What to do when your cities are stuck in the wrong place

The persistence of the location of cities and towns is well recognised in economic geography. People tend to locate where jobs are. New industries (and hence jobs) tend to locate close to where customers are, which unsurprisingly, is where people are. And so, the location of cities in the future is likely to be where cities were in the past.

In order to get substantial change in the location of towns and cities, it looks like you need to generate a collapse in civilization. At least, that might be one tongue-in-cheek take-away from a recent paper by Guy Michaels (London School of Economics) and Ferdinand Rauch (University of Oxford). Michaels and Rauch studied a cool natural experiment - the effect of the fall of the Roman Empire on the location of towns in Britain and France. The key point that makes this natural experiment useful is that the effect of the fall of Rome was much bigger in Britain than in France:

Roman Britain suffered invasions, usurpations, and reprisals against its elite. Around 410CE, when Rome itself was first sacked, Roman Britain's last remaining legions, which had maintained order and security, departed permanently. Consequently, Roman Britain's political, social, and economic order collapsed. From 450-600CE, its towns no longer functioned. The Roman towns in France also suffered when the western Roman Empire fell, but many of them survived and were taken over by the Franks.

• In short, the urban network in Britain effectively ended with the fall of the western Roman Empire; French towns experienced greater continuity.
• The divergent paths of British and French urban networks allow us to study the spatial consequences of the resetting of an urban network, as towns across Western Europe re-emerged and grew during the Middle Ages.

They find that the location of towns changed in Britain, but remained the same in France. But did that even matter? It turns out it did:

The conclusion we draw is that many French towns were stuck in the wrong places for many centuries. They could not take advantage of the new transportation technologies since they had poor coastal access; they were in locations that were designed to fit with the demands of Roman times and not the considerations of the Middle Ages.

So, towns and cities can be stuck in the 'wrong' (from a productivity perspective) location for centuries or longer. There are no barbarian invasions in our near-term future, so we are to a large extent stuck with the urban locations we have now. This has interesting implications for adaptation to climate change. Many cities currently sit in extremely vulnerable locations, in terms of surface flooding, sea level rise, desertification and water stress, etc. The implications of this paper is that there is substantial inertia that will prevent large-scale relocation of people and industries to areas that are more resilient or less vulnerable. In other words, adaptation to climate change in situ is going to be very important - we can't simply rely on moving away from where the problems occur. On a related note, we shouldn't expect large masses of migrants trying to get away from vulnerable cities and countries to suddenly end up on our doorstep. It simply isn't that easy for them to move.

For the full paper (gated), see here.

[HT: Paul Krugman's NY times blog]

How to raise the price AND increase sales

Business Insider Australia reports on the unusual case of Cards Against Humanity:

The people behind card game Cards Against Humanity wanted to get noticed on Black Friday, but they didn’t want to discount their game below $US25. 

So they came up with a strange, perverse offer. For a limited time only, you could buy Cards Against Humanity for…$5 more. 

The plan worked. The absurd offer got a lot of attention and sales spiked.

 According to the chart below, sales increased by around the same amount on Black Friday (the day after Thanksgiving, traditionally a day of big sales in the U.S.) as the previous year, in spite the price increase.

Or was it because of the price increase? Traditional economic theory, as I teach in ECON100 or ECON110, maintains that when the price increases, the quantity demanded (and sales) decrease. But in this case, quantity and price have both increased. Does this mean that the demand curve is upward sloping (as Tyler Cowen cheekily implies here)?

Probably not, as we find out later in the Business Insider piece, quoting Max Temkin (the creator of Cards Against Humanity):

This is a difficult time of year for us because we spend almost no money on marketing, and it’s easy for us to get lost in the noise and money of the holiday season...

 The sale made people laugh, it was widely shared on Twitter and Tumblr, and it was the top post on Reddit. The press picked it up, and it was reported in The Guardian, USA Today, Polygon, BuzzFeed, All Things D, Chicagoist, and AdWeek. It was even the top comment on The Wirecutter’s front page AMA, which had nothing to do with us.

In other words, Cards Against Humanity's publicity stunt of increasing price had the effect of greatly increasing their marketing exposure. So, the observed change in quantity demanded wasn't the result of an upward sloping demand curve, but instead was the result of a shift in the demand curve to the right (an increase in demand).

This raises a more general point about observed price and quantity combinations in the real world. When we see two price-quantity combinations, it is tempting to connect them with a line and think that we have observed the demand curve. However, we face an identification problem - we can't tell for sure whether what we have observed is a movement along a given demand curve, or a shift from one demand curve to another. In other words, we can't identify which portion of the movement from one point to another occurs because the demand curve has shifted.

Graphically, using the Cards Against Humanity example, we have observed the two price-quantity combinations (Q1,P1) and (Q2,P2), and if we assumed they were on the same demand curve we would guess that they were on the dotted demand curve (Da). However, based on the other details we know about the case, we know that what actually occurred was a shift from demand curve Db to demand curve Dc.

 

The identification problem is a serious issue for real-world data. I've had a number of students complete applied projects for me, using for example supermarket data to investigate price elasticities. To get a useful price elasticity estimate though, you must be able to separate the part of a price-quantity change that relates to a movement along a given demand curve, from the part that results from a shift in the demand curve. Usually we try to achieve this by including in our regression models other factors that we know affect demand, but of course we cannot include everything (some things, like consumer tastes and preferences, are not easily measured). So we will always have an imperfect estimate of price elasticity when using real-world data. This is worth keeping in mind any time you are presented with elasticities.

For more on the Cards Against Humanity example, read Max Temkin's Tumblr blog.

HT: Tyler Cowan at Marginal Revolution.

When is an academic like a drug dealer?

Apparently, often. See this blog post. It's not because the teaching and research we generate is addictive, although I suppose it is possible. It's because of the nature of the labour market. From the blog post:

The academic job market is structured in many respects like a drug gang, with an expanding mass of outsiders and a shrinking core of insiders. Even if the probability that you might get shot in academia is relatively small (unless you mark student papers very harshly), one can observe similar dynamics. Academia is only a somewhat extreme example of this trend, but it affects labour markets virtually everywhere. One of the hot topics in labour market research at the moment is what we call “dualisation”. Dualisation is the strengthening of this divide between insiders in secure, stable employment and outsiders in fixed-term, precarious employment. Academic systems more or less everywhere rely at least to some extent on the existence of a supply of “outsiders” ready to forgo wages and employment security in exchange for the prospect of uncertain security, prestige, freedom and reasonably high salaries that tenured positions entail...

In ECON110 we talk about tournament effects, and this is exactly an example of tournament effects at work. A small group of highly successful workers (insiders) get paid high salaries, while many others (outsiders) accept low salaries in exchange for the chance to become one of the highly successful few in the future. For a highly educated person to take a low-paid entry-level job isn't as irrational as it may seem at first. It's a simple benefit-cost decision for the outsiders - the cost is foregone income now (the premium they could have earned outside academia); the benefit is an expected future gain in the form of a cosy academic position with higher salary (maybe?). So, if the returns to becoming one of the successful insiders are high enough, then even a low probability of becoming an insider will induce many recent or nearly-completed PhDs to join the outsider part of the market.

The New Zealand academic labour market is somewhat dissimilar to the U.S. or European markets, in that relatively secure employment is possible even though there is no tenure track. This is similar to the situation in Britain described in the blog post. However, that doesn't mean that there aren't a number of New Zealand beginning academics in precarious work (I had thought our academic union, the TEU, was looking into this, but I can't find anything on their website). My first few years were in rolling fixed-term teaching fellowships.

Now I'm feeling like I'm missing a trick here. I should definitely be exploiting PhD students for more low-paid work.

[HT: Eric Crampton at Offsetting Behaviour]

Zombies, vampires and population modelling

I've been involved in a few climate change-related projects over the last several years. Despite being an economist, my contribution has been in the area of population modelling. These projects have given me numerous opportunities to engage with climate scientists and ecologists, and one of the interesting aspects of these interactions is the doomsday scenarios for future population that many of them hold dear. I have often likened their projections for future population to modelling the zombie apocalypse.

That's why I really enjoyed reading this 2009 paper recently: "When zombies attack! Mathematical modelling of an outbreak of zombie infection" by Munz et al., published in the book Infectious Disease Modelling Research Progress. The authors used an extended SIR epidemiological model to investigate the dynamics of a zombie apocalypse. Their conclusion:

In summary, a zombie outbreak is likely to lead to the collapse of civilisation, unless it is dealt with quickly. While aggressive quarantine may contain the epidemic, or a cure may lead to coexistence of humans and zombies, the most effective way to contain the rise of the undead is to hit hard and hit often. As seen in the movies, it is imperative that zombies are dealt with quickly, or else we are all in a great deal of trouble.

I liked the paper, but I felt there was one flaw. In their model, all dead humans or zombies were able to re-animate. This left me wondering - what would happen in the model if you could permanently kill zombies, through shots to the head or incineration. Maybe the authors will address that in future papers.

Closer to home, Daniel Farhat from the University of Otago has a recent paper on "The economics of vampires: An agent-based perspective". In his paper, Farhat uses agent-based computational models with heterogeneous agents to investigate the dynamics of human-vampire population interaction. There are a number of interesting results from the simulations, from which I quote selectively:

...where vampires are highly visible, the human population suffers terribly for a short period (building up defences which results in starvation) until the vampire population is driven to extinction. Once they have been eradicated, the human community and their corresponding economy proceeds to grow exponentially. Therefore, one reason why we may not come across vampires in modern times is because they have already died out...

 ...where vampires are observable but somewhat hidden, they may flourish provided they are easy to destroy in a confrontation. Cycles of fear then emerge. Therefore, if we do not see vampires today it may be because spotting them is rare..

 ...where vampires are unobservable, their existence persists. Whether they flourish or stagnate depends on their hunger for blood and the speed of human reproduction. If vampires live on the brink of starvation, both vampire and human populations persistently grow despite mild cycles of fear. If humans reproduce easily, both communities languish with extreme cycles of fear keeping both populations in check. The former is more likely given the persistent growth of our planet’s population. If this is the case, we would never encounter vampires (and may even doubt their existence) in reality.

Agent-based models are an excellent tool for modelling population dynamics at very disaggregated levels. The vampire model described above is theoretical, but there are more practical applications of these models as well. For instance, I have a Masters student who is developing an agent-based model of local population dynamics for the Hamilton sub-region, to better project small-area population movements over the next 10-15 years. This highlights that these mathematical models are not just useful for developing cool applications in terms of modelling the undead, but also have real-world applicability.