Technocrats, Rent-seekers, and Tribal Drummers

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Today at Cato, John Samples hosted a discussion of my e-book on the Three-Axes model. One of the interesting questions came from Matt Yglesias, who asked about the role of columnists who are less tribal, or even anti-tribal, in their orientation. My thinking is that their views receive less amplification (as measured, say, by blog citations) than the tribalists. However, as Matt points out, the tribal drummers may have less influence on policy setting, where technocrats and centrists hold more sway. This leads me to posit the following matrix, which captures my views of the relative significance of different types of players in the mediasphere, partisan election results, and policy setting.

Actors Mediasphere Partisan Elections Policy Setting
Tribal Drummers high medium* low
Technocrats low low medium
Rent-seekers medium low high

The Tribal Drummers are folks who can clearly be identified using the three-axis model as progressives, conservatives, or libertarians. I call them tribal drummers because they whip up enthusiasm among those who agree. If you want to have a lot of significance in the mediasphere, it is best to be a tribal drummer. Also, you may have some influence on partisan elections. By partisan elections, I mean the contests between Democrats and Republicans. It is probably easier to argue that the tribal drummers have influence on primaries than on partisan elections.

*Libertarian tribal drummers have a lower influence on partisan elections than progressive or conservative tribal drummers, because libertarians do not have a party.

Technocrats are pundits and policy wonks who tend to be centrist in orientation. I claim that they are not amplified much in the mediasphere. They do get involved in the policy game. I think of Ezra Klein as someone who wants to be both a technocrat and a tribal drummer, and in my opinion he would do better to close off the latter option.

Finally, rent-seekers are folks who know what they want from policy and focus on getting it. Thus, their influence on policy is high. Because they buy influence on both sides, their significance in partisan elections is low. The one exception that comes to mind would be teachers’ unions, who are both rent-seeking and strongly partisan. I also claim that rent-seekers have a lot of influence in the mediasphere, because I think that they are very good at shaping the battle space. What I have in mind is the housing lobby, which is amazing at shaping how housing issues are presented in the media.

This matrix might still leave out the sorts of columnists that Matt Yglesias mentioned, e.g., Thomas Friedman. Friedman is not a technocrat, tribal drummer, or policy wonk. In fact, the category I would put him in is suck-up (and, no, I am not being charitable). I think there is a niche for journalists who write to make important people feel even more important. These journalists go to places like Davos and admire the leaders with whom they rub elbows. The CEOs and politicians write warm blurbs for their books, and so they sell well, even while the tribal drummers and others in the mediasphere dismiss them as insipid.

The Illusion of Explanatory Depth

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Philip M. Fernbach, Todd Rogers, Craig R. Fox and Steven A. Sloman write,

We hypothesized that people typically know less about such policies than they think they do (the illusion of explanatory depth; Rozenblit & Keil, 2002) and that polarized attitudes are enabled by simplistic causal models. We find that asking people to explain policies in detail both undermines the illusion of explanatory depth and leads to more moderate attitudes (Experiments 1 and 2). We also demonstrate that although these effects occur when people are asked to generate a mechanistic explanation, they do not occur when people are instead asked to enumerate reasons for their policy preferences (Experiment 2). Finally, we show that generating mechanistic explanations reduces donations to relevant political advocacy groups (Experiment 3). The evidence suggests that people’s mistaken sense that they understand the causal processes underlying policies contributes to polarization.

This paper was cited in a recent WSJ article by Daniel Akst, forwarded to me by a reader.

Capital Structure Arbitrage

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James Hamilton looks at recent stock market behavior.

Fernando Duarte and Carlo Rosa of the Federal Reserve Bank of New York surveyed 29 different forecasters and models for their calculation of the expected return on stocks relative to that on bonds. Obviously you want to take anybody’s claim that they know where the stock market is headed with a rather large grain of salt. But it’s interesting that the consensus assessment of this group is that stocks will outperform bonds by as high or higher margin as ever would have been expected over the last half century.

Investment-grade corporate bonds yield about 3 to 3-1/4 percent these days in nominal terms. In real terms, that is something closer to 1-1/2 percent or less. Meanwhile, according to Hamilton, Shiller’s backward-looking P/E ratio on stocks is 23.4. Taking 1 over that, you get something like 4 percent as the real yield on stocks.

It would seem as though any corporation (such as Apple, which has been doing this) that can issue bonds at a real yield of 1-1/2 percent and buy back stock. Rinse and repeat until your bond investors get scared and drive up the yield. This lowers your cost of capital–it is nearly an arbitrage. So much for Modigliani-Miller.

There is something going on in financial markets that I do not understand. Is the Fed’s quantitative easing powerful enough to drive the real ten-year rate below zero? Could be, but I doubt it.

Pundits talk about a huge demand for safe assets. But for me, that does not explain ten-year bond yields. Ten-year bonds do not look like safe assets to me. They look dangerous as hell.

If I ran a hedge fund, my main bet would be deep out-of-the-money puts on bonds. I would keep enough cash to keep rolling over this bet for five years or until it pays off, whichever comes first.

Evaluating the Null Hypothesis in Education

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Andrew Coulson links to a study in the UK. The study reports that

From the analysis of the available data, there is no clear correlation between funding and school average performance; for a given level of funding, there is significant variation in performance the calculated correlation coefficient between the two variables was less than 0.1. In our view, this suggests that the level of funding, per se, is almost irrelevant as a predictor of performance.

Having said that, the study does find a correlation between a measure of school quality and average student performance. That result would contradict the null hypothesis. However, the indicator that the researchers use for school quality is not convincingly exogenous.

the Ofsted ‘overall effectiveness’ indicator has been used as our measure of school quality. This is a combined assessment of schools’ performance that contains specific judgements on:
— pupils’ achievement and the extent to which they enjoy their learning;
— how well do learners achieve;
— pupils’ behaviour;
— the quality of teaching; and
— the effectiveness of leadership and management in embedding ambition and driving improvement.
The ‘overall effectiveness’ judgement is then made on a four-point scale: outstanding (1); good (2); satisfactory (3); and inadequate (4)

Coulson also supplies a chart demonstrating the null hypothesis in American education spending.

The Null Hypothesis in Health Insurance

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is that, in the United States, better health insurance produces no difference in health outcomes. Recently, for example, Katharine Baicker, et al, found

This randomized, controlled study [in Oregon] showed that Medicaid coverage generated no significant improvements in measured physical health outcomes in the first 2 years, but it did increase use of health care services, raise rates of diabetes detection and management, lower rates of depression, and reduce financial strain.

Pointer from, well, everyone. All I can say is that this is really separating what David Brooks calls the “detached” from the “engaged.” The latter are making an all-out effort at what I call trying to close minds on your own side.

Somewhat detached commentary includes

Tyler Cowen, Ray Fisman, and Reihan Salam.

Robin Hanson has an even stronger version of the null hypothesis. His version says that differences in health care spending produce no difference in health care outcomes. He and I disagree about how to characterize this result. Let me try to explain how we differ. Let us stipulate that:

1. Some medical procedures improve health, but not in a way that shows up in statistics. For example, if you get your broken arm fixed, you are much better off than not getting it fixed, but this will probably not show up in measured statistics of health outcomes, including longevity.

2. Some medical procedures are a waste (futile care, unwanted care, treatments of non-existent ailments, treatments that do not work, and so on).

3. Some medical procedures have an adverse effect on health.

4. Some medical procedures improve health outcomes, but only with a low probability (e.g., precautionary screening).

5. Some medical procedures definitely improve health outcomes in a measurable way.

Note also, that most studies of medical spending are not controlled experiments. In observational studies, including cross-country comparisons, the results tend to be dominated by a 6th factor, namely that health outcomes are determined much more by individual genes and behavior than by medical intervention.

Robin and I agree that (5) is true. The question becomes, how does (5) wash out in the statistics on differences in spending? His view is that there has to be enough (3) to offset the (5). My view is that it is mostly that (1), (2), and (4) serve to dilute (5). If I am correct, then researchers should find some quantitative differences in health outcomes, but these differences will not be statistically significant. Out of (bad) habit, they will report this as “no difference in outcomes.” This makes it sound as if they have proven the null hypothesis, when they have merely failed to reject it.

Of course, in a large study (as this was), there may not be much difference between failing to reject the null and proving it. The confidence interval around zero could be small (if someone has access to the paper, you can let me know).

The Home Borrowership Crisis

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Christopher L. Foote, Kristopher S. Gerardi, and Paul S. Willen of the Boston Fed write,

the facts refute the popular story that the crisis resulted from financial industry insiders deceiving uninformed mortgage borrowers and investors. Instead, they argue that borrowers and investors made decisions that were rational and logical given their ex post overly optimistic beliefs about house prices

This paper from last year was cited the other day by Scott Sumner.

One quibble I have is that the paper makes it sound as if the only variable that shifted during the run-up to the crisis was house price expectations. In fact, the proportion of loans with down payments less than 10 percent shot up (even the authors have a figure showing that the market share of loans with down payments under 5 percent nearly doubled, to almost 30 percent of loans, in just four years–from 2002 to 2006), the proportion of loans backed by non-owner-occupied properties (i.e., speculative investments) went from roughly 5 percent to roughly 15 percent, and the proportion of loans that went to borrowers with lower credit scores also rose.

Of course, the expectations of rising home prices helped fuel the decline in lending standards, because you cannot be punished for making a bad loan in a rising market. And the deterioration in lending standards helped fuel rising home prices, because it broadened the market to buy homes. Hence the bubble.

Facts About Austerity in the U.S.

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From CBO head Doug Elmendorf. I focused on the 7th slide, comparing 2012 with 40-year averages. The figures are as a percent of GDP.

Category 40-year average 2012
Net Interest 2.2 1.4
All other spending 7.9 9.1
Defense 4.7 4.3
Social Security and Medicare 6.2 7.9
Revenue 17.9 15.8

Pointer from James Hamilton. I hope Mark Thoma will link to me here, because he is forever linking to posts that spin the fiscal data in a way that is very different from how I see it. What stands out to me is this:

Where is the austerity in the budget, i.e., the biggest shortfall in spending from the 40-year average? It is in “net interest.” It is definitely not in domestic discretionary spending (the “all other spending” category).

The “austerity” comes from low interest rates, which cause interest payments to be low. Think about that.

I note that yesterday’s employment report showed that the first four months of 2013, under “austerity,” were much better than 2009, under “stimulus.” I know that other things were not equal. They never are. Any macroeconomist can argue that he is always right, because the interpretation of data has so many degrees of freedom.

What I’m Reading

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1. The New Digital Age, by Eric Schmidt and Jared Cohen. Before I read it, I had modest expectations. Afterward, I regretted buying it.

2. The End of Power, by Moses Naim. So far, this is a candidate for my favorite non-fiction book of the year. Maybe you have to discount some of my enthusiasm as confirmation bias, but I cannot imagine readers of this blog experiencing disappointment with the book. Expect a longer review from me at some point.