scholarly journals Analisis pengaruh pendapatan perkapita dan belanja modal terhadap tingkat kemiskinan di Pulau Sumatera

2019 ◽  
Vol 8 (3) ◽  
pp. 135-148
Author(s):  
Wenny Tri Septiani ◽  
Zamzami Zamzami ◽  
Candra Mustika
Keyword(s):  
Panel Data ◽  
Capita Income ◽  
Capital Expenditure ◽  
Analysis Tool ◽  
Per Capita Income ◽  
Poverty Level ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Poverty Levels

This study aims to: 1) To analyze and determine the development of per capita income, capital expenditure, and poverty levels on the island of Sumatra. 2) To analyze and determine the effect of per capita income and capital expenditure on poverty levels in Sumatra Island. The research analysis tool used panel data regression analysis tools. Based on the results of panel data regression, it can be concluded that per capita income and capital expenditure on the poverty level together have a significant effect. Whereas partially only the per capita income variable had a significant and negative effect on the poverty level, while capital expenditure had no significant and positive effect on the poverty level. Keywords: Poverty rate, Per capita income, Capital expenditures

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 Decentralization and Its Impact on Growth in India

2021 ◽  
Vol 16 (1) ◽  
pp. 130-151
Author(s):  
Fernanda Andrade de Xavier ◽  
Aparna P. Lolayekar ◽  
Pranab Mukhopadhyay
Keyword(s):  
Economic Growth ◽  
Capita Income ◽  
Fixed Effects ◽  
Capital Expenditure ◽  
Per Capita Income ◽  
Reverse Causality ◽  
Social Expenditure ◽  
Causality Test ◽  
Per Capita ◽  
National Growth

We study the effect of revenue decentralization (RD) and expenditure decentralization (ED) on sub-national growth in India from 1981–1982 to 2015–2016 for 14 large (non-special-category) states. Our study provides evidence that both RD and ED play a defining role in India’s sub-national growth in this three-and-a-half-decade period. We use a panel data model with fixed effects (FE) and Driscoll and Kraay standard errors that control for heteroscedasticity, autocorrelation and cross-sectional dependence. To test for causality between growth and decentralization, we use the Granger non-causality test. The regression analysis is supplemented with the distribution dynamics approach. We find that: (a) While decentralization Granger-caused economic growth, the reverse causality effect of growth on decentralization was not significant; (b) Economic growth increased significantly after liberalization; (c) Decentralization, capital expenditure and social expenditure had significant positive impacts on economic growth; and (d) States that had high levels of decentralization also had high levels of per capita income, while states that had low decentralization also exhibited low per capita income.

scholarly journals Pengaruh pendapatan asli daerah, dana alokasi umum dan dana alokasi khusus terhadap belanja modal pada kabupaten/kota di Provinsi Jambi

2019 ◽  
Vol 14 (1) ◽  
pp. 27-36
Author(s):  
Venny Tria Vanesha ◽  
Selamet Rahmadi ◽  
Parmadi Parmadi
Keyword(s):  
Panel Data ◽  
Regression Models ◽  
Capital Expenditure ◽  
Panel Data Regression ◽  
Data Regression ◽  
Directorate General ◽  
Fiscal Balance ◽  
Ministry Of Finance ◽  
The Republic

This study aims to analyze the development of Local Own-Source Revenue (PAD), General Allocation Fund (DAU), Spesific Allocation Fund (DAK), and capital expenditure as well as the influence of PAD, DAU and DAK on capital expenditure in districts/cities in Jambi Province. Data is sourced from the Directorate-General of Regional Fiscal Balance, the Ministry of Finance of the Republic of Indonesia. Data were analyzed using panel data regression models. The results of the study found that simultaneously PAD, DAU, DAK had a significant effect on capital expenditure. However, only partially the DAU influences the capital expenditure of districts/ cities in Jambi Province.

scholarly journals Analysis of Factors Affecting Economic Growth in Bangka Belitung Province, Indonesia with LSDV And FGLS Methods

2018 ◽  
Vol 2 (1) ◽  
pp. 24
Author(s):  
Darman Saputra
Keyword(s):  
Panel Data ◽  
Local Governments ◽  
Capita Income ◽  
Oil And Gas ◽  
Random Effect ◽  
Least Square ◽  
Per Capita Income ◽  
Processing Industry ◽  
Factors Affecting ◽  
Per Capita

The Least Square Dummy Variable (LSDV) method can be used to estimate parameters in the panel data regression model incomplete one-way fixed effect. To produce the best model with GDP data of GRASB. Variables that do not occur heteroscedasticity and models that meet the smallest sum square of error is the variable Mining and Processing Industry, this variable affects the per capita income. The Feasible Generalized Least Square (FGLS) method can be used to estimate the regression parameters for incomplete panel data for a one-way random effect. In this model produce the best model with non-oil and gas GRDP data. The variables that fulfill it are the processing Industry, service, and agriculture of Forestry and Fishery.  Therefore looking at the above model can be concluded non-oil and Gas GRDP has three factors that affect per capita income in Bangka Belitung. This should be a reference of local governments to further improve the quality or production in agriculture and services because this potential is more promising for the future. Software used to analyze data in this paper is with R.

scholarly journals Pengaruh belanja modal dan penanaman modal dalam negeri terhadap pertumbuhan ekonomi di Kabupaten/Kota Provinsi Jambi

2021 ◽  
Vol 10 (2) ◽  
pp. 123-134
Author(s):  
Haikal Redho Fitrah ◽  
Syaparudin Syaparudin ◽  
Emilia Emilia
Keyword(s):  
Economic Growth ◽  
Capital Expenditure ◽  
Capital Expenditures ◽  
Analysis Tool ◽  
Test Results ◽  
Domestic Investment ◽  
Panel Data Regression ◽  
Data Regression ◽  
Regression Test ◽  
Research Analysis

This study aims to: 1) analyze the development of Capital Expenditure, PMDN and Regency and City Economic Growth in Jambi Province; 2) To find out and analyze the effect of Capital Expenditures and Domestic Investment on the Regency and City Economic Growth in Jambi Province. The research analysis tool uses panel data regression analysis tools. Based on the regression test results, the data of the simultaneous data shows that capital expenditure and PMDN have a significant effect on the economic growth of regencies/cities in Jambi Province. While partially leading the two independent variables, only PMDN affects economic growth in Jambi Province districts/cities, while capital expenditure does not affect economic growth in Jambi Province districts/cities during 2011-2017. Keywords: Capital expenditure, PMDN, Economic growth

scholarly journals UKURAN PASAR UKURAN PASAR (Market Size) DAN INVESTASI ASING LANGSUNG DI ASEAN

2016 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Adi Lumadya
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Foreign Investment ◽  
Capita Income ◽  
Random Effect ◽  
Direct Foreign Investment ◽  
Market Size ◽  
Per Capita Income ◽  
Variable Size ◽  
Per Capita

The main objective of this study was to examine the influence of some economic variables that include market size proxied with income per capita, economic growth, and exports to the Foreign Direct Investment in the member countries of ASEAN-9. The analytical tool used is the Least Squares Regression (Ordinary Least Square) and Panel Data. In the Data Panel will look for similarities in effect is Fixed (Fixed Effect) and the effect is Random (Random Effect). The results of the analysis are: Based on the analysis of OLS concluded that the variable size of the market (market size) were proxied with Per Capita Income (GDPP), Economic Growth (EG), and exports (EG) significantly affects the Direct Foreign Investment. Based on the analysis of Panel Data with Fixed Effect Method concluded that the variable size of the market (market size) were represented with per capita income (GDP), Economic Growth (EG), and exports (EG) significantly affects the Direct Foreign Investment. Based on the analysis of Panel Data with Random Effect method concluded that the variable size of the market (market size) were proxied with per capita income (GDP), Economic Growth (EG), and exports (EG) significantly affects the Direct Foreign Investment. Keywords: Foreign Direct Investment, Fixed Effect, Random Effect

scholarly journals DETERMINAN INDEKS KEBAHAGIAAN DI INDONESIA

2021 ◽  
Vol 7 (2) ◽  
pp. 34
Author(s):  
Imawan Azhar Ben Atasoge
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Gini Index ◽  
Well Being ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Subjective States

ABSTRAK  Tolok ukur untuk melihat kemakmuran sebuah Negara dapat dilihat dari GDP yang ada di negara tersebut. Ukuran kesejahteraan tidak hanya diukur berdasarkan substansi akan tetapi diukur berdasarkan keadaan subjektif atau kebahagiaan. Tujuan dari penelitian ini ialah mengetahui faktor yang mempengaruhi kebahagiaan di Indonesia periode 2014 dan 2017. Analisis yang digunakan yaitu model regresi data panel. Penelitian ini menunjukkan bahwa variabel pendidikan, kesehatan, indeks gini serta zis berpengaruh secara terhadap kebahagiaan di Indonesia. Sedangkan variabel PDRB per kapita, kemiskinan, dan Indeks Demokrasi. Kata Kunci: Indeks Kebahagiaan, IPM, Kemiskinan, Indeks Gini, Zakat, Indeks Demokrasi ABSTRACTThe benchmark for seeing the prosperity of a country can be seen from the GDP in that country. The measure of well-being is not only measured based on substance but is measured based on subjective states or happiness. The purpose of this study is to determine the factors that influence happiness in Indonesia for the period 2014 and 2017. The analysis used is a panel data regression model. This study shows that the variables of education, health, Gini index and zis have a significant effect on happiness in Indonesia. While the variables are per capita GRDP, poverty, and the Democracy Index.Keywords: Happiness Index, HDI, Poverty, Gini Index, Zakat, Democracy Index

scholarly journals Analysis of Factors Affecting Poverty in West Nusa Tenggara Province, Indonesia

2018 ◽  
Vol 73 ◽  
pp. 09005
Author(s):  
Supriaman ◽  
Firmansyah ◽  
Yusuf Agung Gunanto Edy
Keyword(s):  
Poverty Alleviation ◽  
National Level ◽  
Work Force ◽  
Poor People ◽  
Poverty Level ◽  
Factors Affecting ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Underdeveloped Region

Poverty is a fundamental and multidimensional problem in economic development, especially in developing countries such as Indonesia and underdeveloped region such as Nusa Tenggara Barat province. The percentage of poor people in Nusa Tenggara Barat is relatively higher compared to the percentage of poor people in national level. This study aims to analyze the factors which influence the poverty level in Nusa Tenggara Barat. By employing a panel data regression of 10 Regencies/Cities along 2010-2015, the study finds that the work force skill, investment and income per capita have significant effect to poverty level. The coefficient of work force skill has the highest impact to the level of poverty, which means that that variable is a major factor in reducing poverty in 10 Regencies/Cities of West Nusa Tenggara Province. Based on the results, the study recommends the policy that promote poverty alleviation need to be strengthened by communities and governments.

scholarly journals MODEL REGRESI DATA PANEL UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI PULAU MADURA

2020 ◽  
Vol 9 (3) ◽  
pp. 355-363
Author(s):  
Artanti Indrasetianingsih ◽  
Tutik Khalimatul Wasik
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Poverty Level ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.

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