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Identification with Latent Choice Sets

Kamat, Vishal (2019) Identification with Latent Choice Sets. TSE Working Paper, n. 19-1031, Toulouse

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Abstract

In a common experimental format, individuals are randomly assigned to either a treatment group with access to a program 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 tools 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 nd 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.

Item Type: Monograph (Working Paper)
Language: English
Date: August 2019
Place of Publication: Toulouse
Uncontrolled Keywords: Program evaluation, latent choice sets, unobserved treatment, program access, multiple treatments, average treatment effect, noncompliance, discrete choice, partial identification, social experiments, head start impact study.
JEL codes: C14 - Semiparametric and Nonparametric Methods
C31 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole
Site: UT1
Date Deposited: 27 Aug 2019 09:29
Last Modified: 27 Aug 2019 09:29
OAI ID: oai:tse-fr.eu:123308
URI: http://publications.ut-capitole.fr/id/eprint/32696

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