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Full Cred and Props to Reinhart & Rogoff and the BEA: They Collected the Data

The other day I dissed the analysis in Reinhart and Rogoff’s Growth in a Time of Debt as being on the level of a blog post from an amateur internet econocrank. I still hold that opinion.

But I want to walk back on that, or at least clarify, and give lots of credit where due. Because they did make a huge contribution, of a quality that you will not find in econoblog posts from even the best bloggers: they assembled a great data set. As I can attest — having spent hundreds of hours assembling data sets that were far less challenging than theirs — this is not a trivial task. And the value of that data set is high, assuming you throw high-quality analysis at it.

Now of course, they didn’t release the goddam data set for years. That seems unforgivable, especially given the paper’s political and policy impact. This paper wasn’t in a peer-reviewed journal (it was a “discussion paper,” which makes its impact even more eyebrow-raising), but I really wonder why those journals (in any field) would publish such papers without requiring that the data sets accompany them, for vetting by other researchers. (Yeah I know: not a new idea.)

I totally understand why this is problematic. This is the researchers’ crown jewels, upon which they can build future papers, at least. Not just the data, but the analysis methods and the coding of those methods (intellectual property?), is often included in the files. At most, you’re looking at corporate/university/personal assets, trade secrets, generally some (at least potentially) damned valuable stuff. (Throw in the issue of partial or complete government funding for the research, and it’s even more complicated.) But providing the data should be the default requirement, with some clear guidelines justifying and explaining why the data is not provided, when it isn’t.

The “damn valuable stuff” double-points to the other topic I want to mention here: the BEA’s move change the NIPAs, to count spending on R&D and the development of creative works as investment spending rather than consumption spending — as real-capital building.

“Double” because 1. the new accounting highlights the real value of this kind of data-gathering and knowledge-creation, and 2. the change itself required (and will continue to require) a huge amount of data gathering.

I was kind of wowed by this line in the Financial Times writeup on the change:

The Internet Movie Database may not seem like a natural source of data for the national accounts, but it was one of many combed by BEA researcher Rachel Soloveichik, who went through film studio records as far back as the 1920s to build a series on investment in movies.

(Another good FT post on this here.)

So when you hear me kibbitz about the structures and methods used in the national accounts, please know that I have wide-eyed respect for the diligence and skill of hundreds of accountants and economists involved in building those structures, and populating them with data from hundreds of diverse sources. (This actually sounds like one of those Google interview/hiring questions: how would you go about estimating the value of every movie made in America since the 1920s? Books? TV programs?)

That information is hugely valuable. It’s a great example of Your Tax Dollars at Work, delivering value far above the government’s cost.

Or at least, I think it is. Somebody should gather some data on that.

Cross-posted at Asymptosis.

Identity Games: Saving ≠ Saving? Whodathunkit?

I finally figured out a simple way to explain my confusion (and that of many others, including many economists) with the whole Saving issue. I may also have figured out a useful solution to that confusion, which I present at the bottom here for my gentle readers’ delectation and denunciation.

Econ profs: I’m really curious. Do you think this post would help your intro students understand this stuff?

First: The accounting’s fine. Of course. But for some not-crazy reasons, the definition of “Saving” changes in the course of the accounting.

Thinking of the “real” sector for the moment, for simplicity and clarity. For each of the economic units at the bottom level of that sector (households and nonfinancial businesses), Saving means money saving:

(1) Saving = Income – Expenditure

But at the top, the level of “sectoral” saving, Saving means saving of real goods:

(2) Saving = Income – Consumption Expenditures

Or in words that more aptly describe what’s being depicted:

(3) Saving = Production – Consumption

(Reminder: Consumption Spending + Investment Spending = Expenditures = Income = Production)

Explanatory aside: There’s Gross or Net Saving, depending on whether Consumption just includes Consumption Spending (on goods that are bought and consumed within the period), or also includes Consumption of Fixed Assets — the very real “depreciation” of those assets. Gross is long-lived goods produced; Net is long-lived goods added, above and beyond what’s “consumed.”

Back to identities: Unlike every other measure in the national accounts, if you sum up the money Saving of all the bottom-level units, it doesn’t equal Saving for the sector. Rather:

(4) Sectoral Saving = Units’ Combined Money Saving + Investment Spending*

Investment spending, of course, causes the creation of real, long-lived goods. But this is the thing that has confused me from the get-go: Saving is (savings are) some combination of money and real goods? Aren’t financial assets supposed to be representative of, proxies for, the real assets? (Equally confusing: economists’ insistence on talking about “capital” as if it were some undifferentiated, homogeneous or vaguely contiguous lump of real and financial capital.)

Here’s what you need to know to sort that out: You know that money saving? It’s zero.

A Bit on Public Debt GDP Growth and Causation

Here I  analyze the data set used by Reinhart and Rogoff (R-R) and by Herndon Ash and Pollin (HAP) in their critique and in particular the stata data set RR-processed.dta with data on public debt to GDP ratios and real GDP growth in 20 developed countries since 1946.

I show evidence that low growth causes a high debt to GDP ratio, so the correlation can’t be interpreted simply as the effect of debt on growth (damn fractions how do they work anyway). Of course this has been shown much better and much more thoroughly by HAP at PERI (warning the sudden traffic killed their hamster) and then their colleague  Arindrajit Dube using non parametric regressions.

The following paragraph is pointless and may be skipped. <pointless nerdo twittosity>My added bit is a bit nerdo twitty — I note you can test whether the debt to GDP ratio is a good regressor or instrument (technically is it weakly exogenous) by considering it and another variable as instruments for it.  The other variable should not affect growth except through the debt to GDP ratio.  This motivates a regression on the debt to GDP ratio and the 5 year lagged debt to GDP ratio.</pointless nerdo twittosity>

This is a regression of one years real GDP growth on the debt to GDP ratio and the 5 year lagged debt to GDP ratio ( l5debtgdp)

OK that’s about it.   If one trusts the standard error calculation, one concludes that there is very very strong evidence that, given debt now, it is much better to have been highly indebted already 5 years ago.  This is the pattern one would expect if low growth caused a high debt to GDP ratio.  Future growth is low if debt is higher than one would predict given debt 5 years ago — presumably because that is the result of disappointing growth and growth rates are serially correlated.  Old debt is not so damaging.  This means that it comes out looking as if old debt is positively a good thing (really the regression doesn’t show this it shows if you have debt it is better for it to be old).

Ooops I motivate the regression by assuming that growth rates are correlated over time, but the standard errors are calculated assuming they aren’t. Fortunately STATA makes it easy to  correct standard errors for correlation of any kind within groups of data — in this case growth rates for the same country at different times.

Just type

. reg dRGDP debtgdp l5debtgdp  if l5debtgdp!=.,cluster(cntry)

gives a new T-statistic on l5debtdgp of 3.19 markedly smaller than the uncorrected t-stat of 5.11 but still very significant.

The regression basically rejects the null (never stated but often insinuated by R-R) that the pooled OLS regression of read GDP growth rate on the debt to GDP ratio gives a valid estimate of a structural causal relationship.

Many more regressions and the batch program I used are here

Note to Reinhart/Rogoff (et. al): The Cause Usually Precedes the Effect

Or: Thinking About Periods and Lags

No need to rehash this cock-up, except to point to the utterly definitive takedown by Arindrajit Dube over at Next New Deal (hat tip: Krugman), and to point out that the takedown might just take even if you’re looking at R&R’s original, skewed data.

But a larger point: I frequently see econometrics like R&R’s, comparing Year t to Year and suggesting — usually only implicitly or with ever so many caveats and disqualifiers — that it demonstrates some kind of causation. I.e. GDP growth in 1989 vs. debt in 1989, ’90 vs. ’90, etc.

Haven’t they heard of looking at lags, and at multiple lags and periods? It’s the most elementary and obvious method (though obviously not definitive or dispositive) for trying to tease out causation. Because cause really does almost always precede effect. Time doesn’t run backwards. (Unless you believe, like many economists, that people, populations: 1. form both confident and accurate expectations about future macro variables, 2. fully understand the present implications of those expectations, and 3. act “rationally” — as a Platonic economist would — based on that understanding.)

By this standard of propter hoc analysis, R&R’s paper shows less analytical rigor than many posts by amateur internet econocranks. (Oui, comme moi.) This is a paper by top Harvard economists, and they didn’t use the most elementary analytical techniques used by real growth econometricians, and even by rank amateurs who are doing their first tentative stabs at understanding the data out there.

Here’s one example looking at multiple periods and multiple lags, comparing European growth to U.S. growth (click for larger).

This doesn’t show the correlations between growth and various imagined causes for the periods (tax levels, debt levels, etc.) — just the difference, EU vs. US, in real annualized growth. You have to do the correlations in your head, knowing, for instance, that the U.S. over this period taxed about 28% of GDP, while European countries taxed 30–50%, averaging about 40%.

But it does show the way to analyzing those correlations (and possible causalities), by looking at multiple periods and multiple lags. (I’d love to see multiple tables like this populated with correlation coefficients for different “causes.”)

Dube tackles the lag issue for the R&R sample beautifully in his analysis. In particular, he looks at both positive and negative lags. So, where do we see more correlation:

A. between last year’s growth and this year’s debt, or

B. between last year’s debt and this year’s growth?

The answer is B:

Figure 2:  Future and Past Growth Rates and Current Debt-to-GDP Ratio

(Also: if there’s any breakpoint for the growth effects of government debt, as suggested by R&R, it’s way below 90% of GDP. More like 30%.) See Dube’s addendum for a different version of these graphs, using another method to incorporate multiple lags.

Here’s what I’d really like to see: analysis like Dube’s using as its inputs many tables like the one above, each populated with correlations for a different presumed cause (“instrumental variable”). Combine that with Xavier Sala-i-Martin’s technique in his paper, “I just ran four million regressions“.

That paper looks at fifty-nine different possible causes of growth/instrumental variables (not including government debt/GDP ratio) in every possible combination, to figure out which ones might deliver robust correlations. I’m suggesting combining that with multiple periods and lags for each instrumental variable. IOW, “I just ran 4.2 billion regressions.” Not sure if we’ve got the horsepower yet, but…

Cross-posted at Asymptosis.


Reinhart/Rogoff Shot Full of Holes Updated X3

This story has rapidly made the rounds in the blogosphere, and it is indeed a big deal. One of the most significant economics papers underlying the argument for why high government debt (especially over 90% of gross domestic product) is bad for growth was published in 2010 by Carmen Reinhart and Kenneth Rogoff, “Growth in a Time of Debt” (ungated version here).

The basic finding of this paper was that if debt exceeds 90% of GDP, then on average growth turns negative. But as Thomas Herndon, Michael Ash, and Robert Pollin report in a new paper (via Mike Konczal at Rortybomb), there are substantial errors including data omitted for no reason, a weighting formula that makes one year of negative growth by New Zealand equal to 19 years years of decent growth by the UK, and a simple error on their spreadsheet that excluded five countries from their analysis altogether (see Rortybomb for the screen shot).

The authors say that with these errors corrected, the average growth rate for 20 OECD countries from 1946 to 2009 with debt/GDP ratios over 90% is 2.2%, not the -0.1% found by Reinhart and Rogoff. This is a huge difference. We still have a negative correlation between debt/GDP and growth rate, but it is much smaller, as we can see from Figure 3 from their paper:

Debt/GDP Ratio     R/R Results     Corrected Results
Under 30%            4.1%               4.2%

30-60%                 2.8%               3.1%

60-90%                 2.8%               3.2%

Over 90%             -0.1%               2.2%

As Paul Krugman (link above) argues, what we are likely seeing is reverse causation: slow growth leads to high debt/GDP ratios. That is certainly what EU countries are finding as they implement austerity measures and slip back into recession. But even if high debt/GDP did cause slower growth, we can see it is nowhere near the crash that Reinhart and Rogoff’s paper made it out to be.

The bottom line here is simple: the focus on deficits and debt that have dominated our political discourse is completely misplaced. We need to do something about the unemployment crisis by increasing growth, something that is even truer in the European Union where the unemployment rate in Spain and Greece exceeds 26%.

Update: Reinhart and Rogoff have responded in the Wall Street Journal. They emphasize that there is still a negative correlation, and that having debt/GDP above 90% for five years or more reduces growth by 1.2 percentage points in developed countries, which is still substantial for developed economies.

Update 2: Paul Krugman’s response to Reinhart and Rogoff is here.  He pronounces it very disappointing, saying they are “evading the critique.”

Update 3:  Reinhart and Rogoff have a new response in the Financial Times (registration required). Here, they admit they committed the Excel error, but claim there was nothing nefarious in their disputed data choices:

The ‘gaps’ are explained by the fact there were still gaps in our public debt data set at the time of the paper. Our approach has been followed in many other settings where one does not want to overly weight a small number of countries that may have their own peculiarities.

This is a very odd response from two authors who equated one year of New Zealand to 19 years of the far larger UK economy. Worse still when you add the fact that by excluding several years when New Zealand had a debt/GDP ratio over 90%, they got an “average” (actually only one year) growth rate of -7.6%, when the correct average, with all relevant years over 90% included, was 2.58%, a 10.18 point swing!

It’s obvious that the austerity crowd is still going to defend this paper, but that doesn’t mean anyone else should be taken in by them.
Cross-posted from Middle Class Political Economist.

Empirical Methods and Progress in Macroeconomics

Mark Thoma, among many others, discusses some implications for readers to consider for macro overall: Empirical Methods and Progress in Macroeconomics

(Quote)The blow-up over the Reinhart-Rogoff results reminds me of a point I’ve been meaning to make about our ability to use empirical methods to make progress in macroeconomics. This isn’t about the computational mistakes that Reinhart and Rogoff made, though those are certainly important, especially in small samples, it’s about the quantity and quality of the data we use to draw important conclusions in macroeconomics. Everybody has been highly critical of theoretical macroeconomic models, DSGE models in particular, and for good reason. But the imaginative construction of theoretical models is not the biggest problem in macro – we can build reasonable models to explain just about anything. The biggest problem in macroeconomics is the inability of econometricians of all flavors (classical, Bayesian) to definitively choose one model over another, i.e. to sort between these imaginative constructions. We like to think or ourselves as scientists, but if data can’t settle our theoretical disputes – and it doesn’t appear that it can – then our claim for scientific validity has little or no merit. There are many reasons for this. For example, the use of historical rather than “all else equal” laboratory/experimental data makes it difficult to figure out if a particular relationship we find in the data reveals an important truth rather than a chance run that mimics a causal relationship.(unquote)

Okay Fine, Let’s Call Investment “Saving.” Or…Not

I really like Hellestal’s comment and linguistic take on this whole business:

I’m comfortable changing my language in order to communicate. I have very little patience for people who aren’t similarly capable of changing their definitions.

This discussion is really about the words we use to describe different accounting constructs. Nick totally gets that as well.

So I’m ready to say, “fine, let’s call investment saving.” That’s perfectly in keeping with the very sensible understanding found in Kuznets, father of the national accounts. He characterized real capital — the actual stuff we can use to create more stuff in the future — as “the real savings of the nation.” (Capital in the American Economy, p. 391.)

So when you spend money to produce something that has long-lived (and especially productive) value, you’re “saving.”

But still, I gotta wonder: why don’t we just call it…investment?

Because this S=I business confuses the heck out of everyone. Some of the smartest econobloggers on the web have spilled hundreds of thousands of words over the last several years trying to sort out this confusion. I’ve read most of them, and I’m still confused. And I’m quite sure that all non-economists who’ve looked at this (and many or even most economists) are as well.

And that’s not a surprise. Here are a few reasons why:

1. When you invest in real assets, you’re spending. That’s why it’s called investment spending. So spending = saving. Really?

2. When you pay someone to build you a drill press, you’re saving. When you don’t eat some of this year’s corn crop, you’re saving. When you pay off some of your money debt, you’re saving. When you don’t spend some of the money in your checking account, you’re saving. Each of these is true within a given (usually implicit) balance-sheet/income-statement accounting construct. But are they anything like the same thing?

3. As I showed in my last post, f you look at the “real” domestic private sector — households and nonfinancial businesses (most people’s implicit default context) — the amount of saving (income minus expenditures) has absolutely no relationship to the amount of investment spending. Saving is always insufficient to “fund” investment. And the changes in the two measures don’t move together, either in magnitude or direction. (Aside from the long, multi-decadal growth in both as the economy grows.)

4. When you “save” by investing, you decrease the amount of money on the left-hand (asset) side of your balance sheet, while increasing the amount of real assets on that tally. Your total assets are unchanged. Have you saved?

5. When you pay someone to write a piece of software, you get a long-lived real asset. You’ve saved. But the money you gave them is income for them, so it contributes to their (money) savings as well. Do you double-count those savings, or did “the economy” get that software for free?

6. Investment means “gross investment” — all the money spent on long-lived goods, including replacement of long-lived assets that have been consumed in the period (through use, decay, and obsolescence, and — for inventory of consumer goods — actual consumption). But in KuznetsWorld, shouldn’t we be talking about net investment — the additions to our stock of long-lived assets? Gross consumption minus consumption of fixed assets (and inventory changes)? Shouldn’t we call net investment “saving”?

I know: there’s (at least apparent) confusion in some of these, but that’s rather my point. And there are answers to all of these in the context of S=I. (All of them, I think, based on the flawed [neo]classical accounting constructs embodied in the NIPAs. That’s my next post.) I’ve read them all, every which way from Sunday. But do they help anybody understand how the economy works, or…quite the contrary? If they do, why do all those econobloggers feel the need to worry at this, constantly?

I’m not sure this really solves the problem, but I’d like to suggest that saving should mean what everybody in a monetary economy means when they use the word: money saving. Monetary income minus money expenditures. In dollars, or whatever. (And while we’re about it, when you take out a loan or spend out of your savings, let’s call those “borrowing” and “spending,” not “dissaving.”)

Meanwhile investment (in economics discussions) should mean what economists mean when they use the word: “spending to create fixed assets and inventory.” (Because the national accounts only count spending on structures, equipment, software, and inventory as investment.)

And actually, that’s what it already means.

Why do we need to call it saving?

Cross-posted at Asymptosis.

Criticizing the IMF staff and Ryan Avent

Lifted from Robert Waldmann’s Stochastic Thoughts:

In the post below, I vigorously criticize IMF staff and Ryan Avent for claiming that central banks adopted low inflation targets in the early 80s without noting that the Fed did not adopt an inflation target until January 25 2012.  I have now read Avent’s post as patiently as I can (meaning I skipped ahead).
Avent wrote “That the disinflation of the 1980s has generated a flattening of the Phillips curve is precisely what the IMF demonstrates:”

This claim is illustrated by a figure which does not show that.  Even if a curve hasn’t changed at all, the slope depends on where you are (that is the curve is not a straight line).  The figure does suggest that  the IMF staff are willing to assume that the Phillips curve is a straight line, or rather that they are willing to support their argument by presenting a graph which tends to convince people willing to make that assumption.

The graph does not demonstrate any change at all in the Phillips curve (I’m not saying it didn’t change just that the question can’t be answered with the graph).  You can’t see if different points lie on the same curve by plotting changes on changes, because the slope of a curve isn’t constant.

In particular, inflation is much lower now than it was in the early 80s.  It is possible that the slope of the Phillips curve is lower now, because the Phillips curve is a curve.  The pattern from 2007 on is clearly different from the pattern in the 1930s.   It is not clear that it is different from what would have happened from 1980 to 1994 if inflation had been around 2% in 1980.  

Oh and the 30s were different. In most developed countries, the unemployed don’t risk starvation any more.  The welfare state was quite different back when high unemployment caused sharp deflation.

I swear that this post has been edited to make it less rude.  You don’t want to read the first draft.
Also I deleted a draft conclusion to the update to the post below, because it was too inflammatory.  I am trying to be as polite as I possibly can without actually lying.

update:  Now I am going to make some graphs.  They are totally unlabeled only partly because I am lazy but also because I want the reader/graph eyeballed to try to guess what they are.  They are US analogs of the IMF graph with the change in core inflation on the y axis and the change the civilian unemployment rate on the x axis.  All graph 17 data points (as in the green series from the IMF).  Two  show data from after the Fed flattened the Phillips curve in the “early 80s”.  Which two show the new flat Phillips curve?

Figure 1 (chosen from three figures at random by the eyes closed point and click method)

Figure 2 (not chosen at random)

Figure 3

Don’t peek 

Come on it’s more fun if you guess than look ?

OK the answer is that figures 1 and 3 show the new flat post early 80s Phillips curve which is due to inflation targeting.  

Did you guess without peeking ?

Figure 1 shows 17 quarterly inflation changes from 1985q1 minus 1984q4 on.  They are the first data which came undeniably after the early 80s.  Figure 3 shows the most recent 17 quarterly changes.   It is not markedly different from figure 1 because of auto scaling (not “not *just* because I am lazy” does not imply “I am not lazy”) but it is much flatter (the range of unemployment changes is 4 times as large and the range of core inflation changes is about the same).

Figure 2 is the first 17 available quarters from Fred from 1958q2 – 1958q1 (when the core CPI series starts).  The first of those data were collected before Phillips published his famous scatter (with labeled axises even) .  The last in the first quarter of 1962 rather before the modern advances in monetary theory.  It is very flat indeed.  If Phillips had relied on FRED, he wouldn’t have gotten published at all.  Inflation bounced around way back then, but there is almost no relationship between the change in inflation and the change in unemployment.

This is what Phillips saw for extremely low inflation rates.  The rediscovery of the fact that the Phillips curve has a low slope at inflation rates near zero is not path breaking progress.

Reading Mankiw in Seattle

A while back Nick Rowe challenged amateur internet econocranks (my word, not Nick’s) like me to actually go read an intro econ textbook. (He was specifically targeting the author of Unlearning Economics — who I, at least, don’t consider to be an econocrank, he’s far better-versed than I am, though Nick might.)

I took him up on the challenge, and am finally writing up my thoughts because I need to reference this from another post.

Figuring I ought to go straight to the belly of the beast, I picked up a used copy of Greg Mankiw’s Principles of Microeconomics. I didn’t read every word — I’ve been poring through various econ textbooks online, plus innumerable papers and blog posts, for years, so I knew a lot of it already. But I did go through it fairly carefully (especially the diagrams), and it had some of the effect that Nick was hoping for. Some of the things that I didn’t think were (sensibly) covered in intro econ, in fact are. And not surprisingly given my autodidact’s typical spotlight (and spotty) pattern of knowledge, I learned quite a few new things.

But still, my overall impression was amazement at what is not covered, and in particular what is not covered right up front.

In place of Mankiw’s nostrums about tradeoffs, opportunity costs, margins, incentives, etc., I would expect to see discussion of the fundamentals that underpin all that:

Value. What in the heck is it? How do we measure it? This was the topic of the opening class in my one accounting class, at the NYU MBA school. Basically: accounting for non-accountants, teaching us to deconstruct balance sheets and income statements into flows of funds. A darned rigorous course, taught by a funny and cranky old guy, formerly on the Federal Accounting Standards Board, with a young assistant prof playing the straight man and the enforcer. That first class was one of the most valuable (?) I’ve ever sat through.

The phrase “theory of value” doesn’t even appear in Mankiw’s text, even though he uses the term “value” constantly, and it’s obviously a term that has some import in economics. Imagine an undergraduate who’s had zero exposure to the ideas of subjective versus objective value, or the centuries of (continuing) discussion and debate on the subject, trying to parse the following sentence, and think critically about what it really means.

…we must convert the marginal product of labor (which is measured in bushels of apples) into the value of the marginal product (which is measured in dollars).

Money. What is it? What’s its value relationship to real goods, and in particular real capital? How is it embodied in financial assets? Where did it come from? (Hint: from credit tallies and for coins, military payments of soldiers, not barter between the butcher and the baker. That’s an armchair-created fairy tale.) The phrases “medium of account” and “medium of exchange” don’t appear in the book. Since economics is all about monetary economies, this seems like a significant omission.

Utility. The most fundamental construct in economics — the demand curve — is derived from utility maps. But Mankiw doesn’t even mention the term until page 447, where it’s discussed as “an alternative way to describe prices and optimization.” Alternative? There’s no discussion of ordinal and cardinal utility, or of the troublesome doctrine of revealed preferences (which 1. is the doctrine that allows economists to avoid talking about utility, 2. constitutes a circular definition, and 3. is never mentioned in the book).

All this gives me a feeling of indoctrination into a self-validating, hermetically sealed body of beliefs floating in space, with no egress outside that bubble, into thinking about the thinking going on therein. There are huge and not-wacky bodies of thinking out there that seriously question what goes on inside, often refuting it on its very own terms, and in the words of its own most eminent practitioners.

Yes, you could argue that I’m asking too much of undergraduates, but I would suggest that you’re asking too little (or the wrong thing) of undergraduate professors.

Is Mankiw teaching his “customers” to understandthe hallmark of the North-American higher-education system, in my opinion, compared to most other countries — or is he teaching them to adopt an undeniably ideological world view (no, neoclassical economics is not purely “positive,” not even close), and to just go obediently through the motions as prescribed in the textbook? In my opinion, he’s doing the latter.

I’m tempted to suggest that this is all true because (neoclassical?) economists don’t have a coherent or non-circular theory of value, and money, and utility. (Neither do I, but I’m working on it!) But saying that would make me sound like an internet econocrank.

Cross-posted at Asymptosis.

Solow on Bernanke (and both, on Libertopians)

I’m just sayin’. (Emphasis mine, words Solow’s):

[Bernanke’s] preferred answer is better and more system-oriented regulation. One has to ask then why regulation failed to see the crisis of 2007–2008 coming and take action to head it off. Bernanke suggests that regulators were lulled into inattention by the so-called Great Moderation. Our masters are all too eager to take the Panglossian view that a system of “free markets,” including financial markets, is self-regulating and self-stabilizing. Bernanke is surely right about this. The scholar of the 1930s has to be aware that there was similar talk about the New Era in the years before 1929. Dr. Pangloss has lots of helpers among the sharpshooters who profit most from the absence of effective oversight, and among simpleminded ideologues. They are still with us.

Cross-posted at Asymptosis.