The CDC gets life expectancy wildly wrong

According to a CDC spokesman, U.S. life expectancy has fallen by a year as a result of Covid. A little arithmetic shows that that cannot be close to correct.

Total deaths so far are about 500,000 out of a population of about 330,000,000. The average death cost 12 years of life. Multiply that out and the average person lost not one year but .018 year of life. That’s an error of almost two orders of magnitude. Including deaths indirectly caused and additional deaths over the next few months might increase it a little, but there is no way it can be one year or even close.

Dr. Peter Bach explains the error on his blog. What the CDC apparently did was to calculate what the effect on life expectancy would be if mortality rates stayed at their 2020 level,  how much Covid would reduce life expectancy if the pandemic was repeated every year forever.

Source: The CDC gets life expectancy wildly wrong

Have Teachers Unions Finally Overplayed Their Hand?

According to the most recent data from School Digger, a website that aggregates test score results, 23 of the top 30 schools in New York in 2019 were charters. The feat is all the more impressive because those schools sported student bodies that were more than 80% black and Hispanic, and some two-thirds of the kids qualified for free or discount lunches. The Empire State’s results were reflected nationally. In a U.S. News & World Report ranking released the same year, three of the top 10 public high schools in the country were charters, as were 23 of the top 100—even though charters made up only 10% of the nation’s 24,000 public high schools.

We are told constantly by defenders of the education status quo that the learning gap is rooted in poverty, segregation and “systemic” racism. We’re told that blaming traditional public schools for substandard student outcomes isn’t fair given the raw material that teachers have to work with. But if a student’s economic background is so decisive, or if black students need to be seated next to whites to understand Shakespeare and geometry, how can it be that so many of the most successful public schools are dominated by low-income minorities?

Some will argue that charter schools obtain these results by picking the best students, which isn’t true. Of the 43 states that have charters, all but three—Arizona, Colorado and Wyoming—mandate that lotteries be used to choose students randomly. Washington Post education writer Jay Mathews reports that even states that don’t officially require the use of lotteries use them anyway or employ “other impartial ways of admitting students.”

A second popular argument against charter schools is that they benefit from having motivated students, which is true but misleading. Numerous empirical studies have shown that charter students outperformed similarly motivated peers in traditional public schools who applied to a charter but weren’t admitted. But there’s an even more fundamental problem with the “motivation” explanation of charter success, as Thomas Sowell explains in his most recent book, “Charter Schools and Their Enemies.”

“While those parents who enter their children’s names in the lotteries for admission to charter schools may well be more motivated to promote their children’s education, and to cooperate with schools in doing so, those who win in these lotteries are greatly outnumbered by those who do not win,” Mr. Sowell writes. “When charter schools take a fraction of the children from motivated families, why does that prevent the traditional public schools from comparably educating the remaining majority of children from those motivated families?”

Source: Have Teachers Unions Finally Overplayed Their Hand?

The disparate impact of a national $15 minimum wage

Even if you believe Janet Yellen’s recent testimony that a $15 federal minimum wage would have a “very minimal” impact on overall employment, it is hard to imagine that raising the federal minimum wage from $7.25 to $15 an hour would not significantly impact business costs and employment in at least some parts of the country. But which parts, and by how much? I use publically available data to provide a rough answer to this important question.

Source: The disparate impact of a national $15 minimum wage

California secretly struggles with renewables

California has hooked up a grid battery system that is almost ten times bigger than the previous world record holder, but when it comes to making renewables reliable it is so small it might as well not exist.

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Mind you the PG&E engineers are not that stupid. They know perfectly well that this billion dollar battery is not there to provide backup power when wind and solar do not produce. In fact the truth is just the opposite. The battery’s job is to prevent wind and solar power from crashing the grid when they do produce.

It is called grid stabilization. Wind and solar are so erratic that it is very hard to maintain the constant 60 cycle AC frequency that all our wonderful electronic devices require. If the frequency gets more than just a tiny bit off the grid blacks out. Preventing these crashes requires active stabilization.

Grid instability due to erratic wind and solar used to not be a problem, because the huge spinning metal rotors in the coal, gas and nuclear power plant generators simply absorbed the fluctuations. But most of those plants have been shut down, so we need billion dollar batteries to do what those plants did for free. Nor is this monster battery the only one being built in California to try to make wind and solar power work. Many more are in the pipeline and not just in California. Many states are struggling with instability as baseline generators are switched off.

Source: California secretly struggles with renewables

Ivermectin looks promising

From the Daily Mail:

In leaked slides published ahead of the study’s release next month, the scientists behind the research combined results from 11 trials of the drug involving more than 1,400 patients.

This revealed only eight Covid-19 patients out of 573 who received the drug died, compared to the 44 out of the 510 who received a placebo. 

Daily Mail

This looks very promising — a death rate of 1.4% in patients on Ivermectin vs. a death rate of 8.6% in patients on a placebo.

Let’s run some numbers.

One tool we have is what’s known as the “standard error of the proportion”. If we have some number that is a proportion of some population, say, 1.4%, we can calculate the standard error — the likelihood that the “real” proportion is some other number. (Well, really, the likelihood that, if we did the same measurement on another group the same size, we’d get some other number.)

So the standard error of the proportion is given as s = (pq/n)^(1/2), or the square root of the proportion of the population that meets a criterion, times the proportion that doesn’t, divided by the sample size.

So, if 8 out of 573 Covid-19 patients on Ivermectin died, that’s a proportion of about 1.4%. The standard error is sqrt(0.014 * 0.986 / 573) = 0.00491.

Multiply that standard error by 573, and the standard error works out to 2.81 above or below the 8 test group patients who died. So if we run the same test on a bunch of groups of 573 patients, we’d expect to see between 5.2 and 10.8 deaths about two thirds of the time. Two standard error units, which is usually a publishable result, would encompass 95% of the results in other tests. That range is between 2.4 and 13.6.

The same analysis of the placebo group yields s = sqrt(0.0863 * 0.9137 / 510) = 0.01243. So our wiggle room here is 6.34. Taking the two standard deviation range again, we expect other replications of this test to fall between 31.2 deaths and 56.7 deaths.

Gee, these ranges don’t overlap. So we conclude the proportions are in fact different.

It turns out there’s a modification for when we’re comparing two proportions:

images

So for our treatment and placebo groups, we have

s = sqrt (0.014 * 0.986 / 573 + 0.0863 * 0.9137 / 510) = 0.01337.
So what we want to know is whether the proportions are statistically different from each other. Is 0.014 significantly different from 0.0863?
The difference between the two is 0.0723. Divide by the standard error, and we get 5.41 standard error units. That’s pretty darn significant.

If the numbers reported in the Daily Mail are right, I’d have to say Ivermectin works.

Tennessee Data for Masked vs. Unmasked Counties

Tennessee is a great place to compare mask-mandated places with ones that aren’t and see if such governmental overreach really does contribute to slowing the spread of COVID-19. If they help, surely some evidence of that would be in the data, right? Since I’m a resident of the eastern part of the state, I decided to run the numbers myself. When I first started getting the data together, I didn’t know entirely what to expect. If there was a sharp difference showing masked counties doing better than unmasked ones, it would be a truth I would have to report, even if it went against my preconceived (in this case anti-mask) notions.

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And boy was I correct. While we’re not allowed to insert tweets or images in op-eds, you can view my data here. Since COVID appears to be seasonal and often hits different parts of the nation at different times and Tennessee is a WIDE state, I stuck with only the east Tennessee area for this particular analysis. For simplicity, I also left out counties that chickened out and issued mask mandates in the middle of the time period. Then I ran the numbers for 17 contiguous counties for the period from October 1 to December 22. Nine did not have a mask-mandate in place, while eight did.

Over the allotted period, counties with mask mandates saw 4.7% of their population infected while those without them saw a 4.6% infection rate. Interestingly, Hawkins County, which let its mandate expire at the end of September, had 4.3% of its population infected, while Carter County had 5.1% infected with a mandate in place. Both have nearly identical populations.

Townhall.com
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A Pandemic of Covid Myths

(Don Boudreaux) Tweet For an example of just how pathetically sloppy are not only the media but also scientists themselves when it comes to Covid-19, see this Facebook post by Phil Magness . Here’s the text that Phil has in his post (but do click on the link to see the entire post, which features the killer sentence from the JAMA paper):

On Dec. 16 the Journal of the American Medical Association published a headline-grabbing article that claimed COVID deaths among people under age 45 were severely underreported, and could be determined by comparing this year’s excess death totals to the same age group in 2018, using the latter as a baseline to exclude opioid deaths. The NY Times & dozens of other media outlets ran with stories about how COVID was a grave danger to young people, contrary to what we’re seeing in the actual fatality statistics.
Although it was barely noticed, JAMA article contained a startling concession in a single line at the end: it was possible that opioid deaths were also up for reasons related to COVID (e.g. depression caused by the lockdowns), in which case the main claim of the article fell apart. The authors did not investigate this possibility any further, nor did the JAMA make them address what appears to be a highly consequential complication to their study.
Two days later the CDC released a report on opioid and other substance abuse deaths, the main chart of which is reproduced below. Guess what shot way up during COVID: opioid and other substance abuse deaths, thereby negating the headline-grabbing JAMA article.

Source: A Pandemic of Covid Myths

COLORADO: COVID-19: Feared Post-Thanksgiving Mega-Spike Didn’t Happen. “Fresh data from the Colorado…

COLORADO: COVID-19: Feared Post-Thanksgiving Mega-Spike Didn’t Happen. “Fresh data from the Colorado Department of Public Health and Environment shows that while cases, hospitalizations and deaths related to the novel coronavirus remain well above viral low points recorded over the summer, they didn’t surge over the Thanksgiving holiday by as much as officials had feared, offering hope for the upcoming yuletide season.” And yet the most populated parts of the state remain under severe lockdown restrictions.

Source: COLORADO: COVID-19: Feared Post-Thanksgiving Mega-Spike Didn’t Happen. “Fresh data from the Colorado…

Comparing Covid Cases to Covid Deaths

(Don Boudreaux) Tweet Here’s a letter to a reader of this blog: Mr. Horn: Thanks for your e-mail. You’re unhappy with my linking to a December 10th Facebook post by Nobel-laureate economist Vernon Smith – a post in which Vernon shares a graph, from Our World in Data, comparing the number of daily Covid-19 cases to the number of daily Covid-19 deaths .

The fact that the number of daily Covid deaths is minuscule relative to the number of daily Covid cases is the relevant reality to which Vernon points and which the graph he shares illustrates vividly. And it’s an important reality. If knowledge of this reality were more widespread, I believe that the irrational hysteria over Covid-19 would be much muted.

Source: Comparing Covid Cases to Covid Deaths