scholarly journals Main Determinants Influencing Inward and Outward Foreign Direct Investments in Thailand: Comparisons and Vital Implications

2021 ◽  
pp. 1-25
Author(s):  
Hsiang-Hsi Liu ◽  
Pitprapha Dejphanomporn
Keyword(s):  
Foreign Direct Investment ◽  
Panel Data ◽  
Data Model ◽  
Direct Investment ◽  
Bilateral Trade ◽  
Important Determinant ◽  
Trade Agreements ◽  
Panel Data Model ◽  
Market Size ◽  
Main Determinants

Abstract Foreign direct investment (FDI) has played an important role in the evolution of globalization and is the cornerstone of industrial expansion and economic development. From 1988 to 1990 till now, Thailand has been one of the main destinations of FDI, namely inward FDI (IFDI). However, outward foreign direct investment (OFDI) increased rapidly from 2003 to 2011, and it continues to grow. Although initially a net importer, Thailand has transformed into a net exporter of direct investment in 2011. Since 2003, Thailand has entered a stage of re-emergence of OFDI, and this growth trend of OFDI will continue in the future. The purpose of this study is to investigate the main determinants of Thailand's IFDI and OFDI, and apply a panel data model to determine which determinants have a significant impact on Thailand's IFDI and OFDI. We considered the FDI flows between Thailand and its five FDI partners (Japan, Hong Kong, the Netherlands, Singapore and the United States) from 1997 to 2014, where IFDI was 1997-2014, and 2004-2014 was OFDI. Regarding the determinants of Thailand's IFDI and OFDI, the market size (relative per capita GDP), Thailand's openness, relative R&D intensity and bilateral trade agreements have a positive impact on FDI decisions for both IFDI and OFDI. Relative wages and geographic distance have opposite effects on Thailand's IFDI and OFDI. Specifically, our empirical results show that market size is the most important determinant of IFDI inflows into Thailand, while the most important determinant of Thai OFDI is bilateral trade agreements. JEL classification numbers: F14, F23, F43. Keywords: Foreign Direct Investment, Panel Data Model, Fixed Effects, Generalized Least Squares (GLS).

DETERMINAN PENANAMAN MODAL ASING LANGSUNG DI ASEAN

2012 ◽  
Vol 7 (1) ◽  
pp. 75
Author(s):  
Joko Susanto
Keyword(s):  
Foreign Direct Investment ◽  
Panel Data ◽  
Data Model ◽  
Direct Investment ◽  
Multinational Enterprises ◽  
Statistical Data ◽  
Regression Equation ◽  
Panel Data Model ◽  
Market Size ◽  
Research Analysis

This research analysis the factors’ that determine the foreign directinvestment (FDI) in ASEAN’s countries especially Indonesia, Malaysia, Philippine and Thailand during 1990-2009. Multinational Enterprises’ (MNE) must decideto choose a locationfor relocating its’ factory by market seeking dan resources seeking strategy. Based on this statement, it can be obtained the regression equation with foreign direct investment is a function of market size, worker’s productivity and infrastructure of road. Statistical data of UNESCAP was used in this research. The regression was base on the panel data model, while the estimation was based on common effects model. This results showthat the market size, worker’s productivity and availability of infrastructure road could be an importance consideration for MNE’s in their choice for FDI.Keywords: foreign direct investment, market size, worker’s productivity, infrastructure of road

scholarly journals Allocation of foreign direct investment across brazilian states

2013 ◽  
Vol 43 (2) ◽  
pp. 241-269 ◽  
Author(s):  
Maurício Mesquita Bortoluzzo ◽  
Sergio Naruhiko Sakurai ◽  
Adriana Bruscato Bortoluzzo
Keyword(s):  
Foreign Direct Investment ◽  
Panel Data ◽  
Foreign Investment ◽  
Direct Investment ◽  
Transport Infrastructure ◽  
Market Size ◽  
Consumer Market ◽  
Determining Factors ◽  
Econometric Study

Foreign direct investment (FDI) has become increasingly important for the Brazilian economy: the ratio of FDI inflow to the country's gross domestic product (GDP) increased from a 0.6% average in the 1980's to 2.5% from 2001 to 2010, according to data from UNCTAD. However, there is great inequality in the distribution of this investment among Brazilian federation units. This study aims at investigating the determining factors for the location of foreign direct investment across Brazilian states, based on an econometric study with panel data for the years 1995, 2000 and 2005. The results showed that foreign investment responded positively to consumer market size, quality of labor and transport infrastructure, but negatively to cost of labor and tax burden.

Empirical Analysis of the Impact of FDI on China‘s Environment

2013 ◽  
Vol 726-731 ◽  
pp. 1001-1005
Author(s):  
Yang Xu ◽  
Wen Bin Shang
Keyword(s):  
Foreign Direct Investment ◽  
Environmental Effects ◽  
Data Model ◽  
Direct Investment ◽  
Panel Data Model ◽  
The West ◽  
Fdi Inflows ◽  
Policy Suggestions ◽  
Negative Environmental Impact ◽  
The Impact

In this paper, the panel data model of China's 29 provinces from 2000 to 2008,to study the environmental effects of foreign direct investment, and analysis the differences of the environmental effects of foreign direct investment.from the eastern, central and western three economic belts. Empirical results show that, FDI inflows have a negative environmental impact to China, and this effect is weaker in the east and stronger in the west. Relative policy suggestions are given based on the research.

scholarly journals Determinants of foreign direct investment outflow from India to Poland

2020 ◽  
Vol 56 (2) ◽  
pp. 109-117
Author(s):  
Robert Dygas
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Bilateral Trade ◽  
Trade Agreements ◽  
Empirical Method ◽  
Outward Fdi ◽  
Eu Countries ◽  
Trade Relations ◽  
Best Management ◽  
Visegrad Group

AbstractThis article concerns the determinants of foreign direct investment (FDI) outflow from India to Poland with some insights to other European countries. This topic strongly relates to globalization of foreign trade and especially new economic initiatives between European Union (EU) and India, which was one of the first countries to develop trade relations with EU. According to CEIC data – Financial Data and Economic Indicators, India’s FDI outflow increased slightly to 1.4 billion USD in September 2019 in comparison with 996.5 million USD in September 2018, but it is still below the average of 1.8 billion USD for a period of 2007–2019.Available at: https://www.ceicdata.com/en/india/foreign-direct-investment-outflow/foreign-direct-investment-outflow (accessed October 19, 2019). Very limited number of the scientific research can be found in European literature about India’s FDI outflow to EU countries in period of 2004–2019. Indian economists made some research on that topic. Professor J. Ramachandran (listed among the Best Management Thinkers for the year 2015, the first Bain Fellow in India) from Indian Institute of Management Bangalore in 2004 and Professor Jaya Prakesh Pradhan from Central University of Gujarat in 2008 explored the evolution in Indian outward FDI, referring to a shift in the pattern of overseas expansion and basis of competitiveness of Indian companies. The main goal of this article is to explain what really triggers Indian investors to go to Poland and what kind of businesses they form. Some examples of the Indian-based companies are mentioned to support the analysis. The author of this article also researched on different governmental bilateral trade agreements and initiatives, trying to find any direct impacts of that on the India FDI outflow to Poland and other EU countries. He used empirical method of the analysis based on accessible data for period 2004–2019 and literature in that topic and also direct interviews with private Indian investors who made decision to start and run their business in Poland or other EU countries. The main conclusion is that Poland the leader of Visegrad Group is an interesting investment for India and India can be for Poland a counterpart investment partner to China.

scholarly journals Challenges of financial inclusion policies in Peru.

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Guillermo Boitano ◽  
Deyvi Franco Abanto Aranda
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Financial Inclusion ◽  
Panel Data Model ◽  
Two Stage ◽  
Remote Areas ◽  
Main Variable ◽  
Main Determinants ◽  
Bank Concentration ◽  
Over Time

The purpose of the research is to identify the main determinants of financial inclusion in Peru at the department level (without Lima) from 2010 to 2017, aiming to analyze the challenges faced by financial inclusion policies. A two-stage GMM method was used to estimate a panel data model where the endogenous variable is Sarma’s financial inclusion index (2008). The results indicate that bank concentration is the main variable affecting financial inclusion. At the same time, although the effect of technology is positive, it is diminishing over time due to the inefficient deployment of such technology and, in many cases, a lack of knowledge on how to use it, especially in the remote areas of Peru.  

Determinants of foreign direct investment inflow to the agricultural sector: a panel-data analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seyed Reza Zeytoonnejad Mousavian ◽  
Seyyed Mehdi Mirdamadi ◽  
Seyed Jamal Farajallah Hosseini ◽  
Maryam Omidi NajafAbadi
Keyword(s):  
Developing Countries ◽  
Foreign Direct Investment ◽  
Panel Data ◽  
Host Country ◽  
Direct Investment ◽  
Agricultural Sector ◽  
Fdi Inflows ◽  
Content Type ◽  
Main Determinants ◽  
Determinants Of Fdi

PurposeForeign Direct Investment (FDI) is an important means of boosting the agricultural sectors of developing economies. The first necessary step to formulate effective public policies to encourage agricultural FDI inflow to a host country is to develop a comprehensive understanding of the main determinants of FDI inflow to the agricultural sector, which is the main objective of the present study.Design/methodology/approachIn view of this, we take a comprehensive approach to exploring the macroeconomic and institutional determinants of FDI inflow to the agricultural sector by examining a large panel data set on agricultural FDI inflows of 37 countries, investigating both groups of developed and developing countries, incorporating a large list of potentially relevant macroeconomic and institutional variables, and applying panel-data econometric models and estimation structures, including pooled, fixed-effects and random-effects regression models.FindingsThe general pattern of our findings implies that the degree of openness of an economy has a negative effect on FDI inflows to agricultural sectors, suggesting that the higher the degree of openness in an economy, the lower the level of agricultural protection against foreign trade and imports, and thus the less incentive for FDI to inflow to the agricultural sector of the economy. Additionally, our results show that economic growth (as an indicator of the rate of market-size growth in the host economy) and per-capita real GDP (as an indicator of the standard of living in the host country) are both positively related to FDI inflows to agricultural sectors. Our other results suggest that agricultural FDI tends to flow more to developing countries in general and more to those with higher standards of living and income levels in particular.Originality/valueFDI inflow has not received much attention with respect to the identification of its main determinants in the context of agricultural sectors. Additionally, there are very few panel-data studies on the determinants of FDI, and even more surprisingly, there are no such studies on the main determinants of FDI inflow to the agricultural sector. We have taken a comprehensive approach by studying FDI inflow variations across countries as well as over time.

Sign in / Sign up

Export Citation Format

Share Document