Relevant and even prescient commentary on news, politics and the economy.

Not A Great Week to be Rupert Murdoch

The week began with the New York Post reporting “a scoop,” and then putting a track coach and a high school kid on the front page as “suspects.”

You would think it couldn’t get worse for NewsCorp.  I would think it couldn’t get worse for NewsCorp, especially after CNN went out of their way to make people forget the early errors with new ones of their own.

But NewsCorp owns several properties, which means they have “cross-branding” possibilities.  For instance, when Aaron Sorkin had SportsNight on ABC, he had Dana (Felicity Huffman) bubbling over for an entire episode about how great The Lion King was on Broadway.  One Disney property promotes another.  “Cross-branding.”

The thing is, cross-branding is supposed to be positive. Sorkin would never have said anything negative about Disney–and didn’t, until the final episode, when he had Clark Gregg say, “You would have to be an idiot not to make money on SportsNight.” By which point it had been cancelled.

Fox, however, decided to end its Boston coverage the way it began it.  Via Josh Malina’s Twitter feed, their Closed Captioning experts on Fox4 posted:


Good thing for Mr. Murdoch this happened after the market closed. No word yet on whether this will affect next season’s episodes of The New Girl.


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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.


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Chechnya and 2004

Reader Matthew McOsker sends a note pointing us to 2004 and reading for context:

Who are the Boston Marathon terrorists? Some early reports state the men are Chechen. So where does Chechnya fit into global terrorism? I found the following piece that gives a nice summary:

” On September 1, 2004, a group of Chechen terrorists took hostage and two days later murdered at least 335 schoolchildren and parents in Beslan, a town in the Russian republic of North Ossetia. The atrocity focused world attention on Chechnya. The Russian government used the event to reiterate its arguments that Chechen terrorists and foreign jihadists supporting them have ideological, financial, and operational ties with Islamist terrorist organizations such as Al-Qaeda.[1] Although President Vladimir Putin and top Russian security officials provided evidence of links between Chechen fighters and Al-Qaeda, European politicians and mainstream Western journalists focused instead upon the Russian army’s brutality and dismissed Putin’s claims as an attempt to gain sympathy in the West and deflect criticism of Russia’s handling of a nationalist insurgency. ”

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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.

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Just so y’all know …

Most of the formatting in my posts–blockquotes, paragraph spacing, italics–did not transfer to the new platform, so my old posts are pretty much a mishmash to read.  At least those posted since about early December, when I began drafting and posting using my then-new Chromebook, which uses only Google’s word processor, not MS Word.  I’m not sure whether the formatting in my posts from before December transferred properly; I haven’t checked.

As those of you who read my posts know, I use a lot of blockquotes, including as ledes, so the lack of blockquote formatting is not pretty.   I just manually indented the blockquotes from a post from Apr. 2 that I randomly checked, and I’ll do it with any other posts I reread for whatever reason, but otherwise I guess my past posts will remain a mishmash.  Which I know some AB readers think they were even with the proper formatting.

Oh, well.

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Old and new feedburner subscriptions…are they working now for you?

MEV has written that this should be happening for our subscribers:

Feedburner issue: You currently have 2 different feeds setup through feedburner. We created a redirect for the old feedburner URL to the new one and all RSS feeds should be working properly at this point.

•All old subscribers will be redirected to the newer feedburner
•All old registrations are tied to the old URL which is not visible to us because it is linked to a different gmail account than the one you have given us access to.
•All new registrations will be linked to new URL in the account which you sent us

Please let me know.

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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)

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Hi folks….let me know if there are problems with your feeds or old links. And also if you find our new format easier to read and what functions we might add once we settle down into our new functioning environment.

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I posted all I can say publicly at Skippy. And even that wouldn’t work at a family blog like this one.

The rest are jokes that I am told–undoubtedly correctly, but lapsed traders are difficult to retrain*–are “too soon.”

The Phantom Scribbler came out of her retirement (first post in more than eleven months), though, so you should Go Read Her.


As for the other Issue of the Day, I repeat what I said chez Duy (whose summary here is concise and informative):

If I told you just those three “Stylized Facts”:
1. Average of -0.1
2. Median of 1.0
3. Positive:Negative ratio is 5:2
would you (a) immediately start talking about the “clear lack of growth”? or (b) even more immediately say, “There must be an outlier in the data. What’s the kurtosis?
I don’t believe I know anyone who would do (a).

If I were teaching Econ 301 and someone presented R&R’s data–see the Konczal link above–and came to their conclusion, they would be lucky to be told to do the assignment over.

*What’s the difference between a bond and a bond trader?  A bond matures.

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Farm subsidies and entrenched wealth

Lynne Kiesling writes Farm subsidies and entrenched wealth at Knowledge Problem:

Veronique de Rugy has a great argument for ending farm subsidies in the April issue of Reason (and yes, do read the whole thing, well worth your time). Farm subsidies are the canonical example of the dynamics of Mancur Olson’s Logic of Collective Action — concentrated benefits and dispersed costs lead to the persistence of inefficient government policies. So canonical, in fact, that I used them just last week in my micro principles class to teach my students about public choice theory and applying economic tools and reasoning to studying decisions we make collectively through political processes.

One feature of Vero’s argument that distinguishes it from others is that it follows this process to its logical, disturbing conclusion for income distribution. Farm subsidies have existed for 80 years, and while their initial intent was to assist struggling farmers during the Depression, their success at doing so has created an entrenched group of land-owning farmers who are now wealthy, but fight against attempts to reduce their subsidies.

While the number of farms is down 70 percent since the 1930s—only 2 percent of Americans are directly engaged in farming—farmers aren’t necessarily struggling anymore. In 2010, the average farm household earned $84,400, up 9.4 percent from 2009 and about 25 percent more than the average household income nationwide.

What’s more, a handful of farmers reap most of the benefits from the subsidies: Wheat, corn, soybeans, rice, and cotton have always taken the lion’s share of the feds’ largesse. The Environmental Working Group (EWG) reports that “since 1995, just 10 percent of subsidized farms—the largest and wealthiest operations—have raked in 74 percent of all subsidy payments. 62 percent of farms in the United States did not collect subsidy payments.”

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