This guide to small area estimation aims to help users compile more
reliable granular or disaggregated data in cost-effective ways. It
explains small area estimation techniques with examples of how the
easily accessible R analytical platform can be used to implement
them, particularly to estimate indicators on poverty, employment,
and health outcomes. The guide is intended for staff of national
statistics offices and for other development practitioners. It aims
to help them to develop and implement targeted socioeconomic
policies to ensure that the vulnerable segments of societies are
not left behind, and to monitor progress toward the Sustainable
Development Goals.
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