Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries.
Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries.
复制标题
DOI：
10.1186/1742-7622-5-3
复制
发表时间：
2008-01-30
影响因子：
2.3
通讯作者：
Huttly, Sharon R A
中科院分区：
文献类型：
--
作者：
Howe, Laura D;Hargreaves, James R;Huttly, Sharon R A
流行病学研究往往需要社会经济地位的措施（SEP）。对资产所有权数据进行主成分分析（PCA）是家庭SEP测量的一种流行方法。支持者认为，该方法提供了一个合理的方法，加权资产数据在一个单一的指标，捕捉最重要的方面，SEP的健康研究，是基于数据，是现成的和/或简单的收集。然而，使用PCA的资产数据可能不是最好的方法SEP测量。人们仍然担心这种方法可能会模糊最终指数的含义，并且在统计上不适合用于离散数据。此外，要纳入的资产的选择以及财富指数与消费支出等更传统的可持续就业指标之间的一致程度仍不清楚。我们讨论这些问题，说明我们的例子，从马拉维综合住户调查2004-5年的数据。财富指数是利用人口与健康调查收集数据的资产编制的。指数构建使用五种加权方法：PCA，PCA使用二分版本的分类变量，相等的权重，权重等于拥有该项目的家庭比例的倒数，以及多重对应分析。评估了指数之间的一致性。指数与人均消费支出进行了比较，并评估了在使用不同方法调整家庭规模和组成的消费支出时的一致性差异。所有指数都显示出与消费支出类似的适度一致。使用二分数据构建的指数表现出很强的一致性，因为使用分类数据构建的指数。使用不同方式编码的数据的指数之间的一致性较低。当采用不同的消费等值表时，财富指数和消费支出之间的一致程度没有差别。这项研究质疑财富指数作为消费支出替代指标的适当性。所选数据对财富指数的影响大于对数据加权的方法。尽管PCA有局限性，但替代方法也都有缺点。
Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004–5. Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.