An Exorbitant Privilege in the First Age of International Financial Integration

The exorbitant privilege literature analyzes the positive differential returns on net foreign assets enjoyed by the United States in the last quarter of the twentieth century as the issuer of the global reserve currency. In the first age of international financial integration (1870-1914), the global reserve currency of the period was the British pound sterling. Whether the United Kingdom enjoyed a similar privilege is analyzed with a new dataset, encompassing microdata on railroad and government financial securities. The use of microdata avoids the flaws that have plagued the US studies, particularly the use of incompatible aggregate variables. New measures of Britain’s net external position provide estimates on capital gains and dividend yields. As the issuer of the global reserve currency, Britain received average revenues of 13.4% of GDP from its international investment position. The country satisfied the necessary condition for the existence of an exorbitant privilege. Nonetheless, Britain’s case is slightly different from the American one. British external assets received higher returns than were paid on external liabilities for each class, but British invested mostly in securities with low profile of risk. The low return on its net external position meant that, for most of the time, Britain would not receive positive revenues from the rest of the world if it were a net debtor country, but this pattern changed after 1900. The finding supports the claim that, at least partially, exorbitant privilege is a general characteristic of the issuer of the global reserve currency and not unique to the late twentieth century US.

Modeling the Effects of Habitat Fragmentation on the Movements of the Mongolian Gazelle in the Eastern Mongolian Steppe

The Mongolian gazelle, Procapra gutturosa, resides in the immense and dynamic ecosystem of the Eastern Mongolian Steppe. The Mongolian Steppe ecosystem dynamics, including vegetation availability, change rapidly and dramatically due to unpredictable precipitation patterns. The Mongolian gazelle has adapted to this unpredictable vegetation availability by making long range nomadic movements. However, predicting these movements is challenging and requires a complex model. An accurate model of gazelle movements is needed, as rampant habitat fragmentation due to human development projects - which inhibit gazelles from obtaining essential resources - increasingly threaten this nomadic species. We created a novel model using an Individual-based Neural Network Genetic Algorithm (ING) to predict how habitat fragmentation affects animal movement, using the Mongolian Steppe as a model ecosystem. We used Global Positioning System (GPS) collar data from real gazelles to “train” our model to emulate characteristic patterns of Mongolian gazelle movement behavior. These patterns are: preferred vegetation resources (NDVI), displacement over certain time lags, and proximity to human areas. With this trained model, we then explored how potential scenarios of habitat fragmentation may affect gazelle movement. This model can be used to predict how fragmentation of the Mongolian Steppe may affect the Mongolian gazelle. In addition, this model is novel in that it can be applied to other ecological scenarios, since we designed it in modules that are easily interchanged.