An application of LASSO and multiple imputation techniques to income dynamics with cross‐sectional data

This paper introduces, validates, and applies a Least Absolute Shrinkage and Selection Operator with multiple imputation by Predictive Mean Matching (LASSO‐PMM) method to estimate intra‐generational income dynamics from cross‐sectional data. We validate the method using 36 harmonized panel data sets in four Latin American countries and apply it to cross‐section data from 43 countries across the world. Results show that LASSO‐PMM predictions are statistically indistinguishable from actual household poverty rates, mobility indicators, and income or consumption changes. These findings suggest that estimating economic mobility using a LASSO‐PMM approach may accurately approximate actual income dynamics when panel data are unavailable.