Friday, November 22, 2013

Medical Professionals, Political Preferences, and Party Identification

It started with this tweet from Matt Y.
My good friend Bryan Caplan remarked:
 Click through for the rest of the conversation. The upshot is that I got to wondering how well self-reported political preferences predict party affiliation among doctors. If you're stopped by here before, you might recognize this question as one I've applied to immigrants.

For medical professionals, there are different effects than with typical immigrants. For doctors, one of the big treatments is education. There's also a bit of natural teamsmanship that happens when a person is a member of various professional organizations (AMA, eg), so my hunch is that political preferences (liberal vs conservative) would do a worse job predicting partisan affiliation for docs compared to everyone else. Let's see if I'm right.

A quick note on variable definitions

For this, I defined medical professionals as GSS respondents who had Census occupation codes related to the medical profession, and let me tell you, it was kind of frustrating, since there are three code batches in the data coming from 1970, 1980, and 2010. I can't fault the census for not making up its mind, since the types of jobs out there have changed a lot in those 40 years (shout out to my man Izzy K!). So the 'medicalprofessional' variable isn't strictly physicians, but includes dentists and diagnosticians and all that. Since the original question was about medical professionals, I judged this to be fair. If you're unhappy with these inclusions, please feel free to write me a referee report and I'll adjust my approach in the final published piece (snicker).

Econometric specification (I can't be bothered to write out the equations, so forgive me my sins, gentle reader):

oprobit [Party Identification] [Political Views] [Medical Profession Dummy]

margins [Political Views]#[Medical Profession Dummy], predict(outcome([0-7])


The graphs below are the margins plots for each of the seven results, starting with strong democrat, all the way to Strong Republican, plus a bonus, Other Party. Please note that for these plots, the original regression is uncontrolled, and it has no fancy standard errors. It's as plain-jane, bare-bones as an ordered probit regression gets. Which is a weird way to describe it, since ordered probits are pretty abstruse to non-specialists.

But please don't be intimidated. It's just a nice, clear way to express probability. You want to know what the odds are that a randomly-chosen moderate doctor will identify as a Democrat? I got you covered, bro. Check out graphs 1-3. Easy! You can also get a pretty good idea of base rates of docs vs non-docs by just looking at whether or not the series are above each other. Also easy!

So avoiding further ado, here are the Democratic Party margins plots
Fig 1: Strong (D)

Fig 2: Not Strong (D)

Fig 3: Independent, Near (D)

Okay, so what are we looking at with these? First off, in overall terms, medical professionals (the series in red) tend to be less likely overall (compared to all other respondents) to identify with the Democratic Party, with the excepetion of liberal docs going independent with (D) tendencies. So, even if doc voters are leaning (D) in the past few elections, they're still not identifying (D) in response to GSS surveys. That's fine, it doesn't prove Caplan wrong. Indeed, when you look at voting patterns, it shouldn't surprise you that well-educated people buck party lines in the voting booth. Not a particularly noteworthy result, ladies and gentlemen.

But that little crossover there sure is interesting, isn't it? Let's see what the probabilities for straight Independents are:
Fig 4: Independent

The effect is even stronger. Compared to other respondents, liberal docs are more likely to be independent than their conservative pals. Good gravy. Of course, strictly speaking, the 95% intervals cross, so it's not a statistically significant effect, especially considering that I didn't use robust standard errors in the original regression and my eyeball checks suggested some heteroskedasticity (anyone else have to look the spelling of that word up every damn time?).

That leaves the Republican Party margins plots.
Fig 5: Independent, Near (R)

Fig 6: Not Strong (R)

Fig 7: Strong (R)

Medical professionals are more likely to be Republicans. But pay attention to where the largest departures are. Liberal/very liberal docs are more likely than liberal/very liberal ordinary citizens to go the independent/(R)-leaning route. Very conservative docs are even more likely than regular citizens to identify strongly with the Republican Party.

What's all that about? Well, to find out, let's run a controlled regression and see if results change.

oprobit partyid i.medicalprofessional i.polviews i.immcat i.wordsum i.female i.race, vce(robust)

You have no way of knowing if this is true, but I am liveblogging these results. The stuff above I ran before I started writing this post, but from here on out, I am composing this blog post without first looking at the results. How daring of me! Whatever, here's the ordered probit results in handy-dandy HTML table form no:

Iteration 0:   log pseudolikelihood = -47539.228  
Iteration 1:   log pseudolikelihood = -45102.581  
Iteration 2:   log pseudolikelihood = -45100.282  
Iteration 3:   log pseudolikelihood = -45100.282  

Ordered probit regression                         Number of obs =      24342
Wald chi2(23) =    4100.19
Prob > chi2 =     0.0000
Log pseudolikelihood = -45100.282                 Pseudo R2 =     0.0513

partyid           Robust
       Coef.     Std. Err.   z      P>z          [95% Conf. Interval]
      .037221   .0372723     1.00   0.318 -.0358314    .1102734
2    -.0042378   .0572077    -0.07   0.941 -.1163628    .1078872
3     .2543847   .0558183     4.56   0.000 .1449828    .3637866
4     .4465486   .0541506     8.25   0.000 .3404153    .5526818
5     .7458233   .0557274    13.38   0.000 .6365996     .855047
6     1.073725   .0568146    18.90   0.000 .9623707     1.18508
7     1.071495   .0686654    15.60   0.000 .9369131    1.206077
1     .0759376   .0297705     2.55   0.011 .0175884    .1342868
2    -.2237079   .0358137    -6.25   0.000 -.2939016   -.1535143
3     -.094237   .0322473    -2.92   0.003 -.1574406   -.0310334
1    -.0183601   .1146955    -0.16   0.873 -.2431592    .2064389
2    -.1406865   .1074688    -1.31   0.191 -.3513215    .0699486
3    -.1088001   .1033581    -1.05   0.293 -.3113783    .0937782
4    -.0730759   .1012778    -0.72   0.471 -.2715768    .1254249
5    -.0292433    .100435    -0.29   0.771 -.2260924    .1676058
6     .0542896   .1000425     0.54   0.587 -.1417901    .2503694
7     .0885638   .1003957     0.88   0.378 -.1082082    .2853357
8     .1268125   .1010346     1.26   0.209 -.0712117    .3248366
9     .1211953   .1016877     1.19   0.233 -.078109    .3204995
10     .0712821   .1027608     0.69   0.488 -.1301253    .2726896
1.female   -.1058552   .0134334    -7.88   0.000 -.1321842   -.0795261
2    -.8595719   .0219832   -39.10   0.000 -.9026582   -.8164856
3    -.2088881   .0355149    -5.88   0.000 -.2784961   -.1392802
/cut1   -.7557187   .1122173 -.9756606   -.5357768
/cut2   -.0160698   .1121541 -.2358878    .2037481
/cut3    .3430329   .1121672 .1231892    .5628765
/cut4    .7309348   .1122396 .5109493    .9509203
/cut5    1.022414   .1123512 .8022098    1.242618
/cut6     1.72903   .1128808 1.507788    1.950273
/cut7    2.782055    .116267 2.554176    3.009934

You'll want to do yourself a favor and look at that on a proper computer monitor. For obvious reasons. Category definitions are what you'd imagine. Medicalprofessional = 1 for medical professionals; polviews are from 1=extremely liberal to 7=extremely conservative; female = 1 for female; race: 1=white, 2=black 3=other. Anyway, let's redo the Independent affiliation margins to see if it's any different (are you excited? I'm excited! Let's go!)

Fig. 8: Independent, with Controls

Wow, that took a long time to run. I have more important things to do than run all of these over again, so I won't. Anyway, let's see what we've got... hm. Same results, with less statistical significance. How anti-climactic.

Maybe we can salvage this pig though. How does medical profession stack up against, say, gender for predicting party affiliation? In other words, let's find interactions of [medical professional]#[political views]#[gender]

Fig 9: Independent, with Controls, by Gender

Okay, so that's not so easy to see, but the red and yellow series are for females and the blue and green are for males. Note that the professional gaps (red-to-yellow and blue-to-green) are modest compared to the gender gaps (blue-to-red and green-to-yellow). I did not expressly choose the colors, if that matters.

So what's the takeaway of this last graph? Well, from where I sit, there's nothing particularly compelling about the medical profession when it comes to party identification, at least not compared to gender (and there are a few other horse races I could run, provided I could muster the interest, which I can't).

But that does not mean that docs necessarily agree with the general population on specific issues. That might be worth a closer look. I imagine I could chase this down a pretty deep rabbit hole, which is why I think I should stop for now.

Which is exactly what I'm doing now. Stopping.

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