To be of real value to governments and development agencies, poverty maps should
go beyond describing the distribution of poverty, to help explain and thence
predict its spatial distribution. Poverty maps are traditionally produced by
exploiting links between extensive census data and intensive socio-economic
household survey data; relationships found within the survey data are extended
to census data, through variables common to both data sets. Many of the
dimensions of poverty are environmentally related; people are poor because they
are unhealthy, or underfed, or without access to fuel and water etc. We suggest
that a more useful approach to poverty mapping might be first to identify its
(environmental) causes. In this analysis, we explore a novel approach that
combines household survey data from Uganda with a suite of environmental
variables that are either direct measures of key climatic variables (such as
temperature), descriptor variables of key ingredients of poverty-generating
processes (such as agricultural production systems) or proxies for constraints
on the health and well-being of the human populations (such as disease-causing
pathogens). This potentially allows us to move beyond description, to explain
and then predict the distribution of poverty at the spatial resolution of the
predictor variables. Whilst correlation obviously does not automatically imply
causation, we suggest an environmental approach to poverty mapping is more
likely to reveal causes than the traditional, small area approaches. This paper
takes the first steps towards establishing the predictive accuracy of an
environmental approach to poverty mapping.
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