> This still leaves open why the distance elasticity is what it is (between -1.2 and -1.9)
If we take -1.55 as the point estimate, is the following interpretation correct?
"Increasing the distance between a city by 1% leads to a -1.55% reduction in trade"
> So technology acts like an upwards shift on the curve of distance vs trade that increases the amount of goods sent to all locations proportionally, rather than increasing long distance trade more than short distance trade.
It's still surprising that this increase would apply proportionally to *all* locations. Selling girl scout cookies door to door or setting up a lemonade stand isn't much different from local trade in the ancient world, but the development of ocean-spanning freighters, dismantling of trade barriers, and spread of theories of trade and capitalism should have all disproportionately boosted long-distance trade over short-distance.
> Then, they try to fit the model’s predictions over the shares of trades between each city pair in the network to the observed data by choosing different latitudes, longitudes, and productivities for each city.
What does this mean? Does it mean using multiple distance readings from city X (e.g. A is 500km away, B is 600km away, C is 400km) to trilaterate the position of X, like a GPS positional fix?
This great fun. Now I am wondering what distance elasticity of trade is in the US, how state lines matter, and a comparison of that with modern Europe.
This is an incredible paper, thanks for writing it up Max!
> This still leaves open why the distance elasticity is what it is (between -1.2 and -1.9)
If we take -1.55 as the point estimate, is the following interpretation correct?
"Increasing the distance between a city by 1% leads to a -1.55% reduction in trade"
> So technology acts like an upwards shift on the curve of distance vs trade that increases the amount of goods sent to all locations proportionally, rather than increasing long distance trade more than short distance trade.
It's still surprising that this increase would apply proportionally to *all* locations. Selling girl scout cookies door to door or setting up a lemonade stand isn't much different from local trade in the ancient world, but the development of ocean-spanning freighters, dismantling of trade barriers, and spread of theories of trade and capitalism should have all disproportionately boosted long-distance trade over short-distance.
> Then, they try to fit the model’s predictions over the shares of trades between each city pair in the network to the observed data by choosing different latitudes, longitudes, and productivities for each city.
What does this mean? Does it mean using multiple distance readings from city X (e.g. A is 500km away, B is 600km away, C is 400km) to trilaterate the position of X, like a GPS positional fix?
This great fun. Now I am wondering what distance elasticity of trade is in the US, how state lines matter, and a comparison of that with modern Europe.