There's a simple answer: Offer a lottery with a high payout rate and flatten jackpots to the extent they don't cause a shift into other worse sorts of gambling. The total amount of money transferred to support government programs makes up less than 0.5% of total government revenue; it's trivially replaceable.
If we assume the main problem with lotteries is that they make some people poorer, we need to solve two things: The payout being less than the cost due to both high overhead and transfers and the payouts tending to concentrate wealth since even in a lifetime of play few win the largest jackpots.
One can't make them too flat because consumers like large jackpots which is why they've grown over time and become harder to win. Presumably there's a level below which consumers would switch to other types of gambling regardless of legality.
I assume that would reduce lottery proceeds significantly so is a nonstarter. I know the numbers, but I'm not so sure it's always illogical (in a human/emotional sense) and you get quite a bit if middle class people playing when the payouts are significant. YOLO
Sounds like NS&I 'premium bonds' in the UK. Expected rate of return is 1.4% (I think, something like that anyway) but it's paid as prizes monthly ranging from £25 to £1M - or no prize.
Increasing payouts should bring more players though it would be offset by eliminating the advertising that currently consumes some of that revenue. Flattening jackpots would have a negative effect but we just need to optimize for being above the point at which there's competition instead of maximizing lottery revenue.
These criteria are such a low bar that merely stating them upfront would have changed the discussion last week.
I wonder if these were the original criteria or if this is a partial walk back. The SBA usually defines a small business as having less than 500 employees even if it has significant revenue.
If I had been asked to guess the criteria based on the initial announcement, I would have guessed 500 employees, $100MM in revenue, or $5MM in money raised with some exceptions for top-tier investors or other judgmental criteria.
That’s true for most adjustments. Buffett also suggests investors ignore income statement effects of marking securities to market.
“I must first tell you about a new accounting rule – a generally accepted accounting principle (GAAP) – that in future quarterly and annual reports will severely distort Berkshire’s net income figures and very often mislead commentators and investors.
The new rule says that the net change in unrealized investment gains and losses in stocks we hold must be included in all net income figures we report to you. That requirement will produce some truly wild and capricious swings in our GAAP bottom-line.”
https://www.berkshirehathaway.com/2017ar/2017ar.pdf
Chinese Government announced it was investigating Didi for national security, which is usually code for corruption/ scaring management into greater compliance.
Didi was a massively valuable Chinese company Uber owned a significant amount of. When the stock value crashed (-23% this quaarter), it destroyed a significant amount of book-value for uber who owned it.
The key challenge with this system is merchant adoption. Since merchants are forced to charge the same prices in the less valuable currency, they'll drop out if its exchange rate falls by more than their profit margin (and perhaps before then).
"every time I convince a Stripe customer to raise their prices ... we benefit directly" sounds like the exact words of an illegal cartel. While I doubt that that is Patrick's intent, if I were an antitrust attorney, this would convince me that the notion of payment processors as clearinghouses for price-fixing merits investigation.
This is a great example of how one can do something well-meaning at small scale (Advise tiny, tiny businesses that they would be better served having more confidence in their value) that can turn into something illegal at scale (Advise price leaders that they can safely lead by less and so-on).
A stripe customer is free to tell Stripe "Your fees are too high, and I'd prefer switching processors than raising prices".
I could be wrong. Is there an example of an illegal cartel, trying to persuade customers from whom it makes transaction fees, to raise prices? Surely this isn't a new thing under the sun.
Of course the customer can say that. And nowhere does Patrick suggest that taking his advice is a condition of continuing a relationship with Stripe. That's not the issue.
The issue is that coordinating pricing is illegal in any form. In any industry, each company could make more money if they knew their competitors would raise prices. Even calling a competitor to tell them that you are raising prices is illegal if they raise prices.
If there's even a single incident of two companies being advised to raise prices on products which compete with each other, it's about as close as it comes to an open and shut price fixing case. And this post makes it easier to prove because it suggests that companies know he's giving the same advice to their competitors. Whether that's said explicitly or not won't matter.
Ultimately consumers foot the bill. It's why antitrust legislation gets pursued in the first place.
It's the raising of prices in the absence of market forces that gives room for the payment processor to increase fees. Theoretically all payment processors benefit from advocating that their clients raise prices. If some companies raise their prices, no issue, but the coordinated effort to do it is where you "smell a rat".
It's then up to the litigating party to try to find collusion between companies to do this. This is like how the anti-competitive hiring practices of Google/Apple/Facebook/etc were surfaced. They all had a set of practices that stood out as anomalous and during the subsequent investigation it was found that they colluded with each other to set the market (rate for hiring talent).
It's probably not the case here...and also patio11 has little risk saying so while living in Japan where industries are highly vertically-integrated/monopolistic.
The idea that any form of price optimization among firms violates antitrust laws would put basically all management and marketing consulting at antitrust risk. This doesn’t sound plausible.
You will find that management consulting firms (and the companies that hire them) have fairly clear rules about how they approach and talk about pricing and competition to avoid the appearance of impropriety. Most notably, they would never ever: 1. Indicate that their advice always is to raise prices 2. They would never share the pricing advice they gave to a competitor.
I don't know if what Patrick does is problematic. It wouldn't surprise me if he was trying to be glib and his actual advice is more nuanced.
If one interprets it literally: Someone who receives advice from him to raise prices can look down Stripe's customer list and find its competition and feel confident that the competition will receive the same advice.
Again, I don't know what he does. All I know is that what he says if read by the right person would be sufficient to trigger their curiosity to ask for records of his correspondence and find out.
This is an antibody test which will only be positive days after the onset of symptoms.
This is principally useful for several purposes:
1. Figuring out who to isolate in hospitals if the RNA test isn't available in sufficient quantity
2. Understanding who has already recovered from COVID-19 and is thus immune with all that implies in terms of inability to spread the disease and reduced need for PPE
3. Enabling us to confirm continued immunity later this year and understand how long the recovered will remain immune
This is not unique to this company. It's unclear to me whether the price is meaningfully less than competitors.
Bear in mind in relation to immunity -- "Positive results may be due to past or present infection with non-SARS-CoV-2 coronavirus strains, such as coronavirus HKU1, NL63, OC43, or 229E."
Those are the most common corona viruses strains that cause symptoms of the common cold (which itself is caused by something like ~200 known different viruses, the most prevelant being rhinoviruses). I was under the impression many people already had these antibodies
Some health professional floated the theory that kids aren’t getting it as much as the rest of the population because they’ve recently been hammered with all the coronaviruses at daycare.
I’m hoping this is true, since it suggests new parents are mostly OK too.
The theory I've heard: Children mostly rely on their innate immune system compared to adults, where the adaptive immune system is more fully developed. In elderly people, the immune response of both systems is slower/weaker.
I keep searching for animal studies on this and come up empty - all I can find is some studies on serological cross response when testing people for SARS exposure.
Vaccines are obviously the best bet, but I wonder whether deliberately exposing the healthy to common cold Coronavirus might improve herd immunity against this Coronavirus. E.g. maybe in late summer it can be used to forestall a second wave of Coronavirus spread in the winter.
From the last paragraph before conclusion of their article:
"Certainly, this test cannot confirm virus presence, only provide evidence of recent infection, but it provides an important immunological evidence for physicians to make the correct diagnosis along with other tests and to start treatment of patients. In addition, possible cross-reactivity with other coronaviruses and flu viruses were not studied, and the change level of antibody was not compared in the different stages of SARS-CoV-2 infection."
This is an antibody test which will only be positive days after the onset of symptoms.
This isn't really what your Twitter link says. That links says: "Both IgM & IgG (those are two different kinds of antibodies, with IgA being a third) were low or undetectable at day 0, but increased by day 5 in nearly all patients (N=16)"
-- Reports strongly indicate there are many infected people who are asymptomatic after five days.
Which is to say, this test could be extremely useful if applied widely and systematically to many people; food service workers, health care workers and so-forth.
Currently the virus test has a week turn around time. So both tests effectively find people with a week's exposure.
I think the acceptable value for N depends on the size of the effect being studied.
For example if it’s a massive effect, maybe 10 people is sufficient, but if it’s tiny enough to get swallowed up in statistical noise until you have 1000, then you need N >= 1000
> 2. Understanding who has already recovered from COVID-19 and is thus immune with all that implies in terms of inability to spread the disease and reduced need for PPE
I've been looking forward to seeing this kind of test just for this reason so we can whitelist people. But since a large percentage of people don't have symptoms how can you tell if you're still shedding the virus?
It's ok to take that on faith. If you recover from the virus, it's because your immune system knows how to kill the virus. We don't know of any immune system interactions that don't work that way.
There's a chance that immunity is short-lived, or that there are multiple strains of the virus which do not produce equivalent antibodies, but 100% of scientists will believe you develop immunity of some sort.
Isn't that a case of having multiple strains of the virus, where recovering from one strain still provides immunity to that strain? The gimmick here is that it decreases resistance to the other strain, possibly after some time.
I was hearing some doctors saying on the radio that any flu immunity is short lived, this is why we need to have the anti-flu vaccinations every year, that cover the typical/usual strains. If immunization lasted forever there wouldn't be a need for annual vaccination.
There have been reports, but it’s not to my knowledge clear whether the people who’ve tested positive after being considered recovered were experiencing something else, like a false positive before/after, or weren’t in fact fully recovered.
I'm interested in the frontline response here as if you already have immunity that person can serve in "risky" positions like working at airports, etc.
I am pretty sure there is a list of things that employers (in the US) can't discriminate on, and immunity to Coronavirus isn't one of them.
I think it will ultimately be in the hands of workers to decide whether or not they want to do the job. Maybe I'm immune to Coronavirus so I should work in a pharmacy... but I get paid 10x as much writing software, so I'm probably going to do that instead.
What if you’re immunocompromised with a hereditary illness? If there’s reasonable grounds it makes sense but reasonable and what employers will try and get away with are two different things.
A version of what you suggest has been the law since ERISA was passed in 1974.
The problem is what to do if investment performance doesn’t meet expectations, lifespan increases, or future assumed yield decreases. Generally companies had to pay enough to be back to even within seven years. Newspapers sought the right to have thirty years to fully fund. McClatchy was larger than Congress was comfortable with and found itself unable to pay the required fraction of the difference between the NPV and finding amount. Hence, it declared bankruptcy.
There's huge conflicts of interest with defined benefit pensions. It wasn't until PPA of 2006 that standards really tightened up. But imagine being an employer in 1970s. Instead of paying people higher wages, you could create a pension plan on paper, pay the plan however much you wanted (there was wide discretion on what assumptions could be used to calculate the cost of benefits), and get the recipients of the benefits to work for you today for an unknown benefit tomorrow.
If you're one of the decision makers, you're likely to be on the older side. So you're likely to start receiving the benefits soon anyway, and so any underfunding wouldn't affect you, since there would be a couple decades of money available before it started to run out. So the decision makers can easily choose to shortchange those 20 to 40+ years in the future in exchange for enriching themselves in the now (1970s, 80s, etc).
Seems like society should've seen it coming. Of course, if everyone kept having 4 kids, maybe those fantasy numbers could have been met, but who has the ability to predict numbers decades in the future?
The report is misleading because AP tends to be used far more on controlled access roads which have fewer crashes per mile. There are other biases too but this alone could easily explain more than the observed 1/3 risk reduction.
Unbiased means that if I draw infinitely many random samples from a population and average a statistic (in this case standard deviation) across all the samples, the answer will be the statistic computed from the population itself. If one divides by n instead of n-1, the estimate for standard deviation will be be (n-1)/n too small. One reading this might think, "Wait! We're going to infinity so the ratio converges to 1." That's true if the size of each sample also goes to infinity but not if we draw millions of ten item samples.
As for using up a degree of freedom, the easiest way to build intuition for why this is a useful concept is to think about very small samples. Let's say I draw a sample of 1 item. By definition the item is equal to the mean so I receive no information about the standard deviation. Conversely, if someone had told me the mean in advance, I could learn a bit about the standard deviation with a single sample. This carries on beyond one in diminishing amounts. Imagine I draw two items. There's some probability that they're both on the same side of the mean, in that case, I'll estimate my sample mean as being between those number and underestimate the standard deviation. Note that I'd still underestimate it even with the bias correction, it's just that that factor compensates just enough that it balances out over all cases.
A simple, concrete way to convince yourself that this is real is to consider the standard deviation of a variable that has an equal probability of being 1 or 0. The standard deviation is 0.5. But if we randomly sample two items, 50% of the time they'll be the same and we'll estimate the standard deviation as zero. The other 50% of the time, we'll get the right answer. Hence, our average is half the right answer (n/(n-1)=2/1). The correction makes the standard deviation double what it should be half the same while remaining zero in the other cases. This also suggests why dividing by n is referred to as a the maximum likelihood estimator.
This is very helpful, especially the example at the end, thanks. I think the difficult part to understand is that dividing by n leads to an estimate that is somehow too small. The intuition tells you that dividing by n would just give you the true average.
