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Improving Targeting of Conditional Cash Transfer Programs
Through Posterior Simulation and Multilevel Modeling
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Conditional Cash Transfer (CCT) programs are a recent and popular approach in developing countries to improve health and education prospects of poor families through conditional financial incentives. However, due to sub-optimal transfers and administrative difficulties in identifying needy families, many CCT programs suffer from incomplete coverage of needy families and/or low financial efficiencies. De Janvry and Sadoulet (2003) have shown that their targeting method (the DJS method), which uses binary regression models to identify needy families and optimal transfer amounts, can effectively overcome these problems. This dissertation tackles two issues encountered in implementing the DJS method: the need for an appropriate methodology to compare binary models, and the need to develop binary models with high external validity. For targeting of CCT programs, binary models need to be analyzed comprehensively beyond the overall goodness-of-fit. For example, policy makers may need to know the rates of false positive and false negative predictions of various models and choose the most appropriate model for given policy priorities.
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