Robust FDI Determinants: Bayesian Model Averaging In The Presence Of Selection Bias

Abstract

The literature on Foreign Direct Investment (FDI) determinants is remarkably diverse in terms of competing theories and empirical results. We utilize Bayesian Model Averaging (BMA) to resolve the model uncertainty that surrounds the validity of the competing FDI theories. Since the structure of existing FDI data is known to induce selection bias, we extend BMA theory to HeckitBMA to address model uncertainty in the presence of selection bias. We then show that more than half of the previously suggested FDI determinants are no longer robust and highlight theories that receive support from the data. In addition, our selection approach allows us to highlight that the determinants of margins of FDI (intensive and extensive) differ profoundly in the data, while FDI theories do not usually model this aspect explicitly.