Application of Small Area Estimation for Global Hunger Index at Regency/Municipality Level in Papua Island

Randy Aditya; Azka Ubaidillah

doi:10.20956/j.v22i2.46784

Authors

Randy Aditya Politeknik Statistika STIS
Azka Ubaidillah Politeknik Statistika STIS

DOI:

https://doi.org/10.20956/j.v22i2.46784

Keywords:

stunting, wasting, SAE, GHI, hunger, SAE EBLUP FH, SAE HB Beta

Abstract

Reducing hunger is one of the primary targets of the Sustainable Development Goals (SDGs), particularly Goal 2: Zero Hunger. The Global Hunger Index (GHI) is a key indicator used to measure hunger, comprising four components: the prevalence of undernourishment (PoU), child mortality rate, child stunting, and wasting. While PoU and child mortality data are available at the district/city level across Indonesia, limited data on stunting and wasting in several makes it difficult to calculate the GHI at the local level. Data limitations hinder the formulation of locally targeted policies. This study aims GHI in Papua Province using the Small Area Estimation (SAE) approach. Data sources include the 2023 Indonesia Health Survey and Podes 2021. , while wasting is estimated using the Hierarchical Bayes Beta approach. that SAE improves estimation precision compared to direct estimation, as reflec by . Estimates reveal GHI may vary in category between serious to extremely alarming, with Jayapura City having the lowest and Dogiyai as the highest GHI in Papua.

References

[1] Amrullah, E. R., Ishida, A., Pullaila, A., & Rusyiana, A., 2019. Who suffers from food insecurity in Indonesia?. International Journal of Social Economics, Vol. 46, No. 10, 1186–1197. doi.org/10.1108/IJSE-03-2019-0196

[2] Badan Pusat Statistik, 2023. Statistik Kemiskinan di Papua 2023. Jakarta: Badan Pusat Statistik.

[3] FAO, IFAD, UNICEF, WFP, & WHO, 2023. The State of Food Security and Nutrition in the World 2023: Building resilience for food security and nutrition. Food and Agriculture Organization of the United Nations.

[4] Hakim, M. A. C., & Muchlisoh, S., 2019. Penerapan Model Fay-Herriot Pada Estimasi Prevalensi Stunting Level Kecamatan Di Nusa Tenggara Barat Tahun 2017. Seminar Nasional Official Statistics, Vol. 2019(2019), No. 1, 74–83. doi.org/10.34123/semnasoffstat.v2019i1.105

[5] Hidayat, F., & Hanri, M., 2023. Analisis Regional Dampak Program Beras Sejahtera dan Bantuan Pangan Non Tunai terhadap Tingkat Ketahanan Pangan Keluarga Penerima Manfaat. Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara Dan Kebijakan Publik, Vol. 8, No. 4, 371–386. doi.org/10.33105/itrev.v8i4.677

[6] Hoddinott, J., Alderman, H., Behrman, J. R., Haddad, L., & Horton, S., 2013. The economic rationale for investing in stunting reduction. Maternal and Child Nutrition, Vol. 9, No. 2, 69–82. doi.org/10.1111/mcn.12080

[7] Hutagaol, M. P., & Sinaga, R., 2022. Pengaruh Pendapatan Dan Harga Pangan Terhadap Diversifikasi Pangan Di Pulau Jawa. SCIENTIFIC JOURNAL OF REFLECTION : Economic, Accounting, Management and Business, Vol. 5, No. 3, 702–715. doi.org/10.37481/sjr.v5i3.524

[8] Jones, A. D., & Foulds, M. S., 2021. Malnutrition and economic productivity: Evidence from a cross-country analysis. Journal of Development Economics, Vol. 150, 102–115. doi.org/10.1016/j.jdeveco.2021.102115

[9] Kementerian Kesehatan., 2024. SKI 2023 Dalam Angka. Jakarta: Badan Kebijakan Pembangunan Kesehatan, Kementerian Kesehatan

[10] Liu, B., 2009. Hierarchical Bayes Estimation and Empirical Best Prediction of Small Area Proportions. University of Maryland.

[11] Purwa, T., 2019. Penerapan Model Spatial Logit-Normal pada Small Area Estimation dengan Metode Hierarchical Bayes: Studi Kasus Estimasi Proporsi Penduduk dengan Asupan Kalori Minimum di Bawah 1.400 kkal / kapita / hari per kecamatan di Provinsi Bali. Seminar Nasional Official Statistics 2019: Pengembangan Official Statistics Dalam Mendukung Implementasi SDGs, 59–66.

[12] Rao, J. N. K., & Molina, I., 2015. Small Area Estimation (2nd ed.). John Wiley & Sons, Inc.

[13] Tanziha, I., Syarief, H., Kusharto, C., Hardinsyah, & Sukandar, D., 2005. Analisis Determinan Kelaparan. Media Gizi Dan Keluarga, Vol. 29, No. 2, 14–23.

[14] Thiele, J., & Markussen, B., 2012. Potential of GLMM in modelling invasive spread. CABI Reviews, 1–10. doi.org/10.1079/PAVSNNR20127016

[15] Thrane, E., & Talbot, C., 2019. An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models. Publications of the Astronomical Society of Australia, Vol. 36, No. 10. doi.org/10.1017/pasa.2019.2

[16] Ubaidillah, A., 2014. Small Area Estimation dengan Pendekatan Hierarchical Bayesian Neural Network untuk Pemetaan Kemiskinan di Kota Jambi [Tesis]. Surabaya: Institut Teknologi Sepuluh Nopember.

[17] von Grebmer, K., Bernstein, J., Geza, W., Ndlovu, M., Wiemers, M., Reiner, L., Bachmeier, M., Hanano, A., Ni Cheilleachair, R., Sheehan, T., Foley, C., Gitter, S., Larocque, G., & Fritschel, H., 2023. 2023 Global Hunger Index: The Power of Youth in Shaping Food System.

[18] Wahyuni, R. N. T., & Damayanti, A., 2014. Faktor-Faktor yang Menyebabkan Kemiskinan di Provinsi Papua: Analisis Spatial Heterogeneity. Jurnal Ekonomi dan Pembangunan Indonesia, Vol. 14, No. 2, 128-144.