NiCE Working Paper 12-107

Version 2

December 2013

The International Wealth Index (IWI)

Jeroen Smits

Roel Steendijk

Nijmegen Center for Economics (NiCE)

Institute for Management Research

Radboud University Nijmegen

P.O. Box 9108, 6500 HK Nijmegen, The Netherlands

http://www.ru.nl/nice/workingpapers

 

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Abstract In this paper we present the International Wealth Index (IWI), the first strictly comparable asset based index for household’s long-term economic status that can be used for all low and middle income countries. IWI is similar to the widely used wealth indices included in the Demographic and Health Surveys and UNICEF MICS surveys, but adds the property of comparability across place and time. IWI is based on data from 2.1 million households in 97 developing countries. With IWI we provide a stable and understandable yardstick for evaluating and comparing the economic situation of households, social groups and societies across all regions of the developing world. A household’s ranking on IWI indicates to what extent the household possesses a basic set of assets, valued highly by people all across the globe. IWI is tested thoroughly for reliability and validity. National IWI values are highly correlated with the Human Development Index, life expectancy, national income and educational outcomes and IWI-based poverty measures are highly correlated with Poverty Headcount Ratios. Jeroen Smits is director of the Global Data Lab (www.globaldatalab.org) and associate professor Inequality and Development at the Nijmegen Center for Economics. Roel Steendijk is consultant at Steendijk Statistics (www.steendijk-statistics.nl). We are grateful to MeasureDHS, the UNICEF MICS department, the Pan Arabic Project for Family Health (PAPFAM), the Integrated Public Use Microdata Series (IPUMS) department of the Minnesota Population Center, the National Statistical Offices of Brazil, Chile, Costa Rica, Sudan, Uruguay and Venezuela, the Statistical Information and Monitoring Programme on Child Labour (SIMPOC) of ILO-IPEC, and the Carolina Population Center at the University of North Carolina at Chapel Hill for making the datasets available that have been used in this project. Contact: Jeroen Smits, Global Data Lab, Nijmegen Center for Economics, Radboud University Nijmegen. PO Box 9108, 6500HK Nijmegen, The Netherlands, phone +31 24 3612319/5890 j.smits@fm.ru.nl, j.smits@globaldatalab.org Website International Wealth Index: iwi.globaldatalab.org

 

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1. Introduction Since the late 1990s, wealth indices have become widely used instruments for measuring economic status of households in low and middle income countries. Hundreds of research papers have appeared in which wealth indices were used for studying variation in health, mortality, poverty, education, work and other outcomes in almost all countries of the developing world (e.g. Gwatkin et al., 2007; Howe et al., 2008; Filmer & Scott, 2012; Falkinham & Namazie, 2002). Wealth indices are considered effective indicators of long-term socio-economic position, living standard or material well-being of households (Filmer & Pritchett, 1999, 2001; Sahn & Stifel, 2000, 2003; McKenzie, 2005; Howe et al., 2008). They often perform as well or better than expenditure data in explaining variation in education, child mortality, nutrition, fertility and health care use (Filmer & Pritchett, 2001; Bollen et al., 2002; Sahn & Stiefel, 2003; McKenzie, 2005; Filmer & Scott, 2012). Important reasons for the success of these indices are their ease of computation, intuitive appeal, and their wide availability in household surveys for developing countries like the Demographic and Health Surveys (DHS) and UNICEF MICS surveys. Also the fact that the required data can be more reliably measured than those needed for computing income or expenditure measures, the most obvious alternatives, has contributed to their success (Sahn & Stifel, 2003; McKenzie, 2005; Filmer & Scott, 2012). In spite of these positive properties, wealth indices suffer from one great disadvantage: they are not comparable among countries and time points (McKenzie, 2005; Gwatkin et al., 2007). For each survey usually a separate wealth index is constructed on the basis of the assets available in the survey data. Such a separate index is tailored completely towards the specific wealth distribution in the survey year in the country on which it is based. This means that it is a valid indicator of wealth differences in that specific country-year combination, but -- as the wealth distributions in other country-year combinations generally will be different – cannot be used to study wealth differences in other countries and years. The scores on survey-specific wealth indices are therefore interpreted as relative wealth levels (Rutstein & Johnson, 2004; Gwatkin et al., 2007). In most applications the wealth distribution is divided into quintiles, with the lowest 20 percent of the population defined as the poor and the upper 20 percent as the rich. For analyzing within country inequalities in education, health, or other outcomes, comparing the lowest and highest wealth quintiles does indeed make sense. However, cross-national or cross-temporal comparisons of groups with similar levels of wealth or poverty are not possible with these relative measures, as the average wealth level of the wealth quintiles differs among countries and years. To solve this problem, a general wealth index is needed that uses the same criteria for rating households independent of country or year. The International Wealth Index (IWI) is such a general wealth index. Whereas other wealth indices are constructed on the basis of data from one or a restricted number of household surveys, IWI is based on data derived from 165 household surveys, held between 1996 and 2011 in 97 low and middle income countries. Together these surveys included information on 2.1 million households, covering all regions of the developing world. Using this broad database, IWI was constructed in the same way as most other wealth indices. Information on households’ possession of consumer durables, access to basic services and housing characteristics was entered into a principal component analysis (PCA), from which the asset weights of the first component were derived. These asset weights were subsequently brought together into the IWI formula, which constitutes the basic instrument for providing households with an IWI value.

 

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2. The International Wealth Index (a) Material well-being The central idea behind IWI is that households across the globe can be placed on an underlying dimension of material need satisfaction (or living standard) for which we will use the term “material wellbeing”. This dimension runs from a situation in which a household has no possessions at all that may help satisfy their material needs, to a situation in which the household possesses all assets that are broadly considered necessary for living an easy and comfortable life. The kinds of assets that are most relevant for a household’s material well-being depend on the household’s economic situation. For very poor households, material well-being is associated with the satisfaction of the basic needs of food, clothing and safety/shelter, which have to be met to survive. One step higher, material well-being refers to the possession of goods and access to basic services that make life easier and more comfortable. There are all kinds of relatively cheap utensils that reduce the workload people have (pots, pans, plates, cutlery, tools) or make it more comfortable (tables, chairs, carpets, beds). A major step is made when the household gets access to electricity, because this opens up infinite new possibilities for increasing material well-being in relatively cheap ways. With electric light, the time that can be spent on useful and leisure activities increases considerably. A refrigerator reduces daily shopping time. Electric tools and utensils reduce time spent on cooking and on work around the home. If the household gets access to clean water, the workload is reduced even more, as this may save an often considerable amount of time spent on fetching water. The quality of the house in which the household lives is another an important aspect of material well-being. The kind of building and flooring material determines how much maintenance there is to the house, whether rain, wind and pests are kept outside well, and how comfortable the house is. Having more than one room, a separate kitchen and bathroom, and a decent in-house toilet facility greatly enhances quality of living. Besides by technical equipment that makes life easier, material wellbeing can also be improved by means of transportation and communication equipment. With a bike, cart, boat, motorbike or car transportation of heavy loads becomes easier and travelling time is reduced. Radio and TV bring the world into the home and phones, computers and the internet greatly enhance communication and access to information. Given that everywhere in the world households tend to buy the assets and ask for the basic services mentioned above, it seems that there is some kind of globally shared consensus about the material requirements needed for living a decent life. IWI is meant to tap into this dimension of material need satisfaction and to indicate the degree to which a household’s material basic needs are met. (b) Measuring material well-being Like other asset based wealth indices, IWI measures a household’s level of material well-being by looking at the household’s possession of durables, access to basic services, and characteristics of the house in which it is living. Households that own more expensive durables, have a better quality house, and have access to basic services are considered to have a higher level of material well-being than household with less expensive durables, worse housing and no access to services. Any household for which the required asset information is available can be given a value on IWI and any household with the same combination of assets obtains the same IWI score. The IWI scale is additive. If a household owns a specific durable, has better access to public services, or has a higher value on a housing characteristic, its IWI value is raised by a specific amount (the re-scaled asset

 

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weight). The IWI scale runs from 0 to 100. If a household has all durables and highest quality housing and services, its IWI value is 100. If it has none of the durables and lowest quality housing and services, its IWI value is 0. Households with the same value on the IWI scale are assumed to have reached the same level of material need satisfaction. This does not mean that they own exactly the same assets. Depending on individual preferences and the context a household is living in, households may reach the same level of material need satisfaction with different portfolios of assets. Ownership of a phone increases a household’s value on the IWI scale to the same extent as having a high quality instead of a medium quality toilet facility. A household’s level of material well-being is closely related to the household’s economic situation. The assets required to satisfy material needs come at a price, hence wealthier households have more possibilities to satisfy these needs. This close relationship between material and economic well-being has stimulated the strong growth in the use of information on asset ownership to indicate the economic welfare of households (e.g. Filmer & Pritchett, 1999; Sahn & Stiefel, 2003; Gwatkin et al., 2007; Howe et al., 2008). Table 1 presents an overview of the assets on which IWI is based, their raw weights and the coefficients to be used in the IWI formula. The assets include seven consumer durables (possession of a TV, refrigerator, phone, bicycle, car, cheap utensil and expensive utensil), access to two public services (water and electricity) and three housing characteristics (number of sleeping rooms, quality of floor material and toilet facility). This set of assets was selected because of its wide availability in household surveys and because it differentiates well across the wealth range needed for a wealth index covering the complete developing world. (c) Clumping and truncation Differentiating well between wealthier and less wealthy households is important to prevent clumping and truncation, two problems of which asset based wealth indices may suffer (McKenzie, 2005; Vyas & Kumaranayake, 2006). Clumping (or heaping) means that there are many households with the same asset combinations, leading to a high percentage of cases in the same category. Clumping can be prevented by including more assets in the index. As IWI includes eight two-category (yes-no) items and four three-category (low, middle, high) items, the total number of possible combinations is over 20,000. Even though many of these combinations are less likely (having a car and flush toilet, but a floor of earth), the number of likely combinations is so big that no clumping is expected. Truncation of the wealth distribution means a lack of discriminative power at the top or bottom end of the scale. This is a problem that cannot completely be prevented, because the wealth range covered by the index is restricted by the number and values of the included assets. However, by choosing the included assets strategically, an index can be computed that allows for enough differentiation at the top and bottom end of the scale to prevent excessive truncation. For IWI this was done by including both at the top and at the bottom of the distribution enough assets to differentiate among households. Households with an IWI value of 100 have a TV, fridge, phone, car, a house with piped drinking water, electricity, a flush toilet, good quality floor material and three or more rooms. These households have reached a standard of material well-being that, even from the perspective of a high-income country, can be considered very reasonable. For an index meant to measure household wealth in developing countries, more discriminative power at the top of the scale does not seem necessary.

 

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Households with an IWI value of 0, on the other hand, own none of the included items -- not even a cheap utensil like a chair, watch or radio -- have a floor of earth or dung, have no or bad quality toilet, no electricity, only one room, and water from an unprotected source. From any reasonable perspective these households are considered to be extremely poor. Differentiating further within this group would probably be possible (e.g. by including nutritional items), but their situation is already so miserable that from a policy perspective it does not seem relevant to subdivide them further. As this group falls below any reasonable poverty line, policies should focus on improving the situation of all of these households.

Table 1: Mean and standard deviations of asset indicators, raw asset weights, and coefficients of IWI formula (N=2189221)

Consumer durables Mean Std. Deviation Raw indicator weight IWI Formula

weight Television 54.25 49.82 0.798552 8.612657 Refrigerator 36.99 48.28 0.781531 8.429076 Phone 38.74 48.72 0.660869 7.127699 Car 11.68 32.12 0.431269 4.651382 Bicycle 29.12 45.43 0.171238 1.846860 Cheap utensils 74.48 43.60 0.381851 4.118394 Expensive utensils 28.16 44.98 0.603345 6.507283 Housing characteristics Floor material: Low quality 34.97 47.69 -0.700809 -7.558471 Medium quality 36.08 48.02 0.113815 1.227531 High quality 28.95 45.35 0.566271 6.107428 Toilet facility: Low quality 40.13 49.02 -0.689810 -7.439841 Medium quality 17.57 38.06 -0.101100 -1.090393 High quality 42.29 49.40 0.754787 8.140637 Number of rooms: Zero or one 38.44 48.65 -0.343028 -3.699681 Two 32.64 46.89 0.035609 0.384050 Three or more 28.92 45.34 0.319416 3.445009 Public utilities Access to electricity 62.30 48.46 0.747001 8.056664 Water source: Low quality 32.13 46.70 -0.584726 -6.306477 Medium quality 23.85 42.62 -0.213440 -2.302023 High quality 44.02 49.64 0.737338 7.952443 Constant 25.004470 Minimum value -2.318374 0 Maximum value 6.953466 100

 

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The foregoing does not preclude that in very poor or very rich countries the majority of households is concentrated at the lower or upper end of the distribution. But that is precisely what can be expected of a comparable indicator of the economic situation of households and groups. In very poor countries, there are many households at the bottom of the wealth distribution, independently of whether their economic situation is measured by income, expenditure, or an asset-based wealth index.

In wealthier countries, the reverse is true. However, there the situation is somewhat more complex. Whereas monetary income can increase infinitely, the number of assets that can be included in a wealth index is limited. When the basic needs of a household are met, the range of more luxurious goods and services on which additional income can be spent is so wide that it becomes practically impossible to include them all in a questionnaire. The use of a wealth index is thus restricted to countries where for a substantial number of households not all basic needs are met. In practice this means that IWI can be used for all low income countries and the majority of middle income countries.

3. Constructing IWI To be able to create a comparable wealth index like IWI, several important choices have to be made. First, the number and type of assets to be included in the index have to be chosen. Second, the number of datasets and countries on the basis of which the index will be computed has to be decided on. Third, a choice has to be made on how these countries should be weighed when constructing the index, as some of the countries are much larger than others. Fourth, the method to be used for computing asset weights has to be chosen. In the next sections, these choices and their outcomes are discussed in detail.

(a) Number of assets and number of surveys

A major challenge in constructing a comparative wealth index is to find a reasonable compromise between number of surveys and number of assets. Because the number of asset questions used in surveys is restricted, and the type of asset on which information is collected varies among surveys, including more assets in the index would mean that less surveys could be used. Nevertheless, a reasonable set of assets had to be included to reduce the risk of clumping and truncation. This asset set should preferable include assets from different domains of household needs, like household chores, transport, communication, access to basic services, and hygiene.

The compromise we came upon was the use of a series of twelve assets, including seven consumer durables, three housing characteristics, and access to two public services. With this set of assets, we could compute IWI on the basis of data from 165 national representative household surveys. The consumer durables included are the possession of a TV, refrigerator, phone, bicycle, car, a cheap utensil and an expensive utensil. The housing characteristics are the number of sleeping rooms, quality of the floor material and quality of the toilet facility. The basic services are access to clean water and electricity.

(b) Measurement

The consumer durables included in the construction of IWI are measured with two-category variables. These variables have value ‘1’ if the household or one of its members owns the durable and value ‘0’

 

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if this is not the case. A similar two-category variable is used to indicate whether (1) or not (0) the household has access to electricity.

Quality of water supply, of floor material and of toilet facility are measured with three categories: (1) low quality, (2) middle quality, and (3) high quality. For the number of sleeping rooms also a three-category variable is used: (1) zero or one sleeping rooms, (2) two sleeping rooms, and (3) three or more sleeping rooms. Zero and one rooms are combined, because it is in a substantial number of surveys not possible to distinguish between households that have one sleeping room and households that only have one room, and hence use the living room for sleeping.

The categories of the ‘quality’ variables need further explanation. Floor material, water sources and toilet facilities may differ among countries, depending on local availability and traditions. In the survey data for different regions, therefore, different categories may be used. For constructing a comparative wealth index, however, it is necessary that a variable is measured with the same categories in each survey. To solve this problem we recoded the substantial categories used in the different surveys into the three general quality categories. In doing so, the following guiding principles were followed:

Water supply: - high quality is bottled water or water piped into dwelling or premises;

- middle quality is public tap, protected well, tanker truck, etc;

- low quality is unprotected well, borehole, spring, surface water, etc.

Toilet facility: - high quality is any kind of private flush toilet;

- middle quality is public toilet, improved pit latrine, etc.;

- low quality is traditional pit latrine, hanging toilet, or no toilet facility.

Floor quality: - high quality is finished floor with parquet, carpet, tiles, ceramic etc.;

- middle quality is cement, concrete, raw wood, etc.;

- low quality is none, earth, dung etc.

The variables ‘cheap utensils’ and ‘expensive utensils’ need further explanation, as these are constructed variables. Early experience with the use of wealth indices revealed a lack of discriminatory power at the lower end of the wealth distribution (Rutstein, 2008). To be able to better differentiate among the poorest groups, some cheaper assets, like having a chair, table, clock, watch, water cooker, radio, fan or mixer were included in later surveys. However, the kind and number of these cheap asset questions varies considerably among surveys. It is therefore not possible to include them as separate items in a comparative wealth index.

As an alternative we have created a more general indicator ‘cheap utensils’ that is based on the information on any cheap (roughly under 50 US Dollar) item that is present in the data. This indicator can be created if information for one or more of such items is available. Household owning one or more cheap utensils get value ‘one’ and other households value ‘zero’ on this indicator.

The indicator ‘expensive utensils’ is meant to create more discriminatory power at the upper end of the wealth distribution. It is constructed in a similar way as the cheap utensils variable, but with respect to the possession of expensive (roughly over 300 US Dollar) items, like having a washer, dryer, computer, motorbike, motorboat, airconditioner, or generator. If information on the possession of at

 

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least one of these items is available, the indicator ‘expensive utensils’ can be created by giving households owning one or more expensive utensils the value ‘one’ and other households the value ‘zero’.

The inclusion of the cheap and expensive utensil indicators in the construction of IWI introduces some variation among countries and time points. However, we consider the increased discriminatory power at the upper and lower end of the scale more important than this loss of generalizability. As our test analyses later will show, the removal of either indicator has hardly any influence on the overall performance of the index.

(c) Survey datasets

The major data sources used for the construction of IWI are the Demographic and Health Surveys (DHS), funded by USAid and collected under responsibility of Measure DHS (www.measuredhs.com), and the Multiple Indicator Clusters Surveys (MICS) collected by UNICEF (www.childinfo.org). Other data sources are World Health Surveys (WHS) collected under supervision of the World Health Organization (www.who.int/healthinfo/survey), the Integrated Public Use Microdata Series (IPUMS) of the Minnesota Population Center (international.ipums.org), the Pan Arabic Project for Family Health (PAPFAM) surveys, with the League of Arab States (www.papfam.org) as major sponsor, the Statistical Information and Monitoring Programme on Child Labor (SIMPOC) surveys of ILO-IPEC (www.ilo.org/ipec), and the 2004 Chinese Health and Nutrition Survey (www.cpc.unc.edu/projects/china).

We aimed to use at least two surveys for each country. Our preferred choice among the data sources were DHS and MICS surveys, because these are large series of comparable datasets of high quality, including a substantial number of assets variables. Only if for a country none or only one DHS or MICS survey was available, we opted for other sources. Given this preference, we ended up with a database with combined data from 165 surveys held between 1996 and 2011 in 97 low and middle income countries. Of these surveys, 99 were DHS, 36 MICS, 16 WHS, 7 IPUMS, 3 PAPFAM, 3 SIMPOC and one Chinese Health and Nutrition Survey. The total number of included households was 2,189,221. Information on the data sources and country-year combinations used is presented in Appendix A.

To get an as broad as possible coverage of the developing world, in some cases datasets with a missing item were accepted. The missing items were replaced by the values for another item, or by zero or one, depending on what was most likely (e.g. electricity is 1 for relatively developed countries as Malaysia or Uruguay). Test analyses revealed that this procedure had a negligible effect on the final index. Information on where this happened is available in Appendix A.

(d) Weighing the countries

An important issue that has to be decided on when constructing a comparable wealth index is how to handle the differences in population size among countries. The countries included in our database range from small states like Belize, Suriname, and Sao Tome Y Principe, with population sizes below one million, to India and China with over one billion inhabitants. Weighing them by population size

 

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does not seem a good choice, as IWI then would be almost completely determined by India and China. However, weighing the countries equally, the most obvious alternative, also is problematic, as it does not seem reasonable to let the smallest countries have the same influence on IWI as countries with a thousand times larger population.

To solve this issue, we have estimated two test versions of IWI, one with the countries weighed by population size and one with the countries weighed equally. It turned out that the wealth indices derived from these opposite approaches were very much alike. The Pearson correlation between the two indices was 0.999, thus indicating that IWI is very robust to differences in the way the countries are weighed.

Given that both extremes have their drawbacks and that there is hardly any differences between the wealth indices computed with each of them, we decided to opt for an intermediate position. Instead of weighing the countries by population size or weighing them equally, we have weighed them by the square root of the population size. This means that the larger countries weigh heavier in computing IWI, but that the difference is much smaller than when absolute population weights would have been used. With our square root weights, the difference in influence between the largest and smallest country is a factor 78, whereas it was a factor 6000 for absolute population weights. The correlations between the IWI version based on the square root weight and the versions based on population weight and equal weight are over .999.

If in the datasets case weights were available to create representative country samples, these weights were also used when constructing IWI.

(e) Computing the asset weights

The easiest way to compute an asset index is to add up the number of assets a household owns (McKenzie, 2005). This method, which has been used in some earlier studies (e.g. Montgomery et al., 2000; Guiley & Jayne, 1997), has the disadvantage that it weighs each item equally. This would imply that possession of a table, a bicycle, a car, or a flush toilet would contribute equally to a household’s wealth score. As this obviously is not realistic, it is recommendable to use a more advanced method to determine the relative weights of the assets included in a wealth index.

Since the landmark papers of Filmer an Pritchett (1999, 2001), almost all asset based wealth indices have used principal component analysis (PCA) for computing the asset weighs. There have been a few attempts to use other techniques for this purpose, but the outcomes differed very little from those using PCA (Booysen et al., 2008, multiple correspondence analysis; Sahn and Stifel, 2000, factor analysis). In line with the tradition in the field, we therefore have chosen to use PCA for estimating the weights.

PCA is a multivariate statistical technique that can be used to reduce the number of variables in a dataset by converting them into a smaller number of components; each component being a linear weighted combination of the initial variables (Vyas and Kumaranayka 2006). The first component, which explains the largest part of the variation in the data, is chosen as the wealth index (Filmer and Pritchett 2001, Sahn and Stifel 2003, McKenzie, 2005). For IWI, this first factor explains 30 percent of the variation in the assets, which is somewhat higher than the percentages generally obtained using

 

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country-specific indices (26% Filmer & Pritchett, 2001; 27%, McKenzie, 2005; 24-27%, Cordova,2008 ).

PCA estimates a weight for each initial variable, and these estimated weights form the basis for computing the wealth index. The weights reflect the possibility that a household that owns one specific asset also owns one of the other assets in the analysis. The estimated indicator weights for IWI are presented in Column 3 of Table 1. We call them ‘raw’ indicator weights to distinguish them from the rescaled weights used in the final IWI formula.

As can be seen in Table 1, more valuable assets do not necessary have a higher weight than cheaper assets. This is because the weight of an asset indicates the increase in the household’s IWI value on top of the contribution of the other assets. Because households owning a car also possess most of the other assets, even with a relatively small weight for car ownership their IWI values will be higher than those of households without a car.

Because there is some discussion in the literature about the use of PCA for discrete data like our asset indicators (Sahn & Stifel, 2000; Howe et al., 2008; Booysen et al., 2008), we have repeated our analysis using categorical PCA (with SPSS CAPCA), Multiple Correspondence Analysis, and Factor Analysis. These analyses gave weights that were the same up to the 8th decimal.

(f) Scaling IWI

The raw indicator weights provided in Table 1 show the relative contribution of each asset to a household’s wealth score. On the basis of these weights we computed a raw wealth score, which we subsequently rescaled to the 0-100 range. To obtain the raw wealth score, the asset weights multiplied by the asset indicator variables had to be summed up. This led to the following equation, where is the raw wealth score, the estimated indicator weight of the Th asset and the indicator variable of the Th asset.

∙

When applying this formula to our data, we obtained a household wealth distribution with a minimum score of -2.318 and a maximum score of 6.953. Households with the minimum score are those with the lowest value on all assets items. They own none of the consumer durables, have no electricity, lowest quality water supply, floor material and toilet facility and no more than one sleeping room. Households with the maximum score own all consumer durables, have electricity, highest quality water supply, floor material and toilet facility, and have three or more sleeping rooms.

To obtain a scale with a more intuitively understandable range, we transformed the wealth distribution to the range 0-100, with an IWI value of 0 for households having none of the durables, no access to public services and lowest quality housing and the value of 100 for households having all durables and highest quality services and housing. This new scale was created by adding the opposite of the lowest value (2.318) to each household score, to put the minimum at ‘0’. The maximum value

 

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then becomes 9.271 (=6.953+2.318). To put the maximum at 100, the scale values are multiplied by 100 and divided by the (new) maximum. Hence:

100 ∙ ∑ ∙ 2.3189.271 25.004′ ∙

Where ′ are the rescaled asset weights. These are obtained by multiplying the original weights by 10.785. Together with the constant 25.004, the rescaled asset weights make up the IWI formula. The exact values (with six decimal places) of the constant and the rescaled weights are presented in the fourth column of Table 1.

Unlike income and consumption expenditure data, asset based wealth indices are generally not adjusted for household size. The reason is that the assets used for constructing these indices consist almost completely of household public goods (Filmer & Scott, 2012). Housing characteristics, access to basic services and durables like a TV, fridge, clock or car tend to benefit all household members (Filmer & Pritchett, 2001; Rutstein & Johnson, 2004; McKenzie, 2005; Howe et al., 2008). Empirical comparisons of unadjusted and size-adjusted wealth indices showed either no substantial differences or less plausible results for the adjusted indices (Sahn & Stifel, 2000; Rutstein & Johnson, 2004; Howe et al., 2008; Filmer & Scott, 2012). We therefore have chosen not to adjust IWI for household size.

After computing a household’s IWI score, it is rounded to one decimal place to prevent the number of IWI combinations from becoming unrealistically large. Although the number of possible asset combinations is over 20,000 (28+34), many of those combinations are very unlikely (having a car and a flush toilet, but a floor of earth). An index with 1000 unique values is rich enough to address this variation. The IWI formula and help files to compute IWI are made available at the IWI website iwi.globaldatalab.org.

4. Testing IWI Now we have constructed IWI, we would like to see what its distribution looks like and conduct a number of performance tests. In those tests, we first determine to what extent IWI depends on the inclusion of specific assets in the index. This is done by comparing the IWI version based on all assets with IWI versions with one or more of the assets removed. Second, we test to what extend IWI depends on data from specific parts of the world or time periods, by correlating the original IWI with reduced IWI versions, computed on datasets with one of the world regions removed, or for specific years. Third, we test for households within a specific world region to what extent their IWI values are correlated with their values on a wealth index computed on data for only that world regions. Fourth, we compare IWI values for households in the DHS datasets with the values of those households on the original DHS wealth index.

 

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(a) Distribution of IWI

The distribution of IWI is displayed in Figure 1. As can be seen, the 2.1 million households on which IWI was constructed are – except for some overrepresentation at the lower end of the distribution -- more or less regularly distributed across the IWI scale.

The clumping at the lower end of the distribution is a common feature of all asset based wealth indices. It is caused by the fact that the first and last steps on the scale cannot be smaller than the lowest asset weight. In the case of IWI, this lowest asset weight is the weight of 1.8 for having a bicycle. Hence after the households with a value of 0, the first possible IWI score is 1.8. The next possible score is 4.0, for households that have middle quality water (protected public source) and none of the other assets. Then comes the value 4.1, for households possessing only a cheap utensil (like a watch, fan, or radio) and none of the other assets. Together these households with value 4.0 and 4.1 create the high spike of 107,103 respondents at the (rounded) 4 score in Figure 1.

Above value 5, the number of possible combinations increases rapidly and there are no large empty spaces any more until above value 95, where we observe a similar phenomenon as between 0 and 5: Below the households with an IWI value of 100 come the households lacking only a bicycle. Given the bicycle weight of 1.8, these households obtain an IWI score of 98.2. Next come the households who possess all assets but have two instead of three sleeping rooms, with an IWI score of 96.9, etc. This clustering at the extremes, from which all asset-based indices suffer, is not very problematic. Households with an IWI value of, say, under 10, are extremely poor. In most practical applications, these households therefore will be combined. The same is probably true for the households with a very high IWI value.

Figure 1 also reveals that besides this clumping at the extremes, the observations are rather evenly distributed across the further range of the index (say between 10 and 90). As there is in that range no intermediate category containing more than a few percent of the households and there are neither at

0

20000

40000

60000

80000

100000

120000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Figure 1. Distribution of IWI (N= 2,189,221)

 

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zero nor at 100 indications of truncation (only 3% of cases has value zero and only one percent has value 100), we can conclude that with regard to truncation and clumping IWI performs well.

(b) Dependency on specific items

To find out to what extent IWI depends on the inclusion of specific assets in the index, we have computed twelve reduced IWI versions, each with one of the assets removed from the index. For this purpose, twelve new PCA analyses were conducted on our database, leading to twelve reduced IWI formulas. The reduced wealth indices created with these formulas were all scaled to run from 0 to 100, in the same way as this was done for IWI. In Table 2, Pearson correlations between the reduced indices and the original IWI are presented.

Table 2. Pearson correlations between IWI based on all 12 assets and reduced IWIs based on 11 assets IWI without: Correlation - water 0.986 - toilet 0.987 - rooms 0.996 - floor 0.991 - electricity 0.996 - TV 0.996 - refrigerator 0.996 - phone 0.996 - car 0.999 - bicycle 0.999 - cheap utensil 0.999 - expensive utensil 0.997

The correlations show that removing one item has not much effect on the wealth index that is produced. The weakest correlations, those for the three-category items ‘quality of water supply’ and ‘quality of toilet facility’, are .986 and .987. This is such a strong correlation that there seems to be hardly any difference between the original and reduced versions of IWI. The correlations between the other reduced versions and IWI are with .99 and over even higher. Hence we can conclude that IWI is robust against removal of an asset. This also implies that for datasets in which only eleven of the twelve assets are available, computing IWI on the basis of those 11 assets will provide a good approximation of IWI based on 12 assets.

We also have tested the effect of removing any combination of two assets from the index. For the combination without water supply and toilet facility, the correlation of the reduced index with IWI was .961. Without water supply and floor material and without toilet facility and floor material it was .972. Removing water supply or toilet facility with any other asset gave correlations of .975 or

 

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higher. For any other combinations of two assets removed, the correlation became in the order of .99 or higher.

We can thus conclude that even with two assets removed, the reduced wealth indices rate households almost the same as IWI. This means that even in situations where two assets are lacking in a dataset, we still can approach the households’ real IWI score very well by applying the formula for the reduced index on the data. The approximation will be best if information on water supply and toilet facility is available in the data, given that removal of these assets has the largest influence on the IWI scores. The reduced IWI formulas are made available through the IWI website: iwi.globaldatalab.org.

(c) Dependency on specific regions or time period

To find out whether IWI depends strongly on the data for a specific region of the developing world, we have created four reduced versions of IWI on the basis of our database. For each of these versions, the data for a specific global region – Latin America, sub-Saharan Africa, Middle East and North Africa (MENA), and Asia without the Middle East – were removed and a PCA analysis was performed on the reduced database. The reduced IWI versions were subsequently applied to all countries in our database. It turned out that the reduced IWI versions were all very high (over .999) correlated with the original IWI (Table 3, middle column). This result makes clear that the data from none of these global regions exert an unreasonable strong influence on IWI.

Table 3: Pearson correlations between IWI and wealth indices with data for a global region removed and between IWI and region-specific wealth indices

Global region Region removed Region-specific

index

Asia 0.999 0,998

Sub-Saharan Africa 0.999 0,996

Latin America 0.999 0,998

Middle East and North Africa (MENA) 0.999 0,999

We also wanted to know to what extent the IWI values for a specific region of the developing world are similar to those of a reduced wealth index, computed only on the data for that region. We therefore have constructed four regional wealth indices, for Latin America, sub-Saharan Africa, the MENA region and Asia (without Middle East), by running PCA analyses on the data for these regions. Table 3 (right column) shows that the regional wealth indices constructed in this way were highly correlated with IWI for these regions. Pearson correlations were .998 for Latin America and Asia, .996 for sub-Sahara Africa and .999 for the MENA region. Hence regional indices hardly perform better than IWI for their own regions.

 

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To test the degree to which IWI is influenced by the time period for which data is used, we have split our database into three time periods: 1996-2000, 2001-2005, 2006-2011. For each time period a separate wealth index was constructed. The Pearson correlations between these separate indices and IWI turned out to be .999, .999 and .997 respectively. We therefore can conclude that, within the time range of our data, IWI is hardly influenced by the period for which data is used.

(d) Association with national wealth indices

To test the performance of IWI further, we have compared the IWI distributions within countries with those of the country-specific wealth indices available in the DHS household surveys. Because IWI in the first place is meant to be a comparable wealth index, it was not developed specifically to fit the wealth distribution of an individual country. Still, the principle behind the index – rating households on the basis of their assets – is similar to that behind the country-specific indices. Hence we would expect IWI to rate the households within countries more or less in the same way as the country-specific indices; the scores on both indices should be positively correlated. As the country-specific indices are generally based on all assets available in a dataset (Rutstein & Johnson, 2004), they provide the best available wealth rating of households in a country. We therefore would like IWI to be well correlated with the country-specific indices.

Appendix C shows for the DHS surveys the correlations between the IWI scores of households within a country and the same household’s values on the DHS country-specific indices. We could include 96 DHS surveys with a country-specific wealth index. In 73 (76%) of these surveys, the Pearson correlation between IWI and the local wealth index was over .90 and in 92 (96%) of these surveys it was over .80. The average correlation was .92. Hence we can conclude that in the large majority of DHS surveys, IWI ranks the households very similar to the country-specific wealth index.

5. Associations with welfare and poverty measures The final proof of the pudding is the eating. Our test analyses have made clear that IWI is a stable index, that does not depend much on the inclusion of specific assets, nor on data for specific regions of the developing world. IWI is also highly correlated with regional wealth indices and with the country-specific wealth indices available in the DHS surveys.

These are favorable test outcomes that indicate that IWI is a reliable measuring instrument. However, being a reliable instrument does not necessarily mean being also a useful instrument. Given the fact that IWI was constructed on the basis of items related to material need satisfaction, it seems likely that IWI will be a reasonable indicator of a household’s level of material well-being. However, in most practical applications, asset-based wealth indices are used as – and were found to perform well as -- indicators of the economic status of households (e.g. Filmer & Pritchett, 1999, 2001; Sahn & Stifel, 2000, 2003; McKenzie, 2005; Howe et al., 2008; Filmer & Scott, 2012).

In this section we aim to test the performance of IWI as such an economic indicator. Given that IWI is meant to be an index that is comparable among countries, we will do so by performing a cross-national analysis. If IWI is an effective indicator of the economic status of households, we would expect national IWI values to be a good indicator of level of economic development of a country and

 

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effective poverty measures. This is tested by comparing national IWI values of developing countries with those countries’ values on established indicators of economic development, human development, education, health and poverty.

The national indicators with which IWI is compared were downloaded from the UNDP Human Development Report website (hdr.undp.org) and from the Worldbank website (data.worldbank.org). From UNDP, we downloaded the development, education and health indicators. The definitions of these indicators given below were derived from the UNDP website or from UNDP (2011, p.130) Economic development is measured by Gross National Income per capita (GNIc). Human development is measured by the Human Development Index (HDI). Education in measured by two indicators: Mean years of schooling and expected years of schooling. Mean years of schooling is the average number of years of education received by people aged 25 and older, converted from education attainment levels using official durations of each level. Expected years of schooling is the number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates would persist throughout the child’s life. A country’s health situation is measured by life expectancy at birth: the number of years a newborn infant could expect to live if prevailing patterns of age-specific mortality rates at the time of birth stay the same throughout the infant’s life.

The poverty indicators were downloaded from the Worldbank website (data.worldbank.org). We use the Poverty Headcount Ratios at $1.25 and $2.00 a day (PPP), defined as the percentage of the population living on less than $1.25 or $2.00 a day at 2005 international prices. From the Worldbank website we also derived the Gini coefficient for income inequality, that is used to split up the countries in high and low income countries.

The indicators of UNDP and Worldbank were not available for all years for which we have an IWI value. To fill in the missing years we used linear interpolation when possible. If interpolation was not possible, we used the value from a nearby year. If the nearest year was more than five years apart, the country/year combination was removed from the data. For each country data for one year was used for the analysis. This was generally the latest year for which valid data was available, but priority was given to DHS and MICS data. Appendix B presents the values of the indicators used in the welfare and poverty analyses, together with information on the country-year combinations that were used.

(a) Welfare measurement

The Pearson correlations between national IWI values for the last available year and the HDI and its components are presented in Table 4. The figures make clear that there are very strong positive correlations with all of the indices. The correlation with HDI is strongest, with a value of .899, but also the correlation with life expectancy is with .841 impressive. IWI and national income are correlated somewhat weaker with .788. The correlations with the educational indicators are with .720 and .658 weakest, but nevertheless still substantial. The correlations of IWI with HDI, health and education are all stronger than those of national income with these indicators.

 

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Table 4. Pearson correlations of IWI with HDI and its components

IWI

Life expectancya .841 Life exp

GNIca .788 .672 GNIc Exp. years of educationa

.720 .651 .682 Exp. edu

Mean years of educationb

.658 .559 .538 .728 Mean edu

HDIb .899 .870 .835 .833 .808 a N=87 b N=85

The finding that IWI is more strongly correlated with human development, health and education than national income is important. It suggests that IWI is a broader index than GNIc and represents more than only the economic situation of households. There are two likely reasons for this difference. First, IWI does not necessarily rise when the income of the rich increases, as per capita income does. The reason for this is that the questions on ownership of consumer durables used for constructing IWI are yes/no questions; in most surveys the households were asked whether they owned at least one item of the durable. A household owning two or more TV’s or cars therefore counts the same for IWI as a household owning one TV or car. This is not the case with per capita income, for which the prices of all TV’s and cars are added up. Compared to per capita income, IWI thus gives a country a lower value in situations of inequality.

Second, a household’s IWI value to a certain extent depends on the provision of public goods -- like supply of water and electricity -- in the area where the household lives and does therefore not completely depend on the household’s income. The increase in household’s welfare due to the access to public services is thus better captured by IWI than by per capita income. The fact that also HDI is less sensitive to inequality and better captures access to public goods than national income (Stanton, 2006) may to a certain extent explain the high correlation between IWI and HDI.

In Figure 2, the associations between IWI and the welfare indices are depicted graphically. The plots confirm the picture that was already given by the Pearson correlations: IWI is strongly and positively correlated with all other measures. To find out whether the difference between IWI and GNIc is to a certain extent due to a different sensitivity to inequality, as suggested above, in the top-right plot (B) the countries are split up into low and high inequality countries. This was done on the basis of the Gini coefficient for income inequality. The dark dots represent countries with above average Gini and the light dots the countries with below average Gini. In almost all cases, the dark dots are situated above the light dots, thus indicating that for a given IWI value the more unequal countries have higher levels of national income than the more equal countries. This confirms the idea that IWI differs more from national income in more unequal countries.

 

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Figure 2. Plots of national IWI values against national welfare measures (A-F) and of IWI-based

poverty measures against Poverty Headcount ratios (G-H)

A B

C

E

G H

F

D

 

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(b) Poverty measurement

A second important test of the usefulness of IWI involves its performance in measuring poverty. To assess this performance, we have defined the 20th, 30th, 40th, 50th and 60th percentiles of the IWI distribution as IWI poverty lines. Table 5 presents Pearson correlations between the national percentages of people with an IWI value below these lines and the Poverty Headcount Ratios (PHR) at $1.25 and $2.00 a day (PPP). Again we see strong correlations, all above .8, which makes clear that IWI-based poverty measures perform well in comparison with these established measures. The lines differ not very much in the strength of the correlations, but the IWI poverty line at the 30th percentile is most strongly correlated with the PHR at $1.25 a day and the line at the 50th percentile most strongly with the PHR at $2.00 a day. When using IWI for poverty measurement, these percentiles therefore seem to be usable IWI poverty lines. We call them the IWI-30 and IWI-50 poverty lines.

Table 5. Pearson correlations of IWI-based poverty lines with headcount ratios (N=76)

Headcount 1.25$ Headcount 2.00$ IWI-20 Poverty line .845 .839 IWI-30 Poverty line .875 .886 IWI-40 Poverty line .874 .906 IWI-50 Poverty line .860 .914 IWI-60 Poverty line .835 .906

In Panels G and H of Figure 2 the associations between these IWI poverty lines and the headcount ratios are displayed graphically. There are a few deviations from linearity, mostly due to countries with more households underneath the IWI poverty lines than underneath the headcount-based lines. In those countries thus being above a dollar based poverty line does not always mean being able to buy enough assets to cross the IWI-based lines. However, the deviations are small and in particular the correlation of .914 between the IWI-50 and the PHR at $2.00 is impressive.

To test the performance of the IWI poverty lines further, Table 6 presents for the 2.1 million households in our data the percentages of households below and above these lines that own a specific asset. The poverty lines behave as could be expected. In all cases, except for the bicycle and the cheap utensil, the differences in asset ownership and quality of facilities is very large. Of the households below the IWI-30 line only 5% owns a TV and 0.3% owns a refrigerator, whereas this is the case for 84% and 59% of the households above this line. Of the households below the IWI-30 line only 4% has high quality floor material, 2% a high quality toilet facility, and 5% high quality water supply. Of the households above this line, these figures are 44%, 67% and 68% respectively. Similar, but less extreme, differences in asset ownership can be observed between households below and above the IWI-50 poverty line.

 

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6. Conclusions Asset-based wealth indices are widely used instruments for measuring the economic status of households and studying inequality and poverty in low and middle income countries. The indices used so far suffered however from one great problem, they were not comparable among countries and time points. Although there have been a few studies in which more general wealth indices were created (e.g. Sahn & Stifel, 2000; Booysen et al., 2008), the geographic coverage of these indices was restricted and they were not turned into broadly usable instruments.

In this paper we introduce the International Wealth Index (IWI), the first strictly comparable asset based wealth index that can be used for all low and middle income countries. IWI is constructed by applying Principal Component Analysis on data for over 2.1 million households, derived from 165 household surveys held between 1996 and 2011 in 97 low and middle income countries. With IWI we provide a stable and understandable yardstick for evaluating and comparing the economic situation of households, social groups and societies across all regions of the developing world.

A household’s position on IWI indicates to what extent the household or its members own a basic set of assets that is valued highly by people across the globe. These assets include consumer durables, housing characteristics, and access to public utilities. The IWI scale runs from 0 to 100, with 0 indicating that the household owns none of the consumer durables, has lowest quality housing and no connection to public utilities, and 100 indicating that the household owns all included consumer durables, has highest quality housing and good access to public utilities.

To assess the performance of IWI as a comparable indicator of household wealth, a number of test analyses were conducted. On these tests IWI performed very well. The 2.1 million households were distributed rather evenly across the IWI scale, without problematic clumping or truncation. Removing one or even two assets from the index hardly influenced the rating of households; the same was true for removing data from specific regions of the developing world. Within DHS countries high correlations between IWI and national DHS wealth indices were obtained.

Comparisons of IWI at the national level with established welfare indices revealed very high correlations of .90 with the Human Development Index (HDI), .84 with life expectancy, .79 with Gross National Income per capita (GNIc), and .66 and .72 with educational indices. IWI is more highly correlated with HDI than with GNIc. This is probably due to the fact that -- just as HDI -- IWI is less affected by inequality and captures welfare effects of access to public goods (services). Another reason might be found is that asset based wealth indices like IWI are more than monetary or expenditure based welfare measures indicators of longer-term, more stable, aspects of household’s economic status (Sahn & Stifel, 2003; Howe et al., 2009; Filmer & Scott, 2012). The correlations between IWI and educational indices (mean and expected years of education) are somewhat lower than those with the other indices, but still clearly higher than those between national income and these educational indices (.54 and .68). Hence, IWI seems to be a better predictor of human capital than national income. Overall we can thus conclude that in comparison with national welfare indices IWI performs very well.

To test the usefulness of IWI for poverty measurement, we compared several IWI-based poverty measures with the Poverty Headcount Ratios (PHR) at $1.25 and $2.00 a day (PPP). These

 

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comparisons revealed high correlations. The percentage of households with an IWI value below 30 was most strongly correlated (.88) with the PHR at $1.25 and the percentage of households with an IWI value below 50 showed a very high correlation (.91) with the PHR at $2.00. These IWI-based poverty measures thus measure poverty almost similarly to PHR, which means that in situations where these other measures are not available, using IWI might constitute a good alternative.

Given the excellent performance of indices derived from IWI as welfare and poverty measures at the national level, it seems plausible that such IWI-based indices will also perform well when aggregated to the sub-national level. This implies that with the introduction of IWI for the first time sub-national indicators can be constructed for measuring in a comparable way the welfare level and degree of poverty of sub-national areas across the developing world.

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Appendix A. Information on datasets used and average national IWI values

Code Country Year Source N IWI value AFG Afghanistan 2010 DHS 21986 32.9 AGO Angola 2011 DHS 8028 33.4 AGO Angola 2000 MICS2 6244 20.2 ARM Armenia 2005 DHS 6562 78.1 ARM Armenia 2010 DHS 6653 77.2 AZE Azerbaijan 2000 MICS2 5859 56.6 AZE Azerbaijan 2006 DHS 7123 67.1 BDI Burundi 2005 MICS3 8150 10.6 BDI Burundi 2010 DHS 8517 15.8 BEN Benin 2001 DHS 5718 26.5 BEN Benin 2006 DHS 17330 28.7 BFA Burkina Faso 1998 DHS 4741 15.6 BFA Burkina Faso 2003 DHS 9042 19.4 BGD Bangladesh 2006 MICS3 62127 25.0 BGD Bangladesh 2007 DHS 10381 24.8 BLZ Belize 2006 MICS3 1821 71.2 BOL Bolivia 2003 DHS 19100 48.2 BOL Bolivia 2008 DHS 19300 54.6 BRA Brazil 1996 DHS 13151 66.7 BRA Brazil 2000 IPUMS 50301 67.7 BTN Buthan 2010 MICS4 14670 56.1 CAF Central African Republic CAR 2006 MICS3 11655 15.8 CHL Chili 2002 IPUMS 41016 83.5 CHN China 2003 WHS 3962 72.5 CHN China 2004 CHNS 4044 64.1 CIV Cote d'Ivoire 1999 DHS 2101 31.0 CIV Cote d'Ivoire 2006 MICS3 7514 41.6 CMR Cameroon 1998 DHS 4618 26.6 CMR Cameroon 2004 DHS 10358 27.2 COD Congo Democratic Republic 2007 DHS 8748 19.4 COD Congo Democratic Republic 2010 MICS4 11258 15.7 COL Colombia 2005 DHS 37211 72.6 COL Colombia 2010 DHS 51415 76.9 COM Comoros 1996 DHS 2163 25.4 COM Comoros 2003 WHS 1640 37.7 CRI Costa Rica 2000 IPUMS 28705 68.0 DOM Dominican Republic 1996 DHS 8772 56.4 DOM Dominican Republic 2002 DHS 26886 65.0 DOM Dominican Republic 2007 DHS 32076 72.4 DZA Algeria 2002 PAPFAM 8228 76.8 ECU Ecuador 2000 SIMPOC 14055 61.8 EGY Egypt 2000 DHS 16869 74.4 EGY Egypt 2003 DHS 20128 80.4 EGY Egypt 2005 DHS 21810 78.3 EGY Egypt 2008 DHS 18838 77.7 ETH Ethiopia 2005 DHS 13607 11.5 ETH Ethiopia 2011 DHS 16612 15.3 GAB Gabon 2000 DHS 6068 45.4 GEO Georgia 2003 WHS 2723 71.3 GEO Georgia 2005 MICS3 11883 64.7 GHA Ghana 1998 DHS 5964 25.7 GHA Ghana 2006 MICS3 5909 35.1 GHA Ghana 2008 DHS 11669 43.0 GIN Guinea 2005 DHS 6172 16.9 GMB Gambia 2000 MICS2 4489 35.4 GMB Gambia 2006 MICS3 5978 42.8 GNB Guinea Bissau 2006 MICS3 4993 31.8 GTM Guatemala 1999 DHS 5434 44.4 GTM Guatemala 2003 WHS 4408 55.2

 

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HND Honduras 2005 DHS 18636 56.5 HTI Haiti 2005 DHS 9899 27.1 IDN Indonesia 2003 DHS 32577 48.0 IDN Indonesia 2007 DHS 40131 48.7 IND India 1999 DHS 92306 31.5 IND India 2006 DHS 108714 37.3 IRQ Iraq 2006 MICS3 17868 74.1 JOR Jordan 2002 DHS 7825 85.8 JOR Jordan 2007 DHS 14547 87.3 KAZ Kazakhstan 1999 DHS 5816 62.8 KAZ Kazakhstan 2006 MICS3 14564 74.0 KEN Kenya 1998 DHS 8242 18.6 KEN Kenya 2003 DHS 8480 21.1 KEN Kenya 2008 DHS 9018 27.7 KGZ Kyrgyzstan 1997 DHS 3647 52.8 KGZ Kyrgyzstan 2006 MICS3 4893 64.9 KHM Cambodia 2005 DHS 14171 29.6 KHM Cambodia 2010 DHS 15622 40.6 LAO Laos 2003 WHS 4838 38.0 LBR Liberia 2007 DHS 6634 20.7 LKA Sri Lanka 2003 WHS 5768 48.8 LSO Lesotho 2010 DHS 9385 30.0 MAR Morocco 2003 DHS 10964 65.0 MAR Morocco 2003 WHS 4696 61.7 MDG Madagascar 1997 DHS 7141 15.2 MDG Madagascar 2009 DHS 17744 22.1 MDV Maldives 2009 DHS 6402 80.0 MEX Mexico 2003 WHS 38537 79.8 MLI Mali 2006 DHS 12768 22.0 MMR Myanmar 2003 WHS 5880 43.0 MNG Mongolia 2000 MICS2 5981 42.0 MNG Mongolia 2005 MICS3 6219 46.7 MOZ Mozambique 1997 DHS 9052 13.2 MOZ Mozambique 2003 DHS 12249 13.7 MRT Mauritania 2007 MICS3 10095 28.6 MUS Mauritius 2003 WHS 3748 88.1 MWI Malawi 2004 DHS 13495 13.8 MWI Malawi 2006 MICS3 30290 12.7 MWI Malawi 2010 DHS 24612 16.4 MYS Malaysia 2003 WHS 5947 90.7 NAM Namibia 2000 DHS 6295 36.8 NAM Namibia 2006 DHS 9086 45.3 NER Niger 1998 DHS 5831 11.0 NER Niger 2006 DHS 7589 12.4 NGA Nigeria 1999 DHS 7254 24.8 NGA Nigeria 2003 DHS 7091 29.6 NGA Nigeria 2008 DHS 33621 36.0 NIC Nicaragua 1998 DHS 11172 40.0 NIC Nicaragua 2001 DHS 11245 43.3 NPL Nepal 2006 DHS 8697 26.5 NPL Nepal 2011 DHS 10820 41.6 PAK Pakistan 2003 WHS 6096 45.9 PAK Pakistan 2007 DHS 9150 52.9 PAN Panama 2000 SIMPOC 9177 55.8 PER Peru 2000 DHS 28671 47.6 PER Peru 2004-2008 DHS 45998 53.9 PHL Philippines 1998 DHS 12257 52.0 PHL Philippines 2008 DHS 12371 61.1 PRY Paraguay 2003 WHS 5072 62.3 RWA Rwanda 2010 DHS 12479 19.8 SDN Sudan 2000 MICS2 24791 22.6 SDN Sudan 2008 IPUMS 36856 18.4 SEN Senegal 1997 DHS 4722 29.7 SEN Senegal 2011 DHS 7902 49.3 SLE Sierra Leone 2005 MICS3 7054 18.0 SLE Sierra Leone 2008 DHS 7177 22.2

 

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SLV El Salvador 2001 SIMPOC 11953 59.2 SOM Somalia 2006 MICS3 5707 18.6 SSD South Sudan (urban) 2000 MICS2 1553 14.1 SSD South Sudan 2008 IPUMS 7442 11.4 STP Sao Tome & Principe 2000 MICS2 3252 31.3 STP Sao Tome & Principe 2009 DHS 3529 42.9 SUR Suriname 2006 MICS3 5603 76.6 SWZ Swaziland 2000 MICS2 4309 37.2 SWZ Swaziland 2006 DHS 4806 40.9 SYR Syria 2006 MICS3 19006 82.3 TCD Chad 2004 DHS 5301 8.6 TGO Togo 2006 MICS3 6484 28.8 THA Thailand 2006 MICS3 40483 77.5 TJK Tajikistan 2000 MICS2 3696 46.3 TJK Tajikistan 2005 MICS3 6684 51.2 TLS Timor Leste 2009 DHS 11454 31.9 TUN Tunisia 2001 PAPFAM 6048 72.6 TUN Tunisia 2003 WHS 4863 74.4 TUR Turkey 2003 DHS 10738 75.7 TZA Tanzania 2004 DHS 9660 15.3 TZA Tanzania 2010 DHS 9569 21.9 UGA Uganda 2006 DHS 8748 14.8 URY Uruguay 2003 WHS 2938 89.7 URY Uruguay 2006 IPUMS 19954 80.1 UZB Uzbekistan 1996 DHS 3687 53.3 UZB Uzbekistan 2005 MICS3 10127 62.4 VEN Venezuela 2001 IPUMS 26815 76.8 VNM Vietnam 1997 DHS 6998 33.0 VNM Vietnam 2002 DHS 6985 43.8 VNM Vietnam 2006 MICS3 8354 55.3 YEM Yemen 1997 DHS 9669 35.1 YEM Yemen 2003 PAPFAM 11146 38.2 YEM Yemen 2006 MICS3 3562 48.4 ZAF South Africa 1998 DHS 11886 53.9 ZAF South Africa 2003 WHS 2129 70.0 ZMB Zambia 2002 DHS 7072 18.8 ZMB Zambia 2007 DHS 7088 24.1 ZWE Zimbabwe 1999 DHS 6308 33.4 ZWE Zimbabwe 2006 DHS 9201 34.8 ZWE Zimbabwe 2011 DHS 9756 38.5

In the following cases missing asset variables were replaced by other variables or by imputation of a value: floor replaced by rooms for Brazil 2000, India 1999; phone replaced by refrigerator for Angola 2000, Azerbaijan 2000, Brazil 1996, Gambia 2000, Mongolia 2000, Sudan 2000, South Sudan 2000, Sao Tome Y Principe 2000, Swaziland 2000, Tajikistan 2000; bicycle replaced by car for Brazil 1996, Brazil 2000; Costa Rica 2000, Jordan 2002, by 0 for Jordan 2007, Venezuela 2001, by motorbike for Tunisia 2001, Uruguay 2006, by 1 for El Salvador 2001; rooms replaced by water for Benin 2001, Burkina Faso 2003, Guinea 2005, Indonesia 2003, Kazakhstan 1999, Zambia 2002, Zimbabwe 1999; cheap utensils put at 1 for China 2003, Georgia 2003, Mexico 2003, Mauritius 2003, Malaysia 2003, Paraguay 2003, Tunisia 2003m Uruguay 2003; electricity put a t 1 for China 2004, Georgia 2003, Mauritius 2003, Malaysia 2003, Uruguay 2003. In Brazil 1996 and 2000, Geaorgia 2003, Mauritius 2003, Malaysia 2003, and Uruguay 2003 two items were missing.

Data Sources: DHS Demographic and Health Survey (www.measuredhs.com) MICS UNICEF Multiple Indicator Cluster Surveys (www.childinfo.org). MICS2 is 2000 round, MICS3 is 2005-2006

round, MICS4 is 2010-2011 round WHS World Health Surveys collected under supervision of the World Health Organization

(www.who.int/healthinfo/survey) PAPFAM Surveys of the Pan Arabic Project for Family Health (PAPFAM), sponsored by among others the League

of Arab States (www.papfam.org) IPUMS Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 6.1 [Machine-

readable database]. Minneapolis: University of Minnesota, 2011 (international.ipums.org) SIMPOC Surveys of the Statistical Information and Monitoring Programme on Child Labor (SIMPOC) of ILO-IPEC

(www.ilo.org/ipec) CHNS Chinese Health and Nutrition Survey 2004 (www.cpc.unc.edu/projects/china).

 

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Appendix B. Data used for computing associations between IWI and welfare measures and between IWI-30 and IWI-50 and poverty Headcount Ratios

ISO_code Year IWI-value HDI GNIc Life exp.

Exp. eduyrs

Mean eduyrs GINI

IWI-30

IWI-50

HR $1.25

HR $2.00

AFG 2010 32.9 0.4 1351 48.3 9.1 3.3 27.8 - - - - AGO 2000 - - - - - - - 76.8 90.2 54.3 70.2 AGO 2011 33.4 0.5 4874 51.1 9.1 4.4 - - - - - ARM 2010 77.2 0.7 5009 74.1 12.0 10.8 30.9 0.2 4.4 1.3 12.4 AZE 2006 67.1 - 3940 69.0 11.5 - 34.7 2.2 18.9 2.1 9.8 BDI 2010 15.8 0.3 359 50.0 10.5 2.7 - 90.7 96.1 81.3 93.5 BEN 2006 28.7 0.4 1311 54.3 9.2 3.0 38.6 62.3 81.9 47.3 75.3 BFA 2003 19.4 0.3 996 51.6 4.2 1.3 39.6 83.5 91.1 56.5 81.2 BGD 2007 24.9 0.5 1256 67.6 8.0 4.4 32.8 69.0 84.9 47.6 75.4 BLZ 2006 71.2 0.7 5765 74.8 12.6 7.8 - - - - - BOL 2008 54.6 0.7 4320 64.7 14.0 8.3 56.3 23.3 40.5 15.6 24.9 BRA 2000 67.7 0.7 7698 70.1 14.5 5.6 60.0 8.2 19.3 11.6 21.5 BTN 2010 56.1 0.5 5060 66.8 11.0 2.3 38.1 12.9 44.4 10.2 29.8 CAF 2006 15.8 0.3 660 44.4 5.8 3.2 56.3 89.8 96.8 62.8 80.8 CHL 2002 83.5 0.8 10483 77.6 13.4 9.0 54.6 1.7 4.8 2.1 5.2 CHN 2004 64.1 0.6 3832 71.9 10.5 7.0 42.5 5.0 26.3 20.3 41.7 CIV 2006 41.6 0.4 1492 52.1 6.3 3.1 43.8 39.8 63.4 23.6 46.5 CMR 2004 27.2 0.4 1866 49.5 8.6 5.3 39.7 62.1 82.5 10.2 31.4 COD 2010 15.8 0.3 270 48.1 8.2 3.5 - 84.4 91.4 87.7 95.2 COL 2010 76.9 0.7 8043 73.5 13.6 7.3 55.9 2.3 7.2 8.2 15.8 COM 1996 25.4 - 1118 56.9 7.8 - - - - - - CRI 2000 68.0 0.7 7467 77.8 10.7 8.0 46.5 3.5 13.6 5.5 10.9 DOM 2007 72.4 0.7 6632 72.5 11.9 6.9 48.7 3.5 14.4 3.8 11.5 DZA 2002 76.8 0.6 6209 70.7 12.0 5.9 - - - - - ECU 2000 61.8 0.7 5005 73.4 12.9 6.9 56.6 9.8 25.6 20.7 37.7 EGY 2008 77.7 0.6 4917 72.4 11.0 6.0 30.8 0.6 3.1 1.7 15.4 ETH 2011 15.3 0.4 971 59.3 8.5 1.5 - 84.1 91.7 39.0 77.6 GEO 2005 64.7 0.7 3650 72.8 12.6 12.1 41.1 2.9 22.2 16.0 33.5 GHA 2008 43.0 0.5 1329 62.7 9.7 6.9 - 35.8 63.0 28.6 51.8 GIN 2005 16.9 0.3 860 51.1 7.5 1.6 39.8 81.0 91.3 49.8 75.2 GMB 2006 42.8 0.4 1075 56.9 8.4 2.4 - 34.4 67.5 33.6 55.9 GNB 2006 31.8 0.3 955 46.4 8.9 2.3 35.5 56.5 83.0 48.9 78.0 GTM 1999 44.4 0.5 3861 67.2 8.4 3.4 55.0 36.2 55.3 14.1 27.7 HND 2005 56.5 0.6 3120 71.4 10.9 5.9 59.7 20.7 42.7 26.4 40.1 HTI 2005 27.1 0.4 959 59.9 7.6 4.5 59.2 63.7 82.5 61.7 77.5 IDN 2007 48.7 0.6 3122 67.8 12.4 5.5 34.0 21.9 52.6 24.2 56.1 IND 2006 37.3 0.5 2474 63.7 10.0 4.1 33.4 48.4 66.7 39.8 74.2 IRQ 2006 74.1 0.6 2578 68.4 9.8 5.4 30.9 1.7 8.0 2.8 21.4 JOR 2007 87.3 0.7 4770 72.9 12.9 8.2 35.8 0.3 0.9 0.2 2.8 KAZ 2006 74.0 0.7 8264 65.5 14.9 10.2 30.8 0.2 7.7 0.4 3.3 KEN 2008 27.7 0.5 1407 55.2 10.4 6.8 - 62.3 84.3 43.4 67.2 KGZ 2006 65.0 0.6 1728 66.8 12.4 9.2 38.7 0.9 20.0 5.9 32.1 KHM 2010 40.6 0.5 1753 62.7 9.8 5.8 37.9 39.5 67.7 22.8 53.3 LBR 2007 20.7 0.3 254 53.7 11.0 3.6 38.2 76.1 93.6 83.8 94.9 LSO 2010 30.0 0.5 1643 47.6 9.9 5.9 - - - - - MAR 2003 65.0 0.5 3203 69.7 9.4 3.7 40.7 15.0 27.7 5.0 20.9 MDG 2009 22.1 0.5 846 66.0 10.4 5.2 44.1 80.2 93.0 78.6 92.0 MDV 2009 80.0 0.7 4828 76.1 12.4 5.6 - - - - - MLI 2006 22.0 0.3 978 49.4 6.9 1.8 39.0 76.1 89.1 51.4 77.1 MNG 2005 46.7 0.6 2550 66.0 12.6 8.2 34.7 - - - - MOZ 2003 13.8 0.3 571 47.8 7.2 1.0 47.1 90.3 95.3 74.7 90.0 MRT 2007 28.6 0.4 1762 57.7 7.8 3.5 40.5 58.9 78.6 23.9 48.9

 

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MWI 2010 16.4 0.4 730 53.5 8.9 4.2 - 86.3 94.3 73.9 90.5 NAM 2006 45.3 0.6 5442 59.4 11.6 7.1 63.9 44.7 57.8 31.9 51.1 NER 2006 12.4 0.3 605 52.2 3.9 1.3 34.6 91.0 95.2 48.0 75.3 NGA 2008 36.0 0.4 1806 50.4 8.9 5.0 46.9 47.8 70.3 66.3 84.0 NIC 2001 43.3 0.5 2058 70.2 10.0 4.8 43.1 36.1 58.9 14.4 34.4 NPL 2011 41.6 0.5 1160 68.8 8.8 3.2 32.8 37.3 62.6 24.8 57.3 PAK 2007 52.9 0.5 2347 64.5 6.6 4.7 31.4 25.8 44.8 21.8 60.6 PAN 2000 55.9 0.7 7721 74.2 12.8 8.5 57.3 21.7 36.9 15.2 22.8 PER 2000 - - - - - - - 34.6 49.5 12.4 24.1 PER 2004/8 53.9 0.7 5803 72.1 12.9 8.1 - - - - - PHL 2008 61.1 0.6 3195 68.1 11.8 8.8 43.0 14.4 31.2 19.8 42.7 RWA 2010 19.8 0.4 1086 55.1 11.1 3.3 50.8 83.5 94.8 65.0 83.4 SDN 2008 18.4 0.4 1706 60.5 4.4 3.0 35.3 80.4 89.6 19.8 44.1 SEN 2011 49.3 0.5 1708 59.3 7.5 4.5 - 31.1 49.5 33.5 60.4 SLE 2008 22.2 0.3 680 46.2 7.2 2.8 - 74.9 90.8 53.4 76.1 SLV 2001 59.2 0.6 5153 70.1 11.5 5.9 53.6 20.1 38.0 14.4 23.0 SOM 2006 18.6 - - - - - - - - - - SSD 2008 11.4 - - - - - 45.5 - - - - STP 2000 - - - - - - - 60.2 82.1 28.2 54.2 STP 2009 42.9 0.5 1380 63.3 10.2 4.2 - - - - - SUR 2006 76.6 0.7 6213 69.1 12.6 7.2 - - - - - SWZ 2006 40.9 0.5 4601 46.4 10.0 6.7 51.1 41.7 66.7 46.2 69.6 SYR 2006 82.3 0.6 3830 74.9 11.0 5.7 35.8 0.4 3.1 1.7 16.9 TCD 2004 8.6 0.3 1010 48.2 5.8 1.5 39.8 96.1 98.4 61.9 83.3 TGO 2006 28.8 0.4 766 55.6 9.7 4.9 34.4 64.5 82.9 38.7 69.3 THA 2006 77.5 0.7 6625 73.4 12.2 6.0 42.4 0.4 4.1 1.0 7.6 TJK 2005 51.2 0.6 1430 65.4 11.0 10.0 33.6 9.8 54.3 18.7 45.6 TLS 2009 31.9 0.5 2867 62.0 11.2 2.8 - - - - - TUN 2001 72.6 0.6 5371 72.8 13.4 5.0 40.9 5.5 15.0 2.3 11.9 TUR 2003 75.7 0.7 10208 71.1 10.8 5.9 43.4 2.2 10.6 2.5 10.0 TZA 2010 21.9 0.5 1272 57.4 9.1 5.1 - 76.6 88.9 67.9 87.9 UGA 2006 14.8 0.4 924 50.9 10.2 4.4 42.6 87.1 95.7 51.5 75.6 URY 2006 80.1 0.8 10051 76.0 15.3 8.0 47.2 0.5 4.0 0.7 3.6 UZB 2005 62.4 0.6 2000 67.2 11.5 10.0 36.7 - - - - VEN 2001 76.8 0.7 9449 72.4 10.5 5.9 47.2 3.0 9.0 9.6 20.8 VNM 2006 55.3 0.6 2214 74.0 10.4 5.0 35.8 9.7 46.8 21.4 48.1 YEM 2006 48.4 0.4 2025 63.2 8.6 1.9 37.7 31.8 49.0 17.5 46.6 ZAF 1998 53.9 0.6 7401 56.8 13.1 8.2 41.3 29.2 46.0 24.3 41.7 ZMB 2007 24.1 0.4 1117 46.0 7.9 6.4 54.6 71.5 82.8 68.5 82.6 ZWE 2011 38.5 0.4 376 51.4 9.9 7.2 - - - - -

Meaning of abbreviations: HDI Human Development Index, source hdr.undp.org GNIc Gross National Income per capita (PPP), source hdr.undp.org Life exp Life expectancy at birth, source hdr.undp.org Exp. eduyrs Expected years of schooling a child of school entrance age can expect to receive, source hdr.undp.org Mean eduyrs Mean years of education received by people aged 25 and older, source hdr.undp.org GINI Gini coefficient for income inequality, source data.worldbank.org IWI-30 Percentage of households with an IWI value below 30 IWI-50 Percentage of households with an IWI value below 50 HR $1.25 Poverty Headcount Ratio at $1.25 a day (PPP) , source data.worldbank.org HR $2.00 Poverty Headcount Ratio at $2.00 a day (PPP) , source data.worldbank.org Data sources are the UNDP Human Development Report website (hdr.undp.org), for HDI, GNIc, life expectancy, expected years of schooling, and mean years of education, and he Worldbank website (data.worldbank.org), for GINI and the Poverty Headcount Ratios. Websites were approached in December 2012. The indicators of UNDP and Worldbank were not available for all years for which we have an IWI value. Missing years were filled in with linear interpolation when possible. If interpolation was not possible, values from a nearby year were used. If the nearest year was more than five years apart, the country/year combination was removed from the data.

 

28  

Appendix C. Pearson correlations between DHS wealth index and IWI for DHS countries

ARM 2005 0.856 ARM 2010 0.784 AZE 2006 0.863 BDI 2010 0.939 BEN 2001 0.927 BEN 2006 0.914 BFA 2003 0.951 BFA 1998 0.923 BGD 2007 0.933 BOL 2003 0.958 BOL 2008 0.951 BRA 1996 0.925 CIV 1999 0.958 CMR 1998 0.968 CMR 2004 0.935 COD 2007 0.938 COL 2005 0.900 COL 2010 0.846 COM 1996 0.965 DOM 1996 0.908 DOM 2002 0.928 DOM 2007 0.873 EGY 2000 0.877 EGY 2003 0.862 EGY 2005 0.870 EGY 2008 0.829 ETH 2005 0.950 ETH 2011 0.961 GAB 2000 0.933 GHA 1998 0.958 GHA 2008 0.937 GIN 2005 0.959

GTM 1999 0.935 HND 2005 0.917 HTI 2005 0.940 IDN 2003 0.895 IDN 2007 0.915 IND 1999 0.935 IND 2006 0.944 JOR 2002 0.894 JOR 2007 0.700 KAZ 1999 0.920 KEN 1998 0.965 KEN 2003 0.891 KEN 2008 0.894 KGZ 1997 0.921 KHM 2005 0.914 KHM 2010 0.939 LBR 2007 0.915 LSO 2010 0.922 MAR 2003 0.950 MDG 1997 0.890 MDG 2009 0.880 MDV 2009 0.761 MLI 2006 0.843 MOZ 1997 0.953 MOZ 2003 0.950 MWI 2004 0.941 MWI 2010 0.936 NAM 2000 0.980 NAM 2006 0.972 NER 1998 0.970 NER 2006 0.968 NGA 1999 0.920

NGA 2003 0.929 NGA 2008 0.937 NIC 1998 0.967 NIC 2001 0.951 NPL 2006 0.924 NPL 2011 0.898 PAK 2007 0.925 PER 2000 0.962 PER 2004-8 0.952 PHL 1998 0.933 PHL 2008 0.935 RWA 2010 0.928 SEN 1997 0.943 SEN 2011 0.939 SLE 2008 0.938 STP 2009 0.921 SWZ 2006 0.941 TCD 2004 0.899 TLS 2009 0.898 TUR 2003 0.750 TZA 2004 0.899 TZA 2010 0.936 UGA 2006 0.934 UZB 1996 0.942 VNM 1997 0.967 YEM 1997 0.949 ZAF 1998 0.972 ZMB 2002 0.971 ZMB 2007 0.923 ZWE 1999 0.938 ZWE 2006 0.966 ZWE 2011 0.939