Friday, 12 October 2018: 4:50 PM
Foreign direct investment (FDI) is marked with model uncertainty due to different theories and conclusions as to what variables should be included in the model. We utilize the Bayesian model averaging (BMA) technique to determine the robust variables that explain two modes of FDI: Greenfield investment (GFI) and mergers & acquisitions (M&A). The GFI data is gathered from Financial Times Limited and the M&A data from Thomson ONE database. This paper examines whether the GFI and M&A modes of FDI respond differently to the proposed determinants of FDI and compares and contrasts the alternative robust determinants. The BMA method is used to tackle separately model uncertainty in the two modes. We also examine robust determinants for two sets of countries: the global sample of countries and developing countries. We find that some of the variables identified by previous literature as determinants of FDI are not robust for each mode separately. We find that GFI and M&A respond in different ways to the potential determinants. The two modes only share six robust determinants with GFI being robustly determined by more variables than M&A. The shared variables are the source country's market size, voice and accountability, urban concentration, distance, host country's land area and common border. We also find that while GFI is influenced by both source and host country characteristics, M&A is mostly affected by source country characteristics. The results obtained from the developing countries follow a similar pattern. However, GFI to developing countries is influenced by the market size of both the source and host country and only six variables are important for explaining M&A in developing countries. They are source country's urban concentration, population, land area, GDP growth, and host country's urban concentration, political instability and population. This study provides a better understanding of the robust determinants of FDI and the different ways GFI and M&A respond to policies.