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. 2010 Sep 3;7(1):7.
doi: 10.1186/1742-7622-7-7.

Comparison of two approaches for measuring household wealth via an asset-based index in rural and peri-urban settings of Hunan province, China

Julie Balen  1 , Donald P McManus, Yue-Sheng Li, Zheng-Yuan Zhao, Li-Ping Yuan, Jürg Utzinger, Gail M Williams, Ying Li, Mao-Yuan Ren, Zong-Chuan Liu, Jie Zhou, Giovanna Raso
Affiliations

Comparison of two approaches for measuring household wealth via an asset-based index in rural and peri-urban settings of Hunan province, China

Julie Balen et al. Emerg Themes Epidemiol. .

Abstract

Background: There are growing concerns regarding inequities in health, with poverty being an important determinant of health as well as a product of health status. Within the People's Republic of China (P.R. China), disparities in socio-economic position are apparent, with the rural-urban gap of particular concern. Our aim was to compare direct and proxy methods of estimating household wealth in a rural and a peri-urban setting of Hunan province, P.R. China.

Methods: We collected data on ownership of household durable assets, housing characteristics, and utility and sanitation variables in two village-wide surveys in Hunan province. We employed principal components analysis (PCA) and principal axis factoring (PAF) to generate household asset-based proxy wealth indices. Households were grouped into quartiles, from 'most wealthy' to 'most poor'. We compared the estimated household wealth for each approach. Asset-based proxy wealth indices were compared to those based on self-reported average annual income and savings at the household level.

Results: Spearman's rank correlation analysis revealed that PCA and PAF yielded similar results, indicating that either approach may be used for estimating household wealth. In both settings investigated, the two indices were significantly associated with self-reported average annual income and combined income and savings, but not with savings alone. However, low correlation coefficients between the proxy and direct measures of wealth indicated that they are not complementary. We found wide disparities in ownership of household durable assets, and utility and sanitation variables, within and between settings.

Conclusion: PCA and PAF yielded almost identical results and generated robust proxy wealth indices and categories. Pooled data from the rural and peri-urban settings highlighted structural differences in wealth, most likely a result of localized urbanization and modernization. Further research is needed to improve measurements of wealth in low-income and transitional country contexts.

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Figures

Figure 1
Figure 1
Distribution of the standardized asset-based proxy wealth index scores created using exploratory factor analysis with the principal components analysis (PCA) extraction method (a, c) and the principal axis factoring (PAF) extraction method (b, d) in rural (Wuyi village) and peri-urban (Laogang village) settings, Hunan province, P.R. China.
Figure 2
Figure 2
Correlation of the standardized asset-based proxy wealth index scores created using exploratory factor analysis with the principal components analysis (PCA) and the principal axis factoring (PAF) extraction methods in (a) rural (Wuyi village) and (b) peri-urban (Laogang village) settings of Hunan province, P.R. China. Lines vertical to the axes define the respective wealth quartiles of each index for rural (dashed) and peri-urban (dotted) settings, respectively.
Figure 3
Figure 3
Mean asset-based index scores, derived either by principal components analysis (PCA), according to income and savings categories. Values shown are for rural (Wuyi village) (filled) and peri-urban (Laogang village) (blank) settings, Hunan province, P.R. China.
Figure 4
Figure 4
Proportion of households within each proxy wealth quartile that are in the rural (Wuyi village) (blue) and peri-urban (Laogang village) (green) settings of Hunan province, P.R. China, respectively. Quartiles represent (a) most poor (n = 127); (b) below average (n = 126); (c) above average (n = 126); and (d) most wealthy (n = 125) categories for pooled data from both villages.
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