Why We Need A Competitiveness Audit

In a new PPI policy brief, Diana Carew and I have proposed that the government undertake a ‘Competitiveness Audit’:

In this global economy, we need to know which industries are internationally competitive, which ones aren’t, and whether the gaps are closing or widening. Unfortunately, the reality is this data currently does not exist. And what we don’t know hurts us, because it prevents us from pursuing effective strategies for boosting US jobs.

Although the government collects reams of economic data, it doesn’t measure what’s most vital to our ability to reverse America’s jobs decline: how our goods and services stack up against those of China and other competitors in terms of price.

You can’t fix what you can’t measure. We need a new national jobs strategy that begins with an accurate way of measuring America’s competitive prowess, on an industry-by-industry basis.

We suggest that at a relatively low cost, a Competitiveness Audit can be used as the basic of a carefully targeted job strategy on both the national and regional levels. If we know what industries are ‘near-competitive’, those are the ones where targeted government help can have the biggest bang for the buck.

China Imports: SF Fed Research Misses the Point

Recently the San Francisco Fed released a new study  entitled “The U.S. Content of “Made in China””.  The study argues that “[g]oods and services from China accounted for only 2.7% of U.S. personal consumption expenditures in 2010.”  This figure was cited approvingly by a large number of publications and bloggers,  including the LA Times, the WSJ,  Fortune, The Street.com,  and  Matt Yglesias,  who writes:

When Americans go buy stuff, they’re overwhelmingly buying things that are made in America:

Sorry, Matt. I’m going to explain why the SF Fed study shouldn’t be taken seriously. In fact, the study has two main flaws:

• The authors did not distinguish between dollar shares and quantity shares of imports. When imported goods are much cheaper than domestic goods, then the quantity share can be much larger than the dollar share.
• The input-output tables used by the authors contain no actual information about how much of Chinese imports are going to personal consumption.  In fact, all imports are  divided among sectors by a simple rule known as the “proportionality assumption.”  So in reality,  Chinese imports could constitute much more of PCE–or much less–than the SF Fed economists calculate.

Dollar Shares versus Quantity Shares

So first let me explain the difference between dollar shares and quantity shares. I’ve just finished the second edition revision of my intro economics text, Economics:The Basics.  When explaining the basic concepts of supply and demand to students, it’s always important to clearly differentiate between quantity (as measured in physical units) and cost (as measured in dollars).  The same cost  can correspond to higher or lower quantities, depending on the price.

The same distinction applies to Chinese imports. It is clearly true that Chinese imports are priced lower per unit than the domestic-made products that they replace.  Similarly, China-made imports are much cheaper than the Japan-made or European-made imports that they replace–that’s why U.S. retailers changed their sourcing over the past ten years.

As a result,  if we measure the share of Chinese-made products in PCE, our answer is going to be much different if we calculate the dollar share, versus calculating the quantity share. An example will make this clear. Suppose that a U.S. shirt factory sells 100 shirts at $50 a piece, for a total cost of $5000.  Now suppose a Chinese manufacturer comes into the market and offers to sell an identical shirt for $5 a piece. In the first year, the Chinese manufacturer sells 50 shirts and the American manufacturer sells 60 shirts.  What share of the market do the Chinese shirts have?

Measured in dollars,  the Chinese have 7.7% of the market ($250/($250+$3000)).

Measured in quantity of shirts,  the Chinese have 45% of the market (50/110)

Which share is right?  For sizing the  impact of imports on U.S. jobs and manufacturing, the quantity share is much more relevant than the dollar share.

In fact, it’s very easy to construct examples where the dollar share of imports goes down, but the quantity share goes up. If China offered its imports to the U.S. for a near-zero price, then China’s dollar share of the U.S. would be close to zero (assuming that there was some U.S. manufacturing left) but the quantity share would be close to 100%.

The authors of the SF study are calculating the dollar share, not the quantity share.  That’s why their number seems so low.

In order to calculate the quantity share, we need to know the relative price of Chinese imports compared to equivalent U.S. products. It would also be useful to be able to compare the price of Chinese imports with imports from other countries.  (See a recent article in the  Journal of Economic Perspectives, Offshoring Bias in U.S. Manufacturing). It makes an enormous difference whether Chinese made imports are 5% cheaper than the equivalent U.S. products, or 50% cheaper.

However,  the Bureau of Labor Statistics does not collect such relative price data across countries.  At  no point does the BLS measure the difference in price between a shirt made in China versus one made in Italy or the U.S.  In fact, when the sourcing of a particular  good changes from one country to another,  the import price index often treats it as a new product, even if it is functionally identical.  (Take a look at the BLS explanation of its import price methodology here).

Proportionality Assumption

The second problem with the study is that the government statisticians have no information–repeat no information–about whether an imported goods or service is going to consumption, to capital spending, or being used as an intermediate input.  How could they? That question is never asked on any economic survey form.

Here’s the description of the problem from the official BEA ‘bible’,  Concepts and Methods of the U.S. Input-Output Accounts

Unfortunately, data on the use of imports by industries and final uses are not available from our statistical data sources. Thus, to develop an import matrix, we make the assumption that imports are used in the same proportion across all industries and final uses.

This is what’s known as the “proportionality” assumption. The proportionality assumption is *not* harmless, especially when it comes to calculating the contribution of a single country, such as China, to PCE (see for example the paper here).  It might very well be that Chinese imports are much more concentrated in PCE than the proportionality assumption suggests, especially in areas such as computers where the Chinese are more likely to have a low-end product (Best Buy does not sell U.S.-made supercomputers, do they?)  Or Chinese imports could go much more into investment goods than anyone realizes. The point is that there is no information to make a judgement.

So the calculations of the SF Fed economists are fundamentally based on a huge assumption which may or may not be true.  At a minimum, they should  have offered up their calculation as a range.

BTW, if the SF Fed economists still are prepared to defend their calculations, I’m happy to debate them in any forum.

On the willingness of economists to believe data that doesn’t make sense

Suppose I told you that the economy was about to go through its greatest financial convulsion in 75 years, causing a vicious disruption in the credit system and the global trading system. Consumers and small businesses would suddenly be cut off from access to credit, and large businesses would go into a fetal crouch and stop investing. A priori , would you expect that such a scenario would lead to

a) An acceleration in productivity growth,  or

b) A deceleration in productivity growth or even a drop in productivity.

My guess is that a majority–perhaps even a great majority–of economists would have answered (b) if I had thought to put the question to them.  After all, disruptions to credit, trade, and investment are terrible for the normal operations of business.

Why, then, were economists willing to accept the productivity surge of 2007-09 as gospel? It made no sense that businesses should suddenly function better  in the face of a financial crisis that nearly dragged down the whole economy.

I’ve noticed this willingness of many economists (and journalists, for sure) to accept the official data as gospel before.  It’s not a good thing.

P.S. You may guess that I’m revising my textbook.

 

 

 

Replying to a critic

[Added: See Karl's response in the comments ]

Karl Smith writes  In Which I Disagree With Almost Every Word Mike Mandel Says . It’s a long post (though not nearly as long as mine), and I just wanted to reply to two points.

First, Karl says:

The only way to get GDP wrong is either to miscount the number of goods and services sold in the US or to misestimate the price index of final goods – not intermediate goods.

Um, no. This statement is simply wrong.

gross domestic product = exports + gross domestic  purchases – imports.

Imports, as it turns, out, include a lot of imported intermediate inputs ( according to  this piece in the February 2011 Survey of Current Business, “BEA estimates that about 40 percent of imported commodities are used as intermediate inputs by businesses”).   So that getting the price index wrong for imported intermediate inputs slides right into GDP.

More fundamentally,  remember that GDP is a value-added measure. However, the fundamental unit of observation for the BEA each quarter is  revenues/shipments  for various industries, which is a gross output measure. Then the BEA has the herculean task–which I never fully appreciated before–of figuring out how much of each industry’s  revenues is final product, and how much is intermediate input.  A simple example:  Each month the revenues of law firms are reported each quarter  by the Census Bureau. Part of those revenues are final product (personal consumption of legal services), and part are intermediate inputs (legal services to business). Taking the real growth rate of observed revenues as given,  any error in estimating the real growth rate of intermediate inputs of legal services will translate directly into an error in estimating the real growth rate of personal consumption of legal services.

This sort of error does not wash out in final GDP.   Consider the related question  of whether R&D should be treated as business investment or as intermediate inputs. Currently, R&D is treated as an intermediate input, but the BEA has calculated that treating R&D  as investment would boost real GDP growth.

Second, Karl says:

An improvement in the terms of trade, which is what Mandel is identifying, is a productivity improvement for US workers. Its not based on US innovation, but it does lead to more output per US worker

The question is an important one: Is a ‘terms-of-trade’ productivity improvement equivalent to a ‘domestic’ productivity improvement? Here I’m going to cheat: The short answer is that I’m about to finish a theoretical paper showing  the specific sense in which they are not equivalent.  But you will have to wait a couple of weeks for that one…I kind of overdid the last post.

How much of the productivity surge of 2007-2009 was real?

[This post just grew and grew and grew, until it turned into something  ridiculously long. Sorry about that--I'm probably guilty of blog abuse.   If you want, here's a PDF version. I look forward to comments ]

Summary

In the 2007-2009 period, the U.S. economy experienced its worst recession since the Great Depression. Nevertheless, despite this deep downturn,  the near-collapse of the financial system and unprecedented global economic turmoil,  U.S. productivity growth actually seemed to accelerate in the 2007-2009 period, or at least maintain its previous pace.

The 2007-2009 productivity gain had a major impact on both economic policy and political discourse. First,  it gave the Fed a free hand to feed mammoth amounts of liquidity into the system without worrying about inflation.  Second, it convinced the economists of the Obama Administration that the economy was basically sound, and that the big problem was a demand shortfall. That’s why they expected things to get back to normal after the fiscal stimulus.

However, I’m going to show in this post that the  productivity gain of 2007-2009 is highly suspect.  Using BEA statistics,  I identify the industries that contributed the most to the apparent productivity gain, including primary metals, mining, agriculture, and computers and electronic products. Then I analyze  these high-productivity growth industries in detail using physical measures such as barrels of oil and tons of steel. I conclude  these ‘high-productivity’ industries did not deliver the gains that the official numbers show.

Based on my analysis, I estimate that the actual productivity gains in 2007-2009 may have been very close to zero.  In addition, the drop in real GDP in this period was probably significantly larger than the numbers showed.  I then explore some implications for economic policy.

Overview

I start by giving several  data points.

*From 2007 to 2009,  business productivity rose at a 2.4% rate, according to the official data. By comparison, the productivity growth rate was only 1.2% over the previous two years (2005-2007).

*If you like your numbers quarterly, business productivity grew at a 1.8% annual rate from the peak in 07IV to the trough in 09II.  That’s somewhat faster than the  1.6% growth rate of the previous 3 years.

*Looking at  total economy productivity–real GDP divided by full-time equivalents (FTE)–we see that productivity growth in 2007-2009, at 1.6% per year,  was double that of the previous two years (0.8% per year).

This chart breaks down productivity growth by business cycle, using the official statistics. You can see that the productivity growth of the 2007-2009 period–the worst recession in 70 years–appeared to be basically a continuation of the productivity gains during the boom. You can’t tell from this chart that anything bad happened to the economy.

The strong measured productivity growth during the crisis years reflects a steep drop in employment (-5.7%) combined with an apparently mild two-year decline in real GDP (-2.6%).  Of course, ‘apparently mild’ is still nastier than any post-war U.S. recession, but a 2.6% decline in real GDP translates into a 4.4% decline in per capita GDP, which puts the U.S. at the low end of  financial crises described by Reinhart and Rogoff.

What’s more, the 2007-2009 productivity gain had a major impact on both economic policy and political discourse. First,  it gave the Fed a free hand to feed mammoth amounts of liquidity into the system without worrying about inflation. 

Second, the productivity gains convinced the economists of the Obama Administration that the economy was basically sound and there was nothing wrong with  the ‘supply-side of the economy.  Instead, the Obama Administration concluded that the big problem was a demand shortfall, which is why they expected things to get back to normal after the fiscal stimulus.

Consider this April 2010 speech from Christina Romer, then head of the CEA. She said:

The high unemployment that the United States is experiencing reflects a severe shortfall of aggregate demand. Despite three quarters of growth, real GDP is approximately 6 percent below its trend path. Unemployment is high fundamentally because the economy is producing dramatically below its capacity. That is, far from being “the new normal,” it is “the old cyclical.”

Perhaps more important from a political perspective, the productivity surge  helped convince the Obama economists that the job loss was ‘normal’ in some sense. That is, the rise in productivity suggests that the financial crisis apparently just accelerated the normal process of  making U.S. businesses leaner and more competitive, and there was no structural problem. In fact, that’s what Romer said in her speech:

In short, in my view the overwhelming weight of the evidence is that the current very high — and very disturbing — levels of overall and long-term unemployment are not a separate, structural problem, but largely a cyclical one. It reflects the fact that we are still feeling the effects of the collapse of demand caused by the crisis. Indeed, at one point I had tentatively titled my talk “It’s Aggregate Demand, Stupid”; but my chief of staff suggested that I find something a tad more dignified.

If productivity was rising, then the job loss was due to a demand shortfall and could be dealt with by stimulating aggregate demand. That, in turn, helps explain why the “job problem”  didn’t seem so urgent to the Obama administration, and why they spent more time on other policy issues such as healthcare and regulation.

Is the Defense Industrial Base Disappearing?

Richard McCormack of manufacturingnews.com writes:

The shift of U.S. manufacturing to foreign nations has become an important issue to the U.S. intelligence community. The Director for National Intelligence is undertaking a National Intelligence Estimate (NIE) on the state of American manufacturing. Growing concern over loss of domestic capability and dependence on foreign nations for key high-tech materials, components and systems has led the DNI office to start such an effort.

I’m glad to hear that, but I certainly hope that the DNI realizes that the manufacturing statistics are pretty much out to lunch.

The Real Ireland Question

I like Michael Lewis. I’m envious of his writing and reporting skills–and I really learned a lot from The Big Short.

But I have to say that I was disappointed in his latest Vanity Fair piece “When Irish Eyes Are Crying” on Ireland’s financial collapse.  A well-written and engrossing piece, for sure, but he managed not to fully address one of the greatest puzzles about Ireland’s economy–how did it ever get such a high per capita income *before* the construction boom started? Here’s what Lewis wrote:

At the bottom of the success of the Irish there remains, even now, some mystery. “It appeared like a miraculous beast materializing in a forest clearing,” writes the pre-eminent Irish historian R. F. Foster, “and economists are still not entirely sure why.” Not knowing why they were so suddenly so successful, the Irish can perhaps be forgiven for not knowing exactly how successful they were meant to be. They had gone from being abnormally poor to being abnormally rich, without pausing to experience normality.

Lewis accepts that Ireland went from poor to rich, and then proceeded to fumble it away. But what if Ireland was never really as rich as it seemed? What if the statistics that said “rich” were wrong? Maybe the real problem came earlier.

Let’s start by looking at the numbers. Remember that Ireland was called the “Celtic Tiger’ because it grew so fast in the 1990s.  The chart below has three lines– per capita real GDP in Ireland,  per capita real GNP in Ireland, and per capita real GDP in Germany (a certifiably real economy that makes things).  Per capita real GNP  subtracts out the net income flow of profits out of Ireland–that makes a big difference  because of Ireland’s role as a pharma and electronics production hub by multinationals.

The salient fact is that according to the official stats, Ireland’s real per capita income appeared to reach Germany’s level around 2000, before the construction boom started.  (Construction was important to the economy in 2000, but not dominant like it was later in the decade. Home completions in Ireland totalled around 50,000 in 2000, compared to 93,000 in 2006). That’s really an astonishing run-up. In 1995, Ireland’s per capita income was 25% less than Germany’s. By 2000 Ireland had made up the gap. Truly a Celtic Tiger not to be trifled with.

Now, let’s stop here.  As regular readers of this blog know, I’m very concerned that the conventional economic statistics are systematically mismeasuring national output, because of problems handling trade. Ireland happens to be one of the most open economies in the developed world.  In fact, exports in magnitude are almost as large as GDP, with imports not far behind. (I don’t know who has a more open economy…but it isn’t China).

With exports and imports so large, it only makes sense that changes in exports and imports actually drive growth in Ireland. In fact, everything else–including capital invesment in things like homes–is really a secondary source of growth. 

So I used the data from the Central Statistics Office in Ireland to calculate the contribution of exports and imports to growth. First I started with the supposed period of strong growth, 1995-2000.  You can see that the positive contribution of exports and the negative contribution of imports is enormous.

Here is a similar chart for the 2000-2005 boom period.  Note that exports and imports are still relatively huge, even during the construction boom.

The implication–and you knew I’d get to the implication someday–is that in Ireland, in particular, relative small mismeasurement errors in real exports and/or imports can have outsized effects.

So this Celtic Tiger may have been a Celtic Kitten instead. And the shift from real boom to fake boom may not have been as abrupt as it seemed.

A Bizarre Labor Market (with data)



As we start the New Year, we face what is perhaps the most unpredictable and bizarre labor market I can remember. This morning the Conference Board released the December Help-Wanted Online report, which apparently shows a sharp increase in labor demand over the past year in most occupations. However,  the BLS  employment by occupation data shows no corresponding gain, even in occupations with soaring want ads.  Nor does the unemployment by occupation data show any corresponding movements. 

I have my own thoughts about what the data means, which I’ll share below. But first let me present the full array of data, by occupation,  so you can make your own judgments.

The first column of data in the table below is the Supply/Demand Rate, as calculated by the Conference Board. That indicates the ratio of unemployed workers to ads, so a small number is better.  The second column is the change in online ads over the past year, as measured by the Conference Board, so a big number shows that demand has ramped up. The third and fourth columns are the changes in occupational employment and unemployment over the past year, respectively, as measured by the BLS.

The ordering of occupations by supply/demand rate feels more or less right. But when it comes to the link between changes in demand, employment, and unemployment, there’s little  consistency. We’ve got occupations with soaring demand and no gains in employment (management, transportation).  We’ve got occupations with good supply/demand ratios and no gains in demand (health practioners).  And so forth and so on.

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A Bizarre Labor Market
		Supply/Demand	yr/yr percentage change**
		Rate*	Want Ads	Employment	Unemployment
Healthcare practitioner and technical		0.3	2%	-1%	8%
Computer and mathematical		0.4	27%	-3%	21%
Life, physical, and social science		0.8	40%	4%	-3%
Architecture and engineering		1.0	47%	1%	-18%
Management		1.4	56%	-3%	-5%
Legal		1.5	-6%	1%	-32%
Business and financial operations		2.1	6%	2%	10%
Community and social services		2.3	19%	-8%	6%
Arts, design, entertainment, sports, and media		2.6	9%	-1%	12%
Healthcare support		2.6	2%	0%	7%
Sales and related		3.5	-2%	2%	-1%
Office and administrative support		3.9	21%	0%	3%
Installation, maintenance, and repair		4.0	36%	3%	3%
Education, training, and library		4.3	22%	-1%	-7%
Personal care and service		5.5	8%	5%	14%
Transportation and material moving		7.4	61%	2%	-2%
Protective service		8.0	34%	1%	36%
Food preparation and serving		9.2	26%	2%	6%
Production		10.8	59%	10%	-6%
Building and grounds cleaning and maintenance		14.1	39%	-2%	0%
Farming, fishing and forestry		28.4	38%	2%	52%
Construction and extraction		29.7	31%	-8%	-15%
*Supply/demand rate, calculated by the Conference Board, is the number of unemployed workers
divided by number of want ads based on latest data.
**December 2009-December 2010 for online wants ads, November 2009-November 2010 for other data
Data: The Conference Board, Bureau of Labor Statistics
Calculations: South Mountain Economics LLC

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My interpretation: The labor market is getting ready for a massive rise in employment over the next year,  as companies finally start hiring for positions they’ve been advertising for. 

We’ll find out soon.