Happy Holidays!

One of my all-time favourite blog posts, that I regularly use in the readings for my ECON110 class, is the 2010 Dr Seuss parody by Art Carden titled "How economics saved Christmas". I use it to teach alternative policy options for dealing with externalities. Enjoy!

Christmas bonus: Tyler Cowen at Marginal Revolution talks talks about the inefficiency (or otherwise) of Christmas.

Happy holidays!

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.