The fundamentals and the midterm

The current conventional wisdom is for the midterm to be somewhat better than average for the president’s party in the House. (I set aside the Senate here.) The fundamentals doubt that, as we’ll see.

The “fundamentals” provide a helpful baseline, even if “non-fundamentals” such as polling, candidate quality, unique issues, may modify that baseline. So let’s only look at historical relationships here.

The starting point is that Democrats currently hold 220 seats in the House, Republicans have 212 and 3 seats are vacant. 218 seats are the minimal majority with no vacancies. 2 of the vacancies were held by Democrats, 1 by a Republican, so call it 222-113 now.

The average loss for the presidents party since 1946 is 26.4 seats. That would put Dems at 196 and Reps at 239. (Note there is very little difference in 1st and 2nd midterm losses on average.)

Losses tend to be larger with less popular presidents. Biden average approval is 41.5% at FiveThirtyEight.com today and 42.4% at RealClearPolitics.com. Let’s call it 42%. It is now October. See the orange line for seat loss by October approval. That fit is a 40 seat loss.

Losing 40 of 222 seats would give Dems 182 seats are Reps 253 seats, considerably worse than an “average” loss of 26 seats. So 196 Dem seats if average, 182 Dem seats if as presidential approval would suggest.

Do note the variation around the orange line. It includes far larger losses, as 1994, and far smaller losses, as 2014. While the best estimate is -40 seats, for a president at 42% approval we see a lot of variation in seat loss, hence uncertainty.

A third fundamental approach combines the loss of popular vote for the House candidates of the president’s party in the prior presidential year and in the midterm. In 2020 Dem House candidates won 51.6% nationwide. But that implies they win only 47.8% in the midterm.

Again notice the variation around the blue line, and we haven’t seen a presidential year close to 51.6% since 1946. So more uncertainty here, but best estimate is a drop on nearly 4 percentage points in popular vote.

So how does popular vote translate to share of seats?

DemSeats% = -25.07 + 1.47*DemVote%

At 47.8% of the vote we’d expect Dems to win 45.2% of the seats, or 197 seats. That is back to an “average” loss, not the larger one based on approval.

There are other factors, even fundamentals, not considered here. The size of the current majority is rather small historically, at least for Democrats. So there are fewer seats and risk, and Dems lost rather than gained seats in 2020.

But there are issues pushing one way (inflation) and issues pushing the other way (abortion). Those are fit topics for a “beyond the fundamentals” analysis, but are not my topic here.

The conclusion is that simple fundamentals suggest a loss of 25 to 40 seats for the Democrats, giving them between 182 and 197 seats and the Republicans between 253 and 238 respectively. Anything in that range would be a strong GOP majority.

I stressed twice above the uncertainty in these estimates. For a given approval or a given national vote share there is considerable uncertainty in the share of seats that result. But if you want to consider the fundamentals, that’s what this gives.

For a “non-fundamental” take, consider the latest CBS News model, based on polling but with a sophisticated model for seats from that poll. As of Oct 16, CBS News estimates 211 Dem seats, a loss of just 11, to 224 Rep seats.

CBS News link here.

That is a much better result for Dems than the fundamentals expect. We’ll know in early November which was closer to the mark.

Confidence & Doubt in 2020 vs 2022 elections

Confidence or doubt in the accuracy of the 2020 election has persisted as an issue since that vote. While substantial majorities of registered voters in Wisconsin are confident the election results were accurate, among Republicans the opposite is true– a majority of Republicans doubt the accuracy of the 2020 election. There has been little change in views of that election over the past nineteen months.

In April, Wisconsin held elections for a variety of state and local offices, including judges, mayors, school boards and other nonpartisan positions. These elections were not followed by widespread claims of fraud or manipulation by either party or by the losers of those elections.

How do Wisconsin registered voters perceive the accuracy of the April 2022 elections compared to the November 2020 election? If “faith in elections” has been seriously damaged by claims that 2020 was a fraud, we should see similar doubts of the 2022 election. If doubt in the 2020 election is primarily a sign of support for former President Donald Trump and his allies, then the reality is not doubt in elections generally but specifically only in the election Trump lost. The April 2022 Wisconsin vote gives us a chance to look at the evidence.

The Marquette Law School poll conducted April 19-24, 2022 followed the April 5 elections in the state. The sample size was 805 registered voters with a margin of error of +/-4.1 percentage points. Full results are available here. Respondents were asked parallel questions about this and the 2020 election:

On April 5, Wisconsin held elections for school boards, judges, local and county positions and other offices. How confident are you that, here in Wisconsin, these votes were accurately cast and counted in the April election?

Concerning the 2020 election they were asked

How confident are you that, here in Wisconsin, the votes for president were accurately cast and counted in the 2020 election?

Response options to both are “very confident”, “somewhat confident”, “not too confident” and “not at all confident.” In the tables below very and somewhat confident are combined as “confident” responses and “not too” or “not at all” confident are combined as “not confident.”

Table 1 shows the results for the April 2022 and November 2020 elections. Confidence in the April 2022 election is 84% compared to 64% confident in the 2020 vote. Only 13% doubt the April results while almost three times as many, 35%, say they doubt the November 2020 election results.

Response April 2022 November 2020 
Confident 84 64 
Not confident 13 35 
DK/Ref 
Table 1: Confidence in the accuracy of the April 2022 and November 2020 elections, Wisconsin registered voters, Marquette Law School poll, April 19-24, 2022

Views of the 2020 election differ dramatically by party, while partisan differences in confidence in the April 2022 elections is much more muted. Large majorities of each partisan group are confident in the April election results, including about three-quarters of Republicans and over 80% of independents. While 22% of Republicans still profess doubt in the April election that contrasts sharply with the 65% of Republicans who say they doubt the 2020 results.

Among independents, confidence is substantially higher in the April election, 82%, than in the 2020 outcome, 65%, though substantial majorities of independents are confident in both elections.

Democrats are nearly unanimous in their confidence in both elections.

Table 2 shows confidence by party for the April 2022 and November 2020 elections.

Table 2: Confidence in the April 2022 and November 2020 elections by party identification, , Wisconsin registered voters, Marquette Law School poll, April 19-24, 2022

Party ID Confident Not confident DK/Ref 
Republican 74 22 
Independent 82 14 
Democrat 97 
(a) April 2022 confidence

Party ID Confident Not confident DK/Ref 
Republican 33 65 
Independent 65 34 
Democrat 96 
(b) November 2020 confidence

Among those who are not confident in the 2020 vote, almost two-thirds, 63%, are confident in the 2022 outcome, with 33% who are not confident in the 2022 result. Virtually everyone confident in 2020 is also confident in 2022. Table 3 shows confidence in 2022 by confidence in 2020.

Confidence in 2020 Confident Not confident DK/Ref 
Confident 96 
Not confident 63 33 
Table 3: Confidence in the April 2022 by confidence in November 2020 elections, Wisconsin registered voters, Marquette Law School poll, April 19-24, 2022

We can also look at confidence in both elections as a percentage of all registered voters, shown in Table 4. The entries here are the “cell percentages”, the size of each cell as a percent of all respondents.

Confidence in 2020 Confident Not confident DK/Ref 
Confident 61 
Not confident 22 11 
Table 4: Percentage of all registered voters confident or not confident in both 2020 and 2022 elections, Wisconsin registered voters, Marquette Law School poll, April 19-24, 2022

Of all registered voters, 11% lack confidence in both elections, while 61% are confident in both. Twenty-two percent are not confident in 2020 but are confident in 2022. This is the crucial segment of the population who doubt Trump’s loss, but are still confident in an election he had no role in and which is not disputed by either party. Just 2% are confident in 2020 but not in 2022.

Conclusion

Supporters of Donald Trump have blamed his loss in 2020 on “election fraud” of some sort. But when considering a different election, one not disputed by either party, they are quite confident in the outcome. While there is some residual increase in doubt of 2022 among those who doubt the 2020 election, it is far short of a widespread “lack of faith in elections” generally.

While the April non-partisan elections in Wisconsin are revealing, the November partisan contests will provide another test of the inclination of parties to blame their losses on “fraud.”

PollsAndVotes Public Data Resources

Updated 2022-09-05

These are links to public data resources for polls and votes, plus census, GIS and software.

I put these here to help those new to polling and political analysis find professional quality data that they can download freely and analyze. For those in high school or college these can be the basis for learning and for building a portfolio of work for Twitter (the source of all knowledge) and for class projects and ultimately for job applications. The data here are also used professionally, but I assume the professionals and graduate students already know all these, though if they are helpful I’m pleased by that.

This is far from comprehensive. If you have suggested additions, or corrections, please ping me @PollsAndVotes

Software

Statistical: R is free, has a huge user community and is complex
Home: https://cran.r-project.org

RStudio provides a free IDE for R that is widely used and a lot of packages and support for R, including RMarkdown for publishing

Home: https://www.rstudio.com
IDE Download: https://www.rstudio.com/products/rstudio/
See the Resources menu on homepage for much more including books and packages

GIS: For Mapmaking

I find R has most of the mapmaking tools I need in various packages. Among many resources see
https://geocompr.robinlovelace.net/adv-map.html
https://r-spatial.org/r/2018/10/25/ggplot2-sf.html

For great integration with Census data see
https://walker-data.com

For a pretty complete understanding of using and mapping census data see this excellent book by Kyle Walker that includes use of his tools for accessing the data as well as mapping it.

https://walker-data.com/census-r/index.html

But if you really need a full blown GIS system

QGIS Extremely capable, not to be learned in a weekend

https://www.qgis.org/en/site/

Tableau Public
A variety of analysis and display options

Home: https://public.tableau.com/en-us/s/

SDA at Berkeley
Online analysis of many datasets

Home: https://sda.berkeley.edu

Polls:

American National Election Studies (ANES)
National election surveys since 1948

Home: https://electionstudies.org
Data: https://electionstudies.org/data-center/

CES (Previously CCES)
Very large political surveys since 2005

Home & Data: https://cces.gov.harvard.edu

Voter Study Group:
Includes the VOTER survey and the Nationscape survey

Home: https://www.voterstudygroup.org
Data: https://www.voterstudygroup.org/data

Pew
Many, many surveys. Recent ones delayed several months.

Home: https://www.pewresearch.org
Data: https://www.pewresearch.org/tools-and-resources/

General Social Survey (GSS)
Huge variety of social topics since 1972

Home: https://gss.norc.org
Data: https://gss.norc.org/Get-The-Data

NORC
One of the oldest polling organizations, currently doing AP polls
Allows download of raw survey data after a delay of about 6 months

Home: https://apnorc.org
Data: https://apnorc.org/download-data/

AP VoteCast is a huge survey pre-election and election day, alternative to exit polls
Data: https://apnorc.org/download-data/politics/?search=votecast&order-by=chronological

BYU Center for the Study of Elections and Democracy

Polls on an interesting variety of social and political topics
https://dataverse.harvard.edu/dataverse/csed

Chicago Council on Global Affairs
Mostly surveys of US opinion on global affairs, but some domestic issues as well

Home: https://www.thechicagocouncil.org
Data: https://www.thechicagocouncil.org/research/lester-crown-center-us-foreign-policy/chicago-council-survey

PRRI: Public Religion Research Institute
Survey data released after 1 year

Home: https://www.prri.org
Data: https://www.prri.org/data-vault/

The ARDA: Association of Religion Data Archives
Includes survey data along with membership and other aggregate Data

Home: https://www.thearda.com
Data: https://www.thearda.com/Archive/browse.asp

AEI Survey Center on American Life
Data available after 6 months

Home: https://www.americansurveycenter.org
Data: https://www.americansurveycenter.org/data/download-data/

Votes

United States Elections Project
Michael McDonald research, lots on turnout and voting eligible population

Home: http://www.electproject.org

Harvard Dataverse
This can be a fantastic resource but it is huge and complex. Various projects archive their data here in a named “dataverse”. You can search for keywords to find things.

Here are examples of projects that have dataverses

MIT Data + Science Lab
Election returns at various levels and for several offices

Home: https://electionlab.mit.edu
Data: https://electionlab.mit.edu/data
Data: https://dataverse.harvard.edu/dataverse/medsl

Voting and Election Science Team
Precinct returns for all states, 2012-2020

https://dataverse.harvard.edu/dataverse/electionscience
2020 National Precinct Level Vote Returns:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/K7760H

OpenElections
A project to collect, clean and distribute official results since 2000, down to precinct
This is a project you can help on, from simple to complex. A great way to give back & to learn.

Home: http://openelections.net
Status page: http://openelections.net/news/
GitHub: https://github.com/openelections

ROAD: The Record of American Democracy
National precinct-level vote returns for the 1980s

Home: https://road.hmdc.harvard.edu
Data: https://road.hmdc.harvard.edu/data

State Legislative Election returns
Data and other files collected by Carl Klarner

Dataverse search: https://dataverse.harvard.edu/dataverse/cklarner?

Data: State legislative returns 1967-2016
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DRSACA

Census Data

IPUMS at University of Minnesota

This is the most wonderful, extremely complex, source of current and historical Census data, for all geographies down to the block level. Also offers current and historical GIS shape files. A significant learning curve but well worth it.

Home: https://www.ipums.org

IPUMS: NHGIS provides census tabular data and GIS boundary files from 1790. This is what most people think of as “Census data”
https://www.nhgis.org

IPUMS: CPS provides the individual level Current Population Surveys (monthly) which is the basis of unemployment statistics, annual demographic estimates, and special supplements including the Voter Supplement for registration and turnout every 2 years
https://cps.ipums.org/cps/

IPUMS USA The individual level micro-data from Census and American Community Surveys. Massive. Not for the faint of heart.
https://usa.ipums.org/usa/

Comparative Data Resources
(Not my specialty but this will get you started.)

Afrobarometer
Public opinion across Africa

Home: https://afrobarometer.org
Data: https://afrobarometer.org/dataBritish Election Study

British Election Study

Many elections with both cross-section and panel designs

https://www.britishelectionstudy.com/data/

CLEA: Constituency-Level Elections Archive
Election returns from around the world

Home: https://electiondataarchive.org
Data: https://electiondataarchive.org/data-and-documentation/
GeoReferenced Districts: https://electiondataarchive.org/data-and-documentation/georeferenced-electoral-districts-datasets/

Eurobarometer
Long-running and frequent surveys of the EU

Home: https://europa.eu/eurobarometer/screen/home
Data: https://europa.eu/eurobarometer/surveys/browse/all

LAPOP: AmericasBarometer
34 nations including all of North, Central, and South America

Home: https://www.vanderbilt.edu/lapop/
Data: https://www.vanderbilt.edu/lapop/data-access.php

World Values survey
What the whole world thinks, many countries with same survey items

Home: https://www.worldvaluessurvey.org/wvs.jsp
Data: https://www.worldvaluessurvey.org/WVSContents.jsp

The Texas Border Shift

Most of the country saw modest shifts in vote margin from 2016 to 2020. The Texas border stands out for the intensity and breadth of the pro-Republican shift. Miami is also attention getting, but here we focus on Texas.

It isn’t that the Texas border counties gave majorities to Trump. Most didn’t. But the swing in these counties, many with large Hispanic populations, was unexpected.

Democratic gains in urban and suburban counties were partially offset by GOP gains along the border.

The Texas border counties stand out nationally, not just in the state. This chart shows 2020 vote margin by 2016 margin, with the Texas border counties highlighted in red.

And I mentioned Miami earlier. It’s the big gray circle below the diagonal near the Texas counties.

For Texas to become a competitive state would be quite a thing. To do so, Democrats must consolidate gains and stop the loses. For Republicans, the prospect of balancing urban/suburban loss with border and western gains is a possible solution for continued hold on statewide offices.

Vote Surge and Decline

Hmmmm. I wonder what this is about? (OK: tl;dr it is about midterm elections and what to expect in 2022).

“Surge and decline” is the title of a 1960 article by Angus Campbell:

Abstract

The tides of party voting are as fascinating as the fluctuations of economic activity. Regularities in the ebb and flow of voting with the alternation of Congressional and Presidential elections challenge the analyst to find an explanation. This article seeks it in propositions rooted in survey data.

Campbell’s explanation was that presidential elections are largely driven by “short term forces” that provide a temporary advantage to one party, usually the winner of the presidency. Then in the midterm, with no presidential election, short term forces are less important and the electorate shrinks with lower turnout (a “low stimulus election” relative to presidential) and the “long-term force” of partisanship becomes relatively more dominant in the midterm. The result is a return to the partisan balance after the surge favoring the presidential winner two years earlier.

This is a beautifully elegant theory. It is rooted in a simple model of partisanship, short-term forces and the inevitable decline of turnout in midterms. I love it.

Alas, it no longer commands general acceptance as a theory of midterm seat loss. More emphasis is now given to presidential approval, economic conditions and incumbency. Those theories bring substantial empirical evidence, and are certainly sensible. But to me they lack Campbell’s beautiful simplicity.

I’m not here to argue theories of midterm loss, but rather to simply illustrate the votes side of midterm losses. That the president’s party almost always loses house seats in midterms is a fact. Here I look at the decline in votes for the president’s party from presidential to midterm.

For fun, I’m going to walk you through the puzzle and steps that end with the figure at the top of this post. Here is the first step. What IS this??

As my poor former students know, I enjoy starting a class example with a mystery. Show the chart above, invite speculation as to what it might be. I love this one because it looks like random noise with no relationship at all.

Next step: Revealing the variables.

OK here are the variables. National Democratic percentage of the 2-party House vote in the midterm by the national 2 party vote in the previous presidential election. Not much of a relationship. Some votes go up (above the diagonal) and about as many go down (below diagonal.)

Ahh, but what about control of the presidency? The midterm-loss of seats by the president’s party is well known. The national vote, here, shows the same pattern. Dems do better (above diagonal) with GOP president, and do worse with a Dem president, almost always (except 2002.)

This is the surge and decline of votes. Almost always the president’s party wins a smaller share of votes in the midterm than they did in the presidential year. Not surprisingly, fewer votes translate into fewer seats, but that’s not our topic here. For that see this post.

The pattern is clear if we fit a regression for midterms with Rep presidents (red line) and one for Dem pres (blue line). Now the upward slope is clear (midterm performance IS related to prior pres vote) and the party of president shifts the lines up (Rep pres) or down (Dem pres).

FWIW the red and blue lines are nearly parallel. A test of a pooled model finds the difference in slopes to be statistically insignificant (p=.9465). I’m using the separate regressions here, but there would be minimal difference for a pooled model.

So what does the model tell us about, say 2018? The red line estimates the Dem 2-pty vote in 2018 to be 53.1%, up from the Dem 2-pty 2016 vote of 49.5%. In fact, Dems got 54.4%, 1.3 points better than the model and 4.9 points over their 2016 performance.

We don’t know how Dems will do in 2022, but we do know how they did in 2020 and that there is a Dem president. The vertical black line shows the actual Dem share of 2020 House vote, and the blue arrow shows the fit: a predicted 47.8% in 2022, down from 51.6% in 2020.

This is the dilemma of every presidential party: they are almost certain to lose votes in midterm elections. For closely divided congresses (looking at you 117th) this imperils majorities. 2002 was an exception, with 1998 and 1990 almost being exceptions.

How do votes translate into seats? The relationship shifted after 1994 undoing a long standing Dem advantage. The votes-to-seats model expected Dems to hold 50.6% or 220 seats in 2021 (actual post-election was 222). For 2022 the estimate is 45.0% of seats. That would be 196 seats, a loss of 26 from the post-2020 election total.

The president’s party gained house seats in 1934, 1998 and 2002, and lost share of seats in every other midterm since 1862. Reps gained seats in 1902 as the House expanded but actually lost share of seats as Dems gained more that year.

If the historical pattern applies in 2022 the Democrats are unlikely to hold control of the House. In addition there will also be the effect of redistricting. Both parties will have an incentive to gerrymander for every advantage possible where they control the process.

Of course the past pattern may change. Political skill or folly might shift the balance away from the models. The model is useful because it gives us a basis for our expectations. We can judge party performance by whether outcomes exceed or fall short of model expectations. 12/12

Seats and Votes in the House

How votes are converted to seats in the House of Representatives and how that has changed.

In a perfectly proportional legislature the percent of seats should equal the percent of votes received by a party. Electoral systems based on proportional representation come close to ensuring this by design.

Two-party, plurality, systems are rarely if ever proportional. They tend to reward votes disproportionately, giving more seats than votes to one party and fewer seats than votes to the other. They also often award a majority of seats for less than a majority of votes.

One measure of the bias in a system is the “representation ratio,” the percent of seats divided by percent of votes. A value over 1.0 means a party gets more seats share than votes share, and values less than 1.0 means underrepresentation.

In the US from 1942 until 1994 the Democratic party was advantaged, with a representation ratio typically around 1.1 with variation across elections. After 1994 that reversed, with the Republican party enjoying an advantage, a bit smaller than Dems had.

Part of this was the “solid South”, dominated by Democrats until the 1980s coupled with very low turnout which made winning Dem vote totals smaller than in competitive elections. The transformation of parties in the South after 1980 became a GOP advantage.

Gerrymandering also plays a role in the vote-to-seats relationship, with advantages to parties that control legislatures and governorships that create the districts. Courts imposed some limits on districting beginning in the 1960s.

From 1942 to 1994 Democrats were advantaged in all but two elections. Since 1994 Republicans have been advantaged in all but one election (2008). The advantages were persistent in each era.

The RepRatio is a simple measure of advantage, but what about how votes are converted to seats across elections? This chart shows the percent of seats won by percent of national vote won in each election. 1942-94 is different from 1996-2020.

One measure of bias is the percent of votes required to win 50 percent of seats. In 1942-94, Dems needed 48.4% of votes to reach 50% of seats. Since 1994, Dems need 51.2% of votes to reach a majority of the House. There is uncertainty but these are the expected outcomes.

Another measure is the “swing ratio”, the slope of the regression lines, measuring how much seat share changes for a 1 point change in vote share. In 1942-94 Dems got 1.80 percent more seats for a 1 percentage point increase in vote share. After 1994 it has been 1.47.

Post 1994 Republicans gained an advantage in votes required for half the seats & reduced the swing ratio to lessen the effect of votes on seats. Both eras have swing ratios over 1.0 meaning seats are more responsive to votes than pure proportionality. This is common in 2 party single-member district systems.

If we shift to the relationship between national presidential vote and seats in the House we can extend the time frame back to 1900. I divide into two partisan eras, 1932-1992 for Dems, and 1900-1928 plus 1996-2020 with a GOP House advantage.

Interestingly, the relationship of seats and votes is essentially the same for the 1900-28 and 1996-2020 eras of GOP advantage. A test of different slopes & intercepts gives p=.69 so I combine them here.

In the 1932-1992 Dem era, a Democratic presidential vote of just 36.3% was enough to expect a 50% Dem House. In the GOP eras, a Dem president needed 51.2% of the national vote to expect half of the House.

The Democratic solid South again provided a huge advantage in the 1932-92 period. In the two eras that Reps were advantaged in the House, their advantage is much smaller, requiring Dems to get 51.9% of the pres vote, 50.3 in 1900-28 & 52.7 since 1996.

The takeaway is that Republicans converted a long time disadvantage in winning House seats to a smaller but persistent advantage after 1994. Once control of the House was won in 1994, the GOP has held an advantage, despite one reversal in 2008.

The size of the current Democratic disadvantage is important, but it should be recognized that the GOP disadvantage from 1932-1994 was far greater. Changes in regional party dominance plays a big role in that and shows party advantage can be altered.

Midterm Seat Loss

I’ve been shocked to hear several sources I respect get the midterm seat loss story wrong. So here is my effort to clarify.

The president’s party almost always loses House seats, but there have been 4* exceptions since 1862: 1902, 1934, 1998 & 2002. *HOWEVER in 1902 the House expanded so while Reps gained seats Dems gained more, thus Reps won a smaller percentage of seats that year. So the presidents party has lost strength in all but 3 midterms since 1862.

In the Senate the president’s party usually loses seats, but not as reliably as in the House. There have been 6 exceptions since 1960.

There is little difference, on average, in House seat losses in 1st vs 2nd midterms. An average -26.4 in 1st and -28.1 in 2nd. NO SIX YEAR ITCH! NO 1ST MIDTERM CURSE EITHER, for that matter.

2nd midterms HAVE been worse in the Senate: -2.3 in 1st, -6.0 in 2nd.

So PLEASE stop saying the president’s party only gains seats “once in the last 100 years”– you know who you are. The right answer is “three times in the last 100 years.”

And don’t imply the Senate is as predictable as the House. They aren’t the same.

And… 1st term vs 2nd? Nah. This is another rant as many people bring up “first midterm” (and in a 2nd term almost always talk about the “second midterm”) as if that mattered. It doesn’t, on average. It does vary across presidencies with some bigger losses in 1st and some in 2nd midterm.

And will 2022 be different? I don’t know. But we should get the history right.

Data details

These seat changes reflect the immediate outcome of the November election. Sometimes members die, change party or resign before the Congress is sworn in, and of course changes can occur during the Congress.

Brookings hosts Vital Statistics on Congress. Note they have a typo for 1998 indicating a loss rather than a gain. I use them here with that fix

Here is the Vital Statistics table.

Small differences if you use the Clerk of the House table, p59

Hello World!

Sixteen years ago this week a hurricane hit New Orleans and I launched PoliticalArithmetik, my first blog. This week a hurricane hit New Orleans and I’m (re)launching a website, PollsAndVotes.com.

After a year of PoliticalArithmetik, Mark Blumenthal (@mysterypollster) and I launched Pollster.com (with the support of Doug Rivers) and spent several years explaining polling and providing tracking of races, presidential approval and other topics in public opinion. In 2010 HuffPost bought Pollster and Mark had a good run with that. I departed and started PollsAndVotes.com in 2011, but have not maintained the site for a while. This is the relaunch of PollsAndVotes.com.

For some while now I’ve primarily posted analysis of polling on Twitter at @PollsAndVotes. As much as I like Twitter (most of the time) I think it is time to again have a PollsAndVotes website that allows longer posts, in one place, that can be easily found and searched for older posts, like from last week or last month. Having an editor to fix typos is also welcome.

I’ll be building out this site at a somewhat deliberate pace. I’ve decided not to import the old posts from the previous PollsAndVotes.com let alone from PoliticalArithmetik. I’ll update some of those, such as partisanship trends, but start fresh with the current data.

There will be a mix of topics here, but I’ll not be trying to replicate what Pollster.com did and what FiveThirtyEight.com and RealClearPolitics.com do well already. Most of the analysis here will be deeper dives into the national and state polling data that goes beyond trends. I also hope that my fellow academics will find graphics that may be useful in teaching.

The menu topics at the top of the page will (eventually!) provide a quick guide to analysis of “Polls” and “Votes” but also Wisconsin politics, party id, voter turnout, roll call votes and the US Supreme Court. Those first two will be something of a catch-all category. <;-)

Sixteen years ago I spent Labor Day weekend at home instead of the American Political Science Association annual meeting, keeping up with news of Katrina and launching PoliticalArithmetik. What started that weekend changed my life. I’ve still got a few days until this Labor Day weekend, and am not attending APSA, though I’ve been following the news on Ida. I hope you find the site interesting and useful.