International Location Index for the Medium-Sized Companies
The strengths and weaknesses of an index depend heavily on the quality of their underlying variables. Hence, the selection not only considered the relevance of a variable, but also its accuracy, reliability, comparability and availability. Only data from official international sources were used for the selection of the sub-indicators. Sources such as the World Bank, the OECD, the United Nations or the International Monetary Fund are distinguished for making reliable data for a large number of countries - if not all of them - publicly available. Data width of the individual indicators was also emphasised, i.e in order to examine the largest country panel possible, a variable should be available for as many countries as possible. These international data are processed in such a way that they enable a comparison between countries, despite different national definitions and statistics such as the unemployment rate. At the same time, the use of these data increases the indicator's transparency, since the sub-indicators can be tracked individually at any time.
The study includes 174 countries in 17 regions on all continents, as listed in Appendix A. Based on the classification of the United Nations, the analysis contains the following regions: Eastern Africa, Central Africa, Northern Africa, Southern Africa, Western Africa, Northern America, Latin America and Caribbean, Central Asia, Eastern Asia, Southern Asia, South-East Asia, Western Asia, Eastern Europe, Northern Europe, Southern Europe, Western Europe, Oceania. As detailed in Appendix B, countries with a population of less than 150,000 were excluded, or generally countries where there is a lack of available data such as Cuba, North Korea, Somalia, Western Sahara, Gaza and the West Bank. Luxembourg was also excluded, because as a centre for financial services, the country exhibits some peculiarities in the variables considered here, such as the per capita inflow of foreign direct investments. These characteristics would have made the distribution of data extremely lopsided and thus distorted the normalisation. The variables we utilised for the index formation are listed in detail in Appendix C.
The indicators presented in paragraph 3.1 are a combination of simple variables, such as per capita income, and composite indicators, such as rule of law, health, investment freedom or infrastructure quality. Whether such a combination is permissible and sensible, is a legitimate question. From a statistical point of view, there is no contra-indication, provided that no variable, which is already part of an indicator that aggregates several individual factors, is considered as a singular indicator. The consequence of this would be that the same aspect would be used twice in the index calculation, i.e it would be weighted doubly in the end result. The selected indicators that aggregate a number of variables are a sensible choice, because it is impossible to reflect the relevant aspects in a single variable. For instance, the indicator "quality of trade and transport-related infrastructure" portrays more than just the length of the road or rail network. To put it another way, these indicators were compiled precisely in order to enable the mapping of a multi-dimensional construct.
1 Cf. OECD (2008), 45.
2 The regions of Melanesia, Micronesia, Polynesia, and Australia and New Zealand were grouped together as Oceania, because fewer data were available for the small (island) countries.
3 Cf. OECD (2008), 32.
4 These indicators include "political stability", "regulatory quality", "rule of law" and "control of corruption" from the Worldwide Governance Indicators, "health" and "education" from the Human Development Index, "business freedom", "trade freedom", "investment freedom" and "labour freedom" from the Index of Economic Freedom, and the "infrastructure quality" from the Logistics Performance Index.