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

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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More on Markets and Neoliberalism from Crooked Timber

Actual markets in the American economy are extremely rare and unusual beasts. An economics of markets ought to be regarded as generally useful as a biology of cephalopods, amid the living world of bones and shells. But, somehow the idealized, metaphoric market is substituted as an analytic mask, laid across a vast variety of economic relations and relationships, obscuring every important feature of what actually is. And, then we wonder why the “thinking” and policy debates that result are stupid and corrupt.
—  Bruce Wilder

Emphasis added.   This is in the context of a critique of neoloberalism, here described by Henry Farrell:

In fact, it is not free markets with vigorous competition among producers, but instead, a mixture of big firm oligopoly and cosy and frequently corrupt relationships between state officials, who have been told to subcontract out parts of government, and the businesses which supply these new services, in what is at best a murky approximation to a real marketplace. You can read this as a statement that classical liberalism has some good points as well as some bad ones. You can equally well read it as saying (and this is the more fundamental point), that regardless of whether or not classical realism had some good arguments, these don’t have anything much to do with actually-existing-neoliberalism which is a crony capitalist fantasy.

This lays bare the greed, dishonesty, corruption and manipulation inherent to neoliberalism, and simultaneously exposes the concept of “the market” as an absurd quirk of the typical economist’s imagination.

Each of these meaty comments is highly worthy of recognition.  The cephalopod reference made the first one utterly irresistible, and prompted this post.

The bad news is that there doesn’t seem to be any way out.

Here, John Quiggin provides a good functional definition of neolibealism – the first I’ve ever seen – and a very thoughtful critique of neoliberalism as a political cum economic ideology.

The core of the neoliberal program is
(i) to remove the state altogether from ‘non-core’ functions such as the provision of infrastructure services
(ii) to minimise the state role in core functions (health, education, income security) through contracting out, voucher schemes and so on
(iii) to reject redistribution of income except insofar as it is implied by the provision of a basic ‘safety net’.

Quiggin judges neoliberaism to be a failure, for different reasons in different places.  I’m going to quibble with his definition of failure, type iii, though: a failure to deliver the promised outcomes.  With a focus in the inherent dishonesty and corruption inherent to neoliberalism, I can only view it as highly successful in the U.S.  This is because there is a real hidden agenda lurking behind the false public agenda.
Wilder describes how it works in a follow-up comment: (Be sure to read the whole thing.)

Neoliberalism, it seems to me, uses the myth of the market, to rationalize rule-making, which serves the rentiers (is dynamically inefficient) and which promotes authoritarian, and therefore unfair, resolution of conflict.

Quiggin describes the type iii failure in the U.S:  “The basic problem is that, given high levels of inequality, very strong economic performance is required to match the levels of economic security and social services delivered under social democracy even with mediocre growth outcomes.”  Of course, no such strong economic performance is forthcoming.

However, the real agenda is not general economic security.  Quite to the contrary, it is to maximize and maintain a high level of inequality, such that the small, elite minority has absolute control over the impoverished majority, precisely because their economic security is severely limited.  I cite as evidence the extreme form of 21st Century Republican party neoliberalism, which even attacks the existence of a basic safety net.  Note also their ongoing attacks against labor unions, health care reform, and education at all levels.

The job is not yet complete, but I have to view the record of neoliberalism in the U.S., to date, as a smashing success.

I posted this on my blog in slightly different form as a Quote of the Day entry. But it makes such a fitting companion piece to Dan’s from earlier today that I decided to put it up here, as well.

 H/T to Unlearningecon

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Where Has All The Money Gone, Pt IV – Dividends

We’ve already seen in previous installments of this series that since about 1980, I: corporate profits have soared, II: the slice of profits going to finance has soared even more, and III:  wages have stagnated.  Here we see what corporations have done with all that money.   There is a limited selection set: pay taxes, distribute as dividends, pay down debt, invest, make acquisitions, speculate, and hold as cash.

Here is a look at taxes through 2008 and dividends through 2010, as percentages of profits; data from BEA table 7.16, lines 19, 20 and 38. For my purposes, profits are divided among taxes, dividends, and all the other things mentioned above, which I’ll call the Residual.

Dividends/ Profits are in green; Taxes/Profits in red.  I’ve added 13 year moving averages to clarify the trends over time.  The Dividend percentage bottomed in 1978 at 20.6%.  I’ve marked that year on both curves with a yellow dot.  After that, dividend payments took off sharply and have been mostly in the 40 to 50 % range since 1989.  The tax rate on dividends was reduced to 15% in 2003, also marked with a yellow dot, but I don’t think that change has had much effect on dividend payout.  The gyrations in the payout percentage since 2003 are largely due to the denominator affect, as profitability increased after the 2001-2 recession, and plummeted during the recent Great Recession.  Notably, 2010 profits are the highest ever. 
The tax payout drop lagged the dividend increase by several years, and didn’t start dropping until 1987.   In 1986, the tax payout rate was 45.2%.  After a sharp drop to 27.7% in 1992, the payout rate increased throughout the Clinton administration, topping at 34.5% in 2000.  Then, there was another sharp drop.  It has since leveled off, averaging 25% since 2004.

In 1978, the 13 year averages were 24.2% for dividends and 42.4% for taxes.  Those averages are now 28.3, and dropping; and 45.7 and rising, respectively  45.7 and rising for dividends; and 28.3% and dropping for taxes – essentially a reversal of positions.  The net result is a massive funneling of money from government to dividend recipients who now are paying only 15% tax on their dividend income.

This is not only “Starve the Beast” in action, it is a massive redistribution of wealth into the hands of those who already have the most.   Say what you will about the relative efficiencies of the private and public sectors in using resources, the public sector places money into the hands of people who will spend it and keep the economy moving.  The private sector largely funnels it into rent seeking.

For the sake of completeness, here is a look at the Residual – as defined above – with a 13 year moving average and a best fit straight trend line.

This provides a partial explanation for Jon Hammond’s observation that net corporate investment has been down over the duration.  There is less residual to invest.

Bottom line:  Corporate profits have been skewed to dividend payments, to the detriment of worker salaries, government tax revenues, and corporate investment.

Cross posted at Retirement Blues.

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Income and Consumption

This is another look at the idea I put forth here, that – contra the standard economic idea that consumption depends on wealth – I believe that consumption depends on income.  It’s worth stressing that wealth and income are not independent variables.  Wealth is the accumulation of unspent income plus returns generated on that wealth over time.  Is it proper to say that wealth is a stock, and income is a flow? 

I believe the evidence very strongly indicates that consumption – also a flow – is tied tightly and directly to income.  This does not mean that wealth cannot play a part in consumption decisions.  People make all kinds of decisions about all kinds of things, for all kinds of reasons.  But consumption decisions are constrained, and there is no reason why they can’t be constrained in more than one way. 

I think the idea that consumption depends primarily on wealth is intuitively weak because consumption is aggregated over the population, while wealth is concentrated in a small segment of that population.  A person with little or no wealth will spend the next dollar meeting some unsatisfied need, while the person with lots of wealth has the option of devoting it to rent-seeking or accumulation in an off-shore shelter.  According to data now more than a decade old, the richest 1% of households owned 38% of all the wealth; the top 5% owned over half, and the top 20% owned over 80% of the wealth.  The trend towards rising inequality started in the mid 70’s.

A couple of proxies for wealth are home and common stock ownership.  Excluding home-ownership, the wealth concentration is even more extreme, with the top 1% owning 50% of the non-home wealth.  It’s difficult to determine the actual amount of stock ownership in private hands.  A number arrived at by elimination leaves 36% among households, non-profits, endowments and hedge funds.   Therefore, realistically, the bottom 99% of individuals share about 18% of all stocks with those other institutions.  At the bottom end, the lowest 20% have either no wealth, or negative net worth.

People at the low end live close to subsistence.  People in the middle live pay check to pay check.  For the vast majority of the population, the next marginal dollar has a high probability of being used as a consumption expense. 

That is my narrative to support the idea that consumption must necessarily be strongly dependent on income.  Now, let’s look at some data, through 2009, from the U.S. Census Bureau, Table 678.  The first graph shows Disposible Income (green) and personal Consumption Expenditures (red) back to 1929.

A careful look suggests a narrative about this relationship.  First, consider the depression years.  From 1932 to ’34, consumption averaged 99% of disposable income.  People had needs, and used their limited incomes to satisfy them, as best they could.   Then, during WW II, with rationing and other constraints, saving was forced, and consumption was artificially low.  Consumption reached an all-time low of 73.3% of Disposable Income in 1944.  Since shortly after WW II, changes in Disposable Income and Consumption have been in virtual lock-step.   I’ve put lines in a contrasting color connecting selected points in the Disposable Income curve, and dropped parallel lines for the same years onto the Consumption curve.  Since 1951, very wiggle in Income corresponds to a wiggle in Consumption.

Here is a scattergram of the two subject variables, with a best-fit straight line provided by Excel.

As has been pointed out to me, correlation is not causation.  But – when one can construct a rational narrative that explains the data, the two series display absolutely congruous motion over several decades, and R^2 is over 0.99, I’m willing to go out on a limb and say the burden of proof is on the denialists.

Here is a look at Consumption as a percentage of Disposable Income, since 1951.

I’ve expanded the Y-axis.  In a view of the entire 0 to 100% scale, the post-1950 line barely wiggles.  Over a span of 6 decades, Personal Consumption has averaged 90.1% of Disposable Income, with a standard deviation of 2.12%. 

The data points, average, and an envelope one Std Dev above and below the average are all displayed on the graph.  Despite having two clearly defined and opposite tending trends, this is still a well behaved data set, with 39 of 58 (67%) of the points within the envelope.

The two minima are in 1982 and 1984, and the bottom trend lines converge in 1982, so that is a reasonable time to define as the break point.  This also suggests a narrative.  During the post WW II golden age, typical wage earners moved incrementally above the subsistence level.  This gave them the opportunity to save a little bit.   Since 1982,  as wages stagnated, it became necessary to devote a higher percentage to Consumption.  Sure enough, savings grew through the mid 70’s, and have dropped dramatically since 1982 (or a bit earlier,) as this FRED graph demonstrates.

I won’t say that Consumption Spending is solely dependent on Income.  But I will say that it is strongly, and even predominantly, dependent on income.   Wealth might enter into the decision for those who actually have some, but they are in the minority and have few needs that can be satisfied by the next dollar of consumption.

My conclusion is that the best solution to the aggregate demand shortfall problem is to put money into the hands of the people who will actually spend it, and that the best way to do that is to give them jobs.  As stop-gaps, various relief and welfare programs also have their place. This is the rational for fiscal stimulus.  Federal spending programs provide real jobs for real people, and they will spend their earnings.  Arguing about whether this is hole-filling or pump-priming strikes me as being just about as important as arguing about how many angels can dance on a pin head.

Cross posted at Retirement Blues

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Where Has All The Money Gone – Pt. III, Not to You and Me

Part I showed the money going to corporate profits, not to the salaries of working people.  Part II showed that the finance sector has captured an increasing slice of the profit pie.  Here is a different look at where the money hasn’t gone.

The first graph shows Real GDP/capita and Real Disposable Income/Cap since 1950 on a log scale.  (Data through 2009, from The Census Bureau. Table 678 at the link.)

I’ve left the 50’s out of the argument (but not the graph,) as a courtesy to Ike, since his relative performance suffers due to the post war baby boom.  The population grew at an above normal rate for over a decade, and that skews the GDP/Cap data.

If you’ve been paying any attention to time series economic data, you know there are break points in almost any econ measure, somewhere in the vicinity of 1980.  I’ve added trend lines, breaking the data sets arbitrarily at 1980.  These trend lines here tell the same story – it’s deja vu all over again.  Pre-1980 trend lines start with 1960 data.  I stopped the post ’80 trend line data sets at 2007, to avoid the influence of The Great Recession, which would have have further deceased their slopes.

What I want to emphasize here is the difference between the two lines.  Though both have a knee, the Disposable Income break is much sharper.   Here is a graph of the difference between the two, linear scale.  And, BTW, this time I left the ’08 and ’09 data in the trend line determination. 

Well – since 1980(-ish) not only has GDP growth slowed, the amount captured in disposable income has decreased, quite dramatically.

That’s a whole lot of wealth that is NOT ending up in the hands of ordinary people.   Which is why it doesn’t get spent.  But that is another story.

An earlier version of this post was published at Retirement Blues, back in June.

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