FT: IMF urges eurozone to boost spending to fuel economic recovery

In this article from the Financial Times we learn that the International Monetary Fund (IMF) is recommending: 

“Eurozone countries should increase government spending by an extra 3 per cent of gross domestic product over the next year to mitigate the economic impact of the coronavirus pandemic…’As monetary policy — close to the effective lower bound in several economies — becomes less effective in boosting output, fiscal policy needs to play an increasingly larger role,’ the IMF said. ‘Fiscal measures to stimulate investment and to facilitate job creation and reallocation would speed up the recovery.’”

This is the essence of Keynesianism, whereby the belief that government spending is the panacea against market corrections – which in fact in this case was the creation of government policy. In Europe, like practically all over the world, the economic mess they find themselves in is purely self-inflicted. The current economic malice was not strictly due to the Covid-19 virus, but rather from the draconian economic lockdown policies enacted and enforced by government. One can argue that the government policies have affected more people than the Covid-19 virus will ever have. 

The fallacy of Keynesianism is the belief that spending begets wealth. Who would ever tell anyone that in order to prosper they need to spend? That’s right, no one! Saving and investment are the key to accumulating wealth. Using the flawed IMF logic, why stop at “extra 3 per cent”? Why not extra 4 or 5, 6, heck, why not 20 per cent extra? 

Government spending to cure economic malice is at best a short-term play. Ultimately, the economic forces will converge and clear the excess at fire sale prices – which is another way to say, an economic correction.

How to Lie with Statistics – Part 5: The Gee-Whiz Graph

Continuing with my previous post, here I summarize Chapter 4 of the book How to Lie with Statistics. This chapter is titled, The Gee-Whiz Graph.

If this chapter could be summarized in six words it would be the following: It is not what it seems. Here we are illustrating how by merely adjusting the scaling of a graph you can get it to say whatever it is that you are trying to prove. Ten percent growth in one year is a “drop in a bucket” when illustrated in a 50-year graph; but when compared versus, say a 2 year graph, the 10-percent rate can be made to look fantastic. Adjust to quarters, or months, or days, and you can get even more stunning results – just like the doctor ordered. In other words, what would visually be a handsome yearly return is lost when viewed in the context of a longer time frame; and vice versa. So, whenever anyone shows you some graph (yours truly included) always check the scaling. Do not be deceived.

Who’s to Blame for America’s Decline? - by Bill Bonner

Note: The article below is courtesy of Bill Bonner. In the article below (original link here) he briefly describes the decline of the US, which really started to look more obvious right after 9/11. The last 20 years are synonymous with war, spending, and immorality – from War on Terror to the ongoing Covid War; from mad spending to crazy spending; and from a society with some resemblance of civility to one now marred with incivility.

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Our view, for the benefit of new readers, is that the U.S. went badly off the rails around 20 years ago.

Since then, by almost every measure, it has been slipping and sliding downward. In everything, from life expectancies to income to GDP growth to freedom… to marriage rates and church attendance… America has lost ground.

Here’s the latest from Wolf Street:

The global share of US-dollar-denominated exchange reserves dropped to 59.0% in the fourth quarter, according to the IMF’s COFER data released [at the end of March]. This matched the 25-year low of 1995. These foreign exchange reserves are Treasury securities, US corporate bonds, US mortgage-backed securities, US Commercial Mortgage Backed Securities, etc. held by foreign central banks.

Since 2014, the dollar’s share has dropped by 7 full percentage points, from 66% to 59%, on average 1 percentage point per year. At this rate, the dollar’s share would fall below 50% over the next decade.

On the global stage, in other words, the role of the U.S. is in decline.

Politically Correct Approach

But “declinism” has been, well, on the decline in academic circles. It suggests a moral failing… as if things might have turned out much differently had not people done stupid things.

“Risism,” on the other hand, is perfectly acceptable. Nothing negative or prejudicial about it.

Even the “fall” of Rome is now viewed not so much as a “fall” but a “transition.”

Yes, after the decline came the collapse. And then, the Barbarians took over and perhaps a million people died…

The Vandals, the Goths, the Sueves, and the Alans enslaved many more… burned down cities… destroyed libraries (they couldn’t read or write, so what good were the ancient manuscripts?)…

…and Europe fell into a “dark age” for at least three centuries.

But that is a very “judgmental” view.

Even the word “civilization” is no longer considered intellectually respectable.

All cultures are equal. All languages are equal. All people are equal. None, according to this politically correct approach, are more “civilized” than others.

Different View

Here at the Diary, we take another view.

In the eyes of God (and sometimes, the courts) all men may be created equal. But we humans look at every single one of them differently.

Equality is neither a fact… nor a useful goal. After all, if we were all equal, we would be bored to death. No jokes, no lovers, no jackasses, no geniuses.

But don’t worry… Equality is just what we don’t have and don’t want. We are always comparing… contrasting… sizing up and looking down…

One is more handsome… One is smarter… One chose the wrong spouse… One has no sense of color coordination!

There are roughly 250,000 adjectives in the English language… and every one is a way of making distinctions. Not even identical twins are the same.

Humans are never equal, one to another. (Otherwise, why would some be judged and others do the judging? Why would some lead and others follow? Why would some govern… and others allow themselves to be governed?)

All human life is unequal… and governed by moral rules, based on unequal conduct.

You make decisions. Decisions have consequences. You leave a nail sticking up on the job site. Inevitably, someone will step on it.

And there are always cycles – cycles of learning and forgetting… cycles of building up and tearing down… of civilizing and uncivilizing.

Most of the time, most people go about their business… doing their win-win deals… exchanging goods and services as best they can.

And then, along come the jackasses… just when you need them, to rob, murder, and legislate… and thus complete the full cycle – the rise and fall, the ups and downs, the booms and busts.

The cycles are inevitable. But it’s still a “moral” world, in the sense that somebody left the damn nail sticking up!

Who’s to Blame?

Who’s to blame for America’s decline?

American economist Milton Friedman forged one of the nails. That is, he was instrumental in creating the new money system put in place in 1971.

People were already limping in the late 1970s – U.S. inflation was already in the double digits.

But then, Federal Reserve chairman Paul Volcker rescued the money system in 1980.

Then, quietly – and to the delight of millions – the new money did its damage, undermining the nation’s economy and its political institutions for the next 40 years.

Today, thanks to all the feds’ fake money, U.S. GDP growth rates are barely half of those from the 1970s and 1980s… and the nails are getting tossed out like confetti.

Last month, Congress passed a $1.9 trillion “relief” program… and is now considering $2.3 trillion more.

And last month, U.S. debt passed the $28 trillion mark, an increase of $4.7 trillion in the last 13 months.

But back in the 1990s, the momentum of growth and progress was so strong that the nation continued on an upward trend, until finally reaching the apogee of its imperial glory in 1999.

Then, measured in gold, U.S. stocks hit their highest levels ever. They began a decline in 2000, and have never recovered.

Bad Emperors

Alas, then came a succession of bad emperors.

George W. Bush launched the War on Terror – $7 trillion down the drain.

Barack Obama bailed out Wall Street after the crisis of 2008-2009, and added nearly $10 trillion to the national debt over his eight-year term.

The third in this parade of clowns was Donald J. Trump, who went on the biggest spending spree in U.S. history… with another $8 trillion added to federal debt in just four years.

Government spending, as a portion of GDP, rose to over 40% during his term in office.

Worst of a Bad Lot

But The Donald’s contribution went far beyond the numbers.

He also remade the Republican Party in his own image. No longer a party of ideas or principles… it is now just another group of hacks and grifters with a nativist/corporatist bent.

This is especially important because now, we have the fourth – and perhaps, worst – of an awful lot, in the White House.

And, with no effective conservative opposition, there is no one to stop the federales’ boondoggles or America’s eventual collapse.

Watch where you step.

Regards,

Bill

How to Lie with Statistics – Part 4: Much Ado About Practically Nothing

Continuing with my previous post, here I summarize Chapter 4 of the book How to Lie with Statistics. This chapter is titled, Much Ado About Practically Nothing.

This brief chapter explains the critical importance of understanding how representative is a sample metric of the population as a whole. We can actually calculate that answer. First, we must understand that there are two figures that can be used to give us a sense of that representation: the probable error and the standard error. My summary will focus on the Standard Error because it is the most commonly used measure these days.

The Standard Error is used to construct confidence intervals, whereby if the calculation is sound then the true value would be contained within those intervals. This means that there is some probability that the true value will in fact be outside those intervals (range of values). For example, you are 95% confidence that the true value is within some confidence interval. Said another way, there is a 5% chance that the true value is outside the range depicted by the confidence interval.

Also of importance is to recognize that the wider the intervals, the less confidence we have as relates to the estimated value. For instance, saying some value is within a range of 5 to 10 will carry greater weight if you are told some value is within 1 to 100. The latter may be more accurate, but precision is way off. This would be a polite way of saying, “take our results with a ‘grain of salt’”; or putting it another way, the results are close to meaningless.   

As you can imagine, rarely you see reported estimates this way. That is why it is important to go to the source documents, not just uncritically receive the headline media reports. Do not be fooled.

Covid-19 Cases and Social Restrictions – What does the Chart Say?

Let’s play a game for a moment. I went to the Center for Disease Control and Prevention (CDC) and pulled up 6 regions in the US to see the trend in new COVID-19 cases being reported. Specifically, I downloaded the seven-day moving average of new cases (per 100K), by number of days since .01 average daily cases (per 100K) first recorded.

Take a look at the following chart: Can you tell which line or lines (which represents a State) has or have implemented the LEAST restrictive lockdown measures?

If you are like most people you would have said the top lines. If you did you are wrong. 

Here are the places in the Chart: 

Texas and Mississippi have lifted all COVID restriction for almost a month since the writing of this post, yet the trend is down since that time. 

Please note that the data has already been adjusted to accounted for population size and density; therefore such pretext cannot be use in this example. Adjusting the data per 100K (or a higher number if the size is larger) is the standard way that these types of analyses are constructed in order to properly compare population-related data that varies by size.  

The data from the Chart comes directly from the COVID Data Tracker from the Center for Disease Control and Prevention. Check it out or yourself.

Blowing Bubbles: The Bigger They Grow the Harder They Pops

I have written earlier about the money printing impact to the equity market by looking at price/earning (PE) ratio and the dividend yield of major US equity markets. The long story short is that the ratios continue on the rise. In a bubble market there are two kinds of people who eventually lose money: those who do not see the bubble and go on blindly investing, and those who after investing think the bubble will pop when some metric is breach. The reality is, to paraphrase what John Maynard Keynes once said, the market can remain irrational longer than you can remain solvent. Bubble can and will do things unimaginable.   

Here is the latest table:

How to Lie with Statistics – Part 3: The Little Figures That Are Not There

Continuing with my previous post, here I summarize Chapter 3 of the book How to Lie with Statistics. This chapter is titled, The Little Figures That Are Not There.

The key thought of this chapter is about what is left unsaid when a particular statistic is illustrated – an average without a range, for example. Today’s post about COVID metric reporting was also illustrative of this point. Take an “average” temperature of any given city and that tells you nothing if a range is excluded. Or better yet, when no information is given as relates to sample size or method of deriving the statistic.

Many times a sample statistic may be passed as representative of the whole, but in fact the sample being singled out is merely one of many taken and conveniently left out (if it doesn’t fit the agenda being pushed, of course).  In addition, “how likely it is that a test figure represents a real result rather than something produce by chance”? In these instances what you should be told is some sort of degree of probability telling you something about the statistical significance of the results. In plain English, how likely are the results truly indicative of the population as a whole? In no uncertain terms you should be told what that likelihood is.

Furthermore, be skeptical of charts that do not have proper scales or at worst deliberately exclude information.