HIERARCHICAL BAYESIAN SMALL AREA ESTIMATION ON OVERDISPERSED DATA: WORKERS WITH DISABILITIES IN INDONESIA

Danardana Muhammad; Halim Nur Jamaluddin; Mira Octavia; Rohimma Arisanti; Nofita Istiana

doi:10.14710/jfma.v0i0.28526

DOI: https://doi.org/10.14710/jfma.v0i0.28526

HIERARCHICAL BAYESIAN SMALL AREA ESTIMATION ON OVERDISPERSED DATA: WORKERS WITH DISABILITIES IN INDONESIA

*Danardana Muhammad - Applied Statistics, Politeknik Statistika STIS, 64C Otto Iskandardinata, Jatinegara, Kota Jakarta Timur, Jakarta, Indonesia 13330, Indonesia

Halim Nur Jamaluddin - Applied Statistics, Politeknik Statistika STIS, 64C Otto Iskandardinata, Jatinegara, Kota Jakarta Timur, Jakarta, Indonesia 13330, Indonesia

Mira Octavia - Applied Statistics, Politeknik Statistika STIS, 64C Otto Iskandardinata, Jatinegara, Kota Jakarta Timur, Jakarta, Indonesia 13330, Indonesia

Rohimma Arisanti - Applied Statistics, Politeknik Statistika STIS, 64C Otto Iskandardinata, Jatinegara, Kota Jakarta Timur, Jakarta, Indonesia 13330, Indonesia

Nofita Istiana - Applied Statistics, Politeknik Statistika STIS, 64C Otto Iskandardinata, Jatinegara, Kota Jakarta Timur, Jakarta, Indonesia 13330, Indonesia

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

BibTex Citation Data :

@article{JFMA28526,
    author = {Danardana Muhammad and Halim Nur Jamaluddin and Mira Octavia and Rohimma Arisanti and Nofita Istiana},
    title = {HIERARCHICAL BAYESIAN SMALL AREA ESTIMATION ON OVERDISPERSED DATA: WORKERS WITH DISABILITIES IN INDONESIA},
    journal = {Journal of Fundamental Mathematics and Applications (JFMA)},
  volume = {8},
    number = {2},
    year = {2025},
    keywords = {Workers with disabilities, Small Area Estimation, Hierarchical Bayesian Poisson-Gamma},
    abstract = { Persons    with disabilities   encounter  difficulties in accessing   essential  services,   including   employment, healthcare, information, and political participation.   In   line with   the target 8.5 of the SDGs, efforts   have been made   to   promote  full, productive, and decent employment   for all  , including for persons with disabilities.   However,   t  he   majority of  workers   with disabilities in Indonesia   remain   concentrated in the informal sector   during the period of   2022–2023. Unfortunately, data on workers with disabilities is   currently   only available at the national level.   This   limitation arises     because the sample size of   workers   with disabilities is insufficient to meet the   minimum   requirements for direct estimation at the provincial level    . Therefore, a Small Area Estimation approach is   necessary to assess   the   participation  of persons with disabilities in the workforce at   more granular   level, such as provinces. In   this study  , auxiliary variables such as the sex ratio, the number of residents who are shackled, and the availability of computer skills infrastructure were   incorporated to the   Small Area Estimation (SAE)   framework  . T  he Hierarchical Bayesian Poisson-Gamma   was employed   to improve the precision of direct   estimat  ion  .   The research results show that the HB Poisson-gamma estimator has better precision compared to the direct estimator.  },
   issn = {2621-6035},   pages = {204--218}  doi = {10.14710/jfma.v0i0.28526},
    url = {https://ejournal2.undip.ac.id/index.php/jfma/article/view/28526}
}

Citation Format:

Abstract

Persons with disabilities encounterdifficulties in accessing essentialservices, including employment, healthcare, information, and political participation. In line with the target 8.5 of the SDGs, efforts have been made to promotefull, productive, and decent employment for all, including for persons with disabilities. However, the majority ofworkers with disabilities in Indonesia remain concentrated in the informal sector during the period of 2022–2023. Unfortunately, data on workers with disabilities is currently only available at the national level. This limitation arises because the sample size of workers with disabilities is insufficient to meet the minimum requirements for direct estimation at the provincial level. Therefore, a Small Area Estimation approach is necessary to assess the participationof persons with disabilities in the workforce at more granular level, such as provinces. In this study, auxiliary variables such as the sex ratio, the number of residents who are shackled, and the availability of computer skills infrastructure were incorporated to the Small Area Estimation (SAE) framework. The Hierarchical Bayesian Poisson-Gamma was employed to improve the precision of direct estimation. The research results show that the HB Poisson-gamma estimator has better precision compared to the direct estimator.

Fulltext View|Download

Keywords: Workers with disabilities, Small Area Estimation, Hierarchical Bayesian Poisson-Gamma

Article Metrics:

Article Info

Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS

Language : EN

In Vol 8, No 2 (2025)

Recent articles

SOME PROPERTIES OF MODULAR TOPOLOGY IN THE ORLICZ SEQUENCE SPACE HIERARCHICAL BAYESIAN SMALL AREA ESTIMATION ON OVERDISPERSED DATA: WORKERS WITH DISABILITIES IN INDONESIA Sharper Upper Bounds for Roots of Polynomials Generated by Positive Sequences More recent articles

Badan Pusat Statistik (BPS), Indikator Pekerjaan Layak di Indonesia 2024. 2025
D. Erissa dan D. Widinarsih, “Akses Penyandang Disabilitas Terhadap Pekerjaan: Kajian Literatur,” J. Pembang. Mns., vol. 3, no. 1, 2022, doi: 10.7454/jpm.v3i1.1027
WHO, Global report on health equity for persons with disabilities. 2022
Maghfirah, “Geospasial Tingkat Kesempatan Kerja dan Faktor yang Berhubungan dengan Partisipasi Kerja Penyandang Disabilitas Pada Masa Awal Pandemi Covid-19 (Geospatial Job Opportunity Rate and Factors Related to Work Participation of Disable People in The Early Time,” Semin. Nas. Off. Stat. 2022, vol. 19, hal. 21–30, 2022
S. P. Estika dan Rumayya, “Partisipasi Penyandang Disabilitas dalam Pasar Kerja di Indonesia,” J. Ilm. MEA (Manajemen, Ekon. dan Akuntansi), vol. 8, no. 3, hal. 501–510, 2024, doi: doi.org/10.31955/mea.v8i3.4476
A. Putri, “Disabilitas dan Partisipasi di Pasar Tenaga Kerja Indonesia: Analisis Data Sakernas 2018,” J. Ketenagakerjaan, vol. 14, no. 2, hal. 138–152, 2019
R. R. Ridwansyah et al., “Penerapan Small Area Estimation dalam Melakukan Pendugaan Angka Partisipasi Sekolah Disabilitas di Pulau Jawa Tahun 2023,” vol. 2024, no. Senada, hal. 30–41, 2024
D. A. Puspitasari et al., “Pendugaan Area Kecil Persentase Anak Usia 0-17 Tahun yang Hidup di Bawah Garis Kemiskinan Tingkat Kabupaten/Kota di Pulau Jawa Tahun 2023,” 2024, doi: 10.34123/semnasoffstat.v2024i1.1967
S. I. Siregar, “Utilizing Night-Time Light and Hierarchical Bayesian Small Area Model for Consumption Expenditure Estimation,” Citiz. J. Ilm. Multidisiplin Indones., vol. 4, no. 1, hal. 61–71, 2024, doi: 10.53866/jimi.v4i1.534
D. N. F. A. Puspita dan A. Ubaidillah, “Pendugaan Area Kecil Tingkat Pengangguran Terbuka Level Kecamatan Di Provinsi Kepulauan Riau Tahun 2022,” Semin. Nas. Off. Stat., 2024
I. Wulandari, K. A. Notodiputro, A. Fitrianto, dan A. Kurnia, “Hierarchical Bayesian Models for Small Area Estimation under Overdispersed Count Data,” Eng. Lett., vol. 31, no. 4, hal. 1333–1342, 2023
S. N. Rohmah dan N. Hajarisman, “Penerapan Small Area Estimation dengan Metode Empirical Bayes dalam Menduga Risiko Relatif Penyebaran TBC di Kabupaten Karawang Tahun 2021,” Bandung Conf. Ser. Stat., vol. 2, no. 2, hal. 490–498, 2022, doi: 10.29313/bcss.v2i2.4765
Badan Pusat Statistik (BPS), Potret Penyandang Disabilitas di Indonesia: Hasil Long Form SP2020. 2024
Washington Group on Disability Statistics, “An Introduction to the Washington Group on Disability Statistics Question Sets,” no. June, hal. 1–17, 2020
N. Fortune et al., “Do Area-Level Environmental Factors Influence Employment for People with Disability? A Scoping Review,” Int. J. Environ. Res. Public Health, vol. 19, no. 15, hal. 1–16, 2022, doi: 10.3390/ijerph19159082
A. Ubaidillah, I. Y. Wulansari, dan Z. Y. Perwira, “saeHB: Small Area Estimation using Hierarchical Bayesian Method,” Compr. R Arch. Netw., no. November, hal. 40, 2022, doi: 10.13140/RG.2.2.23850.13761
R. Arnab, Unequal Probability Sampling. 2017. doi: 10.1016/b978-0-12-811848-1.00005-4
A. Agresti, Categorical Data Analysis, vol. 47, no. 4. 2013
J. N. K. Rao dan I. Molina, Small Area Estimation. 2015. doi: 10.1002/9781118735855

Last update:

No citation recorded.

Last update:

No citation recorded.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish articles in this journal agree to the following terms:

Authors grant the journal the right of first publication that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

For more detailed information about the copyright transfer, please refer to this page: COPYRIGHT TRANSFER FORM

HIERARCHICAL BAYESIAN SMALL AREA ESTIMATION ON OVERDISPERSED DATA: WORKERS WITH DISABILITIES IN INDONESIA

EDITORIAL OFFICE

INDEXED IN: