4 appears, therefore, to have resulted in better coverage of households with fewer or no employed males.

Hourly earnings are defined as reported monthly earnings divided by 4.33 and then divided by reported weekly hours of work. Table 1 reports summary descriptive information for each year on log (hourly earnings), along with descriptive statistics on years of education, years of experience<sup>2</sup> (defined as age - (6 + years of schooling)) and the regional, metropolitan, urban and racial composition of the samples. Real hourly earnings fall between 1988 and 1992 but rise thereafter. The standard deviation in hourly earnings drops somewhat over the period, particularly between 1988 and 1992. This narrowing in the male earnings distribution is further apparent in the movement in the gap between the 75<sup>th</sup> and 25<sup>th</sup> percentiles, and in particular in the narrowing in the gap between the 90<sup>th</sup> and 10<sup>th</sup> percentiles, shown at the bottom of the Table.

Sample average years of schooling increase by 0.9 years between 1988 and 1998. Undoubtedly this is the result of younger more educated male cohorts entering the labor force. There is also a reduction in average experience of just over 1 year between 1988 and 1998. In part this is consistent with the increase of the average length of time spent in education. However the mean and median product of education and experience both show a marked increase.

Figures 1 to 3 describe the log hourly earnings distribution in each year through the estimation of univariate Kernel density functions.<sup>3</sup> Vertical lines of each figure identify the position of the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles of the distribution in each case. Visual inspection reveals that the dispersion of these percentile points narrows over time. A further feature of note is that in 1988 and in 1992 in particular the shape of the distribution reveals a mode at almost exactly the 25<sup>th</sup> percentile. However by 1998 the shape of the distribution changes considerably, with the mode moving much closer to the median.

# **4. Empirical Results**

Key results (coefficients _a_ 1 to _a_ 5) for earnings function estimates for 1988, 1992 and 1998 are presented in Table 2. For each year the table firstly shows OLS estimates and then simultaneous quintile regression estimates for the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> quintiles. For each year a Wald test for the null hypothesis of equality of the coefficients across the quintiles is decisively rejected. The reported coefficients suggest considerable variation in the education-earnings and experience-earnings profiles at the different points in the earnings distribution. We shall discuss rates of return to education and experience shortly.

The education-experience interaction coefficient is negatively signed and statistically significant at the sample mean (OLS) and for the 10<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> conditional quintiles for 1988 and 1992. However at the sample mean (OLS) and for every quintile below the 90<sup>th</sup> the coefficient is positively signed and statistically significant. This suggests that prior to the mid 1990s for those at the very bottom and those in the higher part of the earnings distribution 2 The 1992 and 1998 surveys provide information on experience with the current employer. However the explanatory power of the earnings equations are substantially lower using this definition.

3 Estimated using the Epanechnikov kernel estimator with 200 evaluation points over range of the distribution.