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

Why Spending/GDP is a Terrible, Horrible, No Good, Very Bad Metric For Judging Obama’s Performance

A post like this really shouldn’t be necessary, but part of the right wing canard that Obama has been a profligate spender is based on spending as a percentage of GDP.

It looks like this – Graph 1.

Graph 1.  Fed Expenditures/GDP

Sure enough, by the end of Clinton’s term the ratio had fallen from Reagan’s high of 24% to a modern low of 19%.  But note that the 19% value wasn’t typical.  It was the end point of a decade-long decline.  And, yep, there’s Obama with an all-time-high approaching 26%.

What otherwise intelligent, and sometimes even famous people seem to ignore though, is that every ratio has not only a numerator but also that ol’ devil denominator.   Let’s have a look at both of them.  Graph 2 shows GDP and Expenditures since 1980, expressed in $ Billions.  I’ve also added a line representing 5* Expenditures, since 20% of GDP is a reasonable rough estimate for the post WW II era.

Graph 2.  Expenditures and GDP Compared

Actually, the 5x Expenditures line runs pretty consistently above the GDP line, telling us two things that we should have already known from looking at Graph 1.  First, Expenditures greater than 20% of GDP have been the norm since before 1980, and 2) Clinton’s final number is not representative of anything other than a single year.  Using it as a comparator is cherry-picking and fundamentally dishonest.

The 5x line also emphasizes that the majority of the spending increase under Obama unavoidably occurred during the officially designated recession.  The GDP line shows that, post recession, GDP growth has not recovered to the pre-recession trend line.  In fact, growth has established a new trend line with a lower slope.  This is unprecedented in the scope of FRED historical data.  My guess is that insufficient Federal spending has been a big drag on this recovery.  But it’s also true that GDP growth has been in secular decline since the Reagan administration.  Note that skewing the denominator down will automatically skew the ratio up.  This is what Bill Clinton calls “arithmetic.”

Slicing across this a different way, Graph 3 gives us year-over-year percentage growth in Expenditures and GDP, dating back to the Eisenhower administration.

Graph 3.  YoY % Change in Expenditures and GDP

A few simple observations:
– The spending increase during the recent recession was modest by any standard, and dwarfed by earlier surges.
– That increase, coupled with the most severe GDP decline since the other Great Depression gave our beloved ratio a terrible, horrible, no good, very bad double whammy.
– GDP growth during this recovery is only marginally better than it was during the 2001-2 low, and far below Clinton era levels.
– Clinton was the most consistently frugal president of the post WW II era – until now.
– Since the recession was declared over, B. Hoover Obama has been miserly.

One can legitimately argue that Obama’s approach to the economy has been excessively conservative.  Krugman has made this point repeatedly.  I often say that Clinton governed to the right of Eisenhower – who was a genuine deficit hawk – and that Obama is to the right of Clinton. That is intended to be slightly hyperbolic, but using this data as the benchmark, it’s dead on.

Any questions?

Cross posted at Retirement Blues

Tags: , , , , , , , Comments (54) | |

A Quick Look at Federal Spending

Over at Plain Blog, an anonymous wing nut made this off-topic comment.

Now, yes, Bill Clinton and his 2000 federal spending level of 18% of GDP doesn’t put him on the fringe, which makes it surprising that you lefties are celebrating him, even as you hysterically condemn anybody who resists the Left’s current massive spending levels, which are nearly 50% greater than Clinton’s and are spending the nation into debt obvlivion.

This once again raises the regressive canard that Obama has been a profligate and fiscally irresponsible spender.

Let’s have a look.

Here is a graph of current expenditures that took place in the years of the current century.

First observation is that anon’s math isn’t very good.  Current expenditures are roughly 100% greater than when Clinton left office, not a mere 50%.

Second observation is that the vast majority of that increase – from about $1900 billion to about $3200 billion – took place under the previous administration.

Third observation is that the bulk of the Obama increase occurred during the recession – as it should – from a bit under $3200 billion to a bit under $3600 billion.  Since then it’s crept up to about $3800 billion, and has recently flat-lined.

A more subtle point is that spending, like many time series data sets, increases exponentially over time, following population growth.  So, saying that a value at time B is some percentage greater than the value at time A communicates essentially zero information.  Context matters.

Let’s look at expenditures in terms of year over year increase.

Yep, there was a big increase in 2009 and 2010, as social safety net programs kicked in.

Since then, expenditure growth fell precipitously and now has actually gone negative.  The last time that happened was in the Eisenhower administration.  Clearly, Obama has not been profligate.  Would it be an exaggeration to say he’s been miserly?

Bill Clinton did a great job of exposing Republican lies in his speech at the Democratic convention last night.  But really, it’s easy.  All you have to do to refute a regressive is have a quick look at facts and data.

Cross-posted at Retirement Blues.

Tags: , , , , , Comments (21) | |

Debt, Recession, and That Ol’ Devil Denominator

Krugman recently presented this graph, showing household debt as a percentage of GDP.

and made this comment.

Second, a dramatic rise in household debt, which many of us now believe lies at the heart of our continuing depression.

There are those who seem to believe that if Krugman says it, it must be wrong.   Here is Scott Sumner’s reaction.

What do you see?  I suppose it’s in the eye of the beholder, but I see three big debt surges:  1952-64, 1984-91, and 2000-08.  The first debt surge was followed by a golden age in American history; the boom of 1965-73.  The second debt surge was followed by another golden age, the boom of 1991-2007.  And the third was followed by a severe recession.  What was different with the third case?  The Fed adopted a tight money policy that caused NGDP growth to crash, which in turn sharply raised the W/NGDP ratio.  Krugman has another recent post that shows further evidence of the importance of sticky wages.  Forget about debt and focus on NGDP.  It’s NGDP instability that creates problems, not debt surges.

Bold emphasis is provided by Marcus Nunes, who goes on to say:

Why does the share of debt rise? I believe it reflects peoples “optimism” about future prospects. In the chart below I break down Krugman´s chart and separate mortgage and non-mortgage household debt as a share of NGDP. I also add the behavior of the stock market (here represented by the Dow-Jones Index).

[See the linked Nunes post for his chart.]

Eye of the beholder, indeed.  Nunes makes an expectations-based argument, and adds:

Non-mortgage debt remains relatively stable after 1965, fluctuating in the range of 17% to 22% of NGDP. No problem there.

But the reality is that non-mortgage debt has grown quasi-exponentially in the post WW II period.

Sumner, as always, beats the NGDP drum. 

My friend Art takes a jaundiced view of the Sumner-Nunes interpretation.  He gets it exactly right.  To see why, let’s go back and have a look at the data.  Here is straight CMDEBT (Household Credit Market Debt Outstanding,) presented as YoY percent change – not distorted by a GDP divisor.

Sumner sees a debt surge from 1952 to 1964.  I see a secular decrease in the YoY rate of debt growth from over 15% to under 5% by about 1966.

Sumner sees a debt surge from 1984 to 1991.  I see a decrease in the YoY rate of debt growth from over 15% to about 5% over that same span.

Sumner sees a debt surge from 2000 to 2008.  I see a modest rise into a broad peak between 2003 and 2006, with a net decrease in the rate of debt growth over the 2000 to 2007 period.  In CY 2008 debt growth goes negative.  Here’s a close-up view.

So much for optimism-fueled debt growth. 

Between the non-existent debt surges Sumner sees a golden age from 1965 to 1973.  I’m a bit puzzled by a golden age boom that straddles one recession and leads directly into another; though I will admit that average GDP growth then looks impressive compared to the GDP growth of the last decade.  But the thing that Sumner misses within his “golden age” is the big debt surge from 1971 to 1974. 

By my reckoning, Sumner is incapable of identifying either a debt surge or an economic boom.  

So what is going on here?  Sumner and Nunes either fail to realize or deliberately ignore that the quantity CMDEBT/GDP has a denominator.  Let’s look at GDP.  Here is YoY GDP growth over the post WW II period.  And, of course, this is NGDP – not inflation adjusted – the very quantity to which Sumner ascribes so much gravitas.

The average GDP growth over the period 1948 to 2007 is 7.04%
The average over the “debt surge” period 1952 to 1964 is 5.35%
The average over the “debt surge” period 1984 to 1991 is 6.85%
The average over the “debt surge” period 2000 to 2007 is 5.24%

What we have are three periods of below average GDP growth, two of them substantially so.  The middle one is only slightly below average, but that is misleading since there is a steep decline in GDP growth over the period.

Consider C = A/B.  If B is small or decreasing, it will tend to make C large or increasing.  To ascribe all of the changes in C to changes in A is to ignore that Ol’ Devil Denominator.  

Sumner does bring up NGDP growth late in the passage quoted above, but I don’t get his point.  If I’m reading him correctly, he claims that NGDP growth crashed between 2000 and 2008, and that caused the Debt/GDP ratio to rise.  But NGDP growth was sharply up from 2001 to 2003, relatively steady through 2006, and never crashed until 2008.  If there is any sense in his argument, somebody will have to explain it to me.   

What actually happened was a real debt surge – but it was between 1997 and 2004.  Meanwhile, GDP growth both before and after the 2000-2003 dip was around 6 to 7%.  Then, in 2006, household debt growth and GDP growth both started to slump, and in 2008 took a nose dive together.

Sumner and Nunes have made a very fundamental error – not so much in the math itself as in the application of logic.  This is sloppy thinking, and any conclusions drawn from it must be highly suspect.

To get a handle on what is really going on, let’s look at debt growth and GDP growth together.

 
They don’t move in lock-step, but the similarity is striking.  Specifically, every recession except 2001 corresponds exactly to a minimum in debt growth.  So Sumner’s advice to “forget about debt” looks like it’s missing something very important – specifically that the household component of spending [aka GDP growth] has been debt financed.  To put it in context, have a look at Krugman’s first graph in the article linked above.   It shows what we all know, but some chose to ignore – that median wages have stagnated for 40 years.

In my narrative, the reason household debt grew to almost 100% of GDP is that stagnating incomes have not been able to support the cost of the American life style – due to decades of inflation, but probably largely driven by the costs of health care and education.  Remember – contra the prevailing view of economists today – spending, and therefore GDP growth, is directly dependent on income, not on wealth

Debt is a useful tool that develops into a problem when it becomes too burdensome to service.  Looking at debt as a percentage of GDP provides a clue as to how serviceable the debt is.  When you also consider that all of the GDP growth over several decades has gone to the top income earners, you can see that the debt servicing problem is made that much worse for the average person. 

Nunes thinks debt rises when people are optimistic about the future, and he weaves a narrative based on that idea.  He then blames the 2008 collapse on bad policy, including a contractionary Fed.   He appears to want spending growth, but refuses to recognize the exhausted ability of ordinary people to spend.

In my view – and I think the data supports it – Krugman and Art have this exactly right.  And, as is nearly always the case, those who disagree with PK on what is happening in the real word have to invent a fantasy-world explanation – or, if I can borrow an especially tortured metaphor from Nunes,  pull a red herring out of a hat.

Cross-posted at Retirement Blues.

Tags: , , , , , Comments (25) | |

Who Determines Short Term Interest Rates?

Do you think it’s the Fed?

It’s not.

The market determines short term interest rates.

Really.

The Federal Funds Rate, which is set by the Fed, FOLLOWS 3 month T-Bill rates.  It does not lead the economy.  Here are some looks.  First the whole data set, going back to 1954, presented in Graph 1.

Federal Funds data from FRED.

T-Bill rates from a different Federal Reserve site

These are tabulated monthly values.  But the T-Bill rate is set in a weekly auction, and the Fed Funds rate is set by the Fed Open Market Committee, on an arbitrary schedule, at their discretion. 

Graph 1  Fed Funds and 3 Mo. T Bill Rates, 1954-2011

Not exactly lock step, but they are a couple of clinging vines.  At this scale, it’s pretty hard to tell who leads and who follows.  Let’s look closer at the last few decades.  First, the all-time highs of the early 80’s, in Graph 2.

Graph 2  Fed Funds and 3 Mo. T Bill Rates, 1978-84

Here, the Fed Funds are in green and the T-Bill rate in orange, with the moves off of tops and bottoms highlighted in other colors.  Fed Funds tend to run a bit above T-Bills.  From this data, T-Bill rates generally change direction in the same month or the month prior to a Fed Funds change.

Graph 3  Fed Funds and 3 Mo. T Bill Rates, 1978-84

Same story in Graph 3: either concurrent motion or T-Bills are slightly ahead.  For the two downward moves at the beginnings of 1990 and 1995, they are three to four months ahead.

The story is similar for the most recent decade, shown in Graph 4.

Graph 4  Fed Funds and 3 Mo. T Bill Rates, 2000-2008

Looks like the Fed is a close follower of T-Bill rates, usually within a month or so.  Coming off a diffuse top, the lag can be a little longer.

Graph 5 shows a close up of 2001-5, without the odd colors.  T-Bill leadership is easily seen.

Graph 5  Fed Funds and 3 Mo. T Bill Rates, 2001-05

Two questions present themselves:

1) Does the Fed have any real power to influence interest rates?
2) What would happen if they attempted to move counter to the market?

In my mind, this casts serious doubt on the usefulness of interest rate manipulations as a monetary policy lever.   What do you think?
.

Tags: , , Comments (35) | |

A Different Look at GDP and Inflation

At Illusion of Prosperity, Stagflationary Mark posted this scatter-graph of quarterly GDP YoY growth and CPI data from Q1, 1948 through Q4, 2011.  Each point represents the differences from the medians of each data set for each of the variables, respectively.  This gives you a picture of time spent above and below what might be considered normal performance.

I wondered how this would look if each point were identified by presidential administration, and if this would suggest any particular narrative.  So I redid the graph, data from FRED, using mean instead of median as the determinant.  It is presented here as Graph 1, with each data point (256 total) color-coded by presidential party; red for Republicans, blue for Democrats.  The calendar quarter of each president’s inauguration is allotted to the previous administration.

I’ve labeled the quadrants as follows, and indicated the frequency of data points populating each quadrant.

Here are the Mean and Standard Deviation values.

 

Graph 1  CPI and GDP, data from FRED

The GDP data has something close to a normal distribution, with approximate symmetry around the mean. The CPI data does not. For CPI, the highest frequency is 2 percentage points below the mean, and there is a long tail on the high side, so the distribution looks more like a Poisson type.

I’ve broken out presidential administrations, 3 or 4 to a graph, to avoid excessive clutter.  Graph 2 shows the administrations of Truman (light blue), Eisenhower (red), and Kennedy-Johnson (dark blue.)

Graph 2  CPI and GDP, Truman, Eisenhower, Kennedy-Johnson

Results during the Truman administration were erratic, with both inflation and deflation occurring, and GDP growth widely variable as the nation made post WW II adjustments, and several million G.I.’s reentered the work force.  Ike was an inflation hawk, and one of only two presidents to achieve below average inflation in every quarter of his administration.  (Take your guess now as to who the other might be.  All will be revealed in due time.)  Still, the road was bumpy, with GDP growth highly variable, and two rather severe recessions during his term.  The Kennedy-Johnson administration enjoyed superior economic performance and relatively low inflation, with only 6 quarters of below average GDP growth, and only five quarters of above average inflation during the entire 8 years.  This was one of only two administrations to avoid recession for an entire 8-year term.

Graph 3 shows the Nixon-Ford (orange), Carter (blue), and Reagan (red) administrations.

Graph 3  CPI and GDP, Nixon-Ford, Carter, Reagan

Here we find three increasingly extreme excursions into stagflationary territory, two under Nixon-Ford (remember Whip Inflation Now buttons?) and one under Carter. The first and mildest was in 1970, the second in 1974-5, and the last, in 1979-80 probably played a part in holding Carter to a single term.  Inflation far above average plagued both of those administrations.  Each spent time above and below average in GDP growth with term averages very close to the grand average.  However, Carter’s last two years were consistently below average, and coupled with high inflation, earning him his moribund reputation.  Early in Reagan’s first term, Volker finished slaying the inflation dragon.  But the cost was high in terms of depressed GDP growth, and during that time Reagan was extremely unpopular.  But, as the economy recovered, so did his reputation, and he is now remembered, for good or for ill, as one of America’s most beloved presidents.  The remainder of his presidency resided along at least one of the two average lines, including four consecutive quarters of exceptional GDP growth coupled with only slightly above average inflation, spanning 1983-4.

Graph 4 shows the Bush Sr. (orange), Clinton (light blue), Bush Jr.(red), and Obama (dark blue) administrations.

Graph 4  CPI and GDP, Bush Sr., Clinton, Bush Jr., Obama

During the Bush Sr. administration, 11 of 16 quarters had below average GDP growth, 10 quarters had above average inflation, 8 of these quarters had both.  Clinton’s term began and ended with below average GDP growth, but during his 8 years here were only 9 below average quarters.  Four of them occurred in sequence from Q2, 1995 to Q1, 1996, but the remainder of 1996 was quite strong, and Clinton was granted a second term. Clinton was both the other president who avoided having even a single quarter of above average inflation, and the other president who avoided having a recession during an entire 8-year term.  During the 8-year term of Bush Jr. there were only 4 quarters of only slightly above average GDP growth, occurring from 2003 to 2005.  There were 7 quarters of above average inflation, 3 of them just barely so in 2005-6, and the other 4 in 2007-8, just prior to the economic collapse.  The remainder of his term was in the mild doldrums region.  The collapse ushered in the Obama administration.  Within his first year, the economy was back into the mild doldrums area that has so far been typical of the current century. 

Here is one more graph, showing how each administration performed, as an average over its entire term.  Starting with Truman, the yellow line leads us to each successive administration, up to Obama.

Obama’s position suffers from the recession he inherited.  Whether he gets reelected or not, his average will move up each remaining quarter of his presidency.  If he gets a second term, we can expect more of the doldrums we have experienced over the last two years.

This clearly belies the Romney claim that Obama’s economic policies have failed.  His policies have moved us from near-depression to mere mediocrity.  That counts as some sort of success.

So, here is my narrative.  First off, one can argue that the president does not directly determine the economic fate of the country, and that is partly true.  The other part is that the president sets the policy and the tone, and that both of these things matter.

–  The only presidents to have achieved term averages in the prosperity quadrant were Democrats.
–  The only Republican to achieve above average real GDP growth was Reagan, and that was only by an increment.
–  The only president since Reagan to achieve higher GDP growth than his predecessor was Clinton, other than that, it’s been a downward spiral.
–   Carter had below average GDP growth by a slight margin, but he beat every Republican other than Reagan, and he didn’t trail him by much.
– The last 44 years have been characterized by secular decreases in both CPI inflation and GDP growth.
– They have also been characterized by Republican presidencies 64% of the time, decreasing regulation, lowered tax rates, safety net erosion, loss of labor union strength and participation, and the systematic undoing of of New Deal policies.

What I conclude is that New Deal (dare I say Keynesian?) policies were successful in generating real prosperity, and free market policies have been far less successful.  Over time, Reaganomic trickle-down, free market policies have given us first, the Great Stagnation, and ultimately the worst economic crisis in 80 years.  These policies were, by no coincidence at all, quite similar to those in effect when the Great Depression of the 30’s happened – and also all the other earlier depressions that are no longer very prominent in people’s memories.

As I said, policy matters – and it matters profoundly.

With that in mind, here is my question to the Fed:  Since the average of CPI inflation since WW II is 3.7%, and there is ample evidence that we can have very reasonable economic performance with inflation in that range, why have you set an inflation target that is effectively half of that level, while ignoring high unemployment –  the other half of your alleged dual mandate?

Of course, I’m being rhetorical.  It’s because they are bankers, and inflation favors creditors borrowers, not lenders.  The fact is they don’t care one whit about unemployment.

Policy.

It matters.

Cross-posted at Retirement Blues.

Tags: , , , Comments (11) | |

Another Look at Wealth and Consumption – Pt 2

Correlations and Slopes Over Time

In Part 1, we looked at the ratio of consumption spending to net worth, and how it changed over time.  This time we’ll look at the correlation between net worth and consumption.

Here is the big picture: personal consumption expenditures (FRED Series PCE) plotted against Net Worth (FRED series TNWBSHNO) Data is per calendar quarter.

Graph 1  Consumption vs Net Worth, 1959 – 2011

The data set is divided into two segments, with a break point at the beginning of 1997, with net worth at $30,315 and consumption at $5,467.  In a close up view, there is a clear slope change there.  Still, the selection is a bit arbitrary, since the high point of Q3, 1994, could also have been chosen, with net worth at $25291, and PCE at $4856.  But that is a detail, and no other reasonable breaks stand out.

Notably, both the slope of the data line and R^2 are significantly less after the break.  Visually, it’s obvious that in the later data, there is a lot more scatter.  Also note that that big data moves post 1997 return to the continuation of the best fit line, pre-1997.
Slope and R^2 measurements for the entire data set and the two segments are presented in Table 1. These numbers were generated using the linear trendline function in Excel.

 Table 1
 

Not so visually obvious are the declining slopes during the earlier portion of the data set.  Table 2 presents the same characteristics for data chunks of approximately 20 year duration.

Table 2

We saw in part 1 of this series that the relationship of consumption to net worth was not stable, so this result is not surprising.  And we can now see that as net worth increases, the sensitivity of consumption to increasing net worth decreases.

I can think of two contributing factors.  As wealth increases, the need to spend on basic necessities captures a smaller portion of that wealth, so the propensity to spend decreases.  I’ll defer consideration of the other factor for now.

Here is a detailed look at how the slope of the PCE vs net worth line varied over time.  Graph 2 shows the 34 quarter slope values for the data points of graph 1.  The slope is plotted in dark blue, with certain time spans highlighted in contrasting colors: recessions are in orange, and the stock market and housing bubbles are in yellow.

Graph 2 Slope of Consumption vs Net Worth

Observations:
1) Except for the bubbles and the spike in the post-bubble recession of 2008, the values are mostly contained between a low of 0.168 and a high 0.246.
2) There was an upward trend that ended in the mid-70’s, underscored with a blue line.
3) Values after the mid-70’s, including the two bubbles, are contained in a down-sloping channel, outlined in green.
4)  Except for the early 80’s and 90’s events, recessions are marked by sharp, temporary slope increases.
5) The average slope is 0.184, with a standard deviation of .038
6) The bubbles highlighted in yellow in Graph 2 correspond exactly to the data points in Graph 1 that fall below the red best fit line.
7) The post-bubble recessions brought the slope back into the range described in Observation 1.  This is illustrated in Graph 1 by the returns to the blue best fit line.

If the normal relationship between net worth and consumption is described by a slope in the range of around 0.17 to 0.25, what is there about bubbles that causes drops into the range of 0.11 to 0.12 at the peaks?  I think the answer is the second factor that I defered until now.  The stock bubble and the housing bubble represented wealth increases that were not shared equally across the population.  Specifically, as I pointed out earlier, these assets are mostly owned by the richest population segment, and growth in wealth has excessively favored the top 1% of the population.   They have the least propensity to spend, and this tendency drives the PCE slope into the low range.

This FRED graph illustrates the point in a different way.

Graph 3 PCE, Net Worth and Disposable Income

There are four lines, Net Worth in green (divided by 5 to put it on the same scale); Disposable Income in purple, PCE in red, and Disposible Income multiplied by 0.931 in blue.  Note that the last two overlap almost perfectly, as I also pointed out earlier (see link above.)

The conclusions I’m drawing are
1) Since the bubbles increased wealth in a highly skewed fashion, the relationship between average wealth and consumption broke down.
2) When the bubbles burst, the normal relationship between wealth and consumption reasserted itself.
3) The underlying cause is that during the bubbles the relationship between wealth and income broke down, and afterwards reasserted itself.
4) The relationship between disposable income and consumption is robust across time and most extraordinary financial events.
5) All the foregoing suggest that if wealth distribution were more even across the population (and thus more closely tied to disposable income,) then the relationship between wealth and consumption would be more robust.

Cross-posted at Retirement Blues.

Tags: , , , Comments (2) | |

The Brute Economics of Slavery

Preramble: I posted this on my blog exactly a year ago today, in slightly different form.  Dan linked to it once, from here, just a few weeks before I started writing for Angry Bear.  Recent comments got me thinking about it again. 

 In thinking about the economics of slavery, I’m considering slavery and serfdom to be economic near-equivalents. Of course, I recognize that there are qualitative differences between chattel-slavery and serfdom:

–  In slavery, the master owns the person of the slave; in serfdom the master owns the labor output of the serf, either as a stated labor quantity, a stated output quantity, or some combination.

–  Serfs enjoy some measure of freedom, and can accumulate personal wealth, after the rents are paid; slaves do not and cannot.  (The point, though is to keep rents so high that accumulation is prohibitively unlikely.)

–  It might be easier to gradually and incrementally impose serfdom on an existing population. First generation slaves need to be captured, conquered, or in some other way removed from – and deprived of – their native state. Thus, serfdom is imposed on the indigenous population, slaves are more typically imported.

–  The individual slave is a depreciating asset.  But, as a population, slaves are self-renewing, since, unlike Shakers, they reproduce.   Serfs are factor inputs rather than assets.  (On the other hand, the master also owes the serf protection, and sustenance in times of famine.  In that sense, the serf resembles an asset that requires maintenance.)

These are significant differences, to be sure, but mostly from a sociological or political perspective.  In terms of the brute economics, they are somewhere between second order and trivial.

The necessary conditions for reducing a population to serfdom are as follows.

– A large wealth and power disparity between the haves and the have-nots.

– Perhaps more significantly, the ownership of virtually all assets by an elite class, with severely limited opportunities for the general population to own or accumulate assets.

– A poorly educated population with limited skill sets.

– Severely impaired individual mobility, due to an impossible debt and/or tax burden and legal restrictions.

– Government of the masters, by the masters, for the masters, with little or no sense of worth or justice for the serfs.  This enforces and reinforces the previous point.

– A social and/or religious system that recognizes the inherent meritocracy of the master class.

– A population that is scared or coerced into ceding their freedom to the masters in exchange for security.

– The political will to deprive people of their fundamental human dignity.

Via Krugman, we find Delong’s repost of a short treatise on slavery and serfdom by Evrey Domar.

Domar points out additional requirements, and a mechanism for serfdom to develop.

– Low population density: Labor scarcity favors slavery/serfdom, since the cost of freeman labor will be high.  I’ll admit I didn’t get this until is was stated the other way around.  Population growth favors freeman labor since the competition for jobs drives wages down.  (Note the implicit denial of the “Lump of labor fallacy” canard.)

– A large class of what Domar calls “servitors” who owe allegiance, taxes, and military support to a higher authority.  They are the equivalent of medieval vassals of a liege lord, who extract from the local peasant population not only their own means of existence, but that of their liege, as well.   This is the beginning of, and most literal sense of “rent-seeking.”  The process is that, starting with a free population, by taxation or other forms of indebtedness, the freedom of the common people is eroded.  Those whom Domar calls “servitors” I call leaches.

– Explicit Government complicity in restricting mobility, via legal structures. Besides limiting the population’s mobility in a gross sense, it also eliminates the possibility of competition among different servitors.

In this way, serfdom developed in depopulated* Western Europe during or after the late Roman Empire, and in Eastern Europe many centuries later – in fact, long after serfdom has disappeared in the West.  In each case, the critical enabling factor was low population density, resulting in a critical shortage of labor.

Basically, it comes down to an economic evaluation of costs and returns.   But these are not easy to determine with any precision in the abstract, and probably not in the actual event, either, unless the increment is quite large.  The slave, and even the serf, needs maintenance in a way that the free laborer does not.  The serf can be compelled to work past his willingness in way that the free man cannot.  On the other hand, the free man might have higher willingness and unit productivity.  The wild card here is what the free man can demand as wages, and that depends on the competition for available jobs.  The bottom line is that serfdom will dominate whenever the profit (revenues less costs) of keeping a serf is greater than that of hiring a free laborer.

Of course, all of this was long ago – pre-industrial revolution in fact, and centered on a low-technology agrarian system.  What message does it have for us today?   Here, Krugman wonders** why, after the the plagues of the mid-14th century, serfdom wasn’t reestablished in Western Europe, since the population was greatly depleted.  Domar has no clear answer, and Delong won’t hazard a guess. I will — but it’s only a guess.  Perhaps society had moved on, and the culture was no longer accepting of serfdom as a social institution.  Serfdom had faded away from lack of interest and due to population growth many decades before the plague epidemics occurred around 1350.  There were sufficient numbers of artisans, craftsmen, guilds, merchants, and bankers, such that tying people back to the soil might not have been easy, or even desirable.   The growth of towns might have played a part.  Another social factor is that in late Eastern European serfdom, the servitor’s status was determined by the number of serfs he controlled.  I don’t think that was ever the case in the West.  Sometimes social factors trump economics.

Also, as Barbara Tuchman points out in A Distant Mirror (Ch 11, frex.), though the population decreased due to the plague, total wealth in coins and material possessions did not, and they were largely in the hands of the elite.  It could be that with this wealth maintained, the brute economic drive for serfdom was absent, or severely attenuated, despite the labor shortage.

Krugman also wonders: “And an even bigger question: why hasn’t indentured servitude made a comeback in the modern era? Yes, I know, human rights and all that – but if it was profitable to have indentured servants in the modern world, I’m sure that Richard Scaife’s think tanks would have no trouble finding justifications, and assorted Christian groups would explain why it’s God’s will.”  

Well, that was in 2003, when Scaife was well known and the Koch brothers weren’t. This statement also gets a lot of ridicule in comments at Delong’s Domar post. But, there were certainly many Christian apologists for slavery, and you can see today that tea-baggers and the Christian Right do not exactly align themselves on the side of human rights vs the brute force of the elite.

So Krugman’s question remains, hanging over us like the sword of Damocles.  Here is the way I see it. First off, you need to be skeptical about translating a socio-economic phenomenon from a different place and time to the here-and-now.  Our population is not sparse nor badly educated (yet), and we do not have a pre-industrial agrarian economy.  But these differences effect the possibilities and modes of implementation.  They don’t effect the ongoing defects of human nature that Krugman obliquely alludes to.  These are greed, ego, and the lust for power, and you can see them manifesting themselves right here in the U.S. today in the struggle between labor and the minions of the wealthy elite.

When I think about serfdom, I also think about more modern analogs – sharecroppers, coal miners who owed their soul to the company sto’e, child laborers in early industrialized England, indentured servants, the exploitation of illegal immigrants, and the union busting practices that have been highly successful here since 1980.

In evaluating the conditions that favor and disfavor serfdom as such, something is missing from the analysis.  That is that somewhere along whatever spectrum of conditions makes serfdom more or less economically favorable to the elite, there is a point (or region) of indifference.  If working people are reduced to the point where the economics are no less favorable to the elite than serfdom, then actually going through the formality of making them serfs simply isn’t worth the effort, and doesn’t make any economic difference.

What do we have today?

– The largest wealth disparity since before the great depression – at every stratum of society, growing larger every day.

– An all out assault by the moneyed elite on the wealth and status of working people.   Union busting is one of the tools.

– Deliberate undermining of public education.

– Segments of the population tied to the land by under-water mortgages or the inability to unload a property.

– Popular social movements with religious backing that favor the interests of the elite over the interests of the people.

– Constant fear-mongering as a pretext for inducing people to give up their basic rights.

– A moneyed elite that effectively owns government.

Krugman’s apparent underlying assumption, which I share, is that – for the servitors at least, and possibly for the serfs as well – serfdom is a strategy of least resistance, and therefore the default social order, whenever the conditions for it are right.

One of the things that can make conditions not right for serfdom is regulated entrepreneurial capitalism – inventiveness, innovation, industry, and real competition.  Capitalism generates wealth, increases wages, opportunities and the standard of living, and reinforces concepts of freedom, liberty, and fair practices.  Effective regulation assures that fair practices are maintained, keeps the playing field even, and increases the likelihood that reward is in some way proportional to a combination of skill and effort.  Capitalism is expansionist by nature, serfdom is static.

Unfortunately, over time, capitalism transmogrified into Corporatism.

Corporatism, for all its acquisitiveness, is a very different phenomenon.  Ownership is remote.  Assets are used in large part for executive bonuses, dividends, and mergers and acquisitions.  Though the track record of M&A in meeting stated goals is dismal, the real net effect is monopolization – corporatists hate competition.  Corporatism seeks always and everywhere to decrease wages, and is utterly indifferent to the living standards, freedom, and opportunities of anyone outside the elite.  Ethics and fairness are non-existent.  Rewards are in proportion to rapacity.  In other words, Corporatism is the new feudalism.

This is why I say that the goal of the Republican party, as servitors to Scaife, the Koch’s and their ilk, is to take us back to the 12th century – or whatever it’s 21st Century near-equivalent might be.  I’ve stated that trans-national corporations with no loyalty to anyone or anything constitute the real road to serfdom, in contradistinction to what Hayek said.   That is a bit inaccurate, though. Once wage scales are reduced to the par value of slave maintenance, it doesn’t matter what the correct technical description of our condition is, and the elite won’t care.

__________________________________________________
* Antonine Plague of 165-180, Cyprian Plague of 250-270, Justinian Plague of 541-2
** The link to the Surowiecki article that Krugman mentions is broken.  It can be found here.

Tags: , , , , Comments (72) | |

Another Look at Wealth and Consumption – Pt 1

 Part 1 – Spending as a fraction of Net Worth

Tim Duy weighed in on the output gap debate – not my topic, but he presented this chart of net worth as a percentage of GDP.

Graph 1 Net worth as a Percentage of GDP 

That got me thinking again about the issue of whether consumption spending is determined by income or wealth. Specifically, if consumption is determined by wealth, there should be peaks in consumption corresponding to the dot-com and housing bubbles shown on Graph 1.  However, as Graph 2 shows, there were no such peaks.

Graph 2 Personal Consumption Expenditures

I’ve argued already that, contrary to standard economic thought, consumption is directly determined by income.  (Posted at RB and at AB.) One observation was that consumption, as a fraction of income, didn’t vary much over time, averaging 90.1% with a standard deviation of 2.1%. 

I took a similar look at consumption and net worth, data from Fred.  The next three graphs show personal consumption expenditures (PCE) as a decimal fraction of net worth (blue, left scale) along with net worth (NW) (red, right scale) over different time spans.

Graph 3A  Expenditures/Net Worth and Net worth, 1959-79,

Graph 3A spans from 1959 – the beginning of the data set – to 1979.  Net worth rises exponentially as the population grows.  Adjusting for population growth does not change the shape of the net worth curve, so, in the aggregate, we were becoming richer during those years.  Note that PCE/NW follows a generally similar, though far bumpier trajectory.  As I pointed out in the prior post, the personal savings rate also increased during this period, so the average worker was able to both save and spend more.

Graph 3B  Expenditures/Net Worth and Net worth, 1975-90

Graph 3B spans from 1975 to 1990.  Net worth continues on its exponential track.  But, after about 1979, PCE/NW drops, reversing the prior trend.  By 1990, PCE/NW is no greater than it was in the early 1960’s.  Meanwhile, the personal savings rate also dropped – to a range below that of the early 60’s.

Graph 3C  Expenditures/Net Worth and Net worth, 1989-2011

Graph 3C spans from 1989 through October, 2011.  The exponential growth of net worth falters before and during the two most recent recessions.  After about 1994, PCE/NW is a roller coaster ride.  Of particular interest is the exactly contrary motion at a detail level between NW and PCE/NW, after about 1998.  During the housing bubble of mid-last decade, PCE/NW hit an all time low.

What narrative makes sense of these three graphs?  Here’s my attempt.

Through the 60’s and 70’s, the standard of living was increasing, as incomes and net worth rose together.  This allowed more discretionary spending, and therefore, the fraction of NW that was spent increased.

In the 80’s, aggregate net worth continued to rise, but consumption spending, quite dramatically, failed to keep pace.  Lane Kenworthy has repeatedly pointed out that middle class income growth has decoupled from general economic growth as the upper income percentiles have captured an increasing slice of total income.  As the wealthy grew wealthier and the middle class fell behind, the fraction of NW that was spent declined – exactly the opposite of what should happen if increasing wealth determined spending.  But exactly what should happen if increased wealth is diverted to the already wealthy who have less of a propensity to consume.

During the 90’s, growth in median family income and GDP per capita were close to parallel (see graph at the Kenworthy link)  so there was a lull in the decoupling.  For most of that decade, PCE/NW was close to constant at 0.18-.19.  But while spending was kept level, the personal savings rate continued to fall. 

During the current century, median family income has flat-lined, while GDP/Capita has continued to increase. The decoupling has resumed and the wealth disparity has widened.   During the two wealth bubbles, PCE/NW declined dramatically.  When the bubbles burst and net worth declined, PCE/NW increased  back into the 0.18-.19 range.  Most strikingly, from about 1998 on, the two lines in graph 3C exhibit exactly contrary motion at a detail level.

Conclusions:

There was a tight relationship between Net Worth and consumption through the 60’s and the 70’s, when earnings growth kept up with GDP and wealth disparity was slight by current standards.

This relationship broke down during the 80’s – though one could argue as early as the mid 70’s – as aggregate wealth and working class income decoupled.

Most recently, the relationship between NW and PCE/NW is inverse.  The big swings in NW that the bubbles provided also demonstrated that consumption spending does not depend on net worth.

As I indicated in the earlier post linked above, consumption spending does depend on disposable income, throughout the entire post war period.  A simple look at readily available data casts grave doubts on the idea that wealth, and not income, determines consumption spending.

For the longer perspective, here is the data of Graphs 3 A-C on a single graph.

 Graph 4  Expenditures/Net Worth and Net worth, 1959-2011

In part 2, we’ll look at how spending and Net Worth correlate.

Cross-posted at Retirement Blues.

Tags: , , , Comments (5) | |

World Trade

Mark J. Perry reports on the latest world trade data from The CPB Netherlands Bureau for Economic Policy Analysis.  He presents a graph from 2000 on showing that the levels of world trade and world industrial output have both reached new post-recovery highs.

He takes this to be very good news, and draws some broad conclusions.

Bottom Line: Both world trade volume and world industrial output reached fresh record monthly high levels in January. Trade and output are now far above their pre-recession levels, providing evidence that the global economy has made a complete recovery from the 2008-2009 recession. For the U.S., the annual growth rates for exports (10%) and industrial output (3.5%) reflect the underlying strength in America’s manufacturing sector.

The graph tells me rather a different story.  I went to the source, got the raw data back to 1991, and made my own graph.

It’s true that there has been a V-shaped recovery from the staggering decline that occurred during the 2008 financial crisis.  It’s also true that there is a new post-recovery high.  But I tend to look at graphs of time series data in terms of trends, and have decorated the graph accordingly.

The green straight line is a lower trend line boundary, approximately connecting all the dips.  The yellow straight line is an upper trend line boundary, connecting the tops.  The purple line is an exponential best fit through the peaks, indicated with purple dots.  Of course, in a finite universe, an exponential trend must eventually end.  Even a straight line expanding envelope probably can’t go on forever. 

Now, it looks as if there might be a new top limit to growth.  The red line connects the top just before the crash with the new top that Mr. Perry finds so exciting.   If this holds, then going forward the data will be contained in a collapsing envelope.

Here’s a close up view of the crash and recovery.  I’ve added some purple lines connecting detail level peaks during the recovery.

The purple lines appear to be approaching the red line as an asymptote.  Alternatively, the metric these points represent might be rolling over and approaching another decline.  Either way, there is a clear loss of momentum as the recovery ages. 

I don’t have a crystal ball, and  I’m not going to make a prediction about the future of world trade. But it’s clear that the historical trends no longer apply, and I do not share Mr. Perry’s optimism.

Cross-posted at Retirement Blues.

Tags: , Comments (5) | |

Has America Lost It’s Drive? – Pt. 4

In Part 3 of this series, I wondered a couple of things.

 – With the vehicle/1000 people number in the range of 825 to 845 since 2004, is the market near saturation?
– Is the January sales number of 14.2 SAAR (seasonally adjusted at annual rate) enough to maintain the vehicle/1000 people number?

For the first question, I have to again credit Roger Chittum for pointing me to this 2007 paper, Vehicle Ownership and Income Growth, Worldwide: 1960-2030, by Dargay, Gately and Sommer (32 page pdf, data through 2002.)  There’s a lot to this paper, including projections into the future for vehicle sales and fuel consumption, worldwide.   My immediate interest is in their use of a Gompertz function to estimate vehicle market saturation as a function of per-capita income.

Here is one of their graphs.

 Graph 1  Vehicle/1000 Gompertz Function of Per-Capita Income

Their model indicates flattening above about $30K per year, and leads to a saturation point in the U.S. of about 852 vehicles per 1000 population.  Saturation points for various countries also depend on urbanization and population density.  See the paper for details and background.

This indicates that the U.S market is about 97% saturated, give or take a point.

What does that suggest for vehicle sales going forward?   Karl Smith led off the month pointing to this graph from Calculated Risk, estimating light vehicle SAAR for February at 15.1 million.  With that, on to question 2.
 
I already have the data in hand for vehicles/1000 population (see part 3.)  The data for the Calculated Risk SAAR graph comes from BEA, Table 7.2.5S.  Plotting a scattergram of YoY change in Vehicles/1000 population vs annual average SAAR for the years 1990 to 2009 gives us this picture.  (See notes, below.)

 Graph 2   SAAR and Change in Vehicles/1000 Population

This suggests that the break even point for vehicles per 1000 is right around 14.7 million annual average SAAR.

The official vehicle/1000 numbers are only available up to 2009.  But we have the SAAR data for 2010 and 2011.  Annual average SAAR for 2010 is 11.77; for 2011, it’s 13.05.  You probably don’t want to take the values suggested by Graph 2 too literally, but seeing the vehicle/1000 number slip to around 815 for 2011 should be a reasonable expectation.  This is still slightly above the 95% saturation level.

Average light vehicle SAAR for the first two months of this year is 14.65 – right at the break even point for vehicles/1000 population.  

 Notes on Graph 2

The red dots represent data for 2001 and 2002.  The SAAR values look reasonable.  The changes in vehicles per 1000/population do not.  An increase of 25 in one year, from 800 to 825, followed by a decrease of 10 in the following year with SAAR, nearly identical (17.46 and 17.15) makes no sense.  An average of the two, plotted for both years as yellow dots, by some odd coincidence, lies exactly on the best fit line.

The R^2 value of .43 is less than stellar, but not terrible.

Eliminating the two questionable points raises R^2 to a respectable .65.

Cross posted at Retirement Blues.

Tags: , , , Comments (4) | |