Unfortunately, the need to relate the results to a reference cluster makes the interpretation of multinomial logits cumbersome. Two graphical options exist that help to shed more light on the estimation results. The first option is presented in Figure 3. It measures the probability (vertical axis) of belonging to each of the four clusters as a regressor (horizontal axis) is changed. At any level of a regressor, the sum of the resulting probabilities in all four charts adds up to one. But what matters is how the probability in each cluster changes as the regressor changes. Four examples are provided in Figure 3. It is clear that an increase in trade openness will increase the probability of being in the higher growth and higher vulnerability (H) cluster, and this is also the case for the lower growth and higher vulnerability (V) cluster for levels of trade openness between 0 and 120 percent. In contrast, the probability of belonging to cluster L or G declines or remains stable as trade openness increases. A similar pattern is observed for financial openness, though the impact on cluster V seems sharper. Other examples are on monetary tightening and exchange rate flexibility, the latter measured by the Reinhart and Rogoff (2004) exchange rate regime classification.<sup>9</sup> Specifically, tighter monetary policies lead to a greater probability of a country ending in cluster G while an increase in exchange rate flexibility leads to cluster H. In fact, exchange rate flexibility reduces the likelihood of the worst outcome (i.e., cluster V).

Still, Figure 3 does not provide a metric to gauge the impact of the changes in regressors that could be considered more likely. Undoubtedly, changes in regressors may have an effect on the growth or the vulnerability dimensions (or concurrently on both dimensions). For example, if the change in a regressor shifts a country from the L and V clusters to the G and H clusters (or vice versa), then the effect is on the growth dimension. In contrast, if the shift takes place from the L and G clusters to the V and H clusters (or vice versa), then the effect is on the vulnerability dimension. More involved cases also exist. For example, a change in a regressor may lead to a sharp increase in the G cluster: an optimal outcome. Or lead to a sharp increase in the V cluster: the worst of all possible outcomes. But in all of these cases it remains unclear how much of a change in the regressor it is sensible to expect.

Against this background, Figure 4 provides a second option for presenting the results of estimations. This figure shows the impact of a one standard deviation shock (that is, an increase) on each of the regressors and how this affects the likelihood of being in any one of the four clusters (again, lower growth and lower vulnerability (L), lower growth and higher vulnerability (V), higher growth and higher vulnerability (H), and higher growth and lower vulnerability (G)). The discussion below presents the main estimation results for degree of openness, economic structure, economic policies, and global conditions.

# _<u>Degree of Openness</u>_

An increase in trade openness raises the likelihood of being in a higher vulnerability cluster. Indeed, a one standard deviation shock increases the probability of being in the higher growth and higher vulnerability (H) cluster by slightly over 0.15. It also marginally increases the probability of being in the lower growth and higher vulnerability (V) cluster and reduces the probability of being in the lower growth and lower vulnerability (L) cluster by about 0.20 percent. Thus, much of the effect appears to occur by shifting countries from a lower growth and lower vulnerability cluster to a higher growth and higher vulnerability cluster. Thus, trade openness has an effect on both the growth dimension and the vulnerability dimension.

> 9 This is extended to 2008 using the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions.

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