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.