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

TL;DR

This paper introduces a novel mixed effects random forest approach for small area estimation that uses aggregated auxiliary data instead of detailed population data, demonstrated through estimating opportunity costs of care work in Germany.

Contribution

The paper presents a new method combining mixed effects random forests with calibration-weights that avoids the need for unit-level auxiliary data, enhancing practical applicability.

Findings

Effective estimation of opportunity costs using aggregated data

Method performs well in simulation studies

Applicable to small area estimation problems

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.