Modelling failure rates with machine-learning models: Evidence from a panel of UK firms

In this study, we investigate the ability of machine-learning techniques to predict firm failures and we compare them against alternatives. Using data on business and financial risks of UK firms over 1994–2019, we document that machine-learning models are systematically more accurate than a discrete hazard benchmark. We conclude that the random forest model outperforms other models in failure prediction. In addition, we show that the improved predictive power of the random forest model relative to its counterparts persists when we consider extreme economic events as well as firm and industry heterogeneity. Finally, we find that financial factors affect failure probabilities.     

Government ideology and international migration*

We provide the first empirical evidence that government ideology affects the choice of migration destinations. As ruling political parties differ in their discourse, policies, and positions on migration, the ideology differential between the host and home country governments can shape the relative generosity of the welfare system, the degree of tolerance towards out-groups, and the restrictiveness of migration policies, all acting as important drivers of international migration. Using data on bilateral migration and government ideology for OECD countries between 1990 and 2016, we show that migration flows increase when the government at the destination becomes more left-wing relative to the government at the origin, particularly when both countries are members of the European Economic Area.