Central Perk: 6 mar. 2015

vineri, 6 martie 2015

Mish's Global Economic Trend Analysis

Rush to Judgment and Extremely Inaccurate Reporting
Trends in Employment: What Age Groups Get the Jobs?
Diving Into the Payroll Report: Establishment +295K Jobs; Household +96K Employment, Labor Force -178K
No, No, No Says Draghi to Greece; Spanish Economy Minister Insists 3rd Bailout Talks Underway

Rush to Judgment and Extremely Inaccurate Reporting

Posted: 06 Mar 2015 07:35 PM PST

Rush to Judgment

The moment Russian opposition leader Boris Nemtsov was gunned down last Friday, Western media rushed to judgment. Heck, even friends who should know better rushed to judgment.

One friend sent me the New York Times article After Boris Nemtsov's Assassination, 'There Are No Longer Any Limits' along with this comment:

"The world cannot stand by and let the formula of repression and stealth special forces intervention and sowing contrived disruption succeed, as next there will be little green men in the Baltic states sowing dissention -- we are not going to go through a rinse, repeat and shampoo cycle again in those countries."

I replied...

The headline is ridiculous because

No one knows who did it.
It's none of our business anyway
If we have any moral responsibility it should not be to corrupt puppet governments, but rather the people of Ukraine
The people of Ukraine do not need 4 more years of war nor a mass US invasion
The people of the US do not need and cannot afford a war with Russia

To which I heard "Of course you think the US did it. That was predictable. One does not need to think too hard to figure out what happened here. There is a clear pattern. Europe and the liberal world order are too precious. This has to stop now."

If that's not rush to judgment, what is?

Numerous Possibilities

It would not surprise me in the least to find out the US or Ukraine had some involvement in this. Given disastrous US foreign policy everywhere, including involvement in the Ukraine Maidan uprising, how anyone can be sure of anything is beyond me.

I am not saying "Putin did not do it." Rather I am saying "I don't know".

I do know that Nemtsov could be considered washed out. Russians dropped him and his party in droves when he supported Ukraine in the Ukrainian civil war. I also know his mistress was Ukrainian and Nemtsov flew her to Switzerland to have an abortion.

There are any number of possibilities here, including the strong possibility that making Nemtsov a martyr made him worth more alive than dead to Putin, and more dead than alive to the anti-Putin movement.

Could Nemtsov have been setup by his mistress? The only "no" answer I can come up with is along the lines of "dead women tell no tales". Why would someone leave her as a witness except by accident?

Extremely Inaccurate Reporting

With rush to judgment out of the way, let's turn our focus on some extremely inaccurate headlines.

For example, Yahoo!Finance reported on February 28, Nemtsov Admitted Fears for Life Weeks Before Murder.

The headline, the body of the article, and the actual interview do not match.

From Yahoo!Finance

Russian opposition leader Boris Nemtsov, gunned down on Friday in a contract-style killing, gave an interview this month admitting he had feared for his life over his opposition to President Vladimir Putin.

In an interview with weekly Sobesednik, Nemtsov was asked: "Have you started worrying that Putin could personally kill you in the near future or do it through middle men?"

He replied: "You know... yes. A little.

"But all the same I'm not that scared of him. If I was that afraid, I would hardly have headed an opposition party and would hardly be doing what I'm doing now," he said in the interview published in early February.

In a light-hearted exchange, the Sobesdenik journalist told Nemtsov: "I hope that common sense will prevail after all and Putin won't kill you."

"God willing. I hope so too," Nemtsov replied.

Actual Interview

The actual interview went nothing like the above.

Nemtsov never admitted fear of being killed. Rather he commented his mother (not he) feared for his life.

That link is to the full interview in Russian. Run it through any translator you want. What follows is my edited Yandex translation.

Nemtsov: When I called her regularly, she says, "Son, when will you stop criticizing Putin? He'll kill you" (Nemtsov laughs).

Reporter: Finally, I will ask you, are you afraid of Putin? More cautious?

Nemtsov: Slightly afraid. [See my note below for a more accurate translation]

Reporter: But a little fear, yes?

Nemtsov: "Well listen, I'm kidding. If I was afraid, I would hardly have headed an opposition party and would hardly be doing what I'm doing now."

Not Really Afraid

Note: Reader Jacon Dreizin informs me, that "slightly afraid" better translates as "not really". The context and the reporter's followup question both indicate "not really" is a better translation.

Nowhere was a question asked "Have you started worrying that Putin could personally kill you in the near future or do it through middle men?"

Reader Andrei Chimes In

I also pinged this post off reader Andrei who speaks Russian and graciously offered help with Russian translations. He confirms what Jacob had to say.

Reader Andrei went on ...

Nemtsov says he is "afraid a little bit" or "not really afraid". In Russian both are quite close to each other. But then he follows up with "if I was afraid I would not be leading the opposition" etc.

The actual question from interviewer should have been translated as "And the last question I want to ask you - are you afraid of Putin? Or are you going to be more careful now?" To which Nemtsov replies that if he was afraid he would not be doing what he does.

Nowhere in interview there is a line from the reporter about "let's hope Putin won't kill you" neither Boris reply about god willing. The whole interview is about his relationship with his mom with some small bits about how she does not like Putin.

Hope this helps. Let me know if you need any further elaboration.

Cheers, Andrei

The critical question was made up by someone. So was the answer. So was the exchange about "God willing". Or, if you prefer, the posted interview is a lie.

Which is it?

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Trends in Employment: What Age Groups Get the Jobs?

Posted: 06 Mar 2015 11:15 AM PST

One interesting fact in today's jobs report (see Diving Into the Payroll Report: Establishment +295K Jobs; Household +96K Employment, Labor Force -178K) was a drop in teenage unemployment of 1.7 percentage points while overall the unemployment rate fell by only 0.2 percentage points.

The only reason the overall rate fell was a plunge in labor force of 178,000. Household survey employment only rose by 96,000 vs. the establishment survey gain of an alleged +295,000.

The decline in teenage unemployment got me wondering: Where are the jobs, and what age groups got them? Here are a few seasonally adjusted charts from the St. Louis Fed.

Employment 16-19 Month Over Month

Employment 20-24 Month Over Month

Employment 25-54 Month Over Month

Employment 55+ Month Over Month

Age Categories

25-54 is a rather broad category. So is 55+. I would have liked to see finer breakdowns.

Additional data is available on the BLS data site directly, but even there, not all of the seasonally adjusted numbers I wanted were available. However, all of the age groups I wanted to see on a "not seasonally adjusted" basis were available.

Let's take a look at the two sets of tables I created from BLS data.

Not Seasonally Adjusted Employment Growth Year-Over-Year

Age Group	Employment Growth Y/Y NSA	Population Growth Y/Y	Employment Relative to Population Growth
16-19	456,000	-34,000	490,000
20-24	409,000	-26,000	435,000
25-34	866,000	617,000	249,000
35-44	69,000	108,000	-39,000
45-54	464,000	-207,000	671,000
55-59	175,000	279,000	-104,000
60-64	296,000	543,000	-247,000
65+	228,000	1,534,000	-1,306,000

Note the huge outsized job gains in age groups 16-19 and 20-24. On an age-adjusted basis, the job gains are even greater.

Also the demographic shift to age group 25-34 puts the 866,000 job gain in that group in proper perspective. Relative to population growth, age group 35-44 actually lost jobs.

Retirement explains age groups 60-64 and 65+. Retirement (and forced retirement), along with rising disability fraud, also explains the drop in participation rate.

By forced retirement I mean people who want a job but do not have one, so they retire to collect Social Security because they need the income.

Seasonally Adjusted Employment Growth Month-Over-Month

Age Group	Employment Growth M/M SA	Population Growth M/M
16-19	86,000	-6,000
20-24	103,000	-20,000
25-34	108,000	37,000
35-44	-86,000	2,000
45-54	78,000	-55,000
55+	-187,000	-6,000

Perspective on the 96K Household Survey Gain

Of the 96,000 gain in employment this month, 189,000 of it came in the age group 16-24 even though that population group dropped by 26,000!

Please stop and think about that for a second.

Yes, retirement affected the overall results, but even so, age group 35-44 lost 86,000 jobs. Overall it seems reasonably safe to assume more high-paying jobs were lost this month than gained.

Still think this was a good jobs report?

Close scrutiny of both month-over-month and year-over-year data suggests we keep adding low wage jobs while boomers retire en masse.

These job reports are nowhere near as strong as most think.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Diving Into the Payroll Report: Establishment +295K Jobs; Household +96K Employment, Labor Force -178K

Posted: 06 Mar 2015 08:51 AM PST

Initial Reaction

Once again we see the pattern of a strong establishment survey but a poor household survey. The latter varies more widely, and the tendency is for one to catch up to the other, over time. The question, as always, is which way?

Here is one stat that really stands out: The unemployment rate for teenagers 16-19 fell 1.7 percentage points. BLS Jobs Statistics at a Glance

Nonfarm Payroll: +295,000 - Establishment Survey
Employment: +96,000 - Household Survey
Unemployment: -274,000 - Household Survey
Involuntary Part-Time Work: -175,000 - Household Survey
Voluntary Part-Time Work: +15,000 - Household Survey
Baseline Unemployment Rate: -0.2 at 5.5% - Household Survey
U-6 unemployment: -0.3 to 11.0% - Household Survey
Civilian Non-institutional Population: +176,000
Civilian Labor Force: -178,000 - Household Survey
Not in Labor Force: +354,000 - Household Survey
Participation Rate: -0.1 at 62.8 - Household Survey

January 2015 Employment Report

Please consider the Bureau of Labor Statistics (BLS) November 2014 Employment Report.

Total nonfarm payroll employment increased by 295,000 in February, and the unemployment rate edged down to 5.5 percent, the U.S. Bureau of Labor Statistics reported today. Job gains occurred in food services and drinking places, professional and business services, construction, health care, and in transportation and warehousing. Employment in mining was down over the month.

Click on Any Chart in this Report to See a Sharper Image

Unemployment Rate - Seasonally Adjusted

Nonfarm Employment January 2011 - February 2015

Nonfarm Employment Change from Previous Month by Job Type

Hours and Wages

Average weekly hours of all private employees was stationary at 34.6 hours. Average weekly hours of all private service-providing employees was flat at 33.4 hours.

Average hourly earnings of production and non-supervisory private workers was flat at $20.80. Average hourly earnings of production and non-supervisory private service-providing employees was flat at $20.61.

Since November, Average hourly earnings of production and non-supervisory private workers rose $0.03, from $20.77 to $20.80 (about a penny a month).

Since November, average hourly earnings of production and non-supervisory private service-providing employees rose $0.04 from $20.57 to $20.61 (about 2 cents a month).

From this perspective, wages are rising about 1% a year.

For discussion of income distribution, please see What's "Really" Behind Gross Inequalities In Income Distribution?

Birth Death Model

Starting January 2014, I dropped the Birth/Death Model charts from this report. For those who follow the numbers, I retain this caution: Do not subtract the reported Birth-Death number from the reported headline number. That approach is statistically invalid. Should anything interesting arise in the Birth/Death numbers, I will add the charts back.

Table 15 BLS Alternate Measures of Unemployment

click on chart for sharper image

Table A-15 is where one can find a better approximation of what the unemployment rate really is.

Notice I said "better" approximation not to be confused with "good" approximation.

The official unemployment rate is 5.5%. However, if you start counting all the people who want a job but gave up, all the people with part-time jobs that want a full-time job, all the people who dropped off the unemployment rolls because their unemployment benefits ran out, etc., you get a closer picture of what the unemployment rate is. That number is in the last row labeled U-6.

U-6 is much higher at 11.0%. Both numbers would be way higher still, were it not for millions dropping out of the labor force over the past few years.

Some of those dropping out of the labor force retired because they wanted to retire. The rest is disability fraud, forced retirement, discouraged workers, and kids moving back home because they cannot find a job.

For further discussion of a more accurate measure of the unemployment rate, please see Gallup CEO Calls 5.6% Unemployment Rate "The Big Lie": What's a Realistic Unemployment Rate?

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

No, No, No Says Draghi to Greece; Spanish Economy Minister Insists 3rd Bailout Talks Underway

Posted: 06 Mar 2015 08:05 AM PST

Spanish Economy Minister Insists 3rd Bailout Talks Underway

Spanish economy minister, Luis de Guindos, has reiterated his position that Third Greece Bailout discussions are in play.

Luis de Guindos put back on the table the possibility that the Troika is preparing a new bailout for Greece. "The four month extension of the aid program will show us the real situation and establish the need for a third rescue," he assured an information forum. "Next week, the Eurogroup meeting will hopefully expose the discussions that have occurred," he added.

Jean-Claude Juncker denied this week that it was negotiating a third bailout for Greece, but it's the second time that the economy minister made statements in this regard. In particular he spoke of a package of between 30 and 50 billion euros of which Spain would guarantee between 13% and 14%. Our country has already contributed 26 billion euros in respect of guarantees and loans for the Greek economy.

Whom to Believe?

Once again this is a question of whom to believe.

I don't know for the simple reason that no one involved merits the benefit of the doubt.

I discussed this issue at length in Greecification of Spanish Politics and the Lies of Spain's Ministers.

The actual amount of Spain's loan to Greece is €6.65 billion. Everything beyond that is a loan guarantee, not current paid out of pocket. The guarantee is real, but it's not spent money ... yet.

See the preceding link for discussion.

No, No, No Says Draghi to Greece

Keep Talking Greece has anther interesting post today: Varoufakis says "We have Plan B" after ECB Draghi's says No,No,No to liquidity.

"We have Plan B" Greek Finance Minister Yanis Varoufakis told private Mega TV on Thursday, just a couple of hours after ECB head Mario Draghi linked ECD funding with Greece's compliance to the bailout and austerity program, righting the conditions for liquidity.

At a press conference today, Mario Draghi distributed money around, but to the Greek, he said three times "No".

NO, ECB will not allow Athens to sell additional T-bills total worth 8 billion euro.

NO, ECB will not buy Greek bonds under its new assets-buying program.

NO, ECB will not accept Greek bonds as collateral.

"The ECB is a rule-based institution. It is not a political institution. It cannot provide monetary financing to governments, either directly or indirectly. We cannot give money to banks to fund governments," Draghi said.

Odd, that he did not add that he had no problem to fund banks and put the burden on taxpayers around Europe, when it comes to fund the oh-so-dear banks.

P.S. No, Varoufakis did not elaborate on the Greek Plan B.

Musical Tribute

I believe I see four distinct no's in the above article, and I have the perfect musical tribute.

Link if video does not play: Ringo Starr - The No-No Song

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Damn Cool Pics

Snapchats That Have Hit the Nail on the Head

Posted: 06 Mar 2015 10:44 AM PST

The World's Most Dangerous Selfie

Posted: 06 Mar 2015 10:33 AM PST

After taking a selfie with a group of hungry, rabid polar bears, this girl is lucky to be alive.

What Deep Learning and Machine Learning Mean For the Future of SEO - Whiteboard Friday - Moz Blog

What Deep Learning and Machine Learning Mean For the Future of SEO - Whiteboard Friday

Posted on: Friday 06 March 2015 — 01:15

Posted by randfish

Imagine a world where even the high-up Google engineers don't know what's in the ranking algorithm. We may be moving in that direction. In today's Whiteboard Friday, Rand explores and explains the concepts of deep learning and machine learning, drawing us a picture of how they could impact our work as SEOs.

For reference, here's a still of this week's whiteboard!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we are going to take a peek into Google's future and look at what it could mean as Google advances their machine learning and deep learning capabilities. I know these sound like big, fancy, important words. They're not actually that tough of topics to understand. In fact, they're simplistic enough that even a lot of technology firms like Moz do some level of machine learning. We don't do anything with deep learning and a lot of neural networks. We might be going that direction.

But I found an article that was published in January, absolutely fascinating and I think really worth reading, and I wanted to extract some of the contents here for Whiteboard Friday because I do think this is tactically and strategically important to understand for SEOs and really important for us to understand so that we can explain to our bosses, our teams, our clients how SEO works and will work in the future.

The article is called "Google Search Will Be Your Next Brain." It's by Steve Levy. It's over on Medium. I do encourage you to read it. It's a relatively lengthy read, but just a fascinating one if you're interested in search. It starts with a profile of Geoff Hinton, who was a professor in Canada and worked on neural networks for a long time and then came over to Google and is now a distinguished engineer there. As the article says, a quote from the article: "He is versed in the black art of organizing several layers of artificial neurons so that the entire system, the system of neurons, could be trained or even train itself to divine coherence from random inputs."

This sounds complex, but basically what we're saying is we're trying to get machines to come up with outcomes on their own rather than us having to tell them all the inputs to consider and how to process those incomes and the outcome to spit out. So this is essentially machine learning. Google has used this, for example, to figure out when you give it a bunch of photos and it can say, "Oh, this is a landscape photo. Oh, this is an outdoor photo. Oh, this is a photo of a person." Have you ever had that creepy experience where you upload a photo to Facebook or to Google+ and they say, "Is this your friend so and so?" And you're like, "God, that's a terrible shot of my friend. You can barely see most of his face, and he's wearing glasses which he usually never wears. How in the world could Google+ or Facebook figure out that this is this person?"

That's what they use, these neural networks, these deep machine learning processes for. So I'll give you a simple example. Here at MOZ, we do machine learning very simplistically for page authority and domain authority. We take all the inputs -- numbers of links, number of linking root domains, every single metric that you could get from MOZ on the page level, on the sub-domain level, on the root-domain level, all these metrics -- and then we combine them together and we say, "Hey machine, we want you to build us the algorithm that best correlates with how Google ranks pages, and here's a bunch of pages that Google has ranked." I think we use a base set of 10,000, and we do it about quarterly or every 6 months, feed that back into the system and the system pumps out the little algorithm that says, "Here you go. This will give you the best correlating metric with how Google ranks pages." That's how you get page authority domain authority.

Cool, really useful, helpful for us to say like, "Okay, this page is probably considered a little more important than this page by Google, and this one a lot more important." Very cool. But it's not a particularly advanced system. The more advanced system is to have these kinds of neural nets in layers. So you have a set of networks, and these neural networks, by the way, they're designed to replicate nodes in the human brain, which is in my opinion a little creepy, but don't worry. The article does talk about how there's a board of scientists who make sure Terminator 2 doesn't happen, or Terminator 1 for that matter. Apparently, no one's stopping Terminator 4 from happening? That's the new one that's coming out.

So one layer of the neural net will identify features. Another layer of the neural net might classify the types of features that are coming in. Imagine this for search results. Search results are coming in, and Google's looking at the features of all the websites and web pages, your websites and pages, to try and consider like, "What are the elements I could pull out from there?"

Well, there's the link data about it, and there are things that happen on the page. There are user interactions and all sorts of stuff. Then we're going to classify types of pages, types of searches, and then we're going to extract the features or metrics that predict the desired result, that a user gets a search result they really like. We have an algorithm that can consistently produce those, and then neural networks are hopefully designed -- that's what Geoff Hinton has been working on -- to train themselves to get better. So it's not like with PA and DA, our data scientist Matt Peters and his team looking at it and going, "I bet we could make this better by doing this."

This is standing back and the guys at Google just going, "All right machine, you learn." They figure it out. It's kind of creepy, right?

In the original system, you needed those people, these individuals here to feed the inputs, to say like, "This is what you can consider, system, and the features that we want you to extract from it."

Then unsupervised learning, which is kind of this next step, the system figures it out. So this takes us to some interesting places. Imagine the Google algorithm, circa 2005. You had basically a bunch of things in here. Maybe you'd have anchor text, PageRank and you'd have some measure of authority on a domain level. Maybe there are people who are tossing new stuff in there like, "Hey algorithm, let's consider the location of the searcher. Hey algorithm, let's consider some user and usage data." They're tossing new things into the bucket that the algorithm might consider, and then they're measuring it, seeing if it improves.

But you get to the algorithm today, and gosh there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, "Hey, we only ant to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this." What does that even mean? But that's what the algorithm is designed to do. The machine learning system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.

Then, over time, the idea is that in the future even the inputs aren't given by human beings. The machine is getting to figure this stuff out itself. That's weird. That means that if you were to ask a Google engineer in a world where deep learning controls the ranking algorithm, if you were to ask the people who designed the ranking system, "Hey, does it matter if I get more links," they might be like, "Well, maybe." But they don't know, because they don't know what's in this algorithm. Only the machine knows, and the machine can't even really explain it. You could go take a snapshot and look at it, but (a) it's constantly evolving, and (b) a lot of these metrics are going to be weird conglomerations and derivatives of a bunch of metrics mashed together and torn apart and considered only when certain criteria are fulfilled. Yikes.

So what does that mean for SEOs. Like what do we have to care about from all of these systems and this evolution and this move towards deep learning, which by the way that's what Jeff Dean, who is, I think, a senior fellow over at Google, he's the dude that everyone mocks for being the world's smartest computer scientist over there, and Jeff Dean has basically said, "Hey, we want to put this into search. It's not there yet, but we want to take these models, these things that Hinton has built, and we want to put them into search." That for SEOs in the future is going to mean much less distinct universal ranking inputs, ranking factors. We won't really have ranking factors in the way that we know them today. It won't be like, "Well, they have more anchor text and so they rank higher." That might be something we'd still look at and we'd say, "Hey, they have this anchor text. Maybe that's correlated with what the machine is finding, the system is finding to be useful, and that's still something I want to care about to a certain extent."

But we're going to have to consider those things a lot more seriously. We're going to have to take another look at them and decide and determine whether the things that we thought were ranking factors still are when the neural network system takes over. It also is going to mean something that I think many, many SEOs have been predicting for a long time and have been working towards, which is more success for websites that satisfy searchers. If the output is successful searches, and that' s what the system is looking for, and that's what it's trying to correlate all its metrics to, if you produce something that means more successful searches for Google searchers when they get to your site, and you ranking in the top means Google searchers are happier, well you know what? The algorithm will catch up to you. That's kind of a nice thing. It does mean a lot less info from Google about how they rank results.

So today you might hear from someone at Google, "Well, page speed is a very small ranking factor." In the future they might be, "Well, page speed is like all ranking factors, totally unknown to us." Because the machine might say, "Well yeah, page speed as a distinct metric, one that a Google engineer could actually look at, looks very small." But derivatives of things that are connected to page speed may be huge inputs. Maybe page speed is something, that across all of these, is very well connected with happier searchers and successful search results. Weird things that we never thought of before might be connected with them as the machine learning system tries to build all those correlations, and that means potentially many more inputs into the ranking algorithm, things that we would never consider today, things we might consider wholly illogical, like, "What servers do you run on?" Well, that seems ridiculous. Why would Google ever grade you on that?

If human beings are putting factors into the algorithm, they never would. But the neural network doesn't care. It doesn't care. It's a honey badger. It doesn't care what inputs it collects. It only cares about successful searches, and so if it turns out that Ubuntu is poorly correlated with successful search results, too bad.

This world is not here yet today, but certainly there are elements of it. Google has talked about how Panda and Penguin are based off of machine learning systems like this. I think, given what Geoff Hinton and Jeff Dean are working on at Google, it sounds like this will be making its way more seriously into search and therefore it's something that we're really going to have to consider as search marketers.

All right everyone, I hope you'll join me again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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