, Bangladesh 

A final comparison between HWI and CWI in Bangladesh is shown in Table 4, which gives the distribution 

of household members (pooled across surveys) in any given quintile of the HWI that are in a quintile of the 

CWI. Note that for the purposes of this table CWI quintile is computed based on pooled data in the same 

way that HWI is. If the two metrics categorized members into the exact same quintiles we should see 20 
17 

percent of household members in each cell of the diagonal. However, perfect cross-classification is not 

ideal: if 100 percent of the population was on the diagonal, then the HWI would be entirely redundant with 

an existing metric. To the extent that cross-classification differs, it is not clear which metric is more 

accurate. 

Table 4 shows that 60 percent of household members are categorized in the same quintile on both metrics, 

and an additional 35 percent are one step away on either metric (for example, in the third quintile of HWI 

but the fourth or second quintile of CWI, or vice versa). As anticipated, there are no cases out on the corners 

of the matrix; for example no household members are classified at the top of the CWI and the bottom of the 

HWI, or vice versa. 

Table 4. Correspondence between pooled CWI and HWI quintiles in Bangladesh, 1993-2011 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 12 7 1 0 0 20 

second 6 8 6 1 0 20 

third 3 4 9 5 0 20 

fourth 0 1 4 13 2 20 

highest 0 0 0 2 18 20 

Total 20 20 20 20 20 100 

weighted n 65,328 65,389 65,266 65,330 65,324 326,637 

3.2. Bolivia 

For Bolivia, Table 5 shows a fairly consistent minimum and maximum for the HWI, as for Bangladesh, but 

shows a different maximum for the CWI in 1994 (1.64) compared with later surveys (3.1+). Additional 

analysis shows that an average of 18 percent of the household population in subsequent years is scored to 

be wealthier than the wealthiest household in 1994, which suggests a problematic displacement of CWI 

scores in later years unrelated to sampling variation. 

Table 5. Summary of CWI and HWI in Bolivia, 1994-2008 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1994 40,926 -0.18 1.06 -2.13 1.64 -0.18 1.03 -2.05 1.21 0.963 

1998 52,451 0.32 1.48 -2.77 3.16 0.01 1.01 -2.09 1.21 0.950 

2003 81,090 0.04 1.45 -2.61 3.39 0.00 0.98 -2.04 1.21 0.934 

2008 77,081 0.31 1.33 -2.43 3.10 0.17 0.92 -1.92 1.21 0.949 

Figure 5 shows that HWI more closely tracks the trend in GDP per capita than does CWI, which finds a 

dramatic increase in asset wealth in 1998 and a subsequent drop in 2003. The distribution of CWI, shown 

in Figure 6, suggests a relatively plateaued distribution of wealth in Bolivia. The distribution is generally 

bimodal, but the peak on the right hand side is larger in 1998 and 2008 than is the peak on the left (low) 

end of the distribution. The histograms of HWI scores, shown in Figure 7, have edge peaks, which suggest 

truncation of HWI scores at both ends of the distribution. 
18 

Figure 5. Mean CWI and HWI versus GDP per capita (PPP), Bolivia 1994-2008 

Figure 6. Distribution of comparative wealth index (CWI) in Bolivia by year 

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

 $-

 $1,000

 $2,000

 $3,000

 $4,000

 $5,000

 $6,000

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
19 

Figure 7. Distribution of harmonized wealth index (HWI) in Bolivia by year 

The compression of HWI scores is also clearly evidenced by the Q-Q plot in Figure 8. We see that in 1998, 

2003, and 2008 there is a small variation in HWI at the top of the distribution and a large distribution of 

CWI on those values. Additionally, there is some apparent compression at the bottom of the distribution in 

1998 only. Interestingly, the HWI in Bolivia explained the most amount of variance in assets of any of the 

case study countries. 

In the original computation of the Bolivia DHS Wealth Index in 2008, some of the most salient factors 

differentiating households were mobile phone, computer, Internet access at home or near home, and trash 

collection. None of these factors could be included in the harmonized index due to omission in earlier 

surveys, which helps explain the apparent truncation of scores in the HWI. Additionally, the HWI for 

Bolivia contains only one inexpensive asset (radio) to help distinguish households at the low end of the 

economic distribution. At the same time, the harmonized assets that remained across survey years 

apparently captured a good amount of variation in assets across households. 
20 

Figure 8. Quantile-Quantile plots of HWI versus CWI, Bolivia 

Table 6, which compares joint quintile classification, shows a fairly strong correspondence between 

categories on both measures. Overall, 72 percent of household members were categorized into the same 

quintile in both metrics; this is higher than any other case study country except Egypt. Nearly all of the 

remaining household population was classified within one step of the pooled quintile on either metric. 

Table 6. Correspondence between pooled CWI and HWI quintiles in Bolivia, 1994-2008 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 17 3 0 0 0 20 

second 3 14 3 0 0 20 

third 0 3 13 4 0 20 

fourth 0 0 4 12 4 20 

highest 0 0 0 4 15 20 

Total 20 20 20 20 20 100 

weighted n 50,312 50,308 50,311 50,376 50,242 251,548 
21 

3.3. Cameroon 

Table 7 shows a varying range of minimum and maximum CWI scores in Cameroon. The maximum score 

of 3.15 in 1991 is higher than the maximum scores in subsequent years, including a maximum of 2.47 in 

2011. Additionally, the minimum score declined from -1.63 to -1.82 over the 20-year period. Analysis of 

these patterns reveals that these aberrant cases are only a small share (<2 percent) of household members 

in any given survey. 

Table 7. Summary of CWI and HWI in Cameroon, 1991-2011 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1991 19,783 -0.48 1.07 -1.63 3.15 -0.12 0.97 -1.25 3.27 0.966 

1998 25,757 -0.54 1.10 -1.78 2.88 -0.10 0.94 -1.23 3.27 0.960 

2004 49,758 -0.45 0.91 -1.51 2.33 0.04 0.99 -1.27 3.27 0.947 

2011 70,882 -0.33 0.82 -1.82 2.47 0.18 1.02 -1.28 3.30 0.945 

Cameroon suffered an economic crisis in the late 1990s, which reduced GDP per capita by at least 10 

percent. CWI showed a small average decline, while HWI stagnated. The trends in subsequent years are 

similar to each other and to GDP per capita. 

Figure 9. Mean CWI and HWI versus GDP per capita (PPP), Cameroon 

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

 $2,200

 $2,250

 $2,300

 $2,350

 $2,400

 $2,450

 $2,500

 $2,550

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
22 

Figure 10, which shows the distribution of CWI scores among the household population in each survey 

year, reveals a nearly L-shaped distribution of wealth, with a high peak toward the left (bottom) end of the 

distribution. By 2011, however, the distribution begins to plateau around the middle, suggesting an 

emerging lower/middle class in the country. The distribution of HWI scores shown in Figure 11 reveals a 

similar, though lumpier, clustering of HWI scores toward the left side of the distribution with a long right 

tail. In 2004 and 2011 there appears to be not just a plateau in HWI but a slightly bimodal distribution, with 

wealth peaking toward the middle of the spectrum. 

Figure 10. Distribution of comparative wealth index (CWI) in Cameroon by year 
23 

Figure 11. Distribution of harmonized wealth index (HWI) in Cameroon by year 

The Q-Q plot of HWI versus CWI scores shown in Figure 12 reveals relatively little compression of scores 

except at the top end of the distribution in later years. Table 8, which shows the proportion of household 

members in each combination of CWI and HWI scores, indicates that more than two-thirds are in the same 

category in both metrics. 
24 

Figure 12. Quantile-Quantile plots of HWI versus CWI, Cameroon 

Table 8. Correspondence between pooled CWI and HWI quintiles in Cameroon, 1991-2011 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 14 6 1 0 0 20 

second 6 11 4 0 0 20 

third 1 4 13 3 0 20 

fourth 0 0 3 13 4 20 

highest 0 0 0 3 17 20 

Total 20 20 20 20 20 100 

weighted n 33,239 33,234 33,243 33,238 33,225 166,179 

3.4. Egypt 

Egypt, the focal country with the largest number of harmonized assets (16), has a higher average correlation 

between the HWI and the CWI (and, by extension, between the HWI and the DHS Wealth Index) than any 

other case study country, consistently above 95 percent (Table 9). The mean CWI and HWI scores track 

closely to each other and to GDP per capita, which increased over the course of the survey years. 
25 

Table 9. Summary of CWI and HWI in Egypt, 1995-2008 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1995 82,826 0.60 1.33 -2.68 3.42 -0.35 1.15 -3.06 2.07 0.970 

2000 88,865 0.86 1.10 -2.20 3.16 -0.13 1.01 -3.08 2.07 0.961 

2005 107,300 1.33 0.98 -2.15 4.05 0.20 0.84 -2.90 2.04 0.959 

2008 87,480 1.45 0.94 -1.60 4.32 0.28 0.80 -2.90 2.01 0.954 

Figure 13. Mean CWI and HWI versus GDP per capita (PPP), Egypt 1995-2008 

The distribution of wealth among household members using CWI (Figure 14) and HWI (Figure 15) is nearly 

identical, except that HWI is somewhat more uneven than CWI. Figure 16, the Q-Q plot of HWI and CWI, 

reveals a fairly linear relationship with relatively little compression of HWI, except toward the top of the 

distribution in 2005 and 2008. 

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

 $2,000

 $3,000

 $4,000

 $5,000

 $6,000

 $7,000

 $8,000

 $9,000

 $10,000

 $11,000

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
26 

Figure 14. Distribution of comparative wealth index (CWI) in Egypt by year 

Figure 15. Distribution of harmonized wealth index (HWI) in Egypt by year 
27 

Figure 16. Quantile-Quantile plots of HWI versus CWI, Egypt 

Table 10, which shows the proportion of household members in each combination of CWI and HWI scores, 

shows the largest share of household members of any focal country, 75 percent, classified in the same 

quintile for CWI and HWI. 

Table 10. Correspondence between pooled CWI and HWI quintiles in Egypt, 1995-2008 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 17 3 0 0 0 20 

second 3 13 4 0 0 20 

third 0 4 13 3 0 20 

fourth 0 0 3 15 2 20 

highest 0 0 0 3 17 20 

Total 20 20 20 20 20 100 

weighted n 73,304 73,285 73,299 73,752 72,831 366,471 
28 

3.5. Ghana 

Table 10 shows that in Ghana the 2008 survey has a minimum CWI score of -2.45, whereas earlier surveys 

have a minimum of -1.39. As discussed earlier, 12.7 percent of the household population in Ghana in 2008 

was scored as poorer than the poorest household members in 1993, a finding that suggests some problem 

in the displacement of CWI scores. 

Table 11. Summary of CWI and HWI in Ghana, 1993-2008 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1993 21,900 -0.58 0.90 -1.27 4.25 -0.25 0.86 -0.97 3.56 0.990 

1998 21,665 -0.38 0.83 -1.37 2.29 -0.10 0.94 -0.97 3.51 0.971 

2003 25,154 -0.42 0.95 -1.39 2.88 0.07 1.04 -0.97 3.51 0.969 

2008 44,080 -0.17 0.98 -2.45 3.55 0.28 1.04 -0.98 3.88 0.936 

Both the HWI and the CWI in Ghana are strongly correlated with GDP per capita, but the HWI tracks 

slightly better with GDP, revealing no decline in average economic status from 1998 to 2003 (Figure 17). 

Figure 17. Mean CWI and HWI versus GDP per capita (PPP), Ghana 1993-2008 

In Figures 18 and 19, the distribution of wealth among the household population appears similar in both 

metrics in the three earlier surveys, 1993, 1998, and 2003. In 2008, however, the CWI scores are centralized 

and are more of a plateau shape than in prior years, whereas the shape of the distribution of the HWI in 

2008 is similar to that of prior years. One possible explanation is that additional assets measured in the 2008 

survey provided a more complete picture of household living standards. At the same time, the share of 

variance explained by the pooled HWI in Ghana (14.1 percent, Table 2) was higher than the share of 

variance explained by the 2008 wealth index in Ghana (10.3 percent, not shown here). 

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

 $3,000

 $3,500

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
29 

Figure 18. Distribution of comparative wealth index (CWI) in Ghana by year 

Figure 19. Distribution of harmonized wealth index (HWI) in Ghana by year 
30 

The Q-Q plot of HWI versus CWI (Figure 20) indicates little compression in HWI scores relative to CWI 

until the 2008 survey, when scores are compressed at both the upper and lower ends of the wealth 

distribution. Overall, however, both metrics classify more than two-thirds of the household population into 

the same CWI and HWI quintile (Table 12). 

Figure 20. Quantile-Quantile plots of HWI versus CWI, Ghana 

Table 12. Correspondence between pooled CWI and HWI quintiles in Ghana, 1993-2008 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 14 6 1 0 0 20 

second 6 11 4 0 0 21 

third 1 3 12 4 0 19 

fourth 0 0 4 14 2 20 

highest 0 0 0 2 18 20 

Total 20 20 20 20 20 100 

weighted n 22,595 22,630 22,439 22,516 22,545 112,725 
31 

3.6. Indonesia 

Despite a relatively high number of harmonized assets (12), Indonesia exhibits the weakest correlation 

between CWI and HWI of any of the focal countries studied (Table 13). Even so, the correlation between 

the two indices is above 92 percent. The mean HWI tracks quite closely to GDP, whereas CWI has a sharper 

increase in 2007 and levels off in 2012 (Figure 21). 

Table 13. Summary of CWI and HWI in Indonesia, 1997-2012 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1997 147,673 0.17 1.15 -2.20 3.26 -0.39 0.92 -2.65 1.74 0.920 

2002-03 142,861 0.08 1.24 -2.71 3.45 -0.06 0.97 -2.65 1.84 0.933 

2007 167,002 0.72 1.05 -2.17 2.56 0.07 1.00 -2.65 1.84 0.961 

2012 174,977 0.75 0.88 -2.16 3.65 0.38 0.93 -2.65 1.84 0.942 

Figure 21. Mean CWI and HWI versus GDP per capita (PPP), Indonesia 

In Indonesia, the distribution of wealth in the household population appears similar in the CWI and the 

HWI (Figures 22 and 23, respectively). Household member wealth initially follows a nearly normal 

distribution before tending to skew leftward. The Q-Q plot shown in Figure 24 indicates some compression 

of the HWI in 2002-03 and a moderate degree of compression in 2012. 

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

 $-

 $1,000

 $2,000

 $3,000

 $4,000

 $5,000

 $6,000

 $7,000

 $8,000

 $9,000

 $10,000

1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
32 

Figure 22. Distribution of comparative wealth index (CWI) in Indonesia by year 

Figure 23. Distribution of harmonized wealth index (HWI) in Indonesia by year 
33 

Figure 24. Quantile-Quantile plots of HWI versus CWI, Indonesia 

Table 14 shows that the cross-classification of wealth quintiles between HWI and CWI in Indonesia is 

weaker than in most other countries (62 percent of the household population). An additional 36 percent are 

within one quintile of cross-classification. 

Table 14. Correspondence between pooled CWI and HWI quintiles in Indonesia, 1997-2012 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 16 4 0 0 0 20 

second 4 11 5 1 0 20 

third 0 5 10 5 0 20 

fourth 0 1 5 10 4 20 

highest 0 0 0 5 15 20 

Total 20 20 20 20 20 100 

weighted n 126,505 126,503 126,555 126,473 126,476 632,513 
34 

3.7. Nepal 

In Nepal, the focal country with the fewest harmonized assets (8), there is a fairly consistent minimum 

CWI, and an increasing maximum over the course of the 15-year period (Table 15). The correlation between 

CWI and HWI ranges from 92.9 to 97.4 percent across the four surveys. The trends in the mean CWI and 

HWI scores are almost perfectly parallel to each other (Figure 25) and to GDP per capita over the course 

of the survey years. 

Table 15. Summary of CWI and HWI in Nepal, 1996-2011 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1996 44,559 -1.14 0.43 -1.69 0.86 -0.45 0.69 -1.18 2.86 0.948 

2001 45,511 -1.13 0.62 -1.59 1.47 -0.27 0.85 -1.18 2.88 0.974 

2006 42,256 -0.82 0.84 -1.68 2.56 0.17 1.01 -1.18 2.88 0.935 

2011 48,123 -0.41 0.98 -1.96 2.32 0.55 1.07 -1.18 2.88 0.929 

Figure 25. Mean CWI and HWI versus GDP per capita (PPP), Nepal 1996-2011 

The distribution of wealth scores across the household population is fairly similar in 1996, 2001, and 2006 

across the two measures (Figure 26 and 27)—an L-shaped distribution with a long right tail. The distribution 

of wealth in 2011 is much more evenly plateaued in both metrics. The distribution of the HWI appears 

somewhat lumpy in later surveys, particularly in 2011, where it shows a slight bimodality toward the right 

(upper) end of the distribution. 

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
35 

Figure 26. Distribution of comparative wealth index (CWI) in Nepal by year 

Figure 27. Distribution of harmonized wealth index (HWI) in Nepal by year 
36 

The Q-Q plots of wealth in Nepal (Figure 28) suggest that the only major compression of HWI is in 2006 

at the top end of the distribution. The cross-classification of wealth quintiles between CWI and HWI (Table 

16) finds the lowest identical classification of the focal countries, at 60 percent. 

Figure 28. Quantile-Quantile plots of HWI versus CWI, Nepal 

Table 16. Correspondence between pooled CWI and HWI quintiles in Nepal, 1996-2011 

Proportion of household members in pooled CWI and pooled HWI quintile 

CWI Quintile

HWI 

Quintile lowest second third fourth highest Total 

lowest 15 9 2 0 0 26 

second 3 6 5 1 0 14 

third 1 5 9 5 0 21 

fourth 0 1 4 12 2 20 

highest 0 0 0 2 18 20 

Total 20 20 20 20 20 100 

weighted n 36,267 35,935 36,075 36,082 36,090 180,448 
37 

3.8. Zimbabwe 

During the first decade of this century, Zimbabwe experienced a severe economic crisis and hyperinflation. 

GDP per capita declined by more than 40 percent. Meanwhile, as Table 17 shows, the mean value of assetbased measures of living standards stagnated or increased. Figure 29 shows the mean value of each index 

against GDP per capita. The trends go in opposite directions, which underscores the difference between 

expenditure- and income-based measures of economic standing, versus asset-based measures of wealth. 

There tends to be an inherent momentum in asset accumulation. For example, existing houses do not 

become more poorly constructed during times of economic collapse. 

Table 17. Summary of CWI and HWI in Zimbabwe, 1994-2011 

 Comparative Wealth Index (CWI) Harmonized Wealth Index (HWI) 

weighted 

n mean sd min max mean sd min max 

correlation 

CWI-HWI 

1994 27,949 -0.43 1.09 -1.85 2.97 -0.15 0.97 -1.08 3.25 0.989 

1999 26,666 -0.35 1.06 -1.81 2.26 0.05 0.99 -1.08 3.25 0.924 

2005-06 41,323 -0.20 1.15 -1.78 2.05 0.06 1.03 -1.08 2.87 0.967 

2010-11 40,401 -0.09 0.94 -1.55 4.64 0.05 0.96 -1.08 3.25 0.946 

Figure 29. Mean CWI and HWI versus GDP per capita (PPP), Zimbabwe 1994-2011 

The distributions of CWI and HWI in Zimbabwe are displayed in Figures 30 and 31, respectively. CWI 

indicates a bimodal distribution of wealth in 1994 and 1999, and a trimodal distribution in later years. 

Meanwhile the HWI echoes the bimodal trend in 1994 and 1999 and the trimodal trend in 2010-11. The 

HWI scores show a characteristic ‘comb’ pattern of unevenness throughout the distribution, indicating a 

smaller number of distinct values of the wealth index. 

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

 $3,000

1990 1995 2000 2005 2010

Mean CWI

GDP Per Capita (PPP USD 2000)

GDP per capita PPP

CWI (mean)

HWI (mean)
38 

Figure 30. Distribution of comparative wealth index (CWI) in Zimbabwe by year 

Figure 31. Distribution of harmonized wealth index (HWI) in Zimbabwe by year 
39 

The Quantile-Quantile plots of CWI versus HWI (Figure 32) suggest relatively little compression of the 

HWI until 2010-11, when the top of the range appears substantially compressed. Table 18 shows moderate 

correspondence between CWI and HWI quintile classifications. 

Figure 32. Quantile-Quantile plots of HWI versus CWI, Zimbabwe 

Table 18. Correspondence between pooled CWI and HWI quintiles in Zimbabwe, 1994-2011 

Proportion of household members in pooled CWI and pooled HWI quintile 

 CWI Quintile

HWI Quintile lowest second third fourth highest Total 

lowest 16 4 0 0 0 20 

second 4 11 5 0 0 20 

third 0 5 13 4 0 21 

fourth 0 0 3 13 3 19 

highest 0 0 0 3 17 20 

Total 20 20 20 20 20 100 

weighted n 27,268 27,269 27,268 27,266 27,267 136,339 
40
41 

4. Discussion and Conclusions 

The Harmonized Wealth Index (HWI) approach described in this paper has promising applications for 

intertemporal analysis of health and poverty that combine data from the early- and mid-1990s with more 

recent data. It is particularly well suited to comparisons within a given country where a unique asset such 

as a solar heater or a sewing machine may have been considered salient even in earlier years when the DHS 

contained very few questions on household assets. 

The analysis of eight focal countries finds that, as expected, the HWI produces a bounded range of wealth 

scores that is nearly constant across all surveys. This is in contrast to the CWI, where there appear to be 

some problematic distortions, for example in Ghana 2008 or Bangladesh 2011. This is because the HWI is 

inherently uniform. Households in any given survey with the same basic assets will, by definition, score 

exactly the same. Additionally, the HWI has the less obvious advantage of implementing the rural-urban 

adjustments to the wealth index in every survey rather than just in the most recent surveys. 

But the HWI is not without its flaws. It requires analysts to compute based on the surveys selected for 

inclusion, and, if used to compare early and late DHS surveys it should be computed within a single or 

small number of countries; its reliability declines substantially when the number of harmonized assets is 

reduced. Harmonizing assets obviously leads to a loss of information that can differentiate households from 

one another. Histograms of the distribution of wealth across households reveal some degree of clustering 

in wealth categories in the HWI. The Q-Q plots show that the loss of information and compression of scores 

typically appears at the top of the distribution in more recent surveys. Non-focal countries had slightly 

fewer harmonized assets on average than focal countries, which may result in greater compression, but no 

country eligible for the study had fewer harmonized assets than Nepal (eight). To the extent that analysts 

use percentile categories of the wealth index as opposed to continuous scores, the compression of 

harmonized index scores should not be generally problematic. 

Comparisons of harmonized asset-based wealth and poverty over time from DHS surveys cannot measure 

any increase in quality or quantity of particular assets— for example, the make of a car or truck and whether 

it is old or new. More recent surveys sometimes differentiate between black-and-white versus color 

televisions, for example, and—in countries such as Jordan may quantify the number of expensive assets—

but earlier surveys did not. Yet, despite its name, the DHS Wealth Index was not intended to differentiate 

among the very wealthy, but rather to differentiate gradations of poverty. At the low end of the economic 

distribution in the focal countries studied, the Harmonized Wealth Index was not generally compressed 

relative to the CWI. The main exceptions were Bolivia in 1998 and Ghana in 2008. 

The continuous HWI and CWI (and, by extension, the DHS Wealth Index) scores within each individual 

survey are highly correlated: without exception there was over 90 percent correlation between the scores in 

all 34 DHS surveys studied. The classification of quintiles of economic status was relatively consistent, 

with over 60 percent of household members cross-classified in the same quintile. Some variation is to be 

expected; if the two metrics of relative economic status overlapped perfectly then there would be no need 

to explore an alternative to the CWI. Moreover, when the two metrics differ, it is unclear which is more 

accurate. 

One important lesson from this comparison for country stakeholders and survey managers is that countries 

should work to ensure consistency in asset categories and questions over time across household surveys. 

While new assets, particularly computers and Internet access, are salient markers of economic well-being, 

efforts should be made to ensure backward compatibility of categories and measures. In many countries it 

was not so much the earliest survey that lacked a relevant question, but middle surveys (for example, 

number of sleeping rooms in Bangladesh 1996-97 and 1999) that made it more difficult to produce a 
42 

harmonized wealth index. In other cases, assets that have become nearly ubiquitous have been phased out 

of recent surveys (for example, a watch or clock in Bangladesh) and thus could not be used to compare 

households over time. 

The HWI approach is a promising avenue for analysts and policymakers interested in intertemporal 

comparisons of wealth and health. Based on initial results, compression of the index remains a concern, 

although in almost every country studied the compression occurred primarily among the wealthiest 

household members in the most recent survey. To the extent that most research uses quintile classifications, 

clustering of raw scores should not be unduly problematic; nonetheless, analysts are encouraged to review 

computed indices for compression. 

This study has only evaluated the HWI method for the very particular challenge of comparing earlier and 

later DHS surveys with few harmonized assets. Applications of this method to a set of more recent surveys 

with a larger number of harmonized variables will yield an index that captures a greater degree of variations 

in household economic status than an HWI that includes earlier DHS surveys with relatively few asset 

questions. 

Overall, the HWI methodology should prove useful for intertemporal analysis. However, additional 

research and validation are needed. Some researchers (Booysen et al. 2008; Sahn and Stifel 2000) have 

advocated the use of multiple correspondence analysis or factor analysis instead of principal component 

analysis to compute the asset index factor scores. The HWI retained principal component analysis for the 

sake of consistency with the existing DHS Wealth Index, but alternate computational methods could 

certainly be explored. Applications of the HWI to health indicators, alternate approaches to harmonizing 

asset categories (for example, using a quality index), and computations for additional countries should be 

explored further. 
43 

Appendix 
44 

Table A1. Example of Asset Harmonization Table, Bangladesh 

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

DRINKING 

WATER 

hv201 source of drinking water 

hv201 source of drinking water 

hv201 source of drinking water 

hv201 source of drinking water 

hv201 source of drinking water 

hv201 source of drinking water 

hv201_r source of drinking water 

11. piped inside dwel 11. piped inside dwel. 11. piped inside dwel. 11. piped inside dwelling 11. piped into dwelling 11. piped into dwelling 11. piped into dwelling 12. piped outside dwel. 12. piped outside dwel. 12. piped outside dwel. 12. piped outside dwel. 12. piped to yard/plot 12. piped to yard/plot 12. piped to yard/plot 

13. public tap/standpipe 13. public tap/standpipe 96. other 

21. tubewell 21. tubewell 21. tubewell 21. tubewell 

21. tube well or borehole 

21. tube well or borehole 21. well 

22. surface /other well 22. surface /other well 22. surface/other well 22. shallow tubewell 21. well 

 23. deet tubewell 21. well 

24. surface well/other well 31. protected well 31. protected well 21. well 

32. unprotected well 32. unprotected well 21. well 

41. protected spring 41. protected spring 96. other 

42. unprotected spring 96. other 

31. pond /tank/lake 31. pond /tank/lake 31. pond/tank/lake 41. pond/tank/lake 

43. river/dam/lake/ ponds/stream/canal/i 

43. river/dam/lake/ ponds/stream/canal/i 

43. river/dam/lake/ ponds/stream/canal/ tank 

32. river /stream 32. river /stream 32. river/stream 42. river/stream 

43. river/dam/lake/ ponds/stream/canal/ tank 

41. rainwater 41. rainwater 51. rainwater 51. rainwater 96. other 

61. tanker truck 96. other 

62. cart with small tank 62. cart with small tank 96. other 71. bottled water 71. bottled water 96. other 

96. other 96. other 96. other 96. other 96. other 96. other 96. other 

missing missing missing missing missing 

FLOOR 

hv213 main floor material 

hv213 main floor material 

hv213 main floor material 

hv213 main floor material 

hv213 main floor material 

hv213 main floor material 

hv213_r main floor material 

11. earth 11. earth /bamboo 11. earth/bamboo 11. earth/bamboo 11. earth, sand 11. earth, sand 

11. earth, sand, palm, bamboo 

22. palm, bamboo 22. palm, bamboo 

11. earth, sand, palm, bamboo 

21. wood 21. wood 21. wood 21. wood 21. wood planks 21. wood planks 21. wood 

31. parquet, polished wood 

31. parquet, polished wood 21. wood 

33. ceramic tiles 33. ceramic tiles 96. other 

31. cement/concrete 31. cement /concrete 31. cement/concrete 31. cement/concrete 34. cement 34. cement 31. cement 

35. carpet 35. carpet 96. other 

96. other 96. other 96. other 96. other 96. other 96. other 96. other 

missing missing missing missing 99.00 99.00 missing 

(Continued...) 

44 
45 

Table A1. – Continued

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

WALLS 

hv214 main wall material 

hv214 main wall material 

hv214 main wall material 

hv214 main wall material 

hv214 main wall material 

hv214 main wall material 

hv214_r main wall material 

11. no walls 11. no walls 96. other

11. jute /bamboo/mud 11. natural walls 11. natural walls 11. jute/bamboo/mud 13. dirt 13. dirt 11. jute/bamboo/mud 

 22. bamboo with mud 21. bamboo with mud 11. jute/bamboo/mud 

12. cane / palm / trunks

12. cane / palm / trunks 11. jute/bamboo/mud 

21. wood 21. rudimentary walls 21. rudimentary walls 21. wood 23. stone with mud 22. stone with mud

21. rudimentary: wood, stone, cardboard

24. plywood

21. rudimentary: wood, stone, cardboard

25. cardboard 25. cardboard

21. rudimentary: wood, stone, cardboard

32. tin 32. tin 32. tin 32. tin 31. tin 31. tin 31. tin

31. brick /cement 31. brick /cement 31. brick/cement 31. brick/cement 32. cement 32. cement 32. brick/cement

33. stone with lime / cement

33. stone with lime / cement 32. brick/cement

34. bricks 34. bricks 32. bricks35. wood planks / shingles

36. wood planks / shingles 96. other or wood

96. other 96. other 96. other 96. other 96. other 96. other 96. other or wood

missing missing missing missing 99 99 missing

ROOF 

hv215 main roof material 

hv215 main roof material 

hv215 main roof material

hv215 main roof material

hv215 main roof material

hv215 main roof material

hv215_r main roof material

11. no roof 11. no roof 96. other

11. jute /bamboo/mud 11. natural roof 11. earth/bamboo 11. bamboo/thatch 12. thatch / palm leaf 12. thatch / palm leaf 11. earth

 23. bamboo 22. palm / bamboo 11. earth 21. rudimentary roof 21. rudimentary roof  21. wood 23. wood planks 21. rudimentary roof  32. wood 32. wood 21. rudimentary roof  24. cardboard 21. rudimentary roof 21. tin 21. tin 31. tin 31. tin 21. rudimentary roof  31. finished roof 31. Finished Roof 36. roofing shingles 31. Finished Roof31. cement /concrete 31. cement/concrete

31. cement/ concrete/tiled 34. cement 35. cement 31. Finished Roof

 33. ceramic tiles 34. ceramic tiles 31. Finished Roof96. other 96. other 96. other 96. other 96. other 96. other 96. Other

missing missing missing missing 99.00 99 missing

 (Continued...) 

45 
46 

Table A1. – Continued

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

TOILET 

hv205 type of toilet facility 

hv205 type of toilet facility 

hv205 type of toilet facility 

hv205 type of toilet facility 

hv205 type of toilet facility 

hv205 type of toilet facility 

hv205_r type of toilet facility 

11. septic tank/ modern 11. septic tank/toilet 11. septic tank/toilet 

11. septic tank/modern toilet 

11. flush - to piped sewer system 

11. flush to piped sewer system 11. flush toilet 

12. flush - to septic tank 

12. flush to septic tank 11. flush toilet 

13. flush - to pit latrine 13. flush to pit latrine 11. flush toilet 14. flush - to somewhere else 

14. flush to somewhere else 11. flush toilet 

15. flush - don’t know where 

15. flush, don’t know where 11. flush toilet 21. ventilated improved pit latrine (vi 

21. pit latrine with slab 

21. water sealed /slab 

21. water sealed/ slab la 

21. water sealed/slab la 

21. water sealed/slab latrine 

22. pit latrine - with slab 

22. pit latrine with slab 

21. pit latrine with slab 

23. pit latrine - without slab / open p

23. pit latrine without slab/open pit 22. pit latrine/open 

22. pit latrine 22. pit latrine 22. pit latrine 22. pit latrine 22. pit latrine/open 23. open latrine 23. open latrine 23. open latrine 23. open latrine 22. pit latrine/open 24. hanging latrine 24. hanging latrine 24. hanging latrine 24. hanging latrine 

43. hanging toilet/latrine 

43. hanging toilet/latrine 23. hanging latrine 

31. no facility /bush 31. no fac. /bush/field 31. no facility, bush 

31. no facility/bush/field 

31. no facility/bush/field 

31. no facility/bush/field 31. no facility, bush 

 41. composting toilet 96. other  42. bucket toilet 42. bucket toilet 96. other 96. other 96. other 96. other 96. other

 missing missing missing 99.00 99 missing 

SHARED 

TOILET 

N/A N/A N/A N/A 

hv225 share toilet with other households 

hv225 share toilet with other households 

hv225_r share toilet with other households 

 0. no 0. no 

 1. yes 1. yes Not usable 

 9 missing 

 missing 

# SLEEPING 

ROOMS 

hv216 number of rooms for sleeping 

hv216 number of rooms for sleeping 

hv216 number of rooms for sleeping 

sh36 number of rooms for sleeping 

hv216 number of rooms for sleeping 

hv216 number of rooms for sleeping 

hv216_r number of rooms for sleeping 

1 though 41 n/a n/a 1 through 12 1 through 16 1 through 11 Not usable missing missing 99 99 

 (Continued...) 

46 
47 
48 

Table A1. – Continued

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

OWNS 

LAND 

sh26i agricultural land 

sh25 hh owns agricultural land 

sh27a household owns any other land sh42 owns land 

hv244 own land usable for agriculture 

hv244 own land usable for agriculture hv244_r owns land 

0. no 0. no 0. no 0. no 0. no 0. no 0. no 

1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

missing 9 0. no 

HECTARES 

N/A N/A 

sh28 amount of land owned (number) 

sh43a amount of land owned (acres) 

hv245 hectares for agricultural land 

hv245 hectares for agricultural land 

hv245_r hectares for agricultural land 

0 through 8100 0 through 50 0 through 40 0 through 40 Not usable 

9996 missing 99 999 

 9998 missing missing 

missing 

ASSETS 

hv206 has electricity hv206 has electricity hv206 has electricity hv206 has electricity hv206 has electricity hv206 has electricity 

hv206_r has electricity 

0. no 0. no 0. no 0. no 0. no 0. no 0. no 

1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

hv207 has radio hv207 has radio hv207 has radio hv207 has radio hv207 has radio hv207 has radio hv207_r has radio 0. no 0. no 0. no 0. no 0. no 0. no 0. no 

1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

hv208 has television hv208 has television hv208 has television hv208 has television hv208 has television hv208 has television 

hv208_r has television 

0. no 0. no 0. no 0. no 0. no 0. no 0. no 

1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

hv210 has bicycle hv210 has bicycle hv210 has bicycle hv210 has bicycle hv210 has bicycle hv210 has bicycle hv210_r has bicycle 0. no 0. no 0. no 0. no 0. no 0. no 0. no 

1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

hv211 has motorcycle 

hv211 has motorcycle/ scooter 

hv211 has motorcycle/ scooter 

hv211 has motorcycle/ scooter Not usable 

 hv212 has car/truck Not usable 

hv221 has telephone hv221 has telephone hv221 has telephone 

hv221 has telephone (land-line) Not usable 

hv226 type of cooking fuel 

hv226 type of cooking fuel 

hv226 type of cooking fuel Not usable 

 (Continued...) 

47 
49 

Table A1. – Continued

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

ASSETS, 

Cont’d 

hv241 food cooked in the house/in separate building/outdoors 

hv241 food cooked in the house/in separate building/outdoors Not usable 

hv242 hh has a separate room for kitchen Not usable 

sh39 type of cooking stove 

sh111 food cooked on stove Not usable 

 sh40 place of cooking 

sh113 place cooking usually done Not usable 

sh26a almirah sh24a almirah sh22b almirah 

sh31b almirah (wardrobe) 

sh109g hh own a almirah or wardrobe 

sh110g has almirah/wardrc 

hv211_r has almirah/wardrobe 

0. no 0. no 0. no 0. no 0. no 0. no 0. no 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes sh26b table/chair/bench 

sh24b table, chair or bench sh22c table or chair sh31c table 

sh109h hh own a table 

sh110h has table or chair 

hv212_r has a table or chair 

0. no 0. no 0. no 0. no 0. no 0. no 0. no 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 1. yes 

sh110j has electric fan Not usable sh110k has dvd/vcd player Not usable sh110 has water pumps Not usable 

 hs118 has autobike Not usable 

sh109p hh own a rickshaw/van sh118b has rickshaw Not usable 

sh26c watch/clock sh24c watch or clock sh22e watch or clock sh31e watch or clock Not usable sh26d cot/bed sh24d cot or bed sh22f cot or bed sh31f cot or bed Not usable 

sh22k sewing machine 

sh31k sewing machine Not usable 

sh27 household owns homestead 

sh41 owns any homestead 

sh121 hh own any homestead 

sh122a owns homestead Not usable 

hv243a has a mobile telephone 

hv243a has a mobile telephone Not usable 

 hv243b has a watch Not usable 

(Continued...) 

48 
50 

Table A1. – Continued

1993-94 1996-97 1999 2004 2007 2011 Harmonized variable 

ASSETS, 

Cont’d 

hv243c has an animal-drawn cart Not usable 

hv243d has a boat with a motor Not usable hv246 livestock, herd or farm animals 

hv246 livestock, herd or farm animals Not usable 

hv246b cows, bulls own 

hv246b cows, bulls own Not usable hv246g owns bulls/buffaloes Not usable 

hv246g cs own goats/sheep 

hv246h owns goats/sheep Not usable 

hv246h cs own chickens/ducks 

hv246i owns chickens/ducks Not usable hv247 has bank account Not usable 

49 
51 
52 

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