This paper uses quantitative methods to measure the impacts from selected shocks and evaluates the sensitivity of different welfare indicators to those shocks. The data sets from 2004--05 household and community surveys in the Central Highland of Angola are used for this study. Oaxaca decomposition analysis is conducted to examine how much of the differential in the values of welfare indicators can be explained by group differences in characteristics, and how much may be due to shock impacts. This study then examines the sensitivity of each welfare indicator to different shocks and illustrates the distribution of shock impacts on indicators using the nonparametric density estimation method. The inferences from the above study are later verified with the help of Artificial Neural Network (ANN) analysis. Suitable welfare indicators for the vulnerability assessment under each selected shock are identified. The study results suggest that there is no all-purpose welfare indicator in existence for vulnerability analysis. The groups of population at risk of falling below the poverty line or having their poverty status deteriorated with shock impacts are identified using the welfare indicators with ex ante data. The methods used in this paper are experimental yet innovative in vulnerability study. They are expected to improve the efficiency and accuracy of vulnerability assessment, and to facilitate aid/after-shock relief distribution and policy making / acase@tulane.edu
| Identifer | oai:union.ndltd.org:TULANE/oai:http://digitallibrary.tulane.edu/:tulane_27130 |
| Date | January 2010 |
| Contributors | Lin, Yingge (Author), Mock, Nancy B (Thesis advisor) |
| Publisher | Tulane University |
| Source Sets | Tulane University |
| Language | English |
| Detected Language | English |
| Rights | Access requires a license to the Dissertations and Theses (ProQuest) database., Copyright is in accordance with U.S. Copyright law |
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