A Case for 1) Not Worrying About Inequality, but 2) for Offering Minimal Income Support

As (mostly) business students (and as a business professor), we tend to support equality of opportunity but tend not to worry about equality of outcomes.

Equality of opportunity means that we want to gear society so that anyone can succeed. But actually succeeding is up to the individual.

Equality of outcomes means that we look at how peoples’ lives actually turn out, and then make adjustment afterwards.

The orientation of “business people” tends to be that if you have equality of opportunity that you shouldn’t correct for inequality of outcomes. Unequal outcomes are viewed as a result of not exploiting the opportunities that are available to everyone.

Except … what if … outcomes depend on your last name?

Gregory Clark has a new book out, entitled The Son Also Rises. Clark’s last book was … hmmm … controversial.

The new book argues that income mobility hasn’t improved … ever. What Clark did was construct a data set of last names for a large group of countries. He then showed that the names are highly correlated with your position in the income distribution … both now … and centuries ago. More specifically, the rich are likelier to have last names today — such as Percy, Talbot, Montgomery, Neville, or Darcy — and were likelier to have those last names several hundred years ago.

There are two implications of this sort of result. One is that inequality is exceptionally persistent. If that’s the case, social policy aimed at reducing inequality, say, within an electoral cycle or a generation, are probably a waste of money. The second implication is that if inequality is something that we just have to live with, then there’s a role for aid over the course of a lifetime that is based not on your current status … but on your birth status.

This dovetails in an interesting way with a recent Swiss policy proposal. It has been derided in many parts, but leftists in Switzerland have a referendum on the ballot to guarantee a minimum income for everyone in the country (of about $35K). Perhaps this is something to take more seriously.

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FWIW: Clark published a paper on Swedish surnames. I’m 1/4 Swedish (I’m a Benson), and 1/4 Norwegian (I’m a Tufte). The paper contains this tidbit:

Nina Benner, a reporter for Sverige Radio, has a nice story from her own family of  how  such  surname  changes  took  place.    Her  grandfather  and his  four brothers changed  their  surname  from  Andersson  to  Benner  in  1916,  when  her  grandfather was 16. His oldest brother was studying to become a doctor, and his professor made it clear that Andersson wasn´t a suitable name in that profession. The name Benner stems from the small village of Bennebo, where her great-grandfather grew up.

This caught my eye, since Tufte is not a patronymic. Instead, it’s the name of a place. Perhaps my Norwegian ancestors were peasants, who tried to make it big at one point by changing their last name.

Bank Run?

This bears watching over the next several days. HSBC, one of the largest banks in the world, is in a bind.

Like most banks, some of its customers use it to launder money. U.S. regulators found that some HSBC employees were making it easier to launder money.

In response to ongoing pressure from regulators to better monitor movements of cash, HSBC has been blocking some large cash withdrawals.

This is probably a good idea. But to the extent that it freaks out customers, HSBC could have a big problem. The press coverage probably isn’t helping.

A run on HSBC would be a very big deal. And, since most of HSBC is non-U.S., it would be regulators in other countries that would have to manage this, not FDIC.

High R-Squared

An ongoing theme this semester has been that we need to respect the possibility that there’s good reason to think that some aspects of macroeconomics should not be predictable.

Econbrowser (the blog of Nobel Prize medium lister James Hamilton and Menzie Chen) has an excellent post by Hamilton on this, entitled “On R-squared and Economic Prediction”. More specifically, they discuss how you can get both high R-squared and low R-squared, not just from the same data, but from the same regression.

This is an idea that we’ll be brushing the surface of in February and March.

N.B. Dave Berri and I have a discussion that devolves to this about once a semester. Dave does mostly cross-sectional econometrics, where high R-squared is more or less the acid test of decent results. I do mostly time series (which is pretty common amongst macroeconomics and finance professors). And in time series, you have this issue that R-squared doesn’t mean what you think it does. So if Dave tells you something different, it doesn’t mean that either one of us is wrong.

Anyway, back to Hamilton’s piece. He’s showing examples of something called the Engle-Granger Representation Theorem (both Engle and Granger won Nobel Prizes). This is just a little bit of algebra applied to econometrics (where it makes a huge difference).

In algebra, if you have the following equation:

X = a + bY

you wouldn’t think anything of rearranging it to get:

X – Y = a + (b-1)Y

Seems OK, right?

But, what if you’re doing a regression, and X and Y are the same variable measured at two different points in time? That sounds weird, but it would just mean that you’ve got a spreadsheet with X in one column, and Y in another column, and every cell for X is repeated one row down for Y.

Why might you have that? Well, you might be regressing stock price today on stock price yesterday.

In this case, the first equation above becomes:

P(t) = a + bP(t-1)

Note that the t in parentheses is just keeping track of the passage of time, or the row in your spreadsheet.

You’d probably suspect that this would fit really well, and you’d be right: the R-squared is really high, indicating that (perhaps) the best predictor for today’s price is yesterday’s price. Big deal, right?

But, the Engle-Granger Representation Theorem says that you can rearrange the third equation the same way you did the first one, to get:

P(t)-P(t-1) = a + (b-1)P(t-1)

Now, if you’ve learned a little about how to do regressions, you might say that you can’t have a variable like that on the left hand side. Well, you can, and the way to do that is just to create a third column where you do the subtraction, and then use that in your regression. So … no problem … just an inconvenience.

And now, maybe you’re ready to go back and read Hamilton’s post and get more out of it. Because Hamilton actually runs a few of those regressions and shows that the first one has high R-squared, the second one has low R-squared, and yet the coefficients in them are either the identical or split into two parts.

Read the comments too … some people get Hamilton’s point, and some are in denial.

This insight is huge in finance (where it’s related to why you can make money, but are unlikely to beat the market), and very big in macroeconomics (where it underlies why Obamacare isn’t working out as planned).

It’s also huge in politics, where we have a lot of people who obsess about controlling things. If what they desire to control can’t be controlled … are they just wasting their time (and our money)?

The Topic for Spring 2014: Income Inequality, Income Mobility and the Minimum Wage

N.B. This is the item I mentioned in Friday’s class that I’d written but forgotten to post.

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Part of ECON 3020 “Macroeconomics for Business Decisions” is keeping abreast of current events. Each semester it’s something new.

For Spring 2014, the big current event is somewhat contrived. Politicians and the media around the world are pushing two related topics:

• Income inequality (is the distribution of the flow of income across people at a point in time “fair”
• Income mobility (are the chances of a poor person moving to a better part of the income distribution “fair”)

In the U.S., this is tied up with a third issue:

• Should the minimum wage be raised (since it isn’t raised often, it is usually raised in huge steps … and frankly, it’s not unreasonable to think we’re due)

And there’s a fourth issue that gets confounded with the others:

• Wealth inequality (is the distribution of the stock of wealth across people “fair”)

The confounding of the last issue is a big deal: it’s a common dodge to argue that differences in wealth are a reason to adjust incomes. This isn’t necessarily a dumb thing to do, but like any comparison relating stocks and flows, we’d better be careful.

Note that I’ve also put fair in quotes above. This is because fairness is not something that economists use as a criterion. Fairness is a subjective criterion: you either feel something is fair, or you don’t. This doesn’t make it unimportant, but it does make it hard to pin down. It also makes it very hard to compare across people. Think about it: every argument about fairness in your life has existed because one side thought something was unfair and the other side didn’t. Solutions to these arguments frequently involve making the aggrieved party happier at the expense of the party satisfied with the current situation. This is a problem.

Economists talk much more about efficiency. An allocation of stuff is efficient if we can’t improve upon it, in an objective sense. For example, perfect competition is efficient in the sense that the sum of consumer and producer surplus is maximized. We can set the price at something other than the equilibrium value (as in ceilings or floors), or the quantity at something other than the equilibrium value (as in monopoly). But if we do so, we know that while one of the surpluses might be larger, the sum of the them must be smaller.

Economists also talk about things by adding the word Pareto as a modifier to some adjective: as in Pareto optimal, Pareto efficient, Pareto improving, and so on. (Pareto is always capitalized, because the idea was conceived by the Italian economist Pareto). Pareto argued that because we can’t make interpersonal comparisons of things whose value to us is subjective (and that’s pretty much everything), that fairness could only be conceived of in one way: situation A is a Pareto improvement over situation B because A makes at least one person better off, and makes no one worse off. For example, if you have french fries and can’t finish them, offering them to others at the table is Pareto improving. This is not the same as offering french fries that you do want to your friends out of friendship: that’s called a reciprocal gift exchange because there’s an expectation that you’ll be treated the same way some other time. And it’s definitely not the same as sneaking/taking/”borrowing” a french fry off of someone else’s plate.

FWIW: you should be very suspicious when you see someone make an argument in public based on “fairness” rather than Pareto improvement. For my part, the mere fact that the Pareto concept is not used often in discussions of inequality is a sign there’s something wrong with the argument being put forward.

Having said that, do note that the Pareto condition is very strong, and unlikely to be easily satisfied. So you might view it as a gold standard, rather than something that should be achievable in all situations.

In turn, Pareto efficiency is related to the two fundamental theorems of welfare economics, summarized here:

• First Fundamental Theorem of Welfare Economics: perfectly competitive equilibrium are Pareto efficient
• Second Fundamental Theorem of Welfare Economics: any allocation that is (desirable because it is known to be) Pareto efficient can be achieved by a perfectly competitive market.

Of course, the problem here is competitive market. The conditions for a market to be perfectly competitive are very strict, so essentially no market meets them in practice. But, we also know in reality that most markets are well characterized by the outcome of a competition (in short, if you can understand it with supply and demand, it’s competitive). Nobel Prize winner Joseph Stiglitz has criticized the importance of these theorems, arguing that most markets have imperfect information, and thus aren’t perfectly competitive. Others argue that we just need to be close enough. This is an open case, so there’s no right answer.

Anyway … the Obama administration has always been interested in pushing towards income equality, improved income mobility and wealth equality … as well as a boost in the minimum wage. They’ve been pushing these heavily since last Fall, and they will be a centerpiece of his State of the Union address this week.

And … world financial and social leaders are meeting in Davos at something called the World Economic Forum this weekend. (No one calls it the World Economic Forum; if someone says Davos, you’re supposed to know what that means). A major topic for discussion this year is global income inequality, global income mobility, and global wealth inequality.

The last two may be happening because progressives recognize that this may be their moment, and that it may be slipping away. Political winds shift rapidly, and most political gains are made fairly quickly, and then defended. Consider conservatism in the U.S. It was close to non-existent in the early 1970’s, it rose rapidly, but peaked early in Reagan’s second term. The twenty years that followed that were largely defending the turf that had been won, rather than advancing it further. Progressives aren’t talking this way yet, but if they’re smart they’d recognize that they may be peaking right now, and will be fighting a rear guard action to defend their ideas over the next generation.

Income Mobility

The hot topics this winter are income inequality and income mobility. Obama is expected to stress these in his State of the Union address. And this weekend, world financial leaders are meeting in Davos where those subjects are on the table.

Income mobility refers to the ability of people to move out of the income strata that they are born into.

In researching this, the population is divided into fifths: quintiles. Perfect income mobility would mean that no matter which quintile you were born into, you had a 20% chance of ending up in any of the quintiles.

One problem with discussions about this is the lack of an answer of “compared to what?” No one has a theory of why perfect income mobility would be desirable that’s much deeper than … well … that it sounds nice. Anyway, that’s what we have to go on.

Also, to the extent that wealth can earn income, it isn’t clear that we should ever expect perfect income mobility. To the extent that the children of the wealthy can inherit that wealth, and its income earning potential, we shouldn’t see perfect income mobility. (On the other hand, some people see that as a reason for confiscatory estate taxes, even though those tend to be unpopular across the spectrum of wealth: everyone wants to preserve the chance that if they get rich, they can leave the money to their kids).

Anyway, there’s new research out by some huge names in the field. Their most important finding is that the progressive trope that it’s gotten harder for the poor to become rich over the last generation is not true. The authors even admit that they believed this prior to doing the research.

Instead, their results show that while it is harder for the poor to become rich in the U.S. than in many other developed countries, it hasn’t gotten any harder. This article is summarized in both the January 23 issue of The New York Times entitled “Upward Mobility Has Not Declined”, and in The Wall Street Journal’s “New Data Muddle Debate on Economic Mobility".

The results won't fit neatly into either party's political arguments.

"One way to look at this is 'We fought this whole thing to a bloody draw,' " said David Autor, an economics professor at the Massachusetts Institute of Technology who reviewed the study. "Someone else could say, 'Public policy accomplished nothing.' That leaves lots of room for people to think their favorite hypothesis is correct."

Just like poverty measures, we can also talk about relative mobility (can you be rich if your parents aren’t?), or alternatively we can talk about absolute mobility (can you be richer than your parents?).

The chart shown above is about relative mobility: if you’re born poor, you can get rich, but the odds aren’t great … but at least they haven’t gotten worse. Of course, recall the point I made above: it’s not clear how far off of 20% each of these should be.

But, when we look at absolute mobility, the data suggests that the odds are still very good that you’ll be richer than your parents:

Absolute mobility has continued to improve in recent decades because incomes have risen; median family income is about 12 percent higher today than in 1980, adjusted for inflation. As a result, most adults today have more income at their disposal than their parents did at the same age.

Yet the growth rate of absolute mobility has slowed, as economic growth has slowed to a disappointing level over the last 15 years.

FWIW: Earlier research with the same database showed (with awesome interactive graphics) that Salt Lake City (and Utah generally) is one of the best areas of the country for income mobility. The full paper is here. And it’s well-known that Utah has long had one of the most equal distributions of income in the country.

A Forecasting Example

We’ll be covering this topic in February.

This chart forecasts of traffic (you’ll learn about trends, and forecasting, and about applying those to GDP … and this will help with the first two). Each year, a new forecast of traffic is made, dependent on the current amount of traffic.

You should try to identify 1) whether these forecasts are based on the assumption that traffic has a deterministic or stochastic trend, and then 2) consider what that assumes about the ability and willingness of drivers to change their behavior.

The source article is here. It is not required reading.

Economic Possibilities for Our Children

“Economic Possibilities for Our Children” is the title of a lecture given by Larry Summers that appeared in NBER Reporter. It is a play on words based on Keynes’ 1930 essay “Economic Possibilities for Our Grandchildren”. (BTW: If you’ve been persuaded the Keynes is some sort of swear word, you’d do well to read this essay too).

First off, who’s Larry Summers? He is a very well-known macroeconomist, professor at Harvard, and former Chief Economist at the World Bank. He’s also the most prominent economist connected to the Democratic Party. He served in the Clinton administration, and ended up as Secretary of the Treasury. He was also involved early in the Obama administration, where he served as Director of the White House’s National Economic Council.

But, Summers is also the macroeconomist who is not popular with the progressive side of the Democratic party. First, Summers was President of Harvard, and was forced out for voicing an opinion that is politically incorrect but not terribly controversial outside of academia. Second, Summers offended many White House staffers by pointing out (repeatedly) that, for want of a better term, they needed more adult supervision. Third, Summers was Obama’s first choice to head the Federal Reserve, but his name was removed because progressives wouldn’t support him.

So, what’s in Summers’ essay? Broadly, he’s worried that technological improvement is reducing opportunities for those with less skills. Historically, this is a new thing. Technology has generally augmented the productivity of workers. But we’re starting to see signs (disturbing if they hold) that technology is starting to substitute for labor.

This relates to my first post of the semester, where I argued that as a country we’re in the unusual position of getting more output with less effort. Everywhere in our lives that’s considered a good thing, but when the macroeconomy does that … we’re no longer quite sure.

Part of this relates to an unusual change in who works. It used to be that the richer you were, the less likely you were to work: your income would be high enough that earning more wasn’t as worthwhile as having more leisure. This has changed over the last few decades: now the people who are the most productive work the most hours. Again … this is good in almost all contexts … but when it happens at the macroeconomic level, we’re not so sure.

This has led to a new and evolving problem: non-employment rather than unemployment. Consider the chart (sorry about the sizing, it’s embedded inside a very large frame):

The unemployment rate hasn’t changed that much over the last 60 years, but non-employment rate has increased fairly steadily.

The “what” of this is an amazing feat: we can support more people who aren’t working. That’s been a goal of human existence forever. But the “how” of this is problematic: how is it that, given that comparative advantage says there’s a role for everyone to specialize in something, we can’t find a worthwhile use of these folks’ time.

Summers’ explanation for this is that technology is allowing us to create capital that doesn’t complement labor, but rather substitutes for it.

Now, what makes economics a solid social science is when you take an assumption like that and figure out what new results emerge:

• Real GDP will rise.
• Wage will fall.
• The share of GDP paid to owners of capital will rise.

Of course, this is the story of the last 30 years in the U.S. and many other countries.

Then, he splits his argument into three parts (and, I think, does a poor job of noting that for readers). There are:

• Sectors where capital doesn’t substitute well for labor, because skills are important. This is the traditional story. People in these sectors do well when the macroeconomy gets richer. Think accounting.
• Sectors where capital can substitute for labor, because skills don’t matter much. This is the new story outlined above. Here, production goes up, but incomes fall, and employment declines. This might work out well, unless our demand is limited for that production. Think agriculture.
• Sectors that accumulate labor. By construction, this has to be ones that 1) don’t require too many skills, and 2) have weaker productivity gains so that demand isn’t readily met. Think healthcare.

Plausibly, the third sector becomes more important. But it also becomes expensive because productivity growth is hard to come by, making it easy to bid up prices.

People in the first sector do OK in this world. People in the second sector do badly, but they’re a vanishing breed. People in the third sector don’t do well either: their wages stagnate because productivity gains are hard to come by, but both the prices of their sector and the first sector may get harder to afford.

I’m not sure what to make of all of this. But I found it to be an interesting and provocative point of view.

Macro vs. Micro

This is from an opening video provided for economics majors at the London School of Economics.

You don’t have to watch the whole thing, but I thought you’d enjoy the joke just after the 20 second mark.

Via Economists Do It with Models.

Inflation and Unemployment

Here’s a cool interactive graph from The New York Times. It plots the inflation and unemployment rates against each other over the last 85 years, and then lets you highlight 7 different episodes (it’s interactive, so you much click through to play wit it).

Be careful in your interpretation: 1) unemployment is definitely worse as you go to the right, but 2) both inflation and deflation are problems, so the best spot is along the horizontal axis rather than at zero.

Why just above zero? Economists have told politicians to prefer a little inflation to a little deflation. The reason is that inflation makes your employees cheaper in real terms, while deflation makes them more expensive. Since a lot of our adjustment in labor is through firing people, it makes sense to bias a little away from that. How far: most people tend to think 1-3% is a tolerable inflation rate. The gray horizontal line in the graph is at 2%.

With unemployment, there’s a limit to how low we can go. Given current demographics (i.e., a larger than typical proportion of young people in the labor force), it seems sensible to think that 5% is about as low as we can go. The New York Times is less optimistic (or they’re trying to set a lower hurdle for Obama to reach): they’ve put the vertical line at 6%.

The chart puts things in perspective: we may complain a lot, but we’re not far off the best we can hope to be, and we’re considerably closer than in much of the last 85 years.

If You’re So Smart Why Aren’t You Rich–DSGE Edition

Noah Smith writing at Noahpinion offers up “The Most Damning Critique of DSGE”.

This is heady, but fascinating stuff for students on the borders of macroeconomics and finance. The comment thread is where the real action is, and it drew some big name people in.

Here’s some background. First there was Keynes. Decades later Keynesians built big macroeconometric models that didn’t work well, but were better than nothing. There’s a number of names for these: FRB-MIT or Klein are common. Klein won a Nobel Prize in 1980, in part, for developing these models.

In the 1970’s, academic macroeconomists started shooting holes in Keynesian theory and in Klein-type macroeconometric models. The big hole was shot by Lucas with what is now known as the Lucas’ critique. It says that to be useful, an econometric model needs to estimate coefficients that can be viewed as constants and used for future policy decisions. For example, a Keynesian might argue that an increase in government spending of a dollar always causes GDP to go up by 2 times as much. Lucas argued that the theory underlying the Keynesian models led to econometrics in which you’d get that estimate of 2, but that using it would cause it to change to, say, 3 … so that your policy never did what you thought it would [DIGRESSION: You may have noticed that politicians have a lot of trouble devising policies that work according to plan. Maybe Lucas was on to something.]

By the late 1980’s there were two strains of thought about how to go forward that we still use.

One was based on Sims vector autoregression (VAR for short, or its sibling the VECM). Sims won a Nobel Prize for this work in 2011. The second was Kydland and Prescott’s dynamic stochastic general equilibrium model (or DSGE). Kydland and Prescott won a Nobel Prize for this work in 2004.

The thing is, none of these three methods work very well. You may have noticed that macroeconomics can be really complex, and this is probably why.

So the view of Noahopinion is that DSGE models have failed a market test. If they were better, they’d have been adopted widely by financial firms trying to gain an edge to earn higher returns. And then he asked professionals and academics to chip in with their thoughts. This is where it gets interesting.

What follows is a lot of (seriously) informed, troll-free, discussion of how seriously we should take macroeconomics. You may not see it all clearly, but for me it touches on about a dozen different parts of the text I wrote for your class. Here’s a primer:

• Is the macroeconomy well-forecastable at all? No one is saying that it can’t be forecast, but everyone says that our ability to forecast it well is lousy. No one thinks that weather forecasts are useless just because they’re not very accurate. So perhaps we need to take the same approach to macroeconomics: the problem isn’t the models and their forecasts but our expectations of what others are able to produce for us.
• Should we expect a macroeconomy to be forecastable? This is related to efficiency in financial markets. If we can figure out what will happen in the future, and then take action to avoid what we don’t like, then it will never happen … and our forecasts are wrong. This is odd, but it’s no different than asking why you didn’t forecast your last traffic accident: sharp people recognize that the accident occurred because it couldn’t be forecast, and the accidents that didn’t occur are the ones we could forecast. Perhaps the problem is our insistence that we should be able to forecast the unforecastable. There’s a fascinating World War II story about how this came up in statistics in the footnote.*
• Can we make passable and somewhat useful forecasts without thinking too hard about the theory, by just being observant instead? We do this all the time: you don’t need to understand meteorology, or even check a weather report, to know when to take a coat with you. By the same token, can financial professionals get a lot of the benefit that’s to be had by incorporating macroeconomics into their financial decisions … by just knowing a little bit about the data, the trends, and which data go together? Formally, these are called unconditional forecasts. Often, they are based on reduced forms (regressions showing the correlations between data that don’t impose any structure on how the series relate to each other), or charts.
• Is it useful to impose Keynesian structure to understand how the data works? Seventy years ago, economists working for the Cowles Commission recognized that the data and relationships we observe are consistent with more than one story of the underlying causality. In football, this insight would be that winning teams run the football; but do they run to win the game, or run because they are winning the game? In econometrics, this is called an identification problem. Large-scale (hundreds of equations) macroeconometric models became available in the 1960’s that solved the identification problem by imposing structure from Keynesian theory. These are better than nothing, but their performance at forecasting wasn’t great and plateaued early on. One of the first shots at Keynesian macroeconomics was made by Monetarists working at the Federal Reserve Bank of St. Louis in the late 1960’s, who showed that you could match the performance of a huge and complex Keynesian model with a small and simple Monetarist model. Later, these ideas merged in the FRB-MIT-Penn model; the FRB is the Monetarist part, while the Keynesian part came out if MIT and the University of Pennsylvania. Part of the gist of the comment thread is that a lot of private firms, and most governments still use either this model, or it’s cousin, Klein’s structural Keynesian model (known as the Wharton model, and still marketed by WEFA, a division if IHS Global Insight).
• Academics started discarding these Keynesian models in the 1970’s, and by the 1980’s had started to develop dynamic stochastic general equilibrium models (DSGE’s). They had recognized that there were theoretical problems with the underlying Keynesian macroeconomics in those big models, and they reworked the theory from the bottom up to be robust to the Lucas’ critique. The football analogy is that in those big models the offensive coach drew up the play on the chalkboard, but it didn’t work out as well in the game. DSGE’s address this by arguing how the defense is going to respond to the play the offensive coach drew up, which changes how that coach would draw the play, which changes how the defense will respond, and so on. Solve that out far enough, and you have a better description of the structure underlying the data you observe. The thing is, it’s a lot of work. Noahopinion is asserting that the work wasn’t worth it because there wasn’t enough improvement in performance for private firms and government agencies to switch over to these models. Later in the semester, when we build a growth model, we’re starting down a path then ends with DSGEs.
• Both of the above approaches are structural, and they produce what Noahpinion calls policy-conditional forecasts (and which I’ll just call conditional for short). They’re called conditional because they depend on the underlying theory being correct. Forecasts are unconditional when they use less (or no) theory to relate the data together. John Cochrane’s comment argues that unconditional forecasts are OK for figuring out how to invest, but that you need a conditional forecast to figure out the variables are going to respond to a change in policy (e.g., introducing Obamacare). The football analogy is that you can probably bet on football without knowing much about the game and do OK, and that someone who digs deeper into the football data might get some edge, but not much, because unconditional forecasts work well. But, you can’t win a football game (as opposed to just betting on it) without knowing something about the structure of how the game works, and making conditional forecasts: for example, the quick kick is still legal but has largely disappeared as a football play because it doesn’t offer an advantage in the contemporary game which is structured to make it’s easier for offenses to earn yardage.
• Sims was involved in the early part of the research program that eventually produced DSGE’s, but split off quite early. His position was that the assumptions necessary to impose structure on the data were never likely to be realistic, no matter what the theory. It’s like an econometric model is a water balloon: if the Lucas’ critique is one end of the balloon, and you squeeze it to hold it still, you create a problem at the other end … and you can’t squeeze all parts of the water balloon at the same time. His approach was to impose a minimal structure on the reduced forms to produce a somewhat improved unconditional forecast. The football analogy is that a minimal structure might be that a team runs the ball early in the game because they think it will help them win, but they run at the end of the game because they are already winning. And, you don’t need to know a lot about the structure of football — what running play to call, what blocking scheme to use, and so on — to use that insight. For about 30 years it’s been known that the resulting VAR’s can match the forecasting ability of either kind of structural model, with a lot less work. When we do time series analysis is class, we are on the path that leads to VAR’s.
• If macroeconomics is both hard to understand, and hard to get something useful out of, why bother with it? Heck, why bother with macroeconomists like Tufte? This comes up in the middle of the comment thread, and leads to this other post on Noahpinion. There’s an aphorism that if your performance is going to be measured, you should give the evaluator a ruler of your choosing for them to use … because otherwise you don’t know what ruler they’ll choose. A constructive view of this problem is found by noting that people are going to discuss policy and make policy decisions whether or not there are macroeconomists around … and those policymakers often have some pretty goofy ideas.

… So if there were no academic and Fed macroeconomists around to advise policymakers, who would policymakers listen to on economic matters?

My guess: Some very dangerous people. 

For all the talk of academic macro being politicized, it's much less politicized than the macroeconomic discussion outside of the research community. My own experience is that most macroeconomists are pretty apolitical, and research supports that...but even if my sample is biased, macro's interventionist and laissez-faire schools are pretty close to each other ideologically, compared to, say A) armchair-theorizing politicians, B) TV commentators, C) the denizens of internet forums. It really is a jungle out there. You have David Stockman. You have Ron Paul and his followers. You have David Graeber and his followers. And worse. You have "Austrians" who think all of economics can be deduced from some vague derp. You have Marxists who think - well, I'm not sure, because they tend to denounce and vilify you if you even ask them what they mean, but it sounds nuts. In short you have a cavalcade of vast unending wackitude, often with a proven track record of wrecking economies and societies.

So it's possible to see macroeconomists as doing plenty of good, simply by sitting there not being absolute wackaloons. A million DSGE models from which it is impossible to select sounds a lot better to me than three or four totally nutcase worldviews, the selection of any one of which is likely to cause human tragedy on a vast scale. (Note: This idea, of macroeconomists as a vaccine against macro-lunacy, was first suggested to me by Justin Wolfers.)

• A parallel point is that perhaps the advances in macro models aren’t used by people in finance because they are far more incompetent than we’re willing to admit, and they can’t conceive that the macro models can improve on what they already “know”:

… Financial companies are run by people who don't have a very good intuition for (macro)economics. …

DSGEs will only really be accepted if they match these managers' intuitions, which will only happen if they are also broken and useless.

• Maybe financial firms don’t use macroeconometric models because macroeconomists aren’t building them to sell. I think this view is a bit childish, but there’s a big name economist in the thread making it. One of the commenters notes that his career is based on selling the output of Klein-style models, and he can’t find anyone coming out of school even trained to use them.
• Perhaps all we want is stories that seem plausible rather than theory and data that takes work. The weather analogy might work well here: why are so many TV weather people either “big personalities” or unusually attractive eye candy? Maybe it’s because we know the weather is somewhat unpredictable, so why not get a plausible story from someone we like to listen to or watch, rather than the deeper analysis you’d find on The Weather Channel. So, in the realm of policy, perhaps Obama is exactly the sort of macroeconomist that many people want.

* In World War II, England hired a statistician to help them figure out how to keep their planes from being shot down. Prior to this, the planes had come back full of holes, they’d added extra armor where the holes were, sent the planes out again … and many of them were still shot down. I’m not making this up: the statistician immediately said that they’d done the armoring backwards. The places where the planes had holes was where a bullet hit could be survived. They weren’t seeing any bullet holes in the other spots because they were leading to immediate crashes. The British military was incredulous, but followed the advice, and shifted armor to the spots without hole … and increased their rate or return on planes. This is a similar argument to why you should look for an edge by applying macroeconomics to finance, despite the fact that using macro is unlikely to lead to an edge.

A Retrospective on the “War On Poverty”

On the 50th anniversary of President Johnson’s bizarre declaration of “unconditional war on poverty”, here’s Robert Rector discussing the results.

Please note that Rector is a fellow at the Heritage Foundation, so he is very definitely giving the conservative point of view.

The federal government currently runs more than 80 means-tested welfare programs that provide cash, food, housing, medical care and targeted social services to poor and low-income Americans. Government spent $916 billion on these programs in 2012 alone, and roughly 100 million Americans received aid from at least one of them, at an average cost of $9,000 per recipient. (That figure doesn't include Social Security or Medicare benefits.) Federal and state welfare spending, adjusted for inflation, is 16 times greater than it was in 1964. If converted to cash, current means-tested spending is five times the amount needed to eliminate all official poverty in the U.S. [emphasis added]

Let’s think about the scale of this:

• 80 different programs, all with offices and bureaucrats
• $900 B spent per year — that’s 1/4 of Federal outlays
• 100 million receiving aid — that’s a 1/3 of the population
• 16 times greater is a 1500% increase — you need to sustain a 5.7% growth rate for all 50 years to get that. In fairness, this should be adjusted for population growth too: so the 5.7% should be reduced by the 1.1% per year population growth rate.
• 5 times the official amount needed — the sad fact is that most of the anti-poverty spending goes to the paychecks of people who administer the programs: if we just gave 1/5 of the spending straight to the poor in cash we could raise all of them above the poverty line at once.

Rector points out one of the ways in which we can still officially have poverty in spite of these efforts:

  … The country has invested $20.7 trillion in 2011 dollars over the past 50 years [in anti-poverty programs]. What does America have to show for its investment? Apparently, almost nothing: The official poverty rate persists with little improvement.

That is in part because the government's poverty figures are misleading. Census defines a family as poor based on income level but doesn't count welfare benefits as a form of income. Thus, government means-tested spending can grow infinitely while the poverty rate remains stagnant. [emphasis added]

This is bad, but not as bad as it sounds. It is useful to measure poverty before welfare. But that doesn’t tell us anything about whether welfare is helping or not. What you really need to do is pair that with a second statistic. It turns out the government does produce that figure, but what’s goofy is that no one talks about it. Here they are charted together:

The blue line is the official poverty rate: the peaks roughly correspond to the period after a business cycle trough when most of the economy is recovering but the poor are still lagging. The red line is the poverty rate adjusted for taxes the poor pay, transfers they receive, and income and expenses not measured in the blue line. And this shows substantial improvement over the last 50 years: over a third of poverty eliminated.

Interestingly, Rector doesn’t mention a huge consideration: should we evaluate poverty by relative or absolute measures? Our government uses the relative measure, so this is all you hear about.

Relative poverty is where your income is judged relative to the national average. For example, if average income is $40K, perhaps we’d define anyone with income half that or less as poor. Fair enough. The problem is that this doesn’t account for economic growth. Suppose that average real income goes up to $60K. Then the threshold for poverty is now $30K. The problem is that someone who earns $30K now is considered poor, while someone who earned that much before was not considered poor. So this definition is really more about inequality than it is about poverty.

Absolute poverty is where we measure income against some standard: will you have enough to eat, enough shelter, enough leisure, and so on. Measured this way, all developed countries continue to have some poverty, but far less than in the past.

For policy purposes, the extent to which voters interpret the lack of improvement in the poverty rate as lack of improvement in absolute poverty is problematic. People are very sensitive to absolute poverty, and don’t mind supporting efforts to alleviate it. With relative poverty … not so much.

What’s Wrong with America’s Right Now? Is It the Economy, or Is It Us?

The situation is much the same as the past 4 years. But it’s a little bizarre. Consider this chart:

This chart shows the important pattern I want to illustrate, but it is somewhat flawed. Since both series grow through time, a better chart would use the natural logs of both series (see the Excel file in the class folder on the G drive). When I do this, it’s far more important that the spaces between tick marks be the same size (.05 or about 5%) on each axis.

The main point in the chart above is that real GDP is up, while employment is not. That point can also be seen in the chart below.

But, by plotting natural logs of the data instead of the raw numbers, the slopes in the chart below can be interpreted as growth rates. And, it we look at the blue line in the two expansionary periods, the slope is about the same (a little flatter on the right, indicating the weakness of the recovery). Employment follows the same pattern: the expansions are about the same, with the current one a tad weaker. The blue line being generally steeper than the red line indicates that real GDP is growing faster than employment; this is another way of saying that productivity is increasing.

But this is just a description of the data: what in (a bit more serious) econometrics are called reduced forms. What we really want to be able to do is tell a story/theory about what causal mechanism is working here.

Analogies help sometimes.

So let’s think about preparing and eating a holiday meal with an extended family. The family gets a little bigger every year. But the quantity and quality of the food almost always grows faster than the family does. This year is no exception. But what’s different is that there are less people working in the kitchen.

This is a huge point that should not be missed: it’s always good to produce more with less effort. When discussing macro, people get weird and forget this. But we would not fail to notice this at a holiday meal.

Anyway, how is that the people in the kitchen are able to produce more food with less people. There’s a couple of possible reasons. One might be that they got up earlier and worked more hours. Another is that they have better equipment: like a new pressure cooker for vegetables this year. Another might be an advance in technology: for example, cooking meat sous-vide requires some extra equipment, but it’s also a relatively new technology for home use.

Now here’s an odd thing to think about: some of the people sitting in the living room are potentially productive — are they there because they’re avoiding the work in the kitchen, or are not needed in the kitchen, or not wanted in the kitchen? And if they’re not wanted in the kitchen … is it possible that this is because someone is a cleanliness ogre, and just doesn’t want some people near the food because they don’t wash their hands enough … or they drink a bit and get sloppy?

Lastly, we’d probably think the family was dysfunctional if the people in the kitchen doing the work got angry with the people who weren’t working … and told them they couldn’t have dessert because they didn’t help cook.

All of these things are causal/structural explanations of what is happening with the holiday meal. But, to an outside observer, what’s easiest to see is the reduced forms: there’s a few more people than last year, there’s a lot more food, and there’s less people making it. Note that those observations are consistent with a variety of causal/structural explanations.

Well, this is what this graph is telling us about the U.S. economy. I haven’t shown population going up, because it’s slower than real GDP, and steady. This is the size of the family. Real GDP is up too: this is the amount of food prepared. But there’s fewer people in the kitchen: this is the red line that hasn’t quite gotten back to its previous peak yet. Capital and technology have both grown over the last 10 years, but do we really think they’ve grown enough to push people out of jobs? Probably not. Instead, it seems like there’s fewer people in the kitchen because they all got up earlier and worked more hours: average hours worked by those with jobs is the highest it’s been in a few generations.As to workers, there’s a lot of talk about people avoiding work, but no one denies that there seems to have been a big jump in the number of people who aren’t needed or wanted too. But we lump all those together and act dysfunctionally when the people with work try to limit the share of GDP going to those who are not working. And in our zeal to tar most people who aren’t working because there are some bad apples in the mix … we’ve completely missed the amazing fact that somehow we’ve got more people in the living room watching football, and more food on the table at the same time. I’m not claiming that there’s wholly positive reasons for that, but I’m astounded at how twisted we are not to even acknowledge that getting more leisure and more goods is having your cake and eating it too. In all seriousnesss, what are we, idiots?

How Are the PIIGS Doing?

The PIIGS are Portugal, Ireland, Italy, Greece, and Spain. Along with Cyprus, they have been the focus of concern for the economic future of Europe for the last 4 years in this class.

And, at least on paper, the PIIGS are doing OK right now.

Sovereign debt is debt issued by the government. The interest rate on sovereign debt is a key indicator because it shows (roughly) the burden on taxpayers for bad government policies in the past.

A further problem, is that most poorly governed countries exhaust the willingness of their own citizens to lend them money: the citizens come to know that their own government is a bad risk before anyone else does. In turn, this means that many countries end up paying high rates on sovereign debt to investors from other countries. This has been the problem in the European Union: German citizens left holding the bag by the Greek government (and other similar stories).

So, this chart looks pretty good.

But … how did this happen? Unfortunately, a lot of it was from LTRO money from the ECB.

The ECB is the European Central Bank (their Fed).

An LTRO is a long-term-refinance-operation: basically, a long-term, lower interest rate loan.

In Europe over the last few years, this has meant the citizens of the countries that are doing well contributing extra funds to the European Central Bank, which in turn lends it to struggling private banks in countries with weaker economies. And what do they do with it? Well, it’s a cheap source of funds, so they buy riskier assets with it … mostly sovereign debt from their own countries.

Think about that: it’s weird.

The financial crisis in Europe was largely about governments being unable to make payments on debts owed to citizens in other countries (and the countries on the losing end being unwilling to defend their own citizens by enforcing those debt contracts the old fashioned way, with guns).

So, their plan to address the problem of, say, German citizens lending to the government of Greece, is … to have German citizens loan to their own government, which loans the money to the ECB, which makes LTROs with it to private banks in Greece, who then loan the money to the government of Greece. In other words, just inserting additional layers of financing to cover their tracks. In short, it’s money laundering done to maintain the plausible deniability of government officials in Germany who’d like to get reelected. On the plus side, the plans were only approved when some enforcement mechanisms (whose strength must be OK for now) on governments like the one in Greece.

What should we make of all of this? It’s a gamble that the problems of the last few years were special, and unlikely to be repeated. If they’re not repeated, this sort of scheme will work. If they are, everyone will just end up looking stupid.

FWIW: AN ANALOGY

This is sort of like a family with one sibling with a drug problem, one not, and everyone co-dependent. The kid on drugs is, the government of Greece. The clean sibling is the citizens or banks of Greece. And the parents are Germany.

The kid on drugs gets into trouble, and first tries to borrow money from the sibling.

When the sibling stops lending, the kid on drugs goes to the parents.

When the parent stops lending, the kid on drugs is in real trouble with their dealers.

But the parents want to hold the moral high ground, but also the contradictory position of helping their kid in trouble.

So the loan money to the clean kid, and don’t ask where it goes. And perhaps have a talk with the clean kid about how they need to support their sibling. You can imagine where the money ends up.

Here’s the thing to recognize. We all think this behavior is stupid and counterproductive. But most of us will end up doing the same thing in the same position. It’s a flawed, but very human, response.

And so is the response of the countries in the European Union that are doing well to the problems of government mismanagement in the PIIGS. Get it?

The thing is, in the family analogy, is that it doesn’t address the underlying drug problem, which, after all, is much tougher to fix.

And the thing is, in the European reality, is that it doesn’t address the underlying problem of bad government, which, after all, is much tougher to fix.

FWIW: Some of my earliest memories of network news (which was the only news to watch in the early 1970’s) was about how screwed up the government of Greece was: communist revolutionaries (who in retrospect look like terrorists or morons), military thugs, coups, and picking fights with bigger neighbors (Turkey).

How Are the PIIGS Doing?

The PIIGS are Portugal, Ireland, Italy, Greece, and Spain. Along with Cyprus, they have been the focus of concern for the economic future of Europe for the last 4 years in this class.

And, at least on paper, the PIIGS are doing OK right now.

Sovereign debt is debt issued by the government. The interest rate on sovereign debt is a key indicator because it shows (roughly) the burden on taxpayers for bad government policies in the past.

A further problem, is that most poorly governed countries exhaust the willingness of their own citizens to lend them money: the citizens come to know that their own government is a bad risk before anyone else does. In turn, this means that many countries end up paying high rates on sovereign debt to investors from other countries. This has been the problem in the European Union: German citizens left holding the bag by the Greek government (and other similar stories).

So, this chart looks pretty good.

But … how did this happen? Unfortunately, a lot of it was from LTRO money from the ECB.

The ECB is the European Central Bank (their Fed).

An LTRO is a long-term-refinance-operation: basically, a long-term, lower interest rate loan.

In Europe over the last few years, this has meant the citizens of the countries that are doing well contributing extra funds to the European Central Bank, which in turn lends it to struggling private banks in countries with weaker economies. And what do they do with it? Well, it’s a cheap source of funds, so they buy riskier assets with it … mostly sovereign debt from their own countries.

Think about that: it’s weird.

The financial crisis in Europe was largely about governments being unable to make payments on debts owed to citizens in other countries (and the countries on the losing end being unwilling to defend their own citizens by enforcing those debt contracts the old fashioned way, with guns).

So, their plan to address the problem of, say, German citizens lending to the government of Greece, is … to have German citizens loan to their own government, which loans the money to the ECB, which makes LTROs with it to private banks in Greece, who then loan the money to the government of Greece. In other words, just inserting additional layers of financing to cover their tracks. In short, it’s money laundering done to maintain the plausible deniability of government officials in Germany who’d like to get reelected. On the plus side, the plans were only approved when some enforcement mechanisms (whose strength must be OK for now) on governments like the one in Greece.

What should we make of all of this? It’s a gamble that the problems of the last few years were special, and unlikely to be repeated. If they’re not repeated, this sort of scheme will work. If they are, everyone will just end up looking stupid.

FWIW: AN ANALOGY

This is sort of like a family with one sibling with a drug problem, one not, and everyone co-dependent. The kid on drugs is, the government of Greece. The clean sibling is the citizens or banks of Greece. And the parents are Germany.

The kid on drugs gets into trouble, and first tries to borrow money from the sibling.

When the sibling stops lending, the kid on drugs goes to the parents.

When the parent stops lending, the kid on drugs is in real trouble with their dealers.

But the parents want to hold the moral high ground, but also the contradictory position of helping their kid in trouble.

So the loan money to the clean kid, and don’t ask where it goes. And perhaps have a talk with the clean kid about how they need to support their sibling. You can imagine where the money ends up.

Here’s the thing to recognize. We all think this behavior is stupid and counterproductive. But most of us will end up doing the same thing in the same position. It’s a flawed, but very human, response.

And so is the response of the countries in the European Union that are doing well to the problems of government mismanagement in the PIIGS. Get it?

John Cochrane* On the Weak Recovery

From an interview in the Federal Reserve Bank of Richmond’s Econ Focus:†

EF: What do you think are the biggest barriers to our own economic recovery?

Cochrane: I think we've left the point that we can blame generic "demand" deficiencies, after all these years of stagnation. The idea that everything is fundamentally fine with the U.S. economy, except that negative 2 percent real interest rates on short-term Treasuries are choking the supply of credit, seems pretty farfetched to me. This is starting to look like "supply": a permanent reduction in output and, more troubling, in our long-run growth rate.

Long-term growth is like a garden. You have to weed a garden; you don't just pile on fertilizer — stimulus — when it's full of weeds. So let's count up the weeds. A vast federal bureaucracy is going to be running health care and has cartelized the market. Dodd-Frank is another vast federal bureaucracy, directing the financial brains in the country to compliance or lobbying. The alphabet soup of regulatory agencies is out there gumming up the works. Then there are social programs. The marginal tax rates that low-income people face, along with other disincentives to move or work, mean that many of them are never going to work again. When the economy was steaming ahead, this didn’t really cause much trouble, but now many recovery mechanisms have been turned off. If a Martian economist parachuted down, would he not be struck by the vast number of disincentives and wedges the government places between willing employer and employee? Would he or she really say "the one big wedge between you hiring someone to make something and sell it is the zero bound on nominal Treasury rates"? Finally, uncertainty is surely a part of it. Investing and hiring has some fixed, irreversible costs, and the chance that policy could be even worse gives people an incentive to delay.

This is all really hard to quantify, and it's time somebody did. Unfortunately, it's much easier to focus on "demand," or the zero bound, or fiscal stimulus — one big magic bullet, and not the thousands of weeds.

Cochrane’s argument that the weak recovery looks like a supply problem echoes the posts I’ve made on this blog over the last two years that it looks like the U.S. economy has managed to cut loose a part of the labor force that (presumably) wasn’t carrying its weight.

I like the Martian metaphor, but I don’t think it’s well stated. Let me try and do better (and let me know if I don’t). If a Martian came to Earth and tried to figure out why the recovery was weak, would they accept the 1) simpler, single explanation that businesses aren’t expanding because our near zero interest rates aren’t low enough to justify the investment, or 2) the more complex set of explanations about why it’s harder for managers to hire people to expand production. Cochrane’s point is that a lot people accept the first argument because they don’t want to expend the effort to understand the second one.

* John Cochrane is a macroeconomist working on the borderline of macro and finance (sort of like me, except he’s a superstar). He has been on the faculty at the University of Chicago for close to 30 years, wrote one of the top graduate finance texts, and may win a Nobel Prize in the next 20 years or so. When he talks, you should listen.

† Each Federal Reserve Bank publishes free several things, weekly to annually, that cover macroeconomics and finance at a level that undergraduates can understand. If you’re curious, start looking.

John Cochrane* On Recessions with Financial Crises

From an interview in the Federal Reserve Bank of Richmond’s Econ Focus:†

EF: Do you think there's any reason to believe recessions following financial crises should necessarily be longer and more severe, as Carmen Reinhart and Kenneth Rogoff have famously suggested?

Cochrane: Reinhart and Rogoff only showed that recessions following financial crises have been, on average, longer and more severe — not even "always," let alone "necessarily." I don't believe they advanced a theory, either, so they really just documented a historical regularity, a correlation and not a cause. So no, I don't believe that, at least not yet. Lots of people tell a story in which it takes a long time to "deleverage," "restore balance sheets," and to work "excess debt" out of the system, but just what that means and why it takes a long time hasn’t been adequately modeled and tested yet.

An alternative explanation for the correlation is that governments tend to do particularly bad things in the wake of financial crises. They tend to bail out borrowers at the expense of lenders, overregulate finance, pass high marginal tax rate wealth transfers, alter property rights, and introduce other distortions. Mortgage foreclosure used to take a few months, and now it can take two years. And then people wonder why lenders aren’t willing to lend at low rates anymore. The Great Depression seems like a classic case of counterproductive policies being put in place after a financial crisis that made the whole episode much deeper and longer. [emphasis added]

Students need to recognize that recessions can occur without financial crises (most recently in 2000-1), that financial crises can occur without recessions (most recently in 1987-9, or 1998). But, they can also occur together, as in 2007-2009. Most people think that we’re worse off if they occur together, and that makes a lot of sense, but surprisingly the data aren’t strong enough to call that a settled question.

* John Cochrane is a macroeconomist working on the borderline of macro and finance (sort of like me, except he’s a superstar). He has been on the faculty at the University of Chicago for close to 30 years, wrote one of the top graduate finance texts, and may win a Nobel Prize in the next 20 years or so. When he talks, you should listen.

† Each Federal Reserve Bank publishes free several things, weekly to annually, that cover macroeconomics and finance at a level that undergraduates can understand. If you’re curious, start looking.

John Cochrane on Dodd-Frank

From an interview in the Federal Reserve Bank of Richmond’s Econ Focus:†

EF: Does the 2010 Dodd-Frank regulatory reform act meaningfully address runs on shadow banking?

Cochrane: It tries, but I don’t think it actually does much about runs. I think Dodd-Frank repeats the same things we've been trying over and over again that have failed, in bigger and bigger ways. The core idea is to stop runs by guaranteeing debts. But when we guarantee debts, we give banks and other institutions an incentive to take risks. In response, we unleash an army of regulators to stop them from taking risks. Banks get around the regulators, there is a new run, we guarantee more debts, and so on.

The deeper problem is the idea that we just need more regulation — as if regulation is something you pour into a glass like water — not smarter and better designed regulation. Dodd-Frank is pretty bad in that department. It is a long and vague law that spawns a mountain of vague rules, which give regulators huge discretion to tell banks what to do. It’s a recipe for cronyism and for banks to game the system to limit competition.

Runs are a feature of how banks get their money, not really where they invest their money. So a better approach, in my view, would be to purge the system of run-prone financial contracts — that is, fixed-value promises that are payable on demand and cause bankruptcy if not honored, like bank deposits and overnight debt. Instead, we subsidize short-term debt via government guarantees, tax deductibility, and favorable regulation, and then we try to regulate financial institutions not to overuse that which we subsidize.

One of the points in my “Why Is Macroeconomics So Hard?” lectures is that most of what passes for government policy is a rehash of policies tried before. “They’ve always done it this way” is a useful lens for students to use to figure out why government does what it does. Cochrane is saying that Dodd-Frank is a prime example.

I found the point in the third paragraph both brand new, yet blindingly obvious … and I’ve been thinking about this stuff for 30 years. We’re most worried about runs, but runs occur because of only half the business that financial institutions do, and we mostly regulate the other half. That’s goofy if you think about it. An insight like that makes me think that regulators have other motives.

* John Cochrane is a macroeconomist working on the borderline of macro and finance (sort of like me, except he’s a superstar). He has been on the faculty at the University of Chicago for close to 30 years, wrote one of the top graduate finance texts, and may win a Nobel Prize in the next 20 years or so. When he talks, you should listen.

† Each Federal Reserve Bank publishes free several things, weekly to annually, that cover macroeconomics and finance at a level that undergraduates can understand. If you’re curious, start looking.