Average Length Of School Research Articles

Penerapan Autoregressive Distributed Lag (ARDL) Dalam Memodelkan Pengaruh Lama Sekolah Dan Tingkat Pengangguran Terbuka Terhadap Kemiskinan di Kota Manado

The increase in poverty rates in Manado City, North Sulawesi, is influenced by various factors such as the average length of schooling and the open unemployment rate. This study aims to determine a model and to determine whether the average length of schooling and the open unemployment rate can affect the poverty rate in Manado City from previous time periods using the autoregressive distributed lag (ARDL) model. The data used in this study are secondary data, namely the percentage of poverty rates, average length of schooling, and the percentage of open unemployment rates taken from the Central Bureau of Statistics (BPS) of Manado City, North Sulawesi Province, from the annual period starting from 2003-2021. The results showed that the best model offered by Akaike Information Criteria (AIC) was ARDL (2,2,2) with the smallest AIC value of -0.36 with the model equation with the results that both independent variables, average length of schooling and open unemployment rate, can affect poverty in Manado City.

Regresi Data Panel untuk Memodelkan Tingkat Kemiskinan di Provinsi Papua

The purpose of this research was to model and determine the factors that influence the poverty rate in the Province of Papua from 2018 to 2020 by applying panel data analysis. The independent variables in this research were life expectancy, average length of schooling, open unemployment rate, and labor force participation rate. The estimation model used was the common effect model (CEM), fixed effect model (FEM) and random effect model (REM). The best model to determine the factors that influence the poverty rate in the Province of Papua was Random Effect Model and (REM) and the independent variable that has a significant effect on the poverty rate in Papua Province in 2018-2020 was the average length of school.

The determinants of poverty in the West Papua province

Poverty has become a serious development problem, including in West Papua Province. This research has become a fundamental issue because West Papua tends to be difficult to get out of poverty problems, which has the second-highest percentage of poverty in Indonesia. This study aims to analyze the determinants of poverty in West Papua. The dependent variable is poverty, and the independent variables are population density, unemployment, human development index, and the average length of schooling. This research uses multiple regression analysis time series, 11 years period. The finding of this study is that unemployment has a positive and significant effect on poverty. Meanwhile, the human development index and the average length of schooling have a negative and significant effect. The implication is that the human development index and the average length of schooling must be expanded in scope to open access to people living in remote areas. The region's characteristics and the population tend to live in the interior, so accessibility is an important factor in alleviating poverty in West Papua.

POVERTY PANEL DATA MODELING IN SOUTH SUMATERA

Poverty is still a major problem on Sumatra's island, despite abundant natural resources potential, such as mining and plantation products. Sumatra Island consists of 10 provinces divided into three regions: Northern Sumatra, Central Sumatra, and Southern Sumatra. The island of Sumatra has the highest number of poor people in the Southern Sumatra region, which reaches 2.88 million people. Poverty is an integrated concept with five dimensions: poverty, powerlessness, vulnerability to emergencies, dependency, and alienation, both geographically and sociologically. One method that can be used to analyze poverty data problems is panel data regression analysis, which combines two data, namely cross-sectional data and time series data. It is expected to produce more in-depth and comprehensive information, both the interrelationships between the variables and their development within a certain period. The panel data was related to poverty and included 60 districts in the southern Sumatra region from 2018 to 2020. This study aimed to model poverty panel data in Southern Sumatra. Three estimation methods were used in the panel data regression, including the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM. The results of the model specification test show that the best model for estimating the percentage of poor people in the Southern Sumatra region is the Fixed Effect Model (FEM), with a value of R2 = 75.57%. The results of the significance test show that the variables that significantly influence the percentage of poverty in the Southern Sumatra region using the FEM model are the open unemployment rate (), life expectancy (), and the average length of schooling ().

SPATIAL MODELING IN DATA PANELS WITH LEAST SQUARE DUMMY VARIABLE TO IDENTIFY FACTORS AFFECTING UNEMPLOYMENT IN INDONESIA

Unemployment is a serious issue that must be addressed. Unemployment has a negative impact on the national economy, making economic growth unpredictable. In 2015, Indonesia was ranked third with the highest unemployment rate in ASEAN. It is estimated that the unemployment rate in each province of Indonesia is influenced by the surrounding provinces. Therefore, spatial modelling on panel data with Least Square Dummy Variable (LSDV) is needed to identify factors that influence unemployment in Indonesia. The data used is Open Unemployment Rate (OUR) data and influencing factors are population, average length of schooling, Gross Regional Domestic Product (GRDP) rate, and Human Development Index (HDI) in 34 provinces of Indonesia from 2015 to 2020. Spatial model on panel data with appropriate LSDV for OUR data are Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM). The SAR model with fixed effects has an value of 90.289%, which is greater than the SEM model with fixed effects (82.708%) and LSDV model (87.864%). The root mean square error value for SAR model with fixed effects is 0.58951, less than SEM model with fixed effects (0.78669) and LSDV model (0.65903). The best model is the SAR model with fixed effects. Based on this model, the factors that influence OUR in Indonesia from 2015-2020 are obtained, namely the rate of GRDP and HDI.

Determinants Of Human Development Index In Papua Province 2012-2021

The Human Development Index (HDI) aims to emphasize that a country's development assessment should be based on the capabilities of its population. Papua, as the province with the lowest HDI in Indonesia, reflects the poor level of living and human development in Papua. Although Papua is a special autonomous region that receives an allocation of funds, its public services and people's welfare still lag behind other provinces in Indonesia. The purpose of this study is to understand how Special Autonomy Funds, Sharia Financing, Information and Communication Technology (ICT), and Average Length of School (ALS) affect the Human Development Index. This study uses Error Correction Model (ECM) as an analysis method. The results showed that ICT did not have a significant effect on HDI, Special Autonomy Fund had a negative influence in the short term and a positive influence in the long term, Average School Year had a positive influence in the short term and a negative influence in the long term, while Islamic Bank Financing had a positive influence both in the short and long term.

Analisis Cluster Fuzzy C-Means dan Diskriminan untuk Pengelompokan Data Kesejahteraan Rakyat

Cluster analysis is a multivariate method that aims to classify objects into a cluster based on different characteristics. The statistical technique that can be used for clustering is Fuzzy C-means (FCM). The FCM uses membership degree to determine the existence of each data point in a cluster. To ensure accurate clustering and that the criteria are met, cluster validation must be done to produce good data clustering. Moreover, this study uses a discriminant analysis test to validate the results of cluster solutions. The study aims to classify 34 provinces in Indonesia based on the level of people's welfare. The data used in this study is data on indicators of people's welfare in Indonesia in 2021. The variables used in this study were age/life expectancy, the average length of schooling, the 16-18 years of enrollment, and households with proper access to water. Based on the study results, two clusters were formed: cluster 1, areas with low people’s welfare indicator values in 16 provinces, and Cluster 2, regions with high people's welfare indicator values in 18 provinces.

Political Communication Realization of Sragen Regent in the 2nd Period of the Second Year (2020/2022)

This research focuses on the realization of political communication delivered by the Sragen Regent during the campaign at the 2020 Sragen regent election democratic party. Political communication is basically political messages containing the Regent's vision and mission, political communication is a political promise demanded by the community to be realized by the Regent of Sragen. The realization of the Regent's vision and mission is outlined in the main performance indicators of Sragen Regency. The realization of the Sragen Regent's political communication is important to continue to be monitored by the community so that the benefits can be felt by the people of Sragen Regency. This type of research is descriptive qualitative. Data collection methods are interviews and documentation. Data types are primary and secondary data, to analyze data using interactive models. The results of this research are the realization of the Sragen Regent's political communication for the second period of the second year 2022, namely: 1. Human Development Index (expected length of school, average length of school, life expectancy, gender development index, predicate of child-friendly district) target 74.25 % realization 74.65% achievement level 100.54 % 2. Bureaucratic Reform Index (Regency performance accountability predicate, community satisfaction index, level of regional financial independence, SPBE index) target 60.59% realization 60.36 achievement level 99.62%. 3. Growth (per capita income, realized value of capital investment, score on food expectations) target 4.29% realization 5.76% achievement level 134.27%. 4. Poverty rate (reducing poverty rate) target 12.28%, realization 12.94%, achievement level 94.63%. 5. Open unemployment rate (labor force participation rate) target of 4.37%, realization of 4.69%, achievement rate of 92.68%. 6. Environmentally Friendly Infrastructure Index (infrastruktus index. Environmental quality index) target 72.49% realization 73.80% achievement level 101.81%.

Estimation Of Parameter Regression Panel Data Model Using Least Square Dummy Variable Method

Panel data regression is a set of techniques for modeling the effect of independent variable on the dependent variable of panel data. The parameter estimation in the panel data regression model used the least squares method, but the difference between the intercept and the slope could not be known between time and between cross-section. One of the methods used is the Least Square Dummy Variable method (LSDV). The LSDV method is a method that has the same stages as the least squares method, but uses dummy variable to get different intercept score. This research uses the LSDV method to explain the differences in intercept between cross-sections using balanced panel data, namely the Human Development Index (HDI) data in South Sulawesi 2011-2017 to get fixed effect panel data regression model parameters on that data and the regencies with Average Length of School (ALS) and Life Expectancy (LE) variable that has the most influence on HDI based on the coefficient of determination criteria. According to the results of this research, the score of the coefficient of determination in the panel data regression model using the fixed effect model in each cross-section (regency), there are also three regencies with the highest coefficient of determination, respectively, Gowa, Pare-pare and Bantaeng regency that ALS and LE are able to explain the HDI variables 98.942%, 98.089% and 97.444%.

The Effect of the Tourism Sector, Human Capital, And Economic Openness on Total Productivity Factors in Bali Province 1985 – 2019

In order to improve economic performance, increasing labor productivity is very important, because the higher the productivity of the workforce, the more its competitiveness will increase. This study aims to analyze the effect of the tourism sector, human capital, and economic openness on the total factor productivity in Bali Province. The data used is secondary data obtained from the Central Statistics Agency in the form of data on capital formation, labor, average length of schooling, and export-import in Bali Province from 1985 - 2019. The analysis technique used is multiple linear regression. The results showed that the tourism sector and the quality of human capital had a positive effect on total factor productivity in Bali Province during 1985 – 2019, while economic openness had a positive but not significant effect.

ANALISIS PENGARUH PERTUMBUHAN PENDUDUK, TINGKAT PENDIDIKAN DAN TINGKAT PARTISIPASI ANGKATAN KERJA (TPAK) TERHADAP KEMISKINAN DI PROVINSI PAPUA

The problem of poverty has a very urgent nature and has no clear boundaries. This is due not only to its continued upward trend, but also to its impact that transcends the economic sphere, encompasses social aspects and even threatens political stability within the country. Most countries with large populations also tend to have higher poverty rates compared to countries with smaller populations. Likewise, with the level of education and TPAK where inmany communities with low economies, opportunities to get adequate education, ranging from elementary to higher, are often limited, furthermore, if TPAK increases this can have a positive impact on economic growth. This study uses time series data with variables of population growth, education level, , TPAK, and poverty rate in the last 26 years, namely 1996-2021 until the number of observations is 26 observation points. Based on the results of data processing and testing simultaneously shows that population growth, education rate, and labor force participation rate (TPAK) affect simultaneously in Papua Province. Population growth has a negative and significant effect on poverty. The level of education, that is, the average length of schooling, has a negative and insignificant effect on poverty. The labor force participation rate (TPAK) has a negative and significant effect on poverty.
 keyword: Population Growth, Education Level, TPAK, Poverty

Analysis Of Unemployment Clusters In Indonesia Using The Self Organizing MAP Method

Unemployment is a situation where someone is not working or is trying to find a job but unable to find work. The spread of unemployment in Indonesia has different characteristics in each region, so it is necessary to classify the unemployed so that each government policy program can be carried out in a more focused and directed manner. This study discusses cluster analysis of unemployment using the Self Organizing Map (SOM) method in classifying the unemployed in Indonesia in 2020. The SOM method is able to show dominant patterns and variables in clusters. The variables used in this study consisted of school enrollment rates, average length of schooling, labor force participation rates, and the percentage of the population using computers. The results of this study formed 3 unemployment rate clusters with cluster 1 being a low unemployment group consisting of 144 districts/cities, cluster 2 with a medium level consisting of 287 districts/cities, and cluster 3 with a high level consisting of 83 districts/cities. The grouping using the SOM method on district/city unemployment data in Indonesia is good because it has a minimum standard deviation ratio of 0.529.

PERAN PEREMPUAN TERHADAP TINGKAT PENGANGGURAN DI KABUPATEN/KOTA PROVINSI BALI

This phenomenon shows people's efforts to achieve gender equality in the currenti erai of globalizationi. The ipurpose iof this study iwas toi look at the simultaneous and partial effects of the GEM variables, women's lifei expectancyi, average lengthi of schoolingi for girls, and district/city Labor Force Participation Rate on response rates in districts/cities in Bali Province. This study uses paneil datai which is a icombination of time seriesi datai and cross isection data. This research was conducted within the last ten years, namely 2010-2020 in 9 regencies/cities of Bali Province. The data collected was ithen analyzed iusing panel idata iregression analysis techniques. The results of the study show that simultaneously the Gender Empowerment Index, women's life expectancy, the average length of schooling for women and the Labor Force Participation Rate have a significant effect on the level of stimulation in the districts/cities of the Province of Bali. Meanwhile, the Gender Empowerment Index, women's life expectancy, the average length of schooling for women and the Labor Force Participation Rate partially have a significant negative effect on response rates in the districts/cities of Bali Province.
 keyword: GEM, women’s life expectancy, average length of schooling for women, labor force participation rate

Poverty Modeling in East Java Province Using the Spatial Seemingly Unrelated Regression (Sur) Method

Poverty is a complex problem because it relates to various aspects of human life. In Indonesia, there is one province that has a very high percentage of poverty, namely East Java Province. Although from year to year the poverty rate has decreased, when viewed from the national level it is still very far from the government's expectations of reducing the poverty rate. Cases of poverty can be modeled by Econometrics. Econometric models are often applied to problems involving one or more related equations. One method that can be used to solve several interrelated equations because there is a correlation error regression between one another, namely Seemingly Unrelated Regression which is usually abbreviated as SUR, in this case Spatial Seemingly Unrelated Regression (SUR-Spatial) is development that takes into account the spatial influence between locations. From the results of tests conducted in the SUR-Spatial Lagrange Multiplier model, the poverty data generated by the East Java Province is the SUR-Spatial Autoregressive Model (SUR-SAR). So with the SUR-SAR model it can be seen that the variable that has a significant effect on the percentage of poor people is the growth rate of Gross Regional Domestic Product based on the constant price of the minimum wage for each district, as well as the average length of school years. Meanwhile, the Poverty Depth Index has an effect because of the growth rate of Gross Regional Domestic Product on the basis of constant prices and the average length of schooling. The Poverty Severity Index is influenced by the growth rate of Gross Regional Domestic Product at constant prices and average years of schooling.

The Influence of Government Spending in Education, Health, and Economics on Indicators for Calculation of Human Development Index in Bangka Belitung Islands Province

A country can be said to be successful in carrying out human development if its people enjoy the results of development. One of the indicators used to measure the extent to which development has affected results in a country is using the Human Development Index. In the Human Development Index there are three dimensions that are used as standardization of a decent life including the dimensions of education, health, and a decent standard of living or spending. To increase the Human Development Index based on the dimensions of education, health, and a decent standard of living, government spending is needed from the education, health and economic sectors. This study aims to analyze and determine the effect of government spending on education, health, and the economy on the indicators for calculating the Human Development Index in the Bangka Belitung Islands Province. This research uses a quantitative approach with secondary data. The analysis technique used is panel data regression with lag distribution. The results showed that there was a positive and significant effect between government spending on education in the same year and the previous year on the average length of schooling and there was a negative and significant effect between government spending on education in the same year and the previous year on the expected length of schooling. positive and significant effect between government spending on health in the same year and the previous year on life expectancy, and there is a negative and significant effect between government spending on the economy on per capita spending in the same year and in the previous year government spending on the economy is not effect on per capita spending.

How is the Spatial Linkage of Inclusive Economic Development in Java Island?

This study aims to analyze the spatial relationship of inclusive economic development between provinces in Java and how the open unemployment rate, inflation and average length of schooling affect the inclusive economic development index. The analysis was carried out using spatial concepts or not by using the value of the inclusive economic development index as the dependent variable and the open unemployment rate, inflation and average length of schooling as independent variables. The analytical methods and tools used in this study include Moran index analysis to see the spatial relationship of inclusive economic development between provinces, and panel data regression analysis to see the effect of the open unemployment rate, inflation and average length of schooling on the inclusive economic development index. . Based on the results of the study, it shows that there is no spatial linkage of inclusive economic development between provinces in Java. The open unemployment rate, inflation and the average length of schooling have a significant effect on the inclusive economic development index.

Distribution of Poverty in West Kalimantan

Poverty in each region is caused by various factors. Therefore, the initial step in overcoming it can be grouping regions based on their characteristics. In this study, an analysis was carried out using all provinces in Indonesia and after that, the urban districts in West Kalimantan province were grouped with urban districts from other provinces in cluster 1. The indicators used in this study were the percentage of poor people, poverty severity index, depth index poverty, human development index, expected length of schooling, average length of schooling, and adjusted per capita expenditure. This study uses k-means clustering analysis. The results of phase 1 research obtained 3 clusters of poverty levels, namely cluster 1 is a medium poverty rate of 26 provinces, cluster 2 is a high poverty rate of 5 provinces, and cluster 3 is a low poverty rate of 5 provinces. The results of phase 3 research obtained 3 clusters of poverty levels, namely Cluster 1 is a moderate poverty rate of 65 urban regencies, cluster 2 is a high poverty rate of 103 urban regencies, and Cluster 3 is a low poverty rate of 224 urban regencies.

Modelling Gender Development Index in Central Java In 2021 with Geographically Weighted Regression

Regression analysis is a statistical analysis used to determine and model the influence of the relationship between one response variable and one or more predictor variables using the Ordinary Least Squares (OLS) method. In some cases involving spatial data, it can lead to violations of heterogeneity and autocorrelation, indicating the presence of spatial effects. One regression analysis that can address spatial effects is Geographically Weighted Regression (GWR). The Gender Development Index is an index used to measure the achievement of basic human development capabilities in various areas within a region, taking gender into consideration. This study aims to model the GDI of Central Java Province in 2021 using the GWR approach. The data used in this study are secondary data from 35 Districts/Cities in Central Java Province in 2021. Based on the GWR modeling analysis above, it can be concluded that the GWR model is the best model for modeling the GDI in Central Java Province in 2021 with an R2 value by 51.82% and AIC value of 159.0621. In the formed GWR model, the significant variables are X1 or average length of schooling, X4 or school participation rate, and X5 or gender ratio.

Pesantren and the Human Development Index in Indonesia Post Law Number 18 of 2019

This research intends to determine the extent to which the potential of pesantren is based on the mandate contained in Law no. 18 in 2019 about Pesantren in an effort to increase the Human Development Index (IPM) in Indonesia. This research is qualitative research using a literature study approach in the data collection process, both primary and secondary. The primary data in the form of the Pesantren Law was then analyzed in terms of the Human Development Index indicators with the assistance of several secondary sources such as journal articles, web articles, and books. The results of this research indicate that Pesantren has the potential in efforts to increase the Human Development Index (HDI) in Indonesia through two dimensions: 1) Pesantren have the potential to increase the Knowledge dimension (Expected School Length and Average School Length) in its function as an educational institution through the provision of formal education Pesantrens in the form of the Satuan Pendidikan Muadalah (SPM), Pendidikan Diniyah Formal (PDF), and Ma'had Aly; 2) Pesantren have the potential to increase the dimension of a Decent Standard of Living through their function in community empowerment with various training programs and economic empowerment and entrepreneurship.

DETERMINAN TINGKAT PENGANGGURAN TERBUKA (TPT) KABUPATEN/KOTA DI PROVINSI BALI

The purpose of this study was to determine the simultaneous and partial effects of number of tourist, investment, dan average length of schooling on the open unemployment rate in the Bali Province regencies/cities. This research was conducted in nine regencies/cities in Bali Province. The data used is secondary data obtained from the Bali Province Central Bureau of Statistics and Bali Province Department of Tourism from 2017-2021. The number of samples in this study was 45. Data collection was carried through non-participant observation. The analysis technique used is panel data regression. Based on the result of the study it was found that the number of tourist, investment, and average length of schooling have a simultaneous effect on the open unemployment rate. The number of tourist has a negative and significant effect on open unemployment rate. Investment has a positive and insignificant effect on open unemployment rate. The average length of schooling has a positive and significant effect on open unemployment rate. 
 keyword: number of tourist, investment, average length of schooling, open unemployment rate