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No, Really: Justice Roberts cares about law

UPDATE: With a note on the Roberts concurrence at the end

Justice Roberts sided with the Court's liberals today in a (somewhat surprising, and really important) 5-4 decision by Justice Breyer striking down a Texas abortion law nearly identical to one the Court struck down in 2016.  Justice Roberts is not a fan of abortion.  But he is a fan of the law!  Today, he basically joined in a smackdown of the ultra-conservative 5^th Circuit, which forgot that it was an appellate court, and not a trial court.  Justice Breyer writes:

“The Court of Appeals agreed with the District Court’s interpretation of the standards we have said apply to regulations  on  abortion.    It  thought,  however,  that  the  District  Court  was  mistaken  on  the  facts.    We  disagree.   We  have  examined the extensive record carefully and conclude that it supports the District Court’s findings of fact.  Those findings mirror those made in Whole Woman’s Health [the 2016 case] in every relevant  respect  and  require  the  same  result.  We  consequently hold that the Louisiana statute is unconstitutional.” (op. slip, 3)

Yes, but before we get there, let’s remember that the job of the Court of Appeals is not actually to disagree about facts when it doesn’t like a precedent.  Breyer begins with a review session on what Court is supposed to do what:

Title VII Protects LGBTQ Individuals!

UPDATE: First, I'm being loose with terminology here - "originalism" specifically refers to a theory of Constitutional interpretation; what Gorsuch et al are advocating is "textualism" (for statutory construction).  The distinction between public meaning and expected application is important in the originalism debate - but I think it's clearly at work in the debate here.  Second, Andrew Koppelman's commentary here is worth reading on the different ways of understanding the plain meaning of a text.

Up until today, if your boss wanted to fire you for begin gay or trans, you had no recourse in federal law – so unless your state happened to protect you, you had no recourse at all.  Today, the Supreme Court issued a landmark ruling in Bostock v. Clayton County that not only hands the LBGTQ community a huge, huge set of legal protections, it does so in a straightforward way.  And the opinion was authored by Justice Gorsuch and joined by Roberts.

Gorsuch delivers a seminar on originalism.  Does Title VII prohibit discrimination against trans and homosexual individuals? Well, let’s see what it says.  It says that it prohibits discrimination because of sex.  What does “sex” mean here?  Well, according to the public meaning (more on this in a sec.) of the term when the law was enacted, it means “status as either male or female [as] determined by reproductive biology” (op. slip, 5).  Ok.  You can’t discriminate “because of sex.”  What does “because of” mean?  It establishes a but-for causality.  Gorsuch goes on to explain that an event can have multiple but-for causes, which means that sex need only be a necessary part of the decision.  As he puts it, “when it comes to Title VII, the adoption of the traditional but-for causation standard means a defendant cannot avoid liability just by citing some other factor that contributed to its challenged employment decision. So long as the plaintiff’s sex was one but-for cause of that decision, that is enough to trigger the law.” (6).  Gorsuch notes that this is sweeping, and “Congress could have taken a more parsimonious approach” (6).  But they didn’t.  He proceeds to similarly characterize discrimination.

On the intersection of masks and racist policing

Black men have to decide whether the risk of being harassed and profiled by police for wearing a mask is greater than the risk of contracting Covid for not wearing one.

Covid-19 Came to the U.S. later than we thought

Research into the spread of Covid-19 continues, with an important new preprint by Michael Woroby et al up today (tl;dr see the writeup in Stat News).  The standard narrative about the arrival of Covid-19 in the U.S. is that a patient arrived in Seattle, WA from Wuhan on January 15^th.  He felt ill, and aware of CDC messaging about a new virus circulating in Hubei, sought medical help.  On Jan. 19, he became the first confirmed case of Covid in the U.S. Then, a few weeks later on Feb. 24, another patient turned up with what appeared to be community spread.  Containment had failed.  Per Woroby:

“On February 29th, 2020, a SARS-CoV-2 genome was reported from a second Washington State patient, ‘WA2’, whose virus had been sampled on February 24th as part of a community surveillance study of respiratory viruses. The report’s authors calculated a high probability that WA2 was a direct descendent of WA1 [the first patient, from January], coming to the surprising conclusion that there had by that point already been six weeks of cryptic circulation of the virus in Washington State. The finding, described in a lengthy Twitter thread on February 29th, fundamentally altered the picture of the SARS-CoV-2 situation in the US, and seemed to show how the power of genomic epidemiology could be harnessed to uncover hidden epidemic dynamics and inform policy making in real time” (3, internal citations omitted). 

Then there was more genetic sequencing, and it turned up a strange anomaly: the cases that appeared to stem from WA2 had mutated from WA1 in two places, and there was neither evidence of a transitional virus strain between them or of other infections in Washington whose genetic sequencing matched WA1.  Woroby et al then ran a computer simulation – 1000 times! – of the epidemic, seeding it with WA1.  The result was a surprise: “when we seeded the Washington outbreak simulations with WA1 on January 15th, 2020, we failed to observe a single simulated epidemic that has the characteristics of the real phylogeny” (7).  In other words, the narrative about the early spread in Washington is almost certainly wrong.  Patient WA1 was not the source, and the virus was not spreading covertly for weeks before emerging again in WA2.  Rather, the spread around WA1 was contained, and WA2 represented a new infection, and was either the source of the actual epidemic, or near it.  Specifically, the virus arrived a second time from Hubei, around Feb. 13 (95% probability between 2/7 and 2/19) (9).

Testing Nonsense Redux

As most folks know by now, there's two kinds of Covid tests.  One of them tests for whether you have the disease now.  The other tests for the presence of antibodies in your blood, indicating that you have had the disease at some point.  You might think that an outfit like, say, the CDC, would distinguish between them in its count of Covid-19 testing.  Alas, you'd be wrong, as The Atlantic reported today.  Why would the CDC (and Georgia, Texas, and some other states) do this?  Well, it lets you (a) claim to be doing a lot more testing for current infections than you currently are (because the addition of antibody tests to the viral tests drives up the total number of "tests"), and (b) will drive down the percentage of positive test results (because a small number of people have the antibodies, generally a much smaller number than test positive in most places in the U.S., especially those that conduct relatively few viral tests).

And if you do massive numbers of tests with low percent positive, then those annoying scientists say you can reopen sooner!  It puts you on the good green side of charts like the one above (source), not the bad yellow one.

Trump has been in office too long not to assume that this is the work of Trumpian political operatives and not CDC scientists, at least until proven otherwise.  Trump himself is obviously too stupid and incurious to know the difference between the testing types, but like Henry II, he makes it clear enough what he would like to see happen.  And that's lots of tests, lots of opening, and lots of loyal servants who translate his impulses into "reality."

More on Covid Transmission

One of the widely-discussed metrics for understanding Covid-19 transmission is its R0 number - the average number of infections that a given person causes.  An R0 of 3, for example, means that each person infects an average of 3 others.  In order to stop the disease from eventually spiraling out of control you need to keep the R0 number at or below 1.  But of course R0 represents an average - and a recent write-up in Science elaborates on the emerging evidence that a relatively small number of cases account for most of the spread.  Understanding why that is could of course go a long way toward making it possible to restore some sense of normalcy, even if figuring this out is incredibly difficult.

One thing that does appear to be the case is that indoor transmission is more likely than outdoor, and that mixing large numbers of people together in small spaces is a bad idea.  That's why Michael Sorrell, president of Paul Quinn College, writes in the CHE that the desire to have in-person college in the fall is nonetheless a bad idea.

On the other hand, a group of German medical organizations just issued a statement advocating the immediate and full return of K-12 schools.  There is some nuance - older kids can do some social distancing, and there is an assumption of testing and contact-tracing - but they summarize quite a bit of research saying that there is little evidence that outbreaks occur in groups of kids, and some evidence that kids don't readily transmit the disease.  We are all about to owe France and Germany, which are both competent at testing and are gradually reopening schools, a debt for better understanding how Covid transmits among kids.

In the meantime, the dishes here keep piling up and the racist sociopath pretending to be President is deporting children who aren't sick cuz the #MexicanCaravanWuhanOBAMAChinaWTOFAKENEWS is obviously the reason 93,000+ Americans have died!  But that's ok, because we have done lots of tests, very good tests, and "per capita" is one of those phrases that libtards use to confuse you with their elitism.

On understanding what "Covid cases" means

With reference to malaria tracking, I've tried to suggest some of the reasons we don't really know what "Covid cases" means, either insofar as that is measured by positive tests (because we don't know how many more cases there are beyond the tested ones, so tested cases is at best an rough guide to Covid incidence) or symptoms (a task that gets harder by the day, as Covid continues to present in new and strange ways).  Here's a nice piece that starts with how they deal with malaria incidence, and works through a lot of the problems in translating from test results to meaningful information about Covid.

Getting a more nuanced understanding of Covid Transmission

It’s fairly clear that one of the keys to living with Covid-19 is understanding the dynamics of transmission: absent something more nuanced than what we have, “stay 6 feet away from everyone at all times!” becomes the only public health advice that can be given.  Getting past the initial maximin strategy requires better data on everything.  There’s an interesting Twitter thread by Dr. Muge Cevik (St. Andrews) that collates some of the recent studies about transmission within and through social groups.

 

A lot of discussion recently about transmission dynamics, most of which are extrapolated from viral loads & estimates. What does contact tracing/community testing data tell us about actual probability of #COVID19 transmission(infection rate), high risk environments/age?
[thread]

— Dr Muge Cevik (@mugecevik) May 4, 2020

It’s not a formal meta-analysis or anything like that but there’s emerging confirmation for the idea that most transmission depends on sustained, close contact, and that children do not appear to be a primary vector for the disease (this latter one is enormously important, because nobody knows for sure what should be done about schools).  She summarizes at the end (the paragraph breaks are mine):

“While the infectious inoculum required for infection is unknown, these studies indicate that close & prolonged contact is required for #COVID19 transmission. The risk is highest in enclosed environments; household, long-term care facilities and public transport. High infection rates seen in household, friend & family gatherings, transport suggest that closed contacts in congregation is likely the key driver of productive transmission. Casual, short interactions are not the main driver of the epidemic though keep social distancing! Increased rates of infection seen in enclosed & connected environments is in keeping with high infection rates seen in megacities, deprived areas, shelters. A recent preprint demonstrates that #COVID19 epidemic intensity is strongly shaped by crowding.

“Although limited, these studies so far indicate that susceptibility to infection increases with age (highest >60y) and growing evidence suggests children are less susceptible, are infrequently responsible for household transmission, are not the main drivers of this epidemic.”

“Finally, these studies indicate that most transmission is caused by close contact with a symptomatic case, highest risk within first 5d of symptoms. To note: this preprint suggests that most infections are not asymptomatic during infection.  In conclusion, contact tracing data is crucial to understand real transmission dynamics. Cautionary note: This data & interpretation is based on the available evidence as of May 4th. Our understanding might change based on community testing/lifting lockdown measures.”

Meat is Murder

As Daniele Lorenzini reminds us, the coronavirus pandemic exposes nothing if not the differential precarity of our biopolitics.  Sure, biopolitics is about promoting life, but it’s also about deciding that some people can die in order that others may live.  The most obvious candidates are “essential”  workers in various parts of the supply chain who form what Marx calls the "disposable industrial reserve army" of capitalism.

In case there was any doubt about this, Trump is apparently about to invoke the Defense Production Act to force meat packing and processing plants to stay open.  This is the same Donald Trump who has routinely failed to use the same DPA to get medical supplies to hospitals and caregivers. 

But what about when it’s a question of protecting a bunch of mostly Latinx folks with a median annual wage of under $28,000 and whose working conditions look like this:

“The demographics in the meatpacking industry may be multicultural in the 21st century, but workers share many of the same characteristics. They are uneducated and work in extremely unsafe work conditions. Nearly every worker in meatpacking plants has injuries. Cutting stations are located close to each other, automated lines move too quickly for workers to keep up, and workers must put in long shifts or fear losing their jobs. Men and women must wear goggles, hardhats, stainless steel mesh gloves, rubber aprons and chaps. Still, every worker from the line cutter to the cleaning crew is in danger of suffering amputations, body part crushing, burns, punctures and other traumatic injuries. Though the demographics have opened up to include all segments of society, poor safety conditions in the plants continue to plague workers. According to Human Rights Watch, the poor labor market has caused safety concerns to backslide in the United States. Collective bargaining eroded, and by 2005, the injury rate was twice as high in the meatpacking industry as any other industry in the country.”

Trump wants you to know: sustaining Americans’ egregious overconsumption of cheap meat, which involves the brutal exploitation of vulnerable workers in the best of times, is more important than PPE’s for healthcare workers!  Oh, wait, Trump says (per the Bloomberg piece) that “the government will provide additional protective gear for employees as well as guidance.”

I wonder if that will arrive before, or after, the additional protective gear for hospitals?  But let no one accuse Trump of inconsistency!  He’s using the DPA to ensure that vulnerable workers, many or even most non-white and/or female, are at high risk for Covid.

Who Owns the Law? SCOTUS Speaks

Recall that before Covid (so about 300 years ago), there was an interesting copyright case percolating through the federal courts.  The question concerned the Official Georgia Code Annotated (OGCA), which contains the text of the Georgia Code as well as various annotations.  There were two potentially conflicting principles at work.  On the one hand, the law is public domain.  On the other hand, annotations and supplemental materials by third parties are often copyrightable.  Georgia managed to produce a hybrid system: the legislature established a code commission, which outsourced most of the annotations work to Matthew Bender Corp, which was granted an exclusive license to sell it.  At the same time, the legislature every year officially adopted the GCA, and it was the authoritative source for the Georgia Code in everything from legislative proceedings to cases to public reference.  It was even published with the state seal attached.

The 11^th Circuit ruled that OGCA was not copyrightable because, even if it wasn’t quite the same thing as the statutory text, it nonetheless is “an exercise of sovereign power” (3) and “sufficiently law-like so as to be properly regarded as a sovereign work” (4).  Today, in an opinion by Justice Roberts, the Supreme Court agreed, though for somewhat different reasons.  The SCOTUS opinion basically argues that the relevant question is whether the “author” of something is a judge or legislator; answers that the OGCA is reasonably the work of a legislator, and thus uncopyrightable.

Discouraging News (probably) on Remdesivir

Remdesivir has been one of the most closely watched potential therapies for COVID-19, and a couple of early cohort and observational studies have been encouraging.  But apparently the first results from a randomized controlled trial in China indicate that it did not make a statistically significant difference.  At least, it looks that way: results were accidentally posted on the WHO website and then removed, with a promise of an official publication to come later.  It's all a bit murky, and there's a number of other trials ongoing, but this is not the sort of result you hope to see, even accidentally.

What does it mean to model “social distancing?”

By now it should be apparent just how little we know about the coronavirus pandemic, from how to treat it to basic facts about what the “number of COVID cases” means. Even “deaths due to COVID” turns out to be difficult: both New York and UK have revised their numbers up to accommodate likely cases that hadn’t been counted, and there’s a brewing political battle over how to count them. This ignorance also affects pandemic modeling; the aspect I want to look at here is over what “social distancing” means.

COVID Models are for guiding action: we want to look at them and know when we can leave the house again (or, maybe more importantly, when the kids can leave the house again!). In that sense, models are inherently political, in several ways. First, the model doesn’t tell you what to do; the decision about what to do can be informed by the model, but it requires an entire apparatus of priorities, intuitions, and whatever else goes into a decision about what to do. Part of this is a second-order decision strategy about how to process uncertainty. I’ve suggested before that we’ve been operating on maximin; whether I’m right isn’t important here except to underline that the model is insufficient. Second, the construction of the model is going to embody a number of social judgments. I’m not being Latour here and arguing that whatever comes out of laboratories or complex instruments is broadly political (though he’s right). What I do want to say is that to use a model, you have to answer some antecedent questions what your use of the model is trying to achieve, and how society operates.

U.S. COVID Testing: Still Crap

President Nero wants you to know that the U.S. has conducted a bigly number of coronavirus tests, higher than he can count, and maybe even more tests than there were people at his inauguration!  Anyway, the U.S. is still terrible at COVID testing, as the following chart from Vox reminds us:

As the accompanying article points out, this is completely mediocre.  It is a little better than before, but it nonetheless underscores that we are not ready to go back to a new-normal.  All the actual plans I’ve seen begin with the presumption of widespread testing.  The U.S. is absolutely nowhere close to that.

Cellphone Tracking for COVID Surveillance

Cellphone tracking - whether through geolocation or something like detecting the proximity of bluetooth devices - has been getting a lot of attention for its potential to improve COVID surveillance.  Given that there are estimates that a workforce of upwards of 100,000 people would be necessary to get a good contact-tracing regime going in the U.S., any automated way to reduce this would be welcome.  Apple and Google have announced that they are developing an App for that.  It would enable a user to know if they had been within six feet or so of someone who tested positive for COVID.  Assume for the moment that enough people get tested that such a strategy would generate meaningful data, what are its limitations?  Here are 5 concerns.  Not surprisingly, they echo concerns about other uses of cellphone tracking: not just privacy invasion, but whether they actually generate a lot of false positives (the person in the apartment next to yours might well be within 6 feet of you), whether they promote discrimination (neighborhood hotspots, etc.), and so on.

The EFF has some useful principles here.

Modeling the Future under Coronavirus

UPDATE: Here's a nice piece that talks about the complexities of reducing restrictions, framing the overall need in terms of keeping R0 (the number of new infections a given case leads to) from rising much above 1.

A new article in Science models our future under the new Coronavirus regime.  It is not pretty.  A few takeaways, followed by a thought on social distancing (the whole study is worth a read, because I’m cherry-picking here, and I’m not indicating anything about the modeling process, only some of the implications):

• Social distancing works, BUT when you stop it, COVID will come back. The relation between the two is complex, because effective social-distancing reduces population immunity: “We evaluated the impact of one-time social distancing efforts of varying effectiveness and duration on the peak and timing of the epidemic with and without seasonal forcing. When transmission was not subject to seasonal forcing, one-time social distancing measures reduced the epidemic peak size. Under all scenarios, there was a resurgence of infection when the simulated social distancing measures were lifted. However, longer and more stringent temporary social distancing did not always correlate with greater reductions in epidemic peak size. In the case of a 20-week period of social distancing with 60% reduction in R0, for example, the resurgence peak size was nearly the same as the peak size of the uncontrolled epidemic: the social distancing was so effective that virtually no population immunity was built. The greatest reductions in peak size come from social distancing intensity and duration that divide cases approximately equally between peaks
• You’d better hope COVID is not seasonal: “For simulations with seasonal forcing, the post-intervention resurgent peak could exceed the size of the unconstrained epidemic, both in terms of peak prevalence and in terms of total number infected. Strong social distancing maintained a high proportion of susceptible individuals in the population, leading to an intense epidemic when R0 rises in the late autumn and winter. None of the one-time interventions was effective in maintaining the prevalence of critical cases below the critical care capacity.” And: “One-time social distancing efforts may push the SARS-CoV-2 epidemic peak into the autumn, potentially exacerbating the load on critical care resources if there is increased wintertime transmissibility”

 

Immunity Certificates reading

The latest trendy idea.  It sounds great in some limited contexts, especially as a way to protect healthcare workers.  It also sounds suspiciously like a way for Trump and Co. not to do the actual work of testing and contact tracing, or to guarantee a general social safety net.  It's like some funhouse mirror version of a Deleuzian control society, coupled with the neoliberal destruction of everything except the imperative to generate surplus value.  Now you need a certificate to join the industrial reserve army...

What could go wrong?  Hank Greely shows that the answer is quite a lot, while Sam Hull (no relation) outlines the problems from a legal point of view, focusing on discrimination, and concludes by suggesting that they only begin to make sense when paired with a jobs guarantee program.  Both Greely and Hull highlight that any immunity certificate program will generate not just black markets in certificates, testing and so on, but perverse incentives to deliberately get Covid and risk death in order to get the certificate.  Of course... these folks will just be investing in their human capital!

Republicans still hate voting

Why would you let a pandemic get in the way of voter suppression?  Much better to use it as a tactic of voter suppression.  At least, I can't see any other other coherent way to read the Wisconsin GOP and SCOTUS refusal to either delay Wisconsin's vote today, or allow absentee ballots to be delayed.  Wisconsin GOPs shenanigans to keep itself in power are well-known; we shouldn't forget that the Roberts Court is systematically hostile to voting rights.  The Onion has the right tweet, and reminds us that Wisconsin has 77 Covid deaths today.  Check that space in 2-3 weeks.

Unless it's that Republicans hate democracy so much they don't even care who wins, as long as fewer people vote.

Plus ça change...

More on Late Capitalism and COVID

By Gordon Hull

I argued a few days ago that late capitalism, with its fetishization of efficiency, leaves us unprepared for a pandemic because of vulnerabilities in the supply chain. In a recent blogpost, Frank Pasquale adds some healthcare-specific texture to the point, noting how our healthcare system is almost designed to fail in a pandemic. He cites one of his own papers from 2014:

“The reduction in hospital facilities and other resources, although “efficient” in normal times, may prove disastrous if there is an epidemic. For example, one national-preparedness plan for pandemic flu estimated that, in a worst-case scenario, the United States would be short over 600,000 ventilators. “To some experts, the ventilator shortage is the most glaring example of the country’s lack of readiness for a pandemic,” one journalist noted. The lack of “surge capacity” throughout the health care industry is a major infrastructural shortcoming, likely to cause tremendous, avoidable suffering if a pandemic emerges” (179).

The quotes are from… 2006 and 2007, and refer to warnings coming after the SARS epidemic. In other words, we’ve been as unprepared as possible for 14 years, despite a near-miss epidemic and constant warnings from epidemiologists. So Trump is an idiot and an imposter, and his son-in-law supply czar is a feckless idiot who understands nothing about supply, but, as Pasquale underscores, there is another, longer timeline to our pandemic preparation failure.

In Pasquale’s paper, he notes that part of the problem is how we frame healthcare in terms of aggregate costs (and the need to keep costs down), a construction that makes it impossible to notice that some things are over-funded and others under-funded. In particular, not only can mantras of cost-cutting shield wasteful allocations of resources like those to hedge-fund managers from scrutiny, but it can also hide the fact that some aspects of healthcare (let’s see, hmm. Pandemic prevention!) are radically under-funded. Worse, there is absolutely no way to guarantee that money saved here will actually be reallocated to something more socially useful:

“If the health care cost-cutters had a plan for reallocating excess health sector spending to pay for care that is now undercompensated or absent, they would merit the influence they have now achieved. But in reality, money freed up by cost-cutting is much more likely to be retained as profit or claimed by capital and rentiers in some other way” (191)

We see at least three versions of these problems playing out now.

COVID and Geolocation: Google edition

Geolocation data is getting increasing attention as a way of tracking social distancing in particular.  Google has just released a bunch of its geolocation data, which tracks changes in trips to retail, parks and other places.

In the meantime, a new paper in Science says that a good contact-tracing App, if sufficiently robust and adequately deployed, could avoid the need for lock-downs. 

Of related interest, Zeynep Tufekci has a smart piece in The Atlantic, pointing out that disease modeling isn't useful so much for producing truth or knowledge, but as a guide for how to avoid worst outcomes.  This seems absolutely right to me, and is in line the way health policy folks are pursuing what I've called a maximin strategy.

Late Capitalism Makes COVID-19 Worse

By Gordon Hull

I know there’s a lot of ways to develop that thesis!  Let me focus on one: the fetishization of “efficiency,” and its corollary, just-in-time supply chains.  In a recent piece in The Atlantic, Helen Lewis argues that a lot of the disruption in consumer goods (toilet paper, etc.) is initially attributable not to hoarding, but tiny, unanticipated fluctuations in demand.  Stores don’t carry extra product in the back any more; rather, they keep their shelves stocked by way of a very elaborate, data-intensive logistical operation that delivers enough product to keep items on the shelf, but no more.  Hence the name “just-in-time” capitalism.  Why would you prefer such a system?  Excess stock is inefficient: it just sits there taking up space when it could be sold elsewhere, and you had to pay people to make it, even though you’re not getting paid for selling the product that’s sitting in the back of the store.

The innovations to supply-chain that enable just-in-time capitalism go well beyond the idea that stores should keep minimal stock, however.  If neoliberal financialization has taken a lot of the theoretical attention since 2008, it’s important to remember that financialization hasn’t been the only point of late capitalism.  For example, a pair of McKinsey reports in the early 2000s looked back on productivity growth in the late 1990s, widely attributed at the time to developments in IT.  As one argues, IT was not the cause: within the sectors that grew, “the most important cause of the productivity acceleration after 1995 was fundamental changes in the way companies deliver products and services.”  In “The Wal-Mart Effect,” McKinsey’s Bradford Johnson argues that:

“More than half of the productivity acceleration in the retailing of general merchandise can be explained by only two syllables: Wal-Mart. In 1987, Wal-Mart had a market share of just 9 percent but was 40 percent more productive than its competitors as measured by real sales per employee (the measure used for all company-level analyses in this study). A variety of Wal-Mart innovations, both large and small, are now industry standards. Wal-Mart created the large-scale, or “big-box,” format; “everyday low prices”; electronic data interchange (EDI) with suppliers; and the strategy of expanding around central distribution centers. These innovations allowed the company to pass its savings on to customers. By 1995, it commanded a market share of 27 percent and had widened its productivity edge to 48 percent” (McKinsey Quarterly 2002:1, p. 41).

In other words, our current retail scene – the one existing before Amazon – is attributable to Wal-Mart.  The efficacy of this logistical innovation was evident in the immediate aftermath of Hurricane Katrina, in which Wal-Mart was able to deploy trucks of aid to the New Orleans area much faster the George W. Bush’s mismanaged FEMA.   Amazon, in a sense, represents the intensification of this trend: maintaining brick-and-mortar stores is inefficient, if the logistical operation can be made sufficiently nimble and efficient.