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"We argue that the postwar baby boom [in the United States] caused substantial fluctuations in both the economic rewards to education and educational attainment over the last 3 decades. If substitutability between young and old workers diminishes with education, the present value of lifetime earnings for a boom cohort is depressed more for highly educated workers, reducing incentives for educational attainment. The opposite is true for pre- and postboom cohorts. The diminishing substitutability hypothesis explains the declines in both the returns to college and college completion rates in the 1970s and predicts a substantial increase in educational attainment for postboomers."

When zero mean measurement error is added to the dependent variable for the nonlimit observations of the censored normal regression model, the conventional maximum likelihood estimator (Tobit) is inconsistent. Correct maximum likelihood estimation appears to be computationally difficult under various specifications for the distribution of the measurement error. Estimators based on either the expectation function or the conditional expectation function for uncensored observations remain consistent in the presence of measurement error. Eight such estimators are examined. The results of a numerical experiment suggest that several of these estimators are substantially more efficient than the conventional maximum likelihood estimator when measurement error exists and that they also will do reasonably well when it does not.