Abstract
We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices,
which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian
approach which yields probabilistic (confidence type) intervals for the point estimates of country scores—a vital, and often
missing, feature in cross-national comparisons. We demonstrate our approach using the United Nations Development Programme’s
Millennium Development Goals (MDGs), via the Maternal and Neonatal Program Effort Index (MNPI) data (Ross et al. in Trop Med
Inter Health 6(10):787–798, 2001), and Human Development Index (HDI) (2010) as examples.
which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian
approach which yields probabilistic (confidence type) intervals for the point estimates of country scores—a vital, and often
missing, feature in cross-national comparisons. We demonstrate our approach using the United Nations Development Programme’s
Millennium Development Goals (MDGs), via the Maternal and Neonatal Program Effort Index (MNPI) data (Ross et al. in Trop Med
Inter Health 6(10):787–798, 2001), and Human Development Index (HDI) (2010) as examples.
- Content Type Journal Article
- Pages 1-27
- DOI 10.1007/s11205-011-9946-y
- Authors
- Kobi Abayomi, ISyE, Statistics, Georgia Institute of Technology, 765 Ferst Dr., 444 Groseclose, Atlanta, GA 30332, USA
- Gonzalo Pizarro, MDG Support Team, Poverty Group, BDP UNDP, FF Building, 304 E 45th St. of 1052, New York, NY 10017, USA
- Journal Social Indicators Research
- Online ISSN 1573-0921
- Print ISSN 0303-8300