DAGStat 2022 - Analysing Opportunity Cost of Care Work using MERFs under Aggregated Census Data

Abstract

Reliable estimators of the spatial distribution of socio-economic indicators (like opportunity cost of care work) are essential for evidence-based policy-making. As sample sizes are small for highly disaggregated domains, the accuracy of the direct estimates is reduced. To overcome this problem small area estimation approaches are promising. In this work we propose a small area methodology using machine learning methods. The semi-parametric framework of mixed effects random forest combines the advantages of random forests (robustness against outliers and implicit model-selection) with the ability to model hierarchical dependencies. Existing model-based methods using random forest for small area estimation require access to auxiliary information on population-level. We present a methodology that deals with the lack of population micro-data. Our strategy adaptively incorporates auxiliary information through calibration-weights - based on empirical likelihood - for the estimation of area-level means. In addition to our point estimator, we provide a non-parametric bootstrap estimator measuring its uncertainty. Extensive model-based simulations show the advantages of the proposed point estimator and its uncertainty measure. Finally, the proposed methodology is applied to the 2011 Socio-Economic Panel and aggregate census information from the same year to estimate the average opportunity cost of care work for 96 regional planning regions in Germany.

Dr. Patrick Krennmair

Research Associate in Applied Statistics

I am working as a research associate at the Chair of Applied Statistics at Freie Universität Berlin and as a consultant for the statistical consulting unit fu:stat.