I think the difference is that black market is strictly illegal, while dark market includes legit businesses that sometimes buy stolen goods (like copper lines, etc.) on the side.

It's not just like that in Asia. When I came to Canada from Argentina, I had just finished grade 3, and I was given a bunch of tests to see what my level was. In particular, I was tested at a Grade 9 level in Math, and I wasn't privately tutored or anything like that.

Of course, my parents could afford to send me to a good private school, where I worked very hard. Argentina as a whole did rather poorly on this test because those whose parents don't have money go to schools with no budget for anything and teachers that are on strike half the time.

That said, I never had any homework, and the school didn't filter incoming students with tests or whatever, anyone who could pay could get in. However, the work was rather hard. For example, in grade 3, one was expected to write 3-5 pages by hand for a 1-hour test. In Canada, I only started to see that kind of crunch in grade 10.

Just because people with money have access to very good care in the US does not make a good system, which involves the whole population. On average, pretty much all other OECD get better results for far less money (both as a %age of GDP, and in absolute terms).

No, in many cases it's the exact opposite. If you took the stuff people say to each other in Argentina at face value, you'd think the country was made of the most racist and belligerent people on the planet.

Japan's economy has been crippled because of deflation over the last 2 decades, not inflation as you suggest. Any reasonable economic indicator shows that if anything, inflation is too low right now.

I get the feeling Musk & Tesla are purposely drumming up the fire non-story as a way to draw attention away from how they missed expectations last quarter. In particular they lost 38 million last quarter, which is not as much as a year ago. However, with the heavy R&D done, since they got a good car to market, they should start actually generating revenue soon, but they also cut sales forecasts. So the stock tanked because of missing the expectations, but Musk is probably trying to get people to think the drop was due to the fires.

Especially if you load it with rock salt (I had kind of a wild childhood).

Actually, there is a really good reason to use least-squares regression. A model that minimizes squared error is guaranteed to minimize the variance of error, obviously. Now assume that in a model you have taken into account all variables that have real predictive value, and are fairly independent. Then your error should be normally distributed, and randomly over the range of your data by the Central Limit Theorem. So if your data looks like that after fitting the model, your model probably has very good real predictive value. Note that this definitely may not hold for data where there is no clear causative link, I assume that the variables chosen to predict the response have clear reasons to provide predictive value. For example, if trying to predict the yield of a farm, the soil type, rainfall, sun coverage, and so forth clearly have a part in the resulting yield, but what the farmer drinks on a Sunday night might not, so it's best to exclude from the model even if the variable has a p-value0.05.

The Central Limit Theorem doesn't state that the samples are normally distributed, but their mean (average). So the average surface area, volume, and diameter will all be normally distributed for a large sample of independent berries (ie. not from same plants, and so forth).

Seoul is close enough to North Korea that it can be hit with artillery. Hard to imagine them having nuclear weapons but lacking the ability to deliver a EMP weapon a few tens of kilometers away.

In fact, your local Department of Insurance wouldn't allow the insurance company to lower premiums by that much unless there was very strong evidence that the computers would cut claims by at least that. (Rules like that are so that Ponzi schemes can't disguise themselves as insurance companies. That is, a company could undercut all its competition massively without the regulations, and it could pocket big profits in the short term, but long term, as the bulk of the covered people die, and so forth, it would go broke.)

Ontario has no-fault insurance as the standard car insurance now. That means that if you're injured in a car accident, if you get a note from a doctor saying you need something, you get it pretty much right away, and the insurance companies sort out the liability between themselves. So insurance like that would still be useful for automatic cars, especially in a place like the US where with the glacial courts and lack of a proper health-care system it might take years to get the money for physiotherapy, replacement of lost wages, and whatnot.

I live in Ontario, and when I buy from Amazon, I still have to pay HST.

Everyone needs health-care insurance of some form. There's no such thing as a person with no change of getting cancer or something. So catastrophic insurance at least is essential.

IAAA (I Am An Actuary) and, although I do Property & Casualty instead of Health insurance, I just want to set a few things straight.

While some have said that traditionally insurance is based around spreading risk over groups, that still holds currently, even when some are denied coverage. It's just that the risk is spread over a portfolio of similar people. This is the ideal way to set up insurance for companies because by being able to select the cheaper people to insure and putting them all in a portfolio together, they can sell them insurance at a much lower price than someone who has a broad portfolio and charges everyone the same.

For example, if you just get a life insurance policy that requires practically no info, you'll get a lousy policy since there might be a lot of smokers, morbidly obese people, cancer patients, and so forth in the portfolio so of course premiums will be high. However, if you go through a medical check, answer lots of questions, and are found to be very healthy, you'll get a good rate.

Clearly, companies that offer such highly-differentiated policies will steal all of the healthy people from other companies. The other companies will be left with the expensive people by being adversely selected, will lose tons of money, and will either have to differentiate more or go out of business. Note that more expensive people to insure can usually still get policies, but they'll have to pay something more in line with their expected costs. So the net result is that people are charged fairer rates, but are still protected. So with car insurance, better drivers (both with a good track record and those whose profiles make them less likely to get in accidents, going by age, gender, income and so forth) get lower rates and riskier drivers get higher rates.

Applying this to health insurance, healthy people will be protected against the unexpected (cancer, emergency room treatment, and so forth) but will have to pay little since it's unlikely they'll incur high costs. This is good for both those consumers, who get low rates, and for the insurance companies, who undercut the competition by better identifying the people who are cheaper to insure. The flip side of the coin is that more expensive people are deemed uninsurable, as the article states, by smart insurance companies. But this phenomenon isn't unique to health insurance. If you have terminal cancer, good luck getting life insurance. If you live in a place that gets flooded every year, no way a (non-governmental or non-subsidized) insurance company will cover flood damage for your house.

Of course, this ignores arguments about if such things are fair, right, or how a country's health system should be run (and for the record, I live in Canada and think it's ridiculous how the US still doesn't have proper healthcare). Also, the auto insurance industry here in Ontario has undergone a huge change in the last 5 years. Many companies have closed or had to make significant changes due to staggering losses. The losses were due to both increases in costs from accidents, especially in Toronto, and other companies using much better statistical models to undercut the competition and get all the good drivers. Now, in order to stay competitive, companies have to use predictive models to estimate future trends as opposed to just assuming things will stay roughly constant proportionately.