Asset Details
Do you wish to reserve the book?
Enhancing a geographic regression discontinuity design through matching to estimate the effect of ballot initiatives on voter turnout
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Enhancing a geographic regression discontinuity design through matching to estimate the effect of ballot initiatives on voter turnout
Do you wish to request the book?
Enhancing a geographic regression discontinuity design through matching to estimate the effect of ballot initiatives on voter turnout
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Enhancing a geographic regression discontinuity design through matching to estimate the effect of ballot initiatives on voter turnout
2015
Overview
Ballot initiatives allow the public to vote directly on public policy. The literature in political science has attempted to document whether the presence of an initiative can increase voter turnout. We study this question for an initiative that appeared on the ballot in 2008 in Milwaukee, Wisconsin, using a natural experiment based on geography. This form of natural experiment exploits variation in geography where units in one geographic area receive a treatment whereas units in another area do not. When assignment to treatment via geographic location creates as-if random variation in treatment assignment, adjustment for baseline covariates is unnecessary. In many applications, however, some adjustment for baseline covariates may be necessary. As such, analysts may wish to combine identification strategies—using both spatial proximity and covariates. We propose a matching framework to incorporate information about both geographic proximity and observed covariates flexibly which allows us to minimize spatial distance while preserving balance on observed covariates. This framework is also applicable to regression discontinuity designs that are not based on geography. We find that the initiative on the ballot in Milwaukee does not appear to have increased turnout.
