An Alternative Approach to Estimating Who is Most Likely to Respond to Changes in Registration Laws

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.