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Identification with Latent Choice Sets: The Case of the Head Start Impact Study

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  • Vishal Kamat

Abstract

This paper studies identification of program effects in settings with latent choice sets. Here, by latent choice sets, I mean the unobserved heterogeneity that arises when the choice set from which the agent selects treatment is heterogeneous and unobserved by the researcher. The analysis is developed in the context of the Head Start Impact Study, a social experiment designed to evaluate preschools as part of Head Start, the largest early childhood education program in the United States. In this setting, resource constraints limit preschool slots to only a few eligible children through an assignment mechanism that is not observed in the data, which in turn introduces unobserved heterogeneity in the child's choice set of care settings. I propose a nonparametric model that explicitly accounts for latent choice sets in the care setting enrollment decision. In this model, I study various parameters that evaluate Head Start in terms of policies that mandate enrollment and also those that allow voluntary enrollment into Head Start. I show that the identified set for these parameters given the information provided by the study and by various institutional details of the setting can be constructed using a linear programming method. Applying the developed analysis, I find that a significant proportion of children voluntarily enroll into Head Start if provided access and that Head Start is effective in terms of improving short-term test scores across multiple policy dimensions.

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  • Vishal Kamat, 2017. "Identification with Latent Choice Sets: The Case of the Head Start Impact Study," Papers 1711.02048, arXiv.org.
  • Handle: RePEc:arx:papers:1711.02048
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    References listed on IDEAS

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