
The goal of this project is to develop new methods to solve and estimate discrete stochastic dynamic programming (DS-DP) models, and use these to study decision making in areas such as human capital investment, occupational choice, and investment in health. In recent years, it has become common in economics to model individuals who are making choices in dynamic environments as if they were solving a DS-DP problem to determine their optimal decisions. Empirical implementation of such models has been hampered because their solution and estimation requires that very high order numerical integrations be performed. These researchers are investigating the use of simulation methods to circumvent these integration problems.
The researchers are applying these methods to two problems. The first is an ongoing project on the effects of welfare policies on educational and occupational choices of young women. This is a major extension of the basic Keane-Wolpin model developed for young men, because it brings in fertility and marriage as additional decisions. When this model is completed, it will be useful for predicting a number of important questions about how public welfare policies aimed at women and children affect behavior. For example, the model can simulate how changes in these policies might affect teenage pregnancy rates, out-of-wedlock birth rates, and high school dropout rates. Preliminary results suggest that welfare benefits modestly increase high school dropout rates, while raising teenage pregnancy rates and only slightly raising out-of-wedlock birth rates.
The second project is a new study into the return-to-work decisions of previously employed women after they give birth. In the model, the women decide on a quarterly basis whether or not to return to work and whether to place the child in day care. The model can be used to assess the impact of day care and mother’s employment on child outcomes such as early test scores.
Research Group and Collaborator
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