ISSN 1471-0498
DEPARTMENT OF ECONOMICS
DISCUSSION PAPER SERIES
THE EVOLUTION OF MARKETS IN EARLY MODERN EUROPE,
1350-1800: A STUDY OF GRAIN PRICES
Victoria N. Bateman
Number 350
September 2007
Manor Road Building, Oxford OX1 3UQ
The evolution of markets in early modern Europe, 1350-1800:
A study of grain prices
Victoria N. Bateman1
Abstract
This paper attempts to establish the trend of market development in Europe in the
centuries before the industrial revolution, by applying three different measures of
market integration to a compilation of monthly and annual price data. In contrast to
much of the existing work, which suggests that markets generally improved during
this time, the findings propose that markets were as well integrated in the fifteenth
century as in the late eighteenth century. In between these two times, markets are
found to have suffered a severe contraction. These results provide a challenge to our
current thoughts on economic growth.2
JEL Classification: F15, N13, O10, O52
Keywords: market integration, economic growth, European economic history, price
data, fixed effects panel model
1
University of Oxford, Lincoln College. Address for correspondence: Lincoln College, Oxford, OX1
3DR. Email: [email protected] & [email protected]
2
This paper is based on work presented in Chapter 1 of my DPhil thesis, ‘Market Integration and
Growth in Europe: The Early-Modern period’ (2006), examined by C.K. Harley and C. O’Gráda, and
supervised by R.C. Allen to whom I am extremely grateful for support and encouragement. The
research was funded in part by the ESRC and the Department of Economics, Oxford. I am grateful for
this support. The results have been presented at seminars in Oxford, Cambridge, The Institute of
Historical Research and the L.S.E, and at the Economic History Society annual conference at the
University of Reading. I thank seminar and conference participants for their comments.
1
Introduction
Markets are at the very centre of economics. Beginning with Adam Smith, economists
have argued that well developed markets are essential for economic growth, with
undeveloped markets supposedly having restrained growth in history and in the
poorer economies of today.3 According to this view, the eventual development of
markets in European history led to the division of labour and rising returns to capital,
stimulating the take-off to modern economic growth. For example, North and
Thomas’s The Rise of the Western World (1973) has argued that an increase in
population density and increasing political order (including property rights) were
responsible for the emergence of markets and market supporting institutions,
providing the incentives necessary for capital accumulation, technological change and
economic growth. This market-based view also forms the basis of much of the policy
advice given to the poorer and developing economies of today by such bodies as the
World Bank. Recently, however, research in economic history has begun to down
play the importance of markets, with some even suggesting a return to mercantilist
ideas (Masschaele, 1993; O’Brien, 2000; Acemoglu, Johnson and Robinson, 2002;
Chang, 2002; Ormrod, 2003; Keller and Shiue, 2004).
In an attempt to resolve this burgeoning debate, it would be helpful to more firmly
establish the course of market development in history. In general the existing
literature emphasises the later eighteenth century onwards as the time that market
integration was achieved (Unger, 1983; Allen and Unger, 1990; O’Rourke and
3
North and Thomas, 1973, North, 1990 and Smith, 1961 [1776], World Bank, 2002. See also the
literature on free trade and its relation to economic growth: Dollar, 1992; Sachs and Warner, 1995;
Ben-David, 1996; Edwards, 1993, 1998; Harrison, 1996; Krueger, 1998; Frankel and Romer, 1999;
Dollar and Kraay, 2004. This literature is critiqued by Krugman, 1994b; Rodrik, 1995; Rodriguez and
Rodrik, 1999; Rodrik et al, 2004; Freeman, 2003; Dowrick and Golley, 2004; Stiglitz, 2005, 2006.
2
Williamson, 2001; Findlay and O’Rourke, 2002), with evidence of increasing
European integration between the 1600s and the time of dramatic transformation in
the 1800s (Persson, 1999; Jacks, 2004). In contrast to this established view, there is a
minority literature which places greater emphasis on the earlier centuries, with the
1500s and 1600s (or possibly even earlier) being identified as the time at which
market integration emerged (Abel, 1980; Achilles, 1957; Masschaele, 1993; Clark,
2002; Galloway, 2000).4 Each of these two strains leads to strikingly different
conclusions as to the relationship between markets and growth: the first view suggests
that markets could indeed have been at the root of the modern economic growth takeoff, whilst the second view implies that they developed too early on to be held
responsible. The debate arises from the fact that whilst many studies of market
integration exist, they tend to focus on a restricted set of countries and restricted time
periods, and to use divergent econometric methodology, which can lead to markedly
different findings. As such, this paper aims to arrive at firmer conclusions as to the
path of market development in history by applying a common econometric
methodology to a large time frame of European countries across the long span of the
early modern era.
The findings suggest that markets were as well developed in the fifteenth century as
on the eve of the industrial revolution, but suffered from a serious setback between
the late sixteenth century and first half of the seventeenth century. After this time,
markets recovered to their previously higher levels of development, leading to a U-
4
According to Brenner (1976, 1978), the development of markets and transition to capitalism was an
outcome of the late medieval economic crisis. Other literature attempting to explain the transition
includes Dobb, 1946, Sweezy, 1976, Wallerstein, 1974, Mendels, 1972, and Epstein, 2000. It should be
noted that there is a literature on the commercialisation of the economy even before this time (e.g.
Campbell and Britnell, 1995; Campbell, 2000).
3
shaped trend in market development, a finding which enables us to reconcile the two
contrasting views in the existing literature. The results may be interpreted as casting
doubt on the view that markets were at the root of the growth take-off. Instead, it
could suggest that markets by themselves are insufficient for economic growth,
providing an important lesson for the developing countries of today. An alternative
and equally as challenging interpretation is that had it not been for the factors which
caused the market deterioration in the late sixteenth and seventeenth centuries,
modern economic growth would have begun as far back as the fifteenth or sixteenth
centuries. Given the market deterioration, one could therefore argue that economic
growth was effectively ‘postponed’ until the eighteenth century market recovery had
taken place.
The paper is organised as follows. Section 1 discusses the econometric method and
data used to measure market integration in Europe, 1350-1800, together with
providing an overview of the results at the level of the individual countries. Section 2
applies panel data techniques to the set of results in an attempt to provide a more
general picture of European market development in the time period. Finally, Section 3
concludes.
1. Econometric Method and Data
1.1 Econometric Method
A number of different econometric methods for measuring market integration have
been used in the literature. In order to provide a sufficiently broad perspective, the
current work measures three aspects of market integration: price convergence, price
volatility and speeds of market adjustment, each of which is outlined below.
4
Price Convergence
The law of one price suggests that if the market for a good is efficient and there are no
transaction costs, the price of the good in two different locations (cities i and j) will be
equal at any point in time
Pi,t = Pj,t
(1)
This will come about through the process of arbitrage which works instantaneously to
ensure that any difference in price between the two cities is eliminated. If the market
is efficient but transaction costs are positive then the prices will no longer be equal,
but will differ by the transaction cost wedge. A condition for market efficiency is that
the resultant price gap is stationary (as can be tested using a Dickey-Fuller test) and
does not consistently exceed the transaction cost. Ceterus paribus, as markets become
more integrated – through either increasing efficiency or falling transaction costs one would expect to see a reduction in the price-gaps between cities, and so a
convergence of prices.
Volatility
The evolution of market integration can also be gauged by considering the extent of
price volatility, and, in particular, its change over time. In theory, market integration
involves increasing arbitrage and trade, which has the effect of smoothing prices. In
segmented markets, local variations in the output of the good (such as those caused by
harvests) will lead to large effects on the price,5 most notably for goods such as food
5
Persson, 1999, p.106
5
products which display inelastic demand. Hence, if the output of the good is subject to
volatility, then so will be the price. However, as the locality becomes more integrated
with other geographical locations (through improvements in market efficiency and
declines in transaction costs) then alternative sources of the good become available to
the local market, smoothing the degree of output volatility, and so also the degree of
price volatility. 6 In other words, if one can find evidence that the price of a good has
become less volatile over time, then this would be suggestive of an increase in market
integration.
A useful way of measuring price volatility is by using the coefficient of variation of a
price series (p) in a particular interval of time (t):
C.V. p, t = standard deviation p, t / mean p, t
(2)
If the coefficient of variation of a price series in a particular location falls over time,
then it would suggest a reduction in volatility and so an improvement in the
integration of the location with the outside world. The coefficient of variation is a
particularly useful measure of integration in the sense that it is easy to compare across
places and time.7
Speed of market adjustment
A further useful measure of market integration involves a consideration of the shortrun market price dynamics. We know that in the short run, market disruptions may
result from shocks such as the weather or war, which lead to a departure away from
6
Integration will be price-stabilising if the ‘world’ output varies less than ‘local’ output of the good, as
is generally the case for agricultural products.
7
Federico, 2006
6
the long run (transaction cost adjusted) equilibrium or price-gap. It is also therefore of
interest to examine the short-run dynamics of price adjustment back to equilibrium
between two cities over time (i.e. how quickly the market price equilibrium is reestablished in the event of a shock that affects the price in either cities). This speed of
adjustment back to equilibrium is therefore an additional useful measure of market
integration, and can be understood in terms of the simple error correction model
below:
∆P1,t = b1∆P2,t – λ( P1,t-1 - P2,t-1 - T ) + εt
(3)
where ∆P1,t is the change in price in city 1, ∆P2,t is the change in price in city 2, λ is
the speed of market adjustment and ( P1,t-1 - P2,t-1 - T) is the price disequilibrium,
measuring the extent to which prices are away from their transaction cost adjusted
equilibrium, P1,t-1 = P2,t-1 + T. The model states that the change in price in city 1 will
be a function of the change in price in city 2, and the extent of disequilibrium. The
negative sign on λ indicates that the price in city 1 changes in a direction opposite to
the disequilibrium, and so moves in a way that will eliminate the price disequilibrium
between the two cities, restoring the equilibrium. In fact, if λ is exactly equal to unity,
then all of the disequilibrium will be eliminated quickly, but if λ is zero then no
disequilibria in the market are being eliminated, and so prices can drift away from
each other, which would indicate no integration in the market. Hence, the closer is λ
to unity, the higher is the adjustment from disequilibrium, and so the more integrated
is the market. If market integration improves over time, then one would expect to see
a rise in the speed of market adjustment, and so a rise in λ toward unity. Given the
issues concerning the potential endogeneity of any pair of price series, the Johansen
7
(1988) vector-error-correction-model (VECM) – which allows for the possible
endogeneity – is used to estimate the speeds of adjustment.8 At the same time, this
method provides clear evidence as to whether the prices are determined in a leaderfollower or a simultaneous fashion (with evidence of the latter indicating a greater
degree of integration than the former).
To summarise, three different ways of measuring market integration have been
outlined. This three pronged approach involves, firstly, calculating the (absolute or
percentage) price differences between pairs of cities and examining whether prices
have converged over time. Secondly, calculating the coefficient of variation for each
price series for different intervals of time to examine whether volatility has fallen
across time, and, thirdly, estimating the speed of market adjustment between cities for
different intervals of time to examine whether it has increased towards unity. In
addition, we can examine trends in price-leadership over time. If signs are found of
falling price gaps, falling volatility, rising speeds of adjustment and decreasing price
leadership, then it would provide consistent evidence of improving market integration
over time. By contrast, if we find little change in the price-gaps, volatility and speeds
of adjustment, it would suggest that market integration did not improve. In what
follows, these methods will be applied to price data from European cities between the
years of 1350 and 1800. The price data are discussed below.
1.2 Data
The choice of the price data to be used in the analysis is foremost dictated by
availability, with historical data that has sufficient coverage largely restricted to grain
8
Studies using this approach include Persson (1999), which also discusses the method in greater detail.
8
prices. However, there are a number of strong justifications for using grain data as a
basis for examining markets. Firstly, agriculture comprised the vast majority of
national output in this period of history, and grain in particular was both supplied and
demanded in all regions of Europe, making it a good product with which to study
market integration. Secondly, if the grain market becomes more integrated, then this
allows areas to specialise according to their comparative advantage – whether this be
industrial or agricultural, generating the associated welfare gains.9 Without grain
market integration, all regions would have to invest a large number of resources in
producing grain in order to meet the necessities of life, starving industry of potential
resources, and so restricting economic development in general. Hence, grain market
integration is essential for the broader processes of development. Thirdly, given that
the potential causes of grain market integration (including transport and institutional
improvements) are likely to be similar to those for other products, then the trend in
grain market integration will likely reflect the trend in the integration of other
markets. These three reasons provide a strong case for studying grain prices, and
using the results to form broader conclusions on markets more generally.
The core price data set used in the analysis consists of monthly wheat price data. The
monthly data has the great advantage that it is high frequency, and so provides a good
basis for measuring market integration in the three ways discussed above. The data
has been gathered from a broad set of sources, which are detailed in Appendix 1.1,
and summarised in the table below. The cities and time periods which can be
examined are clearly restricted by the availability of the data. Furthermore, as each
city’s prices are quoted in a different monetary and physical unit, it was necessary to
9
For example, see the example of Dutch agriculture discussed in Jacks (2004).
9
convert the original series into a single unit, grams of silver per litre.10 This process
has enabled me to provide a core database of comparable monthly grain prices for a
number of cities in early modern Europe: Antwerp, Brussels, Cologne, London,
Munich, Paris, Pisa, Ruremonde, Siena, Toulouse, Utrecht and Vienna.
Table 1
city
Antwerp
Brussels
Cologne
London
Munich
Paris
Pisa
Ruremonde
Sienna
Toulouse
Utrecht
Vienna
source
Verlinden (1959)
Verlinden (1959)
Ebeling (1976)
Beveridge (1965)
Elsas (1936)
Baulant (1960)
Malanima (see Appendix 1.1)
Ruwet (1966)
Parenti (1942)
Freche (1967)
Sillem (1901), Posthumus (1964)
Pribram (1938)
start year
from 1608
from 1568
from 1550
from 1683
from 1690
from 1550
from 1550
from 1650
from 1550
from 1563
from 1550
from 1692
monetary unit
Brabantse stuivers
Brabantse stuivers
albus
shilling
denar
setier
lira
Rurem. stuivers
soldi
setier
stuivers
kreuzer
physical unit
viertel
setier
malter
quarter
scheffel
livre tournois
72kg unit
malder
staio senese
livre tournois
modius ('mud')
Wiener Metzen
Whilst this monthly data has the strong advantage that it is high frequency, greater
time coverage can be provided by supplementing it with the analysis of annual grain
price data. This data has the advantage of allowing us to measure market integration
as far back as the fourteenth century, two hundred years before that which can
feasibly be carried out with the monthly data. Most of this data is available from one
source, namely the Allen-Unger grain price data set.11 However, one issue which had
to be addressed in the process of assimilating the data was that some series are
provided for harvest years whilst others are provided for calendar years. As a result, it
was necessary to establish this classification for each series through a close
examination of the source data. On this basis, the following cities were selected,
covering Belgium, Holland, England, Italy, France, Spain, Germany and Austria
respectively: Antwerp, Bruges, Lier, Brussels, Amsterdam, Leiden, Ruremonde,
10
11
Appendix 1.1 provides the details of these conversions.
Available online at: http://www2.history.ubc.ca/unger/htm_files/new_grain.htm.
10
Utrecht, Exeter, London, Winchester, Chester, Siena, Pisa, Toulouse, Paris, Grenoble,
Angers, Valencia, Madrid, Cologne, Munich, Wurzburg, Leipzig, Vienna and Wels.12
Clearly, to make all of the series comparable, it was necessary to convert the harvest
year series into calendar year series. The procedure for doing this was to take the
average of the prices in the two harvest years which overlap with each calendar year,
as was used in the seminal work of Phelps-Brown and Hopkins (1959).13 Whilst this
is by no means perfect, it does at least provide us with as much annual data as
possible to make a comparative analysis.
1.3 Results
In order to arrive at the estimates of market integration, we apply the three different
measures of market integration (price-gaps, volatility and the speed of adjustment) to
the grain price data discussed above.
Firstly, in order to measure the price-gaps, and so examine the extent of price
convergence, we take all of the possible pairwise combinations of the cities based on
the monthly data and calculate the mean price gap between each pair for each 10
yearly time period. This is then supplemented with a similar analysis of the annual
data, which provides for a larger number of city-pairs and a longer time period. Using
this annual data, we take pairwise combinations of Bruges, Exeter, Toulouse,
12
The annual data for Cologne, Paris, Pisa, Brussels, Antwerp, Utrecht and Ruremonde, is formulated
on a calendar year basis using the monthly data. Data on the other cities is from the Allen-Unger grain
price data set. Of this data, the Belgian, English and German data were established to be given for
harvest years (along with the series for Vienna). See, van der Wee, 1963, Beveridge, 1965, Elsas, 1936,
Pribram, 1938, p.267. The other series were established to be in calendar years. See, Hauser, 1936,
Hamilton, 1977, p.148, Freche, 1967, p.9, Posthumus, 1964, Parenti, 1942, Pribram, 1938, p.XI.
13
p.31. For example: Calendar Price for 1500 = (harvest price for 1499-1500 + harvest price for 15001501)/2
11
Cologne, Valencia, Siena, Ruremonde, Utrecht and Wels14 in order to formulate a
series of cross-country pairs. We then use data on the additional cities within each of
the countries to formulate a series of within-country pairs (e.g. Exeter-London,
Bruges-Brussels). This division enables a consideration of the price-gaps both within
and between the different economies. The estimates for these city-pairs are reported in
Appendix 1.2 on a twenty-five yearly basis. In order to apply our second measure of
market integration, volatility, the coefficient of variation for each of the monthly price
series are calculated for rolling five year periods, enabling us to see whether prices
have become less volatile over time. The monthly data is particularly useful here
given the high frequency of the data. The results are presented graphically in
Appendix 1.2. Finally, we apply the Johansen vector-error-correction-model (VECM)
approach to each pair of cities for each fifty yearly time interval (using the higher
frequency monthly data), in order to track the evolution of the speed of market
adjustment over the early modern period, and also to track the trends in price
leadership. The results of this third measure of market integration are also reported in
Appendix 1.2. Further estimates of the speed of market adjustment based on the
annual price data can be found in Bateman (2006).
At the level of the individual European countries, a number of findings can be
suggested on the basis of these three sets of market estimates. In particular, three
different types of market experience can be broadly outlined when comparing the start
14
These particular cities were chosen such that there is coverage of each of the eight European
countries for which we have data, and in such a way as to maximise the time coverage. For Holland, it
was necessary to use two cities (Ruremonde and Utrecht) rather than a single city, as the Utrecht data
has a substantial gap that is covered by the later Ruremonde data.
12
and end of the time period under study: market stagnation, market deterioration and
market improvement. 15
The first type of market experience (stagnation) is exemplified by German and
Spanish markets which show few signs of improvement between the start and end of
the period. This is seen most clearly in the price-gaps between Cologne and other
European cities (Figure 1 below), between Valencia and many other European cities
(Figure 2 below), between Valencia and Madrid, and between Leipzig and Wurzburg
(see appendix 1.2). The rolling coefficients of variation for Cologne and Munich
(depicted in Appendix 1.2) further support this finding, displaying little evidence of a
decrease over time.
Figure 1
Integration of Cologne with Europe: percentage price
difference measure
1.4
1.2
Cologne-Bruges
1
Cologne-Exeter
Cologne-Valencia
0.8
Cologne-Siena
Cologne-Toulouse
0.6
Cologne-Wels
Cologne-Utrecht
0.4
Cologne-Ruremonde
0.2
0
1550- 1575- 1600- 1625- 1650- 1675- 1700- 1725- 175074
99
24
49
74
99
24
49
74
15
A more detailed discussion of these and other economies can be found in Bateman (2006).
13
Figure 2
Integration of Valencia with Europe: percentage price
difference measure
1.4
1.2
Valencia-Bruges
1
Valencia-Exeter
Valencia-Siena
0.8
0.6
Valencia-Toulouse
Valencia-Wels
Valencia-Cologne
0.4
Valencia-Utrecht
Valencia-Ruremonde
0.2
14
50
14 7 4
75
15 9 9
00
15 2 4
25
15 -4 9
50
15 7 4
75
16 9 9
00
16 2 4
25
16 4 9
50
16 7 4
75
17 9 9
00
17 -2 4
25
17 4 9
50
17 7 4
75
-9
9
0
The second type of market experience (deterioration) occurs primarily in the Low
Countries, where there is clear evidence of a decline in the level of market
development between the start and end of the period. This is most apparent when
considering the price-gaps between the Belgian and Dutch cities (depicted below) and
between Bruges and the other European cities (Figure 5 below). This finding is of
particular interest as the Low Countries began the period as one of the most advanced
regions of Europe, with the highest level of agricultural productivity and one of the
most urbanised economic structures.16 However, in the successive centuries,
16
De Vries, 1984; Allen, 2001, 2003.
14
beginning with the sixteenth century, Belgium lost her predominance to the
Netherlands, which subsequently later lost her leadership to England.17
Figure 3
Belgium: percentage price gaps
0.8
0.7
0.6
0.5
0.4
0.3
Bruges-Brussels
Bruges-Antwerp
Brussels-Antwerp
Bruges-Lier
0.2
0.1
14
25
14 4 9
50
14 7 4
75
15 -9 9
00
15 2 4
25
15 4 9
50
15 -7 4
75
16 -9 9
00
16 2 4
25
16 -4 9
50
16 7 4
7
17 5-9
00 9
-1
7
17 24
25
17 -4 9
50
17 7 4
75
-9
9
0
17
Discussed in Van der Wee, 1963; Van Houtte, 1977; Veraghtert, 1983; Israel, 1989; Van Zanden,
1993, 2002; Blomme et al, 1994; De Vries and Van der Woude, 1997; Blom and Lamberts, 1999;
O’Brien, 2000.
15
Figure 4
Netherlands: percentage price gaps
0.9
0.8
0.7
Utrecht-Ruremonde
0.6
Utrecht-Leiden
0.5
Ruremonde-Leiden
0.4
Amsterdam-Ruremonde
Amsterdam-Utrecht
0.3
Amsterdam-Leiden
0.2
0.1
0
1550- 1575- 1600- 1625- 1650- 1675- 1700- 1725- 1750- 177574
99
24
49
74
99 1724 49
74
99
Figure 5
Integration of Bruges with Europe: percentage price difference
measure
1.6
1.4
Bruges-Exeter
1.2
Bruges-Valencia
1
Bruges-Siena
Bruges-Toulouse
0.8
Bruges-Wels
0.6
Bruges-Cologne
Bruges-Utrecht
0.4
Bruges-Ruremonde
0.2
1775-99
1750-74
1725-49
1700-24
1675-99
1650-74
1625-49
1600-24
1575-99
1550-74
1525-49
1500-24
1475-99
1450-74
1425-49
1400-24
1375-99
0
16
A number of factors were involved in the economic decline of Belgium, including the
war for independence of the Dutch from the Spanish (1566-1648). One of the most
severe consequences of this war was that the Scheldt – the coastal access in the north
of Belgium - was closed to navigation by the Dutch navy, as legally confirmed by the
Treaties of Westphalia in 164818. This placed control of the waterway in the hands of
Dutch shipping and customs dues19, with the implication that key Belgian cities such
as Antwerp lost their trading predominance in favour of the Netherlands. In addition,
many craftsmen emigrated from Belgium to Holland in order to escape absolutist
Spanish rule, taking essential skills and knowledge of techniques with them.
According to Veraghtert (1983), Belgium became a ‘sleeping beauty’ who only
awoke once again at the very end of the eighteenth century, when the Scheldt was reopened in 1794. By contrast, and in part due to the decline in Belgium, the
Netherlands experienced a ‘Golden Age’ in the seventeenth century, although by the
eighteenth century they were clearly experiencing economic problems and being
challenged by the British.20 The deteriorating market experience identified herein for
the Low Countries sits well with this story of changing fortunes in the period.
The third – and rarest – type of market experience that can be identified (market
improvement) is found in France and to some extent Italy. These are the only
European economies examined which show consistent signs of an improvement in
market integration between the start and end of the time period, albeit small. For
France, this is most clear in the price-gaps between many of the French cities (Figure
6 below), the price-gaps between Toulouse and a number of other European cities
18
Van Houtte, 1977, p.204
ibid.
20
Israel, 1989, pp.292-358; See O’Brien, 2000, and Ormrod, 2003, for some possible explanations,
including competition from England, a mounting energy crisis, a fragmented state and fiscal problems
(including rising taxation).
19
17
(although by no means all, see Figure 7 below), and in the rolling coefficients of
variation for Toulouse (depicted in Appendix 1.2). O’Gráda and Chevet (2002) also
find signs of a decline in the coefficient of variation between 1690 and 1790 when
using a comprehensive data set of wheat prices for thirty-five towns and cities.21
However, they argue that the pace of change was slow, not helped by poor transport
infrastructure, geography, a threat of sabotage to grain shipments and the many
weights and measures that were in use in France (O’Gráda and Chevet, 2000).
Figure 6
France: percentage price gaps
0.6
0.5
Paris_Toulouse
0.4
Toulouse-Grenoble
Paris_Grenoble
0.3
Paris_Angers
Toulouse_Angers
0.2
Angers_Grenoble
0.1
1775-99
1750-74
1725-49
1700-24
1675-99
1650-74
1625-49
1600-24
1575-99
1550-74
1525-49
1500-24
0
21
Also see the results in Chevet and St. Amour, 1992. Usher, 1913, provides a map which details grain
movements within France. Grain movements are also discussed in Meuvret, 1977.
18
Figure 7
Integration of Toulouse with Europe: percentage
price difference measure
1.2
1
Toulouse_Bruges
Toulouse_Exeter
0.8
Toulouse_Valencia
Toulouse_Siena
0.6
Toulouse_Wels
Toulouse_Cologne
0.4
Toulouse_Utrecht
Toulouse_Ruremonde
0.2
0
1775-99
1725-49
1675-99
1625-49
1575-99
1525-49
1475-99
1425-49
1375-99
The improvement in French markets may in part be associated with the development
of the French canal network between the early seventeenth century and the early
eighteenth century, which aided the cheaper water transport of goods.22 The network
included the opening of the Midi canal connecting Toulouse to the Mediterranean in
1681, and the Briare canal connecting the Loire with the Seine River in 1642.23 In
addition, France was home to the pro-liberal Physiocratic school, which in the
eighteenth century campaigned for deregulation of the grain trade. There were a
number of moves in this direction, starting with the Edicts of 1763 and 1764, and later
in 1774 under Turgot.24 Such reforms, which included the repeal of tolls levied on
grain, were the result not only of Physiocratic and liberal thinking, but also of
growing contemporary concern for French agricultural production, a desire for fiscal
22
For evidence on declining transport costs in France originating from other sources (such as the
establishment of middlemen), see Letaconnoux, 1908/9.
23
Geiger, 1994
24
Persson, 1999, p.4-6; Miller, 1998
19
reform and growing cities.25 Unfortunately, these moves suffered serious setbacks as a
result of poor harvests – which brought the usual popular unrest and saw a return to
regulative policies - and the outbreak of the French Revolution26.
For Italy, the evidence of improvement is clear in the declining price-gaps between
Pisa and Siena, between Siena and a number of the other European cities and in the
rolling coefficients of variation for Pisa (depicted in Appendix 1.2). This positive
trend may have been further encouraged by the Tuscan reforms of 1767-75 which
permitted international trade in grain in the region without the customary
restrictions.27 The findings for Italy should, however, be subjected to some caution,
as they may not be representative as the country as a whole.
In addition to these three broadly different types of market experience, what is
common to most of the economies considered is some form of market deterioration
around the late sixteenth and early seventeenth centuries, displayed most clearly in the
rising price-gaps and falling speeds of market adjustment experienced by many of the
city-pairs at these times. These dates correspond well with a number of events in
Europe that are likely to have adversely affected markets, including the Wars of
Religion in France (1560-1598), wars in the Spanish Netherlands (1566-1648), the
Thirty Years War in Germany (1618-48) and ‘The Little Ice Age’. Indeed, the finding
provides quantitative support for the idea of a ‘seventeenth century crisis’ in Europe,
as discussed in the seminal historical work of Hobsbawm (1954).28
25
Such measures are discussed in detail in Miller, 1998.
Persson, 1999, p.143; Grab, 1985; Miller, 1987
27
Persson, 1999, p.141; Grab, 1985.
28
Also see the more recent work of Parker and Smith (1997).
26
20
2. The general European market trend
2.1 Econometric analysis
Section 1 outlined the way in which we are able to arrive at market estimates based on
the various cities across Europe for which price data is available. On the basis of these
estimates, three different types of market experience have been suggested at the level
of the individual economies, ranging from deterioration for Belgium to a small
improvement for France. We can form a more general picture of the underlying
market trend in Europe by pooling the individual market estimates of the pricedifference, the C.V. and the speed of adjustment into three respective panel data sets.
It is then possible to perform a separate panel regression on each of these three
separate sets of data in order to arrive at a picture of the general European trend.
These three regression models are as follows:
Price-gapit = a + {10 yearly time dummies} + ui + eit
(regression model 1)
Volatilityit = a + {10 yearly time dummies} + ui + eit
(regression model 2)
Adjustmentit = a + {50 yearly time dummies} + ui + eit
(regression model 3)
For regression models 1 (the price-gap) and 3 (the speed of adjustment), the crosssectional units (i) are represented by the market (or, ‘city’) pairs. The time periods (t)
are respectively the ten and fifty yearly intervals between 1550 and 1800, and ui is the
‘individual effect’ (i.e. the cross-section specific and time-invariant effect). For
regression model 2 (price volatility), the cross-sectional units are the cities and the
time periods (t) are the ten yearly intervals between 1550 and 1800. All estimates
used in the analysis are based on the monthly price data. In order to control for the
effects of the different distances between our city-pairs and the effect that this may
21
have on the underlying level of market integration, the fixed effects panel estimator
has been used. The results of these regressions, presented in Table 2 and discussed in
detail below, can be used to indicate the general course of market integration over the
time period 1550-1800.
Table 2
reg: 1a
reg: 1b
price difference GTS
coef.
t-value coef.
t-value
TIME DUMMIES:
1560-69
-0.072
1570-79
0.005
1580-89
0.020
1590-99
0.167
1600-09
0.009
1610-19
-0.037
1620-29
0.019
1630-39
0.047
1640-49
0.058
1650-59
0.041
1660-69
0.009
1670-79
-0.031
1680-89
-0.096
1690-99
-0.018
1700-09
-0.058
1710-19
-0.033
1720-29
-0.089
1730-39
-0.097
1740-49
-0.069
1750-59
-0.058
1760-69
-0.017
1770-79
0.007
1780-89
-0.005
1790-99
0.024
constant
0.220
obs:
r quared (within):
-2.11
0.14
0.6
5
0.28
-1.12
0.59
1.44
1.76
1.23
0.27
-0.94
-2.95
-0.58
-1.72
-0.99
-2.61
-2.96
-2.12
-1.82
-0.53
0.23
-0.14
0.68
7.93
605
0.2871
-0.080
-3.66
0.159
7.93
-0.044
-2.35
0.039
0.051
0.034
2.09
2.58
1.79
-0.038
-0.102
-0.024
-0.062
-0.037
-0.093
-0.102
-0.074
-0.063
-1.93
-5.8
-1.65
-3.23
-1.94
-4.71
-5.87
-4.28
-3.97
0.226
38.86
605
0.2813
reg: 2a
reg: 2b
coeff of variation GTS
coef.
t-value
coef.
t-value
-0.003
-0.012
-0.061
-0.031
-0.096
-0.098
-0.070
-0.033
-0.017
-0.087
-0.096
-0.067
-0.070
-0.031
-0.015
-0.033
-0.075
-0.110
-0.064
-0.058
-0.113
-0.108
-0.096
-0.066
0.261
-0.08
-0.34
-1.69
-0.84
-2.66
-2.79
-1.94
-0.92
-0.46
-2.4
-2.72
-1.85
-1.97
-0.87
-0.41
-0.95
-2.09
-3.14
-1.83
-1.67
-3.16
-3.06
-2.61
-1.43
9.39
183
0.2727
reg: 3a
adjustment speed
coef.
t-value
TIME DUMMIES:
1600-49
-0.027
1650-99
0.026
1700-49
0.003
1750-99
-0.020
-0.055
-0.057
-2.26
-2.5
-0.044
-0.054
-1.83
-2.36
-0.065
-3.04
-0.069
-0.063
-0.052
-3.02
-2.9
-2.09
0.218
38.5 constant
0.108
183 obs:
0.1805 r quared (within):
-0.86
0.8
0.07
-0.61
4.43
104
0.064
Price Difference
The first set of results in the table, reg 1a, provides the results for the price-gap
regression model (1). Reg 1b presents the outcome of applying a general to specific
(GTS) procedure to this model, eliminating the insignificant dummies and so leading
to a more efficient specification. For each of the ten yearly time periods, we can sum
together the value of the coefficient on the associated time dummy (if it is significant)
and the constant term in order to give a value of the price-gap for that ten yearly
period, and then from this draw out a picture of the general trend of the price-gap in
Europe, 1550-1800. This trend is illustrated in Figure 8 below.
22
Figure 8
Trend of the absolute price difference (based on the fixed
effects regression results)
0.45
0.4
0.35
0.3
0.25
0.2
"trend of the abs price
difference"
0.15
0.1
0.05
15
50
-1
5
15 60
70
15 7 9
90
16 9 9
10
-1
16 9
30
16 3 9
50
-5
16 9
70
16 7 9
90
17 9 9
10
-1
17 9
30
17 3 9
50
17 5 9
70
-7
17 9
90
-9
9
0
According to these results, price-gaps at the end of the eighteenth century were
actually on a par with those in the sixteenth century. In other words, and in contrast to
the established view, there was no overall improvement in European market
integration during the early modern period when considering the price-gap measure.
Only by restricting the study to the years after 1590 (as some studies have done)
would one appear to find evidence of an improvement in markets that took Europe to
new heights of market development by the eighteenth century. Clearly, this finding is
falsified when looking further back in time. It is unfortunate that there is insufficient
monthly data to enable us to go back in time further than 1550. However, this can be
carried out using the alternative annual wheat price data. Using this supplementary
data, and as was discussed in the previous section, we have been able to calculate the
price-gaps for a large number of city-pairs. Figures 9 and 10 below depict these gaps
23
(in absolute and percentage terms) for the pars of cities for which we have the longest
data coverage, those involving Bruges, Exeter, Valencia, Toulouse and Wels.29
Figure 9
Cross-European price differences: 1375-1800
1.4
1.2
Bruges_Exeter
Bruges_Valencia
1
Bruges_Toulouse
Bruges_Wels
0.8
Exeter_Valencia
Exeter_Toulouse
0.6
Exeter_Wels
Valencia_Toulouse
0.4
Valencia_Wels
Toulouse_Wels
0.2
13
75
-9
9
14
25
-4
9
14
75
-9
9
15
25
-4
9
15
75
-9
9
16
25
-4
9
16
75
-9
9
17
25
-4
9
17
75
-9
9
0
Figure 10
Cross-European percentage price differences: 13751800
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
Bruges_Exeter
Bruges_Valencia
Bruges_Toulouse
Bruges_Wels
Exeter_Valencia
Exeter_Toulouse
Exeter_Wels
Valencia_Toulouse
Valencia_Wels
Toulouse_Wels
13
75
-9
9
14
25
-4
9
14
75
-9
9
15
25
-4
9
15
75
-9
9
16
25
-4
9
16
75
-9
9
17
25
-4
9
17
75
-9
9
0
Note: 0.1 = 10% price gap
29
A plot of the actual wheat prices for the five cities can be found in Appendix 1.3, which also clearly
displays the increase in the price gaps.
24
As may be inferred from these figures, there is little evidence of a general fall in
either the absolute or the percentage price gaps between the start and end of the
period. These results help to confirm the finding from the panel analysis, that when
we compare the sixteenth century with the later eighteenth century there are few signs
of an improvement in the level of market integration. The other clear finding is that
there were two periods of market deterioration, when both the absolute and
percentage price gaps rose, the first at the very end of the sixteenth century and the
second in the middle of the seventeenth century. The possible factors involved in this
deterioration were referred to in section 1.3 above.
In simplified terms, the evidence from the price gaps suggests that markets followed
an approximately U-shaped trend between the fifteenth and eighteenth centuries,
deteriorating and then recovering. This represents a marked contrast to the picture of
market improvement which is presented in much of the existing literature.
Coefficient of Variation
The results of estimating the regression model for our second measure of market
integration, the coefficient of variation, are shown in Table 2 above as reg 2a. The
model was as follows:
Volatilityit = a + {10 yearly time dummies} + ui + eit
The general to specific version of the model is presented in the table as reg 2b. As
above, we can use the coefficients from this regression to draw a picture of the
general trend in market integration, this time in terms of the coefficient of variation.
This is depicted in Figure 11 below.
25
Figure 11
Trend of the coefficient of variation (based on the fixed effects
regression results)
0.25
0.2
0.15
trend of the coefficient of
variation
0.1
0.05
15
50
-1
5
15 60
70
15 7 9
90
16 9 9
10
16 1 9
30
16 3 9
50
16 5 9
70
16 7 9
90
17 9 9
10
17 1 9
30
17 3 9
50
17 5 9
70
17 7 9
90
-9
9
0
As with the findings above, these results suggest that no increase in market integration
took place generally in Europe between 1550 and 1800. Rather, the C.V. at the start of
the period was the same as that at the end of the eighteenth century. Appendix 1.2
includes a chart of the coefficient of variation for each city on a rolling five year
basis, from which this can be further verified. Paris, Cologne, Munich, Siena, Vienna,
London, Utrecht and Ruremonde show little change in volatility when comparing the
earlier and later parts of the period, with only a couple of cities (Pisa, Toulouse and
arguably Brussels) showing signs of a possible improvement. Once again, we
therefore find little evidence of a general improvement in European markets between
the start and end of the early modern period.
Adjustment Speed
The final regressions in Table 2 above document the results of the regression model
for the third measure of market integration, the speed of market adjustment:
26
Adjustmentit = a + {50 yearly time dummies} + ui + eit
Reg 3a in the table presents the initial results, whilst reg 3b presents the general to
specific results. Notice that all of the time dummies are insignificant. Once again,
there therefore appears to be no improving trend in this measure of market integration,
providing us with little evidence of the supposed improvement in markets during the
early modern period. The constant term gives a value of the speed of adjustment of
0.104, implying that around 10% of disequilibria are eliminated within a month,
equating to a half life of 6.34 months.
The results of the VECMs used in this regression may also be used to track the trend
in price leadership during the period. As markets become more integrated, one should
see price-leadership being replaced by simultaneous price adjustment (i.e. full price
endogeneity). Table 3 below provides details of the number of market pairs which
displayed a leader-follower relationship in the VECM models relative to the number
which revealed a simultaneous relationship.
Table 3
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
leader-follower simultaneous total pairs
8
9
17
10
8
18
10
11
21
8
10
18
16
14
30
Figure 12 below shows the proportion of pairs that exhibited simultaneous adjustment
(i.e. simultaneous/total pairs), and suggests that there was no disappearance of priceleadership during the period, once again suggesting no improvement in the level of
market integration in the early modern period. Furthermore, the relatively low
proportion of pairs displaying simultaneous adjustment in the 1600-50 period (44%
27
relative to 53% in 1500-1599) is suggestive of a market deterioration at this time, in
line with the other results presented above.
Figure 12
Proportion of price pairs exhibiting simultaneous adjustment
0.6
0.5
0.4
0.3
simultaneous proportion
0.2
0.1
0
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
2.2 Discussion of results
The evidence of the price-gaps, coefficients of variation and speeds of adjustment
suggests that there was no clear improvement in market integration in Europe between
1550 and 1800. Altogether, this demonstrates little support for the established view of
initially undeveloped markets, which gradually became more integrated over time,
supposedly achieving a level of integration in the late eighteenth century previously
unseen (Persson, 1999). Rather, the evidence suggests a level of market integration in
the fifteenth century that was generally on a par with that in the eighteenth century,
with episodes of disintegration in the late sixteenth century and seventeenth century,
suggesting an approximate U-shaped trend. The trend of rising integration that has
been documented in previous studies results purely from restricting the period under
study to the years after 1590 (i.e. the latter half of the U). If one goes back further in
28
time, one finds that this process of integration was merely a recovery to earlier
sixteenth century levels following the market deterioration in the late sixteenth and
seventeenth centuries. It is this identification of disintegration that therefore enables
the current study to reconcile the literature emphasising high levels of integration in
the early years with the literature emphasising a rise in integration between the
seventeenth and end of the eighteenth century.
If correct, the market trend that has been uncovered here is also consistent with a
number of important historical hypotheses: firstly, with the idea of a higher-level
income equilibrium being achieved in the fourteenth century, resulting from such
factors as greater jurisdictional integration, the flourishing of monetary unions and the
unification of measurements30, and secondly, with the idea of a crisis in the
seventeenth century (Hobsbawm, 1954; Parker and Smith, 1997), typically argued to
be associated with factors such as the Thirty Years War and the ‘Little Ice Age’.31 In
fact, the current work implies that this crisis started in the late sixteenth century,
coinciding with the Wars of Religion in France (1560-98) and the wars in the Spanish
Netherlands (1566-1648).
The findings arrived at in this paper may be interpreted in two very different ways.
The first interpretation rests on the the way in which markets appear to have been in
many ways as developed at the start of the early modern era as they were at the end of
the era, just before the take-off to modern economic growth. By itself, this suggests
that markets alone are insufficient for economic growth. This is further supported by
the evidence of Keller and Shiue (2004) which provides a cross-continent analysis of
30
Epstein, in Prak, 2000
Brooks, 1949, identified a period of climatic decline, circa. 1550-1850, termed The Little Ice Age.
For a discussion of these and other factors see Duplessis, 1997
31
29
market integration in China and Europe. They find that markets were at a similar
level of development in Europe and China on the eve of the European industrial
revolution, implying that well developed markets could not have been the factor
enabling Europe to have an industrial revolution and push ahead of other parts of the
world. In addition, the historical studies of markets in England conducted by
Masschaele (1993), Clark (2002) and Galloway (2000) suggest that markets were
relatively well developed in the medieval and early early-modern era, centuries before
the start of modern economic growth.32 Pushing the issue even further, Chang and
Ormrod have each separately and perhaps controversially argued that interventionist
and mercantilist policies beginning in the seventeenth century – rather than a reliance
on the market – were responsible for the economic success of both the early and late
industrialisers (Chang, 2002; Ormrod, 2003). Hence, it would be unsurprising if
relatively well developed markets did not precipitate growth in the earlier centuries.
The alternative interpretation of the results, and one which may be more appealing to
pro-marketeers, arises from juxtaposing the finding of initially well developed
markets with the identification of subsequent market deterioration. Along this vein,
one could argue that had it not been for the factors which caused the market
deterioration in the late sixteenth and seventeenth centuries, the onset of modern
economic growth would have begun much earlier, as far back as the fifteenth or
sixteenth centuries. This interpretation of the results is clearly in many ways as
challenging to our existing historical and economic views as the first.
32
On markets and commercialisation in the medieval period see Britnell (1981), Britnell and Campbell
(1995), Kowaleski (1995), Campbell (2000) and Langdon and Masschaele (2006).
30
3. Conclusion
The aim of this paper has been to track the evolution of markets in Europe in the
centuries preceding the era of modern economic growth. Whilst the established view
suggests a trend improvement in these centuries – argued to be at the root of the
takeoff to modern economic growth – it has been based on a restricted set of countries
and restricted time periods. In stark contrast, this paper has found evidence that
European markets were generally as well developed in the fifteenth and earlier
sixteenth centuries as they were at the end of the eighteenth century, on the eve of the
industrial revolution. In between these times, markets appear to have witnessed a
deterioration followed by recovery. This newly identified trend of market
development, which could broadly be described as U-shaped, is consistent with two
different but equally as challenging conclusions. Either, firstly, that markets are
insufficient for growth, or, secondly that modern economic growth would have started
as far back as the fifteenth century had it not been for the adverse shocks which
destabilised many of the European markets in the sixteenth and seventeenth centuries.
31
APPENDIX 1.1: Monthly Data and Conversion Details
1. Antwerp, Belgium (Verlinden, 1959, monthly prices, pp.504-512)
Unit of account: Brabantse stuivers per viertel.
One viertel = 77 litres, until the 18th century, when it becomes 80 litres.33 Brabantse
stuivers conversion source: according to Keller and Shiue (2004),1 Dutch guilder = 20
Brabantse stuiver.34 Dutch guilder conversion to silver:
http://www2.history.ubc.ca/unger/CURRCONV/GUILDER.XLS
2. Brussels, Belgium (Craeybeckx35 in Verlinden, 1959, monthly prices, pp.481 495)
Unit of account: Brabantse stuivers per setier.
One setier = 48.76 litres.36 Brabantse stuiver conversion source: see Antwerp above.
3. Cologne, Germany (Ebeling and Irzigler, 1976, monthly prices, pp.530-662).
Data for the years 1549-1700 is available on the IIHS website:
http://www.iisg.nl/hpw/poynder-germany.php
Unit of account: Albus per Maltern.
One maltern = 164 litres.37 Albus conversion source (also in Ebeling and Irzigler,
1976): http://www.iisg.nl/hpw/poynder-germany.
4. London, England (Beveridge, 1965, monthly prices, pp.566-568).
Unit of account: Shillings per Winchester Quarter.
One Winchester quarter = 285 litres.38 Shilling/pence conversion source:
http://www2.history.ubc.ca/unger/CURRCONV/English%20pence.xls
5. Munich, Germany (Elsas, 1936, monthly prices, pp.671-674).
Unit of account: Den per Scheffel.
One scheffel = 223 litres.39 Den conversion source:
http://www2.history.ubc.ca/unger/CURRCONV/Munich%20denar.xls
6. Paris, France (Baulant and Meuvret, 1960, monthly prices, pp.154 onwards)
Data for the years 1548-1698 is from the iihs website:
http://www.iisg.nl/hpw/poynder-france.php
Unit of account: Livre Tournois per Setier.
One setier = 156 litres.40 Livre Tournois conversion source:
http://www2.history.ubc.ca/unger/CURRCONV/livre%20tournois.xls
7. Pisa, Italy (Malanima; data for the years 1548-1818 from the iihs website:
http://www.iisg.nl/hpw/malanima.php)
Unit of account: lire per sacco.
One sacco = 72kg.41 Lire conversion source: http://www.iisg.nl/hpw/malanima.php
33
Verlinden, 1959, p.6
Also see: http://www.pierre-marteau.com/currency/converter/fla-hol.html
35
‘De prijzen van graan en van brood te Brussel’
36
Verlinden, 1959, p.6, Keller and Shiue, 2004.
37
http://www2.history.ubc.ca/unger/WHEAT/CologneW.xls
38
http://www2.history.ubc.ca/unger/WHEAT/LondonW.xls
39
Keller and Shiue, 2004
40
http://www2.history.ubc.ca/unger/WHEAT/ParisW.xls. Also see Keller and Shiue, 2004
41
http://www.iisg.nl/hpw/malanima.php. The kg to litre conversion is 0.772 kg per litre (from
http://gpih.ucdavis.edu/files/Weight_vs_volume.xls)
34
32
8. Ruremonde, Netherlands (Ruwet, 1966)
Unit of account: Ruremonde stuivers per Malder.
One malder = 170 litres.42 Ruremonde stuivers conversion source: 1 Ruremonde
stuiver = 74.988% of a Brabantse stuiver. From this, and using the fact that 20
Brabantse stuivers = 1 Dutch guilder,43 we can then use the guilder conversion rate:
http://www2.history.ubc.ca/unger/CURRCONV/GUILDER.XLS
9. Siena, Italy (Parenti, 1942, monthly prices in the appendix).
Unit of account: Soldo per staio.
One staio = 22.8litres.44 Soldo conversion source:
http://www2.history.ubc.ca/unger/WHEAT/SienaW.xls
10. Toulouse, France (Freche, 1967, monthly prices, pp.38-74).
This source uses different units of account in different periods. This study uses the
data from the period 1563-1800.
Unit of account: Livre Tournois per Setier. One setier = 93.2litres.45
Livre Tournois conversion source:
http://www2.history.ubc.ca/unger/CURRCONV/livre%20tournois.xls
11. Utrecht, Netherlands (Sillem, 1901, monthly data, tables III-VI; Posthumus,
1964, monthly data, p.420)
Unit of account: Dutch stuivers and guilders per modius.
One modius = 120 litres.46 Dutch guilder conversion source (20 stuivers in a guilder):
http://www2.history.ubc.ca/unger/CURRCONV/GUILDER.XLS
12. Vienna, Austria (Pribram, 1938, monthly data, pp.386-388)
Unit of account: Kreuzern per wiener metzen. For June 1752 onwards, the physical
unit becomes the land-metzen.
For metzen conversion rates, see Keller and Shiue (2003).47 Kreuzern conversions
source: http://www2.history.ubc.ca/unger/CURRCONV/KREUZER.XLS
Notes
All prices are market prices, except for those from London which are from a navy
victualling source. Occasional missing observations were replaced by interpolated
values. There are a number of different methods for dealing with missing
observations, discussed in Allison (2002), and Rubin and Little (2002). Listwise
deletion (which ignores rather than imputes the missing observations) is one of the
simplest and most risk-free methods of dealing with the problem, as whilst it may
discard some of data on either side of the missing observation in some statistical
exercises, it does not introduce a bias, unlike other conventional methods (Allison,
42
Keller and Shiue, 2004
Keller and Shiue, 2004; http://www.pierre-marteau.com/currency/converter/fla-hol.html
44
Keller and Shiue, 2004
45
http://www2.history.ubc.ca/unger/WHEAT/ToulouseW.xls
46
Posthumus, 1964, vol.2, p.34
47
pp.29-30
43
33
2002, pp.84-5). However, for the estimation of the speeds of adjustment, the standard
software does not allow gaps in the data, and so occasional missing values had to be
imputed by some means. The method selected for the monthly data was a simple
linear interpolation, as this fits well with the theoretical model of Samuelson,
discussed in Persson (1999, p.67) on the way in which prices show a gradual trend
upward at an approximately constant rate for the twelve months after the harvest.
Clearly, for the annual data this isn’t a particularly precise or accurate method, and so
the method of imputing missing values for the annual series was to use surrogate data.
This involves establishing a relationship between the wheat price data and the price
data of another cereal (e.g. rye or oats), and then using the statistically estimated
relationship to fill in the missing data. Denis Leung argues in favour of this approach
and it has been used, amongst others, in the work of Jacks (2005). The surrogate data
used for the various annual wheat price series are as follows: Exeter – London wheat
prices; Vienna – Vienna rye prices; Wels – Wels oat prices; Valencia – Madrid wheat
prices; Leipzig St.J – Leipzig Gr. wheat prices (all available in the Allen-Unger grain
price data set). For other annual series there were either no gaps in the data (e.g. Siena
and Toulouse) or the r-squared between the possible surrogate and actual data series
was judged as too low to enable an accurate imputation (i.e. Bruges, Grenoble,
Munich, Wurzburg, Leiden).
34
B
time
1350-74
1375-99
1400-24
1425-49
1450-74
1475-99
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Bruges-Valencia 1450-74
1475-99
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Bruges-Siena
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Bruges-Toulouse 1500-24
1525-49
A
pair
Bruges-Exeter
0.0776649
0.0917832
0.1701139
0.1067457
0.1584962
0.0539205
0.1597877
0.2925642
0.870878
0.5034613
0.8023571
0.5656746
0.6915522
0.5993898
0.5004211
0.4733735
0.4070585
0.1006837
0.13658
0.0915952
0.1035525
0.113474
0.4448839
0.1798924
0.309403
0.2785336
0.463225
0.3437183
0.2656314
0.160752
0.2127884
0.2352365
0.6492881
0.3456355
0.7138142
0.7249204
0.8012299
0.687174
0.5192191
0.5399693
0.0643758
0.1219883
C
abs.p-gap
0.2891399
0.3095809
0.5210741
0.3834501
0.5054402
0.2263133
0.6066524
0.6402085
0.9099845
0.5653933
0.6920858
0.5181736
0.6609026
0.58508
0.5873258
0.4879176
0.3744244
0.2900965
0.4234089
0.2887758
0.2544634
0.1659318
0.3079817
0.1566234
0.2140971
0.2002838
0.3701179
0.2714052
0.2490408
0.1412168
0.1788958
0.3469375
0.5738324
0.3556486
0.64784
0.7296098
0.8460392
0.7426338
0.6251241
0.6350518
0.2999458
0.4037211
D
E
abs.%p-gap obs
25
25
25
25
25
25
25
25
20
23
25
23
25
25
25
25
17
25
24
25
25
25
21
24
25
24
25
25
25
25
15
25
21
24
25
24
25
25
25
16
25
25
A
B
pair
time
Bruges-Toulouse (cont.)
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Bruges-Wels
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Bruges-Cologne
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Bruges-Utrecht
1550-74
1575-99
1600-24
1625-49
1750-74
1775-99
Bruges-Ruremonde 1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
0.205961
0.712383
0.5772039
1.002886
0.8370247
0.8298277
0.7003418
0.6603258
0.6028108
0.5032273
0.0911174
0.1754529
0.3132382
1.012297
0.6434772
1.161479
1.053339
0.9924447
0.8431598
0.6680353
0.8450679
0.2751735
0.9796709
0.7528915
1.048338
0.9961897
0.9112567
0.8102427
0.7271813
0.8120532
0.0692362
0.7066953
0.4659497
0.718276
0.8653939
0.8786511
0.8142904
0.8122859
0.768756
0.651906
0.6913053
0.6735848
0.4136908
0.6502067
0.705178
1.060358
0.9212685
0.8831725
0.7950576
0.8918619
0.6874881
0.4965805
0.4676529
0.6556323
0.7470548
1.224028
0.8479342
1.334299
1.3906
1.222642
1.047788
0.9162985
1.213188
0.5615206
1.092424
1.053858
1.083412
1.224716
1.03933
0.9607058
1.03991
1.07123
0.114894
0.6412851
0.5140069
0.6326073
1.211
1.111101
0.8537042
0.8588268
0.882195
0.8700882
0.8263828
0.7392715
C
D
E
abs.p-gap abs.%p-gap obs
25
21
24
25
24
25
25
25
25
18
23
24
25
20 Exeter-Siena
23
25
24
25
25
25
20
25
21 Exeter-Toulouse
24
25
24
25
25
25
25
25
22
24
18
21 Exeter-Wels
17
24
25
25
25
25
18
A
pair
Exeter-Valencia
B
time
1450-74
1475-99
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
C
D
E
abs.p-gap abs.%p-gap obs
0.1399499
0.4955369
25
0.1646433
0.6213425
24
0.1251012
0.4265324
25
0.2232649
0.7590256
25
0.320898
0.6956685
25
0.5861321
0.7626501
25
0.4899949
0.5771464
25
0.5480686
0.5200342
25
0.3604547
0.3735516
25
0.2502921
0.3106079
25
0.2858591
0.3541107
25
0.296631
0.4006769
25
0.3782502
0.4041181
25
0.2924778
0.2843191
15
0.2863472
0.5865117
25
0.248011
0.3837194
25
0.236047
0.3246306
25
0.2471225
0.3082942
25
0.2312014
0.3195391
25
0.1832089
0.2745831
25
0.1394308
0.2272031
25
0.140394
0.232176
25
0.1487708
0.2295438
16
0.0524148
0.2535131
25
0.091374
0.3823195
25
0.1427193
0.3310754
25
0.2376419
0.3652457
25
0.1573584
0.2662826
25
0.2991544
0.4592614
25
0.3215938
0.4761202
25
0.1758301
0.2724181
25
0.1607235
0.2899247
25
0.1809987
0.3355896
25
0.1377655
0.2133166
25
0.148884
0.1757615
18
0.0712027
0.384658
23
0.0616153
0.3325308
24
0.1751889
0.5237767
25
0.1568043
0.3285161
24
0.1872706
0.3368089
24
0.3735473
0.6482952
25
0.4647203
0.8529872
25
0.3200666
0.5917623
25
APPENDIX 1.2: Market estimates
(a) Price gaps (based on annual grain data)
The tables below provide the mean absolute price-gap and mean absolute
percentage price gap for each of the city pairs discussed in the text.
35
36
A
B
pair
time
Exeter-Wels (cont.)
1700-24
1725-49
1750-74
Exter-Cologne
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Exeter-Utrecht
1550-74
1575-99
1600-24
1625-49
1750-74
1775-99
Exeter-Ruremonde1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Valencia-Siena 1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Valencia-Toulouse1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
0.2475925
0.2114525
0.3570027
0.1010692
0.1141712
0.2433003
0.2773966
0.40296
0.2308235
0.2054143
0.2315445
0.3314291
0.241274
0.1869428
0.0905757
0.1208809
0.4008663
0.4735342
0.216384
0.1706564
0.1723059
0.1608052
0.2153697
0.2722875
0.2415835
0.3731396
0.3205159
0.4757887
0.4928612
0.3488483
0.3858547
0.3103246
0.3712662
0.1313342
0.1692132
0.2403082
0.4031571
0.5614896
0.7630233
0.6011695
0.389178
0.3799071
C
abs.p-gap
0.465703
0.4067619
0.6896086
0.3054277
0.2018242
0.4691615
0.4215059
0.681407
0.3962563
0.3634969
0.4582171
0.5728783
0.550005
0.3176375
0.1384073
0.1522419
0.7366006
0.7410857
0.3101057
0.2658856
0.2994413
0.2976032
0.3358392
0.3718879
0.357862
0.4137759
0.3396411
0.483566
0.5696768
0.4887009
0.5199738
0.4316559
0.4765377
0.4946785
0.521399
0.4719861
0.4662606
0.704101
0.8943881
0.7547646
0.5382375
0.5491609
D
E
abs.%p-gap obs
25
25
20
25
25
25
25
25
25
25
25
25
25
25
25
18
21
17
25
25
25
25
25
18
25
25
25
25
25
25
25
25
16
25
25
25
25
25
25
25
25
25
A
B
pair
time
Valencia-Toulouse (cont.)
1725-49
1750-74
1775-99
Valencia-Wels
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Valencia-Cologne
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Valencia-Utrecht
1550-74
1575-99
1600-24
1625-49
1750-74
1775-99
Valencia-Ruremonde1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Siena-Toulouse
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
0.4514312
0.4924641
0.3919556
0.1814858
0.247958
0.3351285
0.7200069
0.6356625
0.9216159
0.8145189
0.540063
0.5227251
0.4591407
0.7247832
0.3031057
0.6757017
0.7332717
0.8084754
0.7527585
0.4588751
0.489808
0.5182867
0.7017065
0.1433732
0.4384208
0.4405602
0.4836682
0.7648135
0.7239576
0.5661826
0.3733425
0.4483214
0.4430115
0.5809586
0.5342402
0.302892
0.1946025
0.2866761
0.311684
0.1307391
0.1210765
0.1227883
0.6983937
0.5835448
0.4126963
0.7532203
0.8405924
0.7863643
1.035069
0.8520376
1.168329
1.215749
0.8653034
0.8018914
0.7228303
1.097427
0.6096797
0.9194227
1.046256
0.9174429
1.044169
0.6819913
0.714809
0.846442
0.9672873
0.2255814
0.4929679
0.4978229
0.4616317
1.114445
0.9872332
0.6728672
0.5158052
0.6362983
0.6766199
0.7224396
0.6305146
0.5290972
0.2508907
0.4203375
0.4355158
0.2179884
0.1870378
0.2064656
C
D
E
abs.p-gap abs.%p-gap obs
25
25
15
23
24
25
24
24
25
25
25
25
25
20
25
25
25
25
25
25
25
25
25
25
25
25
18
21
15
25
25
25
25
25
15
25
25
25
25
25
25
25
A
B
pair
time
Siena-Toulouse (cont.)
1725-49
1750-74
Siena-Wels
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Siena-Cologne
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Siena-Utrecht
1550-74
1575-99
1600-24
1625-49
1750-74
Siena-Ruremonde 1650-74
1675-99
1700-24
1725-49
1750-74
Toulouse-Wels
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Toulouse-Cologne 1550-74
0.1559685
0.1032243
0.3345115
0.3640002
0.3496631
0.4497517
0.3216577
0.2002439
0.1733029
0.1724244
0.2949906
0.2881957
0.3351701
0.4368861
0.3576484
0.2650984
0.1470591
0.1247231
0.2165262
0.2342552
0.2392555
0.2036654
0.1915232
0.18048
0.2648348
0.1462984
0.1332029
0.0890084
0.1609814
0.1852032
0.0656243
0.1064134
0.1957434
0.3259017
0.1688002
0.2102431
0.229199
0.1780715
0.1739348
0.1439695
0.2286949
0.1253005
0.2937563
0.1700349
0.7350546
0.6329079
0.5463306
0.6914021
0.646072
0.3938255
0.3349165
0.3403688
0.6061312
0.5407232
0.5340171
0.7313144
0.4823928
0.4855237
0.2492809
0.2224112
0.4357494
0.4403779
0.3546856
0.2677622
0.2460992
0.2369392
0.586362
0.2209278
0.1987092
0.1559005
0.3043509
0.3181621
0.3338212
0.4199078
0.4981319
0.5825276
0.3133137
0.3794589
0.4909076
0.3680946
0.3314911
0.314018
0.4813643
0.2626261
C
D
E
abs.p-gap abs.%p-gap obs
25
16
25
24
24
25
25
25
25
25
16
25
25
25
25
25
25
25
25
16
25
25
25
18
12
25
25
25
25
16
23
24
25
24
24
25
25
25
25
25
20
25
37
A
B
pair
time
Toulouse-Cologne (cont.)
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Toulouse-Utrecht 1550-74
1575-99
1600-24
1625-49
1750-74
1775-99
Toulouse-Ruremonde
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Wels-Cologne
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Wels-Utrecht
1550-74
1575-99
1600-24
1625-49
1750-74
Wels-Ruremonde 1650-74
1675-99
1700-24
1725-49
1750-74
Cologne-Utrecht 1550-74
1575-99
1600-24
0.2856085
0.1830148
0.2079747
0.1690928
0.1009527
0.1306564
0.1021292
0.2179358
0.1556343
0.138668
0.1977541
0.2463819
0.2550904
0.3525984
0.1528611
0.0897371
0.1053758
0.0904833
0.1175706
0.1779448
0.1141489
0.0925486
0.0980087
0.1292492
0.0995847
0.1055133
0.1103276
0.1011791
0.0845353
0.2767405
0.2966129
0.2066895
0.376478
0.0421933
0.2483363
0.1895391
0.1239406
0.1033065
0.15649
0.2279819
0.2452541
0.2927115
C
abs.p-gap
0.4719435
0.3676578
0.3399588
0.3275069
0.192144
0.2215178
0.229799
0.4019227
0.3298099
0.1886955
0.3247531
0.3885272
0.5104074
0.5963443
0.2453582
0.1494745
0.1801319
0.186773
0.1956682
0.2502391
0.3393442
0.2161241
0.2085418
0.2828612
0.2595562
0.2263851
0.2410627
0.2363529
0.2217649
0.6758127
0.5651465
0.3655733
0.6323937
0.1230273
0.5428816
0.3793697
0.260625
0.2292867
0.3641894
0.4721974
0.4356871
0.5484333
D
E
abs.%p-gap obs
25
25
25
25
25
25
25
25
25
25
25
18
21
18
25
25
25
25
25
18
25
24
24
25
25
25
25
25
20
25
24
24
18
16
25
25
25
25
20
25
25
25
A
B
C
D
E
A
B
abs.p-gap abs.%p-gap obs
pair
time
pair
time
Cologne-Utrect (cont.)
Bruges-Antwerp (cont.)
1625-49
0.2584568
0.3785732
18
1625-49
1750-74
0.0770438
0.1978558
21
1650-74
Cologne-Ruremonde 1650-74
0.186576
0.3713014
25
1700-24
1675-99
0.0989709
0.1805028
25
1725-49
1700-24
0.0462979
0.0889482
25 Brussels-Antwerp 1600-24
1725-49
0.0752752
0.169822
25
1625-49
1750-74
0.1207479
0.2448477
25
1650-74
DOMESTIC ('within-country') PAIRS
1775-99
Lier-Bruges
1425-49
Austria
1450-74
Vienna-Wels
1525-49
0.0445996
0.2149353
17
1475-99
1550-74
0.064845
0.2049207
22
1500-24
1575-99
0.0889412
0.2384175
22
1525-49
1600-24
0.1465958
0.2252803
24
1550-74
1625-49
0.1753058
0.4142494
22
1575-99
1650-74
0.076398
0.2024196
22 Italy
1675-99
0.120808
0.2796411
24 Pisa-Siena
1550-74
1700-24
0.0763243
0.1798612
25
1575-99
1725-49
0.0812409
0.1890447
25
1600-24
1750-74
0.0506796
0.1360395
20
1625-49
Spain
1650-74
Valencia-Madrid
1500-24
0.1163175
0.4032255
23
1675-99
1525-49
0.1011018
0.2737405
25
1700-24
1550-74
0.1816905
0.3194813
19
1725-49
1575-99
0.3297394
0.3491157
22
1750-74
1600-24
0.282082
0.2563665
24 France
1625-49
0.3457991
0.3068729
23 Paris-Toulouse
1550-74
1650-74
0.6573507
0.5912186
25
1575-99
1675-99
0.3352868
0.4697378
24
1600-24
1700-24
0.450093
0.6576563
23
1625-49
1725-49
0.4515962
0.6950577
23
1650-74
1750-74
0.4419926
0.5121593
24
1675-99
1775-99
0.2634106
0.2831459
10 Toulouse-Grenoble 1500-24
1525-49
Belgium
Bruges-Brussels
1575-99
0.6000197
0.5227978
1550-74
1600-24
0.4886417
0.5522675
24
1575-99
1625-49
0.6321021
0.5053355
25
1600-24
1650-74
0.6387652
0.6045788
24
1625-49
1675-99
0.5954736
0.6028582
21
1650-74
1725-49
0.5429049
0.680077
21
1675-99
1775-99
0.5910332
0.606047
18
1700-24
Bruges-Antwerp
1600-24
0.3853448
0.4280124
12
1725-49
0.4708417
0.6660952
0.6889616
0.6849361
0.1461506
0.0306222
0.0623556
0.0791826
0.243743
0.3058225
0.2340264
0.134016
0.1568898
0.3410835
0.5793752
0.3087194
0.1840448
0.1258136
0.0994058
0.0908983
0.080113
0.0989898
0.1449408
0.1574705
0.3621435
0.3902917
0.3349666
0.5411952
0.4301038
0.3825095
0.4256394
0.3561643
0.3025085
0.3295156
0.2948249
0.3444664
0.2134122
0.1542976
0.2724101
0.1830738
0.5844638
0.806431
0.9737608
0.6446654
0.0988917
0.0283135
0.0553952
0.0537666
0.0943981
0.0879653
0.0852928
0.0346959
0.053849
0.1887438
0.6509817
0.1890633
0.1331497
0.102054
0.0810446
0.0623036
0.0504703
0.0598124
0.086672
0.1030103
0.1892097
0.4117987
0.2139968
0.3828343
0.3213585
0.2757922
0.1163307
0.117917
0.1455029
0.3169292
0.1781209
0.2091532
0.1295743
0.0875421
0.13626
0.0864826
C
D
E
abs.p-gap abs.%p-gap obs
23
25
25
23
23
23
24
25
24
25
24
24
24
25
25
23
25
25
25
25
25
25
25
25
16
7
5
6
19
12
7
6
18
23
22
24
25
25
25
20
38
A
B
pair
time
Toulouse-Grenoble (cont.)
1750-74
1775-99
Paris-Grenoble 1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
Paris-Angers
1575-99
1600-24
1625-49
1650-74
1675-99
Toulouse-Angers 1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
Angers-Grenoble 1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Germany
Munich-Leipzig 1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Munich-Wurzburg 1500-24
1525-49
0.1608984
0.1160524
0.1747431
0.2741779
0.1505031
0.2834836
0.2293416
0.3670219
0.331341
0.3252134
0.5152816
0.4187508
0.5132344
0.2890857
0.2169187
0.2370631
0.2574389
0.200419
0.3463227
0.2054074
0.2218067
0.373399
0.2593767
0.2942975
0.2739691
0.2597958
0.2958922
0.200566
0.2076067
0.1827045
0.3677759
0.4310287
0.5033294
0.2800517
0.3631698
0.2391796
0.2561096
0.3998619
0.2450317
0.2465639
0.2447549
0.1876245
0.2708662
0.3243686
0.1143747
0.1460594
0.1267242
0.1320628
0.2568786
0.0980091
0.0434435
0.0580844
D
E
abs.%p-gap obs
0.09559
0.0847663
0.1161417
0.3226027
0.1144839
0.2483703
0.1985335
0.27039
0.4017583
0.2118755
0.3981398
0.3052049
0.343024
0.2481015
0.1210641
0.1515118
0.1647647
0.1079976
0.1740846
0.0932443
0.1219867
0.3914936
0.1613154
0.1914305
0.1813875
0.1427634
0.1390826
0.0866558
0.1056873
0.1031999
C
abs.p-gap
18
20
23
12
21
22
16
13
12
7
6
25
6
22
25
24
22
22
23
20
25
23
23
23
20
25
25
25
25
25
25
25
20
24
24
24
25
25
23
25
6
A
B
pair
time
Munich-Wurzburg (cont.)
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Leipzig-Wurzburg
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Holland
Utrecht-Ruremonde 1750-74
1775-99
Utrecht-Leiden
1550-74
1575-99
1600-24
1625-49
1750-74
1775-99
Ruremonde-Leiden 1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Amsterdam-Ruremonde
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Amsterdam-Utrecht 1625-49
1750-74
0.4191015
0.3708225
0.1514996
0.1081239
0.1571266
0.1546773
0.1445438
0.1271238
0.0929504
0.5078181
0.3945758
0.3658823
0.254135
0.081087
0.0811139
0.0710307
0.7863271
0.72704
0.289859
0.1659612
0.2627734
0.1783221
0.1992906
0.1612629
0.0526501
0.0507682
0.0594884
0.5352183
0.4678105
0.2265103
0.0965443
0.1725579
0.340195
0.2754742
0.0907498
0.0511666
0.0901391
0.0785251
0.0821586
0.0841269
0.0730611
0.243248
0.4128819
0.2977382
0.3756715
0.4036822
0.4366986
0.1962101
0.20608
0.1798741
0.2388472
0.192034
0.210335
0.3125196
0.3119652
0.269211
0.3196737
0.1812962
0.274079
0.2990206
0.4566702
0.2522546
0.1608918
0.2659374
0.2707775
0.1490187
0.0861913
0.1242261
0.1032766
0.1466859
0.0921553
0.0897717
0.2185176
0.1978437
0.091241
0.1302878
0.0813425
0.1326586
0.1667963
0.2318911
C
D
E
abs.p-gap abs.%p-gap obs
6
12
11
18
24
18
7
21
8
21
17
24
24
23
28
8
15
22
13
20
9
17
16
17
12
17
14
13
12
12
24
21
15
24
19
15
19
24
24
A
B
pair
time
Amsterdam-Utrecht (cont.)
1775-99
Amsterdam-Leiden 1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
England
Exeter-London
1350-74
1375-99
1400-24
1425-49
1450-74
1475-99
1500-24
1525-49
1550-74
1575-99
1600-24
1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
Exeter-Chester
1375-99
1400-24
1450-74
1475-99
Chester-London
1375-99
1400-24
1450-74
1475-99
Exeter-Winchester 1625-49
1650-74
1675-99
1700-24
1725-49
1750-74
1775-99
0.49448
0.1929738
0.3046553
0.1375081
0.2174288
0.2823547
0.238528
0.1616861
0.1325754
0.1761965
0.1527988
0.1849771
0.1841536
0.2303087
0.1720747
0.3148284
0.1571692
0.1120624
0.1596257
0.1119119
0.1641085
0.1262306
0.0906749
0.0713793
0.0621894
0.2501198
0.2361237
0.2359252
0.2766341
0.2320933
0.3087029
0.4191756
0.3279854
0.1864926
0.2205726
0.1404508
0.1252901
0.0906969
0.0713793
0.0621894
0.2842932
0.153172
0.2238856
0.0686147
0.1645703
0.246212
0.1882151
0.0573239
0.0318708
0.0452931
0.0354894
0.0367781
0.0352206
0.046766
0.0343155
0.10234
0.0846825
0.0780285
0.1346252
0.090161
0.1288072
0.0875592
0.0507715
0.0543787
0.0709142
0.0663483
0.0687015
0.0557406
0.059969
0.0648399
0.0848628
0.0867617
0.0662078
0.1395116
0.1524229
0.1043409
0.0868495
0.0507794
0.0543787
0.0709142
C
D
E
abs.p-gap abs.%p-gap obs
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
19
25
12
24
19
25
12
24
20
25
25
25
25
25
25
8
17
13
4
7
9
39
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1684
1687
1690
1693
1696
1699
1702
1705
1708
1711
1714
1717
London: 5 year rolling coefficient of variation
1720
1723
1726
1729
1732
1735
1738
1741
1744
1747
1750
1753
1756
1759
1762
1765
1768
1771
1774
1777
1780
1783
1786
1789
1792
1795
London
(b) Coefficients of variation (computed on a rolling five-year basis)
Utrecht
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Utrecht: 5 year rolling coefficient of variation
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
1740
1735
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
1550
40
Ruremonde
1795
1790
1785
1780
1775
1770
Ruremonde: 5 year rolling coefficient of variation
1765
1760
1755
1750
1745
1740
1735
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1650
41
Brussels
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
1740
1735
Brussels: 5 year rolling coefficient of variation
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1550
42
Cologne
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
1740
Cologne: 5 year rolling coefficient of variation
1735
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1550
43
Antwerp
1793
1788
1783
1778
1773
1768
1763
1758
1753
Antwerp: 5 year rolling coefficient of variation
1748
1743
1738
1733
1728
1723
1718
1713
1708
1703
1698
1693
1688
1683
1678
1673
1668
1663
1658
1653
1648
1643
1638
1633
1628
1623
1618
1613
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1608
44
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Munich: 5 year rolling coefficient of variation
1770
1765
1760
1755
1750
1745
1740
1735
1730
1725
1720
1715
1710
1705
1700
1695
1690
45
Munich
Pisa
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
1740
1735
1730
Pisa: 5 year rolling coefficient of variation
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1550
46
Siena
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
1740
1735
Siena: 5 year rolling coefficient of variation
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1550
47
Vienna
1792
1787
1782
1777
1772
Vienna: 5 year rolling coefficient of variation
1767
1762
1757
1752
1747
1742
1737
1732
1727
1722
1717
1712
1707
1702
1697
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1692
48
Toulouse
1795
1790
1785
1780
1775
1770
1765
1760
1755
1750
1745
Toulouse: 5 year rolling coefficient of variation
1740
1735
1730
1725
1720
1715
1710
1705
1700
1695
1690
1685
1680
1675
1670
1665
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1550
49
Paris
1695
1690
1685
1680
1675
1670
1665
Paris: 5 year rolling coefficient of variation
1660
1655
1650
1645
1640
1635
1630
1625
1620
1615
1610
1605
1600
1595
1590
1585
1580
1575
1570
1565
1560
1555
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1550
50
(c) Speeds of adjustment (‘alphas’)
The table below provides the estimates of the speeds of adjustment for each
pairwise combination of cities in the monthly data set. As discussed in the text,
these values are arrived at using VECMs. The table provides the following:
Column A = city pair
Column B = time period
Column C = other time period used (if there are a limited no. of observations)
Column D = rank (which is 0 if there is no cointegration)
Column E = the no. of lagged terms in the VECMs
Column F and I = the two alpha (i.e. speed of adjustment) estimates from the
VECMs
Column G and J = the t-values of the alpha estimates
Column H and K = the standard errors of the alpha estimates
Column L = total alpha value for the city pair (see the notes below)
Column M = indicates (with ‘yes’) which pairs display weak exogeneity (on
the basis of the t-values of the alpha estimates)
Notes
Alpha values that are not reported are due to either (i) autocorrelation in the
VECM that couldn't be eliminated (and, as a result, the alpha values could not
be trusted), (ii) specification problems with the cointegration equation, or (iii)
insufficient observations. The total alpha value is the sum of the two separate
alpha values from the pairwise VECMs, unless the pair display weak
exogeneity, when a single alpha value is used (see the discussion of this in the
text on pp.25-7). In addition, the alpha value is reported as a zero if the two
price series are not cointegrated (i.e. the markets are not cointegrated).
The VECM results reported here were checked against equivalent results from
a VECM specification that also included monthly dummy variables. For the
vast majority of cases, there was little significant difference in the results.
However, for the Utrecht-Brussels pair (in the period 1600-1649) the VECM
that also included monthly dummy variables proved to be superior, and so was
used to arrive at this estimate.
51
52
B
Time
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-COL
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-PISA
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-VIE
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
A
City-Pair
TOUL-PAR
Nov.1683-Mar. 1693
May 1704-July1724
Apr. 1764-Feb. 1783
to Mar. 1682 only
Aug. 1728 onwards
Jul. 1772-Dec.1789
to end of 1789 only
Sep. 1568-end of 1590
0
2
2
0
2
1
2
2
2
2
2
1692 onwards
to end of 1789 only
to Nov. 1777 only
April1563 onwards
2
2
2
2
0
2
2
2
2
2
16 -0.028749
1 -0.085101
-0.004414
-0.052572
-0.107612
-0.059064
-0.040409
-0.024418
-0.042088
-0.86
-3.14
-1.37
-3.76
-3.81
-4.38
-3.19
-1.38
-1.85
-2.95
-1.87
24 -0.062842
27 -0.056356
18
2
14
13
14
6
2
-0.87
-5.53
-4.86
-0.47
-4.86
-4.34
14 -0.010032
12 -0.13894
3 -0.059657
10 -0.005692
3 -0.055059
10 -0.068804
-0.004517
-0.003077
-0.047904
0.004246
0.033812
0.083075
0.058996
0.0334 0.100461
0.0271 0.036336
0.0032
0.0140
0.0282
0.0135
0.0127
0.0177
0.0228
0.0213 0.029413
0.0301 0.101266
0.0115 0.025308
0.0251 -0.027681
0.0123 0.01026
2.96
1.47
-3.11
-0.5
-0.81
0.39
2.61
3.28
3.53
2.69
3.59
3.41
-1.72
1.02
4.38
-1.4
0.63
0.074221
0.083075 yes
0.101084
0.004517 yes
0.052572 yes
0.0339 0.100461 yes
0.0247 0.085101 yes
0.0015
0.0062
0.0591
0.0109
0.0130
0.0253
0.0167
0.0109 0.092254
0.0282 0.157623
0.0074
0 yes
0.0161 0.166621
0.0101 0.059657 yes
0.0037 0.016122 yes
0.0049
0.0094 0.068804 yes
I
J
K
L
M
alpha 2
t-stat
(st. error) total alpha weak exog
0.199263
3.84
0.0519 0.239845
0.04884
3.43
0.0142 0.093482
0.164897
3.27
0.0504 0.227598
0.0121 0.016122
0.0113 -0.006886
0.0159 0.005942
D
E
F
G
H
rank VECM lagsalpha 1
t-stat
(st. error)
2
4 -0.040581
-2.34
0.0173
2
13 -0.044642
-3.08
0.0145
2
15 -0.062701
-2.03
0.0309
April 1563 onwards
C
Other Time
April 1563 onwards
to Aug 1642 only
Aug 1663-Aug 1698
53
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
COL-PISA
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
COL-VIE
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
COL-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
COL-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PISA-SIENNA 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PISA-VIE
1550-1599
1600-1649
COL-MUN
Nov. 1683-Mar. 1693
May 1704-Jul. 1724
Apr. 1764-Nov. 1777
May 1555 onwards
to Mar. 1682 only
Aug 1728 onwards
Sep. 1568-Jan. 1591
Aug. 1690 onwards
1
2
2
2
2
2
2
2
2
14
13
39
27
0
2
2
2
2
2
2
4 -0.160185
15 0.003661
2 -0.088184
-0.058414
36 0.001109
28 -0.14924
16 -0.103953
14 -0.207194
-2.62
0.1
-2.83
-2.57
0.14
-3.28
-3.69
-1.53
-3.94
0.07
-2.72
-2.47
-2.32
-1.96
16 -0.038568
-0.081576
0.000556
-0.04943
-0.022797
-0.062361
-3.94
-0.86
14 -0.047849
18 -0.00187
2
2
2
2
2
0
2
0.0611
0.0366
0.0312
0.0227
0.0079
0.0455
0.0282
0.1354
0.0207
0.0079
0.0182
0.0092
0.0269
0.138302
0.249608
0.05569
0.071016
0.031358
0.099978
0.004543
0.160386
-0.010897
-0.104217
0.090819
-0.031215
-0.133708
0.0197 0.103093
0.0121 0.030559
0.0022 0.017424
1.35
2.87
1.3
2.68
3.33
2.03
0.12
0.85
-0.74
-2.74
2.74
-1.97
-2.3
3.33
1.12
4.31
0.1024
0.0870
0.0428
0.0265
0.0094
0.0493
0.0379
0.1887
0.160185
0.249608
0.088184
0.12943
0.031358
0.249218
0.103953
0
yes
yes
yes
yes
yes
0.0147 0.081576 yes
0.0380 0.104217 yes
0.0331 0.140249
0.0158
0.0581
0.0310 0.141661
0.0273 0.047849 yes
0.0040 0.017424 yes
54
1650-1699
1700-1749
1750-1799
PISA-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PISA-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
VIE-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
VIE-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
ANT-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
BRU-LON
1550-1599
1600-1649
1650-1699
1700-1749
0
2
2
0
2
0
2
2
2
2
1
1
Nov. 1683-Mar. 1693
May 1704-Jul. 1724
Apr. 1764-Nov. 1777
Aug. 1728 onwards
Jul. 1772-Dec. 1792
May 1704-July 1724
Nov. 1751-Nov. 1762
June 1608-Dec. 1620
Jan. 1658-June 1664
July 1772-Dec. 1792
Nov. 1683-Mar. 1693
Nov. 1737-Apr. 1748
to Mar. 1682 only
Aug. 1728 onwards
Jul. 1772-Dec. 1792
Sep. 1568-Jan. 1591
0
2
0
2
2
2
2
2
2 -0.117358
27 -0.290782
30 -0.091922
16 0.098052
15 0.149767
7 -0.050678
2 -0.019276
16 0.011095
2 -0.069806
4 -0.073693
21 -0.156374
25 0.00906
37 -0.040847
45 0.003734
-2.25
-2.05
-0.37
1.28
0.59
-3.47
-2.53
1.53
-2.25
-2.97
-3.96
1.74
-2.2
0.91
0.43122
0.0522 0.273638
0.1418 -0.028323
0.2484
0.0766 0.161578
0.2538 0.500493
0.0146 0.024852
0.0076 0.044727
0.0073 0.028479
0.0310 0.07155
0.0248 0.079457
0.0395 0.001553
0.0052 0.037057
0.0186 0.046851
0.0041 0.010528
4.16
-0.19
2.11
2.48
2.79
0.61
3.94
1.64
2.9
1.84
0.04
2.13
2.4
2.87
0.15315
0.43122 yes
0.0658 0.390996
0.1491 0.290782 yes
0.2044
0.0652 0.161578 yes
0.1794 0.500493 yes
0
0.0407 0.050678 yes
0.0114 0.064003
0.0174 0.028479 yes
0.0247 0.141356
0.0432
0.0388
0.0174
0.0195
0.0037 0.010528 yes
55
1750-1799
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PAR-PISA
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PAR-VIE
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PAR-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
PAR-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
TOUL-SIEN 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
COL-SIEN
1550-1599
PAR-COL
2
2
2
0
2
2
2
2
2
2
0
2
May 1557 onwards
to Aug 1642 only
Aug. 1663-Aug. 1698
Sep. 1568-Jan. 1591
to Aug. 1642
Aug.1663-Mar.1682
Apr.1563+
May 1709 onwards
to Dec. 1765
May1555 onwards
2
2
2
2
July 1772-Feb.1783
May 1557 onwards
to Aug. 1642 only
Aug 1663-Aug. 1698
-3.05
-5.05
-2.98
-2.27
-3.26
7 -0.033091
-3.42
-3.57
-4.1
-4.01
-2.39
-4.24
-0.5
-3.87
-3.12
-0.112781
-0.066335
-0.048365
-0.036089
27
6
12
24
13 -0.054425
3 -0.075037
14 -0.123205
4 -0.055602
15 -0.03698
4 -0.29005
11 -0.002054
13
-0.064
34 -0.108068
-0.025506
0.003051
0.029368
0.032231
0.0102 0.053099
0.0370
0.0131
0.0162
0.0159
0.0159 -0.025511
0.0210 -0.01611
0.0300 0.019302
0.0139 0.023057
0.0155 0.017753
0.0684 -0.20902
0.0041 -0.001565
0.0165 0.00085
0.0346 -0.011538
2.06
-1.27
0.27
2.48
2.64
-1.96
-0.81
3.11
2.29
2.51
-2.01
-3.85
0.11
-0.8
0.0201 0.112781 yes
0.0113 0.066335 yes
0.0118 0.077732
0.0122 0.06832
0
0.0258 0.08619
0
0.0130
0.0199 0.075037
0.0062 0.142507
0.0101 0.078659
0.0071 0.054732
0.1040
0.0004 0.001565 yes
0.0077
0.064 yes
0.0144 0.108068 yes
56
1600-1649
1650-1699
1700-1749
1750-1799
SIEN-VIE
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
SIEN-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
SIEN-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-PAR
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-TOUL
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-COL
1550-1599
1600-1649
1650-1699
0
0
0
2
2
2
2
2
2
2
Jan. 1683-Dec. 1696
Aug. 1728 onwards
Nov. 1683-Feb. 1693
May 1709-July 1724
Nov.1751-Nov.1762
May1557 onwards
to Sep. 1641
Apr.1563 onwards
to Sep. 1641
Jan. 1760-Dec. 1789
1550 onwards
to Sep. 1641
Sep.1568-Jan.1591
0
2
0
0
2
1
0
Jan. 1692 onwards
May1709 onwards
May1709 onwards
to Dec. 1765
2
2
2
0
5 -0.091632
10 0.003616
12 -0.048347
13 -0.016966
11 0.000886
8 -0.019477
6 -0.046069
13 -0.042488
5 -0.157919
15 -0.031827
4 -0.004072
12 -0.000758
17 -0.043218
-4.63
1.38
-2.95
-1.97
0.39
-2.54
-2.67
-4.27
-5.04
-3
-1.33
-0.13
-3.49
0.0198 0.098567
0.0026 0.005568
0.0164 0.018617
0.0086 0.077917
0.0023 0.014543
0.0077 -0.111597
0.0173 0.096259
0.0100 -0.000298
0.0313 -0.107289
0.0106 0.013072
0.0031 -0.019451
0.0058 0.027764
0.0124 -0.021751
3.12
4.1
1.63
4.27
3.98
-2.24
3.54
-0.03
-2.11
1.86
-3.26
3.91
-1.05
0.0316 0.190199
0.0014 0.005568 yes
0.0114 0.066964
0.0182 0.094883
0.0037 0.014543 yes
0.0498
0.0272 0.142328
0
0
0.0070 0.044899
0
0
0.0099
0.0508
0
0.0060 0.019451 yes
0.0071 0.027764 yes
0.0207
0
57
1700-1749
1750-1799
UTR-PISA
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-SIEN
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-VIEn
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-BRUSS 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
UTR-LON
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_UTR
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
2
2
2
2
2
0
2
2
2
2
2
2
Jan. 1760-Nov.1777
1550 onwards
to Sep. 1641
Jan.1760-Dec. 1791
May 1555 onwards
to Sep. 1641
Sep.1752-Dec. 1758
Jan. 1760-Dec. 1791
Sep.1568-Jan.1591
to Sep. 1641
July 1772-Dec. 1791
Apr.1764-Feb. 1783
Sep. 1752-Dec. 1758
Jan. 1760-Dec. 1791
14 -0.071816
17 -0.115102
15 -0.135818
13 -0.112956
14 -0.009664
13 -0.056847
13 -0.064908
41 -0.044083
4 -0.040289
18 -0.04231
9 -0.035938
-3.22
-3.47
-3.08
-4.04
-1
-2.9
-3.25
-2.96
-3.24
-3.24
-2.86
-0.00147
0.0223 0.023307
0.0332
0.0441 -0.028684
0.0280 -0.007672
0.0097 -0.11408
0.0196 0.048357
0.0200 0.093003
0.0149 0.01375
0.0124 0.028887
0.0131 0.013917
0.0126 0.038612
1.46
-0.03
-0.45
-0.35
-3.69
1.99
3.01
0.73
1.98
0.88
2.65
0.04231 yes
0.07455
yes
0.0160 0.071816 yes
0.0490
0.0637
0.0219 0.112956 yes
0.0309
0.0243 0.105204
0.0309 0.157911
0.0188 0.044083 yes
0.0146 0.069175
0.0158
0.0146
58
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_TOUL 1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_COL
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_PISA
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_SIEN
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_VIE
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
RUR_BRUSS 1550-1599
1600-1649
RUR_PAR
28 -0.030221
37 -0.084745
61 -0.05232
47 -0.01319
18 -0.053535
2
2
2
2
2
0
to Nov. 1777
1692 onwards
0
2
2
14 0.001601
2
2
2
to Dec. 1789
1709 onwards
to 1765
8 -0.043126
2
2
1
29 -0.082836
9 -0.095979
14 -0.078154
15 -0.007034
2
Aug. 1663-Aug. 1698
-3.74
-0.81
-3.75
-1.86
-4.22
-1.71
1.1
-4.46
-3.95
-3.38
-0.59
0.0045
0.0221 0.034711
0.0163 0.048081
0.0143 -0.00637
0.0162 0.05672
0.0201 0.009232
0.0306 0.04534
0.0015
0.0215 -0.004144
0.0198 0.053179
0.0128 0.060545
0.0119 0.085146
2.78
3.24
-0.34
3.77
0.62
1.34
3.1
-0.22
2.19
3.45
3.16
0.10367
0.0045 yes
0.0125 0.117547
0
0.0148 0.048081 yes
0.0187
0
0.0150 0.086941
0.0149 0.084745 yes
0.0338 0.05232 yes
0.0015
0.0188 0.095979 yes
0.0243 0.131334
0.0175
0.0269 0.085146 yes
59
RUR_ANT
UTR_ANT
PAR_SIEN
RUR_LON
1650-1699
1700-1749
1750-1799
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
1550-1599
1600-1649
1650-1699
1700-1749
1750-1799
2
2
2
May 1772-Dec. 1791
Apr.1708-Aug.1715
May 1772-Dec. 1792
2
1
2
2
2
2
2
1
2
2
June 1608-Dec. 1620
Nov.1683-Feb. 1693
May 1704-jul 1724
Apr. 1764-Feb. 1783
May 1557 onwards
to Aug. 1642
Aug.1663-Aug.1698
Jan. 1683-Dec. 1696
Aug.1728-1750
1772-Dec. 1792
-0.178575
-0.116914
-0.000573
-0.13719
-0.053632
-0.063415
17 -0.048518
12 -0.201219
14 -0.167777
13 -0.754518
14
13
10
15
4
12
12 -0.178302
5 -0.046864
16 -0.041598
-1.37
-3.29
-3.4
-3.59
-2.6
-2.14
-0.14
-4.01
-4
-3.58
-2.65
-2.31
-0.92
-0.014923
0.178777
0.016251
0.016899
0.009122
0.015658
0.0354 -0.165738
0.0612 -0.067645
0.0493 0.002922
0.2102 0.275417
0.0687
0.0546
0.0041
0.0342
0.0134
0.0177
0.0673 -0.081211
0.0203 0.107459
0.0452 0.115222
-3.41
-1.17
0.04
1.08
-0.12
2.2
3.03
2.45
0.92
1.88
-0.81
3.35
3.24
0.178575 yes
0.295691
0.016251 yes
0.15409
0.053632 yes
0.079073
0.0486
0.0578 0.201219 yes
0.0731 0.167777 yes
0.2550 0.754518 yes
0.1244
0.0813
0.0054
0.0069
0.0099
0.0083
0.1003 0.178302 yes
0.0321 0.154323
0.0356 0.115222 yes
60
0
0.5
1
1.5
2
2.5
3
3.5
1366
1378
1390
1402
1414
1426
1438
1450
1462
1474
1486
1498
1510
1522
Wheat prices: 5 European cities
1534
1546
1558
1570
1582
1594
1606
1618
1630
1642
1654
1666
1678
1690
1702
1714
1726
1738
1750
1762
1774
1786
Exeter
Bruges
Valencia
Toulouse
Wels
APPENDIX 1.3: Annual wheat prices for five cities
BIBLIOGRAPHY
Abel, Wilhelm, Agrarkrisen und Agrarkonjunktur. Eine Geschichte der Land- und
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