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.

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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.


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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.

Regression to the mean?

It was suggested to me that the fact that very few teachers remained in the 90th percentile for two years in a row in NYC’s value-added madness (my description, not his) is simply yet another case of regression to the mean. That’s a phenomenon where very tall parents tend to have kids that are a bit shorter than they are, and very short parents have children that are taller than them.

Perhaps. But we definitely from ordinary observation that that height, hair color and skin color (but not tattoos or most illnesses) are rather inheritable: tall parents tend to have kids that are taller than most, and short parents have kids that are shorter than most, and so on.

But when the data is a blob showing almost no correlation at all, then regression to the mean doesn’t really mean the same thing. I mean, it starts to become just random variation. Yaknow whatI mean?

OK, let’s look at NYC again.

I just figured out how to get Excel to count some stuff for me in a neat and efficient manner. It counted for me all of the NYC public school teachers who were at or above the 80th percentile in the value-added measurement scheme they'[ve been using there for sy 0506; (That would be considered excellent.) I also had it check to see whether they were also in the 80th percentile during SY 0708, two years later. Or not.

If we trust my programming of Excel, there were exactly 161 such teachers who were in the 80th percentile rank or higher during both years.

But I also had it count how many teachers “dropped”, so to speak. I found there were 545 who were below the 80th percentile the second measured year. Oh, well, that’s nothing – that’s just regression to the mean, you say.

Well, what about those who go BELOW the mean the second year? That’s more than just regression to the mean. After all, the children of congolese pygmies do not get tall enough to play for the NBA. It would take a lot of intermarriage for several generations for that to happen (sorry about that, Bugsy Malone).

In New York city, I found that 146 teachers who were high-flyers in 2005-6 (at or above the 80th percentile) were distincly sub-par, scoring at or below the 50th percentile, two years later.

That’s close to the number of high flyers, nand is about 1/3 of those who “dropped.”


My conclusion:

This ‘value added’ stuff is worthless. It has no real predictive value. It doesn’t tell us anything we really want to know, even on its own terms. Plus, it’s measuring the wrong things — but that’s the subject for many more columns to come, and not just by me.


Published in: on March 5, 2012 at 10:27 pm  Comments (3)  
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More on the Utter Stupidity of NYC’s Value-Added Machinations

I just discovered another weird feature of New York City’s value-added process for teachers.

According to NYC’s own data, a teacher’s percentile ranks for “effectiveness” vary ON AVEARAGE, and for the SAME YEAR, SAME STUDENTS, SAME SUBJECT, SAME CLASS by over 50 percentile ranks.

In other words, a teacher at the median for this variability, about 57 points, could be scoring anywhere from the 20th percentile (very low) on up to the 77th percentile (rather high).

You probably think I’m making this up.


Let me give you some raw data and names to chew on.

The following teachers, who are named in the spreadsheets that were obtained by the NYC media, have scores as follows. All of these are NYC PS mathematics teachers. I give you the grade level, followed by what NYCPS says is their lowest value-added percentile rank for 2009/2010, and then their highest possible value-added percentile rank for the same year. In other words, they can’t tell how “good” these teachers really are, even by their own murky methodology.

These are not the exceptional, weird cases. The MEDIAN range of scores for the entire city is 57 points, and if you do a little subtraction, you will notice that in every single one of these cases, their top and bottom scores are 57 percentile points apart.

RHONDA DUFF BAPTISTE              5th Grade            7              64

KRISTIN  DUNBAR                         5th Grade         25           82

DANIELLE DUNNE                        4th Grade            30           87

TONIA EDWARDS                        4th Grade            28           85

ELAINE ELFOND                           8th Grade            36           93

KATHLEEN ESTES MILANO          4th Grade            3              60

STEPHANIE FAIELLA                   5th Grade            8              65

CORDELIA FAULKNER                 5th Grade            7              64

GLORIA FEIERSTEIN                    8th Grade            31           88

SCOTT FLATOW                           4th Grade            29           86

MORGAN FLUSSER                    8th Grade            23           80

DONNA FOSTER                          6th Grade            8              65

PATRICK FOY                             4th Grade            33           90

JENNIFER FRANCKLIN                4th Grade            4              61

ALIZA    FUENTES                       8th Grade            17           74

MARYANN  GANCI                      4th Grade            32           89

(By the way, I don’t know any of these folks, how old they are, what they look like, whether they are strict or lenient, give lots of homework, are tough graders, coach basketball, or anything else about them. But I know that they are real people, real teachers, college grads, and probably a lot like my and my fellow DC teachers except that many of them probably talk funny because they have Noo Yawk accents. Instead of talking normal like y’all do here in DC. ;=) They don’t need to be treated as if their life’s work revolves around a single number — one that nobody seems to be able to pin down very well, at that. Same thing with their students!)

OK, you might be thinking that this only applies to math teachers.


Same deal with English Language Arts teachers, as I show you here below, just like the list up above, which was for math teachers. Again, these teachers have the median (normal) differences between their highest and lowest possible value-added numbers, so they are not exceptional cases. THESE ARE THE TYPICAL CASES.

JULIE BOLAND                               4th Grade            38           95

SHARON BOONE                             4th Grade            3              60

JENNIFER  BRANDES                      8th Grade            0              57

SHARON CANNELLA                       4th Grade            38           95

MONIQUE CARMICHAEL                  4th Grade            40           97

ANATEA CARPENTER                     7th Grade            41           98

REGINA CARROLL                            5th Grade            0          57

CHRISTINA CASSASE                      5th Grade            0           57

CHRISTOPHE CECIL                         5th Grade            39           96

CHEZ DAVIS                                     4th Grade            1              58

JUANITA DAWSON                          4th Grade            6              63

KAREN DOHERTY                           4th Grade            2              59

MICHAEL DONOGHUE                     8th Grade            41           98

JAIME DRAGOON                            4th Grade            2              59


If you’ve forgotten what a percentile rank is, it goes like this: if you are at the 10th percentile for height, that means you are only taller than 10% of your peers, and about 90% of them are taller than you. I.e., you are kinda short. If you at the 86th percentile for height, that means that about 86% of your peers are shorter than you, and you are only shorter than roughly 14% of your peers. In other words, you are rather tall.

If no-one can pinpoint your height any better than by saying you are somewhere between the 41st and 98th percentile, then they haven’t said diddly.

Still don’t believe me? Look at the exact same spreadsheet that I did, I posted it as a google doc at the following URL:


This range of values is probably their confidence interval, most likely one standard deviation on either side of the theoretical value. However, I don’t see where they actually state that, so I didn’t, either.

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