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Evidence of Hampered Monetary Policy Transmission Channel in the Euro Area

Evidence of Hampered Monetary Policy Transmission Channel in the Euro Area

by Rebecca Wilder

Mario Draghi cautioned on the ‘hampered’ transmission channel of monetary policy in his now famous London speech last week:

To the extent that the size of these sovereign premia hampers the functioning of the monetary policy transmission channel, they come within our mandate.

I referred to the clogging of rates policy back in April via evidence from mortgage lending rates.

I address Draghi’s point that the ECB 1% refi rate will support economic activity through the lens of the mortgage market. Specifically, I find that the interest rate channel is clogged in the economies that are in most desperate need of lower rates: Spain, Portugal, and Italy.

Here I show that on a relative basis, while the household lending rate is quietly trending down for key periphery markets, the real problem lies in the non-financial corporate rates transmission channel. Specifically, rates in Portugal, Italy, and Spain have seriously diverged from both the trend in the refi rate (ECB policy rate) and those of other countries in the Euro area.

The trend in key periphery household mortgage rates is consistent with the ECB rate cuts: down
Note: All ECB refi rate data is through June 2012, so the latest rate cut to 75 bps is not included in the charts.

The magnitude still favors the core – the drop in German mortgage rates is 91 basis points since the max mortgage rate of the Euro area as a whole in August 2011 – but the trend is down for all countries.

In stark contrast to the trend in household mortgages relative to the ECB refi rate, non-financial corporate lending rates in Portugal, Spain, and Italy diverged from the other country trends.

If the ECB means business on improving the monetary transmission channel, they’ll need to attack the price of corporate loans in the Periphery markets.

Rebecca Wilder

Data Note: All non-financial corporate AAR lending rates is the annualized agreed rate on new business loans with a maturity of greater than 5 years and amount between €0.25 bn and €1 bn. Irish data is not available in Ireland and the Greek data is too sporadic.

cross posted with The Wilder View…Economonitors

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Real business fixed investment.

Yesterday I compared real private GDP in cycles.

Today I would like to look at  real business fixed investment.

If you listened to CNBC — otherwise known as the Republican Propaganda Channel — or the campaign claims you would think that all the uncertainty created by Obama was destroying business confidence and that real business fixed investment was collapsing.

But the data shows a different story.  Three years from the economic bottom  real business fixed investment is up some 18.0%, or about the same as during the Clinton years.  In contrast, during  the
“Bush Investment lead Boom” it was up only up 6.9%, or about a third of the current cyclical rise.

(Update…slightly edited for readability…Dan)

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Tax Policy Center* Says Romney Lies

Mitt Romney proposes reducing tax brackets by 20% and cutting the estate tax (to zero IIRC). He will keep or expand favored treatment of capital gains and dividends. He claims that he doesn’t plan to cut taxes for the rich. He claims that he will avoid such cuts by eliminating deductions, credits and exclusions. One of his claims must be false as they are arithmentically inconsistent.

update: Beyond parody. Romney aid Lanhee Chen says that the Tax Policy Center conclusion that Romney’s proposal is gives to the rich is “biased” because it ignores Romney’s proposed corporate income tax cuts

“The study analyzes only half of Governor Romney’s tax program, ignoring the reforms that would make America’s corporations more competitive by moving from the highest corporate tax rate in the industrialized world to one that is comparable to our trading partners.”

Yep that’s the way to convince the public. Note that, in addition to tax cuts for rich people, there are tax cuts for corporations. Importantly Chen does not deny that Romney lied when he said he didn’t seek tax cuts for the rich. Chen’s line is the usual supply side trickle down line that tax cuts for the rich and for corporations will help the non rich by causing greater growth. I know of no evidence which supports this claim.

I almost feel sorry for the Romney campaign.

In other breaking news (from 2002) Romney claimed he was resident in Utah in 1999 and 2000 before he claimed he was resident in Massachusetts in 1999 and 2000.

I can’t keep track of his lies (hell Steve Benen has trouble). Wouldn’t it be easier to keep track of his non-lies ?

A new report from the Brookings Institution and the Tax Policy Center includes the following.

The key intuition behind our central result is that, because the total value of the available tax expenditures (once tax expenditures for capital income are excluded) going to high-income taxpayers is smaller than the tax cuts that would accrue to high-income taxpayers, high-income taxpayers must necessarily face a lower net tax burden. As a result, maintaining revenue neutrality mathematically necessitates a shift in the tax burden of at least $86 billion away from high-income taxpayers onto lower- and middle-income taxpayers. This is true even under the assumption that the maximum amount of revenue possible is obtained from cutting tax expenditures for high-income households.

Amazingly, even if they accept Greg Mankiw’s estimates of the effect of rate cuts on growth (which assumes no increase in the deficit even in the short run). They still conclude that a Romney claim must be false.

Nevertheless, even if one were to use the model from Mankiw and Weinzierl (2006) and assume that after five years 15 percent of the $360 billion tax cut is paid for through higher economic growth, the available tax expenditures would still need to be cut by 56 percent; on net lower- and middle-income taxpayers would still need to pay higher taxes.

This analysis will come as a complete shock to exactly zero Angrybear readers (including the conservatives) but might stimulate discussion in comments.

* Title corrected. The Tax Policy Center is a joint center of the Brookings Institute and the Urban Institute not a separate entity.

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Does the Tea Party support Tax Dodgers?

by Linda Beale

Does the Tea Party support Tax Dodgers?

Andrew Leonard’s July 27 article in Salon, Tea Party Shields Tax Dodgers, looks at the way Jim DeMint and Rand Paul are carrying water for the big banks–suggesting that Treasury shouldn’t be implementing the “FATCA” legislation passed as part of the HIRE act because it might cost the big banks some paperwork they’d rather not do.

Here’s the issue with FATCA. It requires banks to report on substantial accounts held abroad–more than $50,000 for individuals or more than $250,000 for entities.

The Treasury believes it can generate almost $9 billion in additional tax revenues through the implementation of FATCA over the next ten years. But Paul, DeMint and other right-wingers have complained about the cost to banks to carry out the law.

 As Leonard puts it,

FATCA isn’t about making life hard for expatriates. It is targeted at big players who are moving huge amounts of cash overseas for the specific purpose of avoiding their U.S. tax obligations. And Rand Paul and Jim DeMint are defending these upstanding members of the 1 percent by carrying water for banks who want to avoid the paperwork costs involved in ferreting out the tax dodgers. 

Is that how the Tea Party wants this country to be run — in the best interests of the richest Americans and the banks?

cross posted with ataxingmatter

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Romney’s Weird Plan to Decouple Military Spending From National Security Needs and to Tie It Instead to … GDP??

Romney’s plan calls for linking the Pentagon’s base budget to Gross Domestic Product, and allowing the military to spend at least $4 dollars out of every $100 the American economy produces.”

In a post here two weeks ago I noted that peculiar proposal of Romney’s, and also mentioned my dismay that neither the Obama campaign nor (to my knowledge) any other mainstream-media outlet had mentioned it.  I titled the post Crony Capitalism and Its Variety of Flavors.  The occasion for the post was the Romney campaign’s then-newly-invigorated “crony capitalism” schtik featuring, of course, Solyndra. 

I said in the post that, given Romney’s open (if unnoticed) proposal to untether actual national-security needs from national-security spendingand attaching it instead to GDP, its purpose is utterly unrelated to national security.  It’s unabashedly “a stunningly perverse pinstriped-patronage version of Keynsian economics,” I said. 

My post got about as much attention as the May 10 CNNMoney report.  But now the Obama campaign has a new ad out highlighting the absurdity of Romney’s plan to increase defense spending—presumably (although the ad doesn’t say this) so that we’ll be prepared when Romney clumsily gets us into an unnecessary war.  (Current candidates as enemies: Britain and the Palestinians, in addition to Iran.)  The ad is excellent.  But it would be even better if it mentioned that Romney’s spending plan doesn’t even purportto be tied to defense needs, but instead to, um, GDP.

It really should point this out and should note the only possible explanation for that.  So should Obama himself.

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Statisticians aren’t the problem for data science. The real problem is too many posers

I met Cathy for coffee in Cambridge when she was presenting at MIT awhile ago.  I liked her style and knowledge.  Re-posted with permission from the author.

by Cathy O’Neil
a data scientist who lives in New York City and writes at

Statisticians aren’t the problem for data science. The real problem is too many posers 
(Crossposted on Naked Capitalism)

Cosma Shalizi

I recently was hugely flattered by my friend Cosma Shalizi’s articulate argument against my position that data science distinguishes itself from statistics in various ways.

Cosma is a well-read broadly educated guy, and a role model for what a statistician can be, not that every statistician lives up to his standard. I’ve enjoyed talking to him about data, big data, and working in industry, and I’ve blogged about his blogposts as well.

That’s not to say I agree with absolutely everything Cosma says in his post: in particular, there’s a difference between being a master at visualizations for the statistics audience and being able to put together a power point presentation for a board meeting, which some data scientists in the internet start-up scene definitely need to do (mostly this is a study in how to dumb stuff down without letting it become vapid, and in reading other people’s minds in advance to see what they find sexy).

And communications skills are a funny thing; my experience is communicating with an academic or a quant is a different kettle of fish than communicating with the Head of Product. Each audience has its own dialect.
But I totally believe that any statistician who willingly gets a job entitled “Data Scientist” would be able to do these things, it’s a self-selection process after all.

Statistics and Data Science are on the same team
I think that casting statistics as the enemy of data science is a straw man play. The truth is, an earnest, well-trained and careful statistician in a data scientist role would adapt very quickly to it and flourish as well, if he or she could learn to stomach the business-speak and hype (which changes depending on the role, and for certain data science jobs is really not a big part of it, but for others may be).

It would be a petty argument indeed to try to make this into a real fight. As long as academic statisticians are willing to admit they don’t typically spend just as much time (which isn’t to say they never spend as much time) worrying about how long it will take to train a model as they do wondering about the exact conditions under which a paper will be published, and as long as data scientists admit that they mostly just redo linear regression in weirder and weirder ways, then there’s no need for a heated debate at all.
Let’s once and for all shake hands and agree that we’re here together, and it’s cool, and we each have something to learn from the other.


What I really want to rant about today though is something else, namely posers. There are far too many posers out there in the land of data scientists, and it’s getting to the point where I’m starting to regret throwing my hat into that ring.

Without naming names, I’d like to characterize problematic pseudo-mathematical behavior that I witness often enough that I’m consistently riled up. I’ll put aside hyped-up, bullshit publicity stunts and generalized political maneuvering because I believe that stuff speaks for itself.

My basic mathematical complaint is that it’s not enough to just know how to run a black box algorithm. You actually need to know how and why it works, so that when it doesn’t work, you can adjust. Let me explain this a bit by analogy with respect to the Rubik’s cube, which I taught my beloved math nerd high school students to solve using group theory just last week.


First we solved the “position problem” for the 3-by-3-by-3 cube using 3-cycles, and proved it worked, by exhibiting the group acting on the cube, understanding it as a subgroup of S_8 \times S_{12}, and thinking hard about things like the sign of basic actions to prove we’d thought of and resolved everything that could happen. We solved the “orientation problem” similarly, with 3-cycles.

I did this three times, with the three classes, and each time a student would ask me if the algorithm is efficient. No, it’s not efficient, it takes about 4 minutes, and other people can solve it way faster, I’d explain. But the great thing about this algorithm is that it seamlessly generalizes to other problems. Using similar sign arguments and basic 3-cycle moves, you can solve the 7-by-7-by-7 (or any of them actually) and many other shaped Rubik’s-like puzzles as well, which none of the “efficient” algorithms can do.

Something I could have mentioned but didn’t is that the efficient algorithms are memorized by their users, are basically black-box algorithms. I don’t think people understand to any degree why they work. And when they are confronted with a new puzzle, some of those tricks generalize but not all of them, and they need new tricks to deal with centers that get scrambled with “invisible orientations”. And it’s not at all clear they can solve a tetrahedron puzzle, for example, with any success.

Democratizing algorithms: good and bad

Back to data science. It’s a good thing that data algorithms are getting democratized, and I’m all for there being packages in R or Octave that let people run clustering algorithms or steepest descent.

But, contrary to the message sent by much of Andrew Ng’s class on machine learning, you actually do need to understand how to invert a matrix at some point in your life if you want to be a data scientist. And, I’d add, if you’re not smart enough to understand the underlying math, then you’re not smart enough to be a data scientist.

I’m not being a snob. I’m not saying this because I want people to work hard. It’s not a laziness thing, it’s a matter of knowing your shit and being for real. If your model fails, you want to be able to figure out why it failed. The only way to do that is to know how it works to begin with. Even if it worked in a given situation, when you train on slightly different data you might run into something that throws it for a loop, and you’d better be able to figure out what that is. That’s your job.

As I see it, there are three problems with the democratization of algorithms:

  1. As described already, it lets people who can load data and press a button describe themselves as data scientists.
  2. It tempts companies to never hire anyone who actually knows how these things work, because they don’t see the point. This is a mistake, and could have dire consequences, both for the company and for the world, depending on how widely their crappy models get used.
  3. Businesses might think they have awesome data scientists when they don’t. That’s not an easy problem to fix from the business side: posers can be fantastically successful exactly because non-data scientists who hire data scientists in business, i.e. business people, don’t know how to test for real understanding.

How do we purge the posers?
We need to come up with a plan to purge the posers, they are annoying and making a bad name for data science.

One thing that will be helpful in this direction is Rachel Schutt’s Data Science class at Columbia next semester, which is going to be a much-needed bullshit free zone. Note there’s been a time change that hasn’t been reflected on the announcement yet, namely it’s going to be once a week, Wednesdays for three hours starting at 6:15pm. I’m looking forward to blogging on the contents of these lectures.

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