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Demography

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01.02.2012

Assessing Asset Indices

verfasst von: Deon Filmer, Kinnon Scott

Erschienen in: Demography | Ausgabe 1/2012

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Abstract

The use of asset indices in welfare analysis and poverty targeting is increasing, especially in cases in which data on expenditures are unavailable or hard to collect. We compare alternative approaches to welfare measurement. Our analysis shows that inferences about inequalities in education, health care use, fertility, and child mortality, as well as labor market outcomes, are quite robust to the economic status measure used. Different measures—most significantly per capita expenditures versus the class of asset indices—do not, however, yield identical household rankings. Two factors stand out in predicting the degree of congruence in rankings. First is the extent to which expenditures can be explained by observed household and community characteristics. Rankings are most similar in settings with small transitory shocks to expenditure or with little random measurement error in expenditure. Second is the extent to which expenditures are dominated by individually consumed goods, such as food. Asset indices are typically derived from indicators of goods that are effectively public at the household level, while expenditures are often dominated by food, an almost exclusively private good. In settings in which individually consumed goods are the main component of expenditures, asset indices and per capita consumption yield the least similar results.

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For a fuller account of the methodological literature to date, see Online Resource 1. Key issues and readings include comparisons of asset indices to expenditure measures, variations in aggregation methods, and sensitivity to specific indicators used in the index (Bollen et al. 2002; Case et al. 2004; Das et al. 2004; Ferguson et al. 2003; Filmer and Pritchett 2001; Houweling et al. 2003; Lindelow 2006; Lubotsky and Wittenberg 2005; Montgomery et al. 2000; Montgomery and Hewett 2005; Morris et al. 2000; Mukherjee 2006; Organisation for Economic Co-operation and Development (OECD) 2004; Paxson and Schady 2007; Sahn and Stifel 2000, 2003; Stifel and Christiaensen 2007; Wagstaff and Watanabe 2003; Wittenburg 2005).

In previous papers (e.g., Filmer and Pritchett 2001), the index was often referred to as a wealth index. The term wealth in those papers was used to distinguish it from an expenditures-based measure. In order to use a term that more accurately reflects the measure, we refer to it as an asset index in this article.

In addition, asset indices typically exclude productive assets that reflect household investments, assets that are not usually collected in the types of data sets for which analysts turn to an index approach.

We use the term expenditures to refer to what is sometimes more precisely called consumption expenditures, the value of household consumption regardless of whether purchased or home-produced, excluding expenditures for nonconsumption purposes such as investment. This is also sometimes just called consumption. For consistency and compactness, we use the term expenditures throughout.

For a discussion of the comparison between asset indices and total household expenditures (as opposed to per capita expenditures), see the discussion in the section devoted to congruence and divergence in rankings.

These aggregates were graciously made available by those authors for Brazil 1996/1997, Nepal 1996, Panama 1997, South Africa 1993, and Vietnam 1992/1993. The other data sets we use were not available to those authors at the time of their analysis.

Albania 2002, Ghana 1991/1992, Nicaragua 2001, and Papua New Guinea 1996 are available online (http://www.worldbank.org/lsms). Uganda 2000 and Zambia 2004 were made available by the agencies responsible for data collection or analysis. Note that all of these data sets use a methodology consistent with Deaton and Zaidi (2002) to calculate total expenditures. The questionnaire in Papua New Guinea was structured and collected slightly differently: the survey took place over two rounds, with expenditure data collected in the second round using the time between the first and second rounds as the reference period. Other surveys typically collected expenditure data based on fixed recall periods (e.g., last seven days, last month, last year).

Note that this is consistent with the way the approach is often carried out (such as in Stifel and Christiaensen 2007)—although not with the “poverty mapping” approach, which includes area-level aggregates as well as interaction terms (in Alderman et al. 2002; and Elbers et al. 2002, 2003). Including various interaction terms into our construction of the predicted per capita expenditures measure typically yields an estimate that has slightly higher congruence with reported per capita expenditures but does not much affect the other comparisons.

See Jolliffe (2002) for a useful textbook treatment of principal components analysis.

Factor analysis allows for an indicator- and household-specific error term in these equations.

Principal components analysis produces k components. These are typically ordered from the one that has the largest variance (the first principal component) to the one that has the least. There is no theoretical basis for labeling the first component as representing economic status. The assumption underlying this interpretation is that it is economic status that explains the maximum variance (and covariance) of the various indicators. The evidence presented in this article lends credence to this assumption. It is much harder to interpret higher-order components. As discussed in Filmer and Pritchett (2001), visual inspection of the results suggests that the second component frequently “captures” rich rural households (in the sense that asset indicators associated with these households get high weight, and those that are not get low, or negative, weight). But this is not true across all the countries in the present analysis. Note that these higher-order components are, by construction, orthogonal to the first, so they will not bias the bivariate comparisons of the asset index and outcomes of interest. (See Filmer and Pritchett (2001) for further discussion.)

The open-source software ICL was used for estimating the IRT model. It is available at http://www.b-a-h.com/software/irt/icl. Note that only binary variables can be included, which means that rooms per person is dropped. Moreover, only assets whose ownership increases with the latent factor can be considered in this index. Some assets or housing characteristics are therefore dropped in the construction of the IRT index. When we repeat the principal components on this slightly reduced set of indicators, the results are extremely similar. See van der Linden and Hambleton (1997) and Baker and Kim (2004) for textbook treatments of IRT.

This index, like the count index, uses the same reduced set of indicators as the IRT index because only binary assets or characteristics whose ownership increases with the index can be included.

In rare cases, parts of the country were excluded. For example, the Brazilian survey covers only the southeast and northeast regions of the country. In Uganda, one region of the country was not sampled because of security reasons. Surveys typically used cluster sampling; robust standard errors are used for inference in this analysis.

Importantly, our results are robust to reducing the list of indicators to the set of “durable goods” only, which are more similar across countries. Also, the degree of congruence between expenditures and asset indices is not related to the number of asset indicators used.

The full list of indicators for each data set is in Online Resource 2.

Correlations of rankings in this article refer to Spearman rank correlations.

Some of the differences between the PC index and the IRT, share-weighted, and count indices come from the fact that the latter set uses a slightly reduced list of indicators. Repeating the analysis but using only the same set of indicators for the PC index increases the rank correlation coefficients by between 0 and .07 points, with an average increase of .02 points.

We use linear probability models throughout this analysis. We experimented with probit and logit models and compared marginal and predicted probabilities. The results are extremely similar and the conclusions are unaffected.

Statistical significance for the estimates underlying Figs. 1, 2, 3, 4 and 5, based on robust standard errors that allow for clustering, are in the tables in Online Resource 3. Tests of the significance of differences in the estimates of coefficients across models are based on simultaneous equations modeling using seemingly unrelated regressions estimation (SURE).

Consistent with Dow (1996), these estimates do not condition on self-reported health status, which may be systematically related to socioeconomic status. We do not study self-reported illness directly because of the potential problem of biased self-reporting: if the poor are less likely to recognize illness—perhaps because the implicit cost of doing so is high—then it is unclear what the economic status gradient in self-reported illness actually represents. This issue is discussed in Butler et al. (1987), Deolalikar (1998), Sindelar and Thomas (1991), and Strauss and Thomas (1996).

The definition of urban differs across countries. For the purpose of this analysis, we use the definition as described in the context of each data set.

The dependency ratio is defined here as the ratio of the number of household members less than 16 years old plus those aged 60 or older, divided by total household size.

We also explored the relationships with other potential country and data set characteristics that one might expect to be associated with congruence: the number of assets and the share of their covariance explained by the first principal component; overall poverty in the country; and whether or not education or health expenditures are included in expenditure aggregate. While poverty is weakly negatively related to congruence and the share explained by the first component is weakly positively related to congruence, none of these are significant correlates of congruence. The results are described in Online Resource 4.

The share reported in Table 4 is the total sum of squares explained in these regressions divided by the overall sum of squares. Clusters are the lowest sampling unit used in the survey: they are typically the primary sampling unit (PSU) from which 15–30 households are randomly selected.

When asset indicators are included in the set of household characteristics, the share of variance in each country increases by a small amount, but the association with the rank correlation between the assets index and expenditures is similar: the correlation coefficient is .710.

In our application, children are defined as household members aged 15 years or younger, and adults are defined as household members aged 16 and older.

Drèze and Srinivasan (1997) similarly found that poverty rankings across groups of households (e.g., male-headed, female-headed, single widow) are not substantively affected by adjusting for equivalence scales.

Other studies use subjectively reported measures of welfare to document the existence of economies of scale (Pradhan and Ravallion 2000). However, as discussed in Deaton and Zaidi (2002), a formal comparison of measured and subjective welfare often yields unbelievably small values of θ.

Deaton and Paxson (1998) showed how one would expect that, holding per capita total expenditures constant, larger household size should be associated with higher per capita food expenditures. This is because households would be able to exploit the economies-of-scale aspect of the public-goods (i.e., nonfood) portion of expenditures, which would effectively leave more money per person to be allocated to food. If true, this would allow estimation of the economies-of-scale parameter. However, they found exactly the opposite result: larger households are associated with lower per capita spending on food, a puzzle that they were unable to resolve.

One might be worried that the results on the predictability of expenditures are also being driven by the share of food in expenditures since both approaches to prediction use nonfood-related variables to predict overall expenditures (columns 3 and 4 of Table 4). However, the results are barely affected if the prediction models are estimated for nonfood expenditures only (columns 6 and 7 of Table 4), indicating that predictability and household public goods are indeed separate issues.

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Titel: Assessing Asset Indices
verfasst von: Deon Filmer
Kinnon Scott
Publikationsdatum: 01.02.2012
Verlag: Springer US
Erschienen in: Demography / Ausgabe 1/2012
Print ISSN: 0070-3370
Elektronische ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-011-0077-5

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