Relevant and even prescient commentary on news, politics and the economy.

Employment Summary: 195,000 Payroll Jobs Added in June; Household Survey Shows 432,000 More are just Working Part Time

by reader rjs

Employment Summary: 195,000 Payroll Jobs Added in June; Household Survey Shows 432,000 More are just Working Part Time

FRED Graph

On its face, the headline increase of a seasonally adjusted 195,000 new jobs reported by the Bureau of Labor Statistics in its June Employment Situation Summary was a respectable monthly increase and better than expected, and with the revisions of April’s payroll jobs from 149,000 to 199,000 and May’s from 175,000 to 195,000, this report represents 265,000 net new jobs compared to last month’s report, adding up to give us the best first half of private job creation since the Clinton administration. However, as we’ll see, the jobs that have been added for the most part aren’t the types that would typically support a decent middle class lifestyle, and almost twice as many new jobs were only part time, strongly suggesting that full time jobs had actually declined in June..

Considering the large revisions, we’d do well to start our look at the establishment survey with a reminder that the 90% confidence interval for this survey is on the order of +/- 90,000 jobs, and that the average change from the initial estimate to the 3rd review has been averaging 46,000, so none of the numbers in this report are cast in concrete.

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An Important New Book on Income and Wealth Inequality

I just got an email from LIS (the group that runs the Luxembourg Income Study and Luxembourg Wealth Study) giving notice of a new book:

Income Inequality: Economic Disparities and the Middle Class in Affluent Countries

Contrary to the title, there’s a whole section on wealth inequality.

The book’s 17 chapters by 17 established researchers/research teams all draw on the extensive LIS databases of micro-level income and wealth data from 28 affluent countries 1980–2004. That large-sample, carefully normalized database holds promise of delivering insights that have been unavailable from previous data sets.

Given my interest in inequality and growth in advanced countries, I’ve been watching LIS for a while. I’ve tried working with their data, but it’s so micro-level that a great deal of work would have been required — more than I as an interested amateur was prepared to devote. I’m excited to see what these researchers have done with it.

Unfortunately it’s $65, and I don’t see any indication that the researchers have made their compiled/analyzed data sets (much less spreadsheets/stata files,etc.) available in electronic form for vetting and consideration by the likes of me (and more-competent others). But I’ll probably break down and buy it anyway.

Cross-posted at Asymptosis.

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State GDP shows a manufacturing rebound in 2012

by Rebecca Wilder

State GDP shows a manufacturing rebound in 2012

State GDP shows the following in 2012: durable goods manufacturing and finance and insurance are primary drivers of cross-sectional growth. This confirms the national story, according to the BEA.

A state-level breakdown shows strong (a surge in) economic activity in North Dakota, Oregon, Texas, and Utah, as 2012 real GDP was the highest in these economies compared to a long-term average (since 1997): 55%, 33.2%, 26%, and 25.3%, respectively (see Table in appendix). In contrast, 2012 real GDP in Missouri, Ohio, Michigan, and Connecticut were the worst performers compared to their long-term averages at 5.6%, 4.2%, 2.3%, and -2.4%, respectively.

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The Appalachia Map, Yet Again

Lots of desperation talk these days by Republicans hoping to win future national elections by increasing their share of the “missing” white vote, while ignoring all those brown people. (Sean Trende’s piece seem to be the epicenter at this moment.)

Nate Cone drives a very effective stake through the heart of that zombie ambition here, with a single map (below). Yes: that (“Southern“) strategy worked brilliantly for decades. (Johnson said that civil-rights legislation would lose the South to Democrats “for a generation,” and he was only wrong in underestimating the duration.) And it’s the only thing that’s kept Republicans from utter humiliation and abject collapse over the last decade or so. But Judis and Texeira will be right eventually; demographics is destiny, and there are only so many white people — an ever-decreasing percentage. Courting whites may be the most effective method of stemming the hemorrhage, but it’s nothing more than that.

Faithful readers will remember seeing this basic map here multiple times. This latest version shows Obama’s gains/losses in share of the white vote compared to Gore (this by a black man):

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Humans’ Comparative Advantage: Wanting Things

Frances Coppola sums up and expresses a great deal of great thinking in her recent Pieria piece, The wastefulness of automation.

I’d like to highlight one point, one that I’ve been pondering for a long time. There’s one area where machines — until they get sentient — can’t replace people (here from her response in comments):

only humans desire to consume in excess of basic living needs. Show me a robot that wants a Gucci handbag. Or a Shire horse that wants an iPad.

Or…an expert system or drill press that wants a massage. As she concludes the article:

Maybe capitalists DO need a large labour force. Their survival depends on it.

Which leads me, once again, to wonder why I’m not hearing a lot more people discussing Ed Lambert’s work on “effective demand” and its relation to labor’s share of income.

Cross-posted at Asymptosis.



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The So-Called Credit Crunch, Again Some More

It’s really hard to kill this meme. Note the label on this graph from today’s Free Exchange post:

Now change that heading to read “Business borrowing.” Sort of gives a different impression, right?

The idea that the problem’s on the supply side is pervasive, and false or at least wildly overblown. Lending rates are at historic lows. But the credit-crunch storyline gives very effective aid and cover to the financial industry in justifying its inordinate size and power.

I tried to drive a stake through the heart of this vampire squid back when we saw that first dive, back in 2009, and the situation is much the same today. (See also Related Posts at the bottom of that post.)

The Sky Is Falling! Business Lending Down 1.2 Percent!

Cross-posted at Asymptosis.


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The Standard Deviation of NGDP Growth Since 1950

This is a follow up to The Standard Deviation of NGDP Growth During the Great Inflation.  In that post I showed this 100 point scatter graph of the 12 Quarter average Compounded Annual Rate of Return [CARC] of NGDP vs 12 quarter Standard Deviation [Std Dev] of CARC from 1954 to 1978.  It then occurred to me that some of those red dots that have fallen down close to the yellow trend line might be misallocated.  What they represent are 3 quarters in 1957 when Std Dev had a chance to settle down between recessions, and the tumble down of Std Dev in the early 60’s as the high Std Dev values of the the 1960 recession fell out of the 12-point data kernel.

This is illustrated in Graph 1.


Graph 1 CARC vs Std Dev 1954-78, With Points Reallocated


The red dots are data points from 1954 through Q1 ’62.  The yellow dots are from Q1 ’64 on. The blue dots are the three low Std Dev points from 1957, and the pink dots represent the transitions in and out of the 1957 blue-dot data, and the tumble down in Std Dev from Q2 ’62 to Q4 ”63.  The original blue trend line is retained for comparison.  Note that removing these three blue and 8 transitional data points from the pre-1964 data set causes the negative correlation of that period to completely evaporate.

This might seem a bit arbitrary; but now we can observe a more tightly packed red data set, and the behavior of the pink data points does seem to be unusual.  The string of high side outliers in the yellow data set occurs in 1971-2, and is associated with the 1970-71 recession.

This piqued my curiosity, so I took a look at the bigger picture – all 253 quarterly data points from Q1 1950 through Q1 2013, shown in Graph 2.


 Graph 2 – CARC vs Std Dev 1950 to 2012


I see the great majority of these points clustering or stringing out along imaginary upward sloping lines that suggest coherent data subsets, and a relatively small number of points [39, or 15.4% of the total] where the data is in transition between sets.

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Productivity & Effective Demand: An Intriguing and Disturbing Story . . .

Edward Lambert at Effective Demand; Effective Demand = Effective Labor Income/(cu*(1-u)) points to the result of an economy left to maximize Profits at the expense of Labor. I have my own version or underlying causes of this issue and Edward gives the economic side of it.

I am going to show a graph of Productivity against Effective demand. It is an intriguing and a disturbing graph. Let me start by giving the equation for the productivity used in the graph.

Productivity = real compensation per hour: business sector/(labor share: business sector * 0.78)

The data for this equation comes from this graph at FRED.

The equation for effective demand is…

Effective Demand = real GDP * (labor share: business sector * 0.78)/TFUR

TFUR (total factor utilization rate) = capacity utilization * (1 – unemployment rate)

Let me just show the graph and then start explaining . . .

Productivity and Effective Demanda

The graph shows quarterly data from 1967 to the 1st quarter of 2013. The red dashed line is a trend line for the data. We can see that from 1967 to 1997, the plot stayed very tight on the trend line. There were deviations from this line during times of shocks and recessions. But it is very interesting how closely the plot followed the trend line before 1997.

Before 1997, the plot going below the trend line was associated with a recession. The explanation of this is that effective demand rises more during a recession because of more available capacity of labor and capital. At the same time. productivity tends to fall behind the trend line due to rising labor share, not falling real compensation.

When the plot goes above the trend line, productivity is ahead of effective demand. Productivity rises due to labor share settling down and real compensation rising. Effective demand tends to stay still during the expansionary phase of a business cycle. The economy grows up to the effective demand limit and then gets set for a contraction.

We used to have a balance between productivity and effective demand. The economy moved directly on top of the trend line for many many quarters. And now the economy has lost that balance. Since the late 90’s it is a fleeting moment when productivity and effective demand come together on the trend line.

Before 1997, there was very little movement away from the trend line. Then something unusual happened between 1997 and 2001, the dotcom bubble years. The plot went progressively below the trend line even though there was no recession. Productivity was rising during these years, but effective demand was rising at such an unusual rate that productivity could not keep up with it. Effective demand was being artificially created and inflated. The recession of 2001 followed the same unusual path as before the recession.

In 2002, the economy had to make an adjustment. Productivity had to rise or effective demand had to fall. During the housing bubble years (from 2002 to the quarter right before the 2008 recession), productivity rose, while effective demand basically stayed steady. The plot went back above the trend line showing that productivity was beyond the capacity of effective demand and that productivity was at a non-sustainable level. The economy sustained this high level of productivity in the face of low effective demand for a few years, but eventually the correction would come in 2008. The correction was a collapse.

Look at where the economy is now. Since the end of 2010, the plot has barely moved from a productivity just below 1.4 and an effective demand around $14.1 trillion. The plot is way above the trend line and has been just sitting in the same spot for over 2 years. Effective demand is too low for the current productivity in the US. This is an economic bomb building energy that will eventually go off when real GDP approaches $14.1 trillion.

A friend of mine had a dream a few nights ago, where a spirit said that the economy is dying. The graph above would lead one to think the same.
Think about it… where can the economy go now from here?
There are 2 options . . .

Option #1 looks at the equation for Productivity: You have to lower productivity by increasing labor share in relation to real compensation.

1. If you lower real compensation, labor share would fall, but it would have to fall slower than real compensation. Keep in mind though that a lower labor share would lower effective demand too, which would work against the objective. However, if labor share actually rose in the face of lowering real compensation, you would see an economic contraction. So lowering real compensation is not a good option.
2. On the other hand, if you raised labor share faster than raising real compensation, productivity would come down as effective demand increased from higher labor share. This is a safe and sensible way to correct the huge imbalance we find ourselves in.

Option #2 looks at the equation for Effective Demand: You have to increase effective demand back up to $16 trillion. There are two options here as well.

1. Utilization of labor and capital would have fall. (TFUR in the equation above would have to fall.) This would mean a rise in unemployment, which would mean another collapse.
2. Labor share would have to rise. This would also have the beneficial effect of lowering productivity, as long as real compensation rose moderately.

As we can see, the only real option to avert another collapse is to raise labor share of income. This is not likely as businesses are even now fighting an increase in just the minimum wage. Businesses are trying to maximize their profits and do not want to raise labor costs. Yet this objective of theirs is going to kill the economy.

The graph above shows that there is a bomb ticking, and it is a bigger bomb than we saw in 2008. Higher productivity in the face of low effective demand is unsustainable. Yet, we have been sustaining it for over 2 years now with an incredible expansionary monetary policy in the face of an incredibly low labor share of income.

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Where Did the Risk Go? (2007)

Lifted from comments from reader Juan:

Where Did the Risk Go?

How Misapplied Bond Ratings Cause Mortgage Backed Securities and Collateralized Debt Obligation Market Disruptions

Joseph R. Mason. Louisiana State University – Ourso School of Business; University of Pennsylvania – Wharton Financial Institutions Center and Josh Rosner,  Graham Fisher & Co.    May 3, 2007

”We show that the big three ratings agencies are often confronted with an array of conflicting incentives, which can affect choices in subjective measurements of risk. Of even greater concern, however, is the fact that the process of creating RMBS and CDOs requires the ratings agencies to arguably become part of the underwriting team, leading to legal risks and even more conflicts.” Mason and Rosner were slightly ahead of the curve, made sense, well documented but, for most part, ignored.

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The Standard Deviation of NGDP Growth During the Great Inflation

This post is a side bar to the Remarkably Stable GDP Growth series

Part 1


Part 3

Once again I have to thank Mark Sadowski for goading me into digging deeper, staring longer, and thinking harder about this topic than I otherwise would have.  In comments to Part 3,  Mark informs us that: 

In three year periods ending in 1954 to 1978, which overlaps with the Great Inflation, the 12 quarter standard deviations of the compounded annual rate of change in NGDP are significantly *negatively* correlated with the average rate of change in NGDP. In other words NGDP became *less volatile* as its average rate of change *increased*.

Let’s have a look.  Graph 1 is a scattergram of 12 Qtr average NGDP growth from this FRED page, measured as Compounded Annual Rate of Change [CARC] vs Std Dev for the years 1954 through 1978.  A linear trend line is included.


 Graph 1 – 12 Q Avg CARC vs Std Dev

At first glance it appears that Mark is right.  But there is something strange about that data distribution.  Do you see it?

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