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Europe’s Many Integrations: Geography and

Grain Markets, 1620-1913 David Chilosi, Tommy E. Murphy and Roman Studer

Working Paper n. 412

This Version: September 6, 2011

IGIER – Università Bocconi, Via Guglielmo Röntgen 1, 20136 Milano –Italy http://www.igier.unibocconi.it

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Europe’s Many Integrations: Geography and Grain Markets, 1620-1913

David Chilosi d.chilosi@lse.ac.uk

Dep. of Economic History LSE

Tommy E. Murphy tommy.murphy@unibocconi.it IGIER and Centro Dondena

Università Bocconi

Roman Studer r.studer@lse.ac.uk

Dep. of Economic History LSE

This version: September 6, 2011

ABSTRACT

This article documents and examines the integration of grain markets in Europe across the early modern/late modern divide and across distances and regions. It relies on principal component analysis to identify market structures. The analysis finds that a European market emerged only in the nineteenth century, but the process had earlier roots. In early modern times a fall in trading costs was fol-lowed by an increase in market efficiency. Gradually expanding processes of inte-gration unfolded in the long-run. Early modern regional integration was wide-spread but uneven, with North-Western Europe reaching high levels of integration at a particularly early stage. Low-land European markets tended to be larger and better integrated than in land-locked Europe, especially within large, centralised states. In the nineteenth century, national markets grew in old states, but conti-nental and domestic dynamics had become strictly linked. Keywords: International and Domestic Trade, Transport costs, Geogra-

phy, Economic Integration, Grain Markets, Factor Analysis, Europe, Pre-1913.

JEL Classification: N13, F15, C38 1. Introduction

Ever since Adam Smith (1937) wrote the Wealth of Nations, market inte-

gration has been one of the most popular explanations of economic growth, but

there is still considerable uncertainty on when and how European markets inte-

grated. A first set of authors stress that regional and national markets were

well-integrated already by the sixteenth and early seventeenth-centuries (Achil-

les, 1957; Abel, 1980; Clark, 2002). Others point to the eighteenth century as a

period of increasing market integration between and within regions (Unger,

1983; Allen and Unger, 1990; Persson, 1999; Jacks, 2004). By contrast, the pro-

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ponents of the increasingly influential “big-bang” hypothesis claim that the surge

in trade before 1800 did not result in market integration; long-distance integra-

tion, at any rate, began only around the 1820s (O’Rourke and Williamson 2002:

39-45 ff.; Özmucur and Pamuk, 2007; Bateman, 2011; Federico, 2011a).

As stressed by Federico (2011a: 93-4), progress in the field has been ham-

pered by the fact that most studies either focus on the early modern period or the

nineteenth century, thereby blurring the picture about the evolution of markets.

Moreover, key geographical areas and dimensions have been neglected.1 Stem-

ming from these gaps in the literature, a satisfactory synthesis combining the re-

gional and international levels is still lacking. This article contributes to ad-

dressing these gaps by examining a newly compiled database of grain prices from

one-hundred cities between 1620 and 1913, studying European market integra-

tion across various distances and regions. Following in the footsteps of Sanchez-

Albornoz (1974), we identify these regions through principal component analysis.

After justifying the methodological approach and presenting our data, we

provide an overview of European-wide trends. Here we emphasise how nine-

teenth-century market integration had early roots: trading costs declined be-

tween the late seventeenth and the first half of the eighteenth centuries and a

marked increase in efficiency followed. The analysis of how distance shaped dy-

namics of integration highlights that, instead of a “big-bang”, there were multiple

processes of integration with a gradually expanding geographical reach. Princi-

pal component analysis detects segmentation of the early modern European

market, with geography looming large as a determinant of the markets’ size and

location: coastal markets tended to be larger. A continental market emerged in

the nineteenth century, but the process was accompanied by the growth of na-

tional markets in old states. Comparing integration across trading regions and

areas shows that even if regional integration in the early modern years was

widespread, there was heterogeneity in its levels and timing: North-Western

1 For the early modern period, there is strong bias towards lowland Northern Europe (Unger, 1983; Allen and Unger, 1990; Jacks, 2004; Kopsidis, 1999). Typical sample sizes range from 5 to 26 markets, with most studies focussing on trade over long or very long distances (Persson, 1999; Özmucur and Pamuk, 2007; Shiue and Keller, 2004, 2007; Bateman, 2011). Across the early modern/late modern divide only few quantitative stud-ies on regional market integration exist to date (Göttman, 1991, Vögele 1998; Kopsidis 1999, 2002; Brandenberger, 2004).

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Europe reached high levels of integration at a particularly early stage, and other

low-land European markets caught up in the eighteenth century, with state for-

mation contributing to the process. By the nineteenth century national and con-

tinental integrations had become inextricably linked.

2. Three measures

We examine market integration from three perspectives: price conver-

gence, co-movement and volatility. These measures are widely used in the litera-

ture and their logic stems directly from the “law of one price”: in integrated

markets, where trade participants share information and transport costs are

small, price differences are quickly arbitraged away and prices converge. Relying

on different measures is advisable both because all three measures exhibit limi-

tations and, as Federico (2011a: 95-98) stresses, they refer to different aspects of

market integration.

In a competitive equilibrium, price differentials are equal to trading costs

between trading markets (Anderson and van Wincoop, 2004: 740). If trading

costs were greater than price differences, traders would incur in a loss and face

an incentive not to trade; if they were smaller, there would be a missed opportu-

nity for profit. Thus, there would be an incentive to trade more and undercut the

price in the importing city. However, the former constraint does not apply to

markets that are not trading and in such cases estimates of trading costs based

on price differentials are negatively biased, a bias that is bound to increase with

distance.2 Moreover, price differences may merely reflect variations in quality

(Persson, 2004: 136 f.). Whilst this issue is less serious for grain than for other

(e.g. manufactured) goods, it still constitutes a source of noise that at present can

be acknowledged but not systematically controlled for.

2 This issue is emphasised, for example, by Anderson and Wincoop (2004: 740) (cf. also Chilosi and Volckart, 2011: 13). In spite of their theoretical reservations, though, empiri-cally they find a strong positive relationship between price dispersion and trade costs (Anderson and Wincoop, 2004: 744). Moreover, the assumption of on-going trade is rela-tively undemanding when we limit the analysis to areas with highly correlated prices, as we do in section 7.

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Co-movements aim at measuring if and how regularly arbitrage opportu-

nities between markets are exploited by traders, what is known as “operational

efficiency”. In fact, information sharing and exploitation of arbitrage opportuni-

ties imply that whenever price series move away from equilibrium, they swiftly

adjust. A first implication of this condition is that operational efficiency can be

measured by the speed with which prices adjust after a shock. However, such an

approach demands that the frequency of data matches that of adjustment (Tay-

lor, 2001) which in the context of this paper that uses yearly data is not a viable

option. A second implication is that prices between integrated markets develop

stable linear relationships; that is, they become highly correlated (Studer, 2008:

401 f.). Our approach specifically assumes –like Uebele (2009) does– that prices

adjust within a year in integrated markets. Any a priori criterion to demarcate

integrated markets is necessarily arbitrary to some degree. Still, our assumption

is consistent with recent estimates for early modern wheat markets based on

monthly data. According to these, on average it took about 6 months for a shock

to be reduced by a half (Bateman, 2011: 456); in the nineteenth century, when

there was an obvious increase in efficiency, the time frame was substantially

shorter: e.g. in Italy pairwise price differences adjusted in a matter of weeks

(Federico, 2007). Hence, for all our period we can be fairly confident that if prices

did not adjust within a year they embodied relatively independent price regimes.

As well as by com-movement, operational efficiency can be measured by price

volatility in individual localities: efficient markets imply better protection from

local shocks, so that prices become stable (Persson, 1999; Epstein, 2000; Bate-

man, 2011).

Like those of trade costs, measures of efficiency based on prices are not

without caveats. Highly correlated prices may merely reflect similar seasonal

patterns, such as, for example, those due to weather conditions. Similarly, de-

clines in volatility can be due also, for example, to the introduction of new tech-

niques that imply less variability in the yields. Moreover, as efficient markets be-

come more exposed to regional and continental shocks, under certain conditions,

efficiency may in fact increase price instability. Another difficulty is caused by

the fact that increases in price volatility imply improved co-movement between

markets (Forbes and Rigobon, 2002; Corsetti et al., 2005). In consequence, in-

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creases in volatility may lead to detecting spurious improvements in efficiency on

the basis of trends in co-movement.

Measurement errors imply that discrepancies across measures of integra-

tion are to be expected; in the interest of robustness it is therefore important to

rely on a battery of measures. However, error is not the only possible source of

inconsistency across measures. Marked differences between dynamics of price

convergence, on the one hand, and price co-movement and volatility on the other,

signal that different processes are at work: since there is an imperfect correspon-

dence between trading costs and information flows, markets may be highly effi-

cient even if trading costs are high, and the other way round.

3. One-hundred cities

Our analysis is based upon a newly compiled database of yearly grain

price data from a hundred European cities between 1620 and 1913. All the cities

included in our sample are plotted on the map in Figure 1. This database covers

a longer time period, is substantially bigger, and features a more balanced set of

markets in terms of geography and distance than in most previous studies. The

average number of years a price series covers is 169, the minimum being 65, and

for 13 cities we have a complete balanced series from the beginning to the end of

the period.3

[Figure 1 about here]

Most prices were taken from the secondary literature, but many were also

collected from primary sources. Thanks to recent scholarship, some price series

were even available online and in standardised form (mostly expressed in grams

3 Those 13 cities are Arnhem, Berlin, Cambridge, Dordrecht, London, Lucerne, Milan, Newcastle, Paris, Rome, Segovia, Toulouse and Udine. For this and the other balanced samples used in the analysis we used interpolation with TRAMO for series having about 5 per cent or less missing observations. As this selection highlights, despite obvious im-provements with respect to previous datasets, we were only partly able to cover develop-ments in Eastern Europe. This is a task for further research. Moreover, the balanced sample is obviously biased towards North-Western Europe; this needs to be taken into account in interpreting the results.

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of silver per gram, litre, or kilogram of grain), making them directly comparable

internationally (e.g. Allen and Unger database; Jacks database). This is particu-

larly true for most cities in the lowlands of Europe. Considerably less data were

readily available, however, for regions of the interior of the continent. To address

this neglect, we collected a number new price series from archives and old statis-

tical journals. The Appendix describes in detail these sources.

The annual average prices for calendar years are for wheat, or in some

cases for spelt, which is a special type of wheat that was –especially in the early

modern period– the predominant grain in parts of Europe’s interior. The focus

on wheat is primarily justified by the dominant position of grain in consumption

and trade in the pre-twentieth-century world. But the pre-eminence of grain is

also manifest in the availability of data; for this period it is by far the good for

which we have the most information at our disposal and spanning an equal range

of time and space for any other good would be impossible.

Apart from its dominant position, grain is in also particularly fitting to as-

sess the course of commodity market integration for its representativeness. In

terms of “transport suitability” it could be referred to as an intermediate good.

On the one hand, it has a high bulk-to-value ratio, meaning that transport costs

and capacities are central to the extent of grain markets. On the other, grain

markets were heavily regulated (Federico, 2011a). As a result, once grain mar-

kets become integrated, the markets for most other goods also became integrated,

as most of them were easier and cheaper to transport. Other goods, more perish-

able or inherently difficult to transport (e.g. eggs), were never traded over long

distances. Grain, as an intermediate case, makes for a good proxy to infer about

the overall process of expanding markets.4

4. Early roots

We begin the analysis by looking at European-wide trends in market inte-

gration. Following standard approaches, we measure price convergence with the 4 This is not to deny that the integration of other markets such as textiles is more directly related to, and thus arguably better suited to examining the Smithian origin, or lack thereof, the industrial revolution (Daudin, 2010: 717).

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coefficient of variation (plotted in Figure 2), co-movement with the average 21-

years rolling correlation coefficient between the price in each city and the aver-

age price (Figure 3), and volatility with the average 11-years rolling coefficient of

variation of prices in each city (Figure 4). The roll is longer in the former case

because correlation analysis is more demanding than that of volatility in terms of

sample size. To address spurious correlation due to e.g. similar inflationary

movements, we differentiate the series before carrying out the correlation analy-

sis.5 A balanced sample of cities is desirable, or else variations across time may

merely reflect alterations in the sample composition. Yet limiting the analysis to

thirteen cities raises obvious issues of representativeness and efficient use of the

available information. We therefore check the robustness of the results produced

by the balanced sample against those of the full database.6 As we expect the se-

ries to exhibit markedly non-linear behaviour, we examine long-term trends with

the Epanechnikov kernel, which has become a standard tool in market integra-

tion studies (e.g. Federico, 2007; Chilosi and Volckart, 2011).

[Figures 2 to 4 about here]

A long-term trend towards market integration in the early modern period

is evident from the perspective of price dispersion. Specifically, we find that

trading costs declined markedly between the late seventeenth and the first half

of the eighteenth centuries; 7 from then until the French Revolution they re-

mained stationary. While the latter result is consistent with Federico’s (2011a)

analysis and on the whole we find ourselves in agreement with González and

Guerrero (2010), there is an obvious discrepancy with Bateman (2011), who de-

5 Here and in later analyses we checked the robustness of our results by also studying the behaviour of series differentiated twice and we found no substantial difference. 6 The same approach is used in the subsequent analysis for analogous reasons. Fixed ef-fects panel analysis of price dispersion is another way of addressing the shortcomings of analyses of convergence based on unbalanced panels (Bateman, 2011). Yet this technique did not produce new results in our case. 7 Bai-Perron structural break tests (Bai and Perron, 1998, 2003) on the yearly rate of change of the coefficient of variation find drops in 1707 for the unbalanced sample and in 1663 and 1729 for the balanced sample. Subsequent break dates are as follows; balanced sample: 1793 and 1847; unbalanced sample: 1787 and 1838. These signal a marked in-crease in price dispersion in connection with the Napoleonic Wars and a sudden decrease with the demise of the mercantilist system of protection (cf. below).

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tects stationary trading costs for the whole of the early modern period. This is

partly due to her focus over the very long period, which obscures important me-

dium-term fluctuations. It also suggests, however, that an improved coverage

significantly changes the picture of early modern European integration. The bal-

anced sample in particular indicates that by c. 1750 price dispersion had reached

levels similar to those of the mid-nineteenth century, making it difficult to argue

that, at least for Western Europe, the downward trend merely represented a par-

tial recovery of previous levels.8

Price dispersion became lower after the end of the economic crisis of the

seventeenth century, whose origin was intimately linked to widespread conflict

(Hobsbawm, 1954). As shown by Schulze and Volckart (2011), for example, the

Thirty Years War implied marked disintegration for Central Europe. Still, the

pattern of decline suggests that there is more to early modern price convergence

than alternating periods of peace and war. For example, convergence was par-

ticularly marked during the War of Spanish Succession (1701-1714); conversely,

the decades after 1750 were relatively peaceful for Europe, but, as we saw, price

dispersion did not decrease. The results are, on the other hand, broadly consis-

tent with the hypothesis that state formation led to a long-run decline in transac-

tion costs during the early modern era (Epstein, 2000; Jacks, 2004).

Turning to efficiency, the analysis highlights that for the early modern pe-

riod there are substantial differences across measures. A long-term trend to-

wards market integration continues to hold for volatility, albeit not as markedly

as for price convergence. Discounting for short-term fluctuations, co-movement

remained on the whole trendless until the end of the eighteenth century.9 The

8 For the balanced sample, the eighteenth-century minimum was reached in 1749, when the coefficient of variation was 0.132. This compares with 0.127 in 1851 and 0.125 in 1872. The difference, though, is more marked in the unbalanced sample, for which the eighteenth-century minimum was 0.256 in 1751 as compared to 0.197 in 1854 and 0.187 in 1873. 9 Thus, the rolls centred in 1789 detect levels of co-movement higher than in 1630, but also lower than in 1700. A rise in co-movement in the later seventeenth century followed by a sudden drop at the beginning of the eighteenth century stands out. The latter period was also characterised by a heightened British-French rivalry. In accordance with the mercantilist doctrines of the time, this resulted in a sharp decline of trade between the two states (Nye, 2007: 25 ff.); correspondingly, French and English cities experienced a sudden fall in co-movement at around 1719.In the balanced samples, the figures for the

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lack of long-term gains should not, however, blind us to substantial increases in

efficiency from c. 1750 in the case of co-movement, somewhat earlier for volatil-

ity. This trend is in line with other analyses (Allen and Unger, 1990, Persson,

1999; Jacks, 2004), and shows that it is in the mid-eighteenth century that the

modern rise in price co-movement originates: from then until the middle of the

nineteenth century co-movement continued to rise almost without interruptions.

This periodisation matches well Persson’s (1999) argument that modern

European integration had its roots in earlier physiocratic reforms, as these initi-

ated a period of market de-regulation whereby restrictions on the export of grains

from localities were lifted. Still it is difficult to reconcile this perspective with

stationary trading costs in the second half of the eighteenth century. A more

plausible explanation for the pattern that we identify is therefore that the initial

decrease in trading costs subsequently caused an increase in trade and thus op-

erational efficiency. In fact, as trade intensifies and market thickens, operational

efficiency is bound to increase.10

From 1789, the results offer few surprises. As with those of others (Jacks,

2005; O’Rourke, 2006; González and Guerrero, 2010; Federico, 2011a), our data

show that the Napoleonic Wars and associated trading policies led to a marked

increase in trading costs. As expected, prices became very unstable at the same

time amidst a rampant inflation. Surprisingly, though, the correlation analysis

does not identify a similar drop in price co-movement, which on the contrary con-

tinued to rise.11 The analysis of convergence – whose series being formed of

rolls centred in 1718 and 1719 are as follows; Cambridge: 0.73 vs. 0.64; London: 0.73 vs. 0.54; Newcastle: 0.91 vs. 0.78; Paris: 0.90 vs. 0.71; Toulouse: 0.78 vs. 0.63. That this dis-integration does not show up in the trading cost analysis may owe something to the im-pact of bounties, such as those provided to British grain exporters by the Corn Laws of 1689. 10 Needless to say, however, information flows could have also originated from other sources, such as, for example, the spread of business journals publishing prices or in-creased literacy. 11 Information flows within the French Empire may have implied that the destabilising effect of the Wars – a continental-wide shock –, were transmitted swiftly across markets. As argued by Crouzet (1964), the Napoleonic Wars had an ambivalent effect on European trade: while sea- and long-distance trade suffered, the abolition of internal trade barriers fostered the formation of larger domestic markets. Yet Federico (2011a: 109) finds only slight grain price convergence within the Empire, and increased price volatility contrib-uted to the progress in co-movement. Hence, the actual improvement in efficiency during this period was smaller than implied by the correlation figures.

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yearly data rather than rolls are not smoothed – highlights that nineteenth-

century grain market integration was a sudden, rather than gradual, process.

The big drop happened in just over a decade between the mid-1830s and c. 1850,

and little was gained thereafter. Although volatility fell markedly in the 1860s

and 1870s, co-movement also suggests little further integration after the mid-

nineteenth century.

These observations are in line with recent works (Jacks, 2005, 2006; Fede-

rico, 2011a), where the pattern is seen as evidence that the integration of the

European market was due primarily to liberal policies, rather than transport

technology. In fact, as recently shown by Accominotti and Flandreau (2005) (cf.

also Nye, 2007; Federico, 2009), a pan-European movement toward international

trade liberalisation began in the 1830s, and not in the 1860s as previously

thought. Conversely, the cost of water transport fell from the later eighteenth

century, thanks to the building of new canals and the introduction of the steam-

ship; but the big decline in freight rates was concentrated in the 1850s and

1860s, at the same time as the railway was spreading across Europe (Ville, 1990;

Federico, 2011a: 113-14). As we shall see in the next section, however, integra-

tion between very distant cities did improve significantly from the later nine-

teenth century. This dynamic is obscured by a concomitant rise in trading costs

over shorter distances.

An increase in price dispersion from the 1870s, when American and Rus-

sian grain “invaded” the continent fostering a protectionist revival, is well-

documented by the literature (O’Rourke, 1997; Klovland, 2005; Jacks, 2005).12

Jacks (2005: 399) qualifies this view by stressing that protectionism only imper-

fectly accounts for the reversal: states that continued to be liberal were affected,

too. Consistent with this qualification, we find that the reversal was particularly

evident for the balanced sample, almost half of which is formed by cities in Eng-

land and the Low Countries. By contrast, protectionist Spain is particularly

12 Again, that disintegration is more consistent for price convergence than that for co-movement owes something to the positive effect of increased volatility on the correlation coefficients. In fact, volatility spiked up in the 1890s; at around the same time, wheat prices were rapidly increasing and our measure of co-movement began rising or at least ceased falling.

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well-represented in the unbalanced sample in these years, and we find no evi-

dence of widening price gaps here. We shall return to the link between tariff pol-

icy and late-nineteenth century disintegration in section 7.

To sum up, while by and large the emerging consensus is confirmed from

the Napoleonic Wars onwards, this is not the case for the early modern period

where we detect a long-term trend towards market integration. The analysis

suggests that trading costs markedly fell between the late seventeenth and the

first half of the eighteenth century. A significant increase in operational effi-

ciency followed, implying that the beginning of the modern rise in co-movement

dates back to c. 1750.

5. Multiple integrations

These early roots of market integration are further explored in this sec-

tion, whose focus is on the role played by distance in shaping this process. To do

so we split the sample into five historically meaningful periods: from the begin-

ning to the end of the Spanish Succession War (1620-1714), from then until the

French Revolution (1715-1789), the Napoleonic Wars (1790-1815), the age of lib-

eralism (1815-1870), and “the grain invasion” (1871-1913). As before, we con-

sider both the balanced sample and the whole dataset for robustness. The bal-

anced sample can be seen as more representative of the European core, while the

whole datasets covers developments in the periphery. Across the periods we

compute average pairwise price ratios (in Table 1) and correlation coefficients (in

Table 2) by distance so as to gauge who integrated, or disintegrated, when; to-

gether with the other results, t-statistics testing if each mean differs compared to

that of the subsequent period and their levels of significance are shown in brack-

ets.13

[Table 1 and 2 about here]

13 Binary correlations are considered only when 20 or more observations are available, and distance ranges with less than four pairs are not reported. The number of city-pairs examined in each period and sample is given in square brackets on top. Distance was calculated as the straight physical line between cities; using approximate road (based upon current –not highways– roads) increased the average distance about 20% but did not change the main results.

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Distance is confirmed as the basic determinant of the extent of integration

in the pre-railway period: nearly uniformly in all time periods, bar the late nine-

teenth century, price ratios increase and correlations decrease with distance; in

the late nineteenth century, price ratios and, to a lesser extent, correlation coeffi-

cients become similar across distances. Consistent with the previous analysis, in

the early modern period, co-movement increased only after price dispersion fell:

price ratios decreased substantially between the first and the second period, and

correlation coefficients increased in the third period. The unbalanced sample de-

tects strong evidence of falls in trading costs between short distances (<300 km).

Both samples agree that this holds for larger distances (300-1000 km), too. In-

deed, for the balanced samples trading costs declined also between cities distant

over 1000 km. The same groupings experienced increased co-movement in 1790-

1815 as compared to 1715-1789. The unbalanced sample, and to a lesser extent

the balanced one, detects similar movements for very distant cities (>1000 km),

but this largely reflects a return to seventeenth-century levels, rather than long-

term progress.14

For the balanced sample, there is hardly any long-term reduction in price

ratios for cities within 1500 km of each other between the eighteenth and the

nineteenth centuries. On the contrary, these were slightly higher in 1816-1870

and significantly higher in 1870-1913 than in 1715-1789. It is only very distant

cities (>1500 km) that experienced a progressive, albeit not statistically signifi-

cant, reduction in price ratios. The pattern is confirmed for close cities (<150 km)

by the unbalanced sample and at least in part by the correlation figures. Co-

movement also indicates, however, that efficiency progressed significantly in the

nineteenth century across larger distances – in the central part for the balanced

sample and throughout for the unbalanced one. Moreover, for the latter, this

holds for trading costs, too; indeed, here –more markedly than for the balanced

14 This is consistent with the claim that a temporary decline of sea-trade was responsible for the early eighteenth-century drop in co-movement (cf. footnote 9). Similarly, during the Napoleonic Wars, the unbalanced sample detects no evidence of increased trading costs at the local and regional levels. Although there are discrepancies across samples, the price ratio figures are broadly consistent with the argument that it was mainly the cost of international trade that increased at the time (cf. footnote 11).

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sample– the large drop in the costs of trading over large and very large distances

occurred only in the later nineteenth century.

Thus, the results suggest that: firstly, consistent with the previous analy-

sis, the nineteenth-century growth in trade had roots in the earlier reduction in

trading costs; secondly, short-distance integration was largely completed in the

early modern period; thirdly, the nineteenth century saw a limited fall in the

costs of trading over relatively large distances, too, within the European core, but

a substantial one between the core and the periphery and within the periphery;15

fourthly, in the late nineteenth century it was mainly the costs of trading over

relatively large distances within the core that increased; and, lastly, this was at

least in part offset by the spread of new transport technologies that strengthened

links between distant and very distant places in the core and the periphery and

within the periphery. The end product was remarkably homogenous price ratios

across distances by the end of our period. In short, there was no “big-bang” ei-

ther in the late eighteenth or early nineteenth centuries; in the long-run there

were multiple processes of integration, not without setbacks, but with a gradu-

ally expanding geographical reach.

6. The decline of geography

Typically, studies of European integration split the sample into countries

(Jacks, 2005; Uebele, 2009; Bateman, 2011; Federico, 2011a). This is certainly a

meaningful observation unit, as several relevant factors like trade policy, curren-

cies, legal systems or measurement units are often determined by state borders.

But then state borders over the almost three centuries under study are not con-

stant but changing considerably. What is more, there are other potential catego-

risations that do not follow national borders; geographical features may well be

more important dividing lines, particularly in early modern Europe. Thus a

novel approach is required. The solution proposed here about how to break up

the newly compiled database is to let the data speak for itself.

15 This can explain why Jacks (2004), who focuses on North and Baltic Seas, emphasises international integration in the early modern period, while Persson (1999), whose geo-graphical coverage is wider, sees international integration as a nineteenth-century phe-nomenon.

14

One way to do this is to use principal component analysis (PCA), which is

a popular analytical technique to classify variables according to patterns of co-

movement (Lawley and Maxwell, 1971; Dunteman, 1989). In a nutshell, PCA

uncovers latent structures, so that the essential information of the data set can

be expressed with a few variables, called factors. The strength of the correlation

between each variable and component is measured by the so-called factor or com-

ponent loadings. As here parsimony is not an overwhelming concern, we follow

the Kaiser criterion in selecting the number of components. To facilitate inter-

pretation we rotate the component matrix with the varimax method. To assign

cities to the various groupings we consider the component with the highest factor

loading that is also higher than 0.4. In some cases cities exhibit factor loadings

greater than 0.4 with more than one component, pointing to the existence of over-

lapping markets –here we decide on a case by case basis, depending on the

strength of the association and the plausibility of the resulting grouping.

As PCA is comparatively demanding in terms of data quality16 we only use

balanced samples, and to avoid detecting spurious correlation, we de-trend all the

price series with a Hodrick-Prescott filter.17 Our approach assumes stationary

market conditions. Obviously there is a trade-off between the extent to which this

assumption is tenable and the sample size. Keeping these concerns in mind and

in the light of the previous results, we choose the three following periods: 1620-

1789, 1715-1789 and 1835-1900. Since our variables are grain prices of different

cities, our observational units are geographic in nature. Hence, a good option to

make sense of the factors loadings is to visualise the resulting groupings in the

various periods in three maps, which we do in Figures 5 to 7.18

16 An implication is that the exact contours of the markets are sensitive to the composi-tion of the sample. In this respect we chose the largest possible sample that produced consistently plausible results. Only in the second period (1715-1789) this implied rela-tively significant reductions in the sample size (i.e. more than one or two). 17 As this technique can only be applied to balanced samples, it was used only in this part of the analysis that places particular high demands on the quality of the data. 18 The labels we choose for the different groups are meant to be simply illustrative; they convey continuity of the evolution of markets across periods, and have an only loose con-nection to the geographical location they relate to.

15

[Figures 5 to 7 about here]

The PCA demonstrates a major shift between early modern and nine-

teenth-century Europe, whereby regional markets gave way to a continental

market in the latter period. Where the first component explains between a mere

fifth or fourth of price variation in the early modern period, the same figure for

the European component in the nineteenth century is almost two-thirds. PCA

vindicates its usefulness: in the early modern period geography, more markedly

than political borders, loomed large as determinant of the shape, size and loca-

tions of markets. Both the 1620-1789 and the larger 1715-1789 samples show

that markets typically cut across borders and, especially in large states, frag-

mented polities.19 Only in England and Switzerland there is consistent evidence

across samples of a broad congruence between state and market, but there also

links were broader than those dictated by political borders. Although the Apen-

nine peninsula anticipated the formation of an Italian state in the nineteenth

century, the picture was complicated by the overlap with the Northern Mediter-

ranean market. Similarly, trade links spanned across German states, but we fail

to identify a common German market at the time.

The early modern picture is one of segmentation with markets tending be

located within relatively short distances. The broadly similar picture portrayed

by the two early modern samples is consistent with the overall stationary levels

of com-movement discussed earlier (cf. sections 4 and 5). Beside distance, prox-

imity to a particular sea such as the North Sea or the Mediterranean Sea clearly

emerges as a major dividing line. Land-locked markets tended to be smaller.

Helvetia and Central Europe were by far the smallest markets in 1620-1789.

The same remark applies to Lower Saxony West, Lower Saxony East, Helvetia

and Central France in 1715-1789.20 It therefore appears that physical geogra-

19 This is consistent with Daudin’s (2010) recent assessment on French eighteenth-century markets being larger than Britain –both because markets like Western Europe and the Northern Mediterranean were bigger than the North-Western European market and because he concentrates on commodities with a higher value per weight than grain. 20 Average distances are provided in the next section. While similar weather conditions may account for, at least to some extent, similar price movements in landlocked Europe, this appears a less powerful explanation for lowland Europe, where even distant markets show co-movements. Temperature patterns in particular do show very large scale geo-

16

phy and access to the sea in particular is not only an important dimension to add,

but that is actually the most characteristic feature of grain markets in early

modern Europe.21

In contrast to the early modern picture, the emergence of a European

market in the nineteenth century signals that physical geography mattered

much less in modern times. As suggested by the recent literature, a European

market thus emerged only in the course of the nineteenth century, driven by

steam, railways and trade liberalisation that eroded away long-standing differ-

ences across regions and geographies. The same literature, however, often over-

looks that nineteenth century was not only the age of Europe, but also that of the

nation-state.22 For France and Switzerland links with Europe emerged at the

same time as national markets came into being or asserted themselves – to para-

phrase the famous title (Weber, 1976), peasants became Frenchman at the same

time they were becoming European. Jacks’ (2005: 392 f.) negative assessment on

national integration in Spain also needs to be qualified: we find evidence of na-

tional integration here, too.

While it is tempting to see the formation of national markets as the prod-

uct of protectionism, the argument fits well only the case of Spain (Jacks, 2005:

397; Federico, 2009: 2 ff.). Indeed, in Spain protectionism remained so strong

that it prevented this country from fully becoming part of the emerging European

market, which hence was yet to become truly continental.23 The fit, however, is

graphical similarities, but rainfall patterns in Europe normally varied more across dis-tance than such an interpretation would permit (cf. e.g. Pfister, 1984). Moreover, surely, weather conditions are of no help in explaining the emergence of an absolutely dominat-ing component covering most of the continent in the nineteenth century. 21 The notion is actually not new. The idea that coastal areas were better integrated than landlocked areas in early modern Europe dates back at least to the work of Achilles (1957). It was broadly corroborated by Abel (1980) and later Allen and Unger (1990). The tentative results of these studies, however, are hardly present in today’s literature on European market integration. Jacks (2007), for example, explicitly rejects the idea that physical geography played a central part in the history of European market integration. 22 One notable exception is represented by Persson (1999), who dates both national and continental integrations to the nineteenth century. 23 The isolated case of Rome as the other example of city remaining peripheral is not suf-ficient to justify broad generalisations, particularly in view of the sui generis character of

17

less good for France. The image of “fortress France” for the early part of the cen-

tury has been recently revised probably for good by Nye (2007: 12 ff.), who high-

lights how the decline in wheat duties followed remarkably closely that of Brit-

ain. As for Switzerland, Accominotti and Flandreau’s (2006: 166 ff.) comparison

leaves little ambiguity: for the great part of our period it stood out as a beacon of

liberalism. All these countries, on the other hand, were characterised by a long

history of statehood. Thus the emergence or assertion of trading networks span-

ning their territories in the nineteenth century can be seen as stemming from the

early modern decline in trading costs referred to above, particularly as these in-

volved also relatively distant cities (cf. section 4 and 5).24

In short, we find that trade networks in early modern Europe were shaped

by geography more directly than by political borders, with access to seas provid-

ing major dividing lines. The importance of geography in shaping trading rela-

tions clearly declined in the nineteenth century, in the wake of liberalism and

developments in transport infrastructure and technology. The early modern sig-

nificance of geography does not necessarily imply that it is safe to neglect state

formation at the time; the emergence of national markets in old states alongside

a European market in the nineteenth century can be linked to a previous decline

in trading costs. Still, our finding raises the question of what we can learn about

grain market integration over the long run if we shift the focus from states to the

regions and trading areas that we have endogenously identified. As we can be

fairly confident that these corresponded to networks of actually trading cities, the

exercise promises to provide more meaningful results than those produced by the

analysis of states or the continent as a whole.

7. From many to one Europe (almost)

To address this question we compare our three dimensions of market inte-

gration across the markets identified by the PCA. For robustness we assign cit-

the city, which was home of the Papacy. Still it suggests that Spain may conform to a wider Southern European pattern. 24 Here the obvious exception is Britain, suggesting that recent revisions may have gone too far in downplaying its exceptional levels of openness.

18

ies on the basis of geographical proximity to the various markets to construct un-

balanced samples and study the long-term development across the regions identi-

fied for 1620-1913 as well. Our measures of trends of convergence and volatility,

which appear in Table 3, refer to the yearly rate of change of the coefficient of

variation between prices and of the average 11-years rolling coefficient of varia-

tion of individual price series in each region and period.25 As before, co-

movement is measured by the average pairwise correlation coefficient.

[Figure 8 and Table 3 about here]

The trend figures for 1620-1913 detect obvious increases in integration

over the long run, across measures and regions. There are, however, significant

differences in terms of level and one result stands out: overall North Western

Europe was considerably better integrated than all the other regions. The coeffi-

cient of variation was by far the lowest,26 the correlation coefficient was the high-

est and volatility was lower only in the Apennine peninsula. This can only be

partly explained by a relatively small market and cannot be reduced to the in-

dustrial revolution. On the contrary, the long-term trend of the yearly rate of

change of the coefficient variation was less steep than anywhere else. Only in

Central Europe (where the average distance was nevertheless shorter) and the

Apennine Peninsula was the fall in volatility slower.

There is little doubt that between 1620 and 1789 regional market integra-

tion was widespread; virtually all measures concord on this. There were signifi-

cant differences in levels and timing, though. North Western Europe, again,

stands out as a beacon of integration. The balanced and unbalanced samples

concord in showing by far the lowest level of price dispersion in this region; the

average correlation coefficient was also the highest for the balanced sample and

25 The discussion also draws on visual inspection of the trends, not reproduced here for reasons of space. Following Federico (2011b), to take into account mean-reverting behav-iour, for the trend in price convergence we compute the long-term with the error correc-tion model used by Razzaque et al. (2007). 26 Here and later, visual inspection finds confirmation in statistical testing: all t-statistics comparing the mean coefficient of variation between North-Western Europe and the other regions are significant at the 1 per cent level. The same applies to the 1620-1789 period, but not to the 1715-1789 samples that find e.g. similar levels of integration in Western Europe and the Northern Mediterranean.

19

volatility was lower than anywhere but the Apennine and Iberian peninsulas.

However, this is less evident in 1715-1789. During this time in Central France

and Lower Saxony East prices were less dispersed and as or more highly corre-

lated than in North-Western Europe. The figures also consistently detect similar

levels of integration in Lower Saxony West, Western Europe, and the Northern

Mediterranean.

Hence, although as Bateman (2010) stresses at times integration in the

early modern Mediterranean was comparable to that of North-Western Europe,

this was not invariably the case. It looks as if North-Western Europe reached

comparatively high levels of integration at an earlier stage than the other re-

gions. Moreover, the inclusion of Amsterdam and Dordrecht in the later sample

is largely responsible for the discrepancy between the trend in price convergence

in North-Western Europe between 1620-1789 and 1715-1789.27 If one considers

England alone it becomes clear that the second half of the eighteenth century,

just before the industrial revolution, there was substantial progress.28 Therefore

market integration emerges as more plausible as a cause of the industrial revolu-

tion than recent works suggest (Daudin, 2010; Bateman, 2010, 2011). Not only,

as González and Guerrero (2010) point out, market integration preceded the

events in England, rather than simply being one of its consequences. Both over

the early modern period and on the eve of the industrial revolution, market inte-

gration was particularly advanced in the very place that unleashed it.

Very low distance is clearly a factor for particularly high integration in

eighteenth-century Central France and the markets in Lower Saxony. Discount-

ing for this factor, only Western Europe and the Northern Mediterranean exhib-

ited levels of market integration that were comparable to those of North Western

27 On the one hand, comparatively high integration in the early modern period for the Dutch cities fits well the case for the significance of sea transport and that of state forma-tion. On the other hand, their relative failure to build on the initial advantage may be, at least in part, traced to the smallness of the Dutch state (cf. below). 28 In 1715-1789 considering England alone implies that the long-term trend for the bal-anced sample goes from -0.075 to -0.771 and becomes significant at the 5 per cent level. A Bai-Perron test on the yearly rate of change of the coefficient of variation in England be-tween 1620 and 1789 finds a break in 1755; the trend is negative and significant at the 1 per cent level both before and after the break, but the pace of integration increases over tenfold: the yearly rate of change goes from -0.0022 to -0.0277.

20

Europe at the time. These were also the only markets, together with the Iberian

Peninsula where we find strong and consistent evidence of increased integration

in 1715-1789. Thus, only regions that were relying heavily on sea transport con-

sistently experienced increased integration in the early modern years. This con-

firms a key role for geography in shaping patterns of market integration at the

time. It also provides support to the idea that the development and spread of

new shipbuilding technologies (cf. e.g. Unger, 1978) contributed to this integra-

tion. Sea transport not only afforded a wider reach, but also greater depth and

scope for advancement. It is only in the nineteenth century that new transport

technologies ensured a more level playing field in terms of the potential for ex-

change, specialisation, and progress across geographical areas.

Even in the early modern years, though, access to the sea was not a suffi-

cient for integration, as the Cantabrian Sea and the Appenine peninsula testify.

On the other hand, all regions that included French cities experienced consistent

integration, or at least reached particularly high levels, suggesting that state

formation contributed to regional integration. Similarly to Europe as a whole,

the “French” regions experienced particularly marked price convergence between

the later seventeenth and the first half of the eighteenth centuries, i.e. after Col-

bert’s absolutist reforms in the 1660s (Ertman, 1997: 125-39). The early integra-

tion of English cities sits well with Granger and Elliott’s (1967) assessment that

by the beginning of the eighteenth century there was a national grain market in

England. It also lends support to Epstein’s (2000) argument that the public good

character of the market implies that sovereignty, rather than constitutional rep-

resentation, represents the crucial link between state formation and market in-

tegration. By the same token large states like France and Britain should be par-

ticularly well placed to enjoy the benefits of centralisation.29 In other words, in

line with Chilosi and Volckart’s (2011) analysis, our results suggest that water

29 That this occurred only to a lesser extent in Spain matches a milder form of absolutism than in France (Ertman, 1997: 110 ff.). The timing of progress in Switzerland, strongly concentrated as it was in the seventeenth century, also matches closely that of political reform, with the eighteenth century being characterised by stasis, after the dynamism of the previous century (Martin, 1931: 140 ff.). A result of state formation there was that, as seen earlier (cf. section 6), in Switzerland, more markedly than anywhere else, we find a close congruence between state and market already by the eighteenth century.

21

transport and institutional change were major sources of integration dynamics in

the pre-industrial era.

[Figure 9 and 10 about here]

Turning to the nineteenth century, while price volatility decreased signifi-

cantly, the evidence is not as strong or consistent for price convergence. Sub-

stantial price convergence was achieved in Europe towards the end of the 1840s,

but there was a reversal from c. 1870 onwards (cf. section 4). The revival of in-

ternational trade barriers had repercussions also for national markets in France

and especially Switzerland, demonstrating that national and international dy-

namics were by then inextricably linked, though not in Spain that was never lib-

eral and thus remained unaffected by these developments: here there was steady

convergence.30 The contrast confirms that absence of tariffs was not sufficient to

shelter places from the increase in trading costs from late-nineteenth century,

pointing to the existence of other contributory factors. In addition, it suggests

that in fact trade liberalisation in the earlier period implied exposure to the ef-

fects of what appears to have been a continental-wide shock at the time.

[Figure 13 about here]

All in all, despite regional integration being a widespread process, hetero-

geneity in levels and timing across regions needs to be stressed for the early

modern period, with again geography emerging as an important factor. North-

Western Europe reached comparatively high levels of integration earlier than the

other regions, a fact at least consistent with the claim that market integration

was a cause of the industrial revolution. It is only in the course of the eighteenth

century that other coastal regions caught up. That this applied to French cities

in particular suggests state formation contributed to regional integration. By the

nineteenth century, national and continental integrations followed parallel

30 Yet – and here we agree with Jacks (2005: 397) - this was not sufficient for the country to catch up with other European states. Despite comparatively low levels of price volatil-ity, overall price dispersion was significantly higher than in France and Switzerland. The same applies to price co-movement. Its level was similar to that of the European market where on average distances were almost twice as big.

22

paths; only protectionist Spain could diverge significantly from wider European

trends.

8. Conclusion

The results in this paper suggest important qualifications need to be made

to the increasingly influential “big-bang” hypothesis of market integration. A

European grain market emerged only in the first half of the nineteenth century,

but the roots of this process were in the early modern period. A decrease in trade

costs between the late seventeenth and early eighteenth centuries was followed

by an increase in operational efficiency from the second half of the eighteenth

century. It is at this time that the modern rise in price co-movement dates. Over

the long run we find multiple processes of market integration that unfolded over

gradually expanding geographical spans. During the early modern period, there

was a marked and widespread progress in regional integration. The nineteenth

century mainly witnessed improvements in operational efficiency and long and

very long-distance integration. Thus it appears that the picture of early modern

market integration can change considerably when one expands the coverage and

systematically examines patterns of integration across distances. Future re-

search should thus seek to examine similarly large datasets for earlier periods,

too. Moreover, in the nineteenth century a European market emerged alongside

national markets in old states like France and Switzerland; in Spain the growth

of a national market was not accompanied by the development of similarly strong

links with Europe.

Endogenous identification of regional markets highlights limitations with

the use of states as units of analyses for the early modern period. At the time ge-

ography, more markedly than political borders, emerges as the key determinant

of the size and location of markets. Access to a common sea was clearly more im-

portant than shared statehood in shaping commercial networks. Sea transport

influenced also market sizes, with a clear tendency for landlocked Europe to pro-

duce smaller markets than the coastal areas, as well as dynamics of integration.

North-Western Europe became comparatively better integrated at an earlier

stage than the other regions, and in the course of the eighteenth century other

23

coastal regions caught up. Only steam and the modern transport systems seem

to have eradicated long-standing differences between regions with a more be-

nevolent endowment for transportation and the ones in the interior with no ac-

cess to maritime transport and plenty of obstacles to overcome.

This is not to say that politics did not matter. Regions in Italy and else-

where show that access to the sea was not sufficient to reach particularly high

levels of integration. It looks as if being part of large and centralised states fa-

voured regional integration for cities in Britain and France in the early modern

period. As recent studies have stressed, liberal reform obviously was a key factor

behind international integration in the nineteenth century. Notably, though, this

proved to be a double-edged sword. By the later nineteenth century national and

continental integration dynamics had become inextricably linked; judging from

the Spanish experience, only states that had remained protectionist throughout

the century seem to have been sheltered by the process of disintegration that en-

sued at the time.

Last but not least, not only the process of European integration began ear-

lier than the industrial revolution, and thus was not simply one of its conse-

quences. England, where the industrial revolution originated, was also part of a

trading region – North-Western Europe - that reached high levels of market inte-

gration considerably earlier than any other. The process of national integration

further progressed in the decades before the industrial revolution. Hence, paying

attention to the timing and extent of market integration across trading regions

suggests that Smithian growth is more plausible as an explanation of the indus-

trial revolution than recent analyses imply.

24

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Elsas, Moritz J. [1936]; Umriss einer Geschichte der Preise und Löhne in Deutschland vom ausgehenden Mittelalter bis zum Beginn des neunzehnten Jahrhunderts, vol. 1 (Leiden)

Feavearyear, A. E. [1931]; The Pound Sterling: A History of English Money (Oxford)

Frêche, Georges and Genevieve Frêche [1967]; Les prix des grains, des vins et des legumes à Toulouse, 1486-1868 (Paris)

Friis, A. and K. Glamann [1958]; ‘A History of Prices and Wages in Denmark 1660-1800’ vol. I. (Copenhagen)

Furtak, F. [1935]; Ceny w Gdansk w Latach, 1701-1815 (Lwow)

Gerhard, H.-J., and K. H. Kaufhold (eds.) [1990]; Preise im vor- und frühindustriellen Deutschland (Göttingen)

Gorkiewicz, M. [1950]; Ceny w Krakowie w Latach, 1796-1914 (Poznan)

Göttmann, Frank [1991]; Getreidemarkt am Bodensee (St. Katharinen)

Great Britain, House of Commons [1830]; House of Commons Parliamentary Papers 1826-27, Vol. XVI.

Haas-Zumbühl, F. [1903]; ‘Die Kernenpreise und Brotpreise in Luzern von 1601 bis 1900’, Zeitschrift für schweizerische Statistik, 39, pp. 370-372

Hamilton, E. J. [1934]; American Treasure and the Price Revolution in Spain, 1501-1650 (New York)

Hamilton, E. J. [1947]; War and Prices in Spain, 1650-1800 (New York)

Hanauer, A. C. [1876]; Etudes économiques sur l'alsace ancienne et moderne -2 vols.- (Paris), vol. II, pp. 82-86

Hauser, Henri [1936]; Recherches et Documents sur l’Histoire des Prix en France de 1500 à 1800 (Paris)

Jacks, David [2006]; ‘What drove nineteenth century commodity market integration in the Atlantic Economy,’ Explorations in Economic History, Vol. 43, No. 3, pp. 383-412 (see Jacks [odb, data])

Jacobs, A. and H. Richter [1935]; ‘Die Grosshandelspreise in Deutschland von 1792-1934,’ Sonderheft des Instituts für Konjunkturforschung, No. 37 (Berlin)

29

Körner, Martin [2008]; ‘Währungsbewertung’, Teil 2-3, Historisches Lexikon der Schweiz (see Körner [odb: 2008])

Körner, Martin, Furrer Norbert and Niklaus Bartlome [2001]; Währungen und Sortenkurse in der Schweiz 1600-1799 (Lausanne)

Mitchell, B. R. [1971]; Abstract of British Historical Statistics (Cambridge)

Müller, C. K. [1978]; Joh. Heinrich Waser, der zürcherische Volkswirtschaftler des 18. Jahrhunderts (Zürich)

Pfister, Christian [1989]; BERNHIST, unpublished source collection (Bern)

Posthumus, N. W. [1946]; Inquiry into the History of Prices in Holland, vol. 1 (Leiden)

Pribram, A. F [1938]; Materialen zur Geschichte der Preise und Löhne in Österreich, vol. 1 (Vienna)

Priester, Peter R. [1998]; Geschiedenis van de Zeeuwse landbouw circa 1600–1910 (Netherlands)

Radeff, Anne [1978]; ‘Le prix des céréales à Lausanne de 1550 à 1720,’ Revue historique vaudoise, pp. 15-19

Seuffert, Georg K. L. [1857]; Statistik des Getreide- und Viktualienhandels im König-reiche Bayern (München)

Siegel, S. [1949]; Ceny w Warsawie w Latach 1816-1914 (Poznan)

Sillem, J. A. [1901]; Tabellen van Marktprijzen van Granen te Utrecht in de Jaren 1393 to 1644 (Amsterdam)

Tijms, W. [2000]; “Groninger graanprijzen,” in Historia Agriculturae, 31 (see Tijms [odb])

Tomaszewski, E. [1934]; Ceny w Krakowie w Latach, 1601-1795 (Lwow)

United States [1902]; Statistical Abstract of the United States (Washington)

United States [1914]; Statistical Abstract of the United States (Washington), p. 491

United States, Executive Document Committee [1979 (1878)]; International Mone-tary Conference, Paris 1878 (Washington), published originally as United States Senate Executive Document No. 58, 45th Congress, 3rd Senate

Van der Wee, H. [1963]; The Growth of the Antwerp Market and the European Economy, Fourteenth-Sixteenth Centuries (The Hague)

Verlinden, C. and J. Craeybeckx [1959-1973]; Documents pour l'histoire des prix et des salaires en Flandres et en Brabant (Brussells)

Vierteljahreshefte zur Statistik des Deutschen Reichs, 44 (1935), pp. 319-321.

Vögele, Jörg [1989]; Getreidemärkte am Bodensee im 19. Jahrhundert (St. Katharinen)

30

Online databases (odb)

Allen, Robert C. [odb, antwerp]; “Wage and Prices History: Antwerp,” URL: http://www.nuff.ox.ac.uk/users/allen/studer/antwerp.xls

Allen, Robert C. [odb, krakow]; “Wage and Prices History: Krakow,” URL: http://www.nuff.ox.ac.uk/users/allen/studer/krakow.xls

Allen, Robert C. [odb, london]; “Wage and Prices History: London,” URL: http://www.nuff.ox.ac.uk/users/allen/studer/london.xls

Allen, Robert C. [odb, warsaw]; “Wage and Prices History: Warsaw,” URL: http://www.nuff.ox.ac.uk/users/allen/studer/warsaw.xls

Allen, Robert C. and Richard Unger [odb]; “Allen-Unger Global Commodity Prices Database,” URL: http://www.history.ubc.ca/faculty/unger/ECPdb/

GPIH (Global Price and Income History) Group [odb]; “Portugal, 1750-1855,” URL: http://gpih.ucdavis.edu/files/Portugal_1750-1855.xls

Jacks, David [odb, data]; “Data Underlying Journal Publications,” URL: http://www.sfu.ca/~djacks/data/publications/publications.html

Jacks, David [odb, prices]; “Miscellaneous Price Data,” URL: http://www.sfu.ca/~djacks/data/prices/prices.html

Körner, Martin [odb: 2008]; “Währungsbewertung,” URL: http://hls-dhs-dss.ch/index.php, downloaded on January 21, 2008

Measuring Worth [odb]; “Groninger graanprijzen” URL: http://www.rug.nl/let/onderzoek/onderzoekcentra/nahi/download

Officer, Lawrence H. [odb]; “Measuring Worth: The Price of Gold, 1257-2009,” URL: http://www.measuringworth.org/gold/

Tijms, W. [odb]; “Groninger graanprijzen” URL: http://www.rug.nl/let/onderzoek/onderzoekcentra/nahi/download

31

Appendix: Sources of grain prices The database of European grain prices we use includes price series from 100 markets across Europe. The prices series represent annual average prices for wheat, if not mentioned otherwise. The list below shows all the sources used for the analysis. Along with those documents detailed in the references, some series (or parts of them) came from a database that was kindly made available to us by Giovanni Federico who is lead-ing the Market Integration and the Welfare of Europeans project at the European University Institute. We refer to this source as “MIWE database”. A few other series were taken from another dataset collected by Stephan R. (Larry) Epstein at the London School of Economics shortly before his regrettable death in 2007; this source is labelled “LSE Epstein database”. The conversions used to standardise the original prices are shown for the indi-vidual series. If not noted otherwise, conversions used to turn capacity into weight measures are 76kg/hl for wheat and 72kg/hl for spelt. See Göttmann [1991: 486]. For the often used conversion of Pound Sterling into grams of silver, the following sources were used:

1600-1816: Feavearyear [1931: 346, 348-349]; 1817-1829: United States Executive Document Committee [1979: 611-

613]; 1830-1832: values were interpolated; 1833-1900: United States [1902: 65].

The conversions are available at Allen [odb, london]. Table A.1: List of sources

City, period Source

Amsterdam, 1700-1900

Posthumus [1946]. Prices in grams of silver per litre in Allen and Unger [odb]. The holes in the series were filled with regression analysis using rye prices in Groningen from Tijms [2000], available at Tijms [odb]

Appenzell, 1656-1810

Spelt prices. Göttmann [1991: 480-84]. Gulden/hl, average market prices for calendar years. Litre/kg conversion as noted above (using wheat). Silver content of the gulden from Tijms [odb]

Ancona, 1700-1825

Prices in grams of silver per litre in Allen and Unger [odb]; [1] 314-8

Angers, 1620-1789

Prices in grams of silver per litre in Allen and Unger [odb]; [53]

Antwerp, 1750-1913

1750-1826: Van der Wee [1963]; Verlinden and Craeybeckx [1959, 1973]. Original prices in groats/viertel and in francs/hl or per 100kg. Calendar years. Prices in grams of silver per kg are available from Allen [odb, antwerp]. 1896-1913: Jacks [2006]; monthly data avail-able from Jacks [odb, data]. Annual prices calculated as simple aver-age of monthly prices.

32

Arnhem, 1620-1901

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306].

Arras, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Augsburg, 1745-1820

Elsas [1936]. Prices in grams of silver per litre in Allen and Unger [odb]

Bar-le-Duc, 1806-1908

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Bayeux, 1690-1913

1690-1789: Hauser [1936]. 1806-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Berlin, 1624-1912

Vierteljahreshefte zur Statistik des Deutschen Reichs [1935: 319-321], original prices in Reichsmark/1000kg. Average market prices for cal-endar years, based on monthly average prices. The mark was on a gold standard, 2790 Mark equalled 1 kilogram of gold. Silver prices were obtained using the gold-silver price ratio given at Officer [odb]

Bern, 1739-1901

Spelt prices. Pfister [1989]. Average market prices for calendar years based on monthly prices. New Swiss francs/100kg. Conversion to Grams Ag/kg: Körner [2008].

Bilboa, 1700-1914

Great Britain, House of Commons [1830: 207]. Prices in grams of sil-ver per litre in Allen and Unger [odb]

Bordeaux, 1729-1913

1729-1824: Great Britain, House of Commons [1830: 72-73]; 1825-1913: Drame et al. [1991]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Boulogne-sur-Mer, 1700-1826

Great Britain, House of Commons [1830: 72-73]

Braunschweig, 1620-1850

Gerhard and Kaufhold [1990]

Bruges, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Brussels, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Buis-les-Bronnies, 1656-1789

Hauser [1936]

Burgos, 1814-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Cambridge, 1620-1913

1620-1799: LSE Epstein database. 1800-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

33

Celle, 1727-1871

Gerhard and Kaufhold [1990]

Chateaudun, 1620-1787

De Belfort [1881]

Chateau-Gontier, 1713-1913

Hauser [1936]

Châteauroux, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Cologne, 1669-1913

[2], [16] Vierteljahreshefte zur Statistik des Deutschen Reichs [1935: 296-299], original prices in Reichsmark/1000kg. The mark was on a gold standard, 2790 Mark equalled 1 kilogram of gold. Silver prices were obtained using the gold-silver price ratio given at Officer [odb]

Cordoba, 1814-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Coutance, 1676-1778

Hauser [1936]

Detmold, 1767-1909

Gerhard and Kaufhold [1990]

Dordrecht, 1620-1901

Priester [1998]

Dresden, 1750-1815

Great Britain, House of Commons [1830: 444]. Prices in grams of sil-ver per litre in Allen and Unger [odb]

Duderstadt, 1694-1850

Gerhard and Kaufhold [1990]

Frankfurt, 1731-1913

1731-1799: Elsas [1936]. Prices in grams of silver per litre in Allen and Unger [odb]. 1879-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Gdansk, 1703-1913

1703-1815: Furtak [1935]. Prices in grams of silver per litre in Allen and Unger [odb]. 1879-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Geneva, 1820-1900

Brugger [1968: 320-321, 326-332]. Swiss franc/100kg, average prices for calendar years, based on weekly market prices. Conversion to Grams Ag/kg: Körner [2008].

Ghent, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Girona, 1620-1907

1600-1799: LSE Epstein database. 1814-1907: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Göttingen, 1631-1867

Gerhard and Kaufhold [1990]

34

Granada, 1642-1907

1642-1789: LSE Epstein database. 1814-1907: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Grenoble, 1620-1913

1620-1780: Hauser [1936: 365-369]. Prices in grams of silver per kg in Allen and Unger [odb].

Hamburg, 1736-1901

1736-1825: Great Britain, House of Commons [1830: 148-149]. 1826-1901: Vierteljahreshefte zur Statistik des Deutschen Reichs [1935: 296-299]. Original prices in Reichsmark/1000kg. The mark was on a gold standard, 2790 Mark equalled 1 kilogram of gold. Silver prices were obtained using the gold-silver price ratio given at Officer [odb]

Hannover, 1620-1850

Gerhard and Kaufhold [1990]

Herford, 1771-1913

1771-1850: Gerhard and Kaufhold [1990].

Königsberg, 1700-1913

1700-1825: Great Britain, House of Commons [1830: 146-147]. 1826-1913: Vierteljahreshefte zur Statistik des Deutschen Reichs [1935: 296-299]. Original prices in Reichsmark/1000kg. The mark was on a gold standard, 2790 Mark equalled 1 kilogram of gold. Silver prices were obtained using the gold-silver price ratio given at Officer [odb]

Krakow, 1772-1913

1772-1901: Tomaszewski [1934]. Grams Ag/litre, calendar years. Available at Allen [odb, Krakow]. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306]. 1902-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

La Coruña, 1700-1907

1700-1826: Great Britain, House of Commons [1830: 67-69]. Prices in grams of silver per litre in Allen and Unger [odb]. 1841-1907: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Langres, 1620-1870

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306].

Lausanne, 1674-1902

1674-1719: Radeff [1978: 15-19]. Florin Lausannois/quarteron, aver-age institutional prices for harvest years. 1 quarteron = 13.7 l. Li-tre/kg conversion for wheat as noted above. 1700-1802: Chevallaz [1949: 140-3]. Batzen/quarteron. Average market prices for calendar years. 1803-1902: Brugger [1968: 320-321, 326-332]. Swiss franc/100kg, average market prices for calendar years, based on weekly market prices. For the currency conversion to Grams Ag/kg: Körner [2008] and Körner et al. [2001].

Leeds, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Leon, 1814-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Lerida, 1817-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

35

Limoges, 1620-1860

LSE Epstein database

Lisbon, 1750-1913

1750-1855: Source: Coruche [1894]. Reis/hl, calendar years. Avail-able at Jacks at [odb, price: Iberia]. Litre/kg conversion as noted above. For the currency conversion: Grams Ag/Reis available at GPIH Group [odb].

Liverpool, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

London, 1620-1900

1620-1793: MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre pro-posed by Brugger [1968: 306]. 1800-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Lucerne, 1620-1900

Spelt prices. Haas-Zumbühl [1903: 370-372]. Swiss francs/100kg, calendar years. Based on different sources, most recording average market prices and some recording average institutional prices. For the currency conversion to Grams Ag/kg: Körner [2008].

Lüneburg, 1766-1913

Gerhard and Kaufhold [1990]

Lwow, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Lyon, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Madrid, 1620-1774

1600-1650: Hamilton [1934]. 1650-1774: Hamilton [1947]. Prices in grams of silver per kg in Allen and Unger [odb]

Manchester, 1800-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Marseille, 1700-1913

1700-1789: Prices in grams of silver per litre in Allen and Unger [odb]; [22]. 1806-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Mende, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Milan, 1620-1913

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306].

Minden, 1775-1913

1775-1850: Gerhard and Kaufhold [1990].

36

Munich, 1650-1901

1700-1800: Seuffert [1857: 123]. Kreuzer/Schäffel. Average market prices for calendar years, based on average monthly prices. 1 Schäf-fel = 362 l; Litre/kg conversion for wheat as noted above. 1 gulden = 60 kreuzer = 240 pfenning. The silver content of the pfennig is from Jacks [odb, prices: Metals]. Source for 1800-1914: Jacobs and Richter [1935]. Reichsmark/1000kg. Average wholesale prices for calendar years. For the currency conversions see Berlin.

Munster, 1620-1847

Gerhard and Kaufhold [1990]

Naples, 1620-1894

1600-1803: Prices in grams of silver per litre in Allen and Unger [odb]; [13]. 1862-1894: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Newcastle, 1620-1913

1600-1799: LSE Epstein database. 1800-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Osnabrück, 1642-1861

Gerhard and Kaufhold [1990]

Oviedo, 1814-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Oxford, 1620-1793

LSE Epstein database

Paderborn, 1640-1850

Gerhard and Kaufhold [1990]

Pamplona, 1620-1913

1620-1799: Barquín Gil [2001], LSE Epstein database.

Paris, 1620-1913

1600-1805: Hauser [1936: 173-77]. Prices in grams of silver per kg in Allen and Unger [odb]. 1806-1913: Jacks [2006]; monthly data avail-able from Jacks [odb, data]. Annual prices calculated as simple aver-age of monthly prices.

Pau, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices

Pesaro, 1716-1825

Prices in grams of silver per litre in Allen and Unger [odb]; [1] 314-8

Pisa, 1620-1896

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306].

Prague, 1655-1912

1655-1872: MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre pro-posed by Brugger [1968: 306]. 1894-1912: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices

Romans-sur-Isère, 1668-1789

Hauser [1936]

37

Rome, 1620-1894

1620-1789: MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre pro-posed by Brugger [1968: 306]. 1862-1894: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Saint-Brieuc, 1806-1913

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Santander, 1821-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Schaffhausen, 1700-1880

Spelt prices. 1700-1800: Göttmann [1991: 480-84]. Gulden/hl, aver-age market prices for calendar years. Litre/kg conversion as noted above (using wheat). Silver content of the gulden from Tijms [odb]. Source for 1800-1880: Brugger [1968: 349-350]. Swiss francs/100kg. Calendar years, but no average price; prices based on ‘Martinischlag’. For currency conversions see Lucerne.

Segovia, 1620-1907

1620-1808: LSE Epstein database. 1814-1907: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Senigallia, 1729-1825

Prices in grams of silver per litre in Allen and Unger [odb]; [1] 314-8

St Gall, 1814-1904

Brugger [1968: 313-314]. Swiss francs/100kg. Calendar years. For currency conversions see Lucerne.

Strasbourg, 1620-1875

Hanauer [1876: 91-101]. Prices in grams of silver per litre in Allen and Unger [odb]

Toledo, 1651-1907

1651-1799: LSE Epstein database. 1836-1907: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Toulouse, 1620-1913

1620-1792: Frêche [1967: 85-91]. Centimes/hl., monthly prices. An-nual average prices in grams Ag/litre in Allen and Unger [odb]. Li-tre/kg conversion for wheat as noted above. 1806-1913: Drame, et al. [1991]. Centimes/hl., biweekly market prices. We calculated average annual prices for calendar years from the monthly prices of this dataset provided by David Jacks in Dollars/100kg at Jacks [odb, price: France]. The silver contents of the dollar are also his.

Überlingen, 1719-1901

1719-1810: Göttmann [1991: 480-484]. Spelt prices. Gulden/hl, av-erage market prices for harvest years. Litre/kg conversion for spelt as noted above. Silver content of the gulden from Tijms [odb]. 1820-1902: Vögele [1989: 233-234]. Rye prices. Mark/Doppelzentner, av-erage market prices for calendar years. For currency conversions, see Berlin.

Udine, 1620-1913

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306].

Utrecht, 1668-1792

Sillem [1901]. Prices in grams of silver per litre in Allen and Unger [odb]

38

Valencia, 1620-1789

Prices in grams of silver per litre in Allen and Unger [odb]; [20]

Vercelli, 1750-1906

MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre proposed by Brugger [1968: 306]

Vienna, 1621-1913

1621-1699: MIWE database. Prices in grams Ag/hl. Conversion into grams Ag/kg using a conversion (for wheat) of 76 kg/100 litre pro-posed by Brugger [1968: 306]. 1700-1890: Pribram [1938: 371-373]. Kreuzer/Metzen, annual average market prices for calendar years, based mostly on weekly prices (‘Markttage’). Prices in grams Ag/litre available at Allen [odb, krakow]. Litre/kg conversion for wheat as noted above. 1891-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Waake, 1748-1913

1748-1850: Gerhard and Kaufhold [1990].

Warsaw, 1816-1913

1816-1901: Siegel [1949]. Prices in grams Ag/litre available at Allen [odb, warsaw]. Litre/kg conversion for wheat as noted above. 1902-1913: Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Zaragoza, 1814-1907

Jacks [2006]; monthly data available from Jacks [odb, data]. Annual prices calculated as simple average of monthly prices.

Zurich, 1620-1877

Spelt prices. Müller [1978: 50, 52]. Francs/100kg. For the currency conversions see Lucerne.

39

Figure 1: The 100 cities included in the sample

40

Figure 2: Convergence of wheat prices in Europe: Coefficient of variation, 1620-1913

41

Figure 3: Co-movement of wheat prices in Europe: Average 21-years rolling correlation coefficient, 1630-1902

42

Figure 4: Volatility of wheat prices in Europe: Average 11-years rolling coefficient of variation, 1625-1908

43

Figure 5: Wheat markets in Europe, balanced sample 1620-1789

44

Figure 6: Wheat markets in Europe, balanced sample, 1715-1789

45

Figure 7: Wheat markets in Europe, balanced sample, 1835-1900

46

Figure 8: Levels of integration across markets and average distances, un-balanced sample, 1620-1913 Average CV Correlation

Average volatility Average distance (km.)

Figure 9: Levels of integration across markets and average distances, balanced and unbalanced sample, 1620-1789 Average CV Correlation

Average volatility Average distance (km.)

47

Figure 10: Levels of integration across markets and average distances, balanced and unbalanced sample, 1715-1789 Average CV Correlation

Average volatility Average distance (km.)

Figure 11: Levels of integration across markets and average distances, balanced and unbalanced sample, 1835-1900 Average CV Correlation

Average volatility Average distance (km.)

48

Table 1: The role of distance: Price ratios Distance (in km)

1620- 1714

1715- 1789

1790-1815

1816-1870

1871-1913

1620- 1714

1715- 1789

1790-1815

1816-1870

1871-1913

Balanced sample Unbalanced sample

[n=74] [n=74] [n=74] [n=74] [n=74] [n=1595] [n=2850] [n=3564] [n=3534] [n=2654]

0-35 1.16 1.16 1.21 (0.40) (-1.16)

35-70 1.42 1.23 1.20 1.23 1.16 (2.15)** (0.69) (-0.72) (1.20)

70-150 1.52 1.29 1.25 1.25 1.37 (2.61)** (0.92) (-0.03) (-2.33)**

150-300 1.63 1.44 1.44 1.31 1.30 (4.08)*** (-0.06) (4.51)*** (0.44)

300-600 1.40 1.27 1.31 1.33 1.59 1.71 1.52 1.51 1.42 1.32 (5.31)*** (-1.81)* (-0.41) (-3.11)*** (6.02)*** (0.80) (4.78)*** (6.34)***

600-1000 1.57 1.32 1.41 1.35 1.52 1.72 1.63 1.68 1.56 1.39 (6.43)*** (-4.13)*** (1.89)*** (-1.70) (2.16)** (-1.44) (5.45)*** (9.52)***

1000-1500 1.60 1.42 1.46 1.43 1.51 1.74 1.74 1.81 1.69 1.52 (3.83)*** (-1.27) (0.90) (-0.68) (0.00) (-1.54) (4.10)*** (7.50)***

>1500 1.57 1.50 1.49 1.47 1.42 1.71 1.85 2.20 1.96 1.60 (1.01) (0.14) (0.27) (0.52) (-1.18) (-3.82)*** (4.39)*** (9.70)***

Notes: Table shows average price ratios and t-statistics testing if each mean differs from that of the subsequent period appears in brackets (1%, 5%, and 10% levels of significance indicated with *, **, and ***, respectively).

49

Table 2: The role of distance: Correlation coefficients Distance 1620-

1714 1715- 1789

1790-1815

1816-1870

1871-1913

1620- 1714

1715- 1789

1790-1815

1816-1870

1871-1913 (in km)

Balanced sample Unbalanced sample

[n=74] [n=74] [n=74] [n=74] [n=74] [n=1121] [n=2614] [n=1201] [n=2911] [n=1572]

0-35 0.38 0.69 0.82 (-2.23)* (-1.30)

35-70 0.56 0.56 0.70 0.75 0.81 (0.00) (2.56)** (-0.85) (-0.66)

70-150 0.44 0.43 0.57 0.75 0.70 (0.23) (-2.85)*** (-4.82)*** (1.03)

150-300 0.31 0.26 0.42 0.67 0.74 (1.44) (-5.61)*** (-9.92)*** (-2.83)***

300-600 0.31 0.24 0.44 0.64 0.54 0.30 0.17 0.36 0.63 0.69 (1.44) (-3.52)*** (-4.52)*** (1.92)* (8.23)*** (-13.2)*** (-19.6)*** (-4.47)***

600-1000 0.12 0.14 0.22 0.48 0.48 0.19 0.11 0.25 0.51 0.61 (-0.49) (-2.38)** (-6.16)*** (-0.06) (7.54)*** (-12.1)*** (-20.4)*** (-7.62)***

1000-1500 0.11 0.07 0.11 0.37 0.43 0.12 0.06 0.14 0.40 0.46 (1.58) (-0.74) (-6.19)*** (-1.56) (4.60)*** (-10.2)*** (-16.5)*** (-4.89)***

>1500 0.15 -0.06 -0.02 0.30 0.42 0.08 0.02 0.09 0.28 0.28 (4.86)** (-0.38) (-2.28) (-1.43) (3.01)*** (-3.17)*** (-7.68)*** (-0.04)

Notes: Table shows average correlation coefficient and t-statistics testing if each mean differs from that of the subsequent period appears in brackets (1%, 5%, and 10% levels of significance indicated with *, **, and ***, respectively).

50

Table 3: Trend in price convergence and volatility in European markets, balanced and unbalanced sample, various periods

Group CV trend Vol. trend CV trend Vol. trend

1620-1913 Unbalanced sample

Northwestern Europe -0.311 *** -0.195 ***

Western Europe -0.418 *** -0.299 ***

Central France -0.612 *** -0.317 ***

Helvetia -0.352 *** -0.333 ***

Central Europe -0.441 *** -0.147 ***

Apennine Peninsula -0.562 *** -0.152 ***

Iberian Peninsula -0.466 *** -0.271 ***

1620-1789 Balanced sample Unbalanced sample

Northwestern Europe -0.318 *** -0.126 ** -0.055 -0.144 ***

Western Europe -0.528 *** -0.465 *** -0.513 *** -0.453 ***

Central France -0.285 ** -0.386 *** -0.175 ** -0.470 ***

Helvetia -1.338 *** -0.718 *** -0.396 *** -0.545 ***

Central Europe -0.953 *** -0.126 ** -0.404 ** -0.129 **

Apennine Peninsula -0.503 *** -0.295 *** -0.284 *** -0.278 ***

Iberian Peninsula -0.201 * -0.228 *** 0.147 ** -0.469 ***

1715-1789 Balanced sample Unbalanced sample

Northwestern Europe -0.075 -0.309 ** -0.091 -0.279 *

Western Europe -0.539 *** -0.891 *** -0.758 *** -1.132 ***

Central France -0.073 -0.344 ** -0.042 -0.470 ***

Helvetia -0.285 0.906 *** -0.159 0.384 **

Lower Saxony West 0.556 * 0.190 0.829 ** 0.244

Lower Saxony East -0.294 1.197 *** -0.140 1.17 ***

Central Europe 0.007 0.332 * 0.232 0.196

Apennine Peninsula -0.350 0.011 -0.271 0.099

Northern Mediterranean -0.894 *** -0.701 *** -0.765 *** -0.657 ***

Iberian Peninsula -1.197 *** -0.424 *** -0.889 *** -0.631 ***

Cantabrian Sea 0.820 -1.298 *** 2.837 *** -1.393 ***

1835-1900 Balanced sample Unbalanced sample

Northwestern Europe -0.278 -1.387 *** -0.516 -1.451 ***

Western Europe 0.095 -1.418 *** -0.560 -1.522 ***

Helvetia 0.827 ** -1.876 *** 0.739 * -2.010 ***

Iberian Peninsula -0.942 *** -1.099 *** -1.481 *** -1.118 ***

Notes: 1%, 5%, and 10% levels of significance indicated with *, **, and ***, respectively.