Determinants of Foreign Direct Investment in ASEAN

2008 ◽  
Vol 43 (3) ◽  
pp. 21-51 ◽  
Author(s):  
P.R. Bhatt
Keyword(s):  
National Income ◽  
Least Square Method ◽  
Fixed Effect Model ◽  
Positive Influence ◽  
Fixed Effect ◽  
Least Square ◽  
Negative Sign ◽  
Significant Variable ◽  
Fdi Inflows ◽  
Effect Model

The ASEAN region has become one of the most attractive investment locations in the developing world. It attracted FDI to the tune of US$19 billion in 2003. Among ASEAN countries, Singapore topped the list with US$11 billion FDI inflows followed by Malaysia (US$2.5 bn), Brunei (US$.0 bn), and Vietnam (USɁ5 bn) in 2003. An attempt has been made in this paper to understand the determinants of FDI in ASEAN. The empirical model is estimated for five countries of ASEAN and ASEAN region as a whole for the period 1976-2003. The estimation of the model shows that there is a positive influence of the size of the economy (GNI) on FDI inflows in the case of Indonesia and Singapore. The infrastructure is significant for Indonesia and Malaysia in attracting FDI. Exchange rate had influence on FDI for Malaysia. The openness of the economy was significant in attracting FDI for Indonesia. The model is estimated for panel data of ASEAN region by pooled least square method and fixed effect model. In the case of pooled least square method, gross national income (GNI) is very significant and having expected sign. This implies that market size attracted FDI in the ASEAN region. The depreciation of the currency attracted FDI inflows in ASEAN as indicated by the variable XR which is significant with negative sign. Infrastructure is another significant variable in this model. Openness variable is significant but negative sign. When the model was estimated by fixed effect model it is found that all variables are significant with expected sign except in the case of the variable openness.

scholarly journals Analisis Regresi Panel pada Pemodelan Tingkat Kematian Bayi di Provinsi Sulawesi Selatan

2020 ◽  
Vol 2 (2) ◽  
pp. 115
Author(s):  
Syafruddin Side ◽  
S. Sukarna ◽  
Raihana Nurfitrah
Keyword(s):  
Panel Data ◽  
Infant Mortality ◽  
Regression Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian bayi di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2015. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian bayi, berat bayi lahir rendah, persalinan yang ditolong oleh tenaga kesehatan, penduduk miskin, bayi yang diberi ASI ekslusif dan rumah tangga berperilaku bersih sehat di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. Analisis data dilakukan dengan menggunakan penghitungan manual dan dengan menggunakan software EViews 9. Pembahasan dimulai dari melakukan estimasi parameter model regresi data panel, menentukan model regresi data panel terbaik, , menguji asumsi model regresi data panel, pengujian signifikansi parameter dan interpretasi model regresi. Dalam penelitian ini diperoleh kesimpulan yaitu estimasi model regresi data panel terbaik dengan pendekatan fixed effect model.Kata kunci:Regresi Data Panel, Kematian Bayi, Fixed Effect Model, Least Square Dummy Variable. This research discusses about parameter estimation of panel data regression model of infant mortality level modelling in South Sulawesi from 2014 to 2015. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of infant mortality, low weight of infant, childbirth rescued by health workers, poor population, infants who were given exclusive breast milk and household that behaves well in the whole district/town in South Sulawesi year 2014-2016. Data analysis was performed using the calculation manually and by using EViews 9 software. The discussion started from doing parameter estimation of panel data regression model, determining the best panel data regression model, testing the assumption of panel data regression model, testing the signification of parameter and interpretation of regression model. Conclusion of this research are the estimation of regression model is the best panel data regression model with fixed effects model approach.Keywords:Panel Data Regression, Infant Mortality, Fixed Effect Model, Least Square Dummy Variable.

scholarly journals Pengaruh Perkembangan Usaha Mikro, Kecil, dan Menengah Terhadap Pendapatan Nasional Pada Sektor UMKM di Indonesia

2019 ◽  
Vol 8 (2) ◽  
pp. 127-135
Author(s):  
Lies Maria Hamza ◽  
Devi Agustien
Keyword(s):  
Panel Data ◽  
Small And Medium Enterprises ◽  
National Income ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Effect Model ◽  
Medium Enterprises

This study aims to analyze the influence of the development of Micro, Small, and Medium Enterprises on the national income of the UMKM sector in Indonesia. This research used a panel data method with Fixed Effect Model. The data used are secondary in the value of GDP of UMKM, Labours of UMKM, investment of UMKM, and the number of units of UMKM from the 2000-2013 period. The results showed that labors of UMKM and placement of UMKM have a positive and significant effect on the national income of the UMKM sector in Indonesia. While for the number of units of UMKM not affect the national income of the UMKM sector in Indonesia.

scholarly journals The Effect of E-Commerce on National Income in 114 Countries

2019 ◽  
pp. 148-155
Author(s):  
Rasi Tamadhika Fajar Ramadhan ◽  
Bambang Juanda ◽  
Muhammad Findi Alexandi
Keyword(s):  
Purchasing Power Parity ◽  
National Income ◽  
Least Square Method ◽  
Positive Influence ◽  
Error Component ◽  
Least Square ◽  
Information Communication ◽  
Power Parity ◽  
Economic Activities ◽  
Section Data

Information, Communication and Technology (ICT) was growing rapidly. E-commerce creates new economic activities through technology, especially in the field of trade. The main objective of the studied was to analyze the effect of e-commerce on national income. The analysis was performed by EC2SLS (Error Component-Two Stage Least Square) method. Analysis using panel data with cross section data of 114 countries in the 2016-2017 period. The data used were GDP data based on purchasing power parity (PPP); previous year's GDP; e-commerce users; ICT services exports and imports; government spending; investment (FDI); country dummy. The analysis shows that e-commerce had a significant effect on national income. E-commerce for 113 other countries had a positive influence of 0.0026% while e-commerce for indonesia had a positive influence of 0.0080%.

scholarly journals PENGARUH TINGKAT PUTUS SEKOLAH, KEMISKINAN DAN PENGANGGURAN TERHADAP KRIMINALITAS DI INDONESIA

2018 ◽  
Vol 7 (2) ◽  
pp. 147
Author(s):  
Maulidya Rahmi ◽  
Melti Roza Adry
Keyword(s):  
Elementary School ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Drop Out ◽  
Research Use ◽  
Estimation Result ◽  
Effect Model ◽  
School Drop Out ◽  
The Law

This research aims to know and analyze determine drop out, poverty, and unemployment to criminality in Indonesia. This research use panel least square and Fixed effect model. The estimation result should that elementary school drop out has a positive and significant effect on criminality in Indonesia, poverty doesn’t h ave effect to criminality in Indonesia, unemployment doesn’t have effect to criminality in Indonesia. From the result of this research, goverment and the police should be do some sosialization at school and also give sosialization in front of citizen. The sosialization is about the law of criminality and increasing parents educations and anticipate negativity on environment.

scholarly journals Banks Efficiency and the Determinants of Non-Performing Financing of Full-Fledged Islamic Banks in Indonesia

2017 ◽  
Author(s):  
chandra setiawan
Keyword(s):  
Data Envelopment Analysis ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Var Model ◽  
Data Envelopment ◽  
Islamic Banks ◽  
Temporal Relationships ◽  
Effect Model ◽  
The Mean

This paper investigates the efficiency of Indonesian Full-fledged Islamic Banks by employing Data Envelopment Analysis (DEA) approach; the determinants of Non-Performing Financing (NPF) during the period 2012(Q1) to 2016 (Q2) by using Panel Least Square for fixed effect model; the inter-temporal relationships between banks efficiency and NPF are run using VAR model to test the two hypotheses: Bad Debts and Bad Management. The finding of DEA indicates the mean score on Full-fledged Islamic Banks in Indonesia is approach to efficient with the score 96.54% and Bank Rakyat Indonesia Syariah (BRIS) is the most technical efficient. The result shows that Full-fledged Islamic Banks in Indonesia is not support the bad management and Bad Luck hypothesis, probably NPF of full-fledged is average 4.5% that is still below of the critical standard of Bank Indonesia.

scholarly journals APLIKASI REGRESI DATA PANEL DENGAN PENDEKATAN FIXED EFFECT MODEL (STUDI KASUS: PT PLN GIANYAR)

2014 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
NI PUTU ANIK MAS RATNASARI ◽  
I PUTU EKA NILA KENCANA ◽  
G.K. GANDHIADI
Keyword(s):  
Panel Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Panel data regression has three approaches. One of these approaches is Fixed Effect Model (FEM). FEM is common estimated using Least Square Dummy Variable. The use of dummy variable in FEM is based on assumption that slope coefficients are constant but intercept varies over individuals. One of application of FEM is to find out motivation of employees at PT PLN Gianyar for non-outsourcing and outsourcing employees based on existence, relatedness, and growth. This research yields the following two models:with 67% motivation non-outsourcing employees represented by existenceand73% motivation non-outsourcing employees represented by existence and growth.

scholarly journals Determinan Kemiskinan di Provinsi Jawa Tengah Tahun 2011-2014

2018 ◽  
Vol 7 (3) ◽  
pp. 235-242
Author(s):  
Emi Megawati ◽  
Lesta Karolina Br Sebayang
Keyword(s):  
Significant Influence ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Simultaneous Test ◽  
Central Java ◽  
Effect Model ◽  
Independent Variable ◽  
Central Java Province

Berdasarkan data dari BPS, kemiskinan di Provinsi Jawa Tengah pada tahun 2011-2014 masih berada di peringkat kedua setelah DI Yogyakarta di Pulau Jawa-Bali. Penelitian ini menggunakan data panel dengan pendekatan Fixed Effect Model (FEM) dengan metode Generalized Least Square (GLS). Sumber data yang diperoleh dari Badan Pusat Statistik (BPS) dan Direktorat Jendral Perimbangan Keuangan Indonesia. Hasil penelitian menunjukan bahwa variabel IPM berpengaruh negatif dan signifikan terhadap kemiskinan di Provinsi Jawa Tengah. Sedangkan variabel PDRB dan pembiayaan pendidikan berpengaruh tidak signifikan terhadap kemiskinan di Provinsi Jawa Tengah. Hasil uji secara bersama-sama menunjukan bahwa secara keseluruhan variabel bebas secara bersama-sama dapat menunjukan pengaruhnya terhadap kemiskinan. nilai dari Adjusted R2 sebesar 0,995 yang berarti 99,5 persen kemiskinan dapat dijelaskan oleh variabel bebas. Sedangkan sisanya 0,50 persen dijelaskan oleh variabel di luar model. Based on data from BPS, during years 2011-2014 Central Java Province are in number 2 after DI Yogyakarta in Java-Bali. This research use panel data with Fixed Effect Model (FEM) approach and by using Geberalized Square (GLS) method. The data source is secondary data are obtained from the Central Statistics Agency and the Directorate General of Financial Balance Indonesia. The result of this research show that HDI variable give the negative and significant influence to the poverty in Central Java province. GDRP and financing of education not significant influence to the poverty in Central Java province. Simultaneous test results showed that, overall, the independent variable (HDI, GDRP and financing of education) together can show its effect on poverty. the value of Adjusted R2 of 0,995, which means 99,5 percent of poverty can be explained by the independent variable. While the remaining 0,50 percent is explained by variables outside the model.

scholarly journals The impact of corruption on the economic growth in Bangladesh, India and Pakistan: An ARDL approach

2021 ◽  
Vol 12 (8) ◽  
pp. 2079-2093
Author(s):  
Md. Mamun Miah ◽  
Tahmina Akter Ratna ◽  
Shapan Chandra Majumder
Keyword(s):  
Economic Growth ◽  
Correction Term ◽  
Negative Relationship ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Positive Influence ◽  
Fixed Effect ◽  
Ardl Model ◽  
Effect Model ◽  
The Impact

Purpose of the study: Main purpose of the paper is to find out the impact of corruption on the economic growth of Bangladesh, India, and Pakistan. At the same time, our other objectives are to find the long and short-run effects of corruption on growth in these countries. Methodology: For conducting the study, we have taken the data from Bangladesh, India, and Pakistan. For this study necessary secondary data have been collected from 1990 to 2016 based on countries like Bangladesh, India, and Pakistan. Data for economic growth (dependent) and trade (independent) are collected from World Development Bank and data for corruption are taken from International Country Risk published by the PRS Group. The study has used ECM ARDL Model and the Fixed Effect Model.  Findings: The result of the fixed effect model shows a 1percent increase in corruption decreases GDP by 0.07 units and shows a negative relationship with economic growth. Again if trade increases by 1 percent then growth will increase by 0.09 units on average and shows a positive relationship with economic growth. ECM ARDL Model shows the positive coefficient of corruption but not significant but trade has a long-run positive influence on economic growth. The error correction term indicating that the adjustment is corrected by 70% in these three countries. Contributions: This paper may be helpful for existing literature gap and also for further research. It will be helpful for policy makers to control corruption in three countries.

scholarly journals ANALYSIS OF FACTORS AFFECTING POVERTY IN NORTH SUMATERA PROVINCE (CASE STUDY IN 33 DISTRICT / CITY)

2019 ◽  
Vol 8 (2) ◽  
pp. 69
Author(s):  
Yudistira Avandi ◽  
Setyo Tri Wahyudi
Keyword(s):  
Economic Growth ◽  
Poverty Reduction ◽  
Least Square Method ◽  
Fixed Effect Model ◽  
Negative Influence ◽  
Positive Influence ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Factors Affecting ◽  
Level Of Education

The equitable poverty reduction in North Sumatera became one of the unresolved issues until now. The decreasing of poverty percentage in the last five years in North Sumatera can not represent the level of public welfare. In fact , in 2013, there were 22 out of 33 districts in North Sumatera which have the poverty percentage more than ten percent and known as hardcore poverty. The highest poverty percentage was found in North Nias and Gunung Sitoli by 30.94 %, while the lowest was found in Deli Serdang by 4.71 %. This research stated the problem “How is the influence of the economic growth, level of education, and the minimum regional wages toward the level of poverty in North Sumatera Province? The objective is to analyze the influence of economic growth, the level of education and the minimum wages toward the poor population in North Sumatera Province. This research used 165 samples that spread in 33 districts in North Sumatera from 2009 until 2013 with panel data and using Fixed Effect Model Method.  The result of the Ordinary Least Square Method (LOS) through the multiple linear regretion estimated model showed that the economic growth and the minimum regional wages had negative influence, while the level of education had positive influence toward poverty in North Sumatera. The coefficient of determination (R2) is 0.948157 which means the variable of economic growth, minimum regional wages and level of education can define the poverty in North Sumatera by 94.82 %, and 5.18 % defined by other economic variables outside the model.

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