Resource information
In much of the developing world, the
demand for high frequency quality household data for poverty
monitoring and program design far outstrips the capacity of
the statistics bureau to provide such data. In these
environments, all available data sources must be leveraged.
Most surveys, however, do not collect the detailed
consumption data necessary to construct aggregates and
poverty lines to measure poverty directly. This paper
benefits from a shared listing exercise for two large-scale
national household surveys conducted in Liberia in 2007 to
explore alternative methodologies to estimate poverty
indirectly. The first is an asset-based model that is
commonly used in Demographic and Health Surveys. The second
is a survey-to-survey imputation that makes use of small
area estimation techniques. In addition to a standard base
model, separate models are estimated for urban and rural
areas and an expanded model that includes climatic
variables. Special attention is paid to the inclusion of
cell phones, with implications for other assets whose cost
and availability may be changing rapidly. The results
demonstrate substantial limitations with asset-based
indexes, but also leave questions as to the accuracy and
stability of imputation models.
