The 19th century farmer had much less control of the environment in which his crops were growing compared to modern farmers. But did weather risk get translated into different rents? After all, you'd expect a farmer who was wanting to rent land in an area where the weather was very changeable to try to negotiate a lower rent. It seems that the amount of rainfall in July matters a large amount for a grain crop such as wheat or barley. The coefficient of variation is a useful (and somewhat overlooked statistic). It is the standard deviation of a set of observations divided by their mean. So there are no units: it is just a percentage. Perfect for measuring "changeability" of weather. Malcolm kindly got me the CoV for July rainfall for 10 weather stations. I used GIS to interpolate between the stations, and then get the stretched values into the observations for our parishes. Then I ran a regression and --- wonderful news! --- CoV has a strong negative effect on rents. Greater CoV means lower rents. Below is a map of the southwest, showing the parishes and a raster of stretched CoV values. The highest CoVs are in the extreme southwest---that is Cornwall, well known for wet summers.

The pinkish bits have the lowest CoVs and it is no coincidence that wheat is grown there. The very wet areas are suitable for livestock and so that is where cattle and sheep raising went on.

## Monday, May 14, 2012

## Friday, May 11, 2012

### Rent and distance from railway station

I am still working on the Railways Paper, trying to quantify the difference that an extra kilometre of railway track had on agricultural rents. The draft is very nearly finished, and I am hoping I can get the article into a journal such as Economic Geography.

So far I have found a significant relationship using two measures of the availability of railway track. One method is what I call the 'nearest station' method, which involves the measuring of the distance from the farm to the nearest railway station on an annual basis. As the railway was being built, it approached the farm. This meant that the farmer could put his animals into wagons for transport to market. He saved on the costs involved in droving the animals along roads (loss of weight, expenses and risk). So a reduction in distance to nearest station increased the rent...which is what we found. The other measure is the 'buffer' measure, which involves counting the total kilometres of railway track within a 40km radius of the farm. More track nearby raises the rent---it is easier to get your stuff to market. Greater "connectivity" in modern parlance.

Here are a couple of interesting graphs:

We have the rent rolls for 31 large estates. The graph shows the average rent and distance from nearest station. As theory predicts, rent is a declining function of distance. Now take a look at this one:

This is the distance between estate and nearest station over time. By 1860 or so, all the estates were within 10km of the nearest station. That accounts for the clustering around the 10 km mark in the first graph.

So far I have found a significant relationship using two measures of the availability of railway track. One method is what I call the 'nearest station' method, which involves the measuring of the distance from the farm to the nearest railway station on an annual basis. As the railway was being built, it approached the farm. This meant that the farmer could put his animals into wagons for transport to market. He saved on the costs involved in droving the animals along roads (loss of weight, expenses and risk). So a reduction in distance to nearest station increased the rent...which is what we found. The other measure is the 'buffer' measure, which involves counting the total kilometres of railway track within a 40km radius of the farm. More track nearby raises the rent---it is easier to get your stuff to market. Greater "connectivity" in modern parlance.

Here are a couple of interesting graphs:

We have the rent rolls for 31 large estates. The graph shows the average rent and distance from nearest station. As theory predicts, rent is a declining function of distance. Now take a look at this one:

This is the distance between estate and nearest station over time. By 1860 or so, all the estates were within 10km of the nearest station. That accounts for the clustering around the 10 km mark in the first graph.

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