scholarly journals Determinants of Structural Transformation in ASEAN

2020 ◽  
Vol 35 (2) ◽  
pp. 137
Author(s):  
Kalies Sirieh Puspitowati ◽  
Deden Dinar Iskandar
Keyword(s):  
Panel Data ◽  
Structural Transformation ◽  
Value Added ◽  
Fixed Effect Model ◽  
Dependency Ratio ◽  
Service Value ◽  
Income Per Capita ◽  
Effect Model ◽  
Per Capita ◽  
Asean Countries

This study aims to analyze the determinants of the structural transformation in ASEAN countries. This study uses quantitative panel data from 9 countries in ASEAN from 2000 to 2017, thus makes up for 162 observations. This study employs panel data regression analysis with fixed effect model approach. In this study, the shifting of sectoral value added away from agriculture sectors indicates structural transformation. In particular, sectoral value added consists of the industrial value added and service value added. The results of this study shows that dependency ratio, income per capita, education, and trade significantly affect the increase of industrial value added during observation period. On the other hand, total population, dependency ratio, income per capita, education, control of corruption, and trade significantly increase the service value added over time.

scholarly journals ANALISIS FAKTOR YANG MEMENGARUHI AGLOMERASI INDUSTRI UNGGULAN DAERAH DAN HUBUNGANNYA DENGAN DAYA SAING INDUSTRI DAERAH

2018 ◽  
Vol 2 (2) ◽  
pp. 113-134
Author(s):  
Fikanti Zuliastri ◽  
Wiwiek Rindayati ◽  
Alla Asmara
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Manufacturing Industry ◽  
Value Added ◽  
Fixed Effect Model ◽  
Industrial Sector ◽  
Location Quotient ◽  
Industry Competition ◽  
Effect Model ◽  
Panel Data Regression

The manufacturing industry sector is a major driver of economic growth in Indonesia with the largest contribution to the components of Gross Domestic Product is 25.60 % in 2012. But the globalization and liberalization of international trade requires industries to be more competitive. Improving the competitiveness of the industry can be done through the development of regional-based industrial sector main industry that area. The purpose of this study was to analyze the competitiveness and industrial agglomeration, the causality relationship between competitiveness and agglomeration industry and the factors that influence agglomeration of province main industries. This study was using large and medium scale industry raw data. The data analysis using Location Quotient, Hoover Balassa Index, Granger Causality method and panel data method with Fixed Effect Model. The result of panel data regression shows factors that influence the agglomeration of province main industries are firm size, value added, the diversity of industry, industry competition index, competitiveness index, wages and road infastructure. Keywords: Agglomeration, Granger Causality, Industrial Competitiveness, Panel Data

scholarly journals ANALISIS FAKTOR YANG MEMENGARUHI AGLOMERASI INDUSTRI UNGGULAN DAERAH DAN HUBUNGANNYA DENGAN DAYA SAING INDUSTRI DAERAH

2018 ◽  
Vol 2 (2) ◽  
pp. 113-134
Author(s):  
Fikanti Zuliastri ◽  
Wiwiek Rindayati ◽  
Alla Asmara
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Manufacturing Industry ◽  
Value Added ◽  
Fixed Effect Model ◽  
Industrial Sector ◽  
Location Quotient ◽  
Industry Competition ◽  
Effect Model ◽  
Panel Data Regression

The manufacturing industry sector is a major driver of economic growth in Indonesia with the largest contribution to the components of Gross Domestic Product is 25.60 % in 2012. But the globalization and liberalization of international trade requires industries to be more competitive. Improving the competitiveness of the industry can be done through the development of regional-based industrial sector main industry that area. The purpose of this study was to analyze the competitiveness and industrial agglomeration, the causality relationship between competitiveness and agglomeration industry and the factors that influence agglomeration of province main industries. This study was using large and medium scale industry raw data. The data analysis using Location Quotient, Hoover Balassa Index, Granger Causality method and panel data method with Fixed Effect Model. The result of panel data regression shows factors that influence the agglomeration of province main industries are firm size, value added, the diversity of industry, industry competition index, competitiveness index, wages and road infastructure. Keywords: Agglomeration, Granger Causality, Industrial Competitiveness, Panel Data

scholarly journals PELUANG PARIWISATA DALAM MENURUNKAN KEMISKINAN DI ERA MASYARAKAT EKONOMI ASEAN (MEA)

2016 ◽  
Vol 16 (2) ◽  
pp. 199
Author(s):  
Dody Harris Darmawan ◽  
Adi Yunanto
Keyword(s):  
Economic Growth ◽  
Poverty Reduction ◽  
Fixed Effect Model ◽  
Economic Community ◽  
Estimation Model ◽  
Income Per Capita ◽  
Effect Model ◽  
Tourism Sector ◽  
Per Capita ◽  
Asean Economic Community

ABSTRACTIn 2015, the ASEAN Economic Community (AEC), or better known as Masyarakat Ekonomi ASEAN (MEA) have agreed to jointly deal with the benefit expectations each member state.One of those opportunities to alleviate poverty related MEA is on tourism sector as a result of their visa-free between MEA member countries.Tourism development and economic growth have a mutualism relationship in poverty alleviation.This study analyzes the effect of tourism sector and income per capita on poverty reduction by panel data in 30 provinces of Indonesia in the period 2004 - 2012. Method of analysis uses Least Squaremethod and the estimation model used is Fixed Effect Model (FEM). The empirical results shows the tourism sector and income per capita have a significant effect to poverty reduction. Every 1% increase of tourism sector contribution effects on 0.005% poverty reduction, and every 1% increase of income per capita effects on 0.085%. poverty reduction

scholarly journals ANALISIS REGRESI DATA PANEL TERHADAP INDEKS PEMBANGUNAN MANUSIA (IPM) JAWA TIMUR TAHUN 2006-2015

2018 ◽  
Vol 18 (2) ◽  
pp. 69
Author(s):  
Muhammad Jamil Hidayat ◽  
Alfian Futuhul Hadi ◽  
Dian Anggraeni
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Cross Section Data ◽  
Section Data ◽  
Effect Model ◽  
Per Capita

Panel data is a combination of time series and cross section data. Panel data regression is used because in a time there is time researchers can’t perform analysis only by using time series data and cross section data only. This is because the number of factors used in the analysis phase, so that if the researcher only uses cross section data then the researcher can’t see the influence of factors that affect as well as on the growth of HDI that occurs from time to time in a certain period. Whereas it is quite possible that the conditions between one year and another will be different. Based on the model estimation, it is used with fixed effect model (FEM) approach. Modeling HDI with FEM in 2006-2015 period resulted in R2 value of 94.23%. The results showed that from 2006-2015 the ratio of student-teacher (RST), health facilities (HF), percentage of expenditure per capita by group of food (PPF) and regional per capita expenditure (PPE) have significant effect to HDI. Keywords: HDI, Panel, Fixed Effect Model

scholarly journals The Impact of Air Quality on Population Migration

2020 ◽  
Vol 15 (2) ◽  
pp. 23-32
Author(s):  
Ke Wang ◽  
◽  
Yiwei Wang ◽  
Chun-Ping Chang
Keyword(s):  
Air Quality ◽  
Panel Data ◽  
Negative Impact ◽  
Fixed Effect Model ◽  
Education Level ◽  
Population Migration ◽  
Control Factors ◽  
Effect Model ◽  
Per Capita ◽  
The Impact

Based on annual panel data of OECD countries from 1995 to 2014, this paper analyzes the impact of air quality (including per capita CO2, PM2.5, and SO emissions) on the immigrant population through a panel fixed-effect model, while employing control factors such as GDP, unemployment rate, and education level. Overall, we provide evidence that air quality is a key determinant of immigration in the selected countries, and in particular the host country’s emissions have a negative impact on immigrants. Greater emissions imply fewer immigrants, while fewer emissions denote more immigrants. Our findings provide countries with a way to more accurately estimate migrant inflow and offer an idea for OECD members on how to attract immigrants via an improvement in environmental quality.

scholarly journals Population, Poverty and Economic Development Nexus: Empirical Study of Some Selected Developing Countries

2018 ◽  
Vol 6 (4) ◽  
Author(s):  
Muhammad Sibt e Ali ◽  
Syed Muhammad Faraz Raza ◽  
Syed Muhammad Faraz Raza ◽  
Naeem ul Din ◽  
Syed Zain Ul Abidin
Keyword(s):  
Developing Countries ◽  
Economic Development ◽  
Panel Data ◽  
Developing Economies ◽  
Negative Impact ◽  
Positive Impact ◽  
Value Added ◽  
Fixed Effect Model ◽  
Effect Model ◽  
Positive Effect

The major objective of this research is to examine the connection among poverty, population growth and its impact on economic development of different developing countries. This research comprised of panel data for period of 2002-2015. The data has been taken World Bank Indicator (WDI) for twenty six developing countries. To find out the results we use panel data. For the analysis of data we have applied Hausman and Fixed Effect Model in this study. Findings of the study indicate that the consumption of government, export, gross capital formation and industrial value added have positive impact on growth of developing economies. The results show that the variation in these variables has positive effect on dependent variables. On the other hand, economic growth increases due to positive changes in this variable. It is seen in this study that population and poverty has negative impact on GDP per capita in selected developing countries.

scholarly journals ANALISIS DETERMINAN BALITA PENDEK DAN SANGAT PENDEK DI INDONESIA 2015-2018 DENGAN REGRESI DATA PANEL

2020 ◽  
Vol 8 (2) ◽  
pp. 56-61
Author(s):  
Astrid C. A. Pangaribuan ◽  
Kuncoro Dwi Dhanutama ◽  
Miko Oktavio Wijaya ◽  
Putri Tareka Navasha ◽  
Rani Nooraeni
Keyword(s):  
Panel Data ◽  
Cross Section ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Individual Effect ◽  
Capita Expenditure ◽  
School Participation ◽  
Effect Model ◽  
Per Capita

Balita pendek dan sangat pendek (kerdil) adalah kondisi dimana balita memiliki panjang atau tinggi badan yang kurang dibandingkan dengan umur. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi persentase balita kerdil di Indonesia pada tahun 2015–2018. Penelitian ini menggunakan data sekunder berupa data panel yang bersumber dari website Badan Pusat Statistik dan publikasi Kementerian Kesehatan Republik Indonesia. Variabel bebas dalam penelitian ini adalah angka partisipasi sekolah, rata-rata pengeluaran per kapita rumah tangga untuk makanan, tingkat pengangguran terbuka, dan persentase balita gizi buruk dan kurang.  Metode analisis yang digunakan adalah regresi data panel dengan Fixed Effect Model (FEM). Setelah dilakukan estimasi model terpilih, didapatkan hasil bahwa rata-rata pengeluaran per kapita rumah tangga untuk makanan dan persentase balita gizi buruk kurang berpengaruh signifikan. Sementara itu, berdasarkan hasil Individual Effect atau Cross-Section Fixed Effect, persentase balita kerdil tertinggi berada di Provinsi Sulawesi Barat sedangkan yang terendah berada di Provinsi Kepulauan Riau. Kata kunci: Pengeluaran perkapita, partisipasi sekolah, tingkat pengangguran terbuka, balita gizi buruk  Abstract Toddler short and very short (dwarf) is a condition where toddlers have a length or height less than age. This study aims to analyze the factors that influence the percentage of stunted toddlers in Indonesia in 2015-2018. This study uses secondary data in the form of panel data sourced from the website of the Central Statistics Agency and the publication of the Ministry of Health of the Republic of Indonesia. The independent variables in this study are school participation rates, the average per capita household expenditure for food, open unemployment rates, and the percentage of malnourished and under-aged children. The analytical method used is panel data regression with the Fixed Effect Model (FEM). After estimating the selected model, the results show that the average per capita expenditure of households for food and the percentage of malnourished children under five is not significantly influential. Meanwhile, based on the results of the Individual Effect or Cross-Section Fixed Effect, the highest percentage of dwarf children was in West Sulawesi Province while the lowest was in Riau Islands Province. Keywords: Per capita expenditure, school participation, open unemployment rate, malnutrition toddlers 

scholarly journals Daya Saing dan Pertumbuhan Ekonomi Negara Berkembang ASEAN

2020 ◽  
Vol 9 (1) ◽  
pp. 37-44
Author(s):  
Fadeli Yusuf Afif ◽  
Ukhti Ciptawaty
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Panel Data ◽  
Direct Investment ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Labor Participation ◽  
Effect Model ◽  
Asean Countries

The purpose of this study is to look at the condition of the country's competitiveness and its influence on ASEAN economic growth. The data used consists of panel data consisting of time series data for 2009 - 2019 and a cross section of five ASEAN countries with the highest level of competitiveness. The variables used are economic growth, competitiveness, labor participation, and foreign direct investment. The analysis tool used is panel data regression, the Fixed Effect Model (FEM). The results show that competitiveness, labor participation, and foreign direct investment have a positive and significant effect on economic growth in the five developing ASEAN countries.   Keywords: ASEAN, Competitiveness, Economic Growth, and Fixed Effect Model (FEM).

scholarly journals POVERTY RATE DURING THE 1998 AND 2008 CRISIS PERIOD IN 5 ASEAN COUNTRIES

2018 ◽  
Vol 3 (1) ◽  
pp. 49
Author(s):  
Ridho Firmansyah ◽  
Sri Kusreni
Keyword(s):  
Population Growth ◽  
Government Spending ◽  
Fixed Effect Model ◽  
Poverty Rate ◽  
Gdp Per Capita ◽  
Effect Model ◽  
Panel Data Regression ◽  
Per Capita ◽  
Asean Countries ◽  
Distribution Of Income

This study aims to examine and analyze the effect of GDP per capita, Inequal distribution of income, unemployment, population growth and government spending on education on poverty in five ASEAN countries. This study uses panel data regression equation using the Fixed Effect Model (FEM). The results showed that the effect of GDP per capita, Inequal distribution of income, unemployment, population growth and government spending on education affects simultaneously on poverty. While partially each independent variable have different effect on poverty in five ASEAN countries.

scholarly journals Government Expenditure and Human Development in Indonesia

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Johan Beni Maharda ◽  
Bunga Zharfa Aulia
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Human Development ◽  
Human Development Index ◽  
Panel Data Analysis ◽  
Government Expenditure ◽  
Fixed Effect Model ◽  
Effect Model ◽  
Per Capita

This study aims to estimate the association between government expenditure and human development index (HDI) in Indonesia. Due to inequal HDI attainment, this study focuses on 12 provinces which categorized as provinces with low level of HDI in Indonesia. This study employs fixed effect model (FEM) panel data analysis on provincial level datasets from 2010 to 2018. This study found that the increase of government expenditure on education significantly increases HDI, while government expenditure on health has no significant association with HDI. Major finding of the study highlights the role of gross regional domestic product (GRDP) per capita in increasing HDI on 12 provinces in Indonesia. Keywords: Government expenditure on education, government expenditure on health, HDI, FEM.

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