ECON 102B is a class that is both highly frustrating and incredibly useful. Indeed, the power to perform regressions and gain what seems like the power to predict almost anything based on just a few data points can be incredibly intoxicating. Given that I am currently at that stage, where the power to perform regressions seems awesome as opposed to mundane and since, as I have demonstrated, I am an ASSU political junkie, I decided to crunch a few numbers based on last year’s elections to give a little taste of what we might see this year. First, I’ll show each regression (only one in this post, however) and then explain what it means (for those interested, everything was done in Stata, which is on all of the computer clusters; I have cut out extraneous information, but I’m happy to email out anything if someone wants it). So what did I find?
This is the regular votes regression (uploaded as a picture because the blog platform was uncooperative)
This regression shows the effect of the number of petitions received by a candidate in the pre-election round, their year in school (at the time that they are running; thus a freshman running has a 0 for both sophomore and junior), and their “political affiliation” as a member of the Students of Color Coalition and/or Students for a Better Stanford on the number of votes that they received in the election. The bold figures represent significant effects, which, for readers without a background in statistics, means that these variables have an effect that is too large to attribute to chance alone. Basically, for each additional petition that one received in the initial round, one was likely to get 4.414 more votes in the election. Being a member of SOCC meant a 247.797 vote boost and SBS meant a 269.52 vote boost. I’m not making any claims about whether membership is these groups was responsible for the vote boost or whether it was just a function of the fact that these groups selected already experienced candidates, but it’s interesting to note here.
As a side-note, I ran another regression with the variables Petitions*SOCC and Petitions*SBS to try to explore this by seeing if membership in these organizations had any particular effect on well-prepared (high petition) candidates or less prepared (low petition) candidates, but nothing came out as significant. In both cases, however, the coefficient was negative, suggesting that these organizations did in fact work to bring up the least prepared members rather than solely relying on the effect of choosing the “best” candidates already.
I also performed the more interesting analysis of actually predicting election winners and losers, but I’ll save that for a future edition. I will say that the best model correctly predicted 14/15 candidates, which is, one might argue, actually quite a feat (albeit one with the benefit of hindsight). I may even wait to post the model until the SOCC candidates are announced and/or until we find out if any form of SBS is re-emerging so that I can try my hand at guessing this year’s results as well. Ah, that intoxicating (albeit likely foolish) power of regressions…
Below, I ran the regression while excluding candidates with fewer than 400 votes (i.e. excluding non-serious campaigns), but the results are not particularly different (SBS becomes marginally significant instead of just significant), so I’m just putting them here because they were requested by a reader.
This is the regression for votes>400.
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Otis — brilliant work as usual. Econ 102B can be useful! Anyway, some thoughts/ideas:
- Way to only include sophomore and junior as dummy variables. I was about to criticize you for leaving senior out but then realized that you were just avoiding the dummy variable trap. A budding econometrician you are, Mr. Reid.
- An interesting next step (before you start predicting) would be to analyze some of the other endorsements. Try including dummies for the Women’s Coalition, Jewish Student Association, Queer Coalition, and Stanford Democrats.
- Finally, could be interesting to take a look at the issues that people campaigned on. What translated into more votes: sustainability or wellness? Transparency or accountability?
- Oh, and how about a dummy for use of Lolcats in fliers?
Two other ideas:
- number of Stanford Facebook friends. One problem that you’re sure to encounter is that it’s now impossible to know how many friends candidates had last year at the time of elections. But you may want to collect that data now so that you can some even more advanced regressions once this year’s elections have passed.
- dummy for being an incumbent.
you guys are ridiculous
also, dummy variable for public endorsements in the media, i.e. the Review or the Daily?
I agree with you guys saying 102B is a major ass-kicking experience, but you can’t live without econometrics…you just can’t
ps…us forming a study group for the final –> win.
You are redonk…I love it.
@Alex: lots of great ideas there… We should chat about data collection. Also, regarding the dummy variables, the reason that senior is missing is actually because the class year dummies reflect the class year of the person running for office and seniors as a rule do not run for ASSU Senate. The default position here is freshman (and I didn’t include that as a dummy for the reason that you mentioned).
@Rubi: agreed.
@Fausto: thanks!
Also, I’ve now updated the post to include both a version that excludes non-serious candidates and reflects new data about the last election.
hahaha awesome.
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