This announcement from Jay Lee, Matthew Yancheff, and Mia Leung, three Reed students who were in the Data and Election Sciences course that I taught along with Prof. Andrew Bray this spring. They have released the results of their work to CRAN.

Thanks to Rob Richie and Theo Landsman of FairVote for helping push this forward.

Hello,

Just wanted to let you know that the first version of our RCV package is now submitted to CRAN, the R package archive! Going forward we’ll be updating this work, so if you have any comments or bug fixes please feel free to submit a pull request or issue to our GitHub repository, or just email us directly.

I’ve included a few lines of code at the bottom of this email to install the package locally and go through an example election (San Francisco Board of Supervisors, District 7). You’ll need at least version 3.3 of R installed to run these. If you don’t have this installed and don’t want to, some of the examples are available at our GitHub repo (scroll down to the README).

Again, thank you so much for your interest in our work and any help you’ve given us in regards to this project. We look forward to hearing any comments or critiques you might have on your experience using our package.

Thank you,

Jay Lee

Reed College

install.packages("rcv")

library(rcv)

sf_cleaned <- clean_ballot(ballot = sf_bos_ballot, b_header = T, 
                        lookup = sf_bos_lookup, l_header = T, 
                        format = "WinEDS")

results7 <- rcv_tally(sf_cleaned, "Board of Supervisors, District 7")

The results table for this election is stored in the `results7` object. It can be printed in the console with the first line of code provided below, or viewed in the RStudio window with the second line:

results7

View(results7)

We also have a functionality for producing an interactive type of flowchart called a Sankey diagram. This is done with the networkD3 package, which you must install separately to produce the visualization. The code for that is again provided here, but if you don’t want to install it we have an example on our GitHub repo.

install.packages("networkD3")

library(networkD3)

d3_7 <- rcv::make_d3list(results = results7)

networkD3::sankeyNetwork(Links = d3_7$values, Nodes = d3_7$names,
                         Source = "source", Target = "target",
                         Value = "value", NodeID = "candidate", units = "voters",
                         fontSize = 12, nodeWidth = 20)

Maricopa County, AZ is the second largest election jurisdiction in the United States (after Los Angeles County) and is contemplating a move to all-mail ballot delivery, with ballot returns by mail, drop box, or use of a “ballot center.”

This story from the Arizona Republic is lengthy, and it illustrates a lot of the concerns that will be raised in other localities who may contemplate the switch:

Is it secure?
It is efficient?
Is it fair to everyone?
I like voting in person, can’t I continue to do so?

Here’s the link: http://www.azcentral.com/story/news/local/phoenix/2017/06/20/county-recorder-adrian-fontes-changes-voting-system-fall-2017/391279001/

EVIC (or at least a report we worked on) is in the news!

https://www.nytimes.com/2017/06/13/opinion/increasing-voter-turnout-2018.html

I am pleased to announce NSF support of the Election Science, Reform, and Administration Conference (electionsciences.net), jointly offered by the Early Voting Information Center at Reed College and the Center for Public Service at Portland State University.

Thanks to the staff at Reed and PSU who helped get the proposal out and to Brian Humes and Timothy Nokken of the NSF for supporting this new and important field of scientific inquiry.

AWARD NOTICE

Award Date: June 8, 2017
Award No. (FAIN): 1727461
Proposal No.: 1727461
Managing Division Abbreviation: SES

The National Science Foundation hereby awards a grant to Portland State University for support of the project described in the proposal referenced above as modified by revised budget dated June 2, 2017.

This project, entitled “Election Sciences Workshop,” is under the direction of Phil Keisling, in collaboration with the following proposals

Proposal No: PI Name/Institution

——————- ——————————————————————————————————-

1727458 Paul . Gronke, Reed College

I’ll let the OPB story speak for itself, since I was one of the co-authors of the report.

The research team at the Elections Research Center at the University of Wisconsin, Madison, have a new paper analyzing the partisan impact of early voting laws, in combination with a set of other election reforms. The abstract is provided below; the piece is gated at the Political Research Quarterly but may be available from the authors.

Abstract

Conventional political wisdom holds that policies that make voting easier will increase turnout and ultimately benefit Democratic candidates. We challenge this assumption, questioning the ability of party strategists to predict which changes to election law will advantage them. Drawing on previous research, we theorize that voting laws affect who votes in diverse ways depending on the specific ways that they reduce the costs of participating. We assemble datasets of county-level vote returns in the 2004, 2008, and 2012 presidential elections and model these outcomes as a function of early voting and registration laws, using both cross-sectional regression and difference-in-difference models. Unlike Election Day registration, and contrary to conventional wisdom, the results show that early voting generally helps Republicans. We conclude with implications for partisan manipulation of election laws.

The piece is a follow up from the team’s widely cited 2014 piece (conveniently available because it is part of the North Carolina case)that shows that early voting may have a mixed effect on turnout, depending on the mix of other election reforms that are already in place.

I like what the authors have done here, and I don’t find it particularly surprising. I’ve never been convinced by the conventional political wisdom that early voting always helps Democrats. That just doesn’t comport with the longstanding findings that Republicans use no-excuse balloting at higher rates than Independents or Democrats. The reasons for this are complex, including what I suspect is a historical legacy of the emergence of direct mail mobilization by Richard Viguerie in the late 1970s, tied to higher rates of absentee voting among older, more conservative, more Republican voters, and Reagan’s roots in California politics.

This kind of suspicion led to some criticism of the 2014 piece because the team coded “early voting” as a single administrative procedure, not discriminating between no-excuse absentee and early in-person. They’ve fixed that here, and the results hold. A key table of results is reproduced below.

I would still caution against overinterpreting these results as providing a roadmap for election law gamesmanship. Burden et al. spend a bit too much time, in my judgment, opining about how partisan actors may or may not misestimate the political impact of reforms to election laws, without acknowledging the highly contingent and dynamic nature of the legal and administrative environment.

For example, it’s almost certain than when a new voting method is made available, strategic political actors from both parties look at these changes, look at what groups opt for one or another method, and start to change their campaigns accordingly. Capturing this kind of institutional dynamic is nearly impossible to do in a national study like this, and can easily make gamesmanship seem a lot simpler than it actually is.

I posted this query on the Political Methdology listserv:

Hello all, I have some students in an election sciences class who want to do some visualizations using CCES data. I’d like them to use the survey weights if possible, but don’t know an easy way to do this in R.

I have come across this package that claims to support graphics and complex survey weights, but can’t find a reference or vignette that uses any graphics: http://r-survey.r-forge.r-project.org/survey/

Thanks!

Paul Gronke
Reed College

And have provided a review of the answers:

https://docs.google.com/document/d/1VmVeVJ5kzj7OBhWRmH7VHOW4DxGBQ4KYNeTqDSTOaoE/edit?usp=sharing

Thanks to Jay Lee of Reed College for helping me assemble this.

Thanks to those participants in the Political Methodology listserv who responded to a query that I posted a month ago about how to produce survey “toplines” using either Stata or R. The attached document provides a detailed summary of the responses; I have posted the most useful reply here.

Original Question

From: Paul Gronke <gronkep@reed.edu>

Quick question for the list: Lisa Bryant (CU Fresno) and I are preparing some “top lines” and “tabs” for a client with whom we conducted a survey.

If you have seen these before, they are usually organized so that categorial survey responses are reported on the rows, and the columns report the overall responses, then responses “tabbed” or “crossed” by various demographic and political categories. Roll your eyes if you will that this is just a big set of exploratory cross tabs, but a lot of folks expect to see them to help digest the survey results.

A typical “tab” looks like this:

VARIABLE Total GOP IND DEM MEN WOMEN …

Category 1 N % N % N % N % N % N %

Category 2 N % …

Original Question

TOP SOLUTION

Stata Special

EVIC TEAM

EVIC SOCIAL

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