Measures of inequality and mobility based on self-reported earnings reflect attributes of both the joint distribution of earnings across time and the joint distribution of measurement error and earnings. While classical measurement error would increase measures of inequality and mobility, there is substantial evidence that measurement error in earnings is not classical. In this paper, we present the analytical links between nonclassical measurement error and some summary measures of inequality and mobility. The empirical importance of nonclassical measurement error is explored using the Survey of Income and Program Participation (SIPP) matched to tax records. We find that the effects of nonclassical measurement error are large. However, these nonclassical effects are largely offsetting when estimating mobility, as measured by the intertemporal correlation in earnings. As a result, SIPP estimates of the correlation are similar to estimates based on tax records, though SIPP estimates of inequality are smaller than estimates based on tax records.

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 United States? We apply standard methods developed by Bai and Perron to identify structural breaks in time series data. Applying these methods to wage histories allows us to identify when each person experiences 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 are 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 are not corrected for measurement error.

This paper uses data from the Luxembourg Income Study to explore the role of differences in supply shifts in explaining cross-national differences in the rise in earnings inequality. Changes in returns to age and education are estimated for eight countries using a common specification of earnings functions across years and countries. We find that the small overall increase in earnings inequality in many countries reflects large but offsetting changes in returns to skill and changes in inequality within age education cells. Furthermore, these differences in returns to skill can largely be explained by differences in supply shifts.