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

More on Police Shootings and Race

In my last post, I linked to a post by Peter Moskos noting that:

People, all people, are 1.6 times more likely, per capita, to be shot and killed by police in states that are less than 10 percent black compared to states more than 10 percent African American. Blacks are still more likely than whites, per capita to be shot overall. But this ratio (2.6:1) doesn’t change significantly based on how black a state is.
For both whites and blacks, the likelihood of being shot by police is greater in states with fewer blacks. And the difference is rather large. There are seven states less than two percent black. In 2015 and 2016, zero blacks were shot and killed in Maine, New Hampshire, Utah, Vermont, Wyoming, Idaho, and Montana. But if you think cops don’t shoot people in these states, you’re wrong. Compared to the four states with the highest percentage of African-American (Mississippi, Louisiana, Georgia, and Maryland are more than 30 percent black), the overall rate of police-involved killings in states with few blacks is higher. And this is despite a lower rate of overall violence.

It seems an odd result, so I have given it a bit of thought. I think I know what is happening and will try to provide a bit of an explanation over a few posts. I will start by noting that this is what the homicide rate looks like by state when put against the rate of killings by police:

Homicides v killings by police, figure 1
(Click to embiggen. Note that data sources are shown on the chart.)

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The Murder Rate – A Regression with Many Variables

In this post, I want to look at the murder rate, by state. I ran a regression with the state murder rate for 2015 as the dependent variable, and literally threw the kitchen sink at it: demographics, weaponry, income, education, population density, etc. Basically, if its something some reasonable percentage of the population believes matters, and I could find data for it, I threw it into the hopper.

I also included variables relating to immigration status. The latter stems from some from some debate in the comments section to other posts in which I stated my belief that illegal immigrants drive up the crime rate. Several detractors insisted that illegal immigrants have lower, not higher crime rates than the rest of the population, and that I am racist to boot. Before presenting results, I will note – I am not too proud to admit the regression results did not fit with my preconceptions. I am also not too proud to admit the regression results did not fit with the preconceptions of my detractors. Finally, while I am always interested in whatever the data has to say, I suspect my detractors will really, really not the results.

So… without further ado, the output from R:

r output 20170402a

What does this all mean? Simply put, only two variables are statistically significant at the 5% (or even 10%) level: percent of the population made up of non-Hispanic Whites, and population density. The greater the share of the population made up of non-Hispanic Whites, the lower the murder rate. On the other hand, the greater the population density, the higher the murder rate. To those who don’t use statistics very often, remember – this is taking into account all other variables.

Now, there are a few variables that come close to being statistically significant at the 10% level. In other words, it is possible (not necessarily likely, just possible) that under other circumstances – with a better defined model, or more precise variables – these variables would prove to be statistically significant as well. These variables are:

1. Percent of the population made up foreign citizens here legally. That variable would have a negative effect on the murder rate if it were statistically significant.
2. Percent of the population that is Asian. This variable also would have a negative effect on the murder rate if it were statistically significant.
3. Percent of the population age 18 to 64. Obviously, most of the murders are committed by people within a subset of this range – probably around 18 to 30. If I had the data to separate out this cohort, I believe we would find that the more people in this cohort, the greater the murder rate.

So… what doesn’t matter? First, the percentage of the population made up of illegal immigrants. Ditto the percentage of the population made up of naturalized citizens. These did not increase the murder rate nor lower it. If the murder rate parallels the crime rate in general, then the media narrative that illegal immigrants have lower crime rates than the population as a whole is not supported and to some extent contradicted by the data.

Second, race & ethnicity don’t matter, at least once you pull out non-Hispanic Whites and maybe Asians. Holding all other variables (including education and income) constant, it doesn’t appear that the murder rate differs in a statistically significant way from one non-Hispanic White or Asian racial/ethnic group to another.

Median income doesn’t matter. Neither does the percentage of the population with an income under 20K. Or the percentage of the population with an income over 100K. Or education level. The murder rate is not affected by these variables.

Another thing that doesn’t matter is the degree to which the population happens to be armed. And Lord knows, there are all sorts of variables here. These include “destructive devices” (think grenades, rockets, missiles, mines, poison gas, explosives, or incendiary devices – apparently all these and more are registered by the ATF), machine guns, silencers, short barreled rifles, short barreled shotguns, or other. The innocuous sounding other group includes your garden variety revolvers and pistols.

So essentially, in summary – accounting for education, income, nativity. immigration status, the regression suggests that having more non-Hispanic Whites decreases the murder rate, and having a greater population density increases the murder rate. No other variables in this regression are statistically significant.

Anyway, I can babble on about the results. For example, it would be interesting to see immigrants (both legal and illegal) broken up with enough granularity to see if the results of non-Hispanic Whites and Asians apply to immigrants as well.

But enough of my prattling. What are your thoughts?

As always, if you want my spreadsheet, drop me a line. If you contact me within a month of the publication of this post, I will send it to you and possibly make some sort of witty remark. Since I am adorable, I probably will send you my spreadsheet after that date as well, but I reserve the right to have a file crash, lose my computer, acquire dementia, or die if too much has elapsed. My contact info is my first name (mike) and a dot, then my last name (kimel – only one m there) at gmail dot com.

Links and details to the data are in my spreadsheet.  But if you want to replicate it yourself (it was a pain in the butt, but who am I to stop you?) the data are listed below. Where possible (which was the case for only a few exceptions, as noted below), I tried to use 2015 data to match the murder rate.

2014 data on firearms came from Exhibit 8 from this document produced by the Bureau of Alcohol, Tobacco and Firearms.

Population from the Census. 2015 data was used for most purposes. 2014 data was used for firearms per capita data.

Population density from 2010 was obtained from the Census.

2015 median hh income came from the Census.

A number of other variables came from the Census CPS Table Creator. This was used for data on race, income, native v. naturalized citizens v. foreigner, educational attainment, age, and gender.

Pew estimates on illegal immigrants, including Mexican v. non-Mexican, were available for 2014.

Finally, the number of 2015 murders originated with the FBI, but was present in this handy dandy file compiled by the Murder Accountability Project.

 

Update…  April 2, 2017  4:01 PM

I forgot to mention a couple corrections to the data:

1. The Pew data on % of illegal aliens that come from Mexico included a few NAs, in each case for states with a very low percentage of the population being made up of illegal immigrants.  In those instances, I assigned the national average share (i.e., 52% of the unauthorized aliens are from Mexico).

2.  The CPS table information on race and ethnicity had a few examples where no information was given for a given combination of race & ethnicity.  In each case, it was possible to determine that the number was very small because the sum total of the other race & ethnicity combinations came close to 100%.  In those instances, I simply replaced the NA with a zero.

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The rest of the dinner table deficit/debt discussion: Equity

I promise, there are numbers here, but lets have some fun first and write a screen play to set up the point. It is long, but…

 
“Dear, I’m getting nervous. We seem to keep adding to how much money we owe and our income hasn’t changed for the better. What can we do?”
 
At this point of the conversation, the conservative ideology (Republican and Democratic Parties) suggests and encourages you to believe that the answer is something like: “Well Honey, as I look over the horizon I see no possibility for improving our current position. The only thing we can do is cut back on our spending. We have to stop spending on anything we don’t need to live. If we are willing to sacrifice then eventually we’ll have savings that we can then use to invest such that we have more income.”
 
Now, for most Americans at this moment in the euphemistically labeled “business cycle” Honey’s response would be: “But I don’t know where else we can cut!” Of course to the conservative there is always something that money is being spent on that is in actuality an indulgence for which one should repent and thus cut from their spending if said spending is greater than one’s income. This is true because no righteous individual would ever let the devil of consumption tempt them from the path to wealth heaven. Redeem one’s self through the power of restraint of consumption urges.

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Reproduction, Income, and the Future

by Mike Kimel

Reproduction, Income, and the Future

A comment on a post at EconLog, a very libertarian website…

I can say that in my lifetime, I have not earned enough money for me to have kids. I’m 36. I know some have had kids on my income or less but it certainly is not enough money for me. I can’t imagine how it would have been if I had kids.

And this comment on a post by Paul Krugman, on the liberal side of the spectrum:

I personally have never had the stability in my life where I thought it was okay for me to go off and spawn a family the way my parents did beginning in 1953. My father worked hard, and loved to work, had good employment and good pay, without a college degree.

Where as, he sent me to private schools, I got honor roll grades, bachelors degree, doctorate degree, I re-engineered manufacturing systems at some of America’s largest companies, but still I never felt secure in my employment, nor was I.

I am unemployed now, as I was in 2010, as I was in 2006, as I was in 1991, as I was for a time in 1984, 1983 and 1982 – when I graduated.

The current generation is facing a geometrically greater challenge than I did. So I can see why traditional family life is in decline.

Interesting how the problem is the framed the same way at different ends of the political spectrum. I note that this framing also fits my life – my wife and I got married late and had one child (one and done) very late. Economic worries were a big part of the decision making process. On paper, my wife and I are doing relatively well financially, but we are extremely aware that a job loss – something that has become extremely common in recent years – or one financial mis-step could mean the difference between whether our son will have far greater opportunity in life than either my wife or I did growing up, or far less opportunity. There doesn’t seem to be much in-between. In talking with my parents, they also seem to believe outcomes are more stark for families today. Many of my friends tell me the same thing. And when I talk to people ten or twenty years younger than I, in general, their costs seem to be higher than those I faced, and the potential opportunities fewer.

The result is that most people I know within ten years of my age have between zero and two kids. While there are exceptions, in general, people I can name with three or more kids either are in the top 2% or so, income-wise (and most of them are in finance or have inherited wealth or both), or are well below the median. (Note – I went looking for data on number of children by income level in the US but couldn’t find any.) I wonder if that’s a portent of things to come, and what it implies for the stability of society going forward.

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Wait. Canadians make more money than Americans? Yep.

David Zetland passes along some income figures via Aguanomics (re posted).  (Dan here…There are more American billionaires and millionaires, so not to worry) :

Cornelia and I were discussing household income and living standards, and I mentioned that median wages in the US were around $35,000.* She was shocked, saying that they were much higher in Canada.
Wait. Canadians make more money than Americans? Yep.

[1] USD** Median household income
50,050  USA
71,300 Canada
Household size
2.6  USA
2.5 Canada
[2] USD GDP per capita (2011 nominal)
48,400 USA
50,400 Canada
USD GDP per capita (2011 Purchasing-power-parity)
48,400 USA
40,500 Canada
[3] Gini Coefficient (CIA, 100 = most unequal)
45 USA
32 Canada
So here’s how I reconcile these numbers:

  1. Median household income is nearly 40 percent higher in Canada, even after adjusting for the number of people in the household.
  2. PPP GDP per capita is higher in the US by nearly 25 percent. This number, mind you, refers to total economic activity and cost of living, not income to individuals.
  3. These numbers are reconciled via inequality: Canada is more egalitarian (similar to Spain and Italy) than the US (similar to Bulgaria and Iran), circa 2005-2007. I reckon that inequality has recently worsened in the US.

So it seems that the income derived from economic activities in the US is skewed in distribution — with more going to rich people than the average person — compared to Canada.**

Bottom Line: Canadians are well known for their higher levels of social harmony. This harmony may be due to a fairer distribution of income, but it’s also accompanied by a higher average incomes.*** Americans are both poorer AND less equal than their neighbors.


* Average wages in 2011 are $42,000 in the US and $32,600 in Canada, but those numbers do not account for employment (66.7% and 71.5%, respectively) or the distribution of wages/capital gains. Right, Mitt?
** Gross income is not the same as income net of taxes, and total taxes are 27% of GDP in the US and 32% of GDP in Canada, but those rates ALSO do not take the distribution of the tax burden into account.
*** My definition of the American Dream — “being able to do what you want” — does not match common definitions that include upward mobility. That dream is — relative to the past and relative to other countries — more dream than reality [pdf].

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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.

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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.

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.

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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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