New York, Abu Dhabi, London or Stay at Home? Using a Cross-Nested Logit Model to Identify Complex Substitution Patterns in Migration

We propose a cross-nested logit (CNL) approach to address the question of how people revise their migration decisions when facing changes in the global environment. Compared with the popular logit model, the CNL allows for more sophisticated substitution patterns between destinations. Using migration aspiration data from India, we show that the CNL approach outperforms competing approaches in terms of quality of fit and predictive power, implies stronger heterogeneity in responses to shocks, and highlights complex and intuitive substitution patterns. We document the low degree of substitutability between the home and foreign alternatives as well as within subgroups of particular countries. This approach allows to capture spillover effects of a given immigration policy on immigration pressures faced by other countries of destination