The global poverty count uses a common global poverty line, often referred to as the dollar-a-day line, currently $1.25 at 2005 international prices, whose construction and application depends on purchasing power parity (PPP) exchange rates for consumption. The price indexes that underlie the PPPs used for this purpose are constructed for purposes of national income accounting, using weights that represent patterns of aggregate consumption, not the consumption patterns of the global poor. We use household surveys from 62 developing countries to calculate global poverty-weighted PPPs and to calculate global poverty lines and new global poverty counts. (JEL C43, E21, F31, I32, O15)

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To date, the nutrition community's role in most HCES has been as a passive user of secondary data. The nutrition community must become more involved in the design, implementation, and analysis of HCES by identifying criteria for prioritizing countries, establishing assessment criteria, applying the criteria in retrospective assessments, identifying key shortcomings, and recommending alternatives to ameliorate the shortcomings. Several trends suggest that this is a propitious time for improving the relevance and reliability of HCES.

The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.

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