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

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.

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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.


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.

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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.

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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.

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Insurance and Birth Control

In this Forbes article, Tim Worstall says he agrees with generally available birth control, but questions why health insurance should pay for it.    Specifically he says:  “But I really cannot see the point of trying to have health care insurance which then covers a multitude of treatments that aren’t really insurable matters, contraception being just one of these (regular shots, ‘flu vaccines, general everyday low level treatments in fact).”  This is after he points out that, per Wikipedia, “Insurance is a form of risk management primarily used to hedge against the risk of a contingent, uncertain loss.

The first question this prompts in my mind regarding insuring against (unwanted) pregnancy is what loss does that pregnancy entail?  The answer is large measures of personal freedom, hundreds of thousands of dollars in unplanned child-rearing expenses, and quality of life for the potential child resulting from the pregnancy.  These are significant risks, with ramifications for society at large, involving the employability and  productivity (in a purely economic sense) of the involved woman, and her uses of discretionary income.  Furthermore, unwanted children, particularly if the mother is single, are more liable to live in poverty and neglect, suffer from illness and abuse, become delinquent, and develop into adults that are psychologically disturbed, draw welfare, and commit crimes.

So, there are significant personal hardships and costly negative externalities that result form unwanted pregnancies. 

Is it ironic that in the past right-wingers have proposed the forced sterilization of welfare mothers

Also, the hormone therapy we call “birth control” has many other preventive medial uses, including controlling endometriosis.  So a focus in the merely contraceptive attributes is misplaced.

Currently, more than half the states mandate that hormonal birth control be included in prescription coverage, and since 1998, this coverage has been included for all federal employees.  There is ample precedent for this coverage, and insurance companies are well equipped to handle it.

Worstall conducts this thought experiment.

 Just to make up some numbers, say that the preferred method costs $30 a month. But that having the contraception covered by insurance will raise the premium by $50 a month. The insurance company does, after all, have certain costs associated with taking the premium then paying it straight back out again to buy the pill. Why would anyone do this? Why not purchase the pill for $30, stiff the insurance company bureaucrats the $20 and spend it on a couple of cocktails at a place where you might meet someone who thinks that your being a contraceptive user is a good idea?

Of course, when you’re just making up numbers, its easy to have them suit whatever fell purpose you have in mind.  Suppose a much more realistic $16 per month, and the whole make-believe argument  collapses.

Besides, in terms of brute economics, the insurance company is better off paying for decades of contraception in $30 increments than one avoidable pregnancy at many thousands of dollars, if everything goes smoothly.   Let’s just say you can’t count on that. 

So far, this has been a basically economic argument.  Now let’s look at an issue of fairness and parity.  More than half of the prescription programs cover Viagra.   Need I say more?

Worstall’s argument has some logical consistency – he seems opposed to insurance coverage of other types of preventive care.  But this is ignorant and short-sighted.  A flu-shot is less than $25.  An office call is $90 and up.  Prevention, in general,  is cheap, and treatment is expensive.  If cost minimization is your goal, then the clear focus should be on prevention.

Worstall’s problem, I think, is in relating contraception to a free-market business model, though, as I have indicated, he isn’t even understanding that properly.  The proper focus also includes the high costs of the externalities that he conveniently ignores.

The real root problem though, is in trying to force-fit any health insurance system into a for-profit model.  There is simply no way to reconcile the conflicting goals of profit maximization and providing the needed services.  In fact, there are only three avenues to profit maximization: raise premiums, deny coverage, or emphasize prevention.

Whether this leads to the conclusion that a single payer, government mandated program is the best overall approach is left as an exercise for the interested reader.

UPDATE: I forgot the great positive externality of contraception – it prevents hundreds of thousands of abortions every year.

Cross posed at Retirement Blues.

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More on Michigan Voting

The U.S. Election Atlas shows the Michigan county by county results for the general election in 2008.  Note that they have inexplicably reversed the normal Red-Blue color coding.   Contrast those results with the 2012 Republican primary results.

In the Lower Peninsula, the counties that went for Romney in a big way generally went for Obama in a big way in 2008.  Wealthy, densely populated Oakland county went for Obama by 56% to 42% (660,000 total votes.)  Romney crushed Santorum there by 50% to 29%. (116,000 total votes.) Romney tended to win the counties that were close between Obama and McCain four years ago.   Along the west coast, though, many counties that were solidly in Obama’s camp in 2008 went overwhelmingly for Santorum in the Republican primary.  But these counties had big margins on Tuesday with small turnouts.

Commenter CSH at Johnathon Bernstein’s blog remarked, “I can’t recall seeing the rich-poor, East-West gap in that state as strongly represented as last night.”  CSH also pointed out that Romney won the State by 32,000 votes.  Coincidentally, he won Oakland County by 32,000 votes.  The rest of the State was a wash.

The Upper Peninsula as always, has its own different story.  Santorum carried all but two counties, and generally by large margins, while the the ’08 vote was split among counties between Obama and McCain.  The primary was closest in the eastern section of the U.P., which McCain carried in ’08.  But vote counts in the U.P. on Tuesday were very sparse – in the range of a few hundred to about 3,000 total, per county. 

It’s far from one-to-one, but Romney’s results vs Santorum more or less parallel Obama’s results vs. McCain four years ago.  Romney’s best showings were in places where he has virtually no chance in the general election.  Santorum’s best showings were in less populated areas that are likely to vote Republican, regardless.

The other significant factor is voter turnout.  Romney and Santorum together collected 787,420 votes, Statewide.  In 2008, McCain got over 20 2 million votes in Michigan, and lost the State by 16%.

Despite the hype, the stark differences between the two front runners, and Romney’s alleged home field advantage, the total turnout was less than half of McCain’s votes in ’08 this looks like a lot of less Republican apathy than I first thought, but still a significant lack of interest.  Can that bode well for their prospects in November?  (Corrections made in last 2 paragraphs.)

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Michigan Primary Results

Mitt Romney won the Michigan Republican primary yesterday by a margin of 41.1% to 37.9%, the remainder going to the rest of the overpopulated field – Herman Cain, Jon Huntsman and others were on the ballot. Romney and Santorum each gained 11 delegates.

This Huffpo article has an interactive map showing results by county.

The spread in the results is interesting. Along the west coast of the Lower Peninsula is Michigan’s bible belt. Santorum carried most of those counties by Margins of 10 to 20%. Kent county, which contains the city of Grand Rapids, it the exception. Santorum won that county by only 42.4% to 40.3%. This illustrates the other part of the Michigan dynamic. Romney did better in urban areas, while Santorum did better in places where cows or deer outnumber the people. Santorum won many more counties, but lost the total vote count.

This population effect shows up in the victory margins of the counties that Romney won. In the 5 by 2 band of counties that Romney won in the southern part of the state, Romney’s take generally decreases while Santorum’s generally increases as you move west. Then, when you reach the bible belt, it flips to Santorum. Along the Ohio border is a band of sparsely populated counties that Santorum swept. Monroe, Lenawee and Branch counties have towns of significant size in them, and in those counties Romney did better by a couple of percentage points.

Ron Paul got between 10 and 12% of the vote almost everywhere. This illustrates something about the modern Republican party. It is an unholy alliance of far-right Christian fundamentalists, pro-business (pseudo-fiscal) conservatives and libertarians – and the cracks are starting to show. If nothing else, the endless campaign of Republican debates has cast these differences into bold relief.

Logically, the fundamentalists and libertarians should hold each other in contempt. The libertarians and the pro-business faction can agree on many things, but not isolationism and the gold standard. To the business crowd, the fundamentalists are prey.

For decades, the Republicans have drawn the religious right into their fold with emotional hot button issues that have very little actual relevance, like abortion and gay marriage. The recent campaign against birth control has been an over-reach that is finally causing a back-lash.

 In my dreams, the Republican party tears itself apart, and becomes a marginalized political minority. The Michigan results give me hope that this dream might become reality.

H/T to my lovely wife.

Cross posted at Retirement Blues.

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Is America Losing Its Drive? – Pt. 3 Vehicles per 1000 Persons

In private communication, Roger Chittum got me thinking about the vehicle component of gasoline consumption. I’m going focus on the gross vehicle numbers, and not get too deeply into the car/truck/SUV product mix detail.   Data is from the Department of Energy TRANSPORTATION ENERGY DATA BOOK: EDITION 30—2011.   (Warning:  414 page pdf.)

According to Table 3-5 on page 3-9, vehicle ownership, measured as vehicles (both cars and trucks) per 1000 population, peaked in 2007 at 843.57, and dropped by 1.88% to 828.04 in 2009, two years later.  Data presented in the source is from 1900 to 2009.  This graph shows the data from 1950 to 2009.  Recessions are highlighted in red.

During the post WW II era up to 1982, recessions might have slowed the growth of vehicle ownership, but they did not cause a decline.  Even the severe recession of 1958 only caused a flat spot on the curve.


This changed with the double dip recessions of 1980 to 1982.


The reduction from 1981 to ’82 is miniscule.  But since then, every recession has led to a significant reduction from the previous year.  As an aside, this is one more time series that shows a change in character right around 1980.

This source indicates the most recent value for the U.S. is 765, though it’s not clear what “most recent” means.  If this is accurate, then ownership has back-tracked to the 1994 level.   This would correspond to a 7.6% drop from 2009, and an astounding 9.3% drop from the 2007 peak.  I don’t believe it; but that value is indicated with a red dot on the next graph, as a point of reference.

I’ve also included some best fit straight lines to show how the slope has changed over time.  The decreasing slope and more serious response to recessions might result from a market being close to saturation, but that’s just a guess.

One of the reasons I’m skeptical of the red dot point is that new vehicle sales have recovered substantially from the 2009 low, as this graph from Calculated Risk demonstrates.  (The August, 2009 spike is the cash for clunkers event.)

This might not be enough to stop a continuing slide in the vehicles per 1000 population number, but I think it’s enough to keep it from falling off a cliff.

Another perspective on vehicle use comes from Table 3.3 on page 3-5 of the Data Book.  This graph shows the Federal Highway Administration estimate of vehicles in use.

Except for recessions (highlighted in red on the total line) growth of the total vehicle count has been been quite constant over four decades.  But, since the mid 80’s, car sales have been stagnant.  All of the growth since then has come from truck sales.  It will be interesting to see how these trends develop over the next few years.

Part 2
Part 1
Cross posted at Retirement Blues

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Has America Lost its Drive? Part 2

I made a mistake in my original post.   Graph 4 in that post was based on the wrong data set.  As Roger Chittum pointed out in comments, that graph only covers a subset of total gasoline deliveries.

This is the correct graph.  (Source.)  Thanks, Roger!

Graph 1 Gasoline Supplied

The fall off in gasoline delivery is not as extreme as I indicated, but it is still real.  Here is a close-up view of data for the current century, from the same source.

Graph 2 Gasoline Supplied This Century

Seasonal changes are dramatic.  Peaks occur in July or August, valleys in January or February of most years.  May values, highlighted with blue dots, and September values, highlighted with yellow dots, are recurring secondary peaks and valleys, respectively.  July values are highlighted with red dots.  The years 2008 and 2010 are accented with contrasting blue line segments.

In 2008, gasoline consumption dropped dramatically.  May was down slightly, compared to ’07, while July and September were down a lot.  Through 2009 and ’10 there was a slight recovery, with all three highlighted months showing increases.  The 12 month moving average, in pink, stopped falling, but failed to increase very much.

Then, in 2011, gasoline deliveries turned down again. This can be seen clearly in the highlighted months and the moving average. Some of the standard explanations are changing demographics and retail habits. An aging population with more retirees might tend to drive less – though this is not my personal experience. Kids these days cruise on social media rather than pleasure drive through the streets of town as we did in my day. On-line shopping, though only about 5% of total retail, is growing rapidly.

You can’t gainsay any of these trends.  They are probably affecting the big picture.  But it would a stretch to say that they can account for less gasoline use in 2011, but not 2010 or 2009.  Especially so, since this past year was supposed to be a recovery from the previous economic doldrums, and the expectation would be for the improvements of the previous two years to continue.  But it looks like something is happening, economically or culturally, to cause another downturn in travel – though not as dramatically as I suggested in the original post.

The estimate of vehicle miles driven, from the December, 2011 report by the Federal Highway Administration, tells a similar story.

Graph 3 Vehicle Miles Driven – Moving Total

The years 2008 and 2010 are highlighted in yellow.  The pink line traces the November, 2011 low back through the Summer of 2004.  Again we see recovery in 2009 and ’10, and a resumption of the slide in 2011.

The slope change in mid-2005 is intriguing.   This precedes the April 2006 peaks in the Case-Shiller Composite-10 and Composite-20 Indexes by several months, and the October 2007 peak in the S&P 500 by over 2 years.  The new slope remains relatively constant right up to the peak in November, 2007. 

Meanwhile, gasoline prices have increased again in the last month, after sliding about 70 cents from the high in May, 2011.  This gloomy article at Seeking Alpha blames part of the price increase on “stronger demand, courtesy of a growing economy.”  The data simply does not support this opinion. Instead of text book supply-demand behavior, gasoline prices and miles driven exhibit basically similar motion

I still contend that the prices of petroleum products are manipulated on the supply side.  All the data I’m aware of supports this.

I expected the original post to be a one-off, but the current picture is interesting, with no obvious explanation.  This might bear looking into in another 6 months, or so.

Cross posted at Retirement Blues.

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Has America Lost its Drive?

Yesterday,  Karl Smith posted on Oil and the Structural Recession.  This seems to be one of Karl’s thinking-out-loud posts, with more questions than answers, some convoluted reasoning, and a conclusion that higher gasoline prices are in our future.  If I read him right, this will be due to a demand pull.

He included this graph from Calculated Risk.

Graph 1.  U.S. Vehicle Miles

The number of miles driven tends to flatten during a recession, then recover quickly when the recession is over.  At least, that’s the way it used to be.  The Miles Driven curve seems to have been losing slope since the late 90’s, and was close to flat-line during the housing bubble last decade, when everyone supposedly felt rich.  There has been no recovery after the recent Great Recession, which officially ended 32 months ago.

 The same CR post cited above also includes this next graph.

Graph 2.  YoY Change in Vehicle Miles

This confirms my eye-ball assessment that the slope in the first graph has been in decline since long before the oil price bubble of recent years.

But here is a contrary development.  Calculated Risk also reports that the truck tonnage index is way up for all of 2011, and especially in December, when it posted an all time high.

Graph 3. Truck Tonnage Index

Truck traffic is way up, but total miles driven, per graph 2, has been mostly in decline for four years.

This suggests that discretionary personal driving has been sharply curtailed.  I’m having a hard time coming up with any alternative explanation.  Can anybody suggest one?

Just in the last couple of months, it seems that discretionary driving has taken a deep plunge that has not yet shown up in the data posted above.  Deliveries to retail gas stations have been slumping for well over a decade, and now have fallen off a cliff.  If gasoline delivery is just-in-time, as I believe it is, then deliveries are an excellent proxy for consumption.

By the Way, improved fuel economy cannot account for more than a small fraction of this change.  The big improvements in fuel economy happened during the 80’s, when fuel deliveries were in an upswing.  Since 1990, fuel economy improvements for the actual fleet on the road have been on the order of 0.5% per year.

 Graph 4.  Gasoline Retail Deliveries

I made my own graphs of the retail delivery data (not posted,) and there is, surprisingly, no particular response to the recessions of 1991 and 2001.  It’s not easy to find any recession on Graph 4.  Deliveries were slumping even before the Great Recession, so whatever effect it might have had on its own was subsumed by the general trend.  The above graph is noisy, due to lack of seasonal adjustment.  The lowest row of dots over most of this graph represents January data.  Summer months cluster at the top of the array, as you would expect.  Those two lonely points in the lower right corner are October and November, 2011, the most recent data shown.

It’s remarkable that gasoline deliveries are now substantially lower than at any time available in this data set. spanning about 30 years.  And I would never have guessed that anything like this was happening, based on my many trips on I-75 between Detroit and Toledo.  That route must not be a representative sample.

In a comment on Karl’s post, I said that I see all petroleum prices as highly manipulated on the supply side, with demand as a follower.  This data makes me think that the same is true of gasoline, in particular.  But it can’t be the entire story for the decline in consumption.  There is no clear connection between deliveries and gasoline prices over the last several years.

I don’t know where gasoline prices are going.  Karl might be right that they are going up.  But I don’t see any way that this can be due to a demand pull.

Mish also has a couple of recent posts relevant to this topic.

H/T to commentor rjs at Karl’s post, who got me thinking about this, and provided a key link.

Cross-posted at Retirement Blues.

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