The CalPERS approach is an example of investability definitions at one end of the extreme insofar as attractiveness does not (in theory) affect the investability of a country at all. Most investment guidelines allow for some feedback from current investment opportunities into the list of investable countries. In other words, should an attractive investment opportunity arise for a country “outside” the list, this is in many cases ground for revision of the list, even if at the outset it is based on criteria unrelated to profitability. Cross-over investors often very simply limit the investable list of countries by credit quality, setting a minimum rating which has to be fulfilled before investments can be made in any country and/or type of security. At the other end of the extreme are funds that have no limits, that can invest in or short everything without restrictions. These are typically funds (such as hedge funds) where a lot of discretion is left to the individual fund manager. But even these kinds of managers usually operate with a list of investable countries based on market liquidity considerations and access to leverage the investments. Of course these funds will not invest in countries under international investment embargo leaving even this extreme end of the spectrum with a list, albeit wide, of investable countries.

_4.2 THE ROLE OF DATA AND MODELS FOR INVESTMENT DECISIONS_ At the beginning of the analytical process of creating an investable set of countries, investors canvass all countries that have 1) actual or potential assets to invest in and 2) available economic data. At a minimum, they create a standard data sheet that gives an overall macroeconomic picture of each candidate country, and at the extreme, feed the data into a specific statistical model to produce a country risk scoring system. Hence, one of the first basic criteria determining whether a country can make the investable list is whether there exist regularly published, quality data for the model’s key variables. A non-trivial number of countries were excluded by nearly all of our sample’s investors because of a lack of comprehensive data, an inadequate time series, or due to a lack of faith in the accuracy of published figures. In addition, many investors required data availability from a non-government source (EIU, IIF, etc.), for further verification before analyzing a country.

The use of a statistical ranking model in the investment decision-making methodology has grown with the trend toward greater global data dissemination, which in turn has been fostered by both investor demand (especially after a few emerging market crises) and the issuance of the IMF standards for data dissemination. While the models clearly differ among different investors, they all share basic similarities. First, the models all rely on macroeconomic variables to paint a broad picture of the stability and economic growth path of the country. These often include GDP growth, inflation figures (level and change), current account and fiscal deficit, as a share of GDP, and the major debt ratios (external debt/GDP and debt service as a share of exports). Second, many models include variables that attempt to describe the liquidity situation of the country, in terms of the monetary system and interest rate indicators (real interest rates, M2/GDP, etc.). Finally, and most important to local currency investors, the models incorporate variables which proxy for currency devaluation risk. These can include real exchange rate valuations, dummy variables representing an exchange rate regime, and international reserve data. Furthermore, many of these variables are used in papers seeking to identify the indicators or predictability of crises, see Kaminsky and Reinhart (1999) and Ades, Masih and Tenengauzer (1998).

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