This paper estimates sharp bounds on treatment effects for the Job Corps program, a large federally-funded job training program in the United States. The study addresses the challenge of estimating the wage effects of the program in the presence of sample selection, a common issue in applied micro-econometric research. The paper develops a trimming procedure to bound average treatment effects, which does not require exclusion restrictions or bounded support for the outcome variable. The method involves identifying the excess number of individuals induced to be selected (employed) due to the treatment, and then trimming the upper and lower tails of the outcome distribution by this number, yielding a worst-case scenario bound. The bounds suggest that the Job Corps program raised wages, consistent with the notion that the program increases human capital rather than solely through encouraging work. The estimator is generally applicable to treatment evaluation problems with non-random sample selection or attrition. The paper uses data from the National Job Corps Study, a randomized evaluation funded by the U.S. Department of Labor. The results show that the program had a positive effect on wages, with bounds suggesting a treatment effect of 4.2 to 4.3 percent at week 90 after random assignment, and -2 to 9 percent by the end of the 4-year follow-up period. The study highlights the importance of addressing sample selection in treatment evaluation and provides a method for estimating sharp bounds on treatment effects without requiring exclusion restrictions.This paper estimates sharp bounds on treatment effects for the Job Corps program, a large federally-funded job training program in the United States. The study addresses the challenge of estimating the wage effects of the program in the presence of sample selection, a common issue in applied micro-econometric research. The paper develops a trimming procedure to bound average treatment effects, which does not require exclusion restrictions or bounded support for the outcome variable. The method involves identifying the excess number of individuals induced to be selected (employed) due to the treatment, and then trimming the upper and lower tails of the outcome distribution by this number, yielding a worst-case scenario bound. The bounds suggest that the Job Corps program raised wages, consistent with the notion that the program increases human capital rather than solely through encouraging work. The estimator is generally applicable to treatment evaluation problems with non-random sample selection or attrition. The paper uses data from the National Job Corps Study, a randomized evaluation funded by the U.S. Department of Labor. The results show that the program had a positive effect on wages, with bounds suggesting a treatment effect of 4.2 to 4.3 percent at week 90 after random assignment, and -2 to 9 percent by the end of the 4-year follow-up period. The study highlights the importance of addressing sample selection in treatment evaluation and provides a method for estimating sharp bounds on treatment effects without requiring exclusion restrictions.