Country risk maps, with their bright colors and seemingly precise numerical values, are an excellent way to help clients visualize the data and the analysis that these maps are based on. They are easily digestible tidbits of information that are often used to compare one location to another.
While very useful, it is important to remember such maps also have flaws that should be taken into consideration.
The key concept is an old computer science term called “GIGO” that means “Garbage In, Garbage Out.” The quality of the output is dependent upon the quality of the input. Country risk maps and country risk grades are just simplified representations of processed data. If bad data is used in its preparation and bad analysis is used in it’s processing, then it is simply garbage.
So how does garbage creep into country risk maps?
Unreliable data. Economic data from national governments are usually reputable, but not always. Some countries like China, or Saudi Arabia, or Albania sometimes massage their economic figures for political ends. Some economic figures from a few other countries are not meant to be wrong or misleading but are simply incompetent collected and assembled.
Data lost in translation. Data about crime rates in different countries may be correctly collected and assembled but may suffer from differences in definition. What may be a crime in one country may not be in another or there may be differences in gradations of crime that do not make figures comparable.
Difficulties in quantification. Some data that are fed into a country risk map may be more qualitative than quantitative. Expert opinions about a country’s political risk are an example. Expert opinions are very useful because they are already distillations of years of study and experience. They are also effective in filling in the gaps that quantitative data does not cover. Expert opinions are also useful as a check on conclusions arrived at from pure data that may not pass the “smell” test.
Who are your experts? All this benefit from expert opinions, however, are dependent upon the correct choice of expert and the knowledge of that expert’s possible biases. Choose a lousy expert and you get lousy risk maps. Choose a biased expert and you get biased information.
Some risk managers have tried to address these problems by turning to incident collection. For example, a company putting together a travel risk profile for Peru might simply collect all incidents of road deaths due to traffic accidents and then make a conclusion from the data. This approach seems solid because there is real data backing the conclusion. Yet even then flaws can creep in. Unless the firm does the collection itself (impracticable and close to impossible), it has to rely on sources which may not be entirely accurate or complete.
In the end, country risk maps are mere estimates, the best guesses available. When looking at one, it is important to note the provenance of the data, the possible biases of the map creator, and the source of the expert opinions used. A good bit of your own judgment will come in handy as well.
