Dataset Information

Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata.

ABSTRACT: In this study, we produce a valid and consistent variable for socioeconomic status at the household level with census microdata from ten developing countries available from the Integrated Public Use Microdata Series - International (IPUMS-I), the world's largest census database. We use principal components analysis to compute a wealth index based on asset ownership, utilities, and dwelling characteristics. We validate the index by verifying socioeconomic gradients on school enrollment and educational attainment. Given that the availability of socioeconomic indicators varies considerably across samples of census microdata, we implement a stepwise elimination procedure on the wealth index to identify the conditions that produce an internally consistent index. Using the results of the stepwise methodology, we propose which indicators are most important in measuring household socioeconomic status. The development of the asset index for such a large archive of international census microdata is a very useful public resource for researchers.

SUBMITTER: Davila RL

PROVIDER: S-EPMC9603640 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata.

Davila Rodrigo Lovaton RL McCarthy Aine Seitz AS Gondwe Dorothy D Kirdruang Phatta P Sharma Uttam U

Journal of demographic economics 20210303 1

In this study, we produce a valid and consistent variable for socioeconomic status at the household level with census microdata from ten developing countries available from the Integrated Public Use Microdata Series - International (IPUMS-I), the world's largest census database. We use principal components analysis to compute a wealth index based on asset ownership, utilities, and dwelling characteristics. We validate the index by verifying socioeconomic gradients on school enrollment and educat ...[more]

PMID: 36313399

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Project description:BackgroundNowadays, the burden of non-communicable diseases including obesity has been an increasing public health concern. This menace can be monitored using indexing method like body mass index. Studies indicate that socioeconomic indicators such as income, biology, behavior, and demographic factors associated with body mass index. In Ethiopia, few studies associate wealth index with body mass index in people of ages between 15 and 49. This study was aimed to assess the association of body mass index with wealth index, and behavioral and sociodemographic population characteristics.MethodsA cross-sectional population-based study was conducted using the 2016 Ethiopian Demographic and Health Survey population of ages 15-49. A total of 10,245 individuals were considered to detect the effect of socioeconomic, biological, behavioral, and demographic factors on body mass index using logistic regression.ResultsThe prevalence of underweight, overweight, and obesity among men are 23.8%, 6.6%, and 2.0%, respectively, which is lower than that of women (underweight 25.3%, overweight 9.1%, and obesity 2.9%). The poorest men had higher odds of being underweight (adjusted odds ratio = 2.395%; 95% confidence interval = 2.020-3.544) as compared to the richest men. Merchants and government employees have lower odds (adjusted odds ratio = 0.744; 95% confidence interval = 0.588-0.899) compared to men whose occupation is farming and labor, indicating that merchants and government employees are more likely to become overweight and obese compared to men who are farmers and laborers.ConclusionIt is concluded that wealth index is an important socioeconomic determinant of body mass index among men and women of age 15-49 in Ethiopia. A high prevalence of underweight, and overweight, and obesity is observed, which increases instances of non-communicable diseases. Effects of socioeconomic, biological, behavioral, and demographic indicators on body mass index differed according to sex.

Project description:BackgroundThe DHS wealth index?-?based on a statistical technique known as principal component analysis?-?is used extensively in mainstream surveys and epidemiological studies to assign individuals to wealth categories from information collected on common assets and household characteristics. Since its development in the late nineties, the index has established itself as a standard and, due to its ease of use, has led to a large and welcome increase in the analysis of inequalities. The index is, however, known to present some serious limitations, one being a bias towards patterns of urban wealth: the so-called "urban bias".MethodsWe use 10 data sets - 5 MICS (Multiple Indicator Cluster Survey), 4 DHS (Demographic and Health Survey) and one HBS (Household Budget Survey) - to demonstrate that urban bias continues to be a prominent and worrying feature of the wealth index, even after several methodological changes implemented in recent years to try to reduce it. We then propose and investigate an approach to improve the performance of the index and reduce the urban bias. This approach involves the use of ordinal rather than dummy variables, of a polychoric instead of a product-moment correlation matrix, and the use of two principal components rather than one. These approaches are used jointly to produce the polychoric dual-component wealth index (P2C).ResultsThe P2C index enables a larger proportion of the variance of the asset variables to be accounted for, results in all assets contributing positively to the wealth score, exploits added analytical power from ordinal variables, and incorporates the extra dimension of wealth expressed by the second principal component. It results in a better representation of typically rural characteristics of wealth and leads to the identification of more plausible distributions of both the urban and rural populations across wealth quintiles, which are closer to expenditure quintiles than the standard DHS index.ConclusionsThe P2C wealth index can be easily applied to mainstream surveys, such as the MICS and DHS, and to epidemiological studies; it yields more credible distributions of rural and urban subpopulations across wealth quintiles. It is proposed as an alternative to the DHS wealth index.

Dataset Information

Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata.

Publications

Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata.

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