17 advantage.<sup>37</sup> This corresponds closely to the<sup>ratio of average male earnings to average female</sup> earnings in the sample.<sup>3</sup> s Second, differences<sup>in endowments refer to the difference in the</sup> average values of a particular characteristic<sup>in the sample between men and women.39 Third,</sup> differences in rewards refer to the difference in<sup>the corresponding coefficients as reported in the</sup> earnings functions. Again this difference<sup>is calculated between men and women.</sup> According to the data, the male pay advantage<sup>in the present data set varies between</sup> about 15-20 percent (in Colombia, Mexico and<sup>Peru) and 40-50 percent (in Argentina, Bolivia,</sup> Ecuador and Jamaica). This gives an unweighted<sup>average of male pay advantage in the region</sup> of about 30 log-percentage points (or female/male<sup>pay of about 75 percent). The two most</sup> obvious conclusions that can be drawn from<sup>Table 5 are the following.</sup> First, the selectivity corrected estimates<sup>for the part of the pay gw attributable to</sup> differences in rewards (upper bound of discrimination)<sup>are lower than the results derived from</sup> the uncorrected estimates. Correcting for<sup>selectivity reduces the upper bound of discrimination</sup> from 97 percent to 80 percent. The reason<sup>that selectivity correction (lambda) reduces the</sup> unexplained part of the wage gap in the region<sup>is because in most country studies the coefficient</sup> on lambda was negative.<sup>40</sup> This implies that<sup>the difference in the wage offers of women and</sup> men is smaller than the difference in atual<sup>wages. Though it is not strictly appropriate to</sup> calculate averages from percentages, especially<sup>when these percentages refer to countries which</sup> differ in population size (and characteristics)<sup>as much as Jamaica differs from Brazil, the</sup> magnitude of the corrected and uncorrected results<sup>may be taken to suggest that only a small part</sup> of the difference in the actual wages between<sup>women and men reflects self-selection of women</sup> in the labor force. Our figures imply that,<sup>if the average female worker had the characteristics</sup> 3'The reason for expressing the pay gap in terms of male advantage is because the decomposition is formulated in this way. Recall that our decomposition refers to the difference in the average logarithms of earnings between men and women, that is,<sup>log(W,)-log(Wf):</sup> see equation 6. 3'For example, in Argentina the pay gap is indicated<sup>as 43.2 percent and this should be taken to mean</sup> that men earn on average 43.2 percent more than<sup>women. In other words the pay differential is not</sup> expressed in one of the more conventional vays<sup>-such as in terms of the relative female/male wage</sup> (which is 69.8 percent) or the underpayment of women (which is 30.2 percent).

39Again with reference to Argentina working women have on average 9.4 years of schooling compared to 8.8 years of schooling for men (see Appendix<sup>Table A3). In this case the relevant difference</sup> is -0.6 years of schooling and the negative sign suggests<sup>that, had women and men been equally endowed</sup> in terms of schooling, the male pay advantage would<sup>have been even greater.</sup> 'he coefficient on lambda was positive and significant<sup>only in the case of Uruguay. Insignificant</sup> positive coefficients are reported for Ecuador, Peru<sup>(1986) and Bolivia. Insignificant</sup> negative results are reported for Argentina, Colombia (1979), Costa Rica,<sup>Peru (1990) and Venezuela (1989) (see Appendix</sup> Table A6).