  Reliable estimators of the spatial distribution of socio-economic indicators
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
random forest-based methods 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 aggregated
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. The performance of the proposed point estimator and its
uncertainty measure is studied in model-based simulations. 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.
