How Random are Marginal Election Outcomes? 

     

  Dan Carpenter  came by the  workshop  yesterday to talk about  his paper  (with SSS-pal  Justin Grimmer ,  Eitan Hersh , and Brian Fienstein) on close elections and their usefulness for estimating causal effects. A few recent papers exploit these marginal elections to answer the a general class of questions: how does being elected to office affect a person&#8217;s outcomes? At the least, office getting into office is a fairly big boost to resume and, further, while in office there are various (ahem) business opportunities that  may  or  may not  be entirely legal. Fans of politics (including political scientists) have a keen interest in the effect of office-holding on re-election, commonly known as the incumbency advantage.  

 Obviously, simply looking at winners and losers is a problematic strategy, to say the least. So, instead, we look for winners and losers whose elections were  randomly  determined.  And extremely close two-candidate elections seem to fit the bill. Poor weather, ballot miscounts, and voting errors can all push a narrow election margin to either candidate. Thus, the argument goes, vote share counts right around 50-50 essentially assign the office by coin flip. Thus, comparing winners and losers around the that cutoff can actually estimate causal effects.  

 What Dan and his coauthors point out, though, is that some candidates might have more control than others over that coin flip. And candidates and parties are likely to devote more of their resources to those close elections than to safe ones. In fact, they show that candidates with  structural advantages  in these resources are far more likely to win these close elections. In the above graph, you can see that winners of House elections in the U.S. are much more likely to share the party of the current governor of the state, even when we restrict the sample to +/- 2% around the 50-50 mark. This indicates that there may be deeper imbalances between winners and losers, even very close to the 50-50 mark. 

 They suggest the fundamental differences between winners and losers in these close elections could come from two sources: ability to get-out-the-vote pre-election or successful legal challenges post-election. If those ballot miscounts get recounted or thrown out in favor of a candidate due to better legal maneuvering, then those aren&#8217;t terribly random are they? The main critique here is that causal effects are hard to find when we compare winners and losers in close elections and we have to make sure that our proposed &#8220;randomizations&#8221; make sense theoretically and hold empirically.