Download Do Banks Improve Financial Market Integration

Asset Prices, Banks and
Financial Market Integration in
the British Industrial Revolution
Liam Brunt1 & Edmund Cannon2
Abstract3
Using a large panel of weekly wheat prices, we infer the annual rate of return on capital
in each county in England and Wales in the period 1770-1820. Throughout this period
markets were efficient in the sense that weekly returns were serially uncorrelated. We
show that the interest rate differential between London and each county can be explained
by the density of bank coverage in that county. The explosion in provincial banking in
England and Wales during the industrial revolution significantly reduced regional
differentials in interest rates. This is direct evidence that the depth of financial
intermediation determines the degree of capital market integration.
Keywords: banks, financial integration, industrial revolution.
JEL Classification: O16, N13, G21.
St. John’s College, Oxford OX1 3JP. United Kingdom. [email protected]
University of Bristol, Department of Economics, 8 Woodland Road, Bristol BS8 1TN. United Kingdom.
[email protected]
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The authors gratefully acknowledge funding by ESRC Research Grant No. R000220371 and a Royal Economic
Society Small Grant to help buy some additional software. The authors would also like to thank Ludivine
Jeandupeux for research assistance; Rob Brewer, Anna Chernova, Becca Fell, Saranna Fordyce, Dave Lyne, Olivia
Milburn, Hannah Shaw, Derick Shore, Liz Washbrook and Alun Williams for helping with obtaining, entering or
checking the data set; Colin Knowles for computing assistance; the Bristol City Library for allowing us to borrow
copies of the London Gazette not normally available for loan; and Cliff Attfield, Giam Pietro Cipriani, Suro Sahay,
Lucy White for other assistance and helpful comments.
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Introduction. Britain was distinguished from other economies during the Industrial Revolution
by the sophistication of her financial markets (Neal, 1990). This is often invoked as an important
cause of her industrial success in the eighteenth and nineteenth centuries (see, for example,
Cameron, 1967). The importance of efficient capital markets, the difficulty of achieving them
and their rôle in generating economic growth has also received considerable attention from
development economists (Ikhide, 1996; Sial & Carter, 1996; MacKinnon, 1976; King & Levine,
1993). The growth of commercial banking in Britain was truly spectacular, rising from 120
banks in 1784 to 660 banks in 1814 (Pressnell, 1956). So if banking does indeed have a positive
effect on the real economy then we should certainly be able to observe it in action in the British
Industrial Revolution. In fact, there is very little direct evidence linking financial market
integration to changes in the cost of capital during the Industrial Revolution; and a link between
the cost of capital and the rate of investment during the Industrial Revolution has never been
established empirically. Without these two links in the chain it is difficult to estimate the effect
of financial market integration on British economic growth.
The purpose of this paper is to quantify the effect of commercial banking on financial
market integration in Britain during the Industrial Revolution. The fundamental problem in
analysing financial markets in this period is that we have very little information on the cost of
capital. Hence it is difficult to estimate the effect of any changes that might take place on the
demand or supply side of the market, or any change in the nature of financial intermediation. The
recent analysis by Buchinsky and Polack was based on two series of deeds on property
transactions, one for Yorkshire and the other for Middlesex, from which they inferred regional
building, presumed to be determined partly by the cost of capital. Whilst their results are
suggestive, one would not want to place too much weight on them alone.
Our departure point is to estimate the rate of return on capital in each county. We can do
this by looking at the appreciation of a real asset through the year. The only asset that is
sufficiently well documented through the period is grain. Bearing in mind that agriculture was
the largest sector in the British economy until 1840 (Crafts, 1986) - and that grain was the single
most important output - the rate of return on holding grain is probably the single best indicator of
the cost of capital in the economy. McCloskey & Nash (1984) and Taub (1987) show how the
seasonal variation in grain prices is related to the interest rate: grain is an asset, and in
equilibrium the holders of grain must be compensated for storage and interest costs. We use the
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appreciation of grain prices through the harvest year to estimate the rate of interest prevailing in
each county from 1770 onwards. We have previously used this technique successfully on data
from earlier periods (Brunt & Cannon, 1999). Our estimates are based on a large panel of weekly
grain prices collected by the British Government between 1770 and 1820; this enables us to
estimate year-specific county-specific rates of return on capital. We compare the movement of
our interest rate series over time to that of the Consol rate, and find a positive relationship.
We then explain the geographical and temporal variation in interest rates with reference
to the spread of banks outside London (the so-called ‘country banks’). At this time the Bank of
England enjoyed great privileges in the commercial banking market. As a result, other banks
were restricted to being partnerships (as opposed to joint stock companies) with a maximum of
six partners all of whom had unlimited liability. Hence the size of individual banks was greatly
circumscribed and the geographical reach of each bank was inevitably very limited. We take
advantage of this fact by using the number of banks as a measure of the availability of banking
services within each geographical area. There is county-level data available for country banks
from 1800 onwards. We show that the density of country banks has a significant effect on
narrowing the differential between the rates of return on grain traded in London and each county
outside London.
The remainder of the paper is organised as follows. Section 1 discusses the merits and
problems of using grain prices to infer the cost of capital. Section 2 discusses our data set in
more detail and illustrates the seasonal pattern of prices more closely. Section 3 begins with a
discussion of market efficiency in the sense used in the finance literature - namely that returns
should be serially uncorrelated and prices follow a martingale process (weak market efficiency).
This is important because it influences our interpretation of the price data. We then estimate the
gross rate of return on grain for each county-year. Section 4 outlines the institutional structure of
British banking in the period 1770 to 1820, which informs our model of financial market
integration. Section 5 uses the county-level interest rate estimates to analyse the level of financial
market integration, and shows how this was influenced by the spread of banks. Section 6
concludes.
1. Inferring the cost of capital from grain prices. There are several conceptual and practical
difficulties with inferring the cost of capital from the appreciation of grain prices. This has
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generated considerable scepticism amongst economic historians about the value of the approach.
In this section we address these issues and explain why this is the most practical and effective
method of estimating the cost of capital. In the following section we discuss the technical details
of our estimation procedure and implement it.
There are two lines of argument which we can adopt to defend grain prices as a data source
for inferring the cost of capital. First, the grain price approach has the advantage that it is based
upon a considerable body of data and provides high frequency data which is at least available on
a consistent basis and is as good quality as anything else available. Second, there is qualitative
evidence that markets behaved in the way assumed by the grain price approach and that thus it
gives us an accurate and representative estimate of the cost of capital. Hence it is perfectly
reasonable to use grain prices, whether or not there are other sources. We now consider each of
these arguments in turn.
1.1 Options for measuring the cost of capital. The usual approach to measuring the cost of
capital is to look at the rate of return on a riskless asset or, if that is unavailable, to use widely
traded assets whose risk can be quantified easily. The usual asset in these circumstances would
be short-dated treasury bills or government bonds, since such government-backed securities are
free of firm- or individual-specific risks. However, if we want to measure interest rate variation
within a country that has poorly integrated financial markets then our task becomes more
difficult, since government bonds are generally issued and traded only in the capital city. The
best that we can hope to do is to find an asset which is widely traded and is equally risky in each
place; this would then allow us to measure genuine variations in the cost of capital, rather than
variations in risk across the country. The second best would be to look at assets in each place
which have different but known levels of risk, which would allow us, in principle, to infer riskadjusted rates of return.
When dealing with historical economies, there are three data sources that offer a
reasonable chance of allowing us to estimate the local cost of (riskless) capital.
The first source is bank records. James (1978) has used this source for the United States
in the late nineteenth century. Unfortunately, it is exceedingly difficult to get good data for
earlier time periods and other countries. Moreover, there is a huge limitation with this approach.
If we want to examine the effect of banks entering a local capital market ab initio, then we need
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to observe the cost of capital both before and after the banks exist. But it is clearly impossible to
use the records of non-existent banks to measure the local cost of capital, and hence we can
never observe the cost of capital prior to the advent of banks. This is highly likely to be a binding
constraint; when we are dealing with any economy of the nineteenth century (and many
developing economies today), particular localities will have no banks during at least some of the
period of analysis.
The second source of data on the cost of capital is mortgages on land, which is both
widely traded and relatively low risk. Allen (1988) has used this source for England in the early
modern period. Unfortunately, it is difficult to get a large and geographically dispersed sample of
mortgages. Moreover, there may be severe sample selection problems. For example, we have
often been left mortgage documents from the largest landowners, who may have had peculiar
risk characteristics. Also, the large landowners may well have had exceptionally easy access to
the largest financial markets. So the mortgage rate recorded for a piece of land in the north of
England may actually reflect the cost of capital in London (where the landowner raised his loan),
rather than the cost of capital in the north of England.
The third data source on the cost of capital is grain prices. The grain production process
results in grain being harvested once per annum (generally in August in England) and then stored
through the next 12 months. Whilst it was stored, the grain had to appreciate enough to offset the
physical cost of storage, the physical losses in storage, and the cost of the capital invested in the
purchase of the grain. Otherwise, rational economic agents would not have stored the grain,
instead finding some other investment opportunity for their savings. Following the work of
McCloskey & Nash (1984) and Brunt & Cannon (1998), we can use this price appreciation to
estimate the local cost of capital. Grain prices offer several advantages over other sources. First,
before the Second World War every locality produced and traded grain for local consumption.
Second, governments keenly monitored the price of grain in every locality because they were
worried about the possibility of excessive price rises causing social unrest. This has left us with a
huge amount of price data, usually with weekly observations (or sometimes even higher
frequency). Third, holding grain was roughly equally risky in every locality, so we do not have
to worry about the risk premium when looking at regional variations.
Given the relative merits of the three sources for estimating the local cost of capital (bank
loans, mortgages and grain prices), the grain price approach is a clear winner. The potential
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pitfalls in using grain data are no greater than the potential pitfalls with the other sources; and
grain prices are the only source which are likely to give a wide and consistent spatial and
temporal coverage.
1.2 Accuracy and representativeness of cost of capital estimates based on grain prices. If
our estimates of the cost of capital are to be both accurate and representative of the wider
economy, then certain conditions need to be met at the micro level and the macro level. First, at
the micro level the appreciation of wheat stocks can be interpreted as a return on investment –
that is, a rate of interest – only if the grain price is determined by the activities of homo
economicus. Economic agents must be (at least approximately) rational, far-sighted, selfinterested and operating in a market economy. If agents are pursuing some other kind of
behaviour or maximand then grain prices will clearly not exhibit a return on investment. Komlos
& Landes (1991) have argued strongly that agents in the grain market cannot be assumed to be
homo economicus. This criticism focussed on the medieval economy (rather than the eighteenth
century) but many economic historians will have similar reservations about farmers in the later
period and we need to address their fears. Second, suppose that we can answer the first question
in the affirmative and treat the appreciation of grain prices as a rate of interest. Can we then go
further and interpret this return on investment as the rate of interest? This depends on the extent
to which product and financial markets were integrated; and the extent to which agriculture,
industry and services were integrated.
We begin by noting that the conditions required for the appreciation of grain stocks to
reflect a rate of interest are actually quite mild. We do not require everyone in the economy to be
a rational, far-sighted, self-interested optimising agent. It could be the case that many people
held grain irrespective of whether they were expecting to receive a return on the money that they
had invested. But as long as agents at the margin required a market rate of return, then the price
of grain would exhibit a cycle that ensured a rate of return for everyone. That is to say, the
existence of people who are willing to arbitrage between grain markets and other markets
(especially financial markets) is sufficient to ensure that grain holdings earn the market rate of
return. However, it would obviously strengthen our case if we could show that most or all
participants in the grain market were likely to seek a market return on their stocks of grain. So
we now consider each type of agent operating in the grain market. We show that in each case
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they acted as rational economic agents and were well-integrated with financial markets,
arbitraging between the grain and financial markets as necessary.
Let us start with the farmer. English agriculture in the late eighteenth century was very
commercialised. The standard pattern was for large landowners to rent out substantial farms (say,
200 acres) to tenant farmers on long term contracts (usually lasting 10 years and sometimes 20
years). These tenant farmers provided all the movable capital (animals, tools, seed, etc.) and
hired workers as required from an active labour market (both on annual contracts and on a daily
basis). The tenant farmers were geographically mobile and often moved from one locality to
another between tenancies. They were used to evaluating investment opportunities and
calculating rates of return; and generally they were not capital constrained. In fact, agricultural
profits were very high in the late eighteenth century and the investment funds for industry came
from the agricultural sector. It is therefore no surprise to find in 1796 that farmers were well
aware of the wheat price cycle, and they arranged their grain disposals accordingly.
[T]here are a set of wealthy farmers who have it in their power to retain a part of
their growth in those natural and best of granaries, their ricks. Was it otherwise, as the
Corn Laws now stand, we might often, even with a most plentiful harvest, be in the
utmost danger of famine.
The argument made use of is, that the little farmer is, through necessity, obliged
to thresh out his corn and bring it to market; but that the opulent man will not produce
his, until it comes to a certain price. (Arbuthnot, 1796, vol.27, pp.21-22)
We have evidence also from the testimony of farmers and merchants appearing in the
Parliamentary enquiry of 1828 (British Parliamentary Papers, 1928, vol.18, pp.284-9). The
witnesses to the committee discuss both ‘normal’ trade conditions and those pertaining during
the agricultural depression of the 1820s. We can see that farmers commonly financed the storage
of grain through bank lending, in anticipation of higher prices later in the year.
During the war, the landlords easily raised money at the banks on discount, and
consequently were not under the necessity of opposing the speculations of the farmers;
since however, the failure of so many banks in the south of Ireland, the landlords,
generally, have been unable to continue this indulgence. We find accordingly, by the
notes of the different markets, that the delivery of crops is every where, not only quite
unreserved, but much earlier than during the war.
And again,
The want of money has obliged them generally to bring their Corn to market as
early as possible of late years, and but few of the more wealthy have seen sufficient
prospect of advance to induce them to hold [Corn].
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This account is particularly interesting because we can see how higher interest rates in the
banking sector are reflected in higher rates of return on holding grain. The tightening of credit
rates makes it difficult for farmers to borrow money to finance the holding of grain; so they bring
their crop to market earlier, and this drives down the price of grain early in the season. This
generates a sharper increase in grain prices over the year, reflecting the higher interest rate
pertaining in the banking sector.
Grain factors, millers and mealmen (that is, flour merchants) also sought a market rate of
return on their stocks; and they were also commonly financed through bank loans. Hence in the
depression of 1828 we find that:
The capital employed in the Corn trade has been less of late years from less
accommodation by country bankers and others…
The involvement of English banks in the grain trade was apparently very extensive, and the state
of the grain trade was consequently sensitive to conditions in the capital market:
The capital employed in the Corn trade in the South of Ireland is very much
supplied from England, the corn being bought on commission. The failures of the banks
have certainly lessened the capital in the Corn trade in Ireland, by lessening the
accommodation afforded to persons who have no capital of their own.
Two further points are worth emphasising. First, agents who were intimately involved in
the grain trade were perfectly willing to withdraw their capital from the grain trade and redirect it
to more remunerative avenues. This is important because if the rate of return on capital is to be
equalised between the grain market and the banking sector then it is essential that capital can
flow out of the grain market as well as into it.
The capital varies according to the price of Corn. Many millers have laid out a
part of their capital in the funds [government securities]…
Similarly,
Most of the capital employed by merchants or middle men in former years has
been lost, and the remainder withdrawn from the trade, there being now no capital
employed in the trade in Corn.
Second, we also find that the holding of grain purely as an investment activity was not limited
merely to farmers and grain factors. It was also seen as a reasonable target for other investors
seeking a return on their capital:
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…but many capitalists and merchants unconnected with the [grain] trade, were
wont to speculate in Grain, through the factors…
Finally, if we move further forward in time to the 1870s then we find Bagehot (1873)
describing how the bill brokers directed funds to flow into and out of the grain market as
necessary.
Their bill cases [i.e. portfolios] as a rule are full of the bills drawn in the most
profitable trades, and ceteris paribus and in comparison empty of those drawn in the less
profitable. If the iron trade ceases to be as profitable as usual, less iron is sold; the fewer
the sales the fewer the bills; and in consequence the number of iron bills in Lombard
Street is diminished. On the other hand, if in consequence of a bad harvest the corn trade
becomes on a sudden profitable, immediately ‘corn bills’ are created in great numbers,
and if good are discounted in Lombard Street. Thus English capital runs as surely and
instantly where it is most wanted, and where there is most to be made of it, as water runs
to find its level.
In order for the rate of return in the grain market to reflect the rate of return in the
financial market it is not necessary for there to be direct links between the two sectors (i.e. banks
investing in the grain market). But the existence of direct links obviously greatly strengthens our
case. The evidence presented above demonstrates that capital could easily flow into and out of
the grain market and that there were strong direct links between the grain market and banks.
First, grain market capital came from individual agents who were able to trade directly in both
grain markets and financial markets (i.e. their investment in grain was not mediated through
putting their money into a bank). We can see this process occurring when individuals bought
grain stocks through grain factors. Second, agents who were themselves involved in the grain
trade held large grain stocks if they expected a high rate of return; but they were also willing to
withdraw their capital from the trade and find alternative investments if there were higher
expected returns outside the grain trade. This suggests strongly that capital could flow out of the
grain trade just as easily as it could flow into the trade. Third, financial institutions were involved
in financing both the grain trade and other trades – so they were willing and able to arbitrage
between the two markets. We can see this process occurring when country banks financed
farmers and traders who wanted to hold grain stocks. All this evidence demonstrates that grain
markets and financial markets were linked directly by a host of rational economic agents, drawn
from inside and outside the agricultural sector. Therefore it is reasonable to interpret the
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appreciation of grain stocks as a rate of interest, and we would expect this to reflect the rate of
interest.
1.3 The nature of capital and the rate of return in the British economy. We have argued that
we can estimate the rate of return on capital using grain prices because there were strong links
between the grain market and other markets (and hence the returns should move together). But
this really understates our case. Suppose that markets were segmented and rates of return
therefore differed from one to another (returns in the shipping industry differed from iron and
steel, which differed from agriculture, which differed from the insurance industry, and so on).
Moreover, suppose that markets were geographically segmented and rates of return differed from
place to place (returns on shipping were not the same in London and Liverpool, and so on). Then
if we wanted to measure geographical variations in the cost of capital, what could we reasonably
take to be the rate of return in each locality? It would be no use comparing coal in
Northumberland to grain in Norfolk because the coal and grain markets would have different risk
characteristics. And it would be no use trying to compare coal in the two places because Norfolk
does not produce any coal. In modern economies it might be natural to take the return on
equities, or the rate of interest on housing loans (bearing in mind that we cannot use government
debt because we are interested in regional variation). But both of these markets were small in the
British economy until the late nineteenth century. We need a market that attracted a large volume
of capital everywhere. Which market attracted the most capital in the eighteenth and early
nineteenth centuries? It was, of course, the grain market.
Grain had to be stored throughout the year. If we assume that the rate of consumption
was constant from one harvest to the next, then grain stocks would decline linearly. So on
average the amount of grain in storage would be equal to half of the total quantity harvested.
Since we know the size of the harvest and the price of grain, we can thus calculate the average
value of circulating capital which was invested in grain stocks on any day of the year. We made
this calculation for 1760 and 1860 and then compared the value of circulating capital invested in
grain stocks to that invested in the non-farm sector using the data in Feinstein (1978). The results
are reported in Table 1 below:
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Table 1. Circulating Capital in the British Economy, 1760 and 1860 (£m at 1861 prices).
1760 1860
Total for the non-farm sector
40
210
Total for the farm sector
140
240
Grain stocks
48
61
In 1760 the value of circulating capital invested in grain stocks was greater than that invested in
the whole of the non-farm sector; even as late as 1860, the capital invested in grain was around
one third of the entire non-farm sector. Any particular industry (even leading sectors such as
cotton or steel) would have been easily out-classed by the grain market, in terms of circulating
capital. Moreover, the leading sectors were heavily regionally concentrated in Lancashire and
Yorkshire, whereas the grain market was important in every locality. So as a barometer of the
rate of return on capital across the country, the grain market is a far-superior instrument.
The immense influence of agriculture on the prosperity of the British industrial sector
was being emphasised by Bagehot as late as the 1870s:
But every such industry is liable to grave fluctuations, and the most important –
the provision-industries – to the gravest and the suddenest. They are dependent on the
seasons. A single bad harvest diffused over the world, a succession of two or three bad
harvests, even in England only, will raise the price of corn exceedingly, and will keep it
high. And a great and protracted rise in the price of corn will at once destroy all the real
part of the unusual prosperity of the previous good times. It will change the full working
of the industrial machine into an imperfect working; it will make the produce of the
machine less than usual instead of more than usual; instead of there being more than the
average of general dividend to be distributed between the producers, there will
immediately be less than average.
So if we are trying to estimate the interest rate in the British economy during the
Industrial Revolution, then it makes most sense to take the grain market as a benchmark case.
The grain market absorbed more capital than any other market or sector; and the rate of return
that it generated is the one by which all others may be judged.
2. Discussion of the data. Our analysis is based on weekly price data for wheat collected by the
Receiver of Corn Returns and published in the London Gazette for each county in England and
two regions in Wales: this is fully discussed in Brunt & Cannon (2001).
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Weekly price data is available for the 40 English counties, London, North Wales and
South Wales – resulting in 43 price series for each grain. For simplicity, we refer to these 43
geographical areas as “counties”. We analyse each of these series from 10 November 1770
(when the data begin) to 30 September 1820. The Welsh data published in the London Gazette
are disaggregated further after 10 November 1790, at which point the data are reported for each
of the twelve Welsh counties individually. We have simply averaged the six Northern and six
Southern Welsh counties respectively to extend the two series through to 1820. There were
inadequate data for analysis for Scotland.
Data for the period after 30 September 1820 are also available, but some of the inland
counties are unrepresented until a further change in the data set in 1828, apart from some other
changes in the lists of towns, so 1820 forms a natural break-point.
The county prices are weighted averages of prices in several market towns in each county
(where the volume of trade was used as weights). Suffice it to say here that the number of towns
monitored in each English county was almost always three. London was obviously a singleton,
and the tiny county of Rutland had only two monitored towns; some of the counties have more
towns (for example, Norfolk has twelve). By law, the average price for a county could be
reported in the London Gazette if and only if returns had been received from at least two thirds of
the towns in that county in that particular week.
Some of the price series have missing observations, but the scale of this problem is
minimal. For the years 1770 to 1820 we do not know why the prices are missing. For the period
after 1820 we know that most missing price observations occur because there was no trade in
that particular product in that particular week (so there was simply no market price). Over the
entire period, the number of missing observations for wheat is very small, and is zero for some
counties. The worst offender is Herefordshire where there are 62 missing observations out of
2604 weeks (so data is available for 97.6% of all potential observations).
Since we do not have quantity data to correspond to these price data, we cannot draw
strong conclusions about the regional pattern of trade or production, but the pattern of missing
price observations suggests that wheat was widely grown in all areas, even if the climate were
unsuitable. This is what we would expect, given the high transport cost of moving wheat.
Figure 1 illustrates the time series price of wheat over the period 1770-1820. The inner
line represents the mean average price of England and Wales: the two outer lines represent the
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maximum and minimum price of the 43 counties in each week. This graph illustrates that prices
in different regions moved closely together. This fact alone does not allow us to infer
immediately that markets were highly integrated. It would be quite possible for the random
shocks affecting prices to be highly correlated across counties (notably the weather), resulting in
similar prices in markets that were only very weakly linked.
Figure 1: Weekly Wheat Prices
Mean, Min and Max (pence per Qtr)
A further feature of the data is the tendency for the underlying levels and year-on-year
variability of prices to be constant until about 1794. After this time there is both a secular
increase in prices and much greater variability, due to the problems of the Napoleonic Wars.
Ideally we should approach this problem by deflating the price series to obtain the real prices of
the grains, but the lack of a suitable price deflator makes this impossible. Our analysis of rates of
return will thus be confined to nominal rates of return – but since we are using the nominal rates
of return in all places, this will not influence our results.
We now turn to the seasonal pattern of the grain prices. The simplest way to describe the
seasonal pattern is to conduct a regression of the form
13
1820
1817
1814
1811
1808
1806
1803
1800
1797
1794
1791
1788
1785
1782
1779
1776
1773
1770
350
300
250
200
150
100
50
0
52
(1)
ln Pt  a0   ai  et subject to
i 1
52
a
i 1
i
 0.
where a0 is a constant and the ai are 52 dummies for each week of the year. Equation (1) can be
applied to each of the 43 county series. The result is shown in Figure 2, which displays the
average of all these county estimates. From this graph it appears that there is a strong seasonal
pattern which is similar in all counties. However, care must be exercised in interpreting the
graph, since in most counties the regression is insignificant using conventional standard errors.
Chambers & Bailey (1995) argue on this basis that there is no seasonal effect at all (although
inconsistently they maintain that there is a significant price fall in September). However, the use
of conventional tests in this context is inappropriate. From Figure 1, it can be seen that the most
of the temporal variation in prices is not the seasonal effect within years but the combination of
the year-on-year effect throughout the sample and the large inflationary component after 1794.
Any attempt to conduct tests on the basis of equation (1) would need to account for the immense
autocorrelation and heteroskedasticity present in the resulting residuals et. For this reason we
present Figure 2 as only illustrative.
Figure 2 Wheat Mean County Seasonal Effect
Nov 1770 - Aug 1820
0.05
0.04
0.03
0.02
0.01
0
-0.01 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
-0.02
-0.03
-0.04
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The graph reveals that the average seasonal pattern of prices is broadly consistent with
the McCloskey-Nash characterisation: prices rise for most of the year and then fall at the point of
the harvest. The gross rate of return for the period when prices are rising is about 6 per cent, but
this is for a period of about 30 weeks: expressed at an annual rate the returns would be 10.5 per
cent. In one aspect, however, the seasonal patterns for wheat depart from the McCloskey-Nash
pattern: the period of the year when prices are falling is much longer. In the simple McCloskeyNash hypothesis, price falls are only possible if stocks of grain are relatively low, or if they are
unanticipated. Neither of these cases can be relevant on a consistent basis for the period of three
to four months immediately after the harvest.
It is necessary to have a more sophisticated description of the pattern of production to
understand pricing behaviour in this part of the year. Part of the reason for the pattern is that the
timing of the harvest was itself highly variable and thus new stocks of grain could become
available at any time between late July and early September. More importantly, grain had to be
threshed before it was brought to market. Labour costs were highly seasonal, with the highest
wages in the period July-September. This is because labour was needed for the urgent tasks of
harvesting and then ploughing before the weather deteriorated in the autumn. After this period,
the demand for labour fell considerably and only then did farmers allow much of their labour
force to be diverted to threshing, most of which took place in November and December. This
remained true into the late nineteenth century (see Young, 1770; Chalmers 1868 for discussion).
For this reason the falls in the price of wheat during this period do not fully reflect price rises in
the underlying asset and we prefer to concentrate on the period from late December, by which
time threshing was usually completed.
This section has provided a brief description of the data. We have shown that the seasonal
pattern of wheat broadly follows the pattern suggested by McCloskey-Nash and hence we shall
be able to use these data to estimate the rate of return on capital.
3. Econometric Estimates of the Gross Rate of Return. In this section we present further
evidence that wheat prices behaved in a way that we should expect for a weakly efficient asset
market and we obtain estimates for annual rates of return.
In financial time series analysis it is conventional to use a definition of efficiency known
as weak market efficiency, where changes in prices must be unpredictable from past information
15
on prices alone. Tests for this sort of efficiency can be based entirely on regressions involving
just price data and it is this definition that we consider here. If wheat prices do behave in the way
of normal asset markets then we should expect (the logarithm) of prices to follow a random
walk. Consider the example regression,
(2)
ln Pt    1 ln Pt 1   2 ln Pt 2  3 ln Pt 3  ut .
Under the hypothesis of weak market efficiency, we should obtain the results that 1 = 1, 2 = 3
= 0 and ut be serially uncorrelated. The estimate of  would then be the rate of return. In
financial time series it is common to model the variance of the residual to be evolving over time
(in the simplest case using just an ARCH specification), but our analysis of prices within a year,
with at most 40 observations, precludes this approach. Confining ourselves to OLS means that
our estimates may be inefficient and conventional standard errors unreliable. In these
circumstances, we use Heteroskedastic Consistent Standard Errors and test explicitly for
evidence of ARCH in the residuals (although with so few observations the power of this test will
be weak). In fact we find little evidence of heteroskedasticity within years.
Our more important problem is in deciding the time frame over which to use regression
(2). Since we should like to estimate changes in interest rates over time, the natural approach is
to treat each year as an individual time series, resulting in substantially different estimates of 
for each year, which we may refer to as y, where the y subscript denotes the year to which we
are referring (reserving the t subscript for the week of the year). However, this makes it less
straightforward to interpret the tests for 2 = 3 = 0 under the strict assumption of weak market
efficiency. Weak market efficiency assumes that returns are uncorrelated given information on
past prices alone, i.e., not including y. If this parameter is not known, then lnPt-1 provides
useful information about y and we should not expect returns to be uncorrelated. For this reason
our tests of market efficiency are not, strictly speaking, tests of weak-market efficiency, since
they condition upon y. It should also be noticed that we do not allow for changes in the rate of
return within year, since such changes could not be reliably detected with our data set.
We start our tests by using Augmented Dickey-Fuller (ADF) tests using the reparameterized and extended equation
16
(3)
 ln Pt    bt    1ln Pt 1  1 ln Pt 1  2  ln Pt 2  ut :
Under the alternative hypothesis that there is no unit root the data would have to follow a
deterministic trend and hence omitting the bt term would bias the test towards failure to reject the
null, even if the null were false. We conduct this test on data for each county for each harvest
year from 24 December to 8 August, resulting in 50 tests of a unit root for each county (harvest
years 1770-71 through to 1819-20). To check the robustness of these tests we varied the start and
end dates of the period within year and varied the number of lags in the ADF test.
Given the number of tests, we should expect to reject the null hypothesis about 5% of the
time, so it is desirable to summarise the information from all of these tests. Since within each
county the different years are independent, it is possible to combine the ADF tests using a
technique suggested by Fisher (1932) and advocated by Maddala and Wu (1999) in the similar
context of evaluating test statistics within panel data. If the independent probability values of the
ADF tests are denoted i, then the statistic
N
(4)
 2 ln  i
i 1
has a 2 distribution with 2N degrees of freedom. Maddala & Wu’s (1999) Monte Carlo tests
show that the test statistic has almost exactly the right size when N = 50 and T = 25, which
corresponds very closely to the test that we are conducting here, although the power is not
particularly high (26%). These statistics are shown for each of the 43 counties in Table 2: the
probability values of the Dickey-Fuller statistic were calculated from a Monte Carlo experiment
with 60,000 observations. Only one Fisher test is significant out of 43, so we cannot reject the
null hypothesis of a unit root.
17
Table 2. County Tests for a Unit Root.
Bedford
1.000 Essex
0.963
Berks
1.000 Gloucester 1.000
Bucks
0.049 Hampshire 0.076
Cambridge 1.000 Hereford
0.987
Cheshire
0.064 Hertford
0.068
Cornwall
0.994 Huntingdon 1.000
Cumberland 0.998 Kent
1.000
Derby
0.989 Lancashire 1.000
Devon
1.000 Leicester
1.000
Dorset
0.972 Lincoln
0.997
Durham
0.997 Middlesex 1.000
Monmouth
Norfolk
Northampton
Northumberland
Nottingham
Oxford
Rutland
Salop
Somerset
Stafford
Suffolk
0.987
1.000
1.000
0.240
1.000
0.998
1.000
0.993
1.000
0.988
0.103
Surrey
Sussex
Warwick
Westmorland
Wilts
Worcester
York
London
N Wales
S Wales
1.000
1.000
1.000
1.000
0.994
0.997
1.000
1.000
1.000
1.000
Notes. Fisher Tests for a Unit Root: for each county based on 50 (London 21 only) ADF tests of 30 observations
each. Entries are p-values of the chi-squared statistic.
Proceeding on this basis, we imposed this restriction and estimated the further regressions
(5)
 ln Pt    1 ln Pt 1  2  ln Pt 2  ut
In the first case we can test for market efficiency by considering either the individual tests for 1
= 0 and 2 = 0 or the joint test = = 0: in all three cases we use the Heteroskedastic
Consistent Moment Estimator of White using a small sample correction suggested by Davidson
and McKinnon (1993) of multiplying the elements by T/(T – 2): the individual tests have a tdistribution and the joint test a chi-squared. In fact tests for general heteroskedasticity using the
White test, for ARCH and for autocorrelation suggested that the residuals were well behaved and
F and t tests for market efficiency using conventional moment estimators gave qualitatively the
same results in all but a few cases. These provide strong evidence that we cannot reject the
hypothesis of market efficiency once we have allowed for changes in the rate of return over time.
It would be possible to estimate the gross rate of return using /(1 – 1 – 2) from
equation (5). However, since we cannot reject the null hypothesis that = = 0, this method is
unnecessary and will actually make the estimates less precise due to estimation error in or.
For this reason we in fact estimated the gross rates of return by just using the simple means of
the weekly price rises. Tests for heteroskedasticity and autocorrelation suggested that there was
no problem with this simpler specification.
18
4. The British banking system. The classic analysis of British banking in the eighteenth and
nineteenth centuries is that offered by Bagehot (1873). Bagehot starts from a surprisingly modern
standpoint, emphasizing the importance of capital in promoting economic growth:
In countries where there is little money to lend, and where that little is lent tardily and
reluctantly, enterprising traders are long kept back, because they cannot at once borrow
the capital, without which skill and knowledge are useless. (p. 14).
Britain was in a fortunate position because:
We have entirely lost the idea that any undertaking likely to pay, and seen to be
likely, can perish for want of money; yet no idea was more familiar to our ancestors, or is
more common now in most countries. A citizen of London in Queen Elizabeth’s time
could not have imagined our state of mind. He would have thought that it was of no use
inventing railways (if he could have understood what a railway meant), for you would not
have been able to collect the capital with which to make them. (p. 7).
By contrast,
Taking the world as a whole – either now or in the past – it is certain that in poor
states there is no spare money for new and great undertakings, and that in most rich states
the money is too scattered, and clings too close to the hands of the owners, to be often
obtainable in large quantities for new purposes. (p. 7-8).
Bagehot argues that this ready availability of capital in Britain was due to her unique banking
system:
Concentration of money in banks, though not the sole cause, is the principal cause
which has made the Money Market of England so exceedingly rich, so much beyond that
of other countries. (p. 6).
Even the other advanced economies were then labouring under a rudimentary banking system
and a shortage of capital:
If you take a country town in France, even now, you will not find any such system
of banking as ours. Cheque-books are unknown, and money kept on running account by
bankers is rare. People store their money in a caisse at their houses. (p. 76).
So how exactly did the British banking system work? Joint stock banking was outlawed
in England and Wales until 1826. The only exception to this was the Bank of England, which
enjoyed privileged joint stock status in return for being the Government’s banker. Up to 1826,
the banking system was therefore based on the country banks. These were private partnerships,
restricted to a maximum of six partners who had to endure unlimited liability. Most banks were
19
consequently small and generally possessed only one outlet (i.e. there were very few banks with
branches). To overcome their extremely limited geographic reach, the country banks developed
correspondent relationships with a bank in London. In this way, they could send excess deposits
to London and still earn a reasonable return on them; or they could borrow from London when
they needed more funds locally. Hence country banks played the central role in recycling capital
from surplus to deficit regions:
[T]here are whole districts in England which cannot and do not employ their own
money. No purely agricultural county does so. The savings of a county with good land
but no manufactures and no trade much exceed what can be safely lent in the county.
These savings are first lodged in the local banks, are by them sent to London, and are
deposited with London bankers, or with the bill brokers. In either case the result is the
same. The money thus sent up from the accumulating districts is employed in discounting
the bills of the industrial districts. Deposits are made with the bankers and brokers in
Lombard Street by the bankers of such counties as Somersetshire and Hampshire, and
those bill brokers and bankers employ them in the discount of bills from Yorkshire and
Lancashire. (p. 12).
Moreover, the country bankers were heavily engaged in this pursuit:
All country bankers keep their reserve in London. They only retain in each
country town the minimum of cash necessary to the transaction of the current business of
that country town. Long experience has told them to a nicety how much this is, and they
do not waste capital and lose profit by keeping more idle. They send the money to
London, invest part of it in securities, and keep the rest with the London bankers and bill
brokers. (p. 30-31).
In this way, the banks linked each of their localities directly to the London money market. It is
interesting to note that there was little or no interaction between local banks. Instead there was a
sophisticated network of bilateral relationships with London, and an almost complete absence of
regional networks.
5. Gross Rates of Return and Financial Markets. We previously estimated annual series for
the gross rates of return for London and each county. In this section we consider the relationship
between these rates of return and the effect of financial institutions. Our country bank data is
taken from the Shannon MSS. Unfortunately systematic bank data is available only from 1800,
so our analysis will be confined to the period 1800 to 1820. An alternative source of data is the
20
British Parliamentary Papers, but data in this source are available only from 1808. However, we
compared the two sources and found them to be very similar.
The total number of banks grew considerably in this period, from 370 in 1800 to 656 in
1811: thereafter there was a modest fall and the number of banks fluctuated. We also know the
number of banks in each of a few years prior to 1800, but only at random intervals and on an
aggregate basis: suffice it to say that there was considerable growth in country banking from
1784. It is clear that the raw data are not very informative about the availability of financial
institutions, since counties vary considerably in size: unsurprisingly, the county with the most
banks is Yorkshire (the largest county) and the one with the least is Rutland (the smallest
county). Accordingly we scaled the data by alternative measures of the “size” of each county.
Ideally our scale measure would be GDP, but no such data exist. Our two measures were the
surface area and the annual population: the latter was obtained for each year by linearly
interpolating between the censal years of 1801, 1811 and 1821. It is also noteworthy that there
was great variation in the density of bank coverage across England and Wales. We can see this
clearly in Figure 3 below.
21
Figure 3. Density of Bank Coverage by County, 1800-20.
10
0.14
9
0.12
8
7
0.10
6
0.08
5
4
0.06
3
0.04
2
0.02
0
Bedford
Berks
Bucks
Cambridge
Cheshire
Cornwall
Cumberland
Derby
Devon
Dorset
Durham
Essex
Gloucester
Hampshire
Hereford
Hertford
Huntingdon
Kent
Lancashire
Leicester
Lincoln
Middlesex
Norfolk
Northampton
Northumberland
Nottingham
Oxford
Rutland
Salop
Somerset
Stafford
Suffolk
Surrey
Sussex
Warwick
Westmorland
Wilts
Worcester
York
North Wales
South Wales
1
Banks scaled by Area
Banks scaled by Population
The problem with using county land area to scale the number of banks is that it takes no
account of increased demand for banking services, and thus demonstrates a greater degree of
mismeasurement. Even with the population measure we expect there to be a considerable degree
of mismeasurement in the cross-sectional dimension, although less in the time series dimension.
Hence we anticipate more accurate results from within-group estimation than between-group
estimation. Figure 4 below demonstrates that the counties experienced very different time-series
changes in banks over this period.
22
0.00
Figure 4. The Change in County Bank Densities, 1800-20.
Beds
Berks
Bucks
Cambs
Cheshire
Cornwall
Cumberland
Derbs
Devon
Dorset
Durham
Essex
Gloucs
Hants
Hereford
Herts
Hunts
Kent
Lancs
Leics
Lincs
Mids
Norfolk
Northants
Northumberland
Notts
Oxon
Ruts
Salop
Somerset
Staffs
Suffolk
Surrey
Sussex
Warks
Westmorland
Wilts
Worcs
Yorks
N Wales
S Wales/Mons
Number of Banks per County
Scaled by Population
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
23
1820
1819
1818
1817
1816
1815
1814
1813
1812
1811
1810
1809
1808
1807
1806
1805
1804
1803
1802
1801
1800
0.00
We now consider whether there is any relationship between gross rates of return
in the counties and the number of country banks. Assuming that banks should arbitrage
differences in interest rates, we should expect the number of banks to reduce the
differences between interest rates. This possibility seems especially interesting since it is
known that each country bank maintained links primarily with a correspondent bank in
London, and can thus be interpreted as channelling sources of funds to or from the main
financial centre.
To test the hypothesis that banks reduce the differential between local interest
rates and London, we considered the following panel regression
(6)
 it  ht  i   Bit  vit ,
where  it is the country interest rate, ht is the London interest rate and Bit is a measure of
Banks/Size of county. Results for these regressions are shown in Table 3 below, both for
the whole sample and for selections of counties to check for parameter stability. Because
the series are trending, we ran regressions with a time trend, which proved significant and
strengthened the significance of the Banks variable. This may be because the economy as
a whole was growing and hence the trend picks up the increased demand for banking
services.
Table 3. Explaining the London-County Interest Rate Differentials, 1800-20.
Full
Sample
Banks/Area
Hcse
Banks/Pop
Hcse
Trend
Hcse
N
R-squared

Full
Sample
-0.073
0.032
0.006
0.0014
779
0.13
0.18
Half
Sample
Half
Sample
-0.0968
0.04544
-1.270
0.406
0.00645
0.00129
779
0.14
0.18
-0.00582
0.00194
399
0.15
0.18
Notes. Here we report the within-group estimates.
24
-1.577
0.5543
-0.00645
0.00176
399
0.16
0.18
Agricultural Agricultural
Counties
Counties
Only
Only
-0.0854
0.0344
-1.4303
0.42732
-0.00536
-0.00563
0.001
0.00142
608
627
0.13
0.16
0.18
0.18
The two main results from Table 2 are clear: the differential between the rate of
return in London and the rate of return in the provinces is negatively related to the density
of country banking, both over time and across counties. The average measure of
Banks/Area rose from 0.59 to 0.97 over the period, so an estimated coefficient of about
0.08 suggests that the overall effect of banks was to reduce the differential by about 3
percentage points. The Banks/Population variable rose on average from 0.045 to 0.059
and the average coefficient is 1.3, suggesting a fall of about 2 percentage points in the
differential between the rates of return in London and the provinces, which is very
plausible.
6. Conclusion and Discussion. In this paper we have analyzed county wheat prices in the
period 1770-1820. The seasonal pattern of wheat prices is consistent with that suggested
by McCloskey and Nash and hence we can attempt to estimate rates of return from that
part of the year when prices are rising. Our analysis shows that markets were efficient
over this period: prices followed a random walk with drift and the return on wheat was
not serially correlated once changes in the rate of return had been taken into account.
The county rates of return on wheat differ considerably from the London rate.
However, the differential between the county rates of return and the London rate is
negatively correlated with the density of country banks. Consequently, the increase over
time in the number of country banks reduced the interest rate differential between
London and the provinces. Hence we can infer that banks were providing conduits for
excess funds to be invested in London and enabling areas where credit was short to
benefit from the London market. This is strong evidence that county banks played an
important rôle in facilitating financial market integration in the Industrial Revolution.
Given the importance of finance in promoting economic growth, Britain’s exceptionally
advanced banking system may well have been one of the key factors which distinguished
her in the eighteenth and nineteenth centuries from competitor countries such as France
and Germany.
25
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