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11.2. EARNINGS IN LOGS OR NOT? As mentioned earlier, the dependent variable in the standard Mincer earnings equation is the log, as opposed to the level, of earnings. While logs are typically used in econometric models for reasons of convenience or fit, there is a strong theoretical rationale for using log earnings in a human capital earnings regression. As pointed out by Mincer (1958), education should have a multiplicative effect on earnings in a simple model where identical individuals maximize the present value of future income which is equalized for all education levels in equilibrium. The reason is that investments in human capital, like other investments, are only undertaken as long as the rate of return (not the absolute return) on the investment exceeds the discount rate. Log-linearity of earnings as a function of years of schooling is in fact a key empirical implication of the human capital model with identical individuals proposed by Mincer (1958). The existing evidence generally supports the log-earnings specification. For example, James Heckman and Polachek (1974) estimate a Box-Cox model and could not reject the log specification. More recently, Nicole Fortin and Thomas Lemieux (1998) use a more flexible ‘‘rank regression’’ model in which earnings are specified as a relatively unrestricted monotonic transformation of a Mincer-type human capital index (sum of a linear function of education, polynomial function of potential experience, few other regressors and a normally distributed error term). Using large samples from the 1979 and 1991 outgoing rotation group (ORG) supplement of the CPS, they find that the log wage is close to a log-linear function of the human capital index for values of the wage above the minimum wage. Their findings are repro- duced for men in Figure 10.2.4 The human capital index appears to have a smaller effect on wages around the minimum wage, which is consistent with minimum wages compressing the wage distribution at the low end of the skill distribution.5 Figure 11.2 shows, nevertheless, that the assumption of log-linearity is very accurate for most of the range of the wage distribution.