Personality & Partisanship in Congress

Really neat research here from Adam Ramey, on the interaction of personality and partisanship in Congress.  The basic thesis is that partisanship isn’t everything – that personality also matters.  Ramey used some “recent methods” in text analysis to analyze floor speeches and generate personality scores for each Member of Congress.  He then uses the model to generate simulations of how these personality traits interact with partisanship to explain cosponsorship, absences, etcetera. For example, there’s one of his simpler charts – describing how measured conscientiousness predicts the number of absences.  Much as we might expect, the more conscientious Congresspeople miss fewer votes.

Predicted Absences Based on Conscientiousness

One thing I’m very curious about is the alignment of Congresspeople with their districts.  You’ll occasionally see these maps of personality traits as they vary across the country – New England is more neurotic and more open, Minnesota is more agreeable.  It would be interesting to examine congruence between districts and their Congresspeople.  Especially given historical data, there’s some very interesting work to be done.  Perhaps with the ideological sorting of the late 20th century, Congresspeople came into better congruence with their districts.  Or perhaps the personality traits of Congresspeople turn out to be independent of their districts.

One thing I would worry about with this study is the data source – Ramey is using floor speeches from Congress.  There are many things that might best represent the true spirit or personality of a person, but I’m not sure that floor speeches are the best way.  They’re formal, they’re pre-composed, and they’re mostly written by staff committee.  Even if they genuinely represented a Congressperson’s personality, we might still have selection bias if Congresspeople choose when to speak or not to speak.  For example, a particularly ill-tempered Congressperson might appear milder on paper if his Chief of Staff has any sense at all.  I’m not able to comment on the specific methods Ramey is using, but this does seem like a large potential source of concern in drawing conclusions.

What Solar Power Tells Us About Ukraine

Awesome and welcome news about the price of solar power – it has now reached price parity with fossil fuels in certain markets.  Today it’s developing economies in Asia – but it’s coming for everywhere else too.  The march of solar power continues apace, and short of a shocking and unpredicted slowdown in its development it will very quickly become the obvious choice for new power generation installation.  As Ambrose Evans-Pritchard points out in the Telegraph, a secular decline in oil demand and thus prices will completely hammer rentier petrostates like Saudi Arabia, the Gulf States, and yes, Russia.  For the last forty years or so, oil was the only thing propping up the economy of Russia and the USSR before that.

At the margin, this should probably make us more frightened about Russia’s intentions in Eastern Europe.  Vladimir Putin is not a stupid man, and unless his intelligence service is completely dysfunctional, they have likely been mulling over the alarming price charts of solar for the past four or five years.  As oil demand starts to slacken, it will completely destroy the government finances and military capability of the Russian state.  Without an endless river of oil money, it might prove difficult to keep buying off elites and regular Russians.  It will certainly prove difficult to maintain its relatively high degree of regional military power, especially after the extremely expensive professionalization and modernization reforms of the past few years.

Russia might at this moment be more relatively powerful than it will be for the foreseeable future – or ever again, perhaps.  Steven van Evera writes that we should be concerned about wars as great powers decline, in order to seize advantages before they disappear.  It’s certainly hard to imagine that Russia will ever be better-positioned for a geopolitical power grab against its neighbors than it is in April, 2014.  If Putin wants to regain Novorossiya, the clock is ticking.

The Limits of Statistics on Gender & Pay

Today is apparently “Equal Pay Day”.  The idea is that women earn 77% as much as men, so if a woman and a man both started work on January 1st of last year, she would have to work until today to match what he earned last year.  So the headline disparity in pay is quite glaring, but a lot of people don’t like that idea.  If indeed women are paid less than men, that suggests that discrimination can persist even in the face of market pressures.  It might even call for government action - quelle horreur! The most common argument in the face of this is that men and women select into different career tracks – Mark Perry and Andrew Biggs have a well-sourced piece arguing this in today’s Wall Street Journal.

Unfortunately for Drs. Perry and Biggs, this is a great example of a classic social science mistake known as “controlling for post-treatment covariates”.  When you are performing a basic analysis, you are often studying a single item, the “treatment”, and can “control” for variables by including them in your regression function.  In this case, the “treatment” is gender* but careers, promotions, etcetera are all covariates.  The problem here is that just including those covariates in the regression doesn’t help you unless they are independent and independently distributed.  See, if your gender affects whether or not you became a Wall Street financier or an elementary school teacher, a simple regression where you include an “occupation” variable doesn’t necessarily help you.  In fact, there’s good reason to think it might make accuracy worse.

Unfortunately, it’s often really hard to tell which way the estimates here might be biased.  If gender is substantially driving career choice, then my guess is that the estimates Drs. Perry and Biggs give are biased downwards – that is to say, they are understating the gender pay gap.    It’s really hard to get at the true answer to this question without a different approach – one that exploits a natural experiment, or a real experiment, to impose a condition where gender really is the only difference between two groups of people.  We should be pretty dubious of “controlling” for things that seem immediately obviously correlated with both our independent variable of interest (gender) and our dependent variable of interest (pay).  So in conclusion, hooray math!

*: Yes that sounds super-weird, just bear with me here.

Statistical Inference & “Running the Numbers”

I identify a lot with Frederik deBoer, who has a liberal arts background and is getting a crash course in quantitative methods as a grown-ass adult.  He has a nice piece on “Big Data” and its broken promises.  One of the most important is the simple fact that basically anything is “statistically significant” in a large-enough dataset, for which he offers a pretty good layman’s explanation:

Think of statistical significance like this. Suppose I came to you and claimed that I had found an unbalanced or trick quarter, one that was more likely to come up heads than tails. As proof, I tell you that I had flipped the quarter 15 times, and 10 times of those times, it had come up heads. Would you take that as acceptable proof? You would not; with that small number of trials, there is a relatively high probability that I would get that result simply through random chance. In fact, we could calculate that probability easily. But if I instead said to you that I had flipped the coin 15,000 times, and it had come up heads 10,000 times, you would accept my claim that the coin was weighted. Again, we could calculate the odds that this happened by random chance, which would be quite low– close to zero. This example shows that we have a somewhat intuitive understanding of what we mean by statistically significant. We call something significant if it has a low p-value, or the chance that a given quantitative result is the product of random error. (Remember, in statistics, error = inevitable, bias = really bad.) The p-value of the 15 trial example would be relatively high, too high to trust the result. The p-value of the 15,000 trial example, low enough to be treated as zero.

With a big dataset, everything has a very low p-value.  When you run it through most predictive models and test it on every possible variable, you will get the result that every independent variable impacts the result, and the p-value is low enough you can discard the possibility that it is a result of random chance.  This is especially troublesome when your sample, or n, is extremely large yet represents only a small portion of the overall result.  You will not face the issue that your results are driven by random variations of the sample, but you are very likely to face the problem that your sample isn’t representative.  If there is some selection bias in what datapoints are sampled, you could be producing very poor models of reality.  But judging whether your data is representative is difficult, because it’s difficult to make statements about missing data.  For really big datasets, making sure that your data is representative is a devilishly difficult and extremely important problem.

The promise of “Big Data” is that “n=All”, a common statement amongst its proponents.  Yet this is rarely the case, often because the data capture is process and often because of computational practicality.  The most fancy machine learning models in the world won’t help you if your data is of poor quality, and pretty simple methods like linear regression can be incredibly powerful when faced with the right methods.  Fancy machine learning techniques do have their uses, though – for example, regression offers little help in the problem of “feature selection“, or choosing which variables to use in constructing a predictive model.  This is a pretty important problem when you are looking at a problem without strong theoretical avenues of investigation.

The most important lesson I have learned from my intensive study of statistics is that certainty is elusive.  To people with no serious quantitative training (including myself nine months ago), you imagine that statistical inference works like high-school math problems.  You “run the numbers”, and an answer pops out.  But this is almost never the case, and successful inference involves many layers of judgment in data gathering, data processing, and model-building.  There are definitely wrong answers – for example, when faced with a problem that calls for a prediction of probability, you can’t assume linearity.  If you do, some predicted values might be negative and negative probabilities are a nonsensical idea.  But answers can’t be “right”, they can only be defensible.  And beyond that, reasonable intelligent people can disagree violently about which answers are defensible.

Learning statistics has been invigorating as I realize just how much is possible with a dataset and an old laptop, and humbling as I realize that statistical investigation is a lot more difficult than learning the commands in R.

The Impossible Dream of Federalized Education

On pure policy terms, I like the idea of federalizing education spending, as Felix Salmon.  It will almost certainly result in more educational equity, a good thing in and of itself.  In terms of knock-on effect, it’s definitely a good thing that federal money usually comes with strings attached.  It is unlikely to affect states like Massachusetts that take education seriously already, and seems like a good way to prod Mississippi and Alabama in better directions.  It is unlikely that Deep South state governments will take a deep interest in the education of poor black children without an awful lot of federal coercion.

That being said, I worry that the political economy of federalized education spending is unsustainable.  Property taxes are an inherently unfair way to fund schools and virtually guarantee that education will be inequitably provided.  But it does serve the very important function of a clear and visible social contract, where residents are both funding and receiving public goods.  If you’ve ever lived in a state like New Hampshire, with “donor” and “receiver” districts, you know that redistribution of school funding can make politics pretty bitter and divisive.  Taking it to a federal level will make this problem worse, in much the same way that the fight over healthcare has gone – people with money and power really hate redistribution.

Furthermore, education spending is a form of social investment, and the federal government generally seems to underinvest.  Just look at the generally-deplorable levels of public infrastructure.  If we can’t trust the federal government to adequately provide structurally sound bridges and roads, how can we expect them to adequately prepare the minds of the next generation.  Especially given that metrics of education quality are necessarily more abstruse and poorly understood, it is a lot easier for the feds to skimp on spending without immediately seeing worse results.  The looser feedback loop (compared to, say, collapsing bridges) suggests that the federal government won’t be particularly responsive to declining education quality resulting from budget cuts.

I don’t know that there’s a first-best resolution to this issue.  Locally funded education has a lot of problems, principal among them inequity.  This is both an inequity of resources and of attention – well-educated districts are likely not only to be wealthier, but more committed to the principle of generously-funded schools, and inequality is entrenched on many levels.  On balance, federalizing the system might work somewhat better – but it’s not immediately apparent that’s the case.  And even if equity rises, I think it’s entirely possible that overall school quality falls a lot.  One result of our extremely unequal status quo is a relatively large number of very good suburban public schools that would be devastated by the loss of resources.  This is a policy issue with a pretty rough political economy, and while there are better possible worlds out there it’s definitely not immediately clear how we get there from here.

“Hashtag Activism” or Hashtag “Activism”?

I was vaguely aware of the little #CancelColbert Twitter tempest-in-a-teapot. Sorry, should I say #TwitterTeapotTempest?  Apparently while mocking Dan Snyder’s miserable efforts to make “Washington Redskins” socially acceptable, Colbert (in character) used some words that annoyed some people.  So a young “hashtag activist” struck out to #CancelColbert.  She obviously didn’t succeed in anything but getting some people riled up and earning herself some marvelous publicity.  Which kudos to her, I suppose.  No such thing as bad (free) press.

I’ve never understood the intended mechanism of action for “hashtag activism”.  By this I mean the use of social media to rally around some sort of sentiment and then…what?  In this case, it consisted of one “activist” grabbing a lot more followers and some headlines for herself.  As far as I understand, the implicit model for this tactic is:

  1. Make provocative statements and hit Twitter’s trending list
  2. ???
  3. Profit!

The aim certainly can’t be to change minds, because Twitter is mainly used for communicating with pre-existing ideological communities.  It can’t be to spread awareness generally, for the same reason.  It could plausibly be defended as a method for ideological activation or to increase issue salience in order to drive real actions.  But in order for that the plausible mechanism of political change, a furious Twitter campaign needs to be followed up by…actions.  Organizing a boycott of Colbert’s advertisers, for example.  But this wasn’t happening because said activist’s goal wasn’t even to get Colbert canceled.  The hope was to “start a conversation”, which inevitably centered around the messenger rather than…well, I’m not sure what the message was.

I admire our valiant hero’s gumption, but this is a pretty silly mental model of political change.  It reminds me of Corrine McConnaughy’s article from the Monkey Cage a few days ago asking us to forget Susan B. Anthony.  The reason is that idealizing Ms. Anthony ignores the what actually made change happen in America – the boring and dull work of partisan competition, angling for advantage, and picking tactical fights.  The women who picked up her cause organized pressure campaigns, corralled voters, traded horses, rolled logs, worked the streets and most importantly built institutions for sustained political movement.

Finally, the New Yorker article I cite up top includes the absolutely infuriating phrase “after speaking to [our hero] about what she hoped to accomplish with all this (a paternalistic question if there ever was one)…”.  That’s not a paternalistic question, it’s a reasonable adult one.  I hope the activists that represent my political ideals are actually trying to accomplish things, especially when deciding where to donate my time or money.  The idea that we should ask the partisans of our cause what they hope to accomplish seems like a basic question, one which all activists be able to answer.

It’s Not Really Okay to Fail


Megan McArdle says that young Americans have become more risk-averse, avoiding challenging experiences if it might even slightly tarnish their records. Increasingly, high school and college students today are conservative and grade-grubbing, shying away from more challenging classes that might give them a bad grade that will dog them for years. Or more challenging careers where they might fall through the cracks. I am sympathetic to this idea, but I was annoyed to see Noah Smith wrote the same post I was going to: even if risk aversion has remained flat, the cost of failure has increased greatly:

1. No social safety net. America has less of a social safety net than most rich European and Anglophone countries. Especially since welfare reform in 1996, economic failure has dire consequences. If your business or risky career path falls through, you’ll have no health insurance (well, at least til Obamacare kicks in), and you could even find yourself on the street. Jacob Hacker calls this the Great Risk Shift.

2. Income inequality + income stagnation + social preferences. The big runup in inequality since 2000 has mostly been about the top 0.1%. But in the 1980s – when the rate of new business formation started to fall – there was a much broader increase in inequality. The middle class spread out, and that raises the penalty of failure. If you don’t break into that $100k-and-up income bracket, you’ll be stuck in $30k service-sector-land. Add to that the fact that incomes for the lower part of the distribution have flatlined since 1980, while incomes for the top brackets have soared; a career failure means, more than ever, that you’ll be much poorer than your peers.

3. Labor market segmentation. In America, if you’re unemployed for longer than six months, you have a much worse chance of getting a job Also, the college wage premium has increased – if you don’t go to a decent school, good luck getting a decent job. This is a weaker version of what has happened in Japan. It seems like both college and the duration of unemployment have become important signaling devices. Fall off the “success” wagon, and good luck getting back on.

Whether or not actual utility functions (or “cultural preferences”, whatever makes you happy) have changed, these reasons are indubitably true. Increasing inequality has made the costs of failure higher, and increasingly ruthless meritocracy has made non-traditional professional career paths increasingly difficult. And if things really go south and young workers fall off the professional path entirely then they face a long and grinding existence working poverty-level service jobs with little social safety net. In these circumstances it would be pretty nuts to jeopardize your chances at professional success by “challenging yourself” and “not being afraid to fail”.

A nice chaser is a piece from Crooked Timber, mocking a survey where Millennials don’t believe that Social Security will be there when they retire. The tone is snarky and dismissive and completely neglects why Millennials believe this. I don’t believe that Social Security will be there when I retire, and neither does anyone I’ve ever asked. All are intelligent people, most of them are at least vaguely aware that the math is perfectly sustainable. But this belief is fundamentally one of political economy. First and foremost, of course we expect the Boomers to pull the ladder up after them as one last gesture of selfishness. Secondly, we’ve all seen over the last 5 years what has happened to people who believed the government would look out for them. The political world has gone to war on the safety net. The article also fails to mention the probably-relevant fact that the government tried to end Social Security a mere nine years ago, and draconian Social Security cuts remain a key policy goal of the GOP. The Democrats have been more than willing to entertain the possibility as well.

To people of our generation, the idea that “you should be okay with failure” is incredibly, unspeakably naïve. We’ve grown up in an era where the safety net has been shredded, and the penalties for failure have grown ever steeper. Furthermore, the rat race for professional careers is continually creeping closer and closer to birth, and right now I’d peg it as starting right around 6th grade. We all know people who have fallen off the track, and the future is very clearly quite bleak. McArdle bemoaning our personal conservatism reads a lot like this masterpiece from the Onion. Maybe our friends who have failed will end up beating us all in the end – but we have to work from what we see, and it sure seems that those friends have been consigned to a life working for shit wages at shit jobs for shit respect. Better hit the books or get back to your Excel models because there are plenty of people who would kill to take your spot.

I suspect that the Millennials will remain a generation of very deeply seated personal conservatism. If you’ve paid even the slightest attention to the last decade, as we came into political consciousness, the lessons have been pretty clear. If you work hard and succeed, you will get money, you will get respect, and you will get autonomy. If you fuck up, you are pretty much fucked. A spouse won’t even help you much, because the elite don’t marry losers. And the idea that Social Security & Medicare will at least keep us comfortable in retirement is laughably absurd. The economy is basically the state of nature, no one is coming to help you, and the consequences of failure are for life. 


Keeping The Internet’s History Alive

Dr. Greg Brannon is running for Senate in North Carolina.  Dr. Brannon has some unorthodox beliefs. Those beliefs include some unusual opinions about flouride and brain-implanted microchips.  Dr. Brannon used to expound those beliefs on his website.  Dr Brannon no longer wishes these beliefs to be public. Now, he has the much more reasonable belief that his other beliefs might be a hindrance in a Republican Senatorial primary.  So Dr. Brannon has a problem and he would like FoundersTruth to go away – the website is down, but caches are forever.  So Dr. Brannon requested that the Internet Archive (a private nonprofit) take down the cached copy of his site.  The Internet Archive has, apparently, complied.

There is a serious and unresolved policy question here – as more and more keeping of “public” records devolves to private firms, what is the public interest here?  It seems that keeping Internet history both stored and generally available is a matter of public concern, yet right now this isn’t done.  I understand the Library of Congress does some of this, but not in a easily-searchable desktop version or anything like that.  And the question is even more pressing as the internet is increasingly accessed through apps and other closed services.  Twitter is mostly on the public internet, Facebook somewhat less so – but in either case, the information’s accessibility and retention is dictated entirely by private companies.

It would be nice to see Internet archival and accessibility treated as a matter for public concern and thus public funding.  Surely we all need to know about Dr. Brannon and his bold ideas. However, it seems more likely that information accessibility will either go unaddressed or be a topic for heavy-handed government regulation of internet firms.  It’s kind of a shame, because the costs of this are so low compared to feeding the hungry, caring for the sick, or launching ill-advised military interventions abroad.

Should we ask Supreme Court Justices to Retire?

Seth Masket ably makes the case for lobbying elderly Supreme Court Justices to step down.  Ginsburg and Breyer are quite elderly, and to be frank about it, are unlikely to make it to the next Democratic President after Obama.  And we know that Supreme Court Justices are a type of politician, albeit much more principled than most.  Supreme Court Justices are a lot like the type of politicians most people would say they want, who act with judgment and foresight to advance a vision of what is best for the country based on their experience, insight and wisdom.  Frankly, as a liberal, I would be pretty relieved if either of them decided to step down tomorrow and make way for a younger and healthier liberal Justice.  I see the case for pressuring them to step down.

And yet.

The authority the Supreme Court relies on is in some way a sleight of hand.  It is the pretense they are above politics and sit in sober disinterested judgment – which we all kind of know is a pretense but still badly want to believe.  Their legitimacy isn’t from popular acclamation and it certainly isn’t from their political activism – but they both have and need legitimacy.  In order to properly perform their constitutional duties, Supreme Court Justices need to be unafraid to take on political authorities when it is truly necessary.  There is some evidence that when their legitimacy is in question by the public, they are less willing to do so.

So while political scientists know that Supreme Court justices are really political actors, it’d probably be best if that didn’t become conventional wisdom.  Frankly, the normative issues seem pretty conflicted and there’s not necessarily an easy answer.

The “Gig Economy” Isn’t Long for This World

There’s a fantastic story about the “gig economy” in Fast Company, meaning companies like Taskrabbit, Exec, and so on.  These are companies that promise to let you work the hours you want on the jobs you want and provide a more flexible way to work.  So how is it trying to cobble together a living income walking dogs and delivering sandwiches via your smartphone?  Well, it kind of sucks.

This makes sense.  Wages are a function of supply and demand, and demand doesn’t just mean the demand of companies for workers.  It also refers to how willing workers are to perform the work – workers demand a wage premium for work that is difficult or demanding, which is a large part of why plumbers and garbagemen get paid better than most blue-collar laborers.  Imagine I was to offer you two jobs doing roughly equivalent unskilled labor. One is on a regular schedule and relatively simple but the other requires you to scramble constantly, take orders via the phone, prone to cancellation on very short notice and with no visibility into schedules beyond the next day.  You’d demand a much higher wage for the second job.  Yet the Taskrabbits of the world are offering less money than minimum wage.

I think that these companies are mostly a fleeting phenomenon of the financial crisis.  The economics of these things mostly work out when you can pay people insultingly small amounts.  It works great when the gigs are digitized like Odesk or Elance, and you can take advantage of wage differentials to have everything done in Bangladesh or the Philippines.  But that’s extremely hard to do with meatspace work, where you have to rely on First World labor and pay them more money to make up for the hassle and unpredictability of the work.  It seemed, in the slack labor markets of the last five years, that you could get the economics to work out because of the hordes of unemployed.

But the Fast Company article suggests that when the slack finally tightens a bit, the cost structures of these ventures will turn prohibitive pretty quickly.


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