Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System - Next Generation (SIKS-NG)

Abstract

Malang Regency is the second-largest region in East Java with a diversity of geographical, social, and economic conditions. Data from the Malang Regency Social Service in 2018 shows that the number of impoverished families in Malang Regency reached 230,081 families. The highest number of impoverished families is found in the Dampit District, totaling 12,053 families, while the lowest is in the Kromengan District with 3,061 families. This indicates that the Dampit District has a significant number of impoverished families, surpassing other districts in Malang Regency. This makes the Dampit District a relevant location for research on poverty and its impacts. This research has dual objectives: first, to provide reliable, accurate, and valid information on existing poverty indicators, which will serve as a strong foundation for supporting poverty alleviation programs in Malang Regency. Second, this research aims to formulate strategies in the form of activity guidelines that will enhance the competence of employees in conducting poverty data collection, ultimately supporting poverty alleviation efforts. Data collection methods include interviews, documentation, observation, and Focus Group Discussions to obtain more in-depth information. In-depth interviews were conducted using purposive sampling techniques involving stakeholders related to poverty alleviation in the Dampit District Office, Malang Regency. The analysis results indicate that the government of the Dampit District and the villages in the area have not fully succeeded in implementing the Next Generation Social Welfare System (SIKS-NG) optimally. The poverty data collection has predominantly focused on collecting data on the poor only. Therefore, improvements in the approach and implementation of SIKS-NG are needed to enhance the effectiveness of poverty alleviation programs. The findings of this research highlight the importance of a participatory approach in collecting poverty data, involving village governments, utilizing information infrastructure like BDT, and the data consolidation process to ensure the effectiveness of poverty alleviation programs. Additionally, appropriate training for data collection teams is crucial in improving the accuracy of poverty data.