Regional income disparities, distributional convergence, and spatial effects: evidence from Indonesian regions 2010–2017

Santos‐Marquez, Felipe; Gunawan, Anang Budi; Méndez, Carlos

doi:10.1007/s10708-021-10377-7

Cited by 14 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The regional disparities in poverty rates identified in our study align earlier research that has documented spatial variations in poverty within countries 40 , 41 . This suggests the need for targeted regional policies and interventions to address localized poverty challenges and promote equitable development.…”

Section: Discussionsupporting

confidence: 88%

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia

Hassan,

Muse,

Chesneau

2024

Sci Rep

View full text Add to dashboard Cite

Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predict poverty in Somalia. Utilizing data from the first-ever 2020 Somalia Demographic and Health Survey (SDHS), a cross-sectional study design is considered. ML methods, including random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression, are tested and applied using R software version 4.1.2, while conventional methods are analyzed using STATA version 17. Evaluation metrics, such as confusion matrix, accuracy, precision, sensitivity, specificity, recall, F1 score, and area under the receiver operating characteristic (AUROC), are employed to assess the performance of predictive models. The prevalence of poverty in Somalia is notable, with approximately seven out of ten Somalis living in poverty, making it one of the highest rates in the region. Among nomadic pastoralists, agro-pastoralists, and internally displaced persons (IDPs), the poverty average stands at 69%, while urban areas have a lower poverty rate of 60%. The accuracy of prediction ranged between 67.21% and 98.36% for the advanced ML methods, with the RF model demonstrating the best performance. The results reveal geographical region, household size, respondent age group, husband employment status, age of household head, and place of residence as the top six predictors of poverty in Somalia. The findings highlight the potential of ML methods to predict poverty and uncover hidden information that traditional statistical methods cannot detect, with the RF model identified as the best classifier for predicting poverty in Somalia.

show abstract

Section: Discussionsupporting

confidence: 88%

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia

Hassan,

Muse,

Chesneau

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…As seen in the model above, it is necessary to identify the weight matrix in the spatial econometric model because it is the fundamental element of spatial analysis (Florax & Folmer, 1992). This study used an inverse distance matrix following some studies on spatial econometrics in Indonesia (Vidyattama, 2014;Miranti, 2021;Santos-Marquez et al, 2021). The centroid is defined based on the pure physical distance based on the coordinates data of the centroid of each region.…”

Section: Discussionmentioning

confidence: 99%

Spatial Spillover Impact of Sectoral Government Expenditure on Poverty Alleviation in South Kalimantan Province

Novitasari

Iskandar

2022

JISDeP

View full text Add to dashboard Cite

Poverty is still one of the forefront issues in developing countries. It could hamper the achievement ofsustainable development goals, thereby triggering a recurring call on the government's role in mitigatingpoverty. This paper contributed to the debate on the role of sectoral government spending under thefiscal decentralization policy to combat poverty. Using a case study in South Kalimantan Province, weemployed a spatial panel data analysis covering 13 districts from 2010-2020. This study investigated thepresence of spatial dependency on poverty and the spatial spillover impact of government expenditure—education, health, housing, public facilities, and social protection—on poverty. The research found theexistence of spatial autocorrelation on poverty and the significant high-cluster poverty in the agriculture-based region. The direct estimation from the Spatial Durbin Model uncovered that governmentexpenditure on education, health, and social protection significantly alleviated poverty, while housing andpublic facilities expenditure remained insignificant in reducing the poverty rate. Besides, educationspending also has a significant indirect effect on poverty, indicating the spatial spillover impact ofeducation spending by the neighbors on poverty in a region.

show abstract

“…For province‐level data, applying contiguity criteria to define neighbors results in a few provinces – island‐type provinces (i.e., Riau Islands, Bangka Belitung, Maluku, North Maluku, Bali, West and East Nusa Tenggara) – ending up with zero neighbors. Thus, following Santos‐Marquez et al (2021), in this study we first build a Thiessen polygon tessellation for the provinces, which converts the original polygon of provinces (Figure 6) into artificial polygon (for more details on Thiessen polygon, see Anselin et al, 2010). In our application, we use the centroid of provinces to build a Thiessen polygon (Figure 7 (a)).…”

Section: Methodology and Datamentioning

confidence: 99%

Spatiotemporal analysis of regional inflation in an emerging country: The case of Indonesia

Aginta

2022

Regional Science Policy & Practice

View full text Add to dashboard Cite

show abstract

Regional income disparities, distributional convergence, and spatial effects: evidence from Indonesian regions 2010–2017

Cited by 14 publications

References 39 publications

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia

Spatial Spillover Impact of Sectoral Government Expenditure on Poverty Alleviation in South Kalimantan Province

Spatiotemporal analysis of regional inflation in an emerging country: The case of Indonesia

Contact Info

Product

Resources

About