I've done a time-series regression of amount of track within a 40km radius of Holkham Hall against land rent, controlling for the price of wheat and cattle. I deflated the rent and the commodities to adjust for changes in the cost of living. The result is statistically significant, and I'd love to show you the output but can't work out how to paste it into the blog. Track and cattle have positive signs, but wheat is negative. Why should a drop in the wheat price result in increased rent..I don't know yet! I'll get there. The positive sign for Track is what we had expected to see: more track means increased rent. The savings are being extracted by the landowner. Recall that the equation for locational rent is
where m is the market price of the commodity, c the cost of production, E the yield, f the cost of transportation per unit distance and d the distance. As the distance increases, the right hand side gets smaller, so the rent gets smaller. Eventually the rent would be zero right on the edge of the cultivatable land. By increasing the amount of track, in effect the distance is getting smaller...so the rent goes up.
Encouraged by this, we're going to build a larger dataset. Malcolm is calculating the track for three more estates: Petworth, Thorndon and Dalemain. A graph of their rents is here:
You can see that there is a bump in the rents in the period around 1850----what a coincidence! Once we have the track data in, I'll do the same type of regression, but this time it will be a panel-data longitudinal regression. This is a very powerful technique, which I'd urge you to learn if you see the chance.
Tuesday, January 4, 2011
Sunday, January 2, 2011
Track in 40km radius of Holkham Hall
Malcolm has done a wizard job of calculating the amount of track on an annual basis within a 40km radius of Holkham Hall in Norfolk. The graph is here:
Our hypothesis is that the availability of track would reduce the costs of farming to the tenant farmer, but that the landowner would grab the savings in the form of higher land rents---the 'resource extraction' theory.The next step is for me to test this using a time-series regression, controlling for other variables such as the price of wheat and the price of livestock. We need to hold steady the other variables so that we can isolate the effect of the reduced transport cost. Rent is the dependent variable and then the amount of track and the various prices are the independent (or 'explanatory' variables). The equation looks like this:
The regression is a time-series, and so we have to remove the effects of auto-correlation over time. I've omitted all the subscripts for time for clarity. We can use ARIMA for the regression. This is exciting and a great way to spend the holiday! Thanks Malcolm for your speedy work. I'll be back with some statistical output shortly.
Our hypothesis is that the availability of track would reduce the costs of farming to the tenant farmer, but that the landowner would grab the savings in the form of higher land rents---the 'resource extraction' theory.The next step is for me to test this using a time-series regression, controlling for other variables such as the price of wheat and the price of livestock. We need to hold steady the other variables so that we can isolate the effect of the reduced transport cost. Rent is the dependent variable and then the amount of track and the various prices are the independent (or 'explanatory' variables). The equation looks like this:
The regression is a time-series, and so we have to remove the effects of auto-correlation over time. I've omitted all the subscripts for time for clarity. We can use ARIMA for the regression. This is exciting and a great way to spend the holiday! Thanks Malcolm for your speedy work. I'll be back with some statistical output shortly.
Saturday, January 1, 2011
New Year's update
So, we have four papers and a book (yes!) to finish off this year. Here is a little run-down job by job:
1. The 'political' paper is under peer review at the moment. Let me know if you want a copy. We showed that there was a strong statitistical relationship between the type of crops grown in a political constituency, the attendance at church of the residents, and how the MP for that constituency voted. The parliament of 1841 was very much about 'church and state' and so these findings make sense. We used some novel statistical techniques to get round the 'missing voter' problem. Many MPs didn't turn up for divisions and so our sample size is small. But we know all we need about the MP except for how he would have voted had he turned up. We want to keep this information, not toss it out. We used Anne Sartori's improvement on the Heckman procedure (he won a Nobel for that). Special kudos to Hugh (Salway) from the University of York in the UK for volunteering a huge amount of time and helping us with hard to get data. Come and visit us, Hugh!
2. The 'Devon rents' paper. Here we are using very old data: from the 1836 Tithe Commission. We find that in the county of Devon, there is an amazingly robust relationship between arable rent, wheat yields, elevation and distance to market town. This fits all the 'locational rent' theories: von Thunen, Ricardo etc. So not content with that, we want to see whether this relationship holds in neighbouring counties. Malcolm has built up a large dataset (n>600) of parishes in six contiguous (look it up!) counties. What is interesting is that some parts of the relationship change as we move east, seeming at first sight to indicate less reliance on local markets as we get closer to London. Makes sense. To test this, we'll be using a relatively new statistical technique called Geographically Weighted Regression. In regular regression, we are trying to estimate some 'global' parameter that fits throughout the statistical population. In GWR, we allow the parameter estimates to vary according to local conditions. I am keen to measure the elasticity between rent and what the tenant farmer could take home to his wife and kids: in other words, were some landlords greedier than others, and if so, why? I am half-hopeful that we'll see the hand of the Anglican Church behind all this, but mustn't get my hopes up.
3. The 'railways and rents' paper. A huge amount of track construction went on in 'our' period, see the graph below. This must have had some impact on farming. James Caird, writing in 1851, makes an intriguing reference to a Norfolk farmer saving four hundred pounds a year ( a lot. You could have bought a Bentley if they had made them then) because his cattle didn't lose weight when they went by rail; when he walked them to market they ended up quite thin. But amazingly there isn't much published on this. And I can see why. Getting the data is like pulling hen's teeth. Malcolm is figuring out the total amount of track in a 40km radius of Holkham Hall, Norfolk for the years 1836 to 1866 on an annual basis. And Mi is scouring the libraries of the world for any sort of references that might help. Here is the graph:.
Growth like this must have had consequences! We hypothesise that the tenant-farmer's savings would have been transferred to the landlord via an increased rent. This is the phenomenon of 'surplus extraction'. Generally you want to be the extractor, not the extracted. But the tenants were in a weak position....so we would expect to see their rents going up in tandem with better transportation. Mi has got us the rents, now we await Malcolm's annual track data. Then I'll use a time-series analysis procedure called ARIMA to test for linkage. If the Norfolk estate gives us a YES, we'll extend the procedure to other estates and use a panel-data approach. Nice cutting edge stuff.
4. The 'supply response' paper. In the 1870s, the price of wheat fell dramatically because those blasted Americans opened up their railroads and shipped in wheat below the domestic price. See the graph of wheat prices below. Quite shocking: halved in price in less than two decades.
Look what has happened: wheat has halved but livestock numbers have shot up. This look like a structural shift in agriculture. But this is at the national level....individual farmers won't all have been able to shift out of wheat and into livestock. We hypothesise that those estates which were more flexible with what the tenants did with their land probably didn't need to drop their rents as much as those estates which were more rigid. Farmers are not (always) stupid and can adapt pretty well to changing market conditions. Different matter if they can't adapt because of regulations on land use. We will test for a 'breakpoint' in rents, and then use that year as an indicator. Going to borrow from medical statistics, normally for use in working out how long a patient has got to live. Again, nice cutting edge stuff.
5. A book! New idea, inspired by Sarah, one of my two fantastic sisters. This is historical fiction, in other words a story based on real people. The working title is 'Breaking Free From Dr Malthus' and the theme is how the heck did the farmers increase yields enough to allow enough folk to escape from the hard scrabble of agriculture to start off the Industrial Revolution. A new format...left hand side page is fiction, the facing right hand side is economic analysis and commentary on the the fiction. Including the highly exciting new field of neuro
1. The 'political' paper is under peer review at the moment. Let me know if you want a copy. We showed that there was a strong statitistical relationship between the type of crops grown in a political constituency, the attendance at church of the residents, and how the MP for that constituency voted. The parliament of 1841 was very much about 'church and state' and so these findings make sense. We used some novel statistical techniques to get round the 'missing voter' problem. Many MPs didn't turn up for divisions and so our sample size is small. But we know all we need about the MP except for how he would have voted had he turned up. We want to keep this information, not toss it out. We used Anne Sartori's improvement on the Heckman procedure (he won a Nobel for that). Special kudos to Hugh (Salway) from the University of York in the UK for volunteering a huge amount of time and helping us with hard to get data. Come and visit us, Hugh!
2. The 'Devon rents' paper. Here we are using very old data: from the 1836 Tithe Commission. We find that in the county of Devon, there is an amazingly robust relationship between arable rent, wheat yields, elevation and distance to market town. This fits all the 'locational rent' theories: von Thunen, Ricardo etc. So not content with that, we want to see whether this relationship holds in neighbouring counties. Malcolm has built up a large dataset (n>600) of parishes in six contiguous (look it up!) counties. What is interesting is that some parts of the relationship change as we move east, seeming at first sight to indicate less reliance on local markets as we get closer to London. Makes sense. To test this, we'll be using a relatively new statistical technique called Geographically Weighted Regression. In regular regression, we are trying to estimate some 'global' parameter that fits throughout the statistical population. In GWR, we allow the parameter estimates to vary according to local conditions. I am keen to measure the elasticity between rent and what the tenant farmer could take home to his wife and kids: in other words, were some landlords greedier than others, and if so, why? I am half-hopeful that we'll see the hand of the Anglican Church behind all this, but mustn't get my hopes up.
3. The 'railways and rents' paper. A huge amount of track construction went on in 'our' period, see the graph below. This must have had some impact on farming. James Caird, writing in 1851, makes an intriguing reference to a Norfolk farmer saving four hundred pounds a year ( a lot. You could have bought a Bentley if they had made them then) because his cattle didn't lose weight when they went by rail; when he walked them to market they ended up quite thin. But amazingly there isn't much published on this. And I can see why. Getting the data is like pulling hen's teeth. Malcolm is figuring out the total amount of track in a 40km radius of Holkham Hall, Norfolk for the years 1836 to 1866 on an annual basis. And Mi is scouring the libraries of the world for any sort of references that might help. Here is the graph:.
Growth like this must have had consequences! We hypothesise that the tenant-farmer's savings would have been transferred to the landlord via an increased rent. This is the phenomenon of 'surplus extraction'. Generally you want to be the extractor, not the extracted. But the tenants were in a weak position....so we would expect to see their rents going up in tandem with better transportation. Mi has got us the rents, now we await Malcolm's annual track data. Then I'll use a time-series analysis procedure called ARIMA to test for linkage. If the Norfolk estate gives us a YES, we'll extend the procedure to other estates and use a panel-data approach. Nice cutting edge stuff.
4. The 'supply response' paper. In the 1870s, the price of wheat fell dramatically because those blasted Americans opened up their railroads and shipped in wheat below the domestic price. See the graph of wheat prices below. Quite shocking: halved in price in less than two decades.
Look what has happened: wheat has halved but livestock numbers have shot up. This look like a structural shift in agriculture. But this is at the national level....individual farmers won't all have been able to shift out of wheat and into livestock. We hypothesise that those estates which were more flexible with what the tenants did with their land probably didn't need to drop their rents as much as those estates which were more rigid. Farmers are not (always) stupid and can adapt pretty well to changing market conditions. Different matter if they can't adapt because of regulations on land use. We will test for a 'breakpoint' in rents, and then use that year as an indicator. Going to borrow from medical statistics, normally for use in working out how long a patient has got to live. Again, nice cutting edge stuff.
5. A book! New idea, inspired by Sarah, one of my two fantastic sisters. This is historical fiction, in other words a story based on real people. The working title is 'Breaking Free From Dr Malthus' and the theme is how the heck did the farmers increase yields enough to allow enough folk to escape from the hard scrabble of agriculture to start off the Industrial Revolution. A new format...left hand side page is fiction, the facing right hand side is economic analysis and commentary on the the fiction. Including the highly exciting new field of neuro
Sunday, December 26, 2010
Focus for the 'supply response' paper
I have only just been able to work out a suitable 'motivation' for our third paper, British agriculture from about 1870 onwards. We've been collecting all the data, county by county, but with no fixed purpose. I think I have it now: regional differences in response to the American 'grain invasion' of the late 1870s. In that period, the American railroads opened up and importing wheat became competitive. The price of grain fell dramatically and many farmers were ruined. The brighter farmers switched to livestock and used the cheaper grain as their input, making good profits. Not all regions adapted in the same way. Why? That is what we are going to find out.
Saturday, December 25, 2010
Some railway stats found
Mi and I have been looking for statistics of agricultural commodities carried by rail, just got a bit lucky. I checked the 19th Century House of Commons Parliamentary Papers under 'railway returns'. And found very detailed statistics for all sorts of carriage. Cattle are lumped in with other goods etc until about 1860. Then we get detailed record by head of cattle, pigs, etc. We now have to work out how much effort is required to extract the data and whether the end result will be worthwhile.
Tuesday, December 21, 2010
Neural Network Results
I am trying out some software which builds a neural network from a dataset. It looks for connections---like our own brains. I used the data Malcolm has been working on for three counties. The results look like this:
| |||||||||||||||||||
| It is a surprise to me that distance from the market town is so important---I'll run the test again for different counties. | |||||||||||||||||||
The two papers we`re working on
The task has been to write three papers for peer-review and publication in academic journals. So far one paper (on the politics behind some aspects of Peel`s ministry of 1841) is in its final draft stage. The other two are:
agricultural rents in the southwest of England in 1836; and changes in agriculture in nine counties as a result of changes in transport of agricultural commodities by the `new`railway systems.
For the rents paper, Malcolm is getting data from the 1836 Tithe Commission into a format for statistical analysis. So far we have three counties done: Devon, Wiltshire and Gloucestershire. There are two more to go: Dorset and Herefordshire. I have noticed that the relationship between arable rent and wheat yield is very strong, as one would expect. More interesting is the relationship between arable rent and distance to market town. According to von Thunen`s theories of location, the greater the distance, the lower the rent. This holds for Devon, which is far to the west. It doesn`t hold so well for counties more to the east, or closer to larger cities such as London. The distance to market seems to become less important as we approach large centres of population. This implies more integration into the national economy.
To test this hypothesis, Malcolm is creating buffers around six market towns in a line (roughly) from west to east. In each buffer we will have fifty data points (or parishes). If the hypothesis holds, then the significance of distance to market will decrease as we move to the east.
Map above shows Devon with the 96 datapoints (parishes) marked. Devon was relatively isolated from the rest of the British economy as a result of its geography. Moving east from Devon towards London, proximity to market town diminishes in importance for the calculation of rents.
Mi is building up a large dataset of crops and prices for nine counties in England for a period of approximately twenty years. The counties are: Bedford; Berkshire; Buckinghamshire; Cambridgeshire; Cheshire; Cornwall; Cumberland; Derby; and Essex. Soon, Malcolm will begin to build a database of construction of railways in those counties. We will prepare an animation which shows how the amount of each crop produced in each county changed over time as the railway altered the costs of bringing goods to market. This will give us some insights as to how we might make a formal statistical test of the relationship between crop production and transportation,
agricultural rents in the southwest of England in 1836; and changes in agriculture in nine counties as a result of changes in transport of agricultural commodities by the `new`railway systems.
For the rents paper, Malcolm is getting data from the 1836 Tithe Commission into a format for statistical analysis. So far we have three counties done: Devon, Wiltshire and Gloucestershire. There are two more to go: Dorset and Herefordshire. I have noticed that the relationship between arable rent and wheat yield is very strong, as one would expect. More interesting is the relationship between arable rent and distance to market town. According to von Thunen`s theories of location, the greater the distance, the lower the rent. This holds for Devon, which is far to the west. It doesn`t hold so well for counties more to the east, or closer to larger cities such as London. The distance to market seems to become less important as we approach large centres of population. This implies more integration into the national economy.
To test this hypothesis, Malcolm is creating buffers around six market towns in a line (roughly) from west to east. In each buffer we will have fifty data points (or parishes). If the hypothesis holds, then the significance of distance to market will decrease as we move to the east.
Map above shows Devon with the 96 datapoints (parishes) marked. Devon was relatively isolated from the rest of the British economy as a result of its geography. Moving east from Devon towards London, proximity to market town diminishes in importance for the calculation of rents.
Mi is building up a large dataset of crops and prices for nine counties in England for a period of approximately twenty years. The counties are: Bedford; Berkshire; Buckinghamshire; Cambridgeshire; Cheshire; Cornwall; Cumberland; Derby; and Essex. Soon, Malcolm will begin to build a database of construction of railways in those counties. We will prepare an animation which shows how the amount of each crop produced in each county changed over time as the railway altered the costs of bringing goods to market. This will give us some insights as to how we might make a formal statistical test of the relationship between crop production and transportation,
Subscribe to:
Posts (Atom)






