  In this paper we investigate 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. In
specific, 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. Thus, having a
tail index estimator that explicitly accounts for this sample characteristic is
of importance to better understand and compute the tail index dynamics in the
censored right tail of the wage distribution. 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 finite sample performance and
compare it with competing methods; ii) we highlight that the factor values used
to adjust the top-coded wage have changed over time and depend on the
characteristics of individuals, occupations and industries, and propose
suitable values; and iii) we provide 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.
