Human Development Index Data Research Articles

Analisis Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia Di Indonesia Tahun 2023

The Human Development Index (HDI) is a composite indicator that reflects the quality of life of a population through three primary dimensions: health, education, and a decent standard of living. Improvements in HDI are supported by advancements across all its constituent factors. The dimension of long and healthy life is represented by the increase in life expectancy (LE), while the education dimension is captured through the rise in the mean years of schooling (MYS) and expected years of schooling (EYS). Additionally, the standard of living dimension is indicated by improvements in adjusted per capita expenditure. This study aims to analyze the factors that determine HDI in Indonesia in 2023 using multiple linear regression analysis. The data used in this study are secondary data sourced from the Central Statistics Agency in the form of Human Development Index data and the factors that influence it. The findings reveal that variables such as Life Expectancy (LE), Expected Years of Schooling (EYS), Mean Years of Schooling (MYS), and Adjusted Real Per Capita Expenditure significantly affect the Human Development Index in 2023. This is evidenced by an R-Square value of 0.9957, indicating that the independent variables collectively explain 99.57% of the variance in the dependent variable.

Association of the national level of human development with the incidence and mortality of congenital birth defects in 2019: A cross-sectional study from 189 countries.

Congenital birth defects (CBD) play a significant role in causing child mortality globally. The incidence and mortality of CBD vary widely across countries, and the underlying causes for this divergence remain incompletely comprehended. We conducted an analysis to investigate the relationship between the incidence and mortality of CBD in 189 countries and their Human Development Index (HDI). In this study, CBD data from 189 countries was used from the Global Burden of Diseases Study (GBD) 2019, and HDI data was collected for the same countries. Later, the relationship between CBD and HDI was analyzed, and the impact of gross national income (GNI) per capita, expected years of schooling, mean years of schooling and life expectancy at birth was quantified using principal component regression. The age-standardized incidence rate (ASIR) varied between 66.57 to 202.24 per 100,000, with a 95% uncertainty interval (UI) of 57.20-77.51 and 165.87-241.48 respectively. The age-standardized mortality rate (ASMR) also showed a rang from 1.38 to 26.53 (14.03-39.90) per 100,000, with the 95%UI of 0.91-2.09 and 14.03-39.90 respectively. Both the incidence and mortality rates of CBD decreased with the increased HDI (incidence: r = -0.38, p < 0.001, mortality: r = -0.77, p < 0.001). Our investigation revealed significant variations in the incidence and mortality of CBD among countries with different development levels. In conclusion, the global incidence and mortality of CBD vary significantly among countries, possibly due to differences in the accessibility of health services.

Analisis Regresi Data Panel Dengan Model Efek Umum, Model Efek Tetap Dan Model Efek Acak (Studi Kasus: Inflasi Dan Indeks Pembangunan Manusia)

Panel data regression analysis is a method for modeling the influence of independent variables on dependent variables, on a combination of cross-section and time-series data. This research aims to estimate a panel data regression model with a generalized effects model using the least squares method, estimate a fixed effects model with the Least Square Dummy Variable and estimate a random effects model with Generalized Least Square on inflation and human development index data. The results obtained show that the factors that have a significant influence at the 5% level on the inflation rate in 2014-2019 are the dollar exchange rate with a coefficient of determination of the general effects model of 61.06%, then the HDI level in South Sulawesi in 2011-2017 is significantly influenced by factors such as average length of schooling and life expectancy with a coefficient of determination of the fixed effects model of 89.73%, and the HDI level in South Sulawesi in 2016-2019 is significantly influenced by the factors of life expectancy, per capita expenditure and poverty with a coefficient of determination of the random effects model amounting to 63.07%.

Pengaruh Kinerja Keuangan Daerah Terhadap Kesejahteraan Masyarakat di Kabupaten/Kota Se-Provinsi Kalimantan Tengah Tahun 2020 dan 2021

This research aims to determine the influence of regional financial performance on community welfare in districts/cities in Central Kalimantan Province. Regional financial performance is measured by the regional financial independence ratio and the effectiveness ratio of Regional Original Income, while community welfare is measured by the Human Development Index (HDI). This research was conducted in 14 districts/cities in Central Kalimantan Province. The research method used in this research is multiple linear regression. The analytical tool used is the Statistical Program for Social Science. The type of data used in this research is secondary data. The data used in this research is the realization of regional income and expenditure budgets from 2020-2021. Realization of regional income and expenditure budgets is obtained through the website of the Directorate General of Regional Financial Balance. HDI data is obtained through the Central Statistics Agency website. The results of this research show that the regional financial independence ratio has a partial effect on community welfare, while the regional original income effectiveness ratio has no partial effect on community welfare in all regencies/cities in Central Kalimantan Province.

Comparison of Weights in Weighted Least Square Method For Handling Heteroscedasticity on Multiple Regression Model

Regression analysis is the most popular and commonly used to determine causality between two or more variables. In regression analysis there are several assumptions that must be held, so that the property of the best linear unbiased estimator (BLUE) is still guaranteed. In fact, we often found violations of the assumptions. One of them was violations of the homoscedasticity or occurs heteroscedasticity. The impact of heteroscedasticity in the regression model is that the ordinary least square (OLS) estimator no longer has a minimum variance although still linear and unbiased. To handle this, weighted least square (WLS) regression is used instead, which giving weights on the observations. But the problem often encountered is choosing which the best weight in WLS method. This paper aimed to compare and determine the best weight among 1/X, 1/ , 1/Y and 1/s in multiple regression model. Human development index factors data, which were obtained from the Indonesian Central Bureau of Statistics, were used. The results showed that the best weight on human development index data was 1/s. The coefficient of determination was 98.7% indicating that the model was very good.

Influencing factors for the human development index in West Java using geographically and temporally weighted regression kernel functions

Human Development Index (HDI) is a competitive index that serves as one of the crucial metrics for evaluating the effectiveness of enhancing the quality of human resources. HDI values from different areas can be compared. This study aims to spatially and temporally explore the HDI data from districts or cities in West Java and examine the factors that influence HDI in each of these districts or cities using the GTWR Great Circle Distance Fixed Kernels model. In this study, we used a combination of cross-sectional data from districts or cities in West Java and time series data with seven annual periods from 2015-2021. The GTWR Great Circle Distance Fixed Kernels model was expected to display coefficient values at each location and time simultaneously, providing more in-depth information and analysis results at each location and time. The analysis results using the GTWR Great Circle Distance Fixed Kernels model show that HDI in West Java carries a positive influence on the location and time. This finding should be of particular concern to the relevant government, particularly the factors presenting a natural effect on HDI based on location and time. The positive influence obtained by an area at a particular time will also have a positive impact on other regions, and if there is a negative influence, it will undoubtedly affect other regions as well. Analysis of the HDI model in West Java using the GTWR Great Circle Distance Fixed Exponential Kernel model also presents better results in comparison to the Global OLS model and the GTWR model without the Great Circle Fixed Exponential Kernel. The final parameter estimator results are displayed in the form of a geographic map to facilitate ease of understanding.

Institutional Quality and Economic Performance: A Study of Asian Countries

The objective of this article is to investigate the influence of institutional quality on the economic performance of 47 Asian countries, categorized into four income levels (low-income, lower-middle-income, upper-middle-income and high-income countries) over the period 1981–2021. The study utilizes six governance indicators, and a composite governance index is constructed to evaluate institutional quality. Economic performance is assessed through three key indicators: real gross domestic product (GDP), real GDP per capita (GDPPC) and the human development index (HDI). Except for HDI data, all variable data were sourced from the World Bank, while HDI data were obtained from the UNDP. The study employed panel regression, panel co-integration techniques and the autoregressive distributed lag model. The findings indicate that governance indicators have a positive and significant impact on GDP, GDPPC and HDI in Asian countries. Moreover, the study reveals that the influence of governance indicators on economic performance is more pronounced in high-income countries compared to other income levels in Asia. Consequently, the research emphasizes the importance of focusing on governance indicators for fostering economic growth and enhancing HDI in Asian countries.

The Effect of Fiscal Decentralization, Balanced Budget, Provincial Financial Aid, and Public Investment on Human Development Index with Population as a Moderation Variable in Regency/City Regional Government in North Sumatra Province

This research aims to analyze the influence of Fiscal Decentralization, Balancing Funds, Provincial Financial Assistance, and Public Investment on the Human Development Index partially and simultaneously. This research also analyzes the influence of Fiscal Decentralization, Balancing Funds, Provincial Financial Assistance, and Public Investment on the Human Development Index with Population as a moderating variable. The population of this research is the Regency/City Government in North Sumatra Province, totaling 33 Regency/Cities. Data collection methods were carried out using documentation techniques using secondary data, namely APBD Realization Reports, Population Data, and Human Development Index data for 2017-2021. The sampling technique used was purposive sampling based on specific criteria. Based on these criteria, a total of 145 analysis units were obtained. The data analysis methods used in this research are descriptive statistical analysis methods, classical assumption tests, multiple linear regression analysis, sensitivity test analysis, hypothesis testing, and residual tests using SPSS version 25. This research shows that Fiscal Decentralization, Balancing Funds, Provincial Financial Assistance, and Public Investment affect the Human Development Index. Partially, fiscal decentralization and provincial financial assistance have positively affected the human development index. The Balancing Fund has a negative effect on the Human Development Index, and Public Investment does not affect the Human Development Index. This research also concludes that population is a moderating variable in strengthening and weakening the relationship between Fiscal Decentralization, Balancing Funds, Provincial Financial Assistance, and Public Investment on the Human Development Index. Keywords: fiscal decentralization, balancing funds, provincial financial assistance, public investment, human development index, population.

Panel Data Spatial Regression Modeling with a Rook Contiguity Weighting Function on the Human Development Index in West Sumatera Province

The achievement of the level of welfare of a region or country can be seen from the level of human development as measured by the Human Development Index (HDI). West Sumatra is one of the provinces with HDI achievements above the national average. However, there are still regencies/cities in West Sumatra Province that have HDI achievements below the national average and HDI achievements in West Sumatra Province Regencies/Cities have changed in 2017-2021. Therefore, in this study, spatial analysis of panel data was used. The aim of this research is to find out the general description of the HDI of West Sumatra Province, obtain a panel data spatial regression model and obtain variables that significantly influence on HDI in West Sumatra Province 2017─2021because differences in HDI achievement were suspected to have influences from areas that were side by side and the area was observed more than once. The model formed from this analysis using the rook contigutiy weighting function is Random Effect Spatial Autoregressive because the spatial interactions formed in human development index data in West Sumatra Province are real at lag. This model is a suitable model based on panel spatial model selection and has an R2 value of 92.94%. Analysis of human development index data in regencies/cities in West Sumatra Province using spatial regression panel data obtained results that expectations of school length (X1), average length of schooling (X2), and population density (X3) significantly directly influenced the human development index in regencies/cities in West Sumatra Province.

The Influence of Regional Original Income (PAD) and General Allocation Funds (DAU) on the Human Development Index (HDI) in North Sulawesi 2004 - 2022

HDI (Human Development Index) is an indicator in measuring the level of success in improving the quality of human life and determining the ranking/level of development of a region. Increasing human resources is also defined as increasing the basic capabilities of the population, therefore increasing opportunities to be able to participate in the human development process which requires several basic indices such as expenditure, education and health, allocation of local original income and general allocation funds in making human development a success in Infrastructure, health and education matters play an important role in efforts to increase human development. This research aims to examine the influence of local revenue and General Allocation Funds on the Human Development Index in North Sulawesi Province. The research method used is a quantitative method. This research uses PAD, DAU and HDI data for 2004 - 2022. The type of data used is secondary data taken from the Central Statistics Agency (BPS) and this research was tested using the ECM model.

Analisis Deskriptif Indeks Pembangunan Manusia Indonesia Periode Tahun 2020 - 2023

Human development is one of the national development goals outlined in Law Number 25 of 2004 concerning the National Development Planning System. The Human Development Index (HDI) serves as a key indicator to measure the success of human development, comprising three dimensions: longevity and healthy life, knowledge, and a decent standard of living. During the period 2020-2023, Indonesia's HDI experienced an improvement. In 2023, Indonesia's HDI reached 74.39, a rise of 0.62 points (0.84 percent) compared to the previous year (73.77). This increase was observed across all dimensions, including longevity and healthy life, knowledge, and a decent standard of living. The objective of this research is to provide a descriptive analysis of Indonesia's HDI during the period 2020-2023. Secondary data from the Central Statistics Agency (BPS) were utilized, encompassing HDI data, life expectancy, years of schooling expectancy, and per capita expenditure per year. The research findings reveal a significant improvement in Indonesia's HDI during the 2020-2023 period. The HDI increase is evident in all dimensions: 1) Longevity and Healthy Life Dimension, Life expectancy at birth increased from 73.7 years in 2020 to 73.93 years in 2023. 2) Knowledge Dimension, Years of schooling expectancy rose from 12.6 years in 2020 to 12.8 years in 2023. 3) Decent Standard of Living Dimension, Per capita expenditure per year increased from Rp11,293,000 in 2020 to Rp11,899,000 in 2023.

Integrating Satellite Imageries and Multiple Geospatial Big Data for Granular Mapping of Spatial Distribution of Human Development Index in East Java, Indonesia

The availability of data on the Human Development Index (HDI) is crucial as a gauge of regional performance, particularly in terms of assessing the development of human resources. In Indonesia, the collecting of HDI data usesthe conventional method, such as undirect estimation, National Socio-Economic Survey (SUSENAS), The Ministry of Religion, or inventory of sectoral data that used the large cost, time, and effort. Additional data are required to provide more detailed poverty data at a lower cost and with more recent information to overcome these limitations. According to recent studies, the quality of life for measuring HDI can be identified down to the granular level using geospatial big data. Therefore, the contribution of this research is to implement the use of geospatial big data, such as integrated multi-source satellite imagery data and Point of Interest (POI). Besides that, this study develops the relative spatial human development index in 11 km x 11 km resolution for the granular mapping of the quality of life to measure the HDI in East Java, Indonesia. The kinds of weighted sum models used in this study such as equal weight (EWS), Pearson (PCCWS), Spearman (SCCWS) correlation-based weight, and Principal Component Analysis (PCA)-based weight (PCAWS). The best RSHDI PCCWS for representing the human development index in East Java in 2022, which was determined using a weight-sum model based on Pearson correlation, has a correlation coefficient of 0.7858 (p-value = 5.078 x 10-9) and is highly correlated with official HDI data. The use of this RSHDI as a predictor variable in the estimation of HDI data shows the ideal model had an RMSE of 3.098% and an R2 of up to 61.75% using RSHDI PCCWS. According to the findings of the descriptive analysis of this map, areas with low RSHDI scores typically in some regencies areas in Madura Island and the east area of East Java with geographically depressed, while areas with high RSHDI scores typically have dense populations and have better accessibility such as urban area in Surabaya and Kota Malang. As a result, the official human development index data can be supported by the RSHDI's ability to map spatially deprive areas

UNVEILING THE HIDDEN LINK: HUMAN DEVELOPMENT AND VOTER TURNOUT IN BALOCHISTAN, PAKISTAN

The province of Balochistan in Pakistan provides an intriguing opportunity to analyze the complex correlation between human development and voter engagement. This study aims to determine if there is a direct relationship between the Human Development Index (HDI) and voter turnout in Balochistan. The study utilizes a quantitative research approach, analyzing data from the Human Development Index (HDI) and voter participation records for districts in Balochistan, Pakistan. The HDI data was acquired from the United Nations Development Programme (UNDP), whilst the voter turnout data was gathered from the Election Commission of Pakistan (ECP). The study's findings indicate no direct correlation between HDI and voter turnout. Some districts with low Human Development Index (HDI) values have a high voter turnout, whereas, on the other hand, certain districts with high HDI values have a low voter turnout. These findings indicate that other factors beyond HDI substantially influence voter turnout in Balochistan. Further research would be helpful to thoroughly understand the intricate relationship between HDI and voter turnout in Balochistan. These research efforts have the potential to find strategies that can increase voter turnout in the province.

INFLUENCE OF GROSS REGIONAL DOMESTIC PRODUCT AND OPEN UNEMPLOYMENT RATE ON THE HUMAN DEVELOPMENT INDEKS IN LAMPUNG PROVINCE FOR THE YEARS 2017-2021

This research aims to determine the influence of Gross Regional Domestic Product (GRDP) and Open Unemployment Rate (OUR) on the Human Development Index (HDI) in Lampung Province for the years 2017-2021. The data used is secondary data obtained from the Central Statistics Agency (BPS), which includes data from each of the 15 districts/cities in Lampung Province. The research method employed is quantitative. The data was analyzed using panel data regression method by combining the data of the 15 districts/cities in Lampung Province along with GRDP, OUR, and HDI data from 2017 to 2021. The t-test results indicate that Gross Regional Domestic Product has a positive and significant impact on the Human Development Index in Lampung Province for the years 2017-2021. The Open Unemployment Rate has a positive and significant impact on the Human Development Index in Lampung Province for the years 2017-2021. The f-test results show that both Gross Regional Domestic Product and Open Unemployment Rate have an impact on the Human Development Index in Lampung Province for the years 2017-2021. The panel data regression results indicate that the city of Mesuji has the highest constant at -57.34%. Additionally, the coefficient of determination (R2) in this study is 97%, meaning that the variables of Gross Regional Domestic Product and Open Unemployment Rate collectively explain 97% of the variation in the Human Development Index, while the remaining 3% is influenced by other variables.

ANALISIS VARIABEL INDEKS PEMBANGUNAN MANUSIA MENGGUNAKAN ANALISIS REGRESI LINIER BERGANDA DI PROVINSI JAWA TIMUR

According to the Central Statistics Agency, HDI is formed by three basic dimensions, namely life expectancy, knowledge, decent living standards. So researchers are interested in analyzing the influence of these 3 basic dimensions on HDI using multiple linear regression analysis. By determining the independent variables and dependent variables, human development index data in East Java in 2021. Then determining the estimated value of the multiple regression model. Next, carry out testing, starting with a simultaneous test. After that, a partial test (t test) was carried out. Then the classical assumption test is carried out, which is a statistical requirement that must be met in multiple linear regression analysis. In multiple regression analysis, classic assumption tests are carried out, including the normality test, multicollinearity test and heteroscedasticity test. Then look at the coefficient of determination value to find out how far the model is able to explain variations in the dependent variable. The final step in this research is to interpret the model with multiple linear regression, namely to see and explain the results of the best model analysis and find out the independent variable that has the most significant influence on the dependent variable. Research data analysis was carried out using SPSS software. The multiple linear regression model formed in the analysis of the human development index in East Java obtained a contribution value from the independent variable and the dependent variable of 99.7%.

Model Regresi Nonparametrik Spline Truncated Pada Indeks Pembangunan Manusia di Indonesia

Truncated spline nonparametric regression is a nonparametric regression analysis using a segmented polynomial model. This segmented nature provides flexibility so that the regression model can adapt more effectively to the local characteristics of the data. The purpose of this study was to obtain a regression model and determine the factors that influence the Human Development Index (HDI) in all provinces in Indonesia using multivariable truncated spline nonparametric regression. The Human Development Index is an important indicator in measuring success in efforts to build the quality of human life. The Human Development Index can determine the rank or level of development of a region and a country. In development, a high Human Development Index is something that is expected to be achieved, especially for developing countries. The Human Development Index data used in this study is based on BPS data published in 2020 from all provinces in Indonesia. In this study, based on the results of the analysis, the best nonparametric truncated spline regression was obtained using 1 knot point, 2 knot point and 3 knot point. Based on the minimum Generalized Cross Validation (GCV) value, the best truncated spline regression model is 3 knots with an R2 value of 83.70%. The factors that influence the Human Development Index are the variables expected length of schooling, life expectancy at birth, and population

The Effect of Government Expenditure on Education, Health and Road Infrastructure Sector on Human Development Index in Kabupaten Sidoarjo

Human development in East Java continues to grow, one of which is Kabupaten Sidoarjo. The Kabupaten Sidoarjo Human Development Index (HDI) in 2021 is ranked 4th in East Java. The Human Development Index in Kabupaten Sidoarjo has experienced a good increase every year. With this increase, a study was conducted that aims to determine and analyze the effect of government spending in the education, health and infrastructure sectors on the human development index in Kabupaten Sidoarjo. This research was conducted in Kabupaten Sidoarjo by collecting and processing periodic human development index data from 2011-2022 (12 years). The approach is done using descriptive quantitative. For data analysis methods using classical assumption tests, regression analysis and hypothesis testing. From the results obtained that government spending in the education and health sectors has a significant effect and a positive relationship while the infrastructure sector has no significant and positive effect on the value of HDI in Kabupaten Sidoarjo in 2011-2022.

Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).

Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province

Spatial data are data containing information on the location or geography of a region on the representation of objects on earth. Geographically Weighted Regression (GWR) is a development of the Ordinary Least Square (OLS) theory into a weighted regression model that considers spatial effects, resulting in a parameter estimation that can only be used to predict each location where the data are observed. The Human Development Index (HDI) is an essential indicator for measuring success in efforts to build human quality of life. HDI data regencies/cities in Central Java are interconnected, so it is said to be spatial data and there are spatial effects in it. Therefore, the GWR method was applied to obtain faculties affecting HDI in Central Java Province. The data used were secondary data in 2020. The determination coefficients of the GWR model ranged between 76.09% and 87.16%. If the variable values of population density and Gross Regional Domestic Product (GRDP) increase by one unit in each district/city in Central Java Province, the HDI variable value increases. These results were visualized on a dashboard providing information about the characteristics of HDI and independent variables, GWR parameter estimates, and the significance of independent variables in each regency/city.

Determining sustainability using the Environmental Performance Index and Human Development Index – An alternative approach to the Environmental Human Index through a holistic quantitative dynamic framework

The Environmental Human Index (EHI) was recently proposed and demonstrated as a new sustainability assessment tool which uses data from the Environmental Performance Index (EPI) and the Human Development Index (HDI). However, the EHI has potential conceptual and operational issues in respect to its consistency with established concepts and principles of the coupled environment-human system and sustainability. Specifically, the thresholds of sustainability the EHI uses, the bias towards the anthroposphere, and the absence of unsustainability. These issues raise potential questions concerning the EHI's value and approach adopted to utilise the EPI and HDI data to determine potential or actual sustainability outcomes. Therefore, to demonstrate how the EPI and HDI can be used to determine sustainability outcomes, the Sustainability Dynamics Framework (SDF) is applied in respect to the case study of the United Kingdom 1995–2020. The results indicated strong sustainability occurring throughout the specified period, within a S-value range of [+0.503 ≤ S(t) ≤ +0.682]. The Pearson correlation analysis showed a significant negative relationship between E and HNI-values and between HNI and S-values, and a significant positive relationship between E and S-values. The Fourier analysis indicated a three-phase change in the nature of the environment-human system dynamics over the 1995–2020 period. The SDF application to the EPI and HDI data has shown the importance of using a consistent holistic conceptual and operational framework to determine and evaluate sustainability outcomes.