This study assesses the redistributive impacts of fiscal instruments in a 2014 Mexican household budget survey (ENIGH) correcting for potential top-income measurement problems. We use two correction methods based on within-survey information to re-estimate the redistributive impacts of contributory pensions and cash-like transfers; direct taxes; indirect taxes and subsidies; and in-kind transfers. The two methods are: survey-sample reweighting for households' nonresponse probability, and replacing of top incomes using synthetic values from the Pareto distribution. This replacing is implemented either on all core income concepts, or on net market income from which it is passed onto other incomes through fiscal rules. These corrections yield higher inequality as measured by the Gini (0-9 pc.pt. increase) and the top 1% and 10% income shares (0-5, and 1-5 pc.pt. increases), consistently between the reweighting and replacing methods, and consistently across all income concepts. Moving from pre-fiscal market income to post-fiscal final income, corrections for nonresponse fall slightly, while corrections for mismeasurement rise. Taxable income is subject to the highest inequality, which further undergoes the highest upward correction for top income problems, potentially consistent with evidence of earnings misreporting among the rich. Conversely, nontaxable income has a strong equalizing impact of 3.3-4.5 points of the Gini further accentuated under the top-income corrections. The corrections confirm the inequality-neutral impact of pensions in Mexico, and equalizing impacts of transfers, direct taxes, indirect taxes and subsidies, and in-kind transfers. In-kind transfers, cash-like transfers and direct taxes have the strongest equalizing impacts of 4.7-5.7, 1.6-1.9, and 1.2-2.2 points of the Gini, respectively. Indirect taxes and subsidies are weakly equalizing, by 0.4-0.6 points. Finally, top-income measurement challenges retain their magnitude across the 2010, 2012 and 2014 ENIGH, but household nonresponse becomes more positively selected over time, causing more serious biases.
