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

Abstract

The question of how people revise their decisions about whether to emigrate, and where to, when facing changes in the global environment is of critical importance in migration literature. We propose a cross-nested logit (CNL) approach to generalize the way deviations from the IIA (independence from irrelevant alternatives)) hypothesis can be tested and exploited in migration studies. Compared with the widely used logit model, the structure of a CNL model allows for more sophisticated substitution patterns between destinations. To illustrate the relevance of our approach, we provide a case study using migration aspiration data from India. We demonstrate that the CNL approach outperforms standard competing approaches in terms of quality of fit, has stronger predictive power, implies stronger heterogeneity in responses to shocks, and highlights complex and intuitive substitution patterns between all possible alternatives. In particular, we shed light on the low degree of substitutability between the home and foreign alternatives as well as on the subgroups of countries that are considered by potential Indian movers as highly or poorly substitutable.