Publication Bias In Climate Science

Some recent research I've been doing has lead to an interesting experience. I'm always frustrated at the way science is communicated to the public. This was another example of something that disappointed me.

I was trying to figure out if there is publication bias in climate science. More specifically, I was looking for a funnel plot for the "climate sensitivity," something that would quickly and graphically show that there is a bias toward publishing more extreme sensitivity values.

Climate sensitivity is the response of the Earth's average temperature to the concentration of CO2 in the atmosphere. The relationship is logarithmic, so a doubling of CO2 will cause an X-degree increase in average temperature. To increase it another X-degrees would require another doubling, and so on. Obviously there are diminishing returns here. It takes a lot of CO2 to keep increasing the Earth's temperature.

If we focus on just the contribution from CO2 and ignore feedback, this problem is perfectly tractable and has an answer that can be calculated by paper and pencil. In fact Arrhenius did so in the 19th century. (He even raved about how beneficial an increase in the Earth's temperature would be, but obviously many modern scientists disagree with his optimism.) A doubling of atmospheric carbon gets you a 1º Celsius increase in average temperature. The problem is that carbon is only part of the story. That temperature increase leads to there being more water vapor in the atmosphere, and water vapor is itself a very powerful greenhouse gas. So the contribution from water vapor amplifies the contribution from carbon, so the story goes. This doesn't go on forever in an infinite feedback, but "converges" to some value. There are other feedbacks, too, but my understanding is that water vapor is the dominant amplifier.

This is a live debate. Is the true climate sensitivity closer to 1º C per doubling of CO2, or 3º (a common answer), or 6º (an extreme scenario)? This is what I was looking for: a funnel plot of published estimates for the climate sensitivity would reveal publication bias.

I found this paper, titled "Publication Bias in Measuring Climate Sensitivity" by Reckova and Irsova, which appeared to answer my question. (This link should open a pdf of the full paper.)

Figure 2 from their paper shows an idealized funnel plot:

If all circles are actually represented in the relevant scientific literature, there is no publication bias. But if the white circles are missing, an obvious publication bias is present. The idea here is that for lower-precision estimates (with a higher standard error), you will get a big spread of estimates. But journal editors and perhaps the researchers themselves are only interested in effects above a certain size. (Say, only positive effects are interesting and negative effects are thrown out. Or perhaps only climate sensitivities above 3º per doubling of CO2 will ever see the light of day, while analyses finding smaller values will get shoved into a file drawer and never be published.) In fact, here is what the plot looked like for 48 estimates from 16 studies:

It looks like there is publication bias. You can tell from the graph that 1) low-precision low-sensitivity estimates (the lower-left part of the funnel) are missing and 2) the more precise estimates indicate a lower sensitivity. The paper actually builds a statistical model so that you don't have to rely on eye-balling it. The model gives an estimate of the "true" climate sensitivity, correcting for publication bias. From the paper: “After correction for publication bias, the best estimate assumes that the mean climate sensitivity equals 1.6 with a 95% confidence interval (1.246, 1.989).” And this is from a sample with a mean sensitivity of  3.27: “The estimates of climate sensitivity range from 0.7 to 10.4, with an average of 3.27.” So, at least within this sample of the climate literature, the climate sensitivity was being overstated by a factor of two. The corrected sensitivity is half the average of published estimates (again, from an admittedly small sample).

I read this and concluded that there was probably a publication bias in the climate literature and it probably overstates the amount of warming that's coming. Then I found another paper titled "No evidence of publication bias in climate change science." You can read the entire thing here.

My first impression here was, "Oh, Jeez, we have dueling studies now." Someone writes a paper with a sound methodology casting doubt on the more extreme warming scenarios. It might even be read as impugning the integrity or disinterestedness of the scientists in this field. Of course someone is going to come up with a "better" study and try to refute it, to show that there isn't any publication bias and that the higher estimates for climate sensitivity are more plausible. But I actually read this second paper in its entirety and I don't think that's what's happening. We don't have dueling studies here. Despite the title, the article actually does find evidence of publication bias, and it largely bolsters the argument of the first paper. Don't take my word for it. Here are a few excerpts from the paper itself:

Before Climategate, reported effect sizes were significantly larger in article abstracts than in the main body of articles, suggesting a systematic bias in how authors are communicating results in scientific articles: Large, significant effects were emphasized where readers are most likely to see them (in abstracts), whereas small or non-significant effects were more often found in the technical results sections where we presume they are less likely to be seen by the majority of readers, especially non-scientists.

 Sounds kind of "biased" to me.

Journals with an impact factor greater than 9 published significantly larger effect sizes than journals with an impact factor of less than 9 (Fig. 3). Regardless of the impact factor, journals reported significantly larger effect sizes in abstracts than in the main body of articles; however, the difference between mean effects in abstracts versus body of articles was greater for journals with higher impact factors.

So more prestigious journals report bigger effect sizes. This is consistent with the other study linked to above, the one claiming there is publication bias.

From the Discussion section of the paper:

Our meta-analysis did not find evidence of small, statistically non-significant results being under-reported in our sample of climate change articles. This result opposes findings by Michaels (2008) and Reckova and Irsova (2015), which both found publication bias in the global climate change literature, albeit with a smaller sample size for their meta-analysis and in other sub-disciplines of climate change science.

I found the framing here to be obnoxious and incredibly misleading. The Michael’s and the Reckova and Irsova paper (the later linked to above) both found significant publication bias in top journals, and the “No evidence of publication bias” paper found essentially the same thing. In fact, here is the very next part:

Michaels (2008) examined articles from Nature and Science exclusively, and therefore, his results were influenced strongly by the editorial position of these high impact factor journals with respect to reporting climate change issues. We believe that the results presented here have added value because we sampled a broader range of journals, including some with relatively low impact factor, which is probably a better representation of potential biases across the entire field of study. Moreover, several end users and stakeholders of science, including other scientists and public officials, base their research and opinions on a much broader suite of journals than Nature and Science.

So this new paper looking at a larger collection of publications and published estimates confirmed that top journals publish higher effect sizes. It’s almost like they said, “We did a more thorough search in the literature and we found all those missing points on the funnel plot in Reckova and Irsova.” See the effect size plot, which is figure 3 in the paper:

Notice that for the full collection of estimates (the left-most line marked "N = 1042"), the average estimate is close to the 1.6 estimate from the other paper. Essentially, the first paper said, “We found a bias in top-level, high-visibility journals. We filled in the funnel plot using a statistical model and got a best estimate of 1.6.” And the second paper said, “We found a bias in top-level, high-visibility journals. We filled in the funnel plot by looking at more obscure journals and scouring the contents of the papers more thoroughly and got a best estimate of 1.6.” The later paper should have acknowledged that it was coming to a similar conclusion to the Reckova and Irsova paper. But if you just read the title and the abstract, you’d be misled into thinking this new “better” study refuted the old one. If you Google the name of the paper to find some media reports on it, you will see that some reviewers read the title only, or shallowly skimmed the contents and didn’t read the papers it’s commenting on.

 Here is more from the Discussion section:

We also discovered a temporal pattern to reporting biases, which appeared to be related to seminal events in the climate change community and may reflect a socio-economic driver in the publication record. First, there was a conspicuous rise in the number of climate change publications in the 2 years following IPCC 2007, which likely reflects the rise in popularity (among public and funding agencies) for this field of research and the increased appetite among journal editors to publish these articles. Concurrent with increased publication rates was an increase in reported effect sizes in abstracts. Perhaps a coincidence, the apparent popularity of climate change articles (i.e., number of published articles and reported effect sizes) plummeted shortly after Climategate, when the world media focused its scrutiny on this field of research, and perhaps, popularity in this field waned (Fig. 1). After Climategate, reported effect sizes also dropped, as did the difference in effects reported in abstracts versus main body of articles. The positive effect we see post IPCC 2007, and the negative effect post Climategate, may illustrate a combined effect of editors’ or referees’ publication choices and researchers’ propensity to submit articles or not.

Remember, this is from a paper titled “No evidence of publication bias in climate change science.” Incredibly misleading. This entire paragraph is about how social influences and specific events have affected what climate journals are willing to publish. 

“What is the true climate sensitivity?” is really a central question to the climate debate. The 3⁰ C figure is frequently claimed by advocates of climate interventionists (people pushing a carbon tax, de-industrialization, etc.), but the 1.6⁰ C figure is more plausible if you believe there’s a publication bias at work. The actual concentration of carbon has gone from 280 parts per million in pre-industrial times to 380 parts per million today, and the global average temperature has risen by about 0.8⁰ C. (Maybe it's actually more than 0.8⁰ C; 2015 and 2016 were record years and some commentators are extremely touchy about this point. Apologies if I'm missing something important here, but then again any conclusion that depends on two data-points is probably not very robust.) If the sensitivity is low, then we can keep emitting carbon and it’s really no big deal. If water vapor significantly amplifies the effect of carbon, then we’ll get more warming per CO2 doubling. There is a related question of “How much warming would it take to be harmful?” To do any kind of cost-benefit analysis on carbon reduction we’d need to know that, too. But clearly the sensitivity question is central to the climate change issue. If there’s any sort of publication bias, we need to figure out how to correct for it. People who cite individual papers (because they like that particular paper) or rely on raw averages of top journals need to be reminded of the bias and shamed into correcting for it, or at the very least they need to acknowledge it.

This is just the beginning of a new literature, I’m sure. There will be new papers that claim to have a “better” methodology, fancier statistics, and a bigger sample size. Or perhaps there will be various fancy methods to re-weight different observations based on…whatever. Or different statistical specifications might shift the best point estimate for the climate sensitivity. (I can imagine a paper justifying a skewed funnel plot because the error is heterosketastic: “Our regression assumed a non-normal distribution, because for physical reasons the funnel plot is not expected to be symmetric…”) I’m hoping this isn’t the case, but I could easily imagine a world where there are enough nobs to tweak and levers to pull that we’ll just get dueling studies forever. There are enough "researcher degrees of freedom" that everybody can come to their preconceived conclusion while convincing themselves they are doing sound statistics. Nobody will be able to definitively decide this question of publication bias, but each new study will claim to answer the critics of the previous study and prove, once and for all, that publication bias does exist (oops, I mean doesn’t exist). My apologies, but sometimes I’m an epistemic nihilist. 

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It seems weird to me that there are only a few publications on publication bias in the climate sciences. "Publication Bias in Measuring Climate Sensitivity" was published in September 2015, and "No evidence of publication bias in climate change science" was published in February 2017. I remember trying to search for the funnel plot in early 2015 and not finding it. Possibly the September 2015 paper was the first paper ever to publish such a plot for climate sensitivity. If there is a deeper, broader literature on this topic and it comes to a different conclusion, I apologize for an irrelevant post. (Sometimes the literature is out there, but you just don't know the proper cant with which to search it.) But it looks like these two papers are the cutting edge in this particular vein. If more studies come out, I'll try to keep up with them. 

Nobody Is “Forced” To Live Under Capitalism

 I saw the phrase “…being forced to live under capitalism...” on Facebook recently. It was part of a meme from one of those click-baity left-wing pages, probably “Being Liberal” or something similar. Possibly a gullible friend had shared it. I immediately thought, Wow, what a whopping non sequitur of a concept.

If we take “capitalism” to mean free markets and free association between consenting adults, then no dear, you aren’t being “forced” in any meaningful sense. Rather, you live in a world where basic human freedoms are respected and you don’t care for the shape that it takes. You dislike some of the features of this world, but reshaping it to meet your approval would require actual force. You perhaps don’t approve of some of the choices and decisions that other adults make. But that’s the flip-side of freedom: other people get to exercise it, too. Freedom is a grand compromise. I cannot dictate the terms of your marriage contract, and you cannot dictate the terms of my labor contract. Your basic freedoms of association with other adults do not suddenly come to an end the moment money changes hands. Supposing you have a basic human right to privacy and freedom of association, you retain those rights when you transact commercially.

Of course the term “capitalism” is loaded. It has many definitions and carries a lot of baggage. Some use the term to mean “crony capitalism”, a system in which the government explicitly grants favors to certain businesses and industries at the expense of everyone else. This is nearly the opposite of what free-market supporters mean when they say the word. So any argument about “capitalism” should specify which sense of the word they mean. If it does mean “crony capitalism”, then indeed I am forced to live under “capitalism” and I object to it, too. I’d rather do away with the special privileges granted to certain players (import quotas, subsidies, implicit insurance via government bailouts, etc.). But a business operating in a truly free market is not being "privileged" in any meaningful sense. Businesses operating under free-market capitalism can only make offers to their customers and potential employees; the customers and employees have the power to unilaterally deny them the terms offered. You may dislike some of the terms being offered, but it is bizarre to describe this state of affairs as "forced to live under capitalism."

Perhaps to some the term does indeed mean “free markets” but it somehow implies an obsession with material wealth or betrays sympathy with businesses and capital owners. Such insinuations about motives and sympathies are beside the point (in addition to being extremely rude). Suppose I offer an argument that minimum wages and other labor “protections” are bad for workers. They restrict worker options and force them into terms that they otherwise wouldn’t choose for themselves, and they fail to transfer income from capital owners to workers. So the argument goes, anyway. Suppose I make an extended, data-rich presentation of this argument replete with historical examples. Does it matter that deep down in my dark heart I secretly carry a torch for the capital owners? Or that I hold some sinister antipathy toward the working man? Do you have to worry that such insidious sympathies have biased my analysis? No, you can check my work. We can talk impersonally and disinterestedly about the merits of policy without implying a wicked motive or perverse sympathies. Now, if I simply asserted “Free markets are for the best. Trust me, I’m some kind of expert!” and that was my entire appeal, you’d be right to point out something questionable about my motives. But if I’ve offered an impersonal argument for my position, you can check it for yourself. Motive-questioning is a bad faith move.

I actually have no idea what the person who shared this was thinking. Maybe s/he just flippantly hit the "share" button without giving it any thought. Maybe the main point was some other part of the quote, and I'm fixating on an irrelevant, throw-away piece that stuck out like a sore thumb. But I am increasingly seeing denunciations of "capitalism" and support for full-on socialism on my Facebook feed and it disturbs me. It's like some people don't realize that the 20th century happened. 

Income Inequality Is a Nonsense Concept

I’m imagining someone comparing me to one of my peers and describing the difference in life outcomes as “income inequality.” This is essentially what is happening when someone discusses inequality as a statistical abstraction. It’s always in a tone of “See this! There are huge discrepancies and it’s a big mystery why they exist.” I usually don’t share this, but my gut reaction is usually something like, “You and I went to the same school at the same time as me. Any divergence between you and me is a result of our different choices. I graduated high school, went to college, picked a STEM major, finished grad school with good grades, and completed a series of grueling industry exams. For whatever reasons, you did something else.”

I respect other adults and I don’t want to second-guess anyone’s decisions. I assume that if someone picks a bullshit major in college or picks an easy career path that doesn’t require much technical knowledge or specialization, they have a good reason. This person is simply picking a different mix of leisure and income than I picked. Or this person chose not to “sacrifice” the best party years of their late teens and early twenties hunkered down studying in pursuit of a real career. Someone with similar options and advantages made a different series of trade-offs.

The “income inequality” framing misses all of this. It implicitly blames the high-earners for the low earnings of everyone else. It strongly implies a zero-sum worldview where the wealth of the wealthy derives from the poverty of the poor. It assumes away all the choices that people make that actually determine their future career path (and thus their annual income). The inequality framing pretends that there is some fixed basket of stuff that gets divided up based on some arbitrary statistical distribution, and that we (“We, as a society…” as so many of these conversations start) can simply change the shape of that distribution by fiat.

I want to say, “Hey, man, I’m sorry your life didn’t turn out the way you wanted. Maybe we could have talked about this stuff back when you switched from a math major to a P.E. major. I didn’t realize I was on the hook for your bad decisions. Had I known at the time, I would have insisted on some changes.” That’s not to say I want to dictate the terms of anyone’s career trajectory. I really don’t. Nor is this to say I don’t want to be on the hook for someone else’s bad luck. I quite willingly put myself on the hook for the bad luck of thousands of other people, and I will effectively pay them a huge sum if they have a crippling injury, house fire, early death, or devastating car accident. I do this through various intermediaries: my health, homeowners, life, and auto insurance policies. And I’m fine with offering some sort of charitable aid to people who have uninsured misfortunes happen to them. What I’m not fine with is being put on the hook for the predictable bad consequences of poor decision making, and then being told that those consequences are my fault. 

All Races Have Two Mammae!

When I was in high school I played Shadowrun with a few friends. It’s a roleplaying game that takes place in a cyberpunk future. You could play as a human, elf, orc, troll, or dwarf. I remember my friends giggling over the Shadowrun rule book’s descriptions of the different races. Each race had a description of game-relevant stats (+4 strength, +6 body, -2 charisma, etc.), along with various other attributes like average height and weight. One item listed for each race was “2 mammae,” mammae being an obscure term for mammary gland. That is, FASA corporation (Shadowrun’s creator) saw fit to remind Shadowrun players that each race had two boobs. They did this even though this was a common feature to all races. I’m imagining a committee meeting at FASA as the player’s guide was being written:

Committee Note-taker: Okay, next race. Elf. Average height 6’1”,  average weight 160 lbs, 32 teeth. Anything else, guys? (a hand shoots up, note-taker emits a long-suffering sigh) Yes, Jenkins? 

Jenkins: Two mammae. 

CN: Dammit, Jenkins! All races have two mammae. 

Jenkins: Not necessarily! 

CN: Look, if we do the “two mammae” thing, the players are going to think FASA is staffed by a bunch of incorrigible boob-fiends. 

Jenkins: I’m just saying, people will be wondering. Like, does a troll just have two human-like boobs, or two long rows of nips like a nursing sow? 

CN: Okay, show of hands on the “2 mammae” thing? (Jenkins’ hand goes up, nobody else’s does). Overruled. (Pulls a sheet of paper out of a manila envelope.) Next race, the… twelve-titted wood nymph? Dammit Jenkins!

The Economics of Careless Employees and Misplaced Orders

I witnessed a scene recently that made me uncomfortable, but it inspired quite a lot of thought. Here’s what happened. I ordered sushi at a local place and went to pick it up. When I got my order, the girl who handed it to me called off the items I had ordered. The last item was “tekka-don”, tuna over rice. I had actually ordered “sake-don”, salmon over rice, and I said so. The owner, who is also the sushi chef, looked annoyed and started making my order. The girl apologized, “I’m sorry.” The owners said, “No ‘sorry.’ You pay.” As in, he was going to make her pay for the wrong order, presumably unless someone else ordered it in the next hour or so. It seemed really unfair. It was certainly uncomfortable and cast a pall over the entire restaurant.

So I felt really sorry for this poor girl, who made an honest mistake. At first I felt bad and thought, geez, I should just pay for the tekka don. But I remembered specifying over the phone, “Sake-don. It’s salmon over rice.” The two sound similar over the phone, especially if you are working in a busy kitchen. That’s why I explained what the item actually was after saying its name. There’s quite a lot of employee turnover at this place, and I’ve had to explain my order to a lot of new employees. It wasn’t my fault. The girl really did screw up because she wasn't being very attentive. Still, I thought the owner, not the employee, should eat the cost of the occasional misheard order.

Then I thought about all those stupid “outrage” stories that show up on my Facebook feed, where some supposed injustice happens over a trivial infraction. If you dig into these stories, you often find that the infraction that triggered the outrageous response was just the straw that broke the camel’s back. In other words, maybe this was the third or fourth wrong order. Sushi is expensive. Time spent wasted on a $15 menu item is time not spent making another $15 menu item. That’s lost revenue. This guy is always moving when I’m there to pick up my order, so wasting his time is a big deal. Plus there is the cost of the fish that gets wasted. (Economics quiz: Am I double-counting here to add the lost revenue to the wasted fish?) A sushi chef with employees has to somehow make sure they are making as few mistakes as possible. Sometimes that means being very blunt and punitive with employees. They are handling valuable merchandise, so they need to show appropriate care.

I doubt that he ever charged her anyway. Maybe he said it to scare her, or maybe he intended to make her pay but thought the better of it. I’m guessing she got to the end of her shift and just left. He didn’t actually pull her aside and ring her up. Or maybe he did. Or maybe he actually made some kind of deduction on her pay stub. But this potentially runs afoul of some sort of labor law, and maybe he thought the better of creating a paper trail proving he violated such a law. At any rate, I think someone in his situation can’t just say, “Oh, that’s okay” when a careless employee costs him $15. He has to make sure that those correctable errors get corrected, as much as is feasible. She’s still working there, and she double-checks my order every time now. As unfair and humiliating as the treatment seemed, she decided to keep her job. Whatever "mistreatment" she had to endure, she apparently decided that the job was worth it.

Is it even legal to charge an employee for a misplaced order? Should it be? Shouldn’t I, as a libertarian, oppose any such labor laws restricting this practice? Is there an implied contract between employer and employee that forbids such a punishment? What is the “common law” that rules here? If she were to sue over the violation of such an implied contract, how would that case be adjudicated? And is the existing common law correct? I don’t know the answers to any of the legal questions, but these were some stray thoughts that came into my head. If you came here looking for moral clarity, I don’t have any to offer. But I’m quite certain that my initial reaction of “That’s completely unfair!” was mistaken. Or at the very least, it was far too simple. 

Health Insurance Should Be More Like Auto and Life Insurance

Health insurance would be a hell of a lot more affordable and the market would be a lot more competitive if its policies had some features that are common to auto and life insurance.

Longer Policy Terms

I have a life insurance policy whose term is measured in decades. It will pay my wife about three years’ worth of my annual salary if I die during that term. They cannot cancel the policy or change my premiums if my health status changes and the likelihood of a payout suddenly increases dramatically. My premium is locked in for the term. This works fine, because the premium already has integrated into it the chance that I will get sicker and die at some point during the policy term. Since my policy is priced based on my all-cause mortality risk at the beginning of the term, if I suddenly get cancer my insurer says, “It’s fine, I planned for this. You’re covered, buddy!” They might secretly wish I would cancel my policy or forget to collect, but the only reason they have customers in the first place is that they actually pay out as promised when their policyholders die. They certainly don’t want a reputation of weaseling out of their contractual obligations.

We’re talking about an insurance policy here with a huge payout (six figures), a very long policy term, and premiums at just over $20 per pay period. In other words, it costs me just over $500 annually, easily affordable for a middle class family. A catastrophic health plan that does the same would probably have a higher frequency (major health episodes are considerably more frequent than actual deaths) but lower severity (the average surgery is probably closer to four or five figures, not six). The premium, which is based on the expected cost (frequency of a claim x average severity of a claim, plus operational expenses), could be higher or lower than for a life policy, but should be in a similar range. The example of life insurance suggests that catastrophic health insurance with long policy terms should be affordable. Such insurance policies would not be budget-straining disasters like existing health “insurance” policies, which cover a lot of elective and routine expenses.

Liabilities Don’t Transfer To A New Insurance Policy

If I maim someone with my car today, my current auto insurance policy owns that liability. If the guy I maimed has a series of surgeries related to his injuries for the next ten years, my insurer still owns that liability forever (at least until his expenses exceeds the limits of liability on my auto policy). If I shop around next year and switch insurance companies, that liability does not follow me. My new policy will cover future maimings that occur during the policy period, but not past maimings. I will be charged slightly more because I now have a bad driving history, which is predictive of future accidents. But it will be nowhere near the cost of paying my victim’s medical bills. This is how health insurance should work. It’s absolutely crazy that it doesn’t actually work this way. If I get a diagnosis for a chronic disease or a specialist decides I need an expensive surgery, the liability for the related costs follow me if I switch to anther insurer next year. If people are dragging their existing liabilities around with them, insurers will be hesitant about insuring them and try to find any reason to say “No.” The adverse selection problem looms large in the health insurance market not because of some inherent market failure unique to health insurance, but because liabilities that have already been incurred follow people to their new insurer.

If you have very long-term and hard-to-cancel policies, this should rarely be a problem. Changing health insurers should be a once-or-twice-in-a-lifetime event, not an annual shopping experience. But when it does happen, your new insurer is only insuring for new changes to your health status, unrelated to past diagnoses. Your previous insurer owns the liability for every expense already incurred.*

Once again, this kind of policy is affordable for most households. I pay around $8 per pay-period for the medical liability portion of my auto insurance (slightly more for the other coverages I get), just shy of $200 per year.  A catastrophic health insurance policy would likely be higher in claims frequency, so the premium would surely be higher than this. But so what? Conservatively multiply by ten and you still get something that’s quite a bit more affordable than the average existing health policy.

I discuss this topic in greater detail here.  

Deductibles and Coverage Exclusions

Health “insurance” policies have been trending toward higher deductibles in recent years, so this is something that’s actually working right at the moment. A deductible is basically an announcement to your insurer that “I’m not going to bug you for the small stuff. I’m only going to use my policy if something big and financially destructive happens.” Coverage exclusions are another way of doing this, but unfortunately these are explicitly illegal in the health insurance market. You can’t buy a policy that excludes, say, routine doctors checkups, birth and neonatal care, or inexpensive medications (although a high enough deductible can act as a coverage exclusion for some of these). There are laws that mandate “You must buy this coverage if you buy health insurance; you may not opt out.” You should be able to buy a plan that excludes everything that is routine, low-cost, or elective, but still promises to pay for large bank-breaking health events. If voters want to subsidize childbirth or prescription purchases or some other form of non-catastrophic health care, they should do it on budget with actual tax dollars and an explicit government program. They shouldn’t do it off-budget in a round-about way by hobbling health insurance markets.**

Residual Markets

Auto insurance premiums are based on the risk being insured. If you are a young male with a DUI and several recent accidents, you will pay a great deal more than a middle aged woman with a great driving record. Risk is quantified and priced. There are a few people for whom the expected cost of an auto policy is unaffordable. A bad combination of factors can put annual premiums in the $5,000+ range. We want these people to carry liability insurance, because they are a serious risk to other drivers. But with premiums so high, we run the risk that some of them will drive without insurance and cause uninsured accidents. There are usually residual market mechanisms to handle these high-risk drivers. They are a small segment of the population. It depends on exactly how you count them, but at the very most it’s in the 10% of population range. Auto insurance is regulated separately by each state, and each state has its own mechanism for these very bad risks. Sometimes a risk is just assigned to a random insurer (assignment is based on market share) and the state says, “Charge him exactly this rate, and eat the loss if he has a claim.” Sometimes a state creates a special risk pool, and the profits or losses from the pool are shared based on market share or some special formula. The state of Maryland runs its own auto insurance company, which serves a lot of high-risk insureds. Here we have the hand of government, but it is a small addition to a private market with competitive prices and sophisticated risk-pricing. That would work just as well for a market in health insurance: allow a humming free market that will work for 90% of the population along with a tiny bit of government assistance for the very few who aren’t served well. It could involve assigning risks to random insurers, subsidizing the purchase of insurance through a general fund, creating special subsidized risk pools, or something I haven’t even thought about. There are many tools to work with here. We don’t have to hobble 90% of the private market to serve those rare exceptional people who it doesn’t work for.

Health Insurance Isn’t Special

I’m writing this post to make the point that health insurance is fixable, and the policy features that would fix it are already out there in the insurance marketplace. We already have insurance policies with extremely long terms and very large six- and seven-figure payouts. We already have insurance policies that cover medical expenses based on when the liability is incurred, rather than when the bills are paid. And it’s all pretty affordable. Both the auto and life insurance markets involve sophisticated risk-pricing. There are residual markets for extremely high risks with unaffordable premiums. We wouldn’t have the death-spirals we see in today’s health insurance pools, where adverse selection drives premiums through the roof as all the good risks leave. We wouldn’t have the issue of health insurers not wanting you as a customer. Risk pricing solves these problems.

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* In property and casualty insurance, we sometimes talk about “incurred” vs “paid” claims or “accident year” vs “calendar year.” The idea is that we have a concept for when the liability arises, such as an automobile accident or a house fire; we have a separate concept for describing when the payments are made for that liability. For example, an injury was incurred in December 2010, but payments were made to cover the injured party’s surgeries in 2012, 2013, and 2015. The liability was “incurred” in “accident year” 2010. But claims were “paid” in “calendar years” 2012, 2013, and 2015. The insurer will cover those expenses regardless of which year they are paid in, even if the at-fault driver jumps to another insurer. This is a useful concept , and discussions of healthcare policy desperately need it.

**For example, a $1,000 deductible on your auto physical damage coverage means that you will pay the first $1,000 of any claim, and the insurer will pay the remainder. For all those minor scrapes and door dings that cost less than $1,000 to repair, you just eat the cost. If you hit a large animal and your car is a total loss, your insurer will pay you the value of your car minus $1,000. This allows for a huge savings in administrative costs because the insurer isn't using its claims teams to settle piddly $50 or $100 repairs. It also gets people used to paying out of pocket for the small stuff, which is a good habit.

Existential Nihilism At Work

You’re an accomplished surgeon with a long career. Then someone published a paper proving, via randomized controlled trials and placebo surgeries, that what you’ve been doing for the past twenty years is not helpful. In fact, it’s more likely harmful.

You’re an excellent teacher beloved by the vast majority of your students. Then somebody publishes an iron-clad study showing that teacher quality does not matter to life outcomes. You could have been phoning it in the whole time and your students would have been no worse off. In fact, most of your students don’t remember a single factoid you taught them, just that you were “a pretty cool teacher.”

You've been an employee for twenty years at a government agency that rehabilitates ex-convicts after they emerge from prison. A very carefully controlled trial finds that the "rehabilitation" you've been running is completely useless and has no effect on recidivism rates or future employment or lifetime earnings or anything other measure of "successful rehabilitation."

I’m a property and casualty actuary. I run big predictive models that measure the risk of an auto policy holder, given things like age, prior accidents, credit history, etc. The statistics for this are sophisticated and always getting more sophisticated. Sometimes I’m hit with the thought that this is all a big arms race with no net social value. Sure, I convince myself, if we make the price high for high-risk people, they might forego driving altogether. If insurance pricing gets these people off the road, that’s a good thing. But it’s likely this is a small effect, or that those people just drive anyway but do so without insurance. Or here’s another justification: If we don’t price for relative risk, we can very suddenly bring on a lot of very bad risks without knowing it. Rapid growth is the biggest cause of insurer insolvencies, and an insolvency means some people don’t get the money they were promised. So maybe we’re creating net social benefit by guarding against this kind of social cost? But again I find myself saying “Meh.” If my employer were to go under, someone else would simply take their market share. At any rate a fairly crude form of predictive model could guard against this kind of insolvency risk, provided you don’t have one or two insurers who are light-years ahead of their competitors. Insurers can make their predictive analytics more and more sophisticated in order to outdo each other. In this arms race, my model identifies a population that is overcharged by the market, so my company offers them a discount and we add that population to our market share. Competitors lose. A competitor’s model identifies a risky population and surcharges them accordingly; our model fails to identify them as a high-risk population and so we take them on at a price that’s too low. We lose. Perhaps we'd all be better off if we called a cease-fire and stopped experimenting with clever ways to improve predictive analytics. Maybe all the resources that get dumped into this arms-race are a dead-weight loss from the viewpoint of society as a whole.

I worry about these kinds of things that nullify a person’s social contribution. We like to believe that we’re not just “making money,” but actually providing something of social value to the world. I don’t know a good way to guard against this. Even if you actually build something physical, it doesn’t guarantee that you’ve created real value. I imagine this exchange:

Person 1: “Look, I built a house! And an electric car!”

Person 2: “Yeah, but the house-building was based on an artificial demand created by bad macroeconomic policy. Your house sits empty. And demand for the electric car is based on a subsidy that has since expired. With the subsidy gone, we can see demand for such cars isn’t real.”

Basically, it doesn't take that many people to make all the "stuff" we have, so most of the value added comes from "services." Maybe 3% of the population grows all the food, and another 10% or so make widgets and gadgets, cars and homes. The rest of us have to find some useful way to add value to the lives of our fellow creatures. Some of these schemes really do create value, and some are nice tries but turn out to be useless in the end. Sometimes a nice try isn't wasteful. Nobody would have known it wouldn't pan out until someone tried it, right? Still, it can be maddening to think you've wasted your time, even an entire career, on such a failed project.

I don’t have any great advice or deep thoughts here, just sharing a thought that I’m guessing others have had. Try to do the best you can, and make sure you’re happy doing it. 

Coda: If this is a depressing thought, don't despair. There is low-hanging fruit. I think people have a lot of untapped opportunity to make their personal lives better. Make your spouse and children happy, do more useful stuff around the house, be pleasant to your friends and co-workers, improve your personal health, acquire healthy habits and stop the unhealthy ones, take up a hobby, etc. There is a lot of "value" to add in this much-overlooked realm. Maybe you think of all those service jobs as just bullshit make-work to occupy the 85% of us who aren't making physical stuff. That's fine. Take a job, any job, even if it's a bullshit job, and think of it as a way to finance your "real" project of creating a happy home. That's what I've told people who have hobbled their own careers because they wanted the most perfect most meaningful job in existence. I think it's probably good advice.