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This paper specifies a general set of conditions under which the impacts of a policy can be identified using data generated under a different policy regime. We show that some of the policy impacts can be identified under relatively weak conditions on the data and structure of a model. Based on the identification result we develop estimators of policy impacts. We discuss a nonparametric method to implement the estimation but also discuss semparametric methods in order to reduce the conditioning dimension. We then provide an empirical example of the impact of tuition subisdies using the ideas. While the framework used in this paper is fairly narrow, we believe this approach can be applied to a broad set of problems.
Authors
![Hidehiko Ichimura](/sites/default/files/styles/square_desktop/public/2022-07/Hidechiko%20Ichimura.jpg?itok=qozO-upv)
Research Associate University of Arizona, University of Tokyo
Hidehiko is a Professor of Economics at the Eller College of Management, University of Arizona and a Research Associate at the IFS.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Christopher Taber
Working Paper details
- DOI
- 10.1920/wp.ifs.2000.0005
- Publisher
- IFS
Suggested citation
Ichimura, H and Taber, C. (2000). Direct estimation of policy impacts. London: IFS. Available at: https://ifs.org.uk/publications/direct-estimation-policy-impacts (accessed: 30 June 2024).
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