Fiscal Decentralization and Poverty in Papua Province

Suwandi, Suwandi

doi:10.22610/jsds.v6i4.855

JSDS

2015

DOI: 10.22610/jsds.v6i4.855

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Fiscal Decentralization and Poverty in Papua Province

Suwandi Suwandi¹

Abstract: In Papua province, fiscal decentralization policy had a positive impact on the potential development and regional government creativity. The effectiveness in managing regional wealth will affect to the regional revenue which can further determine the effect of fiscal decentralization and economic growth to improve on poverty. The method used is multiple linear regression analysis technique. The results showed simultaneous fiscal decentralization and economic growth has a significant effect on poverty. In parti… Show more

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2017

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Cited by 1 publication

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Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

Munandar

Azhari

Mushdholifah

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Disparities in regional development methods are commonly identified using the Klassen Typology and Location Quotient. Both methods typically use the data on the gross regional domestic product (GRDP) sectors of a particular region. The Klassen approach can identify regional disparities by classifying the GRDP sector data into four classes, namely Quadrants I, II, III, and IV. Each quadrant indicates a certain level of regional disparities based on the GRDP sector value of the said region. Meanwhile, the Location Quotient (LQ) is usually used to identify potential sectors in a particular region so as to determine which sectors are potential and which ones are not potential. LQ classifies each sector into three classes namely, the basic sector, the non-basic sector with a competitive advantage, and the non-basic sector which can only meet its own necessities. Both Klassen Typology and LQ are unable to visualize the relationship of achievements in the development clearly of each region and sector. This research aimed to develop a new approach to the identification of disparities in regional development in the form of hierarchical clustering. The method of Hierarchical Agglomerative Clustering (HAC) was employed as the basis of the hierarchical clustering model for identifying disparities in regional development. Modifications were made to HAC using the Klassen Typology and LQ. Then, HAC which had been modified using the Klassen Typology was called MHACK while HAC which had been modified using LQ was called MACLoQ. Both algorithms can be used to identify regional disparities (MHACK) and potential sectors (MACLoQ), respectively, in the form of hierarchical clusters. Based on the MHACK in 31 regencies in Central Java Province, it is identified that 3 regencies (Demak, Jepara, and Magelang City) fall into the category of developed and rapidly-growing regions, while the other 28 regencies fall into the category of developed but depressed regions. Results of the MACLoQ implementation suggest that there is only 1 regency which falls into the basic-sector category (Banyumas), while the other regencies fall into the non-basic non-competitive sector category.

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Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

Munandar

Azhari

Mushdholifah

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

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Fiscal Decentralization and Poverty in Papua Province

Cited by 1 publication

References 12 publications

Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

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