Enjoy! I will try to keep this updated annually.
Sunday, June 16, 2019
Integrated All-Cause Mortality
Don't pay for fancy actuarial tables anymore. Here it is for free:
Enjoy! I will try to keep this updated annually.
Enjoy! I will try to keep this updated annually.
Monday, June 3, 2019
Beware the Man of No Theory
Scott Alexander has a great post from a few years ago titled
Beware the Man of One Study. You should read the whole thing yourself, or listen to it on
the SlateStarCodex podcast, which is basically a podcaster named Jeremiah
reading Scott’s posts. (Did you know there was such a thing? Pretty sweet,
right?)
If a minimum wage advocate said "We can increase the minimum wage from $7.25/hour to $29/hour and it will only cause a 4% reduction in employment," something has gone wrong. Even pro-minimum wage economists who advocate increasing the minimum tend to suggest modest increases and issue caveats about how an elasticity measured for a small increase doesn't necessarily apply to a large increase. Read what the pro-minimum wage economists actually say. They don't extrapolate the elasticity estimates very far beyond the (usually modest) minimum wage increases that they are calculated from. Arin Dube suggests half of the median wage as a reasonable target for the minimum wage. And his congressional testimony offers many suggestions for how businesses deal with minimum wage hikes without having to fire workers. He and economists like him are clearly grounded in the Econ 101 framework. They feel some need to explain why that story doesn't apply to the low-wage labor market, and the careful ones will often caution that it does apply when you raise the minimum high enough. In other words, they are grounded in theory. They're not coming from a place where any result makes as much sense as any other. They are not naively empirical.
(Read Dube's written testimony to congress. There is a discussion of how employers can adjust by hiring higher-skilled workers. There is discussion of how employees engage in more intense job search and hang on to jobs longer than they otherwise would, absent a minimum wage hike. It's like he's acknowledging that the Econ 101 story should be true, that it's a perfectly reasonable a priori assumption, and the apparent null effect on employment is a mystery that needs explaining. He doesn't pretend like the null result needs no explanation at all, as if we can just totally ditch economic theory and common-sense intuitions about how the labor market should work.)
Not all points on that funnel plot are created equal. It turns out that there is some very recent research, using a richer and more detailed dataset than anything that was previously available, studying a relatively large increase in the minimum wage, that shows significant disemployment effects. It yields an elasticity greater than 1, which would make it an outlier on the funnel plot above. But that same study also duplicates the "no significant effect" result when it restricts itself to the data available to other studies. The new study has access to everyone's actual wages (hours worked and total earnings) before and after the minimum wage hike. It doesn't have to rely on proxies for "minimum wage worker", like "restaurant workers" or "teenage workers", not all of whom are minimum wage workers. And it can detect changes in "hours worked", which is presumably more responsive than actual job losses. Maybe I'm ignoring Scott's advice and being a "man of one study" here (actually two separate papers by the same group of researchers on Seattle). But when a ground-breaking new study 1) uses a much richer dataset 2) finds a result that is more consistent with theory than previous work and 3) replicates results of prior work when it restricts its dataset to the detail available in previous studies, I tend to put a lot of faith in the new study. You need some theory to tell you which studies are more credible. I believe the following: "Having access to actual wages of individual workers gives me a better estimate of the effects on low-wage workers than using a crude proxy, like 'teenagers' or 'restaurant workers'." I could do some crude back-of-the-envelope, assuming a known real effect of a minimum wage increase, and showing that the crude proxy for minimum wage workers yields a smaller, less statistically significant result. I'd be using a little bit of math and some statistics. I'd be using theory to inform my worldview.
Scott says of the funnel plot,
"All these minimum wage studies show that raising the minimum wage has no effect on employment."
"All these minimum wage studies show that 'teenage workers' and 'restaurant workers' are a poor proxy for 'minimum wage workers'."
"All these minimum wage studies show that jurisdictions only raise the minimum wage when the local labor market is ready for a hike. Jurisdictions that are likely to have negative labor market impacts anticipate this and decline to raise the minimum wage."
"All these minimum wage studies show that businesses can effectively anticipate a minimum wage hike, given the time it takes to make it through the political process and the phase-in attached to most legislation."
"All these minimum wage studies show that reality is very messy, with many causal factors coming together at once. Real effects can be socially significant but still be numerically small, swamped by noise in our measurements. In this kind of world, it's hard to measure the effect of one thing on another thing."
All of these explanations are consistent with the new minimum wage research. You need theory, a worldview informed by some kind of prior belief, to decide which ones are most appropriate.
Scott warns against putting too much faith in any single study.
He even points out that you can’t trust any single meta-study and points to conflicting meta-studies on the minimum
wage reaching opposing conclusions. In the academic minimum wage wars, one side
can present a letter signed by 500 economists opposing a minimum wage increase,
while the other can present a letter signed by 600 economists supporting it. There is simply no consensus about whether the minimum wage is good or bad on net. No single study, in fact no single body of work, proves definitively one way or the other.
Scott then presents a funnel plot, which is evidence of
publication bias. (I wrote about this topic wrt climate sensitivity.) Take a look at the figure in Scott's post; it references Doucouliagos and Stanley (2009), which I presume is a paper titled Publication Selection Bias in Minimum Wage Research? (with the question mark as part of the title).
When I looked at this figure, I thought the scale on the x-axis was crazy. It spans
from -20 to +10. It looks like there’s a point at +5, meaning a 10% increase in
the minimum wage results in a 50% increase in employment. Of course that’s
nuts, and its position low on the y-axis tells you it’s not a credible
estimate. Neither are the very large negative values. The academic debate about
the minimum wage isn’t about whether the elasticity is -5 or +1. It’s about
whether the elasticity is closer to -0.1 or zero, or perhaps even very slightly positive. The
scale of the x-axis obscures where the action is at. Just eye-balling the
figure won’t tell you whether the bias-corrected average is zero or -0.1 (an
estimate preferred by Neumark and Wascher’s book Minimum Wages), because the scale of the x-axis doesn't allow your eye to make out differences that small.
I don’t know where
Scott got that figure, but here’s the same chart from a published version of
the (presumably same) paper.
On this scale it’s clearer that the bias-corrected
estimate should be close to zero. The paper actually gives some estimates of
the bias-corrected elasticity and a discussion of their statistical model. See
Table 3. Depending on the exact model specifications, this is telling us that a
10% increase in the minimum wage results in a 0.09% decrease in employment, or
a 0.04% decrease, or a 0.06% decrease…
Let’s say I totally buy the “publication bias” story and
want to use these as my bias-corrected estimates. Does anyone really think we
can increase the minimum wage by 100% and it will only cause a 0.9% reduction
in employment? In other words, doubling the minimum wage will not even cause 1%
of the affected workers to lose their jobs? Will a 300% increase (raising the federal minimum to $29/hour) only cause a 3.6% decrease in employment? Does anyone think these are remotely sensible estimates for the size of the effect?
I'll augment Scott's warning about the "Man of One Study." Beware the Man of No Theory. Don’t trust anyone who says
that their opinions are all “evidence based” or that they’ve crafted their
worldview simply by looking at “the data.” There is no such thing as a theory-free interpretation of "the data." We always need some kind of grounding in common sense, mathematics, logical consistency, and various academic disciplines to inform our interpretation of the data presented to us. In this case, the common-sense notion that "When the price goes up, people buy less" should ground us in reality. (I hope it's very clear that I'm not accusing Scott of being a "Man of No Theory." As far as I can tell he doesn't fall for naive empiricism.)
If a minimum wage advocate said "We can increase the minimum wage from $7.25/hour to $29/hour and it will only cause a 4% reduction in employment," something has gone wrong. Even pro-minimum wage economists who advocate increasing the minimum tend to suggest modest increases and issue caveats about how an elasticity measured for a small increase doesn't necessarily apply to a large increase. Read what the pro-minimum wage economists actually say. They don't extrapolate the elasticity estimates very far beyond the (usually modest) minimum wage increases that they are calculated from. Arin Dube suggests half of the median wage as a reasonable target for the minimum wage. And his congressional testimony offers many suggestions for how businesses deal with minimum wage hikes without having to fire workers. He and economists like him are clearly grounded in the Econ 101 framework. They feel some need to explain why that story doesn't apply to the low-wage labor market, and the careful ones will often caution that it does apply when you raise the minimum high enough. In other words, they are grounded in theory. They're not coming from a place where any result makes as much sense as any other. They are not naively empirical.
(Read Dube's written testimony to congress. There is a discussion of how employers can adjust by hiring higher-skilled workers. There is discussion of how employees engage in more intense job search and hang on to jobs longer than they otherwise would, absent a minimum wage hike. It's like he's acknowledging that the Econ 101 story should be true, that it's a perfectly reasonable a priori assumption, and the apparent null effect on employment is a mystery that needs explaining. He doesn't pretend like the null result needs no explanation at all, as if we can just totally ditch economic theory and common-sense intuitions about how the labor market should work.)
Not all points on that funnel plot are created equal. It turns out that there is some very recent research, using a richer and more detailed dataset than anything that was previously available, studying a relatively large increase in the minimum wage, that shows significant disemployment effects. It yields an elasticity greater than 1, which would make it an outlier on the funnel plot above. But that same study also duplicates the "no significant effect" result when it restricts itself to the data available to other studies. The new study has access to everyone's actual wages (hours worked and total earnings) before and after the minimum wage hike. It doesn't have to rely on proxies for "minimum wage worker", like "restaurant workers" or "teenage workers", not all of whom are minimum wage workers. And it can detect changes in "hours worked", which is presumably more responsive than actual job losses. Maybe I'm ignoring Scott's advice and being a "man of one study" here (actually two separate papers by the same group of researchers on Seattle). But when a ground-breaking new study 1) uses a much richer dataset 2) finds a result that is more consistent with theory than previous work and 3) replicates results of prior work when it restricts its dataset to the detail available in previous studies, I tend to put a lot of faith in the new study. You need some theory to tell you which studies are more credible. I believe the following: "Having access to actual wages of individual workers gives me a better estimate of the effects on low-wage workers than using a crude proxy, like 'teenagers' or 'restaurant workers'." I could do some crude back-of-the-envelope, assuming a known real effect of a minimum wage increase, and showing that the crude proxy for minimum wage workers yields a smaller, less statistically significant result. I'd be using a little bit of math and some statistics. I'd be using theory to inform my worldview.
Scott says of the funnel plot,
The bell skews more to left than to the right, which means more studies have found negative effects of the minimum wage than positive effects of the minimum wage. But since the bell curve is asymmetrical, we interpret that as probably publication bias. So all in all, I think there’s at least some evidence that the liberals are right on this one.
"All these minimum wage studies show that raising the minimum wage has no effect on employment."
"All these minimum wage studies show that 'teenage workers' and 'restaurant workers' are a poor proxy for 'minimum wage workers'."
"All these minimum wage studies show that jurisdictions only raise the minimum wage when the local labor market is ready for a hike. Jurisdictions that are likely to have negative labor market impacts anticipate this and decline to raise the minimum wage."
"All these minimum wage studies show that businesses can effectively anticipate a minimum wage hike, given the time it takes to make it through the political process and the phase-in attached to most legislation."
"All these minimum wage studies show that reality is very messy, with many causal factors coming together at once. Real effects can be socially significant but still be numerically small, swamped by noise in our measurements. In this kind of world, it's hard to measure the effect of one thing on another thing."
All of these explanations are consistent with the new minimum wage research. You need theory, a worldview informed by some kind of prior belief, to decide which ones are most appropriate.
By the way, the Congressional Budget Office (CBO) got a
larger estimate for elasticity when adjusting for publication bias:
On the basis of that review, CBO selected a central estimate of that elasticity of -0.075; in other words, a 10 percent increase in the minimum wage would reduce employment among teenage workers by three quarters of one percent.
…
Second, CBO considered the role of publication bias in its analysis. Academic journals tend to publish studies whose reported effects can be distinguished from no effect with a sufficient degree of statistical precision. According to some analyses of the minimum-wage literature, an unexpectedly large number of studies report a negative effect on employment with a degree of precision just above conventional thresholds for publication. That would suggest that journals’ failure to publish studies finding weak effects of minimum-wage changes on employment may have led to a published literature skewed toward stronger effects. CBO therefore located its range of plausible elasticities slightly closer to zero—that is, indicating a weaker effect on employment—than it would have otherwise.
Monday, May 27, 2019
In Defense of Price Optimization In Insurance Pricing
Traditional Insurance Price Regulation
There is a long tradition of regulation in personal lines insurance. The state department of insurance, or DOI, has the authority to approve or disallow the insurer's rates, depending on statutory authority, although sometimes they go well beyond their statutory authority in restricting how insurers can rate. I wrote about the process here and here. The insurer must file a "rate filing" with the DOI whenever they wish to change their rates, and all rate changes require some kind of actuarial justification. Insurers set the overall price, as in the amount they need to cover total expenses and claims and earn a (usually slim) profit margin, and also the relative price of their various customers, as in the rate differential between 16-year-olds and 40-year-olds or the difference between people who have recently had accidents and people who haven't.
Traditionally the justification is based on expected cost. Setting the overall rate level depends on how well historical premiums have covered claims and expenses. A calculation based on these numbers, along with some sensible adjustments (trending costs for inflation, adjusting historical premiums to the current rate level, etc.) will tell the insurer, for example, "We need to increase rates in Illinois by 3% this year." (This calculation that yields the indicated rate increase is often simply called "the indication.") The relative prices between insurance customers is usually determined by some kind of predictive model. "My generalized linear model tells me that 16-year-olds are 3 times as likely to have an accident as my 40-year-olds. Multiply the base premium by a factor of 3 to get the 16-year-old's rate. Repeat for all rating characteristics..." There is often controversy about the use of certain rating characteristics. Some people think it's unfair to use credit history in insurance pricing, even though from a purely predictive standpoint it is a highly significant predictor of future claims. Certainly it's not allowable to use race anywhere in rating, although there is some discussion of whether some rating variables are a proxy for race. Zip code correlates with race, for example, so some people argue that insurers are sneakily pricing for race without admitting they're doing so, using their territorial rates as a proxy. Some people make the same argument about credit: credit correlates with race, with some races having generally poorer credit scores compared to whites. I happen to think this is wrong; allowing credit-based and location-based pricing means you can identify good and bad risks regardless of their race. In other words, it allows insurers to, say, write a lot of business in predominantly black zip codes because credit history allows them to identify the good risks in those zip codes. They'll even write the bad risks in those zip codes if they can determine the right price for them, and credit history makes this a lot easier to do. Absent credit, the same insurer might avoid that zip code by not placing an agent there or not directing its marketing activity there. (States can regulate the pricing, but there's no way they can tell them "You must place an agent in this zip code, and you must direct a marketing campaign to this one." I don't think there's any version of this mandate that would pass constitutional muster.) All this controversy aside, most insurers have a relatively free hand in using credit history and location (zip code-based or otherwise) to set prices, so long as they can show that their rates correlate strongly with actual claims.
Price Discrimination and Price Optimization
Enter price discrimination. Price discrimination is the practice of charging two otherwise identical people different prices based on their willingness to pay, their "elasticity of demand." That is, I'm going to offer a price lower than my base price to that guy, because otherwise he won't buy what I'm selling, even though I already get a lot of customers at the base price. Unfortunately, most people describe this practice using the converse and feel moral indignation at the thought of ever getting charged more than the lowest possible price. As in, "I'm going to charge you more, because you are willing to pay more for this service." Both framings are technically accurate, but the second is so fraught with emotional baggage that I prefer to avoid it. Price discrimination actually lowers overall costs for customers as a whole. If I can attract more customers by offering different prices, that means I have a bigger customer base over which to distribute my fixed costs. The overall price level is lower, even though in some particular cases some individuals might be paying more than they would in a world of flat prices. That person who is seething with indignation over being denied a discount is probably paying a lower price than he would if that discretionary discount didn't exist.
In property and casualty insurance (P&C, meaning home and auto in this context), price discrimination is a hot topic. It's called Price Optimization (although the two terms aren't exactly synonymous; more on that below). To many regulators it's a big no-no. Because pricing is so heavily based on traditional actuarial methods, the language of statutes and regulations typically reference "loss costs" and expenses and "actuarially sound rates" (which implicitly means something that is cost-based and not demand-based). Many regulators are actuaries, and they are relying on the language of actuarial standards of practice for guidance. In this sense, actuarial accrediting societies (and I'm a member of one) can be regulators by proxy. If the standards of practice only ever reference "loss cost" and never mention propensity to buy ("elasticity of demand"), then regulators relying on these standards of practice will not allow for it. Indeed, under a strict reading, the relevant standards don't allow for price discrimination. When the CAS (Casualty Actuarial Society) tried to rewrite a standard of practice to allow for rating based on considerations other than loss costs, some prickly "watchdog" groups complained loudly. (The CAS was in a tough spot here. On the one hand, they didn't want to be in a position to say price optimization is contrary to actuarial principles, such that actuaries using it should be sanctioned for malpractice. On the other hand, they didn't want to be the ones to green-light price optimization everywhere. I suppose this is one of the hazards of being a guild; you are sometimes the de facto regulator of an industry and it falls on you to make difficult decisions.) States differ in how strict they are, but some states have statutes that explicitly forbid price optimization and others have regulators who assume it is implicitly forbidden by traditional standards of practice (perhaps interacting with existing regulation, which might reference "actuarially sound rates" or "unfairly discriminatory rates" from those standards).
By the way, I understand why people don't like price optimization. I hinted at this above: people hate the feeling that they aren't getting the lowest possible price for something. If I explained price optimization to the average insurance customer, I'm sure they'd balk. So regulators and "watchdog" groups are responding to the impulses of typical insurance customers. (Scare quotes around "watchdog" because many of these agencies act in ways that are contrary to the interests of consumers, as is the case here.) Don't get the impression that I'm some ideologically blinkered libertarian saying, "Gee, why would anyone ever want to regulate markets?" Or some antisocial economist saying, "Gee, why wouldn't consumers want a perfectly efficient market?" Or some morally compromised data scientist saying, "Gee, why don't consumers appreciate the beauty of my glorious pricing model?" I get it. My response is, What do consumers know anyway? Consumers balk at all kinds of commercial activity, even though economists can usually come up with "efficiency" justifications for those behaviors. In fact, economists often conclude that we'd be much worse off if those unpopular practices were outlawed. (We'd be far worse off if the government banned something every time a consumer felt indignant; read Defending the Undefendable by Walter Block for a long list of legitimate business practices that average people get indignant about.) If "efficiency" sounds bloodless, bear in mind that it usually means lower overall costs for consumers and more products available. Just so with price optimization.
Price Optimization On the Overall Rate Level
Let me start by defending the practice of measuring demand elasticity, which basically means the propensity of a customer to leave one insurer for another based on the magnitude of a price change. Suppose I'm the actuary in charge of rates in the state of Illinois. I do some actuarial calculations and determine that prices need to rise by 10%. I have some rating software that re-rates my book of business (meaning the full set of our insurance customers) at the new, higher rate level. I proudly report that this rate change will increase our Illinois revenue by 10%. Except this is wrong. It assumes that we retain 100% of our customers after the rate increase. I should know how much premium we're actually going to get if I increase rates by 10%, accounting for the propensity of policyholders to shop for insurance elsewhere. In fact, I have a duty to upper management, to the shareholders, and ultimately the customers (who are relying on the company remaining solvent) to accurately estimate the effect on revenues. At the very least, I should calculate elasticity so I can calculate the effect of a rate change on customer retention, which will give me a more accurate estimate of the effect on revenue. (I'm sort of using "premium" and "revenue" interchangeably here, btw, though insurers get revenue from sources other than their customers' premiums.) I don't want to say, "We're increasing premiums by 10%" when it's within my power to provide a better estimate. Suppose I say we're taking 10% but it's only 5% when factoring in retention effects. That's bad. It makes it harder for a company to plan for the long-term. Insurance companies need to be making these decisions with their eyes wide open, not making absurdly unrealistic assumptions, assumptions that can easily be relaxed with a moderately complex calculation. Actuaries are the guardians of capital at insurance companies. We're supposed to analyze risk and safeguard the billions of dollars of stockholder capital. We're supposed to ensure there is enough money held in reserve to pay policyholder claims for the indefinite future. We're supposed to do these kinds of estimates. And we're supposed to make them as accurate as is feasible.
This is where it gets sticky. Suppose my retention calculation affects the company's decision about how much to increase rates. An executive who sees that a 10% rate increase only leads to a 5% premium increase might say, "Okay, let's only take an 8%. Show me what that looks like." Or it could go the other way. Maybe my Illinois customers are relatively inelastic, and I could take a 15% rate increase and get pretty close to the 15%. The executive might use this information to take a rate increase that's larger than what's actuarially justified. In practice this is usually limited by the historical data and the actuarial methods. Actuaries have to calculate an indicated rate increase (once again, "the indication"), and DOIs usually don't allow you to go above them. (There is some amount of play here; maybe I can make a few adjustments and turn a 10% into an 11%. But I can't make the indication go arbitrarily high, and even eking out more than a couple of percentage points is unlikely.) But they don't mind you going below the indication. This question of "How far below my indicated rate increase can I deviate?" is where price optimization comes in. Traditionally insurers use rules of thumb to make these kinds of decisions. The indicated rate increase, based entirely on actuarial calculations straightforwardly applied to historical premium, loss, and expense data, is often higher than what's actually reasonable. That executive might say, "Hmm, a 10% increase is too high and will cause a lot of disruption in our book. Let's take 5% instead." ("Disruption" meaning lots of customers non-renewing their insurance policies.) State DOI's are usually accepting of these hand-waving statements about "We're not taking the full indicated rate increase because we're worried about policyholder disruption." But they are very opposed to us doing an explicit calculation to optimize the rate increase. The "rule of thumb" and the explicit calculation are both forms of price optimization, it's just that the former is much cruder. I don't think DOI's should be in the position of saying, "You can do X, as long as you do it crudely and inaccurately. If you get more sophisticated about doing X, we'll punish you." That is essentially the line some DOI's have taken with respect to price optimization.
Price Optimization at the Individual Customer Level
The previous discussion is about the overall rate level. Do I increase rates by the traditionally-indicated 10% or the elasticity-indicated 5%? A more sophisticated version of price optimization involves adjusting the rate for individual policyholders based on their willingness to pay. This basically takes the overall rate effect as a given, but allocates the rate impact based on retention considerations. I can build a predictive model that tells me "This group of customers will leave if I give them a 2% rate increase, but this group of customers won't leave even if I give them a 5% increase. I'm going to allocate more of the rate impact to the less elastic group." (Of course, these are all probabilistic statements. The model doesn't say, "Joey will definitely leave if we increase his rates 10%", but rather something like "Joey's probability of retention will fall from 90% to 80% if I increase his rates by 10%." Optimization is done on the basis of expected values, not "Will he leave? Yes/No?") This practice inspires some unwarranted fears that insurers will identify inelastic groups of people and permanently charge them a high rate. "Hmm. It turns out that soccer moms and rural single men are very price inelastic. Let's just keep increasing their rates every year." That is wildly implausible. The personal lines insurance market is far too competitive for this to actually happen. There are dozens, often hundreds, of insurers in every market. If there are demographics that are systematically overcharged by the industry, someone will come along and specialize in marketing to that group and take all of the customers. (Contra ProPublica and their atrocious article about territorial pricing in auto insurance.)
Here's a more likely scenario for how price optimization would be used at the individual or demographic grouping level. I spelled it out in an earlier post, but I'll repeat the points here. Suppose I build a new predictive model that tells me the price differentials between my various customers. My 16-year-old rate came down from a 200% surcharge to a 150% surcharge. The differential between the worst and best credit individuals used to be a factor of 2, but now it's a factor of 2.5. With dozens or even hundreds of rating variables changing in terms of their indicated surcharge/discount, each individual customer is likely to get something different from the overall rate impact. Maybe the overall rate effect is neutral, 0%, but almost nobody actually gets exactly 0%. If you build a histogram of customer rate impacts, you'd get something normally distributed around 0%, with a few customers getting large premium increases and a few getting large decreases. Well, just like I have a predictive model that tells me the expected costs for each individual insurance customer, I have a model that tells me each customer's elasticity of demand. I can then adjust my surcharges and discounts to optimize something (something other than the error function of my "expected claim costs" model). I can optimize, say, "growth in policy count", or "overall profit", subject to various constraints. (This is why price optimization is not exactly the same thing as price discrimination. Price discrimination simply refers to charging different prices based on willingness to pay. The term "price optimization" in insurance refers to a broad suite of optimization routines, and demand elasticity is simply one of many inputs.) Given a long enough timeline, insurers will ultimately approach their indicated rate differentials. Price optimization simply smooths the path so as to minimize the number of customers who are lost along the way. If my indicated rate for 16-year-olds drops from a 200% to a 150% surcharge, my price optimization routine might say to make this change over the course of three or four years, rather than doing it in one jump. If my surcharge for prior claims jumps from 30% to 50%, my price optimization routine might effectively say, "You're fine to do that in one jump." And it might be because those customers aren't price sensitive and won't leave, or it might be because they are price sensitive but they're also high-cost and we don't want their business anyway. Some other insurer has the right price for them, but maybe we don't. That's a win-win. It seems unlikely that such an optimization routine would in effect say, "You can permanently overcharge married family households with a single teenage driver by 50% over the model predicted premium, because they are just that price inelastic."
Once again, traditionally DOI's have accepted these practices of deviating from the indicated rates based on concerns about disruption.
Another crude method of price optimization is rate capping. No single policy's premium will increase by more than, say, 15% in any one year. Clearly this is an attempt to mitigate customer disruption. If I just charged everyone the rates indicated by my new predictive model in a "Let 'er rip" fashion, the customers getting big premium increases would leave. Rate capping smooths the transition to higher rates. Again, price optimization is simply a more sophisticated method of doing something that is already a widely accepted practice.
I should point out here that price discrimination is common in every other industry. Airlines use price discrimination to set ticket prices. They might charge one customer a higher price than another on the same flight because their predictive algorithm says that the first customer is willing to pay more. And, more obviously, ticket prices generally get higher closer to the date of the flight. (Does it go in the opposite direction for flights that don't get filled? As in, "This flight isn't filling up. Let's discount the tickets.") Doctors used to charge different rates to different patients, giving away some free or low-price care to their indigent patients and making it up on their more affluent patients. (I find it interesting that this kind of "privatized redistribution" was once standard practice, but that mandatory health insurance effectively eliminates this "the rich pay a greater share of society's healthcare costs" dynamic.) I like to tell a story about my eyeglasses. The original quoted price was $425, and I must have visibly balked at this price. The sales person then said, "Of course, that's with the anti-reflective coating on the lenses. We can save $150 if you go without." I chose to opt out, thinking this was a useless add-on. They ended up making my glasses with the coating anyway, and still gave me the lower price. I assume they default to making the lenses with the coating and that it doesn't actually cost extra to add it. They just use it as a bargaining chip to win price sensitive customers who bridle at the first quoted price.
Price Discrimination Is Economically Efficient
Price discrimination generally enhances economic efficiency, because it means more customers are served. Companies are identifying price-sensitive customers and trying to attract them by offering discounts. In a flat-price world those customers don't get served, because they say "No" to the single flat price. Granted, these are customers on the edge of indifference between the money and the product. Plausibly they are reaping very little consumer surplus, somewhere close to the difference between the sticker price and the discounted price they are offered. But nonetheless the practice means more production and more served customers, which necessarily implies a greater surplus. In the case of insurance, maybe it's less plausible that price discrimination allows more "production", but it is still welfare enhancing. From the point-of-view of the insurer, they need to collect $X from their customers to cover their costs. Insurers are identifying people who don't mind paying and allocating slightly more of the $X to them, and slightly less to people who would mind paying.
Regulators Should Permit Price Optimization
Regulators ought to allow insurance companies to do sophisticated price optimization. They need to stop treating deviations from the pure risk-based price as something sordid or unethical or necessarily contrary to sound actuarial principles. Some states have passed statutes that explicitly ban price optimization. In those states the regulator's hands are tied. In other states, regulators have simply assumed they have the authority to ban price optimization. They will hold up or disapprove filings that employ these methods. Regulators should stop assuming authority that goes beyond the literal language of their state's statutes. As I hinted at above (and described in detail in a previous post), regulators will often broadly interpret statutory language. Often the law that grants the state the authority to regulate insurance will make reference to "actuarially sound rates" or say that rates shall not be "unfairly discriminatory," and this language often echoes actuarial standards of practice. Unfortunately, some regulators decide that anything they don't like is "unfairly discriminatory." Insurance pricing is discriminatory by its very nature, and it has to be. An insurer must charge higher rates to 16-year-olds and people with poor credit, otherwise they will get only 16-year-olds and customers with poor credit, their claims frequency will explode, their losses will spiral out of control, and they will eventually go insolvent (or perhaps become a niche company that only insures 16-year-olds and other very poor risks, but they would needlessly bleed capital in the process of reaching that equilibrium). If you want to see what insurance without risk-based pricing looks like, look at the disastrous market for health insurance. Or look at Medicare and Social Security, which are prone to shocks from changing demographics. Appropriately priced insurance is necessarily discriminatory, so statutes that reference "unfairly discriminatory" rates leave us at the mercy of a regulator's arbitrary opinion of what's "unfair." I have heard current and former regulators describe disapproving or holding up a rate filing because they just didn't like a new rating variable (e.g. an auto surcharge based on prior homeowners insurance claims), even though they had no explicit authority to ban it. Many of these regulators assume price optimization is banned by default. They push back against attempts to use price optimization because they just don't care for it, even if officially they might cite boilerplate statutory language about "unfarily discriminatory" rates to justify their decisions. Insurers need a free hand to charge appropriate rates and manage their books of business. They need to be able to innovate and make decisions about their idiosyncratic risk portfolios without being held hostage by arbitrary regulators. If a state passes legislation that officially bans a particular rating variable or outlaws differential pricing based on demand elasticity, that's another matter. Of course it's the regulator's job to apply the statutes. But other than that, they should stop hindering innovation in the price optimization space by insisting on strictly risk-based pricing. They should resist knee-jerk consumer reactions that such-and-such a surcharge "seems fishy" or is unfair.
Insurance customers generally have dozens or even hundreds of options. It's basically impossible for an insurer to "overcharge" a customer, because there are always other options. Any customer who bothers to get a few quotes will generally find a lower price than what their current provider is charging. It's quite absurd to worry about nickle-and-dime price differences caused by price optimization. But from the point of view of the insurer, price optimization could mean eking out the tiny margin necessary to keep the company solvent. It could spell the difference between solvency and liquidation, which generally means lay-offs and unpaid claims for policyholders.
There is a long tradition of regulation in personal lines insurance. The state department of insurance, or DOI, has the authority to approve or disallow the insurer's rates, depending on statutory authority, although sometimes they go well beyond their statutory authority in restricting how insurers can rate. I wrote about the process here and here. The insurer must file a "rate filing" with the DOI whenever they wish to change their rates, and all rate changes require some kind of actuarial justification. Insurers set the overall price, as in the amount they need to cover total expenses and claims and earn a (usually slim) profit margin, and also the relative price of their various customers, as in the rate differential between 16-year-olds and 40-year-olds or the difference between people who have recently had accidents and people who haven't.
Traditionally the justification is based on expected cost. Setting the overall rate level depends on how well historical premiums have covered claims and expenses. A calculation based on these numbers, along with some sensible adjustments (trending costs for inflation, adjusting historical premiums to the current rate level, etc.) will tell the insurer, for example, "We need to increase rates in Illinois by 3% this year." (This calculation that yields the indicated rate increase is often simply called "the indication.") The relative prices between insurance customers is usually determined by some kind of predictive model. "My generalized linear model tells me that 16-year-olds are 3 times as likely to have an accident as my 40-year-olds. Multiply the base premium by a factor of 3 to get the 16-year-old's rate. Repeat for all rating characteristics..." There is often controversy about the use of certain rating characteristics. Some people think it's unfair to use credit history in insurance pricing, even though from a purely predictive standpoint it is a highly significant predictor of future claims. Certainly it's not allowable to use race anywhere in rating, although there is some discussion of whether some rating variables are a proxy for race. Zip code correlates with race, for example, so some people argue that insurers are sneakily pricing for race without admitting they're doing so, using their territorial rates as a proxy. Some people make the same argument about credit: credit correlates with race, with some races having generally poorer credit scores compared to whites. I happen to think this is wrong; allowing credit-based and location-based pricing means you can identify good and bad risks regardless of their race. In other words, it allows insurers to, say, write a lot of business in predominantly black zip codes because credit history allows them to identify the good risks in those zip codes. They'll even write the bad risks in those zip codes if they can determine the right price for them, and credit history makes this a lot easier to do. Absent credit, the same insurer might avoid that zip code by not placing an agent there or not directing its marketing activity there. (States can regulate the pricing, but there's no way they can tell them "You must place an agent in this zip code, and you must direct a marketing campaign to this one." I don't think there's any version of this mandate that would pass constitutional muster.) All this controversy aside, most insurers have a relatively free hand in using credit history and location (zip code-based or otherwise) to set prices, so long as they can show that their rates correlate strongly with actual claims.
Price Discrimination and Price Optimization
Enter price discrimination. Price discrimination is the practice of charging two otherwise identical people different prices based on their willingness to pay, their "elasticity of demand." That is, I'm going to offer a price lower than my base price to that guy, because otherwise he won't buy what I'm selling, even though I already get a lot of customers at the base price. Unfortunately, most people describe this practice using the converse and feel moral indignation at the thought of ever getting charged more than the lowest possible price. As in, "I'm going to charge you more, because you are willing to pay more for this service." Both framings are technically accurate, but the second is so fraught with emotional baggage that I prefer to avoid it. Price discrimination actually lowers overall costs for customers as a whole. If I can attract more customers by offering different prices, that means I have a bigger customer base over which to distribute my fixed costs. The overall price level is lower, even though in some particular cases some individuals might be paying more than they would in a world of flat prices. That person who is seething with indignation over being denied a discount is probably paying a lower price than he would if that discretionary discount didn't exist.
In property and casualty insurance (P&C, meaning home and auto in this context), price discrimination is a hot topic. It's called Price Optimization (although the two terms aren't exactly synonymous; more on that below). To many regulators it's a big no-no. Because pricing is so heavily based on traditional actuarial methods, the language of statutes and regulations typically reference "loss costs" and expenses and "actuarially sound rates" (which implicitly means something that is cost-based and not demand-based). Many regulators are actuaries, and they are relying on the language of actuarial standards of practice for guidance. In this sense, actuarial accrediting societies (and I'm a member of one) can be regulators by proxy. If the standards of practice only ever reference "loss cost" and never mention propensity to buy ("elasticity of demand"), then regulators relying on these standards of practice will not allow for it. Indeed, under a strict reading, the relevant standards don't allow for price discrimination. When the CAS (Casualty Actuarial Society) tried to rewrite a standard of practice to allow for rating based on considerations other than loss costs, some prickly "watchdog" groups complained loudly. (The CAS was in a tough spot here. On the one hand, they didn't want to be in a position to say price optimization is contrary to actuarial principles, such that actuaries using it should be sanctioned for malpractice. On the other hand, they didn't want to be the ones to green-light price optimization everywhere. I suppose this is one of the hazards of being a guild; you are sometimes the de facto regulator of an industry and it falls on you to make difficult decisions.) States differ in how strict they are, but some states have statutes that explicitly forbid price optimization and others have regulators who assume it is implicitly forbidden by traditional standards of practice (perhaps interacting with existing regulation, which might reference "actuarially sound rates" or "unfairly discriminatory rates" from those standards).
By the way, I understand why people don't like price optimization. I hinted at this above: people hate the feeling that they aren't getting the lowest possible price for something. If I explained price optimization to the average insurance customer, I'm sure they'd balk. So regulators and "watchdog" groups are responding to the impulses of typical insurance customers. (Scare quotes around "watchdog" because many of these agencies act in ways that are contrary to the interests of consumers, as is the case here.) Don't get the impression that I'm some ideologically blinkered libertarian saying, "Gee, why would anyone ever want to regulate markets?" Or some antisocial economist saying, "Gee, why wouldn't consumers want a perfectly efficient market?" Or some morally compromised data scientist saying, "Gee, why don't consumers appreciate the beauty of my glorious pricing model?" I get it. My response is, What do consumers know anyway? Consumers balk at all kinds of commercial activity, even though economists can usually come up with "efficiency" justifications for those behaviors. In fact, economists often conclude that we'd be much worse off if those unpopular practices were outlawed. (We'd be far worse off if the government banned something every time a consumer felt indignant; read Defending the Undefendable by Walter Block for a long list of legitimate business practices that average people get indignant about.) If "efficiency" sounds bloodless, bear in mind that it usually means lower overall costs for consumers and more products available. Just so with price optimization.
Price Optimization On the Overall Rate Level
Let me start by defending the practice of measuring demand elasticity, which basically means the propensity of a customer to leave one insurer for another based on the magnitude of a price change. Suppose I'm the actuary in charge of rates in the state of Illinois. I do some actuarial calculations and determine that prices need to rise by 10%. I have some rating software that re-rates my book of business (meaning the full set of our insurance customers) at the new, higher rate level. I proudly report that this rate change will increase our Illinois revenue by 10%. Except this is wrong. It assumes that we retain 100% of our customers after the rate increase. I should know how much premium we're actually going to get if I increase rates by 10%, accounting for the propensity of policyholders to shop for insurance elsewhere. In fact, I have a duty to upper management, to the shareholders, and ultimately the customers (who are relying on the company remaining solvent) to accurately estimate the effect on revenues. At the very least, I should calculate elasticity so I can calculate the effect of a rate change on customer retention, which will give me a more accurate estimate of the effect on revenue. (I'm sort of using "premium" and "revenue" interchangeably here, btw, though insurers get revenue from sources other than their customers' premiums.) I don't want to say, "We're increasing premiums by 10%" when it's within my power to provide a better estimate. Suppose I say we're taking 10% but it's only 5% when factoring in retention effects. That's bad. It makes it harder for a company to plan for the long-term. Insurance companies need to be making these decisions with their eyes wide open, not making absurdly unrealistic assumptions, assumptions that can easily be relaxed with a moderately complex calculation. Actuaries are the guardians of capital at insurance companies. We're supposed to analyze risk and safeguard the billions of dollars of stockholder capital. We're supposed to ensure there is enough money held in reserve to pay policyholder claims for the indefinite future. We're supposed to do these kinds of estimates. And we're supposed to make them as accurate as is feasible.
This is where it gets sticky. Suppose my retention calculation affects the company's decision about how much to increase rates. An executive who sees that a 10% rate increase only leads to a 5% premium increase might say, "Okay, let's only take an 8%. Show me what that looks like." Or it could go the other way. Maybe my Illinois customers are relatively inelastic, and I could take a 15% rate increase and get pretty close to the 15%. The executive might use this information to take a rate increase that's larger than what's actuarially justified. In practice this is usually limited by the historical data and the actuarial methods. Actuaries have to calculate an indicated rate increase (once again, "the indication"), and DOIs usually don't allow you to go above them. (There is some amount of play here; maybe I can make a few adjustments and turn a 10% into an 11%. But I can't make the indication go arbitrarily high, and even eking out more than a couple of percentage points is unlikely.) But they don't mind you going below the indication. This question of "How far below my indicated rate increase can I deviate?" is where price optimization comes in. Traditionally insurers use rules of thumb to make these kinds of decisions. The indicated rate increase, based entirely on actuarial calculations straightforwardly applied to historical premium, loss, and expense data, is often higher than what's actually reasonable. That executive might say, "Hmm, a 10% increase is too high and will cause a lot of disruption in our book. Let's take 5% instead." ("Disruption" meaning lots of customers non-renewing their insurance policies.) State DOI's are usually accepting of these hand-waving statements about "We're not taking the full indicated rate increase because we're worried about policyholder disruption." But they are very opposed to us doing an explicit calculation to optimize the rate increase. The "rule of thumb" and the explicit calculation are both forms of price optimization, it's just that the former is much cruder. I don't think DOI's should be in the position of saying, "You can do X, as long as you do it crudely and inaccurately. If you get more sophisticated about doing X, we'll punish you." That is essentially the line some DOI's have taken with respect to price optimization.
Price Optimization at the Individual Customer Level
The previous discussion is about the overall rate level. Do I increase rates by the traditionally-indicated 10% or the elasticity-indicated 5%? A more sophisticated version of price optimization involves adjusting the rate for individual policyholders based on their willingness to pay. This basically takes the overall rate effect as a given, but allocates the rate impact based on retention considerations. I can build a predictive model that tells me "This group of customers will leave if I give them a 2% rate increase, but this group of customers won't leave even if I give them a 5% increase. I'm going to allocate more of the rate impact to the less elastic group." (Of course, these are all probabilistic statements. The model doesn't say, "Joey will definitely leave if we increase his rates 10%", but rather something like "Joey's probability of retention will fall from 90% to 80% if I increase his rates by 10%." Optimization is done on the basis of expected values, not "Will he leave? Yes/No?") This practice inspires some unwarranted fears that insurers will identify inelastic groups of people and permanently charge them a high rate. "Hmm. It turns out that soccer moms and rural single men are very price inelastic. Let's just keep increasing their rates every year." That is wildly implausible. The personal lines insurance market is far too competitive for this to actually happen. There are dozens, often hundreds, of insurers in every market. If there are demographics that are systematically overcharged by the industry, someone will come along and specialize in marketing to that group and take all of the customers. (Contra ProPublica and their atrocious article about territorial pricing in auto insurance.)
Here's a more likely scenario for how price optimization would be used at the individual or demographic grouping level. I spelled it out in an earlier post, but I'll repeat the points here. Suppose I build a new predictive model that tells me the price differentials between my various customers. My 16-year-old rate came down from a 200% surcharge to a 150% surcharge. The differential between the worst and best credit individuals used to be a factor of 2, but now it's a factor of 2.5. With dozens or even hundreds of rating variables changing in terms of their indicated surcharge/discount, each individual customer is likely to get something different from the overall rate impact. Maybe the overall rate effect is neutral, 0%, but almost nobody actually gets exactly 0%. If you build a histogram of customer rate impacts, you'd get something normally distributed around 0%, with a few customers getting large premium increases and a few getting large decreases. Well, just like I have a predictive model that tells me the expected costs for each individual insurance customer, I have a model that tells me each customer's elasticity of demand. I can then adjust my surcharges and discounts to optimize something (something other than the error function of my "expected claim costs" model). I can optimize, say, "growth in policy count", or "overall profit", subject to various constraints. (This is why price optimization is not exactly the same thing as price discrimination. Price discrimination simply refers to charging different prices based on willingness to pay. The term "price optimization" in insurance refers to a broad suite of optimization routines, and demand elasticity is simply one of many inputs.) Given a long enough timeline, insurers will ultimately approach their indicated rate differentials. Price optimization simply smooths the path so as to minimize the number of customers who are lost along the way. If my indicated rate for 16-year-olds drops from a 200% to a 150% surcharge, my price optimization routine might say to make this change over the course of three or four years, rather than doing it in one jump. If my surcharge for prior claims jumps from 30% to 50%, my price optimization routine might effectively say, "You're fine to do that in one jump." And it might be because those customers aren't price sensitive and won't leave, or it might be because they are price sensitive but they're also high-cost and we don't want their business anyway. Some other insurer has the right price for them, but maybe we don't. That's a win-win. It seems unlikely that such an optimization routine would in effect say, "You can permanently overcharge married family households with a single teenage driver by 50% over the model predicted premium, because they are just that price inelastic."
Once again, traditionally DOI's have accepted these practices of deviating from the indicated rates based on concerns about disruption.
Regulator: Why is your 16-year-old factor 3.0 when your model says it should be 3.5?That is, they allow us to do so as long as were using crude rules of thumb and not doing an explicit price optimization calculation. Why should we be confined to the cruder version of this calculation? If more sophistication is available, why not allow it?
Insurer: We are moving in the direction of 3.5 with this filing, but due to policyholder disruption considerations we are worried about moving the factor all the way in a single filing.
Regulator: Okay, that makes sense. (Stamps "Approved" on the filing.)
Another crude method of price optimization is rate capping. No single policy's premium will increase by more than, say, 15% in any one year. Clearly this is an attempt to mitigate customer disruption. If I just charged everyone the rates indicated by my new predictive model in a "Let 'er rip" fashion, the customers getting big premium increases would leave. Rate capping smooths the transition to higher rates. Again, price optimization is simply a more sophisticated method of doing something that is already a widely accepted practice.
I should point out here that price discrimination is common in every other industry. Airlines use price discrimination to set ticket prices. They might charge one customer a higher price than another on the same flight because their predictive algorithm says that the first customer is willing to pay more. And, more obviously, ticket prices generally get higher closer to the date of the flight. (Does it go in the opposite direction for flights that don't get filled? As in, "This flight isn't filling up. Let's discount the tickets.") Doctors used to charge different rates to different patients, giving away some free or low-price care to their indigent patients and making it up on their more affluent patients. (I find it interesting that this kind of "privatized redistribution" was once standard practice, but that mandatory health insurance effectively eliminates this "the rich pay a greater share of society's healthcare costs" dynamic.) I like to tell a story about my eyeglasses. The original quoted price was $425, and I must have visibly balked at this price. The sales person then said, "Of course, that's with the anti-reflective coating on the lenses. We can save $150 if you go without." I chose to opt out, thinking this was a useless add-on. They ended up making my glasses with the coating anyway, and still gave me the lower price. I assume they default to making the lenses with the coating and that it doesn't actually cost extra to add it. They just use it as a bargaining chip to win price sensitive customers who bridle at the first quoted price.
Price Discrimination Is Economically Efficient
Price discrimination generally enhances economic efficiency, because it means more customers are served. Companies are identifying price-sensitive customers and trying to attract them by offering discounts. In a flat-price world those customers don't get served, because they say "No" to the single flat price. Granted, these are customers on the edge of indifference between the money and the product. Plausibly they are reaping very little consumer surplus, somewhere close to the difference between the sticker price and the discounted price they are offered. But nonetheless the practice means more production and more served customers, which necessarily implies a greater surplus. In the case of insurance, maybe it's less plausible that price discrimination allows more "production", but it is still welfare enhancing. From the point-of-view of the insurer, they need to collect $X from their customers to cover their costs. Insurers are identifying people who don't mind paying and allocating slightly more of the $X to them, and slightly less to people who would mind paying.
Regulators Should Permit Price Optimization
Regulators ought to allow insurance companies to do sophisticated price optimization. They need to stop treating deviations from the pure risk-based price as something sordid or unethical or necessarily contrary to sound actuarial principles. Some states have passed statutes that explicitly ban price optimization. In those states the regulator's hands are tied. In other states, regulators have simply assumed they have the authority to ban price optimization. They will hold up or disapprove filings that employ these methods. Regulators should stop assuming authority that goes beyond the literal language of their state's statutes. As I hinted at above (and described in detail in a previous post), regulators will often broadly interpret statutory language. Often the law that grants the state the authority to regulate insurance will make reference to "actuarially sound rates" or say that rates shall not be "unfairly discriminatory," and this language often echoes actuarial standards of practice. Unfortunately, some regulators decide that anything they don't like is "unfairly discriminatory." Insurance pricing is discriminatory by its very nature, and it has to be. An insurer must charge higher rates to 16-year-olds and people with poor credit, otherwise they will get only 16-year-olds and customers with poor credit, their claims frequency will explode, their losses will spiral out of control, and they will eventually go insolvent (or perhaps become a niche company that only insures 16-year-olds and other very poor risks, but they would needlessly bleed capital in the process of reaching that equilibrium). If you want to see what insurance without risk-based pricing looks like, look at the disastrous market for health insurance. Or look at Medicare and Social Security, which are prone to shocks from changing demographics. Appropriately priced insurance is necessarily discriminatory, so statutes that reference "unfairly discriminatory" rates leave us at the mercy of a regulator's arbitrary opinion of what's "unfair." I have heard current and former regulators describe disapproving or holding up a rate filing because they just didn't like a new rating variable (e.g. an auto surcharge based on prior homeowners insurance claims), even though they had no explicit authority to ban it. Many of these regulators assume price optimization is banned by default. They push back against attempts to use price optimization because they just don't care for it, even if officially they might cite boilerplate statutory language about "unfarily discriminatory" rates to justify their decisions. Insurers need a free hand to charge appropriate rates and manage their books of business. They need to be able to innovate and make decisions about their idiosyncratic risk portfolios without being held hostage by arbitrary regulators. If a state passes legislation that officially bans a particular rating variable or outlaws differential pricing based on demand elasticity, that's another matter. Of course it's the regulator's job to apply the statutes. But other than that, they should stop hindering innovation in the price optimization space by insisting on strictly risk-based pricing. They should resist knee-jerk consumer reactions that such-and-such a surcharge "seems fishy" or is unfair.
Insurance customers generally have dozens or even hundreds of options. It's basically impossible for an insurer to "overcharge" a customer, because there are always other options. Any customer who bothers to get a few quotes will generally find a lower price than what their current provider is charging. It's quite absurd to worry about nickle-and-dime price differences caused by price optimization. But from the point of view of the insurer, price optimization could mean eking out the tiny margin necessary to keep the company solvent. It could spell the difference between solvency and liquidation, which generally means lay-offs and unpaid claims for policyholders.
Tuesday, April 30, 2019
Critique of the Illinois Economic Policy Institute Report: Raising the Minimum Wage
The Illinois Economic Policy Institute put out this report titled Raising the Minimum Wage. I mentioned this in a previous post but I thought it would be worth giving
it a much more thorough treatment here. I think this report is being used by
policy-makers in Illinois to justify the recent minimum wage hike. If so,
someone needs to dissect the report, vet its various claims, and debunk the
stuff it gets wrong. Honestly, I suspect that Illinois politicians don’t really
bother with the requisite scholarship or policy analysis that they’d need to
actually govern effectively. Maybe a few ranking members read the executive summary of the report, but most probably didn't even get that far. I’m pretty sure that the $15/hour minimum wage was
passed based on political considerations, not a cost-benefit analysis. I
seriously doubt that the people running my state did their due diligence here.
When I contacted my representative in the Illinois House, she forwarded me to
another member of her party who was spearheading this initiative, Will Guzzardi.
He was not responsive. He’s made some public statements that badly misrepresent what the literature on the minimum wages says. I’m
curious if the ILEPI paper is one of his sources. Even if not, I want to make it
a little bit harder for policy think tanks like the ILEPI to just say whatever
they want. If they are going to make bad arguments in a public forum, I think
someone should point out how bad they are. If they are making dishonest or
misleading claims, they should be held to account and publicly embarrassed for
it. Every state probably has institutes like the ILEPI who put out policy
papers. It’s worth taking the time to read what they say and, if necessary,
trying to debunk these papers.
The Executive Summary starts with the sentence: “Illinois should
raise the minimum wage.” To their credit, they are upfront about their intent. It
becomes clear as you read the document that this was their true starting point,
and all the “evidence” was assembled to reach this conclusion. Then follows an
irrelevant statement that 13 states have a higher minimum wage and that nine of
those states have unemployment rates lower than Illinois. As I read this I
braced myself for some really bad econometrics. The report did not disappoint (or should
I say didn’t fail to disappoint?). It
also mentions the irrelevant fact that a majority of voters support increasing
the minimum wage. Okay, but maybe that’s a function of dishonest policy
advocates misleading them? Maybe that’s due to widespread economic illiteracy,
a problem made worse by extremely biased policy papers like this one. In policy analysis, saying that something is democratically popular is a throw-away argument. Nobody decides to be pro-X just because slightly over 50% of the population support X. It's an irrelevant piece of information, so why bring it up?
The second paragraph begins: “Raising the minimum wage
boosts worker incomes while having little or no effect on employment.” This is
a misleading summary of the research. I’ve written about that here.
The ILEPI isn’t alone in making this claim, but they are mistaken.
The report then describes a staged roll-out, eventually
getting to $15/hour by July 1, 2024, along with absurdly optimistic estimates
of how much incomes would rise for Illinois workers.
From the last paragraph of the executive summary: “Illinois’
current minimum wage of $8.25 per hour fails to prevent workers from earning
poverty-level wages.” If the intent is to help poor households, the minimum
wage is an extremely poorly targeted policy for it. $8.25 an hour is a
perfectly good starting wage for a first job. Very few minimum-wage workers
stay at that wage for long. Very few of them are full-time workers in a
single-earner household. A 2014 report by the CBO found that only 19% of the increased wages from a minimum wage hike would accrue to families below the poverty line, with 29% accruing to households above 3x the poverty line.
First sentence of the introduction: “The minimum wage is
intended to ensure that working-class individuals can maintain a decent standard
of living.” Of course, intentions are not results. It goes on:
“Despite this acknowledgement that poverty-level wages
foster reliance on social safety net programs, a full-time worker earning
today’s state minimum wage rate of $8.25 per hour brings home just $17,160 in
annual income. This is $3,620 below the federal poverty line for a family of
three and $7,940 below the federal poverty line for a family of four.”
This is just completely irrelevant. There are very few
full-time minimum wage workers, and most minimum wage workers are in households
that are well above the poverty line. Also, like I’ve
argued before, the notion that our social safety net programs are subsidizing the employers of low-wage
workers is exactly backwards. Safety nets makes the option of not working more
attractive, which means employers have to pay more to attract workers.
Figure 1 is a stunningly bad piece of econometric reasoning.
It lists the states with a $10/hour or higher minimum wage and gives the overall unemployment rate. Most of the
research on the minimum wage focuses on teenagers or restaurant workers, in
other words groups where minimum wage workers are highly represented. Minimum
wage workers only make up a tiny proportion of total workers (2.3% of workers,
according to the BLS). Total unemployment, calculated across the whole population, severely dilutes the effect of the minimum wage, and careful economists have caught on to this problem and adjusted their methods. I don’t know why they even bothered with this chart. It is far below the
standards of modern econometric studies on the effects of minimum wages.
There is a long discussion of Chicago’s minimum wage hike. I’m
not familiar with the attempts to study that particular city’s minimum wage
policy. They claim (citing a paper by one of the report’s authors) that “…the
policy change is working.” If I familiarize myself with the literature on the Chicago episode, I'll write up another blog post on that.
The report notes that many minimum wage studies find small
elasticities: “In their meta-analysis of 64 studies, Belman and Wolfson report
that a 10 percent increase in the minimum wage is statistically associated with
a small 0.2 and 0.6 percent drop in employment or hours.” A couple of reactions
to this. Do they really think that a 100% increase in the minimum wage would
only cause a 2% to 6% increase in unemployment to the relevant workers? Taking the low estimate: Would a 200% increase only cause a 4% increase in unemployment? That
seems implausible, but as we’ll see below they actually do take these estimates
and extrapolate them far beyond where they are appropriate. For another thing,
the disemployment effects are much stronger when you measure not just
employment (as in: Are you employed? Yes or No) but also measure hours worked.
You get much larger elasticities that way, and in fact the loss in hours worked
can be large enough that workers actually lose net income, despite their
higher wages.
Then they turn their attention to Seattle, which I do know a little about: “However, another recent study by researchers at the University of
California, Berkeley found that minimum wage increases in Seattle resulted in
higher earnings for affected workers in food service but had no negative impact
on their employment.” This is incredibly misleading. They fail to cite the two
papers by Jardim et. al. which had a much more detailed dataset. The Jardim group
had access to state unemployment insurance data which had “hours worked” in
addition to earnings, which allowed them to compute the hourly wages for each
worker, before and after the minimum wage hike. This allowed them to 1) accurately identify low-wage workers and 2) track "hours worked" over time at the individual worker level. They found huge disempoyment
effects, but these showed up as lost hours worked and slowed growth of jobs in the
low-wage sector. The Berkley group’s study was too crude to pick up these
effects. In fact, the Jardim et. al. papers effectively replicated the Berkley
group’s findings by only looking at restaurant workers (in other words, by
ignoring some of the rich features of their dataset), which is strong evidence
that all these “null result” papers are hobbled by inadequate datasets. When
you have the data in its full detail, the disemployment effects show up quite
clearly. Pardon me for saying so, but this shows very bad faith on the part of
the authors of the ILEPI report. Clearly the results of the Jardim group
discredit the conclusion the ILEPI would like to reach, so they fail to disclose it
to their readers. (The Jardim et. al. papers were out when the ILEPI published this report.) This is part of the reason why we get so much bad policy.
The paper mentions more intense job search and reductions in
turnover as ways of explaining the low disemployment effects found in (some of)
the minimum wage literature. As I’ve written before, those are costs, not
benefits. People who are trying to justify a higher minimum wage need to be
upfront about this. A standard economic treatment of these issues treats them
as costs, as part of the deadweight loss. (See the last image in this post and the surrounding discussion; the small triangle is the deadweight loss from foregone employment that would have happened at the natural market wage, and the pink rectangle is the potential deadweight loss from extra job search.)
There is then a discussion of who benefits (demographically
speaking) and by how much. All of this is irrelevant if you don’t buy their
assumptions about disemployment effects, but go ahead and read it.
I was perturbed by the discussion and tables under the
heading Economic Impact: Minimum Wage
Hikes Would Grow the Illinois Economy. “Drawing on the economic research,
Figure 4 assumes that every 10 percent increase in the minimum wage causes a
1.1 percent increase in worker incomes and a 0.45 percent decrease in working
hours. These “elasticities” are midpoints between the comprehensive analysis of
dozens of minimum wage studies (Belman & Wolfson, 2014) and the more
recent, and perhaps more relevant, evaluation of the Chicago minimum wage hikes
(Manzo et al., 2018).” I criticized these assumptions and the resulting table,
Figure 4, in a recent post.
Here is an example of a calculation in which someone really is treating the minimum wage like a perpetual motion machine. This study (IMO a terrible one, more on that in a later post) by the Illinois Economic Policy Institute attempts to calculate the effects of a minimum wage on various economic outcomes. See Figure 4 and the associated discussion in the text. They claim that a literature review turns up a result that a 10% increase in the minimum wage results in a 1.1 percent increase in worker incomes and a 0.45 percent decrease in hours-worked (presumably this comes from the various studies measuring the elasticity of demand for low-skilled workers). They apparently think that you can extrapolate those numbers to arbitrarily high increases in the minimum wage, because that's exactly what Figure 4 is doing. I want to say, "Okay, show me what the result will be for a $50/hour minimum wage. Or $1,000/hour for that matter." They get that a $10/hour minimum wage will result in a 1% reduction in working hours and a 2.3% increase in worker incomes (from a starting point of an $8.25/hour minimum). They get this by calculating the change in the minimum wage, 10/8.25-1 = 21.2%, and simply multiplying through by the numbers above. So 21.2%* (1.1%/10%) = 2.3% for the change in worker incomes. 21.2% * (-0.45%/10%) = -1% for the reduction in worker hours. They do exactly the same thing for the $15/hour minimum wage: 15/8.25-1 = 81.8%. So 81.8% * (1.1%/10%) = +9.0% for the change in income and 81.8% * (-0.45%/10%) = -3.7% for the reduction in employment. If the 1.1% and 0.45% can really be extrapolated to arbitrarily high minimum wages, then they have a perpetual motion machine. The increase in incomes keeps going up forever. If asked about a $30 or $50 minimum wage, the authors might demur. "Oh, of course you'd start to see bigger disemployment effects at that point." But why wouldn't they also see it at $13 and $15/hour? The $13 and $15 are minimum wages far large than what the 1.1% and 0.45% numbers are calculated from, so even extrapolating this far is dubious.
This is a general criticism I have of this report and of
other advocates of minimum wages: Okay, so show me what happens with a $50
minimum wage. Or a $1,000 minimum wage. Are your equations and calculations
telling me something sensible? If they are obviously missing something at these
very large values, then isn’t it likely they’re missing something, even if it’s
subtle, at lower values?
The report attempts to quantify “multipliers” using IMPLAN
software, which it refers to as a “’gold standard’ in economic impact analysis.”
I’m not familiar with the software, so someone who has done real scholarly
economic research can chime in and tell me if theirs is an accurate description or legitimate use of the software. I find it highly dubious that some off-the-shelf software can accurately
simulate a real economy after a policy change such as a minimum wage increase. I
don’t know if this kind of thing is common in economic research, but I’m pretty
sure it isn’t valid. Figure 5 shows the results of these simulations.
Unsurprisingly, they find net benefits for the $10, $13, and $15 minimum wage.
Here is where my general critique comes back in: Show me what happens when you
plug in $30 or $50, or $1,000 for that matter. Is there still a “net economic
benefit”, even though no reasonable person believes there would be one? To
their credit, they disclose that there would be a large reduction in hours
worked (again, assuming the IMPLAN computations are right): “The impact on
employment would be a drop of about 220 million labor-hours in Illinois.
However, despite the estimated drops in total hours of employment, the positive
economic impact means the minimum wage hike would positively impact more
workers than those who would be negatively impacted by it.” I am just
incredulous at this line of argument, which I’ve seen elsewhere. Even when you
get minimum wage advocates to admit to some kind of job loss, they dismiss them
in light of the “net benefits” or otherwise assume it will just turn out
alright for the people who lose their jobs. Maybe it’s the most vulnerable
workers with the lowest skill-level who lose their jobs, and the benefits
accrue to the better-off among the minimum wage workers? Indeed that would be a
sensible a priori assumption, and that’s basically what the Jardim et. al.
studies of the Seattle minimum wage hike found. Weighing these job losses
against economic gains simulated in canned software, and siding with the
simulated gains, is highly suspect.
The report claims: “As a result, more than 35,000 low-income
workers in Illinois would be lifted out of poverty if the minimum wage was
increased to $10 an hour. This would represent a 2 percent drop in the total
number of people living in poverty across Illinois.” Again, we’re assuming that
the increased wages aren’t offset by hours reductions or job cuts, which would
plunge some of these workers into even
worse poverty than what they’re now experiencing. See their summary of
poverty reductions in Figure 6. Again, I’d like to see what this table looks
like for very large increases in the minimum wage. If it tells us that there would
be a large reduction in poverty at $20 or $25, it would make me even more
skeptical of what it’s telling me about what’s happening at $10 and $13. Figure
7 in the same section attempts to quantify the impact on the Illinois State
budget. Higher minimum wages, to the extent that they actually increase
take-home pay, might increase income, sales, and property taxes. They’d also
make people less dependent on SNAP and other transfer programs. Once again, the
bigger the minimum wage hike, the more money Illinois saves! I hate to repeat
myself, but let’s see them plug in $50 or some other absurdly high value. If
they think they have a true perpetual motion machine, let them say so. If they
don’t, let them explain what’s fundamentally different about “small” minimum
wage hikes. (Scare quotes around “small” because we’re talking about nearly a
doubling here.)
Obviously this report has a lot of problems, and I hope
nobody is citing to argue for a higher minimum wage. For my readers in other states,
check to see if your state has a counterpart to the Illinois Economic Policy
Institute. There is some low-hanging fruit here in terms of policy advocacy.
Many of these state-level think tanks are poor in resources and can’t afford to
fund high-quality scholarship. They put out stuff like this to influence policy.
Don’t let them succeed, not if they haven’t earned it. If they put out reports
that are full of mistakes, material omissions, poor arguments, and motivated
reasoning, call them on it If there is a local think tank that you are sympathetic
with, do the same for them and help them make more convincing arguments. Most
people in the policy analysis space probably think they can just uncritically
cite a study (like the ILEPI piece) and be believed by their receptive audience.
Don’t make it too easy for them. A lot of published research is just no good,
and a little bit of critical reading can go a long way. People who put out stuff like this under their name should feel some hesitation or embarrassment. They need to do their scholarship with the feeling that, "I can't just say anything. It has to at least make sense. Otherwise that jerk Jubal Harshaw is going to jump on me." David Henderson set an excellent example of what I'm talking about here, here, and here. Maybe the authors of the CEA report were able to brush off Henderson's criticism with a, "Who cares what this Henderson guy thinks." But I think most academics, deep down, are honest and will feel nagged by the idea that they've said something wrong or easily critique-able. Putting out these critiques slightly changes the incentives in an Adam Smith Theory of Moral Sentiments sort of way, if not in a Wealth of Nations sort of way.
Monday, April 22, 2019
The Government Version of Price Discrimination
Local governments often dig themselves into a deep hole. Tax rates need to be high enough to cover overall spending, some of which is impossible to cut. Pensions and other long term liabilities are often a big piece of that. A city can't simply decide not to pay pensions, because those are contractual obligations. The problem is that tax rates that are high enough to pay for spending are so high they deter new businesses from entering. So local governments often offer special tax breaks and other incentives to specific businesses to entice them. They couldn't simply offer everyone the same deal, because they'd go broke. (Broke-er?) But it's still a winning move to give a specific new business a lower tax rate. It's a good idea for such a city to charge different tax rates to different businesses, depending on their willingness to incorporate elsewhere.
It struck me that this looks a lot like the common practice of "price discrimination," which is economists' horrible term for charging different prices for the same product or service based on the buyer's willingness to pay. It's a perfectly defensible practice, even though people get morally indignant about the possibility that they might not get the absolute lowest possible price. Actually, even people paying the high price benefit (generally) from price discrimination. The price discriminating company can attract more customers by offering different prices to different customers, which allows them to spread fixed costs across a larger portfolio of customers. That allows overall prices to be lower than otherwise. Price discrimination is common when there are high fixed costs. It might cost you, say, $10 on average to serve all of your customers, naively dividing total expenses by total customers. But in many cases the marginal cost of servicing one additional customer is lower than the average cost. Maybe you're a restaurant with excess capacity near the end of the lunch hour, and you'll have to throw out a bunch of prepared items if they don't get sold. You might be willing to offer some kind of discount on the fly. Or maybe you're a doctor serving both wealth and indigent populations, and you know your less wealthy patients will simply do without medicine if you don't offer them a low price. It can be much more subtle than these examples.
I should be clear here. I don't think local governments are engaging in clever, well-planned strategies to maximize their revenues. I think they're locked in place by fiscal profligacy, because previous generations of politicians made irresponsible promises to their constituents. Still, there's a superficial resemblance here, so I thought I'd remark on it.
It struck me that this looks a lot like the common practice of "price discrimination," which is economists' horrible term for charging different prices for the same product or service based on the buyer's willingness to pay. It's a perfectly defensible practice, even though people get morally indignant about the possibility that they might not get the absolute lowest possible price. Actually, even people paying the high price benefit (generally) from price discrimination. The price discriminating company can attract more customers by offering different prices to different customers, which allows them to spread fixed costs across a larger portfolio of customers. That allows overall prices to be lower than otherwise. Price discrimination is common when there are high fixed costs. It might cost you, say, $10 on average to serve all of your customers, naively dividing total expenses by total customers. But in many cases the marginal cost of servicing one additional customer is lower than the average cost. Maybe you're a restaurant with excess capacity near the end of the lunch hour, and you'll have to throw out a bunch of prepared items if they don't get sold. You might be willing to offer some kind of discount on the fly. Or maybe you're a doctor serving both wealth and indigent populations, and you know your less wealthy patients will simply do without medicine if you don't offer them a low price. It can be much more subtle than these examples.
I should be clear here. I don't think local governments are engaging in clever, well-planned strategies to maximize their revenues. I think they're locked in place by fiscal profligacy, because previous generations of politicians made irresponsible promises to their constituents. Still, there's a superficial resemblance here, so I thought I'd remark on it.
Wednesday, April 10, 2019
Were Early Climate Scientists "Climate Deniers?"
The question was so deep that almost no one had thought to ask it before: Does a climate exist? That is, does the earth’s weather have a long-term average? Most meteorologists, then as now, took the answer for granted. Surely any measurable behavior, no matter how it fluctuates, must have an average. Yet on reflection, it is far from obvious. As Lorenz pointed out, the average weather for the last 12,000 years has been notably different than the average for the previous 12,000, when most of North America was covered by ice. Was there one climate that changed to another for some physical reason? Or is there an even longer-term climate within which those periods were just fluctuations? Or is it possible that a system like the weather may NEVER converge to an average?
This is from James Gleick’s excellent book Chaos. Earth's climate (at least as it was understood at the time Gleick wrote Chaos) is non-stationary. It doesn't have a long term average, and as the excerpt implies one epoch's "average" climate can be very different from another's. Like a random walk, it doesn't settle on or revert to a long term average value. The book contains a reference to this paper from 1964, to flesh out some of the details. I don't know how much or how fast the Earth's climate changes due to these endogenous drifts (as opposed to forcings like CO2 or aerosols), but it seems like this should color our view of climate change somehow.
I wonder of Gleick would feel uncomfortable writing that paragraph today. The climate of intellectual thought has indeed drifted to a different regime, and some of these basic points about the history of climate science might not be welcome. There is a discussion in Chaos of Earth getting "kicked" into a very different climate and getting stuck in that equilibrium. But it's not the "Venus Earth" scenario or even a very much warmer but still livable Earth. Rather, early climate modelers believed, based on their computer generated scenarios, that a "White Earth" was possible: an Earth in which the oceans are all covered in snow and the continents with ice. Climatologists were scratching their heads that their computer simulations kept falling into this scenario, but the real Earth never seems to have switched to this regime.
Don't read me as having some kind of hidden meaning or implication here. I'm just sharing because I found it interesting. Chaos is well worth a read.
I wonder of Gleick would feel uncomfortable writing that paragraph today. The climate of intellectual thought has indeed drifted to a different regime, and some of these basic points about the history of climate science might not be welcome. There is a discussion in Chaos of Earth getting "kicked" into a very different climate and getting stuck in that equilibrium. But it's not the "Venus Earth" scenario or even a very much warmer but still livable Earth. Rather, early climate modelers believed, based on their computer generated scenarios, that a "White Earth" was possible: an Earth in which the oceans are all covered in snow and the continents with ice. Climatologists were scratching their heads that their computer simulations kept falling into this scenario, but the real Earth never seems to have switched to this regime.
Don't read me as having some kind of hidden meaning or implication here. I'm just sharing because I found it interesting. Chaos is well worth a read.
So Everyone Knows the "Right" Amount of Federal Spending on the Special Olympics?
I was annoyed by the media's pouncing on Besty DeVos for proposing a budget that cut federal funding to the Special Olympics. My first thought was that isn't not the business of government to be funding such things in the first place, no matter how laudable the organization's work. "How much should we fund the Special Olympics with federal money?" is a very different question from "Do we as a society value the work done by this organization?" The answer to the former can be zero, while the answer to the latter can be "Yes, very much so." A society that highly values religion and religious institutions (as in early America) would still be very wise to draw a boundary separating church and state. I don't see why that principle shouldn't apply here.
My slightly snarkier response was, "Oh, really? Suddenly everyone knows exactly the right quantity of federal spending on the Special Olympics? And that it just so happens to be the current amount?" What brilliant policy analysts we all are! How wonderful it is that we all paused, took a deep breath, and performed a cost-benefit analysis of this single budget item. Amazing that we all computed the operating needs of the Special Olympics, estimated the volume of charitable giving that can be counted upon, and came up with precisely $17.6 million as the difference. High-fives for not answering such important policy questions with your emotions!
My even snarkier response was something like: "Why do you all hate the Special Olympics so much?" (I very nearly titled this post "Why Do Betsy DeVos's Critics Hate the Special Olympics So Much?", but then I realized that some people only read the title and don't bother to read the actual post.) Nobody was calling for an increase in spending on the Special Olympics. Nobody was paying attention at all until it became a hot-button news item. There's a lot of room between $17.6 million and infinity. Why weren't you calling for an increase? Here is a crystal ball that shows us a world where that number is twice as high, or another one where it's ten times as high. Would the denizens of that world pounce on you for your insufficient spending on the Special Olympics? Would they scorn you for failing to act to increase it? I'm sure in those worlds, anyone arguing to cut spending on a beloved cultural event would face the same kind of wrath as Betsy DeVos. There is no rational sense of "the right amount," just an emotional reaction that "beloved cultural icons shouldn't be defunded, ever." This should give us pause, because there is no way of deciding that the current amount of spending on something is too high (even if it really is too high). The recent flap over the Special Olympics has nothing to do with actual policy and everything to do with virtue signalling. There is plenty of reason to believe that the Special Olympics would be able to cover the shortfall with additional charitable givings if the federal government cut them out of the budget. See the Reason piece linked to above. The Special Olympics has revenues of $149 million, and federal grants are ~10% of that. A slightly more aggressive fundraising drive would surely cover any shortfall. Unless we really don't value the Special Olympics as a society. In which case forcing people to subsidize it is just wrong.
How to even steelman this? Maybe someone thinks, "Governments are deliberative in setting budgets, so we can be confident that the $17.6 million figure is right. It's vetted by many expert policy analysts. That explains why it doesn't need to be higher." In fact I often hear this "policy is the way it is for a good reason" argument, but I think that's a mistake. If I were trying to make the case that budget decisions are not deliberative and not backed by any kind of rational analysis, the DeVos story would be Exhibit A. "It's $17.6 million. Let's make it zero!" (public outcry) "Okay, we won't cut it at all!"
My slightly snarkier response was, "Oh, really? Suddenly everyone knows exactly the right quantity of federal spending on the Special Olympics? And that it just so happens to be the current amount?" What brilliant policy analysts we all are! How wonderful it is that we all paused, took a deep breath, and performed a cost-benefit analysis of this single budget item. Amazing that we all computed the operating needs of the Special Olympics, estimated the volume of charitable giving that can be counted upon, and came up with precisely $17.6 million as the difference. High-fives for not answering such important policy questions with your emotions!
My even snarkier response was something like: "Why do you all hate the Special Olympics so much?" (I very nearly titled this post "Why Do Betsy DeVos's Critics Hate the Special Olympics So Much?", but then I realized that some people only read the title and don't bother to read the actual post.) Nobody was calling for an increase in spending on the Special Olympics. Nobody was paying attention at all until it became a hot-button news item. There's a lot of room between $17.6 million and infinity. Why weren't you calling for an increase? Here is a crystal ball that shows us a world where that number is twice as high, or another one where it's ten times as high. Would the denizens of that world pounce on you for your insufficient spending on the Special Olympics? Would they scorn you for failing to act to increase it? I'm sure in those worlds, anyone arguing to cut spending on a beloved cultural event would face the same kind of wrath as Betsy DeVos. There is no rational sense of "the right amount," just an emotional reaction that "beloved cultural icons shouldn't be defunded, ever." This should give us pause, because there is no way of deciding that the current amount of spending on something is too high (even if it really is too high). The recent flap over the Special Olympics has nothing to do with actual policy and everything to do with virtue signalling. There is plenty of reason to believe that the Special Olympics would be able to cover the shortfall with additional charitable givings if the federal government cut them out of the budget. See the Reason piece linked to above. The Special Olympics has revenues of $149 million, and federal grants are ~10% of that. A slightly more aggressive fundraising drive would surely cover any shortfall. Unless we really don't value the Special Olympics as a society. In which case forcing people to subsidize it is just wrong.
How to even steelman this? Maybe someone thinks, "Governments are deliberative in setting budgets, so we can be confident that the $17.6 million figure is right. It's vetted by many expert policy analysts. That explains why it doesn't need to be higher." In fact I often hear this "policy is the way it is for a good reason" argument, but I think that's a mistake. If I were trying to make the case that budget decisions are not deliberative and not backed by any kind of rational analysis, the DeVos story would be Exhibit A. "It's $17.6 million. Let's make it zero!" (public outcry) "Okay, we won't cut it at all!"
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