Percentage Of Poor Population Research Articles

Analisis kluster Kabupaten dan Kota di Provinsi Jambi berdasarkan indikator pembangunan

The purpose of this study is to classify Regencies and Cities in Jambi Province based on HDI, economic growth, percentage of population growth, percentage of poor population, percentage of open unemployment rate and Gini ratio. To answer these objectives the hierarchical method used is Average Linkage (Between Groups). The of analysis were obtained, two clusters could be formed, where group 1 consisted of 10 Regencies and Cities, group 2 consisted of 1 Regencies.

Modeling of West Java inflation with Spatial Durbin Model (SDM)

Inflation in Indonesia, in general, can be caused by a combination of various factors, both in terms of demand, supply, and expectations. The contribution of each element that influences inflation is not always the same from time to time. Gordon (1997) explains the determinants of inflation from these three sides, namely demand, supply, and expectations. The importance of expectations in influencing increase was also stated earlier by Chopra (1985). In this study, spatial Durbin for panel data with fixed effects in modeling inflation in the West Java province. The results show that Minimum Work Wage, percentage of poor population, Gross Regional Domestic Product (GRDP), spatial lag of poor population, and spatial lag of GRDP have a significant influence on the Durbins panel spatial model. There is an adjacent regional effect on inflation in the West Java province based on the value of the lambda obtained. Based on the criteria R-square of 94.29%, it can be concluded that the SDM panel data model has an excellent performance in explaining the diversity of data. The AIC and BIC criteria also show that the SDM panel data model well applied in modeling Regency/City inflation in West Java province.

Modeling of Human Development Index in Papua Province Using Spline Smoothing Estimator in Nonparametric Regression

The development goal of a country must be focused on the quality of human life to achieve prosperity. One important indicator for measuring the success of a country’s development is the Human Development Index (HDI). In 2018, Papua was the province with the lowest HDI in Indonesia. Special attention is needed to improve HDI in Papua Province, one of them is by paying attention to the variables that affect HDI such as population growth rate, percentage of poor population, and economic growth. The relationships between HDI and the predictor variables do not have a clear pattern and tend to change at certain subintervals. This case can be approached using Spline Smoothing in multivariable nonparametric regression. Spline Smoothing is a type of estimator in nonparametric regression that has an excellent ability to handle data that tend to change at certain subintervals. Therefore, the purposes of this study are to obtain the form of Spline Smoothing estimator function in multivariable nonparametric regression, estimate the function and apply it to the HDI in Papua Province. The empirical results of modeling HDI in Papua Province show that it can be adequately applied which gives GCV = 58.108, R2 = 99.77% and RMSE = 0.0505.

KETIMPANGAN PENDIDIKAN DI SULAWESI TENGGARA TAHUN 2019 DAN DETERMINANNYA

Kemerataan pendidikan adalah salah satu permasalahan yang perlu ditanggapi dengan serius. Hal ini bersesuaian dengan tujuan keempat dari SDGs (Sustainable Development Goals) yang mengemukakan tentang pendidikan, yaitu pada tahun 2030 memastikan bahwa semua anak perempuan dan laki-laki menyelesaikan pendidikan primer dan sekunder yang gratis, setara dan berkualitas, yang mengarah pada hasil belajar yang relevan dan efektif. Pemerataan pendidikan berkaitan dengan ketimpangan pendidikan di suatu daerah. Ketimpangan pendidikan adalah salah satu permasalahan yang harus diselesaikan karena, sebagaimana tercantum dalam UUD 1945 Pasal 31, setiap warga negara berhak mendapat pendidikan. Sayangnya, pemenuhan kemerataan pendidikan masih belum sepenuhnya terwujud. Hal ini ditunjukkan dengan tingginya Rata-rata Lama Sekolah (RLS) di Kota Kendari yang melampaui tingginya kabupaten/kota di DKI Jakarta yang tidak diikuti oleh kabupaten/kota lainnya. Penelitian ini bertujuan untuk mengetahui gambaran ketimpangan pendidikan di Sulawesi Tenggara yang diukur dengan Koefisien Gini Pendidikan (KGP) tahun 2019 dan determinannya dengan metode regresi linear berganda. Hasil penelitian menunjukkan bahwa Angka Melek Huruf (AMH) signifikan memengaruhi KGP sedangkan persentase penduduk miskin dan persentase anggaran pendidikan tidak signifikan memengaruhi KGP di Sulawesi Tenggara 2019.

Spatial panel data model on human development index at Central Java

Human development is one of the ways to create human resources that have the ability to drives the economy. It is become the focus of Indonesia’s government attention and listed on several programs implemented at the global and national levels. Human development is measured by one composite index known as the Human Development Index (HDI). HDI is often used as a measure of government success in human development. Human development in Indonesia could vary because it depends on the policies of each region, so in a region, it could give an influence between regencies or cities. In this study, focus of attention is Central Java because HDI in this region has always increased from 2017 to 2019. Therefore, this study aims to analyze the factors that affect HDI on regencies or cities in Central Java from 2017 to 2019. Factors used in this study based on previous research are gross regional domestic product, high school participation rates, population density, labor force participation rates, percentage of poor population, and percentage of households with proper sanitation. Based on this study the observed data contains location information, so these observations could be spatially correlated. Method that could handle the issue and also observed in three time periods (years) is spatial panel data model. The spatial panel data model in this study is divided into two, known as spatial lag panel data model and spatial error panel data model. Based on the comparison of two models, it is obtained that the best model could explain the Human Development Index at Central Java from 2017 to 2019 is spatial lag panel data model with all the explanatory variables significant.

СОЦІАЛЬНО-ЕКОНОМІЧНІ НАСЛІДКИ ГЛОБАЛІЗАЦІЇ В УМОВАХ ПАНДЕМІЇ

The article examines the social and economic consequences of globalization and the peculiarities of their manifestation during the COVID-19 pandemic. The meaning of the term "globalization" is revealed. The emergence of the global economy due to the development of globalization processes and their contradictory nature is noted, as there is a big discrepancy between the positive impact of globalization on developed and underdeveloped countries. These aspects, which are under the influence of globalization, interact penetrating each other. Therefore, the economy and politics, social processes and the environment become interconnected. The statistics of world GDP for the period 2005-2020 are presented. The decline of the foresaid indicator during the crisis of 2008-2009 and the crisis of 2020 is compared. The reasons for the downfall of the world GDP in 2020 are given. Attention is drawn to the rising unemployment rate in Ukraine. Attention is paid to the enrichment of the oligarchs of the countries during the crisis caused by the coronavirus. Unequal growth in wealth of billioners and an increase in the average wage of citizens are present in Ukraine. Those factors reveal the decline in incomes during the pandemic and the accumulation of opulence of wealthy people. There is an increase in the number of rich and poor people in the world. The uneven income of different segments of the population is considered. Statistics on the number of poor people, who survive on less than $ 2 a day are provided. The growth in the percentage of poor population is not shown to be dependent on the state of the economy. The problems of job loss and employment in many countries of the world during the pandemic are highlighted, as companies are slowly resuming their work after the abolition of the lockdown. There are some proposals regarding the fair redistribution of wealth and solving the problems of the poor. Despite the above negative consequences, the globalization process in a pandemic has given impetus to the active development of IT technologies. Reorientation of all spheres of human activity to the use of new technologies and their introduction into production processes are continued to be implemented. The development of digitalization in many areas of activity is one of the ways to overcome the problems of unemployment.

Pemodelan Indeks Keparahan Kemiskinan di Indonesia Menggunakan Analisis Regresi Robust

<p>Poverty is one indicator to see the success of development in a country. The Poverty Severity Index can be used as one measure of the magnitude of poverty in an area. In the Poverty Severity Index data in Indonesia, in 2018 there were some outliers, so to analyze it used robust regression. The purpose of this study is to determine the significant factors on the Poverty Severity Index in Indonesia using robust regression with the <em>M</em>-estimation method. The results showed that the Poverty Severity Index model in Indonesia using robust regression was influenced by Gini Ratio, Percentage of Poor Population, and Pure Participation Rate with <em>R-square</em> = 94,8%.</p><p><strong>Keywords: </strong>Poverty Severity Index, robust regression.</p>

Penerapan Path Analysis terhadap Faktor-Faktor yang Mempengaruhi IPM dan Kemiskinan di Indonesia Tahun 2019

Poverty is a complex and multidimensional problem so that it becomes a development priority. One of the measurement tools commonly used in seeing the quality of human life is the human development index. The purpose of this study is to analyze the HDI in Indonesia including poverty which is a general impact. The analysis was conducted using the Path Analysis method approach which is an extension of the regression analysis. The factors that had the greatest influence on HDI values in Indonesia in 2019 were Life Expectancy, then Regional Original Income, then Old School Expectations and who had the smallest influence and had a negative effect on HDI values in Indonesia in 2019 were the Number of Hospitals. The factor that has the greatest influence and has a negative influence on the percentage of poor population in Indonesia in 2019 is the Human Development Index, and another factor that also affects the percentage of poor population in Indonesia in 2019 is Hope for School Duration.

DISTRIBUTION ANALYSIS OF DOCTORS IN INDONESIA

Introduction: The distribution of health workers in Indonesia raises an interesting discussion since Indonesia as an archipelagic country has a wide geography and challenges for fulfilling equitable health services.Aim: This study identified factors related to the distribution of doctors in provinces of Indonesia.Method: Advanced analysis of secondary data was done and obtained from the "Data and Information: Indonesian Health Profile in 2017". The units analyzed in this study were all 34 provinces in Indonesia. The variables analysed were the number of doctors, population, density, percentage of poor population, the number of hospitals, and the number of primary healthcare centers.Results: Variability in the number of doctors was very wide. The more the population is, the more attractive it is for doctors to conduct practices in the provinces. The denser the population is, the more doctors are interested to work in the provinces. It also figured out that doctors tend to opt to work in the provinces which have more hospitals and primary healthcare centers.Conclusions: Out of five independent variables studied, there were four variables related to the number of doctors distributed in the provinces. Population, density, the number of hospitals, and the number of primary healthcare centers were positively related to the number of doctors. The results of this study were important for doctor redistribution policy in Indonesia. Keywords: distribution analysis, doctor distribution, health resources management, health workers.

DETERMINANTS OF DISTRICTS/CITIES EDUCATION ATTAINMENT INEQUALITY IN EAST JAVA PROVINCE DURING 2014-2016

High school enrollment rate can reduce educational inequality. However, East Java Province which has a high school enrollment rate, education inequality is also high. Using data from the National Socioeconomic Survey (Susenas) from 2014 to 2016 and publications from several institutes, this research aims to find out the determinants of districts/cities education inequality in East Java in 2014-2016. The inferential analysis uses panel data regression with the random effect model (REM). The results show that the significant variables increasing the education inequality are the percentage of poor population and percentage of married young women, while the significant variables decreasing the education inequality are Gross Domestic Regional Product (GDRP) growth, the percentage of household head educated minimum secondary level, and teacher-school ratio on the secondary level. There are no significant effects of government spending on education inequality.

Modelling Generalized Poisson Regression in the Number of Dengue Hemorrhagic Fever (DHF) in East Nusa Tenggara

Regression analysis is an analysis used to model the relationship between the dependent variable (Y) and the independent variable (X). If the dependent variable is a discrete random variable, it is developed using the Poisson regression model. Poisson regression models require non-over-dispersion model assumptions. To deal with over-dispersion, a Generalized Poisson regression model was developed. Generalized Poisson regression (GPR) model is an extension of the Poisson regression model. In this study a GPR model is applied to model the number of dengue hemorrhagic fever (DHF) sufferers in East Nusa Tenggara Province in 2018. The independent variables used include percentage of poor population (X1), population density (X2), percentage of proper sanitation (X3), percentage of decent homes (X4), number of doctors (X5), percentage of access to improved drinking water (X6), average length of schooling (X7), human development index (X8). In the resulting model, Poisson regression experiences multicollinearity and overdisception occurs. To overcome multicollinearity, variable selection is performed. Based on the measurement of the goodness of the model using AIC, the GPR model provides better accuracy than Poisson regression to model DHF in East Nusa Tenggara which is 218.5.

PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH DENGAN REGRESI KOMPONEN UTAMA ROBUST

Robust principal component regression is development of principal component regression that applies robust method at principal component analysis and principal component regression analysis. Robust principal component regression does not only overcome multicollinearity problems, but also overcomes outlier problems. The robust methods used in this research are Minimum Covariance Determinant (MCD) that is applied when doing principal component analysis and Least Trimmed Squares (LTS) that is applied when doing principal component regression analysis. The case study in this research is Human Development Index (HDI) in Central Java in 2017 which is influenced by labor force participation rates, school enrollment rates, percentage of poor population, population aged 15 years and over who are employed, health facilities, gross enrollment rates, and net enrollment rates. The model of HDI in Central Java in 2017 using robust principal component regression MCD-LTS provides the most effective result for handling multicollinearity and outliers with Adjusted R2 value of 0.6913 and RSE value of 0.469. Keywords: Robust Principal Component Regression, Multicollinearity, Outliers, Minimum Covariance Determinant (MCD), Least Trimmed Squares (LTS), Human Development Index (HDI).

Pemodelan Kemiskinan Pada Kabupaten/Kota Di Provinsi Jawa Timur Tahun 2015 Dengan Pendekatan Model Regresi Spasial

Regression is a technique that can be used for response variables with one or more predictor variables. The purpose of this study is to model poverty in districts / cities in East Java 2015 with a spatial regression approach. In 2015, poverty in East Java has increased compared to the previous year. Therefore it is necessary to identify the factors that affect poverty. The variables used are the percentage of poor population as the response variable and the predictor variables include last elementary school education (X1), school participation rate 7-12 years (X2), informal sector workers (X3), open unemployment rate (X4), household using bamboo walls (X5), and household users of inadequate drinking water sources (X6). The result of this research is the best model to model the percentage of poor people is Spatial Error Model (SEM) with spatial weighting matrix Queen Contiguity and obtained AIC value 191,02 and R2 equal to 77,47%. Factors that have significant effect on the percentage of the poor are school enrollment (X2), informal sector workers (X3), household users of inadequate drinking water sources (X6) and there is an error dependency on one location to another.