Saturday, 19 August 2017

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]

Wednesday, 16 August 2017

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!

Tuesday, 15 August 2017

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]

Sunday, 13 August 2017

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.

Saturday, 12 August 2017

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:


Thursday, 10 August 2017

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.

Wednesday, 9 August 2017

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:



Monday, 7 August 2017

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!

Sunday, 6 August 2017

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?

Thursday, 3 August 2017

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.

Tuesday, 1 August 2017

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).

Monday, 31 July 2017

The latest evidence supports negative employment effects of the minimum wage

A couple of years ago, I wrote a post about some research that updated our understanding of the effects of the minimum wage on employment. In contrast with many well-publicised studies, such as this one by David Card and Alan Krueger (ungated), that research showed that the minimum wage did reduce employment (see here for the Adam Ozimek piece that summarises that work). In the last month, two new studies have further added to this evidence. Both studies are summarised by Bryan Caplan here, who also raises some additional points that I will address in a follow-up post, probably tomorrow.

The first study is reported in this NBER Working Paper (ungated here) by Ekaterina Jardim (University of Washington) and others. The paper investigates the impact of the rising minimum wage in Seattle:
The minimum wage rose from the state’s $9.47 minimum to as high as $11 on April 1, 2015. The second phase-in period started on January 1, 2016, when the minimum wage reached $13.00 for large employer...
Given the starting point was already well above the U.S. federal minimum wage level of $7.25 per hour, this gives some evidence as to what might happen if a much higher minimum wage was introduced (although as the authors note they probably overstate the effect of more modest minimum wage changes). Jardim et al. find that:
...the rise from $9.47 to $11 produced disemployment effects that approximately offset wage effects, with elasticity estimates around -1. The subsequent increase to as much as $13 yielded more substantial disemployment effects, with net elasticity estimates closer to -3...
In other words, although workers earned more per hour worked after the $11 minimum wage was introduced, they worked fewer hours so that the overall impact on their earnings was approximately zero. When the minimum wage increased further to $13, the effect on hours was greater than the effect on hourly earnings, leading to a reduction in overall earnings. Students of ECON100 will recognise that when demand is elastic (here the price elasticity of demand for labour was estimated at -3, i.e. elastic), an increase in price (here, the wage) is more than offset by a decrease in quantity demanded, leading to a reduction in total revenue (in this case, a reduction in total earnings for workers). Jardim et al. estimate that the overall effect included the loss of over 6,300 low-wage jobs at single-site employers (or approximately 10,000 jobs if multi-site employers are also included, although their dataset could not accurately evaluate the impact on multi-site employers). 

One of the interesting things about this study is that the authors were able to reconcile their results with those of earlier work, which has generally focused on all employees in one or more low-wage industries (whereas this study limited consideration to only low-wage workers, defined as those earning less than $19 per hour - that is, those most likely to be affected by the increased minimum wage). Often the focal industry of earlier studies has been the restaurant industry. Jardim et al. show that these earlier studies "may have substantially underestimated the impact of minimum wage increases on the target population".

The second study is reported in this CEPR Working Paper by Claus Thustrup Kreiner (University of Copenhagen), Daniel Reck (UC Berkeley), and Peer Ebbesen Skov (Auckland University of Technology). This paper investigates the impact of the Danish youth minimum wage, where:
The average hourly wage rate jumps by DKK46, or about $7, corresponding to a 40 percent change in the wage level at age 18 computed using the midpoint method.
That's quite a substantial change when someone turns 18, and Kreiner et al. demonstrate a substantial disemployment effect, summarised in their Figure 1:


Notice that average hourly wage (in the left panel) increases significantly at age 18, while the employment rate (in the right panel) decreases significantly at that same age (along with a small drop off slightly before age 18, which may be explained by employers anticipating the increase in wage that would occur a few months later). When it comes to the elasticity:
We observe that wages are relatively constant around 90 DKK beforehand, and then increase to about 135 DKK after the wage change... we estimate that this 46 DKK increase constitutes a 40 percent increase in hourly wages.
...In our preferred specification... we estimate a 15 percentage point drop in employment, equivalent to a 33 percent decrease in the number of employed workers. In other words, the presence of the wage hike causes roughly one in three workers employed before 18 to lose their jobs when they turn 18. Combining the percentage change in hourly wages and in employment, we obtain the implied elasticity of -0.82...
That's the elasticity of employment, rather than hours worked. Once they also account for reductions in hours for those who remain employed, the elasticity increases to -1.1, which is eerily similar to that for the Jardim et al. paper (though in a completely different context). An elasticity of -1 implies that when the wage rate increases, total earnings pretty much does not change (because the decrease in hours worked would offset the increase in the wage rate).

Kreiner et al. then go on to show that the effect of age 18 is almost entirely driven by labour market exits (lost jobs) at, or just before, age 18, and not by a decrease in hiring. And finally, the effects of that job loss are persistent:
...by one year after job separation at age 18, only 40 percent of separated individuals are employed, compared to just over 75 percent of individuals who did not experience a separation. Even two years after turning 18, individuals who kept their job at age 18 are about 20 percent more likely to be employed than individuals who did not...
Both papers have a similar advantage over earlier work, in that they are able to use the observed change in wage rates for workers to compute the elasticity, rather than relying on an implied increase in wage rates proxied by the percentage increase in the minimum wage. Given that many workers affected by the higher minimum wage would have had wage rates that were higher than the original minimum wage, this means that the increase in wages is overestimated in those earlier studies, leading to under-estimates of the elasticity.

Finally, with increases in minimum wages we can be fairly certain that at least some workers are made better off (those who retain jobs at the new, higher, minimum wage), whereas others are made worse off (those who lose their jobs, or now work substantially fewer hours, at the new minimum wage). Both papers are silent on these distributional impacts, and do not have the data to adequately address them. But those distributional impacts are also important for understanding the impact of the minimum wage.

[HT: Marginal Revolution, here and here]

Sunday, 30 July 2017

More on the gender gap in economics

Last month I wrote a post on the gender gap in economics. A couple of other sources has since come to my attention, starting with this speech by Luci Ellis, who is Assistant Governor (Economic) at the Reserve Bank of Australia (RBA). RBA has come under fire recently for its gender gap. Ellis paints a picture that looks very similar to the situation in New Zealand:
Unfortunately, both in general and for female students, economics is not exactly popular in Australia... for economics, the share of female university students has always been much lower and appears to have fallen further more recently. Even more concerning is that total student numbers in economics appear to have fallen at our universities over the past couple of decades, though some data show a small pick-up more recently.
The picture is even worse at school level... From what we understand, when business studies subjects were introduced, they expanded at the expense of economics.
Those trends are very similar in New Zealand, and especially the growth of business studies at the expense of economics at high school. Ellis makes a good point though, which is also true here:
Of course, it is not essential to have studied economics at school to select it as a major at university.
She argues that mathematics is another pathway, but I would say that even mathematics is not a strict requirement (although aversion to mathematics would be very unhelpful). I can think of many very good economics students who started with no background in economics or strong background in mathematics or statistics. Despite that, Ellis makes many good points, and I encourage you to read the speech in its entirety, especially if you want to understand some of the key points related to the gender gap in occupations more generally.

The second source is this blog post by Leith Thompson, who writes:
In 2016 the Reserve Bank asked me to do some research on how to encourage female students into the field by creating a more inclusive economics...
We don’t just need to encourage female students to study economics, but we also need to adopt innovative, best practice pedagogy that inclusively encourages all students to embrace economics.
Thompson's solutions don't seem to me to be necessarily focused on encouraging more female students to study economics, but seem to be good practice for all students. Clearly, we still have more work to do, and hopefully my Summer Research Scholarship student this coming summer will help us understand this problem further. We also have a group of very keen students at Waikato who are looking at trialing an intervention with high school students, and I hope to have an update on that sometime in the future as well.

Read more:

Thursday, 27 July 2017

Infrastructure costs are going to rise

We've been covering the interactions between markets in ECON100 and ECON110 over the last couple of weeks, so I thought an example might be useful. Brian Roche (chair of the Aggregate and Quarry Association) wrote in the New Zealand Herald on Tuesday:
Aggregate makes up 75 to 90 per cent of all the concrete used in buildings, roads and other infrastructure like airport runways or bridges...
Current total annual demand in Auckland is 13 million tonnes. This will rise to 16.5 million tonnes by 2031, assuming medium growth, or as much as 20 million tonnes, assuming high growth. The latter is likely given the Auckland Unitary Plan allows for more than 100,000 new houses to be built, mainly in newly developed areas.
Demand is good but only if it's matching supply. Get that out of whack and the greywacke that's widely quarried and used in aggregates will rise in price, adding more costs to our already high cost of building.
If more infrastructure is to be built, that will increase the demand for aggregate. The effect is pretty straightforward, as shown in the diagram below - demand for aggregate has increased from DB to DA, and price increases from PB to PA.


How does that affect the 'market' for infrastructure though? The demand for infrastructure (to be built in any given period of time) is downward sloping - if costs rise, we'll built less infrastructure (perhaps deferring some of the least essential projects to sometime in the future). The increase in the price of aggregate causes an increase in the cost of production for infrastructure, which is essentially the same as a decrease in supply. As shown in the diagram below, supply shifts up and to the left, from S0 to S1. This increases the price of infrastructure from P0 to P1, and because infrastructure is now more expensive, we invest in less of it now (deferring some needed infrastructure to the future).


What about if we don't defer investment in infrastructure in response to the increase in the price of aggregate? In that case, the demand curve is vertical (perfectly inelastic) - the quantity demanded doesn't adjust in response to a change in price (that is, the quantity of infrastructure is fixed at Q0). As shown in the diagram below, the decrease in supply from S0 to S1 now leads to a much higher cost of infrastructure (P2), compared with the price if demand was downward sloping (P1).


Overall, regardless of whether we defer infrastructure spending or not, the increased price of aggregate is going to lead to higher costs of building infrastructure.

Tuesday, 25 July 2017

Sunshine, the value of housing and compensation for externalities

In ECON110 today, we discussed hedonic demand theory (or hedonic pricing). Hedonic pricing recognises that when you buy some (or most?) goods you aren't so much buying a single item but really a bundle of characteristics, and each of those characteristics has value. The value of the whole product is the sum of the value of the characteristics that make it up. For example, when you buy a house, you are buying its characteristics (number of bedrooms, number of bathrooms, floor area, land area, location, etc.). When you buy land, you are buying land area, soil quality, slope, location and access to amenities, etc.

In a new Motu working paper, David Fleming, Arthur Grimes, Laurent Lebreton, Dave Maré, and Peter Nunns show that sunshine is one of the important characteristics that contributes to house values. The New Zealand Herald reported a couple of weeks ago:
Motu Economic and Public Policy Research Trust has released what it calls the first research carried out anywhere in the world to specifically evaluate the extra value house buyers put on extra sunshine hours.
Arthur Grimes, a senior fellow at Motu and co-author of the study, said there was a direct correlation between more sunshine and higher values and the study was precise about how much extra value is added.
"Direct sunlight exposure is a valued attribute for residential property buyers, perhaps especially in a cool-climate city such as Wellington. However, natural and man-made features may block sunlight for some houses, leading to a loss in value for those dwellings," the study said.
The effect is quite large. Quoting from the paper:
...each additional hour of direct sunlight exposure for a house per day (on average across the year) adds 2.4% to a dwelling’s market value.
The paper also has some interesting implications in terms of negative externalities. If a high-rise apartment development will block the sunlight from nearby houses, then it will reduce the value of those houses. This constitutes a negative externality imposed on the affected homeowners. Fleming et al. note that these externalities could be dealt with through compensation:
At a policy level, our estimates may be used to facilitate price-based instruments rather than regulatory restrictions to deal with overshadowing caused by new developments. For instance, consider a new multi-storey development that will block three hours of direct sunlight exposure per day (on average across the year) on two houses, each valued at $1,000,000. The resulting loss in value to the house owners is in the order of $144,000. Instead of regulating building heights or the site envelope for the new development, the developer could be required to reimburse each house owner $72,000. In return, the developer would be otherwise unrestricted (for sunlight purposes) in the nature of development. If the development cannot bear the $144,000 then the efficient outcome is that the development does not proceed. Conversely, if the development can bear that sum, then the socially optimal outcome is for the development to occur and, from an equity perspective, the neighbours are compensated for their loss of sunlight exposure.
The idea that compensation can be used to deal with externalities relies on the Coase Theorem - the idea that, if private parties can bargain without cost over the allocation of resources, they can solve the problem of externalities on their own (i.e. without government intervention). In the case of a bargaining solution to an externality based on the Coase Theorem, the solution depends crucially on the distribution of entitlements (property rights and liability rules). In this case, the homeowners have existing rights to sunlight and because an apartment development would infringe on those rights, the developer would be expected to pay compensation to the affected homeowners. This will only be viable if the total amount of compensation paid to affected homeowners is not so great that it makes the development unprofitable.

The study was based on data from Wellington. Given that development in Auckland is happening faster and involves increasing density and greater numbers of taller mixed-use buildings, it would be interesting to see if the results hold there as well. As noted in the New Zealand Herald story:
"For places other than Wellington, the value of sunshine hours may be higher or lower depending on factors such as climate, topography, city size and incomes. Nevertheless, our approach can be replicated in studies for other cities to help price the value of sunlight in those settings," Grimes said. 
So the approach is transferable, even if the results are not. It's almost certainly extendable to considering the value of volcanic viewshafts in Auckland, and hopefully someone is already thinking about undertaking that work.

Monday, 24 July 2017

Reason to be wary if a job in Taumarunui offers an Auckland salary

The New Zealand Herald reported last week:
If you fancy getting away from the rat-race and settling in small-town New Zealand, the perfect role just came up.
Forgotten World Adventures are advertising for a general manager to be based in Taumarunui while receiving an "Auckland salary" - over $150,000 for the "right" candidate...
The advertisement says candidates don't need tourism experience but will "need to be a true leader".
"We are looking for someone who is excited about doubling our revenue over the next three years, passionate about securing our position as a 'bucket list' experience for our target market, and focused on developing our reputation as an industry leader," the advertisement reads.
If you're wondering why a business in Taumarunui is offering an 'Auckland salary', you're right to wonder. That should be a great big red flag. It screams out "compensating differentials!".

Economists recognise that wages may differ for the same job in different firms or locations. Consider the same job in two different locations. If the job in the first location has attractive non-monetary characteristics (e.g. it is in an area that has high amenity value, where people like to live) then more people will be willing to do that job. This leads the supply of labour to be higher, which leads to lower equilibrium wages. In contrast, if the job in the second area has negative non-monetary characteristics (e.g. it is in an area with lower amenity value, where fewer people like to live) then fewer people will be willing to do that job. This leads the supply of labour to be lower, which leads to higher equilibrium wages. The difference in wages between the attractive job that lots of people want to do and the dangerous job that fewer people want to do is called a compensating differential.

So, coming back to the Taumarunui job with an Auckland salary, you really have to ask yourself what is so bad about the job that it requires a high salary to attract someone to work there? Perhaps the business is struggling (but nonetheless trying to double their revenue over the next three years)? Or maybe the owners or co-workers aren't easy to get along with? Or, maybe living in Taumarunui is truly awful? They're almost certainly compensating for some undesirable characteristic of the job.

Whatever it is, I'd be wary of applying. Is there a prospective employee equivalent to caveat emptor?

Read more:


Sunday, 23 July 2017

Are house prices a self-fulfilling prophecy?

Possibly. But let's start from the beginning, which was neatly summarised in this New Zealand Herald article from a couple of weeks ago:
An economist from one of New Zealand's biggest banks has questioned the role of the media in reporting on Auckland's housing market, asking if significant coverage of Auckland house price declines could be "a self-fulfilling" prophecy.
BNZ senior economist Craig Ebert was writing ahead of tomorrow's release of Real Estate Institute data for June and posed a question about the effect of the media's role in the market.
He referred to other recent data that showed prices dropping in some Auckland areas.
"The recent decline in Auckland house prices is now getting significant media coverage. This can be self-fulfilling to the extent that folk fearful that a market might correct are more likely to withdraw from it - buyers that is - and sellers will either delist their properties, simply not sell or, if under pressure, accept lower prices than might otherwise be the case," Ebert wrote.
One of the factors that affects the current demand in a market is expectations about future prices, which may be affected by media coverage. If a consumer (in this case, a home buyer) believes that the price of a good (in this case, a house) will be lower in the future, then they may hold off on purchasing now and wait for the lower future price. This lowers current demand for the good (houses). As shown in the diagram below, demand falls from D0 to D1, and the effect of that is that the equilibrium price falls from P0 to P1 (and the quantity of houses traded falls from Q0 to Q1). So the price falls, which is exactly what the consumer expected. Hence, this becomes a self-fulfilling prophecy.


But wait, there's more. If potential sellers expect prices to fall in the future, they may choose to sell their houses now, which increases the current supply of houses. As shown in the diagram below, this combination of decreased demand (from D0 to D1) and increased supply (from S0 to S2) leads to an even greater drop in prices, to P2. Note that the change in quantity becomes ambiguous - quantity of houses traded could increase (if the increase in supply is greater than the decrease in demand), decrease (if the increase in supply is less than the decrease in demand), or least likely of all the quantity could stay the same (if the increase in supply exactly offsets the decrease in demand).


But maybe sellers aren't that dumb - maybe they recognise that they can hold onto their houses for now instead (and rent them out), and then sell them at some point in the future once prices have recovered. In this case, the supply of houses for sale would decrease rather than increase. As shown in the diagram below, this combination of decreased demand (from D0 to D1) and decreased supply (from S0 to S3) leads to a certain decrease in quantity (to Q3), but an ambiguous change in prices. House prices could increase (if the decrease in supply is greater than the decrease in demand), decrease (if the decrease in supply is less than the decrease in demand), or least likely of all the price could stay the same (if the decrease in supply exactly offsets the decrease in demand).


So, are house prices a self-fulfilling prophecy? It really depends on the reaction of sellers. If sellers choose to cash out before prices start to fall (which I would suggest is probably the case for short-term speculators) then yes. However, if sellers choose to hold onto houses and wait out the downturn (which is more likely the case for owner-occupiers, landlords and long-term investors), then possibly not. At that point, it becomes an empirical question - if the quantity of houses changing hands falls significantly and house prices hold up, then the latter of those two explanations is probably having the greater effect.

Saturday, 22 July 2017

Surge pricing is coming to a supermarket near you

When demand increases, the standard economic model of supply and demand tells us that the price will increase. However, most businesses don't dynamically adjust prices in this way. For instance, ice cream stores don't raise prices on hot days, and umbrellas don't go up in price when it rains.

There are a few reasons that sellers don't automatically adjust prices in response to changes in demand. The first reason is menu costs - it might be costly to change prices (they're called menu costs because if a restaurant wants to change its prices, it needs to print all new menus, and that is costly). The second reason is that changing prices creates uncertainty for consumers, and if they are uncertain what the price will be on a given day, perhaps they choose not to purchase (in other words, the cost of price discovery for consumers makes it not worth their while to find out the price). The third reason is fairness. Research by Nobel Prize winner Daniel Kahneman (and described in his book Thinking, Fast and Slow) shows that consumers are willing to pay higher prices when sellers face higher costs (consumers are willing to share the burden), but consumers are unwilling to pay higher prices when they result from higher demand - they see those price increases as unfair.

Despite this, there are examples of sellers dynamically adjusting prices. For example, Alvin Roth's book Who Gets What - And Why (which I reviewed here) relates a story about how Coke ran a short-lived experiment, where their vending machines increased prices in hot weather. And many of us will be familiar with Uber's surge pricing (which, as noted in this post, is used to manage excess demand).

It seems that soon Uber may not be the only local example that we will see of this. The New Zealand Herald reported a couple of weeks ago:
On demand surge-pricing is making its way to New Zealand.
The country could soon be in the same boat as the UK, Europe and America, with stores and supermarkets adopting digital e-pricing - prices that change hour to hour, based on demand.
Retail First managing director Chris Wilkinson said variants of surge-pricing had already hit New Zealand, particularly around the Lions tour, with accommodation and campsites prices soaring.
While on demand surge-pricing is not a new phenomenon, Wilkinson said the way it was being administered, overseas, was.
"What is new is the ability to manage on-shelf pricing dynamically and tie this to key commercial opportunities - such as busy times, events, weather or other responsive opportunities," he said.
Asked if he thought it would become standard practice in New Zealand supermarkets and on shelves anytime soon, Wilkinson said it would likely hit service stations first.
"We'll likely see this in service stations first, as they will be able to maximise potential around higher margin products such as hot drinks, bakery and other convenience items," he said.
I'd be interested to know how a supermarket would deal with a customer who picks up an item observing one price at the shelf, but then finds that the price has changed by the time they get to the checkout. Would that breach the Fair Trading Act? As Consumer notes here:
In the past, a supermarket has been convicted and fined for charging higher prices at the checkout than were on display.
Despite that particular problem, surge pricing is coming. When you see the traditional price sticker replaced by a small LCD or LED display, you'll know it has probably arrived.

Friday, 21 July 2017

Health economics and the economics of education in introductory economics

Sometimes it's good to receive some affirmation that what you're teaching is also taught in a similar way, and at a similar level, at top international universities. In the latest issue of the Journal of Economics Education, two of the articles have demonstrated to me that the material I teach in the health economics and economics of education topics in ECON110 is current best practice.

In the first paper (sorry I don't see an ungated version), David Cutler (Harvard) writes about health economics:
Health care is one of the biggest industries in the economy, so it is natural that the health care industry should play some role in the teaching of introductory economics... The class that I teach is an hour long...
In his hour-long class, Cutler covers medical care systems, the financing of medical care, and the demand and supply of medical care. In ECON110, I have a whole topic (three hours of lectures, and two hours of tutorials) devoted to health economics, and we cover the peculiarities of health care as a service (peculiar due to derived demand, positive externalities, information asymmetries, and uncertainty), cost-minimisation/cost-effectiveness/cost-utility analysis (including consideration of expected values to deal with uncertainty), the value of statistical life and cost-benefit analysis, and health systems. Obviously I can cover more ground because I have more time available, but it's good to see that the things that Cutler covers at Harvard are part of my topic.

Similarly in the second paper (also no ungated version), Cecilia Elena Rouse (Princeton) writes about the economics of education:
There are many aspects of the “economics of education” that would make excellent examples for introductory economics students... I chose two related topics that are central to the economics of education and to human capital theory: the economic benefit (or “returns”) to schooling and educational attainment as an investment.
Again, those sub-topics that Rouse identifies are part of the ECON110 topic on the economics of education. In that topic, we cover human capital theory and the private education decision (including introducing the concept of discounting future cash flows), the public education decision (including consideration of the optimal subsidy for education, and dealing with credit constraints). Recently, I've also been discussing the economics of MOOCs (Massive Open Online Courses).

Both papers gave me a few small points to follow up on, but overall it looks like the fact that Harvard and Princeton teach similar topics in a similar way is a good sign of the ongoing quality of the ECON110 paper.

Wednesday, 19 July 2017

Why fire protection is (or was) a club good

Goods and services can be categorised on two dimensions: (1) whether they are rival, or non-rival; and (2) whether they are excludable, or non-excludable. Goods and services are rival if one person’s use of the good diminishes the amount of the good that is available for other peoples' use. Most goods and services that we purchase are rival. In contrast, non-rival goods are those where one person using them doesn’t reduce the amount of the good that is available for everyone else. Listening to the radio is a non-rival good, since if one person listens, that doesn't reduce the number of other people who can also listen.

Goods and services are excludable if a person can be prevented from using or benefiting from them. In other words, there is some way to exclude people from using the good. Often (but not always), the exclusion mechanism is a price - to use the good or service you must pay the price. In contrast, non-excludable goods are available to everyone if they are available to anyone - there isn't a way of excluding people from using or benefiting from the good or service. A fireworks display is non-excludable. If you let off fireworks, you can't easily prevent other people from seeing them.

Based on those two dimensions, there are four types of goods as laid out in the table below.


I want to focus this post on club goods - goods (or services) that are non-rival and excludable. With club goods, often the exclusion mechanism is a price - you have to pay the price in order to be a part of the club (and receive the benefits of club membership).

Some goods or services that are categorised as club goods may be contentious. For instance, according to the table fire protection is a club good - it is non-rival and excludable. Provided there aren't large numbers of fires, if the fire service attends one fire, that doesn't reduce the fire protection available to everyone else [*]. So, fire protection is non-rival. Is fire protection excludable? In theory, yes. People can be prevented from benefiting from fire protection. Say there was some sort of fire service levy, and the fire service decided to only respond to fires at homes or businesses that were fully paid up. For the same reason, tertiary education is also in many cases a club good. [**]

I always thought that fire protection as a club good was purely a theoretical case, but this recent Mac Mckenna article notes:
Since 1906 the Fire Service has been universally available to all New Zealanders. Prior to then, the Fire Service was run by insurance companies to mitigate loss. Firefighters would only respond to save houses which had a red rock outside showing the owners had fire insurance cover.
I was a bit surprised by this piece of history, and I haven't been able to confirm it from another source. But it does demonstrate how fire protection at one point was excludable in more than just the theoretical sense, and therefore it was at that time a genuine club good.

However, in practice the government chooses not to exclude any home or business from fire protection, making it non-excludable, and therefore a public good. Of course, non-excludability comes with problems such as free riding (where a person benefits from the good or service without paying for it). As Mckenna notes, by funding the Fire Service by levying only those who take out insurance, the insured will be subsidising the free-riders who choose not to take out insurance.

*****

[*] Of course, in a large-scale disaster, or in summer when there are large forest or bush fires burning, this may not be true.

[**] Tertiary education is a club good provided it is non-rival. For most university and polytechnic courses, this is the case. However, some courses have limited spaces and in the case of those courses tertiary education is a private good (rival and excludable).

Tuesday, 18 July 2017

Caramilk arbitrage and the endowment effect

As 1974 Nobel Prize winner Friedrich Hayek noted, markets allocate goods to the buyers who value them the most, since those are the buyers who are willing to pay the most for them. So, consumers who purchase the good at a low price, may be willing to give up their purchase in exchange for more money from those who value the good more.

Having said that though, the endowment effect doesn't make Hayek's observation automatic. Quasi-rational decision makers are loss averse - we value losses much more than otherwise-equivalent gains. That makes us are unwilling to give up something that we already have, or in other words we require more in compensation to give it up than what we would have been willing to pay to obtain it in the first place. So if we buy something for $10 that we were willing to pay $20 for, we may choose not to re-sell it even if someone offers us $30 for it.

We've seen a graphic example of both of these effects (goods flowing to the buyers who value them the most, and endowment effects) this week, as Newshub reports:
Ever since Cadbury relaunched its iconic Caramilk chocolate in New Zealand last month, our Aussie neighbours have been desperate to get in on the action.  
The chocolate, a solid bar which is a blend of caramelised white chocolate, was a '90s classic, and appeared back on supermarket shelves around New Zealand at the end of June. The limited edition product is a New Zealand-only release and isn't available in Australia.
Now some clever Kiwis have cottoned on to the demand across the ditch and are putting Caramilk blocks on Ebay Australia for Aussies to buy.
And it turns out they're willing to pay quite a bit.
One auction, which closed early on Monday afternoon (NZ time), saw 19 bids for one $3 block, which eventually sold for AU$40 (NZ$42.64). release and isn't available in Australia.
Savvy New Zealand chocolate buyers have been snapping up Caramilk chocolate for a low price in New Zealand, and promptly on-selling the bars to Australians for a higher price (this practice of buying in a low price market and re-selling in a high price market is known as arbitrage). However, in order to overcome the endowment effect, the price must be high enough to induce them to sell and overcome the endowment effect. But NZ$42 can buy a lot of alternative chocolate!

Eventually though, greater quantities of Caramilk being offered to Australians will leave only those Australians with lower willingness-to-pay for it unsatisfied, and the auction prices will fall. Once the buyers who are willing to pay $42 have their chocolate, that only leaves buyers willing to pay $40, and once they've got their chocolate that only leaves buyers willing to pay $38, and so on. So if you're thinking of trying to take advantage of this arbitrage opportunity, you'd better get in fast.

[HT: Memphis from my ECON100 class]

Monday, 17 July 2017

The optimising behaviour of Italian bank robbers

One of 1992 Nobel Prize winner Gary Becker's many contributions to economics was the development of an economic theory of crime (see the first chapter in this pdf). Becker argued that criminals, like other rational decision-makers, weigh up the costs and benefits of their actions, and will take the action that offers the greatest net benefits. That assumes we are talking about a discrete decision (a yes or no decision) based on incremental benefits and incremental costs. The benefits of crime include the monetary gains, and any 'rush' associated with committing the crime. The costs include any punishment that might be received, conditional on the probability of being caught (and convicted).

However, not all criminal decisions are yes/no type decisions. That is, not all decisions are made on the extensive margin. Some decisions are instead made on the intensive margin, such as how long to spend inside a bank while committing a robbery. The trade-off here is that the longer a criminal spends in the bank, the greater their haul of loot, but also the greater the risk of the police arriving and the criminal being caught. When a question is about the optimal amount of something (e.g. the optimal amount of time for the bank robber to spend in the bank), a rational decision-maker will optimise at the quantity where marginal benefit is equal to marginal cost. In this case, that will be whatever time in the bank where the last minute spent there equates the additional loot collected with the disutility (the negative utility) of being caught and punished.

In a recent discussion paper, Giovanni Mastrobuoni (University of Essex) and David Rivers (University of Western Ontario) exploit this equality using data on nearly 5,000 bank robberies in Italy, to estimate the disutility of imprisonment. Their dataset is quite rich and, while it doesn't include data on the robbers, it includes a lot of data about the robbery including, crucially, the exact duration of the robbery (which is often able to be confirmed using CCTV camera footage). They find that:
...the most successful robbers in terms of hauls use weapons, wear masks, and rob banks with fewer security devices and no guards. Those who work in groups, wear masks, target banks around closing time, and target banks with no security guards and few employees, achieve lower rates of apprehension. Offenders who use a mask and target banks without security guards have higher disutilities of prison. Robber ability is also found to be a strong driver of larger hauls, lower probabilities of arrest, and larger disutilities of prison. The latter finding is consistent with higher ability offenders having a larger opportunity cost of prison.
That latter finding is most interesting. Higher ability offenders tend to earn more from crime (and possibly have better earning opportunities outside of crime as well). So, the foregone earnings (from crime or otherwise) are higher for these offenders if they are imprisoned, which explains their higher opportunity cost of prison and their higher disutility of prison. The other results are mostly unsurprising. Mastrobuoni and Rivers also find that:
...heterogeneity in robber ability generates a positive correlation between criminal harmfulness and disutility. An importance consequence of this is that policies designed to affect those with higher disutilities of prison (for example simply raising overall sentences) have the added benefit of disproportionately targeting the more harmful (higher ability) offenders.
What that means is that the offenders who create the most harm (by being least likely to be caught, and generating the greatest hauls of loot) are also those with the greatest disutility of punishment. So, increasing the punishment for bank robbery would disproportionately deter the highest ability criminals, and have a large effect on reducing bank robberies. Whether the benefits to the state of greater punishment (less bank robbery) exceed the costs (more prisoners who cost money to house and feed) is not considered in the paper. However, for Italy it may well be the case, since:
Each year there are more bank robberies in Italy (approximately 3,000) than in the rest of Europe combined, with a 10 percent chance of victimization on average (there are about 30,000 bank branches).
Over the period 2000-2006, on average 8.7 percent of Italian banks were robbed each year. That compares with 2.2 percent in New Zealand (and a surprising 14.1 percent in Canada!). So, even if the benefits of greater prison terms for bank robberies exceed costs in the case of Italy, they may not do so for New Zealand.

[HT: Marginal Revolution]

Sunday, 16 July 2017

Book Review: Economic Ideas You Should Forget

Imagine you gathered together a bunch of economists, and asked each of them to write two pages about their pet hates (in economics). I imagine you would be able to put together a volume that looks very similar to a new book, "Economic Ideas You Should Forget", edited by Bruno Frey and David Iselin. It would be charitable to describe this book as anything more than an excuse for complaint by several well-known (and many lesser known) economists. The editors are up-front in the introduction that "The essays do not idolize models or references..." but it is the lack of references that make many of the essays seem at the same time both lightweight and unsupported by evidence.

To be fair, there are some excellent chapters including those by Daron Acemoglu (Capitalism), Thomas Ehrmann (Big Data Predictions Devoid of Theory), and Dider Sornette (Decisions are Deterministic). But there are some misses like Jurg Helbling (Boundedness of Rationality) and surprisingly (to me) Richard Easterlin (Economic Growth Increases People's Well-Being), which contrasts starkly with research by Betsy Stevenson and Justin Wolfers (see here). It was interesting to read Victor Ginsburgh (Contingent Valuation, Willingness to Pay, and Willingness to Accept), given that I have written on the contingent valuation debate before (see here and here), but I don't think that essay added much to the debate.

Most of the essays are unconvincing and I doubt anyone will be persuaded to change their thinking on the basis of reading two pages in this book. Overall, there were some good bits but really, this is an economics book you should forget.

Wednesday, 12 July 2017

Strip clubs, externalities, and property values in Seattle

Property values tend to reflect not only the characteristics of the property itself, but also the neighbourhood that the property is located in. This is hedonic pricing - the price of a property reflects the sum of the values of all of the characteristics of the property. If the property includes a dwelling, the price reflects the quality and size of the dwelling, number of bedrooms, bathrooms, whether it has off-street parking, and so on. But the price also reflects the access of the property to local amenities, such as good schools, public transport, and so on (for example, see this post from earlier this year).

But not all local amenities are positive. Some features of the neighbourhood might create disamenity, reducing property prices. One example may be strip clubs. If a strip club attracts unsavoury people and petty (and not-so-petty) crimes, then fewer people will want to live in that neighbourhood, reducing demand for properties in that area and consequently reducing property prices. Another way of thinking about this is that the strip club creates a negative externality on local property owners (an externality is the uncompensated impact of the actions of one party - in this case the strip club locating in a particular neighbourhood - on others, in this case the local property owners).

There is evidence to suggest that some facilities do create disamenities that negatively affect property prices, including meth labs and toxin-emitting industrial plants. But what about strip clubs? A recent working paper by Taggert Brooks (University of Wisconsin - La Crosse), Brad Humphreys and Adam Nowak (both West Virginia University) looks at relevant data for Seattle.

Specifically, Brooks et al. looked at repeated property sales (where the same property was sold multiple times) over the period 2000-2013, a period during which a moratorium on new strip clubs in King County (which includes Seattle [*]) was removed. Using repeated property sales gets around the problem of accounting for the different quality of different properties (provided you assume that property quality doesn't markedly change between sales). Their dataset included over 317,000 property sales, of which about 5,400 were within 2000 feet of a strip club.

What did they find? A whole lot of nothing. In their preferred specification of the mode, the results:
...indicate that the presence of an operating strip club is not associated with any differential in residential property prices over this period. These results indicate price dynamics for those properties within K of an operating strip club are no different from price dynamics for properties between K and 1 [mile] of a strip club.
There did appear to be some weaker evidence that condominium prices were lower when a strip club was nearby though:
However, the results using the condominium sub-sample, and the single family home sub-sample, provide weak evidence that strip clubs are associated with residential property price differentials in some cases... condominiums located within 1000 feet of a strip club have transactions prices about 5.5% lower than condominiums located farther from operating strip clubs. Some weak evidence also suggests that condominiums within 500 feet also sell for lower prices...
These results are interesting, but are based on only a small amount of variation in the sample. If I read the paper correctly, there were only 370 properties that were sold multiple times, where there was a nearby strip club at the time of one of the sales and no nearby strip club at the time of the other sale. So, given the small number of 'identifying observations', I'd be much more cautious than the authors about interpreting the lack of statistical significance here as suggesting that strip clubs have no effect on property values. I would be more inclined to say that they may have an effect, but this study didn't have sufficient statistical power to detect the effect. Although statistically insignificant, the point estimate of the effects from their preferred specification suggests that property prices are 6.5 percent lower when there is a strip club within 500 feet, 2.9 percent lower within 1000 feet, and 1.6 percent lower within 2000 feet. That is quite a large effect.

It would also be interesting to see if similar results obtain for other cities in the U.S. and elsewhere. It also suggests to me that we could use a similar approach to evaluate the negative effects of alcohol outlets in New Zealand. Something to follow up later.

[HT: Marginal Revolution last year]

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[*] Yes, as I mentioned in an earlier post I was in Seattle a couple of weeks ago and no, it wasn't to collect observational data on strip clubs. My wife was with me and can attest to the lack of strip clubs in our itinerary.