Tuesday, October 17, 2017

Really Bad Allies on Drug Legalization

A while ago I wrote a post about how progressives and other leftists make pretty flakey allies on drug reform issues. 

Jacob Sullum has a post today at Reason illustrating the same problem
Today Tom Marino, the Pennsylvania congressman whom Donald Trump nominated to head the Office of National Drug Control Policy, withdrew his name because of a bill he was publicly bragging about just a year and a half ago. That bill, the Ensuring Patient Access and Effective Drug Enforcement Act of 2016, was uncontroversial when it was enacted. Not a single member of Congress opposed it. Neither did the Justice Department, the Drug Enforcement Administration (DEA), or President Obama, who signed it into law on April 19, 2016. Yet Marino's sponsorship of the bill killed his nomination because of the way the law was framed in reports by 60 Minutes and The Washington Post.
Here was a bipartisan bill without any congressional opposition. It limited the DEA’s power to keep people away from their pain medication. Thank goodness for that. But the Washington Post and 60 Minutes decide to spin this story about a sinister industry-sponsored bill that limited the DEA’s power to prevent the opioid crisis. “Simpleminded narrative” indeed. If “opioid epidemic” means heroin users accidentally overdosing on fentanyl, that is the DEA’s fault to begin with. Giving them more power to harass pharmaceutical companies wouldn’t help anything. In fact it would almost surely make the problem worse.  If people could buy pharmaceutical grade heroin from a licensed dealer, this rash of overdoses would not have happened in the first place. By cracking down on dealers and disrupting the market, the DEA is making the dosage of street heroin unpredictable.


The Tom Marino thing is really a non-story, but here’s what I think is actually happening. These guys are willing to side with the DEA, against pain patients and their doctors, in order to sink Trump’s nominee and embarrass his administration. And they’re willing to tell this “evil pharmaceutical companies are bamboozling na├»ve patients into taking medicine they don’t need that’s actually bad for them” narrative. Shame on them. The leftist worldview assumes that mere “bigness” grants large companies power over their customers. This makes meaningful drug reform almost impossible, because leftists will try to hold companies (drug manufacturers) responsible for the misdeeds of their customers. I think most leftists are at least nominally in favor of "ending the drug war" and instituting harm-reduction policies. None of this will work if they start berating the first company that sells pure heroin because some fools use it recklessly, become hopelessly addicted, or take way too much and overdose. A stable, legal drug market is necessary if we want to implement meaningful harm reduction. If legal suppliers immediately see themselves on the wrong end of firm-wrecking wrongful deaths suits and theatrical grillings in front of congress, that's not going to work. This "blame the supplier" ideology obliterates the responsibility of the individual. It destroys the very concept of self-ownership, the notion that we have sovereignty over our very bodies. It also indulges a particularly toxic brand of economic populism.

I'd gladly be part of a political coalition for drug policy reform, even if it included people who I profoundly disagree with. But if it means signing on with people who are poised to sabotage any actual progress in order to score some cheap political points (and I think dumping on the Marino bill counts as a shining example), count me out. 

Sunday, October 15, 2017

The Regulatory State in Practice: The Insurance Industry

People would have far less faith in the regulatory state if they saw how it works day-to-day. In this post I'll share some of my experiences with the regime I am familiar with: personal lines insurance regulation.

Sometimes I'll give my standard libertarian argument for limited government, and somebody will make a knee-jerk, unserious comment about how "Of course, we need some regulation. Otherwise unfettered greed will rule." I don't think so. Whether regulation in practice fetters greed or exacerbates it is really an empirical question. It depends on how good your institutions are, how observant and diligent the voting public is about disciplining the regulatory state, whether it's possible to align the incentives of the regulators with the interests of the public, the relative costs of free versus regulated markets, and lots of other things. I think in almost all cases the best regulation is market discipline without any government augmentation. But in this post I want to narrowly focus on the regulation of personal lines insurance and suggest that maybe some of these lessons generalize.

I am an actuary. Part of my job is to defend my employer's rate filings to regulators, who are always looking for reasons to reject them. First, a little bit about how this works. Personal lines insurance (home/renters and auto policies) is regulated at the state level by each of the 50 states, rather than being regulated at the federal level. Each state has a Department of Insurance, or "DOI". (A mean and immature joke is to pronounce that acronym out loud.) Each insurance company has a rate structure that is explicitly written down such that any two people who are identical on paper get exactly the same price. Prices can vary by rating territory (usually groupings of zip codes and/or counties), age, gender, marital status, credit history (surprisingly predictive of auto and home-related accidents!), and prior claim history. But the insurer has to specify exactly how this works in a rate filing, and has to use exactly those rates until it makes another filing amending that structure. Typically it's something like: $500 base rate for the rating territory you live in, times a factor of 2.0 for your age, gender, and marital status, times a factor of 0.5 for your good credit history, times 2.0 for having multiple prior accidents, so your rate is $500 * 2.0 * 0.5 * 2.0 = $1,000. (This is just an example; it would be an extremely simple rating structure that no insurer could actually get away with in today's marketplace.) I can't just say to this customer, "Been shopping around, eh? Can't find anyone else who will write you a policy at under $2,000, huh? That'll be...$2,000!" I have to charge this customer exactly what my rating algorithm calculates or I am in violation of state law. Any insurer found deviating from their filed rates would be severely fined. There might be some ambiguities about what rate to charge. Maybe the Postal Service redefines zip-codes mid year, and the customer's zip code doesn't map to any rating territory, so I have to place them in the most reasonable one. Or maybe their marital or credit status changes and my rate plan failed to specify how quickly I will reclassify them, such that a divorced person gets the "married" rate or a person with improving credit is temporarily being dinged for their poor past credit (or someone with deteriorating credit is temporarily benefiting from their good past credit, which is far more likely in my experience). But these ambiguities are a small part of the game. For the most part, the rate is spelled out clearly and unambiguously.

Typically, a company does a rate filing for every state at least once a year. This means an actuary has to write up a long report full of data (claims paid, premiums received, expenses incurred, investment income received, rate differentials by territory or classification) and submit it to the state DOI. Then a regulator at the DOI looks it over and either 1) approves it or 2) sends the insurer an "objection letter" stating the many things they don't like about the filing. There is a huge difference between state DOIs. Some are extremely lenient and will rubber-stamp approve almost any filing, as long as it's reasonable. Unless you're increasing rates by 100%, or implementing an explicitly racist class plan, these states will approve your filing very quickly. Bless them. (Typical overall rate changes are in the 5-10% range, usually just keeping up with inflation. And rating based on race is explicitly against the law in every state, and probably a violation of federal law, too.) Other states are extremely picky. Sometimes they are nettlesome for no particular reason. Sometimes the regulator does not have any statutory authority for their objection, but rather they are objecting to something that they just don't like. Most states have some catch-all statute regarding insurance regulation that reiterates the definition of an actuarially sound rate: A rate is reasonable and not excessive, inadequate, or unfairly discriminatory if it is an actuarially sound estimate of the expected value of all future costs associated with an individual risk transfer. Emphasis mine. Do you see a problem with this? "Unfair" is extremely subjective. A statute reiterating this principle basically gives the regulator carte blanche to object to anything they don't like. One state (a very, very northern state) will cite statutes in their objections, but when we look them up we always find that they refer to this boilerplate language about actuarially sound rate-making. It's almost never a reference to a law explicitly banning something in our proposed rating structure.

If regulatory overreach is one annoying problem, regulator incompetence is another. Often the regulator is not an actuary or is not otherwise technically savvy. Or sometimes they are actuaries who lack the specific technical expertise to understand the rate filing. Insurers are increasingly sophisticated in their pricing and using increasingly complex methods for segmenting risks. The industry standard for a long time has been the generalized linear model, or "glm". If you've ever found the "line of best fit" through a bunch of points in a math class, the glm is just a more sophisticated version of this, with an arbitrary number of dimensions (not just the two) and with different kinds of penalties for "missing" points on the scatter-plot. A glm is not all that complicated. Using one gives you a rating plan with multiplicative factors, as in my example above. The model tells you: "Multiply by 1.05 for male, 1.2 for unmarried, 0.90 for fair credit history, 1.00 for no prior incidents..." Simple as this is, I would say that most regulators are pretty clueless even when it comes to glms. But insurers are increasingly using things far more complex than these. Gradient boosted models (gbms) are insanely complex decision trees with thousands of branches. Neural nets are extremely complex systems of variable weights and triggering-thresholds. Increasingly these even more complicated models are being used to design (at least to inform) our rating plans, and yet many regulators are still perplexed by the relatively simple glms.

We try to do our best when it comes to justifying our glm results, but honestly most regulators wouldn't know what they were looking at if we gave them a filing exhibit spelling out everything in perfect detail. Sometimes they ask telling questions that betray their lack of understanding. I literally had an objection letter once that asked, "What is 'multivariate analysis'?" Perhaps it's a non-standard term, but anyone even remotely familiar with recent trends in the industry would know this is a reference to glms and related methods. It is in contrast to "univariate analysis", in which the mean for each group is calculated and the relative averages are used to set the rate differentials. For example, "Males cost 1.2 times as much as females to insure, so apply a 1.2 factor to males and a 1.0 factor to females." The "univariate" approach is wrong, because males could have other risk factors driving the difference. Maybe the average male customer for our company is younger, has worse credit, etc. A glm automatically accounts for these correlations between different rating variables. That is why we use them. None of this is terribly obscure, either. The reasons for using glms are described in detail and the methodology is fully fleshed out in several of the actuarial exams (grueling industry exams that people in my tribe have to take to earn our designation). Another typical question is something like, "How do you avoid double-counting if two rating variables overlap?" or "How do you adjust for correlations between rating variables?" The answer is that I don't have to, because I'm using a glm. A colleague once asked me how I answered such questions, and I said something like the previous sentence. We busted up laughing, because my blunt answer (which I would never really give to a DOI) points out how thoroughly the questioner is missing the point.

Another typical question is something like "Please provide the data used in this analysis." Once again, this betrays a complete lack of understanding. The underlying data in a glm is a gigantic table containing millions of records, probably in the tens of gigabyte range for a decent sized insurance company. The regulator doesn't actually want this, and probably doesn't have the technological capacity to even accept a file transfer of this size, and almost certainly could not perform an independent analysis if we sent it to them. At any rate, it would completely compromise our competitive position and (more importantly) our policyholders' privacy/security if we were to send around such a comprehensive database of our customers and their claims payments. (DOIs aren't always so diligent about security. I have seen pages from competitor filings marked with big red letters saying "CONFIDENTIAL", as in "The insurer marked this as confidential but the state DOI did not honor their wishes. They just published it with everything else, because they couldn't be bothered to separate out the 'public' from the 'confidential' files.") My best guess is that the person asking for "supporting data" is still in the univaraite mind-frame. They think they are asking for a few summarized tables showing, say, claim payments by gender (or age or credit), number of policies in each category (termed "exposures" in the industry), and a loss relativity, thus supporting the rating factor for each variable. Unfortunately there is no way to fairly "summarize" the data underlying a glm. The entire database goes in, and the rating factors come out. It's a sophisticated calculation that requires all the data at once.

Sometimes there are "filing forms", which are lists of questions that we have to answer in our filing which are the same each time we file. At least the DOI is telling us ahead of time what it wants, rather than asking for several rounds of clarification after-the-fact. In theory, this can be a time saver and allow us to preempt questions and get the filing approved more quickly. In practice, these are a waste of time and can open up the insurer to further rounds of questioning because they DOI doesn't understand the answer to the question it asks. ("Give me a statistics lecture! Mmm hmm. Mmm hmm. And what is this 'multivariate analysis' you speak of?") These filing forms frequently betray a lack of understanding. One that I helped fill out recently asks about a "test for homoscedasticity." Homoscedasticity means that the points are evenly distributed around the best fit line; they aren't closer to the best fit line for small values and further from the best fit line for large values (or vice versa). The question betrays ignorance about glms, because in a glm you explicitly relax this assumption. A traditional linear model insists on normally distributed residuals with a constant variance; a glm allows one to choose a gamma or poisson or some other kind of error structure, which allows the variance to be a function of the mean (the y-value of the best-fit line). If that's all very confusing, don't worry about it. What's happening here (I think) is that someone copied and pasted a few lines of text from a linear modeling textbook without understanding what they were copying. Many filing forms ask about the R-squared or adjusted R-squared, and ask if the residuals are normally distributed (essentially reiterating the "homoscedasticity" question without realizing they've asked the same thing twice!). Once again, they are failing to understand the very basics of a glm, a standard insurance industry tool. These questions apply only to traditional linear modeling and don't apply to the glm world.

Don't mistake me as saying that regulators should develop a sophisticated understanding of these models so they can really grill insurers about how they are being used. Some moderately sophisticated regulators do ask reasonable questions about methodology. ("Did you control for geography? Did you offset with your limit and deductible factors?") The problem here is that there are a thousand "right" ways to do something. One modeler might think it's absolutely necessary to "offset" your model with your coverage limit factors (which are more appropriately calculated outside of the glm; this is the 50/100/25 or 100/300/50 that you see on your insurance policy in your glove box). Another might think it's okay to not offset, so long as you have the various limits in your model as a control variable. Another might think it's okay not to even bother with this control variable, because every time she's ever done this in the past, she got the same factors with and without controlling for limit. It would be a mistake for a regulator to assemble a list of "best practices" from the actuarial literature and start grilling every insurance company about whether they're complying with those standards or not. (And "Why not!?") I've talked to very senior glm builders, gurus for the profession, who have very different ways of building these models. It's a mistake to think there's a "right" way of doing things. It would be wrong to waste time and resources demanding that a company show the results if the model were built some other way. At best, the technically competent regulator should see their role as a guiding hand, perhaps gently suggesting that an unsophisticated insurer might get a better result if they built their model some other way. But they shouldn't be grand-standing on their checklist of best practices and holding up someone's rate filing.

Regulators vary in their level of rudeness. Some are extremely boorish. I guess they figure you aren't really a "customer." You have to deal with them and accede to their demands. I guess they figure that if courtesy takes any effort at all, it's not worth it. Fortunately, most of these people turn back into human beings once you get them on the phone and they have to talk to you. (Most.) But even in the case of a "polite" regulator, this person is often asking for lots of unnecessary busy-work.  This person wields the power of the state, and can use it to uphold your filing. The resulting busy-work can result in hundreds of man-hours of labor and tens or hundreds of thousands of dollars in lost revenue due to unnecessary delays.

Sometimes incentives are poorly aligned. Many states use outside consulting agencies to review all rate filings. Many of these agencies are paid by the hour, or awarded for each "infraction" they find. So they have an incentive to create busy work to create billable hours and find "infractions" no matter how trivial. A company I worked for once got fined after a "market conduct exam" because our rating manual said we would surcharge customers who paid late, but we never did surcharge them. I think it was just a matter of us wanting to have something to threaten late-paying customers with, but not actually wanting to annoy them every time they paid late. So we never put in place the process to actually surcharge them, or we had a process but never pulled the trigger on it. It's the kind of reasonable latitude that companies grant their customers all the time, but these regulators saw an opportunity to fine us and they pounced.

Every state has an insurance commissioner, who generally oversees the state's DOI. Some are elected and some are appointed. Elected commissioners might face different political incentives than appointed ones. Appointed commissioners usually are older insurance professionals who have some interest in public service. They might be more technically savvy. They typically understand that prices have to go up to keep up with inflation, that price differentiation is necessary to a functioning insurance market, that locking in low rates will make insurance less available, etc. These people may understand things about the realities of insurance pricing that the voting public doesn't. Elected commissioners, on the other hand, might campaign explicitly on a platform of "I will not approve any rate increases." A populist back-wind may allow these commissioners to behave incredibly irresponsibly and compromise the insurance market in their state. They end up not approving reasonable rate increases, or placing unreasonable caps on rate increases, or holding up rate filings for months before finally relenting when things aren't going well.

With all this regulation, what benefit does the insurance customer actually see? Surely they get a rate that's, say, 10% lower, right? No. That would be an absolutely intolerable rate inadequacy and no insurer would stay in that market for long. Insurers actually have higher insurance premiums because of regulation. We have to hire teams of people to stay informed and up-to-date on regulations and various law changes. We occasionally have to physically fly representatives to rate hearings in other states. We have staff dedicated to preempting and responding to regulatory actions. All of this is ultimately paid for by the insurance customer. There is no one else to pay it! The regulatory lag I mentioned above may not actually cost the insurer any revenue. More likely, the insurer assumes this lag in its business process. They either start the process of the rate filing earlier, or they take a slightly higher rate increase to account for the lag. (If my rate filing will take three months of regulatory approval time, for example, I will build in three months worth of inflation into my calculation indicating how much rate to take.) There is also labor on the regulator side. Someone has to pay for the staff or the state's department of insurance, to keep the lights on and to keep the building heated and cooled. This may be paid for with insurance taxes, or it may come from a general state revenue. Either way it comes out of the pockets of insurance customers. And what do they get for all this? At best, maybe some insurers get a 10% lower bill, but at the cost of someone else paying 10% more. Regulation doesn't result in overall lower insurance costs. It just means that some customers pay slightly more and some others slightly less. If a state DOI managed to truly hold down overall prices in their state, insurers would start to exit that state's insurance market.

For an example of insurers exiting the market completely, see the Florida market for homeowner's insurance. Most of the cost of Florida homeowners insurance is due to infrequent but catastrophic hurricanes and other tropical storms. Historical losses will not be truly indicative of future expected losses, so insurers need to use simulations to estimate their actual exposure to hurricane risk. Computer simulations of thousands of storms are run, and the resulting damage to existing homes is estimated based on these simulated storms. The Florida Office of Insurance Regulation is extremely picky about what what kind of hurricane model you can use. The regulation of these models is so onerous as to be punitive. Florida's regulation of hurricane models is an example of regulators being relatively sophisticated but still not adding any value to the insurance market. (Well, adding negative value, in that they've driven insurers out of the state.)

I try to view this all charitably. Maybe even though every action taken by regulators looks like a waste of time and resources, market discipline would totally collapse without them? The marginal action of a regulator looks silly, but maybe the overall effect of regulation is a positive one? It could be, but I find this hard to swallow. There is fierce competition in the market for personal lines insurance. You can get dozens, even hundreds, of quotes if you only have the time to shop around. There are thousands of insurers. It is a very thick marketplace. Some insurers will advertise their financial strength, others will give you a lower price because they lack the reputation of major industry players. Some will sell based on strong "customer service", while others will have no-frills service with a corresponding low-expense and lower premiums. Some will never deny a reasonable claim (thus costing more), and some will fight every marginal claim and even some reasonable ones (thus costing less). I don't think regulation has much of a role to play in such a thick market. Customers know they are taking a chance when they buy from a no-name insurance company with cheap premiums. They also know they can find a better price if they shop around a little. Most customers don't bother. They may complain about their insurance rate going up, but they can't be bothered with the minor annoyance of getting quotes from a few competitors. Oh, some certainly do. And insurers are paranoid about policyholder attrition. Insurers are often trigger-shy on taking the rate increases they need to, because even a necessary rate increase would threaten customer retention. They implicitly feel the discipline of the market when deciding how to set the price. They pour over competitor rates, customer retention statistics, and new customer acquisition numbers. The regulator adds no value to this process.

I don't think any of this is necessarily unique to insurance. I would imagine other industries have similar problems regarding regulatory incompetence and regulatory overreach (or perhaps forbearance). Fundamentally, government just doesn't have much to offer us in terms of market regulation.

Friday, October 6, 2017

Estimates of the Uninsured: Worse than Useless

Every time there is any movement to change health policy at the federal level, I hear estimates that “X million people will lose their insurance under the Republican plan” or that “Y million people gained insurance under Obamacare.” I think these are useless statistics. It’s not like being uninsured implies zero access to health care. People with no coverage and no assets get tons of free treatment all the time. If you’re homeless with no health insurance policy and no money but you go to the ER suffering a heart attack, you will get an angioplasty for free. Conversely, people in other developed nations with “universal healthcare” often have long waits to see a doctor. Often they want a treatment but are told “no.”  Also, as I’ve pointed out before, coverage status just doesn’t appear to correlate well with actual health outcomes. It’s not like those millions of people who got coverage under Obamacare suddenly got healthier. (Are there any empirical estimates of the effects of the ACA showing large, positive, unambiguous health effects? If so, please share.) Likewise it’s not likely that they’ll suddenly get sicker once they lose their so-called coverage. (Several examples of "uninsured" Americans consuming more healthcare than their Canadian neighbors here. If you know of a more systematic comparison of this type, please share.)

I’d like to see something more meaningful than a count (really an estimate) of how many Americans “gain” or “lose” coverage under some health policy proposal. I’d rather see an estimate of wait-times, perhaps broken down by covered versus not-covered. Or an estimate of the likelihood that someone will be treated, or receive some particular treatment. “X million Americans will see their wait-times for an office visit drop by Z-percent.” Or “X million Americans will get Y-percent more MRIs and Z-percent more mammograms.” Ideally this could be turned into a mortality rate estimate, and the estimate could be measured against the actual observed mortality change after the policy passes. The effect of health policy on health outcomes is, after all, an empirical question. We should ultimately have some objective means of deciding whether the policy succeeded or not.

I’m a bit tired of hearing claims that some Republican tweak to the ACA is going to plunge millions of Americans into Dickensian poverty and illness. Not that I’m defending the Republicans or any particular proposal they’ve put forth. (If I were to put forth my own proposal, it would be far more radical and go a lot further than anything the GOP has proposed.) Rather I just don’t think that health policy has that strong an effect on actual health outcomes. 

Wednesday, October 4, 2017

A Simple Value-Neutral Model of Rising Income Inequality

Suppose that the range of options has expanded in both directions. There are more ways to make a lot of money, and there are more ways to live comfortably without earning much or without earning anything at all. Next, suppose that people vary in their preferences. Some prefer more income with less leisure, and some prefer more leisure with less income. Think about what happens to naively-measured “income inequality” in this world.

I’m nearly certain both conditions in the above paragraph are true. Incomes (conditional on working) have risen, and it’s easier and much more common these days to be a “live-in-your-parents’-basement-playing-video-games” man-child. I don’t think that the corporate lawyer and the under-employed man-child were cast into their roles by a cosmic role of the dice. People choose their professions in large part based on their preference for the leisure/income trade-off.

If annual income is the metric on which we’re to measure “inequality” (and it’s a phenomenally bad one), then we should expect it to increase as the world gets richer and more prosperous. If we picked a more relevant measure of economic well-being (like consumption, while perhaps monetizing leisure to put it on the same level as other forms of consumption), we’d see that the world is much more equal. 

I don't have a ton of data to bring to bear on this simple model. I have read that when you measure the activity of unemployed men of prime working age, they are spending a lot of time playing video games (citation needed). Anecdotally, I know a lot of people who could have earned more but deliberately chose not to. They picked a b.s. (lower-case) major in college, or they picked a decent career path but weren't "gunner" enough about it, or they finished their undergraduate degree but decided at the last minute not to go on to law school. As the title says, my explanation is value-neutral. I'm not judging these people for not working harder and I'm not going to insist that they all made mistakes (though I suspect that some of them didn't act in their own long-term self-interest).

Now think for a moment who is likely to attribute their success mostly to chance versus mostly to effort. Think about who will be more apt to notice and remember obstacles to their success. Who is more likely to rationalize bad decisions? I'm guessing that lower-income, lower-status folks are more likely to perceive (imagine?) barriers to their success. 

Really Bad Arguments Against Repealing Drug Prohibition

This will not be a comprehensive argument in favor of drug legalization, just a list of really bad whoppers I have heard and my responses to them.

“There will be a huge surge in drug use.”

This is the most obvious objection, and it’s wrong for a number of reasons. In historical cases where the legal status of a drug has been changed, you just don’t see that large a demand response. In the United States most recreational drugs have been illegal for a very long time, so it's hard to say what demand was "before" and "after." But use rates have failed to respond to massive shifts in drug enforcement efforts. Also, massive changes in use rates of any particular drug have fluctuated wildly despite there not being any change in enforcement effort. In other words, neither the legal status nor the intensity of enforcement appears to affect usage rates by much. (The empirical evidence for this is fully fleshed out in Jeffrey Miron's Drug War Crimes and also in Matthew Robinson's Lies, Damned Lies, and Drug War Statistics. I'll stop there, because I don't want to list every book on my "drug policy" shelf.)

I think the people who say this are implicitly assuming that the only thing holding people back from drug use is the legal status of the drug, which is a very absurd assumption once you say it out loud. The main thing keeping people away from dangerous drugs are the inherent risks of addiction, social dysfunction, drug-related health problems, and overdose. People who are willing to endure these risks are not much affected by adding legal risks on top of these. The people who want do use these substances are already doing them. It is absurd to think that people are undeterred by the pharmacological risks of drug use but then respond strongly to the legal risks of drug use. (Remove the words "pharmacological" and "legal" from that sentence to see the absurdity. To make drug prohibition sound like a good idea, someone has to actually square this circle.) There isn’t an enormous pent-up demand that will surge forth if the dam of drug prohibition bursts.

“Bad guys will just find something else to do.”

I first heard this one at a debate on drug legalization at my undergraduate university, and I’ve heard it a few times since. This is the kind of thing that people can only say if they have not incorporated any economics into their worldview. Proponents of drug legalization often argue that much of the violence in society is due to black market crime. (Again, see Drug War Crimes, which has an entire chapter devoted to this topic.) Drug dealers killing each other over territory, killing witnesses, killing or beating subordinates, drug users retaliating against a dealer who ripped them off, etc. There really is quite a lot of this kind of violence. It makes up a significant fraction of total murders and assaults. This becomes very clear if you look at countries like Mexico or Columbia, where the violence is almost noticeable in everyday life. It exists in the United States, too, even if to a lesser degree.  

When you make something illegal, you don’t actually stop people from producing and selling it. All you do is ensure that the most violent individuals will be in charge of production and distribution. Simply put, there are more bad guys in the world because drug prohibition has made it more lucrative to be a bad-guy. The proponents of this argument are making some kind of daffy assumption that there is a fixed number of wrong-doers, regardless of the relative costs or rewards to being a wrong-doer. Most of these people are “law-and-order” types who love heavy criminal penalties, so it is truly stunning to hear them argue that the bad guys don’t actually respond to incentives.
To anyone who is committed to this viewpoint, we legalizers happily accept your surrender. If, by your own admission, bad guys will do bad regardless of the rewards or penalties they face, legalization is a no-brainer.

I suspect that this argument is simply an ad hoc attempt to deny one of the major benefits of drug legalization, given that it’s (usually) contrary to the speaker’s actual worldview. It’s the kind of argument you get when people try to “wrack up bullet-points” rather than actually think about what they are saying.

“Drug prices won’t fall much, so you’ll still have all the economic crimes by drug users trying to finance their habit.”

I heard this one recently, and it’s new to me. It’s another ad hoc attempt to dismiss an argument in favor of drug legalization, but in fact someone who takes this position seriously is actually making an incredibly strong case for legalizing drugs. The whole purpose of drug prohibition is to make drugs so expensive (in monetary and other costs) that people stop using them. If the drug warriors are ready to admit failure on this front, once again I’d happily accept their surrender. I don’t understand how someone could still favor drug prohibition after insisting that prohibition has failed to achieve its one true objective. Nevertheless, I have heard this claim more than once, and by people who put drug "offenders" in prison. Legalizers like me sometimes make the argument that if drug prices are allowed to fall to their true market value, there will be far less property crime from addicts trying to support a habit. These people can find real jobs and live lives with normal schedules, rather than constantly seeking their next fix and stealing or "hustling" to finance it. I view the "drugs won't get cheaper" argument as a pathetic attempt to deny this benefit. 

In actual fact, drug prohibition has increased the market price of drugs. The black-market markup has been exaggerated by some writers; it’s not in the “factor of 100” range that you sometimes hear. In “The Effect of Drug Prohibition on Drug Prices: Evidence from the Markets for Cocaine and Heroin”, Jeffrey Miron concludes that the black market price of cocaine is 2-4 times the legal price and heroin is 6-19 times the legal price. Not exactly a “factor of 100” (an extreme claim that Jeffrey Miron is attempting to tone down) but still a significant financial relief for the severe addicts who spend most of their resources feeding an expensive habit.

“Drug laws are a good way to arrest real criminals when those crimes are hard to prove.”

This one is shocking to the conscience. It is pretty disturbing to hear law-and-order types suggest that drug laws allow an end-run around the constitution, and that this is a feature rather than a bug. I’m sure they have a point. If you “know” someone is a criminal, it’s probably easier to pat them down and find a baggy of drugs than to actually discover evidence of a real crime. That being said, I’m always disturbed by the confidence that law enforcement types have in their own estimates of who is or isn’t guilty. 

I dearly hope that proponents of this argument aren’t actually saying that we should make something arbitrarily illegal just so the police and prosecutors can arrest and imprison whoever they want to. I suspect this is just a throw-away, “Oh, by the way…” kind of argument. Perhaps it doesn’t, on its own, support the policy of drug prohibition, but is in some sense a mitigating factor to an otherwise bad policy. I don’t approve of this viewpoint at all. In fact, I think that too many resources are diverted from policing real crimes to policing drug crimes, and that’s part of the reason for social decay in some neighborhoods. If not for drug prohibition, there wouldn’t be so many missing young men spending time in prison, there wouldn’t be as many shattered families, and there wouldn’t be so much distrust of the police. Under those circumstances, maybe the communities could actually forge some kind of relationship with the police, and real crimes would actually get solved because of the resulting cooperation.

That's it for now. I hate to do these "fish-in-a-barrel" responses to really stupid things that I've heard. I like Scott Alexander's concept of steel-manning an argument, as in "making the argument under scrutiny as strong as possible, even if the person delivering it wasn't very articulate or reasonable." But I've heard these silly claims so I might as well respond to them and say why they're wrong. I plan to eventually do a long round-up post that unifies arguments in favor of drug legalization made in several earlier posts. 

Sunday, October 1, 2017

Welcome New Readers!

A recent post of mine got picked up by Scott Alexander in a link roundup. I was astonished to see the amount of traffic that came to my blog via that one link. I shudder to think what an entire Slate Star Codex post dedicated to the the topic might have done. I rarely get comments, but I got a few on the post. And I could tell that people were skimming my older posts, and even commenting on a few. Lurkers are of course perfectly welcome, but I appreciate any feedback I can get. I want to welcome new readers I've picked up in the past week or so.

I'm sure curious readers have perused my previous posts. If you're reading this on a computer or tablet, you should see my most-read posts on the right-hand side. I have a large number of posts arguing against drug prohibition starting around February 2016. I have a couple of posts about what thoughtful comments do and don't look like, here and here. I have a few scattered posts about so-called "inequality", how health insurance should work, and "moral outrage" as a debating tactic (one that I am finding increasingly obnoxious).

A few things I noticed.

Most people don't read all that carefully. That post, which attempts to debunk the standard narrative of the opioid epidemic, had at least a dozen links to prior posts by me which contained supporting information. Fewer than 10% of readers clicked on any of those. I'd hope that a larger share of readers would think, "Huh, is that really true? Why does he think that's true? Oh, there's a link arguing that this is true." Of course, many of those links were to places other than by own blog, and maybe people were scrupulously checking the various government documents and other articles I linked to. I promise that I'm doing my best and will never deliberately bend the truth, but I also sincerely hope nobody ever simply takes my word for anything I claim on this blog.

Some of the comments I got were great. And some were terrible. I made a couple of edits on my post after reading those comments (some here and some at SSC). One was to correct an error (one that I thought was not material, even to the very narrow argument in that particular paragraph). One was to clarify something that was not an error. (I called meditation "basically a placebo treatment", which should not be construed to mean I think meditation isn't effective for pain management. Just that I have an expansive definition of "placebo." After all, imagine doing an experiment where one group gets "real" meditation and the other gets "placebo" meditation as the treatment...) One thing I didn't care for was how easily people will conclude that you're deliberately lying. One comment, if I'm reading it correctly, implied that I was "lying about" a statistic cited in the Vox paper. Another implied that one of my claims was "dishonest." Is this how the rationalist community points out mistakes, and even disagreements that can't really be called "mistakes"? Mostly not, but it was a little bit grating to get this treatment over immaterial details. To say that somebody is "lying" implies something about their motives, which usually the accuser doesn't know. Anyway, the good comments outweighed the bad ones, and even the bad ones forced me to think harder about my arguments. (Bad commenters sometimes improve your understanding in the same way as a small child who keeps asking "Why?" to each successive answer.)

There were some excellent comments at Slate Star Codex about how people are actually using opioids. Consider it a small, random sample, but it's still illuminating. Considering the examples given (a broken arm, skin scraped to the bone, oral surgery), I'm very glad these people got powerful painkillers. I really hope that Vox does not have the effect on health policy that it wants to have, which would probably deny a few of these acute pain sufferers the relief they seek.

Free Medicine Doesn't Make People Healthier

This is from Free For All? Lessons from the RAND Health Insurance Experiment by Joseph Newhouse. It's not exactly a page-turner. It's more of an eat-your-vegetables kind of book. I've been thumbing through it recently. I am familiar with the conclusions (which I'll share below) because of the classic article Cut Medicine In Half by Robin Hanson. That piece was the lead essay in a Cato Unbound forum. I had thought that maybe Hanson drew some weird contrarian conclusions from the study. Indeed three other health policy wonks disagreed with him (err...without actually disagreeing with him; you'll have to see what they say and how they fail to meaningfully respond to Hanson).  Not contrarian at all, actually. Hanson was pretty much drawing the most straightforward possible conclusion from the RAND study. This slays some political sacred cows, but people should face the information with their eyes wide open. They shouldn't be engaging in casuistry to avoid the obvious. It's fine to speculate that "The effect of free medicine is clinically important, but it's hard to see in small datasets because of 'statistical significance' issues." But people who take such positions should admit that they are speculating beyond a straightforward interpretation of the best data we have on this question.

 Here's the relevant part (starting on page 201; emphasis mine):
For the average person there were no substantial benefits from free care (Table 6.6). There are beneficial effects for blood pressure and corrected vision only; ignoring the issue of multiple comparisons, we can reject at the 5 percent level the hypothesis that these two effects arose by chance, but we do not believe the caveat about multiple comparisons to be important in this case. We investigate below the mechanisms by which these differences might have arisen; the results from these further analyses strongly suggest that the results did not occur by chance.
For most health status measures the difference between the means for those enrolled in the free plan and those enrolled in the cost-sharing plan did not differ at conventional levels. Many of these conditions are rather rare, however, raising the possibility that free care might have had an undetected beneficial effect on several of them. To determine whether this was the case we conducted an omnibus test, the results of which make it unlikely that free care had any beneficial effect on several conditions as a group that we failed to detect when we considered the conditions one at a time. 
If the various conditions are independent and if free care were, for example, one standard error better than cost sharing for each measure, then of the 23 psychologic measures in Table 6.6 we would expect to see four measures significantly better on the free plan (at the 5 percent level using a two-tailed test), and none significantly worse. Among the insignificant comparisons, 15 would favor free care and only 4 would favor cost sharing. In fact three measures are significantly better on the free plan and none is significantly worse, but 13 of the 23 measures rather than the predicted 4 favor the cost-sharing plan. Hence it is very unlikely that free care causes one standard error of difference in each measure. If the independence assumption is violated, the violation is probably in the direction of positive dependence, in which case accounting for such dependencies would only strengthen our conclusion. Moreover, one standard error of difference is not a very large difference -- about half of the 95 percent confidence interval shown in the fourth column of Table 6 (equal, for example, to one milligram per deciliter of cholesterol). 
The same qualitative conclusions hold for persons at elevated risk (table 6.7). In this group, those on the free plan had nominally significantly higher hemoglobin but worse hearing in the left ear. Again outcomes on 13 of 23 measures favored cost sharing.

Staring at the top of page 204:
Hypertension and vision. Further examination shows that the improvements for hypertension and far vision are concentrated among those low-income enrollees at elevated risk (Table 6.8). Indeed, there was virtually no difference in diastolic blood pressure readings across the plans for those at elevated risk who were in the upper 40 percent of the income distribution. 
Because the low-income elevated risk group is small (usually between 5 and 10 percent of the original sample depending on the health status measure), the outcome differences for that group between the free and cost-sharing groups have relatively large standard errors. These results might be taken to mean that we missed beneficial effects for the low-income, elevated risk group for certain measures. But although this might be the case for a small number of measures, it is unlikely to be generally true. If we apply the same omnibus test just described to the low- and high-income groups shown in Table 6.8, we would expect that if there were a true one standard error favorable difference for the free plan for each measure, 2 of the 13 comparisons in Table 6.8 would be significantly positive and 2 would be negative, but none would be significantly negative. Of the 9 that would be insignificantly positive at the 5 percent level, 6 would have values of significance between 5 and 20 percent. The data in Table 6.8 show that for the low-income group, none (rather than 2) of the 13 comparisons is significantly positive at the 5 percent level; 4 (rather than 6) are significant at the 20 percent level; and 4 (rather than 2) are negative, one (acne) significantly so. For the high-income group, 7 of the 13 results favor the free-care plan, and the results are even "less significant" than one would expect at random (that is, one would have expected 2 or 3 differences "significant" at the 20 percent level among 13 comparisons, even if there were no true differences, whereas only one comparison was significant at this level).
Sorry, you'll need to get the book to see the actual charts. (I typed this while looking at my copy of the book and double-checked it. I sincerely apologize if I mistyped something, but on a double-check what I type matches what's in my book.) I like this concept of an "omnibus test." Note that the question isn't exactly "What dimensions of health improve when we give people free medicine," but rather a much more modest "Does free medicine improve health at all?" I like this exercise of saying, "What would I expect to see if free medicine had a significant effect on health?", comparing that to the observation, and concluding "What we predicted did not match what we observed." Keep in mind that the people with free care consumed something like 30-40% more medicine, apparently to no effect.

There is much more in the book, all in a similar vein. Giving people free medicine, even at-risk, low-income people, doesn't seem to make them any healthier. If someone want to take issue because the sample size is too small, I will join them in asking the RAND study to be redone with a much larger sample size. I won't stand for someone insisting that no data whatsoever, however carefully collected, can ever have policy implications that they don't approve of. That seems to be most of what I get from the popular media. Whenever there is a proposal to change health policy, there is a lot of shrill doom-saying by the proponents of socialized medicine. They speak as if any reductions made to the medical welfare state represent a lethal threat to people in poverty. I get the sense that they don't even realize they're making empirical claims. Well, we have the RAND study, and more recently the Oregon Medicaid Experiment. We have two randomized controlled experiments demonstrating that free medicine just doesn't seem to have health benefits, and we have tons of observational studies coming to the same conclusion.