Analysing opportunity cost of care work using mixed effects random forests under aggregated auxiliary data
提出一种无需个体层面人口数据、仅用聚合辅助信息的小区域估计方法，用于从德国社会经济面板和普查数据估算照护工作的机会成本。
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 unit-level 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.