MOBILIZING POVERTY ALLEVIATION THROUGH A COMMUNITY-BASED DATA MANAGEMENT MECHANISM: A CASE STUDY OF PHANOM DONG RAK DISTRICT, SURIN PROVINCE

Abstract

This research aimed to: 1) Develop the capacity of local volunteers to become community data managers responsible for identifying, verifying, and surveying the livelihood assets of poor households in Surin Province; and 2) Promote participatory community learning processes for poverty data management and contextual poverty analysis. The study employed an action research approach integrating experiential learning (Kolb, 2015) and Design Thinking. The research instruments consisted of: 1) A community data manager training curriculum, 2) A poor household survey questionnaire, and 3) Experiential learning workshop guidelines. The research process was conducted through five stages: curriculum development, capacity preparation, participatory learning workshops, the application of Design Thinking for poverty analysis and problem-solving, and the improvement of local welfare and assistance systems. The purposive sample consisted of 107 local volunteers from Phanom Dong Rak District, Surin Province. The findings revealed that most volunteers were female and aged between 40 - 49 years. The participants were successfully transformed into “community data managers” capable of surveying 2,331 poor households across 55 villages. The study also identified significant discrepancies in government poverty databases, including 607 excluded households (35.21%) and 396 inclusion-error households (22.97%), reflecting limitations in the existing centralized poverty classification system. Furthermore, the participatory learning process enabled community members to analyze poverty conditions through five dimensions of livelihood assets: human, social, financial, physical, and natural capital. This process contributed to linking community data with local decision-making and targeted welfare assistance mechanisms. The findings suggest that community-based data management mechanisms can enhance the accuracy and timeliness of poverty data and strengthen area-based poverty alleviation efforts through participatory and context-sensitive approaches.