published in: Review of Economics and Statistics, 2005, 87 (3), 556-568
This paper presents a new method to correct for measurement error in wage data and
applies this method to address an old question. How much downward wage flexibility is there
in the U.S? We apply standard methods developed by Bai and Perron (1998b) to identify
structural breaks in time series data. Applying these methods to wage histories allows us to
identify when each person experienced a change in nominal wages. The length of the period
of constant nominal wages is left unrestricted and is allowed to differ across individuals, as is
the size and direction of the nominal wage change. We apply these methods to data from the
Survey of Income and Program Participation. The evidence we provide indicates that the
probability of a cut in nominal wages is substantially overstated in data that is not corrected
for measurement error.
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