Abstract
Scholars often seek to understand which individuals are most responsive to the change in some treatment. Such work inevitably faces issues of identification. When the dependent variable is binary, the assumption that the largest effect occurs where p = 0.5 is also encountered. I apply Manski's ((1995). Identification problems in the social sciences. Cambridge: Harvard University Press) non-parametric Bounds approach, which relaxes the func- tional form and distributional assumptions found in traditional models, in an attempt to resolve the long standing debate on which types of individuals are most affected by changes in registration laws. Under the standard assumption that treats the selection of registration laws as exogenous, the results revise the current understanding. By exploring the power of various behavioral assumptions, new insights into the study of policy changes emerge, calling into question some of the assumptions that are standard in the literature.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.