This paper presents a structural estimation of a dynamic model of schooling, work, and occupational choice decisions based on 11 years of data from the 1979 youth cohort of the National Longitudinal Surveys of Labor Market Experience (NLSY). The model is grounded in human capital investment theory and is designed to investigate whether a theoretically restricted model can quantitatively fit observed data patterns. The authors find that an extended human capital investment model performs well in fitting observed data on school attendance, work, occupational choices, and wages, and also produces reasonable forecasts of future work decisions and wage patterns. The structural approach allows for rigorous interpretations of the estimated parameters, including the importance of unobserved skill heterogeneity and the impact of college tuition subsidies on life cycle outcomes. The paper extends previous research by considering self-selection in schooling, work, and occupational choice, and it uses a dynamic programming method to solve the optimization problem at each age. The estimation process involves numerical solution of the dynamic programming problem and likelihood calculation, with the likelihood function involving multivariate integrals. The results show that the basic model generates parameter values within reasonable ranges and fits the data well, both within and outside the sample.This paper presents a structural estimation of a dynamic model of schooling, work, and occupational choice decisions based on 11 years of data from the 1979 youth cohort of the National Longitudinal Surveys of Labor Market Experience (NLSY). The model is grounded in human capital investment theory and is designed to investigate whether a theoretically restricted model can quantitatively fit observed data patterns. The authors find that an extended human capital investment model performs well in fitting observed data on school attendance, work, occupational choices, and wages, and also produces reasonable forecasts of future work decisions and wage patterns. The structural approach allows for rigorous interpretations of the estimated parameters, including the importance of unobserved skill heterogeneity and the impact of college tuition subsidies on life cycle outcomes. The paper extends previous research by considering self-selection in schooling, work, and occupational choice, and it uses a dynamic programming method to solve the optimization problem at each age. The estimation process involves numerical solution of the dynamic programming problem and likelihood calculation, with the likelihood function involving multivariate integrals. The results show that the basic model generates parameter values within reasonable ranges and fits the data well, both within and outside the sample.