In a recent post I outlined some of the problems with insurance regulation. I'll try to briefly mention some things that I either forgot to say or failed to emphasize without trying too hard to wrap it up in a cohesive narrative. Here are some scattered thoughts.
People really do turn back into human beings once you get them on the phone and talk to them like they're people. Kurt, impolite e-mails and official letters turn into, "Oh, hello! How are you today!" A few boorish oafs (or oafish boors?) do not undergo this reversion to human form, but most do. I think my other post overstated the rudeness and understated this very positive point. People like to think they are nice and usually try to treat others with dignity. Professionals like to think of themselves as having an earned sense of integrity. I'm thinking of Russ Roberts' description of Adam Smith: People like to be respected, but also to have an internal sense that they are actually respectable. We like not just to be loved, but to actually be lovely. In a phone conference with one regulator who I had had previous dealings with in a different role at my company, he remembered me and said, "The last time I spoke to you, you were about to run out of the office because your baby was due any day." One regulator rapped with me on the phone for about 15 minutes after we had closed out our filing. Her professional obligation was done, but she wanted to chat, one colleague to another. Another e-mailed me after I received my designation to congratulate me, long after we'd had any kind of ongoing business-relationship. The world isn't just "incentives" all the way down. People are funny. Most of them are kind of pleasant.
There is a serious "rule-by-checklist" mentality in insurance regulation. So long as someone can check something off their list as "handled" or "answered", they don't really care whether it's 1) completely irrelevant (so long as I get my check!) or 2) technically "answered" but still a problem. I think this is a feature of government, and bureaucracy more generally. A private enterprise can keep changing the rules to avoid rule-gamers and adjust to new trends. A government agency doesn't really face any consequence for having an out-of-date checklist. Governments are also bound by statutory authority; they can't just go banning shit they don't like...usually. Private enterprise, being private, can set the rules within the walls of their own buildings and adjust them as quickly as necessary. If their checklists are out of date, they lose money and lose out to competitors. Government regulation is complying with an out-of-date checklist. Market regulation means trying to ensure your internal checklist is up-to-date.
I don't think my previous post truly captured the banal tedium of the back-and-forth objection-and-response process. Regulators will object to a rate filing. The insurer might respond in full detail, or they may give just enough to barely answer the question and hope that's sufficient. Is the regulator asking this question because they are sophisticated and anticipate a specific "wrong" answer? Or is the regulator clueless and confused? Should we get them on the phone to clarify? Or should we simply fire off a borderline adequate response letter and hope they'll accept? Sometimes we'll be waiting weeks or months for approval, and instead yet another objection letter will show up. Sometimes this happens at inopportune times. Maybe we built in plenty of time for the objection/approval process, but the regulator still dragged their feet. So the objection comes when the actuary who needs to respond to it will be on a week-long vacation, or out studying for an actuarial exam, or (as alluded to above) out caring for a newborn baby and exhausted mother. One state will sit on the filing for two months, then send a 20- or 30-question objection letter. The little icon on Microsoft Outlook that shows you have an unread e-mail? At one point it drove me into a near-fit of anxiety every time I saw it because I thought it was an official correspondence from (say) the State of Florida, arriving just in time to ruin my week. (It took another year to unlearn this anxiety after I had moved out of that role.) It's a little unfair to say they "sit on" our filings. I'm sure they are very busy and don't have the resources to thoroughly review all filings in a timely matter. But knowing this, they should show some professional courtesy and grant us a little lenience. Meanwhile, through this delay, our IT folks are waiting to implement the change, and we have to keep telling them to hold off because we don't have approval. If we have a tight schedule for our IT crew, a delay on one state's rate change can have knock-on effects in which all subsequent rate changes are delayed, until they have time to catch up.
I meant to make an "in their defense..." point in my previous post. Maybe all this regulatory oversight looks stupid if you look at the actual actions taken. But maybe it's actually keeping us honest? The marginal regulatory action looks pretty silly and unnecessary, but the existence of this threat is keeping us from cheating our policyholders. In his book Breaking Rank, former police chief Norm Stamper describes a cop cursing out a "sleazy" defense attorney, who always stands up for the bad guys and sometimes get them off when they're really guilty. The defense attorney responds that, if it weren't for him constantly busting the prosecution's balls, the prosecutors and police would get lazy. They'd slack in their investigations, deny their suspect their constitutional rights more often (given that there wouldn't be anyone pushing back), and put more innocent people in prison. So maybe all this regulation looks silly to me but is acting like a back-stop against the bad stuff we would try in an unconstrained world. I don't quite buy this justification, but it had occurred to me so I wanted to share it. The insurance market is highly competitive. There are hundreds, maybe thousands, of personal lines insurers. Any screw-up we make is an opportunity for a thousand other players to steal our business. But, I don't know, maybe we'd all get lazy and slack, in the exact same way, making the exact same infractions, if not for regulation? I'll just shrug and say "Maybe."
There is an actuarial exam on the history of insurance regulation (well, maybe 30-40% of the syllabus is on this topic). The syllabus for this exam contains several papers that in some way or another outline various regulatory failures. There used to be a paper on failed attempts to constrain rate plans. Michigan, for example, said that you could only have a few rating territories, and furthermore that the difference between rates could only be "this much." This was an attempt to keep premiums low in Detroit. It failed, because some companies could write in Detroit while others wrote in the rest of the state. In fact, the same company could write business in two different charters (separate companies under the same parent company), rendering the regulation irrelevant. There is also a long discussion of "risk-based capital", or RBC. This is a set of rules for discounting various kind of assets and comparing them to the company's liabilities. Perhaps government bonds are worth 100% of their face value, but maybe junk bonds are only worth a fraction of their face value, to account for the fact that they aren't reliable. The punch-line: RBC had almost no power to predict insurer insolvency. I mention this because solvency regulation came up in the comments of the previous post. In theory, I admit this superficially seems like a legitimate case for government regulation. In practice, you need an almost omniscient government to actually spot the insolvent insurers.
If I think of more, I'll do another post.
Sunday, October 22, 2017
Friday, October 20, 2017
Thomas Sowell on Checking Citations
This is from Thomas Sowell's excellent autobiography Man of Letters:
Man of Letters is great. It's literally a collection of letters he's written to people over the decades. His narrative autobiography, A Personal Odyssey, is also excellent.
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I'll try to do more posts like this. I have a lot of highlighted pages in my Kindle books and even physical books. I might as well share the things I've found interesting.
I've had similar experiences. Either the citation backing some claim is from an extremely weak paper, or perhaps the cited paper doesn't at all support the claim.[M]y mentor in economics, the late Nobel Laureate George Stigler, once suggested that anyone who spent an afternoon in a library checking up on footnote citations was likely to find the experience disillusioning. Years later, when I had occasion to follow the trail of a footnote on a familiar proposition in labor economics, I found that the evidence for it collapsed like a house of cards. My wife, an attorney, says that she has similar experiences when following up on citations in court cases.
Man of Letters is great. It's literally a collection of letters he's written to people over the decades. His narrative autobiography, A Personal Odyssey, is also excellent.
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I'll try to do more posts like this. I have a lot of highlighted pages in my Kindle books and even physical books. I might as well share the things I've found interesting.
Contradictions
The limited success of a few charter schools and private schools doesn't scale up very well. That's why we should stick with public schools.
We're using up our oil too quickly. Also, the price of gasoline is too high.
You can't reward teachers with merit pay or fire them for poor performance, because nobody knows how to measure teacher performance. Also, you need this certificate to be a teacher in this state, because the certificate packages together all the prerequisites that make someone a good teacher.
Drug users are irrational about the risks of drug use. That's why we need to ban drugs. With legal penalties in place, people will be rationally deterred by the legal risks associated with drug use.
I have a "COEXIST" sticker on my car, right next to my "I BUY LOCAL" sticker.
Others?
Research shows that people behave irrationally in the
marketplace, lack basic information, and are easily misled and fooled. Therefore
we should give them much more power as voters.
We're using up our oil too quickly. Also, the price of gasoline is too high.
You can't reward teachers with merit pay or fire them for poor performance, because nobody knows how to measure teacher performance. Also, you need this certificate to be a teacher in this state, because the certificate packages together all the prerequisites that make someone a good teacher.
Drug users are irrational about the risks of drug use. That's why we need to ban drugs. With legal penalties in place, people will be rationally deterred by the legal risks associated with drug use.
I have a "COEXIST" sticker on my car, right next to my "I BUY LOCAL" sticker.
Others?
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 naïve 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.
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.
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.
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