Nowcasting the BELMOD input dataset: Comparing techniques for an administrative dataset

The BELMOD microsimulation model of the Belgian Federal Public Service Social Security is based on an administrative input dataset. While the model’s policy simulations are updated twice a year, the most recent input dataset currently available refers to 2019. As a result, the discrepancy between the input data and the present situation has grown to several years. This gap can be reduced through nowcasting methods, by updating the outdated input data with more recent information to bring it more in line with the present situation, thereby making the data more suitable for simulations relating to the most recent years. We applied nowcasting to the BELMOD input dataset by incorporating both demographic changes and changes in individuals’ labour market status. To assess which nowcasting approach is most suitable for BELMOD, we developed and compared three different methods. In the first two methods, labour market status transitions are modelled using, respectively, a parametric and a non-parametric approach. For individuals experiencing a labour market status transition, the relevant variables in the dataset are subsequently adjusted. In addition, demographic changes are incorporated in these two methods through reweighting. In the third method, both labour market status and demographic changes are implemented through reweighting. We validated these three methods using external statistics on the number of income and benefit recipients, as well as aggregate income and benefit amounts.