Export credit insurance in a transitional economy: Evidence from China

China is the world’s largest exporter, and is also one of the few economies that have successfully transitioned from central-planned to market-oriented economy. In this paper, we analyze the effect of China’s official export credit insurance on Chinese exports to countries in the world. We describes the background of official export insurance in general and the development of China’s ECI. Meanwhile, we discuss the data and model used to examine the relationship between China’s ECIs and exports. We estimate both the static and dynamic gravity models where exports are a function of country size, transportation costs, and country-risk. Our results of static model suggest that that a 1 percent increase in China’s official ECI coverage stimulates its exports volume by 0.34 percent. Furthermore, Chinese companies export 1.5 times more to countries with ECIs than to those countries where ECIs are not available. Our estimates from the dynamic model are similar to the static model, to a lesser extent, of ECI impacts on exports to trading partners.

Exportsicherheit

The economic shock in Brazil at the beginning of the 1950s posed a massive threat to West Germany’s export trade because Hermes, the export credit insurance provider that had been founded only shortly beforehand, proved to be deficient as it did not sufficiently cover the transfer or conversion risk involved. This case thus created a situation which required drastic action, during the course of which intensive and complex negotiations between countless actors from the state and the private sector collapsed. The subsequent compromise of covering the transfer or conversion risk, which was finally found two years later, then provided the greater security the foreign trade sector desired and demanded.

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.