How Bad is the U.S. Trade Deficit with China?

San Pedro Harbor
Creative Commons License photo credit: prayitno

According to a new study by Asian Development Bank Institute, some of the U.S. trade deficit with China may be a statistical illusion (pdf).  More specifically, they delve into how the iphone, invented in the U.S., actually makes our trade deficit with China worse.

In a nutshell, China is only an assembler of the iphone, yet, beancounters count the whole value of the finished iphone rather than the value-added by Chinese assemblers.  On a value-added basis, the trade deficit in iphones becomes a trade surplus.  Here is an excerpt from the study:

Being solely an iPhone assembly center, the PRC first imports all components and then re-exports them as the final assembled product to the US. The imported components from other countries greatly inflate the export value. If iPhone exports from the PRC to the US were calculated based on the value-added contributed by PRC workers, i.e., the assembling cost, the value of the PRC’s iPhone exports to the US would be much smaller, at only US$73.5 million. Accordingly, the trade deficit associated with iPhone trade would also drop substantially. As mentioned above, the PRC imported US$121.5 million worth of components from the US companies Broadcom and Numonyx for assembling iPhones. Based on the value added approach, the US would have no deficits but a US$48 million trade surplus with the PRC in iPhones related trade. The sharp contrast between the two different trade deficit measurements indicates that conventional trade statistics are not consistent with the trade where global production networks and production fragmentations determine cross-country flows of parts, components, and final products. Bilateral trade imbalances between a country used as a final assembler and its destination markets are greatly inflated by trade in intermediate products. These statistics provide a distorted picture about bilateral trade imbalances.

While this study is interesting, the Chinese are still sitting on $3 trillion dollars worth of foreign exchange reserves. That is no statistical illusion and someone needs to do something about it . . . paging Donald Trump? Sorry, I just couldn’t resist 🙂

Why the Globe Will (Is) Cool(ing), or Will Appear To

Joe Bastardi explains the cyclical nature of global warming/cooling in this video (link opens video).  What I find fascinating is how global forecasters are splicing data from the pre- and post-satellite era (1979).  Anyone who works with data knows that splicing data created from two different methodologies is a very perilous exercise.  In this case, who with a straight face could believe that comparing pre-1979 land-based observations with post-1979 satellite observations is ever a good idea.  Yet, it apparently goes on all the time with nary a whimper of protest.  Such is the state of “global warming” science.

I think I will continue to worry about the next ice age instead . . . at least we know that has actually happened (several times) in the past.  Heck, where my home now stands was once under a glacial lake only a few thousand years ago.  Maybe a little global warming is not such a bad idea 🙂

Will America’s Private Sector Continue to Shrink? A Look at Government Compensation

In my previous blog looking at America’s Private Sector for October, 2010, I stated that I thought over the long-term the private sector share of personal income would continue to fall.  This post will begin to explore why I think that is the case.  Be warned that this journey involves delving into the intricacies of national accounting (which is why it took me awhile to amass this blog) so I’m going to try to keep it a high level.  Links to mind-numbing details will be provided.

So, let’s refresh our memory on how the private sector is measured.  The equation is: Private Sector = Total personal income – (government transfer receipts [Social Security, Medicare, Medicaid, etc.] + government compensation [Federal Civilian and Military, State and Local]).  Today we will look at government compensation.

Government compensation consists of wages and salaries and benefits and are accounted for on a cash-basis.  This is no big deal for wages and salaries since they are paid in cash in the current period; however, benefits are another story.  A large part of benefits for government workers is their defined-benefit (DB) retirement plans–this is where it gets tricky becomes the timing of payments makes a big difference.

Under cash accounting, only the payments being made into the DB plans are counted in compensation.  However, the final value of the DB plan to the employee is worth more than the actual contributions because the return earned on the trust fund will also be used to pay their benefits.  So, ideally, an accrual-based accounting would be better a better system because it would make estimates of the total value of DB plan including the future returns on the trust fund.  However, due to a lack of data and disagreement on the assumptions needed to make it work, accrual accounting is not yet possible.

As such, cash accounting is the de facto measure of government compensation.  This is a serious problem for two reasons:  First, payments to state DB plans rarely equal the annual required contribution (ARC) based on requirements from the Government Accounting Standards Board (GASB) and, second, as economist Novy-Marx and Rauh point out the GASB mandates dramatically underestimate the ARC payments.  In the future, this means ARC payments will dramatically increase dragging government compensation with it.  In Maine, for example, where stronger Constitutional constraints trump GASB; official pension payments are expected to more than double in just four short years from $300 million to over $700 million.

To illustrate how cash accounting affects the timing of DB payments consider the effects of Pension Obligation Bonds (POBs).  States have resorted to risky schemes such as POBs to try and undo the damage of short-changing their DB plans.  Let’s look at the case of Illinois which in 2003 issued one of the largest POBs ever worth approximately $10 billion.  Chart 1 below shows how the POB issuance in 2003 caused state compensation to significantly jump in 2003 under cash accounting .  This one-time infusion created an overestimate of compensation in that one year, but an underestimate in the years (most years) that Illinois neglected to pay its ARC.

Illinois State Compensation 2000 to 2009

More disturbing, states that issue POB’s not only face the escalating cost of their ARC payments due to year’s of underpayment, but they also saddle their taxpayers with the payback schedule of the POB.  Chart 2 below shows the payback schedule of Illinois’s 2003 POB which more than doubles over the 30 year schedule.  Adding insult to injury, note that the first few  years were interest-only payments!  But wait, there’s more, a new study by the Center for Retirement Research at Boston College (pdf), found that:

“while POBs may seem like a way to alleviate fiscal distress or reduce pension costs, they pose considerable risks.  After the recent financial crisis, most POBs issued since 1992 are in the red.”

Illinois Pension Obligation Bond Payment Schedule Issued in 2003

To wrap this up, the point of this is that under the current cash accounting system contributions to state DB plans will soar in the future because of past underfunding, unsustainable assumptions about the future and the use of risky schemes such as POBs.  This means that government compensation will be growing at rates that the private sector will find hard to keep up with, especially if taxes on capital are raised to pay for the growing DB costs.  As a result, government compensation will continue to crowd-out the private sector.

The only way this doesn’t come to pass is if the DB pension burden becomes too much to bear and they are dramatically reformed.  In the case of California or Illinois, that decision may be forced on them because their DB system has effectively bankrupted the state government.  If these DB systems are reformed, willingly or not, then perhaps we will one day see the private sector grow again.  I’m still not optimistic, because we still need to look at the other part of the equation, government transfer receipts, which is also an ugly picture . . . stay tuned.

For mind-number details on how the Bureau of Economic Analysis treats DB plans in their statistics, see:

Ice, Ice Baby . . .

The more data that I explore the more I’ve come to realize that truly objective data is a rarity.  There are omissions, unquantifiable variables and simple biases that all play a role.  Understanding the strengths and weaknesses of a particular data series is vitally important to good analysis.  I’ve seen many a researcher come to bogus conclusions because they failed to understand that such-and-such was an artifact of the data and not of the phenomenon being studied.

So what does this have to do with ice?  In this week’s Monday Morning Sea Ice/Global Temp report (link leads to a video), Joe Bastardi wonders aloud if U.S. arctic/antarctic ice data is not tainted by ideological bias in support of the global warming hypothesis.  This is not a charge that anyone should make lightly, but Bastardi has clearly done his homework with a plethora of additional data in direct conflict with the U.S. data.  As a fellow data-geek, I can appreciate how the dots can slowly begin to connect themselves as one studies one ream of data after another.

This is why I follow one cardinal rule with data work–the researcher  must collect and analyze the data personally.  This may seem a tedious waste-of-time when so much free labor, i.e., interns, may be floating around, but these types of anomalies take time to ferment in the brain before you have that “A-Ha” moment.  I simply don’t understand how anyone can put their name on an analysis and not have done any of the data work . . . at best, you are leaving knowledge on the table and, at worst, opening yourself up to trouble.

Is Bastardi right?  Who really knows . . . since there is another cardinal rule of data work: if you mess-up (purposefully or not), no one will notice.  Or so you hope 🙂

Jobs . . . Now You See Them, Now You Don’t

Not being involved in the financial markets, I don’t usually pay much mind to new monthly jobs data.  I know monthly numbers are extremely noisy and will be significantly revised in the future.  However, lots of people do and, consequently, it influences the psychology of the markets.  So one would think that such a closely monitored metric of economic health would be immune to data games . . . you would be wrong.

John Mauldin’s recent weekly e-letter (free subscription) found that last month’s job report may not be as rosy as the Bureau of Labor Statistics would have us believe.

I was sitting in London when the employment numbers came out last Friday, and I didn’t have time to really get into the data. I did send you Lacy Hunt’s quick analysis as to why it was weaker than it appeared, but something else did not seem right. I follow a few people who are pretty good at predicting the employment numbers (like Philippa Dunne of The Liscio Report). Most were expecting numbers in the 60,000 range. Most unusual for there to be such a big miss from these guys. I read the press release and saw nothing to raise my eyebrows. And then Alan Abelson in Barron’s gave us the following, after reciting the headline number:

“Happily, the always astute Stephanie Pomboy of MacroMavens provided a quickie explanation:

” ‘The seasonal bar which the payroll data must jump was (inexplicably and dramatically) lowered from prior Octobers.

” ‘Thus, in October 2009, the BLS set the bar at 870,000 jobs, similar to the 840,000 it anticipated in October 2008. This year, by contrast, it lowered the bar to 768,000. Mumbo, jumbo, payrolls presented “an upside surprise” of 100,000.’

“According to John Williams at Shadow Government Statistics, the BLS’ fiddling with the figures via what he calls ‘seasonal-factor games’ actually created 200,000 phantom jobs last month. John cites such finagling as the reason his prediction of an October decline and a rise in the jobless rate was wrong. It also explains why seasonally adjusted payrolls were revised upward by 110,000 in September, including 56,000 in August.”

In the opinion of your humble analyst, if they are going to make such changes, they should be announced up-front or noted prominently in the press release. People (foolishly) trade on these numbers and money is made and lost. This is serious stuff.

Why would they do this?  I fear that there appears to be a growing politicization of our economic statistical apparatus.  Why else would the Dating Committee declare on September 20, 2010 that the recession had ended in June 2009, just a few short weeks before the mid-term elections?  The recession doesn’t even look over today, November 15, 2010, much less in June 2009.  Things that make you go HHHHmmmmm . . .