Comparing Election Spending

Everyone knows that campaign spending is out of control, right?

Except it’s not. In raw numbers, of course, the amount just keeps getting bigger, but controlled for inflation, it’s exactly what you would expect, and no more expensive than it was at the turn of the century, as the graph above from Mother Jones shows.

And if you control for eligible voters (remember that women did not get the right to vote until 1920, and 18-21 year olds did not get to vote until the 1970s), Jonathan Bernstein points out that we are spending far less on elections per voter than we did early in the century.

So what’s going on? Dana Houle notes at Rooted Cosmopolitan that the election reforms of 1973 capped contributions at $1,000. Because the cap was not indexed to inflation the cap very quickly moved from being relatively generous to exceedingly tight. You can see the result – as inflation skyrocketed, the real worth of $1,000 plummeted. In 2004, the cap was reset to $2,000 (which frankly only begins to adjust for post ’73 inflation), then in 2008 it was raised to $2,300, and what you see is the amount finally catching up to historical norms after its post-Watergate reform lows.

In the course we’re putting together on Making Fair Comparisons, one of the rules we give students is to control for inflation and population wherever you can, if only to see what happens. It’s hard to figure out how to control for something more complex, but you can get per capita numbers and inflation adjusted dollars pretty easily from WolframAlpha – it takes under 10 seconds to control a couple numbers and see what happens. Yet, no one does. We’d rather throw around “Most expensive campaign ever” nonsense, because it fits our intuitions or our politics.

I’ll make one more comment – it’s often mentioned by Lessig and others how much time politicians have had to spend raising money – a disciplined candidate will spend ten or more hours a week doing direct fundraising calls and as many hours doing fundraising events. Lessig claims that congressmen will spend 30% to 70% of their time raising money, implying this is a new phenomenon – looking at these charts I wonder if the increased fundraising time is a result of the non-indexed cap. In 1973, if you wanted to raise $1 million dollars for a general election race, you’d have to get maximum donations from 200 of those names on your call list. By 2004, you’d have to get more than four times that many maximum donations. No wonder congress members that served through the seventies forward have said that the amount of calls they had to make for money has increased – inflation was pushing them to it.

Incidentally, I favor publicly funded elections, for a variety of reasons. But we should be suspicious of claims that the current spending is exponentially larger than it was in the past. And as we tell our students, we should be ruthless with commentators and pundits who don’t at least attempt to control for relevant variables.

Liberal Arts and Transfer

In a Moneybox post I mostly agree with, Matt Yglesias says this:

In order to do well in courses on 19th Century British Literature or Social Anthropology or Philosophy or American History in a properly running American college, what you need to do is get pretty good at reading and writing documents in the English language…. [And] If you can compose an email that’s 10 percent clearer in 90 percent of the time as the other guy, you’re going to get ahead in a wide range of fields. Outside of office work, a big part of the difference between a hard-working individual who’s pretty good at his job and a person who’s able to leverage his skills and hardwork into an entrepreneurial or managerial role is precisely the ability to research things and write up plans. Everyone knows that a kid growing up in rural India is obtaining valuable skills if he gets better at English, but this is equally true for a kid growing up in Indiana.

To which I would say, yes – but there is not as much transfer in that area as you might think. In many ways, academic writing trains one in habits that have to be unlearned in a business environment (as a former language and linguistics grad student I can attest to this). The quality of business writing among faculty, who have practiced writing more intensely than most, is not any better than that in the general population.

Liberal Arts *can* be very useful to business, but it has to be taught for transfer, in an integrative way.  It requires getting beyond the term paper conception of writing and into something less formal but more regular. And maybe more varied — again, transfer (for many students) requires practice across multiple domains with explicit explanation of how the domains relate. The kid at the top of your class is going to move from Milton to competitive analysis reports just fine, but the kids in the middle need guidance.


Another day, another misguided graph on happiness research. This time Fast Company (tech populations are ground zero for happiness research for some reason) puts up the graph above. Which seems interesting, right?

Except that in the article we find this:

Some countries are significantly happier than others (happiness is, of course, subjective). Indonesia, India Mexico, and Brazil lead the pack in happiness, while Russia, South Korea, and Hungary are all pretty miserable (see the chart). There are other factors as well: People who are under 25 are most likely to say they’re “very happy”; Latin American countries as a whole have the most “very happy” people; and people with high income and extensive education are also most likely to report being “very happy.”

I got interested in how much the age question figures in, because just glancing at the big graphic I could see it looked almost identical to what these nations would look like if ranked by median age.

Turns out it probably figures in a lot. Here are the top five “happy” nations and their median age (from WolframAlpha):

Indonesia: 27.6 yr
India: 25.3 yr
Mexico: 26.3 yr
Brazil: 28.6 yr
Turkey: 27.7 yr

Here are the bottom five:

Italy: 43.3 yr
Spain: 41.1 yr
Russia: 38.4 yr
South Korea: 37.3 yr
Hungary: 39.4 yr

So, in other words, most of what we are seeing in the above graph may be attributable to age — not country at all. Young people say they are happier, countries with a lot of young people will therefore have higher reported happiness, which tell us… well, nothing except those countries are young demographically.

Is it the whole story? Well, probably not. But I have no idea why you wouldn’t control for median age in a graph like the one above.

I’ll also add that I think the sociolinguistics of “happy” are pretty difficult. Young people value happiness (and respond to polls accordingly). Older people, especially those with children, often see happiness as too thin a word for what governs their life — life is partially about sacrifice, a parent hitting a 5-point bubble on a Likert scale may see that as an indication of selfishness (rightly or wrongly). So even with the age difference, I’m not sure what happiness research is really getting at.


Students really don’t get randomness. This is the classic Trick Coin Flip question — I have a trick coin that either comes up heads a bit more than tails, or tails a bit more than heads [They sell trick coins both ways, apparently]. I don’t know whether this particular trick coin tends towards heads or tails, and I don’t know by how much.

We call the tendency of the trick coin to “tilt” results in one direction it’s bias. I have the trick coin in my hand. Which of the following would give me the best idea of the coin’s bias?

  • 10 flips?
  • 100 flips?
  • 1000 flips?
  • or, it doesn’t matter, all of these give you the same idea of the coin’s bias.

The results from class you can see above. More later on this.

I want to do this in a class….

What a neat way of combining two textbooks to get a novel course design (which meshes with current theories of interleaving):

In an effort to maximize spacing and encoding variability, Robert Bjork once taught an honors introductory psychology course twice in one term. Up to the point of the midterm, the basic concepts of introductory psychology were covered using a textbook that adopted a history of psychology approach and emphasized the contributions of key individuals in the history of psychology, such as Pavlov, Freud, and Skinner. After the midterm exam, the basic concepts were covered again, this time using a textbook that adopted a brain mechanisms approach. The goal was to have key concepts come up in each half of the course (spacing) and from a different standpoint (variation).

From here.

Divided Attention During Lecture

I’ve been having some fun reading Bjork and his followers on elements of instruction. It’s good stuff! This comes from Successful Lecturing: Presenting Information in Ways That Engage Effective Processing by  Patricia Ann de Winstanley & Robert A. Bjork:

In addition to its having a strong negative impact on encoding, divided attention has been shown to have much larger effects on direct, or explicit, tests of memory than on indirect, or implicit, tests of memory (MacDonald and MacLeod, 1998; Szymanski and MacLeod, 1996). The implication is that divided attention during a lecture may leave students with a subsequent sense of familiarity, or feeling of knowing, or perceptual facilitation for the presented material but without the concomitant ability to recall or recognize the material on a direct test of memory, such as an examination. As a consequence, students may misjudge the amount of time needed for further study.

Dividing students’ attention during a lecture therefore poses a double threat. First, information is learned less well when attention is divided. Second, one’s feeling of knowing or processing facility remains unaffected by divided attention, which may result in the assumption that information is learned well enough and no further study time is needed (see Bjork, 1999, and Jacoby, Bjork, and Kelley, 1994, for reviews of the literature on illusions of comprehension and remembering).

Concept Inventories and Dan Meyer’s Linear Modeling Exercise

I’ve talked a bit in the past about good concept inventory questions — questions that address difficult conceptual questions but have black and white answers and don’t require any special vocabulary to answer.

Dan Meyer’s Linear Modeling exercise [PDF] is a good example. The first question has a specific answer, and answering it requires the right set of intuitions about linear processes, but it doesn’t matter what terms you are using, and the student does not need to intuit what you are trying to assess to get it right. 

I’ll add the exercise has one other mark of a great inventory question — apart from the title, it contains no hints that this is an application of linear modeling. This jives with what we know from processes like interleaving — that the decision of which model to apply is as important as the model itself. 

One final thing — I can’t help to notice that like many ConceptTests and like many questions on the FCI it is a prediction question. There’s something very powerful about prediction in the way it focuses the mind. More on that later.