Using GIS and Spatial Statistics to Target Poverty and Improve Poverty Alleviation Programs: A Case Study in Northeast Thailand

Abstract

Various poverty alleviation programs have helped reduce poverty in Thailand, yet the poverty gap still remains, specifically in rural areas in the north and northeast of the country. The major barrier to poverty alleviation policies and strategies is the weakness of identifying where the poor are, thereby targeting poverty interventions. This paper investigates the potential of descriptive statistics, the geographic information system (GIS), and spatial autocorrelation in recognizing poverty association of a site selected in the northeast Thailand, including identifying factors that influence rural poverty, and investigating underlying factors and spatial associations of poverty at the rural household level. Results showed that 70% of the households sampled in the study area were poor, and nearly half of their income generated was from farming. Factors influencing farm income were examined by regression statistics and it was found that farm income is related to area cultivated, rice yield, livestock and learning experience of farmers. It was demonstrated that GIS is a useful tool to identify environmental factors that influence poverty and spatial autocorrelation is an effective method in revealing similarities and dissimilarities of poverty in household units. Use of these two technologies to identify factors underlying rural poverty was analyzed and possible use of the findings in poverty alleviation programs was presented. Drawbacks and limitations in Thailand’s poverty alleviation plans and programs were discussed and suggestions were made to improve these programs using GIS and spatial autocorrelation.