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

  1. Jussi Tervola  Is a corresponding author
  2. Joonas Ollonqvist
  1. Finnish Institute for Health and Welfare (THL), Finland
Research article
Cite this article as: J. Tervola, J. Ollonqvist; 2026; Refining the simulation of social assistance with monthly income data and calibration; International Journal of Microsimulation; 19(2); 22-36. doi: 10.34196/ijm.00341
3 figures and 5 tables

Figures

Over- and undercoverage of social assistance recipients using different simulation methods in 2022, and root mean square error compared to actual data.
(a-b) Distribution of social assistance recipient households by income decile and number of benefit months according to data, simulation with official model, and simulation with refined model.
The impact of the 2024-2025 reforms on total disposable income by income decile, simulated with the official and the refined model.

Tables

Table 1
Sources of error in simulating social assistance by the direction of the bias.
UndersimulationOversimulationIndefinite
  • Use of annual income

  • Non-take-up

  • Inaccuracies in simulating first-tier benefits

  • Incomplete data on reimbursed costs and payments (e.g. OOP payments)

  • Overcoverage of population registers (e.g. under-reported emigration)

  • Discretionary decisions are not modelled

  • Inaccurate consideration of the family unit definition in SA (e.g. adult children living in the same household)

  • Incomplete asset data (esp. savings)

  • Changes in household composition or housing costs during the year are not observed

  • Priority treatment of garnished income not considered

  • Tax refunds not included

  • Survey-related biases such as sampling error, errors in survey design and recall bias

  • Taxes simulated according to final taxation, not withholding taxation

  • Sanctions not modelled

  • No data on gifts and informal support

Table 2
Methods for monthly allocation by income item.
Income itemAllocation to monthly level
Unemployment benefitsObserved at monthly level
Part-time labour incomeObserved at monthly level
Study grants and loansEstimated based on additional information
Sickness and parental benefitsEstimated based on additional information
Full-time labour incomeEstimated based on additional information
PensionsEstimated based on additional information
Child benefitsEstimated based on additional information
Capital incomeAnnual average (no allocation)
TaxesAnnual tax rate (applied to monthly income)
General housing allowanceAllocated through simulation
Social assistanceAllocated through simulation
Table 3
Differences between results from the refined model and the official model compared to data reference values in 2022.
DataDifference with dataDifference, calibrated vs. official model
Official modelCalibrated model
Social assistanceExpenditure, M€670+38 %0 %-28 %
Households231 000-3 %0 %+2 %
Indiviudals392 000-14 %-6 %+9 %
General housing allowanceExpenditure, M€1 576-4 %+3 %+7 %
Households504 000-15 %+2 %+21 %
Gini29.2-0.2-0.1+0.1
Median income, eqv. €/yr27 614-202-179+23
AROP rate (60), %13.6+0.4+0.3-0.1
AROP rate (50), %6.8-0.20.0+0.2
AROP gap (60), %16.7-1.3-0.6+0.7
AROP gap (50), %21.2-7.2-5.5+1.7
AROP rate (60), age less than 18, %11.9+0.8+0.2-0.6
AROP rate (60), age 65+, %14.0-0.2-0.1+0.1
Income share of the 1st decile, %3.5+0.4+0.2-0.2
Table 4
The estimated effects of the reform on benefit expenditure and distributional indicators with the official and refined model.
Official modelRefined modelSensitivity test:
Change of overcoverage rate
+20%-20%
Social assistanceExpenditure, M€+313+182+122+242
Households (1000)+50+26+8+43
Individuals (1000)+81+37+12+62
Housing allowanceExpenditure, M€-885-972-972-972
Households (1000)-218-251-251-251
Gini index+0,4+0,6+0,6+0,6
Median income, €/yr+292+270+269+271
AROP rate, %Total pop (60)+1,7+1,9+2,0+1,9
Total pop (50)+1,3+1,8+1,9+1,8
Age less than 18 (60)+1,7+2,7+2,7+2,6
Age 65+ (60)+0,4+0,4+0,4+0,4
AROP gap, %Total pop (60)+1,2+2,6+2,7+2,5
Income share of the 1st decile, %-0,2-0,3-0,3-0,3
  1. Note: In the sensitivity analysis the overcoverage rate after the reform is assumed to be 20% higher or lower than in the reference-year calibration. The increased overcoverage reflects a scenario where oversimulation rises, for example due to unobserved wealth among new recipients, while the reduced overcoverage can reflect higher take-up if increased benefit amounts spur higher claiming probabilities.

Table A1
Coefficients of the logistic regression model analysing overcoverage (1/0) among the simulated social assistance (SA) recipients.
βp
Intercept4.600.00
Simulated months of SA (1–12)-0.120.00
SA as % of hh income, avg per month-0.020.00
Socio-economic position (ref=unknown)
 Upper-level office workers0.810.00
 Lower-level office workers0.230.00
 Employees-0.440.00
 Students0.440.25
 Pensioners-1.010.00
 Other-1.170.00
Household life stage (ref=single parents)
 Single, less than 35 years-0.140.00
 Single, 35–64 years0.450.00
 Single, 65+ years-0.200.00
 Couple, less than 35 years0.250.00
 Couple 35–64 years0.190.00
 Couple, 65+ years0.100.56
 Family with 2 parents and children-0.030.00
 Other-0.240.00
Disposable income (ref=more than 7500 €/year)
 Less than 1500 €3.490.00
 1500–4500 €0.240.00
 4500–7500 €-1.000.00
Income decile (ref=I)
 II-0.460.00
 III-0.510.00
 IV-0.330.00
 V-X0.670.00
Migrated during the data year (0/1)0.480.00
Institutionalized (0/1)2.110.00
Debt for execution (0/1)-0.690.00
Immigrant (0/1)-0.500.00
Rental income (0/1)2.380.00
Extra adults in the household (0/1)-0.490.00

Data and code availability

Code is available by request. Access to microdata requires pertinent data licenses from Finnish Social and Health Data Permit Authority (Findata).

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