endogeneity of schooling.<sup>9</sup> These selection correction terms are a function of observables that affect both participation and earnings, _X,_ and variables that affect participation only, _Z,_ the exclusion-restrictions. Under the null hypothesis, program participation has no impact on earnings, i.e. _βTP_ =0 and _βXTP_ =0 where _βTP_ is the coefficient on the dummy that indicates that a worker is employed in a program (constructor) firm, while _βXTP_ is the coefficient on the interaction term between program participation and observable characteristics, _X,_ intended to capture heterogeneity in the benefits of program participation. Such heterogeneity exist if the null hypothesis of homogenous returns, _βXTP_ =0 is rejected.

Table 8 presents the results from OLS estimation of earnings functions. Column 1 presents the results of regressing monthly income on days worked, a dummy for being a casual worker, gender, age and its square, years of schooling and its square, employment history dummies and firm type dummies, forcing the gains from program participation to be homogenous. The results are consistent with the literature; returns to schooling are convex,<sup>10</sup> wages for casual workers are significantly lower than those for permanent workers, the age-earnings profile is concave, and the negative coefficient on the gender dummy attests to the existence of widespread discrimination against women in the labor market. Turning to the results of central interest, workers in program constructors earn 25% more than workers in non-program constructing firms, though there is a 60% premium associated with working for a contractor. In column 2 the assumption that the returns to schooling are homogeneous is relaxed and interactions between program participation and returns to schooling are included as additional regressors. While these interaction terms are not significant in this specification, their signs suggest that those with the least education benefit the most from partaking in the program.

In column 3 we allow for the endogeneity of education by including the predicted residual of a model of educational attainment and interactions of this residual with program participation as additional regressors. The model of educational attainment is presented in Table A2 in the appendix and uses distance to school at the age of six and parental occupation as exclusion restrictions. The terms are not jointly significant and parameter estimates do not change very much; the endogeneity of schooling is unlikely to be a serious problem as also evidenced by the Hausman test, which comfortably accepts the null hypothesis of no endogeneity of schooling.

We would like to know why program constructors pay more than non-program constructors and consider firm size, capital intensity and input intensity as candidate explanations.<sup>11</sup> Adding a control for firm-size in column 4 does not affect the pattern of results, which suggests that the program premium is not due to a positive association between firm size and wages.<sup>12</sup> To test whether capital- and input-intensity can explain the program premium, the capital-labor ratio and inputintensity are included as explanatory variables in columns 6 and 7. Unfortunately information on these variables is not available for all workers, forcing us to drop about 40% of our sample. For the resulting subsample the null hypothesis of heterogeneity in the treatment effect is rejected since the interaction terms between schooling and program participation are significant at the 10% level. In addition, the estimated program premium increases and is individually significant.<sup>13</sup> Somewhat

> 9 Note that if returns to program participation are heterogeneous and program participation and schooling are both endogenous, we should also control for the endogeneity of the interaction of schooling and participation. In the context of our model this is done by interacting the terms correcting for selection bias with the terms which correct for the endogeneity of schooling.

> 10 Years of schooling and years of schooling squared are jointly significant at the 5% level.

> 11 We also experimented with other firm characteristics, such as the age of the firm, the organizational structure of the firm (for example whether the firm is a cooperative or not), and the activities the firms engage in, but these variables could not explain why program firms pay more.

> 12 Note that the sample has been reduced by 14 workers. However, differences in sample composition are not driving the results; estimates available upon request.

> 13 These are sample selection effects. To rule out the possibility that changes in coefficients are due to changes in the composition of the sample, specification 4 is re-estimated on the subsample of workers for whom information on the capital- and input- intensity of the firms they work in is available (see column 5). This subsample contains only 11 casual workers, which is reflected in the coefficients on days-worked and the casual dummy; both are now insignificant; the casual-dummy because it is imprecisely estimated; the days worked

-8-