Determinants of Foreign Direct Investment: A Bayesian Model Averaging Approach From 55 Countries

Year 2023, , 135 - 158, 25.06.2023

Erhan Çene , Filiz Karaman

https://doi.org/10.46928/iticusbe.1062993

Abstract

Purpose: This study aims to investigate the determinants of Foreign Direct Investment (FDI) for the 55 countries from 4 different continents and 3 different development levels.
Method: A panel dataset is constructed over the period of 1995-2019 with 17 candidate independent variables and Bayesian Model Averaging (BMA) is employed for each continent and for each development level to reveal the determinants of FDI.
Results: Results showed that globalization is the primary factor to attract FDI regardless of the development level and continent. But there are also other factors that affect FDI which differ based on the development level and geographic location of the country. FDI in transition economies benefits from qualified labor force which negatively affects developed countries and has no effect on developing countries. Corruption is only effective in developing countries. Secondary education is effective at European and Asian countries, tertiary education is effective on American countries. This study contributes the literature by using a BMA method in a panel data context. BMA uses weighted average of all the possible models which gives more balanced results than a single model. Applying BMA to the countries in different continents and development levels separately allows to make inferences based on region and wealth.
Originality: This study used a fairly large dataset on terms of selected time period, number of countries and selected variables which most of the studies lack of. Also, the advantage of applying BMA to a panel dataset, is obvious as both controls the cross section variation and also considers a weighted average of all possible linear regression results rather than giving only a single linear regression model result. Thus applying BMA on a panel dataset give a better insight as to which variables should be considered as determinants of FDI.

Keywords

FDI, BMA, Panel Data, Emerging Economies, Transition Economies

DOĞRUDAN YABANCI SERMAYENİN BELİRLEYİCİLERİ: 55 ÜLKE ÜZERİNE BAYESÇİ MODEL ORTALAMASI YAKLAŞIM

Abstract

Amaç: Bu çalışma 4 farklı kıta ve 3 farklı gelişmişlik düzeyine sahip olan 55 ülke için Doğrudan Yabancı Yatırım (DYY)’ı etkileyen etmenleri araştırmayı amaçlamaktadır.
Yöntem: 1995-2019 yılları arasında 17 aday bağımsız değişkenden oluşan bir panel veri seti oluşturulmuş ve doğrudan yabancı yatırımın belirleyicilerini ortaya çıkarmak için her bir kıta ve her bir gelişmişlik düzeyi için Bayesçi Model Ortalaması (BMO) yöntemi uygulanmıştır.
Bulgu: Yapılan analizler küreselleşmenin gelişmişlik düzeyi ya da ait olunan kıtadan bağımsız olarak DYY’ı etkileyen ana etmen olduğunu göstermiştir. Bununla birlikte gelişmişlik seviyesi ve bulunulan coğrafi bölgeye göre değişim gösteren etmenler de mevcuttur. Nitelikli iş gücü geçiş ekonomilerinde DYY’ı olumlu yönde etkilerken, gelişmiş ülkelerde olumsuz etkiye sahipken, gelişmekte olan ülkelerde herhangi bir etkiye sahip değildir. Yozlaşma sadece gelişmekte olan ülkelerde etkilidir. İkinci basamak eğitim Avrupa ve Asya ülkelerinde fark yaratırken, üçüncü basamak eğitim Amerika kıtasındaki ülkelerde etkilidir. Bu çalışma var olan alan yazına panel veriye BMO uygulayarak katkıda bulunmaktadır. BMO olası tüm modellerin bir ağırlıklı ortalamasını kullanarak tek bir modele göre daha dengeli sonuçlar elde edilmesini sağlar. BMO’yu farklı kıta ve gelişmişlik seviyesine sahip ülkelere ayrı ayrı uygulamak bölgeye ve gelişmişliğe göre ayrı ayrı çıkarımlar yapılmasını sağlamaktadır.
Özgünlük: Bu çalışma, seçilen zaman periyodu, ülke sayısı ve seçilmiş değişkenler açısından oldukça büyük bir veri seti kullanmıştır. Ayrıca, BMO'yu bir panel veri setine uygulamanın avantajlı olduğu, hem kesit değişimini kontrol ettiği hem de yalnızca tek bir doğrusal regresyon modeli sonucu vermek yerine tüm olası doğrusal regresyon sonuçlarının ağırlıklı ortalamasını dikkate aldığı için açıktır. Bu nedenle, BMO'yu panel veri setine uygulamak, hangi değişkenlerin DYY'nin belirleyicileri olarak değerlendirilmesi gerektiği konusunda daha iyi bir fikir verecektir.

Keywords

Doğrudan Yabancı Yatırım, Bayesçi Model Ortalaması, Panel Veri, Geçiş Ekonomisi, Gelişmekte Olan Ekonomiler

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