Parametric models of income distributions integrating misreporting and non-response mechanisms

Several representativeness issues affect the available data sources in studying populations' income distributions. High-income under-reporting and non-response issues have been evidenced to be particularly significant in the literature, due to their consequence in underestimating income growth and inequality. This paper bridges several past parametric modelling attempts to account for high-income data issues in making parametric inference on income distributions at the population level. A unified parametric framework integrating parametric income distribution models and popular data replacing and reweighting corrections is developped. To exploit this framework for empirical analysis, an Approximate Bayesian Computation approach is developped. This approach updates prior beliefs on the population income distribution and the high-income data issues pressumably affecting the available data by attempting to reproduce the observed income distribution under simulations from the parametric model. Applications on simulated and EU-SILC data illustrate the performance of the approach in studying population-level mean incomes and inequality from data potentially affected by these high-income issues.