Princeton Election Consortium

A first draft of electoral history. Since 2004

Under GOP rules, 30% before Iowa/New Hampshire implies a delegate majority: Simulating the “proportional” rules

January 13th, 2016, 2:24pm by Sam Wang


Since late December, polls have become predictive enough to point toward Trump as the eventual nominee. New in The American Prospect, I give a detailed analysis of the GOP Presidential delegate-assignment process. This analysis includes a simulation of how vote share translates to delegate share. My principal conclusion is that if his current levels of support hold in a divided field, Donald Trump could well win his party’s nomination in the first round of voting at the Republican National Convention. These same mechanisms cause Marco Rubio’s chances to shrink. Unless the Republicans get their act together soon after New Hampshire and cull the field, it could be too late for anyone but Trump.

Here, I focus on the fine details of how the simulation of delegate selection was done. This is a work in progress, and I will be glad to correspond on improvements to the approach. It is available at GitHub. [Read more →]

→ 26 CommentsTags: 2016 Election

PEC readers chat about the GOP nomination

January 10th, 2016, 3:11pm by Sam Wang


I don’t have the financial resources of ESPN/FiveThirtyEight. But I do have you, my dear readers!

Let me cut-and-paste some of your recent remarks into a PEC chat. Edited for flow. Add your own two cents in the comment thread. [Read more →]

→ 50 CommentsTags: 2016 Election

Does Trump’s ceiling matter?

January 7th, 2016, 9:55am by Sam Wang


Today, Ross Douthat quotes my post yesterday about Donald Trump’s current strength. He also says Trump is doomed because he will hit a ceiling of support around 30%. But even if that ceiling holds, it might not matter – because of how delegates are chosen. [Read more →]

→ 36 CommentsTags: 2016 Election · President

The predictive value of GOP Presidential polls

January 5th, 2016, 10:30pm by Sam Wang



The New Year is not a bad time for a fresh start. So please let me acknowledge that back in July, I was too pessimistic about Donald Trump’s chances. Like Harry Enten, I was led astray by his high unfavorables. Six months into the Season of Trump, I think it is time to examine his chances with a more neutral stance.

Two Nates (Silver and Cohn) have come out with essays arguing that we still can’t extract much predictive value from opinion polls. For the detailed kind of analysis they like, this may be true. However, a slightly different approach has suggestive implications about who is likely to be the eventual Republican nominee. (Spoiler: rhymes with Grump.)

First, let’s examine two current attitudes about polls. One is endemic to journalists, the other to data pundits.

1) Focusing on the leader in polls. Journalists and commentators have been losing their minds over the fact that Donald Trump’s lead has lasted since July. (For an antidote, see a sharp and entertaining takedown at Lawyers, Guns, and Money.) In 2015, one way of coping was to say things like “at this point in 2012, Gingrich led nationally.” Certainly this was good for a cheap laugh. However, focusing only on the leader discards all the information that can be learned by examining lower-ranked candidates. But how to do that? This leads to the second problem.

2) Trying to predict vote share. Analysts often focus on a technical question: what will each candidate’s vote share be? That approach uses tools that are common to econometric analysis, involving the prediction of quantitative parameters. For example, Cohn writes about how far off polls will be, on average, from the exact final outcome in New Hampshire.

But let us take a step back. Do you care about whether Trump wins by five points in New Hampshire, or by ten points – or loses by five points? Maybe what you really want to know is: From polling data, do we have information on who the eventual nominee will be? [Read more →]

→ 37 CommentsTags: 2004 Election · 2008 Election · 2012 Election · 2016 Election · President

Happy New Year!

December 31st, 2015, 6:22pm by Sam Wang


Happy new year, all!

Predictions for 2016, anyone? Ideally with some logical support, but oh heck, it’s New Year’s Eve – let your freak flag fly. Aspirations?

→ 29 CommentsTags: Uncategorized

Attending oral arguments in Harris v. Arizona Independent Redistricting Commission

December 20th, 2015, 11:39pm by Sam Wang


sketch by Art LienDec. 25th: Updated with five more FantasySCOTUS predictions. The median outcome still favors the Commission, by a narrow 5-4 majority.

In my NYT piece proposing statistical standards to detect partisan gerrymandering, I focused on Harris v. Arizona Independent Redistricting Commission, a current Supreme Court case [SCOTUSblog] [Blog for Arizona]. On December 8th I attended oral arguments for this case [Supreme Court transcript and audio]. Bottom line: a majority of justices seems disposed toward the Commission, an outcome that I argue is consistent with the data. However, it is not clear whether they will cite any statistical standards.

Oral arguments can help predict how the Supreme Court will rule. Here is my reaction based on (1) what I saw and heard, and (2) what successful Court-prognosticators think. [Read more →]

→ 7 CommentsTags: Redistricting

The effect of gerrymandering in four states exceeds that of population clustering in all 50 states

December 8th, 2015, 7:00am by Sam Wang


My New York Times piece was specifically focused on the legal question of what a party-neutral standard should look like. It didn’t address the political question: where do such deviations come from? As it turns out, the answer is: post-2010, partisan redistricting accounts for more than half of the total asymmetry in the House. The top four gerrymandered states have an effect that exceeds the effect of population clustering in all 50 states combined. The net effect of all gerrymanders combined is similarly large.

It is a commonly believed that the predominant force in partisan asymmetry is population clustering: groups that tilt Democratic are clustered into cities, generating a natural packing effect. A clustering effect certainly exists. However, as of 2012-2014, this effect has become secondary to gerrymandering in a handful of states.

Population clustering and partisan actions are not mutually exclusive. In fact, partisan gerrymandering relies on the fact that voters are not distributed perfectly uniformly. Using this fact, redistricters lasso voters into districts to suit political ends. For this reason, it is easy to mix up the two processes.

My statistical measures provide a way to separate these effects. As an obvious example of how redistricting can cause asymmetry, see the Michigan example at left. As you can see, the mean-median difference took a sudden jump from 2010 to 2012. The intervening event? Redistricting. [Read more →]

→ 2 CommentsTags: Redistricting

Can Math Help Save Democracy?

December 5th, 2015, 10:41am by Sam Wang


In the New York Times I have a piece in the Sunday Review, “Let Math Save Our Democracy.” It describes some simple statistical standards for partisan gerrymandering, and how they might resolve an upcoming Supreme Court case (Harris v. Arizona Independent Redistricting Commission) quickly and definitively. It’s based on my law article, “A Three-Prong Standard for Practical Evaluation of Partisan Gerrymandering,” which you can read here. My amicus brief in Harris is here.

In a short piece like that, some key points had to be glossed over. An extremely important concept is the “zone of chance.” That is basically a catchy phrase to describe a statistical significance test. When comparing lopsided winning margins (for example, let’s say we have a state where Democratic wins average 80%-20%, whereas Republican wins average 55%-45%), one should compare the Democratic winning percentages (80%) with the Republican winning percentages (55%). This is done by a grouped t-test, “probably the most widely used statistical test of all time,” as VassarStats puts it. The average-median difference can also be evaluated with a version of the t-test.

Other stuff: University of Chicago law professor Nicholas Stephanopoulos and his collaborators have developed a measure of gerrymandered voters that they call the efficiency gap. In the Harris case, they found that small variations in district population were not enough to skew the overall outcome.

Also, there is the analysis of individual districts. This approach has a long history, dating to the work of Gary King and Andrew Gelman’s JudgetIt program. Recently, Jowei Chen and John Rodden have developed automated districting procedures to probe the whole range of possible districting schemes that obey a few basic principles. And of course there is my own “fantasy delegations” approach (PEC essays here and here), which can help estimate the right number of seats for a given statewide vote.

Finally, there is a common objection to claims of partisan gerrymandering: namely that population clustering is the true culprit. Population clustering does indeed have a substantial effect. However, post-2010, partisan gerrymandering in a handful of states shifted more Congressional seats than population-clustering effects in all 50 states combined. See the calculation on pages 36-37 of my law article.

→ 15 CommentsTags: Redistricting

Redistricting battles everywhere

December 4th, 2015, 10:27pm by Sam Wang


Bloomberg Politics has an excellent interactive on redistricting battles (ht/ to reader bks). While we’re on the topic, make sure to read the New York Times this weekend. I have a piece on gerrymandering standards in the Sunday Review section.

Other entertainment: here’s a cool general-election demographic simulator by Aaron Bycoffe and David Wasserman.

→ 3 CommentsTags: 2016 Election · Redistricting

Happy Thanksgiving!

November 26th, 2015, 3:30pm by Sam Wang


I know it’s a time for people to come together, eat, and get into conversation. Sometimes things can get contentious. Here’s a primer on What To Say To Your Frequentist Uncle. Lots of good stuff there, including a question on what “never” means. Happy Thanksgiving, everyone!

Update: [Read more →]

→ 22 CommentsTags: Uncategorized