Causality Analysis of Drivers for Test Market Investments: Practical Implications for Copenhagen Capacity

Abstract

Traditionally the arguments used by various Investment Promotion agencies, for attracting foreign direct investment in test markets have not been sufficiently backed up by empirical data. Instead, these arguments have been based on case studies and different types of anecdotal evidence, and there has yet to be conducted an in depth analysis on the drivers of test market investments. However before it is possible for Investment Promotion agencies, such as Copenhagen Capacity, to develop arguments to attract Foreign Direct Investments in test markets, it is necessary to create a proper empirical foundation for them to use. In this thesis I have chosen to create this foundation by using the theory of Bayesian Networks. Bayesian Networks allows me to investigate conditional dependencies, and together with the causal sufficiency assumptions it is possible to determine causal relationships between different parameters. In the Bayesian network different causal relationships are suggested, however it is not possible to confidently assume causal sufficiency. This means that only conditional probabilities were found. Nonetheless, the data does give Copenhagen Capacity, and other I. P. agencies, a better foundation to build better arguments, which can help attract Foreign Direct Investments in test markets. The analysis concludes that 1) the perception of particular key parameters might be more relevant than the actual state of these parameters, 2) Foreign Direct Investment in a specific industry does not diverge largely from the overall Investment flows and, 3) The attraction of Foreign Direct Investments resembles a “winner takes it all” game.