Fun with Congressional Data: Difference in incumbent and challenger expenditures and who won

If you’ve been reading my Fun With Congressional Data series, you’ve seen a snapshot of the relationship between party majority and GDP, congressional occupations over time, trends of the democratic and republican parties over time in the house and senate, and the minimal representation of independents in either house of congress. In the last edition of this fun with data series, I took a look at the amount of money spent by incumbents and challengers in each election since 1974. This data specifically can be found here. Because there was much more information related to House elections than the Senate elections (many seats up for grabs vs few) let’s first take a look at the difference in total spending for incumbents and challengers for the house.


We see that for all three outcomes of the election (incumbent won handily, incumbent barely won, incumbent lost) that this difference in spending has been increasing over time. However, the difference in spending for races where incumbent’s lost has fluctuated much more over time, and in 1978, challengers actually spent more money than incumbents in these races. We can also see that since 2008, the difference in spending for winning incumbents is drastically higher than that for losing incumbents with this difference being at least 500,000$ more in 2012 (0.63$ million compared to 1.14$ million).

Let’s do the same for the Senate, bearing in mind that there is only 3-14 of each group (Incumbent wins handily, barely wins or loses) each election.

senateWe see a stark contrast to the trend for incumbents losing in the senate compared to the House, but this is likely due to a small sample size. For example in 2012,2004,1996 and 1990, there was only one instance when an incumbent senator lost, which partially explains the huge outlier in 2012. At most, 9 incumbent senators have been outed in an election since 1974, concurring in 1976 and 1980. However, we can get more reliable estimates of the effect of difference of spending for instances when incumbents won barely or by a landslide, which has at least 8 cases for each midterm election. We see that for incumbents that won by over 60 % that the difference in spending has increased over time and has doubled since 2000. Both of these graphs give the appearance that large differences in finance drive incumbent landslides and that in general for challengers to stand a chance they need to spend no less than 500,000$ than their opponent. Let’s take a look at the average amount of money spent by winning candidates since 1984.


For the house, we see a constant trend upwards, with winners spending more at each election than the previous election. The trend for the senate is not as clear as the trend for the house in this regards but since 2002, the average Senate winner spent 8-10 million dollars compared to 6-8 from 1985-2000. All in all, it appears that campaign finance has a direct effect on who wins house and senate races and that incumbents can almost always out fund raise challengers. This is a large part of the reason why we have career politicians and not much change in policy over the past 10-15 years.

I hope you’ve enjoyed my Fun with Congressional Data series and that it sparked thoughts or ideas about changes that need to be made in our governance. And please, go vote on Tuesday but more so for the local elections. If you don’t like either major party presidential nominee at least cast a “protest vote” or just skip the presidential vote. Check out the other posts below:

  1. Party Majority after election in the House/Senate since 1931 and a congressional majority’s connection to changes in Real/Nominal GDP.
  2. Occupation status of congress since 1953.
  3.  Percentages of Congress and House/Senate of Democrats and Republicans since 1857.
  4.  The myth of independent representation and choices in 2016.
  5. This Post: Amount of $$ spent on elections by Incumbents vs. Challengers and it’s effect on re-election since 1974.

If you enjoyed this post, take a look at some of my other Adventures in Statistics.

-Andrew G Chapple





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