The best way to re-open the economy is to defeat the virus. Not by yelling slogans.

By Alex Tabarrok and Puja Ahluwalia Ohlhaver in the Washington Post

May 15, 2020 at 10:06 a.m. EDT

With the unemployment rate at its highest level since the Great Depression — 14.7 percent and climbing — many Americans are clamoring to reopen the economy, even if it means that thousands of daily covid-19 deaths become part of the backdrop to life. It’s time to move on as “warriors,” President Trump has said, because “we can’t keep our country closed down for years.” We, too, favor markets and share the president’s eagerness to stop economically ruinous shutdowns. But the choice between saving lives and saving the economy, the latter of which Trump has endorsed implicitly, is a false one.

In fact, framing the issue that way could kill many Americans and kill the economy.

The dangers of reopening without disease control — or a coronavirus vaccine or therapeutic breakthrough — are illustrated by events at the Smithfield Foods meatpacking plant in Sioux Falls, S.D. Smithfield offered workers a bonus if they showed up every day in April. Normally, bonus pay would increase attendance. But in a pandemic, encouraging the sick to haul themselves into work can be disastrous. The plan backfired. Hundreds of Smithfield employees were infected, forcing the plant to shut down for more than three weeks. If we stay the current course, we risk repeating the same mistake across the whole economy.

The economy consists of people who have hopes and fears. As long as they are afraid of a lethal virus, they will avoid restaurants, travel and workplaces. (According to a Washington Post-Ipsos poll last week, only 25 percent of all Americans want to “open businesses and get the economy going again, even if that means more people will get the coronavirus.”) The only way to restore the economy is to earn the confidence of both vulnerable industries and vulnerable people through testing, contact tracing and isolation.

As covid-19 spreads through Nebraska meat plants, workers feel helpless and afraid

There is already a bipartisan plan to achieve this; we helped write it. The plan relies on frequent testing followed by tracing the contacts of people who test positive (and their contacts) until no new positive cases are found. It also encourages voluntary isolation, at home or in hotel rooms, to prevent further disease spread. Isolated patients would receive a federal stipend, like jurors, to discourage them from returning to workplaces too soon.

But our plan also recognizes that rural towns in Montana should not necessarily have to shut down the way New York City has. To pull off this balancing act, the country should be divided into red, yellow and green zones. The goal is to be a green zone, where fewer than one resident per 36,000 is infected. Here, large gatherings are allowed, and masks aren’t required for those who don’t interact with the elderly or other vulnerable populations. Green zones require a minimum of one test per day for every 10,000 people and a five-person contact tracing team for every 100,000 people. (These are the levels currently maintained in South Korea, which has suppressed covid-19.) Two weeks ago, a modest 1,900 tests a day could have kept 19 million Americans safely in green zones. Today, there are no green zones in the United States.


What antibody tests can teach us about potential coronavirus immunity

Most Americans — about 298 million — live in yellow zones, where disease prevalence is between .002 percent and 1 percent. But even in yellow zones, the economy could safely reopen with aggressive testing and tracing, coupled with safety measures including mandatory masks. In South Korea, during the peak of its outbreak, it took 25 tests to detect one positive case, and the case fatality rate was 1 percent. Following this model, yellow zones would require 2,500 tests for every daily death. To contain spread, yellow zones also would ramp up contact tracing until a team is available for every new daily coronavirus case. After one tracer conducts an interview, the team would spend 12 hours identifying all those at risk. Speed matters, because the virus spreads quickly; three days is useless for tracing. (Maryland, Virginia and Washington, D.C., are all yellow zones.)


A disease prevalence greater than 1 percent defines red zones. Today, 30 million Americans live in such hot spots — which include Detroit, New Jersey, New Orleans and New York City. In addition to the yellow-zone interventions, these places require stay-at-home orders. But by strictly following guidelines for testing and tracing, red zones could turn yellow within four weeks, moving steadfastly from lockdown to liberty.


Getting to green nationwide is possible by the end of the summer, but it requires ramping up testing radically. The United States now administers more than 300,000 tests a day, but according to our guidelines, 5 million a day are needed (for two to three months). It’s an achievable goal. Researchers estimate that the current system has a latent capacity to produce 2 million tests a day, and a surge in federal funding would spur companies to increase capacity. The key is to do it now, before manageable yellow zones deteriorate to economically ruinous red zones.


States can administer these “test, trace and supported isolation” programs — but Congress would need to fund them. The total cost, we estimate, is $74 billion, to be spent over 12 to 18 months. That sum would cover wages and training for contract tracers, the cost of building voluntary self-isolation facilities, stipends for those in isolation and subsidies to manufacture tests.


That amount is a lot, but not compared to the cost of a crippled economy. In Congress’s latest relief package, $75 billion went to struggling hospitals alone, $380 billion to help small businesses and $25 billion toward testing. But hospitals and businesses will continue to hemorrhage money and seek bailouts as long as they can’t open safely. Not spending on disease control means new waves of infection followed by chaotic spikes in disease and death, followed by more ruinous cycles of economic openings and closures. Economists talk about “multipliers” — an injection of spending that causes even larger increases in gross domestic product. Spending on testing, tracing and paid isolation would produce an indisputable and massive multiplier effect.


States have strong economic incentives to become — and remain — green zones. Nations that have invested the most in disease control have suffered the least economic hardship: Taiwan grew 1.5 percent in the first quarter, whereas the United States’ gross domestic product contracted by 4.8 percent, at an annual adjusted rate. (Taiwan was fortunate to have its vice president, Chen Chien-Jen, a U.S.-trained epidemiologist; under his guidance, the island acted quickly with masks, temperature checks, testing and tracing.) The second quarter will be worse: The projected decline for U.S. GDP, at an annualized rate, is an alarming 40 percent.


Looking forward, we will see stark economic contrasts across states, depending on their investment in disease control. With $74 billion, Congress could close the gap between states and relieve pressure on state budgets hamstrung by collapsing revenues. In the spirit of federalism, states would then become laboratories for discovering the best ways to implement testing, tracing and isolation. States might choose to form interstate compacts that pool and move testing resources across state lines as the disease travels and surges; county health officials might tap firefighters or other municipal workers to build regional contact-tracing workforces (as is happening in Tyler, Tex.). When local and state governments become accountable for adopting strategies that work, we can expect more innovation.


How do we know that testing, tracing and supported isolation would work? It already has worked in New Zealand, South Korea and Taiwan — where there have been few to no new daily cases recently. Taiwan never had to shut down its economy, while New Zealand and South Korea are returning to normal. It would work here, too. Since March, Congress has passed relief bills totaling $3.6 trillion to support an economy devastated by a virus — and $3 trillion more is on the table. We should attack the disease directly so we can stop spending to alleviate symptoms. Following this road map, we can defeat the coronavirus and be celebrating life, liberty and livelihood by the Fourth of July.

Perhaps a slight downward trend in new COVID cases?

Prompted by a former colleague, I did some tedious work at the CDC site on the numbers of COVID-19 cases each day, going back to January. I found what looks like a weekly up-and-down oscillation pattern that might have to do with whether offices are open and whether reports are made promptly, or might have to be delayed until the end of the weekend. However, it does appear to me that there might be a slow, but real, downward trend over the last few weeks — mostly because the vast majority of us are practicing self-isolation. Here is the graph I made:

new covid cases in the US, per day

Clearly, we are no longer seeing either a steady increase in the number of new cases each day as we were seeing from week 6 to week 10 nor (God forbid!) exponential growth as we were seeing back in March. If we were having exponential growth, it would show up as a horizontal line in the graph below.

daily rate of increases

However, if we stop the social distancing, if we all stop wearing masks and washing hands, if we all start going to movies and restaurants and museums and bars as if this is all over, and if kids go play on playgrounds and go back to school as normal, then exponential growth will raise its ugly, feverish head, and perhaps millions will die.

By the way, I cannot easily find equivalent data on the CDC website for daily deaths; just new diagnosed cases. The COVID death data may be there, but it’s really difficult to dig out. Maybe someone has a source?

I got tested!

I finally got COVID- tested today. It took quite a lot of phone calls, and leads from a bunch of people, and searches through clinics until I hit pay dirt. Mine was through Kaiser Permanente, our medical plan. I probably could have done it through the DC government as well, again for free.
A few days ago I got a form reply to a request I had made to my KP GP for a test; the reply said that I didn’t fit the profile of someone who needed one. I found a number of places where I could spend $150 to $2200 for one out of pocket.
Today I talked to my doctor, and I checked off enough boxes in the questionnaire he gave me to qualify: 70 yo, Crohn’s disease, immunosupressant (infliximab./Remicade) and plus I had sniffles and a stomach ache…
My reason for testing is to go help with grand-toddlers in NC while my son and DIL are trying to keep their business afloat remotely and – hopefully – reopen in a week or two if all goes well.
I don’t remember whether I got the antigen test or the antibody test, but I guess I’ll find that out tomorrow. on Monday the 18th. EDIT: It was the antigen test.
The testing procedure itself was very efficient: I had a 12:30 appointment. There were several parking spaces set aside with cones, in front of a huge medical van, on 2nd St NE in DC, on the street opposite Kaiser’s Capitol Hill center. I drove in, showed my ID at a distance to somebody in a mask on my right, on the sidewalk; he went back to the van, and less than a minute later a nurse (I guess) in full PPE came out, took a closer look at my face and my ID, checked that against the printout she had; then she stuck a long Q-tip into each nostril, and then she told me I was all done.
That sort of efficient testing is what Trump and Brix promised would happen ‘next week’ when he declared on March 13 a national emergency, for anyone. It’s still only for some people, TWO MONTHS LATER.
Such a fine job. Not.
I just got the results this morning (5/18/2020) for the antigen test, and it was negative. As I strongly suspected.

The Pandemic Is Far From Over

While the rate of increase per day in the number of deaths is generally down, the COVID-19 pandemic is far from over. In general, more people are still dying each day in the US from this disease than the day before, as you can see from this data, which is taken from the CDC. The very tall bar on day 27 is when New York City finally added thousands of poor souls who had in fact died from this virus. (Day 27 means April 9, and Day 41 means April 30, which is today.)

Opening up the economy and encouraging everybody to go back to work, play, and school will mean a rebirth of exponential growth in deaths and in diagnosed cases after about 2 weeks, since this disease takes about that long to be noticed in those who have been exposed. And once everybody is back on the streets and in the stores and schools, the disease WILL spread exponentially. Opening wide right now, when we still can’t test or follow those who may be infected, would be a huge mistake.

us covid deaths per day

Only somebody as clueless as our current Grifter-In-Chief and his brainless acolytes could be recommending something so irresponsible, against the advice of every medical expert. Maybe they think that only the poor, the black, and the brown will get this disease. Wrong.

How do we fix the CV19 testing problem? By re-testing everybody who tested positive!

I guess I’ve re-discovered a form of Bayes’ Theorem  regarding the problem that is posed by the high numbers of false negatives and false positives when testing for the feared coronavirus.  What I found is that it doesn’t really even matter whether our tests are super-accurate or not. The solution is to assume that all those who test negative, really are negative, and then to give a second test to all those who tested positive the first time. Out of this group, a larger fraction will test positive. You can again forget about those who test negative. But re-test again, and if you like, test again. By the end of this process, where each time you are testing fewer people, then you will be over 99% certain that all those who test positive, really have been exposed.

Let me show you why.

Have no fear, what I’m gonna do is just spreadsheets. No fancy math, just percents. And it won’t really matter what the starting assumptions are! The results converge to almost perfect accuracy, if repeated!

To start my explanation, let’s start by assuming that 3% of a population (say of the US) has antibodies to CV19, which means that they have definitely been exposed. How they got exposed is not important for this discussion. Whether they felt anything from their exposure or not is not important in this discussion. Whether they got sick and died or recovered, is not going to be covered here. I will also assume that this test has a 7% false positive rate and a 10% false negative rate, and I’m going to assume that we give tests AT RANDOM to a hundred thousand people (not people who we already think are sick!) I’m also assuming that once you have the antibodies, you keep them for the duration.

This table represents that situation:

math of CV19 testing

If you do the simple arithmetic, using those assumptions, then of the 100,000 people we tested, 3%, or three thousand, actually do have those antibodies, but 97%, or ninety-seven thousand, do not (white boxes, first column with data in it).

Of the 3,000 folks who really do have the antibodies – first line of data – we have a false  negative rate of 10%, so three hundred of these poor folks are given the false good tidings that they have never been exposed (that’s the upper orange box). The other 90% of them, or two thousand seven hundred, are told, correctly, that they have been exposed (that’s the upper green box).

Now of the 97,000 people who really do NOT have any antibodies – the second line of data – we have a false positive rate of 7%, so you multiply 0.07 times 97000 to get six thousand, seven hundred ninety of them who would be told, incorrectly, that they DID test positive for Covid-19 – in the lower orange box. (Remember, positive is bad here, and negative is good.) However, 90,210 would be told, correctly, that they did not have those antibodies. (That’s in the lower green box.)

Now let’s add up the folks who got the positive test results, which is the third data column. We had 2,700 who correctly tested positive and 6,790 who wrongly tested positive. That’s a total of 9,490 people with a positive CV19 antibody test, which means that of that group of people, only 28.5% were correctly so informed!! That’s between a third and a fourth! Unacceptable!

However, if we look at the last column, notice that almost every single person who was told that they were negative, really was negative. (Donno about you, but I think that 99.7% accuracy is pretty darned good!)

However, that 28.5% accuracy among the ‘positives’ (in the left-hand blue box) is really worrisome. What to do?

Simple! Test those folks again! Right away! Let’s do it, and then let’s look at the results:

math of CV19 testing - round 2

Wowser! We took the 9490 people who tested positive and gave them another round of tests, using the exact same equipment and protocols and error rates as the first one. The spreadsheet is set up the same; the only thing I changed is the bottom two numbers in the first data column. I’m not going to go through all the steps, but feel free to check my arithmetic. Actually, check my logic. Excel doesn’t really make arithmetic errors, but if I set up the spreadsheet incorrectly, it will spit out incorrect results.

Notice that our error rate (in blue) is much lower in terms of those who tested positive. In fact, of those who test positive, 83.7% really ARE positive this time around, and of those who test negative, 95.9% really ARE negative.

But 84% isn’t accurate enough for me (it’s either a B or a C in most American schools). So what do we do? Test again – all of the nearly three thousand who tested positive the first time. Ignore the rest.

Let’s do it:

math of CV19 testing - round 3

At this point, we have much higher confidence, 98.5% (in blue), that the people who tested ‘positive’, really are ‘positive’. Unfortunately, at this point, of the people who tested negative, only about 64% of the time is that correct. 243 people who really have the antibodies tested negative. So perhaps one should test that subgroup again.

The beautiful thing about this method is that it doesn’t even require a terribly exact test! But it does require that you do it repeatedly, and quickly.

Let me assure you that the exact level of accuracy, and the exact number of exposed people, doesn’t matter: If you test and re-test, you can find those who are infected with almost 100% accuracy. With that information you can then discover what the best approaches are to solving this pandemic, what the morbidity and mortality rates are, and eventually to stop it completely.

Why we don’t have enough tests to do this quickly and accurately and repeatedly is a question that I will leave to my readers.


Note that I made some starting assumptions. Let us change them and see what happens. Let’s suppose that the correct percentage of people with COVID-19 antibodies is not 3%, but 8%. Or maybe only 1%. Let’s also assume a 7% false positive and a 10% false negative rate. How would these results change? With a spreadsheet, that’s easy. First, let me start with an 8% infection rate and keep testing repeatedly. Here are the final results:

Round Positive accuracy rating Negative accuracy rating
1 52.8% 99.1%
2 93.5% 89.3%
3 99.5% 39.3%

So after 3 rounds, we have 99.5% accuracy.

Let’s start over with a population where only 1% has the antibodies, and the false positive rate is 7% and the false negative rate is 10%.

Round Positive accuracy rating Negative accuracy rating
1 11.5% 99.9%
2 62.6% 98.6%
3 95.6% 84.7%
4 99.6% 30.0%

This time, it took four rounds, but we still got to over 99.6% accuracy at distinguishing those who really had been exposed to this virus. Yes, towards the end our false negative rate rises, but I submit that doesn’t matter that much.

So Parson Tommy Bayes was right.

More on the “false positive” COVID-19 testing problem

I used my cell phone last night to go into the problem of faulty testing for COVID-19, based on a NYT article. As a result, I couldn’t make any nice tables. Let me remedy that and also look at a few more assumptions.

This table summarizes the testing results on a theoretical group of a million Americans tested, assuming that 5% of the population actually has coronavirus antibodies, and that the tests being given have a false negative rate of 10% and a false positive rate of 3%. Reminder: a ‘false negative’ result means that you are told that you don’t have any coronavirus antibodies but you actually do have them, and a ‘false positive’ result means that you are told that you DO have those antibodies, but you really do NOT. I have tried to highlight the numbers of people who get incorrect results in the color red.

Table A

Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 50,000 10% 45,000 5,000
Actually Negative 950,000 3% 28,500 921,500
Totals 1,000,000 73,500 926,500
Percent we assume are actually positive 5% Accuracy Rating 61.2% 99.5%

As you can see, using those assumptions, if you get a lab test result that says you are positive, that will only be correct in about 61% of the time. Which means that you need to take another test, or perhaps two more tests, to see whether they agree.

The next table assumes again a true 5% positive result for the population and a false negative rate of 10%, but a false positive rate of 14%.

Table B

Assume 5% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 50,000 10% 45,000 5,000
Actually Negative 950,000 14% 133,000 817,000
Totals 1,000,000 178,000 822,000
Percent we assume are actually positive 5% Accuracy Rating 25.3% 99.4%

Note that in this scenario, if you get a test result that says you are positive, that is only going to be correct one-quarter of the time (25.3%)! That is useless!

Now, let’s assume a lower percentage of the population actually has the COVID-19 antibodies, say, two percent. Here are the results if we assume a 3% false positive rate:

Table C

Assume 2% really exposed, 3% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 20,000 10% 18,000 2,000
Actually Negative 980,000 3% 29,400 950,600
Totals 1,000,000 47,400 952,600
Percent we assume are actually positive 2% Accuracy Rating 38.0% 99.8%

Notice that in this scenario, if you get a ‘positive’ result, it is likely to be correct only a little better than one-third of the time (38.0%).

And now let’s assume 2% actual exposure, 14% false positive, 10% false negative:

Table D

Assume 2% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 20,000 10% 45,000 2,000
Actually Negative 980,000 14% 137,200 842,800
Totals 1,000,000 182,200 844,800
Percent we assume are actually positive 2% Accuracy Rating 24.7% 99.8%

Once again, the chances of a ‘positive’ test result being accurate is only about one in four (24.7%), which means that this level of accuracy is not going to be useful to the public at large.

Final set of assumptions: 3% actual positive rate, and excellent tests with only 3% false positive and false negative rates:

Table E

Assume 3% really exposed, 3% false positive rate, 3% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 30,000 3% 45,000 900
Actually Negative 970,000 3% 29,100 940,900
Totals 1,000,000 74,100 941,800
Percent we assume are actually positive 3% Accuracy Rating 60.7% 99.9%

Once again, if you test positive in this scenario, that result is only going to be correct about 3/5 of the time (60.7%).

All is not lost, however. Suppose we re-test all the people who tested positive in this last group (that’s a bit over seventy-four thousand people, in Table E). Here are the results:

Table F

Assume 60.7% really exposed, 3% false positive rate, 3% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 45,000 3% 43,650 1,350
Actually Negative 29,100 3% 873 28,227
Totals 74,100 44,523 29,577
Percent we assume are actually positive 60.7% Accuracy Rating 98.0% 95.4%

Notice that 98% accuracy rating for positive results! Much better!

What about our earlier scenario, in table B, with a 5% overall exposure rating, 14% false positives, and 10% false negatives — what if we re-test all the folks who tested positive? Here are the results:

Table G

Assume 25.3% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 45,000 14% 38,700 6,300
Actually Negative 133,000 10% 13,300 119,700
Totals 178,000 52,000 126,000
Percent we assume are really positive 25.3% Accuracy Rating 74.4% 95.0%

This is still not very good: the re-test is going to be accurate only about three-quarters of the time (74.4%) that it says you really have been exposed, and would only clear you 95% of the time. So we would need to run yet another test on those who again tested positive in Table G. If we do it, the results are here:

Table H

Assume 74.4% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 38,700 14% 33,282 5,418
Actually Negative 13,300 10% 1,330 11,970
Totals 52,000 34,612 17,388
Percent we assume are really positive 74.4% Accuracy Rating 96.2% 68.8%

This result is much better, but note that this requires THREE TESTS on each of these supposedly positive people to see if they are in fact positive. It also means that if they get a ‘negative’ result, that’s likely to be correct only about 2/3 of the time (68.8%).

So, no wonder that a lot of the testing results we are seeing are difficult to interpret! This is why science requires repeated measurements to separate the truth from fiction! And it also explains some of the snafus committed by our current federal leadership in insisting on not using tests offered from abroad.



EDIT at 10:30 pm on 4/25/2020: I found a few minor mistakes and corrected them, and tried to format things more clearly.

People are Not Cattle!

This apparently did not occur to William Sanders.

He thought that statistical methods that are useful with farm animals could also be used to measure effectiveness of teachers.

I grew up on a farm, and as both a kid and a young man I had considerable experience handling cows, chickens, and sheep. (These are generic critter photos, not the actual animals we had.)

I also taught math and some science to kids like the ones shown below for over 30 years.

guy teaching  deal students

Caring for farm animals and teaching young people are not the same thing.


As the saying goes: “Teaching isn’t rocket science. It’s much harder.”

I am quite sure that with careful measurements of different types of feed, medications, pasturage, and bedding, it is quite possible to figure out which mix of those elements might help or hinder the production of milk and cream from dairy cows. That’s because dairy or meat cattle (or chickens, or sheep, or pigs) are pretty simple creatures: all a farmer wants is for them to produce lots of high-quality milk, meat, wool, or eggs for the least cost to the farmer, and without getting in trouble.

William Sanders was well-known for his statistical work with dairy cows. His step into hubris and nuttiness was to translate this sort of mathematics to little humans. From Wikipedia:

“The model has prompted numerous federal lawsuits charging that the evaluation system, which is now tied to teacher pay and tenure in Tennessee, doesn’t take into account student-level variables such as growing up in poverty. In 2014, the American Statistical Association called its validity into question, and other critics have said TVAAS should not be the sole tool used to judge teachers.”

But there are several problems with this.

  • We  don’t have an easily-defined and nationally-agreed upon goal for education that we can actually measure. If you don’t believe this, try asking a random set of people what they think should be primary the goal of education, and listen to all the different ideas!
  • It’s certainly not just ‘higher test scores’ — the math whizzes who brought us “collateralization of debt-swap obligations in leveraged financings” surely had exceedingly high math test scores, but I submit that their character education (as in, ‘not defrauding the public’) was lacking. In their selfishness and hubris, they have succeeded in nearly bankrupting the world economy while buying themselves multiple mansions and yachts, yet causing misery to billions living in slums around the world and millions here in the US who lost their homes and are now sleeping in their cars.
  • Is our goal also to ‘educate’ our future generations for the lowest cost? Given the prices for the best private schools and private tutors, it is clear that the wealthy believe that THEIR children should be afforded excellent educations that include very small classes, sports, drama, music, free play and exploration, foreign languages, writing, literature, a deep understanding and competency in mathematics & all of the sciences, as well as a solid grounding in the social sciences (including history, civics, and character education). Those parents realize that a good education is expensive, so they ‘throw money at the problem’. Unfortunately, the wealthy don’t want to do the same for the children of the poor.
  • Reducing the goals of education to just a student’s scores on secretive tests in just two subjects, and claiming that it’s possible to tease out the effectiveness of ANY teacher, even those who teach neither English/Language Arts or Math, is madness.
  • Why? Study after study (not by Sanders, of course) has shown that the actual influence of any given teacher on a student is only from 1% of 14% of test scores. By far the greatest influence is from the student’s own family background, not the ability of a single teacher to raise test scores in April. (An effect which I have shown is chimerical — the effect one year is mostly likely completely different the next year!)
  • By comparison, a cow’s life is pretty simple. They eat whatever they are given (be that straw, shredded newspaper, cotton seeds, chicken poop mixed with sawdust, or even the dregs from squeezing out orange juice [no, I’m not making that up.]. Cows also poop, drink, pee, chew their cud, and sometimes they try to bully each other. If it’s a dairy cow, it gets milked twice a day, every day, at set times. If it’s a steer, he/it mostly sits around and eats (and poops and pees) until it’s time to send  them off to the slaughterhouse. That’s pretty much it.
  • Gary Rubinstein and I have dissected the value-added scores for New York City public school teachers that were computed and released by the New York Times. We both found that for any given teacher who taught the same subject matter and grade level in the very same school over the period of the NYT data, there was almost NO CORRELATION between their scores for one year to the next.
  • We also showed that teachers who were given scores in both math and reading (say, elementary teachers), there was almost no correlation between their scores in math and in reading.
  • Furthermore, with teachers who were given scores in a single subject (say, math) but at different grade levels (say, 6th and 7th grade math), you guessed it: extremely low correlation.
  • In other words, it seemed to act like a very, very expensive and complicated random-number generator.
  • People have much, much more complicated inputs, and much more complicated outputs. Someone should have written on William Sanders’ tombstone the phrase “People are not cattle.”

Interesting fact: Jason Kamras was considered to be the architect of Value-Added measurement for teachers in Washington, DC, implemented under the notorious and now-disgraced Michelle Rhee. However, when he left DC to become head of Richmond VA public schools, he did not bring it with him.


“Slaying Goliath” by Diane Ravitch

I wish I could write half as well as, or as much as, Diane Ravitch manages to do, every single day. I also admire her dedication to fighting the billionaires who have been dictating education policy in the USA for quite some time.

If you are reading this post, you are no doubt aware that only ten years ago, Ravitch did a 180-degree turn on major education issues, admitted she had been wrong on a number of points, and became one of the major forces fighting against the disruptive education-privatization agenda of the billionaires.

Since that time, she has been documenting on her blog, several times a day, nearly every day, the utter failures of the extremely wealthy amateurs who have been claiming to ‘reform’ education, but who have instead merely been disrupting it and failing to achieve any of the goals that they confidently predicted would be won, even using their own yard-sticks.


I found DR’s most recent book (pictured above) to be an excellent history of the past 37 years wherein certain billionaires, and their well-paid acolytes, have claimed that the American public school system is a total failure and needed to be torn down and rebuilt through these steps:

  1. Pretending that American students were at one point the highest-scoring ones on the planet (which has NEVER been true) and that the fact that they currently score at middling levels on international tests like PISA is a cause for national alarm;
  2. Claiming that student family poverty does not cause lower student achievement (however measured), but the reverse: that the schools that have students from poor and non-white populations are the CAUSE of that poverty and low achievement;
  3. Fraudulently assuming that huge fractions of teachers are not only incompetent but actively oppress their students (particularly the poor, the brown, and the black) and need to be fired en masse (as they were in New Orleans, Rhode Island, and Washington, DC);
  4. Micromanaging teachers in various ways, including by forcing all states to adopt a never-tested and largely incomprehensible ‘Common Core’ curriculum and demanding that all teachers follow scripted lessons in lockstep;
  5. ‘Measuring’ the productivity of teachers through arcane and impenetrable ‘Value-Added’ schemes that were devised for dairy cows;
  6. Mass firings of certified teachers, particularly African-American ones (see #2) and replacing them either with untrained, mostly-white newbies from Teach for America or with computers;
  7. Requiring public and charter schools (but not vouchers) to spend ever-larger fractions of their classroom time on test prep instead of real learning;
  8. Turning billions of public funds over to wealthy amateurs (and con artists) with no educational experience to set up charter schools and voucher schools with no real accountability — the very worst ones being the online charter schools.

One great aspect of this book is that Ravitch points out how

  1. All of those claims and ‘solutions’ have failed (for example, a study in Texas showed charter schools had no impact on test scores and a negative impact on earnings (p. 82);
  2. Teachers, parents, students, and ordinary community members have had a good deal of success in fighting back.

I will conclude with a number of quotes from the book in random colors.

“How many more billions will be required to lift charter school enrollment to 10 percent? [It’s now about 5 percent] And why is it worth the investment, given that charter schools, unless they cherry-pick their students, are no more successful than public schools are and often far worse? Why should the federal government spend nearly half a billion dollars on charter schools that may never open when there are so many desperately underfunded public schools?” (p. 276-277)

“Any movement controlled by billionaires is guaranteed […] to preserve the status quo while offering nothing more than the illusion of change.” (p. 281)

“There is no “Reform movement.” The Disrupters never tried to reform public schools. They wanted to disrupt and privatize the public schools that Americans have relied on for generations. They wanted to put public school funding in private hands. They wanted to short-circuit democracy. They wanted to cripple, not improve, the public schools. They wanted to replace a public service with a free market.” (p. 277)

“Our current education policy is madness. It is madness to destroy public education in pursuit of zany libertarian goals. It is madness to use public funds to put young children into religious schools where they will learn religious doctrine instead of science. It is madness to hand public money over to unaccountable entrepreneurs who want to open a school but refuse to be held to high ethical standards or to be held accountable for its finances and its performance. It is madness to ignore nepotism, self-dealing, and conflicts of interest. We sacrifice our future as a nation if we continue on this path of de-professionalizing our schools and turning them over to businessmen, corporate chains, grifters, and well-meaning amateurs. We sacrifice our children and our grandchildren if we continue to allow them to be guinea pigs in experiments whose negative results are clear.” (p. 281)

Ravitch proposes a number of things that billionaires could do that would be more helpful than what they are currently doing. She suggests [I’m quoting but shortening her list, found on page 280] that the billionaires could …

  • pay their share of taxes to support well-resourced public schools.
  • open health clinics to serve needy communities and make sure that all families and children have regular medical checkups.
  • underwrite programs to ensure that all pregnant women have medical care and that all children have nutritious meals each day.
  • subsidize after-school programs where children get exercise, play, dramatics, and tutoring.
  • rebuild the dramatics programs and performance spaces in every school.
  • lobby their state legislatures to fund schools fairly, to reduce class sizes, and to enable every school to have the teachers, teaching assistants, social services, librarians, nurses, counselors, books, and supplies it needs.
  • create mental health clinics and treatment centers for those addicted to drugs.
  • underwrite programs based on “the Kalamazoo Promise.”
  • They could emulate the innovative public school that basketball star leBron James subsidized in Akron, Ohio.

She also quotes Paymon Rouhanifard, who was a “prominent member of the Disruption establishment [who] denounced standardized testing when he stepped down as superintendent of the Camden, New Jersey, public schools […]. He had served as a high-level official on Joel Klein’s team in New York City […] Upon his arrival of the impoverished Camden district [….] he developed school report cards to rank every school mainly by test scores. But before he left, he abolished the school report cards.” She quotes him directly: “[…] most everybody in this room wouldn’t tolerate what I described for their own children’s school. Mostly affluent, mostly white schools shy away from heavy testing, and as a result, they are literally receiving an extra month of instruction […] The basic rule, what we would want for our own children, should apply to all kids.” (p.271)

“Disrupters have used standardized testing to identify and take over or close schools with low scores, but they disregard standardized testing when it reveals the failure of charters and vouchers. Disrupters no longer claim that charter schools and inexperienced recruits from Teach for America will miraculously raise test scores. After three decades of trying, they have not been successful.

“Nothing that the Disrupters have championed has succeeded unless one counts as ‘success’ closing hundreds, perhaps thousands, of community public schools in low-income neighborhoods. Ths Disrupters have succeeded in demoralizing teachers and reducing the number of people entering the teaching profession. They have enriched entrepreneurs who have opened charter schools or developed shoddy new products and services to sell to schools. They have enhanced the bottom line of large testing corporations. Their fling with the Common Core cost states billions of dollars to implement but had no effect on national or international test scores and outraged many parents, child advocates, lovers of literature, and teachers. “

Fortunately, the resistance to this has been having a fair amount of success, including the massive teacher strikes in state after state. As Ravitch writes (p. 266):

“The teachers taught the nation a lesson.

“But more than that, they taught themselves a lesson. They united, they demanded to be heard, and they got respect. That was something that the Disrupters had denied them for almost twenty years. Teachers learned that in unity there is strength.”



Yes, they really do want public education to fail!

A long quote from Peter Greene of Curmudgucation with some emphasis added by me.

…as the Obama administration rolled out policy, I began to realize that this was not going to be the guy to help us, that he was, in fact, going to take some of the worst parts of NCLB and keep them, boost them. Keep high stakes testing, but now judge individual teachers and not just schools. States were encouraged to fight for some additional funding, which they could do by handing over control of their state department of education to the feds. But then all states were encouraged to do the same for free to escape the penalties of NCLB, which Congress seemed completely incapable of fixing, as if– and this seemed to be a recurring theme in the early 10s– as if they actually wanted public schools to fail.

We said it over and over– when we peeked at test questions and saw how bad they were, when we asked for actionable results from last year’s tests, when we looked at the kind of crappy materials the state sent us, when we saw the unattainable goals– do they actually want us to fail??

And the more I dug into things, the more troubling they seemed. Most of what we had been told about the Common Core standards turned out to be a lie. Everywhere there were new groups with “student” and “education” in their names, important rich guys like Bill Gates, the guys in DC that we had voted for, all agreeing that we teachers in public schools, we who were devoting our lives to education and who, mostly, had far more training and experience than any of them– we were stinking up the joint. Public education was failing, and it was our fault.

“We don’t trust you. We don’t believe you or believe in you. We are trying to fix the system that you broke.” They said.

“Is this over that test? That crappy bad test?? Is that what this is about??” We asked incredulously.

“Never mind,” they said. “We’re not talking to you. You’ve done enough already. We think you’re going to need some motivation, like threats or maybe free market competition to get you to stop slacking and screwing up. Don’t like it? Big deal– we can get some of this teacher-proof curriculum in a box, or hire one of those five-week wonders from Teach for America. Your job, even though you suck at it, is not so hard.”

It began to sink in. The newly-required aligned texts. The computer-based practice testing. The test prep materials. The education-flavored businesses designed to make a buck from ed solutions, from charter schools to consulting groups. The data collection. All of those narratives were based on one premise– that public schools were failing and that some combination of solutions and alternatives were needed.

Added to that shock was the feeling of isolation. Who was on the side of public schools? Not politicians– not from either party. Not wealthy and powerful people. Not even our damned unions, which cheerfully endorsed Common Core and implicitly accepted the premise that public schools were failing.

It’s really scary.

More Educational Miracles (Not!)

I have prepared charts and graphs for 8th grade NAEP average scale scores for black, hispanic, and white students in various jurisdictions: the entire nation; all large cities; Washington DC; Florida, Michigan; and Mississippi.

You will see that there was a general upwards trend in math from about 1992 to roughly 2007 or 2009, but the scores have mostly leveled off during the last decade. I included Michigan, since that is the state where current Education Secretary Betsy DeVos has had the mo$t per$sonal influence, but that influence doesn’t look to be positive.

While it’s good that DC’s black students no longer score the lowest in the nation (that would be Michigan – see the first graph), there is another feature of my fair city: very high-performing white students (generally with affluent, well-educated parents) in its unfortunately rather segregated public schools, as you can see in the last graph. Naep 8th grade math, black students, various placesnaep math, hispanic, 8th grade, various places

naep 8th grade math, white students, various places

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