Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia
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, pol...
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| Định dạng: | EJournal Article |
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Komunitas Ilmuwan dan Profesional Muslim Indonesia,
2017-12-02.
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| LEADER | 02659 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | CST_50_25 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Januardi, Ryan Willmanda |e author |
| 700 | 1 | 0 | |a Utomo, Agung Priyo |e author |
| 245 | 0 | 0 | |a Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia |
| 260 | |b Komunitas Ilmuwan dan Profesional Muslim Indonesia, |c 2017-12-02. | ||
| 500 | |a https://cst.kipmi.or.id/journal/article/view/50 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2017 Communications in Science and Technology | ||
| 540 | |a https://creativecommons.org/licenses/by/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Poverty ratio | ||
| 690 | |a education | ||
| 690 | |a spatial error model | ||
| 690 | |a robustness | ||
| 690 | |a MLE | ||
| 690 | |a GMM | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 786 | 0 | |n Communications in Science and Technology; Vol. 2 No. 2 (2017) | |
| 786 | 0 | |n Communications in Science and Technology; Vol 2 No 2 (2017) | |
| 786 | 0 | |n 2502-9266 | |
| 786 | 0 | |n 2502-9258 | |
| 786 | 0 | |n 10.21924/cst.2.2.2017 | |
| 787 | 0 | |n https://cst.kipmi.or.id/journal/article/view/50/25 | |
| 856 | 4 | 1 | |u https://cst.kipmi.or.id/journal/article/view/50/25 |z Get Fulltext |