Saturday, November 28, 2020

Left Coast Correspondent: Twitter and the CCP Work to Ensure Covid Doesn't Produce Another 1644 or 1912

Click here to read the original Cautious Optimism Facebook post with comments

1 MIN READ - The Cautious Optimism Correspondent for Left Coast Affairs and Other Inexplicable Phenomena has been known to dabble in Chinese history and gives credit to the Chinese Communist Party for one thing: they know their country’s history and all the ways thousands of years of emperors and dynasties before them were overthrown and ousted from power.

The last two dynasties, the Ming and the Qing—pronounced “ching,” also referred to as the Manchu dynasty since the rulers came from the northeastern region of Manchuria—fell in 1644 and 1912 respectively, both overthrown by the Chinese masses who were long fed up with decades of dynastic decline, corruption, poverty, and hunger.

But even though the peasantry had suffered throughout the last failing decades of both dynasties, can it be just a coincidence that major plagues broke out right before the rebellions?

A major bubonic plague epidemic spread throughout China from 1641 to 1644, 1644 being the year that armies led by peasant rebel general Li Zicheng marched victoriously into the Forbidden City as the last Ming emperor Chongzhen hung himself from a tree overlooking the palace.

A pneumonic plague outbreak struck the Qing’s home territory of Manchuria in 1910, followed by a bubonic plague epidemic in greater China that lasted from 1911 to 1912. The domestic uprising against the Qing began in 1911 and the dynasty was finished by 1912.

So in 2020 with China ruled by the Communist Party dynasty, is it any wonder that the regime will spread whatever disinformation and propaganda necessary to deflect blame for the modern plague of Covid? 

Despite the CCP’s tight control of media and information, they are not very popular with most everyday Chinese who effectively put up with them so long as the economy keeps growing. 

And the communist leadership isn’t stupid. They know plagues and epidemics at a minimum coincided with the fall of China’s last two dynasties’ 276 and 268 year reigns, and they aren't taking any chances that theirs will be cut short after just 71.

No wonder then that both they and their political allies at Twitter are determined not to let history repeat itself and will do or say whatever it takes to prevent another 1644 or 1912.

Read Fox News story: "Twitter slammed for not acting on Chinese media tweet alleging COVID-19 came from 'imported frozen food'"

Sunday, November 22, 2020

Inflation and Deflation Fallacies Part 4: “My Grocery Bill Went Up Which Proves the Country is Experiencing Inflation”

 Click here to read the original Cautious Optimism Facebook post with comments

4 MIN READ - This is not one that would typically be on the Economics Correspondent’s list, but a lot of comments in previous articles by well-meaning readers made it worth examining.

To define inflation, one has to measure it comprehensively (ie. everywhere, not a single product, industry, or sector).

Going back once again to the Quantity Theory’s Equation of Exchange tautology:

mv = py


m = money supply

v = monetary velocity

p = price level, and

y = total output of goods and services

…changes in “p” reflect macroeconomic changes in prices, ie. across the entire economy—in this case the United States. Or...

p = mv/y

And as we all know, price movements vary across industries or even individual products. 

For example, the price of healthcare has been rising relentlessly for decades, but the price of DVD players has also fallen consistently for years and years. I’ve never heard anyone argue “DVD players have been getting cheaper for fifteen years. That proves the economy is undergoing massive price deflation” and for good reason.

So if we focus on just a single sector then yes, the Economics Correspondent has seen his own grocery bill go up too, particularly during the initial months of the spring pandemic. There was definitely food price inflation in March, April, and May due to massive new demand for at-home eating combined with lower supply from Covid-related processing and supply-chain bottlenecks. 

In fact the Bureau of Labor Statistics reported in April that food prices rose at the fastest monthly pace since 1974.

(the BLS also reports food prices have moderated significantly since supply factors were resolved in the summer, something else the Correspondent has seen reflected in his grocery bill).

But to determine if the dollar is losing its overall purchasing power requires looking at all prices, not just grocery prices.

During the same period energy prices fell far below their pre-pandemic levels. So did airline fares and hotel rates. Auto insurers gave rebates to their policyholders reflecting lower claims costs. Rents in many large cities plummeted including the Correspondent’s own city. Used cars and trucks, and car and truck rental prices fell. Evidently a great deal of men’s apparel fell in price although the Correspondent can’t speak for women’s apparel (women readers feel free to comment). Nancy Pelosi’s hair stylist is even reported to have lowered her prices.

There are undoubtedly other sectors that saw falling prices but the Correspondent doesn’t track them all.

All these falling prices and other rising ones are factored into changes in the general price level (ie. price inflation).

Most people instinctively understand this whether they realize it consciously or not. If they notice the price of baseball cards is going up they typically don’t conclude the country is experiencing a major price inflation. And food prices, while a much larger component of the economy, represent only one segment of the market as well. 

So American food purchases are much larger than baseball card purchases. Food purchases represented a full 9.7% of GDP in 2019, although that includes restaurant dining which plummeted during the spring lockdowns.

But even when food prices rise, the other 90.3% of GDP must still be accounted for to draw final conclusions about inflation.

Incidentally there is another theoretical basis for not overweighting one sector’s prices too much, one we will discuss in greater length in the upcoming final column of this series. It is:

If consumers are forced to spend more on food they will necessarily have less money remaining to buy other products. The corresponding fall in demand for those other products will tend to drive a reduction in prices elsewhere.

The only reason this paradigm will fail to materialize is: 

-The money supply rises

-Velocity rises

-Output falls

….in some combination that results in such a surge in overall prices that other sectors become more expensive too.

Well during this spring that perfect combination definitely did not play out.

-The Q2 money supply measured by M1 rose 22.9% (not exact as M1 is measured by week not by month)

-Q2 real GDP fell 9.0% (-31.4% annualized)

Both of these had massive inflation written all over them, but…

-Q2 velocity plummeted a record 26.5%.

Plug all those unprecedented and volatile numbers into the Equation of Exchange and the price level for Q2 fell by 0.7% (1 x 1.229 ÷ 0.91 x 0.735 =  0.993), close to the BLS’s monthly inflation reports at the time of -0.8%, -0.1%, and +0.6% for April, May, and June.

And that’s why food prices alone can’t tell us if the entire country is undergoing inflation or not.


ps. The Correspondent believes it’s completely understandable that Americans and consumers around the world tend to equate grocery prices with inflation for several reasons:

-Human beings tend to stress what they see going up in price more than what they see going down.

-Grocery shopping is near universal. Almost everyone sees grocery prices but not everyone sees baseball card prices, bulldozer prices, or ethylene prices.

-People tend to buy the same grocery products over and over and are exposed to even minute changes in price in repeated, short intervals. In other words the act of weekly or biweekly grocery shopping provides a more frequent sampling of apples-to-apples pricing than any other consumer activity with the possible exceptions of buying gasoline, cigarettes, or paying one’s monthly cell phone or cable bills.

However people only notice the change in rent once a year when they renew the lease. They usually only notice a change in home prices when they’re home shopping, and the house they’re buying is never exactly the same house in the same location that they previously bought (unlike buying a twelve pack of Budweiser this week and a twelve pack of Budweiser again next week from the same store).

People only notice changes in used car prices when they buy another used car, and even then only if they buy a car of identical make, model, age, mileage, and options. No one concludes major price inflation when they trade in their used Kia for a used Lexus.

And people only notice their auto insurance rebates twice a year, assuming they even associate rebates with lower inflation at all.

In other words, consumers have much better and more frequent comparative information when sampling changes in food prices than they do in many sectors that experienced deflation during the spring outbreak and lockdowns. Therefore they acquire an observation bias with food prices and tend to associate them with a more general inflation.

In the last installment we’ll address a basket of fallacies employed by economists and policymakers everywhere such as “cost push,” “demand pull,” labor unions, and greedy businessmen.

Sunday, November 8, 2020

Left Coast Correspondent: Joe Biden's Miraculous Post-Election Night Comeback That Happened in Only Three States

 Click here to read the original Cautious Optimism Facebook post with comments

3 MIN READ - The Cautious Optimism Correspondent for Left Coast Affairs and Other Inexplicable Phenomena confesses to stirring up quite a discussion last Friday when he suggested that Joe Biden’s miraculous post-Election Night erasure of Donald Trump’s sizable leads in four critical battleground states may have been a little too miraculous.

The huge comeback was credited to record numbers of mail-in ballots that were still being counted after hours, a phenomenon that, if valid, should have occurred in just about every other state too since fears of Covid and social distancing are prevalent everywhere, not just in Wisconsin, Michigan, Pennsylvania, and Georgia.

As before, the four states where Biden enjoyed the largest gains after polls closed--three of them colossal (see chart)--happened to not only all be states he trailed in, but also the very four he needed to save any chance of winning as the window of opportunity had almost closed on him.

Based on the 33 states that mandate waiting until Election Day/Night to begin counting absentee and mail-in ballots, the odds of the four best-performing randomly matching the exact four Biden needed to win are 40,920 to 1.

Therefore such a miracle is not scheduled to occur again in a U.S. presidential election until the year 165,700 A.D. That’s U.S. history beginning in 1776 elapsing another 679 times before this should happen again.

The Correspondent’s friends “shared” the original story and sparked many heated debates elsewhere across Facebook with some skeptical commenters demanding more states and more data.

So Cautious Rockers get first dibs at the exclusive research available at CO Nation. Please enjoy the chart before it's fact-checked out of existence.

Note again that the four states where Biden enjoyed the largest—in three cases absolutely massive—improvement in vote percentages against Donald Trump just happened to be the very four that:

1) Remained uncalled, and...

2) Had 10 or more electoral votes at stake, and…

3) Were already long anticipated to be critical battleground states, and...

4) He trailed Donald Trump in (all four, plus NC), and most importantly…

5) He critically needed to win since Donald Trump was performing far better than polls had predicted and the window to salvage a victory was nearly closed.

ps. Note also how many points Biden needed in each state and how many he got.

WI: Needed 4.9. Got 5.6.

GA: Needed 8.9. Got 9.0.

MI: Needed 10.6. Got 13.3 (the only "not a squeaker" rebound)

PA: Needed [an incredible] 14.4. Got an even more incredible 15.0.

[End of main article. Readers who wish to know more about the source data read on]

I. Here is an example of how a state’s change in margin is calculated:

VIRGINIA: At the point that NBC News had both called a winner and the percentage of precincts in exceeded 75%, Joe Biden held 52.4% of votes and Donald Trump held 46.0%, a lead of 6.4 percentage points.

On Saturday, November 7th, after the bulk of absentee and mail-in ballots had been added to count totals, Joe Biden held 54.0% of votes and Donald Trump held 44.5%, a lead of 9.5 percentage points.

Thus Biden added to his margin of victory and gained 3.1 points (9.5 – 6.4 = 3.1)

OHIO: At the point that NBC News had both called a winner and the percentage of precincts in exceeded 75%, Joe Biden held 45.2% of votes and Donald Trump held 53.3%, a deficit of 8.1 percentage points.

On Saturday, November 7th, after the bulk of absentee and mail-in ballots had been added to count totals, Joe Biden held 45.2% of votes and Donald Trump held 53.4%, a deficit of 8.2 percentage points.

Thus Biden’s margin of defeat worsened and he lost another 0.1 points (8.1 – 8.2 = -0.1)

(It’s worth noting that Ohio was a major battleground state where a large number of mail-in ballots, largely Democratic, were expected and received)

II. Source data:

The Correspondent spent many hours combing through Election Night video from NBC News to find leading or trailing margins of every state that was called and then recorded data when the percent of precincts in exceeded 75%. Using vote percentages with only 2% of precincts reporting was unreliable and could lead to wild changes in margins leading to November 7th.

Then the Correspondent compared the leading or training margins on Saturday, November 7th when AP called the election for Biden and calculated the difference.

For the five critical states that remained uncalled (WI, MI, PA, GA, NC) the Correspondent noted the vote percentages for each candidate at 12AM ending Election Night.

Although he has data for NC, AZ, and NV, those three states permitted counting absentee and mail-in ballots prior to Election Day/Night and therefore don’t appear in the chart. Also, as of this posting North Carolina totals have not changed since late Election Night.

A few states had not yet reached 75% precincts in by the time the NBC livestream ended late at night. Therefore the Correspondent was forced to seek another livestream that ran later. However most livestreams have been taken down from YouTube and the only one he could still find was NTD--not his favorite source but all he wanted was numbers--that had later evening data on just a handful of states.

Alaska and Hawaii closed so late that no data was available in either the NBC or NTD livestreams before they ended. Therefore the Correspondent has no Election Night totals for those two states which he considers uncritical anyway. Hawaii is particularly inconsequential since absentee/mail-in ballots can be counted early in that state.

Finally, the states of Maine. Maryland, Massachusetts, and New Jersey had not reported 75% or more precincts in before either livestream ended. Therefore the Correspondent had to use Election Night data using only 58%, 67%, 73%, and 61% of precincts for those states respectively. Maryland and New Jersey are not included in the chart due to early absentee/mail-in ballot counting.