Representation of voters in the Supreme Court confirmation process

2020 October 06

There is now an updated post following the confirmation of Barrett.

Justices of the US Supreme Court are not selected directly by the people, but nominated by the president and then confirmed by a majority of senators. The president is themself elected by the members of the electoral college, who are elected by the people, as are senators. These layers of indirect selection create the opportunity to distort the representativeness of the end result, as we have become acutely aware of in recent decades.

To measure this distortion, I’ve computed the “implied” popular vote for recent justices by propagating forwards the actual votes cast by Americans through the layers of indirection. This gives two results, corresponding to the nomination and the confirmation. For the nominator, this is simply the popular vote: percentages reported below are the two-party percentage, i.e., votes to third parties are ignored and only the winner and runner-up are compared. For the senate, as confirmation requires as many ‘Yea’ votes as ‘Nay’ votes, I chose to compare the number of votes for senators who voted ‘Yea’ to the number of votes for senators who voted ‘Nay’; votes for senators who did neither were ignored. (Many, but not all, senators who did not vote have their intended votes recorded in the congressional record.) Votes against senators were also ignored; there are several different ways these could be included but I felt it made more sense to not use them. Another alternative would be to consider the number of people each senator represents, rather than the votes they received.

I’ve graphed these percentages for confirmations since 1976, the earliest I have data on the number of votes each senator received. Not included is Robert Bork, whose nomination was rejected in 1987 by 42 - 58. The solid line shows the two-party popular vote for each presidential election; along this line are squares when justices were confirmed. Circular data points are the corresponding implied popular vote for the senators that confirmed them, with the number of ’Yea’s given below.

We see in the figure that nominations became significantly more contentious by 2005; while there have always been contentious confirmation fights, prior to this time most nominees were confirmed without significant dispute, and indeed before 1967 most confirmations were made by simple voice vote. Ignoring Rehnquist’s 1986 nomination for chief justice as he was already an associate justice at the time, we also notice that Republicans appoint justices 50% more frequently than Democrats in the time interval shown; had Obama’s third nomination not been nullified, this would be near parity instead.

Also apparent is that senators voting to confirm Democratic-appointed justices are supported by many more people than those confirming Republican-appointed justices: compare, for example, Elena Kagan who was confirmed in 2010 by 63 - 37 with 67% implied vote to John Roberts who was confirmed in 2005 by 78 - 22 with 64%. In fact, every Democratic-appointed justice currently on the court had more implied vote than every Republican-appointed justice, with four of those five not even reaching 50%. Clarence Thomas is the only current justice appointed by a Republican with more popular support than a Democrat that appointed a current justice, by 0.5%.

I’ve included the full table of each confirmation:

Justice Year Nominator Senate ‘Yea’ ‘Nay’ ‘Yea’%
Marshall 1967 Johnson 61.34% 69 - 11 - - -
Burger 1969 Nixon 50.41% 74 - 3 - - -
Blackmun 1970 Nixon 50.41% 94 - 0 - - -
Powell 1971 Nixon 50.41% 89 - 1 - - -
Rehnquist 1971 Nixon 50.41% 68 - 26 - - -
Stevens 1975 Ford 61.79% 98 - 0 - - -
O’Connor 1981 Reagan 55.31% 99 - 0 81938226 0 100.00%
Rehnquist 1986 Reagan 59.17% 65 - 33 52484264 34602506 60.27%
Scalia 1986 Reagan 59.17% 98 - 0 87086770 0 100.00%
Kennedy 1988 Reagan 59.17% 97 - 0 79603513 0 100.00%
Souter 1990 Bush 53.90% 90 - 9 76500215 11844310 86.59%
Thomas 1991 Bush 53.90% 52 - 48 35475831 44253820 44.50%
Ginsburg 1993 Clinton 53.45% 96 - 3 88261651 2034999 97.75%
Breyer 1994 Clinton 53.45% 87 - 9 81479894 6195598 92.93%
Roberts 2005 Bush 51.24% 78 - 22 76870777 43929082 63.63%
Alito 2006 Bush 51.24% 58 - 42 59162228 60126394 49.60%
Sotomayor 2009 Obama 53.69% 68 - 31 86633780 30182701 74.16%
Kagan 2010 Obama 53.69% 63 - 37 75861452 37123012 67.14%
Gorsuch 2017 Trump 48.89% 54 - 45 54760599 76494514 41.72%
Kavanaugh 2018 Trump 48.89% 50 - 48 53364281 76883828 40.97%

The current justices are in bold. The ‘Yea’ and ‘Nay’ columns indicate the total number of votes received by the corresponding senators. William Rehnquist appears twice as he was appointed as associate justice in 1971 and then chief justice in 1986.

Note that some senators have been appointed to their position, and therefore received zero votes. Here is a list of every such senator that influenced my result:

Justice Year Senator State Vote
Kavanaugh 2018 Cindy Hyde-Smith Mississippi Yea
Kavanaugh 2018 Jon Kyl Arizona Yea
Kavanaugh 2018 Tina Smith Minnesota Nay
Gorsuch 2017 Luther Strange Alabama Yea
Kagan 2010 Michael Bennet Colorado Yea
Kagan 2010 Roland Burris Illinois Yea
Kagan 2010 Kirsten Gillibrand New York Yea
Kagan 2010 Carte Goodwin West Virginia Yea
Kagan 2010 Ted Kaufman Deleware Yea
Kagan 2010 George LeMieux Florida Nay
Sotomayor 2009 Michael Bennet Colorado Yea
Sotomayor 2009 Roland Burris Illinois Yea
Sotomayor 2009 Kirsten Gillibrand New York Yea
Sotomayor 2009 Ted Kaufman Deleware Yea
Alito 2006 Bob Menendez New Jersey Nay
Breyer 1994 Harlan Mathews Tennessee Yea
Ginsburg 1993 Harlan Mathews Tennessee Yea
Thomas 1991 John Seymour California Yea
Souter 1990 Daniel K. Akaka Hawaii Nay
Souter 1990 Dan Coats Indiana Yea
Kennedy 1988 David Karnes Nebraska Yea
Scalia 1986 Jim Broyhill North Carolina Yea
Rehnquist 1986 Jim Broyhill North Carolina Yea
O’Connor 1981 George J. Mitchell Maine Yea

I chose not to do any kind of an ad hoc adjustment to remove this noise. Looking at the table, it seems likely that the main effect of these appointments is to understate the degree of popular support received by Elena Kagan in 2010 and by Sonia Sotomayor in 2009, and to a lesser extent the degree of support for Clarence Thomas in 1991.

Also note that, as California uses a jungle primary system, senator Kamala Harris ran in 2016 against an opponent in the same party as her. She received 7542753 votes while her opponent received 4701417 votes, a much larger percentage than all Republicans in the primary had received combined. The use of the jungle primary system thus likely understated the number of votes Harris would have otherwise received; she went on to vote ‘Nay’ for the confirmations of Neil Gorsuch and Brett Kavanaugh. California senator Diane Feinstein likewise won against a Democratic opponent in 2018, but since then has not voted on any confirmations.

Washington’s jungle primary system has not yet resulted in any general elections between opponents in the same party. Louisiana has long had a complicated jungle primary system that makes it difficult to assess the effect it has on the votes (in some years, there was no general election at all), but the number of votes at stake is much smaller than in California or other states.

When I undertook this analysis, I expected the requisite data to be easily found and so the project would only take a few hours. While the analysis was simple enough, I spent many, many hours dealing with the data. My source (from here) of election data had numerous omissions and errors and did not include any appointed senators, as well as not recording the information needed to determine which senator was replaced in each election. It seems likely there were other errors in the data I did not find. I was able to use the official congressional record for nominations of justices but much of it was not in machine-readable format and had to be manually processed (and there were a few trivial errors). Ultimately it would have been far easier to have just written a scraper to get the data from Wikipedia (which was done to generate some datasets I saw) but I would be uncomfortable using that as a source.

My code is quite messsy due to the repeated changes I had to make as I discovered more problems with my data. Input files are here and here, the former being downloaded directly from the MIT election data linked above and the latter having been manually processed.