The goal of this research is to investigate the determinants and consequences of the training, schooling, and employment decisions of adolescents. Of particular interest will be the decisions leading up to high school completion and the subsequent transitions from secondary school to work, post-secondary education, military service, and vocational training for high school graduates, GED recipients, and high school dropouts. Several large-scale longitudinal data sources will be analyzed to understand the dynamics and interrelationship among these choices. We plan to develop and apply new econometric tools in order to exploit the wealth of information available in the Survey of Income and Program Participation (SIPP), the NLSY, NELs:88, the NLS Young Men and Young Women data sets, and other longitudinal data sources. Explicit dynamic models will be estimated with an eye toward interpreting empirical regularities within articulated choice-theoretic frameworks. We seek to accomplish several specific tasks in the course of our proposed research. We plan to extend previous research on schooling decisions by estimating dynamic models of school attainment which will explain the age-by-age decisions to attend school. We will investigate decisions to drop out and reenter school and to obtain a GED. We will assess the role of opportunity wages, family resources, and tuition costs in explaining the dynamics of school attainment. We will examine the contribution of these factors to differences in the levels and rates of educational attainment between genders and among the major racial and ethnic groups. We plan to consider a broad portfolio of post-secondary options facing high school graduates and non-graduates, including two-and four-year college, military service, vocational training, and work. We will model the joint relationship between schooling and child residential choice decisions. Finally, we will develop a general semiparametric econometric framework with interrelated continuous and discrete endogenous variables that will allow us to more fully exploit the richness of longitudinal data than has been possible to date.
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