Does Medieval Trade Still Matter? - European Historical Economics

Does Medieval Trade Still Matter? Historical Trade Centers,
Agglomeration and Contemporary Economic Development
Fabian Wahl∗
University of Hohenheim
August 2, 2013
Abstract
This study empirically establishes a link between medieval trade, agglomeration
and contemporary regional development in ten European countries. It documents
a statistically and economically significant positive relationship between prominent
involvement in medieval trade and regional economic development today. This finding is robust to inclusion of various historical, economical and geographical control
variables and to controlling for endogeneity via IV estimations. A mediation analysis
shows that, as theoretically postulated, the majority of this long-lasting effect transmits via the impact of medieval trade on contemporary agglomeration and industry
concentration. Thus, this research highlights the long-run importance of medieval
trade in shaping contemporary spatial patterns of economic activity throughout
Europe. The path-dependent regional development processes caused by medieval
trading activity can also provide an explanation for the observed persistence of regional differences in development across the considered European countries.
Keywords: Medieval Trade, Agglomeration, Regional Economic Development, PathDependency, New Economic Geography
JEL Classification: F14, N73, N93, O18, R12
∗
Department of Economics, University of Hohenheim. Chair of Economic and Social History, Speisemeisterflügel, Stuttgart, Germany. [email protected] The author especially would
like to thank Bas van Bavel, Sibylle Lehmann, Alexander Opitz, Alfonso Sousa-Poza, Oliver Volckart
and Nicole Waidlein for the helpful comments and discussions. Additionally he is indebted to T.
Matthew Ciolek for his helpful suggestions and for discussing his medieval European trade route
maps.
1
1 Introduction
There is ample evidence that trade is an important determinant of both long- and shortrun economic development. However, most of the existing literature focuses on the impact of 19th century trade on market integration or the “Great Divergence” (e.g. Galor
and Mountford 2008 or O’Rourke and Williamson 2002) or on the impact of contemporary, Post-World War II trade activities on recent economic growth and development
performance across countries (Dollar and Kraay 2003, Frankel and Romer 1999). There
is only one study (Acemoglu et al. 2005) considering the effect of cross country trade
in earlier periods. They investigate the impact of long-distance overseas trade on institutional developments and the pre-industrial development process across European
countries.
Hence, until now there is no study exploring the possible long-lasting effects of trade
in European cities throughout the High and Late Middle Ages. The importance of
medieval trade for the development of cities and regions in the Middle Ages and the
following centuries is well-known and widely acknowledged. Apart from this, no research acknowledged the fact that medieval trade could have also long-term influences
on regional development persisting until today. This despite the fact that medieval
trade through its influence on agglomeration and spatial concentration of industry could
have lead to path-dependent regional development processes resulting in development
differences outlasting the centuries in between.
The aim of this study is to investigate whether medieval trade had caused differences
in regional development which are still visible today due to its its impact on agglomeration. If this is the case it could provide a new explanation for the uneven distribution of
economic activity and significant spatial concentration of industries throughout Europe
(e.g. Chasco et al. 2012, Koh and Riedel 2012, Roos 2005). Furthermore, it can contribute to the understanding of persistent differences in regional economic development
(Becker et al. 2010, Maseland 2012, Tabellini 2010 or Waidlein 2011).
To establish a link between medieval trade, agglomeration and contemporary performance we link typical characteristics of medieval trade and cities to the determinants
of agglomeration suggested by New Economic Geography (NEG) and agglomeration
economics (e.g. Krugman 1991, Glaeser et al. 1992). In a second step, based on studies combining NEG and endogenous growth models and the theory of path-dependence
(David 2007) we propose a positive connection between agglomeration, industrial concentration and contemporary development.
Afterwards, we test the causal chain from medieval trade through agglomeration to
2
contemporary regional economic development using a rich data set (where we choose a
NUTS-3 region as unit of observation) and a wide range of empirical methods. In general,
the detailed empirical analysis shows indeed medieval trade is robustly associated with
contemporary regional economic performance. Moreover, we also find that the majority
of the effect of medieval trade on contemporary regional development can be explained by
its influence on agglomeration patterns. Most importantly, we show that our hypotheses
are robust to the inclusion of many geographical, political, economical and historical
covariates of development and agglomeration and are not biased by endogeneity.
Finally, a mediation analysis shows that medieval trade activities are strong predictors
of today’s spatial distribution of economic activity and population and that around two
third of the influence of medieval trade on contemporary regional GDP per capita can
be attributed to this influence of medieval trade on agglomeration.
The remainder of the article proceeds as follows. First, we theoretically establish the
link between medieval trade, agglomeration and present-day’s economic development.
Afterwards, we introduce and discuss the most important variables and data and explain
the empirical setting. Next, we conduct our empirical analysis and interpret and discuss
the results in detail. At last, we conclude and summarize the main findings.
2 Theory and Hypotheses
It is a well established idea that trade was a decisive factor in the development of medieval
cities and the revival of city growth during the period of the so called “Commercial Revolution” (e.g. Börner and Severgnini 2012, Epstein 2000, Habermann 1978, Holtfrerich
1999, King 1985, Postan 1952, Pounds 2005 and van Werveke 1952). History provides
many examples of cities owing their importance primarily to their function as centers
of trade, like the German cities of Nuremburg (Nicholas 1997), Frankfurt (Holtfrerich
1999) or Cologne (King 1985) or the Polish city of Gdansk.1
Using concepts developed by NEG (Krugman 1991) and agglomeration economics, one
1
Obviously, there are exceptions from this story, i.e. cities and regions becoming large and important
agglomerations without being important in medieval trade. This is true for example for Stuttgart
(the sixth largest German city today) and Munich two of the richest and economically prosperous
cities and agglomeration areas in present day’s Germany. Stuttgart was not important until after the
Napoleonic Wars it became the capital of the newly founded kingdom of Württemberg. The rise of
Munich (today the third largest city of Germany) followed a similar pattern, albeit the capital of a
kingdom and residence of a bishop (and later archbishop) Munich began to become a large city not
before late 18th century. Again, it experienced large population growth in the nineteenth century
after the Napoleonic Wars until World War I. Even more, Bavaria and Munich as it’s center stayed
relatively poor till the 1950ies (when e.g. the Siemens corporation moved its headquarter from Berlin
to Munich). Additionally, the largest agglomeration in Germany the Ruhr Area largely results from
3
can explain why medieval trade was important for the rise of cities in medieval Europe.
This can be done by linking the characteristics of medieval trade and trade cities to
second nature causes of agglomeration (for an overview over these see e.g. Christ 2009,
Glaeser et al. 1992, Henderson et al. 2001). In medieval times, the economy, especially
the urban economy was characterized by a high degree of regional specialization (Ammann 1955, King 1985, Lopez 1952, Nicholas 1997,Postan 1952, Pounds 2005 and van
Werveke 1963).2 The Southern German cities that became important trade centers in
the later medieval for instance were specialized in textile (Barchent etc.) and paper production, while other areas had specialized in mining (like e.g. the Saxon town of Freiberg
or Liège in today’s Belgium that had the this times most productive coal field), or in
food and salt (where the cities at the French Atlantic coast were the main exporters).
The different regions exported in what they were specialized in – or had an comparative
advantage in e.g. due to natural endowments– and imported what they did not have
themselves.3 This specialization of trade cities on a particular industry or sector gave
rise to the existence of technological (non-pecuniary) externalities like Marshall-ArrowRomer (MAR) externalities (Marshall 1890, Romer 1986) or Porter externalities.4 Those
type of externalities arise as knowledge spillovers between firms in the same industry and
contribute therefore to the growth of both industry and city (Glaeser et al. 1992).5 . Indeed Epstein (1998) and more broadly Epstein and Prak(2008) in an anthology about
the Guilds and Innovation they edited show that the guild as the dominant economic
institution of the later medieval city indeed could have fostered innovation and enable
knowledge spillovers and diffusion within the urban economy (and through migration
also between cities).6
A second important characteristic of medieval trade cities was the comparatively high
the rich endowments with coal and iron making it to one of the most important nucleus of German
industrialization.
2
A comprehensive illustration of medieval trade activities is provided in Postan (1952) and Lopez
(1952).
3
A review of the general geographical patterns of trade and industry specialization in the middle ages
is provided among others by King (1985).
4
Nicholas (1997) additionally points to the fact that over the course of the Middle Ages the industry
dominating in a city e.g. the textile industry did more and more diversify. This intra-industry
diversification could be an additional channel through which technological externalities could had
been arisen.
5
Such knowledge spillovers between firms might appear because of imitations, movements of skilled
workers between the different firms in the industry etc.
6
For evidence about the high mobility of skilled craftsmen in this period see Reith (2008) in this
anthology. Of course, among historians there is no consensus about the role of guild and whether
their negative or positive effects for economic development are more dominant. However, at least
the more recent contributions clearly brought forward evidence that guilds indeed could had large
positive impacts through their positive influence on innovativeness.
4
variety of available goods. Those varieties of goods were available first at the local
markets, then at the big trade fairs in the Champagne and other important trade cities
(like Frankfurt, Cologne, Ulm etc.) and then, in the late medieval age in the branches
and kontors of the Hanseatic League and the trading companies (“super-companies”)
like the Fugger in Augsburg78 Especially the latter two also provide supply with luxury
goods and exotic commodities from far east, as long-distance trade was reestablished at
the beginning of Late Middle Ages. This high variety can be considered as an important
demand-side driven agglomeration force, because it makes it more attractive to settle in
a city.9
Additionally, the large variety of goods and prospering industry gave rise to the selfreinforcing circular causation caused by backward and forward linkages and leading to
agglomeration and core-periphery patterns in NEG models (Krugman 1991, Ottaviano
and Thisse 2004). Because trade cities provided a higher variety of goods, employment
for high-skilled specialized workers and –as consequence of the higher labor demand– also
higher wages, they attracted additional workers. When more and more workers made use
of the opportunity to work in the city as e.g. textile workers or craftsmen, employment
and the number of firms increased. This decreased the price index, raised real wages and
therefore resulted in the immigration of even more workers to the city. Consequently, this
pecuniary externality (forward linkage) caused increased agglomeration and industry
concentration in the city. Supplementary, more workers lead to a higher demand for
goods produced and/or traded in the city. The higher demand once more lead to the
expansion of markets and industries, raising labor demand and real wages resulting again
in additional immigration. This is the so called “home market effect” or the backward
linkage. In short, this amounts to the logic that industry will tend to concentrate
where there is a large market, whereas the market is large at the area where industry
is already located. Thus, forward and backward linkages constitute the virtuous circle
that generates agglomeration and uneven spatial distribution of population and economic
activity.10
7
For a detailed description of the business activities of the Hanseatic League the reader is reffered to
Dollinger (1966). An illustration of the medieval early medieval markets and fairs is found in van
Werveke (1963).
8
A comprehensive description of the medieval super-companies can be found in Hunt and Murray
(1999). An transaction economic analysis of the super companies using the example of the Fugger is
provided by Börner (2002).
9
This follows clearly from love of variety preferences commonly assumed in NEG models. Additionally,
one can make a transaction cost argument, because e.g. when living in a city there are no costs of
transporting the sold commodities back to the village.
10
Of course, the medieval city was a highly cartelized and regulated economy with dominant guilds and
significant rent-seeking activities (e.g. Braudel 1986). However as Braudel (1986) concludes since the
5
Furthermore, after the process of agglomeration lasted for some time other types of
technological externalities occurred. Conditional on certain factors (i.e. geographical
position or natural endowments) other industries located in the previously specialized
cities, e.g. in the Southern German city of Ravensburg (an important trade center in the
15th century) the traditional textiles industry was supplemented by paper production at
the beginning of the 15th century (Schelle 2000). In addition, there were also incentives
to locate in a trade city for firms using special commodities as inputs or that produced
inputs used in the industry the city was specialized in.11 Therefore also Jacobs (1969)
externalities occurred in the late medieval cities.12
A first test whether the story fits to stylized empirical facts about city population and
city growth in the Middle Ages delivers the regressions in Table1. There we regress
However, the main argument of this paper is that medieval trade has significant consequences on economic development today. Reassuringly, the self-reinforcing nature of the
described agglomeration and concentration processes implies a path-dependent process
of city development. This path-dependent development process results in differences in
concentration of economic activity and population that are persistent until today. Cities
involved in medieval trade activities over a sufficient period of time got locked in on a
superior development path as compared to other cities which were not involved. This is
a typical characteristic of processes caused by increasing returns or positive feedback and
that are characterized by multiple equilibria (David 2007). There are many examples of
historical events and phenomenons having long-run impacts on economic development,
e.g. Colonization (e.g. Acemoglu et al. 2001, 2002), Slave Trade (Nunn 2008, 2011),
the Neolithic revolution (e.g. Ashraf and Galor 2011, Olsson and Hibbs 2005 or Putterman 2008), the capacity to adopt and develop new technologies (Comin et al. 2010) or
the timing of human settlement (Ahlerup and Olsson 2012).13 Additionally, Maseland
(2012) shows, that regional development disparities in Germany are persistent and can
13th century something like market integration (to some extent) existed with prices varying in the
markets of cities every week according to supply and demand. Furthermore, the increasing spread
of the “Verlagssystem” sometimes might had limited the power of the guilds. Concerning the urban
rural wage differential evidence in general is limited for this period in time Braudel (1986) notes that
in general, also due to the power of guilds the wages in the city can be considered to be usually
higher than in rural areas. In line with this, Munro (2002) comparing the real wages in England
and Flanders between 1300 and 1500 found that the real wages in the cities were higher than in
rural areas and showed a higher downward rigidity. van Bavel and van Zanden (2004) in addition
notice that in pre industrial societies the relationship between city size and nominal wages usually
was positive.
11
The idea that vertical linkages along the supply chain can lead to agglomeration is developed in
Krugman and Venables (1995).
12
Jacobs externalities are knowledge spillovers arising between firms of different industries.
13
A comprehensive review of such events caused path-dependent developments is Nunn (2009).
6
largely be explained by strong and increasing differences between core areas and the
periphery. We argue that medieval trade can be added to the list of such events.
Finally, the positive connection between agglomeration, industry concentration and
regional economic growth is reported by several theoretical studies (e.g. Baldwin and
Martin 2004, Martin and Ottaviano 2001, Yamamoto 2003 or Bertinelli and Black 2004)
linking growth e.g. through innovations and agglomeration by combining standard NEG
and endogenous growth models. In addition, studies like Hohenberg and Lees (1995)
or Fujita and Thisse (2002) also establish empirically the positive relationship between
agglomeration and regional growth.
In conclusion, we postulate the following two hypotheses about the relationship between medieval trade and contemporary regional development:
Hypothesis 1. There is a positive and significant relationship between involvement in
medieval trade activities and regional economic performance today, i.e. cities that were
centers of medieval trade show a higher GDP per capita today than cities not involved
in medieval trade.
Hypothesis 2. Medieval trade activities influence contemporary regional economic development through their positive effect on agglomeration and industry concentration, i.e.
there is a positive and significant relationship between medieval trade centers, agglomeration and industry concentration measures and current regional economic development.
3 Data and Setting
3.1 Setting and Level of Analysis
Because medieval trade took place in cities and agglomeration is a regional phenomenon,
our empirical analysis is based on regional level data. We stick to the NUTS (“Nomenclature of Units for Territorial Statistic”) regional classification, the official regional
reference unit systematic used in the European Union (EU).14 Furthermore the official
regional statistics of Eurostat are available for those territorial units. Additionally, different regions on the same NUTS level have the advantage of being relatively comparable
to each other since they are defined according to a particular range of inhabitants.15 We
14
A detailed description and overview over the NUTS classification scheme and the regions can be found
in the data appendix and the references mentioned there.
15
Although the population thresholds are defined very widely, e.g. a NUTS-3 region can have 150.000
and 800.000 inhabitants. Again, sometimes there are exceptions so that some NUTS-3 regions show
a larger population. From this it follows also, that more densely populated regions cover on average
a smaller area. To overcome potential biases resulting from the this, we will control for the area of
7
choose to conduct our analysis on the most disaggregated level for which our essential
data (e.g. GDP per capita) is available. Therefore we conduct our analysis with a
NUTS-3 region as observational unit.
NUTS-3 regions are identical to existing administrative units in most of the countries
in our sample, which is an additional advantage of using them. In Germany for example
they are mostly identical to districts or district-free cities, in France to Departments
and in Italy to Provinces. A potential bias resulting from considering regions instead of
actual cities that were subject to medieval trade is limited as heterogeneity within NUTS3 regions should not be of significant size. However, some control variables are available
only at NUTS-2 or NUTS-1 level. In these cases we include the respective variables at the
level where they are provided. Another advantage of sticking to the NUTS classification
is that it enables to use fixed effects for the different NUTS-levels (countries, federal
states etc.). This allows to appropriately handle all kinds of heterogeneity on country
and regional levels. Besides this, one can also account for cross-sectional and spatial
dependence among the regions in the dataset. The latter being a important advantage
of regional empirical analyses especially when compared to country level investigations.16
3.2 Dependent Variables and Agglomeration Measures
As dependent variable we use the natural logarithm (ln) of GDP per capita in a NUTS-3
region, originating from the Eurostat regional statistics database. We take the latest
available values from the year 2009. All other time-variant variables are also taken from
the year 2009 to enable comparability.
As measure of spatial industry agglomeration we follow Roos (2005), Chasco et al.
(2012) and others in using the ln of the relative GDP density as measure for the spatial distribution of economic activity. The measure is calculated by dividing a region’s
share of GDP per capita through its share of the country’s total area. This means it
shows whether the concentration of economic activity in a region is below or above the
country’s average.17 As such this is a more direct measure of economic agglomeration
than population density. Additionally, we present results using the ln of a regions population density in 2009 as a more general measure of agglomeration, i.e. as a variable
identifying more densely populated places. These results are reported in Appendix C.
a region and introduce dummy variables for city districts, city states and districit-free cities (regions
with a high population density, i.e. a large population but a small area).
16
Chasco et al. (2012) discuss further advantages of using NUTS-3 regions as observational units in the
context of spatial economic analyses.
17
The exact formula according to which the relative GDP Density is calculated is shown in the data
appendix.
8
We think that the relative GDP Density is a more direct measure of industry agglomeration and concentration and is therefore should more suitable for our empirical analysis.
However, since population density might capture additional aspects of agglomeration
that might be important for economic activities indirectly and therefore can provide
additional insights.
Table A.1 in the data appendix gives a descriptive overview over all variables used
in the following empirical analysis. The exact sources and further explanations of all
variables are provided in the data appendix.
3.3 Independent Variables
This study aims to investigate the impact of trade between cities during the medieval
age.18 To be able to identify the theoretically assumed effect of medieval trade on agglomeration we focus on the most important trade cities, i.e. cities where trade probably
had the most powerful and long-lasting impact. Since agglomeration is a long-lasting
process unfolding its effects only after some time, it is important to ensure that trade
took place long enough in a city to influence agglomeration there in a sufficient way.
Stated differently, trade had to take place long enough in a city to lock it in on a superior development path. To account for this fact, we focus on important trade cities at
the end of the medieval period (i.e. around 1500 AD). This is due to the fact that cities
important at the end of the medieval period are most likely also having experienced
noticeable trade activities in the years before (i.e. over a longer time period).
Our main source of information about important medieval trade activities are maps
printed in historical atlases or monographs. We focus on maps because they provide
a much more comprehensive source of trade cities and activities then the information
available historical monographs. In addition, their information usually can assigned to
a certain period much clearer than that contained in books. In consequence, we collect
information about cities prominently involved in trade from four historical maps providing evidence about cities located on “major” or “important” trade routes around 1500
18
It is important to note that between the breakdown of the Roman Empire and the early medieval (the
foundation of Francia) there were comparatively small trade activities. Trade began to increase not
before the tenth century (Postan 1952, Braudel 1986). Furthermore, after the end of the medieval in
the course of the 16th century, overseas trade (e.g. with the colonies) and long-distance trade became
increasingly important. Due to this, the character of trade (e.g. rising importance of slaves trade)
as well as the leading trade centers changed (Spain and Portugal came to rise). Compatibly with
that, the leading actors of medieval trade like e.g. the Hanseatic League lost their importance in the
period following the medieval. In consequence, it is possible to isolate the medieval trade activities
in cities from trade activities before and after the medieval. This ensures that the effects we measure
empirically can actually be attributed to medieval trade activities and not trade in general or trade
in other periods.
9
AD (i.e. the late medieval). Due to the fact that there is no consensus or quantitative
evidence about the exact importance of trade cities and trade routes during the medieval period we have to consult several different sources to become sufficiently reliable
data. The first is a map printed in Davies and Moorehouse (2002), the second is a map
printed in King (1985). The third source is a map on Central European trade published
in Magocsi’s (2002) Historical Atlas of Central Europe.19 At last, we consult several
maps included in “Westermanns Atlas zur Weltgeschichte” (Stier et al. (1956). More
information about the kind of information and the geographical and temporal scope of
those maps is provided in the Data Appendix. There, we also list the primary sources
on the basis of which the maps are drawn – if we were able to identify them. We include
a city if it is mentioned in one of these maps. We include only cities located in EU
countries, since only for those the Eurostat regional statistics database provides data.
Despite this, in some cases we included cities in the sample not mentioned by the maps
but by other sources of information. For example, we include the eastern German city
of “Zwickau” because it is prominently recognized in Spufford (2002) standard account
on medieval commerce and is known for its importance in salt trade. In other cases, we
included cities not mentioned in the maps but in other sources for robustness checks.
Furthermore, we stick to other qualitative information in our judgment of the importance
of the included trade cities. For example, we look whether a city was an important
member of the Hanseatic League or a capital of a quarter or a third (like e.g. Dortmund or
Cologne). Information about this is provided by Dollinger (1966). Additionally, we also
look whether, especially for not so prominent trade cities (Paderborn, Soest, Harfleur,
Tarent etc.) they lied on well-known trade routes like the “Hellweg” in German (as it is
the case e.g. for Soest). Moreover, we consult several historical standard sources about
medieval trade activities in different Central European regions (e.g. Dietze 1923, Hunt
and Murray 1999, Schulte 1966, Spufford 2002 etc.) and look whether they mention a
city as being prominently involved in trade or having an over-regional importance as
market, trading place or fair city. Finally we also draw on other historical atlas like
that by Kinder and Hilgemann (1970) and other e.g. regional trade route maps (e.g.
Schulte 1966) as sources for validating the information in the primary maps.In the Data
Appendix (Table A.3) we report and discuss all these source and provide information
about which city is mentioned by which source.
Overall these sources have left us with 119 trade cities located in 10 European coun19
As we are not interested in information about only regionally important trade cities an additional
reason for choosing this particular maps is that they provide cross-national information about trade
activities.
10
tries. Our dataset encompasses all 839 NUTS-3 regions in these countries.20 The Data
Appendix offers a detailed description of how we construct our database of important
late medieval trade cities.
Even with the information in these sources, the relative importance of cities is not
always clear. Additionally, there is also a different degree of uncertainty about the
extent and location of trade activities and the course of main routes, i.e. the actual
importance of a certain trade route at a particular point in time is not always clear.
However, there are cities that undoubtedly were important centers of trade like the
Northern Italian city states (Milan or Genoa etc.), some Southern German imperial
cities (like Augsburg, Nuremburg or Ulm) and the leading centers of the Hanseatic
League (Hamburg, Bremen, Lübeck, Cologne etc.) . On the other hand, there are cases
were only some sources mention the city as important trade center or lying on a main
trade route, like in the case of Paderborn, Minden or some port cities in France e.g.
Harfleur or some smaller cities in Italy (Brindisi,Mantoa or Udine). This uncertainty is
a natural result of the qualitative — and therefore to some extent always subjective —
nature of the collected information and the scares amount of overall information about
the medieval period and the trade activities back then. To account for this uncertainties,
we will re-estimate the most important of our empirical results with alternative samples
of trade cities where we first remove cities mentioned only by one of our sources. Second,
we exclude cities reported in some of the maps or sources but actually do not lay on a
well-known important trade route, where no important member of the Hanseatic League
(according to Dollinger and Stier et al. 1956) or are not mentioned by any of our other
historical sources as being of notable importance in later medieval trade (albeit there
was probably some extent of trade activity). Those cities include e.g. Amberg, Einbeck,
Como, Paderborn, Parma or St. Melo.
21
What is more, we also conduct our empirical
analysis with a sample of trade cities including additional cities (Dijon, Piacenza or
Aigues Mortes) that are mentioned by some of the sources, but for which we — after
consulting several different information about the history of the respecitve places — are
in doubt of their actual importance in medieval trade, at least over a longer period.
At last, we try to ensure that we do not include trade cities that only experienced
significant trade activities for a a short period and therefore not long enough for resulting in a lock-in to a superior development path. To overcome this problem, that would
downward bias our results, we construct a fourth alternative sample of trade centers
20
We exclude the islands of Elba, Corse and Sicily from our sample because they are not comparable
with regions on the continent with respect to trade flows. (This follows Chasco et al. 2012 who also
exclude island regions).
21
A full list of excluded cities is reported in the Data Appendix.
11
only considering cities for which we found records of recognizable trade activities in earlier periods than the late 15th and early 16th century. The sources consulted here are
e.g a volume about medieval trade in the Levant by Heyd (1879a,b) and the already
mentioned volume about the history of German trade by Dietze (1923). Furthermore,
also Dollinger (1966) presents some evidence about trade activities in the periods preceding the late medieval in a map, where he e.g. depicts cities lying on the Hellweg and
“other important trade routes” in the period between 1286 and 1336. Additional, this
map also reports the signers of the treaty of Smolensk in 1229 (i.e. the most important
trade cities in this times Western Dvina trade) and additionally some information of
maps digitized by the Old World Trade Routes Project (OWTRAD) website, primarily containing information about trade in Eastern Europe, especially Poland.22 Exact
information about the construction of this alternative sample is provided in the Data
Appendix. Such information about earlier trade activities could be collected for 70 of
the originally 119 trade cities. As such, this last sample represents the most selective one
and probably contains only cities for which important medieval trade activities are most
sure. Overall, we consulted fifteen different sources to construct our different samples
of trade cities. However, even with this amount of sources one cannot be sure that the
coding of the trade city dummy variables is perfect. Regardless of this fact, there seems
to be no reason why the inclusion of cities that were probably not that important than
other cities or experienced trade activities for only a short period of time should more
than downward bias our estimates. The estimates obtained using this kind of dummy
variable should therefore considered to be a lower bound of the actual long-term effect
of medieval trade.
We will use two different variables as measures of late medieval trade and its impact
on contemporary regional development. First, we will use a dummy variable “Trade
Center” that is equal to one if a region includes at least one medieval trade city. The
lack of quantitative information and the limited availability of qualitative judgments
leads us to use a simple dummy variable coding important trade cities. Of course, this
implies that we treat all trade cities being the same with respect to the scale of trade
activities and agglomeration forces working there. However, since we try to focus on
cities located on “major” or “important” trade cross-national trade routes and also rely
on qualitative judgments of importance —when available– we should be able to reduce
the heterogeneity among the trade cities. Additionally, the construction of a dummy
variable allows also for the construction of a second variable “Distance to Trade Center”
representing the distance (in degrees) between a region and the closest medieval trade
22
http://www.ciolek.com/owtrad.html
12
city.footnoteThe variable is zero in regions that are coded as trade centers. This variable
offers a very useful direct test of our hypothesis that medieval trade contributed to the
emergence of time persistent core-periphery patterns and therefore can act as a notable
explanation for contemporary regional income differences.
Table 1 provides a summary of our trade city data. For each country, the total number
of NUTS-3 regions, the number of regions with trade cities, the share of trade center
regions and the average distance of a region to the closest trade city is listed.
[Table 1 about here]
As reported in the table, the average distance to a medieval trade center is about
1.5 degrees (e0.432 ) that is approximately 170 km. Overall around 14% of all regions
are considered as containing medieval trade centers. A list with the name, NUTS-3
region and country of all trade cities is provided in Table A.2 in the data appendix.
Furthermore Figure 1 shows a map that depicts all included NUTS-3 regions and the
regions with medieval trade centers (reddish colored).23
[Figure 1 about here]
4 Empirical Analysis
4.1 Medieval Trade and Contemporary Development
4.1.1 Descriptive Evidence
Some first insights about the relationship between medieval trade centers, agglomeration
and contemporary economic performance can be obtained from a descriptive look on the
relevant variables.
At first, we consider simple bivariate correlations between the ln of GDP per capita,
the trade center dummy, the ln of the distance to the next trade center and our two
measures of agglomoration, ln population density and ln relative GDP density. These
correlations are shown in Table 2.
[Table 2 about here]
In general, we see that there is a high and significant correlation between all the variables.
Additionally, the sign of the correlation coefficients are as expected (e.g. there is a
23
The geographical distribution of medieval trade cities in the map is largely consistent with what King
(1985) wrote about the location of leading trade and economic centers in medieval Europe (King
1985, p. 220)
13
strong positive relationship between agglomeration measures and GDP per capita. Vice
versa we found a negative association between distance to a trade center and both
agglomeration and GDP). The correlation between GDP per capita and the trade center
dummy is significant and positive, but comparatively low. On the one hand, this low
correlation could be the result of considerable heterogeneity of GDP per capita across
regions and countries in the sample that is not accounted for in these simple pairwise
correlations. On the other hand, the high correlation between the trade center dummy
and the agglomeration measures on the on side and agglomeration measures and GDP
per capita on the other side indicates that the effect of trade centers is largely running
through agglomeration. Therefore the observed correlations provide preliminary support
for our theoretical reasoning.
Another way to illustrate the stylized relationship between medieval trade, agglomeration and present day’s regional economic development is to compare averages values of
GDP per capita and agglomeration measures for late medieval trade centers and nontrade centers. This is done in Table 3 both separately for each country as well as for
the whole sample of regions. From the last line of Table 3 we can infer that in total,
i.e. pooled over all regions and countries in the sample, regions with late medieval trade
cities have a significant “GDP Advantage”, that is, their average GDP per capita is
around 5000 Euro higher than that of regions without trade cities. Furthermore, they
also exhibit significantly higher population and relative GDP densities.24 This result
does also hold within all countries apart from Lithuania where trade center regions show
a higher GDP per capita but the differences is insignificant. For relative GDP Density
the within country results are not that clear. In Belgium and the Netherlands the relative GDP Density is lower, although the difference is not significant.25 However, in
Austria, Germany, France and Poland the countries account for three quarters of the
sample, there is a statistically and economically significant advantage of trade centers
with respect to both regional economic development and relative GDP Density.
[Table 3 about here]
Finally, we estimate the kernel densities of ln relative GDP for all regions, for regions
with medieval trade cities and for regions without them. The kernel density of ln relative
GDP density is shown in Figure 2. The density for regions with and without medieval
24
The significance of the Difference between trade regions and non trade regions is tested by a two-sample
t test.
25
In the smaller countries (like Lithuania, the Czech Republic or Belgium) the insignificance of the
differences is probably attributable to the insufficient total number of regions/ trade centers. Here,
the numbers should be treated with caution.
14
trade centers is depicted in Figure 3. A comparison of those kernel densities reveals
that the variable’s kernel density over all regions is clearly leftly skewed and shows an
additional notable local peak on the right.26 The latter indicates that there is a cluster
of regions showing a relatively high spatial concentration of economic activity.
However, what is more interesting for our argumentation is the comparison of the
density across groups of regions with and without medieval trade cities. One can infer
from Figure 3, that as expected the kernel density across both groups differs considerably.27 Most importantly, the density function for regions with medieval trade centers
clearly show a larger mass in the right tail supporting the idea that agglomeration and
concentration of economic activity are higher in regions with medieval trade centers. We
also run similar estimations using population density as agglomeration measure. The
result of this task are shown in Appendix C (Figure C.1).
In sum, the descriptive analysis of the data delivers strong preliminary evidence for
our hypotheses.
[Figure 2 and 3 about here]
4.1.2 OLS Regressions
To test our main hypothesis, i.e. that regions with cities involved in medieval trade
exhibit higher levels of economic development today we estimate the following regression
using Ordinary Least Squares (OLS):
ln(GDP )cijk = α + βT Ccijk + γ10 Xcijk + γ20 Xcij + δc + θi + λj + cijk
(1)
Where ln(GDP )cijk is the natural logarithm of GDP per capita in NUTS-3 region k
NUTS-2 Region j in NUTS-1 region i of country c. T Ccijk is a dummy variable “Trade
Center” that is equal to one if a NUTS-3 region includes a medieval trade city and zero
otherwise. Xcijk and Xcij are vectors of NUTS-3 or NUTS-2 level covariates, respectively.
δc , θi and λj are country, NUTS-1 and NUTS-2 region fixed effects. At last, cijk is the
error term capturing all unobserved factors.28 Equation (1) is a straightforward way to
establish a significant direct link between late medieval trade activities and contemporary
economic performance. Our expectation is that β > 0 and significantly different from
zero.
26
Accordingly, a Shapiro-Wilk test clearly rejects the null hypothesis of normality for the kernel density
Conversely, a Kolomogorov-Smirnov rejects the equality of both group’s densities.
28
As mentioned before, all time-variant variables are measured in the year 2009 so we do not report an
index for the period of measurement.
27
15
But, even when medieval trade still matters today, does its impact transmit via agglomeration and concentration of economic activities in places where it took place? A
simple way to test this additional hypothesis is to look whether GDP per capita becomes
lower when the distance to medieval trade centers increases. Expressed differently, if the
effect of trade works through agglomeration, then, a “classical” core-periphery pattern
should emerge, with the medieval trade cities as core and the regions far away as periphery. One can therefore modify equation 1 by substituting the trade center dummy
through a variable representing the distance between a region’s centroid and the closest
trade city. Equation 1 can be rewritten as:
ln(GDP )cijk = α + ρln(Dist T C)cijk + γ10 Xcijk + γ20 Xcij + δc + θi + λj + cijk
(2)
Where Dist T Ccijk is the natural logarithm of the distance from a region’s centroid to
the closest trade city measured in degrees. We expect ρ to be negative and significant.
4.1.3 Baseline Results
First, we estimate equations one and two using NUTS-1, NUTS-2 and country fixed
effects. They are included to account for shocks common to all observations at the respective geographical unit. Additionally, they are included to exploit the pure variation
between NUTS-3 regions.29 We also add a set of basic geographical controls, including
latitude, longitude and altitude of a NUTS-3 region. The latter set of variables should
capture the general geographical pattern of development in Central Europe. This means,
that economic development roughly increases from South to North (i.e. with increasing
latitude) and decreases - in our sample- from West to East (i.e. with increasing longitude). Furthermore, it is a well known fact that regions with a higher latitude are more
difficult to reach - what seems relevant for trade- and show a less favorable climate so
that we expect a negative influence of altitude.
The results of these regressions are shown in Table 4. There, we report three different standard errors above each coefficient. At first, in parentheses there are reported
heteroskedasdicity robust standard errors. Below those, in brackets we present standard
errors obtained by multiway clustering on NUTS-1 and NUTS-2 region level according
to the methodology of Cameron et al. (2011). The use of multiway clustering is justified
because it is likely that the development in NUTS-3 regions is not independent of that
29
Overall, there are 49 NUTS-1 regions and 143 NUTS-2 regions in our dataset. In the regression some
of them are omitted, because of multi-collinearity. The multi-collinearity is most often caused by the
fact, that sometimes, like in the case of the German city states Berlin, Hamburg or Bremen NUTS-1,
NUTS-2 and NUTS-3 regions are identical.
16
in NUTS-1 or NUTS-2 regions.30 Supplementary, because multiway clustering allows
for arbitrary residual correlation across both included dimensions, it also accounts for
possible spatial correlation. Finally, the third standard errors (in curley brackets) are
adjusted for two-dimensional spatial correlation using the method proposed by Conley
(1999).31
[Table 4 about here]
A look at the estimation results confirms our expectations and the descriptive evidence brought forward before. Regions with medieval trade centers show a significantly
higher GDP per capita than regions without such cities. The coefficient of the trade
center dummy remains relatively stable and significant at 1 % level, regardless which
combination of control variables and fixed effects is used. According to column (3) of
Table 4, where we include the full set of country and region dummy as well as the basic
geographic controls, regions with medieval trade centers on average have around 30 %
higher GDP per capita than regions without. This means that the effect of medieval
trade is not only statistically but also economically of considerable significance.
This holds also true for the coefficients of the distance to trade center. They are
always highly significant and are quantitatively in the same range as that of the trade
center dummy. Furthermore, they show the anticipated negative sign.
The clear positive relationship between contemporary GDP per capita and medieval
trade centers is also illustrated graphically in Figure 3, a partial regression plot of the
Trade Center Dummy based on the full baseline specification in column (3). And in
Figure 4 the same is done for the negative relationship between the distance to a medieval
trade center and present days GDP per capita.
Regarding the geographical controls latitude and longitude turn out to be insignificant
throughout all estimations. Altitude, to the contrast, is always significant and its coefficient shows the expected negative sign. Furthermore, the NUTS-2 dummies are often
not significant and do – according to the adjusted R2 – add nothing to the explanatory
power of the model. For this reason, they would only introduce additional noise in the
estimation and are therefore excluded from the remaining regressions.
30
It might even be the case that the development of included variables regional variables is correlated
within countries. However, due to the fact that we only have ten countries in our sample and clustered
standard errors are only consistent asymptotically, clustering at country level is no option.
31
Conley’s (1999) standard errors are obtained using a cutoff point of 3 degrees (approx. 330 km) after
which the spatial correlation is assumed to be zero. We experimented with several different cutoff
points and this cutoff produced the most conservative standard errors.
17
The three different types of standard errors in general do not differ substantially. If
any, the standard errors in brackets, adjusted fro multiway clustering are a little bit
larger than the other two. Because of that, we will use standard errors clustered on
NUTS-1 and NUTS-2 level, for all remaining specifications if possible.
[Figures 3 and 4 about here]
4.1.4 Controlling for Determinants of Agglomeration and Development
To ensure that the significant positive relation between medieval trade and contemporary economic development is not driven by omitted variables bias we have to control
for relevant determinants of both agglomeration and economic development. As a next
step, we therefore add several sets of control variables to the baseline specification. In
agglomeration economics, the causes of agglomeration are categorized in first nature
(physical and political geography, climate etc.) and second nature causes of agglomeration (man-made factors, i.e. agglomeration resulting from spatial spillovers or scale
effects) (e.g. Chasco et al. 2012, Christ 2009, Ellison and Glaeser 1999, Krugman 1993,
Roos 2005). This literature assumes that there are direct effects of both types of causes,
as well as an additional indirect effect of second nature through its interaction with first
nature. Because medieval trade is supposed to be a first nature cause of agglomeration,
this indirect effect geography and other natural factors exert on first nature causes is
what we especially have to control for.
Additionally to standard economic agglomeration and growth literature we also have
to account for potentially important historical causes of agglomeration and development. This clearly follows from our argument that medieval trade influenced regional
development processes through its impact on agglomeration and industry concentration.
In conclusion, we decide to group the control variables in four set of variables we add
separately to the baseline specification (without NUTS-2 dummies).
The first set of variables controls for the “geographic centrality” of regions. It includes
variables measuring the distance of a region to the closest important infrastructure facilities (airports, roads and railroads) and to important political and physical geographic
features (coasts and borders).32 Especially, the last two are found to be important first
nature determinants of agglomeration (e.g. Roos 2005, Ellison and Glaeser 1999). Additionally, the ln of the distance of each region to the geographically nearest major river
32
Holl (2004) and Martin and Rogers (1995) establish empirical and theoretical evidence on the importance infrastructure facilities for industry location. This justifies the inclusion of distance to road,
airports and railroads as control variables.
18
is included as control.33 Rivers are geographical features important for both medieval
trade, industry and city location (Börner and Severgnini 2012, Bosker and Buringh 2010,
Ellison and Glaeser 1999, Roos 2005 and Wolf 2009). The idea behind this set of controls is to ensure that we do not simply capture the impact of many medieval trade cities
being located at geographically favorable places today or in the past.
A second set of variables controls for relevant contemporary characteristics of the included regions. It comprises out of dummy variables for district-free cities in Germany
(which are by definition larger or more densely populated places than others), for the
regions that include a country’s capital or the capital of an autonomous region.34 Additionally, a categorical variable identifying the degree to which a region can be considered
as a“mountain regions” is included. Furthermore the set includes dummies for regions
with coal or ore mines (or mining firms), for regions located in the former GDR and for
regions located in Easter European post-communistic transition countries. At last, it
includes the ln of a region’s area. In consequence, this set of controls accounts for many
important first nature causes of agglomeration (political geography and resource endowments) as well as for relevant historical facts that could have influence the contemporary
economic performance of a region (like communism).
The next set of controls captures historical characteristics of the regions that could
matter for both present day’s agglomeration and economic performance. Here we consider dummy variables indicating regions with a university founded before 1500 AD and
regions that adopted printing technology before 1500 AD. As unearthed by Cantoni and
Yuchtman (2012), Dittmar (2011) and Rubin (2011) both universities and printing technology are important factors in explaining the late medieval commercial revolution and
city growth. To account for the positive influence Protestantism probably had on economic development (Woesmann and Becker 2009, Rubin 2011) we also include ln distance
to Wittenberg as variable in this set of controls. Furthermore, we also include dummies
for regions containing at least one imperial city or at least one city that was member
of the Hanseatic League. Finally, we also control for the possible long-lasting influence
of roman heritage and low transport costs for trade and agglomeration in including a
dummy for cities located at an important imperial road (Postan 1952).35
The fourth set controls for the most important covariates of economic growth and
development. Here we use the share of people aged between 25 and 64 with tertiary
33
In Germany for example we consider Elbe, Danube, Rhine and the Oder as major rivers.
An autonomous region is considered to be a Belgian or Italian Region or a German or Austrian federal
state (“Bundesland”).
35
This variable considers the Via Regia, the Via Regia Lusatiae Superioris and the Via Imperii as the
probably important imperial roads more or less following the route of former Roman roads.
34
19
education (on NUTS-2 level) as measure for regional human capital.36 As variable to
measure the quality of regional economic and political institutions we use the quality of
government index developed by the Quality of Government Institute at the university of
Gothenburg which provides a measure for regional institutional quality design similar to
the World Governance Indicators (WGI) of the World Bank.37 As measure for regional
inequality we construct the ratio of average workers compensation to GDP per capita.
As measure of innovative activity in a region we use the number of patents registered by a
region’s firms again at NUTS-2 level. Furthermore, we include a region’s unemployment
rate, ln of the average workers compensation and the ln of the average fixed capital of a
region’s firm.
Finally, the last set of controls include all robust covariates from the regressions before.
The robust controls are obtained by including all variables in one regression that were
significant both times when added with one of the other four sets of controls to the baseline specification. In the next step, we did remove the variables becoming insignificant
in that regression. We repeat this procedure until only significant controls remain in the
specification.38 This procedure results in a set of 12 variables robustly associated with
GDP per capita. These are altitude, the ln distances to airports, railroads and rivers,
dummies for district free cities, capital cities, capital cities of autonomous regions, postcommunistic transition countries, Eastern Germany, the ln of a region’s area, the share
of people with tertiary education, the inequality measure and the printing press before
1500 AD dummy. Once more, this highlights the importance of human capital and political geography. Furthermore the robust influence of printing confirms Dittmar’s (2011)
claim that printing technology fostered - similar to medieval trade- localized spillovers
and forward- and backward linkages.
The results of the regressions are shown in Table 5. There we first add the first four
set of controls separately to the baseline specification and then we include as fifth set
all robust covariates to the country and NUTS-1 region fixed effects. We see that the
coefficient of the trade center dummy and the distance variable remain significant in
every of the specifications although the sizes of the coefficients is considerably reduced
compared to the baseline estimates.
[Table 5 about here]
The coefficient is smallest (e.g. 0.07 in the case of the trade center dummy) in the
36
Again, we take the values for the year 2009.
This variable is for some countries available at NUTS-1 level and for others it is available at NUTS-2
level. For details consult the data appendix.
38
These regressions are not shown but are available from the author upon request.
37
20
specification with all robust covariates added to the baseline model. This is not surprising since in this specification we added only the variables with the highest explanatory
power to the regression. It suggests, that medieval trade center regions have today a
GDP per capita around 7 % higher than other regions. Based on the average regional
GDP per capita in our sample this corresponds to a GDP per capita that is approximately 1200 Euros higher. When looking at the different set of controls it is evident from
the adjusted R2 , that region characteristics and growth covariates add most additional
explanatory power to the model. Apart from mountain and mining region dummies,
each variable in the regional characteristics set is significant and especially the effects
of political geography (capital regions or regions with a capital of a autonomous region) seem to be important. And regarding the growth covariates especially inequality
(with an remarkable negative sign) and human capital exert a strong effect on GDP per
capita.39 In general, the historical region characteristics are least important in explaining
contemporary regional economic development. But regions with universities and cities
adpoted printing technology before 1500 AD seem to have a significantly higher GDP
per capita even today, once again highlighting the importance of human capital.40 However, the university before 1500 AD dummy becomes insignificant when added jointly
with the measure of current regional human capital. This suggests universities lead to
advantages of regions concerning their human capital persisted until today. Finally, the
robustly negative impact of the distance to river variable again shows the already widely
acknowledge role of first nature geography for regional economic development.
Overall, we see that the relationship between medieval trade and contemporary regional development is robust to the inclusion of a wide range of control variables and
other important determinants of agglomeration and economic performance. The one
exception is the estimation in column (10) where distance to trade center becomes insignificant.
4.1.5 Accounting for Endogeneity
Even after controlling for many factors endogeneity of the medieval trade variables remains a serious issue. Endogeneity primarily could arise through unobserved factors,
influencing both contemporary regional development and medieval trade. Geography
39
This finding is for example in line with Simon (1998) and Gennaioli et al. (2013) who highlight the
importance of human capital for regional development and city growth.
40
In the specification with the distance to trade center variable and historical region characteristics
(column (7)) also the other historical region characteristics seem to be significant (at least at 10%
level). This indicates that some of the effects captured in distance to trade centers are in fact e.g.
are attributed to the course of important imperial roads like the Via Regia.
21
might be a prominent factor for which this holds true. However, we can control for
geography in our regressions. But there are many other unobservable factors that might
affect both our right- and left-hand side variables. A prominent example is institutional
quality in medieval cities an important factor in medieval trade and the commercial
revolution (e.g. Greif, 1992, 1993 and 1994).Other cases are cultural differences between
regions and countries –apart from being protestant or not– or historical differences in
politics between regions.
To solve the endogeneity issue, we therefore run IV Regressions using the Limited
Information Maximum Likelihood (LIML) method.41
In order to be able to test the validity of the exclusion restriction we choose two
instrument variables.
The first considered instrument variable is a categorial variable (taking the values
zero, one, two and three) indicating whether a region is classified as a mountain region
by the official EU regional statistics. The variable is zero if a region is not classified
as a mountain region. It is equal to two or three if the region is a mountain region
according to two different set of criteria (for details about the exact definition consult
the Data Appendix).42 The idea behind this variable is intuitively plausible. In mountain regions, characterized by higher trade costs, less favorable climate and many other
adverse features trade activities were lower than in region located at large rivers, the
coast or in low altitude areas with fertile soils and less rugged terrain. Especially in
the medieval age, where no advanced transport technologies are available — especially
for over-land transport — mountains constituted a severe hindrance of trade (Spufford
2002).43 Furthermore, as highlighted by Bosker and Buringh (2010) high elevation (as
well as differences in elevation between places) has a considerable negative effect on city
41
This estimation method has better small sample properties and is most often more efficient than the
standard 2SLS method,especially in the presence of weak instruments. Its confidence intervals are
more reliable and it is unbiased in the median when the instruments are weak (Stock and Yogo 2005).
42
Albeit this variable is of categorial nature we choose to include it as a single variable and not by using
three different dummies as instruments. This is primarily motivated by guaranteeing a parsimonious
set of instruments since the IV estimates are biased towards the OLS estimates when the number
of instruments increases. Furthermore, the test of overidentifying restrictions wouldn’t be valid if
one include several instruments following the same reasoning or originating from the same measure
phenomenon as excluded instruments in the first stage. However, the results are fully robust to using
the three different categories of the mountain region variable as separate instruments. They are also
robust to recoding the three categories to one and include the variable as binary dummy variable.
Results not shown but available from the author.
43
Evidently, the large amount of trade activities between the northern Italian city states and the southern
German cities (Ulm, Ravensburg etc.) require that the traded goods are transported over the alps,
e.g. through the Splügen Pass (Schulte 1966). However, the transport probably took place over only
a few important passes and none of the small villages and populated places along those mountain
routes could develop to an remarkable center of trade.
22
growth and urban potential of a place. The exogeneity of this geographical characteristic
of a region should not be a concern.
The second instrument variable we will use is a dummy variable for cities that were
residential cities of bishops before 1000 AD. The church as political, spiritual and economical power had a significant impact on the development of cities in the medieval
age (e.g. Baker and Holt 2004, Isenmann 1988, King 1985 and).44 Because of this it is
probable that ecclesiastical centers, like residential cities of bishops did grow larger and
had a higher probability of becoming a trade center. In line with this reasoning, Börner
and Svergnini (2012) could show that trading activity (in- and outflows of commodities)
were higher in bishop residence cities. Additionally, Bosker and Buringh (2010) found
that the presence of a bishop was a important factor in the foundation and development
of cities during the Middle Ages. The exogeneity of this measure is not as sure as in
the case of distance to river. But nevertheless, since we can control for geography it
is hard to find a variable that could potentially influence both the location of bishop
residences in 1000 AD and contemporary regional development. First, in 1000 AD most
of the political and economical institutions emerged in the late medieval did not exist.
Even the central political power of our sample countries during the middle age, the Holy
Roman Empire, was found in the second half of the 10th century and couldn’t therefore
have larger influences on bishops residences founded before 1000 AD. This is especially
true because many of the considered dioceses or archbishoprics are already established
when the Empire was found in 962 AD. Second, we control for many other historical factors like being located on an important imperial road or early adoption of printing that
might had influenced both the location of trade cities, bishop residences and economic
development today. Third as explained e.g. in Pounds (2005) the dioceses built in the
early medieval period were virtually identical to the territory of predated Roman cities.
In consequence, their location was determined centuries before the early medieval period
which makes it even more unlikely that they are endogenous to contemporary economic
development.
In other words, there are many reason to conclude that bishop residences before 1000
AD are exogenous and can be used as instrument.
Additional to those instruments, we make use of Lewbel’s (2012) approach that ex44
King (1985) describes the importance of the church for commercial activities and trade, i.e. they
mentioned that in many cases the local fairs and markets are managed and organized by the church.
Pounds (2005) and Nicholas (1997) additionally emphasize the importance of bishops for the development of cities in the early middle age, when traditional trade declined during the economic
depression in the eighth and ninth century. Finally, Hunt and Murray (1999) notice the significance
of the church for city development and commerce arising from fostering ecclesiastical tourism and
pilgrim activities.
23
ploits heteroskedastic first stage errors terms to generate artificial instruments not correlated with the product (covariance) of the first stage’s heteroskedasdic errors.45 This
method can provide more reliable estimates if it is doubtful, that the instruments meet
the exclusion restriction or are weak. Since at least the exogeneity of the bishop seats
can be disputed in principle this method ensures that we do not produce invalid IV
estimates. The strength of these generated instruments depend on the amount of scale
heteroskedasdicity in the error. The presence of heteroskedasdicity in our first stage
regression is tested with a Pagan-Hall test. The test clearly rejects the presence of a
homoskedasdic disturbance (p-value<0.000). Therefore, the method can yield reliable
estimates although first stages statistics are not available.
46
We run LIML IV regressions using the instruments outlined above and using Lewbel’s
(2012) approach with generated instruments for the trade center dummy and the distance
variable. We include the set of robust covariates as well as NUTS-1 region and country
fixed effects as controls, i.e. we reestimate columns (5) and (10) of Table 5. The results
of these estimations are shown in Table 6.
The first important result is that throughout all specifications the trade center dummy
and the distance to trade center variable are significant and retain there signs. Even
more, the size of the coefficients increased remarkably, at least in the case of the conventional IV regressions in columns (1) and (3). Moreover, the distance to trade center
variable that was insignificant before in column (10) of Table 5 regains significance at
1 % level. This can be interpreted as endogeneity downward biased the OLS results,
probably due to measurement error or a negative correlation between an unobserved
factor and our medieval trade measures. Concerning the validity of the instruments the
overidentification tests (Hansen J-statistic) informs us that the validity of the exclusion
restriction cannot be rejected in almost all case at the common levels of significance.
The exception is the last specification where we cannot reject the null at all levels of
significance. Due to this, one should be cautious in interpreting the results from the
last columns here. Nevertheless, in line with our arguments above it seems the case that
the being a mountain region and bishop residences before 1000 AD affect contemporary
levels of development solely through their impact on which cities became medieval trade
centers.47 Furthermore, at least in the case of the trade center dummy, Lewbel’s (2012)
45
The vector of instruments Zj is constructed by multiplying the first stage error terms with each of
the included exogenous, mean-centered regressors (all or a subset of the first stage regressors), i.e.
Zj = (Xj − X) (Lewebel 2012).
46
Lewbel 2012 mention several papers that already applied this method resulting in plausible estimates
e.g Sabia (2007) or Kelly and Markovitz (2009).Thus, the method has proven to provide reliable
estimates in different empirical settings.
47
In fact, it is very likely that geographic characteristics like being a region in the mountains also
24
approach show, that our results hold even when we do not use external instruments but
instruments that are exogenous by construction. However, the coefficients obtained with
LIML IV are much larger as that resulting from Lewbel’s (2012) approach that are in
much closer to the original OLS estimates. Since Lewbel’s (2012) approach relies on
second moment conditions and additionaly produces a comparatively large number of
instruments it is likely that this results reflect the lower bound of the true estimates.
Turning to the first stage results, it emerges that both instruments are indeed significant and strong predictors of medieval trade. The bishop dummy is highly significant
in both specifications. This is also true for mountain region dummy, although it is only
marginally significant when the trade center dummy is instrumented . The underidentification test and the Angrist-Pischke F statistic of excluded instruments always indicate
that the instruments are strong and relevant.
Altogether, the IV estimations show that endogeneity does not affect the detected
significant relation between medieval trade and contemporary economic development. If
anything, endogeneity downward biases the OLS estimations and therefore lead us to
underestimate the true effect.48
[Table 6 about here]
4.2 Further Results - Index of Medieval Commercial Importance
Although the evidence brought forward in the previous section provide robust empirical
support for a significant relationship between medieval trade and contemporary regional
development, the data on which the results are based has its limitations. First and
foremost, the evidence so far is solely based on a dummy variable constructed according
to whether a city was located at an important trade route and few other qualitative
judgments about their importance. In treating all trade cities as equal this approach is
probably not able to capture all the dimensions and factors that made a city an important
center of commercial activity throughout the medieval. In consequence, we possibly do
not catch the true effect of medieval trade or commercial activities on contemporary
development levels. However, based on the data set at hand and historical evidence about
important determinants of trade, economic and commercial activities in the middle ages
one can construct an “Index of Commercial Importance” for each region in our sample.
Among the many potential determinants of medieval commercial activity, we choose eight
influenced which cities became residence cities of medieval bishops but since we include both variables
jointly in the first stage we take into account this correlation.
48
A test of endogeneity of the instrumented variables rejects the null of actual exogeneity in at 1 % level
in every LIML IV regression.
25
to construct the index. At first, we include out trade center dummy, representing cities
located on important trade routes. Second, we consider the variable indicating cities
that were residence of a bishop or archbishop before 1000 AD. As already outlined, the
church was found to be one of the most important factors in the development of medieval
cities and trade. Hence, the presence of a bishop should be a valid proxy variable for
cities of notable commercial importance. Third, we include the ln distance to coast of
each region’s centroid, representing the distance of each city to a potential sea harbor
and the significant trade cities located at the coast (like e.g. many of the Hanseatic
cities). Fourth, we include the dummy variable identifying important members of the
Hanseatic League. Since the Hanseatic League was one of the leading actors in medieval
commerce, its important members cities very likely were subject to significant commercial
activity. Fifth, we adopt the dummy variables representing cities that had the status
of an imperial city or that were located at an important imperial road. As transport
cost were a crucial factor in medieval trade, the presence of a paved and protected road
should be an important economic advantage for the cities located at it (e.g. Spufford
2002). On the other hand, most of the important trade cities in the Holy Roman Empire
that were not member of the Hanseatic League were free or imperial cities. Due to this,
imperial cities, with their political and institutional microcosm can be seen as germ cells
of commercial activity in the medieval period(Cantoni and Yuchtman 2012). Sixth, we
include a variable depicting regions in which medieval mining activties (copper and salt
mining) took place. This accounts for the fact that salt and copper —as raw materials
in general— were some of the major commodities trade in medieval Europe (e.g. Postan
(1952), King 1985, Spufford 2002). Finally, we follow the reasoning of a recent study by
Cantoni and Yuchtman (2012) showing that universities decisively fostered commercial
activities and market establishment in the area around them. Consequently, we include
the dummy variable reporting cities with universities founded before 1500 AD as last
variable. The index is constructed by simply adding up this variables combining them
in one index ranging from zero to eight.Thereafter, we substract the mean of the index
from all its values so that the average region would have a value of zero. Regions with an
below average value therefore have a negative and regions with an above average value
have a positive value. We also construct an alternative version where we include the ln
distance to trade center variable instead of the trade center dummy.49
Clearly, there are other determinants of commercial activity in the middle age. Nevertheless, we choose this set of variables because these variables are significant predictors
49
We recode this variable so that it is positively associated with economic development and agglomeration
as the other seven variables in the index.
26
of the original trade center dummy when jointly included in probit model. Together,
they produce a pseudo R2 of around 0.2.50 This result serves as a initial hint confirming
the relevance of our variables for explaining commercial activity in the medieval age.
We now perform OLS and instrumental variable regressions (as before with the LIML
and Lewbel’s (2012) method) using both versions of the index of medieval commercial
importance as independent and the ln of GDP per capita in 2009 as dependent variable.
We include the complete baseline specification (NUTS-1, NUTS-2 and country fixed
effects as well as the basic geographic controls) and the set of robust covariates employed
in Tables 5 and 6 supplemented by NUTS-1 region and country fixed effects. This ensures
that the results are comparable to that obtained before using the simple trade center
dummy and the distance variable. The results are shown in Table 7.
[Table 7 about here]
All in all, the index of commercial importance, in both the original and the alternative
version, shows up significant with a positive sign in every regression. Reassuringly, the
LIML IV regressions using the same instrumental variables as before and a version of
the index without the bishop before 1000 AD dummy, yield a more significant and
remarkably higher coefficient. This is similar to the IV regressions using the dummy
variable. The coefficient obtained with Lewbel’s (2012) generated instruments is much
closer to the original OLS estimate but keeps its significance. Furthermore, the Lewbel
estimate has to be treated with some care since the overidentification test does reject
the null of a valid exclusion restriction at the marginal significance level.
To sum up, the index of commercial importance confirm the results of the regressions
using a simple dummy variable. Therefore, it is fair to conclude that there is a statistically robust relationship between medieval trade and commerce and today’s regional
economic development.
4.3 Medieval Trade, Agglomeration and Contemporary Economic
Development - Establishing Causality
Until now, we only indirectly show that medieval trade influences present-day’s regional
economic development through its impact on agglomeration. We did so by showing
that the distance of a region to the next trade city is robustly negatively associated
with regional GDP per capita. In this section we will conduct a more direct test of the
50
Regression not shown but available from the author.
27
proposed causal chain going from medieval trade activities to medieval city growth to
contemporary agglomeration patterns and from there to regional economic performance.
4.3.1 Trade and City Growth in the Medieval Age
The first building block of our argument is that there should be a positive association
between involvement in medieval trade activities and city growth during that period. To
illustrate that the theoretically proposed relationship between medieval trade and city
growth does actually exist, we run a set of regressions were we explain ln city growth
in the medieval period by the trade center dummy and other covariates of medieval city
growth identified in the literature.The population data on which the city growth variable
is based originates from Bairoch’s (1988) compilation of European city population data
from 800 to 1850. We include every city for which there is population data in Bairoch
(1988) in 1500 AD and that is located in one of our ten sample countries. This leaves us
with 361 cities from which 90 are coded as trade cities based on the same information
than in the NUTS-3 region sample. A list of all included trade cities is provided in the
Data Appendix.
The estimated results are depicted in Table 8. There, in columns (1) to (3) we run
cross-sectional OLS regressions with the natural logarithm (ln) of city growth between
1500 AD (the end of the medieval period) and 1200 AD, 1300 AD and 1400 AD. We
choose these three variables to demonstrate that the results are not dependent on the
chosen period and furthermore are stronger when we consider a longer period of city
growth. The latter would be an indication that the impact of trade on city growth
works trough agglomeration processes unfolding there effect only after a longer period of
time. In every of the regressions we include country fixed effects as well as a set of other
set of historical determinants of city development as controls. We control for first nature
agglomeration forces by including the distance of a city to the next river or coast and also
a city’s latitude and longitude and whether it is classified as a mountain region and was
therefore difficult to reach(e.g. Bosker and Buringh 2010, Spufford 2002). Furthermore,
we consider several dummy variables indicating whether a city was residence of a bishop
before 1000 AD, had the status of imperial city, was located at an important imperial
road or was a member of the Hanseatic League.51 At last, we always include the ln
of the initial city population at the beginning of the considered growth period. This
51
This variables were already used in the preceding empirical analysis on regional level data. However,
the NUTS-3 level variables do not always fully coincide with the city level variables. This is due
to the fact that a NUTS-3 region could harbor an archbishop in 1000 AD but none of the cities we
consider in this sample and are located in this region.
28
accounts for the fact that city growth is concave in city size and in consequence the
growth rate of a city strongly depend on there initial size.52 This is, we estimate the
following regression specification:
ln(CityGrowth)i, 1500 = α + βT Ci + γP OPt + δ 0 Xi + θc + i
t
(3)
Where ln(CityGrowth)i, 1500 is the ln the growth in population in a city between 1500 AD
t
and period t with t=1200, 1300 or 1400 AD. T Ci is the trade center dummy P OPt is the
ln city population begin of the period and Xi is a set of time-invariant covariates and θc
are country fixed effects.We also estimated this equation using the Index of Commercial
Importance insteas of the trade center dummies. These results, that do not generally
not differ from that reported here are available in Appendix C (Table C.2).
Turning to the interpretation of the results, we clearly find that the trade cities show
significantly higher growth throughout the medieval than non trade cities. This is clear
evidence in favor of our theoretical reasoning that medieval trade contributed to city
growth and agglomeration. Furthermore, we also see a highly significant and negative
effect of the initial population level showing that indeed already large cities did grow
slower.
What is more, in columns (4) and (5) we also run random effect (RE) estimations using
a panel data set comprising out of the same sample and variables as the cross section. In
these estimations we first regress the ln of the city population in every of our considered
years (1200, 1300, 1400 and 1500 AD) on the trade center dummy and the same set of
controls as previously in the cross sectional estimates and additionally we add year fixed
effects. Again, pooled over all years, the population of a city is significantly higher if the
city is a important medieval trade city. At last, we regress the change in ln population
between every of our base years on the trade center dummy and additionally include the
lagged population in the regression (what is similar to the cross sectional estimations).
Once more, we found a significantly positive association between being a trade center
and changes in population throughout the period from 1200 AD through 1500 AD.
In sum, this results suggest that medieval trade can indeed be regarded as an important determinant of city growth and agglomeration during the middle ages. Having
established this, in the following we will focus on a detailed investigation of the relationship between medieval trade activities, contemporary agglomeration patterns and
regional economic growth.
52
A descriptive overview over all variables used in the city level estimations is available in Table A.2 in
the Data Appendix
29
[Table 8 about here]
4.3.2 The Medieval Legacy of Contemporary Economic Agglomeration Patterns
The next step in our causal chain is to link medieval city growth and contemporary
economic agglomeration patterns, i.e. we have to establish that there is a significant
amount of path-dependency in city development throughout the regions in our sample.
To do so, we regress the ln of the relative GDP density of a region on the three medieval
city growth variables used in the previous subsection, the initial city population at
the beginning of the considered growth period and again NUTS-1 region and country
fixed effects and the robust covariates used already in the preceding estimations. More
formally spoken following regression equation is estimated using OLS:
ln(RGDP D)cijk = α+βln(CityGrowth)cijk, 1500 +γP OPcijk,t +δ 0 Xcijk +θc +i +cijk (4)
t
Where ln(RGDP D)cijk is the ln of the relative GDP Density in a NUTS-3 region,
ln(CityGrowth)cijk, 1500 is the ln of a city’s population in 1500 AD divided by its popt
ulation in t with t being either 1200, 1300 or 1400 AD. γP OPcijk,t represents the ln of
the city’s population at the t, i.e. the beginning of the considered growth period. Xcijk
is the set of robust covariates used several times before. θc and i are NUTS-1 or country
fixed effects, respectively. cijk finally is the error term.
The final step, is then to establish the relationship between medieval trade, contemporary economic agglomeration (via path dependent agglomeration processes as shown
above) and regional economic development.
We will achieve this by conducting a causal mediation analysis (estimation of mediation effects) following the method developed by Imai et al. (2010, 2011).53 Mediation analysis enables to disentangle direct and indirect effects –via determining
agglomeration– of medieval trade on contemporary development. Since we cannot rule
out that there are direct effects or –what amounts to the same– indirect effects of medieval trade working via other channels this methodology seems to be appropriate for
our setting. The estimation of mediation effects is based on a set of three different linear
estimation equations (Imai et al. 2010):
53
The method suggested by Imai et al. is a generalization of the traditional mediation analysis (MacKinnon 2008) that implement it as a variant of linear structural equation modeling (LSEM).
30
0
0
Ycijk = α1 + β1 Tcijk + γ11
Xcijk + γ12
Xcij + δc + θi + λj + cijk1
(5)
0
0
Mcijk = α2 + β2 Tcijk + γ21
Xcijk + γ22
Xcij + δc + θi + λj + cijk2
(6)
0
0
Ycijk = α3 + β3 Tcijk + πMcijk + γ31
Xcijk + γ32
Xcij + δc + θi + λj + cijk3
(7)
Where Ycijk represents ln GDP per capita in a NUTS-3 region, Tcijk represents our
variables of interest (treatment variable), i.e. the trade center dummy, the ln distance to
trade center measure and the index of medieval commercial importance. Mcijk represents
the mediating variable, that is ln relative GDP density as measure of the spatial distribution of economic activity. Xcijk is defined as before and stands for a set of NUTS-3
level covariates. Accordingly, Xcij is a set of NUTS-2 level covariates. δc , θi and λj
are again country, NUTS-1 and NUTS-2 region fixed effects. The epsilons represent the
error terms. This means that equation (4) is identical to equation (2) or (3) respectively,
while in equation (5) we regress the medieval trade variables on the agglomeration measures and in equation (6) finally we include both the medieval trade variables and the
agglomeration measures in one regression on ln GDP per capita.
The “average causal mediation effect” (ACME) is estimated by the product of the
coefficients β2 and π (β2 π) and is obtained through a two-step procedure described in
detail in Imai et al. (2011).54 The ACME represents the indirect effect of medieval
trade on contemporary GDP per capita, i.e. that part of the overall effect of medieval
trade running through agglomeration. Correspondingly, β1 measures the total (average)
effect of medieval trade on regional GDP per capita and β3 represents the direct effect
of medieval trade, i.e. that part of the effect not mediated by agglomeration (but maybe
other factors). In consequence, this methodology of separating direct and indirect effects
enables to calculate which amount of the total effect of medieval trade works via increased
agglomeration. We expect β2 > 0 in the case of the trade center dummy and β2 < 0 in
the case of the distance to trade center variable. Even more, we also hypothesize that on
average, the majority of the effect of medieval trade should run through agglomeration.
This leads us expecting the ACME being significantly different from zero and greater
than the direct effect (|β2 π| > |β3 |). Moreover, since it holds that β1 = β2 π + β3
equation (4) is redundant given equations (5) and (6) and therefore we only estimate
54
In the classical case, where the mediation analysis is conducted using LSEM the coefficients are
obtained by separately estimating equations (5) and (6) using OLS.
31
those two equations.55 Last, we assume π > 0, i.e. a significant positive direct effect of
agglomeration on regional GDP per capita.
The results of both the regressions of medieval city growth on ln GDP density and
the mediation analysis are presented in Table 9. Supplementary to those result, we
estimated Table 9 with ln population density as mediating agglomeration measure. The
results are similar and available in Appendix C (Table C.1).
[Table 9 about here]
Columns(1) to (3) show the results for the estimation of equation (4). We clearly
see that there is a robust and positive relationship between medieval city growth in
different time periods and the contemporary relative GDP density of the NUTS-3 regions
in which the cities are located. The smallest estimate, resulting from the estimation
with city growth between 1400 and 1500 AD as regressor, implies that on average, one
percentage of city growth in this period leads to a around 0.17 percent higher relative
GDP density. This shows that there is indeed a considerable amount of path-dependency
in the development of European cities, i.e. cities that grew larger in the medieval age
due to trade are the economic centers and agglomeration areas still today.
Turning to the results of the mediation analysis (columns (4) to (6)) we again find
strong empirical support for our theory. As expected based on the previous empirical
results, all three measures of medieval trade (the dummy, the distance variable and the
index of commercial importance) are strong predictors of contemporary relative GDP
density. The coefficients are both significant from a statistical and economical point of
view. The coefficient of the trade center dummy for instance implies that regions with
an important medieval trade center shows on average a around 40 % higher relative
GDP density than non trade center regions. What is more, the results clearly show that
a higher distance to a trade center largely corresponds to a higher distance to areas
where the economic activity is concentrated.Thus, according to those estimates, there
is a significant and robust positive relation between present day’s spatial distribution
of economic activity and medieval trade. Moreover, from the estimations of equation
(7) we see that the significant effect of the medieval trade measures on the ln GDP
per capita does completely disappear when we include the relative GDP density in the
regression estimation. The relative GDP density in contrast, enters with a positive and
significant sign in each of the three regressions. Thus, areas with a high concentration of
economic activity are also the regions with a higher GDP per capita. Most importantly,
55
Finally, this also implies that the share of the total effect of medieval trade running through agglomeration is (ββ21π) .
32
this also implies that the vast majority of the observed strong effect of medieval trade on
regional development levels works through its impact on the patterns of spatial industry
agglomeration. In line with this, the ACME is always significant and on average above
100 % indicating that the insignificant remaining effect of medieval trade is even negative
in some cases.
Thus, it is fair to conclude that the effect of medieval trade indeed runs through
agglomeration as proposed in this paper.
4.4 Robustness of the Results
Our results have proven to be robust to the inclusion of many important covariates
and to endogeneity issues. However, there remain some additional concerns about the
robustness of the obtained estimates. To account for these objections, we conduct various
robustness checks. The results of these tasks are reported in appendix B (Tables B.1 to
B.8).
At first, we account for the effect some additional variables might have on both the
current level of regional development and/or medieval trading activities. In order to do
so, we add four different variables to the set of control variables used in Tables 5 and 6.56
We add a dummy variable indicating regions with copper mining sites in the medieval
age to look whether such type of economic activities at least partly causes the significant
effects we attribute to medieval trade activities. This could be possible if e.g. mining
activities actually led to higher trade activities in the regions they took place. We add
this variable to the specifications three and eight in Table 6, i.e. we add the variable to
the set of control variables capturing historical region characteristics.
Additionally, we include an interaction term of latitude and longitude of a region’s
centroid to the set of basic geographic controls and re-estimate specifications three and
six of Table 5 including this interaction effect. The justification for this is to look whether
development levels systematically differ when changing latitude and changing longitude
and vice versa. In this way we can for example identify effects of different climatic
conditions varying along different latitudes for countries located at the same longitudes.
Furthermore, we add the share of Roman Catholic people in a country’s population
in 2009 to the set of growth covariates and the re-run the regression in Table 6 columns
(4) and (9). This takes account of the fact that the impact of Protestantism (or religion
in general) on economic outcomes might not be captured adequately by the Distance to
Wittenberg variable, at least not today 500 years after the Reformation.
56
A descriptive overview over these variables is provided in Table B.9. A detailed description of the
variables and their sources is available in appendix B.
33
At last, we add a dummy variable equal to one if a region includes an important
residence city of a clerical or secular ruler. Residence cities of important rulers were
the centers of political and economic power in the territory of the ruler. Therefore, it
is quite likely that they showed high growth rates of population and economic activity
and maybe explain a significant part of medieval trade and its long-lasting effects on
agglomeration and development (e.g. Ringrose 1998).
The results obtained when adding these supplementary variables to the mentioned
regression specifications are shown in Table B.1. The dummy for medieval copper mining
regions and the latitude longitude interactions are not significant (Columns one to four
in Table B.1). Apart from the fact, that some of the included covariates seem to be
significant (e.g. the catholic variable) the trade center dummy and the distance to trade
center variable retain there significance and the size of the coefficients is comparable to
that obtained in the original estimates or larger.
A second robustness check is to look whether our results are sensitive to removing
influential observations. To test this we re-estimate Table 6 but remove regions that show
a high leverage, i.e. have a large impact on the coefficient estimate. This can be done
by computing the DFITS statistics, developed by Belsely et al. (1980). They suggest
p
to consider an observation as influential if |DF IT Sj | > 2 k \ N (with k indicating
the number of regressors and N denoting the number of observations in the sample).
Following their suggestion in each regression the regions having a DFITS statistic above
this threshold are removed from the sample and then the estimations are based on this
reduced sample. The results of this task are shown in Table B.2. Once again, the
exclusion of influential observations does only lead to minor quantitative changes in the
coefficient values (in both directions). Qualitatively, the results seem to be completely
unaffected by influential observations.
As already discussed in the data section, there is a considerable amount of uncertainty in the historical sources and information on which our identification of important
medieval trade centers is based. In consequence, it is adequate to test, whether our
empirical results hold, when alternative sample of trade cities are used in the regressions. We therefore re-estimate the all important results that depend on the trade
center dummy using the four different alternative samples of trade regions introduced
in section 3.3 and further elaborated in Appendix B. For each of this four alternative
trade center dummies we re-run the regression specification in Table 5 column (5) where
we employed all robust covariates from the previous regressions as controls. This specification is used —as in most parts of the analysis above– because it yields the most
conservative estimates. We further repeat the LIML and Lewbel (2012) instrumental
34
variables regressions from Table 6 columns (1) and (2) as well as the estimation in Table
8 column (1) where we regress the ln city growth between 1200 and 1500 AD with the
trade center dummy, the inital population level and appropriate historical controls. At
last, we re-do the mediation analysis with ln relative GDP density as mediator variables
(originally reported in Table 9 column (4)). The results of this re-estimations are shown
in Appendix B, Tables B.3–B.7.
As one can infer from the results in these Tables the results most often do only
marginally change with the alternative trade center variables. They coefficients even
tend to be a little bit larger than with the original sample of trade cities. However,
this does not hold for the estimations from Table 8. At least, with the last sample
of trade cities containing cities with reported trade activities in earlier periods. The
coefficient of the trade center dummy becomes insignificant when using this alternative
sample. However, in sum, none of our conclusions and general results is invalidated by
the alternative samples of trade cities. As such, the results are robust to considerable
changes in the sample due to uncertainty of historical information and underlying data
selection criteria.
5 Conclusion
This paper argues that medieval trade led to agglomeration and concentration of economic activities in the region it took place. It further postulates that the observed spatial
distribution of population and economic activity across Europe today is still shaped by
the self-reinforcing and long-lasting agglomeration processes originating from medieval
trade activities.
An empirical tests of these hypotheses brought forward that, as expected, there is a
statistically and economically significant positive relationship between medieval trade
activities and contemporary regional economic development. The analysis further unearthed that this relationship is indeed caused by the influence medieval trade exerted on
the emerging patterns of agglomeration and spatial concentration of industrial activities
throughout European regions. Based on the result of this paper we are able to confirm a
causal chain running from medieval trade activities through medieval city growth to contemporary industry concentration and regional economic development. Medieval trade
therefore can considered to be an important determinant of modern economic development. Further quantitative analyses of medieval trade activities maybe based on more
detailed historical data can therefore help to significantly improve our understanding of
the sources of long-lasting economic and social prosperity.
35
Tables and Figures
Figure 1: NUTS-3 Regions with Medieval Trade Cities
36
.4
.3
Density
.2
.1
0
2
4
6
ln(Population Density)
8
10
0
.05
.1
Density
.15
.2
.25
Figure 2: Kernel Density Estimate for ln(Relative GDP Density)
-5
0
5
ln(Relative GDP Density)
10
Kernel Density Trade Center=1
Kernel Density Trade Center=0
Figure 3: Kernel Density Estimates for Trade Centers and Non Trade Centers
37
1
e( ln(GDP per capita) | X )
0
.5
-.5
-1
-.5
0
e(Trade Center | X )
.5
1
-.5
e( ln(GDP per capita) | X )
0
.5
1
Figure 4: GDP p.c and Trade Centers - Partial Regression Plot
-1
-.5
0
e( ln(Distance to Trade Center) | X )
.5
Figure 5: GDP p.c. and Distance to Trade Centers - Partial Regression Plot
38
Table 1: The Data on Medieval Trade Centers
Country
No. of No. of Trade
Regions
Centers
Share Trade
Centers
Mean ln(Distance
to Trade Center)
Austria
Belgium
Czech Republic
France
Germany
Hungary
Italy
Lithuania
Netherlands
Poland
35
44
14
94
429
20
90
7
40
66
7
3
4
20
37
2
25
2
7
12
20
6.8
28.6
21.3
8.6
10.0
27.8
28.6
17.5
18.18
0.36
0.41
0.43
0.53
0.39
0.69
0.41
0.56
0.29
0.55
Total
839
119
14.8
0.425
39
Table 2: Bivariate Correlations of the Main Variables
Trade Center ln(Distance to
Trade Center)
Trade Center
ln(Distance to
Trade Center)
ln(Population
Density)
ln(GDP per
capita)
ln(Relative
GDP Density)
1
-0.529***
(0.000)
0.228***
(0.000)
0.12***
(0.108)
0.218***
(0.000)
ln(Population
Density)
ln(GDP per
capita)
ln(Relative
GDP Density)
1
-0.36*** (0.000)
1
-0.356***
(0.000)
-0.303***
(0.000)
0.461***
(0.000)
0.921***
(0.000)
1
0.434***
(0.000)
1
Notes. Correlation coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level.
Reported are pairwise correlation coefficients using the whole sample of NUTS-3 regions.
40
41
27576
8200
Italy
Lithuania
28652.9
Total
23779.2
6822.22
30430.3
6439.99
24095.38
6677.78
26342.86
24513.5
11100
25014.6
26885.71
4873.77***
(1050.28)
10542.28***
(2569.8)
10552.03**
(4669.6)
4850*
(2574.7)
5166.48**
(2267.2)
8038.22***
(1692.8)
6822.23***
(2049)
3480.62***
(1220.9)
1760
(2397.35)
5712.56*
(2883.3)
3652.78***
(921.2)
35.99
42.9
1.81
1.64
3.04
75.51
14.02
137.07
31.94
1.02
19.21
GDP p.c. non “GDP Advantage” Rel. GDP Dens.
trade centers
trade centers
trade centers
5.48
4.16
2.97
0.71
2.23
.174
5.91
13.71
0.247
3.00
0.453
30.51***
(9.7)
18.76**
(8.5)
-1.98
(8.43)
31.7
(18.79)
123.36*
(72.72)
8.1***
(2.5)
75.34***
(18.73)
0.818
(1.73)
0.924
(0.471)
-1.15
(2.0)
38.74***
(9.00)
Rel. GDP Dens.
“Rel. GDP Dens.
non trade centers Advantage” trade centers
Notes. The statistical significance of differences in GDP per capita, population density and relative GDP density between trade centers and non
trade centers is tested by a two-sample t test (assuming equal variances). Differences between trade centers and non trade centers are statistically
different from zero at the ***1 %, **5 % and *10 % level. Standard errors of the t tests are reported in parentheses.
10475
Poland
36142.86
13500
Hungary
Netherlands
34381.08
Germany
29680
France
35566.66
Belgium
15950
37428.71
Austria
Czech Republic
Av. GDP p.c.
trade centers
country
Table 3: Medieval Trade, Agglomeration and Regional Development - Descriptive Overview
Table 4: Medieval Trade and Contemporary Economic Development - Baseline Estimates
Dep. Var.
Trade Center
ln(Distance to
Trade Center)
(1)
(2)
ln(GDP per capita)
(3)
(4)
(5)
0.244*** 0.272***0.264***
(0.026) (0.028) (0.028)
[0.03] [0.033] [0.031]
{0.03} {0.029} {0.27}
(6)
-0.232***
(0.039)
[0.047]
{0.038}
-0.31***
(0.046)
[0.053]
{0.045}
-0.29***
(0.046)
[0.055]
{0.043}
Country Dummies
NUTS-1 Dummies
NUTS-2 Dummies
Basic Geographic
Controls
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Obs.
Adj. R2
839
0.78
839
0.778
839
0.778
839
0.765
839
0.762
839
0.763
Notes. Below each coefficient three standard errors are reported. First, heteroskedasdicty
robust standard errors are reported in parentheses. Second, standard errors adjusted for
two-way clustering within NUTS-1 and NUTS-2 regions are reported in square brackets.
Third, standard errors adjusted for two-dimensional spatialcorrelation according to Conley’s (1999) method are reported in curley brackets. The standard errors are constructed
assuming a window with weights equal to one for observations less than 3 degrees apart
and zero for observations further apart. Coefficient is statistically different from zero at the
***1 %, **5 % and *10 % level. The basic geographic controls include a NUTS-3 region’s
latitude, longitude and altitude. Each regression contains a constant not reported.
42
43
(2)
(3)
(4)
839
0.809
Obs.
Adj. R2
839
0.873
Yes
Yes
Yes
No
Yes
No
No
No
839
0.784
Yes
Yes
Yes
No
No
Yes
No
No
518
0.878
Yes
Yes
Yes
No
No
No
Yes
No
818
0.878
Yes
Yes
No
No
No
No
No
Yes
(9)
(10)
839
0.798
Yes
Yes
Yes
Yes
No
No
No
No
839
0.859
Yes
Yes
Yes
No
Yes
No
No
No
839
0.776
Yes
Yes
Yes
No
No
Yes
No
No
518
0.872
Yes
Yes
Yes
No
No
No
Yes
No
818
0.877
Yes
Yes
No
No
No
No
No
Yes
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is
statistically different from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The basic geographic
controls include a region’s latitude, longitude and altitude. The geographic centrality controls include the ln distances of a region’s centroid
to the nearest airport, railroad, road, border and coast point. Region characteristic controls include a dummies for regions in Germany
that are district-free cities, for regions including a country’s capital, are classified as mountain regions, with ore or coal mines, located in
the former GDR and located in an Eastern European post-communistic transition country. Furthermore it encompasses the ln of a regions
area. The historical region characteristics consist of a dummy variables indicating regions with a university founded before 1500 AD, that
adopted printing technology before 1500 AD, contain cities that were members of the Hanseatic League, with former imperial cities and
were located on an imperial road. Moreover it includes the ln of the distance of a region’s centroid to Wittenberg. The growth covariates
encompass a region’s unemployment rate, number of registered patents, average firm ln fixed capital stock, average worker compensation.
Furthermore, it includes the share of people aged between 25-64 with tertiary education on NUTS-2 level, the quality of government index
on NUTS-1/ NUTS-2 level and the ratio of an average workers compensation to a region’s GDP per capita as inequality measure. The
set of all robust covariates encompasses altitude, the ln distances to airports, railroads and rivers, dummies for district free cities, capital
cities, capital cities of autonomous regions, post-communistic transition countries, Eastern Germany, the ln of a region’s area, the share of
people with tertiary education, the inequality measure and the printing press before 1500 AD dummy. Each regression includes a constant
not reported.
Yes
Yes
Yes
Yes
No
No
No
No
(8)
-0.105** -0.0857* -0.135** -0.138*** -0.0529
(0.044) (0.044) (0.053) (0.041) (0.041)
ln(GDP per capita)
(5)
(6)
(7)
0.175***0.105*** 0.181***0.0701***0.045**
(0.025) (0.024) (0.024) (0.027) (0.021)
(1)
Country Dummies
NUTS-1 Dummies
Basic Geographic Controls
Geographic Centrality Controls
Region Characteristics
Historical Region Characteristics
Growth Covariates
All Robust Controls
ln(Distance to
Trade Center)
Trade Center
Dep. Var.
Table 5: Medieval Trade and Contemporary Economic Development - Adding Further Controls
Table 6: Medieval Trade and Contemporary Economic Development - IV Regressions
(1)
Method
Dep. Var.
Trade Center
ln(Distance to Trade Center)
R2 (centered)
F-value
Overidentification Test
(Hansen J statistic)
p-value
Dep. Var.
Mountain Region
Bishop before 1000 AD
(2)
LIML Lewbel (2012)
(3)
(4)
LIML
Lewbel (2012)
2. Stage Results
ln(GDP per capita)
0.306***
(0.105)
0.0787***
(0.0247)
0.563
55.02
0.307
0.580
0.632
86.43
66.64
-0.519***
(0.173)
0.508
51.52
0.0981
-0.155***
(0.0503)
0.880
131.85
78.26
0.116
0.754
0.008
1. Stage Results
Trade Center
ln(Distance to Trade Center)
-0.0232*
(0.013)
0.2553***
(0.071)
0.0259***
(0.01)
-0.1342***
(0.039)
Country Dummies
NUTS-1 Dummies
All Robust Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs.
Angrist-Pischke F statistic of
excluded IV’s (p-value)
R2 (centered)
Underidentification Test
p-value
818
8.39
818
44.51
818
9.32
818
13.47
0.273
14.06
0.000
0.837
194.6
0.000
0.206
16.25
0.000
0.699
158.2
0.000
Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different
from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The
set of all robust covariates encompasses altitude, the ln distances to airports, railroads and rivers,
dummies for district free cities, capital cities, capital cities of autonomous regions, post-communistic
transition countries, Eastern Germany, the ln of a region’s area, the share of people with tertiary
education, the inequality measure and the printing press before 1500 AD dummy. Each regression
includes a constant not reported. The Overidentification test reporst the Hansen J-statistic and
the Underidentification test reports the Kleibergen-Paap rk LM statistic (null hypothesis: equation
is underidentified). Lewbel’s (2012) approach uses a vector of generated instruments that are
uncorrelated with the product of the heteroskedasdic first stage’s errors as instruments. These
instruments are not included in the table due to space restrictions, but their coefficients and
standard errors are available from the author upon request.
44
Table 7: Medieval Commercial Importance and Contemporary Regional Development
Dep. Var
(1)
(2)
ln(GDP per capita)
(3)
(4)
(5)
OLS
Commercial Importance
Commercial Importance
Alternative
Country Dummies
NUTS-1 Dummies
NUTS-2 Dummies
All Robust Controls
Obs.
Adj.R2 \R2
Underidentification Test
p-value
Overidentificaton Test
p-value
AP F-statistic of excluded
IV’s
p-value
(6)
LIML IV Lewbel (2012)
0.0964***0.0211**
(0.014) (0.009)
0.0972***0.0181*
(0.016) (0.011)
0.153***
(0.055)
0.0232**
(0.01)
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
839
0.776
818
0.877
839
0.77
818
0.877
818
0.502
16.45
0.000
0.129
0.719
9.15
818
0.621
224.5
0.000
69.41
0.0772
32.72
0.000
0.000
Notes.Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are
reported in parentheses. In column (5) and (6) heteroskedasdicity robust standard errors are reported. Coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level. The
unit of observation is a NUTS-3 region. The index of commercial importance of a medieval city is
constructed by adding up the coast region dummy, the trade center, bishop in 1000 AD, imperial
city and road, hanseatic league, medieval mining region and university before 1500 AD dummy
variables. The alternative index of commercial importance includes the distance to trade center
variable instead of the dummy (recoded to be positively related to GDP). In the case of the LIML
IV regression a version of the index is used that does not include the bishop before 1000 AD dummy
since this variable is used as excluded instrument in that estimation. The set of covariates encompasses altitude, the ln distances to airports, railroads and rivers, dummies for district free cities,
capital cities, capital cities of autonomous regions, post-communistic transition countries, Eastern
Germany, the ln of a region’s area, the share of people with tertiary education, the inequality
measure and the printing press before 1500 AD dummy. Each regression includes a constant not
reported. The Overidentification test reporst the Hansen J-statistic and the Underidentification
test reports the Kleibergen-Paap rk LM statistic (null hypothesis: equation is underidentified).
Lewbel’s (2012) approach uses a vector of generated instruments that are uncorrelated with the
product of the heteroskedasdic first stage’s errors as instruments. These instruments are not included in the table due to space restrictions, but their coefficients and standard errors are available
from the author upon request. The first stage regressions are also not reported but are available
from the author.
45
46
86
0.39
0.65***
(0.215)
-0.66***
(0.148)
199
0.398
-0.62***
(0.068)
0.49***
(0.121)
OLS
180
0.222
-0.427***
(0.08)
0.448***
(0.151)
826
0.288
361
0.777***
(0.094)
RE
390
0.369
194
-0.433***
(0.049)
0.393***
(0.072)
opulation1500
P opulation1500
P opulation1500
ln( P
P opulation1200 ) ln( P opulation1300 ) ln( P opulation1400 ) ln(Population) ln(∆ Population)
(1)
(2)
(3)
(4)
(5)
Notes. Robust standard errors are reported in parentheses in columns (1) - (3). Standard errors clustered at city
level are reported in parentheses in columns (4) and (5). Coefficient is statistically different from zero at the ***1 %,
**5 % and *10 % level. The unit of observation is a city. The set of covariates encompasses the ln distances of a city
to the next river or coast, dummies indicating cities that were residence of a bishop before 1000 AD, had the status
of an imperial city, were located at a main imperial road, were member of the Hanseatic League or are classified as a
mountain region by the EU regional statistics. Furthermore, we control for a city’s latitude and longitude and include
country fixed effects. In columns (4) and (4) we additionally include year fixed effects. Each regression includes a
constant not reported.
Obs.
Adj. R2 \overall R2
Number of Clusters
ln(Populationt−1 )
ln(Population 1400 AD)
ln(Population 1300 AD)
ln(Population 1200 AD)
Trade City
Method
Dep. Var.
Table 8: Medieval Trade Activity and City Growth
Table 9: Medieval Trade, Relative GDP Density and Regional Economic Development
(1)
Method
City Growth from to
Dep. Var.
P opulation1500
P opulationt
ln(Relative GDP Density)
(2)
(3)
OLS
1200–15001300–15001400–1500
ln(Relative GDP Density)
0.337*** 0.178*** 0.172***
(0.105)
(0.067)
(0.062)
Trade Center
ln(Distance to Trade Center)
Commercial Importance
R2
ACME
Direct Effect
Total Effect
% of total mediated
0.964
0.955
0.947
(4)
(5)
(6)
Mediation Analysis
Equation (7)
ln(GDP per capita)
0.202*** 0.203*** 0.205***
(0.011) (0.011) (0.011)
0.0048
(0.017)
0.0103
(0.023)
-0.0074
(0.007)
0.919
0.919
0.919
0.0661***-0.0786***0.0317***
0.0054
0.0111
-0.0072
0.0715*** -0.0675** 0.0246***
92.1*** 115.1** 128.1***
Equation (6)
ln(Relative GDP Density)
Trade Center
0.3316***
(0.063)
ln(Relative GDP Density)
Commercial Importance
-0.3799***
(0.103)
0.1565***
(0.023)
Country Dummies
NUTS-1 Dummies
All Robust Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs.
R2
85
179
197
818
0.939
818
0.938
818
0.94
Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3
region. The set of all robust covariates encompasses altitude, the ln distances to airports
and railroads, dummies for district free cities, capital cities, capital cities of autonomous
regions, post-communistic transition countries, Eastern Germany, the ln of a region’s area,
the share of people with tertiary education, the inequality measure and the printing press
before 1500 AD dummy. Each regression includes a constant not reported. ACME is the
“Average Causal Mediation Effect” and means how much of the effect of medieval trade is
mediate, i.e. works indirectly through the relative GDP density.
47
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A. Data Appendix
The level of an observation is a NUTS-3 region ( For example, in Germany this corresponds to the “Landkreise”, in France to the “Departments” and in Italy to the
“Provinicas”). If the variables are defined on an other NUTS level, this is indicated
in the description of the respective variable. City level information is matched to the
NUTS-3 regions by the use of Eurostat (2007). We use the NUTS-2006 classification,
since the most data is available only for this version of the NUTS classification. An
descriptive overview over all variables used in the empirical analysis is given in Table
A.1 below.
Main Variables
Trade Centers. Primarily, the data on historical trade cities is based on four different
maps. The first is a map printed in Davies and Moorhouse (2002) and includes ”Main
trade routes in the Holy Roman Empire and nearby countries” for the period around 1500
AD. It contains the trade routes and the cities located on them. Davies and Moorhouse
(2002) is a book about the history of the Polish city of Wrcolaw written by a renowned
expert for Polish and Eastern European history Norman Davies and his student Roger
Moorhouse. According to google scholar it is cited around 60 times (at 24th June 2013)
e.g. in articles in the Journal of the Royal Statistical Association. Therefore it considered
to be a reliable source for information about medieval trade activities.
Because this map only covers the area of Austria, Belgium ,Czech Republic, Eastern
France, Germany, Hungary Lithuania, the Netherlands, Poland and North Italy we make
use of a second map published in King (1985) including ”Chief trade routes in Europe,
Levant and North Africa 1300-1500 CE”. The map covers a wide area including parts
of North Africa and the Near East. From this map, we primarily take the information
about French trade cities, but we also include cities from other countries that are not
mentioned in the first map. The original map is printed in a chapter about the “Currents
of Trade. Industry, Merchants and Money” in the medieval age as part of a volume
about the “Flowering of the Middle Ages” edited by the Oxford-based medieval art
historian Joan Evans. In this chapter Donald King illustrates the most important goods
of the medieval economy, discusses how they were produced and traded. He lays special
emphasis on the patterns of commerce and trade. He describes the most important
centers of commerce and trade activity (Fair and market cities etc.) and also discusses
the importance of institutions (like contract security) etc. played for trade activities.
Again, this volume seems to be an often cited source with around 50 citations in google
48
scholar (24th June 2013). According to the bibliography of the volume King (1985)
heavily draws on standard sources about medieval trade like Heyd (1879a,b), Lopez and
Raymond (1955) or Postan and Rich (eds.)(1952).
As third source we employ an overview map of late medieval trade printed in Magocsi
(2002) a historical atlas of central Europe and an often cited source for historical information about economic and cultural and political features. He is cited 222 (at 24th June
2013) at google scholar. Among the papers using information provided by the atlas are
the historical economic papers by Börner and Severgnini (2012) and Dittmar (2011) as
well as Becker et al. (2011). It contains information on ”economic patterns” in Central
Europe around the year 1450. From this map, we primarily took the information about
Southern Italian trade cities not included in the other maps. Again, we also include cities
mentioned there but not in the other two sources. From this map, a city is considered
if it is located on a ”major” or ”important” trade route. The map also contains also
information about members of the Hanseatic League (and their importance) as well as
commercial offices and foreign depots of the Hanseatic League. Further, it also depicts
the goods traded over the particular routes and the areas where they are the commodities are typically produced. The map drawn in Magocsi’s atlas relies on other regional
and general historical atlases like the that of Darby and Fuller (eds.)(1978) or Lendl and
Wagner (1963) for Austria. However, Magocsi also consulted books about the history of
certain cities like Dubrovnik (Carter 1972) or Wroclaw (Ochmanski 1982).
At last, we consult several maps included in “Westermanns Atlas zur Weltgeschichte”
(Stier et al. 1956). To be precise, we consider the information of a map depicting the
“Hanseatic League and its Opponents in the 15th century after the piece of Utrecht”.
The map reports the location of Hanseatic cities, contours of the Hanseatic League in
other countries and the main trade routes of the time as well as the traded goods. The
geographical scope of the map is limited to the part of Germany northern of Prague,
the Netherlands, the most part of today’s Belgium and Poland. We include a city,
if it is located at one of the trade routes but regardless of whether it was a member
of the Hanseatic League or not. Second, we draw on a map in this atlas that limns
“Western European Trade” in the late medieval and reports the course of “important
trade routes” and the cities located on them. The scope of the map is south-west
Europe (Spain and France) but it also includes West Germany and the north-western
Italy. Here again, we include a city if it is located on a major trade route. At last, we use
the information contained in a map about “Levant Trade in the Late Medieval and the
Ottoman Invasion”. This map among other information, limns the course of “important”
trade routes (both on land and sea) and the cities located at them. We recognize cities
49
on trade routes in the southern part of Germany, Hungary, Italy and the most parts of
France as well as parts of Poland.
Although not the only sources of information about medieval trade activities, these
four maps seem to contain the most complete cross-national information about important
trade activities in the later medieval period.
To validate the information of these maps and obtaining additional evidence about
medieval trade we consult other sources like a list depicting members of the Hanseatic
league from Dollinger (1966) a standard source for the history of the Hanseatic League.
We only recognize cities that according to Dollinger ”played an important role in the
Hanseatic League” or that were capitals of thirds and quarters. Furthermore we consulted a map containing information about “North-South Trade Routes in the Alps Area
in the Medieval Period” from Schulte (1966), two very general maps printed in Kinder
and Hilgemann (1970) focusing on Baltic Sea and Levant trading activities in 1400 AD, a
map published in Ammann (1955) focusing on trade routes for Southern Germany textile
products (Barchent) and the map “Business Centers and Maritime Trade Routes High
Middle Ages” printed in Hunt and Murray (1999).1 Furthermore, we draw on qualitative
information about the importance of a trade cities from Spufford’s (2002) standard work
about medieval trade and commerce and the monograph about the history of German
trade written by Dietze (1923).
In Table A.2, all trade cities and the corresponding regions for which the dummy variable is equal to one and the source(s) mention the respective city as trade center are
shown. However, due to space restrictions we do not report any of the sources we consulted for becoming information about the validity of our sample of important trade
centers. For example, there is a three volume anthology by Escher and Hirschmann
(eds.) (2005) where a group of researches developed an index of urban centrality for
cities in the “Rhine-Meuse area” in the period from 1000 to 1350 AD (i.e. south-west
Germany, and western Switzerland, east France , large parts of Belgium and the South
of the Netherlands). As part of the index of urban centrality they collected data about
the existence and number of markets, fairs, trade hall and the presence and importance
of long-distance trade activities. They also have data about the presence of certain manufacturing activities also being a good indicator for the presence of trade. They develop
a categorical index of centrality from the qualitative information the collect. From the
trade cities in our sample Aachen, Antwerp, Cologne, Dordrecht, Dortmund, Frankfurt,
Maastricht, Metz, Münster, Paderborn, Rotterdam, Soest and Straßburg are included in
1
Geographical scope, time period and level of generality sometimes differ between these maps, so a
cross-validation is always possible only with limitations.
50
the volume. For every of those cities, one or more markets, a fair or differently important long-distance trade are mentioned. But here, the range goes from Cologne (having
4 markets, and ”very important” fairs and long-distance trade activities) to e.g. Paderborn where it is stated that it have a fair and long-distance trade. Due to this, it is not
an easy task to say, that the information provided by this source can be used to validate
whether a city was important enough to be included in the sample. Furthermore, the
period for which the index is constructed ends in the middle of the 14th century and
therefore earlier than our period of observation. Nevertheless, the information provided
in the anthology of Escher and Hirschmann (eds.) (2005) can be useful to select cities
that were probably not that important because e.g. the markets, fairs or trade there
was comparably limited in scope (i.e. according to the number of markets, halls, fairs
or there importance) or time. Additionally, it provides clear evidence for the outstanding importance of Cologne and e.g. the over-regional importance (“very important”
long-distance trade or fair) of Dortmund, Frankfurt, Münster and Soest.
As already mentioned, the information in those sources primarily is used to validate
that the information printed in the maps. However, as indicated in the main text we
sometimes also include cities mentioned in these sources but not in the maps when we
are in doubt about the actual importance of a city in medieval trade.
Furthermore, we construct several trade center dummies using alternative samples of
trade cities (as discussed in the main text). At first, we exclude cities mentioned by only
one of our sources. These cities are Amberg, Bruck, Fulda, Maastricht, Malbork, Mantoa,
Minden, Orleans, Parma, Pecs, Piotrkow Trybunalski, Plock, ,Rotterdam, St. Melo,
Udine, Utrecht and Zwickau. Second, we exclude cities for which we are not sure about
there importance, altough they are reported in more than one of our sources. Those
cities are Paderborn, Einbeck, Greifswald, Braniewo, Görlitz, Metz, Palanga, Como and
Stargard. For example, we exclude Paderborn because despite the fact that it was a
member of the Hanseatic League and layed on the Hellweg, no other source mentioned it
and Dollinger (1966) did not consider it as being a Hanseatic city of special importance.
Furthermore, the data collected by Escher and Hirschmann (eds.) (2005) group implies
that the existing trade activity in Paderborn was of relatively lower importance compared
to e.g. Cologne, Münster, Dortmund or other leading trade cities. Third, we add some
cities to the original sample of trade cities. These cities are cases were a first look at the
available information lead to the decision not to include the trade city. Even though, the
city is mentioned somewhere in one of the sources as a place of certain relevance for trade.
This is for example the case for Anklam, a member city of the Hanseatic League lying
on an important trade route according to a map in Stier et al. (1956). However, none of
51
the other sources mention Anklam as important trade center and Dollinger (1966) did
not intend a special role for Anklam within the Hanseatic League.
Finally, we build a last alternative sample of trade cities that only includes cities for
which historical sources indicate long-run trade activities (i.e. cities that are important
trade cities around 1500 AD and that were important also in the period before). An
overview over these cities the earliest period in which trade activities are reported and
the source mentioned the respective city are depicted in Table A.4. This re-coding is
based on information primarily derived from the 2 Wilhelm Heyds two volumes about
medieval Levant trade (Heyd 1879a and 1879b). He provides information about medieval
trade activities in the Levant and the most important involved parties in a chronological
order beginning with the end of migration period (“Barbarian Invasions”). We take the
period mentioned in the chapter headings of the chapter where the trade activities of a
city are firstly mentioned as the period with the earliest authenticated trade activities.
If Heyd explicitly reports a date or a period we take this date. Heyd (1879a,b) provides
information about trade activities of Austrian, Belgian, French, German and Italian
cities. Additionally, the monograph about the Hanseatic League written by Dollinger
(1966) includes a couple of maps depicting e.g. the main Hanseatic trade routes and trade
cities before 1250, between 1250 and 1350 and 1350 and 1500 (always AD). Another map
report important trade routes (e.g. the salt way) and the cities that signed the treaty
of Smolensk in 1229 AD a trade agreement between German trade cities and the Duke
of Smolensk. According to Dollinger (1966), this map covers the period from 1286 to
approximately 1336. We stick to the dates given in these maps when assigning the
respective cities the dates when they are mentioned first. All in all, this and the other
maps in Dollinger (1966) contain information about trade activities in France, Germany,
Lithuania and Poland. Finally, for Germany, Italy and France the book of Dietze (1923)
about the history of German trade reports significant trade activities and places since
the ”pre-historical” period. We include a city in the sample if Dietze (1923) reports a
city to be an important player in early and high medieval trade.
For Austria, the Czech Republic and Poland information is provided by three digitized maps from T. Matthew Ciolek’s OWTRAD website. The first is based on a map
printed in Humnicki and Borawska (1969) and shows “Central European Trade Routes
800 – 900 CE”.2 The second map originates from Wojtowicz (1956) and according to the
OWTRAD website reports “Major trade roads in Poland and adjacent border regions
2
The map can be found under the following URL: http://www.ciolek.com/OWTRAD/DATA/tmcCZm0800.
html; accessed at June 11th, 2013.
52
1340 – 1400 CE”.3 Form this map we include information about Polish trade cities. The
last map from the OWTRAD project is based on Rutkowski (1980) and is about ‘Major
trade roads in Poland and adjacent border regions in 1370 CE”.4 From this map we
solely include the German city of Görlitz since all the other relevant cities in the map
were mentioned by another source depicting trade in an earlier period. Overall are able
to found information about 68 of our 115 medieval trade cities.
ln(Distance to Trade Center). This variable is calculated using the ArcGIS Near Tool.
It represents the natural logarithm (ln) of the distance between a region’s centroid and
the closest trade city in degrees. The variable takes the value 0 for regions that contain
medieval trade cities (i.e. for which the trade center dummy is equal to one). Trade City.
Variable used for the city-level regressions in Table 3. The collection of cities coded as
trade cities stem from Bairoch’s (1988) data, as explained in the main text. The cities
are coded according to the procedure described in detail below in the explanation of the
trade center dummy on regional level. The cities coded as trade cities are: Amsterdam,
Antwerp, Augsburg, Avignon, Bari, Berlin, Bordeaux, Braniewo, Brunswick, Bremen,
Brno, Bruges, Budapest, Chalon-Sur-Saone, Como, Deventer, Dordrecht, Dortmund,
Einbeck, Elblag, Erfurt, Florence, Frankfurt (Main), Frankfurt (Oder), Gdansk, Genoa,
Ghent, Görlitz, Graz, Hamburg, Hannover, Hildesheim, Imola, Innsbruck, Kampen,
Cologne, Cracow, Leipzig, Linz, Lübeck, Lucca, Lyon, Maastricht, Magdeburg, Mantoa,
Marseille, Metz, Milan, Minden, Montpellier, Münster, Naples, Narbonne, Nuremberg,
Orleans, Osnabrück, Padoa, Paris, Parma, Perpignan, Plock, Poznan, Prague, Prato,
Ravensburg, Regensburg, Reims, Rome, Rostock, Rotterdam, Salzburg, Soest, St. Malo,
Stralsund, Straßbourg, Torun, Toulouse, Tours, Treviso, Troyes, Udine, Ulm, Utrecht,
Venice, Verona, Warsaw, Vienna, Wismar and Wroclaw.
ln (GDP per capita). The natural logarithm of GDP per capita on NUTS-3 level is
from the Eurostat regional statistics database (http://appsso.eurostat.ec.europa.
eu/nui/show.do?dataset=nama_r_e3gdp&lang=en; accessed at October 10th 2012). It
is in measured in current market prices. We took values from 2009 the latest year for
which data is available.
Commercial Importance. Variable that should measure the commercial importance of a
city according to different, historically relevant characteristics. The exact construction
is explained in the main text. It is the sum of following five dummy variables: trade
3
The original title of the map is (according to the OWTRAD website) “Trade roads at the times
of Casimir the Great”). The map is available at the OWTRAD website under this link http:
//www.ciolek.com/OWTRAD/DATA/tmcPLm1370a.html; accessed at June 11th, 2013.
4
The map can be accessed under the URL http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370.html;
accessed at June 11th, 2013.
53
center, imperial city, hanseatic league, imperial road, medieval mining, coast region and
university before 1500 AD. This variable is constructed by the author.
Commercial Importance Alternative. Identical to the variable commercial importance
but instead of the trade center dummy, it constains the distance to trade center variable, recoded in a way that it is positively associated with the GDP per capita (as the
other variables). ln(Population Density). A region’s Population Density comes from the
Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/
show.do?dataset=demo_r_d3dens&lang=en; accessed at October 10th 2012). The values are from 2009.
ln(Relative GDP Density). This variable is calculated using the following formula (Roos
2005):
rdi =
P
Yi / Yi
P
Ai / Ai
Where rdi is the relative GDP Density of a region. Yi is a region’s GDP (calculated by
multiplying the GDP per capita with the population density) and Ai is a region’s area.
Therefore, the relative GDP Density is the GDP density of a region (GDP per km2 )
relative to the average density of all other regions. Alternatively, it is the ratio of a
regions share of GDP relative to its share of a country’s overall area. In consequence, if
the relative GDP Density is larger than one this means that a region shows concentration
of economic activity higher than the average region in a country (Roos 2005). For the
empirical estimations, we take the natural logarithm of the variable, so that it is greater
than zero for above average levels of spatial economic concentration. GDP per capita,
the population density and the area of a region are all from the sources listed in this
appendix.
Control Variables and Instruments
Altitude. The Altitude of a region is from the website gpsvisualizer.com (accessed at
November 8th 2012) and based on the coordinates of its centroid.
Bishop before 1000 AD. Dummy variable equal to one if a region includes a city
that was seat of a bishop (or in France and Italy of an archbishop) before the
year 1000 AD. The variable is coded according to information from the website
http://www.catholic-hierarchy.org (accessed at November 27th, 2012). For bishoprics in the Holy Roman Empire additionally Oestreich and Holzer (1970b) is consulted.
When there were doubts on whether the diocese or archbishopric was founded before 1000
AD wikipedia and the catholic encyclopedia (http://www.newadvent.org/cathen/;
54
accessed at November 27th, 2012) are consulted.
Capital. A dummy variable equal to one if a region includes the capital of a sovereign
state. Coded by the author.
Capital Autonomous Region. A Dummy Variable equal to one if a region includes the
capital of a partly autonomous administrative unit, i.e. a German or Austrian State
(“Bundesland”) or an Italian or Belgian Region. Coded by the author.
District-Free City. A dummy variable equal to one for German NUTS-3 regions being
district-free cities (“Kreisfreie Städte” or “Stadtkreis”). Coded by the author.
Eastern German Region. Binary variable equal to one if a region in Germany is located
in the former GDR. Coded by the author.
Education. We measure human capital of a NUTS-2 region with the share (in percent)
of persons aged 25-64 with tertiary education attainment. The variable is obtained from
the Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/
nui/show.do?dataset=edat_lfse_11&lang=en; accessed at October 10th, 2012). We
took the values from 2009.
Hanseatic League. Binary variable equal to one if a region contains at least one city
that was a member of the Hanseatic League. Coded according to Dollinger (1966).
Imperial City. A Dummy Variable equal to one if a region includes at least one city
that was an imperial city in the Holy Roman Empire. The variable is coded following
Oestreich and Holzer (1970a).
Imperial Road. Dummy variable equal to one if a region contains at least one city
that was located on an important imperial city, i.e. the Via Imperii, the Via Regia or the Via Regia Lusatiae Superioris.
The variable is coded according to in-
formation provided by Kühn (2005), the entry “Hohe Landstraße” in the online
version of “Meyers Großes Konversations-Lexikon” a general german encyclopedia
(http://www.zeno.org/Meyers-1905/A/Hohe%20Landstra%DFe; accessed at December 18th 2012), a map from a website of the federal government of the German
State Saxony on regional development (http://www.landesentwicklung.sachsen.
de/download/Landesentwicklung/ED-C_III_Via_Regia_Verlauf.jpg;
accessed at
December 18th, 2012) and wikipedia entries.
Inequality. We measure inequality as ratio of average workers compensation to the GDP
per capita. The Sources of GDP per capita and average workers compensation are as
listed in this appendix.
Latitude. The values of this variable represent the latitude in decimal degrees of a
region’s centroid and are obtained from a GIS map of NUTS territories provided by the
Eurostat GISCO Database.
55
(http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_
SH.zip; accessed at November 8th, 2012).
ln(Area). The natural logarithm of a region’s area is taken from the Eurostat regional
statistics database http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=
demo_r_d3area&lang=en; accessed at January 10th, 2013. As always, we use the values
from 2009.
ln(Distance to Airport). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest international airport in degrees. It is calculated using the ArcGIS Near Tool. The coordinates of airports are from the GIS map
“Airports and Ports” from ArcGIS Online Database (accessed at November 9th, 2012).
ln(Distance to Border). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest point of the country’s border. It is calculated
using the ArcGIS Near Tool. The coordinates of borderlines are taken from a GIS map
of EU countries provided by the Eurostat GISCO Database (http://epp.eurostat.ec.
europa.eu/cache/GISCO/geodatafiles/CNTR_2010_03M_SH.zip; accessed at January
10th, 2013).
ln(Distance to Coast). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest point of a country’s coastline. It is calculated using the ArcGIS Near Tool. The coordinates of a country’s coastlines are
taken from the GIS map “Corine land cover 2000 coastline” provided by European
Environment Agency (EEA) (http://www.eea.europa.eu/data-and-maps/data/
corine-land-cover-2000-coastline; accessed at November 8th, 2012).
ln(Distance to Railroad). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest point of a country’s major railroad. It is
calculated using the ArcGIS Near Tool. The coordinates of the railroads are obtained
from the map “World Railroads” from ArcGIS Online Database (accessed at November
9th 2013).
ln(Distance to River). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest point of a country’s major waterway (e.g. in
Germany these are Elbe, Danube, Rhine and Oder). It is calculated using the ArcGIS
Near Tool. The coordinates of the rivers are taken from the GIS map “WISE Large
rivers and large lakes” provided by European Environment Agency (EEA) (http://
www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes;
accessed at November 8th, 2012).
ln(Distance to Road). The variable represents the natural logarithm of the distance
between a region’s centroid and the closest point of a country’s roads. It is calculated
56
using the ArcGIS Near Tool. The coordinates of the roads are obtained from the GIS
Map “World Roads” from ArcGIS Online Database (accessed at November 9th, 2012).
ln(Distance to Wittenberg). Variable containing the geodesic distances between each
region’s centroid and the city of Wittenberg in the German State of Saxony-Anhalt.
The coordinates of Wittenberg are taken from the website geonames.com (accessed at
November 8th, 2012).
ln(Employees Compensation). Natural logarithm of average of employees compensation
(wages, salaries and employer’s social contributions) at NUTS-2 level measured at
current prices and from the year 2009. Data was obtained from the Eurostat regional
statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=
nama_r_e2rem&lang=en; accessed at October 10th, 2012).
ln(Fixed Capital).
2009.
Gross fixed capital formation by NUTS-2 regions measured for
Data is obtained from the Eurostat regional statistics database (http://
appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e2gfcfr2&lang=en;
accessed at October 10th, 2012).
Longitude. The values of this variable represent the longitude in decimal degrees of a
region’s centroid and are obtained from a GIS map of NUTS territories provided by
the Eurostat GISCO Database (http://epp.eurostat.ec.europa.eu/cache/GISCO/
geodatafiles/NUTS_2010_03M_SH.zip; accessed at November 8th, 2012).
Medieval Mining. Binary Variable depicting regions with medieval copper or salt mining
sites. The variable is coded according to a map in Elbl (2007) as well as information in
Spufford (2002).
Mining Region. Dummy variable equal to one if in a region at least one ore or coal
mine (or mining firm) is located. The information on which the coding is based originate from the structural business statistics included in the Eurostat regional statistics
database
(http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_r_
nuts06_r2&lang=en accessed at January 28th, 2012).
Mountain Region. Categorial variable equal to one if in a region more than 50% of their
population living in mountain areas according to the ESPON (European Observation
Network for Territorial Development and Cohesion) regional typologies project. The
variable is equal to one if more than 50% of a region’s population live in a mountain area.
It is two if more than 50% of a region’s surface is covered by mountain areas. At last,
it is three for regions with more than 50% of their surface covered by mountain areas
and with more than 50% of their population living in mountain areas. It is zero when
a region fulfills none of this criteria. The data and an explanation of the classifications
can be downloaded from http://www.espon.eu/export/sites/default/Documents/
57
ToolsandMaps/ESPONTypologies/Typologies_metadata_data_final.xls
(accessed
at November 8th, 2012).
Patents. Total number (over all IPO section and classes) of patent applications to the
European Patent Office (EPO) in each region in 2009. Data available from the Eurostat
regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?
dataset=pat_ep_ripc&lang=en; accessed at October 10th, 2012).
Post Communistic Country. A binary variable equal to one if a region lies in an Eastern
European post communistic transition country, i.e.
the Czech Republic, Hungary,
Lithuania or Poland. Coded by the author.
Printing Press before 1500 AD. Dummy variable equal to one if at least one city in
a region had adopted printing technology before 1500 AD. The coding is based on
information in Benzing (1982), Clair (1976) and the Incunabula Short Title Catalogue
(ISTC) of the British library (http://www.bl.uk/catalogues/istc/index.html; accessed at November 18th, 2012). A region is included if any of these sources mentioned
a city in this region.
Quality of Government. The European Regional Quality of Government Index (EQI)
as developed by the Quality of Government Institute at the university of Gothenburg
in Denmark. The index is constructed in a similar way than the World Governance
(WGI) Indicators of the World Bank (further information on the index design and the
data can be found here: http://www.qog.pol.gu.se/digitalAssets/1362/1362471_
eqi---correlates-codebook.pdf; accessed at January 28th 2013). The data on which
the indix is based are collected in 2009. In Belgium, Germany, Netherlands and Hungary
the index report values at NUTS-1 level in the other countries in our dataset it reports
values at NUTS-2 level. The data can be downloaded from http://www.qog.pol.gu.
se/digitalAssets/1362/1362473_eqi-and-correlates--qog-website-.xlsx
(ac-
cessed at January 28th, 2013).
Unemployment. The average annual unemployment rate (in percent) in a region in 2009
(including people above the age of 15). Data is from the Eurostat regional statistics
database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfst_r_
lfu3rt&lang=en; accessed at October 10th, 2012).
University before 1500. Dummy variable equal to one if at least one city in a region has
a university founded before 1500 AD. Coding according to Eulenburg (1994), Kinder
and Hilgemann (1970) and Rüegg (1993). The a city is recognized if it is mentioned
by any of these sources. If there were doubts on the founding date of a university (or
contradicting dates) Cantoni and Yuchtman (2012) or wikipedia are used as validation.
58
59
Table A.1: Descriptive Data Overview – Regional Level Variables
Variable
Obs
Mean
Std. Dev.
Altitude
Bishop before 1000 AD
Capital
Capital Autnomous Region
Commercial Importance
Commercial Importance Alt.
District-Free City
Eastern German Region
Education
Hanseatic League
Imperial City
Imperial Road
Inequality
Latitude
ln(Area)
ln(Distance to Airport)
ln(Distance to Border)
ln(Distance to Coast)
ln(Distance to Railroad)
ln(Distance to River)
ln(Distance to Road)
ln(Distance to Trade Center)
ln(Distance to Wittenberg)
ln(Employees Compensation)
ln(Fixed Capital)
ln(Population Density)
ln(Relative GDP Density)
Longitude
Medieval Mining
Mining Region
Mountain Region
Patents
Post Communistic Country
Printing Press before 1500
Quality of Government
Trade City
Trade Center
Unemployment
University before 1500
839
839
839
839
839
839
839
839
832
839
839
839
825
839
839
839
839
839
839
839
839
839
839
825
803
839
839
839
839
839
839
803
839
839
839
361
839
582
839
279.230
.064
0.011
0.051
0.67
1.46
0.147
0.122
24.211
0.108
0.069
0.045
1.134
49.460
7.032
-0.645
-0.825
0.308
-2.111
-.675
-4.001
0.432
6.027
9.867
9.141
5.351
-.077
10.228
0.027
0.228
0.479
83.094
0.111
0.199
72.130
.249
0.137
8.237
0.052
320.194
.246
0.103
0.221
0.955
0.866
0.354
0.327
6.319
0.311
0.254
0.208
0.921
3.088
1.297
0.727
1.083
1.204
1.390
1.322
1.376
0.272
0.804
0.924
0.818
1.137
1.262
5.012
0.16
0.420
1.022
89.654
0.314
0.4
17.163
.433
0.344
3.435
0.223
60
Min
Max
-6.200 2472.600
0
1
0
1
0
1
0
5
0
5.357
0
1
0
1
8.4
48.6
0
1
0
1
0
1
0.037
8.425
38.245 55.939
3.575
9.400
-4.142
0.792
-5.532
1.16
-5.566
1.882
-7.365
0.429
-7.185
1.944
-10.868 -1.194
0
1.665
-7.447
7.335
7.086
12.331
6.802
11.494
2.709
9.964
-2.461
6.194
-4.091 25.573
0
1
0
1
0
3
0.286 764.717
0
1
0
1
10.18
97.61
0
1
0
1
1.9
19.1
0
1
Table A.2: Descriptive Data Overview – City Level Variables
Variable
Obs Mean Std. Dev.
Min
Max
Bishop 10000 AD
Imperial Road
Imperial City
Hanseatic League
1
Latitude
Longitude
Mountain Region
ln(Distance to Coast)
ln(Distance to River)
ln(Population 1200 AD)
ln(Population 1300 AD)
ln(Population 1400 AD)
ln(Population 1500 AD)
Trade City
361 0.127
361 0.078
361 0.122
361
0.334
0.268
0.328
0.155
0
0
0
0.363
1
1
1
0
361
361
361
361
361
86
199
180
361
361
3.633
5.048
0.887
1.326
1.504
0.812
1.104
1.063
0.983
.433
40.11
-4.29
0
-5.566
-7.185
6.908
6.908
6.908
6.908
0
54.473
22
3
1.762
1.944
11.608
11.918
12.524
12.324
1
48.453
8.727
0.385
-0.24
-0.541
9.533
9.114
9.053
8.817
.249
61
62
NUTS-3 Region
Östliche
Obersteiermark
Innsbruck
Graz
Linz-Wels
Wien
Klagenfurt-Villach
Salzburg und
Umgebung
Arr. Antwerpen
Arr. Brugge
Trade City
Bruck
Innsbruck
Graz
Linz
Vienna
Villach
Salzburg
Antwerp
Bruges
Belgium
Belgium
Austria
Austria
Austria
Austria
Austria
Austria
Austria
country
Davies and Moorhouse
(2002), King (1985), Stier et
al. (1985)
Davies and Moorhouse
(2002), King (1985), Magocsi
(2002) and Stier et al. (1956)
Magocsi (2002), Stier et al.
(1956)
Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
Magocsi (2002)
Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse
(2002), Stier et al. (1956)
Magocsi (2002)
Map Sources (Primary)
Ammann (1955), Hunt
and Murray (1999),
Kinder and Hilgemann
(1982), Spufford (2002)
Hunt and Murray (1999),
Kinder and Hilgemann
(1982), Spufford (2002)
Schulte (1966), Spufford (2002)
Schulte (1966)
Kinder and Hilgemann
(1982), Spufford (2002)
Schulte (1966), Spufford
(2002)
Other Historical Records
Table A.3: Overview over the included Trade Cities and Regions
63
Jihomoravský kraj
Stredoceský kraj
Olomoucký kraj
Hlavnı́ mesto Praha
Vaucluse
Pyrénées-Atlantique
Gironde
Saône-et-Loire
Seine-Maritime
Haute-Vienne
Rhône
Bouches-du-Rhône
Moselle
Hérault
Brno
Kutna Hora
Olmouc
Prague
Avignon
Bayonne
Bordeaux
Chalon-sur-Saône
Harfleur
Limoges
Lyon
Marseille
Metz
Montpellier
Table A.3 – Continued
Ghent
Arr. Gent
Stier et al. (1956)
France
France
France
France
France
France
King (1985), Stier
et al. (1956)
Davies and Moorhouse (2002)
King (1985)
King (1985), Stier
et al. (1956)
King (1985), Stier et al. (1956)
Stier et al. (1956)
Czech Republic Davies and Moorhouse
(2002), Magocsi (2002)
Czech Republic Magocsi (2002)
Czech Republic Davies and Moorhouse
(2002), Magocsi (2002)
Czech Republic Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
France
King (1985), Stier
et al. (1956)
France
Stier et al. (1956)
France
Stier et al. (1956)
France
Stier et al. (1956)
Belgium
Ammann (1955), Hunt and
Murray (1999), Kinder and
Hilgemann (1982), Schulte
(1966), Spufford (2002)
Kinder and Hilgemann
(1982), Spufford (2002)
Schulte (1966)
Spufford (2002)
Hunt and Murray (1999),
Spufford (2002)
Spufford (2002)
Spufford (2002)
Schulte (1966),
Spufford (2002)
Kinder and Hilgemann
(1982), Spufford (2002)
Spufford (2002)
Hunt and Murray (1999),
Kinder and Hilgemann (1982),
Spufford (2002)
64
Haute-Garonne
Indre-et-Loire
Aube
Amberg,
District-Free City
Augsburg,
District-Free City
Toulouse
Tours
Troyes
Amberg
Braunschweig,
District-Free City
Bremen,
District-Free City
Bremerhaven,
District-Free City
Brunswick
Bremen
Bremerhaven
Berlin
Berlin
Augsburg
France
France
France
France
Pyrénées-Orientales
Marne
Ille-et-Vilaine
Bas-Rhin
Perpignan
Reims
St. Melo
Strasbourg
Germany
Germany
Germany
Germany
Germany
France
France
France
Germany
France
France
France
Table A.3 – Continued
Narbonne
Aude
Orleans
Loiret
Paris
Paris
Davies and Moorhouse
(2002), King (1985), Magocsi
(2002), Stier et al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al. (1956)
Davies and Moorhouse (2002),
King (1985), Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse
(2002), Stier et al. (1956)
Davies and Moorhouse
(2002), Stier et al. (1956)
King (1985), Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse
(2002), King (1985), Stier
et al. (1956)
King (1985)
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse (2002),
Stier et al. (1956)
Stier et al. (1956)
Stier et al. (1956)
Stier et al. (1956)
Magocsi (2002)
Dollinger (1966), Kinder and
Hilgemann (1982), Spufford (2002)
Dollinger (1966), Kinder and
Hilgemann (1982), Spufford (2002)
Dollinger (1966), Kinder and
Hilgemann (1982)
Dietze (1923), Kinder and
Hilgemann (1982), Schulte
(1966), Spufford (2002)
Kinder and Hilgemann (1982),
Schulte (1966), Spufford (2002)
Spufford (2002)
Spufford (2002)
Schulte (1966), Spufford (2002)
Kinder and Hilgemann (1982),
Hunt and Murray (1999), Schulte
(1966), Spufford (2002)
Spufford (2002)
Schulte (1966), Spufford (2002)
65
Dortmund,
District-Free City
Northeim
Erfurt,
District-Free City
Frankfurt (Oder),
District-Free City
Dortmund
Region Hannover
Hildesheim
Leipzig, District-Free
City
Hannover
Hildesheim
Leipzig
Hamburg
Greifswald
Fulda
Görlitz
Frankfurt am Main,
District-Free City
Fulda
Görlitz,
District-Free City
Greifswald,
District-Free City
Hamburg
Frankfurt (Main)
Frankfurt (Oder)
Einbeck
Erfurt
Germany
Konstanz
Constance
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Table A.3 – Continued
Cologne
Cologne,
District-Free City
Davies and Moorhouse
(2002), King (1985), Magocsi
(2002), Stier et al. (1956)
Magocsi (2002), Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al.
(1956)
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al. (1956)
Davies and Moorhouse
(2002), Magocsi (2002), Stier
et al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al. (1956)
Stier et al. (1956)
Magocsi (2002)
Davies and Moorhouse
(2002), King (1985), Stier
et al. (1956)
Davies and Moorhouse
(2002), Stier et al. (1956)
Stier et al. (1956)
Dollinger (1966)
Ammann (1955), Kinder and
Hilgemann (1982), Spufford (2002)
Ammann (1955), Dollinger
(1966), Kinder and Hilgemann
(1982), Spufford (2002)
Dollinger (1966)
Spufford (2002)
Kinder and Hilgemann (1982),
Schulte (1966), Spufford (2002)
Dietze (1923), Kinder and
Hilgemann (1982)
Ammann (1955), Dollinger (1966),
Hunt and Murray (1999), Kinder and
Hilgemann (1982), Spufford (2002)
Dietze (1923), Schulte (1966),
Spufford (2002)
Dollinger (1966)
66
Magdeburg,
District-Free City
Minden-Lübbecke
Münster,
District-Free City
Nuremberg,
District-Free City
Magdeburg
Minden
Münster
Soest
Rostock
Paderborn
Ravensburg
Regensburg
Osnabrück
Soest
Osnabrück,
District-Free City
Paderborn
Ravensburg
Regensburg,
District-Free City
Rostock,
District-Free City
Lüneburg
Lüneburg
Nuremberg
Lübeck,
District-Free City
Lübeck
Table A.3 – Continued
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse, Magocsi
(2002), Stier et al. (1956)
Davies and Moorhouse (2002),
King (1985), Magocsi (2002),
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse
(2002), Magocsi (2002), Stier
et al. (1956)
Davies and Moorhouse
(2002), Magocsi (2002),
Stier et al. (1956)
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse
(2002), King (1985), Magocsi
(2002), Stier et al. (1956)
Dollinger (1966)
Dollinger (1966)
Dietze (1923), Spufford (2002)
Schulte (1966),
Spufford (2002)
Dollinger (1966),Kinder and
Hilgemann (1982)
Ammann (1955), Dietze (1923),
Kinder and Hilgemann (1982),
Spufford (2002)
Dollinger (1966)
Ammann (1955), Dietze (1923),
Dollinger (1966), Kinder and
Hilgemann (1982) and Hunt and
Murray (1999)
Dollinger (1966), Kinder and
Hilgemann (1982), Spufford
(2002)
Dollinger (1966), Kinder and
Hilgemann (1982)
67
Bolzano-Bozen
Como
Firenze
Genova
Lucca
Mantova
Como
Florence
Genoa
Lucca
Mantoa
Pecs
Ancona
Bari
Bologna
Bozen
Baranya
Ancona
Bari
Bologna
Budapest
Italy
Italy
Italy
Italy
Italy
Italy
Hungary
Italy
Italy
Italy
Hungary
Germany
Wismar
Wismar,
District-Free City
Budapest
Germany
Germany
Table A.3 – Continued
Stralsund
Stralsund,
District-Free City
Ulm
Ulm, Urban District
Stier et al. (1956)
Magocsi (2002)
Davies and Moorhouse
(2002), King (1985), Stier
at al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al. (1956)
Magocsi (2002)
Magocsi (2002), Stier et al. (1956)
Magocsi (2002), Stier et al. (1956)
King (1985), Magocsi (2002),
Stier et al. (1956)
Magocsi (2002), Stier et
al. (1956)
Stier et al. (1956)
Magocsi (2002), King (1985),
Stier et al. (1956)
Stier et al. (1956)
Magocsi (2002), Stier et
al. (1956)
Davies and Moorhouse (2002),
Stier et al. (1956)
Dietze (1923), Kinder and
Hilgemann (1982), Schulte (1966)
Schulte (1966)
Dietze (1923), Kinder and
Hilgemann (1982), Hunt and
Murray (1999), Spufford (2002)
Ammann (1955), Dietze (1923),
Hunt and Murray (1999),
Kinder and Hilgemann (1982),
Schulte (1966), Spufford (2002)
Dietze (1923), Spufford (2002)
Spufford (2002)
Spufford (2002)
Schulte (1966)
Spufford (2002)
Dietze (1923), Kinder and
Hilgemann (1982), Schulte
(1966), Spufford (2002)
Dollinger (1966)
Dollinger (1966)
68
Siena
Trento
Treviso
Udine
Venezia
Verona
Klaipedos apskritis
Trento
Treviso
Udine
Venice
Verona
Klaipeda
Padova
Parma
Prato
Roma
Padoa
Parma
Prato
Rome
Siena
Italy
Napoli
Naples
Lithuania
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Table A.3 – Continued
Milan
Milano
King (1985),Magocsi
(2002), Stier et al. (1956)
Magocsi (2002)
Magocsi (2002)
Magocsi (2002)
Davies and Moorhouse
(2002), King (1985),
Magocsi (2002), Stier
et al. (1956)
Magocsi (2002), Stier et al.
(1956)
Davies and Moorhouse
(2002), Magocsi (2002)
King (1985), Magocsi
(2002), Stier et al. (1956)
Magocsi (2002)
Magocsi (2002)
King (1985)
King (1985), Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse
(2002), King (1985),
Stier et al. (1956)
Dietze (1923),Hunt and
Murray (1999), Kinder and
Hilgemann (1982), Schulte
(1966), Spufford (2002)
Schulte (1966)
Schulte (1966)
Schulte (1966)
Spufford (2002)
Hunt and Murray
(1999),Kinder and
Hilgemann (1982),
Spufford (2002)
Spufford (2002)
Dietze (1923), Hunt and
Murray (1999),Kinder and
Hilgemann (1982), Schulte
(1966), Spufford (2002)
Hunt and Murray (1999),
Kinder and Hilgemann (1982),
Schulte (1966), Spufford (2002)
Schulte (1966)
69
Netherlands
Zuidwest-Overjissel
Zuidoost-Zuid-Holland
Noord-Overjissel
Zuid-Limburg
Groot-Rijnmond
Utrecht
Elblaski
Miasto Kraków
Elblaski
Gdanski
Starogardzki
Piotrkowski
Ciechanowsko-plocki
Deventer
Dordrecht
Kampen
Maastricht
Rotterdam
Utrecht
Braniewo
Cracow
Elblag
Gdansk
Malbork
Piotrków Trybunalski
Plock
Poland
Poland
Poland
Poland
Poland
Netherlands
Netherlands
Netherlands
Poland
Poland
Netherlands
Netherlands
Lithuania
Lithuania
Netherlands
Table A.3 – Continued
Kovno
Kauno apskritis
Palanga
Klaipedos apskritis
Amsterdam
Groot-Amsterdam
King (1985)
Davies and Moorhouse (2002)
Magocsi (2002)
Stier et al. (1956)
Stier et al. (1956)
Stier et al. (1956)
Stier et al. (1956)
Davies and Moorhouse (2002),
King (1985), Magocsi (2002),
Stier et al. (1956)
Magocsi (2002), Stier
et al. (1956)
Davies and Moorhouse
(2002), King (1985), Magocsi
(2002), Stier et al. (1956)
King (1985), Stier et
al. (1956)
King (1985), Stier et
al. (1956)
King (1985)
King (1985), Magocsi (2002)
Stier et al. (1956)
King (1985), Stier et al.
(1956)
Ammann (1955), Dietze
(1923), Dollinger (1966),
Kinder and Hilgemann
(1982), Spufford (2002)
Dollinger (1966)
Ammann (1955), Kinder
and Hilgemann (1982),
Spufford (2002)
Kinder and Hilgemann (1982)
Dollinger (1966),
Spufford (2002)
Dollinger (1966),
Spufford (2002)
Dollinger (1966), Kinder
and Hilgemann (1982),
Spufford (2002)
Dollinger (1966)
Kinder and Hilgemann (1982)
70
Poland
Bydgosko-Torunski
Miasto Warszawa
Miasto Wroclaw
Szczeciński
Torun
Warsaw
Wroclaw
Stargard
Poland
Poland
Poland
Poland
Table A.3 – Continued
Poznan
Poznanski
Davies and Moorhouse
(2002),Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse (2002),
King (1985), Magocsi (2002),
Stier et al. (1956)
Davies and Moorhouse (2002),
Magocsi (2002), Stier et al. (1956)
Davies and Moorhouse
(2002), King(1985), Magocsi
(2002), Stier et al. (1956)
Stier et al. (1956)
Ammann (1955) and Kinder
and Hilgemann (1982)
Ammann (1955), Dietze (1923),
Kinder and Hilgemann (1982),
Spufford (2002)
Dollinger (1966)
Dollinger (1966),
Spufford (2002)
Ammann (1955)
71
Wien
Arr. Antwerpen
Arr. Brugge
Jihomoravský kraj
Olomoucký kraj
Hlavnı́ mesto Praha
Vienna
Antwerp
Bruges
Brno
Olmouc
Prague
Vaucluse
Gironde
Haute-Vienne
Rhône
Bouches-du-Rhône
Moselle
Hérault
Aude
Paris
Bas-Rhin
Aube
Augsburg, District-Free City
Berlin
Linz-Wels
Linz
Avignon
Bordeaux
Limoges
Lyon
Marseille
Metz
Montpellier
Narbonne
Paris
Strasbourg
Troyes
Augsburg
Berlin
NUTS-3 Region
Trade City
mentioned earliest by
Humnicki and Borawska
(eds.) (1969)
Austria
Dietze (1923)
Belgium
Heyd (1897b)
Belgium
Heyd (1897b)
Czech Republic Humnicki and Borawska
(1969)
Czech Republic Humnicki and Borawska
(1969)
Czech Republic Humnicki and Borawska
(1969)
France
Heyd (1879b)
France
Dollinger (1966)
France
Heyd (1879a)
France
Dollinger (1966)
France
Heyd (1879a)
France
Heyd (1879b)
France
Heyd (1879a)
France
Heyd (1879a)
France
Dollinger (1966)
France
Dollinger (1966)
France
Dietze (1923)
Germany
Dietze (1923)
Germany
Dollinger (1966)
Austria
country
Table A.4: Medieval Trade Cities and Regions with long-run trade activity
high medieval
15th century
before 12th century
15th century
before 10th century
14th century
before 12th century
before 12th century
15th century
before 1250
before 9th century
before 9th century
15th century
9th century
9th century
before 14th century
14th century
14th century
9th century
9th century
earliest period mentioned
72
Table A.4 – Continued
Brunswick
Bremen
Bremerhaven
Cologne
Constance
Erfurt
Frankfurt (Oder)
Frankfurt (Main)
Görlitz
Greifswald
Hamburg
Hildesheim
Lübeck
Lüneburg
Magdeburg
Minden
Münster
Nuremberg
Osnabrück
Paderborn
Regensburg
Rostock
Soest
Stralsund
Ulm
Wismar
Budapest
Braunschweig, District-Free City
Bremen, District-Free City
Bremerhaven, District-Free City
Cologne, District-Free City
Konstanz
Erfurt, District-Free City
Frankfurt (Oder), District-Free City
Frankfurt am Main, District-Free City
Görlitz, District-Free City
Greifswald, District-Free City
Hamburg
Hildesheim
Lübeck, District-Free City
Lüneburg, District
Magdeburg, District-Free City
Minden-Lübbecke
Münster, District-Free City
Nuremberg, District-Free City
Osnabrück, District-Free City
Paderborn
Regensburg, District-Free City
Rostock, District-Free City
Soest
Stralsund, District-Free City
Ulm, Urban District
Wismar, District-Free City
Budapest
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Hungary
Dietze (1923)
before 9th century
Heyd (1879a)
before 12th century
Heyd (1879a)
before 12th century
Dietze (1923)
before 9th century
Dietze (1923)
before 9th century
Heyd (1879a)
before 12th century
Heyd (1879a)
before 12th century
Dietze (1923)
before 9th century
Rutkowski (1980a)
14th century (1370)
Dietze(1923)
before 14th century
Dollinger (1966)
before 1250
Dollinger (1966)
13th – 14th century
Heyd (1879a)
Treaty of Smolensk (1229)
Dollinger (1966)
13th – 14th century
Heyd (1879a)
before 10th century
Dollinger (1966)
13th – 14th century
Dollinger (1966)
Treaty of Smolensk (1229)
Dietze (1923)
before 9th century
Dollinger (1966) 13th – 14th century
Dollinger (1966)
13th – 14th century
Dietze (1923)
before 9th century
Dollinger (1966)
13th – 14th century
Dollinger (1966)
13th – 14th century
Dietze (1923)
before 14th century
Dietze(1923)
before 9th century
Dollinger (1966)
13th – 14th century
Wojtowicz (1956)
14th century
73
Poland
Poland
Poland
Poland
Poland
Poland
Poland
Poland
Poland
Gdansk
Malbork
Piotrków Trybunalski
Plock
Poznan
Szczecin
Torun
Warsaw
Wroclaw
Gdanski
Starogardzki
Piotrkowski
Ciechanowsko-plocki
Miasto Poznan
Miasto Szczecin
Bydgosko-Torunski
Miasto Warszawa
Miasto Wroclaw
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Italy
Lithuania
Poland
Table A.4 – Continued
Ancona
Ancona
Bari
Bari
Bologna
Bologna
Florence
Firenze
Genoa
Genova
Lucca
Lucca
Milan
Milano
Naples
Napoli
Parma
Parma
Pisa
Pisa
Rome
Roma
Siena
Siena
Venice
Venezia
Kovno
Kauno apskritis
Cracow
Miasto Kraków
Heyd (1879a)
Heyd (1879a)
Heyd (1879b)
Heyd (1879b)
Heyd (1879a)
Heyd (1879a)
Heyd (1879b)
Heyd (1879b)
Heyd (1879b)
Dietze (1923)
Heyd (1879a)
Heyd (1879b)
Heyd (1879a)
Dollinger (1966)
Humnicki and Borawska
(1969)
Dollinger (1966)
Wojtowicz (1956)
Wojtowicz (1956)
Wojtowicz (1956)
Wojtowicz (1956)
Wojtowicz (1956)
Dollinger (1966)
Wojtowicz (1956)
Dollinger (1966)
13th
14th
14th
14th
14th
14th
13th
14th
13th
– 14th century
century
century
century
century
century
– 14th century
century
– 14th century
before 12th century
before 12th century
14th century
14th century
before 12th century
before 13th century
14th century
before 12th century
14th century
before 14th century
before 12th century
13th century (1209)
before 12th century
between 1350 and 1500
9th century
B. Robustness Checks
Robustness to Influential Observations and Additional Controls
In this appendix we report the results of several robustness checks and additional results mentioned in the main text of the study. To be precise, in Table B.1 we re-run some specifications
from Table 5 and 6 in the main text, including additional control variables (a dummy variable for
medieval copper mining regions, an interaction term of latitude and longitude, the country-evel
share of Catholics and a dummy for regions containing important medieval residence cities).In
Table B.2 we look whether the results are sensitive to the exclusion of influential observations,
identified by the DFITS statistics (see main text for a detailed description).
74
75
Table 6
column (6)
839
0.784
0.181***
(0.029)
No
839
0.776
-0.134**
(0.053)
Adding Dummy for
medieval mining regions
Table 6
column (3)
(2)
839
0.778
0.264***
(0.031)
No
(5)
839
0.762
-0.291***
(0.055)
Table 6
column (9)
(6)
518
0.878
0.13***
(0.027)
Yes
518
0.872
-0.138***
(0.041)
Adding share of Catholics
in a country
ln(GDP per capita)
Table 5
Table 6
column (6)
column (4)
(4)
Adding a interaction variable
of latitude and longitude
Table 5
column (3)
(3)
Table 6
column (8)
(8)
839
0.784
0.181***
(0.03)
No
839
0.776
-0.135*
(0.053)
Yes
Adding a dummy for important
residence cities
Table 6
column (3)
(7)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is statistically different from zero at the
***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. For the controls included in each specification consult the main text or the notes to the original
tables mentioned in the third row. Each regression includes a constant not reported.
Obs.
Adj. R2
ln(Distance to Trade Center)
Trade Center
Additional Variable
significant
Modification
Dep. Var.
Modified Specification
(1)
Table B.1: Inclusion of Additional Control Variables
76
(2)
(3)
(4)
40
799
0.844
Yes
Yes
Yes
Yes
Yes
No
No
No
No
45
794
0.891
Yes
Yes
Yes
Yes
No
Yes
No
No
No
40
799
0.829
Yes
Yes
Yes
Yes
No
No
Yes
No
No
41
477
0.911
Yes
Yes
Yes
Yes
No
No
No
Yes
No
47
771
0.901
Yes
Yes
No
No
No
No
No
No
Yes
(7)
(8)
(9)
(10)
40
799
0.837
Yes
Yes
Yes
Yes
Yes
No
No
No
No
45
794
0.887
Yes
Yes
Yes
Yes
No
Yes
No
No
No
41
798
0.816
Yes
Yes
Yes
Yes
No
No
Yes
No
No
43
475
0.904
Yes
Yes
Yes
Yes
No
No
No
Yes
No
44
774
0.899
Yes
Yes
No
No
No
No
No
No
Yes
-0.108*** -0.081** -0.111*** -0.12*** -0.064*
(0.038) (0.039) (0.046) (0.043) (0.038)
ln(GDP per capita)
(5)
(6)
0.17*** 0.11*** 0.153*** 0.117*** 0.0794**
(0.022) (0.024) (0.025) (0.026) (0.021)
(1)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is
statistically different from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The basic geographic
controls include a region’s latitude, longitude and altitude. The geographic centrality controls include the ln distances of a region’s centroid
to the nearest airport, railroad, road, border and coast point. Region characteristic controls include a dummies for regions in Germany that
are district-free cities, for regions including a country’s capital, are classified as mountain regions, with ore or coal mines, located in the
former GDR and located in an Eastern European post-communistic transition country. Furthermore it encompasses the ln of a regions area.
The historical region characteristics consist of a dummy variables indicating regions with a university founded before 1500 AD, that adopted
printing technology before 1500 AD, contain cities that were members of the Hanseatic League, with former imperial cities and were located on
an imperial road. Moreover it includes the ln of the distance of a region’s centroid to Wittenberg. The growth covariates encompass a region’s
unemployment rate, number of registered patents, average firm ln fixed capital stock, average worker compensation. Furthermore, it includes
the share of people aged between 25-64 with tertiary education on NUTS-2 level, the quality of government index on NUTS-1/ NUTS-2
level and the ratio of an average workers compensation to a region’s GDP per capita as inequality measure. The set of all robust covariates
encompasses altitude, the ln distances to airports and railroads, dummies for district free cities, capital cities, capital cities of autonomous
regions, post-communistic transition countries, Eastern Germany, the ln of a region’s area, the share of people with tertiary education, the
inequality measure and p
the printing press before 1500 AD dummy. A region is removed from the estimation if its DFITS value is above the
cut-off of |DF IT Sj | > 2 k \ N (with k indicating the number of regressors and N denoting the number of observations in the sample). Each
regression includes a constant not reported.
No. of removed regions
Obs.
Adj. R2
Country Dummies
NUTS-1 Dummies
NUTS-2 Dummies
Basic Geographic Controls
Geographic Centrality Controls
Region Characteristics
Historical Region Characteristic
Development Covariates
All Robust Controls
ln(Distance to Trade Center)
Trade Center
Dep. Var.
Table B.2: Regressions of Table 5 Without Influential Observations
Results for Alternatively Coded Medieval Trade Variables
In Tables B.3 and B.6 we conduct the OLS, IV and mediation analysis estimations with alternatively coded medieval trade variables, i.e. alternative samples of medieval trade cities. Here,
Table B.3 show the estimation results with when we only consider trade cities mentioned in more
than one of the sources. In Table B.4 we redo this estimations this time excluding cities for which
the actual importance in trade is in doubt. To continue, in Table B.5 we repeat this, using the
original sample and include additional cities for which we think they might be important, albeit
they are not mentioned by our main sources. At last, in Table B.6 we show the results for a
sample of trade cities that only includes cities for which historical sources indicate long-run trade
activities (i.e. cities that are important trade cities around 1500 AD and that were important also
in the period before). An overview over these cities the earliest period in which trade activities
are reported and the source mentioned the respective city are depicted in Table A.4.
77
78
818
0.877
0.0543**
(0.0225)
818
0.629
173.3
0.000
61.09
0.236
46.53
0.000
14.45
0.000
0.000
1.000
8.27
0.000
0.0613**
(0.0260)
Table 6
Column (2)
Lewbel (2012)
(3)
818
0.534
0.363***
(0.133)
Table 6
Column (1)
LIML IV
ln(GDP per capita)
(2)
Table 5
Column (5)
OLS
(1)
86
0.344
0.479**
(0.232)
Table 8
Column (1)
OLS
818
0.938
0.3267***
(0.071)
Table 9
Column (4)
818
0.924
0.0654
-0.0085
0.0569
112.7
-0.00912
(0.0181)
0.203***
(0.0109)
Table 9
Column (4)
Mediation Analysis
Equation (6)
Equation (7)
ln(City Growth) ln(Relative GDP Density) ln(GDP per capita)
(4)
(5)
(6)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is statistically different from
zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. For the controls included in each specification consult the main text or
the notes to the original tables mentioned in the third row. In columns (1) and (2) the adjusted R2 is reported. In column (3) and (4) the centered R2 is shown
and in columns (5) and (6) the R2 . In column (3) the results of the first stage are omitted but available from the author. Each regression includes a constant not
reported.
Obs.
Centered R2 \R2
ACME
Direct Effect
Total Effect
% of total mediated
Underidentification Test
p-value
Overidentification Test
p-value
AP F-statistic of
excluded IV’s
p-value
ln(Relative GDP Density)
Trade Center
Estimated Specification
Method
Estimated Equation
Dep. Var.
Table B.3: Results for Alternative Trade Center Dummy – Without Regions Mentioned by Only One Source
79
818
0.877
818
0.621
160.2
0.000
58.41
0.317
43.92
0.000
15.18
0.000
0.008
0.93
8.57
0.000
0.073***
(0.027)
818
0.544
0.375***
(0.14)
Table 6
Column (2)
Lewbel (2012)
(3)
86
0.342
0.468*
(0.235)
Table 8
Column (1)
OLS
818
0.939
0.3621***
(0.074)
Table 9
Column (4)
818
0.919
0.0724***
-0.0029
0.0694**
102.8**
-0.0036
(0.02)
0.203***
(0.011)
Table 9
Column (4)
Mediation Analysis
Equation (6)
Equation (7)
ln(City Growth) ln(Relative GDP Density) ln(GDP per capita)
(4)
(5)
(6)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is statistically different from
zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. For the controls included in each specification consult the main text or
the notes to the original tables mentioned in the third row. In columns (1) and (2) the adjusted R2 is reported. In column (3) and (4) the centered R2 is shown
and in columns (5) and (6) the R2 . In column (3) the results of the first stage are omitted but available from the author. Each regression includes a constant not
reported.
Obs.
Centered R2 \R2
ACME
Direct Effect
Total Effect
% of total mediated
Underidentification Test
p-value
Overidentification Test
p-value
AP F-statistic of
excluded IV’s
p-value
ln(Relative GDP Density)
0.0670***
(0.023)
Trade Center
Table 6
Column (1)
LIML IV
ln(GDP per capita)
(2)
Table 5
Column (5)
OLS
(1)
Estimated Specification
Method
Estimated Equation
Dep. Var.
Table B.4: Results for Alternative Trade Center Dummy – Cities with Uncertain Importance Removed
80
818
0.878
818
0.623
203.9
0.000
70.56
0.065
56.93
0.000
13.03
0.001
0.192
0.661
7.60
0.001
0.0765***
(0.024)
818
0.552
0.324***
(0.114)
Table 6
Column (2)
Lewbel (2012)
(3)
86
0.358
0.544**
(0.225)
Table 8
Column (1)
OLS
818
0.939
0.3268***
(0.62)
Table 9
Column (4)
818
0.919
0.0652***
0.0044
0.0696***
93.3***
0.0038
(0.016)
0.202***
(0.011)
Table 9
Column (4)
Mediation Analysis
Equation (6)
Equation (7)
ln(City Growth) ln(Relative GDP Density) ln(GDP per capita)
(4)
(5)
(6)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is statistically different from
zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. For the controls included in each specification consult the main text or
the notes to the original tables mentioned in the third row. In columns (1) and (2) the adjusted R2 is reported. In column (3) and (4) the centered R2 is shown
and in columns (5) and (6) the R2 . In column (3) the results of the first stage are omitted but available from the author. Each regression includes a constant not
reported.
Obs.
Centered R2 \R2
ACME
Direct Effect
Total Effect
% of total mediated
Underidentification Test
p-value
Overidentification Test
p-value
AP F-statistic of
excluded IV’s
p-value
ln(Relative GDP Density)
0.0686***
(0.021)
Trade Center
Table 6
Column (1)
LIML IV
ln(GDP per capita)
(2)
Table 5
Column (5)
OLS
(1)
Estimated Specification
Method
Estimated Equation
Dep. Var.
Table B.5: Results for Alternative Trade Center Dummy – Cities with Uncertain Importance Added
81
818
0.877
0.0568**
(0.027)
818
0.62
140.80
0.000
65.7
0.132
78.54
0.000
14.84
0.001
0.406
0.524
9.16
0.001
0.0743**
(0.032)
Table 6
Column (2)
Lewbel (2012)
(3)
818
0.574
0.320***
(0.112)
Table 6
Column (1)
LIML IV
ln(GDP per capita)
(2)
Table 5
Column (5)
OLS
(1)
86
0.305
0.123
(0.253)
Table 8
Column (1)
OLS
818
0.938
0.3218***
(0.088)
Table 9
Column (4)
818
0.919
0.0641***
0.0053
0.0588**
105.0**
-0.0061
(0.024)
0.202***
(0.011)
Table 9
Column (4)
Mediation Analysis
Equation (6)
Equation (7)
ln(City Growth) ln(Relative GDP Density) ln(GDP per capita)
(4)
(5)
(6)
Notes. Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions are reported in parentheses. Coefficient is statistically different from
zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. For the controls included in each specification consult the main text or
the notes to the original tables mentioned in the third row. In columns (1) and (2) the adjusted R2 is reported. In column (3) and (4) the centered R2 is shown
and in columns (5) and (6) the R2 . In column (3) the results of the first stage are omitted but available from the author. Each regression includes a constant not
reported.
Obs.
Centered R2 \R2
ACME
Direct Effect
Total Effect
% of total mediated
Underidentification Test
p-value
Overidentification Test
p-value
AP F-statistic of
excluded IV’s
p-value
ln(Relative GDP Density)
Trade Center
Estimated Specification
Method
Estimated Equation
Dep. Var.
Table B.6: Results for Alternative Trade Center Dummy – Only Cities with Long-Run Trade Activity
Description and Sources of the Additional Variables
Residence city. Binary variable that represents important residence cities (of Dukes, Kings . . . )
in the Holy Roman Empire or the German Reich (after 1871). The coding follows a wikipedia
list at http://de.wikipedia.org/wiki/Residenzstadt (accessed February, 24th 2013) and
Köbler (1988). It also includes residences of electors (“Kurfürsten”) and prince-bishoprics.
Furthermore, it represents the capitals or residence cities of Italian duchies, kingdoms and
republics (like Venice, Lombardy, Sardinia, Parma, Modena, Tuscany, Naples or the Kingdom
of the two Sicilies). For all other countries it marked the capitals of pre-existing states or
kingdoms, duchies etc. (e.g. in Poland it includes the residence of the kings of the Kingdom of
Poland, in Lithuania the residence of the grand duke of Lithuania. . . ). The coding here follows
the author’s information or different versions of Putzgers historical atlas (Bruckmüller (eds.)
2011 and Baldamus et al. (eds.) 1914).
Share of Catholics. The share of people with Roman Catholic denomination (in percent
of total population) in a country is taken from “The World Religion Dataset, 1945 2010” (Zeev and Henderson 2013) available from the “Correlates of War” project website
(http://www.correlatesofwar.org/COW2%20Data/Religion/WRD_national.csv; accessed at
May, 8th 2013). As always, we took the values from 2009.
An overview over the additional variables used for the robustness checks is provided in Table
B.6 above:
Table B.7: Descriptive Overview over the Additional Variables
Variable
Obs
Mean
Latitude*Longitude 839 507.123
Residence City
839 0.067
Share of Catholics 839 49.623
82
Std. Dev.
253.213
0.25
22.29
Min
Max
-197.378 1401.973
0
1
26.85
89.15
C. Additional Results
Here the result of the estimation of Table 9 using the ln population density of a NUTS-3 region as
mediating agglomeration measure is shown. The results are almost identical to that obtained with
the relative GDP density. However, the probably biggest difference between both estimations
is that the average ACME using the population density is clearly lower. Neverthless, since it is
always significant and on average around three quarters of the effect of medieval trade on ln GDP
per capita is mediated by the ln population density our main conclusion does hold. Furthermore
we report the results of estimating Table 8 using the Index of Commercial Importance instead
of the trade city dummy (Table C.2). We see that the result are a little bit weaker (especially
concerning the results for city growth between 1200 and 1500 AD). Nevertheless, the overall
results and therefore also the general implications of the results do stay the same.
83
Table C.1: Medieval Trade, Population Density and Regional Economic Development
(1)
Method
City Growth from to
Dep. Var.
P opulation1500
P opulationt
ln(Population Density)
(2)
(3)
OLS
1200–1500 1300–1500 1400–1500
ln(Population Density)
0.337***
(0.105)
0.178***
(0.067)
0.172***
(0.062)
Trade Center
ln(Distance to Trade Center)
(4)
(5)
Mediation Analysis
Equation (7)
ln(GDP per capita)
0.135***
(0.015)
0.0308
(0.019)
Commercial Importance
R2
ACME
Direct Effect
Total Effect
% of total mediated
0.964
0.955
0.947
(6)
0.139***
(0.015)
-0.007
(0.027)
0.137***
(0.015)
0.0067
(0.008)
0.889
0.888
0.888
0.0405*** -0.0605*** 0.0178***
0.0314
-0.0062
0.0067
0.0719*** -0.0667** 0.0247***
55.7***
90.0**
70.8***
Equation (6)
ln(Relative GDP Density)
Trade Center
0.3043***
(0.053)
ln(Distance to Trade Center)
Commercial Importance
-0.4313***
(0.108)
0.1318***
(0.019)
Country Dummies
NUTS-1 Dummies
All Robust Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs.
R2
85
179
197
818
0.867
818
0.87
818
0.87
Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different from
zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The set of
all robust covariates encompasses altitude, the ln distances to airports and railroads, dummies for
district free cities, capital cities, capital cities of autonomous regions, post-communistic transition
countries, Eastern Germany, the ln of a region’s area, the share of people with tertiary education,
the inequality measure and the printing press before 1500 AD dummy. Each regression includes a
constant not reported. ACME is the “Average Causal Mediation Effect” and means how much of the
effect of medieval trade is mediate, i.e. works indirectly through the relative GDP density.
84
85
86
0.346
0.301*
(0.155)
-0.605***
(0.148)
199
0.381
-0.607***
(0.069)
0.266***
(0.084)
OLS
180
0.173
-0.362***
(0.076)
0.105
(0.093)
826
0.344
361
0.394***
(0.065)
RE
390
0.26
194
-0.416***
(0.05)
0.156***
(0.052)
P opulation1500
P opulation1500
opulation1500
ln( P
P opulation1200 ) ln( P opulation1300 ) ln( P opulation1400 ) ln(Population) ln(∆ Population)
(1)
(2)
(3)
(4)
(5)
Notes. Robust standard errors are reported in parentheses in columns (1) - (3). Standard errors clustered at city level are
reported in parentheses in columns (4) and (5). Coefficient is statistically different from zero at the ***1 %, **5 % and *10
% level. The unit of observation is a city. The set of covariates encompasses the ln distances of a city to the next river or
coast, dummies indicating cities that were residence of a bishop before 1000 AD, had the status of an imperial city, were
located at a main imperial road, were member of the Hanseatic League or are classified as a mountain region by the EU
regional statistics. Furthermore, we control for a city’s latitude and longitude and include country fixed effects. In columns
(4) and (4) we additionally include year fixed effects. Each regression includes a constant not reported.
Obs.
Adj. R2 \overall R2
Number of Clusters
ln(Populationt−1 )
ln(Population 1400 AD)
ln(Population 1300 AD)
ln(Population 1200 AD)
Commercial Importance
Method
Dep. Var.
Table C.2: Medieval Trade Activity and City Growth - Estimations using the Index of Commercial Importance
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