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. 2022 Mar;88(1):79-120.
doi: 10.1017/dem.2020.27. Epub 2021 Mar 3.

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

Rodrigo Lovaton Davila  1 , Aine Seitz McCarthy  2 , Dorothy Gondwe  3 , Phatta Kirdruang  4 , Uttam Sharma  5
Affiliations

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

Rodrigo Lovaton Davila et al. J Demogr Economics. 2022 Mar.

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.

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Figures

Figure A1.1:
Figure A1.1:. Botswana Census 2001, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A1.2:
Figure A1.2:. Botswana Census 2001, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A2.1:
Figure A2.1:. Brazil Census 2000, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A2.2:
Figure A2.2:. Brazil Census 2000, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A3.1:
Figure A3.1:. Cambodia Census 1998, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A3.2:
Figure A3.2:. Cambodia Census 1998, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A4.1:
Figure A4.1:. Dominican R. 2002, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A4.2:
Figure A4.2:. Dominican R. 2002, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A5.1:
Figure A5.1:. Panama Census 1980, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A5.2:
Figure A5.2:. Panama Census 1980, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A6.1:
Figure A6.1:. Peru Census 1993, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A6.2:
Figure A6.2:. Peru Census 1993, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A7.1:
Figure A7.1:. Senegal Census 2002, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A7.2:
Figure A7.2:. Senegal Census 2002, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A8.1:
Figure A8.1:. S. Africa Census 1996, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A8.2:
Figure A8.2:. S. Africa Census 1996, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A9.1:
Figure A9.1:. Thailand Census 2000, Cronbach alpha and Spearman rank correlations
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure A9.2:
Figure A9.2:. Thailand Census 2000, School enrollment regressions
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure 1.1:
Figure 1.1:. Colombia Census 2005, Cronbach alpha and Spearman
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International.
Figure 1.2:
Figure 1.2:. Colombia Census 2005, School enrollment regressions1/
Data source: Minnesota Population Center, Integrated Public Use Microdata Series (IPUMS) - International. 1/ Regressions include controls for child’s sex, age, and age squared, household head’s sex, age, and educational attainment, urban/rural status and dummies for highest level of geography for each country.
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References

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