When a firm chooses to establish a new subsidiary in a foreign country, there are many factors that a company must take into consideration. Whilst there may be short-term benefits of locations with low labour and raw material costs, it’s important to consider longer term factors such as economic stability which is often assessed via a complex interaction between many different factors. Weighing up the pros and cons of these different factors is a difficult task, but it is important that they are all taken into consideration when choosing a new location as mistakes can be very costly indeed.
Using a statistical model, it was possible to formalise a system for choosing new subsidiary destinations that incorporates (and weights appropriately) all of the relevant factors. The model incorporated data on the countries of interest (e.g., reliability, political and economical stability, total cost of the internationalisation process and other local and global socio-economic factors), but also on the company itself (e.g., business model, economic status, growth politics and managerial ability).
Since the number of countries under consideration was high, we used cluster analysis to group them into a smaller number of homogeneous groups according to their individual characteristics. We found, for example, that countries in Eastern Europe and Latin America with high unemployment and low foreign exchange and taxation levels were grouped together, whilst another cluster contained countries in North, West and Central Europe with lower GDP growth. The values of the resulting clusters, together with the business-specific variables were then used to model the likely economic performance of the new subsidiary in each location.
For example, it was found that young SME’s were best located in Eastern Europe or Latin America where they have relatively high unemployment and low foreign exchange and taxation levels, whilst larger and more experienced organisations would most likely flourish in North, West and Central Europe where they have lower GDP growth. As well identifying the best locations, the models were used to explain why performance varied from country to country and also to determine which location would give the most robust performance under different scenarios such as global recession or exchange rate fluctuations.
Statistical models for internationalisation can identify which territorial areas are likely to be the most profitable for firms intending to establish foreign subsidiaries. Considering the profile of firms and the political, economic and social characteristics of each country, these models are able to help firms find the location that finds the best trade-off between minimising risks and maximising profit. Similar models can also be used within countries, to select the best region or state to establish a new subsidiary by using regional rather than national socio-economic data, for example.