Introduction to small area estimation techniques: a practical guide for national statistics offices Asian development bank

By:

Asian development bank

Material type: Text

TextPublication details: Philippines : Asian development bank , 2020.Description: x,99p.: ill.; 28cmISBN:

9789292622220

LOC classification:

DUCE HC79.P6578

Summary: 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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Holdings
Item type	Current library	Call number	Status	Barcode
Books	DUCE LIBRARY Humanities: Shelf HC51. C57 – HF 3887. S64	DUCE HC79.P6578 (Browse shelf(Opens below))	Available	000000172865
Books	DUCE LIBRARY Humanities: Shelf HC51. C57 – HF 3887. S64	DUCE HC79.P6578 (Browse shelf(Opens below))	Available	000000172864

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Previous	DUCE HC79.P6.I.57 Poverty /	DUCE HC79.P6.I.57 Poverty /	DUCE HC79.P152 Poverty: an international glossary	DUCE HC79.P6578 Introduction to small area estimation techniques: a practical guide for national statistics offices	DUCE HC79.P6578 Introduction to small area estimation techniques: a practical guide for national statistics offices	DUCE HC85.1584 Integrated natural resource management: linking productivity, the environment and development	DUCE HC85.1584 Integrated natural resource management: linking productivity, the environment and development	Next

Includes bibliographical reference

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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