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]

Rational ignorance is why we don't check our supermarket receipts

Last week, news.com.au reported:

WHEN you go shopping, do you casually throw your receipt in with the groceries, neglecting to read it over?

Or, worse, do you decline to print one out and walk away, ignorant what you’ve paid for all those carefully-selected items?

If you answered “yes”, you could be ripping yourself off, with scanning errors causing supermarkets to overcharge for everyday items — and many shoppers failing to detect, or act upon the mistakes to recoup what they are owed.

A survey of 2,141 Australians by comparison site finder.com.au found that two in five people had been overcharged at the till in the past year.

But one-quarter said they didn’t bother checking their dockets and, of those who did, they would only bother going for a refund if they were overcharged by more than $10.

There is a good reason why supermarket shoppers don't check their receipts. It takes time and effort to check those receipts, and the chances that the time and effort you spend pays off by your finding an error in your favour is low.

To illustrate, let's construct a plausible numerical scenario. Let's say that "two in five people" have been overcharged once each in the past year (as noted in the quote above), making a 40 percent chance that you've been overcharged once in the past year [*]. So, on average each person has been overcharged 0.4 times in the last year (some have been overcharged more times, and some have been overcharged fewer times). Let's assume each person goes to the supermarket roughly once per week (say 50 times per year). So, on average each shopper is being overcharged 0.4 times in 50 shopping trips, or a 0.8 percent chance that any given shopping trip results in an overpayment. Now let's generously assume that in each shopping trip where a shopper is overcharged, they are overcharged by $10 [**]. The expected value of checking every supermarket receipt is 0.8% x $10 = $0.08 (yes, that's EIGHT CENTS). This is the expected (or average) benefit you would gain from carefully checking every supermarket receipt to make sure you haven't been overcharged.

If the cost, in terms of time and effort, of carefully checking a supermarket receipt is more than $0.08, a rational person wouldn't do so. You would be better off to remain rationally ignorant of whether you overpaid the supermarket. Even at the minimum wage, $0.08 is the pay for about 18 seconds. Taking the minimum wage as the cost of people's time, you wouldn't want to spend any more than 18 seconds scrutinising each supermarket receipt. If your implicit value of time is higher than the minimum wage, you'd want to spend even less time than that.

You might argue that people are loss averse, so that they value losses much more than gains. Losing money to overcharging is a loss, so we should value the expected loss at much more than $0.08. However, it isn't a loss if you don't know about it! You're still better off being rationally ignorant, even if you're loss averse (maybe especially if you're loss averse).

I'm not surprised supermarket shoppers don't carefully scrutinise their receipts to check for overpayments on every item. It simply doesn't pay off for us.

*****

[*] Probably some people had been overcharged multiple times, but I don't think that will make much difference to this example.

[**] Probably most instances of overcharging are much less than $10. In my experience, overpayments I have caught in my own shopping trips (usually because I happened to be looking at the screen when the item was scanned and remembered the price) have been a couple of dollars at most.

The trajectory of the economics major in the U.S.

A couple of years ago, I wrote a post on the depressing state of university economics in Australasia, in particular the closure or downsizing of many university economics departments. However, a recent article by John Siegfried (Secretary-Treasurer Emeritus of the American Economic Association), and published in the Journal of Economic Education (sorry no ungated version), shows that this isn't the case in the U.S. In fact, economics majors are increasing:

The number of undergraduate economics degrees awarded by colleges and universities in the United States was virtually stagnant from 2009–10 through 2012–13. In 2013–14, undergraduate economics degrees began to accelerate, rising about 6.9 percent in just one year, followed by a slightly larger rise in degrees in 2014–15, totaling almost 15 percent over the two years. In 2015–16, however, growth fell back to near a 2 percent increase.

Overall, the number of economics graduates across the 293 universities included in Siegfried's survey has grown over 37 percent between the 2006-07 and 2015-16 academic years. There has been a similar increase over recent years in the U.K. It makes me wonder why Australia and New Zealand should be going so against the trend. Perhaps, as I noted in that earlier post, we really need to be looking closely at what is taught in high schools and reconsider allowing such a dominance of business studies.

One thing appears unfortunately similar across all countries though, and that's the gender gap (which I have covered before, here and here and here). In Siegfried's data, the 2015-16 academic year had the highest proportion of female economics graduates (from the surveyed universities) at 34.0%, up from 31.0% in 2006-07, but not terribly different from the 33.8% in 2001-02. There's still a long way to go to achieve parity.

Read more:

• Have we reached peak economist in Australasia?

Those Machiavellian economics and business students...

Back in March I wrote a post about some research on Dutch students' personality traits by academic major, showing that (among other results):

Extraverted students are more likely to choose to study law, or business and economics, and avoid science, technology, engineering and mathematics (STEM).

A recent paper by Anna Vedel and Dorthe Thomsen (both Aarhus University), and published in the journal Personality and Individual Differences (sorry I don't see an ungated version), looks at a similar question for Danish students. This new paper looks not only at the Big Five personality traits (extraversion, agreeableness, neuroticism, openness, and conscientiousness), but also at the Dark Triad (Machiavellianism, narcissism, and psychopathy), using a sample of 487 incoming Danish university students. They find that:

Male students scored significantly higher on all Dark Triad traits than female students, female students scored significantly higher on neuroticism, agreeableness, and conscientiousness than male students, and non-significant results were found for extraversion and openness. There was a significant effect of academic major for all personality traits except from extraversion and psychopathy...

Economics/business students scored significantly higher on Machiavellianism than all others and significantly higher on narcissism than both psychology and political science students... Psychology students scored significantly higher on neuroticism than economics/business and political science students... They also scored higher than economics/business and law students on both agreeableness and openness... Political science students scored significantly higher on openness and agreeableness than economics/business and law students... both political science and law students scored significantly higher on neuroticism than economics/business students.

In terms of the size of the differences, the largest differences were between economics/business students and psychology students, especially in terms of Machiavellianism. At least there weren't any significant differences in psychopathy. Although many more female students choose psychology than male students, the difference in Machiavellianism was consistent across both genders. According to Wikipedia, Machiavellianism is associated with grandiosity, pride, egotism, and lack of empathy. Maybe it's confirmation bias, but to me that doesn't seem too far from the mark for many business people.

A bit of comfort for university business and economics lecturers should come from these results, since they suggest that, at least in Danish universities, economics and business faculty aren't teaching our students to be Machiavellian, they already are!

Read more:

• Personality traits and field of study choice in university

[HT: Marginal Revolution, back in April]

Superpowers and social incentives

My son has asked the question a few time, of what superpower I would choose to have (if I could choose any). It seems a reasonable (if fantastic) question. In a recent paper in the Journal of Humanistic Psychology (sorry I don't see an ungated version) Ahuti Das-Friebel (of Monk Prayogshala in India) and co-authors provide an answer based on a sample of 572 mainly Indian online respondents:

Of the 302 [quantitative analysis] participants, 94% opted to possess a superpower (n = 285). On average, participants wanted positive powers... significantly more than negative powers...

Only six superpowers were evaluated, made up of three 'positive' powers (healing, invulnerability, and flight) and three 'negative' powers (poison generation, fear inducement, and psychic persuasion). The main part of the research didn't focus on which superpowers the respondents preferred though, but rather how they said they would use those superpowers:

In general, it was found that while most individuals would prefer to hypothetically possess superpowers, their usage would not necessarily be altruistic. Specifically, results from both the quantitative and qualitative analyses showed that majority of the participants would use hypothetical superpowers for personal benefit versus social benefit and social harm. Moreover, both positive and negative powers generated more self-serving purposes than altruistic responses, with a minority indicating using the powers for antisocial purposes. Last, women were more likely to use positive powers for socially beneficial purposes than men, even though men indicated a higher desirability for both positive and negative powers than women.

So, according to this research people say they would most likely use their hypothetical superpowers for personal benefit. But would they? Economists understand incentives, and one could make the case that the incentives for someone who has newly acquired superpowers is to use those superpowers for personal gain - the benefits of using superpowers to benefit oneself are large, and the costs to the individual are probably only small. But purely economic incentives are only one type of incentive. There are also moral incentives (based on what you believe about right and wrong) and social incentives (based on what other people perceive as right and wrong). Moral incentives might be enough to keep many superpower-wielding people in line.

Where economic and moral incentives are not enough, the social incentives to behave well are likely to be very high. A person with superpowers is probably going to be very visible to others, so I imagine it would probably be difficult to hide their actions effectively. A person with superpowers who engages in actions for their own benefit is going to get a very hard time from the general non-super-powered population.

Think about how a lot of people feel about the wealthy right now. Many people believe that the wealthy should be using their wealth for socially beneficial activities, rather than purely for personal gain. Some people strongly believe that the wealth distribution is very unfair. How unfair would the distribution of superpowers be? How would people react to a person with superpowers using those powers for personal gain? It wouldn't be pretty, and even invulnerability wouldn't save the selfish superpower-wielder from the consequences.

Anyway, coming back to the original question at the beginning of this post, my answer to my son has always been qualified by whether lots of other people have superpowers. If lots of other people have superpowers, having a superpower that makes you immune to others' superpowers seems quite valuable (as does being able to replicate or borrow or steal other people's superpowers). It's hard to see that the social incentives would be against using that superpower.

[HT: Marginal Revolution, back in April]

Russ Roberts on the emergent order the underpins markets

Russ Roberts (of EconTalk fame, and co-creator of the Hayek vs. Keynes rap battles - see here and here) wrote an excellent article published on NewCo Shift, on how markets work in terms of emergent order. Here's one bit:

The baker takes the on-going relentless availability of flour as a given — a reality that is as reliable as the force of gravity. The baker goes to bed at night unworried that the earth is going to spin out of orbit. And unworried about the availability of flour.

But who is in charge of the supply of flour around the country and the world?

A bunch of farmers around the world decide independently of each other how much wheat to plant. No one is in charge of the total number of acres of wheat which will be planted or harvested this year. No one is in charge of how much of the resulting flour should go to pasta vs. scones vs. whiskey vs. pizza vs. bread. Yet somehow, even when pizza becomes a world-wide craze around the world, there is still sandwich bread.

I also especially liked this bit:

Understanding and appreciating emergent order, and understanding when it works well and when it doesn’t and it does not always work well, is for me, the essence of economics and the deepest idea that we economists can contribute to helping normal human beings understand the world around us.

Economists call the interaction between buyers and sellers of bread a “market,” but our charts of supply and demand, while often very powerful, don’t get at the richness of how we as human beings manage to cooperate without top-down coordination and do it so peacefully.

I encourage you to read the whole article, but if you're time poor there's a poem version on YouTube:

I don't think the poem's nearly as good as the article (you can read the text of the poem here), but this bit is good:

And here’s the crazy thing, if someone really were in charge

To make sure that bread was plentiful, with the power to enlarge

The supply of flour, yeast and of bakers and ovens, too

Would that person with that power have any idea of what to do?

The power of markets is that there is no need for anyone to be in charge of coordinating supply and demand, since prices do all of the work. If the price of some good rises, sellers will want to sell more and buyers will want to buy less, and if the price falls, sellers will want to sell less and buyers will want to buy more. Simple.

Or at least, simple when there are no market failures (of which there are many, ranging from imperfect competition to imperfect information to externalities, and so on)...

[HT: Marginal Revolution]

Try this: Who to vote for this (NZ) election?

Are you unsure which political party to vote for, and you want to know which party best matches your values? The Design+Democracy Project at Massey University has updated their excellent On The Fence tool for the 2017 New Zealand general election. The results when I took the test just now were spot on (for my first and second choices, but not for the third).

There are other similar tests to On the Fence around, like this one from isidewith.com. This tool from The Spinoff requires a bit more effort than just taking a test, but is well worth it if you have the time.

If you want to know more about your political beliefs you could try the Political Compass test, which my ECON110 class does for extra credit every year. The Political Compass is pretty interesting because it ranks us on two scales: Economic Left-Right, and Authoritarian-Libertarian (see here for more). I also like it because at the end it shows how you compare with famous world leaders. However, I do think that the 'centre' between the economic left and right is not correct for New Zealanders (in my experience based on several years of ECON110 classes, the centre for New Zealand is maybe two points to the left of the centre in that test).

Try them all out, and make a better informed decision this election!

Why sports stars are paid more than teachers

Last September, Don Boudreaux wrote this letter:

Ms. Montgomery:

I regret that you’re offended by my claim that “Only by thinking at the margin can we correctly understand why the wages of life-saving first-responders are lower than are the wages of NFL players and of Hollywood starlets and why this fact is a good thing for society.”  You allege that “Real people know it’s wrong and dangerous that men playing games get paid so much more than men and women who save lives and educate our children.”

I agree that most people are troubled that the likes of Tom Brady and Jennifer Lawrence earn far higher pay than does any firefighter or school teacher.  But this reality reflects not people’s correct understanding of a failing economy but people’s incorrect understanding of a successful economy.  It reflects also a failure of economists to better teach basic economics to the general public.  So let me ask: would you prefer to live in a world in which the number of people who can skillfully fight fires and teach children is large but the number of people who can skillfully play sports and act is very tiny, or in a world in which the number of people who can skillfully fight fires and teach children is very tiny but the number of people who can skillfully play sports and act is large?

Boudreaux's argument is that sports and movie stars are paid more than teachers or firefighters because there are fewer of them relative to the demand for their labour. In comparison, the higher labour supply of teachers and firefighters (compared with demand for them) leads to lower equilibrium wages. However, labour supply is only part of the story. The more important part is that the supply is low 'relative to the demand for their labour'. It isn't so much that there are few potential sports and movie stars (arguably there are many wannabe stars who are nearly as good), but that the demand for them is so high.

Why is demand for sports and movie stars so high? Because of 'superstar effects', which were first described by Sherwin Rosen in the 1980s (and which I have written about before). If a worker can satisfy the demand (for entertainment, in this case) from many consumers, they get paid a higher wage (a 'superstar' wage). Essentially, the worker is rewarded for generating very high revenues for their employer. Since movies or sports are watched (and paid for) by many consumers, this generates a lot of revenue for movie production companies and sports teams, who pass on some of these high revenues to their stars as higher wages. I'm sure that if teachers or firefighters could satisfy the demands of a much greater number of consumers (presumably students or victims of fire, respectively), then they could earn superstar wages as well.

Maybe in the future, teachers who teach on MOOCs (Massive Open Online Courses) will earn superstar wages? After all, "Massive" implies that they will be satisfying the demand from a lot of students. Of course, MOOCs would need to start making some money first (which is another topic I have written on before, see here and here).

[HT: Marginal Revolution, last September]

Inequality and why we need legislated labour protections

The standard rhetoric of why we need labour protections (like health and safety standards, the Holidays Act, etc.) is that without the legal requirement to do so, employers would not otherwise offer these protections to employees. But that doesn't explain why employees would be willing to accept lower labour protections. The theory of compensating differentials suggests that workers will be willing to work in unattractive jobs (such as those with lower labour protections), in exchange for higher wages. If employees are fully informed about the lack of labour protections, then they may make a rational choice to take that job. So why do we need legislated labour protections?

I recently re-read Robert Frank's book Falling Behind: How Rising Inequality Harms the Middle Class, which I have blogged about before, and which offers some additional (and interesting) explanation of why labour protections are necessary.

Frank's argument can be easily summarised as four propositions, from the introduction to the book:

1. People care about relative consumption more in some domains than in others (i.e. context matters);
2. Concerns about relative consumption lead to "positional arms races", or expenditure arms races focused on positional goods;
3. Positional arms races divert resources from non-positional goods, causing large welfare losses; and
4. For middle-class families, the losses from positional arms races have been made worse by rising inequality.

Essentially, because we value our relative status, we over-spend on positional goods such as housing, and under-spend on non-positional goods (goods that don't confer status, or signal our status). One non-positional good is the labour protections (e.g. health and safety protections) that we receive during our work. We don't 'spend' our income on labour protections in the traditional sense, but we do accept lower wages in exchange for having those protections, which is in effect the same thing. In contrast, higher wages allow us to buy more positional goods (such as spending more on housing to get ourselves into a better neighbourhood). Therefore, we may be willing to trade off labour protections to receive higher wages, with which we can spend more on positional goods. Frank writes:

The positional account, by contrast, stresses that even in perfectly informed, competitive labor markets, risks that rational workers find collectively unattractive will often be attractive to them individually. A worker will accept a riskier job at higher pay because doing so will help her buy something important she wants, such as a house in a safer neighborhood with better schools.

Coming back to Frank's fourth proposition above, rising inequality leads the middle-class to spend more on housing (and other positional goods) in order to try not to 'fall behind'. So, when inequality rises (as it did in New Zealand in the 1980s, but not too much since - see here), we may need stronger labour protections. This is not because we are worried about any exploitative power of employers, but because the voluntary actions of workers in being willing to give up non-positional labour protections in exchange for income (that can be spent on positional goods such as housing), inadvertently makes those workers collectively worse off.

Want a living wage for everyone? Raise productivity first

Jim Rose (economic adviser to the Auckland Ratepayers' Alliance) wrote in the New Zealand Herald back in June:

The Auckland Council's new living wage policy will lift the pay of its minimum waged employees by 30 per cent. Of course, no minimum wage worker will be shortlisted for these jobs in the future because these jobs will be paying $20.20 per hour. Minimum wage workers will be crowded out by better quality applicants who already earn a similar pay.

Rose's point is that increases in wages need to be underpinned by increases in productivity. To see why, consider our simple model of labour demand, as shown in the diagram below. The VMPL curve is the value of the marginal product of labour (also called the marginal revenue product of labour) - it's the extra value that one additional hour of work provides to the employer, in terms of additional revenue. Essentially it is the productivity of the marginal worker (the amount they would produce in that additional hour), multiplied by the value of that output. A profit maximising employer will be willing to hire a worker for an hour provided that the VMPL is at least as great as the wage. If the wage is higher than the VMPL, then hiring a worker for that hour would reduce profits. So, if the wage is equal to W0, then the quantity of labour employed will be where VMPL is exactly equal to W0, which in the diagram below is Q0.

Now, consider what happens if you implement a living wage that is higher than W0 (say, at W1). Let's assume that the value of output is the same regardless of which worker produces it (which seems reasonable). When the wage goes up to W1, relatively low-productivity work hours (previously producing VMPL between W0 and W1) will no longer be profitable for the employer, so it will cut back on labour hours (from Q0 to Q1). Fewer people will be employed, or those who are employed will be employed for fewer hours.

Advocates for the living wage argue that it will increase productivity, as Rose notes:

Living wage activists prefer to talk about the costs supposedly being offset by labour productivity gains - higher staff morale, fewer absences and reduced staff turnover. These are supposed to make everything right and low risk.

This is essentially an efficiency wage argument, which I have discussed before. As I noted then, it only works provided not all employers are paying the living wage, since it relies on alternative jobs for employees paying much less. However, if only a limited number of employers are using the living wage, then it could increase productivity for those living-wage-paying employers, by ensuring that the most productive employees go to work for them (and not for other employers). However, other employers would be left with less-productive workers (and would pay lower average wages as a result). So wages on average across the economy remain unchanged, because the overall productivity of the economy remains unchanged.

Rose's overall point is that we can't simply legislate higher wages, through proposals like the living wage:

Living wage activists and unions are right to point out that we live in a low-wage economy compared to Australia. The solution is not to vote ourselves a pay rise.

Increasing productivity, innovation and entrepreneurship is the only way to catch up.

If we want higher wages across the economy as a whole, we have to be more productive first.

Read more:

• The living wage is good for employers; unless lots of employers pay a living wage

This is a nonsense measure of inequality

In The Conversation on Tuesday, Jennifer Chesters (University of Melbourne) wrote an interesting piece on wealth inequality in Australia. Interesting, but also partially misleading because of this bit:

Using the mean and median household wealth figures, it is possible to calculate the ratio of median to mean wealth.

The closer this ratio is to one, the lower the level of inequality. In 2003-04, the ratio was 0.63. In 2013-14, it was 0.57. This also indicates that wealth inequality increased.

The ratio of median to mean wealth doesn't tell you about inequality. It does tell you about how skewed the wealth distribution is, since the smaller the ratio is the greater the average distance from the middle of the wealth distribution the wealth of the people above the median will be. But that is not the same concept as inequality. To see why, consider these two countries:

• In Country A, every person has wealth of exactly $100,000.  The median wealth (the wealth of the person in the exact middle of the distribution) is equal to $100,000. The mean (average) wealth is also equal to $100,000. So the ratio of median to mean is equal to 1.
• In Country B, exactly half of the people have wealth of $200,000, and exactly half of the people have wealth of zero. The median wealth is again equal to $100,000. The mean wealth is again equal to $100,000. So the ratio of median to mean is also equal to 1.

It should be clear to you that in Country A, there is no wealth inequality, because everyone has the same wealth. In contrast in Country B, clearly there is wealth inequality. And yet, the ratio measure is exactly the same for Country B as for Country A. That's because neither wealth distribution is skewed - both distributions are symmetric. Ok, this was a superficial example, but it should be enough to illustrate that the ratio of the median to mean wealth is nonsensical as a measure of inequality, since skewness and inequality are not the same thing.

Fortunately, the ratio of median to mean isn't the only measure that Chesters uses:

The P90 to P10 ratio compares the wealth of households at the 90th percentile with that of households at the tenth percentile. A larger ratio indicates greater levels of inequality.

In 2003-04, households at the 90th percentile held 45 times as much wealth as households at the tenth percentile. In 2013-14, households at the 90th percentile of the distribution held 52 times as much wealth as households at the tenth percentile. This indicates that wealth inequality increased in that decade.

These sorts of ratio methods are crude, but simple to calculate (which is why we use them in my ECON110 class). They also have problems, chiefly that they ignore most of the people in the distribution - for example, the P90 to P10 ratio is the same regardless of whether the top person in the distribution has wealth of $100 million, or $100 billion. However, at least they aren't the nonsense that the ratio of median to mean wealth is.

The deadly consequences of rent control in Mumbai

Back in April, Alex Tabarrok wrote an interesting blog post on rent controls in Mumbai. Rent controls are an example of a price ceiling (a legal maximum price that can be charged in a given market). They have a number of negative effects (a topic that I have written on here and here), one of which is that they reduce the quality of rental housing over time. To see why, it is important to first recognise that the rent control creates an excess supply for rental housing, as shown in the diagram below.

Rent control keeps the price below equilibrium (at R1) – it can’t rise to the equilibrium rent of R0 because of the rent control rules. The lower rent makes renting more attractive relative to owning your own home. Some people would find it cheaper or more convenient to be a renter at this lower rent, so the quantity of rental housing demanded increases (to QD1). However, the lower rents make rental housing a less attractive investment for landlords. Perhaps they convert that rental housing into commercial rentals instead (e.g. offices) or maybe they choose to live there themselves (the opportunity cost of living in the house is now lower). Either way, the quantity of rental housing supplied decreases (to QS1). The difference between QD1 and QS1 represents the excess demand for rental housing at the controlled rent – there are fewer houses available than the quantity people want to rent.

Why does that lead to lower quality housing? Excess demand means that there are many more prospective tenants available than there are rental housing units. That gives landlords a lot of power. They can be choosy about tenants, but tenants can't afford to be choosy about rental properties because they may miss out. And landlords know that tenants can't be choosy. So, if you're a landlord why would you go to the expense of maintaining your property to a high quality, when if the tenant doesn't like it they can leave and it is easy to find a new tenant? You wouldn't.

Coming back to Tabarrok's blog post, that's exactly what he described:

Walking around Mumbai it’s common to see some lovely, older buildings (circa 1920s perhaps) that are rent control in a great state of disrepair. A well maintained building can last for hundreds of years so why are these buildings falling apart? The answer is rent control. Bombay passed a rent control act in 1947 that froze rents at 1940 levels.

More than fifty years later, rents remained frozen at 1940 levels. It wasn’t until 1999 that the Act was modified slightly to lift controls on some new construction and to allow rent increases of 4% per year. After a fifty two year freeze, however, a 4% increase was a pittance. Thus, even today there are thousands of flats where tenants are paying rents of 400-500 rupees a month (that’s $6 to $8 a month!)–far, far below market rates.

The rent control law meant that there was virtually no construction of rental housing (WP) for decades and a slowly dilapidating housing stock. (Ironically, the only free market in rental housing is in the slums.)

The nominal landlords have neither the incentive nor the funds to maintain the buildings so every year during monsoon season some of the buildings collapse and people die.

So, not only to rent controls create deadweight losses (see here), they can have very real and very harmful consequences.

Read more:

• The deadweight loss of rent control
• Why rent controls have worse effects over longer time horizons

School zone is not the whole story of house price differences

From Friday's New Zealand Herald:

Property buyers seeking houses in zones for certain public schools can expect to pay a premium up to 90.5 per cent on homes in the wider area, new data shows.

Homes.co.nz released data comparing the median regional price to the median price for different school zones to find a "school price premium". The prices are the estimated value calculated by Homes.co.nz and are updated monthly.

The median estimate price for property in the Auckland region is $940,610, according to Homes.co.nz.

Houses zoned for Epsom Girls' Grammar School have a median price of $1.79 million, an increase of 90.5 per cent on the Auckland average.

Now, I don't doubt that the price of homes in the Epsom Girls' Grammar School zone are 90.5 percent higher than the Auckland average. However, it would be wrong to describe this as a "school price premium", because school zone is not the only difference between those houses and the median house in the Auckland region. Houses in good school zones might also be in nicer neighbourhoods, closer to amenities (like the CBD), and with better access to services. All of these neighbourhood-level variables are important for house prices - they also have value for home buyers and home owners. Those variables also vary systematically by neighbourhood and are likely to be correlated with the location of good school zones.

On top of that, houses in those areas might also be larger, have more bedrooms and bathrooms, have better views, and differ on many other house-level dimensions that contribute to house prices. Or they may not. The point is that none of those potential differences are controlled for by the Homes.co.nz analysis.

So, while there is almost certainly a price premium for good school zones (for example, see this 2014 working paper by my colleagues John Gibson and Geua Boe-Gibson), it almost certainly isn't as much as 90.5 percent for the Epsom Girls' Grammar School zone. Once your subtract the value of the good neighbourhood, access to amenities, etc., then the marginal value of the school zone is likely to be much less than that. But at least Homes.co.nz didn't claim that the whole price of the house was the price of living in a good school zone!

Uber's drivers are gaming the system

It had to happen sooner or later. The Daily Mail reports:

Uber drivers have been accused of secretly logging out of the app to make prices soar and allow them to charge customers more money, new research suggests.

Researchers interviewed Uber drivers in London and New York and produced a study which claims staff are deliberately making the price more expensive.

It was suggested that drivers working in the same area are logging out of the mobile taxi app which will make the number of available cars drop.

This, as a result, causes a higher demand because there are less cars available and therefore a 'surge' price is introduced with fares increasing.

To be clear, Uber drivers logging out of the app doesn't "cause a higher demand", but it does decrease the observable supply of drivers, which may lead Uber to raise the price (when supply decreases, the equilibrium price is higher). Presumably, drivers can then log back into the app and reap the benefits of the higher price (until it adjusts downwards). As I noted in this 2015 post, Uber's surge pricing is working. But perhaps it is working too well?

The unintended consequences of carless days

One of my favourite parts of teaching ECON110 (ok, there are many, but this is one is a particular favourite) is discussing the unintended consequences of well-intended policies. That's why I enjoyed reading this article on carless days from Sunday's New Zealand Herald:

Today is the anniversary of former Prime Minister Robert Muldoon's introduction of carless days in 1979, a policy his critics consider an emblem of his attempts to control all aspects of the economy.

Carless days were intended to reduce car use and petrol consumption following the second oil shock of the 1970s. Much of New Zealand's crude oil came from Iran, but the Middle Eastern country's output shrank because of the revolution that began there in 1978, causing a worldwide shortage of oil...

Under the carless days rules, owners had to pick one day of the week on which they wouldn't drive their car. They would get a sticker to put on their car's windscreen and there was a different colour for each day of the week.

Driving a car on a carless day risked a fine of up to $400 under the regulations, which were made by the Government under the 1948 Economic Stabilisation Act.

Exemption stickers could be obtained by firefighters, doctors and other "essential users"...

Carless days were scrapped in May 1980 - and motoring and cycling advocates don't want them revived.

"It didn't work at the time because people tried to circumvent it," said Automobile Association spokesman Mike Noon.

"People used another vehicle instead of having a day off because in a lot of cases they still needed a vehicle to get to work; there wasn't an alternative so they flouted it."

If petrol prices are high (as they were in the 1970s), then there is a strong incentive to use a car that gets good mileage (i.e. one that doesn't use much petrol). But, if everyone is wanting cars with better mileage, those cars are going to be a bit more expensive than cars with worse mileage. So, if you buy a second car (to use on your carless day for your efficient car), chances are the second car uses more petrol. Which really defeats the purpose of the carless days.

In the labour market, you can't have the best (or the worst) of both worlds

In yesterday's post about new research on the disemployment effects of the minimum wage, I mentioned this post by Bryan Caplan. One part of Caplan's post in particular caught my attention:

Research doesn't have to officially be about the minimum wage to be highly relevant to the debate.  All of the following empirical literatures support the orthodox view that the minimum wage has pronounced disemployment effects: 

1. The literature on the effect of low-skilled immigration on native wages.  A strong consensus finds that large increases in low-skilled immigration have little effect on low-skilled native wages.  David Card himself is a major contributor here, most famously for his study of the Mariel boatlift.  These results imply a highly elastic demand curve for low-skilled labor, which in turn implies a large disemployment effect of the minimum wage.

This consensus among immigration researchers is so strong that George Borjas titled his dissenting paper "The Labor Demand Curve Is Downward Sloping."  If this were a paper on the minimum wage, readers would assume Borjas was arguing that the labor demand curve is downward-sloping rather than vertical.  Since he's writing about immigration, however, he's actually claiming the labor demand curve is downward-sloping rather than horizontal!

The reason that part of the post caught my attention is that it highlights something I have noticed before. There are at least some people who truly want to believe things about the labour market that are highly likely to be mutually exclusive. Consider these two true/false questions:

1. True or False: Increasing the minimum wage will substantially reduce employment of low-skilled (or low-wage) workers.
2. True or False: An increase in immigration substantially reduce the wages of low-skilled (or low-wage) native-born workers.

The word substantially in both cases is important, since it suggests that the effects are large. You may believe that Statement 1 is true and Statement 2 is false, or you may believe that Statement 1 is false and Statement 2 is true. However, there are at least some people who believe that both of these statements are false - these optimists believe in the best of both worlds (higher minimum wages are good since they raise wages without increasing employment by much; and immigration is good for both immigrants and the native-born population). And there are at least some people who believe that both of these statements are true - these pessimists believe in the worst of both worlds (higher minimum wages are bad because they increase unemployment; and immigration is bad because it reduces the wages of the native-born population). The last two groups of people (the optimists and the pessimists) are unlikely to be correct, and here's why.

Let's say that Statement 1 is true (and the latest research that I blogged about yesterday suggests that may be the case). If minimum wages substantially reduce employment of low-skilled workers, then that suggests the demand for labour is relatively elastic (the labour demand curve is relatively flat). If the labour demand curve is relatively flat, then if there is an increase in the supply of labour (as would occur with an increase in immigration), then the effect on the equilibrium wage would only be small - immigration would not lead to a large decrease in wages of low-skilled workers (and indeed, that is what the Mariel boatlift research by David Card found - see here and here for the latest debate about the effects of the Mariel boatlift). That would make Statement 2 false, so it's unlikely that both statements are true.

Let's say that Statement 1 is false. If minimum wages don't substantially reduce employment of low-skilled workers (and that's what this influential paper by David Card and Alan Krueger (ungated) finds), then that suggests the demand for labour is relatively inelastic (the labour demand curve is relatively steep). If the labour demand curve is relatively steep, then if there is an increase in the supply of labour (as would occur with an increase in immigration), then the effect on the equilibrium wage would be large - immigration would lead to a large decrease in the wages of low-skilled workers. That would make Statement 2 true, so it's unlikely that both statements are false.

Even if you believe that there is monopsony power in the labour market that ensures that increasing the minimum wage would increase employment (a common theoretical argument supporting the findings of Card and Krueger), an increase in the supply of labour in such a market would decrease the marginal cost of labour and decrease wages (consider extending the diagrams here, with an increase in supply). So Statement 1 would be false, but Statement 2 would still be true.

All of this is enough to make one wonder: Given that David Card has research that supports 'false' for both statements (possibly making him an optimist?), what does he really believe about the elasticity of demand for labour? It would be interesting to know (the closest I could find easily was this 2006 interview, which discusses his research on immigration and on the minimum wage, as well as a lot of other labour economics research he has conducted).