Using Small Area Estimation (SAE) Methods for Generating Nutrition Maps in Indonesia: an update

Advisor:
Coordinator:
Team Member: Ridho Al Izzati , Maudita Dwi Anbarani, Sri Murniati
Completion Year:
2020
Area:
Riau, Sumatera Barat, Jawa Barat, Banten, Nusa Tenggara Barat, Kalimantan Timur
Topic:
Food & Nutrition

Collaborating Partners

Project Donor/Funder: Tanoto Foundation
Project Counterpart: TNP2K

 
 

Description & Progress

Background

In 2018, SMERU was requested to conduct technical process off mapping the nutritional status down to village level in six of the Government of Indonesia’s priority districts, under the guidance of the TNP2K (National Team for the Acceleration of Poverty Reduction) and an advisory board composed of some Indonesia’s leading development practitioners and policymakers. Using secondary data analysis, FGD, and householdsurveys, SMERU and TNP2K developed the methodologies for nutrition mapping, and conduct field verification from August 2018 to December 2018. The results are then compared and used for the basis of policy recommendation for the expansion of nutrition mapping for all areas in Indonesia. Based on initial data exercise and literature review, nutrition mapping at village level is necessary for better targeting policy and programs, particularly due to data limitation in health sectors. Field verification comparing village condition and houseold survey is the ideal way to ensure that estimation from nutrition mapping has a reliablle precision.

In the previous study, the SMERU Research Institute has successfully generated the nurition maps for six districts utilizing Riskesdas 2013 dataset. Field verification has proven that the SAE method is valid and reliable to map the nutrition status on the village level in Indonesia.

 

Objective

This study aims to:

  1. apply the SAE method using the latest available Riskesdas and Susenas data set (2018) for mapping nutrition status in seven districts;
  2. analyze factors potentially affect the nutrition status at village and district levels using latest secondary data available.

 

Methodology

ELL methods will be used to combine the information obtained from a household survey with the information collected through a population census. Four sources of data are used in this mapping work: (i) integrated data of Basic Health Survey (Riskesdas) and Susenas; (ii) Population Census; and (iii) PODES (village census). All of them collected by BPS. In the nutritional status model estimation, the data on children height and weight is obtained from the RISKESDAS, the data on household characteristics is obtained from the RISKESDAS, SUSENAS, and Population Census, while the data on village-level characteristics is obtained from PODES.


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