Should landlords offer efficiency rents?

Last week, Ron Goodwin wrote in the New Zealand Herald about the problems of being a landlord:

You think it's easy being a landlord? All we do is sit around and randomly put up rents when our Jags need servicing. Capitalist pigs, feeding from the trough of tenants' misery. Apparently we bunch people's undies too...

Last week, my hired rental inspector and I went to my worst tenants' house. The whole place was a trashed pigsty. Broken windows, the kitchen vinyl torn to bits beyond recognition, the lounge carpet covered in big black stains and torn, rubbish piled up around the place, torn and missing curtains, the deck gate smashed, the main steel driveway gate bent beyond repair.

These are tenants who wrecked an outside tap and then left it running, and who throw much of their rubbish out the windows, over the deck and out the doors into the yards and landscaped gardens...

Anyone want this lot living in their house?

This got me thinking. There are good tenants and bad tenants, and it is difficult for landlords to regulate tenants' behaviour after they have signed the rental agreement. Given this is moral hazard and efficiency wages is one way to deal with moral hazard in labour markets, is there a rental market equivalent of efficiency wages?

First, some context. In ECON100 and ECON110, we discuss moral hazard and agency problems. One such problem is where employees' incentives (after they have signed their employment agreement) are not aligned with those of the employer. The employer wants their employees to work hard, but working hard is costly for the employee so they prefer to shirk. One potential solution to this is efficiency wages (I've previously discussed efficiency wages here). With efficiency wages, employers offer wages that are higher than the equilibrium wage, knowing that this will encourage higher productivity and lower absenteeism from their workers. This is because if workers don't work hard (and avoid absenteeism), they may lose their jobs and have to find a job somewhere else at a much lower rate.

Which brings me to landlords and efficiency rents. As noted above, there is a moral hazard problem for landlords - tenants' incentives (to look after the property) are not aligned with the landlord's incentive (to keep the property in top condition). If the landlord instead offered an efficiency rent (a rent below the equilibrium market rent), then they would have many potential tenants applying for the property, allowing the landlord to pick the best (the least likely to damage the property). It also gives the tenants an incentive to look after the property after signing the tenancy agreement, because if they don't they get evicted and have to find another place to live at a much higher cost.

Maybe landlords offer efficiency rents already and we just don't realise it? There is certainly plenty of evidence for excess demand for rental properties (see here or here for example), so maybe rents are below equilibrium (though they are rising quickly so it's possible that the observed below-equilibrium rents are simply in transition to a higher equilibrium level). Excess demand by itself is pretty weak evidence for efficiency rents. I'd want to hear landlords telling us they offer lower rents to attract good tenants before I found it believable. There's not a lot of evidence in the academic literature on efficiency rent either (see this paper by Basu and Emerson as one example, ungated here).

So, why wouldn't landlords offer efficiency rents? If it is difficult to evict tenants due to tenant protection laws, then that lowers the incentives for tenants to look after the property (since the risk of eviction is lower, or they know it will take a long time before they are eventually evicted, as Ron Goodwin's article suggests). This in turn reduces the incentives for landlords to offer lower rents than the equilibrium rent - why take the risk?

Would efficiency rents be a good thing? Would they have some of the same effects as rent control (which I have discussed here and here, and which we cover in both ECON100 and ECON110)? Perhaps. There would certainly be excess demand at the level of the efficiency rent. However, if not all landlords are offering efficiency rent, there need not be excess demand overall - this might simply be a way of allocating low rent houses to low cost tenants (i.e. those who would look after the properties), with high cost tenants paying higher rents.

Why government should subsidise people to play Pokemon Go

I think I've turned my blog into the economics of Pokemon Go lately (see recent posts here and here). Why another one? The New Zealand Herald reported on Friday:

Pokemon Go players striving to "catch 'em all" are being recruited by Rotorua police to help catch more than the fictional species...

[Rotorua crime prevention manager Inspector Stu] Nightingale said technology was constantly evolving and it was a great tool.

"Having bunches of people roaming around the Government Gardens at all hours chasing Pokemon is a great opportunity for us ... it will deter thieves and also there's more people to report to us what's happening as well."

You read that right. Having more Pokemon Go players in an area reduces crime in contrast with this earlier story). So, Pokemon Go has positive externalities (a benefit that is provided to other people who are not playing the game). When there are positive externalities the market will tend to provide too little of the activity relative to the social optimum. This is because the Pokemon Go players are only considering their own benefits and costs when they decide how much to play (as I discussed yesterday). They fail to consider the benefits that their gameplay provides for others (in terms of reduced crime).

So, to make society better off, the government should find some way to incentivise people to play more Pokemon Go. Subsidies are often used to incentivise production (and/or consumption) of goods that have positive externalities, so that suggests government should subsidise Pokemon Go. Perhaps the government should buy lures and place them in crime hotspots?

There's another good reason for the government to subsidise Pokemon Go. As Dave Schilling wrote in The Guardian, it may help fight obesity:

We might not be able to make fast food healthier or discourage the gluttony our culture reveres, but we can make walking less unpleasant. You might not ever be interested in real birds chirping, but at least maybe you can find the pleasure in the siren song of a wild Togepi or Fearow.

I know anything that gets my kids outdoors and involves fresh air and exercise has to be a good thing. The government should recognise that there are both crime and public health benefits to Pokemon Go, and subsidise the game!

Read more:

• Pokemon Go - Why it might be best not to catch them all
• Pokemon Go - How much are you willing to pay to catch them all?

Pokemon Go - Why it might be best not to catch them all

This is my second post on Pokemon Go this week (earlier I wrote about 'How much are you willing to pay to catch them all?'). This time though, I wonder: is it even best to catch them all? It didn't take long for someone to become the first Pokemon Go Master (by catching them all). But should everyone have this as their goal? In ECON110, we use marginal analysis to identify the optimal quantity of something - the quantity where net benefit of that thing (whatever it is) is optimal. So, I'm going to do the same here.

Finding the optimal quantity of Pokemon to collect requires us to consider the marginal benefits and marginal costs of catching Pokemon. The marginal cost (the additional cost of catching one additional Pokemon) is likely to be upward sloping. This implies that each additional Pokemon caught costs more than the previous Pokemon. It is easy to see why this should be the case. Consider the costs of catching Pokemon. The most obvious cost is time - you get your first Pokemon for free just by starting the game (marginal cost close to zero), and it is pretty easy to find the first few Pokemon just by walking around. However, it then becomes progressively harder to find new Pokemon (i.e. Pokemon that you haven't already caught at least once before). So the amount of additional time (the marginal cost) required for finding one more Pokemon (that you haven't already caught) increases.

The marginal benefit is a little trickier to conceptualise. The benefit of catching Pokemon is the satisfaction the player gains by building their collection, especially with rarer Pokemon. With most activities, the marginal benefit declines as you do more of the activity, due to satiation. Think about an obvious example - eating pizza when you're hungry. The first slice of pizza will provide you with some amount of satisfaction (marginal benefit), but the second slice will provide less additional satisfaction (because you aren't as hungry anymore), and the third slice will provide even less additional satisfaction. So marginal benefit (the additional benefit of eating an additional slice of pizza) declines as you eat more pizza. However, with Pokemon this diminishing marginal benefit might not necessarily be the case. The first Pokemon provide relatively high marginal benefit, but subsequent Pokemon you catch may not necessarily provide lower marginal benefit because they are likely to be rarer Pokemon (and more valuable as a result). So, there are two effects here which work in opposite directions - satiation (reducing marginal benefit for additional Pokemon), and rarity (increasing marginal benefit for additional Pokemon, since those additional Pokemon are likely to be rarer). For simplicity, let's assume that these two effects exactly offset, and so marginal benefit of catching additional Pokemon (that you haven't already caught) is constant. [*]

So, as shown in the diagram below, marginal cost (MC) is upward sloping, and marginal benefit (MB) is constant. For small numbers of Pokemon (less than Q*), the marginal benefit (the additional benefit from catching one more new Pokemon) is greater than the marginal cost (the additional cost of catching that Pokemon). So catching one more Pokemon makes you better off (increasing net benefit). However, once you get to Q* Pokemon and beyond, each additional Pokemon beyond that quantity has a marginal cost that is greater than the marginal benefit - catching additional Pokemon beyond Q* will make you worse off (decreasing net benefit), because the extra benefit (satisfaction) you get from them is less than the extra cost (in terms of time, effort, etc.).

Should you catch all of the Pokemon? Only if Q* is some number greater than all of the around 140 Pokemon available (noting that Q* will be different for each person, because the intrinsic benefit or satisfaction from catching Pokemon will be different for different people). So, for at least some people, it might be best not to catch them all.

*****

[*] One additional point to make here is that, for the very last Pokemon required to complete the entire set and become a Pokemon Go Master, the marginal benefit of that final Pokemon is likely to be very high.

Pokemon Go - How much are you willing to pay to catch them all?

It didn't take long for the entrepreneurial to find opportunities to profit from Pokemon Go. The New Zealand Herald reported on Saturday that:

New ads are popping up on Craigslist nearly every day from people who say they will log on to your "Pokemon Go" account and effectively run up your score while you are stuck at work or sitting in class.

On a recent July afternoon, two 24-year-old Pokemon "trainers," Lewis Gutierrez and Jordan Clark, walked through Brooklyn's Prospect Park with their eyes glued to their phones, tapping and swiping away to catch virtual Pokemon for clients paying about $20 per hour for the service.

Of course, this is nothing new. As the article notes, there are plenty of similar offers on online forums from people willing to level up your World of Warcraft characters, or to sell you high level characters.

Would you be willing to pay $20 per hour for someone else to visit Pokestops, catch pokemon, etc. for you? A rational (or quasi-rational) decision maker weighs up the costs and benefits of their decisions. The cost here is $20 (per hour of gameplay). How large is the benefit, and does it outweigh the cost?

First, we need to recognise that there are two benefits to Pokemon Go: (1) the act of playing itself leads to some satisfaction or happiness (not to mention the benefits of physical activity from all the walking around); and (2) the sense of achievement from having a high-level trainer (which might be kudos from your friends, a type of conspicuous consumption). If you play the game yourself, you receive both benefits, but if you pay someone to play for you, you only receive the second benefit.

Now, if we think about the opportunity cost of playing Pokemon Go, we might compare that $20 with your hourly wage (since for each hour playing Pokemon Go, you forego one hour of working). So if your wage is above $20 per hour, then it is lower cost to have someone else play for you. So provided the benefit from an hour of Pokemon Go leveling-up is worth more to you than $20, it makes sense to pay someone else to do it. However, despite these tips for playing at work, or employers who might encourage you to play the game at work, most people wouldn't take time off working in order to play Pokemon Go.

For most people, the choices are: (1) play Pokemon Go and forego the other leisure activity (but receive both benefits); (2) do the other leisure activity and forego Pokemon Go; or (3) do the other leisure activity and pay someone else $20 to play Pokemon Go for you (but receive only the second benefit).

Think about the costs and benefits of the three options. For Option (1), the benefits are the 'activity benefit' of playing (call it B1) plus the 'conspicuous consumption benefit' (call it B2); the opportunity cost is the benefit of the other leisure activity foregone (call it B3). For Option (2), the benefits are B3 while the opportunity costs are B1 and B2. For Option (3), the benefits are B2 and B3 while the opportunity costs are B1 and B2 and $20.

It turns out that, regardless of whether you prefer Option (1) or Option (2), in order for Option (3) to be preferred, then the conspicuous consumption benefit (B2) must be greater than $20, and perhaps much more if playing Pokemon Go is your preferred leisure activity. [*]

So, people who pay others to play Pokemon Go for them are just valuing the conspicuous consumption benefit at more than $20. Make of that what you will.

*****

[*] Pointless algebra time!

The net benefit of Option (1) is B1+B2-B3.

The net benefit of Option (2) is B3-B1-B2.

The net benefit of Option (3) is B3-B1-$20.

Let's first assume that you value the other leisure activity and playing Pokemon Go yourself equally (so B1+B2 = B3). You would be indifferent between Options (1) and (2), because the costs and benefits are equal (and net benefits of both are zero). Would Option (3) be better? The net benefit of Option (3) is B3-B1-$20, which in this case simplifies to B2-$20 (since B3-B1 = B2). So, you would choose Option (3) only if your conspicuous consumption benefit (B2) is greater than the $20 per hour you are paying someone else to play for you. This is also the case if you prefer Option (2) over Option (1), since the difference in net benefits between Option (2) and Option (3) is the difference between B2 and $20.

What about if you prefer Option (1) over Option (2)? In this case, B1+B2>B3. So, B1+B2 = B3+D (where D is the difference in value between the two options).

If Option (3) is preferred over Option (1), then B3-B1-20 > B1+B2-B3. This simplifies to:
B3 - B1 - 20 > (B3 + D) - B3
B3 - B1 > 20 + D
B2 - D > 20 + D
B2 > 20 + 2D

So to prefer Option (3), the conspicuous consumption benefit has to be more than $20 (plus twice the difference in value between playing Pokemon Go yourself and the alternative activity). Note that, holding B2 and B3 constant, the higher B1 is the less likely it is that B2 will be large enough to choose to pay someone else to play Pokemon Go for you.

Sex in Greece for the price of a sandwich

This coming week in ECON100 and ECON110 we will cover supply and demand. One of the important aspects to understand is how prices adjust when market conditions change. So, let's take a recent example of the market for sex services in Greece, as described in this Washington Post article from last November:

Young Greek women are selling sex for the price of a sandwich as six years of painful austerity have pushed the European country to the financial brink, a new study showed Friday.

The study, which compiled data on more than 17,000 sex workers operating in Greece, found that Greek women now dominate the country’s prostitution industry, replacing Eastern European women, and that the sex on sale in Greece is some of the cheapest on offer in Europe.

“Some women just do it for a cheese pie, or a sandwich they need to eat because they are hungry,” Gregory Laxos, a sociology professor at the Panteion University in Athens, told the London Times newspaper. “Others [do it] to pay taxes, bills, for urgent expenses or a quick [drug] fix,” said Laxos, who conducted the three-year study.

When the economic crisis began in Greece, the going rate for sex with a prostitute was 50 euros ($53), the London newspaper quoted Laxos as saying. Now, it’s fallen to as low as two euros ($2.12) for a 30-minute session...

Ok, so how can we explain this large decrease in price? Consider the market described below. Initially the supply of sex services is S0, and demand for sex services is D0. The equilibrium price is P0 (€50) and the equilibrium quantity is Q0.

Then the severe economic crisis hits Greece, and many Greeks find it increasingly tough to survive financially. Women with few other options may move into the sex industry. This increases the supply of sex services to S1. Now, if the price was to remain at P0 (€50), there would be excess supply (or a surplus of services offered) - the quantity supplied at that price would have increased to Q2 (with the new supply curve S1), but the quantity of services demanded would remain at Q0. Some of the sellers will miss out on clients. So, in order to ensure that they aren't the seller that misses out, some sellers start to accept lower prices. In other words, sellers bid the price downwards, until eventually the price falls to the new equilibrium price P1 (€2). At that price, the quantity of sex services is Q1 (an increase from the original equilibrium quantity).

In essence, that is how we can explain price adjustments following changes in market conditions. Adjustment from one equilibrium price (and quantity) to another happens either through sellers bidding the price downwards, or buyers bidding the price upwards. Of course, this is very simple comparative statics (that is, we assume that the market will reach a new equilibrium), which won't be true in the real world because market conditions are constantly changing. However it is a useful simplification nonetheless, since the general finding that price increases when demand increases (or supply decreases) and price decreases when demand decreases (or supply increases) tends to hold true in most situations.

The impact of business and economics education on moral competence

Last year I wrote a post on whether economics education made politicians more corrupt. It's an interesting question, whether learning about economics alters moral reasoning and makes graduates more corrupt (there are at least some who would quickly buy into this line of argument). Unfortunately, that paper didn't actually answer that question, because it confused correlation with causality. However, a new paper published in the Journal of Business Ethics (ungated version here) by Katrin Hummel, Dieter Pfaff, and Katja Rost (all University of Zurich) gets us a lot closer to understanding whether business and economics education affects moral reasoning.

The authors rightly identify that there are both selection effects (students who choose to study business and economics might be systematically different in terms of moral reasoning from those who study in other fields) and treatment effects (the effect of studying business and economics over and above any difference based on selection). To tease apart the effects, the authors surveyed over 3000 bachelor's and master's students, across six faculties: (1) theology; (2) law; (3) economics and business; (4) medicine; (5) arts; and (6) science. Since most students in Swiss universities progress from bachelor's to master's degrees in the same university (and most stay on to do the master's degree rather than exiting with a bachelor's degree), they essentially observe a cohort of students before, and a cohort of students after, their undergraduate education, as well as students in business and economics, and a range of control disciplines.

They claim to find:

...that both the self-selection as well as the treatment effect of the study of business and economics on students' MJC [Moral Judgment Competence - their measure of moral reasoning] do not exist.

I'd quibble slightly with that summary of their results, because actually there are some statistically significant differences that suggest selection effects - in particular, theology students have significantly higher MJC scores, even after controlling for a range of demographic and other variables, and the size of the effect is about one quarter of a standard deviation. However, regardless of that result there are no treatment effects that suggest that business and economics education reduces moral judgement competence. So, overall nothing to suggest that business and economics education reduces the level of morality in students. Phew!

Some people would (rightly) be worried about the external validity of the results. This study was based on a single university. I'd suggest that this is an invitation for some cross-university comparative research, particularly comparing European universities with British and/or North American universities, to better understand whether the findings are generalisable.

Some of the other results are interesting as well. Quoting (selectively) from the paper:

The results further reveal a significant negative effect of political attitude on MJC, indicating that left-oriented persons have higher C-scores...

Plenty of people would agree with that result, but probably not this one:

Regarding gender, the results suggest that male students have higher MJC.

It seems to contradict plenty of previous research, but apparently the MJC measure is known to be biased towards males. The only treatment effect that was statistically significant was a surprise to me, and somewhat disturbing:

Bachelor's education in medicine in particular seems to significantly reduce students' initially extraordinarily high MJC. This negative impact of medical education on students' MJC is also documented by other researchers... and these researchers explain this finding by the unfavorable learning environment of medical education, which discourages the use of highest-stage moral reasoning.

So medical doctors are less morally competent after their education than before. However, the fact that none of the other fields showed any impact on MJC, let alone a positive impact, leads the authors to conclude:

Today's universities do not offer a learning environment in which optimal moral development can occur. To facilitate moral development, university teachers must encourage students to engage in problem solving rather than offering prepackaged solutions to moral problems.

Ouch!

[HT: Marginal Revolution]

Phishing for Phools

I don't often read book reviews before reading a book (or if I do, I will have mostly forgotten the review before I get around to reading the book). Not so Phishing for Phools, by Nobel Prize winners George Akerlof and Robert Shiller, where Alex Tabarrok's review was still quite vivid in my mind. Tabarrok mostly has the right of it when he says:

Phishing for Phools might have had a bigger impact had it been written 20 years ago but today its examples seem tired. Do we really need another book telling us how supermarkets “phish” us by putting staples like eggs and milk at the back of the store and the impulse purchases like candy and magazines at the front?

The basic premise of the book is this: 'phishing' is "about getting people to do things that are in the interest of the phisherman, but not in the interest of the target" (who is the phool). This leads to "people making decisions that NO ONE COULD POSSIBLY WANT" (the emphasis is the authors). People are susceptible to being phished because they have monkeys on their shoulders, whose preferences differ from what we really want or need, but who have an outsize effect on our decision-making.

There wasn't anything new or startling in the book, and the authors are up-front about that (if you can call it being up-front when they discuss the lack of novelty of their ideas in the afterword!). They also clearly missed a trick - the idea that there is a tension between a person's passions (the monkey-on-the-shoulder) and their rational deliberations dates at least back to Adam Smith's The Theory of Moral Sentiments in 1759!

To be brutally honest, there are much better books around if you want to read about some behavioural economics or market failure (try something by Dan Ariely for starters). However, having said that there are still bits to like about the book, but mostly they come at the end. It struck me that what they were really describing was an alternative type of market failure to the type we discuss in introductory economics. Nothing new in itself, but an interesting way of thinking about the issues that is worth exploring further. The underlying arguments against free markets, and the parallel they draw in the final chapter with an argument against free speech, will not suit some people. However, as one who strongly believes that free markets have gone too far in a lot of cases, it was a point well made. In that vein, the final words from the book are worth noting:

...phishing for phools leads us to quite different conclusions from the usual takeaways of the old economics. The modern economy with its quite-free markets brings those of us who live in developed countries a standard of living that would be the envy of all previous generations. But let us not fool ourselves. It also brings us phishing for phools. And that too is consequential for out well-being.

Why performance pay may underperform

Tim Harford wrote an interesting piece last month about performance pay. He wrote:

Here’s an age-old management conundrum: who should be rewarded for high performance, and how? As Diane Coyle, the economist and former adviser to the UK Treasury, recently observed in this newspaper, the answer to the question is usually self-serving. Simple and easily monitored jobs, such as flipping burgers, are natural candidates for performance incentives. Yet somehow it’s the inhabitants of the C-suite who tend to pick up bonuses, despite the fact that their complex, hard-to-measure jobs are poorly suited to the crude nature of performance-related pay.

Harford (as always) does a great job of summarising the state of research in this area. He correctly identifies that financial rewards don't work in all situations. I'm going to use this post to highlight the factors that must be in place for a performance pay scheme to work well - the absence of one or more of these factors will lead to a performance pay scheme that won't work so well.

First though, some background. Why have performance pay? Employers face a problem we refer to as the principal-agent problem (a specific type of moral hazard). The employer (the principal) engages an employee (the agent) to work on their behalf. However, the goals of the employee are rarely perfectly aligned with the goals of the employer. So, because the employer cannot easily monitor all of the employee's activities, the employee can engage in activities that are not necessarily in the best interests of the employer (like goofing off). Rewarding employees for meeting set targets (and performance pay more generally) is one way of re-aligning the interests of the employee with those of the employer (other options include closer monitoring of employees, and paying efficiency wages, which I have previously discussed here). However, for performance pay to work well a number of things factors need to be present.

First, employees must respond to incentives. This seems like a given, since one of the assumptions economists make is that people respond to incentives. However, it might not always be true. If employees are already highly paid, and the performance payment is small, it might not be enough incentive to encourage greater work effort. So, if you are selling farm machinery, for performance pay to work well your salespeople must be willing to try to earn more sales to capture the performance pay (e.g. commissions).

Second, employees' output must be sensitive to their effort. If employees work harder but the additional effort does not enable them to produce (or sell) more, then rewarding them with performance pay simply won't work well. Why waste your time working harder if it doesn't lead to more output (and higher pay)? So, for performance pay to work well your farm machinery salespeople must be able to sell more tractors if they work harder (at trade shows, visiting potential clients, etc.).

Third, employees' output must be measured easily. If you are going to reward employees for their performance, you must be able to objectively measure their performance. On top of that, employees must be able to believe that the measurement of their performance is accurate. Measurements that are not credible will not encourage employees to work harder. So, for performance pay to work you must be able to know how many tractors each salesperson is selling.

Fourth, employees must not be too risk averse. Risk averse people prefer a higher degree of certainty. Performance pay reduces the certainty of employees' incomes, so if they are highly risk averse they will prefer to work elsewhere (this may or may not be a good thing!). So, your farm machinery salespeople must be willing to accept some likely fluctuations in their salary (as their sales increase or decrease from month to month).

Finally, the level of risk that is beyond the employees' control must be low. If employees' performance depends on their own effort, but also on other factors that are beyond their control, then rewarding high performance may not necessarily increase work effort. This will be a greater problem the greater the share of employee performance that is driven by the external factors. So, if farm machinery sales are driven more by the weather and how farm profits are going, and less by the efforts of the individual salespeople themselves, it will not work so well.

On that last point though, one work-around is to reward employees for their relative performance, i.e. their performance relative to their peers, to industry benchmarks, or to agreed targets. However, it is possible to take performance pay too far - to the extent where competition between different employees is encouraged to an unhealthy extent. In any case, performance pay is one method of dealing with underperforming employees - but if the factors noted above are not present the performance pay scheme itself may well underperform.

The economic non-impact of stadiums and arenas

Peddlers of economic impact studies are modern day snake oil salesmen. Most economic impact studies would not be less credible if they were printed on toilet paper (but they would be more useful!). Exhibit A is the cost-benefit analysis used to justify the upgrade of the white elephant Claudelands Events Centre in Hamilton (see here and here, and note that things are not getting any better). Fudging the figures on cost-benefit analysis to justify public spending on sport and recreation facilities is nothing special to Hamilton either - see this recent post by Tim Harford on measuring (or should that be mis-measuring?) the benefits of the Olympic Games.

A new paper by Sam Richardson, published in New Zealand Economic Papers (sorry I don't see an ungated version anywhere), makes the case against any substantial economic impact of stadiums and arenas, based on New Zealand data. And New Zealand is not an outlier - Richardson's work simply adds to a mountain of existing research that says the same, not just for stadiums and arenas, but also conference centres and large events (on this topic I'm particularly looking forward to reading this book by Andrew Zimbalist, which has been on my to-be-read pile for too long, and which I will review here when done).

Economic impact studies typically go wrong in one (or often more than one) of three ways: (1) they use the wrong counter-factual; (2) they don't account for leakages to outside the local economy; and (3) they fail to consider the opportunity costs. In the first of these, many economic impact studies assume that all spending that relates to stadium events would not have been spent otherwise. Clearly, this is incorrect for local people attending events at the stadium, who probably would have spent that money anyway. In the second, many economic impact studies fail to recognise that not all spending associated with a stadium stays in the local economy. For instance, if a hot dog seller comes from out-of-town to service the event, then their income goes out of the local economy. In the third, almost all economic impact studies fail to consider what the next best use of the money spent on the stadium is, and the net benefits that alternative use could have generated.

Anyway, back to Richardson's work. He looks at the impact of stadium construction across 13 territorial authorities that built or upgraded 24 sports facilities over the period 1997 to 2009. Specifically he looks at the impacts on employment in the construction sector, and local GDP, both during and after construction. He finds:

that there is a statistically significant (judged in terms of a 10% significance level) increase in quarterly employment growth of 1.033 percentage points (p-value D 0.003) for each quarter during facility construction...

Model 1(b) shows the impact of the post-construction period for the combined facility projects, and there is no statistically significant impact...

Results for model 2(a) indicate that the aggregated facility during-construction coefficient is not statistically significant, indicating that facility projects in general did not have any impact on quarterly real GDP during construction. Results from model 2(b) show that the post-construction aggregate facility coefficient is also statistically insignificant.

In other words, there is a small impact on construction sector employment growth during the period of construction (no surprises there), but that the effect does not persist after construction. Probably the construction employment is only temporary because the large construction firms that engage in building stadiums and arenas bring in construction staff, but then move them onto the site of the next large project when the current one is complete. Even worse, there is no effect on local real GDP, either during or after construction. Now, we might expect a lesser impact of

Richardson concludes:

Results from this paper strengthen the conclusions of the majority of research throughout the scholarly literature in this field that sports facilities should not be relied upon as economic stimuli. They do not generate increases in long-term employment, and they have no impact on local area incomes (as measured by real GDP).

For more context on Richardson's work, see his blog (although I note that he hasn't posted for over two years, but there is good stuff there nonetheless).

[HT: Eric Crampton at Offsetting Behaviour]

Winners and losers in population growth

I was quoted at length in a story by Michael Daly published in Stuff last week. My comments were based on my ongoing research programme (with many collaborators) on subnational population projections and migration:

While New Zealand's population was continuing to grow it was becoming much more concentrated in the main centres, Cameron said. "For a lot of regions it really is about managing the decline." 

Declining areas could have a reverse momentum. "You can get young people moving out of the area. You're going to get less natural increase, that's going to reinforce population decline," Cameron said.

Declining rural areas tended to have more older people, while the larger centres had tertiary education opportunities that drew in the young.

"Areas that have more job growth, better income availability, lower unemployment, those tend to be places that are attractive for people to live," he said.

Good amenities were also important. "There's quite a difference between the sorts of things you can do in Auckland from Taumarunui, for instance. People like to be able to be able to do things, and urban centres tend to have more of those opportunities."

Migration was one factor contributing to fast growth in some areas but so was natural increase - the difference between births and deaths.

Although some migrants were retirees, most tended to be younger than average. "Younger people have more babies so that reinforces itself."

Cameron did not expect there would be a tipping point where Auckland's high house prices and traffic congestion would lead to an avalanche of people moving out. "It's a trickle rather than a torrent," he said.

But Auckland's high property prices were benefiting Hamilton and the Waikato District. While some people were commuting north into Auckland, jobs were also spilling over from Auckland into Waikato, where land was much cheaper.

Waikato also had good road and rail links to the ports in Tauranga and Auckland, Cameron said. The dairy boom, although ending 18 months or so ago, had also brought considerable income into Waikato, as well as into Taranaki.

Hamilton did have a similarity with Dunedin that counted against the cities. "They have the university there (Dunedin), which brings in a lot of young people but once they finish they are all heading out of Dunedin. We have the same thing here in Hamilton."

Dunedin's slow growth was a long term trend. It had been New Zealand's largest city in the 19th century and it was hard to pull out the causal factors that had led to its decline in importance.

Queenstown had a booming tourist industry, which was labour intensive, Cameron said. "There's a lot of jobs available. Those jobs pull in people. The more people you have the more hairdressers and things you need. It gets a little bit of momentum going."

Nelson had the same sort of sunbelt migration that Tauranga did, including the arrival of many retired people. Gisborne was "so far away from everywhere. It's very isolated out there."

The notoriety of Wellington's weather didn't seem to be a massive disadvantage, Cameron said. He had looked into the effects of climate on migration, and while it had an effect it wasn't very large.

"People do tend to move to sunny, warmer, less wet places, but the actual size of that effect is pretty small."

Some work had been done on whether regions could arrest population decline by attracting migrants, he said. "But the amount of migration you would need to offset both the ageing population and the fact young people want to move out - it's unrealistic."

Overseas, where areas with declining populations had managed a resurgence, it was usually because of some sort of black swan event. For example, the only thing that turned the population change in North and South Dakota around had been the fracking boom. "It was really a one-off," Cameron said.

One small district that had done a good job of turning around declining fortunes was Otorohanga, which had been losing people for a long time before growing between the last two censuses.

"They managed to retain a lot of their young people," he said. Dale Williams, who was mayor from 2004-2013, had a compact with local employers to make jobs available for young people.

"Because young people could stay in Otorohanga and have a good job, many chose to stay. Then you have more natural increase in the population, as well."

Daly did a good job of collecting and summarising my comments. The key point is that the areas that are already growing fast (especially Auckland, Tauranga, and Hamilton - the so-called 'Golden Triangle' of the upper North Island) are doing so not solely because of migration. Migrants tend to be younger than non-migrants (even for Tauranga a lot of in-migration is young people), and younger people generate additional population growth because they have children. At the other extreme, rural and peripheral areas of the country are experiencing sustained out-migration of the younger population, which is a double-blow (again because there will be fewer children as a result). It could be (and may yet become) worse though - consider the situation in Japan.

The idea that there will be 'winners' and 'losers' in future population growth is nothing new. Consider the discussion of 'zombie towns' in New Zealand (which I discussed here). The Marsden funded project Tai Timu Tangata (led by Natalie Jackson, and including me) will begin producing some final outputs over the coming months. I look forward to outlining some of those outputs here.

Why you can't use taxes to restrict a monopoly's market power

I mark all my own exam papers (whereas many other lecturers out-source this to tutors). I think this gives me a better feel for where students are going wrong and usually I can figure out why. This helps me to improve my teaching for future semesters, especially where it is some passing comment that students have adopted in their answer. Sometimes though, I'm sure it isn't something I've said that has led the students astray. One case of the latter happened in the A Semester ECON100 exam, when I asked a question about the options government has to restrict the market power of monopolies. Many students answered that an appropriate way to restrict market power was to tax the monopoly. Here's why that's not a correct answer.

A monopoly is a sole seller of its product. This gives it market power - the ability to choose a price that will maximise its profit. Consider the diagram below (which assumes a constant-cost firm). The monopoly will operate at the profit-maximising quantity (where marginal revenue (MR) is equal to marginal cost (MC)), which is QM. To sell that profit-maximising quantity they will set a price of PM (because at the price PM consumers will demand exactly QM units of the product).

With that price and quantity, the consumer surplus (the difference between the amount that consumers are willing to pay (shown by the demand curve), and the amount they actually pay (the price)) is the triangle area ABF. The producer surplus (the difference between the amount the monopoly producer receives (the price), and their costs (which are shown by the marginal cost curve)) is the rectangle area FBDG. Total welfare is the combination of consumer and producer surplus, i.e. the area ABDG.

If this was a perfectly competitive market, the market would operate at the point where supply is equal to demand (and note that the supply curve is the marginal cost curve for these constant-cost firms). The perfectly competitive market would operate at the quantity QC and price PC. Consumer surplus would be the triangle AEG, while producer surplus would be zero (because the price PC is equal to the cost of every unit produced), so total welfare is also the triangle AEG.

Now, comparing the monopoly with perfect competition, we see that market power (exercised by the monopoly firm) leads to a higher price (PM) than under perfect competition (PC). That in itself isn't a problem though. What is a problem is the loss of total welfare, which is ABDG with a monopoly firm, but AEG with perfect competition. The difference is the area BED - the deadweight loss of monopoly. It is this deadweight loss that is the main reason why governments might prefer to restrict market power.

So, what happens if you tax the monopoly firm? Consider the diagram below. The tax would likely be levied on the seller since this is administratively easier, so this is like increasing their costs. We represent the tax as a new curve S + tax (or MC + tax in this case for a monopoly). These 'higher' costs for the monopoly move the profit-maximising quantity down to QT (the quantity where MC + tax is equal to marginal revenue), and lead to an even higher price PT.

Consumer surplus with the taxed monopoly is the triangle area AJH, and producer surplus is the area HJKL. The government receives tax revenue equal to the area LKNG (this is part of total welfare for society because the government can use that revenue to pay for roads, schools, etc.). So total welfare with the taxed monopoly is the area AJNG. Notice that the combination of monopoly plus tax has increased the deadweight loss from the area BED to the area JEN. Taxing the monopoly makes the problem of lost welfare worse not better.

And that is why you can't use taxes to restrict the market power of a monopoly. The monopoly can still use its market power to set the price, and in response to the tax it will set an even higher price, increasing the size of the deadweight loss. A better response is to use a price control (a price ceiling - a maximum price that is lower than PM), government ownership (so the government can choose any price it wants, including a price below PM), or using anti-trust laws to prevent firms from merging into larger firms with market power in the first place.

Massage licence fees, sex services, and public health

I've finally gotten around to reading this job market paper, entitled "Optimal regulation of illegal goods: the case of massage licensing and prostitution", by Amanda Nguyen (who was on the job market at the start of the year, from UCLA). It's of interest for a few reasons: (1) it follows on from the paper I wrote about a couple of weeks ago about black market and white market goods; (2) it's a paper about the market for sex services; and (3) it may be one of the most clearly-written job market papers I've ever read (in the sense that even the most technical details in the theoretical section are clearly explained).

In the paper, Nguyen looks at how changes in the costs of licensing in the (legal) market for massage services affect the market for sex services and the associated externalities (in terms of health and crime). She distinguishes between two sectors: (1) the quasi-legal sector (e.g. erotic massage parlours); and (2) the illegal sector (escort services). Only the quasi-legal sector is subject to licensing costs.

So, first some economic theory. If the licensing costs for the quasi-legal sector decrease, we would expect an increase in supply in the quasi-legal sector, and a decrease in price (and increase in the number of services performed) in that sector. Some illegal sector suppliers will shift into the quasi-legal sector because of the lower costs. So, we would expect decreased supply in the illegal sector. Alongside this, because the price of quasi-legal services have reduced (and services from the quasi-legal and illegal sectors are imperfect substitutes for each other), we would expect a decrease in demand in the illegal sector. Overall, the quantity of services in the illegal sector will reduce, but the effect on the price of illegal services is ambiguous (the change will depend on the relative size of the shifts in demand and supply).

Now, because the sex services performed in the quasi-legal sector are typically less risky (more manual stimulation, less intercourse), then we would expect public health benefits from this shift. Nguyen does provide some support for this assertion in her paper. Overall the shift to less risky services should means less transmission of STDs, resulting in lower incidence of gonorrhea and chlamydia (as well as syphilis, HIV, etc.).

Nguyen uses an interesting natural experiment based on California data. Prior to 2009, the licensing fees varied widely for different cities, but in 2009 the fees were standardised. Some cities faced a substantial reduction in fees, while others faced no change. That provides the treatment (decreased fees) and control (no change) groups for the natural experiment. Then in 2015 California unwound the changes, which provides a second natural experiment.

You may be worried (and rightly so, it turns out) that the areas with the highest licensing fees prior to the change are systematically different from those with the lowest licensing fees, in ways that are important. So, Nguyen employs and instrumental variables approach (which I have earlier discussed here). Her instrument of choice is the fees for alternative business sectors (retail and professional services), which can plausibly be related to the fees for massage services but are unlikely to be directly related to the outcome variables (which are price and quantity of services, STD rates, and rape reports).

Nguyen finds support for the simple descriptive demand-and-supply analysis above. In terms of the health and crime externalities, she finds:

In the case of prostitution and massage, reducing licensing costs for the quasi-legal sector increased the total size but also reduced the overall riskiness of the black market for prostitution. For the average massage licensing fee reduction observed in California, gonorrhea rates fell by 16.3% for the general population and by 13.5% for the predominant sex worker demographic, Asian females. Chlamydia rates also fell by 1.73% for Asian females, while forcible rapes declined by 19% for the general population. These improvements to health and crime can be attributed to reductions in illegal prostitution consumption and risk-taking behavior in the quasi-legal prostitution sector.

The trade-off appears to be a significant impact on the legal massage sector (i.e. the massage sector that does not include sex services), which she attributes to competition from the quasi-legal sector. She writes:

...the consequent 138% growth in quasi-legal prostitution also reduced the supply of legal massage by 45.6%. Thus, reducing the barriers to entry makes the black market safer at the expense of the legal sector.

Overall though, the paper provides some food for thought, in terms of alternatives to full legalisation if harm reduction in the sex services sector is a policy goal but full legalisation is politically untenable. Or alternatively, read in reverse it provides some idea of the consequences of increasing licensing fees to try and prevent quasi-legal operations.

[HT: Marginal Revolution, back in December last year]

Try this: Broadway economics

The latest issue of the Journal of Economic Education has a short paper about the website Broadway Economics, by Matthew Rousu (Susquehanna University). From the paper:

Songs from musicals tell stories, and many of the concepts we strive to teach our principles of economics students are illustrated in songs such as “Stars” from Les Misérables (inelastic preferences) and “If I Were a Rich Man” from Fiddler on the Roof (inequality, economic growth). While titled Broadway Economics, the site also includes songs from non-Broadway musicals, such as “Let it Go” from Frozen (which illustrates sunk costs). Topics covered more often in upper-level courses such as signaling and screening and consumer time preferences are also well represented by Broadway musical songs.

I'm not much into show tunes, but perhaps you are or you know some economics students who are. The site has videos of the songs, with associated discussion questions that link the song lyrics or theme to economic concepts. For instance, for "Let It Go" from the Disney movie Frozen, the discussion questions are:

1.) What is a sunk cost?

2.) Why should sunk costs be ignored when considering future decisions?

3.) Provide one example where you’ve earned a sunk cost (Hint – the cost need not be a monetary one – it could be time you’ve invested).

 Enjoy!

Newsflash! Researchers in top departments publish in top journals

I just finished reading this new article in Applied Economics Letters by Tolga Yuret (Istanbul Technical University), titled "Is it easier to publish in journals that have low impact factors?" (sorry I don't see an ungated version online). The short answer to the titular question is yes, at least according to the data that was used.

However, I struggled to get past the 'so what?' question in this article. I guess maybe I was expecting the unexpected. Yuret's measure of difficulty of publishing was the proportion of the authors publishing in the journal who are affiliated with the top 125 departments. He argues:

A journal is less likely to be accepting papers from the researchers from lower ranked departments if most of the authors are from the top departments. Therefore the measure developed by Moore (1972) also reflects the difficulty in publishing in a journal. Therefore we label his measure as the difficulty measure.

I would argue that if you wanted a measure of difficulty of publishing in a journal, you probably want to start with the acceptance rate (the proportion of submitted papers that are eventually accepted). But then you would want to control for selection bias - authors don't send all papers to the top journals, because we know that not all papers will be accepted there and prefer not to waste our time (or that of the editors and reviewers). So, the more difficult journals to publish in may have low acceptance rates, but those low acceptance rates are actually likely to be biased upwards (they would be even lower if every researcher submitted every relevant paper to them).

When Yuret proceeds to show that there is a high correlation (0.62) between impact factor and his difficulty measure for economics journals, he is simply showing that faculty in top economics departments make up a higher proportion of the authors in the highest impact factor economics journals. Given that faculty in top economics departments are probably higher quality researchers, producing higher quality research, this should not be a surprise. This paper could clearly be filed under 'so what'.

A more interesting question to ask (and probably the question this article was trying to answer but really didn't) is, for a paper of a given quality, is it more difficult to get it accepted in a journal with a high impact factor than a journal with a lower impact factor? I think most researchers' experiences (and certainly mine) would suggest that it is - papers rejected at top journals usually eventually find a home at a lower-ranked journal.

What is perhaps more interesting is that the correlations between impact factor and proportion of authors from top departments are much smaller for the other disciplines that Yuret looked at: chemistry (0.49), physics (0.23), and mathematics (0.22). What's going on in those disciplines (especially physics and mathematics)? Do faculty outside the top departments in those disciplines have a better shot at publishing in the top journals? Given his data I suspect that the lower correlations (for physics and chemistry at least) may be an effect of the other disciplines simply having more journals with top impact factors - it's much harder for faculty at top departments to monopolise the pages of many top journals than it is to do so when there are only a few top journals. Still, the correlations are all positive - researchers in top departments publish in top journals. Surprise!

Predicting student success, and failure

Carrie Wells writes in the Baltimore Sun:

Officials at the University System of Maryland have begun to analyze student data — grades, financial aid information, demographics, even how often they swipe their ID cards at the library or the dining hall — to find undergraduates who are at risk of dropping out...

University system officials say the practice, called predictive analysis, will boost graduation rates by enabling educators to intervene with struggling students before failure becomes inevitable.

The whole story is well worth reading, covering how big data analytics can help identify at-risk students, but also the valid privacy concerns that this sort of data mining raise. I was interested in this because of two research projects I've recently been involved in. The first project was one I blogged about last April, and was somewhat similar to the work that Wells was looking at (but not nearly as sophisticated, mainly because we had less data available):

In the final (multivariate) specification of the logistic regression model (which only included data we would have known before the students commenced study, and data that are available for all students):

• Students aged 25 years and over (at first enrolment) had significantly lower odds of degree completion than those aged 19 years and under;

• Male students had significantly lower odds of degree completion than female students;

• Asian students had significantly higher odds of degree completion than all other ethnic groups, and Maori and Pacific Island students had the lowest odds of degree completion;

• Domestic students had significantly lower odds of degree completion than international students;

• Special admission (or provisional entrance) students had significantly lower odds of degree completion than other students; 

• Students who initially completed the Certificate of University Preparation (CUP) had significantly lower odds of degree completion than other students; and

• Students initially enrolled in conjoint degrees had significantly lower odds of degree completion than students enrolled in single degrees.

...at the least there is one take-away from Jacinda's work, which is that maybe we need to target more pastoral care or mentoring and role models for conjoint degree students.

Which brings me to this recent working paper by Papu Siameja and I. In the paper we first identify students at risk of failing ECON100, and then we used a simple randomised experiment to trial two very simple interventions. For the first intervention group (Treatment A), we sent them an email providing information about academic support. The second intervention group (Treatment B) received the email plus a follow up personal phone call. We ran the experiment in 2013 and 2014, but did not persist with it because my initial analyses showed little effect (on test results). However, when we look at pass rates, it appears there was an effect:

Both treatments appear to increase the odds of students passing the course, and the effect of Treatment B is statistically significant. Specifically, the results show that students who are part of the Treatment B group in 2014 had more than seven times higher odds of passing than the control group.

What is interesting is that the effect of Treatment B was only significant in 2014, and not 2013. In 2013 we had a staff member make the phone calls, but in 2014 we had a student (one of the tutors in the paper) make the calls. Maybe the (younger) tutor was simply better at connecting with the at-risk students and impressing on them the importance of remaining engaged in the course? Either way, this intervention turned out to be highly cost effective - I estimate the cost-per-failure-averted at about NZ$69. It's something we'll probably bring back next semester.

[HT for the Wells article: Marginal Revolution]