scholarly journals ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015

2020 ◽  
Vol 4 (1) ◽  
pp. 89-96
Author(s):  
Intan Lukiswati ◽  
Anik Djuraidah ◽  
Utami Dyah Syafitri
Keyword(s):  
Panel Data ◽  
School Enrollment ◽  
Explanatory Variables ◽  
Capita Expenditure ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression ◽  
Enrollment Rates ◽  
Gender Development Index ◽  
Gender Based

The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.  

scholarly journals PENINGKATAN TARIF CUKAI ROKOK DAN DAMPAKNYA TERHADAP PEREKONOMIAN DAN PENDAPATAN SEKTORAL JAWA TENGAH

KINERJA ◽  
2017 ◽  
Vol 19 (2) ◽  
pp. 99
Author(s):  
Fatoni Ashar ◽  
Firmansyah ,
Keyword(s):  
Panel Data ◽  
Household Income ◽  
Fixed Effect Model ◽  
Cigarette Consumption ◽  
Input Output ◽  
Output Analysis ◽  
Central Java ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

This study analyzes the effect of excise of cigarette price changes to the consumption of cigarette and Central Java’s economy and household income. In the first stage, with employing panel data regression model,i.e. fixed effect model (FEM) which include 35 regencies/cities in Central Java Province during 2009-2013, the study examines the effect of cigarette excise to cigarette consumption. On the next stage, the study simulatesthe impact of cigarette consumption shock to the Central Java’s sectoral economy and household income using the Central Java 2013 Input-Output table. The findings indicate that the cigarette excise has a tradeoff effect tohousehold’s cigarette consumption. The increase of cigarette excise reduces cigarette consumption, and next, reduces output and sectoral household income. The cigarettes industries suffered the highest impact of thedecrease of the cigarette consumption, followed by other sectors which is has a high link to cigarette industries such as agricultures and tobacco sectors.Keywords: cigarette, excise, panel data regression, input-output analysis

scholarly journals ANALISIS DETERMINAN KEMISKINAN DI PROVINSI JAWA TENGAH

JEJAK ◽  
2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Dita Wahyu Puspita
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Total Population ◽  
Poverty Level ◽  
Literacy Rate ◽  
Section Data ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression

<p>This study aims to determine factors of poverty in the province of Central Java period 2008 to 2012. Central Java province was chosen because it has the second highest poverty level among 33 provinces in Indonesia. In this study, the factors that infl uence poverty are the numbers of population live in poverty, unemployment, Gross Regional Domestic Product (GDP) and literacy rate. The method used is the panel data regression. Panel data is the data that combines the time series and cross-section data. In this study, it is found that unemployment, GDP and total population have signifi cant aff ect on poverty in the province of Central Java.</p><p>Penelitian ini bertujuan untuk mengetahui determinan kemiskinan di provinsi Jawa Tengah periode 2008 sampai 2012. Dipilihnya Jawa Tengah karena dari 33 provinsi yang ada di Indonesia, Jawa Tengah merupakan provinsi dengan penduduk miskin terbanyak ke dua. Dalam penelitian ini faktor-faktor yang mempengaruhi kemiskinan di antaranya yaitu jumlah penduduk miskin, banyaknya pengangguran, Pendapatan Domestik Regional Bruto (PDRB) dan Angka Melek Huruf dan semua variable tadi dipilih periode 2008 sampai 2012. Metode penelitian yang digunakan yaitu metode regresi data panel. Data panel merupakan data yang menggabungkan antara data time series dan data cross-section. Dalam penelitian ini pula ditemukan bahwa pengaruh pengangguran, PDRB dan jumlah atau populasi penduduk Jawa Tengah signifi kan. Artinya berpengaruh pada kemiskinan di provinsi Jawa Tengah.</p>

scholarly journals PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL

2019 ◽  
Vol 8 (2) ◽  
pp. 220-232
Author(s):  
Siska Alvitiani ◽  
Hasbi Yasin ◽  
Mochammad Abdul Mukid
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Spatial Durbin Model ◽  
Spatial Panel ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data ◽  
Independent Variable ◽  
Poor Population

Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model

Analisis Tingkat Kesenjangan Pendapatan antar Provinsi di Indonesia Menggunakan Regresi Data Panel Model Pengaruh Tetap

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Thooriq Ghaith ◽  
Hari Wijayanto ◽  
Anang Kurnia
Keyword(s):  
Panel Data ◽  
Fixed Effect Model ◽  
Poor People ◽  
Income Disparity ◽  
Explanatory Variables ◽  
Income Disparities ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Using Data

THOORIQ GHAITH. Analysis of Income Disparity Rates among Provinces in Indonesia Using Panel Data Regression. Supervised by HARI WIJAYANTO and ANANG KURNIA.   Income disparities in Indonesia generally and in each province particularly is a serious problem from year to year. It is necessary to find out the factors that affect the income disparity rates (Gini ratio) to be taken into consideration in determining the economic policy. By using data of 33 provinces from 2007 until 2016, panel data regression with provincial fixed effect model approach was used to determine factors that affect Gini ratios in Indonesia and to capture the differences of Gini ratio characteristics of each province in form of intercept. Modeling was done for whole Indonesia and for five regions as well to find out what factors that affect the Gini ratio of provinces in Indonesia generally and what factors affect Gini ratios of provinces in each region particularly. The percentage of poor people is a significant factor to Gini ratio in the model throughout Indonesia and in the model of each region, except in Sumatera. Beside the percentage of the poor people, other explanatory variables affecting Gini ratios are GDP growth rates in Kalimantan, open unemployment rates in Sulawesi, and provincial minimum wage in Nusa Tenggara, Maluku and Papua. All of the predicted models are good enough because they produce MAPE values below 10%.

scholarly journals Pengaruh angka harapan hidup, rata-rata lama sekolah dan pengeluaran perkapita terhadap tingkat kemiskinan pada Kabupaten /Kota di Provinsi Jambi

2021 ◽  
Vol 10 (3) ◽  
pp. 223-232
Author(s):  
Ropikatul Hasanah ◽  
Syaparuddin Syaparuddin ◽  
Rosmeli Rosmeli
Keyword(s):  
Panel Data ◽  
Life Expectancy ◽  
Analytical Method ◽  
Average Length ◽  
Poverty Level ◽  
Capita Expenditure ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Poverty Levels

This study aims to analyze the development of life expectancy, the average length of schooling, expenditure per capita, and poverty level in districts/cities in Jambi Province, as well as analyze the effect of life expectancy, the average length of schooling, and expenditure per capita on poverty levels. The analytical method used is panel data regression. The results of this study indicate that. After the Chow and Hausmant test, the best model is the Fixed effect. Partially, the analysis results of the variable life expectancy and expenditure per capita significantly impact the poverty level. At the same time, the average length of school does not have a significant effect. Simultaneously, life expectancy, the average schooling size, and per capita expenditure significantly impact poverty levels in districts/cities in Jambi province  Keywords: Poverty, Life expectancy, Length of schooling, Expenditure per capita

scholarly journals STRATEGI ALOKASI BELANJA PEMERINTAH DAERAH UNTUK MENINGKATKAN INDEKS PENDIDIKAN DI PROVINSI BANTEN

2018 ◽  
Vol 10 ◽  
Author(s):  
Abdullah Al Mizan ◽  
A. Faroby Falatehan ◽  
Ekawati Sri Wahyuni
Keyword(s):  
High Schools ◽  
Panel Data ◽  
Descriptive Analysis ◽  
Budget Allocation ◽  
Fixed Effect Model ◽  
School Enrollment ◽  
Fixed Effect ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

ABSTRACTHuman development measured by Human Development Index (HDI) is composed of three components: health index, education index, and expenditure index. HDI Banten on year 2016 was ranked 8th in Indonesia, but the education index has the lowest value among the other components. Whereas education is a capital which is very important for people to achieve better welfare. This study aimed to formulate strategies to improve education index through education budget allocation in Banten Province. The analytical methods used were descriptive analysis, panel data regression analysis, SWOT analysis, and QSPM. Descriptive analysis was used to give an overview of the education condition in Banten Province. By using fixed effect model panel data regression, found that income per capita, School Enrollment Rate of high schools level, Pupil-Teacher Ratio of high schools level, and Number of high schools had positive and significant influences on education index in Banten Province. Meanwhile, based on the interview results of key respondents, by using SWOT techniques obtained six grand strategies that could improve the education index. The strategies obtained were analyzed using QSPM. The QSPM results showed the priority strategies for increasing the education index was by the policy improvement and increase in the budget allocation in the education sector.Key words: HDI, education index, SWOT, QSPMABSTRAKPembangunan manusia yang diukur melalui Indeks Pembangunan Manusia (IPM) dibentuk dari tiga komponen yakni indeks kesehatan, indeks pendidikan, dan indeks pengeluaran. IPM Banten Tahun 2016 menduduki peringkat 8 terbesar di Indonesia Namun demikian, indeks pendidikan memiliki nilai yang paling rendah di antara komponen lainnya. Padahal pendidikan merupakan salah satu modal yang sangat penting bagi seseorang untuk menuju kesejahteraan yang lebih baik. Penelitian ini bertujuan untuk merumuskan strategi meningkatkan indeks pendidikan melalui alokasi anggaran pendidikan. di Provinsi Banten Metode analisis yang digunakan yakni analisis deskriptif, analisis regresi data panel, analisis SWOT, dan QSPM. Analisis deskriptif digunakan untuk memberikan gambaran umum pendidikan di Provinsi Banten. Sedangkan dengan menggunakan regresi data panel fixed effect model didapat hasil bahwa pendapatan perkapita, Angka Partisipasi Sekolah (APS) tingkat SMA, Rasio Murid per Guru (RMG) tingkat SMA, dan Jumlah SMA memiliki pengaruh yang positif dan signifikan terhadap indeks pendidikan di Provinsi Banten. Sementara itu berdasarkan hasil wawancara kepada responden kunci, melalui teknik SWOT diperoleh enam strategi yang dapat meningkatkan indeks pendidikan. Strategi yang didapat kemudian dianalisis menggunakan QSPM. Hasil analisis QSPM menunjukkan bahwa strategi prioritas untuk meningkatkan indeks pendidikan yakni melalui penyempurnaan kebijakan dan peningkatan alokasi anggaran di bidang pendidikan. Kata kunci: IPM, Indeks Pendidikan, SWOT, QSPM

A mechanism to improve the allocation of public investment funds

2020 ◽  
Vol 26 (7) ◽  
pp. 1522-1533
Author(s):  
A.V. Larionov
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Public Investment ◽  
Allocative Efficiency ◽  
Investment Funds ◽  
The Public ◽  
Panel Data Regression ◽  
Data Regression ◽  
Efficiency And Effectiveness ◽  
Effective System

Subject. This article deals with the issue of improving the public investment allocative efficiency. Objectives. The article aims to develop an approach to improve the efficiency and effectiveness of public investment in the economy. Methods. The study is based on a panel data regression with random effects. Conclusions and Relevance. All sectors of the economy have different demand for investment resources attracted, determined by operational and technological aspects. The results of the study can be used to develop an effective system of public investment.

Determinants Of Capital Structure For Multinational And Domestic Companies In India: Panel Data Regression Model

2019 ◽  
Vol 118 (7) ◽  
pp. 147-154
Author(s):  
K. Maheswari ◽  
Dr. J. Gayathri ◽  
Dr. M. Babu ◽  
Dr.G. Indhumathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Business Risk ◽  
Return On Assets ◽  
Panel Data Regression ◽  
Data Regression ◽  
Return On Capital ◽  
Domestic Companies ◽  
The Capital Structure

The capital structure refers to the components of capital needed to establish and expand its business activities. The study was made with an objective to examine the determinants of capital structure of multinational and domestic companies listed in S&P BSE automobile sector. The study concluded that there is significant impact on capital structure determinants such as size, business risk, non debt shield tax, return on assets, tangibility, profit, return on capital employed and liquidity on the capital structure of multinational and domestic companies of Indian Automobile Sector.  

FAKTOR DETERMINAN PEMBENTUK STRUKTUR MODAL PADA PERUSAHAAN SEKTOR FARMASI PERIODE 2012 - 2016

2017 ◽  
Vol 2 ◽  
pp. 241-250
Author(s):  
Neng Ria Kanita ◽  
Hendryadi Hendryadi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Firm Size ◽  
Stock Exchange ◽  
Pharmaceutical Manufacturing ◽  
Manufacturing Companies ◽  
Purposive Sampling ◽  
Panel Data Regression ◽  
Data Regression ◽  
Partial Effects

This study aims to examine the simultaneous and partial effects of profitability, liquidity, and firm size on capital structure. The sample is 10 pharmaceutical manufacturing companies listed in Indonesia Stock Exchange period 2012-2016, using purposive sampling. The technique of analysis used is panel data regression (pooled regression). The results showed that the selected model is the fixed effect. Simultaneously NPM, CR, and Firm Size have a significant effect on capital structure. Partially NPM has a negative and significant effect on capital structure. CR partially have a negative and not significant effect on capital structure. Partially Firm Size have a positive and significant effect on capital structure. Variables that have a significant effect on capital structure are NPM and Firm Size. While CR does not significantly affect the capital structure. Keywords: Capital Structure, Profitability, Liquidity, Firm Size

A Study of Multilayered Profitability Analysis by Using DuPont Model: Evidence from Indian Pharmaceutical Industry

2021 ◽  
pp. 097215092199305
Author(s):  
Pinku Paul
Keyword(s):  
Panel Data ◽  
Pharmaceutical Industry ◽  
Profit Margin ◽  
Pharmaceutical Firms ◽  
Sustainable Performance ◽  
Profitability Analysis ◽  
Panel Data Regression ◽  
Data Regression ◽  
Negative Effect ◽  
Positive Effect

Profitability is used as a prime indicator to measure the sustainable performance of an organization. The current study made an attempt to apply the DuPont model to investigate the multilevel profitability determinants for the pharmaceutical industry of India. The study also estimates an empirical model to predict the association of profitability with factors such as profit margin, asset utilization, leverage, interest load and tax load of firms in the pharmaceutical industry of India. For this purpose, a dataset for 170 companies from 2010–2011 to 2018–2019 was analysed initially by using panel data regression followed by stepwise panel data regression. The study successfully applied and tested the DuPont model with respect to the firms of the pharmaceutical industry in India. It was found that the factors such as profit margin, asset utilization and leverage had a significant positive effect on the firms’ profitability and the factor interest load had a significant negative effect on the firms’ profitability. The tax load does not have an impact on the profitability of the pharmaceutical firms in India. These findings are expected to provide a guide for understanding the profitability of the firms in a better way.

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