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

Fowl and Fishy Inflation

It has been suggested that the rapid increase in the prices of fish, fowl, meat and eggs for about two years following October, 2009 was the result of QE causing inflation in these items.  From this Calculated Risk graph, we can get the QE date line.  QE was announced on Nov 25, 2008, and expanded in April 2009.  It ended in May, 2010.  QE II was hinted at in Sept, 2010, announced in Nov 2010, and ended in August 2011.

The timing correspondence is less than stellar, since the YoY increase in prices for those food items dropped like a rock from October, ’08 though Oct. ’09.  It then shot up to a 7 1/2 year high in May of 2011.

This can be seen in the red line of Graph 1, which also shows the CPI for all items except food and energy (CPILFESL) in blue.

 Graph 1 YoY Price increases for Selected Food Stuffs and All Items Less Food and Energy

To assume a cause and effect relationship, you have to account for a time lag of a year from the announcement and 6 months from the expansion of QE to the turn around in those price increases from the Oct ’09 bottom.  Remember, through the first 11 months of QE, the YoY change in those prices dropped dramatically.  Between May and November, 2010, while no QE program was in effect, these prices had the steepest part of their rise.  After QE II ended in August, 2011, the YoY price increase remained high for those items until the end of the year, and then fell rapidly.

A longer view reveals that the increase in those food prices oscillates continuously around the All Items Less Food and Energy line.  The trough to trough period is irregular, averaging 3.52 years with a standard deviation of 0.45 year (5 measurements).   The trough to trough time from May, ’06 to Oct., ’09 was a very typical 3.4 years.  It is very hard to look at that graph and see anything unusual about the 2008-2012 region, other than the depth of the trough shortly after the Great Recession.

It appeared to me that the blue line of Graph 1 might be a crude approximation of a long average of the red line.  This turns out not quite to be the case, since the two lines are measuring different baskets of goods.  What we have is the YoY increase for these food items oscillating around its own mean. That sounds like a tautology, but let’s look a little deeper.

Graph 2 shows the same data, along with some long averages of the food stuffs YoY price increase line.   These are the 5 Yr (light blue), 8 Yr (yellow), and 13 Yr (purple) moving averages, and the average for the whole data set, 2.9 (bright green).  I’ve also included an envelope one standard deviation (3.06) above (5.96) and below (-0.17) the mean in dark green.

Graph 2 YoY Price increases for Selected Food Stuffs with Avgs and All Items Less Food and Energy

This (sort of) resembles a control chart.  The +/- Std. Dev. envelope isn’t a hard barrier, but does tend to turn the data path back toward the mean, unless something strange happens.  Frex, the big rise from late ’02 to early ’04 followed the Iraq invasion and resulting disruption in petroleum pricing.  The ’09 trough was the result of the Great Recession.  These are explainable variations.

Note also that the moving average lines tended to run below the CPILFESL line prior to late 2002, and have tended to run above it since.  This is to be expected since these items are basically the top of the food chain and have several layers of fuel dependent contributors in their cost structure.  Recall that until 2002, fuel prices were low, and since then (except for the Great Recession) have increased steadily.

I’m quite sympathetic to the idea that QE has done very little to help ease the economic doldrums following the GR.  But I see no reason at all to believe that it has contributed to the pain and suffering of ordinary citizens at either the grocery store or the gas pump.

Maybe there have been real downsides to QE.  Any thoughts on what they might be and how to quantify them?

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GDP Growth Caused By Tax Cuts Has Never Happened

Mike’s post here got me thinking.  I’ll telegraph my conclusion.  He dramatically understated his case.

You can see the long range view of nominal and inflation adjusted GPD growth in Graph 1 of FRED quarterly YoY percent change data.

Graph 1 YoY growth Nominal and Inflation Adjusted GDP

Nominal GDP Growth was in a secular up-trend from 1960 through 1980.  However, inflation adjusted GDP growth quickly peaked after the Kennedy-Johnson tax cut, reaching a maximum value of 8.5% in Q4 of ’65 and Q1 of  ’66.  It then dropped dramatically for the next four years.  This peak value has been matched only once since: in 1984, during a sharp rebound from the double dip recession of 1980-82.

Since then, in the wake of numerous tax cuts, the rate of GDP growth has been anemic. To get a look at the rate of growth, I took an 8 year average of the annual percent change data presented above, and then plotted a 5 year rate of change for that data.  This is essentially the 2nd derivative of GDP, or GDP acceleration, as shown in Graph 2.

 Graph 2  GDP Acceleration

 Inflation Adjusted GDP acceleration peaked in Q3, 1966.   Fueled by the inflation of the 70’s, NGDP acceleration stayed high until Q1, 1980, then plummeted for 9 years.  It has been relentlessly negative since.

Inflation adjusted GDP acceleration has not done quite as badly in this disinflationary era, but has been below zero more than half the time since 1970.  This is a little bit worse than coasting.

This all might seem a bit abstract, but the message is clear.  If tax cuts were good for the economy, then GDP growth would be increasing.  In other words, acceleration would be positive and most especially so after a tax cut.  The data is not consistent with this notion.

Clinton’s famous tax increase preceded increased GDP growth by either measure, and an upturn in acceleration.  The Bush tax cuts preceded decreasing GDP growth.

I’m not going to get into a correlation vs causation discussion.  I’ll simply say that tax cuts over 5+ decades have been an utter failure at stimulating real economic growth in any inflationary environment.  Since the real world data correlation is counter to the received conservative wisdom, it might be worth trying an anti-conservative approach.

It might also give the NGDP targeting enthusiasts something to ponder.

Cross posted at Retirement Blues.

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Debt and Growth

Art at The New Arthurian Economics and I are looking at the relationship between debt and economic growth.  Art started with an observation of two FRED series, total credit market debt owed (TCMDO) and Gross Domestic Product (GDP,  nominal or GDPC1, inflation adjusted – take your pick.)

Graph 1, from FRED, shows these data series.  I’ve chosen nominal GDP and, for reference, also included the total Federal Debt.

 Graph 1 TCMDO, GDP and Total Federal Debt

In 1950, TCMDO was about 1.3 times GDP, but growing a bit more quickly.  By 1980, the ratio was 1.6, and by 1987 it was greater than 2.  Now that ratio is approaching 4.  Note that TCMDO is also close to 4 times greater than total public debt.  This is why Art and I agree that private, not public debt is the problem that needs to be addressed, but is largely ignored.

Linked here are Art’s posts with graphs of YoY growth in both factors, pre 1980 and post-1980.  Pre 1980, their moves are similar in magnitude, and pretty well coordinated. Post 1980 there is still some occasional similarity of motion, but the coordination breaks down and debt growth is generally quite a bit higher than GDP growth.  The 80’s in particular stand out as being starkly different from the previous period.

Graph 2 shows the entire data set, since 1952.

Graph 2 YoY % Growth in TCMDO and GDP

These observations led Art to the reasonable hypothesis that, “Output growth slowed when debt became excessive.”  This, in fact, might explain the great stagnation.

I suggested, and Art accepted two corollaries to his hypothesis.

1) There is a non-excessive amount of debt. Let’s call it “just right.”
2) Below the “just right” amount, there might also be “not enough.”

Actually, there is a lower level hypothesis, to which Art’s is corollary: That there is a functional relationship between debt and growth, in which growth is the dependent variable.

This is what I will explore in this post.

Graph 3 is a scatter plot of GDP vs TCMDO YoY % change for each, FRED quarterly data from Q4, 1952 through Q2, 2012, with a best fit straight line included.

Graph 3 GDP vs TCMDO, YoY % Change

The relationship is quite clearly positive.  The R^2 value at .39 is rather low, but not terrible.  There is quite a bit of scatter in the data.  Note the circle of data points around the left end of the line.  More on that later.

Next, I divided the data by decades, frex, 1961-1970.  This admittedly simplistic data parsing reveals that the slope and R^2 values are strongly variable over time.  Graph 4 shows the scatter plot along with the slope and R^2 values for each decade.  These data values are arranged in the chart in chronological order and color matched with the corresponding data points.

 Graph 4 GDP vs TCMDO, YoY % Change by Decade

I’ve added a brown line connecting the dots for the first decade of this century.  The chronology proceeds from a cluster near the center of the graph into a clockwise circular spiral.

Graph 5 shows how the slope and R^2 vary over time.

 Graph 5 Slope and R^2 Over Time for GDP vs TCMDO

After the 60’s, the slope plummets, and by the 80’s R^2 is a laughable 0.035.  Though the slope has remained low, R^2 has since recovered to 0.38, which is near the whole data set value of 0.39, and only slightly less than the 0.40 to 0.44 of the first three decades.

The slope changes can be interpreted as generally less GDP bang for the TCMDO buck, as the TCMDO/GDP ratio increases.  This is totally consistent with Art’s hypothesis.

I have more to say about the GDP -TCMDO relationship, but this post is getting long, so I’ll save it for a follow-up.

For now, I’ll close with a few questions.

1) Do you think we’re on to something?
2) What do you think of the methodology?
3) “Excessive debt” is suggestive, but non-specific.  How should this concept be quantized?
4) How should I go at exploring corollaries 1 and 2 mentioned after Graph 2?
5) Any thoughts on what was there about the 80’s that blew up the prior debt – GDP relationship?
6) Is there such a thing as productive vs non-productive debt, and how would they be characterized?

I look forward to your constructive comments.

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Strategic Lying as Political Art

If you listen to Randi Rhodes, you know she is still livid over Romney being declared the “winner” in last week’s – we’ll call it a “debate” for the nonce.

Alas, though, the reason he won is that poll numbers have moved in his favor.  Whether that bounce is robust remains to be seen.  But it did gain Romney some sort of advantage, at least in the near term.

Randi’s objection is that Romney lied, repeatedly, and about almost everything.  In the process, he flatly repudiated some of the major planks in his platform – the destruction of Medicare as we know it, the $5 Trillion dollar tax cut, the reduction of tax share paid by high income people, and an insurance plan not covering pre-existing conditions stand out in that regard.   And these are but a few of the 27  debate lies that can easily be recognized and refuted.

Indeed, the one rare moment of lucid candor came when he eagerly, gleefully announced that he would send Big Bird to the unemployment line in order to avoid borrowing money from China.  Big NPR whoop!  To put this in perspective, for CY 2012, the Federal Government, via the Corp. for Public Broadcasting, is contributing $26.65 million in support of PBS, or 0.0007% of total Federal expenditures ($3.77 Trillion) for 2012.    In fact, the entire Federal contribution to CPB is $445.2 million, or 0.0118% of total expenditures. That’s sure going to help balance $5 Trillion in tax cuts over ten years. (CPB data from Wikipedia, current expenditure data from the St. Louis Fed.)   Romney isn’t lying about our creditor position with China, but he was certainly misleading.  According to Fox News (!) “China, it turns out, holds less than 8 percent of the money our government has borrowed over the years.”

OK, I get where Randi is coming from – to have a totally unprincipled opportunist in charge of running the world’s greatest super power is not a recipe for any kind of enduring success, either for the U.S.A. specifically, or for the world at large.  There are many historical examples one could cite, but we really needn’t go back any further than the “compassionate conservatism” of unprosecuted war criminal and would-be social security privatizer George W. Bush to make the point.

But what Randi refuses to acknowledge is that what we witnessed last week was not a debate, by any recognizable definition of the term.  Lying will get you disqualified in a real debate – right?  This was political theater – and what is theater but staged fiction? 

And there is nothing unusual here.  I’ve been saying for years that all Republicans do is lie, and then lie about their lies. (I might have gotten that phrase from Randi – the memory is foggy.)  Here is a four-year-old exposé of some of Romney’s shape shifting.  (H/T to Dave Brockington at LGM.)

A more insidious kind of lie is simply denying reality, as characterized by birtherism, New Deal and global warming denialism, and Friday’s epidemic of conspiracy theories surrounding the latest favorable jobs report.   But I digress.

Here is my point.  Brad Delong points us to a 1984 Fay Joyce article in the N. Y. Times uncovered by Michael Moore.  It turns out that lying during a debate is a time honored Republican strategy.  Even 28 years ago, when there was some chance of the main stream media doing actual journalism, they were confident in their lying strategy.

The Republicans are unabashed in their discussion of their ability to use the television medium. “You can say anything you want during a debate and 80 million people hear it,” observed Peter Teeley, press secretary to Vice President Bush. If reporters then document that a candidate spoke untruthfully, ”so what?”

”Maybe 200 people read it or 2,000 or 20,000,” he said.

Now, they have honed it into an art form.  And it’s worth remembering the one reason that always accounts for every person’s lie: their agenda is not compatible with the truth.

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The Effect of Capital Gains Tax on Investment – Appendix

In comments to my previous post, Robert requested the unsmoothed data from Graph 3.  Here it is.   GPDI is plotted against the Capital Gains Tax Rate.

Since the Capital Gains Tax Rate (X-axis) is quantized, the result is columns of data.  Compared to the smoothed version, there is little change in either the slope or intercept of the best fit straight line.  R^2 is, of course, much lower.

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The Effect of Capital Gains Tax on Investment

Matt Yglesias, servitor to our corporate overlords, suggests that the reduced capital gains tax rate paid by rentiers like Willard Romney is really a very, very good thing.  To wit:

The main reason Romney’s effective rate is so low is that the American tax code contains a lot of preferences for investment income over labor income.
. . .
But this is definitely an issue where the conservative position is in line with what most experts think is the right course, and Democrats are outside the mainstream.
.  .  .
That’s the theory, at any rate. It’s a pretty solid theory, it’s in most of the textbooks I’ve seen, and it shapes public policy in basically every country I’m familiar with. Even researchers like Thomas Piketty and Emmanuel Saez (see “A Theory of Optimal Capital Taxation”) who dissent from the standard no taxation of investment income position think capital income should be taxed more lightly than labor income. Empirically, it’s a bit difficult to verify that variations in capital gains tax rates and the like really are making a material difference to investment levels. But then again the data is noisy.

Scott Lemieux at LGM demurs.

Sure, if you 1)accept the premise that reducing or eliminating capital gains taxes will result in productive infrastructure investments rather than worthless accounting tricks, 2)ignore the economic benefits created by consumption, 3)assume that significant numbers of people will forgo money for doing nothing just because the profits will be taxed , and 4)ignore the fact that in most jurisdictions consumption is also “double taxed,” then reducing capital gains taxes looks good.   But since all of these assumptions are (to put it mildly) highly contestable, it’s just question-begging.

My response to Matt is that in my jaundiced opinion, you might as well consult The Necronomicon of Abdul Alhazred as an economics textbook for an issue like this; and that in a world that has on the one hand Krugman, Thoma and Delong, and on the other Fama, Cochran and Cowan, a consensus among experts is about as likely as lions lying down with lambs for some purpose other than a quick snack.

To Scott I say, why assume or ignore anything when that oh-so-noisy data is readily available?

Graph 1 shows the capital gains tax rate and year-over-year growth in gross domestic private investment (GPDI,) each presented as a percent.  If Matt and what he calls “the mainstream” are right, then there should be a negative correlation between the tax rate and investment growth, since higher taxes would be a disincentive to investment.

Graph 1  C G Tax Rate and GPDI, 1954 – 2011

Instead, what we find is that over time, as the capital gains top rate has gone down, so has GPDI.  This is indicated by the downward slope of the best fit straight lines through each data set.  The best fit lines are based on the data through 2008, so the huge 2009 negative in GPDI is not represented.

One way to handle noisy data is to superimpose a moving average.  The dark heavy line that snakes up to a top in 1978 is an 8-Yr moving average.  This top corresponds exactly with the last year of the 40% Cap Gains Tax rate.  The purple horizontal line is the period average of GPDI YoY growth from 1954 through 2011.   Note that until 1986, the 8 Yr line is mostly above the long average line, and since 1986 it is mostly below.

This is not because the bottoms in the GPDI data set are lower since 1986.  A quick look shows that, except for the 2009 plunge, they are not.  It is because the peaks are lower.  The table gives a count of extreme data points for GPDI growth, before and after 1982, the year the Cap Gains rate was reduced to 20%.

Even at a detail level, it appears that a higher tax rate corresponds with a higher rate of investment growth, as both curves peak in 1978.

Graph 2 provides a close-up view of 1985 through 2005.

Graph 2  C G Tax Rate and GPDI, 1985 – 2005

When the Cap Gains tax rate was increased from 20 to 28% in 1987, the rate of investment growth increased from 1.4 to 5.2%, and stayed at about that level until it was derailed by the 1990-91 recession.  Then from 1992 through 2000, 8 of 9 years had GPDI growth above the long average (purple line,) an unprecedented occurrence.  Granted, the last three of these years were at the lower C G Tax rate of 21.19%, instituted in 1998.  But also note that this decrease did absolutely nothing to spur increased investment.

Cutting across the data in a different way, Graph 3 presents a scatter plot of the C G Tax Rate and YoY GPDI growth, each presented as an 8 Yr average.

Graph 3 Scatter Plot of GPDI Growth vs C G Tax Rate, Smoothed

Even with smoothing, there’s a lot of scatter.  No surprise, since many other factors can affect investment: business cycle, commodity price shocks, wars, etc.  I’m tempted to say the obvious relationship is that a higher C G Tax rate leads to higher investment, but I don’t want to get into a correlation-is-not-causation brouhaha.  So I’ll simply say that the idea that lowering C G taxes leads to increased investment – and therefore increased economic growth – is not only unsupported by the data, it is refuted by the data, and therefore contrary to fact.

So, once again, we find a mainstream economic idea that is only valid in some imagined alternate reality.

Capital Gains Rate data can be found here (Returns With Positive Net Capital Gains Table, 1954-2008) and here.  There are a few slight discrepancies between these sources, mostly in transition years.  I have used the maximum tax rate, column farthest to the right in either table.
Gross Domestic Private Investment is FRED series GPDI.

Cross posted at Retirement Blues

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What is the Economic Middle Class?

My lovely wife shared this link with me on Facebook.  I got into a discussion in comments there with a right winger who suggested that $250,000 was a very reasonable estimate for median income in Boston.

As it turns out, median household income in Boston is $51,914, close to the national average, and way below the Mass. State average of $67,950.  But right wingers live in a data-free world, so this is no surprise.

Another contention in comments at that site is that the middle class is undefined and undefinable. Not so.  I define middle class household income as the middle quintile.  This range includes the median and a band around it wide enough to hold 20 percent of the population.  You might wish to concoct your own definition with a wider spread, but you’d better not be asymmetric around the median.  Feel free to use the middle three quintiles, if that is your preference.  But if your of concept of middle class gets very far beyond 50% of the population, you really ought to give more thought to what the word “middle” actually means.

Thinking about all this prompted a look at the various income quintiles.  The data, through 2009, is available at the Census Bureau web site, table 694.  This table provides historical data from 1967 through 2009 on the top income limit for the bottom 4 quintiles, and the bottom income limit for the top 5%, expressed in constant 2009 dollars.

Graph 1 presents this data.  The 3rd quintile – my definition of the middle class – is between the orange line and the yellow line.

In 1967, the threshold for the middle quintile was $32, 848.  By 2009, it had increased by 17% to $38,550.  This is a compounded annual growth rate of 0.38%

In 1967, the top limit for the middle (and threshold to the 4th) quintile was $46, 621.  By 2009, it had increased by 33% to $61,801. This is a compounded annual growth rate of 0.68%.

The threshold value for the fifth quintile increased from $66,481 in 1967 by 80% to exactly $100,000 in 2009.  This is a compounded annual growth rate of 0.98%.

To reach the top 5% required an income of 106,684 in 1967.  By 2009, this had increased by 69% to $180, 001.   This is a compounded annual growth rate of 1.25%.

So my comment sparring partner and the current presidential challenger he seems to support are a bit off base.  $250,000 in household income puts a family well above the 95th percentile.  In fact, that is just enough household income to crack the top 2%.

My ongoing hobby of debunking right wing nonsense aside, the point of this post is mainly to inform.
There are two main observations:
1) While the bottom two quintiles haven’t changed much over the decades, entry to the third quintile has crept up a bit; and into higher categories it’s moved up a lot.  We recognize this as stagnation in the bottom half and growing inequality in the top half, skewed powerfully to the top.
2) This data set stops in ’09, so Obama is outside the discussion.  But we can see that all the way up to the 95th percentile, income growth was dead flat during the Bush administration.  No wonder the 95% percentile feels so poor.

But — surely, some wealth was generated during those 8 years.  GDP growth was positive at least some of the time.  I wonder where it all went?

Cross posted at Retirement Blues.

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Public and Private Investment

One not particularly cute graph; no analysis, explanation, nor editorializing.

GDPI is gross domestic private investment.

NDGIC96 is Real Federal Nondefense Gross Investment.

SLINVC96 is Real State & Local Government Gross Investment.

Source page at FRED.

Note that private investment runs at about 7 times the total of government investment at all levels.

Make of it what you will.

H/T to PK.

Cross posted at Retirement Blues.

UPDATE: In comments, Art raises the valid but trivial point that this graph compares nominal GPDI to real govt investment.  Oversight on my part.

Here is is again, with GPDI adjusted by the implicit GDP Deflator, indexed to 100 in 2005.  This brings the GPDI line down a bit relative to government investment.

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Yes, The Right Wing Lies When They Say Obama is a Profligate Spender

Part III – How to think about time series data.

For reference:
Part II  Federal Spending as a Fraction of GDP

Part I  Federal Spending Growth

Some commentors to the previous posts have rightly concluded that I consider spending under Obama in the context of historical trends.  In fact, if you don’t consider historical trends, you are ignoring the most important element of context that is available.  I only mentioned trends briefly in Part II, but the directional changes in the graphs of Parts I and II implicitly suggest them.

Time series data that relate to the size of the population, the government, or the economy generally follow a quasi-exponential growth pattern.  I say quasi- because a perfect exponential growth pattern  results from a continuous constant rate of growth, while real world growth rates vary from year.  Graph 3 of Part II shows how these variations have occurred over several decades.  Usually this does not result in a large or permanent deformation in the shape of a quasi-exponential curve, since the growth rate typically oscillates irregularly around a mean value that only changes slowly over time.

Graph 2 in Part II shows that the spending and GDP growth curves stay close to exponential tracks over long time spans.  It also illustrates that the recent recession was one of those rare times when growth rates deviated substantially.

Human eye-brain coordination doesn’t deal well with exponential curve shapes.  Straight lines are much easier to comprehend and extrapolate.  Graphing quasi-exponential data on a log scale reduces the curve to a quasi-straight line that is much easier to use and understand.

Graph 1 shows Federal Spending and GDP, since 1995, plotted on a log scale.  Constant growth results in a straight line segment, and a higher growth rate causes a steeper slope.  Zero growth shows up as a horizontal line.   I’ve again included a line for 5 times spending, to get a close overlay with the GDP line.

 Graph 1.  Spending and GDP since 1995 (log scale)

There is an upward bend in the spending line in 2000.  This is most easily seen in the blue line.  During the 90’s, we can see that GDP grew faster than spending.  In 2001-2, GDP growth flat-lined, as expected during a recession. Then, from 20002 to 2008, the growth rates for GDP and spending were close to identical.  Both lines twist during the most recent recession.  Curiously, spending growth was flat for a large portion of 2008.

Since the recession, the spending lines are very close to flat, and GDP growth has been anemic.  Here is a close up.

Graph 2.  Spending and GDP since 2007 (log scale)

Graph 3 provides context, all the way back to 1947.  Ponder the inflection points and slope changes at your leisure.

Graph 2.  Spending and GDP since 1947 (log scale)

Note that there are only two flat-ish spots in the spending lines: now, and during the Eisenhower administration. The current administration has, at least temporarily, broken the decades-long trend in continuous spending increases. 

To emphasize the obvious, spending growth is now very close to zero.  In context, this is remarkable.  Saying Obama is a profligate spender is a lie. 

In this post, I am not suggesting that the rate of spending growth under any president is good, bad, appropriate or inappropriate.  I am only pointing out what was and is. 

So, this is how you think about time series data.
0) Forget your preconceived notions.    (Frex:I had no idea that spending growth has essentially stopped until I looked at the data.)
1) Identify trends. The history of time series data provides meaningful context.
2) Identify break points and trend changes.  These are key data points.
3) Note the directions of these changes.
4) Think hard about what these observations are actually telling you, not what you want them to say.
5) Double down on 4) if you are looking at a ratio.  Ponder that denominator.
6) Don’t cherry pick.  It’s dishonest.

There are a lot of ways to look at a data set: linear and log scales, rate of change, etc.  Chose the one that gives the clearest picture of the data you want to analyze, or simplifies the analysis, or makes it easier to understand.  Studying different views can be informative, as can a comparison of different data sets. 

Here is the working page at FRED for the graphs in this post.  I encourage the interested reader to spend some time working with the capabilities of this very powerful tool.

Editorial Comment:
In case it’s not obvious, I’ll tell you that I write these posts because they interest me and I think they generate some knowledge, or at least information, that is worth sharing.  I have virtually no interest in the fool’s errand of convincing anybody that I am right – either the data analysis convinces you or it doesn’t.  So unless you have better data, or can point out some specific flaw in my reasoning [and then tell me specifically and in detail how to get it right] don’t bother arguing with me.

I appreciate rational discourse, and am always willing to engage thoughtful readers. I’m also willing to be proven wrong by a cogent argument.  That said, though, I don’t really care if anyone comments.  At this point, I’d almost rather nobody did.  But if you chose to, please do me the courtesy of having your comment be somewhere in the general vicinity of on-topic.  And – fair warning: naked assertions and unsubstantiated ideologically approved talking points will be scoffed at, so please check that nonsense at the door.

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