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Using a novel dataset constructed for this study, the spatio-temporal dynamics of income per capita across 34 provinces and 514 districts in Indonesia over the 2010–2017 period are analyzed. First, an exploratory spatial analysis suggests that spatial autocorrelation is only significant at the district level, and it appears to be robust from 2013 t...
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Ecological efficiency mainly emphasizes the importance of balancing the relationship between natural resources,energy,ecological environment and economic growth, which has aroused widespread concern in the world.China's rapid economic development has inevitably accompanied by serious resource exhaustion,environmental pollution and ecological deteri...
Citations
... 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. ...
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, agropastoralists, 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.
... In order to address the methodological constraints arising from the geographical isolation of some Indonesian provinces, we follow Aginta et al. (2021); Miranti and Mendez (2022), and Santos-Marquez et al. (2022) and employ Thiessen polygons to determine the neighbors of each province. Specifically, based on the centroids of the provincial polygons and perpendicular bisectors connecting adjacent points, we derive a new spatial structure in which all polygons are contiguous (see Appendix C for further details). ...
We analyze the space-time dynamics of Indonesia’s provincial unemployment by simultaneously accounting for their serial persistence, spatial dependence, and common factors. The results show that unemployment rates vary widely across provinces, but have similar patterns over time, indicating the presence of common latent factors. Using the average national unemployment rate as a proxy for common factors, the results indicate that the space-time dynamics of provincial unemployment are characterized by both significant serial persistence and spatial dependence. The results also quantify which regions are most sensitive to national unemployment shocks, providing a deeper understanding of regional unemployment heterogeneity.
... Then, Sun et al. (2017) and Wang et al. (2021) also found the significant role of spatial dependence on economic growth among prefectures (from 1992 to 2010) and provinces (from 2009 to 2017) in China. In the Indonesian context, there are several researchers who have found the existence of spatial dependence in interregional economic growth; they are Aspiansyah & Damayanti (2019) at provincial level, Nurjanna et al. (2020) at cross-regional level in Sulawesi, Miranti & Mendez (2022), Day & Lewis (2013), Ervina & Jaya (2018), Aritenang (2014), and Santos-Marquez et al. (2022) across regions in national level, Wibisono & Kuncoro (2015), Rummaya et al. (2005), Laksono et al. (2018) across regions in East Java. ...
Java is inarguably essential for Indonesia’s economy with its 59% contribution to the country’s GDP. However, behind the tremendous participation lies high regional disparity, poverty, and unemployment, which have become challenges to its economic development. This research aims to analyze the role of spatial dependence on regional economic growth in 119 regencies and cities in Java during the 2015-2019 period. Exploratory Spatial Data Analysis (ESDA) and Spatial Durbin Model (SDM) were used to analyze the determinants of the spatial dependence and its impact on regional economic growth. The travel time among regions was utilized for the spatial weight matrix. The existence of spatial dependence in inter-regional economic growth can be identified from all models. The positive value of Moran’s I in the ESDA analysis indicates that the spatial pattern of the growth is clustering. The lambda parameter in the SDM estimation indicates the occurrence of backwash spillover in the effect of the spatial dependence on economic growth. The direct effects of initial per capita income, physical capital investment, road infrastructure, population growth, and education are significant on economic growth. Furthermore, the spillover effect of initial per capita income and education is also significant on the inter-regional economic growth.
... Despite efforts by the Indonesian government to overcome the disparity between Indonesian regions, the problem of economic and development disparity can still be seen [42]. In healthcare, this problem presents itself in various forms, such as the lower rate of utilization of outpatient care facilities in rural areas [43]. ...
Background
Traditional medicine (TM) is commonly used as a treatment in Indonesia. This raises the need for an analysis of its potential development and irrational use. Therefore, we analyze the proportion of TM users among chronic disease patients and its associated characteristics to optimize the use of TM in Indonesia.
Methods
A cross-sectional study of treated adult chronic disease patients was conducted using the fifth Indonesian Family Life Survey (IFLS-5) database. Descriptive analysis was used to identify the proportion of TM users, while a multivariate logistic regression was used to analyze their characteristics.
Results
This study included 4901 subjects and identified 27.1% as TM users. The highest TM use was in subjects with cancer (43.9%), liver issues (38.3%), cholesterol issues (34.3%), diabetes (33.6%), and stroke (31.7%). Characteristics associated with TM users were a perception of one's current health as unhealthy (OR 2.59, 95% CI 1.76–3.81), low medication adherence (OR 2.49, 95% CI 2.17–2.85), age above 65 years (OR 2.17, 95% CI 1.63–2.90), having higher education (OR 1.64, 95% CI 1.17–2.29), and residence outside of Java (OR 1.27, 95% CI 1.11–1.45).
Conclusions
Low medication adherence among TM users highlights the potentially irrational use of treatment in chronic diseases. Nevertheless, the longstanding use of TM users indicates the potential for its development. Further studies and interventions are needed to optimize TM use in Indonesia.
... Indonesia, which has implemented fiscal decentralization since 2001, was able to reduce the poverty rate from 19.14 percent in 2000 to 9.68 percent in 2020 (BPS, 2021). Some studies believed that fiscal decentralization was the primary factor behind this decrease (Abdillah & Mursanto, 2016;Nursini & Tawakkal, 2019;Syamsul, 2020). The theoretical literature on the role of fiscal decentralization in poverty reduction was originally proposed by Oates (1999), who argued that fiscal decentralization through the role of subnational public expenditure can improve the population's welfare. ...
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.
... Other recent studies have quantified the effects of spatial association on regional income inequality and convergence (Gunawan et al., 2019;Vidyattama, 2013;Vidyattama, 2014). For example, Vidyattama (2013) applied the spatial autoregressive lag model (SAR) and the spatial autoregressive error model (SEM) to examine the impact of spatial association at the provincial and district levels on regional convergence for 1999-2008. ...
... These two studies do not address how Indonesia's sub-national regions interact with each other. Estimating the unique district-level per capita GRDP values for 2000-2017, Gunawan et al. (2019) found statistically significant weak but monotonically increasing positive spatial associations. Applying the spatial filtering technique, their modal boxplot convergence analysis found a significant role of spatial autocorrelation in the reduction of inter-district income inequality. ...
... Note that w ijt in Equation (3) is the element of a row-standardized queen contiguity-based spatial weight matrix. We employ the contiguity-based spatial weight matrix data from Gunawan et al. (2019). They use Thiessen polygons to evaluate contiguity based on centroids of districts. ...
Our exploratory spatial data analysis covered Indonesia's district‐level per capita incomes for 2004–2018 and found statistical evidence of a weak but monotonically increasing positive spatial association. The spatial income clusters/outliers were scattered nationwide and expanded geographically. Applying the filtering method, we found that regional cyclical shocks significantly influenced spatial association, largely in resource‐rich districts, and identified the locations of persistent spatial association that were immune to shocks. We also specified new development targets, showing the adjacent coexistence of low‐income agrarian clusters with high‐income mining clusters in the undeveloped Papuan provinces. The overall national average is moving toward the narrowing of inter‐district income disparities. However, nearly half of all districts have widened the income disparities with their neighbors; shocks increased income gaps rather than narrowing them.
... In contrast, others used spatial autocorrelation models to measure convergence and found no spatial patterns or clustering as a result of decentralization (e.g. Santos-Marquez et al., 2021;Vidyattama, 2013). Thus, this paper fills the gap of the previous studies by emphasizing the spatial implications of fiscal decentralization on regional convergence and its underlying explanatory factors. ...
... In this sense, regions would have direct economic impact from longer length of asphalt road and higher share of the urban population. Whilst previous studies on spatial spillovers have suggest region characteristics would have significant effects to its neighbors (Aritenang, 2014;Setiawan and Aritenang, 2019;Santos-Marquez et al., 2021). Third, the study by Pepinsky and Wihardja (2011) suggests decentralization has merely impose to competition of labor and capital due to the immobility of these productive assets across regions in Indonesia. ...
Since 2001, Indonesia established fiscal decentralization to address the growing economic and social inequalities and promote regional economic development. Two decades later, the spatial effects of decentralization in reducing regional inequality in Indonesia are still unclear. Various studies indicate that geographical proximity, spatial linkages, and spillovers have major impacts on economic growth. This paper offers an empirical analysis of the long-run effects of fiscal decentralization and its spatial impacts on regional growth and convergence in Indonesia. The study uses district-level panel data over a 10-year period (2003–2013). We apply regional distribution indices, spatial cluster analysis, convergence analysis, and spatial econometrics. The convergence analysis uses GMM and spatial models. Our findings highlight the persistence of regional divergence and a widening development gap between regions, especially at the provincial level. The results show regional polarization and backwash effects with the strengthening of core regions and weakening of hinterlands. The paper shows that equalization funds are ineffective in reducing vertical and horizontal inequalities. Their distributional effect is unclear except for the revenue sharing fund which boosts economic growth. Furthermore, the paper demonstrates the importance of urban agglomerations, spatial proximity, labor size, and a skilled labor base in increasing regional economic performance. There are two main policy implications of this study. First, place-based economic policies are needed that emphasize endogenous local development and highlight the mobilization of resources (labor and capital). Second, national policies must support regional development based on a thorough understanding of the caveats of liberalization policies.
... Akita (2002) implemented a two-stage inequality decomposition analysis to investigate the share of between-region and between-province inequalities to overall regional inequality, as measured by the Theil index. Using a club-convergence methodology, there is an emerging literature that studies regional disparities at the district-level (Kurniawan et al 2019;Santos-Marquez et al 2021;Aginta et al 2021). ...
This paper evaluates social and economic convergence across 514
districts in Indonesia over the 2010-2018 period. By applying spatial
panel-data methods, this paper re-examines the regional convergence
hypothesis using a novel dataset on the human development index (HDI) and GDP per capita. These two indicators are used as proxies for social and economic progress, respectively. Results show a significant neighborhood effect on the convergence process in both indicators. Specifically, the performance of regional neighbors tends to accelerate the convergence speed of both HDI and GDP per capita. A spatial Durbin model further indicates that the convergence process of HDI is slightly faster than that of GDP per capita. These results are robust to two spatial connective structures: a contiguity-based Thiessen polygon and an inverse distance matrix. Among the determinants of social convergence, share of industry, and share of services sector show statistically significant effects. In contrast, only initial economic size presents a significant effect on economic convergence.
... Furthermore, based on the geographical distribution of the clubs, one is tempted to conclude that real wages in Sumatra Island converge perfectly to club 3, except for the Riau Islands. This might not be the case when the spatial unit used is at the district level, as observed in the context of regional income convergence in Indonesia (Santos-Marquez et al. 2021). Hence, future studies could investigate regional wage convergence across Indonesia's district level, subject to data availability. ...
This study evaluates empirically the convergence in real wages across 34 Indonesian provinces. The authors apply the club convergence test on real wage data at the province level from 2008 to 2020. An overall convergence in real wages has not been found. Instead, three significant club convergences have been identified. Furthermore, regional factors that influence club convergence formation are investigated by using the ordered logit model. It has been found that club convergence formation is jointly influenced by the following factors: share of employment in the manufacturing sector, investment share to GDP, labour force participation rate, and the initial level of wage. The findings support the evidence of club convergence studies that emphasise the role of the initial condition and regional characteristics on the formation of club convergence. The results should alert
national and provincial governments to synchronise policies promoting sound and
competitive labour markets across provinces from a policy standpoint.
... Lastly, to explicitly account for the spatial dimension of the growth process, one could also apply the spatial filter approach of Getis and Griffith (2002) and Getis and Ord (2010). Evaluating the role of spacial dependence through spatial filters may prove useful to delve deeper into the nature of the spatial convergence process (Fischer and Stumpner 2008;Santos-Marquez et al. 2021). ...
This paper studies the evolution of economic and social disparities across South America. By exploiting a novel multi-country subnational dataset, we evaluate the evolution of gross national income per capita (GNI) and the human development index (HDI) across 151 subnational regions over the 1990-2018 period. In particular , regional dynamics are evaluated through the lens of two spatial convergence models. The first model deals with the role of spatial dependence. Results indicate that for both GNI and HDI, there is an overall process of regional convergence. Furthermore , spatial dependence plays a significant role in this process. A spatial error specification suggests that spatial dependence accelerates the speed of convergence in some decades, but decelerates it in others. The second model deals with the role of spatial heterogeneity. Results indicate that for both GNI and HDI, the speed of convergence is largely heterogeneous across space and time. Moreover, economic and social disparities are characterized by multi-country spatial clusters that show both converging and diverging trends. Taken together, these results emphasize the importance of accounting for spatial dependence and heterogeneity when evaluating the dynamics of economic and social inequality in South America.
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