In 2008, there were 19.6 abortions for each 1000 women of fecund age in the U.S. In absolute numbers, his means that 1.21 million induced abortions occur each year which account for 22.4% of pregnancies. (Jones and Kooistra, 2011). When asked about the reasons to get an abortion, women in the U.S. often refer to the conflict between child rearing and their careers or employment (38%), their education (38%), and to not being ready to have a (another) child at the moment (32%) (Finer et al., 2005). The goal of this project is to develop a rigorous empirical analysis of the way in which abortion and contraception decisions are related to the career choices and paths of women in the U.S. In particular, we are interested in answering the following questions: How are the career paths of women affected by the availability of abortion as a method of fertility control? What effect does having an abortion (or not having it) have on a pregnant woman's human capital accumulation through schooling and labor market experience? What is the degree of substitution or complementarity between contraceptive methods and induced abortions? We are particularly interested in predicting the effects on career choices of young women of counterfactual policies that affect the cost of contraception and abortion services, or the access to the latter. To achieve this, we develop a dynamic discrete choice model of schooling, labor supply, marriage and cohabitation, contraception, and abortion decisions of young women who are high school graduates in the U.S.1 In the model, these choices are jointly determined and fully take into account their effect on future outcomes and decisions (e.g. choosing to give birth rather than getting an abortion is not independent from the decision of working and may affect the chances of attending college in the future). We will structurally estimate the parameters of the behavioral model using NLSY97 data. Survey level data, like NLSY97, have rarely been used for the empirical analysis of abortion decisions due to considerable under-reporting of abortions. To address this crucial issue, we apply and extend an estimation method introduced in Keane and Wolpin (2001) and formalized in Keane and Sauer (2010) which is able to account for biased classification error in the data and makes use of aggregate (unbiased) abortion rates to identify the misreporting rate. Structurally estimating the model will allow us to simulate and predict the effects, in terms of schooling, labor supply, and life cycle outcomes, of counterfactual policies that alter the availability of abortion 1 In 2008, 87.7% of abortion patients were at least high school graduates (Jones and Kavanaugh, 2011) services, their cost, and the cost of different contraceptive methods. The relationship between contraception and abortion will be key when predicting the effects of these policies. This is due to the fact that one woman might choose to abort to stop a pregnancy that occurred due to a failure of her contraceptive method, while another one might use abortion as a substitute for the use of contraceptive methods as a form of birth control.