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Daily Kos Elections does a lot of good by compiling information about the politics of different places all over the country.  But there's a real danger if we focus too much on all of those differences.  The danger is that the considerable lack of difference between different places gets lost in all of that information.  While there are definite regional trends and variable effects, most (really, nearly all) of the results of the 2012 election are explained by a very simple equation once you account for the 2008 election.  Just look at most of the Huffington Post's election results pages and you'll see what I mean, but if you like, we can take a closer look.

Note: The state/county approach is not my preferred one, but it's what's most immediately available.  Also, I know the scatterplots are small, but there's a lot to include.  If you click through and make them their original sizes or download them (the files are all quite large), you should be able to read the labels.  

And I tried to get 2012 election results from state sites, where possible, but sometimes I used the U.S. Election Atlas.  Sometimes they're election night results, other times they've been updated.  Click the specific links if you have any questions.


Lately, on DKE, I've been trying to push back against the idea that demographics and demographic change are the big story of 2012--because, in part, I don't think there's all that much to explain about 2012.  Different people might disagree with me about that point, but hopefully everyone can find some of this information useful.

Let's begin with a national perspective.  Here's a scatterplot of the Democratic share of the two-party vote, by state, for both 2008 and 2012, via Dave Wasserman (itself via Plain Blog):

As you can see, there's a very strong overall pattern.  In technical terms, the r-squared rounds to 0.97, and the best-fit line is:

Obama2012 = 1.05*Obama2008 -4.86.
What does that mean?

More polarization: Obama's vote share dropped by a bit more in places where he did badly last time, but held up well in places where he did well last time.  

On the other hand, uniform swing, which would be something like, say

Obama2012 = Obama2008-2.05
is very close to that line, especially in states that aren't extreme (they're equal at about Obama2008 = 56%).  We'll stick with the first line, but a couple of the later graphs will demonstrate how similar the two cases are.

Here's a zoom in to around the 40%-60% range:

Perhaps you can see why I don't think Texas is "trending" Democratic to a particularly significant extent, or that Obama's advantages in the swing states were all that great relative to 2008.

The 2004-2008 fit was considerably worse:

The best-fit line was

Obama2008 = Kerry2004+4.87
but the fit was just 0.88--nearly 10 points worse than the 2008-2012 shift.

I think the dramatic 2004-2008 changes gave a lot of support to a kind of "demographism"--the (caricatured) idea that changing demographics would change the electoral map in a continuous and irreversible way.  But 2012 didn't vary from 2008 the way 2008 varied from 2004, and we should consider the possibility that some extent of the 2004-2008 changes were about Barack Obama--raised here by John Sides, which I found originally via Plain Blog and again via Sean Trende.  That would explain why there was a strong shift when Obama got onto the ballot, but not a strong shift as he remained there, even though the electorate presumably continued to diversify from 2008 to 2012.


Now let's add some very different data: Obama's 2008 and 2012 performances in Kentucky, by county:

Most of Kentucky's county results fit right in around the same best-fit line as for the states, and the biggest exceptions (click through for a bigger image, or see my zoom in) are nearly all in eastern Kentucky, where Obama did considerably worse than the overall pattern.  This almost certainly helped doom Ben Chandler, whose supposedly "shored-up" district included many of those outlier counties (although I'm not sure how populated they are).  But it couldn't have helped.

Let's add Iowa from this time and last time, with the labels in blue:

The best-fit line for Iowa's counties?

IA2012 = 1.05*Obama2008 -5.25.
0.95 r-squared, and very similar to the state results, if a tiny bit more of a drop-off.

Let's throw on three more swing states: Florida, Ohio, and Colorado.  Florida will be in green, Ohio will be in red, and Colorado will be in orange.

Florida results this time and last time from those links, Ohio results from last time and this time from those links, and Colorado results from this time and last time from those links.

Our graph is a bit crowded, so I'll include three new ones (scroll down for a cumulative zoom-in, though).

The best-fit lines for the three states are:

CO2012 = 1.01*CO2008 - 2.68.
OH2012 = 1.06*OH2008 - 4.5.
FL2012  =  1.01*FL2008-1.86.
With fits of--respectively--0.99, 0.96, 0.98.  

We can see that things are a bit different for Colorado and Florida--the very best fit line is closer to uniform swing, suggesting those states had a bit less polarization and a bit more uniformity--but, as the graph makes apparent, the overall "2012 = 1.05*2008 - 5" equation still fits it pretty well.

To emphasize how small a difference this is, here are three graphs with just those swing states, plus the states, plus the best-fit line for each:

One last one: Texas, from 2008 and 2012.  I include this because Texas is a perpetual topic of DKE discussion, and it also has one of Obama's largest over-performances, in tiny Reeves County:

As we can see, Obama really did over-perform in a few Texas counties, some of which--at least Webb County and Hidalgo County--are decently populated.  As far as I can tell, they're all South Texas/border counties.  So demographics really might be changing those regions.  But otherwise, there doesn't seem to be much evidence of a trend.  

(Also note that my decision to cut the graph off at 10% Obama is actually cutting off a few Texas counties!)

The Texas best-fit line is:

Texas2012 = 1.08*Texas2008 - 5.66.
Again, very similar, maybe a bit more polarization.


Does that original best-fit line hold up?  Let's plug it all into a single regression: States, Kentucky counties, Iowa counties, Florida counties, Ohio counties, Colorado counties, Texas counties.  743 data points, by my calculation.

We get:

Obama2012 = 1.04*Obama2008 -4.68.
The r-squared is 0.97.  That's very high!  And it's not very different from the original equation.  

Here's the scatterplot for everything so far, with the original "50 states" best-fit line, the new best-fit line, and Texas in grey:

Looking across (the counties of) four of the biggest swing states and the very different Republican states of Texas and Kentucky added very little to what we knew from just looking at the 50 states.  And what that says to me is:

There was rather little regional variation in the 2012 election once you account for the 2008 election.

So yes: Obama did well in South Texas, in Miami-Dade County and Osceola County, and in Alaska.  And he did very poorly in eastern Kentucky counties, in Utah, in Monroe County OH, and in West Virginia.  But beyond that, there's not much evidence of strong "trends"--from demographics or otherwise--that I can see in the 2012 Presidential election, at least in the counties I've looked at so far.  Overall, either uniform national swing, or a slight variation to account for increased polarization (some of which might come from, yes, demographic changes) would seem to explain the vast majority of the 2012 election results.

That doesn't mean there's no story in the 2012 elections.  I included "almost" in my diary title for a reason.

First of all: This county/state approach might be missing something.  I'll hopefully look at it again once we have results by congressional district, or by state legislative district for a particular state, since those divisions are far more equally populated.

Although, I realized after I wrote most of this diary that there were some 2012 results available by Congressional District, thanks to David Nir and GradyDem: some 65 districts, actually (some of which have provisionals and mail-ins, some of which don't).  With the 50 states plus D.C., the equation is:

Obama2012 = 1.04*Obama2008 - 4.08
with a fit of 0.98.  

Second of all: I have found what might be some other statistically significant factors--yes, demographics, as well as campaign boosts and more--when looking at some of this information.  There's plenty of work to be done in understanding those and in estimating their sizes and so on.  And even in the above scatterplots, there really is variation left to be explained.

But we need to keep it in perspective.

What the strong overall fit does mean is that there really isn't all that much of a mystery left for other factors to explain.  Not nothing--but not all that much.  In my opinion, discussions of local trends, local campaign effects, and so on, should always keep the powerful and consistent national picture in mind, as well as the apparent relative sizes of their effects.  

12:02 PM PT: I gave myself a deadline to publish this one by 3:00 today--so hopefully there aren't any mistakes (there always seem to be mistakes).  Let me know.

12:10 PM PT: The emphasis on scatterplots, and on the outlier of Miami-Dade was also in this Hans Noel post, via Plain Blog.  And the above John Sides post had this great summary line:

What is most remarkable about 2012 is not its radical change but instead enduring stability—very modest shifts in state outcomes relative to 2008, relative even to 2000.
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Comment Preferences

  •  interesting stuff (6+ / 0-)

    that looks like a lot of work!

    "Kossacks are held to a higher standard. Like Hebrew National hot dogs." - blueaardvark

    by louisev on Tue Nov 20, 2012 at 12:14:33 PM PST

    •  Heh. (4+ / 0-)

      And here I am kicking myself for not double-checking everything.  I always seem to have some stupid mistake or something I don't realize.  But here I was just copying and pasting numbers and loading them onto scatterplots.  How much could even I have screwed that up?  (Famous last words.)

      And thanks!

      27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

      by Xenocrypt on Tue Nov 20, 2012 at 12:16:41 PM PST

      [ Parent ]

      •  The problem with Texas (1+ / 0-)
        Recommended by:

        is that areas like Harris, Bexas, Nueces, and even Tarrant, are all badly in need of functioning voter registration and GOTV machines, like the one Obama put together in Florida, and Democrats have put together in Nevada. A while back a very good user did some calculations (it might have been trowaman back on SSP), that estimated Democrats could gain 500,000 raw votes in Harris alone if they simply brought up the participation and turnout of their voter demographics.

        The problem with the Texas Democratic party is that it's a car with no wheels at this point. It needs new technology, new leadership, and a lot of money to do the grunt work of establishing itself again. At this point, Democrats have collapsed to abyssal levels in rural areas (this is partly the reason for Obama's poor performance), and they've hit bottom in many of the suburban areas where Romney played pretty well.

        My point and belief is that Texas is all up for Democrats at this point, the national party just needs to make the substantial organizational and financial investments to make it happen. And local Democrats like Julian Castro and Wendy Davis are the folks to make it happen. Heck, Pete Gallego has a very bright future as a long-term Representative.

        "Once, many, many years ago I thought I was wrong. Of course it turned out I had been right all along. But I was wrong to have thought I was wrong." -John Foster Dulles. My Political Compass Score: -4.00, -3.69, Proud member of DKE

        by ArkDem14 on Wed Nov 21, 2012 at 02:36:04 PM PST

        [ Parent ]

        •  I agree. I volunteered to call voters for (2+ / 0-)
          Recommended by:
          MichaelNY, ArkDem14

          Obama here in Harris County, I spent days and days at it, and two or three out of five numbers were disconnected or belonged to the documents processing department of a bank or went to a Republican who said "I'd rather die than vote for Obama."

          They were burning volunteer hours to clean up their god-knows-how-old call list.

          We heard a lot about "microtargeting" - I got steamed every time I heard that. They put NO EFFORT AT ALL into a blue-and-getting-bluer area. No effort and no money.

          It makes me sick. And angry. And disinclined to help more.

          I'll go all out for candidates I believe in. I'll give them my time and what money I can afford. But the national committees and the state party and, hell, the Tarrant County Democratic Party - kiss off.

  •  I love this nerdy number-cruncher stuff. (3+ / 0-)
    Recommended by:
    Xenocrypt, erush1345, Larsstephens

    There's a lot to be extracted from election data. It's interesting that the statistical analysis backs up the notion that this country is indeed becoming more polarized.

    I'd be interested to see a comparison of the data from 2010 and 2012, midterm vs. Presidential, because I fear that 2014 will look a lot like 2010.

    •  It's a pretty slight effect, though. (2+ / 0-)
      Recommended by:
      Hubbard Squash, Larsstephens

      Obama tended to do a bit better in his biggest strongholds than he did nationally--especially those with strong minority populations--and that logically means he did a bit worse elsewhere.  It's hard to know what's "particularly strong minority turnout", what's "a particularly polarized election", and what's a continuation of a long-term polarizing trend.

      And thanks.  I don't claim this sort of thing explains everything, but my hope is that it can help ground other and more specific discussions in a comprehensive national baseline.  If people are wondering how significant the 2008-2012 change in an area is, I hope they try one of these well-fitting equations, and if there's just a point or two of difference, it might not mean much.

      27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

      by Xenocrypt on Tue Nov 20, 2012 at 12:29:38 PM PST

      [ Parent ]

  •  Tip'd for all your hard work. (5+ / 0-)

    And all the cumulative best-fit r-squared polarity analysis sumthin' sumthin.'

    I simply have nothing to add.

    Things work out best for those who make the best of the way things work out.

    by winsock on Tue Nov 20, 2012 at 12:57:12 PM PST

    •  Thanks! (4+ / 0-)

      I don't know what polarity analysis is, to be honest.  But I was hoping to start a conversation, not to end one.  For example, I'm wondering why Osceola County, FL had a bigger Obama over-performance than Orange County, FL.

      27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

      by Xenocrypt on Tue Nov 20, 2012 at 12:58:44 PM PST

      [ Parent ]

      •  Short answer is (3+ / 0-)
        Recommended by:
        Xenocrypt, 4democracy, Larsstephens

        I don't know.  But there is more going on here than simply "demographics," as you rightly suggest. Demographic shifts and local economic differences count for something, to be sure.  Then too, comparing 2008 to 2012, we have very different candidates that Obama is running against (and voters are voting for and against).  Simple equations become really qualitative in this sea of complexity.

        Things work out best for those who make the best of the way things work out.

        by winsock on Tue Nov 20, 2012 at 01:24:04 PM PST

        [ Parent ]

      •  I'll field that one (4+ / 0-)

        (although it betrays my coming from more of a demographics-is-destiny position): Orange County was 24.8% Latino in 2008, 27.5% in 2011 (according to ACS 1-year estimates). Osceola County was 41.8% in 2008, 46.3% in 2011. Osceola is becoming more Latino more quickly than Orange, hence the more rapid pro-Obama trend.

        Editor, Daily Kos Elections.

        by David Jarman on Tue Nov 20, 2012 at 02:02:38 PM PST

        [ Parent ]

        •  Is that CVAP or population? (2+ / 0-)
          Recommended by:
          MichaelNY, Larsstephens

          27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

          by Xenocrypt on Tue Nov 20, 2012 at 02:15:30 PM PST

          [ Parent ]

          •  Population (3+ / 0-)
            Recommended by:
            MichaelNY, jncca, Larsstephens

            That's a great question, but we're talking primarily about Puerto Ricans migrating to both Orange and Osceola, so CVAP isn't as much of a concern as if we were talking about south Texas or California's Central Valley, as Puerto Ricans are already citizens.

            Editor, Daily Kos Elections.

            by David Jarman on Tue Nov 20, 2012 at 02:27:45 PM PST

            [ Parent ]

            •  So I tried (4+ / 0-)

              among those few counties that have ACS estimates for both 2008 and 2011, and I got this:

              lm(formula = O2012 ~ O2008 + NHW2008 + NHW2011)

                     Min         1Q     Median         3Q        Max
              -0.0163682 -0.0058475 -0.0007363  0.0077624  0.0151617

                          Estimate Std. Error t value Pr(>|t|)    
              (Intercept)  0.08369    0.03045   2.748   0.0143 *  
              O2008        0.94490    0.04142  22.815 1.24e-13 *
              NHW2008      0.05974    0.25273   0.236   0.8161    
              NHW2011     -0.16906    0.24976  -0.677   0.5082

              The fit was 0.99 (or 0.985), which is up quite a bit from the 0.96 you get with just Obama's percentages--but note that the NHW populations aren't statistically significant.  (And "O2008" should have three *'s next to it.)

              Also note: Miami-Dade had one of the smallest drops in NHW percentage on the list, while Osceola had the largest on the list.  But Broward County was second on the "NHW drop" list, and Obama doesn't seem to have over-performed there.  Basically, the shift is very orderly, but the demographic change isn't.

              My takeaway is that you can use the demographic changes to help explain the outlier counties, but you can't extend them into a coherent story about the whole set of counties.  And that's really my objection to the "demographics = destiny" stories--they seem too ad hoc, and I want a comprehensive picture.

              27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

              by Xenocrypt on Tue Nov 20, 2012 at 02:50:09 PM PST

              [ Parent ]

              •  I actually see Broward as an over-performer (1+ / 0-)
                Recommended by:

                of the aggregate trend line now, looking at my pictures--it was right on the regression line of that particular subset of counties I was looking at in Florida, which is what confused me.

                27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

                by Xenocrypt on Tue Nov 20, 2012 at 03:01:03 PM PST

                [ Parent ]

            •  What is possible is that (1+ / 0-)
              Recommended by:

              demographic changes have an effect that isn't linear, in one way or another.  That's an interesting possibility, but even then, I'd want to see a comprehensive case.

              27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

              by Xenocrypt on Tue Nov 20, 2012 at 02:56:15 PM PST

              [ Parent ]

  •  Is there any way you could unskew this? (7+ / 0-)

    It absolutely does not fit my preconceived explanations of the world.

    Howard Dean will always be my president.

    by 4democracy on Tue Nov 20, 2012 at 12:59:26 PM PST

    •  Lol. (4+ / 0-)

      Honestly--I think uniform national swing, or some other kind of linear thinking, would have been a better guide than the polls.  Yes, polling averages and 538-esque aggregators predicted binary outcomes very well--who won, who lost.  

      But, in part because I think uniform national swing and linear models tend to hold very well, I had suspected (if uncertainly) that it wasn't particularly likely for Ohio to suddenly be a light blue state, while Obama dropped huge in Connecticut and in California, or whatever the scenarios were.  

      And I was right to be suspicious.  But I followed the polls, day in and day out, and I thought "maybe this time is different", and it really wasn't.  

      27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

      by Xenocrypt on Tue Nov 20, 2012 at 01:05:05 PM PST

      [ Parent ]

      •  But (4+ / 0-)

        Uniform swing did not work nearly as well from 2004 to 2008. So don't you then have to ask, how should you know whether to use it?

        Here's a related q: How did uniform swing work from 2000 to 2004?

        For what it's worth, I've always agreed with your thesis, more or less. Especially in this era of maximal polarization, I think the top of the ticket really explains most things.

        Political Director, Daily Kos

        by David Nir on Tue Nov 20, 2012 at 03:31:16 PM PST

        [ Parent ]

        •  2000-2004, I prefer to combine Gore and Nader (1+ / 0-)
          Recommended by:

          and look at the Kerry/Bush shares vs. the Bush/(Gore+Nader) shares.  Numbers from Wikipedia state totals.

          Let's see.  The very best-fit line is:

          Kerry2004= 0.97*GoreNader2000 -0.88.
          The r-squared is 0.96 (had to use Wessa here, since I'm away from my R computer in the library).  Obviously, that's quite close to uniform swing.  A uniform swing of 2.8 points from GoreNader is very nearly as accurate, in fact.

          The big misses either way?  Using uniform swing for the errors, Kerry under-performed in:

          Hawaii    (by -5.00 points)
          Rhode Island    -4.40
          New Jersey    -3.29
          Alabama    -3.13
          Connecticut    -3.00
          Tennessee    -2.92
          New York    -2.33
          Utah    -2.23
          and over-performed in:
          Vermont    4.52
          South Dakota    3.50
          North Carolina    3.02
          Idaho    2.54
          District of Columbia    2.33
          Ohio    2.25
          New Hampshire    2.16
          Oregon    2.13
          Montana    2.08
          Nevada    2.03
          Colorado    2.01
          Note that Kerry over-performed in Ohio, then Obama under-performed in it, then Obama over-performed in it.  I think there might just be some mean reversion here.  And yeah, Nevada, North Carolina, and Colorado are on that second list--but below Idaho(?).

          27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

          by Xenocrypt on Tue Nov 20, 2012 at 03:55:27 PM PST

          [ Parent ]

        •  Using 98/00 Presidential results by CD (1+ / 0-)
          Recommended by:

          via, uh, you, and assuming "other" in 2000 is all Nader and "other" in 1996 is Perot and looking at Clinton's share of the two party vote vs. the Gore/Other share in 2000 (phew):

          GoreNader00 = 1.04*Clinton96-8.36.
          In this case, uniform national swing (of about 3.1 points, it'd be) is a bit more inaccurate than with 2000/2004--about 0.6 points or so more of average error.  The r-squared is 0.94 on the above line (I'm not sure what it'd be on uniform swing, but again, not all that different.)  Lots of reasonably big errors, though.

          Oddly, the correlation is considerably worse at the state level--just a 0.91 r-squared, with

          GoreNader00 = 1.1*Clinton96 - 9.54.
          Here, though, a uniform national swing of 3.1 points is somewhat of a closer fit--0.2 points more of average error.

          The big misses of the uniform swing, at the state level, would be Gore+Nader under-performing in:

          Wyoming    -8.85
          Arkansas    -8.27
          West Virginia    -7.61
          Louisiana    -6.77
          South Dakota    -6.58
          North Dakota    -5.52
          Idaho    -5.16
          Montana    -5.00
          Kentucky    -4.28
          Maine    -4.09
          Texas    -3.88
          Oklahoma    -3.58
          Utah    -3.20
          Minnesota    -2.43
          Mississippi    -2.28
          and over-performing in:
          Kansas    4.33
          Maryland    4.04
          D.C.    3.97
          Connecticut    3.81
          California    3.78
          Alaska    3.32
          New Jersey    2.59
          Delaware    2.32
          Colorado    2.28
          (Nevada is just below.)

          So: Home-state effects (Kansas, Connecticut, Wyoming, Texas, Arkansas).  Maybe Alaska was Nader over-performing there more than Gore.

          jncca suggested to me that all presidential re-elections are very highly correlated with the initial election.

          27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

          by Xenocrypt on Tue Nov 20, 2012 at 04:28:32 PM PST

          [ Parent ]

  •  To play the devil's advocate, though.. (5+ / 0-)

    Is it possible to fit a statistical explanatory line around a single causative event in such an autocorallary quagmire as a U.S. national presidential election?  

    You're simply stating that the change in variance between 2004 and 2008 was much more pronounced than between 2008 and 2012, and that the more recent change shows that the difference is explained by already R areas becoming more R in this election.

    But by using standard regression and not multivariate analysis, it's hard to really identify causative factors related (or not related) to demographics without drilling into areas where there has been demographic change vs areas where there has not been.

    Howard Dean will always be my president.

    by 4democracy on Tue Nov 20, 2012 at 01:17:35 PM PST

    •  You're completely right, but (4+ / 0-)

      again, my goal was to start a conversation--and to establish a baseline--not to explain everything.  "Almost" everything--but a lot of what's interesting about politics is indeed in that "almost".

      Multivariate analysis would surely explain more--but there's only so much better the fit will get.

      27, Dem, Dude seeing a dude, CT-04(originally), PA-02/NY-14 (formerly PA-02/NY-12).

      by Xenocrypt on Tue Nov 20, 2012 at 01:33:25 PM PST

      [ Parent ]

      •  It's a really important question, especially (2+ / 0-)
        Recommended by:
        MichaelNY, Larsstephens

        for places like Texas, where the thought is that the shifting demographics represent this magic fertile ground for Dems.  If that is not actually the case, then it would be good to know that before putting a lot of resources into it.

        This is also why a 50 state strategy, or even a precinct-level strategy, makes sense.  In addition to the direct value of getting more people involved at the local level, it will also provide a series of leaves in the tea of democratic campaign efforts that can help refine the strategy and approach.

        BTW in the above comment, I think I meant covariance as opposed to autocorrelation.  Statistics was a long time ago for me now.

        Howard Dean will always be my president.

        by 4democracy on Tue Nov 20, 2012 at 01:42:18 PM PST

        [ Parent ]

  •  Fantastic Diary (4+ / 0-)

    WOW. This needs to be the basis of someone's dissertation.

  •  Love this diary. (1+ / 0-)
    Recommended by:

    As more or less a proponent of the "demographics is destiny" argument, I think it presents a very strong counter-argument.

    I notice, though, that the correlation between 2008 and 2012, while quite strong, is not 100%. And in fact the states where Obama '12 outperformed Obama '08 do tend to have larger minority populations, and Dem-trending demographics (e.g., NJ, MD, VA, NC, FL, etc.). And the states where Obama underperformed in '12 do tend to be whiter and older.

    So it's not like the data, as near as I can tell, is inconsistent with the idea of a slow, demographics-driven shift in the electorate. It's just that individual campaigns and the conditions of any individual election will swamp that effect. But that doesn't mean the demographic effect, over a number of campaigns, isn't significant. It's very much equivalent to global warming: the weather patterns on any given day will explain whether it's hot or cold outside, but over the long term, global warming will gradually raise the baseline - the average temperature for a given day. Likewise, demographic change will gradually raise baseline Dem performance, around which individual campaigns will swing.

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