In this study, we produce a valid and consistent variable for socioeconomic status (SES) 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 SES. The development of the asset index for such a large archive of international census microdata is a very useful public resource for researchers.

The objective of this paper is to determine if achieving the Millennium Development Goals (MDGs) for a country has an impact on the geographical disparities, for some selected indicators, within the country. IPUMS-I data provide variables comparable between countries (while the sources traditionally used by the United Nations are not) and allow examining differences for lower levels of geography and between urban and rural settings. We measure indicators related to education, gender equality, and maternal health. For all of them, we will be primarily interested in disparities by gender across geographical units. Furthermore, we will track changes for these indicators for all censuses available since the 1990's round. Finally, we explore demographic factors related to higher disparities for these indicators.

This research aims to develop a reliable and robust measure for socioeconomic status at the household level using census data available from the Integrated Public Use Microdata Series- International (IPUMS-I), the world's largest census database. First, we use principal component analysis to compute a wealth index based on housing characteristics and asset ownership, with separate measures for rural and urban areas. The validation strategies include comparing our proposed index with the widely used Demographic and Health Survey (DHS) wealth indices and then verifying the predictive power of our index on selected outcomes. Moreover, we attempt to identify general conditions necessary to produce a robust asset index based on census data. Preliminary results based on the wealth indices from four separate samples reveal estimates comparable to the DHS indices. Conditioned on the reliability of the results, this measure will be included in the IPUMS-I database for further research.