This paper investigates potential changes which may have occurred, over the last two decades, in the probability mass of the right tail of the wage distribution, through the analysis of the corresponding tail index. A conditional tail index estimator is introduced which explicitly allows for right-tail censoring (top-coding), which is a feature of the widely used current population survey (CPS), as well as of other surveys. Ignoring the top-coding may lead to inconsistent estimates of the tail index and to under or over statements of inequality and of its evolution over time. The contribution of this paper is threefold: i) We introduce a conditional tail index estimator that explicitly handles the top-coding problem, and evaluate its nite sample performance and compare it with competing methods. Based on this estimator we observe that inequality increased more than what was previously described in the literature; ii) We show that the factor values used to adjust the top-coded wages have changed over time and depend on the characteristics of individuals, occupations and industries, and propose suitable values. Specifically, we observe that the factor values used to adjusted the top-coded wages for women in the Finance industry increased from 1.5 in 1992 to around 2 in 2017 (and similarly in other industries although of smaller magnitude); and iii) an in-depth empirical analysis of the dynamics of the US wage distribution's right tail using the public- use CPS database from 1992 to 2017 is provided. It is shown that the probability of observing an extreme value increased in 2017 by 4.94% for an individual in the Finance industry, which is an indication of an increase in concentration in the right-tail of the wage distribution in this industry (an increase, although of smaller magnitude, of the partial effects is also observed for all other industries except for Public sector which is still negative).