  Multiple Indicator Cluster Surveys (MICS), supported by UNICEF, are one of
the most important global household survey programs that provide data on health
and education of women and children. We analyze the Serbia 2014-15 MICS dataset
using topological data analysis which treats the data cloud as a topological
space and extracts information about its intrinsic geometric properties. In
particular, our analysis uses the Mapper algorithm, a dimension-reduction and
clustering method which produces a graph from the data cloud. The resulting
Mapper graph provides insight into various relationships between household
wealth - as expressed by the wealth index, an important indicator extracted
from the MICS data - and other parameters such as urban/rural setting,
ownership of items, and prioritization of possessions. Among other uses, these
findings can serve to inform policy by providing a hierarchy of essential
amenities. They can also potentially be used to refine the wealth index or
deepen our understanding of what it captures.
