Tag Archives: Project

All Pay Liability

We could raise government revenue much more efficiently than we now do, with less damage to the economy for any given amount of revenue raised. For example, we could tax fixed characteristics like height instead of income, we could tax traffic congestion a lot more, and we could do better at taxing pollution, including carbon. Recently I posted on a more efficient system of property taxes, that allows more revenue to be raised at a lower cost. Today, I’ll post on a more efficient system of accident liability, which similarly raises more revenue at a lower cost.

Some don’t want me to talk about these things. They hope to “starve the beast” by drying up government revenue sources. That seems to me a lost cause, the sort of logic that pushed radicals toward generic destruction, hoping that eventually the masses would get fed up and revolt. I instead expect a better world overall if governments adopt more efficient policies, including more efficient tax policies.

Regarding accident liability, we want a system that will encourage good levels of care and activity by all who can influence accident rates. For example, regarding car accidents we want drivers to pick good car models, speeds, sleep, and maintenance frequencies. We also want them to take into account the possibility of hurting others via accidents when they choose how often they drive. In addition, we want a system that induces fewer actual court cases, which are expensive, and that asks courts to make fewer judgements, in which they might err.

The simplest system is no liability. Courts just don’t get involved. This has the lowest possible rate of court cases, namely zero. It creates good incentives for accident victims to set their care and activity levels well, but gives rather poor incentives for others to set such things well.

The next simplest system is strict liability. This induces good care and activity by potential injurers, but not from potential victims. It also induces a high rate of court cases; nearly every accident results in a lawsuit. While the parties might settle out of court, if a case goes to trial the court must determine responsibility, i.e., who caused the accident, and how much damages the victim suffered as a result.

Relative to strict liability, systems of negligence cut the rate of court cases, but at the cost of asking courts to make more judgements. As with strict liability, courts must judge who is responsible and victim damage levels. But in addition, courts must also ask themselves if that injurer took enough care to prevent the accident. For each of visible parameter, the courts must judge both the actual level of care taken, and the optimal level of care.If the injurer took enough care overall, that injurer does not owe damages. And if that no damages situation is the usual case, there are fewer court cases, as there are fewer lawsuits.

In practice, however, courts can only look at a small number of injurer choice parameters visible enough to them, such as driving speed. Far more parameters, including all injurer activity level parameters, remain invisible, and so are not considered. Negligence doesn’t create good incentives to set all those less visible parameters.

There are standard variations on these systems, such as allowing contributory negligence on the part of the victim. But all of these systems fail to induce optimal levels of care and activity in someone. We have long known, however, of a simple system that gets pretty much all of these things right, and in addition only asks courts to judge who is responsible for an accident and victim damage levels. (I didn’t invent this system; it is mentioned in many law & econ texts.) In this simple system, courts do not need to consider anyone’s actual or ideal levels of care or activity.

This simple system is to make all responsible parties pay the damage levels of all other parties hurt by the accident. The trick is that they pay all of these amounts to the government, instead of to each other. As each party now internalizes all of the damage suffered by all of the parties, they should choose all their private care and activity levels well. And the government gets more revenue to boot.

The big problem with this all-pay liability system is that none of these responsible parties, including the victims, want to report this accident to the government. They’d all rather pretend it didn’t happen. So the government needs some other way to find out about accidents. In dense areas where they government already has access to mass surveillance systems, they can just use those systems. In other areas, governments might offer bounties to third parties who report accidents, and put strong penalties on those who fail to report their own accidents. Or the system might revert to other liability rules in contexts where governments might otherwise detect accidents too infrequently.

With all-pay liability, we expect a lawsuit for every accident. But in that suit the courts only need to judge who is responsible and victim damage levels. No other judgements need be made. So if we could find simple streamlined ways to make these judgements, this system might not be that expensive to administer. And then we’d have both better accident prevention and more available government revenue.

(Yes, people might want to buy insurance against the risk of making these payments. Yes, if multiple parties could coordinate to prevent accidents together, this system might induce them to spend too much on prevention. Hopefully we could identify such efforts and treat them differently.)

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Design A Better Chess

Friday the Wall Street Journal published my review of Garry Kasparov’s new book Deep Thinking. I end with:

I’ve always been a bit skeptical of the high status of chess champions, whom many consider intellectuals (rather than, say, sports stars). But in “Deep Thinking,” Mr. Kasparov has changed my mind. He praises Mikhail Botvinnik, the founder of the Soviet chess school where he trained, for practicing an “intense regime of self-criticism.” Chess champions are rewarded for brutal honesty about their habits and strategies. If only most tenured professors and business executives were this conscious of their limitations and blind spots.

“Few young stars in any discipline are aware of why they excel,” Mr. Kasparov writes. Like Mr. Kasparov, I don’t know why he was great. But I know now why I’m glad we have him. We need at least a few of our most celebrated minds to be this intellectually honest with themselves, and with us.

While all sports reward honesty and self-criticism on your sports performance, in more intellectual sports that honesty can more influence your opinions on more important topics. Which raises the question: can we design a game that promotes even more useful honestly? As I spent some of my youth doing game design, and had a friend who shared that interest, I know that designing games is hard; there are many relevant constraints of which most players are unaware (see the usual literature). For this game design task, all those usual constraints apply, and we must attend to some added criteria:

  • Relevant: We’d like the topics where this game rewards insight and understanding to be closer to the topics that matter, where brutal honesty would be more useful to the world.
  • Fair: Even with relevant topics, the game can’t seem to greatly favor people who by class or culture get much more direct personal info and experience regarding those relevant topics. Anyone should be able to learn the game by playing it.
  • Fragmented: Performance must be broken into many little games, where winning one game gives little or no direct advantage in future games. Thus consistent wins allow strong inferences on underlying ability.
  • Isolated: Players can’t easily get help from hidden allies outside the game.
  • Status: Chess is seen as very high status, because so many high status people have treated it as high status for so long. Somehow this new game needs to have a shot at achieving a status that high.

If these criteria could be met, high capability people might try to achieve status by consistently winning at this game, the opinions they generate on relevant topics might be more honest and accurate, and the rest of us might then be more inclined to listen to those accurate and relevant opinions.

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Trade Engagement?

First, let me invite readers, especially longtime/frequent readers, to suggest topics for me to blog on. I try to pick topics that are important, neglected, and where I can find something original and insightful to say. But I also like to please readers, and maybe I’m forgetting/missing topics that you could point out.

Second, many of my intellectual projects remain limited by a lack of engagement. I can write books, papers, and blog posts, but to have larger intellectual impact I need people to engage my ideas. Not to agree or disagree with them, but to dive into and critique the details of my arguments, and then publicly describe their findings. (Yes, journal referees engage submissions to some extent, but it isn’t remotely enough.)

This is more useful to me when such engagers have more relevant ability, popularity, and/or status. Since I also have modest ability, popularity, and status, at least in some areas, this suggests the possibility of mutually beneficial trade. I engage your neglected ideas and you engage mine. Of course there are many details to work out to arrange such trade.

First, there’s timing. I don’t want to put in lots of work engaging your ideas based on a promise that you’ll later engage mine, and then have you renege. So we may need to start small, back and forth. Or you can go first.

Second, there’s the issue of relative price. If we have differing levels of ability, popularity, and status, then we should agree to differing relative efforts to reflect those differences. If you are more able than I, maybe I should engage several ideas of yours in trade for your only engaging one of mine.

Third, we may disagree about our relevant differences. While it may be easy to quickly demonstrate one’s popularity, status, and overall intelligence, it can be harder to demonstrate one’s other abilities relevant to a particular topic. Yes if I read a bunch of your papers I might be able to see that your ability is higher than your status would suggest, but I might not have time for that.

Fourth, we may each fear adverse selection. Why should I be so stupid as to join a club that would stoop so low as to consider me as a member? The fact that you are seeking to trade for engagement, and willing to consider me as a trading partner, makes me suspect that your ideas, ability, and status are worse than they appear.

Fifth, we might prefer to disguise our engagement trade. When engagement is often a side effect of other processes, then it might look bad to go out of your way to trade engagements. (Trading engagement for money or sex probably looks even worse.) So people may prefer to hide their engagement trades within other process that give plausible deniability about such trades. I just happened to invite you to talk at my seminar series after you invited me to talk at yours; move along, no trade to see here.

These are substantial obstacles, and may together explain the lack of observed engagement trades. Even so, I suspect people haven’t tried very hard to overcome such obstacles, and in the spirit of innovation I’m willing to explore such possibilities, at least a bit. My neglected ideas include em futures, hidden motives, decision markets, irrational disagreement, mangled worlds, and more.

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Community Watchers

In my youth, I was skeptical of things I could not see. Like community social health. Not just physical health, but social health, and not just of individuals, but of communities. But now that I am older and can see more, I am convinced: communities exist, and matter. Not just very visible things like jobs, parks, houses, and stores. But harder to see coalitions, cultures, and norms that influence how people feel about and treat each other.

In some places people more often see when someone is hurting, and try to help. Or stop predators on the prowl. Or see other big changes for mutual gain, and coordinate to achieve them. In other places, these happen less. This sort of community health varies not just from city to city, or firm to firm, but from block to block, and from one cubicle row to cubicle row.

If you live in a place for a while, and you are mature enough to see the local social fabric, then you may see your local social health. And while you might want government to help with this, distant government officials managed by and via formal rules can’t do much. Sincere competent local community activists can do more. But while some can choose to become these, it can be hard for others to tell who they are, to support them. What else can we do?

Many people like to travel, and wish somehow to combine travel with doing good. Many also like the idea of secret societies, especially ones devoted to noble causes. I see an opening here for a secret society of travelers devoted to improving community social health.

The idea is simple: a secret society evaluates the local health of communities they visit, and combines these ratings into a public map. If this map came to be seen as reliable, it could shame poor communities into doing more to improve their health. With residents preferring to move to better communities, land owners would gain stronger incentives to promote improvements.

This would not be easy. Society members must be socially perceptive, stay long enough at each place to evaluate well, overcome temptations to push various other agendas and biases in their evaluations, and avoid detection. And they must find ways to collect new similarly virtuous members, even after their society becomes prestigious. This is a tall order.

But the payoff could be huge: healthier communities. If you try to create this, my only advice is: first collect a big enough map in secret and then test it in many ways for accuracy before going public. It isn’t enough that you hope you will be able to do this; wait until you have actually done it.

From a July 14 conversation with Pete Bertine and Andrew Lockhart.

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Seeking Super Factors

In a factor analysis, one takes a large high-dimensional dataset and finds a low dimensional set of variables that can explain as much as possible of the total variation in that dataset. A big advantage of factor analysis is that it doesn’t require much theoretical knowledge about the nature of the variables in the data or their relations – factors are mostly determined directly by the data.

Factor analysis has had some big successes in helping us to understand how humans differ. As many people know, intelligence is the main factor explaining variation in cognitive test performance, ideology is the main factor explaining variations in political positions, and personality types explain much of the variation in stable attitudes and temperament. These factors have allowed us to greatly advance our understanding of intelligence, ideology, and personality, even while remaining ignorant of their fundamental causes and natures.

However, people vary in far more ways than intelligence, ideology, and personality, and factor analyses have been applied to many of these other human feature categories. For example, there have been factors analyses of jobs, brands, faces, body shape, gait, accent, diet, clothing, writing styleleisure behavior, friendship networks, sleep habitsphysical health, mortality, demography, national cultures, and zip codes.

As my last post on media genre factors showed, factors found in different feature categories are often substantially correlated with one another. This suggests that if we put together a huge super-dataset describing many individual people in as many ways as possible, a factor analysis of this dataset may find important new super-factors that span many of these features domains. Such super-factors would be promising candidates to use in a wide range of social research, and social policy.

Now it remains logically possible that these super-factors will end up being simple linear combinations of the factors that we have already found in each of these feature categories. Maybe we already know most of what there is to know about how humans vary. But I’d bet strongly and heavily against this. The rate at which we have been learning new things about how humans vary doesn’t remotely suggest we’ve run out of new big things to learn. Yes, merely knowing the super-factors isn’t the same as understanding their origins. But just as we’ve seen with factor analysis in more specific areas, knowing the main factors can be a big help.

So I’d guess that the super-factors found in a super dataset of human details will be revolutionary developments. We will afterward see uncovering them as a seminal milestone in our progress in understanding human variation. A Nobel prize worthy level of seminality. All it will take is lots of tedious work to collect a super dataset, and then do some straightforward number crunching. A quest awaits; who will rise to the challenge?

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Failed Singularity Model

Noted Yale economist William Nordhaus has a new paper “Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth”:

Assume that labor is constant, that all technological change is capital-augmenting at 10% per year, and that the elasticity of substitution between labor and information capital is 1.25. Figure 3 shows a typical simulation of the share of capital and the growth rates of output and wages.

NordhausSingularityIn this model, capital slowly gets a larger share of total income and the economic growth accelerates, even though the rate of innovation never changes. Nordhaus lists six empirical predictions for the sign of observed parameters, and finds that four of the six are rejected by our best estimates having the opposite sign. And this doesn’t include the fact that our best estimates find the elasticity of substitution between labor and capital to be less than one. The two sign predictions that match the data suggest it would take a century or more before growth rates exceed 20% per year. Nordhaus says, “The conclusion is therefore that the growth Singularity is not near.”

Of course this is far from the only possible economic model of a singularity. But it sets a good standard for future efforts. Can anyone find a concrete simple economic model of singularity that better fits the data?

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The Data We Need

Almost all research into human behavior focuses on particular behaviors. (Yes, not extremely particular, but also not extremely general.) For example, an academic journal article might focus on professional licensing of dentists, incentive contracts for teachers, how Walmart changes small towns, whether diabetes patients take their medicine, how much we spend on xmas presents, or if there are fewer modern wars between democracies. Academics become experts in such particular areas.

After people have read many articles on many particular kinds of human behavior, they often express opinions about larger aggregates of human behavior. They say that government policy tends to favor the rich, that people would be happier with less government, that the young don’t listen enough to the old, that supply and demand is a good first approximation, that people are more selfish than they claim, or that most people do most things with an eye to signaling. Yes, people often express opinions on these broader subjects before they read many articles, and their opinions change suspiciously little as a result of reading many articles. But even so, if asked to justify their more general views academics usually point to a sampling of particular articles.

Much of my intellectual life in the last decade has been spent in the mode of collecting many specific results, and trying to fit them into larger simpler pictures of human behavior. So both I and the academics I’m describing above in essence present themselves as using these many results presented in academic papers about particular human behaviors as data to support their broader inferences about human behavior. But we do almost all of this informally, via our vague impressionistic memories of what has been the gist of the many articles we’ve read, and our intuitions about what more general claims seem how consistent with those particulars.

Of course there is nothing especially wrong with intuitively matching data and theory; it is what we humans evolved to do, and we wouldn’t be such a successful species if we couldn’t at least do it tolerably well sometimes. It takes time and effort to turn complex experiences into precise sharable data sets, and to turn our theoretical intuitions into precise testable formal theories. Such efforts aren’t always worth the bother.

But most of these academic papers on particular human behaviors do in fact pay the bother to substantially formalize their data, their theories, or both. And if it is worth the bother to do this for all of these particular behaviors, it is hard to see why it isn’t be worth the bother for the broader generalizations we make from them. Thus I propose: let’s create formal data sets where the data points are particular categories of human behavior.

To make my proposal clearer let’s for now restrict attention to explaining government regulatory policies. We could create a data set where the datums are particular kinds of products and services that governments now provide, subsidize, tax, advise, restrict, etc. For such datums we could start to collect features about them into a formal data set. Such features could say how long that sort of thing has been going on, how widely it is practiced around the world, how variable has been that practice over space and time, how familiar are ordinary people today with its details, what sort of justifications do people offer for it, what sort of emotional associations do people have with it, how much do we spend on it, and so on. We might also include anything we know about how such things correlate with age, gender, wealth, latitude, etc.

Generalizing to human behavior more broadly, we could collect a data set of particular behaviors, many of which seem puzzling at least to someone. I often post on this blog about puzzling behaviors. Each such category of behaviors could be one or more data points in this data set. And relevant features to code about those behaviors could be drawn from the features we tend to invoke when we try to explain those behaviors. Such as how common is that behavior, how much repeated experience do people have with it, how much do they get to see about the behavior of others, how strong are the emotional associations, how much would it make people look bad to admit to particular motives, and so on.

Now all this is of course much easier said than done. Is it a lot of work to look up various papers and summarize their key results as entries in this data set, or just to look at real world behaviors and put them into simple categories. It is also work to think carefully about how to usefully divide up the space of actions and features. First efforts will no doubt get it wrong in part, and have to be partially redone. But this is the sort of work that usually goes into all the academic papers on particular behaviors. Yes it is work, but if those particular efforts are worth the bother, then this should be as well.

As a first cut, I’d suggest just picking some more limited category, such as perhaps government regulations, collecting some plausible data points, making some guesses about what useful features might be, and then just doing a quick survey of some social scientists where they each fill in the data table with their best guesses for data point features. If you ask enough people, you can average out a lot of individual noise, and at least have a data set about what social scientists think are features of items in this area. With this you could start to do some exploratory data analysis, and start to think about what theories might well account for the patterns you see.

Now one obvious problem with my proposal is that while it looks time consuming and tedious, it isn’t obviously impressive. Researchers who specialize in particular areas will complain about your data entries related to their areas, and you won’t be able to satisfy them all. So you will end up with a chorus of critics saying your data is all wrong, and your efforts will look too low brow to cower them with your impressive tech. So I can see why this hasn’t been done much. Even so, I think this is the data set we need.

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Imagine Libertopia

In this post I’ll talk primarily to people who, like me, lean libertarian. The rest of you can take a break.

Libertarians want to move more products and services from being provided directly by government, to being provided privately. And for those that are provided privately, libertarians want to weaken regulations. These changes would increase liberty.

Libertarians tend to offer arguments that are relatively abstract and theory-based. That is, they focus more on why more liberty is more moral, or why it should in theory give better outcomes. They focus less on showing that liberty has in practice worked out better. When libertarians do focus on data, they tend to be very broad, or randomly specific. That is, they talk about how West Germany is better than East Germany, or South Korea better than North Korea. Or they pick on very specific examples, like regulations limiting eyeglass ads, and leave audiences wondering how cherry-picked are such examples.

It seems to me that libertarians focus too much on trying to argue abstractly that liberty would be better, and not enough on just concretely describing how liberty would be different. Yes for you the abstract arguments seem best; they persuade you plenty, and they bring the most prestige in your circle. But typical libertarians today are a distinct personality type; most people are not like you. Most people just cannot be comfortable with a proposal for change if they cannot imagine it in some detail, and imagine that they’d like that detail. Such people don’t need more abstract arguments and examples; they instead credible concrete descriptions.

True, people have sometimes written fiction set in libertarian settings. But such fiction doesn’t usually come with a careful analysis of why one should believe in its many details. Yes, part of the attraction of liberty is that it frees up people to innovate in ways that one can’t anticipate in advance. But that doesn’t mean that we can’t go a long way to better describe a world of more liberty.

On reflection, I realize that when I try to imagine more liberty, I mostly draw on a limited set of iconic comparisons, such as comparing airlines, trucks, and phones before and after US deregulation, or comparing public to private schools and mail in the US. Alas, we and our audiences should worry that we cherry-pick such examples to support conclusions we like.

We should be able to do much better than this. By now there are vast literatures discussing many industries in many places before and after regulation or deregulation, and describing specific times and places where certain products and services provided directly by governments, or provided privately. From this vast literature we should be able to identify many concrete patterns and “stylized facts” about how government-provision and heavy-regulation tends to change products and services.

I recall these suggestions for typical features of industries with more liberty:

  1. Less “gold-plating” in materials and methods
  2. More product variety, including more low quality versions
  3. Faster innovation and product cycles
  4. Fewer guarantees to workers or customers
  5. Price, features vary more with customer features
  6. Workers have less school and seniority
  7. Less overhead spend on paperwork
  8. more?

Some people should work to extract patterns like these from our vast related literatures – I’ve looked, and there just aren’t many such summaries today. With such patterns in hand, we would be in a much better position to credibly describe how familiar products and services would concretely change if we were to provide them privately, or to regulate them less. And such credible concrete descriptions might allow many more people to become comfortable with endorsing such expansions of liberty.

This sort of project seems well within the abilities of the median grad student. It doesn’t require great creativity or technical skills. Instead, it just requires methodically surveying and summarizing related literatures. Perhaps some libertarian students should shy away from it in hopes of impressing via more difficult methods. But surely there must be other students for which this sort of project is a good match.

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The Why-Policy-Wiki

“Normative as positive” (NAP) — explaining that the [education] policies actually chosen were chosen because they maximize an individualized social welfare function — fails as a useful general positive model of schooling. While NAP can perhaps accommodate the fact of some direct production of schooling by some governments, the reality is that (nearly) all governments produce education and that, by and large, this is their only support to education. Moreover, NAP fails not just in the large but also the small: there are six additional common facts about educational policies inconsistent with NAP. (more; HT Bryan Caplan)

That is Lant Pritchett, and I share his frustration. People usually explain their government’s policies via scenarios wherein such policies would help the world, or at least their local region. But when you point out details at odds with such simple stories, such people are usually uninterested in the subject. They switch to suggesting other scenarios or problems where policy might help, also with little interest in the details.

This evasive style, i.e., the habit of pointing to a diffuse space of possible scenarios and problems instead of particular ones, is a huge obstacle to critics. If you put a lot of time in critiquing one story, people just note that there are lots of other possible stories you didn’t critique.

This style helps people maintain idealist attitudes toward institutions they like. In contrast, people do the opposite for institutions they dislike, such as rival foreign governments or profit-making firms. In those cases, people prefer cynical explanations. For example, people say that firms advertise mainly to fool folks into buying products they don’t need. But the evasion remains; if you critique one cynical explanation they switch to others, avoiding discussing details about any one.

To solve this evasion problem, I propose we create a new kind of wiki that surveys opinions on policy explanations. In this new wiki readers could find items like ” 68% (162/238) of college graduates say the best explanation of government running schools, instead of subsidizing them, is because educated citizens can pay more taxes to benefit other citizens. 54% (7/13) of economics PhDs surveyed say it is to push propaganda.”

Here is how it would work. There would be three category hierarchies: of policies, of policy explanations, and of people with opinions on policy explanations. Each hierarchy would include a few very general categories near the top, and lots of much more specific categories toward the bottom.

Anyone could come to the wiki to contribute opinions on policy explanations. They would first give some demographic info on themselves, and that info would put them somewhere in the category hierarchy of people. They could then browse the category hierarchy of policies, picking a policy to explain. Finally, they could browse the category hierarchy of explanations, picking their favored explanation of that policy.

Users could start by being shown the most common explanation offered so far for similar policies by similar people, and then browsing away from that. Users could also expand the category hierarchies, to add more specific policies and explanations. For particular policy explanation pairs, users might add links to relevant theory, evidence, and arguments. Users might also upvote links added by others. This would help later readers search for well-voted evidence and theory close in the hierarchies to any given policy explanation.

By using category hierarchies, a wide range of people could express a wide range of opinions. Experts could dive into details while those who can barely understand the most basic categories could gesture crudely in their favored directions. Given such a wiki, a critic could focus their efforts on the most popular explanations for a policy by their target audience, and avoid the usual quick evasion to other explanations. Prediction markets tied to this wiki could let people bet that particular explanations won’t hold up well to criticism, or that popular opinion on a topic will drift toward a certain sort of expert opinion.

Of course this wiki could and should also be used to explain common policies of firms, clubs, families, and even individuals. I expect some editorial work to be needed, to organize sensible category hierarchies. But if good editors start the system with good starting hierarchies, the continuing editorial work probably wouldn’t be prohibitive.

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Why National Med?

People offer many noble rationales for public education, but the data suggest they were adopted to create patriotic citizens for war. I suspect a similar data analysis could show why so many nations have recently adopted national medical systems:

Even as Americans debate … Obama’s healthcare law and its promise of guaranteed health coverage, … many far less affluent nations are moving in the opposite direction – to provide medical insurance to all nations.

China … is on track to .. cover more than 90 percent of the nation’s residents. … Two decades ago, many former communist countries … dismantled their universal health-care systems amid a drive to set up free-market economies. but popular demand for insurance protection has fueled an effort in nearly all these countries to rebuild their systems. Similar pressure is coming from the citizens of fast-growing nations int Asia and Latin America. …

Some countries have set up public systems like those in Great Britain and Canada. But many others are relying on a mix of government and commercial insurance, as in the United States. …

In countries such as India, politicians have learned that one of the surest says to secure votes is to promise better access to health care.  … The Thai system, set up a decade ago, has survived years of political upheaval and a military coup. “No party dares touch it.” …

Columbia’s universal system, set up in 1993, has cost more than twice what as expected.  (Today’s Post, article by Levey, p. A11; link will go here when available)

My guess: for our distant ancestors, medicine was a way to show that they care about each other. So today there is a demand for medicine to be provided by units of organization toward which we, or they, want us to feel solidarity. But I’m not sure what are the most direct and proximate causes of such a need for solidarity.

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