Analysing Opportunity Cost of Care Work using Mixed Effects Random Forests under Aggregated Auxiliary Data

Abstract

Evidence-based policy-making requires reliable, spatially disaggregated indicators. The framework of mixed effects random forests leverages the advantages of random forests and hierarchical data in small area estimation. These methods require typically access to auxiliary information on population-level, which is a strong limitation for practitioners. In contrast, our proposed method - for point and uncertainty estimation - abstains from access to unitlevel population data but adaptively incorporates aggregated auxiliary information through calibration-weights. We demonstrate its usage for estimating opportunity cost of care work for Germany from the Socio-Economic Panel and census aggregates. Simulation studies evaluate our proposed method.