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 scoresacross multiple policy dimensions .
In a common experimental format, individuals are randomly assigned to either a treatment group with program access or a control group without access. In such experiments, analyzing the average effects of the treatment of program access may be hindered by the problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment. Available methods to account for such a problem typically require the researcher to observe the receipt of program access for every individual. However, in many experiments, this is not the case as data is not collected on where any individual received access . In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access . I propose a nonparametric selection model with latent choice sets to relate where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute the identified set for parameters evaluating the average effects of program access in this model. I illustrate the framework by analyzing the average effects of Head Start preschool access using the Head Start Impact Study. I find that the provision of Head Start access induces parents to enroll their child into Head Start and also positively impacts test scores, and that these effects heterogeneously depend on the availability of access to an alternative preschool .
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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,
In a common experimental format, individuals are randomly assigned to either a treatment group with program access or a control group without access. In such experiments, analyzing the average effects of the treatment of program access may be hindered by
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largest early childhood education program in the United States
problem that some control individuals do not comply with their assigned status and receive program access from outside the experiment. Available methods to account for such a problem typically require the researcher to observe the receipt of program access for every individual. However, in many experiments, this is not the case as data is not collected on where any individual received access
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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
paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access
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model that explicitly accounts for
selection model with
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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
to relate where access was received to the treatment assignment status, participation decision and outcome, and a linear programming procedure to compute
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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,
parameters evaluating the average effects of program access in this model. I illustrate the framework by analyzing the average effects of Head Start preschool access using the Head Start Impact Study.
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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 scoresacross multiple policy dimensions
the provision of Head Start access induces parents to enroll their child into Head Start and also positively impacts test scores, and that these effects heterogeneously depend on the availability of access to an alternative preschool