Powerline Economic Analysis: Continuing Their Clue Free Ways

     In a post titled "Talkin' Unemployment Blues" Scott Johnson of the Powerline Blog referenced the following chart from "Shadowstats:" 

So, according to Shadowstats, the real unemployment rate is about 22.5%.  This stands in stark contrast to the U6 and U3 rates of unemployment from the BLS:

     For the sake of argument, let's assume SS is correct.  Wouldn't that mean there could be no wage pressure at all?  At that level of unemployment, employees have, literally, no bargaining power.  An employer could say, "there are, literally, 100 people who will take your job."  Yet, as the following chart from the FRED database shows, wages have increased about 2% Y/Y for most of this expansion.  That pace increased a bit over the last 6-9 months, and is now around 2.5%:

I wonder how Mr. Johnson would explain that rather glaring logical inconsistency?  

     This is but one of the many problems with SS numbers.  According to SS, their "alternate" CPI index based on 1980 calculations is ~7.5%.  If that were really the case, the 10-year would now be trading at a -5.75 yield.  (Just to help Mr. Johnson out: the current 10-year CMT was 1.75% at the close of yesterday's trading.   1.75 - 7.5 = -5.75%.  In case you were wondering, that's called "subtraction" which is a basic mathematical function.)  Trust me on this one: if the 10 year were trading at 5.75%, you'd be hearing about it.

     Here's the point.  The guys at Powerline aren't clueless; that description might imply that, at some point, they had a clue.  No: Powerline's writers are economically clue free.   Over the last few weeks, I pointed out that John Hinderaker -- who is now in charge of a conservative think tank -- thinks Minnesota should change economic policies.  This, when the state's unemployment rate is below 4% and wage gains are between 2.5%-5% Y/Y.  And now Scott Johnson is partially basing his argument on Shadowstats -- a website used by, quite literally, no reputable economist.  And this is before we consider that these guys spent the entire 2014 blog year providing economic commentary that was, literally, 100% incorrect.  That's just dumbfounding.  

     This post will have no impact on Powerline.  They'll continue to argue that they are economic geniuses.  And we'll be here to document the latest economic missive from the cluefree crowd at Powerline as they post.

     

      

Regarding Government Numbers

One of the oldest arguments on the internet is the government -- or non-government numbers for that matter -- are deeply flawed and therefore useless in economic analysis. This argument falls short in one very important way: there is no proof.

Purveyors of this theory first point to the website Shadow Stats as evidence. However they are wrong. For example, according to their "methodology" the US inflation rate was over 5% for the entire decade of 2000-2010.


However during this same time the US 10-year Treasury crossed 5% three times:


Either Shadowstats is correct or one of the most liquid and heavily traded markets in the world is correct. Sorry Shadowstats, but you're way off.

Then there is the issue of "survivor bias." This theory states that surveys are biased in favor of firms that survive economic hard times, thereby skewing the numbers positively. Unfortunately, this theory is also wrong as we debunked here a while ago. Here is an example from the Census Bureau regarding retall sales.

"Births are added to the monthly survey in February, May, August, and November of each year. At the same time, deaths are removed from the survey. To minimize the effect of births and deaths on the month-to-month change estimates, we phase-in these changes by incrementally increasing the sampling weights of the births and decreasing the sampling weights of the deaths in a similar fashion. In the first month, we tabulate the births at one-third their sampling weight and tabulate the deaths at two-thirds their sampling weight. In the second month, we tabulate the births at two-thirds their sampling weight and tabulate the deaths at one-third their sampling weight. In the third month, we tabulate the births at their full sampling weight and the deaths are dropped (sampling weight equal zero)."

Then there's the issue that no one has issued any research on this topic. Here's a fun exercise. Go to SSRN -- the Social Science Research Network -- and start searching for papers from economists and statisticians on the failure of government numbers. If the problem were as widespread as some think, then there should be evidence as in an entire body of work indicting the government statistical system. However -- there is no such body of work; it does not exist.

The short version is people who have absolutely no training in statistics think they know more than the people who staff government and professional forecasting agencies -- who also happen to be trained in statistics.

The reason for this entry is to reiterate a central rule of the comments section:

2.) If you are going to challenge the veracity of government statistics you must provide a reference from a paper written by someone with at least a masters in a relevant discipline (statistics, mathematics, economics etc...). There has been a raging debate in the economic blogosphere about government statistics. The classic debate is about the birth/death model used by the Bureau of Labor Statistics. (To find out more, go to the Bureau of Labor Statistics and type in birth death in the search bar in the upper right hand corner). The BLS uses this to overcome sampling errors in their employment statistics. According to some bloggers this is a bogus adjustment which makes the numbers unreliable. However, go to www.ssrn.com -- the social science research network -- and type in birth/death in the search bar. You'll find 34 hits that center around health care systems. But there is nothing about the BLS' birth/death model. In other words, among academics in the economic and financial world, there isn't a debate (at least not yet). So, the people who should be calling bullshit -- and backing it up with data and information -- are not calling bullshit. When they are, I'll be happy to consider the information.

And please -- Shadowstats is crap.

And a second reason is people have a habit of saying, "the government statistics are wrong" when the statistics disagree with their assertions. But when the statistics confirm their assertions, the government numbers are sacrosanct.

Bottom line: these are the numbers economists use. When I see an economist with a Ph.D. say, "these numbers are flawed and I can prove it" then I'll listen. But when a guy on a blog with a political ax to grind says the same thing, well, let's just say credibility is an issue at that point.

So here's the deal. If you're going to argue that economic numbers are wrong on this blog, please prove it. And, no, your word isn't good enough. Find someone who can prove they know what they are talking about (that's what PhDs are for) to back-up your assertion. And make sure the entire paper is about the topic, not just a sentence you can mis-quote and mis-construe. Better yet, find a group of people to back-up your assertion. Until then, Mish and Daily Kos are open for business.

No Virginia, the Government Isn't Manipulating Economic Statistics

One of the greatest themes around the economic blogsphere is the government is manipulating the numbers to make things look better than they are. However, this is an easily debunked claim.

The latest source for this claim is a story from the New York Times. Here are the first three paragraphs from that story:

A widening gap between data and reality is distorting the government’s picture of the country’s economic health, overstating growth and productivity in ways that could affect the political debate on issues like trade, wages and job creation.

The shortcomings of the data-gathering system came through loud and clear here Friday and Saturday at a first-of-its-kind gathering of economists from academia and government determined to come up with a more accurate statistical picture.

The fundamental shortcoming is in the way imports are accounted for. A carburetor bought for $50 in China as a component of an American-made car, for example, more often than not shows up in the statistics as if it were the American-made version valued at, say, $100. The failure to distinguish adequately between what is made in America and what is made abroad falsely inflates the gross domestic product, which sums up all value added within the country.

Let's look at the third paragraph in detail, especially the opening sentence. "The fundamental shortcoming is in the way imports are accounted for." So, the issue is not with an entire group of statistics but with the way we count imports. The net effect of this problem?

The statistical distortions can be significant. At worst, the gross domestic product would have risen at only a 3.3 percent annual rate in the third quarter instead of the 3.5 percent actually reported, according to some experts at the conference. The same gap applies to productivity. And the spread is growing as imports do.

So -- the very worst reading of the data as it currently exists would have subtracted .2 from US GDP. And that's the worst reading of the distortion -- the worst. And yet, this news article has been cited as proof that the official US government statistics are way off.

Then there is "Shadowstats." . Many people cite them as proof positive the US government is distorting the data. For example, here is a chart of their CPI calculation.


However, there is a big problem with claiming the blue line is the correct measurement of inflation. It comes in the form of bond yields:



If the blue line on the shadowstats graph were the correct inflation measure, bond yields would be at least 400 basis points higher. Why? If shadow stats were correct, then bond investors would have been losing money for most of the 2001-2008 period because inflation was higher than the stated interest rate on the 10-year Treasury bond. Simply put, investors would not put up with that and instead would have sent yields far higher for the last decade. Yet they did not. That tells us that Shadow stats CPI number is wrong.

And if there was a grand conspiracy regarding US economic statistics, where are the academic papers specifically showing why certain numbers are wrong? For example, go the National Bureau of Economic Research and the social science research network. Then search both for "birth/death model." According to literally the entire economic blogsphere this statistical adjustment to the establishment job numbers is an abomination (Except A Dash of Insight who explains why the criticism is unwarranted here, here and here.) Yet there are no papers from academics on why this number is wrong. Type in "US import prices" and you get the same thing -- nothing. Simply put, there is no huge outcry from the people who should be doing that -- academics.

In summation, we have the following points:

1.) The New York Times story that supposedly shows a massive conspiracy regarding government statistics has been taken massively out of context.

2.) Shadow Stats is bunk, plain and simple. Or to quote professor Jim Hamilton:

Why do people continue to give credibility to an operation like Shadowstats? Now that's something that I'd like to hear explained.

3.) The people who should be publishing papers showing the massive distortions in US government numbers aren't publishing those papers.

In short, the claim the numbers are cooked is great for headlines to drive traffic to your site, but that's about it.

Just When You Thought The Boys At Powerline Couldn't Become More Economically Incompetent ...

     I have a confession: the boys at Powerline utterly fascinate me.  On paper, they are smart guys: all of them are ivy league educated and all are lawyers with a large national firm.  And I'm sure that, if you needed a top-notch commercial litigator out of Minnesota, one of these guys would be on the list to call.

     But these guys just can't get anything right when it comes to economics.  I mean nothing.  You'd think that after making nothing but erroneous calls for an entire year, they'd call it quits.  But, you'd be wrong.

     The latest missive is from Scott Johnson, titled, "Talkin' Unemployment Blues."  Here's the opening paragraph:

President Obama made an appearance in the White House press room on Friday to take a victory lap over the fall of the official unemployment rate (U-3) to 4.9 percent (video below, about 15 minutes). Is that number for real? Referring to the hell he saw tending to wounded men during the Civil War, Walt Whitman held that “the real war will never get in the books.” By the same token,I wonder if the real unemployment will ever get in the books.

That's right.  At the beginning of his term, when the financial world was literally collapsing around him, Obama secretly fired the entire staff of the BLS so they could cook the statistical books.  That's why the unemployment rate climbed for the first few years of Obama's term, finally hitting 10% in 2010.  Then, the rate moved lower for the remainder of his term.  If he's going to hire people to manipulate the data, you'd think they'd do a better job than a gradual rate of decline.

     But that's not the best part.  Next, Johnson quotes Shadowstats.  Yes, he actually relies on data from Shadowstats.  Johnson even uses a graph from the website that shows a "real" unemployment rate of about 22.5%.  Note to Mr. Johnson: SS was debunked about 8 years ago by James Hamilton (a leading economist, BTW) and the BLS.  In quoting SS, you've basically demonstrated that you have no idea what you're talking about.

     But using SS isn't the best part.  The best part is Johnson links to a report from the St. Louis Federal reserve on the labor force participation rate (LFPR).  Here's that report's conclusion:

The BLS projections show the LFP rate continuing its decline, reaching 62.5 percent in 2020 (using the 2010-2020 medium-term projection). Since 2000, the BLS has projected the long-term decline in the LFP rate, indicating that the high LFP rate that we saw in 2000 might be a figure of the past. In particular, the decline in women's LFP since 1999 is not expected to reverse. The BLS does not expect the large decline in the LFP rates for the youngest group, 16-24-year-olds, to reverse either. To the extent that the decline for the youngest group is due to the time spent at school, it is possible that these workers will show a higher labor force attachment once they are out of school.

In other words, the reports shows that: demographers and statisticians have known about this decline for some time.  They have also studied it and can explain it.  While Johnson thinks the report bolsters his credibility, it only shows that he's clueless.    As in totally clueless.

     Memo to Powerline: econ is not your thing.  It's really not.

 


   

CNBC Jumps the Shark: Or, Why the Conspiracy Theories About CPI Are Dead WRONG

From CNBC:

Inflation, using the reporting methodologies in place before 1980, hit an annual rate of 9.6 percent in February, according to the Shadow Government Statistics newsletter.

Never mind that Shadowstats has been debunked.

Let's assume that inflation is actually at 9.6%. That means the entire Treasury market is underwater by a little under 6% or 600 basis points. While the markets may not be perfectly efficient, they are not that inefficient either -- not by a long-shot. I haven't watched CNBC for a long time -- as in years. Now I now why. They are a financial news channel that doesn't know anything about finance.

Silver Oz has the following links to share

The effect of adjustments

Owners Equivalent Rent

Some Common Misconceptions about CPI:

When the cost of food rises, does the CPI assume that consumers switch to less desired foods, such as substituting hamburger for steak?

No. In January 1999, the BLS began using a geometric mean formula in the CPI that reflects the fact that consumers shift their purchases toward products that have fallen in relative price. Some critics charge that by reflecting consumer substitution the BLS is subtracting from the CPI a certain amount of inflation that consumers can "live with" by reducing their standard of living. This is incorrect: the CPI's objective is to calculate the change in the amount consumers need to spend to maintain a constant level of satisfaction.

Specifically, in constructing the "headline" CPI-U and CPI-W, the BLS is not assuming that consumers substitute hamburgers for steak. Substitution is only assumed to occur within basic CPI index categories, such as among types of ground beef in Chicago. Hamburger and steak are in different CPI item categories, so no substitution between them is built into the CPI-U or CPI-W.

Furthermore, the CPI doesn't implicitly assume that consumers always substitute toward the less desirable good. Within the beef steaks item category, for example, the assumption is that consumers on average would move up from flank steak to filet mignon if the price of flank steak rose by a greater amount (or fell by less) than filet mignon prices. If both types of beef steak rose in price by the same amount, the geometric mean would assume no substitution.

In using the geometric mean the BLS is following a recognized best practice for statistical agencies. The formula is widely used by statistical agencies around the world and is recommended by, for example, the International Monetary Fund and the Statistical Office of the European Communities.

Is the use of "hedonic quality adjustment" in the CPI simply a way of lowering the inflation rate?

No. The International Labour Office refers to the hedonic approach as "powerful, objective and scientific". Hedonic modeling is just one of many methods that the BLS uses to determine what portion of a price difference is viewed by consumers as reflecting quality differences. It refers to a statistical procedure in which the market valuation of a feature is estimated by comparing the prices of items with and without that feature. Then, for example, if a television in the CPI is replaced by one with a larger screen and higher price, the BLS can make an adjustment to the price difference by estimating what the old television would have cost had it had the larger screen size.

Many of the challenges in producing a CPI arise because the number and types of goods and services found in the market are constantly changing. If the CPI tried to maintain a fixed sample of products, that sample quickly would shrink and become unrepresentative of what consumers were purchasing. Each time that an item in the CPI sample permanently disappears from the shelves, the BLS has to choose another, and then has to make some determination about the relative qualities of the old and replacement item. If it did not--for example, if it treated all new items as identical to those they replaced -- significant upward or downward CPI biases would result.

Critics often incorrectly assume that BLS only adjusts for quality increases, not for decreases, and that hedonic adjustments have a large downward impact on the CPI. On the contrary, BLS has used hedonic models in the CPI shelter and apparel components for roughly two decades, and on average hedonic adjustments usually increase the rate of change of those indexes. Since 1998, hedonic models have been introduced in several other components, mostly consumer durables such as personal computers and televisions, but these newer areas have a combined weight of only about one percent in the CPI. A recent article by BLS economists estimated that the hedonic models currently used in the CPI outside of the shelter and apparel areas have increased the annual rate of change of the All Items CPI, but by only about 0.005 percent per year.

Has the BLS selected the methodological changes to the CPI over the last 30 years with the intent of lowering the reported rate of inflation?

No. The improvements chosen by the BLS that some critics construe to be a response to short term political pressure were, in fact, the result of analysis and recommendations made over a period of decades, and those changes are consistent with international standards for statistics. The methods continue to be reviewed by outside commissions and advisory panels, and they are widely used by statistical agencies of other nations.

Moreover, the sizes and effects of the changes implemented by the BLS are often over-estimated by critics. Some have argued that if the CPI were computed using the methods in place in the late 1970s, the index would now be growing at a rates as high as 11 or 12 percent per year. Those estimates are based on the belief that the use of a geometric mean index lowered the annual rate of change of the CPI by three percentage points per year, and a belief that other BLS changes, such as the use of hedonic models and rental equivalence, have lowered the growth rate of the CPI by four percentage points per year.

Neither belief is supported by evidence. BLS calculations have shown that the geometric mean formula has reduced the annual growth rate of the CPI by less than 0.3 percentage points. Hedonic quality adjustments for shelter regularly increase the rate of change of the CPI, and those for apparel have had both upward and downward impacts at different points in time and for different types of clothing. The BLS estimates that the overall impact of hedonic quality adjustments in use in other categories has been extremely small. Furthermore, if the CPI were using the pre-1983 asset-based method instead of rental equivalence to measure homeowner shelter cost it would yield a sharply lower current measure of shelter inflation, given that house prices are now declining in many parts of the country

No Really -- There Are Rules

First please read the guidelines for making a comment on this blog. They will clear up a lot of misconceptions.

Secondly, don't comment if you have no idea what your are talking about. As an example, if you don't know there are two employment surveys or you think that posting numeric data in a graphic format is "technical analysis," then go elsewhere. I don't have time for that type of illiterate crap.

Third, if you believe that

1.) People are "disappearing from the labor force," or

2.) The government statistics are wrong or

3.) Shadowstats is valid, or

4.) Social Security is rigging unemployment statistics

please fine somewhere else to post.

Again, there are plenty of websites that have no standards and a lack of knowledge about economics. There are also plenty of websites that routinely print ill-informed and researched information on economics. They are (regrettably) a dime a dozen. If that is your cup of tea, fine. Please visit those websites and add to the ever-increasing ill-informed conspiracy theory garbage that routinely fills the internet.

New Rules for the Comments Section

Going forward, these are the new rules for the comments section.

1.) No anonymous comments. If you aren't willing to sign your name to it don't post it. And please, no "Jim Shoos" or "Liz Onnyas" in the name section. Just type in your name. Simple.

2.) If you are going to challenge the veracity of government statistics you must provide a reference from a paper written by someone with at least a masters in a relevant discipline (statistics, mathematics, economics etc...). There has been a raging debate in the economic blogosphere about government statistics. The classic debate is about the birth/death model used by the Bureau of Labor Statistics. (To find out more, go to the Bureau of Labor Statistics and type in birth death in the search bar in the upper right hand corner). The BLS uses this to overcome sampling errors in their employment statistics. According to some bloggers this is a bogus adjustment which makes the numbers unreliable. However, go to www.ssrn.com -- the social science research network -- and type in birth/death in the search bar. You'll find 34 hits that center around health care systems. But there is nothing about the BLS' birth/death model. In other words, among academics in the economic and financial world, there isn't a debate (at least not yet). So, the people who should be calling bullshit -- and backing it up with data and information -- are not calling bullshit. When they are, I'll be happy to consider the information.

And please -- Shadowstats is crap.

And a second reason is people have a habit of saying, "the government statistics are wrong" when the statistics disagree with their assertions. But when the statistics confirm their assertions, the government numbers are sacrosanct.

Bottom line: these are the numbers economists use. When I see an economist with a Ph.D. say, "these numbers are flawed and I can prove it" then I'll listen. But when a guy on a blog with a political ax to grind says the same thing, well, let's just say credibility is an issue at that point.

3.) Assertions require attribution. The topic of the first class I had in graduate school was plagiarism. The lesson was simple: cite everything. I am currently editing my dissertation which has over 2500 footnotes. Why? To show everyone where I get my information from. In fact, formatting the damn thing was by far the hardest part of writing it. The point is there are lots of people who make simple declarative statements about economic facts, working on the assumption that simply saying it (even when proven wrong) somehow makes it true. Here's a great example from another website:

The index of leading indicators has been rising for four months at a strong pace.

#8: This is another convoluted measurement hyped by many. It's too significantly influenced by an intensively flawed GDP, as many have noted in the past; see my--and others'--comments regarding GDP, above.

The fact that the LEIs do not have one element from GDP in them was irrelevant to this writer. Had he gone to the conference board and read the contents of LEIs he would have avoided looking like an ass. Then to make matters worse he cites himself as a source for the reason that LEIs are flawed. This is like Rush Limbaugh citing himself.

I could go on, but you get the idea. When you make assertions, please cite to a source. And an original source is really preferred.

So -- why am I instituting these rules? Here's the deal. The internet is a wonderful tool because it allows us to access vast quantities of data. I tell clients that my law office is a laptop computer, a cell phone and a fax machine. I can access legal databases, practitioner's guides, other countries tax authorities all through the internet. And that is wonderful.

But the internet is the source of a lot of bullshit. And worse, the bullshit becomes common fact even after it is debunked (like "the unemployment rate is not a lagging indicator" nonsense.) So, these rules area to prevent the use of my cite as a way to spread bullshit. And also to elevate the conversation.

Why A Rate Cut Won't Help, pt. IV

Let's take a look at money supply. I'll be using MZM which is:

A measure of the liquid money supply within an economy. MZM represents all money in M2 less the time deposits, plus all money market funds.

Here is a chart from the St. Louis Federal Reserve of the total MZM money supply:



Notice that starting in late 2005 the total started increasing and has been increasing ever since. Let's see what the percentage change chart looks like:



This number has been increasing for the last year and a half, indicating money supply is increasing.

And then there is M3, which the government no longer publishes but which is available from the website shadowstats:



That's a huge increase.

So -- the issue isn't money supply. The facts show there is ample money in the system. The basic problem is no one wants to lend right now.

There's a lot of Money Floating Around Right Now

Here's a chart of the year-over-year change in M2 money growth, seasonally adjusted.



Here's a chart of M3 from Shadowstats. I can't comment on the sites methodology, but it's interesting food for thought.