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Januardi, Ryan Willmanda
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Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia Januardi, Ryan Willmanda; Utomo, Agung Priyo
Communications in Science and Technology Vol 2 No 2 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.2.2017.50

Abstract

Java Island is the center of development in Indonesia, and yet poverty remains its major problem. The pockets of poverty in Java are often located in urban and rural areas, dominated by productive age group population with low education. Taking into account spatial factors in determining policy, policy efficiency in poverty alleviation can be improved. This paper presents a Spatial Error Model (SEM) approach to determine the impact of education on poverty alleviation in Java. It not only focuses on the specification of empirical models but also in the selection of parameter estimation methods. Most studies use Maximum Likelihood Estimator (MLE) as a parameter estimation method, but in the presence of normality disturbances, MLE is generally biased. The assumption test on the poverty data of Java showed that the model error was not normally distributed and there was spatial autocorrelation on the error terms. In this study we used SEM using Generalized Methods of Moment (GMM) estimation to overcome the biases associated with MLE. Our results indicate that GMM is as efficient as MLE in determining the impact of education on poverty alleviation in Java and robust to non-normality. Education indicators that have significant impact on poverty alleviation are literacy rate, average length of school year, and percentage of high schools and university graduates.
Granular Multidimensional Poverty Index Using Grid-Based Spatial Modeling: A Case Study of East Java, Indonesia Januardi, Ryan Willmanda; Paramitasari, Nurina
Communications in Humanities and Social Sciences Vol. 5 No. 2 (2025): CHSS
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia (KIPMI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/chss.5.2.2025.106

Abstract

Capturing multidimensional poverty through conventional poverty statistics is challenging in view of their limited spatial resolution and focus on monetary indicators. In Indonesia, poverty measurement remains largely expenditure-based, potentially obscuring localized deprivations in education, health, and living standards. The objective of this present study is to address this limitation by developing a granular spatial mapping framework for the Multidimensional Poverty Index (MPI) in East Java Province. Employing the Alkire–Foster approach and Susenas 2023 data, the provincial MPI is estimated at 0.0479, and MPI values are spatially predicted at a 3 × 3 km grid resolution by integrating geospatial indicators of infrastructure accessibility, education and healthcare facilities, nighttime light intensity, and population density. The spatial models demonstrate strong predictive performance (R2 ≈ 0.97; AUC ≈ 0.98), revealing pronounced fine-scale variation in multidimensional poverty and identifying deprivation clusters that are not observable in administrative-level statistics. Areas characterized by geographic isolation and limited-service accessibility consistently exhibit elevated predicted MPI values. The findings of this study highlight the significance of high-resolution multidimensional poverty mapping in facilitating the development of more spatially targeted and evidence-based poverty reduction policies at the local level.
Co-Authors Agung Priyo Utomo Paramitasari, Nurina