More on climate risk, insurance, and moral hazard

Nearly two years ago, I posted about climate risk and disaster insurance, noting that insurance premiums for the homes most at risk as a result of climate change, including coastal properties, were likely to face increasing insurance premiums. The most surprising thing is that, nearly two years on, there hasn't yet been a major shift by the insurers. Until now, as the New Zealand Herald reported last month:

A major insurer is eyeing risk-based pricing for coastal erosion, in what's being described as another landmark step by the industry to confront climate threats.

Last year, Tower became New Zealand's first insurer to introduce a new pricing model based on individual homes' risk of flooding from rainfall and rivers – and to make such ratings public.

It meant about 100,000 customers received either a low, medium or high rating for their home, reflecting the potential risk of a flood and the estimated cost of replacing or repairing.

About one in 10 customers received a small hike in the flood risk portion of their premiums – while a few hundred that received a high or very high ratings saw increases of more than $500 a year.

In some cases, the company needed to find customers alternative insurance cover, chief executive Blair Turnbull told the Herald.

 That story came hot on the heels of this one the day before, also from the New Zealand Herald:

Properties worth $1 million on Wellington's Petone foreshore could cost $100,000 a year to insure in 20 years, a climate risk expert says.

The warning came as the Government grappled with whether to set up its own flood insurance scheme to cover people as private insurers become less willing to.

Climate change is driving increasingly common and damaging storms, and it, coupled with sea level rise, means thousands of homeowners in harm's way face spiralling premiums or having cover pulled altogether.

The scary thing about that story is the mere suggestion that the government might get involved in offering insurance to high-risk properties. That is exactly the problem I was concerned about in my post from two years ago. The government offering insurance to coastal properties, or properties on flood plains, or those at risk of severe erosion, or whatever, leads to a problem of moral hazard.

Moral hazard arises when one of the parties to an agreement has an incentive, after the agreement is made, to act differently than they would have acted without the agreement. In this case, the agreement is between the government, and homeowners (or potential homeowners) of high-risk properties. After the government creates an insurance scheme for high-risk properties (or agrees to subsidise insurance premiums in some way), that reduces the costs of owning an at-risk property. When the cost of something decreases, we tend to do more of it than we would otherwise. At the margin, people will be a little more likely to buy or live in at-risk properties, or to construct more at-risk properties. It likely makes the problem of the amount of assets (and people) at risk of sea level rise, coastal inundation, erosion, etc. even worse.

This is not just an issue that New Zealand is grappling with. As this July article in The Conversation, by Brian Cook and Tim Werner (both University of Melbourne) notes in relation to the Sydney floods that month:

In flood risk management, there’s a well-known idea called the “levee effect.” Floodplain expert Gilbert White popularised it in 1945 by demonstrating how building flood control measures in the Mississippi catchment contributed to increased flood damage. People felt more secure knowing a levee was nearby, and developers built further into the flood plains. When levees broke or were overtopped, much more development was exposed and the damages were magnified. “Dealing with floods in all their capricious and violent aspects is a problem in part of adjusting human occupance,” White wrote.

Cook and Werner note that:

To tackle flood risk, we have to respond to the social, political, economic, and environmental factors that drive development and occupation of floodplains.

Surprisingly, Cook and Werner don't note the additional problems that providing additional insurance to at-risk property owners would create. They are right that there are a range of inter-related factors that lead to development in at-risk and largely inappropriate locations. Their solution is to prohibit development in those areas. Prohibition is a very blunt instrument, but at the very least government shouldn't be considering policies that would incentivise more at-risk development. We need to ensure that homeowners and developers adequately take into account the actual climate risks that their properties face. There is evidence that coastal properties are not sufficiently risk-priced (see this post). Only then will we see a reduction in at-risk developments, as well as saving the taxpayer from covering the costs of coastal property owners' and developers' decisions.

*****

Read more:

• Climate change risk, disaster insurance, and moral hazard

Is this increasing gender equity, or gender inequity?

A post at the Dangerous Economist pointed me to this 2020 Medium article by Koen Smets (which is worth reading in its entirety):

Motor insurance in Europe forms a very interesting case study. Traditionally, insurers charged women less, because they tend to be safer drivers, and hence make fewer and smaller claims. Unlike life expectancy, the factors determining the risk here are much more linked to individual choice and behaviour. Since 2012, an EU directive forbids insurers to use gender as an element in the calculation of the premium. So, in Q4 2011, men paid on average 17% more than the overall average premium, while women paid 20% less. In 2018, that difference with the overall average premium had shrunk to a 5% uplift for male drivers, and a 6% discount for female drivers. (The residual difference stems from the fact that men tend to drive more miles per year, in more powerful cars.) So, relatively speaking, the ‘gender equity’ intervention has increased the average premium for the lower-risk women by more than 17%, while it has cut it by just under 10% for the higher-risk men. Is this an improvement? It’s not so sure. [sic]

Insurers charge higher motor vehicle insurance premiums to male drivers, because male drivers cost the insurers more. Male drivers tend to drive more kilometres, and have more severe accidents (see also here). It is reasonable for insurers to charge a higher premium to a more costly segment of the population, especially where those drivers are more costly because of their own behaviour (men could be lower cost to insurers, if they drove differently).

Insurance is subject to an asymmetric information problem that economists refer to as adverse selection. The uninformed party (the insurer) cannot easily tell drivers with 'good' attributes (low-risk drivers) apart from drivers with 'bad' attributes (high-risk drivers). To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that every driver is high risk. This leads to a pooling equilibrium - low-risk drivers are grouped together with the high-risk drivers and all drivers pay the same premium, because they can't easily differentiate themselves.

Since the premium is based on drivers of all risks on average, many low-risk drivers will find the cost of insurance to be too high. They will drop out of the market. The average risk of the remaining pool of insured drivers will increase, so the insurer will need to increase the insurance premium. Medium-risk drivers may then find the premiums too high, and drop out of the market. So, insurers raise premiums again. And so on, until the market fails because only the riskiest drivers would be left, and the insurer surely doesn't want to insure them!

One way of avoiding this adverse selection problem is for insurers to try to reveal how risky a driver each insurance applicant is. That way, we would have a separating equilibrium, where high-risk drivers pay higher premiums, and low-risk driver pay lower premiums. When the uninformed party tries to reveal private information (like how risky a driver an insurance applicant is), we refer to this as screening. In this case, the insurer uses various characteristics of the insurance applicant to estimate how risky they are likely to be. These characteristics might include their insurance history, past driving behaviour, the type of car they are insuring, and their demographic characteristics (including age and gender).

By imposing a law that equalises insurance premiums for men and women, the government is essentially telling insurers that they can no longer use gender as a screening tool for determining which drivers are higher risk. In the absence of that information, the insurer is a bit less informed than before. It moves things back towards the pooling equilibrium (but not all the way, because insurers still know the other details of the applicants). At the margin, insurers will now tend to over-estimate the risk of female drivers, and under-estimate the risk of male drivers. The consequence of this is that the premium for riskier male drivers becomes lower than it would have been without the law, and the premium for female drivers becomes higher than it would have been without the law. Essentially, relatively safe female drivers are cross-subsidising relatively riskier male drivers.

Wait! Wouldn't the intention of the EU directive have been to increase equity between female and male drivers? If you have a policy that equalises insurance premiums for male and female drivers, but in so doing makes male drivers better off and female drivers worse off, is that actually increasing gender equity, or decreasing gender equity? It would be interesting to know whether the policy makers had thought about this at all.

Read more:

• Car insurance, adverse selection and gender

The economics of the government's plan for 'social unemployment insurance'

One of the big (and surprising) announcements in the Budget earlier this year was that the government was developing a 'social unemployment insurance' scheme. This would presumably sit alongside the current unemployment benefit system, but would work in a similar way to accident compensation, paying each person who is made unemployed (and meeting certain conditions) 80 percent of their prior wage up to a certain cap.

This would represent a significant shift in the style of social security system that New Zealand operates. In my ECONS102 class, we distinguish three types (or models) of social security system:

1. A social assistance model - where there is an emphasis on self-reliance and responsibility, and the government provides support (often means tested) where a person would otherwise face hardship;
2. A social insurance model - where social assistance is available and based on previous contributions to a fund (which might be an individual account, or a general account for all insured people); and
3. A social citizenship model - where all citizens have a right to assistance for any contingencies they face (and the assistance is often not means tested).

In reality, most social security systems have features in common with all three types, but New Zealand's system up until now has mostly been a social assistance model, with the exception of accident compensation, which is clearly a social insurance scheme. This proposed introduction of social unemployment insurance would move unemployment assistance into the social insurance model (it would be interesting to see what the government would do with sickness and invalids benefits, or whether they would remain under the old system, along with sole parents and student allowances).

Anyway, there was a great article in The Conversation today by Simon Chapple and Michael Fletcher (both Victoria University of Waikato) that outlines some of the economic issues with a social insurance scheme:

However, there are two problems with the private insurance market, meaning they under-provide relative to people’s real need.

The first problem is called “adverse selection”, meaning people choosing to buy insurance have better information about the risks facing them than insurance businesses do, and no good reason to disclose that information.

To protect themselves from this, insurance companies set premiums higher. In turn, due to the costs, this leads to people being under-insured. Ultimately, society’s best interests aren’t met.

There’s also the problem of “moral hazard” – if a person has insurance they may take on more risk, without the insurer knowing exactly which customers are adopting riskier behaviour.

Again, insurance companies set higher premiums and people are generally under-insured. And again, this isn’t in society’s best interests...

These market failures mean there is potential for well-designed government interventions to meet the social interest. In particular, making everyone join a social insurance scheme would fix the adverse selection problem.

But a compulsory social insurance system also expands the scope for moral hazard. People might change their behaviour to increase their eligibility for an insurance payout. They might take on jobs with higher redundancy risks, or be less motivated to look for work, because the consequences are now less severe.

The problems of information asymmetry (including adverse selection and moral hazard) is among my favourite topics to teach in my ECONS102 class. Chapple and Fletcher are right that the unemployment social insurance scheme would not have an adverse selection problem (provided it is compulsory, in the same way that accident compensation currently is), and the key problems would be moral hazard.

To expand on the moral hazard problems a little bit, workers would be less fearful of losing their jobs, because they would receive a higher unemployment payment than previously. So, at the margin, workers would not work as hard, and productivity might decrease. Similarly, absenteeism might increase, which also reduces productivity. 

On the other hand, wages might increase. To see why, consider a search model of the labour market. This model recognises that each matching of a worker and a job creates a surplus that is shared between the worker and the employer, based on their relative bargaining power. A higher unemployment payment increases the worker's bargaining power, since they can afford to hold out for a better deal. Employers will have to offer slightly higher wages than before, in order to attract workers to leave the unemployment payment and accept the job offer. So, wages will increase, and employers will find that vacancies take a little longer to fill.

Workers may also benefit from better job matches. Since they can afford to stay on the higher unemployment insurance payment for longer, they can afford to wait and find a job they really want, rather than accept the first half-decent offer they receive. The number of unemployed will likely increase, and the average length of unemployment spells will also increase.

Clearly, there is a lot for the government to balance here. Chapple and Fletcher also note that:

If it turns out there are gaps in the current system, advocates of social insurance must also consider:

• such a scheme may simply be substituting for one or several of the existing solutions, which would then reduce if the scheme were introduced

• reforming and improving what already exists may be preferable in terms of cost, effectiveness and equity than introducing an entirely new system

• there may be implications for both equity and erosion of the core welfare system of creating a separate, higher tier of assistance for some.

At this stage, all we have had from the government is an announcement, and a promise of "public consultation later in 2021". Presumably that consultation has been delayed until next year, due to the pandemic. It will be interesting to see what comes out of this.

Climate change risk, disaster insurance, and moral hazard

One problem that insurance companies face is moral hazard - where one of the parties to an agreement has an incentive, after the agreement is made, to act differently than they would have acted without the agreement and bring additional benefits to themselves, and to the expense of the other party. Moral hazard is a problem of 'post-contractual opportunism'. The moral hazard problem in insurance occurs because the insured party passes some of the risk of their actions onto the insurance company, so the insured party has less incentive to act carefully. For example, a car owner won't be as concerned about keeping their car secure if they face no risk of loss in the case of the car being stolen.

Similar effects are at play in home insurance. A person without home insurance will be very careful about keeping their house safe, including where possible, lowering disaster risk. They will avoid building a house on an active fault line, or on an erosion-prone clifftop, for example. In contrast, a person with home insurance has less incentive to avoid these risks [*], because much of the cost of a disaster would be borne by the insurance company.

Unfortunately, there are limited options available to deal with moral hazard in insurance. Of the four main ways of dealing with moral hazard generally (better monitoring, efficiency wages, performance-based pay, and delayed payment), only better monitoring is really applicable in the case of home insurance. That would mean the insurance company closely monitoring homeowners to make sure they aren't acting in a risky way. However, that isn't going to work in the case of disaster insurance. Instead, insurance companies tend to try to shift some of the risk back onto the insured party through insurance excesses (the amount that the loss must exceed before the insurance company is liable to pay anything to the insured - this is essentially the amount that the insured party must contribute towards any insurance claim).

And that brings me to this article from the New Zealand Herald from a couple of weeks ago:

Thousands of seaside homes around New Zealand could face soaring insurance premiums - or even have some cover pulled altogether - within 15 years.

That's the stark warning from a major new report assessing how insurers might be forced to confront the nation's increasing exposure to rising seas - sparking pleas for urgent Government action.

Nationally, about 450,000 homes that currently sit within a kilometre of the coast are likely to be hit by a combination of sea level rise and more frequent and intense storms under climate change...

The report, published through the Government-funded Deep South Challenge, looked at the risk for around 10,000 homes in Auckland, Wellington, Christchurch and Dunedin that lie in one-in-100-year coastal flood zones.

That risk is expected to increase quickly.

In Wellington, only another 10cm of sea level rise - expected by 2040 - could push up the probability of a flood five-fold - making it a one-in-20-year event.

International experience and indications from New Zealand's insurance industry suggest companies start pulling out of insuring properties when disasters like floods become one-in-50-year events.

By the time that exposure has risen to one-in-20-year occurrences, the cost of insurance premiums and excesses will have climbed sharply - if insurance could be renewed at all.

Because insurance companies have few options for dealing with moral hazard associated with disaster risk, their only feasible option is to increase insurance excesses, and pass more of the risk back onto the insured. Increasing the insurance premium also reflects the higher risk nature of the insurance contract. Even then, in some cases it is better for the insurance company to withdraw cover entirely from houses with the highest disaster risk.

I'm very glad that the Deep South report and the New Zealand Herald article avoided the trap of recommending that the government step in to provide affordable insurance, or subsidise insurance premiums for high-risk properties. That would simply make the moral hazard problem worse. Homeowners (and builders/developers) need the appropriate incentives related to building homes in the highest risk areas. Reducing the risk to homeowners (and buyers) by subsidising their insurance creates an incentive for more houses to be built in high-risk locations. However, as the New Zealand Herald article notes:

Meanwhile, homeowners were still choosing to buy, develop and renovate coastal property, and new houses were being built in climate-risky locations, said the report's lead author, Dr Belinda Storey of Climate Sigma.

"People tend to be very good at ignoring low-probability events.

"This has been noticed internationally, even when there is significant risk facing a property.

"Although these events, such as flooding, are devastating, the low probability makes people think they're a long way off."

Storey felt that market signals weren't enough to effect change - and the Government could play a bigger role informing homeowners of risk.

Being unable to insure one of these properties creates a pretty strong disincentive to buying or building them. Perhaps withdrawal of insurance cover isn't a problem, it's the solution to a problem? 

*****

[*] Importantly, the homeowner with insurance doesn't face no incentive to avoid disaster risk, because while the loss of the home and contents may be covered by insurance, there is still a risk of loss of life, injury, etc. in the case of a disaster, and they will want to reduce that risk.

How insurers can use data to beat adverse selection and moral hazard

This week, my ECONS102 class has been covering the economics of information. In particular, we focus on the problems of information asymmetry, and we spend a fair amount of time talking through problems of adverse selection. Adverse selection arises when one of the parties to an agreement (the informed party) has private information that is relevant to the agreement, and they use that private information to their own advantage at the expense of the uninformed party.

A classic example of adverse selection, which I've blogged about many times, occurs in the market for insurance (regardless of whether we are discussing home insurance, car insurance, health insurance, or even life insurance). The insured person knows whether they are high risk or low risk, but the insurer doesn't know - risk is private information. Since the insurer doesn't know how risky any person applying for insurance is, their best option is to assume that everyone is high risk. We refer to this as a pooling equilibrium - all insurance applicants are pooled together as if they are the same risk. The insurer then sets the insurance premium on the basis of the risk pool they think they have (high risk). The low risk people will (rightly) identify that the insurance premium is too high for them, and they drop out of the market, leaving only high risk people buying insurance. The insurance market for low risk people fails - they can't by insurance if they can't credibly convince the insurer that they are low risk. This problem is referred to as adverse selection, because the people who select into applying for insurance are the people that the insurer least wants to insure!

As you know, we do have insurance markets that cater to low risk people, so the markets must have adapted to deal with this adverse selection problem. This involves the private information (about the level of risk) being credibly revealed to the uninformed party (the insurer). If the insurer tries to reveal the private information, or tries to induce the informed party (the person applying for insurance) to reveal the private information, this is referred to as screening.

Insurers can screen applicants on the basis of their demographic and other information that they provide when they apply for insurance, their insurance history or credit history, and details about what they are insuring (house, car, health, life, etc.). However, insurers are increasingly using online data to screen applicants and determine their risk. Take this example, from The Wall Street Journal (gated) last year:

"Did you document your hair-raising rock-climbing trip on Instagram? Post happy-hour photos on Facebook? Or chime in on Twitter about riding a motorcycle with no helmet? One day, such sharing could push up your life insurance premiums.

In January, New York became the first state to provide guidance for how life insurers may use algorithms to comb through social media posts—as well as data such as credit scores and home-ownership records—to size up an applicant’s risk. The guidance comes amid expectations that within years, social media may be among the data reviewed before issuing life insurance as well as policies for cars and property.

If you're not thinking about how much information you reveal on social media, perhaps you should be now that it might cost you more in terms of insurance (on the other hand, if you are a low risk person, then perhaps your social media posts will earn you a lower insurance premium). However, that isn't the end of insurance companies' use of data.

Another information asymmetry problem in insurance happens after the insurance contract is agreed. This is the problem referred to as moral hazard - this problem arises when one of the parties to an agreement has an incentive, after the agreement is made, to act differently than they would have acted without the agreement. In the case of insurance, the insured party might act in a more risky manner when they are insured than they would have acted without insurance. They can do this because they have passed some of the (financial) risk of their actions onto the insurer.

One solution to moral hazard problems is for the uninformed party (the insurer) to monitor the actions of the informed party (the insured) more closely. And, you guessed it - insurers are looking at data to deal with moral hazard problems. As one example, Sven Tuzovic (Queensland University of Technology) wrote in The Conversation last year that:

...wearable devices are not only being embraced by consumers, but also across insurance industries. Health and life insurance companies collect data from fitness trackers with the goal of improving business decisions.

Currently, these business models work as a “carrot” incentive. That means consumers can benefit from discounts and cheaper premiums if they are willing to share their Fitbit data.

But we could see voluntary participation become mandatory, shifting the incentive from carrot to stick. John Hancock, one of the largest life insurance companies in the United States, has added fitness tracking with wearable devices to all of its policies. Though customers can opt out of the program, some industry experts argue that this “raises ethical questions around privacy and equality in leaving the traditional life insurance model behind”.

In terms of moral hazard, the insured is less likely to engage in risky behaviour if they know that their insurer is watching their every move. Insurers can use the data they collect from devices like Fitbit to not only monitor the insured, but also to determine their risk and adjust future premiums. It potentially solves both moral hazard and adverse selection problems at the same time.

And this is just the beginning. Insurers may turn to more sophisticated artificial intelligence tools in the near future, as David Tuffley (Griffith University) wrote in The Conversation last year:

Then you have a car accident. You phone your insurance company. Your call is answered immediately. The voice on the other end knows your name and amiably chats to you about your pet cat and how your favourite football team did on the weekend.

You’re talking to a chat-bot. The reason it “knows” so much about you is because the insurance company is using artificial intelligence to scrape information about you from social media. It knows a lot more besides, because you’ve agreed to let it monitor your personal devices in exchange for cheaper insurance premiums.

This isn’t science fiction. More than three-quarters of insurance executives believe artificial intelligence will revolutionise the industry within a few years. By 2030, according to McKinsey futurists, artificial intelligence will mean your car and life insurance premiums could change based on whether you decide to take one route or another.

If you're starting to think that there is nowhere to hide, you're right. Even if you refuse to let your insurer access your data, you're simply suggesting to the insurer that you are high risk. The insurer may frame it as if those agreeing to share data are receiving a discount, but really they are applying a higher premium to the high risk people who are least likely to want to share their data.

Should we be worried? Arguably no, unless we are high risk people wanting to pass ourselves off to insurers as low risk. Otherwise, we get insurance priced at premiums that is actuarially fair and accurately reflects our level of risk. However, as Tuffley notes, we should be concerned about what happens to the data that insurers collect about us:

An insurer might also be tempted to use the data for purposes other than assessing risk. Given its value, the data might be sold to third parties for various purposes to offset the cost of collecting it. Advertisers, marketers, lobbyists and political parties are all insatiably hungry for detailed demographic data.

It pays to read the fine print on contracts, and if insurers are going to collect much more data about us in the future, we should at least be aware of the risks of what will happen to that data.

[HT: The Dangerous Economist last year, for the Wall Street Journal article]

Read more:

• Your Fitbit will betray you
• The most surprising thing I learned about home insurance this year
• Could your social media posts make insurance more expensive?
• Why your Fitbit or Apple Watch could soon get you cheaper health insurance
• Could technology eliminate moral hazard in car insurance?
• Is there adverse selection in the life insurance market?

Why having a safer car could make your car insurance more expensive

When a car owner buys car insurance, they are looking to shift some of the cost of an accident onto the insurer. In exchange, the car owner agrees to pay an annual (or monthly) premium to the insurer.

Is selling insurance worth it to the car insurer? As with any decision, it depends on weighing up the costs and benefits. If the costs of offering insurance are greater than the benefits, then selling insurance would make the car insurer worse off (they would make a loss), and so they shouldn't sell car insurance. If the benefits are greater than the costs, then selling car insurance makes the car insurer better off (they would make a profit), and so they should sell car insurance.

How does the car insurer decide on the insurance premium? Obviously, they need to know how much it will cost them, and then set the premium to be higher than the cost (to ensure that they make a profit). Let's simplify things and say that all cars are the same, and all have the same chance of being involved in an accident. In simple terms, the cost of selling insurance to a car owner is the repair and other costs that the insurer would have to pay in the case of an accident, multiplied by the probability of there being an accident. For example, if there is a 5% chance on average of there being an accident in any year, and the accident would cost $20,000 in repairs and other costs, then the cost of providing insurance would be $1,000 ($20,000 x 5%) per year. In order to make a profit, the insurer would have to charge an insurance premium of more than $1,000 per year.

So, what happens to the car insurance premium if cars get safer? The probability of an accident probably goes down, but the cost of repairs goes way up. So, does the cost of providing car insurance go down (less chance of having to pay the repair costs), or up (when there is a repair, the cost of the repair is much higher)? We can't tell - the effect on the cost of providing insurance is ambiguous, because we can't tell if the cost goes up or down.

However, as this recent article in Wired reports:

American car insurance rates are going up up up. In the past decade, they climbed 29.6 percent, to an average of $1,548 in 2019 from $1,194 in 2011. The surge, detailed in a new report from insurance shopping site The Zebra, outpaced both inflation (by far) and the increase in average car prices (more narrowly). And it came even as the rate of crashes has fallen year over year.

Aggrieved drivers have plenty of directions to point their fingers. Vehicle theft is on the rise, and extreme weather fueled by climate change can destroy swaths of vehicles in short order...

A more surprising, counterintuitive culprit isn’t the wider world or the person behind the wheel but the car itself. It turns out that new features designed to keep vehicles in their lanes and out of trouble are contributing to rising insurance rates.

That’s because the sensors that power those systems make cars much more expensive to fix when they do crash. Dent a steel bumper, and a few hammer blows gets you back on the road. Smash one on a new car, and it could mean replacing a radar, a camera, and ultrasonic sensors, then calibrating them so they work properly. Replacing a cracked windshield now comes with the extra cost of having someone readjust any cameras that look through the glass. “Technology is playing a bigger role than ever in pricing,” says Nicole Beck, The Zebra’s communications chief. “It’s not actually making it cheaper for people.”

While some studies have shown the effectiveness of emergency braking, insurance companies haven’t yet seen enough evidence to justify a break in rates for most of these features. That’s not to say lane keeping, parking assist, and the rest don’t work. They’re all relatively new, and the actuaries aren’t yet confident that their benefits outweigh the extra costs they incur to repair.

So, these new safety features do reduce accident rates, but because they cost so much more to repair, the cost of providing insurance is increasing. So, insurers are passing the cost onto consumers, in the form of higher insurance premiums. However, this increase in insurance premiums might not last. As the article notes:

The good news for car owners is that the steep upward trend in rates may not last. More data showing the upsides of driver assistance tech may accrue. Repairs should get cheaper as more mechanics learn to replace and calibrate sensors and as the prices of those parts drop. The mystery lies in figuring out how long those trends will take to make their effects felt. “Knowing that it’ll happen eventually is pretty easy,” Carges [the chief actuary at Root Insurance] says. “Knowing exactly when the inflection point is, is not.”

When the costs of repairs come down, the cost of providing insurance comes down, and insurers will start to charge lower premiums for car insurance.

[HT: Marginal Revolution]

Evergreening insulin and market collusion

Yesterday I posted about price discrimination among pharmaceutical firms. However, engaging in price discrimination requires that firms have market power. One way that get market power is if the government grants them an exclusive licence to sell their product, such as through a patent. That market power is time-limited though, because patents expire. Unless, that is, you can argue that you have discovered a new use for your invention. So, pharmaceutical firms spend a lot of time and effort on trying to find new uses for their drugs (see my previous posts here and here, about Viagra), so that the patent can be renewed. This is called evergreening.

Evergreening is pretty widespread, and not just for well-known brand-name drugs. Consider the example of synthetic insulin, as described in this article by James Elliott and Elizabeth Pfiester:

Why aren’t we seeing more companies making insulin? There are many reasons for this, but patent evergreening is a big one. Patents give a person or organization a monopoly on a particular invention for a specific period of time. In the USA, it is generally 20 years. Humalog, Lantus and other previous generation insulins are now off patent, as are even older animal based insulins. So what’s going on? Pharmaceutical companies take advantage of loopholes in the U.S. patent system to build thickets of patents around their drugs which will make them last much longer (evergreening). This prevents competition and can keep prices high for decades. Our friends at I-MAK recently showed that Sanofi, the maker of Lantus, is no exception. Sanofi has filed 74 patent applications on Lantus alone, that means Sanofi has created the potential for a competition-free monopoly for 37 years.

So, don't expect cheap generic insulin to come onto the market for some time. Elliott and Pfiester also reveal a number of other reasons why insulin is expensive, including:

• Just three firms (Eli Lilly, Novo Nordisk and Sanofi) control over 90% of the insulin market worldwide, giving them a cosy oligopoly and a lack of competition on price;
• Collusion (as you would expect in an oligopoly), such as firms paying others not to enter certain markets ("it is actually legal for one insulin producer to pay another one not to enter the market"); and
• Price fixing (again, what you would expect from an oligopoly).

Evergreening is no surprise, since the incentives for that practice are created by the patent laws. However, taking advantage of market power through colluding on prices or market-splitting is illegal in most Western countries. Given that these practices raise prices, reduce the availability of insulin to patients, and reduce total welfare, it is surprising that the pharmaceutical firms can continue to get away with this. When it comes to health policy, this is surely some low-hanging fruit that can be picked to improve health cost-effectively (at the least, by lowering the cost to health funders, who can then divert more resources to other categories of health spending).

In the U.S. though, one problem that is not acknowledged in the Elliott and Pfiester article is that the health insurers, who fund much of the health system in the U.S., have very little incentive to reduce costs. The insurers don't care if the pharmaceutical firms are driving up prices, because the costs are simply passed onto consumers in higher health insurance premiums.

[HT: Marginal Revolution]

Read more:

• Evergreening viagra, revisited
• Evergreening viagra

Your Fitbit will betray you

Yesterday I wrote a post about how home insurers are starting to more accurately price house insurance based on natural hazard risk. Home insurance isn't the only area where insurers are looking at adopting more sophisticated screening methods to deal with adverse selection. Take this story from the New Zealand Herald in June:

Fitbits are already used to track your heart rate, the amount of exercise you do and how much you sleep - essential data that could potentially be used by insurance providers to determine your premiums.

The boom in wearable health tracking technology means we now have more information than ever before on health and well being of people at any given moment.

The Telegraph reports that information collected from these devices is already being used by insurers to calculate insurance premiums and there are concerns that this might lead to only the healthiest customers enjoying lower premiums.

This is serious business. Insurance companies have it in their interests not only to ensure the lowest-risk customers but also to detect potential health conditions before they become severe (and expensive). A study of the insurance market by the Swiss Re Institute, a research organisation, last year found that insurers had filed hundreds of patent applications relating to "predictive insurance modelling".

The issue that an uninformed health insurer or life insurer faces is essentially the same as the home insurer from yesterday's post. They can't tell the low-risk applicants from high-risk applicants. A pooling equilibrium develops, where everyone pays the same premiums (coarsely differentiated based on age, gender, and smoking status). A savvy and entrepreneurial insurer that was better able to tell who the low-risk insured people are could attract them away with lower premiums (knowing that they would cost less to insure because they are low risk).

So, that is effectively what insurers are starting to do. As the Herald article notes:

In making these moves, Insurance companies aim to collect data that could serve to help them make better policy decisions or even tweak existing policies over time.

The Telegraph reported that policy agreements increasingly feature clauses that allow insurers to collect data on their customers.

This is a point that I first made in a post back in 2015 (and an earlier post on technology in car insurance in 2014). We can all look forward to insurers asking for our Fitbit data when we apply for health or life insurance. And if we're fit and healthy, we'll give it to them. The people most likely to withhold that information are the unfit and unhealthy (and those who are most privacy-conscious). Denying access to your Fitbit data would probably be enough to signal to the insurer that you are high risk, and result in a declined application or a higher premium. So, even if you want to opt out of sharing your data, your Fitbit will still betray you.

Read more:

• The most surprising thing I learned about home insurance this year
• Why your Fitbit or Apple Watch could soon get you cheaper health insurance
• Could technology eliminate moral hazard in car insurance?
• Is there adverse selection in the life insurance market?

The most surprising thing I learned about home insurance this year

Home insurance markets are subject to adverse selection problems. When a homeowner approaches an insurer about getting home insurance, the insurer doesn't know whether the house is low-risk or high-risk. [*] The riskiness of the house is private information. In fact, the riskiness of the house is probably not known even to the homeowner, but let's assume for the moment that they have at least some idea. To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that every house is high-risk. This leads to a pooling equilibrium - low-risk houses are grouped together with the high-risk houses and owners of both types of house pay the same premium, because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

The market failure may arise as follows (this explanation follows Stephen Landsburg's excellent book The Armchair Economist). Let's say you could rank every house from 1 to 10 in terms of risk (the least risky are 1's, and the most risky are 10's). The insurance company doesn't know who is high-risk or low-risk. Say that they price the premiums based on the 'average' risk ('5' perhaps). The low risk homeowners (1's and 2's) would be paying too much for insurance relative to their risk, so they choose not to buy insurance. This raises the average risk of the homes of those who do buy insurance (to '6' perhaps). So, the insurance company has to raise premiums to compensate. This causes some of the medium risk homeowners (3's and 4's) to drop out of the market. The average risk has gone up again, and so do the premiums. Eventually, either only highest risk homeowners (10's) buy insurance, or no one buys it at all. This is why we call the problem adverse selection - the insurance company would prefer to insure low-risk homes, but it's the homeowners with high-risk homes who are most likely to buy.

Of course, insurers are not stupid. They've found ways to deal with this adverse selection problem. When the uninformed party (the insurer in this case) tries to reveal the private information (about the riskiness of the house), we refer to this as screening. Screening involves the insurer collecting information about the house and the homeowner in order to work out how risky the house is. With the private information revealed, the insurer can then price accordingly - higher-risk houses attract higher premiums, while lower-risk houses attract lower premiums. We have a separating equilibrium (the high-risk and low-risk houses are separated from each other in the market).

With all this in mind, this story from April surprised me greatly:

Other insurers are likely to follow NZX-listed Tower's lead and increase their focus on risk-based pricing for natural hazards, says an insurance expert...

Thousands of home-owners who live in high-risk earthquake-prone areas and insure via Tower are set to face hikes in their premiums while those in low-risk areas like Auckland will get a cut.

The insurance company, which is New Zealand's third largest general insurer, said it would stop cross-subsidising its policy-holders from April 1 in a bid to send a clearer message to home-owners about the risks of where they lived.

Tower chief executive Richard Harding said at the moment six Auckland households were paying more to subsidise insurance premiums for every one high-risk house in Wellington, Canterbury or Gisborne.

In other words, insurers previously weren't screening for all available private information before pricing their insurance for a given house. Essentially, owners of low-risk houses have been paying premiums that are too high, and owners of high-risk houses have been paying premiums that are too low. It took a little while, but eventually other insurers have also started to use risk assessments in determining insurance premiums, so this discrepancy is disappearing.

Why didn't the market break down due to adverse selection? The issue here is something I noted earlier in the post - the riskiness of a house is private information to both the insurer and the homeowner. If the homeowner doesn't know how risky their house is, owners of low-risk houses can't tell if the insurer is pricing their insurance too high relative to the risk of natural hazard damage. So, the owners of low-risk houses have no reason to drop out of the market. And, if the owners of low-risk houses don't drop out of the market, insurers have no reason to raise premiums.

However, that leaves the market open to disruption. As noted in the April article:

Jeremy Holmes, a principal at actuarial consulting firm Melville Jessup Weaver, said it was hard to say how long this would take. Insurers needed to be as good as their competitors at distinguishing risk.

"Otherwise they risk having their competitors target the lower-risk policyholders whilst they are left with only the higher risks ..."

An entrepreneurial insurer that was able to distinguish the low-risk houses from high-risk houses could start approaching owners of low-risk houses and offering them lower premiums. The remaining insurers would be left with higher-risk houses on average, and would have to raise premiums. This would increase the number of homeowners dropping out of the market (or rather, going to the insurer that was pricing according to risk). Tower was the first insurer to shift to risk-based premiums, so presumably they recognised this issue before any of the other insurers and acted exactly as you would expect - by moving to risk-based premiums before any potential disruptor could enter the market.

Still, it's a little surprising (to me, at least) that pricing based on natural hazard risk wasn't already happening.

*****

[*] For simplicity, I'm going to refer to low-risk houses and high-risk houses, when risk is probably as much a function of location as of the house itself. So, if you must, read 'low-risk house' as 'house with a low risk of damage in an earthquake', and 'high-risk house' as 'house with a high risk of damage in an earthquake'.

Car insurance, adverse selection and gender

Car insurance has an adverse selection problem. The uninformed party (the insurer) cannot tell those with 'good' attributes (low-risk people) from those with 'bad' attributes (high-risk people). To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that everyone is high-risk. This leads to a pooling equilibrium - low-risk people are grouped together with the high-risk people and pay the same premium, because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

To see how the car insurance market fails, consider this from an earlier post about health insurance (it applies just as well to car insurance):

In the case of insurance, the market failure may arise as follows (this explanation follows Stephen Landsburg's excellent book The Armchair Economist). Let's say you could rank every person from 1 to 10 in terms of risk (the least risky are 1's, and the most risky are 10's). The insurance company doesn't know who is high-risk or low-risk. Say that they price the premiums based on the 'average' risk ('5' perhaps). The low risk people (1's and 2's) would be paying too much for insurance relative to their risk, so they choose not to buy insurance. This raises the average risk of those who do buy insurance (to '6' perhaps). So, the insurance company has to raise premiums to compensate. This causes some of the medium risk people (3's and 4's) to drop out of the market. The average risk has gone up again, and so do the premiums. Eventually, either only high risk people (10's) buy insurance, or no one buys it at all. This is why we call the problem adverse selection - the insurance company would prefer to sell insurance to low risk people, but it's the high risk people who are most likely to buy. 

In order to solve an adverse selection problem, the uninformed party can try to reveal the private information - this is referred to as screening. Car insurance companies engage in screening by collecting information about every person who applies for insurance, including their demographic details and accident and insurance history. The insurers know that this information provides some clues as to who is higher risk and who is lower risk to insure. They can then separate the higher-risk and lower-risk groups, and we move from a pooling equilibrium to a separating equilibrium. Higher-risk drivers will pay higher car insurance premiums, and lower-risk drivers will pay lower premiums.

One of the key demographic variables that is associated with car insurance risk is gender. Women drivers are less risky to insure. On average, they have accidents at lower speeds, which are less costly to repair. So, female car owners pay lower car insurance premiums on average. Gender works as one screening tool because it is difficult to fake. Or it was, until this story from CBC in Canada in July:

He wanted a brand new car — a Chevrolet Cruze with all the trimmings.

As a man in his early 20s, he knew his insurance costs would be high.

So he became a woman, though only on paper.

"I have taken advantage of a loophole," said the man — we're calling him David — who spoke on the condition that his identity be kept confidential because of the potential repercussions...

After doing some research, he realized he needed a doctor's note to show the government he identifies as a woman, even though he doesn't.

"It was pretty simple," he said. "I just basically asked for it and told them that I identify as a woman, or I'd like to identify as a woman, and he wrote me the letter I wanted."...

David shipped the note and other paperwork off to the provincial government. And, a few weeks later, he received a new birth certificate in the mail indicating he was a woman.

"I was quite shocked, but I was also relieved," he said. "I felt like I beat the system. I felt like I won."

With the new birth certificate in hand, he changed his driver's licence and insurance policy.

All to save about $91 a month.

"I'm a man, 100 per cent. Legally, I'm a woman," he said.

"I did it for cheaper car insurance."

"David" may have just raised the cost of car insurance for all women in Canada. If car insurers can no longer believe from a drivers licence that an insurance applicant is a woman, then every gender will be pooled together as being the same risk. That means higher premiums for women (and ironically, lower premiums for men, which is what David wanted!).

[HT: Marginal Revolution]

Genetic tests, life and health insurance

Is there an unintended consequence of getting a genetic test? Increasingly, people are undergoing genetic tests to identify whether they are susceptible to various illnesses such as cancer. But, as Jane Tiller and Paul Lacaze (both Monash University) wrote in The Conversation earlier this week, there's a downside to this:

Australian insurers can increase premiums, exclude insurance cover for certain conditions such as cancer, or refuse insurance cover altogether purely based on your genetic test results.

Genetic tests look at DNA, the material that contains the instructions for our bodies to grow, develop and function. Some DNA changes cause diseases such as cystic fibrosis or Huntington’s Disease, while others can make us more susceptible to conditions such as cancer. Doctors can refer patients to a genetics service if they consider such tests might be of value due to family or personal history.

Although cases of genetic discrimination are difficult to identify, they have been documented in Australia. In one case, a woman with a BRCA gene, which is known to increase breast cancer risk, elected to have both breasts removed to reduce her risk. However, the consequent, significant risk reduction wasn’t taken into account by the insurer. When she applied for death and critical illness cover, the insurer excluded any cancer cover and imposed a 50% premium loading for death cover.

As I noted in yesterday's post about car insurance, insurers base premiums on:

1. The risk of the insured person making a claim (higher risk groups pay higher premiums than lower risk groups); and
2. The cost of the claims to the insurer (those who would make more expensive claims pay higher premiums).

Note that this applies to both health insurance, and life insurance. Tiller and Lacaze argue that:

As genetic testing becomes more widespread in our society and offers increased potential to help manage patient risk, we must find a way of regulating the insurance implications.

The Australian government must take action towards an immediate ban (moratorium) on the use of genetic test results in insurance, until adequate long-term regulation is in place.

However, before we jump to the same conclusion, let's think through the implications, because this situation is different from yesterday's example of car insurance. In yesterday's example, safe cars cost more for insurance companies because the cost of claims to the insurer were higher. This information (the cost of claims) is public information - the insurers already know this information. In the case of the results of a genetic test that a person has undertaken privately, that is private information - it is information that the insured person knows, but the insurer does not. There is an information asymmetry.

While not all information asymmetries are problematic, sometimes they can lead to adverse selection and market failure, as is possible in this case. An adverse selection problem arises because the uninformed party (the insurer) cannot tell those with 'good' attributes (low-risk people) from those with 'bad' attributes (high-risk people). Now of course the insurer knows some details about each person, but two people who look similar in terms of the observable characteristics (age, gender, occupation, smoker/non-smoker, etc.) may differ in terms of their genetic risk. Genetic risk is the private information here. To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that everyone is high-genetic-risk. This leads to a pooling equilibrium - low-genetic-risk people are grouped together with the high-genetic-risk people and pay the same premium, because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

Will the market fail in this case? If a person had a genetic test, and the results said they were low-genetic-risk, but the insurer wasn't allowed to take this into account, then the insurer has to charge that person the same premium as a high-genetic-risk person (with the same other characteristics). This is likely to be a bad deal for the low-genetic-risk person, so they may opt out of the market. This leaves only high-genetic-risk people in the insurance market (plus those who haven't had a genetic test). It is easy to see that the market might fail here. High-genetic-risk people are less profitable to insure, and the insurance companies might opt out of providing cover at all (for similar explanations for why the market will fail, see this earlier post about health insurance, or this post on adverse selection in life insurance).

But what about if the low-genetic-risk person reveals the private information to the insurer? When the informed party reveals their private information in a credible way, we refer to this as signalling. This would be one way of solving the adverse selection problem. If low-genetic-risk people started revealing their test results, it wouldn't matter if high-genetic-risk people kept their results hidden, since the insurers could infer that anyone withholding their results would be more likely to be high-genetic-risk. Unless, as Tiller and Lacaze propose, insurers are banned from using genetic test results.

Is there a better option? You might be concerned, as Tiller and Lacaze are, about the unfairness of it all. We have no control over our own genetics (at least, not yet), so it seems unfair that some people would have to pay higher premiums as a result of something they have no control over. But banning the use of genetic tests potentially makes a bad problem worse, because it may mean that everyone pays higher insurance premiums, not just those at high-genetic-risk. This is like a tax on those at lower-genetic-risk who get insurance. A better option, if the government is concerned about reducing unfairness in this market, would be to allow the genetic test results to be used for setting premiums, but subsidising the premiums of those who are at high-genetic-risk. Subsidies have their own problems of course, but at least the cost would be spread over all taxpayers, rather than concentrated on the small number of lower-genetic-risk insured people.

Read more:

• Why your Fitbit or Apple Watch could soon get you cheaper health insurance
• Is there adverse selection in the life insurance market?

Safer cars cost more to insure

According to this recent Wall Street Journal article (gated):

New cars loaded with high-tech crash-prevention gear are having a perverse effect on car-insurance costs: They are soaring.

Safety features such as autonomous braking and systems to prevent drivers from drifting out of their lanes are increasingly available on vehicles rolling off assembly lines. Auto companies and third-party researchers say these features help prevent crashes and are building blocks to self-driving cars. But progress comes with a price.

Enabling the safety tech are cameras, sensors, microprocessors and other hardware whose repair costs can be more than five times that of conventional parts. And the equipment is often located in bumpers, fenders and external mirrors—the very spots that tend to get hit in a crash. Insurance companies, unwilling to shoulder all the pain, are passing some of the cost off to buyers.

Most insurers base their premiums on historical claims data. Essentially this involves assessing two components:

1. The risk of accidents (higher risk groups pay higher premiums than lower risk groups); and
2. The cost of enacting repairs (owners of vehicles that are more costly to repair pay higher premiums).

So perversely, while high-tech systems such as rear view cameras, sensors, autonomous braking, and so on may reduce the number of serious accidents, they actually increase the cost of those accidents. Fewer, but more costly, accidents seem to be leading to an increase in total cost of claims to insurers, which is being passed onto vehicle owners as higher premiums. And this effect isn't limited to the U.S. Here's more from the New Zealand Herald:

At the same time new vehicles were making up a larger proportion of all vehicles assessed and this was impacting claim sizes because of expensive technology, use of modern repair techniques and new vehicle owners having a greater tendency to claim.

"These are positive changes to safety which are embraced by the insurance industry, and should eventually reduce the number of accidents.

"However, in the meantime, the new technology comes at a cost.

[National portfolio manager private motor at IAG, Judith] Harvey told Radio NZ that for some people would could mean double digit increases on their insurance premiums.

An AA Insurance spokeswoman said increasing costs were being reflected in its premiums but increases would be gradual...

Twenty years ago a wing mirror could cost $70 or $80 to fix but now it could be several thousand dollars depending if it had sensors, cameras or a computer inside it.

And the trend is set to increase.

And one more, from Auto News, on why Tesla owners should pay more for insurance:

At least one major insurer, AAA-The Auto Club Group, is raising rates on Tesla vehicles based on data showing that the Model S and Model X had abnormally high claim frequencies and high costs of insurance claims compared with other cars in the same classes.

AAA said premiums for Tesla vehicles could go up 30 percent based on data from the Highway Loss Data Institute and other sources...

"Teslas get into a lot of crashes and are costly to repair afterward," said Russ Rader, spokesman for the Insurance Institute for Highway Safety, which is the Highway Loss Data Institute's parent organization. "Consumers will pay for that when they go to insure one."...

The rear-wheel-drive Tesla Model S is involved in 46 percent more claims than average, and those claims cost more than twice than average, [the Highway Loss Data Institute] said. 

So, before you go with the high-tech vehicle option, it may pay (literally!) to be prepared for an increase in insurance premiums.

[HT: Marginal Revolution, here and here]

Book Review: Weapons of Math Destruction

I just finished reading the book "Weapons of Math Destruction" by Cathy O'Neil, better known online as mathbabe. The premise of the book is simple - our life decisions and outcomes are increasingly influenced if not determined by algorithms or mathematical models, and often not in a good way. O'Neill calls these models 'Weapons of Math Destruction' (WMDs). For O'Neill, a WMD has a number of characteristics, including:

1. The model is opaque, or invisible - people whose data are included in the model (let's call them 'subjects') don't know how the model functions, or understand how the inputs are converted into outputs;
2. The model is unfair, or works against the subject's best interests; and
3. The model is scalable - it can grow exponentially.

The WMDs that are considered in the book include college admissions, online advertising, justice and sentencing, hiring and monitoring of employees, credit, insurance, and voting. The book is generally well-written and doesn't lack for real-world examples. Consider this passage on online for-profit universities in the U.S.:

The marketing of these universities is a far cry from the early promise of the Internet as a great equalizing and democratizing force. If it was true during the early dot-com days that "nobody knows you're a dog," it's the exact opposite today. We are ranked, categorized, and scored in hundreds of models, on the basis of our revealed preferences and patterns. This establishes a powerful basis for legitimate ad campaigns, but it also fuels their predatory cousins: ads that pinpoint people in great need and sell them false or overpriced promises. They find inequality and feast on it. The result is that they perpetuate our existing social stratification, with all of its injustices.

One of the big takeaways for me was how often models are constructed and used on insufficient data. O'Neill gives the example of teacher value-added, which works well in aggregate but when you consider it at the level of an individual teacher, their annual 'value-added' is based on perhaps 25 data points (the students in their class). Hardly a robust basis for making life-changing hiring and firing decisions.

However, there are also parts of the book that I disagree with. In the chapter on insurance, O'Neill argues against using models to predict risk profiles for the insured (so that those of higher-risk would pay higher premiums than those of lower-risk). O'Neill argues that this is unfair, since some people who are really low-risk end up grouped with higher-risk people and pay premiums that are too high (e.g. think of a really-careful young male driver, if there is such a person). O'Neill could do with a greater understanding of the problems of asymmetric information, adverse selection, and signalling. Having larger pools of insured people that include both low-risk and high-risk people paying the same premium leads to higher premiums. Essentially the low-risk people are subsidising the high-risk people (how is that not more unfair?). The low-risk people drop out of the market, leaving only the high-risk people behind, who are not profitable for insurers to insure. The insurance market could eventually collapse (for a more detailed explanation, see this post). The insurer's models are a way of screening the insured, revealing their private information about how risky to insure they are.

Despite that, I really enjoyed the book. The chapter on voting and micro-targeting of political campaigns in particular was very enlightening - particularly given more recent events such as the 2016 U.S. Presidential election and the Brexit vote. The sections on the teacher value-added models have caused me to re-evaluate the strengths and weaknesses of those models.

Finally, O'Neill argues for a more ethical use of models. This isn't so far from what Emmanuel Derman argues in his book "Models. Behaving. Badly." (which I reviewed here back in 2013), and is a cause well worth supporting.

Newsflash! NZ health insurers want us to buy more health insurance

Roger Styles (chief executive of the Health Funds Association of New Zealand, the industry body for health insurers) wrote an opinion piece in the New Zealand Herald last week:

Thanks to an ageing population, healthcare inflation, and the rise of new and costly treatments, New Zealand's health spending has one of the fastest rates of increase in the OECD.

The Treasury has repeatedly advised this unsustainable growth presents a bigger fiscal problem for the Government than the soaring cost of NZ Super...

Currently about 20 per cent of healthcare in this country is privately funded, amounting to about $4b a year. Just over a quarter of this is funded through health insurance, which is held by about 1.36 million New Zealanders.

Health insurance could be playing a bigger role in meeting future healthcare costs and thereby relieving the pressure on government budgets and the public health system.

Private health insurance is ideally placed to be able to routinely fund high-cost treatments, which user charges cannot. We just need to address some of the disincentives that stand in the way of more people taking out cover...

Insurance works by aggregating premiums across a large number of people in order to fund the healthcare costs which might otherwise be unaffordable or cause financial hardship.

The fact that New Zealand can achieve $1.13b annually of healthcare funding through 28.5 per cent of the population having health insurance indicates that there is significant scope to increase the contribution to future healthcare costs by lifting coverage rates...

he Government needs to face up to the unsustainability of future health spending and develop a collaborative strategy to reduce dependency on public financing and move closer to the OECD average for public/private health spending shares. It won't be able to raise the age of eligibility for surgery, and it will have to act before 2040.

It may be true that health care costs will rise in the future, due in part to population ageing but equally or more due to Baumol's cost disease (see here for more on this). However, that in itself doesn't mean that we need more health insurance. Anyone who believes that health insurance is a solution ought to be looking carefully at the continuing mess that is the cost of healthcare in the U.S.

The main problem with insurance is adverse selection - insurers want low-risk people to buy insurance, but the incentives to buy insurance are greater for high-risk people. In the case of voluntary health insurance (as in New Zealand currently), the people who buy health insurance are either: (1) the worried well and risk averse healthy people; and (2) people at high risk of getting sick. The second group are of course quite expensive for insurers, so they rely on getting as many healthy people (who won't make claims) to sign up as possible. One way to do this is the lobby government to make health insurance more attractive in some way, such as making employer-funded schemes tax-advantageous to firms (which is what Styles is arguing for).

However, we need to see this for what it is. A self-serving lobby from a profit-motivated health insurance industry that ought to be ignored by policy makers.

Big data and loyalty to your insurer could raise your insurance premiums

Back in September, I wrote a post about how the most loyal customers are the ones that firms should charge higher prices to, based on this Wall Street Journal article. Last week, the Telegraph had a similar article:

The financial regulator has warned that insurance companies could start charging higher premiums to customers who are less likely to switch by using “big data”.

In a speech to the Association of British Insurers, Andrew Bailey, chief executive of the Financial Conduct Authority, suggested that big data could be used to “identify customers more likely to be inert” and insurers could use the information to “differentiate pricing between those who shop around and those who do not.”...

James Daley, founder of Fairer Finance, the consumer group, said that to some degree big data was already being used to punish inert customers.

He said: “Insurers already know how their own customers are behaving. Those who don’t switch are penalised for their loyalty with higher premiums. Inert customers will be priced partly on risk and partly on what the insurer can get away with.”

To recap, these insurers are engaging in price discrimination -  where firms charge different prices to different customers for the same product or service, and where the price differences don't reflect differences in cost. There are three necessary conditions for effective price discrimination:

1. Different groups of customers (a group could be made up of one individual) who have different price elasticities of demand (different sensitivity to price changes);
2. You need to be able to deduce which customers belong to which groups (so that they get charged the correct price); and
3. No transfers between the groups (since you don't want the low-price group re-selling to the high-price group).

 As I noted in my previous post in September:

If you are an insurance company, you want to charge the customers who are most price sensitive a lower price. If customer loyalty is associated with customers who don't shop around, then customer loyalty is also associated with customers who are less price sensitive. Meaning that you want to charge those loyal customers a higher price.

What about big data? The Telegraph article notes:

Earlier this month Admiral, the insurer, announced that it planned to use Facebook status updates and “likes” to help establish which customers were safe drivers and therefore entitled to a discount.

Campaigners called the proposal it intrusive and the social media giant then blocked Admiral’s technology just hours before it was due to launch.

Just last week a telematics provider, Octo, launched an app that that shares customers' driving data with insurers so that they could bid for custom. It claimed that the safest drivers would get the lowest premiums.

The problem here is that opting out of having your social media profiles available for your insurer to peruse may be an option, but it would also provide a signal to the insurer. Who is most likely to refuse? The high-risk insured, of course. So, anyone who refuses will likely face higher premiums because of the signal they are providing to their insurer. Again, this is a point I made a couple of months ago.

It seems that we will have to accept the reality that big data, and especially our 'private' social media and activity data, is simply going to determine our insurance premiums in future.

Read more:

• Could your social media posts make insurance more expensive?
• Customer loyalty and price discrimination

Could your social media posts make insurance more expensive?

James from my ECON110 class pointed me to this insightful Tamsyn Parker article in the New Zealand Herald with the above title. Parker writes:

Could that Instagram image of you bungy jumping in your 20s result in having to pay higher insurance costs in the future? One insurance expert thinks so.

Michael Naylor, a senior lecturer in finance and insurance at Massey University, says people should expect insurers to mine their social media accounts in the future to determine how much they will charge for insurance premiums and if they will pay out on claims.

"People have to be aware everything they do on social media can be effectively public.

Why would insurance companies want to mine social media data to find out about us? It's because of the adverse selection problem. An adverse selection problem arises because the uninformed party (the insurer) cannot tell those with 'good' attributes (low-risk people) from those with 'bad' attributes (high-risk people). To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that everyone is high-risk. This leads to a pooling equilibrium - low-risk people are grouped together with the high-risk people and pay the same premium, because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

I've written about how the insurance market fails before (this comes from a post about health insurance, but it equally applies to accident insurance or life insurance - see also this post on adverse selection in life insurance):

In the case of insurance, the market failure may arise as follows (this explanation follows Stephen Landsburg's excellent book The Armchair Economist). Let's say you could rank every person from 1 to 10 in terms of risk (the least risky are 1's, and the most risky are 10's). The insurance company doesn't know who is high-risk or low-risk. Say that they price the premiums based on the 'average' risk ('5' perhaps). The low risk people (1's and 2's) would be paying too much for insurance relative to their risk, so they choose not to buy insurance. This raises the average risk of those who do buy insurance (to '6' perhaps). So, the insurance company has to raise premiums to compensate. This causes some of the medium risk people (3's and 4's) to drop out of the market. The average risk has gone up again, and so do the premiums. Eventually, either only high risk people (10's) buy insurance, or no one buys it at all. This is why we call the problem adverse selection - the insurance company would prefer to sell insurance to low risk people, but it's the high risk people who are most likely to buy. 

In order to solve an adverse selection problem, the uninformed party can try to reveal the private information - we call this screening. Mining people's social media posts could reveal to the insurance companies which people are high-risk and which are low-risk. They can then separate the two groups, and we move from a pooling equilibrium to a separating equilibrium. High-risk people will pay higher premiums, and low-risk people will pay lower premiums. Or, as noted in the New Zealand Herald article linked above:

[Michael Naylor] predicts it will be one to three years away in New Zealand and says it may not come from existing insurers but new entrants to the market who will use personal data to cut insurance premiums for less risky customers.

That could leave old-style insurers with more risky customers and the prospect of rising premiums to cover their costs.

He says the change could have implications for people who seek adventure when younger and record it all on their social media pages.

"Of course the internet doesn't die."

They may have to prove they no longer undertake those activities to get insurance in the future or sign exclusion agreements meaning they won't be covered for certain activities, says Naylor.

So, if new insurers use social media mining to offer cheaper insurance to low-risk people, you can bet the large incumbent insurers will follow suit soon after, because insuring low-risk people is much more profitable than insuring high-risk people.

So for now, before you apply for life insurance (or health insurance, accident insurance, or even car insurance), it might be best to lock down your social media accounts. Or at least delete any references to the risky exploits of your youth.

If everyone locks down their social media accounts so that insurers can't access them, things start to get interesting. Will insurers simply ask to see your past social media posts? If I was an insurer, I would. People who say 'no' to such a request are more likely to be high-risk (because low-risk people would have nothing to hide), and the insurer could price their premiums accordingly. Essentially, low-risk people could signal that they are low risk by making their past social media posts available to their insurer, and reap the reward of a lower premium. In fact, low risk people could probably do this right now.

[HT: James from my ECON110 class]

Customer loyalty and price discrimination

This week in ECON100 we discussed price discrimination - where firms charge different prices to different customers for the same product or service, and where the price differences don't reflect differences in cost. There are three necessary conditions for effective price discrimination:

1. Different groups of customers (a group could be made up of one individual) who have different price elasticities of demand (different sensitivity to price changes);
2. You need to be able to deduce which customers belong to which groups (so that they get charged the correct price); and
3. No transfers between the groups (since you don't want the low-price group re-selling to the high-price group).

If these conditions are met, then the firm would sell to groups with relatively more inelastic demand at a higher price than to groups with relatively more elastic demand. Which brings me to this Wall Street Journal article from earlier this year, about the insurance market:

Some car insurers hope to identify people who could potentially be their best, most loyal customers–and charge them more for their insurance.

Wait... what? Surely firms should charge lower prices to their most loyal customers, to reward their loyalty, right? However, the article continues (emphasis added):

Or so contend consumer advocates who track the insurance industry, who say the practice discriminates against low-income drivers and should be forbidden. They maintain that insurers are wrongly seeking to maximize their profits with an advanced analytical technique–known as “price optimization”— under which the companies identify consumers least likely to comparison shop and stick them with higher prices than more price-sensitive drivers. While insurers are targeting policyholders across a range of demographic groups, the activists say many lower-income people fall into that camp.

This is clearly a form of price discrimination, even though the industry sources quoted in the article argue otherwise. If you are an insurance company, you want to charge the customers who are most price sensitive a lower price. If customer loyalty is associated with customers who don't shop around, then customer loyalty is also associated with customers who are less price sensitive. Meaning that you want to charge those loyal customers a higher price.

Now you might argue (as the regulators are in this case) that this price discrimination is unfair. That would put you in good company (with John List, see my post here). The article notes:

A big concern among consumer advocates like Mr. Hunter is that poorer people may not shop around as much as others, because they may have fewer choices for insurance coverage to start with, and they may not have the Internet access that makes it increasingly easy for many Americans to get price estimates online.

However, then the case in favour of price discrimination starts to break down. Where car insurance premiums take up a higher proportion of a consumer's income, their demand should be more elastic. This will offset the price-insensitivity associated with being a loyal customer. And then of course you have pricing based on risk (which is what the insurers are arguing they are really engaging in).

Given all this, if firms are engaging in price discrimination (which they probably are) I would expect to see, for customers at the same level of risk:

• Loyal customers who have higher incomes paying the highest premiums;
• Loyal customers who have lower incomes, and less-loyal customers with higher incomes paying moderate premiums; and
• Less-loyal customers with lower incomes paying the lowest premiums.

It seems to me that helping low income people to shop around for insurance might be a good thing for those consumer advocates to be engaging in, rather than complaining about price discrimination.