| Abstract: |
Rank-rank regressions are widely used in economic research to evaluate phenomena such as intergenerational income persistence or mobility. However, when covariates are incorporated to capture between-group persistence, the resulting coefficients can be difficult to interpret as such. We propose the conditional rank-rank regression, which uses conditional ranks instead of unconditional ranks, to measure average within-group income persistence. This property is analogous to that of the unconditional rank-rank regression that measures the overall income persistence. The difference between conditional and unconditional rank-rank regression coefficients therefore can measure between-group persistence. We develop a flexible estimation approach using distribution regression and establish a theoretical framework for large sample inference. An empirical study on intergenerational income mobility in Switzerland demonstrates the advantages of this approach. The study reveals stronger intergenerational persistence between fathers and sons compared to fathers and daughters, with the within-group persistence explaining 62% of the overall income persistence for sons and 52% for daughters. Families of small size or with highly educated fathers exhibit greater persistence in passing on their economic status.
|
|
Biography:
|
Iván Fernández-Val is a Professor at the Department of Economics in Boston University. His research fields are Econometrics and Labor Economics. He has recently worked on nonlinear panel data, distributional and causal methods, and applications of machine learning to causal inference. His work has been published in top economic and statistics journals such as Biometrika, Econometrica, Journal of the American Statistical Association, Journal of Econometrics, Journal of Machine Learning Research, Journal of Political Economy and Review of Economic Studies. He serves as coeditor and associate editor of several journals.
|
