Data and the Future of the Welfare State
Through DTSEN, the DTKS data, Regsosek, and P3KE are consolidated into a single national database to determine who is eligible to receive social assistance. The government wants to ensure that assistance no longer overlaps, is more accurately targeted, and reaches the people most in need.
Amid the complexity of social problems and fiscal constraints, a data-based approach seems rational. The question is whether the future of the welfare state can be built on data precision alone.
The Welfare State Growing More Digital
In recent years, welfare governance in Indonesia has increasingly moved toward a digital and data-based approach. Citizens are classified into welfare deciles, verified through digital systems, then mapped according to their vulnerability level.
The state is striving to render poverty something that can be calculated, predicted, and intervened administratively.
This approach is not without reason. For years, Indonesia’s social assistance policy has faced classic problems, ranging from duplicate data, exclusion errors, inclusion errors, and mis-targeting.
In this context, DTSEN is promoted as a national data integration solution capable of improving the quality of social policy. The government even emphasises that data updates are carried out periodically and across sectors to improve the accuracy of recipients.
On one hand, this transformation is worth appreciating as it demonstrates progress in welfare state governance. The state becomes more proactive, systematic, and measured in reaching vulnerable groups.
Various assistance programmes are now designed to follow the life cycle of citizens, from pregnant mothers, schoolchildren, productive-age adults, to the elderly. The state no longer appears sporadically, but begins to build a more integrated social protection system.
But at this point a more fundamental question arises: is society sufficiently understood through data?
When Poverty Becomes an Administrative Object
James C. Scott, in Seeing Like a State (1998), explains that modern states tend to make society easier to ‘read’. In the DTSEN context, that logic is evident. The state aims to translate the complexity of social life into numbers, decile categories, and welfare indicators.
The problem is that social life cannot always be simplified into statistics. Poverty is often far more fluid than the administrative definitions used by the state.
There are families who are formally considered not poor, but live in highly vulnerable conditions. There are informal workers whose earnings are only slightly above the poverty line, but can fall into poverty at any time due to illness, layoffs, or an economic crisis.
In the Pocket Book of the Welfare Support Programme 2026, the extreme poverty line is set at below Rp 391,000 per month per capita. Statistically, a person may be considered no longer in poverty when they are slightly above that figure. Yet, are their lives truly safe from vulnerability?
This is where we see how data-driven governance risks creating an illusion of precision. The state feels it knows society better because it has more complete data. In fact, citizens’ lived experiences are often far more complex than what appears on policy dashboards.
DTSEN also reveals an interesting shift in the face of the modern welfare state. The state no longer merely distributes social assistance, but begins to build a data-based welfare ecosystem. Aid is mapped, verified, and calculated digitally. Citizens gradually become part of an increasingly integrated social administration system.
This transformation shows how the welfare state moves toward what can be called a data state, i.e., a state that relies on data integration as the main foundation for social policy decisions.
Preserving the Human Dimension in Social Policy
Such an approach can indeed improve bureaucratic efficiency. However, there are risks when the state becomes overly confident that social problems can be solved mainly through technology and data management. Critics of this kind were voiced by Evgeny Morozov in To Save Everything, Click Here (2013) through the concept of technological solutionism, the belief that the complexity of social problems can be resolved primarily with technological solutions.
In fact, the roots of poverty are often structural. Poverty is closely linked to job quality, unequal access to education, asset ownership, low wages, and limited social mobility. Data can help the state identify the poor, but data does not automatically change the structures that produce poverty.
What is interesting about the Pocket Book of the Welfare Support Programme 2026 is how the state attempts to demonstrate its presence concretely through the details of the social assistance received by families. From a policy transparency perspective, this approach is certainly important. Citizens can see the tangible form of state intervention.
However, social policy cannot stop at administrative calculations alone. Poverty is not only about who falls into a particular decile or who passes the verification for assistance. Poverty is a lived experience of limitation, uncertainty, and vulnerability experienced daily by people.
Therefore, strengthening DTSEN is indeed important as a foundation for more accurate social policy. However, the future of the welfare state cannot be built solely on data-precision logic. Data does help the state read society, but understanding poverty requires something more than mere numbers and administrative categories.
At this point, human-centric governance becomes important.