Modeling Human Development Index of East Java Using Spatial Autoregressive and Spatial Error Ensemble

This study aims to analyse the influence of average years of schooling and life expectancy on Indonesia's Human Development Index (HDI) for the period 2021-2023 and its implications from an Islamic economic perspective based on maqāṣid al-Syarī'ah. This study employs a mixed-method approach, combining quantitative analysis through multiple linear regression tests to analyse the impact of average years of schooling and life expectancy on Indonesia's HDI for the 2021-2023 period, with qualitative analysis through content analysis from the perspective of maqāṣid al-Syarī‘ah. The results of this study indicate that average years of schooling and life expectancy have a positive and significant impact on the improvement of the Human Development Index (HDI) in Indonesia for the period 2021-2023, contributing 73.50 per cent to HDI variation. The increase in average years of schooling reflects improved access to education, thereby supporting workforce quality, while the stability of life expectancy indicates the success of healthcare services and improving living conditions for the population. From an Islamic economic perspective, these findings reinforce the connection between HDI indicators and the five objectives of maqāṣid al-Syarī‘ah, namely the protection of religion, life, intellect, lineage, and property, indicating that HDI improvement supports human development aligned with Sharia values. The implications of these findings underscore the importance of integrating education and health policies with environmental conservation policies to achieve a fair, sustainable, and prosperous Islamic economy for society.

Leprosy, a highly contagious disease caused by the bacterium Mycobacterium leprae, can result in permanent disability if left untreated. It remains a significant public health issue in many regions, particularly tropical countries like Indonesia. Despite ongoing control efforts, incidence rates are still high in some areas. In 2023, East Java had the highest number of leprosy cases in Indonesia, with 2,124 out of 7,166. To understand the factors contributing to these cases, this study explores various influences and offers policy recommendations to reduce leprosy in East Java. The study uses spatial modeling with a weighting scheme based on queen contiguity, selected because leprosy spreads through human interactions and movement, creating spatial dependencies. It examines spatial, social, economic, educational, and environmental factors based on cross-sectional data from 38 regencies/cities in East Java for 2023. Among the regression models tested, the spatial error regression model proved most effective, showing an R-Square value of 67.14% and an AIC of 213.023. Key findings identified () average years of schooling and () healthcare worker ratios as significant factors influencing leprosy cases. These results aim to guide policymakers in developing stronger leprosy control strategies and offer a basis for further research in East Java.

This research examines enduring disparities in human development across the South Asian Association for Regional Cooperation (SAARC) nations, specifically highlighting Afghanistan's structural obstacles. This study utilizes a balanced panel dataset encompassing seven SAARC nations from 2000 to 2023, applying panel econometric techniques including a Fixed Effects model (validated through a robust Hausman test) and Pooled OLS estimators to analyze the influence of critical socioeconomic factors like life expectancy, average years of schooling, expected years of schooling, and gross national income per capita on the Human Development Index (HDI). Clustered standard errors at the country level are used to ensure the robustness of the results. The empirical findings indicate that all four factors exert statistically significant positive influences on HDI, affirming the essential role of health, education, and wealth in determining human development outcomes. The results indicate that Afghanistan consistently lags behind its regional counterparts, even when accounting for fundamental socioeconomic variables, implying that further structural impediments may be hindering its developmental advancement. These findings provide compelling evidence for policymakers to prioritize investments in health, education, and equitable income growth to bridge enduring development disparities and expedite progress towards the Sustainable Development Goals. Keywords: Human Development Index, SAARC, Panel Data, Socioeconomic Determinants, Afghanistan

BackgroundLess developed countries are increasingly afflicted with over–nutrition, and the escalating overweight prevalence has become a global problem. However, a problem as global as this may not be amenable to a general set of remedial interventions applicable to all countries.MethodsI use data from various sources, including the World Health Organization and the World Bank, to test the association of overweight prevalence with economic, social, and demographic indicators. I then split the countries up by human development index to investigate to what extent these associations vary between development levels.FindingsOn a global scale, overweight prevalence is most associated with gross domestic product (GDP) per capita, the proportion of a country that is rural, the proportion of elderly in a country's population, and the average years of schooling. At what magnitude, and even in which direction, these relationships go vary with a country's level of development. Generally, GDP per capita has a positive association with overweight prevalence, with the magnitude of such association for countries of very high human development more than twice of that for countries of low human development. However, proportion rural has a negative association with overweight prevalence, with the magnitude of such association for countries of low human development nearly twice of that for countries of very high human development. All four of these variables have statistically significant association with overweight prevalence in countries with low human development.ConclusionsI make policy suggestions to combat increasing overweight prevalence, based on the models that are developed, paying special attention to the differences in magnitude and direction of the regressors between human development levels.

The Human Development Index (HDI) is a human development index that is used to achieve the development outcomes of a region. HDI is formed by 3 basic dimensions, namely the health dimension as seen from the indicator of life expectancy at birth, the dimension of knowledge seen from a combination of indicators of average length of schooling and expectation of school years and dimensions of decent living standards as seen from the indicator of average per capita expenditure has been adjusted. The development of HDI in Central Java shows an increase every year. In 2018 the HDI figure for Central Java Province reached 71.12% and increased by 0.6% from the previous year. This is because the large HDI figures in an area are influenced by the large HDI numbers in adjacent areas. The location / area factor is thought to have a spatial dependence effect on the HDI figure. This problem can be overcome by using spatial regression by including the relationship between regions into the model. The spatial regression approach used in this study is the Spatial Error Model (SEM). The weighting matrix used in this study is Queen Contiguity (intersection between sides and corners). This study provides results that the variables that significantly influence HDI are poverty and school enrollment rates.

IntroductIonIn 1990, to help public health officials, politicians and key leaders compare human wellbeing, the UN Development Programme created the Human Development Index (HDI).1 The aim was to create an index that was based on available country- specific data, whilst being transparent and suffi- ciently robust enough to withstand scientific scru- tiny. The index is now widely used to compare world countries, and government leaders pay close attention to the individual rank of each country.The HDI is based on the concept that people are the real wealth of a nation. The originators of the HDI recognised that there are three essential components required for humans to develop to their maximum ability: health, education and income. There is general agreement that if any one of these elements is absent, individual achievement will be severely limited. Currently, the composite index is obtained by first normalising each individual component to a maximum value of unity so that all are on a comparable scale. Then the overall HDI is computed by taking the geometric mean of the three individual index val- ues.2 The health component of the index is based solely on life expectancy at birth, and the wealth component is based upon the gross national income per capita. Since 2011, the education part of the HDI is calculated by combining two separate measures: mean schooling years for adults and expected school years for children entering the school system. The geometric mean of these two values comprises the education index.For countries with large numbers of guest workers, mean years of schooling is likely to be low, because the majority of guest workers are unskilled labourers with a lower educational level than the citizen population. In contrast, the expected number of school years component would be relatively unaffected by guest workers because it is based upon students currently enrolled in the educational system.The aim of this report is to determine the impact of guest worker status on the composite HDI ranking score. We focused on the 93 countries classified as high or very high human development because this group has the highest concentration of migrant guest workers.MethodWe used published HDI data from the UN Human Development Programme 2010 or the most recent year to examine country rankings for all the countries ranked in the high or very high HDI groups.2 As an estimate of the number of migrant guest workers, we used the percentage of migrant workers aged 40-45 in the total popula- tion from available data in the UN migration data- base or, if unavailable, data from the World Bank.3,4 To determine the impact of migrant sta- tus on HDI, we plotted the country-specific rank- ing of each of the three individual HDI compo- nents (education, health, income) against migrant status. We also plotted each of the three compo- nents against the overall HDI, using a marker to identify those countries with the highest propor- tion of migrant workers.We used linear least squares analyses to study the relationship between migrant status and HDI as well as the individual components of the HDI. For outcomes that appeared non- linear, we used locally weighted scatterplot smoothing (Lowess) to fit the data. For comparing continuous variables between groups of coun- tries, we used t-tests. All p-values are two-sided, with a predetermined significance level of .05.ResultsData for both HDI and migrant status were availa- ble for 89 of the 93 countries listed in the UN high or very high development group. When we plot- ted the educational component of the HD against the percentage of migrants in these countries, we noted a biphasic curve with an inflexion point when the percentage of migrant workers in the population reached 30%. After this level was reached, there was a robust negative correlation between the educational component of the HDI and percentage of migrant workers (R = 0. …

This study aims to determine the extent to which factors such as poverty, the average number of years spent in school, the district minimum wage, and per capita expenditure all have an impact on the human development index (HDI) in six cities located in Central Java in the years 2010-202. The method used is associative with quantitative from the publication of the Central Java Province Statistics Agency (BPS). The data used is panel data consisting of cross-section data in the form of city and time series data for 2010-2021. The results showed that the minimum wage had a positive and significant effect on HDI, poverty had a positive and insignificant effect on HDI, average years of schooling had a significant positive effect on HDI, and adjusted per capita expenditure had a positive and significant effect on HDI. The coefficient of determination in this study's independent variables influences 99.08% of HDI. At the same time, the other 1.92% is influenced by other variables

Background: Pancreatic cancer (PC) is as the twelfth most frequent cancer and the seventh most important cause of mortality by reason of cancer in the world. Being informed about the incidence and mortality of this cancer and the potential role of development is useful in health policy. The aim of this research is investigating disparities in the incidence and mortality of PC in the world countries in the year 2012.
 Methods: This study was an ecologic study in the World for assessing the correlation between Human Development Index (HDI) and its details (Gross national income (GNI) per capita, average years of schooling and life expectancy at birth) with age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) of PC.
 Results: In total, 337872 new cases of PC occurred in 2012 around the world , that 178116 and 159711 cases take happen in men and women respectively, also at the same year 330391 deaths of PC occurred ,that 173,827 and 156564 cases were in men and women. In assessment the relationship between HDI and ASIR and ASMR of PC there is significant positive correlation equal to 0.767 (p <0.001) between HDI and ASIR of PC, and a significant positive correlation equal to 0.776 (p <0.001) between HDI and ASMR of PC.
 Conclusion: The incidence and mortality of PC has a significant positive correlation with the Human Development Index.

In its quest to realize vision 2030 of middle – income status, the Zambian government instituted several developmental policies. These were enabled in order to accelerate economic growth and improve human development. This paper looked at the Human Development Index (HDI) and its components, Foreign Direct Investment (FDI) and other economic trends. These were analyzed from 1990 to 2017 and also compared with specific countries. Over time there were improvements in the said indicators for Zambia. However, concerns still existed on issues such as insufficient levels of innovation and the slow decline in the poverty levels. The average years of schooling were seven, which also contributed to the country’s low Global Innovation ranking. It was noted that Multi-National Corporations (MNCs) when bringing investments mainly focussed on sectors such as mining and manufacturing. Education and health received insignificant attention in this regard. In support of the Endogenous Growth Model, which advocated for investing in human capital, the priorities of MNCs ought to be revisited. This article applied inductive reasoning to descriptive analysis. Policies and economic trends were reviewed. In order to improve from the current economic trends, investment should be directed towards areas which have a direct impact on welfare. The government through the Zambia Development Agency (ZDA) also ought to redirect investment towards social aspects such as education and health. Lastly, for the impact of external investment to be much more realized, the management of institutions needs improvement.

Spatial regression analysis is a technique employed to examine the relationship between independent and dependent variables in datasets that exhibit regional neighborhood influences or spatial effects. When a spatial effect exists for the independent variable, the Spatial Autoregressive (SAR) regression can be utilized. The Maximum Likelihood Estimation (MLE) is a commonly used parameter estimator for SAR. However, due to the limitations of MLE, the Bayesian method provides an alternative approach for parameter estimation. This study compares the results of SAR estimations using both MLE and Bayesian methods to determine the most accurate estimation model. Both methods were implemented in this research to model the factors affecting the Human Development Index (HDI) in East Java Province for the year 2022. The findings indicate that the Bayesian SAR offers a superior proposed model compared to the MLE SAR. The factors influencing the HDI in East Java Province in 2022 include poverty, per capita expenditure, and the presence of an upper middle-class manufacturing industry.

<p>Human development progress in Central Java. It is characterized by a continued rise in the human development index (HDI) of Central Java. HDI is an important indicator for measuring success in the effort to build the quality of human life. HDI explains how residents can access the development results in obtaining a long and healthy life, knowledge, education, decent standard of living and so on. HDI is affected by four factors, namely life expectancy, expected years of schooling, means years of schooling, and expenditure per capita. Currently the Central bureau of statistics do grouping HDI, using calculation formula then known how the value HDI each regency or city in Central Java. In this research we classified the regency or city in Central Java based on the HDI be high, middle, and under estimate area. We used cluster analysis. Cluster analysis is a multivariate technique which has the main purpose to classify objects based on their characteristics. Cluster analysis classifies the object, so that each object that has similar characteristics to be clumped into a single cluster (group). One of the cluster analysis method is <em>k</em>-means. The result of this research, there are three groups, high estimate area, middle estimate area, and under estimate area. The first group or the under estimate area contained 12 regencies, namely Cilacap, Purbalingga, Purworejo, Wonosobo, Grobogan, Blora, Rembang, Pati, Jepara, Demak, Pekalongan, and Brebes. The second group or the middle estimate area contained 8 regencies, namely Banjarnegara, Kebumen, Magelang, Temanggung, Wonogiri, Batang, Pemalang, and Tegal. The third group or the high estimate area contained 11 regencies, namely Banyumas, Kudus, Boyolali, Klaten, Sukoharjo, Karanganyar, Sragen, Semarang, Kendal, Surakarta, and Salatiga.</p><p><strong>Keywords</strong><strong> : </strong>cluster analysis, <em>k</em>-means, the human development index.</p>

This study aims to determine the effect of economic freedom and national income per capita of the human development index. Countries will be classified into 4 groups according to the classification in the HDI are very high human development (with an HDI of 0788 or above), high human development (with an HDI of 0677 to 0.784), medium human development (HDI of 0488-0669), and low human development (HDI of less than 0488). The analysis method used in this study is a model of data panel to investigate the influence of Economic Freedom and Income per capita of the HDI. The results showed that for the group of countries very high human local development per capita income is only significant effect on the HDI, for the group of countries High human development and medium human development only economic freedom that affects the HDI, while the low human development group showed that income per capita and Economic Freedom does not affect the HDI.

Poverty is still a problem in many developing countries, including Indonesia, especially in eastern Indonesia, such as East Nusa Tenggara, Maluku, West Papua, and Papua. Based on Statistic Indonesia data, the poverty rate in the east of Indonesia shows a downward trend but is still well above the national average. This raises questions about the achievements of poverty alleviation programs. To overcome this problem, the government has created several programs to alleviate poverty. These programs include increasing social assistance, improving access to essential services, empowering communities, and promoting inclusive development. This study aims to measure the ability of poverty alleviation programs to reduce poverty levels in Eastern Indonesia. This study uses the Blundell-Bond System GMM (Sys-GMM) analysis technique to measure these programs' short-run and long-run effects. This study used secondary data from official institutions such as Statistics Indonesia and the Ministry of Finance of the Republic of Indonesia. The data collected includes the government budget for social assistance, average years of schooling, life expectancy, human development index, and Gini ratio. Based on the results obtained in this study during the research period from 2010 to 2022, it shows that (1) the government budget in the field of social protection as a proxy for social assistance programs has no significant effect on poverty, (2) the average length of schooling as a proxy for programs to improve access to essential services in the field of education has no effect on poverty, (3) life expectancy as a proxy for programs to improve essential services in the health sector significantly reduces poverty, community empowerment programs and inclusive development proxied by (4) the human development index has no significant effect on poverty, and (5) the Gini ratio has a significant effect on poverty.

<span lang="EN-US">Poverty is a multidimensional problem related to economic, political, social, cultural, and community participation. This is evidenced by the increasing trend of the number of people living below the poverty line. The main trigger of poverty in Indonesia is the inequality of economic distribution or what is known as economic justice. Whereas all religions prohibit economic monopoly actions that cause injustice and order their people to give charity (zakat) and cooperate evenly. With the aim of reducing the poverty rate of the community even though we still often hear of hunger and hardship for some humans. This means that the welfare that is expected and aspired to has not been realized for people's lives. According to BPS 2022 data, South Sumatra is included in the top 10 with high poverty rates. Therefore, this study aims to determine, explain and analyze the factors that affect the Percentage of Poor Population (PPM) in South Sumatra for the period 2019-2022. This research method uses panel data regression method with individual unit observations as many as 17 districts / cities of South Sumatra province in a four-year period from 2019 to 2022. This study uses one response variable, namely PPM and four explanatory variables, namely the unemployment rate (TPT), average years of schooling (RLS), GRDP per capita, and human development index (HDI). The results of this study show that the fixed effect model with two-way specific effects is the best model of the panel data poverty rate. Variables that have a significant effect are GRDP. RLS. and HDI</span>

This research examines the impact of the open unemployment rate, average years of schooling, and population growth on poverty levels in districts and municipalities across East Java Province during the 2019-2023 period. By employing panel data regression and a quantitative approach, the study identifies key determinants of poverty and visualizes their geospatial distribution. The findings indicate that the open unemployment rate significantly increases poverty, whereas the average years of schooling significantly reduces it. On the other hand, population growth exhibits a positive but statistically insignificant effect. A simultaneous test confirms that the independent variables collectively influence poverty, contributing 99.03% to its variation. The study underscores the importance of improving access to education, creating employment opportunities, and managing population growth as essential strategies for poverty alleviation. These findings are intended to serve as a foundation for strategic and sustainable poverty reduction policies in East Java.