Refining the simulation of social assistance with monthly income data and calibration

As a last-resort benefit, social assistance can cushion the impact of cuts in other benefits on low-income households. Therefore, it may play a crucial role for the effects of benefit cuts on income distribution. Measurement errors in its simulation can significantly affect the estimated distributional indicators. At the same time, simulating social assistance is challenging because it is influenced by many factors, not all of which are typically observed in the data or captured with an existing simulation model. In this study, we refine the simulation of social assistance in the Finnish static tax-benefit model SISU by considering monthly fluctuations in household income. Moreover, we use regression-based calibration technique to account for oversimulation caused by, among other things, non-take-up and unobserved household wealth. Using the refined simulation model, we estimate that social assistance compensates the substantial cuts to housing and unemployment benefits in Finland 2024-2025 much less than without refinements. Based on the analysis, the reforms would increase the number of recipient households of social assistance by approximately 26,000 households – a substantial downward revision from the pre-refinement estimate of 50,000 households. At the same time, the benefit cuts are estimated to increase at-risk-of-poverty rates and income inequality more than without the refinements. The increase of the child at-risk-of-poverty rate are 60% larger than before refinements. Although demonstrated in a Finnish context, the results underline the importance of accounting both monthly income variation and oversimulation when simulating means-tested benefits also in other contexts.