Friday, June 29, 2018

Act like you mean it.

THE hot topic at Reddit, at the moment:

Here's some of what the article has to say:

National debt at highest level since after WWII

Despite President Trump's declaration that he would eliminate the national debt over eight years, the debt-to-Gross Domestic Product ratio has reached its highest level since after World War II, according to a new report from the Congressional Budget Office (CBO)...

"At 78 percent of gross domestic product, federal debt held by the public is now at its highest level since shortly after World War II," the CBO found.
You can be sure they're talking about "debt held by the public", because the "gross" Federal debt is already 100% of GDP or more.

Not that it matters much. But if you're gonna talk about how much debt there is, you ought to be honest about it. If you're one of the people who says the Federal debt doesn't matter, then it doesn't matter whether it's 78% or 100%, right? If it doesn't matter, it doesn't matter. So, why insist on using the smaller number?

The Federal debt matters to some people. You can tell who they are. Those people always use the bigger number, the gross Federal debt. I understand that: It matters to them. What I can't understand is why anyone would insist on calling it 78%. If it doesn't matter, it doesn't matter, so use the bigger number. It doesn't matter, right?

Maybe the people who call it 78% just do it to piss off the people who insist on calling it 100%. Maybe they just do it to be dicks. What other reason could there be? I mean, if it doesn't matter how big the Federal debt is, why else insist on using the smaller number?

You're telling me it doesn't matter. So, act like you mean it.

Thursday, June 28, 2018

Everything was going fine, when all of a sudden ...

It sounded interesting. At Reddit, Anger in America: US Institutions No Longer Fit For Purpose. At Project Syndicate, it comes with a great intro:
US President Donald Trump has exploited popular anger to advance his own interests, but he did not create that anger. America’s elites have spent decades doing that, creating the conditions for a figure like Trump to emerge.
That's exactly right, I think.

Here, the first two paragraphs:
Many blame today’s populist rebellion in the West on the far right, which has won votes by claiming to be responding to working-class grievances, while stoking fear and promoting polarization. But, in blaming leaders who have seized on popular anger, many overlook the power of that anger itself, which is aimed at elites whose wealth has skyrocketed in the last 30 years, while that of the middle and working classes has remained stagnant.

Two recent analyses get to the heart of the issues at play, particularly in the United States, but also in the rest of the world. In his new book Tailspin, the journalist Steven Brill argues that US institutions are no longer fit for purpose, because they protect only the few and leave the rest vulnerable to predatory behavior in the name of the free market. According to Brill, this is an upshot of America’s meritocracy: the best and brightest had the chance to climb to the top, but then essentially pulled the ladder up behind them, as they captured democratic institutions and used them to entrench special privileges for themselves.
And there is my problem: they "pulled the ladder up behind them".

As long as we insist on evaluating problems in terms of MAS (metaphor, analogy, and simile), we will never understand the problems well enough to solve them.

Beyond that, there is the explanation of motives: "the best and brightest ... captured democratic institutions and used them to entrench special privileges for themselves." On second read, I might question my first-read conclusion that this is a description of the motives of others. But no: it is. The sentence not only tells what they did -- captured democratic institutions -- but also why they did it:
to entrench special privileges for themselves.
A description of something that happened is entirely appropriate, as long as it is actual and not metaphorical. But one person's description of another person's motives is never, under any circumstances, appropriate.

Come to think of it, this also is somebody's description of somebody else's motives:
... Trump has exploited popular anger to advance his own interests ...
We hate him because he's advancing his own interests. We hate him for his actions, but we hate him even more for his motives.

His actions, we can see. That's his record. Judge him on his record. His motives are made up by somebody else.

Evaluating Trump based on someone else's description of Trump's motives may make the old clit tingle but will turn out as fruitless as evaluating problems in terms of metaphor, analogy, and simile.

"It is necessary to be gracious as to intentions."

Monday, June 25, 2018

The Holodeck and the grain of salt


1. In Star Trek: Next Generation, when they had a problem they couldn't figure out, they'd take it to the Holodeck and run a simulation. As a fan, I was supposed to believe the computer could figure out things the people couldn't. I never bought that. The computer only does what you tell it.

If you write an economic model for the Holodeck based on Y=C+I+G+NX then the Holodeck economy will be based on consumption and investment and government spending and net exports. But if the real economy is based on a different formula, your model won't model the real economy.

I'm not questioning Y=C+I+G+NX. I'm questioning the confidence that comes out of the holodeck of economic thought.

2. If we assume the program we wrote for the Holodeck accurately mirrors reality, then we can assume the results the Holodeck shows us are correct.

That is a lot of assumption.

3.If you don't know all the relevant information to begin with, you cannot put that information into your holodeck program. You cannot put it into your economic-policy rules. Neither the rules of a simulator nor rules of economic policy can provide guidance when basic principles are unknown or overlooked.

Garbage in, garbage out.

4. The Taylor rule [for example] produces a number that economists think should be the right number, based on their incomplete and flawed understanding of the economy. Confidence in the Taylor rule presupposes that the modern understanding of economic forces is accurate and complete.

The Taylor rule is just one example.

This all comes up now because (Laubach and Williams 2001) write:
Economic theory implies that the natural rate of interest varies over time and depends on the trend growth rate of output. In this paper we apply the Kalman filter to jointly estimate the natural rate of interest, potential output, and its trend growth rate ...
They add:
For this purpose, it is useful to define the natural rate of interest to be the real short-term interest rate consistent with output converging to potential, where potential is the level of output consistent with stable inflation.
Their paper, titled Measuring the Natural Rate of Interest, gave rise to an estimate of the natural rate of interest. That is a very useful piece of information to gain. However, it is best to remember that such information arises not from reality but rather from a form of virtual reality.

Sunday, June 24, 2018

Sounds right to me

JW Mason:
In my opinion, the way forward, certainly for people like me — or, dear reader, like you — who have zero influence on the direction of the economics profession, is to forget about finding the right model for “the economy” in the abstract, and focus more on quantitative description of concrete historical developments.

Maynard Keynes, via Lars P. Syll:
The master-economist must ... study the present in the light of the past for the purposes of the future.

Saturday, June 23, 2018

We call it the Phillips curve because...

because, Bill Phillips.

Here are his opening words:
When the demand for a commodity or service is high relatively to the supply of it we expect the price to rise, the rate of rise being greater the greater the excess demand. Conversely when the demand is low relatively to the supply we expect the price to fall, the rate of fall being greater the greater the deficiency of demand. It seems plausible that this principle should operate as one of the factors determining the rate of change of money wage rates, which are the price of labour services.
It's simple: It's supply and demand. The simplicity makes for a beautiful opening.

But the Phillips curve no longer seems to work.

That's true. And as Bill Phillips said, it seems plausible that supply and demand should operate as one of the factors determining wage rates.

One of the factors. Supply and demand still works. But something else has taken priority and now has more effect even than supply and demand. Something changed. Something changed so that the Phillips curve no longer seems to work.

And we can pinpoint the time of the change, based on when the Phillips curve stopped working. I didn't look into it, but my previous post was about the Phillips curve not working in the 1990s. But it was working in the 1960s and '70s. Was it still working in the 1980s? You can look into that.

So I'm thinking that the changes of the 1980s were the changes that caused Phillips to fail. Just off the top of my head it could have been supply-side economics, which improved conditions for everything on the supply side, everything except the supply of labor.

Thursday, June 21, 2018

What's Happened to the Phillips Curve?

An often-asked question, these days. Unemployment is really low, so where's the inflation?

Makes me tend to doubt the reported numbers. But I don't have anything on that.

It happened before, the "where's the inflation?" thing. I found something on that:

What's Happened to the Phillips Curve?

Flint Brayton, John M. Roberts, and John C. Williams
Division of Research and Statistics
Federal Reserve Board
Washington, D.C. 20551
September 1999

From the abstract:
Two alternative modifications to the standard Phillips curve restore stability. One replaces the unemployment rate with capacity utilization. Although this change leads to more accurate inflation predictions in the recent period, the predictive ability of the utilization rate is not superior to that of the unemployment rate for the 1955 to 1998 sample as a whole. The second, and preferred, modification augments the standard Phillips curve to include an “error-correction” mechanism involving the markup of prices over trend unit labor costs. With the markup relatively high through much of the 1990s, this channel is estimated to have held down inflation over this period, and thus provides an explanation of the recent low inflation.

Monday, June 18, 2018

Not all recessions are the same

At the St. Louis Fed:
May’s unemployment rate dropped to 3.8%, the lowest in 18 years. Does this tell us something about the next recession?
with a link to the Economic Synopses of 2018-06-01: Recession Signals: The Yield Curve vs. Unemployment Rate Troughs by Kevin L. Kliesen.

The article looks separately at the yield curve and unemployment rate troughs. Shows both to be good indicators of recession. Compares the indications and finds they don't agree. Conclusion:
Overall, both indicators tend to be reliable signals of a coming recession. But as with all recession signals, the wise economic analysts should examine many indicators rather than betting the farm on one or two.
Fair enough.

I looked at the yield curve recently myself. And I like to use the change in employment, not terribly far removed from the unemployment rate, as a recession indicator. So I find this stuff worth reading.

But I find a couple odd things. They show a graph: Treasury Yield Curve, 1954 to 2018. And sure enough:
As Figure 1 shows, yield curve inversions have regularly occurred prior to periods of economic recessions since the 1960s.
But there is also a horizontal line on the graph, representing the "average expansion value" for the 1954-2018 period. As if that overall value was somehow relevant to every recession since 1954. Or since the 1960s, or what.

Maybe it is relevant. Maybe it isn't. Not all recessions are the same.

They show another graph: Unemployment Rate, 1954 to 2018. This graph shows a horizontal line at the 4% level. This line is not an average, but is offered to show that
This is only the third economic expansion in the past eight (current included) that has registered an unemployment rate below 4 percent.
Yeh. Recessions differ. Recoveries differ. So I don't really think that an "average expansion value" for the whole 1954-2018 period tells us very much.

A footnote to their comparison of the recession indicators says:
Since June 1954, the average monthly change in the unemployment rate is –0.003 percent, with a standard deviation of 0.18 percent.
They are looking at long-period average values, not only for the yield curve, but also for the unemployment rate. I'm not convinced that these averages are relevant.

Useful? Yeah, they are useful, if you use them to show that they are not relevant. Or if you manage to convince me that I'm wrong about the lack of relevance. Kliesen does neither. If he does, I missed it.

But he dangled that stuff in front of my face: the -0.003 average monthly change, and the 0.18 standard deviation. I had to see if I could duplicate his numbers. I got the -0.003 monthly change all right. But I got a standard deviation of 0.19, not 0.18.

It's probably me. I probably did something wrong: selected the wrong data, or selected the wrong STDEV function. No matter: I already think the number is not relevant.

But there I was, with Excel on my screen, and all that unemployment rate data, thinking that the long-term averages are not relevant numbers. So how could I resist? I figured the average by decade instead. And what the heck, standard deviation by decade too:

Graph #1
Blue: average monthly change in the unemployment rate, by decade. The numbers are large in the 2000s and later; otherwise small. The 1970s and 2000s show positive values, meaning unemployment on average increased; unemployment on average decreased in the other decades.

The three largest decreases in unemployment occur in the 1960s, the 1990s, and the current decade.

Red  Brown: standard deviation of the monthly change numbers, by decade.

The three lowest values occur in the 1960s, the 1990s, and the current decade.

I don't know what that means. But it's a hell of a lot more interesting than a couple overall average values.

Sunday, June 17, 2018


"There is consensus among economists. Therefore, they are right."
Nobody would say that, would they? And yet, almost every time I read something, I find consensus among economists used as part of the argument.

Despite what we seem to think, a consensus is not right by definition. Anyway, a consensus is a shared opinion, not a shared fact.

I think there is general agreement on that.

Saturday, June 16, 2018

Recessions happen when the economy slows down. Recoveries happen when the economy speeds up.

I showed this graph before, comparing the Great Recession and aftermath (red) to the 1990-91 recession and aftermath (gray):

Graph #1: Comparison of Recessions with the 1990-91 Recession in Slow Motion
Imagine: The red line and the X and Y axis are printed on a pane of glass. The gray line, showing data beginning in January 1990, is drawn on a rubber sheet. The glass rests atop the rubber sheet.

I have stretched out the rubber sheet to two and a half times its original width, to make what happens with the gray take 2½ times as long to happen. I went over all that in the earlier post; I won't repeat it here.

What I see on this graph is that the red and the stretched-out gray lines peak together sometime in the middle of 2006, and then decline together until some time in 2012. Call it a six-year decline.

Really, the gray decline took 2½ times less. The 1990-91 recession was smaller and shorter and less severe than the Great Recession. Stretching out the shorter recession allows us to compare its path to that of the longer and more severe Great Recession.

What I'm looking at on this graph is two lines that peak together in mid-2006 and run downhill together for six years. But they don't exactly run downhill "together". The red one goes downhill faster than the gray, because the Great Recession was more severe than the 1990-91 recession.

After the six-year decline, both lines change. The red runs uphill; the gray runs flat. The red line bottoms out in 2012 and ends in 2018, so we have another six-year period here.

During the six years of decline, the red went downhill faster than the gray. During the second six years, the red went up faster than the gray. There was more recovery going on with the red line -- again, because the Great Recession had been more severe.

The graph shows that after the 1990-91 recession, average hourly earnings growth clung to the 2.5% level for six years. In real time, it was about two and a half years, from mid-1992 to the start of 1995.

When we think of the 1990s now, we remember "the new economy" and the "tech boom" and the "high productivity" of the latter 1990s. The early 1990s were not so memorable. The early years were not so great. And if we stretch out those years on the rubber sheet, we have a benchmark: six years during which hourly earnings growth refused to improve.

Seen in the context of that benchmark, the red line runs uphill. The red runs up, and then down, then up and down again, and then up again before the data ends in April of 2018. But each high point of the red reaches the benchmark level. And each new low of the red line is not as low as the one before. The red runs uphill, and then it ends.

What will happen next? That is the question, isn't it.

I took the recent data on Graph #1 and made it dull red, but it looks brown to me. Then I eyeballed in trend lines, bright red to get your attention.

Graph #2: Same, with Markup
Notice that the trend lines show the "V-shaped" recovery everybody was talking about, ten years back. Ironic, isn't it? We actually got that V. Nobody knew. Because our economy... has been moving... in slow motion... for so many years.

It bottomed out in 2012. Things have been getting better since then, despite appearances. And now, at the end of the dull red data, our options are open. Recovery from the Great Recession has caught up with the recovery from the 1990-91 recession -- and caught up at just the point when the economy of the 1990s started to get memorably good.

If this comparison of the two recessions is credible, we should not be surprised to find that the next six years turn out to be very good ones for this economy of ours. No one any longer believes it possible that the economy might be "good" again. I say it is more than possible: It is likely.

If we continue the bright red trend line uphill into the future, it aligns well with the gray data, as the dotted red line shows. But that gray data is on the rubber sheet, stretched out to make things happen slowly. In real time, the gray recovery was much more rapid. And as the graph suggests, our economy is again ready to enter the recovery phase. In the recovery phase, by definition, the pace of economic activity picks up.

Recessions happen when the economy slows down. Recoveries happen when the economy speeds up. In other words: Don't expect things to stay in slow motion. Almost everyone predicts economic growth to remain in the neighborhood of 2% annual. I expect four percent.

Don't expect things to stay in slow motion.

Graph #3: Same, with Optimistic Estimate of Average Hourly Earnings Growth
I took the second graph and added in blue the increase in Average Hourly Earnings from the 2.5% level. The blue increase is the same as the gray increase, but at a "real time" pace. If and when people abandon their reticence and get down to business, hourly earnings could easily improve at that rapid pace.

I'm not such an optimist. The dotted line shows a midway path between the real-time pace and the rubber-sheet pace. This path is certainly possible, and would still be an improvement over what everyone seems to expect. But I will not be at all surprised if things turn out as the blue line shows.

By the way, the red line on the first graph ends in April 2018. The red line ends at the gray line. The dull red line on Graph #3 ends one month later, in May. The dull red crosses the gray and continues rising at a rapid pace, a pace apparently as rapid as the early months of the blue increase.

A pace apparently as rapid as the early months of the blue increase: A good start.

And it's about time.

Thursday, June 14, 2018

The process by which the cycle of civilization moves beyond its "capitalism" phase

Put simply, when U.S. corporate profits are unusually high, it’s typically an indication that households and the government are cutting their savings and going into debt.

Hussman is advising investors. He's offering a bit of wisdom, describing something that "typically" happens.

I'm thinking: If it typically happens, it's a process and it has an end result. In this case the end result affects where we are on the Cycle of Civilization.

Now you know.

Tuesday, June 12, 2018

The oldest profession

Armstrong Economics:
Some say that prostitution is the oldest profession; history actually suggests that the oldest profession may indeed be that of the moneylender.

Sunday, June 10, 2018

No good answers

In On household debt at the FRED Blog, for household debt they use "consumer credit" plus "home mortgages" as a measure of household debt. I always use CMDEBT. What's the difference?

Graph #1: Blue is CMDEBT. Red is what the FRED Blog used.
Ooh, that's a fair amount. I was expecting to say "not much" but it is more than I thought. It varies, but my number is in the neighborhood of 9 or 10 percent higher than their number:

Graph #2 (This graph also shows more years.)

Maybe I should be using the FRED Blog number.


I wonder what the difference is. Just the nonprofit organizations? If it was some other household debt, the FRED Blog graph should have included it. So, maybe yeah. That's not a very good answer. But that's all I got.


Suppose we look at related things. Interest paid:

Graph #3
These are close. This, I've looked at before. Here's the ratio:

Graph #4
This one varies, too. But the interest paid by nonprofits is generally in the neighborhood of 2 or 3 percent of the amount of interest paid by households. Not 9 or 10 percent.

If our assumptions so far are good, then nonprofits borrow about 9 or 10% as much as households borrow, but pay only about 2 or 3% as much interest. That's odd. Do you suppose households on average pay interest rates that are four or five times as high as those paid by nonprofits? That doesn't seem right. Makes me think that the 9 or 10 percent difference we found is maybe only in part the debt of nonprofits, and in part also the debt of households. Not all of it nonprofit debt. But I still don't know.

The information should emerge from the numbers, but in this case it does not.

Suppose we look at effective interest rates: Monetary interest paid, relative to debt owed. Debt for households, using household debt as figured in the FRED Blog post. Debt for households plus nonprofits, using CMDEBT. And if we assume the 9 or 10 percent difference between CMDEBT and the other is the debt of nonprofits -- an assumption I do not trust -- then we can see the effective interest rate paid by nonprofits, too.

Blue for Households, red for Households and Nonprofits, and green for Nonprofits:

Graph #5

FRED has two different series for monetary interest paid: government: federal. There are also two basic versions of the Federal debt: the gross debt, and the one that excludes debt the government owes to itself. Using the lower measure of interest paid and the higher measure of Federal debt gives me the lowest measure I can get for the effective rate of interest paid on the Federal debt. That's the dotted line on the graph above.

Federal interest rates are low, compared to other rates. I've heard it many times. So it doesn't surprise me that the dotted line is lower than the red and blue.

It doesn't surprise me either, really, that the green line is lower than the dotted line. I take it to mean that the green line is wrong. The green line is too low. Either the interest paid by nonprofits is wrong, or the number I used for the debt of nonprofits is wrong.

The latter, obviously.

So here we are at the end of this episode, and all we know is that I still don't know about the debt of nonprofits.

And that means I still don't know about the debt of households.

Saturday, June 9, 2018

Yield Curve, again

The other day I showed this graph:

Graph #1: Excel Polynomial Order 2 Trendline

What happens if we extend that trend line into the future?

Graph #2
The trend line bottoms out above 1% and starts to rise.

Wednesday, June 6, 2018

Debt and GDP on a Log Scale

Graph #1: TCMDO Debt (red) and GDP (blue) on a Log Scale
Accumulated Debt and Nominal GDP appear very much in sync from 1951 to 1981. Then GDP growth slows. Then debt growth slows, but not as much, and the two drift apart until the Great Recession.

Now they appear to be running parallel again. Like in the 1950s, except much farther apart. And at a much higher level.

And not so fast uphill.

Tuesday, June 5, 2018

Monetary interest paid: Domestic: Corporate business: Financial

A FRED search for monetary interest paid turns up 30 series. Look for "financial" in the results, and there are four hits. One of those is for nonfinancial corporate business; I skipped that one. Of the remaining series, one is for financial corporate business, and the other two are the components of that: interest paid "on deposits", and interest paid "on other liabilities".

I took a quick look: Add the interest on "deposits" and "other" together, and they do add up to the number for financial corporate business.

Here's a look at the two parts of financial corporate interest expense, each shown as a percent of the total:

Graph #1: Financial Corporate Business Interest Paid
on Deposits (red) and on Other Liabilities (green)
In the 1960s, 75 to 80 percent of that interest expense was on deposits. Now, 90 percent of it is not on deposits.

This looks like a change that started in the '60s, by the way.

Sunday, June 3, 2018

Experimenting with ways to show the yield curve

An "area" graph:

Graph #1
It looks to me like the increases since mid-2016 are more parallel than not.

The skinny white vertical lines are not really lines. They are gaps in the daily data. I don't know why there are gaps; they're not "regular" enough to be mostly weekends. Anyway there are gaps in the data, and when FRED filled the area with color for the area graph, it kept the gaps as gaps.

This next graph uses the same daily data for the same time period, but shows it a different way.

Graph #2
The blue line here is the spread: the 10-year interest rate minus the Federal Funds interest rate. The red line is the Federal Finds rate, but with a minus sign to make it negative. So if you measure from the zero line up to the blue line, that's the size of the yield spread. And if you measure from the red line up to the blue line, that's the size of the 10-year interest rate. Plus, the red line lets you see when and how the Fed changed the Fed Funds rate. (But of course it has really been going up, not down.)

The changes in the red line are clearly visible in the blue. I had to hunt to find them, but they are there.

I like this way of showing the yield spread.

I want to use this "spread and negative fedfunds" comparison graph to look at the 1990s now. But, oh, apparently the daily data only goes back to the year 2000. So I switched to weekly data for this one:

Graph #3
The Fed Funds rate (red) is shown here a little below -7.5% in 1990; really it was around 8%. By 1993 it appears to be a little below -2.5%; really, around 3%. The FedFunds rate fell (moved toward the zero line) in those years. And the spread (blue) went up.

In 1994 and early 1995, on the graph FedFunds goes from below -2.5% to below -5% (really, it increased from around 3% to around 6%). As FedFunds rose, the spread fell; it reached zero in July 1995, five years before the start of the 1990-91 recession.

You can see the Fed reducing the FedFunds rate from 6% to 5% in late 1995 and early 1996; and you can see the spread rising up from zero in 1996.  Then in early 1997 you can see the FedFunds rate increase to about 5.5% and stay there for more than a year as the spread falls to zero again.

I won't bore you with every excruciating detail of this. But maybe you can tell why I like the graph: It really lets you see what happened with the policy rate and the yield spread.

And the relevant detail? The spread hit zero five years before a recession hit the economy.

Saturday, June 2, 2018

The yield spread since the end of the Great Recession

Looking at the yield spread since June 2009, an optimist might see this:

Graph #1: Excel Polynomial Order 5 Trendline

A pessimist might see this:

Graph #2: Excel Polynomial Order 2 Trendline

A panicking pessimist:

Graph #3: Excel Polynomial Order 3 Trendline

And a realist:

Graph #4: Excel Polynomial Order 6 Trendline

What do you see?

Friday, June 1, 2018

The yield curve (follow-up)

One more graph on the yield spread (again, figured as the 10-year rate less the FedFunds rate, based on what John Cochrane showed). Looking at "change from year ago" values for the spread, in blue. And in red, the Fed's upper target for the Federal Funds rate. Since January 2014:

Graph #1
In January 2014 the spread was one percentage point larger than it was in January 2013. In January 2015 the spread was one percentage point smaller than in January 2014. During that time there was no change in the FedFunds target. The change in the spread was not due to the Fed changing its target.

Over the course of 2015 the blue line moved toward zero. By December it was almost zero, meaning there had been almost no change from a year earlier. At that moment the Fed raised the target a quarter point and held it there for a year.

For six months after the Fed raised the target, the spread narrowed. By July 2016 it was one percentage point smaller than a year earlier. Then (with no additional change by the Fed) the trend turned upward. By the end of 2016, again, there was no change from a year earlier.

In other words, the first quarter-point increase by the Fed, the increase of December 2015, affected the spread for about six months. After that, the prior trend (narrowing less and less) resumed. And in the first two months of 2017 the spread actually started to widen.

As happened a year earlier, the Fed saw it coming. They raised the target once again at the end of 2016 and twice more in the first half of 2017. Despite all these increases, the spread held steady till October 2017, showing little or no change from a year earlier. Then, perhaps anticipating another increase by the Fed, the spread fell in November and again in December when the anticipated increase came to pass.

Those several increases together did hasten the narrowing of the spread. But the low point of the blue line this time was not as low as the two prior lows you see on the graph. And the third low occurred in the same month as the Fed's December 2017 target increase. By January, one month after the target hike, the trend was upward again, in the direction of a bigger spread.

The most recent increase in the target, in March 2018, probably slowed the move toward widening. But the move toward widening continues. And that's where we are as of the April data.

In summary:
  • ONE target increase (Dec 2015) caused six months of increased narrowing. Then,
  • THREE increases (Dec 2016-Jun 2017) stopped the widening of the spread but caused little narrowing. ANTICIPATION of the Dec 2017 increase did increase the narrowing; but despite
  • TWO increases (Dec 2017, Mar 2018) the narrowing grows less, and the widening appears more ready to resume.
If I'm reading the graph right, the economy appears to be growing stronger and better able to withstand the increases in the Fed's target. The only bad thing I see in all of this is that anticipation -- or jitters, as Cochrane says -- seems to have more of an effect that two or three actual increases in the Fed's target rate.

Does this analysis stand up? I think so. Here's a graph of the yield spread plain and simple. No "change from year ago" view to complicate things, this time. The blue line is the spread. The red bumps indicate the Fed bumping up the Federal Funds target.

Graph #2
The December 2015 bump led to a 6-month fall in the spread, followed by a 6-month increase. The increase was interrupted by a series of three bumps up which brought the spread down again. But as soon as the Fed stopped raising the target, the spread started rising again.

The analysis stands up.

The December 2015 target change was a 0.25% increase that led to a 1% decrease in the spread, a decrease four times as big as the increase.

The target change that began in December 2016 was a 0.75% increase that caused a 1% decrease in the spread. This time, the spread changed only a little more than the target.

And the December 2017 target change was a 0.25% increase, despite which the yield spread continued to rise. There was no decrease in the spread.

The longer the Fed has continued this policy of increase, the more the economy has adjusted to the idea that interest rates are finally getting back to normal.

This seems like an appropriate time to remember that we still have many policies which encourage credit use, and no policy that encourages repayment of debt. A tax rate designed to vary with the taxpayer's debt-to-income ratio could encourage repayment of debt without being punitive. Such a policy could reduce the growth (and eventually even the level) of accumulated debt, bringing relief to debtors while encouraging economic growth and vigor.

The best time to implement such a plan would be in the very early stages of what will eventually be a long, strong boom. Now, for example. Right now.