Shiro Kuriwaki

• Wealth, Slave Ownership, and Fighting for the Confederacy: An Empirical Study of the American Civil War. (with Andrew B. Hall and Connor Huff). American Political Science Review. (Published on FirstView)

• Party Loyalty on the Long Ballot: Is Ticket Splitting More Prevalent in State and Local Elections? Submitted.
• Congressional Representation, the Constituent’s Perspective. (with Stephen Ansolabehere). Submitted.
• Sparse Multilevel Regression (and Poststratification). (with Max Goplerud, Marc Ratkovic, and Dustin Tingley).

The ballot image log project is collecting and organizing public ballot image log data (also known as Cast Voter Records) to advance the understanding of voting patterns in federal, state and local elections. One of the project's goals is to establish a relational database for such data. This project is one of the 2018 New Initiative Grants from the MIT Election Data and Science Lab. See the associated paper of this ongoing work here. More information about the queryable database, which is currently in development with help from collaborators, is at this page. Please feel free to contact me for any questions.

• Charleston Exit Poll Technical Report. (with Shun Yamaya).

At Harvard I have taught classes on American Public Opinion, statistics, and programming, at the Undergraduate, Professional Masters, and Ph.D. level.

I taught the Government Math Prefresher for incoming Ph.D. students in 2017 (with Connor Jerzak) and 2018 (with Yon Soo Park). The Prefresher's original text is available in a web book form.

Additionally:

• My statistics notes covering Probability, Inference, and Regression written for a Masters-level Statistics and Quantitative Analysis (source),
• Slides for an introduction to version control with GitHub (source),
• Memo for an introduction to Stata (source), and
• Memo for project management ( source )

• The Cumulative CCES Common Content (2006-2018) is a part of the Cooperative Congressional Election Survey Dataverse. It combines all common content respondents of the CCES and harmonizes key variables, so that researchers can analyze all years of the CCES or merge in standardized variables with their own CCES datasets.