Foreign investment location screening using an investment index

Abstract

The purpose of this research was to develop a decision tool to identify and rank potential locations for making a greenfield investment in flour milling. The driving characteristics of the tool developed are transparency, reproducibility, specificity and clarity. Currently, the approach to selecting countries in which to invest is driven purely by ad hoc frameworks that often lack the characteristics driving this investment index tool. The investment index was designed to have three main components: market conditions, economic environment and supporting infrastructure. Market conditions for the product of interest – in this case flour – were defined to encompass per capita wheat-based food consumption growth rate, wheat production versus wheat consumption and wheat flour imports growth rate. The economic environment was defined to incorporate the growth rate of per capita gross domestic product, corporate tax rate , labor productivity, foreign direct investment growth rates, position on the World Bank’s Doing Business 2012 rankings, and the number and extent of the country’s membership in regional economic and trade groups. Supporting infrastructure included electricity reliability, transportation quality, urbanization rate and the physical presence of the investing company in the country. The rationale for this last variable is that when the investing company already has a presence in the country under consideration, it has already incurred some of the hurdle costs that it would have to include in investments in a location where it does have current physical activities. The study started by filtering the scope of potential opportunities by a set of well-defined criteria: target geographical locations; Doing Business 2012 scores; and quantity of wheat flour imports in 2009. This led to four countries emerging as leading candidates for investment considerations: Brazil, Malaysia, Indonesia and Thailand. The investment index ranked these countries according to their relative suitability for investment. The three components of the index carry different weights because of their effect on the potential investment outcome. There is no data to support these weighting and therefore executives must utilize different probing approaches to weight the components. To this end, a base scenario and two other scenarios based on alternative weights were considered. The robustness of the ranking is revealed by the consistency of the rankings under the alternative weights applied to the components. The results showed that under the base scenario Malaysia had the highest investment index score. The results also showed that varying the alternative weights for the scenarios did not affect the overall outcome with Malaysia leading with the highest overall index score for each of the three scenarios.