Melola Discovery

← Melola Discovery

Journal

Poverty Modeling in Indonesia: a Spatial Regression Analysis

Economics Development Analysis Journal · Vol. 12 No. 4 (2023) · pp. 441–457

DOI
10.15294/edaj.v12i4.66027
ISSN
2252-6560
Published
2023-11-04
Publisher
Economics Development Department, Universitas Negeri Semarang, Indonesia
Language
eng

Abstract

The government has made various efforts to reduce poverty in Indonesia. However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. This research was conducted by comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike's information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. The results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.

Collaboration network of the authors

28 authors · 118 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Airlangga
  • Cenderawasih University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Weni NovandariAffiliation unknown2
Nur ChamidahUniversitas Airlangga2
M. Fariz Fadillah MardiantoAffiliation unknown2
Aini Divayanti ArrofahAffiliation unknown2
Azzah Nazhifa Wina RamadhaniAffiliation unknown2
Aulia RamadhantiAffiliation unknown2
M. Nabil SaputraUniversitas Airlangga2
Muhammad Fariz Fadillah MardiantoUniversitas Airlangga2
Dita AmeliaUniversitas Airlangga2
Elly AnaUniversitas Airlangga2
Aini Divayanti ArrofahUniversitas Airlangga2
Azzah Nazhifa Wina RamadhaniUniversitas Airlangga2
Aulia RamadhantiUniversitas Airlangga2
Toha SaifudinUniversitas Airlangga1
Diah Puspita NingrumUniversitas Airlangga1
Sulasih SulasihAffiliation unknown1
Agus SurosoAffiliation unknown1
Siti Zulaikha WulandariAffiliation unknown1
ABDUL KARIMAffiliation unknown1
Agus RiyadiAffiliation unknown1
Rimuljo HendradiUniversitas Airlangga1
Felix RebaCenderawasih University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/edaj/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Poverty Modeling in Indonesia: a Spatial Regression Analysis</dc:title>
	<dc:creator>'Iffah, Ameliatul</dc:creator>
	<dc:creator>Suliyanto, Suliyanto</dc:creator>
	<dc:creator>Sediono, Sediono</dc:creator>
	<dc:creator>Saifudin, Toha</dc:creator>
	<dc:creator>Ana, Elly</dc:creator>
	<dc:creator>Amelia, Dita</dc:creator>
	<dc:description xml:lang="en-US">The government has made various efforts to reduce poverty in Indonesia. However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. This research was conducted by comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike's information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. The results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.</dc:description>
	<dc:publisher xml:lang="en-US">Economics Development Department, Universitas Negeri Semarang, Indonesia</dc:publisher>
	<dc:date>2023-11-04</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/edaj/article/view/66027</dc:identifier>
	<dc:identifier>10.15294/edaj.v12i4.66027</dc:identifier>
	<dc:source xml:lang="en-US">Economics Development Analysis Journal; Vol 12 No 4 (2023): Economics Development Analysis Journal; 441-457</dc:source>
	<dc:source>2252-6560</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/edaj/article/view/66027/25641</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Economics Development Analysis Journal</dc:rights>
</oai_dc:dc>