are presented here only for the group of Lima wage employees. And for them, because the inclusion of lower-level certificate variables results in nonsignificant regression coefficients and also renders the coefficients for the corresponding years-of-education variables nonsignificant owing to the collinearity between years of education and certification, the Model III equation includes only the university degree certificate variable.

Selectivity Bias. We do not have a sufficient number of theoretically relevant background variables to test for self-selectivity in an individual's choice between VTE and general education, given that this decision could have been made far in the past and the PLSS data set is cross-sectional rather than longitudinal. We do, however, test for selectivity in the present-day choice between wage employment and selfemployment. Economic theory says that an individual will choose whichever sector of employment promises the highest net marginal returns. OLS (ordinary least squares) estimates of the earnings equations specified above will be biased to the extent that there are variables omitted from an earnings equation that determine the probability of being in either the wage or self-employed sector. Their omission implies that the error terms of the earnings and labor supply equations will be correlated.

To take into account the choice between wage employment and selfemployment, i.e., to adjust for selectivity bias, we use the standard econometric procedure introduced by Heckman (1976; 1979). We first estimated a set of probit equations to determine, separately, for all males in Lima, and then for all males in OUAs: (a) the probability of being employed in the private wage sector versus all other options (including, but not restricted to, 3elf-employment), and (b) the probability of being self-employed versus all else (including, but not restricted to, wage employment). The following right-hand variables were used in estimating the probit functions: age, age squared, the five splines representing each level and type of schooling, the three educational certificate variables, the training and marriage vEriables, years in the current place of residence, annual unearned income, father's occupation, the number of children in the household, and hectares of land owned by the household. From each probit an estimate of the inverse Mills-tatio, or "lambda" (), was derived.

In the second step of the adjustment procedure, we re-estimated the wage equations with lambda included on the right-hand side to control for the probability of being in the particular sector. With the exception of the self-employed in Lima, we found no evidence of selectivity bias in the original OLS equations, in which one assumes that wage workers and the self-employed are random subsets of the active population of workers. The addition of lambda did not affect the regression coefficients, and the coefficients on lambda itself were not significantly different from zero.

For the self-employed in Lima, however, the coefficient on lambda was positive and significant, and its inclusion lowered the estimated effects of some variables including those of both general and technical secondary schooling. This points to the existence of unobserved factors in Lima that may increase both the probability of becoming self-employed and 15