4.3 Million New Beneficiaries Added to Social Assistance List

The Ministry of Social Affairs (Kemensos) has recorded approximately 4.3 million new Beneficiary Families (KPM) entering the social assistance recipient list following the utilisation of the Single Socio-Economic and National Data (DTSEN).

Andy Kurniawan, Expert Staff to the Minister of Social Affairs for Planning and Strategic Policy Evaluation, stated that the DTSEN update is necessary to respond to changing socio-economic conditions and various discrepancies found in the field. He noted that all feedback, including from the media, is being addressed as part of the data refinement process during a discussion on poverty measurement methodology at the STIS Polytechnic in Jakarta.

According to Andy, the use of DTSEN provides a broader basis for assessing the socio-economic status of the population. A significant result of this is the inclusion of citizens who were previously unrecorded as social assistance recipients, made possible through the use of decile-based classification.

The DTSEN update is also linked to the process of determining deciles, which serves as a primary consideration for the government in setting social protection programme priorities. Andy added that the government remains open to academic discussions and external input to refine the methodology and the DTSEN system.

Prof. Setia Pramana, Director of Methodology, Statistics, and Science Data at the Central Bureau of Statistics (BPS), explained that the DTSEN is constructed by integrating several government data sources, including DTKS, Regsosek, and P3KE. Each source has different characteristics, making integration a vital part of building the socio-economic database.

Setia emphasised that data must be continuously updated to reflect changes in the population, such as changes in domicile or deaths. In determining deciles, BPS ranks welfare based on socio-economic characteristics rather than income alone, as direct income information is not available for all households.

To address this, BPS utilises Susenas data, which contains household expenditure information, to build estimation models using the Proxy Means Test (PMT) approach and machine learning. These models are developed at the regency/city level to account for regional socio-economic differences.

The results of this modelling are used to establish national socio-economic rankings. Prof. Setia noted that the process is under continuous evaluation to improve model accuracy and reduce errors arising from both the model and data quality, ensuring the database accurately reflects the evolving socio-economic landscape.