BPS Attributes Decile Polemic to Inaccurate Data
Outdated Records Drive Errors in Indonesia’s Welfare Decile Data, BPS Says
Kabarsaji.com – Indonesia’s welfare classification system still faces significant data-quality challenges, with outdated records contributing to cases where people are placed in decile groups that do not reflect their current economic conditions. The issue matters because decile rankings are used to help identify households eligible for social assistance programs.
Setia Pramana, Director of Statistics Methodology and Data Science at Statistics Indonesia (BPS), said data updates are central to improving the accuracy of the Socio-Economic Single Data, known as DSTEN. Without timely revisions, a person’s recorded welfare position may differ from their actual circumstances.
“So, there are instances that do not match an individual's true decile because the data is not yet accurate,” Setia said after a discussion on decile classification and poverty measures at the STIS Statistics Polytechnic in East Jakarta on Friday, September 11, 2026.
Deciles are broader than spending figures
Deciles divide households into welfare groupings, with each category representing a portion of the population. In the current debate, deciles one through four are especially important because they generally cover the lowest 40 percent of households by economic welfare and are commonly linked to eligibility for government assistance.
BPS does not assign those classifications by looking only at how much a household spends. Setia explained that expenditure estimates are part of the assessment, but the methodology also draws on more than 40 indicators. These include housing quality, ownership of assets, household composition, and access to sanitation.
This wider approach is intended to provide a more complete picture of living conditions. A household’s welfare cannot always be understood through one measure alone, particularly when income, spending patterns, basic services, and assets may point in different directions. At the same time, a multi-variable model depends heavily on accurate and current information for every factor it uses.
Regional differences also shape how decile classifications should be understood. BPS has stressed that a decile is a relative welfare measure, not a permanent spending cutoff that applies identically across Indonesia. Economic conditions and household characteristics can vary substantially between regions, meaning a single expenditure figure cannot fully explain why a household is placed in a particular group.
Error rate has fallen, but gaps remain
Setia acknowledged that both the data and the calculation models still leave room for mistakes. The current inclusion and exclusion error rate is 23 percent. In practical terms, this means that some people who should not receive support may appear on beneficiary lists, while some households that meet the criteria may not be included.
Although the figure remains substantial, it marks an improvement from the previous error rate of 37 percent. The reduction indicates progress in the effort to improve targeting, yet it also highlights why regular verification and data renewal remain important for public assistance programs.
Several conditions can create mismatches in the system. Old records may no longer reflect changes in a family’s financial situation. A household can also be classified incorrectly, while difficult terrain and geographic barriers can complicate the collection and confirmation of information. These issues can be especially consequential when data must be gathered across diverse areas with differing levels of access.
Setia also identified another major source of discrepancies: cases in which the intended number of aid recipients exceeds the available quota. When demand or distribution targets are larger than the established allocation, the gap can create pressure on the targeting process and intensify public concern over who is included or left out.
Public controversy over lawmakers’ classifications
The reliability of decile data came under scrutiny after reports that several members of the House of Representatives, or DPR, appeared in decile four. That grouping is associated with the bottom 40 percent of the welfare distribution, making the listings difficult for many members of the public to reconcile with the lawmakers’ positions.
The controversy illustrated a broader concern: a welfare database must be responsive to changes in people’s lives if it is to support fair distribution of assistance. A classification may be based on information collected at an earlier time, while a household’s current situation may have changed by the time the data is used for policy decisions.
For families seeking aid, an inaccurate classification can have direct consequences. Being wrongly placed outside an eligible group may delay or prevent access to support. Conversely, incorrect inclusion can reduce the effectiveness of limited assistance budgets by directing benefits to people who may not be the intended priority.
Work continues on data and statistical models
BPS, the Ministry of Social Affairs, and other ministries are continuing efforts to reduce weaknesses in both the underlying records and the statistical methods used to process them. The work includes improving the model and ensuring the data feeding into it is updated more accurately.
“How we can refine the model and obtain accurate, updated data remains our ongoing homework,” Setia said.
The challenge is not simply to produce a list of recipients, but to maintain a system that can reflect changing household conditions over time. Welfare classifications may need revision when employment, living arrangements, access to services, assets, or other relevant circumstances change.
The latest discussion around DSTEN has reinforced the importance of data maintenance alongside data collection. Better targeting depends on reducing outdated entries, correcting classification errors, and making sure that the criteria used by public institutions are applied with sufficient sensitivity to local conditions.
As agencies work to lower the remaining error rate, the central test will be whether social assistance can reach households that genuinely need it while minimizing the number of people incorrectly included or excluded. The decline from 37 percent to 23 percent shows improvement, but the continuing decile debate makes clear that accuracy remains a major task for Indonesia’s welfare data system.
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