This article is part of a series on Methods for Health Surveys in Difficult Settings, edited by Oleg Bilukha (Centers for Disease Control), Kristof Bostoen (London School of Hygiene and Tropical Medicine), Francesco Checchi (London School of Hygiene and Tropical Medicine), Bridget Fenn (London School of Hygiene and Tropical Medicine), Oliver Morgan (London School of Hygiene and Tropical Medicine) and Anne-Marie ter Veen (London School of Hygiene and Tropical Medicine).

Methodology

Assessing household wealth in health studies in developing countries: a comparison of participatory wealth ranking and survey techniques from rural South Africa

James R Hargreaves^1,2 , Linda A Morison¹ , John SS Gear² , Julia C Kim^1,2 , Mzamani B Makhubele² , John DH Porter¹ , Charlotte Watts¹ and Paul M Pronyk^1,2

¹  London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK

²  Rural AIDS and Development Action Research Programme, PO Box 2, Acornhoek 1360, South Africa

author email corresponding author email

Emerging Themes in Epidemiology 2007, 4:4doi:10.1186/1742-7622-4-4

Published:	1 June 2007

Abstract

Background

Accurate tools for assessing household wealth are essential for many health studies in developing countries. Household survey and participatory wealth ranking (PWR) are two approaches to generate data for this purpose.

Methods

A household survey and PWR were conducted among eight villages in rural South Africa. We developed three indicators of household wealth using the data. One indicator used PWR data only, one used principal components analysis to combine data from the survey, while the final indicator used survey data combined in a manner informed by the PWR. We assessed internal consistency of the indices and assessed their level of agreement in ranking household wealth.

Results

Food security, asset ownership, housing quality and employment were important indicators of household wealth. PWR, consisting of three independent rankings of 9671 households, showed a high level of internal consistency (intraclass correlation coefficient 0.81, 95% CI 0.79–0.82). Data on 1429 households were available from all three techniques. There was moderate agreement in ranking households into wealth tertiles between the two indicators based on survey data (spearman rho = 0.69, kappa = 0.43), but only limited agreement between these techniques and the PWR data (spearman rho = 0.38 and 0.31, kappa = 0.20 and 0.17).

Conclusion

Both PWR and household survey can provide a rapid assessment of household wealth. Each technique had strengths and weaknesses. Reasons for differences might include data inaccuracies or limitations in the methods by which information was weighted. Alternatively, the techniques may measure different things. More research is needed to increase the validity of measures of socioeconomic position used in health studies in developing countries.