  This paper proposes a methodology to obtain estimates in small domains when
the target is a composite indicator. These indicators are of utmost importance
for studying multidimensional phenomena, but little research has been done on
how to obtain estimates of these indicators under the small area context.
Composite indicators are particularly complex for this purpose since their
construction requires different data sources, aggregation procedures, and
weighting which makes challenging not only the estimation for small domains but
also obtaining uncertainty measures. As case study of our proposal, we estimate
the incidence of multidimensional poverty at the municipality level in
Colombia. Furthermore, we provide uncertainty measures based on a parametric
bootstrap algorithm.
