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Category Archives: Random Abstruseness

Dynastic Politics and the 2014 Elections

Dynastic politics was on full display in Great Britain this week with the birth of Prince George Louis Alexander Louis of Cambridge. Although the coverage of the royal birth has begun to calm down, lovers of hereditary politics should not fret! After all, the slate of candidates running in the 2014 Senate elections (and a few House and Governor races too) in the United States is a who’s who of candidates who had politically active progenitors.

Seven of the eleven most competitive races in 2014 for a seat in the “world’s greatest deliberative body” (based on the rankings of Chris Cilizza’s The Fix) feature candidates descended from political lineage. The four most vulnerable Democratic incumbents, Mark Pryor of Arkansas,  Mary Landrieu of Louisiana, Mark Begich of Alaska, and Kay Hagan of North Carolina, all had fathers (or in Hagan’s case, an uncle) who served in public office.

Senator Pryor’s father, David Pryor, once served as a United States Senator from Arkansas, as well as holding a number of other political roles. Senator Landrieu father, Moon Landrieu, once served as mayor of New Orleans (a post now held by Senator Landrieu’s brother Mitch) and was a member of President Jimmy Carter’s cabinet. Before tragically perishing in a plane crash, Nick Begich—father to Senator Mark Begich—was a member of the House of Representatives. Finally, Senator Hagan is the niece of the late Lawton Chiles, who once served as both Governor and U.S. Senator from the State of Florida.

Several candidates running in open seat races or as challengers also have a strong political lineage. This past week, Michelle Nunn—daughter of former Senator Sam Nunn (D-GA)—announced that she would run for her father’s old seat in the U.S. Senate. Kentucky Secretary of State Alison Lundergan Grimes, who is challenging Senator Mitch McConnell (R-KY), is the daughter of former Kentucky Democratic Party Chair and State Senator Jerry Lundergan. Finally, on the Republican side, West Virginia Representative Shelley Moore Capito (R-WV) is the daughter of former Governor of West Virginia Arch Moore.

Democrats also tried, but ultimately did not succeed in their attempts to recruit former Representative Stephanie Herseth-Sandlin (D-SD) or U.S. Attorney Brendan Johnson to run in the South Dakota Senate race. Herseth’s grandfather, Ralph Herseth, served as South Dakota governor and her grandmother, Lorna Herseth, served as Secretary of State of South Dakota. (Herseth’s husband, Max Sandlin, also once served in Congress.) Brendan Johnson is the son of current South Dakota U.S. Senator Tim Johnson.

It is also worth mentioning that two other U.S. Senators who are up for reelection in states that are currently somewhat less competitive— Senators Mark Udall (D-CO) and Tom Udall (D-NM)— also hail from a famous political family. And in Wyoming, Liz Cheney, daughter of former Vice-President Dick Cheney, is running for the seat currently held by Mike Enzi (R-WY).

In keeping with the this theme, several races for other offices also deserve a mention. In Florida’s Second Congressional District, Democrat Gwen Graham—daughter of former Senator Bob Graham (D-FL)—hopes to unseat two-term Republican Steve Southerland. Nevada’s 3rd Congressional District also features the daughter of a former member of Congress running to unseat a Republican incumbent. In this district, Erin Bilbray-Kohn, the daughter of former Congressman James Bilbray (D-NV), is running against Representative Joe Heck (R-NV). Last, but certainly not least, William Daley, a member of the Chicago Daley family is running in a primary against Democratic Illinois Governor Pat Quinn.

Although victory in the Revolutionary War means that Americans will never have Prince George of Cambridge as their King, dynastic politics is still very alive on this side of the pond. While 2014 features an especially large number of candidates with political antecedents, political dynasties have been a part of American politics since the Adamses (and perhaps before). This custom continued with the Roosevelts, the Kennedys, the Bushes, and countless other examples over the next several centuries. So despite initially seeming strange for a country with the popular traditions of the United States to have so many inheritors of political legacies running for office in 2014, these midterm elections are yet another chapter in the American tradition of dynastic politics!

What’s the (Expected) Value of Your Life?

Last week, a friend asked for my opinion on an economics problem where students were asked to estimate the statistical value of a human life.

This will make more sense later…

The procedure blew my mind, and not in a good way. Not because I’m not a fan of quantifying the value of life — it’s weird, but I’d rather governments use good estimates over bad ones — but because the statistics are being used so poorly.

And it wasn’t my friend’s fault. He was following the example of several scholars who used the same framework to answer this question. And he got the problem set question correct, despite it being horrifyingly misguided.

Regression to the Rescue!

Here’s the published article by James Hammitt with the economic theory, here’s an overview of the field by Viscusi & Aldy (2003), and here are some applications. Basically, we’re trying to pin down how much you would spend to reduce your risk of death by some fixed amount. So, for example, how much is it worth to you if I could reduce your chance of death this year by, say, 0.1 percent?

Unfortunately it’s expensive and theoretically questionable to ask people that question. Even if we could afford the survey, do we think people could respond to the hypothetical accurately? I couldn’t.

So we flip the task toward figuring out how much we demand to be paid to undertake dangerous jobs. And since we’re in econometrics land, let’s start with linear regression. With some controls (age, blue collar, race, et cetera), let’s find the relationship between the dangerousness of a job (risk of death) and compensation (weekly wages). Economists call this a hedonic wage model.

And then? Let’s predict wages where risk of death is certain, i.e., p = 1.0. Or, as Moore & Viscusi (1986) put it, “We extrapolate the willingness to pay of the individual worker for a small risk reduction linearly to calculate the collective willingness to pay for a statistical life.”

Voilà! Now we know how much people value their lives.

See the problems yet?

When my friend first showed me this approach, I laughed a bit. His response went something like this:

Yea, I know, it’s not great. We only have a sample of 300. And we don’t have all of the variables we would want, so there may be some omitted variable bias, right? So it’s not perfect, but…

He’s right. Fitting a linear model without including relevant variables results in biased estimates. Proving this is simple but isn’t really where my objections lie.

My problem comes from the ridiculous extrapolation involved here. Let’s assume that we have the relevant variables in hand. Even under this unlikely condition, extrapolating so far beyond the data can make us super confident in an utterly stupid model and its predictions.

A simulated example

To show what I mean, we will use some simulated data. By using data that we create ourselves, we can observe the “truth” in a purely objective way, and thus test our intuition about lots of stuff. For the following demonstration, I’ll give the basic overview and give technical details after the post.

Let’s assume that we have some variable x that represents “risk of death” in various occupations. Now let’s assume that nature defines some true function linking “risk of death” to wages, and the function is arbitrarily complicated:

y_i = \alpha + \frac{(100x_i + 3^{x_i})}{1-x_i} + \epsilon_i

There’s nothing special about the function, other than that I (acting as ‘nature’) defined it myself. It also has a nice asymptote at x= 1, meaning that the limit from the left \displaystyle \lim_{x \to 1 -} f(x) = \infty . This comes from the denominator (since dividing by 0 is undefined), and could match some intuition about nobody accepting any reasonable wage for a risk x \approx 1. The error term \epsilon_i we will assume to be normally distributed with expectation of zero.

With this function in hand, we can randomly generate some values for x that could reasonably be risks of death; in this case, for ease, x \sim Unif(0,1), meaning uniformly distributed on the interval 0 (risk=0%) to 1 (risk=100%).

If we fit the linear model to the full data (because this data is costless, let’s say n=10,000), we’d see pretty quickly that regressing y on x is a terrible idea. The estimated coefficient on risk of death is significant (with 10,000 observations it’s hard not to get ‘significant’ coefficients; see here and here); but the plot of predicted values \hat{y}  against y make it pretty obvious that the model misses the mark:

Here’s the problem: we don’t have the full data. Nobody accepts jobs where the risk of death x \approx 1 , or really anywhere near it. The highest occupational risks seem to have ~120 deaths per 100,000 workers. The data my friend was given for his homework, similarly, had the following risk density (and for amusement, see here for perspective):

Density of “risk of death” in the real data given with a problem set.

So, for verisimilitude, let’s refit the model using only a small subset of the data from the low-end values of x, say those for which 0<x<0.01. Now things get frighteningly fun…

Again, the coefficient estimate is significant, but this time there’s nothing in the residuals that give us pause:

These residuals look pretty normal, don’t they?

In fact, there’s nothing to point out exactly how wrong our model is! For example, check out this image which shows the “true” line and the linear fit line. You can’t distinguish one from the other. In this restricted sample, the root mean squared differences between the linear fit and the true model is only 0.002, compared to more than 13,000 between the true versus linear fit values in the full data.

How bad could this possibly be? Well, let’s plot the true function in black and the linear fit in grey:

Well, as it turns out, we can be really, really far off.

Oh, right. We could be really, really far off. Oops.

That was a fun exercise, but so what?

Why does any of this matter? First, consider this fact: I provided us with one possible true function, just for comparison with our linear model. But the actual relationship between risk and wages, to the extent that we can say it exists at all, could take any form.

Second, and this is the scarier part, it’s entirely possible to fit a linear model that looks perfectly acceptable. Within the small subset of data, the linear model is a decent approximation of the truth, at least insofar as we might be interested in predicting wages from risk. 

In extrapolating so far beyond the data, however, we assume that this decent approximation works for all values of x, even with no evidence to support the assumption. There’s no data out at the extremes to tell us how good or bad the assumption is, and thus not only is our model likely wrong, but there is effectively no bound on how wrong we might be.

This isn’t too different from me telling you that the relationship between, say, age and income, can be approximated by a linear model. We’ll include a squared term since income at upper ages tends to fall below our full earning potential. And then - because the model fits so well for data we have! - I’ll extrapolate to give you the expected income for somebody who is 200 years old (it’s less than negative $500,000… someone should stop researchers from improving life expectancy, stat!).

E(income | age=200) ~ <-$500,000… or something. Scale on the y-axis is categorical from 0 ($0) to 16 ($500,000+). The rug plot at the bottom shows the range of actual data in the CCES. Everything else is just a really bad guess.

Third, no standard ‘fix’ in the econometric toolkit is going to help us. Unlike our toy data, we aren’t ignoring data that might exist out there somewhere. We’re extrapolating beyond data that will likely ever exist. On our subsample, even transforming x with the true function - the function that we’d never know was correct a priori - gives us predictions indistinguishable from the linear fit.

In short, we will never know how wrong we are, in which direction, or how to improve the model.

So what do you propose?

When I pointed out the extrapolation issue to my friend, he responded with some frustration: “We need an estimate, and this is as good a method as any. What do you want governments to do? Guess?”

Well, yea, kinda. I would rather a government or agency guess, and be transparent about it, than use the prediction from a hedonic wage model. The linear extrapolation is just a poorly founded guess anyway, since the method was not designed nor is it suited to the question at hand. Masking it as some scientific method for defining a quantity of interest gives it an air of authority that it doesn’t deserve.

To put it another way, I can buy a car to get us from New York to London. But hey, at least I bought a vehicle of some variety, right? Except that we’ll both drown, and you’ll wish you hadn’t trusted my whole “look, it’s a fancy machine” argument.

Now, I don’t have a better alternative at the moment. Asking people seems silly, and besides, that could also result in dumb linear modeling. Guessing isn’t satisfactory. So what to do?

Well first, please stop defining everything as a regression problem. As a wise man should never have had to write (but did): “Linear regression is not the philosopher’s stone.” Let’s stop treating it like one.

And second… wait, I’ve written 1,500 words and - if you’re still with me - I owe it to the reader to stop.  Also, I don’t have an answer right now. I leave that open to intrepid readers. E-mail or comment, and maybe there’s a follow-up post in the near future.