Inferensi
Vol 8, No 2 (2025)

Modeling Youth Development Index in Indonesia Using Panel Data Regression for Binary Response with Random Effect

Widyangga, Pressylia Aluisina Putri (Universitas Airlangga)
Suliyanto, Suliyanto (Universitas Airlangga)
Mardianto, M. Fariz Fadillah (Universitas Airlangga)
Sediono, Sediono (Universitas Airlangga)



Article Info

Publish Date
02 Jul 2025

Abstract

Indonesia has the largest youth population in Southeast Asia, yet its Youth Development Index (YDI) ranks only fifth in the region. This study aims to fill the gap in empirical research by modeling the YDI in Indonesia using binary logit and binary probit regressions with random effects, based on panel data from 34 provinces during 2020–2022. The YDI categories are defined according to the national target of 57.67 set by the Ministry of Youth and Sports Affairs. The analysis reveals that the binary probit model performs better than the binary logit model, with a classification accuracy of 93.14% and a McFadden R-squared of 0.4064. Gender Inequality Index (GII) and Expected Years of Schooling (EYS) significantly affect the likelihood of achieving the YDI target. These results highlight the critical role of gender equality and education in advancing youth development in Indonesia. The binary probit model provides a practical tool for policymakers to predict and evaluate the effectiveness of development programs targeting youth outcomes. This research not only contributes methodologically to the study of youth development using advanced econometric models but also offers policy-relevant insights that support the strategic goals of Indonesia Emas 2045. By identifying key leverage points such as gender equity and education access, the findings reinforce the importance of inclusive and evidence-based planning to nurture a generation of resilient, empowered, and high-performing youth who can lead Indonesia toward a prosperous future.

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Journal Info

Abbrev

inferensi

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

Description

The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and ...