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, agglomerationand contemporary regional development in ten European countries. It documentsa statistically and economically significant positive relationship between prominentinvolvement in medieval trade and regional economic development today. This find-ing is robust to inclusion of various historical, economical and geographical controlvariables and to controlling for endogeneity via IV estimations. A mediation analysisshows that, as theoretically postulated, the majority of this long-lasting effect trans-mits via the impact of medieval trade on contemporary agglomeration and industryconcentration. Thus, this research highlights the long-run importance of medievaltrade in shaping contemporary spatial patterns of economic activity throughoutEurope. The path-dependent regional development processes caused by medievaltrading activity can also provide an explanation for the observed persistence of re-gional differences in development across the considered European countries.

Keywords: Medieval Trade, Agglomeration, Regional Economic Development, Path-Dependency, New Economic GeographyJEL Classification: F14, N73, N93, O18, R12

∗Department of Economics, University of Hohenheim. Chair of Economic and Social History, Speise-meisterflugel, Stuttgart, Germany. Fabian.Wahl@uni-hohenheim.de. The author especially wouldlike to thank Bas van Bavel, Sibylle Lehmann, Alexander Opitz, Alfonso Sousa-Poza, Oliver Volckartand 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 routemaps.

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1 Introduction

There is ample evidence that trade is an important determinant of both long- and short-run economic development. However, most of the existing literature focuses on the im-pact of 19th century trade on market integration or the “Great Divergence” (e.g. Galorand Mountford 2008 or O’Rourke and Williamson 2002) or on the impact of contem-porary, Post-World War II trade activities on recent economic growth and developmentperformance across countries (Dollar and Kraay 2003, Frankel and Romer 1999). Thereis only one study (Acemoglu et al. 2005) considering the effect of cross country tradein earlier periods. They investigate the impact of long-distance overseas trade on in-stitutional developments and the pre-industrial development process across Europeancountries.

Hence, until now there is no study exploring the possible long-lasting effects of tradein European cities throughout the High and Late Middle Ages. The importance ofmedieval trade for the development of cities and regions in the Middle Ages and thefollowing centuries is well-known and widely acknowledged. Apart from this, no re-search acknowledged the fact that medieval trade could have also long-term influenceson regional development persisting until today. This despite the fact that medievaltrade through its influence on agglomeration and spatial concentration of industry couldhave lead to path-dependent regional development processes resulting in developmentdifferences outlasting the centuries in between.

The aim of this study is to investigate whether medieval trade had caused differencesin regional development which are still visible today due to its its impact on agglomera-tion. If this is the case it could provide a new explanation for the uneven distribution ofeconomic activity and significant spatial concentration of industries throughout Europe(e.g. Chasco et al. 2012, Koh and Riedel 2012, Roos 2005). Furthermore, it can con-tribute 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 perfor-mance we link typical characteristics of medieval trade and cities to the determinantsof agglomeration suggested by New Economic Geography (NEG) and agglomerationeconomics (e.g. Krugman 1991, Glaeser et al. 1992). In a second step, based on stud-ies combining NEG and endogenous growth models and the theory of path-dependence(David 2007) we propose a positive connection between agglomeration, industrial con-centration and contemporary development.

Afterwards, we test the causal chain from medieval trade through agglomeration to

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contemporary regional economic development using a rich data set (where we choose aNUTS-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 withcontemporary regional economic performance. Moreover, we also find that the majorityof the effect of medieval trade on contemporary regional development can be explained byits influence on agglomeration patterns. Most importantly, we show that our hypothesesare robust to the inclusion of many geographical, political, economical and historicalcovariates of development and agglomeration and are not biased by endogeneity.

Finally, a mediation analysis shows that medieval trade activities are strong predictorsof today’s spatial distribution of economic activity and population and that around twothird of the influence of medieval trade on contemporary regional GDP per capita canbe attributed to this influence of medieval trade on agglomeration.

The remainder of the article proceeds as follows. First, we theoretically establish thelink between medieval trade, agglomeration and present-day’s economic development.Afterwards, we introduce and discuss the most important variables and data and explainthe empirical setting. Next, we conduct our empirical analysis and interpret and discussthe 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 medievalcities and the revival of city growth during the period of the so called “Commercial Rev-olution” (e.g. Borner and Severgnini 2012, Epstein 2000, Habermann 1978, Holtfrerich1999, King 1985, Postan 1952, Pounds 2005 and van Werveke 1952). History providesmany examples of cities owing their importance primarily to their function as centersof trade, like the German cities of Nuremburg (Nicholas 1997), Frankfurt (Holtfrerich1999) 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 importantagglomerations 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 prosperouscities and agglomeration areas in present day’s Germany. Stuttgart was not important until after theNapoleonic Wars it became the capital of the newly founded kingdom of Wurttemberg. The rise ofMunich (today the third largest city of Germany) followed a similar pattern, albeit the capital of akingdom and residence of a bishop (and later archbishop) Munich began to become a large city notbefore late 18th century. Again, it experienced large population growth in the nineteenth centuryafter the Napoleonic Wars until World War I. Even more, Bavaria and Munich as it’s center stayedrelatively poor till the 1950ies (when e.g. the Siemens corporation moved its headquarter from Berlinto Munich). Additionally, the largest agglomeration in Germany the Ruhr Area largely results from

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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 tosecond 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, especiallythe urban economy was characterized by a high degree of regional specialization (Am-mann 1955, King 1985, Lopez 1952, Nicholas 1997,Postan 1952, Pounds 2005 and vanWerveke 1963).2 The Southern German cities that became important trade centers inthe later medieval for instance were specialized in textile (Barchent etc.) and paper pro-duction, while other areas had specialized in mining (like e.g. the Saxon town of Freibergor Liege in today’s Belgium that had the this times most productive coal field), or infood 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 comparativeadvantage in e.g. due to natural endowments– and imported what they did not havethemselves.3 This specialization of trade cities on a particular industry or sector gaverise to the existence of technological (non-pecuniary) externalities like Marshall-Arrow-Romer (MAR) externalities (Marshall 1890, Romer 1986) or Porter externalities.4 Thosetype of externalities arise as knowledge spillovers between firms in the same industry andcontribute therefore to the growth of both industry and city (Glaeser et al. 1992).5. In-deed Epstein (1998) and more broadly Epstein and Prak(2008) in an anthology aboutthe Guilds and Innovation they edited show that the guild as the dominant economicinstitution of the later medieval city indeed could have fostered innovation and enableknowledge spillovers and diffusion within the urban economy (and through migrationalso 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 Germanindustrialization.

2A comprehensive illustration of medieval trade activities is provided in Postan (1952) and Lopez(1952).

3A review of the general geographical patterns of trade and industry specialization in the middle agesis provided among others by King (1985).

4Nicholas (1997) additionally points to the fact that over the course of the Middle Ages the industrydominating in a city e.g. the textile industry did more and more diversify. This intra-industrydiversification could be an additional channel through which technological externalities could hadbeen arisen.

5Such knowledge spillovers between firms might appear because of imitations, movements of skilledworkers between the different firms in the industry etc.

6For evidence about the high mobility of skilled craftsmen in this period see Reith (2008) in thisanthology. Of course, among historians there is no consensus about the role of guild and whethertheir negative or positive effects for economic development are more dominant. However, at leastthe more recent contributions clearly brought forward evidence that guilds indeed could had largepositive impacts through their positive influence on innovativeness.

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variety of available goods. Those varieties of goods were available first at the localmarkets, 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 branchesand kontors of the Hanseatic League and the trading companies (“super-companies”)like the Fugger in Augsburg78 Especially the latter two also provide supply with luxurygoods and exotic commodities from far east, as long-distance trade was reestablished atthe beginning of Late Middle Ages. This high variety can be considered as an importantdemand-side driven agglomeration force, because it makes it more attractive to settle ina city.9

Additionally, the large variety of goods and prospering industry gave rise to the self-reinforcing circular causation caused by backward and forward linkages and leading toagglomeration and core-periphery patterns in NEG models (Krugman 1991, Ottavianoand Thisse 2004). Because trade cities provided a higher variety of goods, employmentfor high-skilled specialized workers and –as consequence of the higher labor demand– alsohigher wages, they attracted additional workers. When more and more workers made useof the opportunity to work in the city as e.g. textile workers or craftsmen, employmentand the number of firms increased. This decreased the price index, raised real wages andtherefore resulted in the immigration of even more workers to the city. Consequently, thispecuniary externality (forward linkage) caused increased agglomeration and industryconcentration in the city. Supplementary, more workers lead to a higher demand forgoods produced and/or traded in the city. The higher demand once more lead to theexpansion of markets and industries, raising labor demand and real wages resulting againin additional immigration. This is the so called “home market effect” or the backwardlinkage. In short, this amounts to the logic that industry will tend to concentratewhere there is a large market, whereas the market is large at the area where industryis already located. Thus, forward and backward linkages constitute the virtuous circlethat generates agglomeration and uneven spatial distribution of population and economicactivity.10

7For a detailed description of the business activities of the Hanseatic League the reader is reffered toDollinger (1966). An illustration of the medieval early medieval markets and fairs is found in vanWerveke (1963).

8A 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 isprovided by Borner (2002).

9This 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 oftransporting the sold commodities back to the village.

10Of course, the medieval city was a highly cartelized and regulated economy with dominant guilds andsignificant rent-seeking activities (e.g. Braudel 1986). However as Braudel (1986) concludes since the

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Furthermore, after the process of agglomeration lasted for some time other types oftechnological externalities occurred. Conditional on certain factors (i.e. geographicalposition or natural endowments) other industries located in the previously specializedcities, e.g. in the Southern German city of Ravensburg (an important trade center in the15th century) the traditional textiles industry was supplemented by paper production atthe beginning of the 15th century (Schelle 2000). In addition, there were also incentivesto locate in a trade city for firms using special commodities as inputs or that producedinputs 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 andcity 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 conse-quences on economic development today. Reassuringly, the self-reinforcing nature of thedescribed agglomeration and concentration processes implies a path-dependent processof city development. This path-dependent development process results in differences inconcentration of economic activity and population that are persistent until today. Citiesinvolved in medieval trade activities over a sufficient period of time got locked in on asuperior development path as compared to other cities which were not involved. This isa typical characteristic of processes caused by increasing returns or positive feedback andthat are characterized by multiple equilibria (David 2007). There are many examples ofhistorical 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 Putter-man 2008), the capacity to adopt and develop new technologies (Comin et al. 2010) orthe 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 themarkets of cities every week according to supply and demand. Furthermore, the increasing spreadof the “Verlagssystem” sometimes might had limited the power of the guilds. Concerning the urbanrural wage differential evidence in general is limited for this period in time Braudel (1986) notes thatin general, also due to the power of guilds the wages in the city can be considered to be usuallyhigher than in rural areas. In line with this, Munro (2002) comparing the real wages in Englandand Flanders between 1300 and 1500 found that the real wages in the cities were higher than inrural areas and showed a higher downward rigidity. van Bavel and van Zanden (2004) in additionnotice that in pre industrial societies the relationship between city size and nominal wages usuallywas positive.

11 The idea that vertical linkages along the supply chain can lead to agglomeration is developed inKrugman and Venables (1995).

12Jacobs externalities are knowledge spillovers arising between firms of different industries.13A comprehensive review of such events caused path-dependent developments is Nunn (2009).

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largely be explained by strong and increasing differences between core areas and theperiphery. We argue that medieval trade can be added to the list of such events.

Finally, the positive connection between agglomeration, industry concentration andregional economic growth is reported by several theoretical studies (e.g. Baldwin andMartin 2004, Martin and Ottaviano 2001, Yamamoto 2003 or Bertinelli and Black 2004)linking growth e.g. through innovations and agglomeration by combining standard NEGand endogenous growth models. In addition, studies like Hohenberg and Lees (1995)or Fujita and Thisse (2002) also establish empirically the positive relationship betweenagglomeration and regional growth.

In conclusion, we postulate the following two hypotheses about the relationship be-tween medieval trade and contemporary regional development:

Hypothesis 1. There is a positive and significant relationship between involvement inmedieval trade activities and regional economic performance today, i.e. cities that werecenters of medieval trade show a higher GDP per capita today than cities not involvedin medieval trade.

Hypothesis 2. Medieval trade activities influence contemporary regional economic de-velopment through their positive effect on agglomeration and industry concentration, i.e.there is a positive and significant relationship between medieval trade centers, agglomer-ation 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 (“Nomen-clature of Units for Territorial Statistic”) regional classification, the official regionalreference unit systematic used in the European Union (EU).14 Furthermore the officialregional statistics of Eurostat are available for those territorial units. Additionally, dif-ferent regions on the same NUTS level have the advantage of being relatively comparableto each other since they are defined according to a particular range of inhabitants.15 We14A detailed description and overview over the NUTS classification scheme and the regions can be found

in the data appendix and the references mentioned there.15Although 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 showa larger population. From this it follows also, that more densely populated regions cover on averagea smaller area. To overcome potential biases resulting from the this, we will control for the area of

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choose to conduct our analysis on the most disaggregated level for which our essentialdata (e.g. GDP per capita) is available. Therefore we conduct our analysis with aNUTS-3 region as observational unit.

NUTS-3 regions are identical to existing administrative units in most of the countriesin our sample, which is an additional advantage of using them. In Germany for examplethey are mostly identical to districts or district-free cities, in France to Departmentsand in Italy to Provinces. A potential bias resulting from considering regions instead ofactual cities that were subject to medieval trade is limited as heterogeneity within NUTS-3 regions should not be of significant size. However, some control variables are availableonly at NUTS-2 or NUTS-1 level. In these cases we include the respective variables at thelevel where they are provided. Another advantage of sticking to the NUTS classificationis that it enables to use fixed effects for the different NUTS-levels (countries, federalstates etc.). This allows to appropriately handle all kinds of heterogeneity on countryand regional levels. Besides this, one can also account for cross-sectional and spatialdependence among the regions in the dataset. The latter being a important advantageof 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-3region, originating from the Eurostat regional statistics database. We take the latestavailable values from the year 2009. All other time-variant variables are also taken fromthe 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 spa-tial distribution of economic activity. The measure is calculated by dividing a region’sshare of GDP per capita through its share of the country’s total area. This means itshows whether the concentration of economic activity in a region is below or above thecountry’s average.17 As such this is a more direct measure of economic agglomerationthan population density. Additionally, we present results using the ln of a regions pop-ulation density in 2009 as a more general measure of agglomeration, i.e. as a variableidentifying 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 (regionswith a high population density, i.e. a large population but a small area).

16Chasco et al. (2012) discuss further advantages of using NUTS-3 regions as observational units in thecontext of spatial economic analyses.

17The exact formula according to which the relative GDP Density is calculated is shown in the dataappendix.

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We think that the relative GDP Density is a more direct measure of industry agglomer-ation and concentration and is therefore should more suitable for our empirical analysis.However, since population density might capture additional aspects of agglomerationthat might be important for economic activities indirectly and therefore can provideadditional insights.

Table A.1 in the data appendix gives a descriptive overview over all variables usedin the following empirical analysis. The exact sources and further explanations of allvariables are provided in the data appendix.

3.3 Independent Variables

This study aims to investigate the impact of trade between cities during the medievalage.18 To be able to identify the theoretically assumed effect of medieval trade on ag-glomeration we focus on the most important trade cities, i.e. cities where trade probablyhad the most powerful and long-lasting impact. Since agglomeration is a long-lastingprocess unfolding its effects only after some time, it is important to ensure that tradetook 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 supe-rior development path. To account for this fact, we focus on important trade cities atthe end of the medieval period (i.e. around 1500 AD). This is due to the fact that citiesimportant at the end of the medieval period are most likely also having experiencednoticeable trade activities in the years before (i.e. over a longer time period).

Our main source of information about important medieval trade activities are mapsprinted in historical atlases or monographs. We focus on maps because they providea much more comprehensive source of trade cities and activities then the informationavailable historical monographs. In addition, their information usually can assigned toa certain period much clearer than that contained in books. In consequence, we collectinformation about cities prominently involved in trade from four historical maps provid-ing evidence about cities located on “major” or “important” trade routes around 150018It 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 notbefore the tenth century (Postan 1952, Braudel 1986). Furthermore, after the end of the medieval inthe course of the 16th century, overseas trade (e.g. with the colonies) and long-distance trade becameincreasingly 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 withthat, the leading actors of medieval trade like e.g. the Hanseatic League lost their importance in theperiod following the medieval. In consequence, it is possible to isolate the medieval trade activitiesin cities from trade activities before and after the medieval. This ensures that the effects we measureempirically can actually be attributed to medieval trade activities and not trade in general or tradein other periods.

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AD (i.e. the late medieval). Due to the fact that there is no consensus or quantitativeevidence about the exact importance of trade cities and trade routes during the me-dieval period we have to consult several different sources to become sufficiently reliabledata. The first is a map printed in Davies and Moorehouse (2002), the second is a mapprinted in King (1985). The third source is a map on Central European trade publishedin Magocsi’s (2002) Historical Atlas of Central Europe.19 At last, we consult severalmaps included in “Westermanns Atlas zur Weltgeschichte” (Stier et al. (1956). Moreinformation about the kind of information and the geographical and temporal scope ofthose maps is provided in the Data Appendix. There, we also list the primary sourceson the basis of which the maps are drawn – if we were able to identify them. We includea city if it is mentioned in one of these maps. We include only cities located in EUcountries, 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 mapsbut by other sources of information. For example, we include the eastern German cityof “Zwickau” because it is prominently recognized in Spufford (2002) standard accounton medieval commerce and is known for its importance in salt trade. In other cases, weincluded 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 importanceof the included trade cities. For example, we look whether a city was an importantmember of the Hanseatic League or a capital of a quarter or a third (like e.g. Dortmund orCologne). Information about this is provided by Dollinger (1966). Additionally, we alsolook 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 isthe case e.g. for Soest). Moreover, we consult several historical standard sources aboutmedieval trade activities in different Central European regions (e.g. Dietze 1923, Huntand Murray 1999, Schulte 1966, Spufford 2002 etc.) and look whether they mention acity as being prominently involved in trade or having an over-regional importance asmarket, trading place or fair city. Finally we also draw on other historical atlas likethat 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 DataAppendix (Table A.3) we report and discuss all these source and provide informationabout which city is mentioned by which source.

Overall these sources have left us with 119 trade cities located in 10 European coun-

19As we are not interested in information about only regionally important trade cities an additionalreason for choosing this particular maps is that they provide cross-national information about tradeactivities.

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tries. Our dataset encompasses all 839 NUTS-3 regions in these countries.20 The DataAppendix offers a detailed description of how we construct our database of importantlate medieval trade cities.

Even with the information in these sources, the relative importance of cities is notalways clear. Additionally, there is also a different degree of uncertainty about theextent and location of trade activities and the course of main routes, i.e. the actualimportance 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 theNorthern Italian city states (Milan or Genoa etc.), some Southern German imperialcities (like Augsburg, Nuremburg or Ulm) and the leading centers of the HanseaticLeague (Hamburg, Bremen, Lubeck, Cologne etc.) . On the other hand, there are caseswere only some sources mention the city as important trade center or lying on a maintrade 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 isa natural result of the qualitative — and therefore to some extent always subjective —nature of the collected information and the scares amount of overall information aboutthe 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 samplesof 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 awell-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 otherhistorical sources as being of notable importance in later medieval trade (albeit therewas 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 empiricalanalysis with a sample of trade cities including additional cities (Dijon, Piacenza orAigues Mortes) that are mentioned by some of the sources, but for which we — afterconsulting several different information about the history of the respecitve places — arein 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 experiencedsignificant trade activities for a a short period and therefore not long enough for result-ing in a lock-in to a superior development path. To overcome this problem, that woulddownward bias our results, we construct a fourth alternative sample of trade centers20We 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 alsoexclude island regions).

21A full list of excluded cities is reported in the Data Appendix.

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only considering cities for which we found records of recognizable trade activities in ear-lier periods than the late 15th and early 16th century. The sources consulted here aree.g a volume about medieval trade in the Levant by Heyd (1879a,b) and the alreadymentioned volume about the history of German trade by Dietze (1923). Furthermore,also Dollinger (1966) presents some evidence about trade activities in the periods pre-ceding 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, thismap also reports the signers of the treaty of Smolensk in 1229 (i.e. the most importanttrade cities in this times Western Dvina trade) and additionally some information ofmaps digitized by the Old World Trade Routes Project (OWTRAD) website, primar-ily containing information about trade in Eastern Europe, especially Poland.22 Exactinformation about the construction of this alternative sample is provided in the DataAppendix. Such information about earlier trade activities could be collected for 70 ofthe originally 119 trade cities. As such, this last sample represents the most selective oneand probably contains only cities for which important medieval trade activities are mostsure. Overall, we consulted fifteen different sources to construct our different samplesof trade cities. However, even with this amount of sources one cannot be sure that thecoding of the trade city dummy variables is perfect. Regardless of this fact, there seemsto be no reason why the inclusion of cities that were probably not that important thanother cities or experienced trade activities for only a short period of time should morethan downward bias our estimates. The estimates obtained using this kind of dummyvariable should therefore considered to be a lower bound of the actual long-term effectof medieval trade.

We will use two different variables as measures of late medieval trade and its impacton contemporary regional development. First, we will use a dummy variable “TradeCenter” that is equal to one if a region includes at least one medieval trade city. Thelack of quantitative information and the limited availability of qualitative judgmentsleads us to use a simple dummy variable coding important trade cities. Of course, thisimplies that we treat all trade cities being the same with respect to the scale of tradeactivities and agglomeration forces working there. However, since we try to focus oncities located on “major” or “important” trade cross-national trade routes and also relyon qualitative judgments of importance —when available– we should be able to reducethe heterogeneity among the trade cities. Additionally, the construction of a dummyvariable 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 trade22http://www.ciolek.com/owtrad.html

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city.footnoteThe variable is zero in regions that are coded as trade centers. This variableoffers a very useful direct test of our hypothesis that medieval trade contributed to theemergence of time persistent core-periphery patterns and therefore can act as a notableexplanation for contemporary regional income differences.

Table 1 provides a summary of our trade city data. For each country, the total numberof NUTS-3 regions, the number of regions with trade cities, the share of trade centerregions 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 about1.5 degrees (e0.432) that is approximately 170 km. Overall around 14% of all regionsare considered as containing medieval trade centers. A list with the name, NUTS-3region 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 theregions 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, agglomerationand contemporary economic performance can be obtained from a descriptive look on therelevant 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 twomeasures of agglomoration, ln population density and ln relative GDP density. Thesecorrelations 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 a23The 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 (King1985, p. 220)

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strong positive relationship between agglomeration measures and GDP per capita. Viceversa we found a negative association between distance to a trade center and bothagglomeration and GDP). The correlation between GDP per capita and the trade centerdummy is significant and positive, but comparatively low. On the one hand, this lowcorrelation could be the result of considerable heterogeneity of GDP per capita acrossregions and countries in the sample that is not accounted for in these simple pairwisecorrelations. On the other hand, the high correlation between the trade center dummyand the agglomeration measures on the on side and agglomeration measures and GDPper capita on the other side indicates that the effect of trade centers is largely runningthrough agglomeration. Therefore the observed correlations provide preliminary supportfor our theoretical reasoning.

Another way to illustrate the stylized relationship between medieval trade, agglomer-ation and present day’s regional economic development is to compare averages values ofGDP per capita and agglomeration measures for late medieval trade centers and non-trade centers. This is done in Table 3 both separately for each country as well as forthe 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 tradecities have a significant “GDP Advantage”, that is, their average GDP per capita isaround 5000 Euro higher than that of regions without trade cities. Furthermore, theyalso exhibit significantly higher population and relative GDP densities.24 This resultdoes also hold within all countries apart from Lithuania where trade center regions showa higher GDP per capita but the differences is insignificant. For relative GDP Densitythe within country results are not that clear. In Belgium and the Netherlands the rel-ative GDP Density is lower, although the difference is not significant.25 However, inAustria, Germany, France and Poland the countries account for three quarters of thesample, there is a statistically and economically significant advantage of trade centerswith 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 regionswith medieval trade cities and for regions without them. The kernel density of ln relativeGDP density is shown in Figure 2. The density for regions with and without medieval24The significance of the Difference between trade regions and non trade regions is tested by a two-sample

t test.25In 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 revealsthat the variable’s kernel density over all regions is clearly leftly skewed and shows anadditional notable local peak on the right.26 The latter indicates that there is a clusterof regions showing a relatively high spatial concentration of economic activity.

However, what is more interesting for our argumentation is the comparison of thedensity across groups of regions with and without medieval trade cities. One can inferfrom Figure 3, that as expected the kernel density across both groups differs consider-ably.27 Most importantly, the density function for regions with medieval trade centersclearly show a larger mass in the right tail supporting the idea that agglomeration andconcentration of economic activity are higher in regions with medieval trade centers. Wealso run similar estimations using population density as agglomeration measure. Theresult of this task are shown in Appendix C (Figure C.1).

In sum, the descriptive analysis of the data delivers strong preliminary evidence forour 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 tradeexhibit higher levels of economic development today we estimate the following regressionusing Ordinary Least Squares (OLS):

ln(GDP )cijk = α+ βTCcijk + γ′1Xcijk + γ′2Xcij + δc + θi + λj + εcijk (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. TCcijk is a dummy variable “TradeCenter” that is equal to one if a NUTS-3 region includes a medieval trade city and zerootherwise. Xcijk andXcij 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, εcijk is theerror term capturing all unobserved factors.28 Equation (1) is a straightforward way toestablish a significant direct link between late medieval trade activities and contemporaryeconomic performance. Our expectation is that β > 0 and significantly different fromzero.

26Accordingly, a Shapiro-Wilk test clearly rejects the null hypothesis of normality for the kernel density27Conversely, 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.

15

But, even when medieval trade still matters today, does its impact transmit via ag-glomeration and concentration of economic activities in places where it took place? Asimple way to test this additional hypothesis is to look whether GDP per capita becomeslower when the distance to medieval trade centers increases. Expressed differently, if theeffect of trade works through agglomeration, then, a “classical” core-periphery patternshould emerge, with the medieval trade cities as core and the regions far away as pe-riphery. One can therefore modify equation 1 by substituting the trade center dummythrough a variable representing the distance between a region’s centroid and the closesttrade city. Equation 1 can be rewritten as:

ln(GDP )cijk = α+ ρln(Dist TC)cijk + γ′1Xcijk + γ′2Xcij + δc + θi + λj + εcijk (2)

Where Dist TCcijk is the natural logarithm of the distance from a region’s centroid tothe 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 fixedeffects. They are included to account for shocks common to all observations at the re-spective geographical unit. Additionally, they are included to exploit the pure variationbetween NUTS-3 regions.29 We also add a set of basic geographical controls, includinglatitude, longitude and altitude of a NUTS-3 region. The latter set of variables shouldcapture the general geographical pattern of development in Central Europe. This means,that economic development roughly increases from South to North (i.e. with increasinglatitude) and decreases - in our sample- from West to East (i.e. with increasing longi-tude). Furthermore, it is a well known fact that regions with a higher latitude are moredifficult to reach - what seems relevant for trade- and show a less favorable climate sothat we expect a negative influence of altitude.

The results of these regressions are shown in Table 4. There, we report three differ-ent standard errors above each coefficient. At first, in parentheses there are reportedheteroskedasdicity robust standard errors. Below those, in brackets we present standarderrors obtained by multiway clustering on NUTS-1 and NUTS-2 region level accordingto the methodology of Cameron et al. (2011). The use of multiway clustering is justifiedbecause it is likely that the development in NUTS-3 regions is not independent of that29Overall, 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 thefact, 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.

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in NUTS-1 or NUTS-2 regions.30 Supplementary, because multiway clustering allowsfor arbitrary residual correlation across both included dimensions, it also accounts forpossible spatial correlation. Finally, the third standard errors (in curley brackets) areadjusted 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 evi-dence brought forward before. Regions with medieval trade centers show a significantlyhigher GDP per capita than regions without such cities. The coefficient of the tradecenter dummy remains relatively stable and significant at 1 % level, regardless whichcombination of control variables and fixed effects is used. According to column (3) ofTable 4, where we include the full set of country and region dummy as well as the basicgeographic controls, regions with medieval trade centers on average have around 30 %higher GDP per capita than regions without. This means that the effect of medievaltrade is not only statistically but also economically of considerable significance.

This holds also true for the coefficients of the distance to trade center. They arealways highly significant and are quantitatively in the same range as that of the tradecenter dummy. Furthermore, they show the anticipated negative sign.

The clear positive relationship between contemporary GDP per capita and medievaltrade centers is also illustrated graphically in Figure 3, a partial regression plot of theTrade Center Dummy based on the full baseline specification in column (3). And inFigure 4 the same is done for the negative relationship between the distance to a medievaltrade center and present days GDP per capita.

Regarding the geographical controls latitude and longitude turn out to be insignificantthroughout all estimations. Altitude, to the contrast, is always significant and its coef-ficient shows the expected negative sign. Furthermore, the NUTS-2 dummies are oftennot significant and do – according to the adjusted R2 – add nothing to the explanatorypower of the model. For this reason, they would only introduce additional noise in theestimation and are therefore excluded from the remaining regressions.

30It might even be the case that the development of included variables regional variables is correlatedwithin countries. However, due to the fact that we only have ten countries in our sample and clusteredstandard errors are only consistent asymptotically, clustering at country level is no option.

31Conley’s (1999) standard errors are obtained using a cutoff point of 3 degrees (approx. 330 km) afterwhich the spatial correlation is assumed to be zero. We experimented with several different cutoffpoints and this cutoff produced the most conservative standard errors.

17

The three different types of standard errors in general do not differ substantially. Ifany, the standard errors in brackets, adjusted fro multiway clustering are a little bitlarger than the other two. Because of that, we will use standard errors clustered onNUTS-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 contempo-rary economic development is not driven by omitted variables bias we have to controlfor relevant determinants of both agglomeration and economic development. As a nextstep, we therefore add several sets of control variables to the baseline specification. Inagglomeration economics, the causes of agglomeration are categorized in first nature(physical and political geography, climate etc.) and second nature causes of agglom-eration (man-made factors, i.e. agglomeration resulting from spatial spillovers or scaleeffects) (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 firstnature. 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 iswhat we especially have to control for.

Additionally to standard economic agglomeration and growth literature we also haveto account for potentially important historical causes of agglomeration and develop-ment. This clearly follows from our argument that medieval trade influenced regionaldevelopment processes through its impact on agglomeration and industry concentration.

In conclusion, we decide to group the control variables in four set of variables we addseparately to the baseline specification (without NUTS-2 dummies).

The first set of variables controls for the “geographic centrality” of regions. It includesvariables measuring the distance of a region to the closest important infrastructure facil-ities (airports, roads and railroads) and to important political and physical geographicfeatures (coasts and borders).32 Especially, the last two are found to be important firstnature determinants of agglomeration (e.g. Roos 2005, Ellison and Glaeser 1999). Ad-ditionally, the ln of the distance of each region to the geographically nearest major river

32Holl (2004) and Martin and Rogers (1995) establish empirical and theoretical evidence on the impor-tance infrastructure facilities for industry location. This justifies the inclusion of distance to road,airports and railroads as control variables.

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is included as control.33 Rivers are geographical features important for both medievaltrade, industry and city location (Borner and Severgnini 2012, Bosker and Buringh 2010,Ellison and Glaeser 1999, Roos 2005 and Wolf 2009). The idea behind this set of con-trols is to ensure that we do not simply capture the impact of many medieval trade citiesbeing located at geographically favorable places today or in the past.

A second set of variables controls for relevant contemporary characteristics of the in-cluded 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 theregions that include a country’s capital or the capital of an autonomous region.34 Addi-tionally, a categorical variable identifying the degree to which a region can be consideredas a“mountain regions” is included. Furthermore the set includes dummies for regionswith coal or ore mines (or mining firms), for regions located in the former GDR and forregions located in Easter European post-communistic transition countries. At last, itincludes the ln of a region’s area. In consequence, this set of controls accounts for manyimportant first nature causes of agglomeration (political geography and resource endow-ments) as well as for relevant historical facts that could have influence the contemporaryeconomic performance of a region (like communism).

The next set of controls captures historical characteristics of the regions that couldmatter for both present day’s agglomeration and economic performance. Here we con-sider dummy variables indicating regions with a university founded before 1500 AD andregions that adopted printing technology before 1500 AD. As unearthed by Cantoni andYuchtman (2012), Dittmar (2011) and Rubin (2011) both universities and printing tech-nology are important factors in explaining the late medieval commercial revolution andcity growth. To account for the positive influence Protestantism probably had on eco-nomic development (Woesmann and Becker 2009, Rubin 2011) we also include ln distanceto Wittenberg as variable in this set of controls. Furthermore, we also include dummiesfor regions containing at least one imperial city or at least one city that was memberof the Hanseatic League. Finally, we also control for the possible long-lasting influenceof roman heritage and low transport costs for trade and agglomeration in including adummy for cities located at an important imperial road (Postan 1952).35

The fourth set controls for the most important covariates of economic growth anddevelopment. Here we use the share of people aged between 25 and 64 with tertiary

33In Germany for example we consider Elbe, Danube, Rhine and the Oder as major rivers.34An autonomous region is considered to be a Belgian or Italian Region or a German or Austrian federal

state (“Bundesland”).35This 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.

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education (on NUTS-2 level) as measure for regional human capital.36 As variable tomeasure the quality of regional economic and political institutions we use the quality ofgovernment index developed by the Quality of Government Institute at the university ofGothenburg which provides a measure for regional institutional quality design similar tothe World Governance Indicators (WGI) of the World Bank.37 As measure for regionalinequality 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 aregion’s firms again at NUTS-2 level. Furthermore, we include a region’s unemploymentrate, ln of the average workers compensation and the ln of the average fixed capital of aregion’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 weresignificant both times when added with one of the other four sets of controls to the base-line specification. In the next step, we did remove the variables becoming insignificantin that regression. We repeat this procedure until only significant controls remain in thespecification.38 This procedure results in a set of 12 variables robustly associated withGDP 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, post-communistic transition countries, Eastern Germany, the ln of a region’s area, the shareof people with tertiary education, the inequality measure and the printing press before1500 AD dummy. Once more, this highlights the importance of human capital and polit-ical geography. Furthermore the robust influence of printing confirms Dittmar’s (2011)claim that printing technology fostered - similar to medieval trade- localized spilloversand forward- and backward linkages.

The results of the regressions are shown in Table 5. There we first add the first fourset of controls separately to the baseline specification and then we include as fifth setall robust covariates to the country and NUTS-1 region fixed effects. We see that thecoefficient of the trade center dummy and the distance variable remain significant inevery of the specifications although the sizes of the coefficients is considerably reducedcompared 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 the36Again, we take the values for the year 2009.37This 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.38These regressions are not shown but are available from the author upon request.

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specification with all robust covariates added to the baseline model. This is not surpris-ing since in this specification we added only the variables with the highest explanatorypower to the regression. It suggests, that medieval trade center regions have today aGDP per capita around 7 % higher than other regions. Based on the average regionalGDP per capita in our sample this corresponds to a GDP per capita that is approxi-mately 1200 Euros higher. When looking at the different set of controls it is evident fromthe adjusted R2, that region characteristics and growth covariates add most additionalexplanatory power to the model. Apart from mountain and mining region dummies,each variable in the regional characteristics set is significant and especially the effectsof political geography (capital regions or regions with a capital of a autonomous re-gion) 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 percapita.39 In general, the historical region characteristics are least important in explainingcontemporary regional economic development. But regions with universities and citiesadpoted printing technology before 1500 AD seem to have a significantly higher GDPper capita even today, once again highlighting the importance of human capital.40 How-ever, the university before 1500 AD dummy becomes insignificant when added jointlywith the measure of current regional human capital. This suggests universities lead toadvantages of regions concerning their human capital persisted until today. Finally, therobustly negative impact of the distance to river variable again shows the already widelyacknowledge role of first nature geography for regional economic development.

Overall, we see that the relationship between medieval trade and contemporary re-gional development is robust to the inclusion of a wide range of control variables andother important determinants of agglomeration and economic performance. The oneexception is the estimation in column (10) where distance to trade center becomes in-significant.

4.1.5 Accounting for Endogeneity

Even after controlling for many factors endogeneity of the medieval trade variables re-mains a serious issue. Endogeneity primarily could arise through unobserved factors,influencing both contemporary regional development and medieval trade. Geography

39This finding is for example in line with Simon (1998) and Gennaioli et al. (2013) who highlight theimportance of human capital for regional development and city growth.

40In 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.

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might be a prominent factor for which this holds true. However, we can control forgeography in our regressions. But there are many other unobservable factors that mightaffect both our right- and left-hand side variables. A prominent example is institutionalquality in medieval cities an important factor in medieval trade and the commercialrevolution (e.g. Greif, 1992, 1993 and 1994).Other cases are cultural differences betweenregions and countries –apart from being protestant or not– or historical differences inpolitics between regions.

To solve the endogeneity issue, we therefore run IV Regressions using the LimitedInformation Maximum Likelihood (LIML) method.41

In order to be able to test the validity of the exclusion restriction we choose twoinstrument variables.

The first considered instrument variable is a categorial variable (taking the valueszero, one, two and three) indicating whether a region is classified as a mountain regionby the official EU regional statistics. The variable is zero if a region is not classifiedas a mountain region. It is equal to two or three if the region is a mountain regionaccording to two different set of criteria (for details about the exact definition consultthe Data Appendix).42 The idea behind this variable is intuitively plausible. In moun-tain regions, characterized by higher trade costs, less favorable climate and many otheradverse features trade activities were lower than in region located at large rivers, thecoast or in low altitude areas with fertile soils and less rugged terrain. Especially inthe medieval age, where no advanced transport technologies are available — especiallyfor over-land transport — mountains constituted a severe hindrance of trade (Spufford2002).43 Furthermore, as highlighted by Bosker and Buringh (2010) high elevation (aswell as differences in elevation between places) has a considerable negative effect on city

41This estimation method has better small sample properties and is most often more efficient than thestandard 2SLS method,especially in the presence of weak instruments. Its confidence intervals aremore reliable and it is unbiased in the median when the instruments are weak (Stock and Yogo 2005).

42Albeit this variable is of categorial nature we choose to include it as a single variable and not by usingthree different dummies as instruments. This is primarily motivated by guaranteeing a parsimoniousset of instruments since the IV estimates are biased towards the OLS estimates when the numberof instruments increases. Furthermore, the test of overidentifying restrictions wouldn’t be valid ifone include several instruments following the same reasoning or originating from the same measurephenomenon as excluded instruments in the first stage. However, the results are fully robust to usingthe three different categories of the mountain region variable as separate instruments. They are alsorobust to recoding the three categories to one and include the variable as binary dummy variable.Results not shown but available from the author.

43Evidently, the large amount of trade activities between the northern Italian city states and the southernGerman cities (Ulm, Ravensburg etc.) require that the traded goods are transported over the alps,e.g. through the Splugen Pass (Schulte 1966). However, the transport probably took place over onlya few important passes and none of the small villages and populated places along those mountainroutes could develop to an remarkable center of trade.

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growth and urban potential of a place. The exogeneity of this geographical characteristicof a region should not be a concern.

The second instrument variable we will use is a dummy variable for cities that wereresidential cities of bishops before 1000 AD. The church as political, spiritual and eco-nomical power had a significant impact on the development of cities in the medievalage (e.g. Baker and Holt 2004, Isenmann 1988, King 1985 and).44 Because of this it isprobable that ecclesiastical centers, like residential cities of bishops did grow larger andhad a higher probability of becoming a trade center. In line with this reasoning, Bornerand Svergnini (2012) could show that trading activity (in- and outflows of commodities)were higher in bishop residence cities. Additionally, Bosker and Buringh (2010) foundthat the presence of a bishop was a important factor in the foundation and developmentof cities during the Middle Ages. The exogeneity of this measure is not as sure as inthe case of distance to river. But nevertheless, since we can control for geography itis hard to find a variable that could potentially influence both the location of bishopresidences in 1000 AD and contemporary regional development. First, in 1000 AD mostof 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 HolyRoman Empire, was found in the second half of the 10th century and couldn’t thereforehave larger influences on bishops residences founded before 1000 AD. This is especiallytrue because many of the considered dioceses or archbishoprics are already establishedwhen the Empire was found in 962 AD. Second, we control for many other historical fac-tors like being located on an important imperial road or early adoption of printing thatmight had influenced both the location of trade cities, bishop residences and economicdevelopment today. Third as explained e.g. in Pounds (2005) the dioceses built in theearly medieval period were virtually identical to the territory of predated Roman cities.In consequence, their location was determined centuries before the early medieval periodwhich makes it even more unlikely that they are endogenous to contemporary economicdevelopment.

In other words, there are many reason to conclude that bishop residences before 1000AD are exogenous and can be used as instrument.

Additional to those instruments, we make use of Lewbel’s (2012) approach that ex-44King (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 de-velopment of cities in the early middle age, when traditional trade declined during the economicdepression in the eighth and ninth century. Finally, Hunt and Murray (1999) notice the significanceof the church for city development and commerce arising from fostering ecclesiastical tourism andpilgrim activities.

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ploits heteroskedastic first stage errors terms to generate artificial instruments not cor-related with the product (covariance) of the first stage’s heteroskedasdic errors.45 Thismethod can provide more reliable estimates if it is doubtful, that the instruments meetthe exclusion restriction or are weak. Since at least the exogeneity of the bishop seatscan be disputed in principle this method ensures that we do not produce invalid IVestimates. The strength of these generated instruments depend on the amount of scaleheteroskedasdicity in the error. The presence of heteroskedasdicity in our first stageregression is tested with a Pagan-Hall test. The test clearly rejects the presence of ahomoskedasdic disturbance (p-value<0.000). Therefore, the method can yield reliableestimates 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 distancevariable. We include the set of robust covariates as well as NUTS-1 region and countryfixed effects as controls, i.e. we reestimate columns (5) and (10) of Table 5. The resultsof these estimations are shown in Table 6.

The first important result is that throughout all specifications the trade center dummyand the distance to trade center variable are significant and retain there signs. Evenmore, the size of the coefficients increased remarkably, at least in the case of the con-ventional IV regressions in columns (1) and (3). Moreover, the distance to trade centervariable that was insignificant before in column (10) of Table 5 regains significance at1 % level. This can be interpreted as endogeneity downward biased the OLS results,probably due to measurement error or a negative correlation between an unobservedfactor and our medieval trade measures. Concerning the validity of the instruments theoveridentification tests (Hansen J-statistic) informs us that the validity of the exclusionrestriction 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 ofsignificance. Due to this, one should be cautious in interpreting the results from thelast columns here. Nevertheless, in line with our arguments above it seems the case thatthe being a mountain region and bishop residences before 1000 AD affect contemporarylevels of development solely through their impact on which cities became medieval tradecenters.47 Furthermore, at least in the case of the trade center dummy, Lewbel’s (2012)45The 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)ε (Lewebel 2012).

46Lewbel 2012 mention several papers that already applied this method resulting in plausible estimatese.g Sabia (2007) or Kelly and Markovitz (2009).Thus, the method has proven to provide reliableestimates in different empirical settings.

47 In fact, it is very likely that geographic characteristics like being a region in the mountains also

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approach show, that our results hold even when we do not use external instruments butinstruments that are exogenous by construction. However, the coefficients obtained withLIML IV are much larger as that resulting from Lewbel’s (2012) approach that are inmuch closer to the original OLS estimates. Since Lewbel’s (2012) approach relies onsecond moment conditions and additionaly produces a comparatively large number ofinstruments 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 signif-icant and strong predictors of medieval trade. The bishop dummy is highly significantin both specifications. This is also true for mountain region dummy, although it is onlymarginally significant when the trade center dummy is instrumented . The underidenti-fication test and the Angrist-Pischke F statistic of excluded instruments always indicatethat the instruments are strong and relevant.

Altogether, the IV estimations show that endogeneity does not affect the detectedsignificant relation between medieval trade and contemporary economic development. Ifanything, endogeneity downward biases the OLS estimations and therefore lead us tounderestimate 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 empiricalsupport for a significant relationship between medieval trade and contemporary regionaldevelopment, the data on which the results are based has its limitations. First andforemost, the evidence so far is solely based on a dummy variable constructed accordingto whether a city was located at an important trade route and few other qualitativejudgments about their importance. In treating all trade cities as equal this approach isprobably not able to capture all the dimensions and factors that made a city an importantcenter of commercial activity throughout the medieval. In consequence, we possibly donot catch the true effect of medieval trade or commercial activities on contemporarydevelopment levels. However, based on the data set at hand and historical evidence aboutimportant determinants of trade, economic and commercial activities in the middle agesone 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 variablesjointly in the first stage we take into account this correlation.

48A test of endogeneity of the instrumented variables rejects the null of actual exogeneity in at 1 % levelin every LIML IV regression.

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to construct the index. At first, we include out trade center dummy, representing citieslocated on important trade routes. Second, we consider the variable indicating citiesthat were residence of a bishop or archbishop before 1000 AD. As already outlined, thechurch was found to be one of the most important factors in the development of medievalcities and trade. Hence, the presence of a bishop should be a valid proxy variable forcities of notable commercial importance. Third, we include the ln distance to coast ofeach region’s centroid, representing the distance of each city to a potential sea harborand the significant trade cities located at the coast (like e.g. many of the Hanseaticcities). Fourth, we include the dummy variable identifying important members of theHanseatic League. Since the Hanseatic League was one of the leading actors in medievalcommerce, its important members cities very likely were subject to significant commercialactivity. Fifth, we adopt the dummy variables representing cities that had the statusof an imperial city or that were located at an important imperial road. As transportcost were a crucial factor in medieval trade, the presence of a paved and protected roadshould be an important economic advantage for the cities located at it (e.g. Spufford2002). On the other hand, most of the important trade cities in the Holy Roman Empirethat 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 cellsof commercial activity in the medieval period(Cantoni and Yuchtman 2012). Sixth, weinclude a variable depicting regions in which medieval mining activties (copper and saltmining) took place. This accounts for the fact that salt and copper —as raw materialsin 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 byCantoni and Yuchtman (2012) showing that universities decisively fostered commercialactivities and market establishment in the area around them. Consequently, we includethe dummy variable reporting cities with universities founded before 1500 AD as lastvariable. The index is constructed by simply adding up this variables combining themin one index ranging from zero to eight.Thereafter, we substract the mean of the indexfrom all its values so that the average region would have a value of zero. Regions with anbelow average value therefore have a negative and regions with an above average valuehave a positive value. We also construct an alternative version where we include the lndistance to trade center variable instead of the trade center dummy.49

Clearly, there are other determinants of commercial activity in the middle age. Never-theless, we choose this set of variables because these variables are significant predictors

49We recode this variable so that it is positively associated with economic development and agglomerationas 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 confirmingthe relevance of our variables for explaining commercial activity in the medieval age.

We now perform OLS and instrumental variable regressions (as before with the LIMLand Lewbel’s (2012) method) using both versions of the index of medieval commercialimportance 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 fixedeffects as well as the basic geographic controls) and the set of robust covariates employedin Tables 5 and 6 supplemented by NUTS-1 region and country fixed effects. This ensuresthat the results are comparable to that obtained before using the simple trade centerdummy 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 alternativeversion, shows up significant with a positive sign in every regression. Reassuringly, theLIML IV regressions using the same instrumental variables as before and a version ofthe index without the bishop before 1000 AD dummy, yield a more significant andremarkably higher coefficient. This is similar to the IV regressions using the dummyvariable. The coefficient obtained with Lewbel’s (2012) generated instruments is muchcloser to the original OLS estimate but keeps its significance. Furthermore, the Lewbelestimate has to be treated with some care since the overidentification test does rejectthe null of a valid exclusion restriction at the marginal significance level.

To sum up, the index of commercial importance confirm the results of the regressionsusing a simple dummy variable. Therefore, it is fair to conclude that there is a statis-tically robust relationship between medieval trade and commerce and today’s regionaleconomic development.

4.3 Medieval Trade, Agglomeration and Contemporary EconomicDevelopment - Establishing Causality

Until now, we only indirectly show that medieval trade influences present-day’s regionaleconomic development through its impact on agglomeration. We did so by showingthat the distance of a region to the next trade city is robustly negatively associatedwith regional GDP per capita. In this section we will conduct a more direct test of the

50Regression not shown but available from the author.

27

proposed causal chain going from medieval trade activities to medieval city growth tocontemporary 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 associationbetween involvement in medieval trade activities and city growth during that period. Toillustrate that the theoretically proposed relationship between medieval trade and citygrowth does actually exist, we run a set of regressions were we explain ln city growthin the medieval period by the trade center dummy and other covariates of medieval citygrowth identified in the literature.The population data on which the city growth variableis based originates from Bairoch’s (1988) compilation of European city population datafrom 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 uswith 361 cities from which 90 are coded as trade cities based on the same informationthan in the NUTS-3 region sample. A list of all included trade cities is provided in theData Appendix.

The estimated results are depicted in Table 8. There, in columns (1) to (3) we runcross-sectional OLS regressions with the natural logarithm (ln) of city growth between1500 AD (the end of the medieval period) and 1200 AD, 1300 AD and 1400 AD. Wechoose these three variables to demonstrate that the results are not dependent on thechosen period and furthermore are stronger when we consider a longer period of citygrowth. The latter would be an indication that the impact of trade on city growthworks trough agglomeration processes unfolding there effect only after a longer period oftime. In every of the regressions we include country fixed effects as well as a set of otherset of historical determinants of city development as controls. We control for first natureagglomeration forces by including the distance of a city to the next river or coast and alsoa city’s latitude and longitude and whether it is classified as a mountain region and wastherefore 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 bishopbefore 1000 AD, had the status of imperial city, was located at an important imperialroad or was a member of the Hanseatic League.51 At last, we always include the lnof the initial city population at the beginning of the considered growth period. This

51This 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 dueto the fact that a NUTS-3 region could harbor an archbishop in 1000 AD but none of the cities weconsider 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 thegrowth rate of a city strongly depend on there initial size.52 This is, we estimate thefollowing regression specification:

ln(CityGrowth)i, 1500t

= α+ βTCi + γPOPt + δ′Xi + θc + εi (3)

Where ln(CityGrowth)i, 1500t

is the ln the growth in population in a city between 1500 ADand period t with t=1200, 1300 or 1400 AD. TCi is the trade center dummy POPt is theln 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 CommercialImportance insteas of the trade center dummies. These results, that do not generallynot 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 showsignificantly higher growth throughout the medieval than non trade cities. This is clearevidence in favor of our theoretical reasoning that medieval trade contributed to citygrowth and agglomeration. Furthermore, we also see a highly significant and negativeeffect of the initial population level showing that indeed already large cities did growslower.

What is more, in columns (4) and (5) we also run random effect (RE) estimations usinga panel data set comprising out of the same sample and variables as the cross section. Inthese estimations we first regress the ln of the city population in every of our consideredyears (1200, 1300, 1400 and 1500 AD) on the trade center dummy and the same set ofcontrols as previously in the cross sectional estimates and additionally we add year fixedeffects. Again, pooled over all years, the population of a city is significantly higher if thecity is a important medieval trade city. At last, we regress the change in ln populationbetween every of our base years on the trade center dummy and additionally include thelagged population in the regression (what is similar to the cross sectional estimations).Once more, we found a significantly positive association between being a trade centerand 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 im-portant determinant of city growth and agglomeration during the middle ages. Havingestablished this, in the following we will focus on a detailed investigation of the rela-tionship between medieval trade activities, contemporary agglomeration patterns andregional economic growth.

52A descriptive overview over all variables used in the city level estimations is available in Table A.2 inthe 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 contemporaryeconomic agglomeration patterns, i.e. we have to establish that there is a significantamount 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 medievalcity growth variables used in the previous subsection, the initial city population atthe beginning of the considered growth period and again NUTS-1 region and countryfixed effects and the robust covariates used already in the preceding estimations. Moreformally spoken following regression equation is estimated using OLS:

ln(RGDPD)cijk = α+βln(CityGrowth)cijk, 1500t

+γPOPcijk,t+δ′Xcijk+θc+i+εcijk (4)

Where ln(RGDPD)cijk is the ln of the relative GDP Density in a NUTS-3 region,ln(CityGrowth)cijk, 1500

tis the ln of a city’s population in 1500 AD divided by its pop-

ulation in t with t being either 1200, 1300 or 1400 AD. γPOPcijk,t represents the ln ofthe 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 countryfixed effects, respectively. εcijk finally is the error term.

The final step, is then to establish the relationship between medieval trade, contem-porary economic agglomeration (via path dependent agglomeration processes as shownabove) and regional economic development.

We will achieve this by conducting a causal mediation analysis (estimation of me-diation effects) following the method developed by Imai et al. (2010, 2011).53 Me-diation analysis enables to disentangle direct and indirect effects –via determiningagglomeration– of medieval trade on contemporary development. Since we cannot ruleout that there are direct effects or –what amounts to the same– indirect effects of me-dieval trade working via other channels this methodology seems to be appropriate forour setting. The estimation of mediation effects is based on a set of three different linearestimation equations (Imai et al. 2010):

53The method suggested by Imai et al. is a generalization of the traditional mediation analysis (MacK-innon 2008) that implement it as a variant of linear structural equation modeling (LSEM).

30

Ycijk = α1 + β1Tcijk + γ′11Xcijk + γ′12Xcij + δc + θi + λj + εcijk1 (5)

Mcijk = α2 + β2Tcijk + γ′21Xcijk + γ′22Xcij + δc + θi + λj + εcijk2 (6)

Ycijk = α3 + β3Tcijk + πMcijk + γ′31Xcijk + γ′32Xcij + δc + θi + λj + εcijk3 (7)

Where Ycijk represents ln GDP per capita in a NUTS-3 region, Tcijk represents ourvariables of interest (treatment variable), i.e. the trade center dummy, the ln distance totrade center measure and the index of medieval commercial importance. Mcijk representsthe mediating variable, that is ln relative GDP density as measure of the spatial distri-bution of economic activity. Xcijk is defined as before and stands for a set of NUTS-3level 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 theerror 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 mea-sures and in equation (6) finally we include both the medieval trade variables and theagglomeration measures in one regression on ln GDP per capita.

The “average causal mediation effect” (ACME) is estimated by the product of thecoefficients β2 and π (β2π) and is obtained through a two-step procedure described indetail in Imai et al. (2011).54 The ACME represents the indirect effect of medievaltrade on contemporary GDP per capita, i.e. that part of the overall effect of medievaltrade running through agglomeration. Correspondingly, β1 measures the total (average)effect of medieval trade on regional GDP per capita and β3 represents the direct effectof medieval trade, i.e. that part of the effect not mediated by agglomeration (but maybeother factors). In consequence, this methodology of separating direct and indirect effectsenables to calculate which amount of the total effect of medieval trade works via increasedagglomeration. We expect β2 > 0 in the case of the trade center dummy and β2 < 0 inthe case of the distance to trade center variable. Even more, we also hypothesize that onaverage, the majority of the effect of medieval trade should run through agglomeration.This leads us expecting the ACME being significantly different from zero and greaterthan the direct effect (|β2π| > |β3|). Moreover, since it holds that β1 = β2π + β3

equation (4) is redundant given equations (5) and (6) and therefore we only estimate

54In the classical case, where the mediation analysis is conducted using LSEM the coefficients areobtained 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 ofagglomeration on regional GDP per capita.

The results of both the regressions of medieval city growth on ln GDP density andthe mediation analysis are presented in Table 9. Supplementary to those result, weestimated Table 9 with ln population density as mediating agglomeration measure. Theresults 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 clearlysee that there is a robust and positive relationship between medieval city growth indifferent time periods and the contemporary relative GDP density of the NUTS-3 regionsin which the cities are located. The smallest estimate, resulting from the estimationwith city growth between 1400 and 1500 AD as regressor, implies that on average, onepercentage of city growth in this period leads to a around 0.17 percent higher relativeGDP density. This shows that there is indeed a considerable amount of path-dependencyin the development of European cities, i.e. cities that grew larger in the medieval agedue 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 findstrong empirical support for our theory. As expected based on the previous empiricalresults, all three measures of medieval trade (the dummy, the distance variable and theindex of commercial importance) are strong predictors of contemporary relative GDPdensity. The coefficients are both significant from a statistical and economical point ofview. The coefficient of the trade center dummy for instance implies that regions withan important medieval trade center shows on average a around 40 % higher relativeGDP density than non trade center regions. What is more, the results clearly show thata higher distance to a trade center largely corresponds to a higher distance to areaswhere the economic activity is concentrated.Thus, according to those estimates, thereis a significant and robust positive relation between present day’s spatial distributionof 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 GDPper capita does completely disappear when we include the relative GDP density in theregression estimation. The relative GDP density in contrast, enters with a positive andsignificant sign in each of the three regressions. Thus, areas with a high concentration ofeconomic activity are also the regions with a higher GDP per capita. Most importantly,55Finally, this also implies that the share of the total effect of medieval trade running through agglom-

eration is (β2π)β1

.

32

this also implies that the vast majority of the observed strong effect of medieval trade onregional development levels works through its impact on the patterns of spatial industryagglomeration. In line with this, the ACME is always significant and on average above100 % indicating that the insignificant remaining effect of medieval trade is even negativein some cases.

Thus, it is fair to conclude that the effect of medieval trade indeed runs throughagglomeration as proposed in this paper.

4.4 Robustness of the Results

Our results have proven to be robust to the inclusion of many important covariatesand to endogeneity issues. However, there remain some additional concerns about therobustness of the obtained estimates. To account for these objections, we conduct variousrobustness checks. The results of these tasks are reported in appendix B (Tables B.1 toB.8).

At first, we account for the effect some additional variables might have on both thecurrent level of regional development and/or medieval trading activities. In order to doso, 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 medievalage to look whether such type of economic activities at least partly causes the significanteffects we attribute to medieval trade activities. This could be possible if e.g. miningactivities actually led to higher trade activities in the regions they took place. We addthis variable to the specifications three and eight in Table 6, i.e. we add the variable tothe set of control variables capturing historical region characteristics.

Additionally, we include an interaction term of latitude and longitude of a region’scentroid to the set of basic geographic controls and re-estimate specifications three andsix of Table 5 including this interaction effect. The justification for this is to look whetherdevelopment levels systematically differ when changing latitude and changing longitudeand vice versa. In this way we can for example identify effects of different climaticconditions varying along different latitudes for countries located at the same longitudes.

Furthermore, we add the share of Roman Catholic people in a country’s populationin 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 religionin general) on economic outcomes might not be captured adequately by the Distance toWittenberg variable, at least not today 500 years after the Reformation.56A 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 importantresidence city of a clerical or secular ruler. Residence cities of important rulers werethe centers of political and economic power in the territory of the ruler. Therefore, itis quite likely that they showed high growth rates of population and economic activityand maybe explain a significant part of medieval trade and its long-lasting effects onagglomeration and development (e.g. Ringrose 1998).

The results obtained when adding these supplementary variables to the mentionedregression specifications are shown in Table B.1. The dummy for medieval copper miningregions and the latitude longitude interactions are not significant (Columns one to fourin Table B.1). Apart from the fact, that some of the included covariates seem to besignificant (e.g. the catholic variable) the trade center dummy and the distance to tradecenter variable retain there significance and the size of the coefficients is comparable tothat obtained in the original estimates or larger.

A second robustness check is to look whether our results are sensitive to removinginfluential observations. To test this we re-estimate Table 6 but remove regions that showa high leverage, i.e. have a large impact on the coefficient estimate. This can be doneby computing the DFITS statistics, developed by Belsely et al. (1980). They suggestto consider an observation as influential if |DFITSj | > 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 abovethis threshold are removed from the sample and then the estimations are based on thisreduced sample. The results of this task are shown in Table B.2. Once again, theexclusion of influential observations does only lead to minor quantitative changes in thecoefficient values (in both directions). Qualitatively, the results seem to be completelyunaffected by influential observations.

As already discussed in the data section, there is a considerable amount of uncer-tainty in the historical sources and information on which our identification of importantmedieval trade centers is based. In consequence, it is adequate to test, whether ourempirical results hold, when alternative sample of trade cities are used in the regres-sions. We therefore re-estimate the all important results that depend on the tradecenter dummy using the four different alternative samples of trade regions introducedin section 3.3 and further elaborated in Appendix B. For each of this four alternativetrade center dummies we re-run the regression specification in Table 5 column (5) wherewe employed all robust covariates from the previous regressions as controls. This spec-ification is used —as in most parts of the analysis above– because it yields the mostconservative 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 Table8 column (1) where we regress the ln city growth between 1200 and 1500 AD with thetrade center dummy, the inital population level and appropriate historical controls. Atlast, 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 shownin Appendix B, Tables B.3–B.7.

As one can infer from the results in these Tables the results most often do onlymarginally change with the alternative trade center variables. They coefficients eventend 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 sampleof trade cities containing cities with reported trade activities in earlier periods. Thecoefficient of the trade center dummy becomes insignificant when using this alternativesample. However, in sum, none of our conclusions and general results is invalidated bythe alternative samples of trade cities. As such, the results are robust to considerablechanges in the sample due to uncertainty of historical information and underlying dataselection criteria.

5 Conclusion

This paper argues that medieval trade led to agglomeration and concentration of eco-nomic activities in the region it took place. It further postulates that the observed spatialdistribution of population and economic activity across Europe today is still shaped bythe self-reinforcing and long-lasting agglomeration processes originating from medievaltrade activities.

An empirical tests of these hypotheses brought forward that, as expected, there is astatistically and economically significant positive relationship between medieval tradeactivities and contemporary regional economic development. The analysis further un-earthed that this relationship is indeed caused by the influence medieval trade exerted onthe emerging patterns of agglomeration and spatial concentration of industrial activitiesthroughout European regions. Based on the result of this paper we are able to confirm acausal chain running from medieval trade activities through medieval city growth to con-temporary industry concentration and regional economic development. Medieval tradetherefore can considered to be an important determinant of modern economic develop-ment. Further quantitative analyses of medieval trade activities maybe based on moredetailed historical data can therefore help to significantly improve our understanding ofthe sources of long-lasting economic and social prosperity.

35

Tables and Figures

Figure 1: NUTS-3 Regions with Medieval Trade Cities

36

0.1

.2.3

.4D

ensi

ty

2 4 6 8 10ln(Population Density)

Figure 2: Kernel Density Estimate for ln(Relative GDP Density)

0.0

5.1

.15

.2.2

5D

ensi

ty

-5 0 5 10ln(Relative GDP Density)

Kernel Density Trade Center=1Kernel Density Trade Center=0

Figure 3: Kernel Density Estimates for Trade Centers and Non Trade Centers

37

-.5

0.5

1e(

ln(G

DP

per

cap

ita)

| X )

-1 -.5 0 .5 1e(Trade Center | X )

Figure 4: GDP p.c and Trade Centers - Partial Regression Plot

-.5

0.5

1e(

ln(G

DP

per

cap

ita)

| X )

-1 -.5 0 .5e( ln(Distance to Trade Center) | X )

Figure 5: GDP p.c. and Distance to Trade Centers - Partial Regression Plot

38

Table 1: The Data on Medieval Trade Centers

Country No. ofRegions

No. of TradeCenters

Share TradeCenters

Mean ln(Distanceto Trade Center)

Austria 35 7 20 0.36Belgium 44 3 6.8 0.41Czech Republic 14 4 28.6 0.43France 94 20 21.3 0.53Germany 429 37 8.6 0.39Hungary 20 2 10.0 0.69Italy 90 25 27.8 0.41Lithuania 7 2 28.6 0.56Netherlands 40 7 17.5 0.29Poland 66 12 18.18 0.55Total 839 119 14.8 0.425

39

Table 2: Bivariate Correlations of the Main Variables

Trade Center ln(Distance toTrade Center)

ln(PopulationDensity)

ln(GDP percapita)

ln(RelativeGDP Density)

Trade Center 1ln(Distance toTrade Center)

-0.529***(0.000) 1

ln(PopulationDensity)

0.228***(0.000) -0.36*** (0.000) 1

ln(GDP percapita)

0.12***(0.108)

-0.356***(0.000)

0.461***(0.000) 1

ln(RelativeGDP Density)

0.218***(0.000)

-0.303***(0.000)

0.921***(0.000)

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

Tabl

e3:

Med

ieva

lTra

de,A

gglo

mer

atio

nan

dR

egio

nalD

evel

opm

ent

-Des

crip

tive

Ove

rvie

w

coun

try

Av.

GD

Pp.

c.tr

ade

cent

ers

GD

Pp.

c.no

ntr

ade

cent

ers

“GD

PA

dvan

tage

”tr

ade

cent

ers

Rel

.G

DP

Den

s.tr

ade

cent

ers

Rel

.G

DP

Den

s.no

ntr

ade

cent

ers

“Rel

.G

DP

Den

s.A

dvan

tage

”tr

ade

cent

ers

Aus

tria

3742

8.71

2688

5.71

1054

2.28

***

(256

9.8)

19.2

10.

453

18.7

6**

(8.5

)Be

lgiu

m35

566.

6625

014.

610

552.

03**

(466

9.6)

1.02

3.00

-1.9

8(8

.43)

Cze

chR

epub

lic15

950

1110

048

50*

(257

4.7)

31.9

40.

247

31.7

(18.

79)

Fran

ce29

680

2451

3.5

5166

.48*

*(2

267.

2)13

7.07

13.7

112

3.36

*(7

2.72

)G

erm

any

3438

1.08

2634

2.86

8038

.22*

**(1

692.

8)14

.02

5.91

8.1*

**(2

.5)

Hun

gary

1350

066

77.7

868

22.2

3***

(204

9)75

.51

.174

75.3

4***

(18.

73)

Ital

y27

576

2409

5.38

3480

.62*

**(1

220.

9)3.

042.

230.

818

(1.7

3)Li

thua

nia

8200

6439

.99

1760

(239

7.35

)1.

640.

710.

924

(0.4

71)

Net

herla

nds

3614

2.86

3043

0.3

5712

.56*

(288

3.3)

1.81

2.97

-1.1

5(2

.0)

Pola

nd10

475

6822

.22

3652

.78*

**(9

21.2

)42

.94.

1638

.74*

**(9

.00)

Tota

l28

652.

923

779.

248

73.7

7***

(105

0.28

)35

.99

5.48

30.5

1***

(9.7

)N

otes

.T

hest

atist

ical

signi

fican

ceof

diffe

renc

esin

GD

Ppe

rca

pita

,pop

ulat

ion

dens

ityan

dre

lativ

eG

DP

dens

itybe

twee

ntr

ade

cent

ers

and

non

trad

ece

nter

sis

test

edby

atw

o-sa

mpl

et

test

(ass

umin

geq

ualv

aria

nces

).D

iffer

ence

sbe

twee

ntr

ade

cent

ers

and

non

trad

ece

nter

sar

est

atist

ical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

Stan

dard

erro

rsof

the

tte

sts

are

repo

rted

inpa

rent

hese

s.

41

Table 4: Medieval Trade and Contemporary Economic Development - Baseline Esti-mates

Dep. Var. ln(GDP per capita)(1) (2) (3) (4) (5) (6)

Trade Center 0.244***0.272***0.264***(0.026) (0.028) (0.028)[0.03] [0.033] [0.031]{0.03} {0.029} {0.27}

ln(Distance toTrade Center)

-0.232***(0.039)

-0.31***(0.046)

-0.29***(0.046)

[0.047] [0.053] [0.055]{0.038} {0.045} {0.043}

Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesNUTS-2 Dummies No Yes Yes No Yes YesBasic GeographicControls

No No Yes No No Yes

Obs. 839 839 839 839 839 839Adj. R2 0.78 0.778 0.778 0.765 0.762 0.763Notes. Below each coefficient three standard errors are reported. First, heteroskedasdictyrobust standard errors are reported in parentheses. Second, standard errors adjusted fortwo-way clustering within NUTS-1 and NUTS-2 regions are reported in square brackets.Third, standard errors adjusted for two-dimensional spatialcorrelation according to Con-ley’s (1999) method are reported in curley brackets. The standard errors are constructedassuming a window with weights equal to one for observations less than 3 degrees apartand 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’slatitude, longitude and altitude. Each regression contains a constant not reported.

42

Tabl

e5:

Med

ieva

lTra

dean

dC

onte

mpo

rary

Econ

omic

Dev

elop

men

t-A

ddin

gFu

rthe

rC

ontr

ols

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

Trad

eC

ente

r0.

175*

**0.

105*

**0.

181*

**0.

0701

***0

.045

**(0

.025

)(0

.024

)(0

.024

)(0

.027

)(0

.021

)

ln(D

istan

ceto

Trad

eC

ente

r)-0

.105

**(0

.044

)-0

.085

7*(0

.044

)-0

.135

**(0

.053

)-0

.138

***

(0.0

41)

-0.0

529

(0.0

41)

Cou

ntry

Dum

mie

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sN

UT

S-1

Dum

mie

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sBa

sicG

eogr

aphi

cC

ontr

ols

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

No

Geo

grap

hic

Cen

tral

ityC

ontr

ols

Yes

No

No

No

No

Yes

No

No

No

No

Reg

ion

Cha

ract

erist

ics

No

Yes

No

No

No

No

Yes

No

No

No

Hist

oric

alR

egio

nC

hara

cter

istic

sN

oN

oYe

sN

oN

oN

oN

oYe

sN

oN

oG

row

thC

ovar

iate

sN

oN

oN

oYe

sN

oN

oN

oN

oYe

sN

oA

llR

obus

tC

ontr

ols

No

No

No

No

Yes

No

No

No

No

Yes

Obs

.83

983

983

951

881

883

983

983

951

881

8A

dj.R

20.

809

0.87

30.

784

0.87

80.

878

0.79

80.

859

0.77

60.

872

0.87

7N

otes

.St

anda

rder

rors

adju

sted

for

two-

way

clus

terin

gw

ithin

NU

TS-

1an

dN

UT

S-2

regi

ons

are

repo

rted

inpa

rent

hese

s.C

oeffi

cien

tis

stat

istic

ally

diffe

rent

from

zero

atth

e**

*1%

,**5

%an

d*1

0%

leve

l.T

heun

itof

obse

rvat

ion

isa

NU

TS-

3re

gion

.T

heba

sicge

ogra

phic

cont

rols

incl

ude

are

gion

’sla

titud

e,lo

ngitu

dean

dal

titud

e.T

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ogra

phic

cent

ralit

yco

ntro

lsin

clud

eth

eln

dist

ance

sofa

regi

on’s

cent

roid

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ene

ares

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rpor

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d,ro

ad,b

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egio

nch

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teris

ticco

ntro

lsin

clud

ea

dum

mie

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rre

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Ger

man

yth

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ties,

for

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ons

incl

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ern

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mea

sure

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tco

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tes

enco

mpa

sses

altit

ude,

the

lndi

stan

ces

toai

rpor

ts,r

ailro

ads

and

river

s,du

mm

ies

for

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rict

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ities

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sre

gion

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ansit

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trie

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ster

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erm

any,

the

lnof

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hesh

are

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with

tert

iary

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atio

n,th

ein

equa

lity

mea

sure

and

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prin

ting

pres

sbe

fore

1500

AD

dum

my.

Each

regr

essio

nin

clud

esa

cons

tant

not

repo

rted

.

43

Table 6: Medieval Trade and Contemporary Economic Development - IV Regressions

(1) (2) (3) (4)Method LIML Lewbel (2012) LIML Lewbel (2012)

2. Stage ResultsDep. Var. ln(GDP per capita)

Trade Center 0.306*** 0.0787***(0.105) (0.0247)

ln(Distance to Trade Center) -0.519*** -0.155***(0.173) (0.0503)

R2 (centered) 0.563 0.632 0.508 0.880F-value 55.02 86.43 51.52 131.85Overidentification Test(Hansen J statistic)

0.307 66.64 0.0981 78.26

p-value 0.580 0.116 0.754 0.008

1. Stage ResultsDep. Var. Trade Center ln(Distance to Trade Center)

Mountain Region -0.0232* 0.0259***(0.013) (0.01)

Bishop before 1000 AD 0.2553*** -0.1342***(0.071) (0.039)

Country Dummies Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes

Obs. 818 818 818 818Angrist-Pischke F statistic ofexcluded IV’s (p-value)

8.39 44.51 9.32 13.47

R2(centered) 0.273 0.837 0.206 0.699Underidentification Test 14.06 194.6 16.25 158.2p-value 0.000 0.000 0.000 0.000Notes. 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. Theset 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-communistictransition countries, Eastern Germany, the ln of a region’s area, the share of people with tertiaryeducation, the inequality measure and the printing press before 1500 AD dummy. Each regressionincludes a constant not reported. The Overidentification test reporst the Hansen J-statistic andthe Underidentification test reports the Kleibergen-Paap rk LM statistic (null hypothesis: equationis underidentified). Lewbel’s (2012) approach uses a vector of generated instruments that areuncorrelated with the product of the heteroskedasdic first stage’s errors as instruments. Theseinstruments are not included in the table due to space restrictions, but their coefficients andstandard errors are available from the author upon request.

44

Table 7: Medieval Commercial Importance and Contemporary Regional Development

Dep. Var ln(GDP per capita)(1) (2) (3) (4) (5) (6)

OLS LIML IVLewbel (2012)

Commercial Importance 0.0964***0.0211** 0.153*** 0.0232**(0.014) (0.009) (0.055) (0.01)

Commercial ImportanceAlternative

0.0972***0.0181*(0.016) (0.011)

Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesNUTS-2 Dummies Yes No Yes No No NoAll Robust Controls No Yes No Yes Yes Yes

Obs. 839 818 839 818 818 818Adj.R2 \R2 0.776 0.877 0.77 0.877 0.502 0.621Underidentification Test 16.45 224.5p-value 0.000 0.000Overidentificaton Test 0.129 69.41p-value 0.719 0.0772AP F-statistic of excludedIV’s

9.15 32.72

p-value 0.000 0.000Notes.Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions arereported in parentheses. In column (5) and (6) heteroskedasdicity robust standard errors are re-ported. Coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level. Theunit of observation is a NUTS-3 region. The index of commercial importance of a medieval city isconstructed by adding up the coast region dummy, the trade center, bishop in 1000 AD, imperialcity and road, hanseatic league, medieval mining region and university before 1500 AD dummyvariables. The alternative index of commercial importance includes the distance to trade centervariable instead of the dummy (recoded to be positively related to GDP). In the case of the LIMLIV regression a version of the index is used that does not include the bishop before 1000 AD dummysince this variable is used as excluded instrument in that estimation. The set of covariates encom-passes 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, EasternGermany, the ln of a region’s area, the share of people with tertiary education, the inequalitymeasure and the printing press before 1500 AD dummy. Each regression includes a constant notreported. The Overidentification test reporst the Hansen J-statistic and the Underidentificationtest 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 theproduct of the heteroskedasdic first stage’s errors as instruments. These instruments are not in-cluded in the table due to space restrictions, but their coefficients and standard errors are availablefrom the author upon request. The first stage regressions are also not reported but are availablefrom the author.

45

Tabl

e8:

Med

ieva

lTra

deA

ctiv

ityan

dC

ityG

row

th

Dep

.Va

r.ln

(Po

pu

lati

on

1500

Po

pu

lati

on

1200

)ln(

Po

pu

lati

on

1500

Po

pu

lati

on

1300

)ln(

Po

pu

lati

on

1500

Po

pu

lati

on

1400

)ln(

Popu

latio

n)ln

(∆Po

pula

tion)

(1)

(2)

(3)

(4)

(5)

Met

hod

OLS

RE

Trad

eC

ity0.

65**

*0.

49**

*0.

448*

**0.

777*

**0.

393*

**(0

.215

)(0

.121

)(0

.151

)(0

.094

)(0

.072

)ln

(Pop

ulat

ion

1200

AD

)-0

.66*

**(0

.148

)ln

(Pop

ulat

ion

1300

AD

)-0

.62*

**(0

.068

)ln

(Pop

ulat

ion

1400

AD

)-0

.427

***

(0.0

8)ln

(Pop

ulat

ion t

−1)

-0.4

33**

*(0

.049

)

Obs

.86

199

180

826

390

Adj

.R

2 \ov

eral

lR2

0.39

0.39

80.

222

0.28

80.

369

Num

ber

ofC

lust

ers

361

194

Not

es.

Rob

ust

stan

dard

erro

rsar

ere

port

edin

pare

nthe

ses

inco

lum

ns(1

)-

(3).

Stan

dard

erro

rscl

uste

red

atci

tyle

vela

rere

port

edin

pare

nthe

ses

inco

lum

ns(4

)an

d(5

).C

oeffi

cien

tis

stat

istic

ally

diffe

rent

from

zero

atth

e**

*1%

,**

5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aci

ty.

The

set

ofco

varia

tes

enco

mpa

sses

the

lndi

stan

ces

ofa

city

toth

ene

xtriv

eror

coas

t,du

mm

ies

indi

catin

gci

ties

that

wer

ere

siden

ceof

abi

shop

befo

re10

00A

D,h

adth

est

atus

ofan

impe

rialc

ity,w

ere

loca

ted

ata

mai

nim

peria

lroa

d,w

ere

mem

ber

ofth

eH

anse

atic

Leag

ueor

are

clas

sified

asa

mou

ntai

nre

gion

byth

eEU

regi

onal

stat

istic

s.Fu

rthe

rmor

e,w

eco

ntro

lfor

aci

ty’s

latit

ude

and

long

itude

and

incl

ude

coun

try

fixed

effec

ts.

Inco

lum

ns(4

)an

d(4

)w

ead

ditio

nally

incl

ude

year

fixed

effec

ts.

Each

regr

essio

nin

clud

esa

cons

tant

not

repo

rted

.

46

Table 9: Medieval Trade, Relative GDP Density and Regional Economic Development

(1) (2) (3) (4) (5) (6)

Method OLS Mediation AnalysisCity Growth from to 1200–15001300–15001400–1500 Equation (7)Dep. Var. ln(Relative GDP Density) ln(GDP per capita)

P opulation1500P opulationt

0.337*** 0.178*** 0.172***(0.105) (0.067) (0.062)

ln(Relative GDP Density) 0.202*** 0.203*** 0.205***(0.011) (0.011) (0.011)

Trade Center 0.0048(0.017)

ln(Distance to Trade Center) 0.0103(0.023)

Commercial Importance -0.0074(0.007)

R2 0.964 0.955 0.947 0.919 0.919 0.919ACME 0.0661***-0.0786***0.0317***Direct Effect 0.0054 0.0111 -0.0072Total Effect 0.0715*** -0.0675** 0.0246***% of total mediated 92.1*** 115.1** 128.1***

Equation (6)ln(Relative GDP Density)

Trade Center 0.3316***(0.063)

ln(Relative GDP Density) -0.3799***(0.103)

Commercial Importance 0.1565***(0.023)

Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes Yes Yes

Obs. 85 179 197 818 818 818R2 0.939 0.938 0.94

Notes. Robust standard errors are reported in parentheses. Coefficient is statistically dif-ferent from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3region. The set of all robust covariates encompasses altitude, the ln distances to airportsand railroads, dummies for district free cities, capital cities, capital cities of autonomousregions, 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 pressbefore 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 ismediate, 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 cor-responds 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 indicatedin the description of the respective variable. City level information is matched to theNUTS-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. Andescriptive overview over all variables used in the empirical analysis is given in TableA.1 below.

Main Variables

Trade Centers. Primarily, the data on historical trade cities is based on four differentmaps. The first is a map printed in Davies and Moorhouse (2002) and includes ”Maintrade routes in the Holy Roman Empire and nearby countries” for the period around 1500AD. 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 renownedexpert for Polish and Eastern European history Norman Davies and his student RogerMoorhouse. 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 consideredto be a reliable source for information about medieval trade activities.

Because this map only covers the area of Austria, Belgium ,Czech Republic, EasternFrance, Germany, Hungary Lithuania, the Netherlands, Poland and North Italy we makeuse 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 partsof North Africa and the Near East. From this map, we primarily take the informationabout French trade cities, but we also include cities from other countries that are notmentioned in the first map. The original map is printed in a chapter about the “Currentsof Trade. Industry, Merchants and Money” in the medieval age as part of a volumeabout the “Flowering of the Middle Ages” edited by the Oxford-based medieval arthistorian Joan Evans. In this chapter Donald King illustrates the most important goodsof the medieval economy, discusses how they were produced and traded. He lays specialemphasis on the patterns of commerce and trade. He describes the most importantcenters of commerce and trade activity (Fair and market cities etc.) and also discussesthe 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

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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 andRaymond (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 infor-mation about economic and cultural and political features. He is cited 222 (at 24th June2013) at google scholar. Among the papers using information provided by the atlas arethe historical economic papers by Borner and Severgnini (2012) and Dittmar (2011) aswell as Becker et al. (2011). It contains information on ”economic patterns” in CentralEurope around the year 1450. From this map, we primarily took the information aboutSouthern Italian trade cities not included in the other maps. Again, we also include citiesmentioned there but not in the other two sources. From this map, a city is consideredif it is located on a ”major” or ”important” trade route. The map also contains alsoinformation about members of the Hanseatic League (and their importance) as well ascommercial offices and foreign depots of the Hanseatic League. Further, it also depictsthe goods traded over the particular routes and the areas where they are the commodi-ties are typically produced. The map drawn in Magocsi’s atlas relies on other regionaland general historical atlases like the that of Darby and Fuller (eds.)(1978) or Lendl andWagner (1963) for Austria. However, Magocsi also consulted books about the history ofcertain 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 inother countries and the main trade routes of the time as well as the traded goods. Thegeographical 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 memberof 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 “importanttrade routes” and the cities located on them. The scope of the map is south-westEurope (Spain and France) but it also includes West Germany and the north-westernItaly. Here again, we include a city if it is located on a major trade route. At last, we usethe information contained in a map about “Levant Trade in the Late Medieval and theOttoman 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

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on trade routes in the southern part of Germany, Hungary, Italy and the most parts ofFrance as well as parts of Poland.

Although not the only sources of information about medieval trade activities, thesefour maps seem to contain the most complete cross-national information about importanttrade activities in the later medieval period.

To validate the information of these maps and obtaining additional evidence aboutmedieval trade we consult other sources like a list depicting members of the Hanseaticleague 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 theHanseatic League” or that were capitals of thirds and quarters. Furthermore we con-sulted a map containing information about “North-South Trade Routes in the Alps Areain the Medieval Period” from Schulte (1966), two very general maps printed in Kinderand Hilgemann (1970) focusing on Baltic Sea and Levant trading activities in 1400 AD, amap published in Ammann (1955) focusing on trade routes for Southern Germany textileproducts (Barchent) and the map “Business Centers and Maritime Trade Routes HighMiddle Ages” printed in Hunt and Murray (1999).1 Furthermore, we draw on qualitativeinformation about the importance of a trade cities from Spufford’s (2002) standard workabout medieval trade and commerce and the monograph about the history of Germantrade written by Dietze (1923).In Table A.2, all trade cities and the corresponding regions for which the dummy vari-able is equal to one and the source(s) mention the respective city as trade center areshown. However, due to space restrictions we do not report any of the sources we con-sulted for becoming information about the validity of our sample of important tradecenters. 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 forcities in the “Rhine-Meuse area” in the period from 1000 to 1350 AD (i.e. south-westGermany, and western Switzerland, east France , large parts of Belgium and the Southof the Netherlands). As part of the index of urban centrality they collected data aboutthe existence and number of markets, fairs, trade hall and the presence and importanceof long-distance trade activities. They also have data about the presence of certain man-ufacturing activities also being a good indicator for the presence of trade. They developa categorical index of centrality from the qualitative information the collect. From thetrade cities in our sample Aachen, Antwerp, Cologne, Dordrecht, Dortmund, Frankfurt,Maastricht, Metz, Munster, Paderborn, Rotterdam, Soest and Straßburg are included in

1Geographical scope, time period and level of generality sometimes differ between these maps, so across-validation is always possible only with limitations.

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the volume. For every of those cities, one or more markets, a fair or differently impor-tant long-distance trade are mentioned. But here, the range goes from Cologne (having4 markets, and ”very important” fairs and long-distance trade activities) to e.g. Pader-born where it is stated that it have a fair and long-distance trade. Due to this, it is notan easy task to say, that the information provided by this source can be used to validatewhether a city was important enough to be included in the sample. Furthermore, theperiod for which the index is constructed ends in the middle of the 14th century andtherefore earlier than our period of observation. Nevertheless, the information providedin the anthology of Escher and Hirschmann (eds.) (2005) can be useful to select citiesthat were probably not that important because e.g. the markets, fairs or trade therewas comparably limited in scope (i.e. according to the number of markets, halls, fairsor there importance) or time. Additionally, it provides clear evidence for the outstand-ing importance of Cologne and e.g. the over-regional importance (“very important”long-distance trade or fair) of Dortmund, Frankfurt, Munster and Soest.

As already mentioned, the information in those sources primarily is used to validatethat the information printed in the maps. However, as indicated in the main text wesometimes also include cities mentioned in these sources but not in the maps when weare in doubt about the actual importance of a city in medieval trade.

Furthermore, we construct several trade center dummies using alternative samples oftrade cities (as discussed in the main text). At first, we exclude cities mentioned by onlyone 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 aboutthere importance, altough they are reported in more than one of our sources. Thosecities are Paderborn, Einbeck, Greifswald, Braniewo, Gorlitz, Metz, Palanga, Como andStargard. For example, we exclude Paderborn because despite the fact that it was amember of the Hanseatic League and layed on the Hellweg, no other source mentioned itand 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 impliesthat the existing trade activity in Paderborn was of relatively lower importance comparedto e.g. Cologne, Munster, Dortmund or other leading trade cities. Third, we add somecities to the original sample of trade cities. These cities are cases were a first look at theavailable information lead to the decision not to include the trade city. Even though, thecity 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 lyingon an important trade route according to a map in Stier et al. (1956). However, none of

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the other sources mention Anklam as important trade center and Dollinger (1966) didnot intend a special role for Anklam within the Hanseatic League.

Finally, we build a last alternative sample of trade cities that only includes cities forwhich historical sources indicate long-run trade activities (i.e. cities that are importanttrade cities around 1500 AD and that were important also in the period before). Anoverview over these cities the earliest period in which trade activities are reported andthe source mentioned the respective city are depicted in Table A.4. This re-coding isbased on information primarily derived from the 2 Wilhelm Heyds two volumes aboutmedieval Levant trade (Heyd 1879a and 1879b). He provides information about medievaltrade activities in the Levant and the most important involved parties in a chronologicalorder beginning with the end of migration period (“Barbarian Invasions”). We take theperiod mentioned in the chapter headings of the chapter where the trade activities of acity 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) providesinformation about trade activities of Austrian, Belgian, French, German and Italiancities. 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 tradecities before 1250, between 1250 and 1350 and 1350 and 1500 (always AD). Another mapreport important trade routes (e.g. the salt way) and the cities that signed the treatyof Smolensk in 1229 AD a trade agreement between German trade cities and the Dukeof Smolensk. According to Dollinger (1966), this map covers the period from 1286 toapproximately 1336. We stick to the dates given in these maps when assigning therespective cities the dates when they are mentioned first. All in all, this and the othermaps 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 sincethe ”pre-historical” period. We include a city in the sample if Dietze (1923) reports acity to be an important player in early and high medieval trade.

For Austria, the Czech Republic and Poland information is provided by three digi-tized maps from T. Matthew Ciolek’s OWTRAD website. The first is based on a mapprinted in Humnicki and Borawska (1969) and shows “Central European Trade Routes800 – 900 CE”.2 The second map originates from Wojtowicz (1956) and according to theOWTRAD website reports “Major trade roads in Poland and adjacent border regions

2The map can be found under the following URL: http://www.ciolek.com/OWTRAD/DATA/tmcCZm0800.html; accessed at June 11th, 2013.

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1340 – 1400 CE”.3 Form this map we include information about Polish trade cities. Thelast map from the OWTRAD project is based on Rutkowski (1980) and is about ‘Majortrade roads in Poland and adjacent border regions in 1370 CE”.4 From this map wesolely include the German city of Gorlitz since all the other relevant cities in the mapwere mentioned by another source depicting trade in an earlier period. Overall are ableto 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 andthe closest trade city in degrees. The variable takes the value 0 for regions that containmedieval 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 astrade cities stem from Bairoch’s (1988) data, as explained in the main text. The citiesare coded according to the procedure described in detail below in the explanation of thetrade 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, Gorlitz, Graz, Hamburg, Hannover, Hildesheim, Imola, Innsbruck, Kampen,Cologne, Cracow, Leipzig, Linz, Lubeck, Lucca, Lyon, Maastricht, Magdeburg, Mantoa,Marseille, Metz, Milan, Minden, Montpellier, Munster, Naples, Narbonne, Nuremberg,Orleans, Osnabruck, 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 isfrom 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). Itis in measured in current market prices. We took values from 2009 the latest year forwhich data is available.Commercial Importance. Variable that should measure the commercial importance of acity according to different, historically relevant characteristics. The exact constructionis explained in the main text. It is the sum of following five dummy variables: trade

3The original title of the map is (according to the OWTRAD website) “Trade roads at the timesof 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.

4The map can be accessed under the URL http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370.html;accessed at June 11th, 2013.

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center, imperial city, hanseatic league, imperial road, medieval mining, coast region anduniversity before 1500 AD. This variable is constructed by the author.Commercial Importance Alternative. Identical to the variable commercial importancebut instead of the trade center dummy, it constains the distance to trade center vari-able, recoded in a way that it is positively associated with the GDP per capita (as theother variables). ln(Population Density). A region’s Population Density comes from theEurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_d3dens&lang=en; accessed at October 10th 2012). The val-ues are from 2009.ln(Relative GDP Density). This variable is calculated using the following formula (Roos2005):

rdi = Yi/∑

Yi

Ai/∑

Ai

Where rdi is the relative GDP Density of a region. Yi is a region’s GDP (calculated bymultiplying 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 aregions share of GDP relative to its share of a country’s overall area. In consequence, ifthe relative GDP Density is larger than one this means that a region shows concentrationof economic activity higher than the average region in a country (Roos 2005). For theempirical estimations, we take the natural logarithm of the variable, so that it is greaterthan 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 thisappendix.

Control Variables and Instruments

Altitude. The Altitude of a region is from the website gpsvisualizer.com (accessed atNovember 8th 2012) and based on the coordinates of its centroid.Bishop before 1000 AD. Dummy variable equal to one if a region includes a citythat was seat of a bishop (or in France and Italy of an archbishop) before theyear 1000 AD. The variable is coded according to information from the websitehttp://www.catholic-hierarchy.org (accessed at November 27th, 2012). For bish-oprics 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 1000AD wikipedia and the catholic encyclopedia (http://www.newadvent.org/cathen/;

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accessed at November 27th, 2012) are consulted.Capital. A dummy variable equal to one if a region includes the capital of a sovereignstate. Coded by the author.Capital Autonomous Region. A Dummy Variable equal to one if a region includes thecapital 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 beingdistrict-free cities (“Kreisfreie Stadte” or “Stadtkreis”). Coded by the author.Eastern German Region. Binary variable equal to one if a region in Germany is locatedin 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 fromthe Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=edat_lfse_11&lang=en; accessed at October 10th, 2012). Wetook the values from 2009.Hanseatic League. Binary variable equal to one if a region contains at least one citythat 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 citythat was an imperial city in the Holy Roman Empire. The variable is coded followingOestreich and Holzer (1970a).Imperial Road. Dummy variable equal to one if a region contains at least one citythat was located on an important imperial city, i.e. the Via Imperii, the Via Re-gia or the Via Regia Lusatiae Superioris. The variable is coded according to in-formation provided by Kuhn (2005), the entry “Hohe Landstraße” in the onlineversion of “Meyers Großes Konversations-Lexikon” a general german encyclopedia(http://www.zeno.org/Meyers-1905/A/Hohe%20Landstra%DFe; accessed at Decem-ber 18th 2012), a map from a website of the federal government of the GermanState Saxony on regional development (http://www.landesentwicklung.sachsen.de/download/Landesentwicklung/ED-C_III_Via_Regia_Verlauf.jpg; accessed atDecember 18th, 2012) and wikipedia entries.Inequality. We measure inequality as ratio of average workers compensation to the GDPper capita. The Sources of GDP per capita and average workers compensation are aslisted in this appendix.Latitude. The values of this variable represent the latitude in decimal degrees of aregion’s centroid and are obtained from a GIS map of NUTS territories provided by theEurostat GISCO Database.

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(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 regionalstatistics 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 valuesfrom 2009.ln(Distance to Airport). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest international airport in degrees. It is calcu-lated 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 distancebetween a region’s centroid and the closest point of the country’s border. It is calculatedusing the ArcGIS Near Tool. The coordinates of borderlines are taken from a GIS mapof EU countries provided by the Eurostat GISCO Database (http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/CNTR_2010_03M_SH.zip; accessed at January10th, 2013).ln(Distance to Coast). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s coastline. It is cal-culated using the ArcGIS Near Tool. The coordinates of a country’s coastlines aretaken from the GIS map “Corine land cover 2000 coastline” provided by EuropeanEnvironment 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 distancebetween a region’s centroid and the closest point of a country’s major railroad. It iscalculated using the ArcGIS Near Tool. The coordinates of the railroads are obtainedfrom the map “World Railroads” from ArcGIS Online Database (accessed at November9th 2013).ln(Distance to River). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s major waterway (e.g. inGermany these are Elbe, Danube, Rhine and Oder). It is calculated using the ArcGISNear Tool. The coordinates of the rivers are taken from the GIS map “WISE Largerivers 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 distancebetween 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 GISMap “World Roads” from ArcGIS Online Database (accessed at November 9th, 2012).ln(Distance to Wittenberg). Variable containing the geodesic distances between eachregion’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 atNovember 8th, 2012).ln(Employees Compensation). Natural logarithm of average of employees compensation(wages, salaries and employer’s social contributions) at NUTS-2 level measured atcurrent prices and from the year 2009. Data was obtained from the Eurostat regionalstatistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e2rem&lang=en; accessed at October 10th, 2012).ln(Fixed Capital). Gross fixed capital formation by NUTS-2 regions measured for2009. 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 aregion’s centroid and are obtained from a GIS map of NUTS territories provided bythe 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 miningsites. The variable is coded according to a map in Elbl (2007) as well as information inSpufford (2002).Mining Region. Dummy variable equal to one if in a region at least one ore or coalmine (or mining firm) is located. The information on which the coding is based origi-nate from the structural business statistics included in the Eurostat regional statisticsdatabase (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 theirpopulation living in mountain areas according to the ESPON (European ObservationNetwork for Territorial Development and Cohesion) regional typologies project. Thevariable 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 areasand with more than 50% of their population living in mountain areas. It is zero whena region fulfills none of this criteria. The data and an explanation of the classificationscan be downloaded from http://www.espon.eu/export/sites/default/Documents/

57

ToolsandMaps/ESPONTypologies/Typologies_metadata_data_final.xls (accessedat November 8th, 2012).Patents. Total number (over all IPO section and classes) of patent applications to theEuropean Patent Office (EPO) in each region in 2009. Data available from the Eurostatregional 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 EasternEuropean 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 ina region had adopted printing technology before 1500 AD. The coding is based oninformation in Benzing (1982), Clair (1976) and the Incunabula Short Title Catalogue(ISTC) of the British library (http://www.bl.uk/catalogues/istc/index.html; ac-cessed at November 18th, 2012). A region is included if any of these sources mentioneda 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 Gothenburgin 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 thedata 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 whichthe indix is based are collected in 2009. In Belgium, Germany, Netherlands and Hungarythe index report values at NUTS-1 level in the other countries in our dataset it reportsvalues 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 statisticsdatabase (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 hasa university founded before 1500 AD. Coding according to Eulenburg (1994), Kinderand Hilgemann (1970) and Ruegg (1993). The a city is recognized if it is mentionedby any of these sources. If there were doubts on the founding date of a university (orcontradicting 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. Min Max

Altitude 839 279.230 320.194 -6.200 2472.600Bishop before 1000 AD 839 .064 .246 0 1Capital 839 0.011 0.103 0 1Capital Autnomous Region 839 0.051 0.221 0 1Commercial Importance 839 0.67 0.955 0 5Commercial Importance Alt. 839 1.46 0.866 0 5.357District-Free City 839 0.147 0.354 0 1Eastern German Region 839 0.122 0.327 0 1Education 832 24.211 6.319 8.4 48.6Hanseatic League 839 0.108 0.311 0 1Imperial City 839 0.069 0.254 0 1Imperial Road 839 0.045 0.208 0 1Inequality 825 1.134 0.921 0.037 8.425Latitude 839 49.460 3.088 38.245 55.939ln(Area) 839 7.032 1.297 3.575 9.400ln(Distance to Airport) 839 -0.645 0.727 -4.142 0.792ln(Distance to Border) 839 -0.825 1.083 -5.532 1.16ln(Distance to Coast) 839 0.308 1.204 -5.566 1.882ln(Distance to Railroad) 839 -2.111 1.390 -7.365 0.429ln(Distance to River) 839 -.675 1.322 -7.185 1.944ln(Distance to Road) 839 -4.001 1.376 -10.868 -1.194ln(Distance to Trade Center) 839 0.432 0.272 0 1.665ln(Distance to Wittenberg) 839 6.027 0.804 -7.447 7.335ln(Employees Compensation) 825 9.867 0.924 7.086 12.331ln(Fixed Capital) 803 9.141 0.818 6.802 11.494ln(Population Density) 839 5.351 1.137 2.709 9.964ln(Relative GDP Density) 839 -.077 1.262 -2.461 6.194Longitude 839 10.228 5.012 -4.091 25.573Medieval Mining 839 0.027 0.16 0 1Mining Region 839 0.228 0.420 0 1Mountain Region 839 0.479 1.022 0 3Patents 803 83.094 89.654 0.286 764.717Post Communistic Country 839 0.111 0.314 0 1Printing Press before 1500 839 0.199 0.4 0 1Quality of Government 839 72.130 17.163 10.18 97.61Trade City 361 .249 .433 0 1Trade Center 839 0.137 0.344 0 1Unemployment 582 8.237 3.435 1.9 19.1University before 1500 839 0.052 0.223 0 1

60

Table A.2: Descriptive Data Overview – City Level Variables

Variable Obs Mean Std. Dev. Min Max

Bishop 10000 AD 361 0.127 0.334 0 1Imperial Road 361 0.078 0.268 0 1Imperial City 361 0.122 0.328 0 1Hanseatic League 361 0.155 0.363 01Latitude 361 48.453 3.633 40.11 54.473Longitude 361 8.727 5.048 -4.29 22Mountain Region 361 0.385 0.887 0 3ln(Distance to Coast) 361 -0.24 1.326 -5.566 1.762ln(Distance to River) 361 -0.541 1.504 -7.185 1.944ln(Population 1200 AD) 86 9.533 0.812 6.908 11.608ln(Population 1300 AD) 199 9.114 1.104 6.908 11.918ln(Population 1400 AD) 180 9.053 1.063 6.908 12.524ln(Population 1500 AD) 361 8.817 0.983 6.908 12.324Trade City 361 .249 .433 0 1

61

Tabl

eA

.3:O

verv

iew

over

the

incl

uded

Trad

eC

ities

and

Reg

ions

Trad

eC

ityN

UT

S-3

Reg

ion

coun

try

Map

Sour

ces

(Prim

ary)

Oth

erH

istor

ical

Rec

ords

Bruc

kO

stlic

heO

bers

teie

rmar

kA

ustr

iaM

agoc

si(2

002)

Inns

bruc

kIn

nsbr

uck

Aus

tria

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(1

985)

,Mag

ocsi

(200

2)an

dSt

iere

tal.

(195

6)

Schu

lte(1

966)

,Spu

fford

(200

2)

Gra

zG

raz

Aus

tria

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Linz

Linz

-Wel

sA

ustr

iaD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,St

ier

etal

.(1

956)

Vie

nna

Wie

nA

ustr

iaD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,St

ier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)

Vill

ach

Kla

genf

urt-

Vill

ach

Aus

tria

Mag

ocsi

(200

2)Sc

hulte

(196

6)Sa

lzbu

rgSa

lzbu

rgun

dU

mge

bung

Aus

tria

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Schu

lte(1

966)

,Spu

fford

(200

2)

Ant

werp

Arr

.A

ntwe

rpen

Belg

ium

Dav

ies

and

Moo

rhou

se(2

002)

,Stie

ret

al.

(195

6)A

mm

ann

(195

5),H

unt

and

Mur

ray

(199

9),

Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)Br

uges

Arr

.Br

ugge

Belg

ium

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(1

985)

,Stie

ret

al.

(198

5)

Hun

tan

dM

urra

y(1

999)

,K

inde

ran

dH

ilgem

ann

(198

2),S

puffo

rd(2

002)

62

Tabl

eA

.3–

Con

tinue

dG

hent

Arr

.G

ent

Belg

ium

Stie

ret

al.

(195

6)H

unt

and

Mur

ray

(199

9),

Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Brno

Jiho

mor

avsk

ykr

ajC

zech

Rep

ublic

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2)K

utna

Hor

aSt

redo

cesk

ykr

ajC

zech

Rep

ublic

Mag

ocsi

(200

2)Sp

uffor

d(2

002)

Olm

ouc

Olo

mou

cky

kraj

Cze

chR

epub

licD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

Prag

ueH

lavn

ımes

toPr

aha

Cze

chR

epub

licD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,St

ier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)

Avig

non

Vauc

luse

Fran

ceK

ing

(198

5),S

tier

etal

.(1

956)

Hun

tan

dM

urra

y(1

999)

,Sp

uffor

d(2

002)

Bayo

nne

Pyre

nees

-Atla

ntiq

ueFr

ance

Stie

ret

al.

(195

6)Sp

uffor

d(2

002)

Bord

eaux

Giro

nde

Fran

ceSt

ier

etal

.(1

956)

Spuff

ord

(200

2)C

halo

n-su

r-Sa

one

Saon

e-et

-Loi

reFr

ance

Stie

ret

al.

(195

6)Sc

hulte

(196

6),

Spuff

ord

(200

2)H

arfle

urSe

ine-

Mar

itim

eFr

ance

Kin

g(1

985)

,Stie

ret

al.

(195

6)Li

mog

esH

aute

-Vie

nne

Fran

ceK

ing

(198

5),S

tier

etal

.(1

956)

Lyon

Rho

neFr

ance

Stie

ret

al.

(195

6)A

mm

ann

(195

5),H

unt

and

Mur

ray

(199

9),K

inde

ran

dH

ilgem

ann

(198

2),S

chul

te(1

966)

,Spu

fford

(200

2)M

arse

ille

Bouc

hes-

du-R

hone

Fran

ceK

ing

(198

5),S

tier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)M

etz

Mos

elle

Fran

ceD

avie

san

dM

oorh

ouse

(200

2)Sc

hulte

(196

6)M

ontp

ellie

rH

erau

ltFr

ance

Kin

g(1

985)

Spuff

ord

(200

2)

63

Tabl

eA

.3–

Con

tinue

dN

arbo

nne

Aud

eFr

ance

Kin

g(1

985)

,Stie

ret

al.

(195

6)O

rlean

sLo

iret

Fran

ceSt

ier

etal

.(1

956)

Paris

Paris

Fran

ceD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),S

tier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,H

unt

and

Mur

ray

(199

9),S

chul

te(1

966)

,Spu

fford

(200

2)Pe

rpig

nan

Pyre

nees

-Orie

ntal

esFr

ance

Kin

g(1

985)

Spuff

ord

(200

2)R

eim

sM

arne

Fran

ceSt

ier

etal

.(1

956)

Schu

lte(1

966)

,Spu

fford

(200

2)St

.M

elo

Ille-

et-V

ilain

eFr

ance

Stie

ret

al.

(195

6)St

rasb

ourg

Bas-

Rhi

nFr

ance

Dav

ies

and

Moo

rhou

se(2

002)

,St

ier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,Sc

hulte

(196

6),S

puffo

rd(2

002)

Toul

ouse

Hau

te-G

aron

neFr

ance

Stie

ret

al.

(195

6)Sp

uffor

d(2

002)

Tour

sIn

dre-

et-L

oire

Fran

ceSt

ier

etal

.(1

956)

Spuff

ord

(200

2)Tr

oyes

Aub

eFr

ance

Stie

ret

al.

(195

6)Sc

hulte

(196

6),S

puffo

rd(2

002)

Am

berg

Am

berg

,D

istric

t-Fr

eeC

ityG

erm

any

Mag

ocsi

(200

2)

Aug

sbur

gA

ugsb

urg,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Die

tze

(192

3),K

inde

ran

dH

ilgem

ann

(198

2),S

chul

te(1

966)

,Spu

fford

(200

2)Be

rlin

Berli

nG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,M

agoc

si(2

002)

,Stie

ret

al.

(195

6)Br

unsw

ickBr

auns

chwe

ig,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),

Kin

g(1

985)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

Brem

enBr

emen

,D

istric

t-Fr

eeC

ityG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,Stie

ret

al.

(195

6)D

ollin

ger

(196

6),K

inde

ran

dH

ilgem

ann

(198

2),S

puffo

rd(2

002)

Brem

erha

ven

Brem

erha

ven,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),S

tier

etal

.(1

956)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)

64

Tabl

eA

.3–

Con

tinue

dC

olog

neC

olog

ne,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),S

tier

etal

.(1

956)

Am

man

n(1

955)

,Dol

linge

r(1

966)

,H

unt

and

Mur

ray

(199

9),K

inde

ran

dH

ilgem

ann

(198

2),S

puffo

rd(2

002)

Con

stan

ceK

onst

anz

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),S

tier

etal

.(1

956)

Die

tze

(192

3),S

chul

te(1

966)

,Sp

uffor

d(2

002)

Dor

tmun

dD

ortm

und,

Dist

rict-

Free

City

Ger

man

ySt

ier

etal

.(1

956)

Dol

linge

r(1

966)

Einb

eck

Nor

thei

mG

erm

any

Stie

ret

al.

(195

6)Er

furt

Erfu

rt,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Die

tze

(192

3),K

inde

ran

dH

ilgem

ann

(198

2)Fr

ankf

urt

(Ode

r)Fr

ankf

urt

(Ode

r),

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)Fr

ankf

urt

(Mai

n)Fr

ankf

urt

amM

ain,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

,Sc

hulte

(196

6),S

puffo

rd(2

002)

Fuld

aFu

lda

Ger

man

ySt

ier

etal

.(1

956)

Gor

litz

Gor

litz,

Dist

rict-

Free

City

Ger

man

yM

agoc

si(2

002)

Spuff

ord

(200

2)

Gre

ifswa

ldG

reifs

wald

,D

istric

t-Fr

eeC

ityG

erm

any

Stie

ret

al.

(195

6)D

ollin

ger

(196

6)

Ham

burg

Ham

burg

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Am

man

n(1

955)

,Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)H

anno

ver

Reg

ion

Han

nove

rG

erm

any

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Hild

eshe

imH

ildes

heim

Ger

man

ySt

ier

etal

.(1

956)

Dol

linge

r(1

966)

Leip

zig

Leip

zig,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Am

man

n(1

955)

,Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)

65

Tabl

eA

.3–

Con

tinue

d

Lube

ckLu

beck

,D

istric

t-Fr

eeC

ityG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(1

985)

,Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Am

man

n(1

955)

,Die

tze

(192

3),

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

and

Hun

tan

dM

urra

y(1

999)

Lune

burg

Lune

burg

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)M

agde

burg

Mag

debu

rg,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

,St

ier

etal

.(1

956)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

Min

den

Min

den-

Lubb

ecke

Ger

man

ySt

ier

etal

.(1

956)

Mun

ster

Mun

ster

,D

istric

t-Fr

eeC

ityG

erm

any

Stie

ret

al.

(195

6)

Nur

embe

rgN

urem

berg

,D

istric

t-Fr

eeC

ityG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Am

man

n(1

955)

,Die

tze

(192

3),

Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Osn

abru

ckO

snab

ruck

,D

istric

t-Fr

eeC

ityG

erm

any

Stie

ret

al.

(195

6)D

ollin

ger

(196

6)

Pade

rbor

nPa

derb

orn

Ger

man

ySt

ier

etal

.(1

956)

Dol

linge

r(1

966)

Rav

ensb

urg

Rav

ensb

urg

Ger

man

ySt

ier

etal

.(1

956)

Die

tze

(192

3),S

puffo

rd(2

002)

Reg

ensb

urg

Reg

ensb

urg,

Dist

rict-

Free

City

Ger

man

yD

avie

san

dM

oorh

ouse

,Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Schu

lte(1

966)

,Sp

uffor

d(2

002)

Ros

tock

Ros

tock

,D

istric

t-Fr

eeC

ityG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,K

ing

(198

5),M

agoc

si(2

002)

,St

ier

etal

.(1

956)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

Soes

tSo

est

Ger

man

ySt

ier

etal

.(1

956)

Dol

linge

r(1

966)

66

Tabl

eA

.3–

Con

tinue

dSt

ralsu

ndSt

ralsu

nd,

Dist

rict-

Free

City

Ger

man

yM

agoc

si(2

002)

,Stie

ret

al.

(195

6)D

ollin

ger

(196

6)

Ulm

Ulm

,Urb

anD

istric

tG

erm

any

Dav

ies

and

Moo

rhou

se(2

002)

,St

ier

etal

.(1

956)

Die

tze

(192

3),K

inde

ran

dH

ilgem

ann

(198

2),S

chul

te(1

966)

,Spu

fford

(200

2)W

ismar

Wism

ar,

Dist

rict-

Free

City

Ger

man

ySt

ier

etal

.(1

956)

Dol

linge

r(1

966)

Buda

pest

Buda

pest

Hun

gary

Dav

ies

and

Moo

rhou

se(2

002)

,M

agoc

si(2

002)

,Stie

ret

al.

(195

6)Sp

uffor

d(2

002)

Pecs

Bara

nya

Hun

gary

Mag

ocsi

(200

2)A

ncon

aA

ncon

aIt

aly

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Spuff

ord

(200

2)Ba

riBa

riIt

aly

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Spuff

ord

(200

2)Bo

logn

aBo

logn

aIt

aly

Kin

g(1

985)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)Sc

hulte

(196

6)

Boze

nBo

lzan

o-Bo

zen

Ital

yM

agoc

si(2

002)

,Stie

ret

al.

(195

6)D

ietz

e(1

923)

,Kin

der

and

Hilg

eman

n(1

982)

,Sch

ulte

(196

6)C

omo

Com

oIt

aly

Stie

ret

al.

(195

6)Sc

hulte

(196

6)Fl

oren

ceFi

renz

eIt

aly

Mag

ocsi

(200

2),K

ing

(198

5),

Stie

ret

al.

(195

6)D

ietz

e(1

923)

,Kin

der

and

Hilg

eman

n(1

982)

,Hun

tan

dM

urra

y(1

999)

,Spu

fford

(200

2)G

enoa

Gen

ova

Ital

yD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),S

tier

atal

.(1

956)

Am

man

n(1

955)

,Die

tze

(192

3),

Hun

tan

dM

urra

y(1

999)

,K

inde

ran

dH

ilgem

ann

(198

2),

Schu

lte(1

966)

,Spu

fford

(200

2)Lu

cca

Lucc

aIt

aly

Stie

ret

al.

(195

6)D

ietz

e(1

923)

,Spu

fford

(200

2)M

anto

aM

anto

vaIt

aly

Mag

ocsi

(200

2)

67

Tabl

eA

.3–

Con

tinue

dM

ilan

Mila

noIt

aly

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(1

985)

,St

ier

etal

.(1

956)

Die

tze

(192

3),H

unt

and

Mur

ray

(199

9),K

inde

ran

dH

ilgem

ann

(198

2),S

chul

te(1

966)

,Spu

fford

(200

2)N

aple

sN

apol

iIt

aly

Kin

g(1

985)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)H

unt

and

Mur

ray

(199

9),

Kin

der

and

Hilg

eman

n(1

982)

,Sc

hulte

(196

6),S

puffo

rd(2

002)

Pado

aPa

dova

Ital

yM

agoc

si(2

002)

Schu

lte(1

966)

Parm

aPa

rma

Ital

yM

agoc

si(2

002)

Prat

oPr

ato

Ital

yK

ing

(198

5)Sp

uffor

d(2

002)

Rom

eR

oma

Ital

yK

ing

(198

5),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Hun

tan

dM

urra

y(1

999)

,Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Sien

aSi

ena

Ital

yK

ing

(198

5),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)Sp

uffor

d(2

002)

Tren

toTr

ento

Ital

yM

agoc

si(2

002)

Schu

lte(1

966)

Trev

isoTr

eviso

Ital

yM

agoc

si(2

002)

Schu

lte(1

966)

Udi

neU

dine

Ital

yM

agoc

si(2

002)

Veni

ceVe

nezi

aIt

aly

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(1

985)

,M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Die

tze

(192

3),H

unt

and

Mur

ray

(199

9),K

inde

ran

dH

ilgem

ann

(198

2),S

chul

te(1

966)

,Spu

fford

(200

2)Ve

rona

Vero

naIt

aly

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Schu

lte(1

966)

Kla

iped

aK

laip

edos

apsk

ritis

Lith

uani

aD

avie

san

dM

oorh

ouse

(200

2),M

agoc

si(2

002)

68

Tabl

eA

.3–

Con

tinue

dK

ovno

Kau

noap

skrit

isLi

thua

nia

Kin

g(1

985)

,Mag

ocsi

(200

2)K

inde

ran

dH

ilgem

ann

(198

2)Pa

lang

aK

laip

edos

apsk

ritis

Lith

uani

aSt

ier

etal

.(1

956)

Am

ster

dam

Gro

ot-A

mst

erda

mN

ethe

rland

sK

ing

(198

5),S

tier

etal

.(1

956)

Dol

linge

r(1

966)

,Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Dev

ente

rZu

idwe

st-O

verji

ssel

Net

herla

nds

Kin

g(1

985)

,Stie

ret

al.

(195

6)D

ollin

ger

(196

6)

Dor

drec

htZu

idoo

st-Z

uid-

Hol

land

Net

herla

nds

Kin

g(1

985)

,Stie

ret

al.

(195

6)D

ollin

ger

(196

6),

Spuff

ord

(200

2)K

ampe

nN

oord

-Ove

rjiss

elN

ethe

rland

sK

ing

(198

5)D

ollin

ger

(196

6),

Spuff

ord

(200

2)M

aast

richt

Zuid

-Lim

burg

Net

herla

nds

Stie

ret

al.

(195

6)R

otte

rdam

Gro

ot-R

ijnm

ond

Net

herla

nds

Stie

ret

al.

(195

6)U

trec

htU

trec

htN

ethe

rland

sSt

ier

etal

.(1

956)

Bran

iewo

Elbl

aski

Pola

ndSt

ier

etal

.(1

956)

Dol

linge

r(1

966)

Cra

cow

Mia

sto

Kra

kow

Pola

ndD

avie

san

dM

oorh

ouse

(200

2),

Kin

g(1

985)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Am

man

n(1

955)

,Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Elbl

agEl

blas

kiPo

land

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Kin

der

and

Hilg

eman

n(1

982)

Gda

nsk

Gda

nski

Pola

ndD

avie

san

dM

oorh

ouse

(200

2),K

ing

(198

5),M

agoc

si(2

002)

,Stie

ret

al.

(195

6)

Am

man

n(1

955)

,Die

tze

(192

3),D

ollin

ger

(196

6),

Kin

der

and

Hilg

eman

n(1

982)

,Spu

fford

(200

2)M

albo

rkSt

arog

ardz

kiPo

land

Kin

g(1

985)

Piot

rkow

Tryb

unal

ski

Piot

rkow

ski

Pola

ndD

avie

san

dM

oorh

ouse

(200

2)Pl

ock

Cie

chan

owsk

o-pl

ocki

Pola

ndM

agoc

si(2

002)

69

Tabl

eA

.3–

Con

tinue

dPo

znan

Pozn

ansk

iPo

land

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Am

man

n(1

955)

Toru

nBy

dgos

ko-T

orun

ski

Pola

ndD

avie

san

dM

oorh

ouse

(200

2),

Kin

g(1

985)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

Dol

linge

r(1

966)

,Sp

uffor

d(2

002)

War

saw

Mia

sto

War

szaw

aPo

land

Dav

ies

and

Moo

rhou

se(2

002)

,M

agoc

si(2

002)

,Stie

ret

al.

(195

6)A

mm

ann

(195

5)an

dK

inde

ran

dH

ilgem

ann

(198

2)W

rocl

awM

iast

oW

rocl

awPo

land

Dav

ies

and

Moo

rhou

se(2

002)

,Kin

g(19

85),

Mag

ocsi

(200

2),S

tier

etal

.(1

956)

Am

man

n(1

955)

,Die

tze

(192

3),

Kin

der

and

Hilg

eman

n(1

982)

,Sp

uffor

d(2

002)

Star

gard

Szcz

ecin

ski

Pola

ndSt

ier

etal

.(1

956)

Dol

linge

r(1

966)

70

Tabl

eA

.4:M

edie

valT

rade

Citi

esan

dR

egio

nsw

ithlo

ng-r

untr

ade

activ

ity

Trad

eC

ityN

UT

S-3

Reg

ion

coun

try

men

tione

dea

rlies

tby

earli

est

perio

dm

entio

ned

Linz

Linz

-Wel

sA

ustr

iaH

umni

ckia

ndBo

raw

ska

(eds

.)(1

969)

9th

cent

ury

Vie

nna

Wie

nA

ustr

iaD

ietz

e(1

923)

befo

re14

thce

ntur

yA

ntwe

rpA

rr.

Ant

werp

enBe

lgiu

mH

eyd

(189

7b)

14th

cent

ury

Brug

esA

rr.

Brug

geBe

lgiu

mH

eyd

(189

7b)

14th

cent

ury

Brno

Jiho

mor

avsk

ykr

ajC

zech

Rep

ublic

Hum

nick

iand

Bora

wsk

a(1

969)

9th

cent

ury

Olm

ouc

Olo

mou

cky

kraj

Cze

chR

epub

licH

umni

ckia

ndBo

raw

ska

(196

9)9t

hce

ntur

y

Prag

ueH

lavn

ımes

toPr

aha

Cze

chR

epub

licH

umni

ckia

ndBo

raw

ska

(196

9)9t

hce

ntur

y

Avig

non

Vauc

luse

Fran

ceH

eyd

(187

9b)

high

med

ieva

lBo

rdea

uxG

irond

eFr

ance

Dol

linge

r(1

966)

15th

cent

ury

Lim

oges

Hau

te-V

ienn

eFr

ance

Hey

d(1

879a

)be

fore

12th

cent

ury

Lyon

Rho

neFr

ance

Dol

linge

r(1

966)

15th

cent

ury

Mar

seill

eBo

uche

s-du

-Rho

neFr

ance

Hey

d(1

879a

)be

fore

10th

cent

ury

Met

zM

osel

leFr

ance

Hey

d(1

879b

)14

thce

ntur

yM

ontp

ellie

rH

erau

ltFr

ance

Hey

d(1

879a

)be

fore

12th

cent

ury

Nar

bonn

eA

ude

Fran

ceH

eyd

(187

9a)

befo

re12

thce

ntur

yPa

risPa

risFr

ance

Dol

linge

r(1

966)

15th

cent

ury

Stra

sbou

rgBa

s-R

hin

Fran

ceD

ollin

ger

(196

6)be

fore

1250

Troy

esA

ube

Fran

ceD

ietz

e(1

923)

befo

re9t

hce

ntur

yA

ugsb

urg

Aug

sbur

g,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Berli

nBe

rlin

Ger

man

yD

ollin

ger

(196

6)15

thce

ntur

y

71

Tabl

eA

.4–

Con

tinue

dBr

unsw

ickBr

auns

chwe

ig,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Brem

enBr

emen

,Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

befo

re12

thce

ntur

yBr

emer

have

nBr

emer

have

n,D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)be

fore

12th

cent

ury

Col

ogne

Col

ogne

,Dist

rict-

Free

City

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yC

onst

ance

Kon

stan

zG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Erfu

rtEr

furt

,Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

befo

re12

thce

ntur

yFr

ankf

urt

(Ode

r)Fr

ankf

urt

(Ode

r),D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)be

fore

12th

cent

ury

Fran

kfur

t(M

ain)

Fran

kfur

tam

Mai

n,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Gor

litz

Gor

litz,

Dist

rict-

Free

City

Ger

man

yRu

tkow

ski(

1980

a)14

thce

ntur

y(1

370)

Gre

ifswa

ldG

reifs

wald

,Dist

rict-

Free

City

Ger

man

yD

ietz

e(19

23)

befo

re14

thce

ntur

yH

ambu

rgH

ambu

rgG

erm

any

Dol

linge

r(1

966)

befo

re12

50H

ildes

heim

Hild

eshe

imG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yLu

beck

Lube

ck,D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)Tr

eaty

ofSm

olen

sk(1

229)

Lune

burg

Lune

burg

,Dist

rict

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Mag

debu

rgM

agde

burg

,Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

befo

re10

thce

ntur

yM

inde

nM

inde

n-Lu

bbec

keG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yM

unst

erM

unst

er,D

istric

t-Fr

eeC

ityG

erm

any

Dol

linge

r(1

966)

Trea

tyof

Smol

ensk

(122

9)N

urem

berg

Nur

embe

rg,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Osn

abru

ckO

snab

ruck

,Dist

rict-

Free

City

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Pade

rbor

nPa

derb

orn

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Reg

ensb

urg

Reg

ensb

urg,

Dist

rict-

Free

City

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yR

osto

ckR

osto

ck,D

istric

t-Fr

eeC

ityG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

ySo

est

Soes

tG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

ySt

ralsu

ndSt

ralsu

nd,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

14th

cent

ury

Ulm

Ulm

,Urb

anD

istric

tG

erm

any

Die

tze(

1923

)be

fore

9th

cent

ury

Wism

arW

ismar

,Dist

rict-

Free

City

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Buda

pest

Buda

pest

Hun

gary

Woj

tow

icz

(195

6)14

thce

ntur

y

72

Tabl

eA

.4–

Con

tinue

dA

ncon

aA

ncon

aIt

aly

Hey

d(1

879a

)be

fore

12th

cent

ury

Bari

Bari

Ital

yH

eyd

(187

9a)

befo

re12

thce

ntur

yBo

logn

aBo

logn

aIt

aly

Hey

d(1

879b

)14

thce

ntur

yFl

oren

ceFi

renz

eIt

aly

Hey

d(1

879b

)14

thce

ntur

yG

enoa

Gen

ova

Ital

yH

eyd

(187

9a)

befo

re12

thce

ntur

yLu

cca

Lucc

aIt

aly

Hey

d(1

879a

)be

fore

13th

cent

ury

Mila

nM

ilano

Ital

yH

eyd

(187

9b)

14th

cent

ury

Nap

les

Nap

oli

Ital

yH

eyd

(187

9b)

befo

re12

thce

ntur

yPa

rma

Parm

aIt

aly

Hey

d(1

879b

)14

thce

ntur

yPi

saPi

saIt

aly

Die

tze

(192

3)be

fore

14th

cent

ury

Rom

eR

oma

Ital

yH

eyd

(187

9a)

befo

re12

thce

ntur

ySi

ena

Sien

aIt

aly

Hey

d(1

879b

)13

thce

ntur

y(1

209)

Veni

ceVe

nezi

aIt

aly

Hey

d(1

879a

)be

fore

12th

cent

ury

Kov

noK

auno

apsk

ritis

Lith

uani

aD

ollin

ger

(196

6)be

twee

n13

50an

d15

00C

raco

wM

iast

oK

rako

wPo

land

Hum

nick

iand

Bora

wsk

a(1

969)

9th

cent

ury

Gda

nsk

Gda

nski

Pola

ndD

ollin

ger

(196

6)13

th–

14th

cent

ury

Mal

bork

Star

ogar

dzki

Pola

ndW

ojto

wic

z(1

956)

14th

cent

ury

Piot

rkow

Tryb

unal

ski

Piot

rkow

ski

Pola

ndW

ojto

wic

z(1

956)

14th

cent

ury

Ploc

kC

iech

anow

sko-

ploc

kiPo

land

Woj

tow

icz

(195

6)14

thce

ntur

yPo

znan

Mia

sto

Pozn

anPo

land

Woj

tow

icz

(195

6)14

thce

ntur

ySz

czec

inM

iast

oSz

czec

inPo

land

Woj

tow

icz

(195

6)14

thce

ntur

yTo

run

Bydg

osko

-Tor

unsk

iPo

land

Dol

linge

r(1

966)

13th

–14

thce

ntur

yW

arsa

wM

iast

oW

arsz

awa

Pola

ndW

ojto

wic

z(1

956)

14th

cent

ury

Wro

claw

Mia

sto

Wro

claw

Pola

ndD

ollin

ger

(196

6)13

th–

14th

cent

ury

73

B. Robustness Checks

Robustness to Influential Observations and Additional ControlsIn this appendix we report the results of several robustness checks and additional results men-tioned in the main text of the study. To be precise, in Table B.1 we re-run some specificationsfrom Table 5 and 6 in the main text, including additional control variables (a dummy variable formedieval copper mining regions, an interaction term of latitude and longitude, the country-evelshare of Catholics and a dummy for regions containing important medieval residence cities).InTable 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

Tabl

eB

.1:I

nclu

sion

ofA

dditi

onal

Con

trol

Varia

bles

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)M

odifi

edSp

ecifi

catio

nTa

ble

6co

lum

n(3

)Ta

ble

6co

lum

n(6

)Ta

ble

5co

lum

n(3

)Ta

ble

5co

lum

n(6

)Ta

ble

6co

lum

n(4

)Ta

ble

6co

lum

n(9

)Ta

ble

6co

lum

n(3

)Ta

ble

6co

lum

n(8

)

Mod

ifica

tion

Add

ing

Dum

my

for

med

ieva

lmin

ing

regi

ons

Add

ing

ain

tera

ctio

nva

riabl

eof

latit

ude

and

long

itude

Add

ing

shar

eof

Cat

holic

sin

aco

untr

yA

ddin

ga

dum

my

for

impo

rtan

tre

siden

ceci

ties

Add

ition

alVa

riabl

esig

nific

ant

No

No

Yes

No

Yes

Trad

eC

ente

r0.

181*

**0.

264*

**0.

13**

*0.

181*

**(0

.029

)(0

.031

)(0

.027

)(0

.03)

ln(D

istan

ceto

Trad

eC

ente

r)-0

.134

**-0

.291

***

-0.1

38**

*-0

.135

*(0

.053

)(0

.055

)(0

.041

)(0

.053

)

Obs

.83

983

983

983

951

851

883

983

9A

dj.

R2

0.78

40.

776

0.77

80.

762

0.87

80.

872

0.78

40.

776

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

with

inN

UT

S-1

and

NU

TS-

2re

gion

sar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

For

the

cont

rols

incl

uded

inea

chsp

ecifi

catio

nco

nsul

tth

em

ain

text

orth

eno

tes

toth

eor

igin

alta

bles

men

tione

din

the

thir

dro

w.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

75

Tabl

eB

.2:R

egre

ssio

nsof

Tabl

e5

With

out

Influ

entia

lObs

erva

tions

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)Tr

ade

Cen

ter

0.17

***

0.11

***

0.15

3***

0.11

7***

0.07

94**

(0.0

22)

(0.0

24)

(0.0

25)

(0.0

26)

(0.0

21)

ln(D

istan

ceto

Trad

eC

ente

r)-0

.108

***

-0.0

81**

-0.1

11**

*-0

.12*

**-0

.064

*(0

.038

)(0

.039

)(0

.046

)(0

.043

)(0

.038

)

Cou

ntry

Dum

mie

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sN

UT

S-1

Dum

mie

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sN

UT

S-2

Dum

mie

sYe

sYe

sYe

sYe

sN

oYe

sYe

sYe

sYe

sN

oBa

sicG

eogr

aphi

cC

ontr

ols

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

No

Geo

grap

hic

Cen

tral

ityC

ontr

ols

Yes

No

No

No

No

Yes

No

No

No

No

Reg

ion

Cha

ract

erist

ics

No

Yes

No

No

No

No

Yes

No

No

No

Hist

oric

alR

egio

nC

hara

cter

istic

No

No

Yes

No

No

No

No

Yes

No

No

Dev

elop

men

tC

ovar

iate

sN

oN

oN

oYe

sN

oN

oN

oN

oYe

sN

oA

llR

obus

tC

ontr

ols

No

No

No

No

Yes

No

No

No

No

Yes

No.

ofre

mov

edre

gion

s40

4540

4147

4045

4143

44O

bs.

799

794

799

477

771

799

794

798

475

774

Adj

.R

20.

844

0.89

10.

829

0.91

10.

901

0.83

70.

887

0.81

60.

904

0.89

9N

otes

.St

anda

rder

rors

adju

sted

for

two-

way

clus

terin

gw

ithin

NU

TS-

1an

dN

UT

S-2

regi

ons

are

repo

rted

inpa

rent

hese

s.C

oeffi

cien

tis

stat

istic

ally

diffe

rent

from

zero

atth

e**

*1%

,**5

%an

d*1

0%

leve

l.T

heun

itof

obse

rvat

ion

isa

NU

TS-

3re

gion

.T

heba

sicge

ogra

phic

cont

rols

incl

ude

are

gion

’sla

titud

e,lo

ngitu

dean

dal

titud

e.T

hege

ogra

phic

cent

ralit

yco

ntro

lsin

clud

eth

eln

dist

ance

sof

are

gion

’sce

ntro

idto

the

near

est

airp

ort,

railr

oad,

road

,bor

der

and

coas

tpo

int.

Reg

ion

char

acte

ristic

cont

rols

incl

ude

adu

mm

ies

for

regi

ons

inG

erm

any

that

are

dist

rict-

free

citie

s,fo

rre

gion

sin

clud

ing

aco

untr

y’s

capi

tal,

are

clas

sified

asm

ount

ain

regi

ons,

with

ore

orco

alm

ines

,lo

cate

din

the

form

erG

DR

and

loca

ted

inan

East

ern

Euro

pean

post

-com

mun

istic

tran

sitio

nco

untr

y.Fu

rthe

rmor

eit

enco

mpa

sses

the

lnof

are

gion

sar

ea.

The

hist

oric

alre

gion

char

acte

ristic

sco

nsist

ofa

dum

my

varia

bles

indi

catin

gre

gion

sw

itha

univ

ersit

yfo

unde

dbe

fore

1500

AD

,tha

tad

opte

dpr

intin

gte

chno

logy

befo

re15

00A

D,c

onta

inci

tiest

hatw

ere

mem

bers

ofth

eH

anse

atic

Leag

ue,w

ithfo

rmer

impe

rialc

ities

and

wer

elo

cate

don

anim

peria

lroa

d.M

oreo

veri

tinc

lude

sthe

lnof

the

dist

ance

ofa

regi

on’s

cent

roid

toW

itten

berg

.T

hegr

owth

cova

riate

senc

ompa

ssa

regi

on’s

unem

ploy

men

tra

te,n

umbe

rof

regi

ster

edpa

tent

s,av

erag

efir

mln

fixed

capi

tals

tock

,ave

rage

wor

ker

com

pens

atio

n.Fu

rthe

rmor

e,it

incl

udes

the

shar

eof

peop

leag

edbe

twee

n25

-64

with

tert

iary

educ

atio

non

NU

TS-

2le

vel,

the

qual

ityof

gove

rnm

ent

inde

xon

NU

TS-

1/N

UT

S-2

leve

land

the

ratio

ofan

aver

age

wor

kers

com

pens

atio

nto

are

gion

’sG

DP

per

capi

taas

ineq

ualit

ym

easu

re.

The

set

ofal

lrob

ust

cova

riate

sen

com

pass

esal

titud

e,th

eln

dist

ance

sto

airp

orts

and

railr

oads

,dum

mie

sfo

rdi

stric

tfr

eeci

ties,

capi

talc

ities

,cap

italc

ities

ofau

tono

mou

sre

gion

s,po

st-c

omm

unist

ictr

ansit

ion

coun

trie

s,Ea

ster

nG

erm

any,

the

lnof

are

gion

’sar

ea,t

hesh

are

ofpe

ople

with

tert

iary

educ

atio

n,th

ein

equa

lity

mea

sure

and

the

prin

ting

pres

sbe

fore

1500

AD

dum

my.

Are

gion

isre

mov

edfr

omth

ees

timat

ion

ifits

DFI

TS

valu

eis

abov

eth

ecu

t-off

of|D

FIT

Sj|>

2√k

\N(w

ithk

indi

catin

gth

enu

mbe

rofr

egre

ssor

san

dN

deno

ting

the

num

bero

fobs

erva

tions

inth

esa

mpl

e).

Each

regr

essio

nin

clud

esa

cons

tant

not

repo

rted

.

76

Results for Alternatively Coded Medieval Trade VariablesIn Tables B.3 and B.6 we conduct the OLS, IV and mediation analysis estimations with alter-natively 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 morethan one of the sources. In Table B.4 we redo this estimations this time excluding cities for whichthe actual importance in trade is in doubt. To continue, in Table B.5 we repeat this, using theoriginal sample and include additional cities for which we think they might be important, albeitthey are not mentioned by our main sources. At last, in Table B.6 we show the results for asample of trade cities that only includes cities for which historical sources indicate long-run tradeactivities (i.e. cities that are important trade cities around 1500 AD and that were important alsoin the period before). An overview over these cities the earliest period in which trade activitiesare reported and the source mentioned the respective city are depicted in Table A.4.

77

Tabl

eB

.3:R

esul

tsfo

rA

ltern

ativ

eTr

ade

Cen

ter

Dum

my

–W

ithou

tR

egio

nsM

entio

ned

byO

nly

One

Sour

ce

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)ln

(City

Gro

wth

)ln

(Rel

ativ

eG

DP

Den

sity)

ln(G

DP

per

capi

ta)

(1)

(2)

(3)

(4)

(5)

(6)

Met

hod

OLS

LIM

LIV

Lew

bel(

2012

)O

LSM

edia

tion

Ana

lysis

Estim

ated

Equa

tion

Equa

tion

(6)

Equa

tion

(7)

Estim

ated

Spec

ifica

tion

Tabl

e5

Col

umn

(5)

Tabl

e6

Col

umn

(1)

Tabl

e6

Col

umn

(2)

Tabl

e8

Col

umn

(1)

Tabl

e9

Col

umn

(4)

Tabl

e9

Col

umn

(4)

Trad

eC

ente

r0.

0543

**0.

363*

**0.

0613

**0.

479*

*0.

3267

***

-0.0

0912

(0.0

225)

(0.1

33)

(0.0

260)

(0.2

32)

(0.0

71)

(0.0

181)

ln(R

elat

ive

GD

PD

ensit

y)0.

203*

**(0

.010

9)

Obs

.81

881

881

886

818

818

Cen

tere

dR

2\R

20.

877

0.53

40.

629

0.34

40.

938

0.92

4A

CM

E0.

0654

Dire

ctEff

ect

-0.0

085

Tota

lEffe

ct0.

0569

%of

tota

lmed

iate

d11

2.7

Und

erid

entifi

catio

nTe

st14

.45

173.

3p-

valu

e0.

000

0.00

0O

verid

entifi

catio

nTe

st0.

000

61.0

9p-

valu

e1.

000

0.23

6A

PF-

stat

istic

ofex

clud

edIV

’s8.

2746

.53

p-va

lue

0.00

00.

000

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

with

inN

UT

S-1

and

NU

TS-

2re

gion

sar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

For

the

cont

rols

incl

uded

inea

chsp

ecifi

catio

nco

nsul

tth

em

ain

text

orth

eno

tes

toth

eor

igin

alta

bles

men

tione

din

the

thir

dro

w.

Inco

lum

ns(1

)an

d(2

)th

ead

just

edR

2is

repo

rted

.In

colu

mn

(3)

and

(4)

the

cent

ered

R2

issh

own

and

inco

lum

ns(5

)an

d(6

)th

eR

2.

Inco

lum

n(3

)th

ere

sults

ofth

efir

stst

age

are

omitt

edbu

tav

aila

ble

from

the

auth

or.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

78

Tabl

eB

.4:R

esul

tsfo

rA

ltern

ativ

eTr

ade

Cen

ter

Dum

my

–C

ities

with

Unc

erta

inIm

port

ance

Rem

oved

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)ln

(City

Gro

wth

)ln

(Rel

ativ

eG

DP

Den

sity)

ln(G

DP

per

capi

ta)

(1)

(2)

(3)

(4)

(5)

(6)

Met

hod

OLS

LIM

LIV

Lew

bel(

2012

)O

LSM

edia

tion

Ana

lysis

Estim

ated

Equa

tion

Equa

tion

(6)

Equa

tion

(7)

Estim

ated

Spec

ifica

tion

Tabl

e5

Col

umn

(5)

Tabl

e6

Col

umn

(1)

Tabl

e6

Col

umn

(2)

Tabl

e8

Col

umn

(1)

Tabl

e9

Col

umn

(4)

Tabl

e9

Col

umn

(4)

Trad

eC

ente

r0.

0670

***

0.37

5***

0.07

3***

0.46

8*0.

3621

***

-0.0

036

(0.0

23)

(0.1

4)(0

.027

)(0

.235

)(0

.074

)(0

.02)

ln(R

elat

ive

GD

PD

ensit

y)0.

203*

**(0

.011

)

Obs

.81

881

881

886

818

818

Cen

tere

dR

2\R

20.

877

0.54

40.

621

0.34

20.

939

0.91

9A

CM

E0.

0724

***

Dire

ctEff

ect

-0.0

029

Tota

lEffe

ct0.

0694

**%

ofto

talm

edia

ted

102.

8**

Und

erid

entifi

catio

nTe

st15

.18

160.

2p-

valu

e0.

000

0.00

0O

verid

entifi

catio

nTe

st0.

008

58.4

1p-

valu

e0.

930.

317

AP

F-st

atist

icof

excl

uded

IV’s

8.57

43.9

2

p-va

lue

0.00

00.

000

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

with

inN

UT

S-1

and

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TS-

2re

gion

sar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

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the

cont

rols

incl

uded

inea

chsp

ecifi

catio

nco

nsul

tth

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ain

text

orth

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tes

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igin

alta

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men

tione

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dro

w.

Inco

lum

ns(1

)an

d(2

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ead

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edR

2is

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rted

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colu

mn

(3)

and

(4)

the

cent

ered

R2

issh

own

and

inco

lum

ns(5

)an

d(6

)th

eR

2.

Inco

lum

n(3

)th

ere

sults

ofth

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age

are

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ble

from

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auth

or.

Eac

hre

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sion

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udes

aco

nsta

ntno

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port

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79

Tabl

eB

.5:R

esul

tsfo

rA

ltern

ativ

eTr

ade

Cen

ter

Dum

my

–C

ities

with

Unc

erta

inIm

port

ance

Add

ed

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)ln

(City

Gro

wth

)ln

(Rel

ativ

eG

DP

Den

sity)

ln(G

DP

per

capi

ta)

(1)

(2)

(3)

(4)

(5)

(6)

Met

hod

OLS

LIM

LIV

Lew

bel(

2012

)O

LSM

edia

tion

Ana

lysis

Estim

ated

Equa

tion

Equa

tion

(6)

Equa

tion

(7)

Estim

ated

Spec

ifica

tion

Tabl

e5

Col

umn

(5)

Tabl

e6

Col

umn

(1)

Tabl

e6

Col

umn

(2)

Tabl

e8

Col

umn

(1)

Tabl

e9

Col

umn

(4)

Tabl

e9

Col

umn

(4)

Trad

eC

ente

r0.

0686

***

0.32

4***

0.07

65**

*0.

544*

*0.

3268

***

0.00

38(0

.021

)(0

.114

)(0

.024

)(0

.225

)(0

.62)

(0.0

16)

ln(R

elat

ive

GD

PD

ensit

y)0.

202*

**(0

.011

)

Obs

.81

881

881

886

818

818

Cen

tere

dR

2\R

20.

878

0.55

20.

623

0.35

80.

939

0.91

9A

CM

E0.

0652

***

Dire

ctEff

ect

0.00

44To

talE

ffect

0.06

96**

*%

ofto

talm

edia

ted

93.3

***

Und

erid

entifi

catio

nTe

st13

.03

203.

9p-

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e0.

001

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verid

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catio

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st0.

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70.5

6p-

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e0.

661

0.06

5A

PF-

stat

istic

ofex

clud

edIV

’s7.

6056

.93

p-va

lue

0.00

10.

000

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

with

inN

UT

S-1

and

NU

TS-

2re

gion

sar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

For

the

cont

rols

incl

uded

inea

chsp

ecifi

catio

nco

nsul

tth

em

ain

text

orth

eno

tes

toth

eor

igin

alta

bles

men

tione

din

the

thir

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w.

Inco

lum

ns(1

)an

d(2

)th

ead

just

edR

2is

repo

rted

.In

colu

mn

(3)

and

(4)

the

cent

ered

R2

issh

own

and

inco

lum

ns(5

)an

d(6

)th

eR

2.

Inco

lum

n(3

)th

ere

sults

ofth

efir

stst

age

are

omitt

edbu

tav

aila

ble

from

the

auth

or.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

80

Tabl

eB

.6:R

esul

tsfo

rA

ltern

ativ

eTr

ade

Cen

ter

Dum

my

–O

nly

Citi

esw

ithLo

ng-R

unTr

ade

Act

ivity

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)ln

(City

Gro

wth

)ln

(Rel

ativ

eG

DP

Den

sity)

ln(G

DP

per

capi

ta)

(1)

(2)

(3)

(4)

(5)

(6)

Met

hod

OLS

LIM

LIV

Lew

bel(

2012

)O

LSM

edia

tion

Ana

lysis

Estim

ated

Equa

tion

Equa

tion

(6)

Equa

tion

(7)

Estim

ated

Spec

ifica

tion

Tabl

e5

Col

umn

(5)

Tabl

e6

Col

umn

(1)

Tabl

e6

Col

umn

(2)

Tabl

e8

Col

umn

(1)

Tabl

e9

Col

umn

(4)

Tabl

e9

Col

umn

(4)

Trad

eC

ente

r0.

0568

**0.

320*

**0.

0743

**0.

123

0.32

18**

*-0

.006

1(0

.027

)(0

.112

)(0

.032

)(0

.253

)(0

.088

)(0

.024

)ln

(Rel

ativ

eG

DP

Den

sity)

0.20

2***

(0.0

11)

Obs

.81

881

881

886

818

818

Cen

tere

dR

2\R

20.

877

0.57

40.

620.

305

0.93

80.

919

AC

ME

0.06

41**

*D

irect

Effec

t0.

0053

Tota

lEffe

ct0.

0588

**%

ofto

talm

edia

ted

105.

0**

Und

erid

entifi

catio

nTe

st14

.84

140.

80p-

valu

e0.

001

0.00

0O

verid

entifi

catio

nTe

st0.

406

65.7

p-va

lue

0.52

40.

132

AP

F-st

atist

icof

excl

uded

IV’s

9.16

78.5

4

p-va

lue

0.00

10.

000

Not

es.

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dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

with

inN

UT

S-1

and

NU

TS-

2re

gion

sar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

For

the

cont

rols

incl

uded

inea

chsp

ecifi

catio

nco

nsul

tth

em

ain

text

orth

eno

tes

toth

eor

igin

alta

bles

men

tione

din

the

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dro

w.

Inco

lum

ns(1

)an

d(2

)th

ead

just

edR

2is

repo

rted

.In

colu

mn

(3)

and

(4)

the

cent

ered

R2

issh

own

and

inco

lum

ns(5

)an

d(6

)th

eR

2.

Inco

lum

n(3

)th

ere

sults

ofth

efir

stst

age

are

omitt

edbu

tav

aila

ble

from

the

auth

or.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

81

Description and Sources of the Additional VariablesResidence 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 wikipedialist at http://de.wikipedia.org/wiki/Residenzstadt (accessed February, 24th 2013) andKobler (1988). It also includes residences of electors (“Kurfursten”) and prince-bishoprics.Furthermore, it represents the capitals or residence cities of Italian duchies, kingdoms andrepublics (like Venice, Lombardy, Sardinia, Parma, Modena, Tuscany, Naples or the Kingdomof the two Sicilies). For all other countries it marked the capitals of pre-existing states orkingdoms, duchies etc. (e.g. in Poland it includes the residence of the kings of the Kingdom ofPoland, in Lithuania the residence of the grand duke of Lithuania. . . ). The coding here followsthe author’s information or different versions of Putzgers historical atlas (Bruckmuller (eds.)2011 and Baldamus et al. (eds.) 1914).Share of Catholics. The share of people with Roman Catholic denomination (in percentof 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 atMay, 8th 2013). As always, we took the values from 2009.

An overview over the additional variables used for the robustness checks is provided in TableB.6 above:

Table B.7: Descriptive Overview over the Additional Variables

Variable Obs Mean Std. Dev. Min MaxLatitude*Longitude 839 507.123 253.213 -197.378 1401.973Residence City 839 0.067 0.25 0 1Share of Catholics 839 49.623 22.29 26.85 89.15

82

C. Additional ResultsHere the result of the estimation of Table 9 using the ln population density of a NUTS-3 region asmediating agglomeration measure is shown. The results are almost identical to that obtained withthe relative GDP density. However, the probably biggest difference between both estimationsis that the average ACME using the population density is clearly lower. Neverthless, since it isalways significant and on average around three quarters of the effect of medieval trade on ln GDPper capita is mediated by the ln population density our main conclusion does hold. Furthermorewe report the results of estimating Table 8 using the Index of Commercial Importance insteadof the trade city dummy (Table C.2). We see that the result are a little bit weaker (especiallyconcerning the results for city growth between 1200 and 1500 AD). Nevertheless, the overallresults 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) (2) (3) (4) (5) (6)

Method OLS Mediation AnalysisCity Growth from to 1200–1500 1300–1500 1400–1500 Equation (7)Dep. Var. ln(Population Density) ln(GDP per capita)

P opulation1500P opulationt

0.337*** 0.178*** 0.172***(0.105) (0.067) (0.062)

ln(Population Density) 0.135*** 0.139*** 0.137***(0.015) (0.015) (0.015)

Trade Center 0.0308(0.019)

ln(Distance to Trade Center) -0.007(0.027)

Commercial Importance 0.0067(0.008)

R2 0.964 0.955 0.947 0.889 0.888 0.888ACME 0.0405*** -0.0605*** 0.0178***Direct Effect 0.0314 -0.0062 0.0067Total Effect 0.0719*** -0.0667** 0.0247***% of total mediated 55.7*** 90.0** 70.8***

Equation (6)ln(Relative GDP Density)

Trade Center 0.3043***(0.053)

ln(Distance to Trade Center) -0.4313***(0.108)

Commercial Importance 0.1318***(0.019)

Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes Yes Yes

Obs. 85 179 197 818 818 818R2 0.867 0.87 0.87

Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different fromzero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The set ofall robust covariates encompasses altitude, the ln distances to airports and railroads, dummies fordistrict free cities, capital cities, capital cities of autonomous regions, post-communistic transitioncountries, 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 aconstant not reported. ACME is the “Average Causal Mediation Effect” and means how much of theeffect of medieval trade is mediate, i.e. works indirectly through the relative GDP density.

84

Tabl

eC

.2:M

edie

valT

rade

Act

ivity

and

City

Gro

wth

-Est

imat

ions

usin

gth

eIn

dex

ofC

omm

erci

alIm

port

ance

Dep

.Va

r.ln

(Po

pu

lati

on

1500

Po

pu

lati

on

1200

)ln

(Po

pu

lati

on

1500

Po

pu

lati

on

1300

)ln

(Po

pu

lati

on

1500

Po

pu

lati

on

1400

)ln

(Pop

ulat

ion)

ln(∆

Popu

latio

n)(1

)(2

)(3

)(4

)(5

)M

etho

dO

LSR

E

Com

mer

cial

Impo

rtan

ce0.

301*

0.26

6***

0.10

50.

394*

**0.

156*

**(0

.155

)(0

.084

)(0

.093

)(0

.065

)(0

.052

)ln

(Pop

ulat

ion

1200

AD

)-0

.605

***

(0.1

48)

ln(P

opul

atio

n13

00A

D)

-0.6

07**

*(0

.069

)ln

(Pop

ulat

ion

1400

AD

)-0

.362

***

(0.0

76)

ln(P

opul

atio

n t−

1)-0

.416

***

(0.0

5)

Obs

.86

199

180

826

390

Adj

.R

2 \ov

eral

lR2

0.34

60.

381

0.17

30.

344

0.26

Num

ber

ofC

lust

ers

361

194

Not

es.

Rob

ust

stan

dard

erro

rsar

ere

port

edin

pare

nthe

ses

inco

lum

ns(1

)-(

3).

Stan

dard

erro

rscl

uste

red

atci

tyle

vela

rere

port

edin

pare

nthe

ses

inco

lum

ns(4

)an

d(5

).C

oeffi

cien

tis

stat

istic

ally

diffe

rent

from

zero

atth

e**

*1%

,**5

%an

d*1

0%

leve

l.T

heun

itof

obse

rvat

ion

isa

city

.T

hese

tof

cova

riate

sen

com

pass

esth

eln

dist

ance

sof

aci

tyto

the

next

river

orco

ast,

dum

mie

sin

dica

ting

citie

sth

atw

ere

resid

ence

ofa

bish

opbe

fore

1000

AD

,had

the

stat

usof

anim

peria

lcity

,wer

elo

cate

dat

am

ain

impe

rialr

oad,

wer

em

embe

rof

the

Han

seat

icLe

ague

orar

ecl

assifi

edas

am

ount

ain

regi

onby

the

EUre

gion

alst

atist

ics.

Furt

herm

ore,

we

cont

rolf

ora

city

’sla

titud

ean

dlo

ngitu

dean

din

clud

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untr

yfix

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ects

.In

colu

mns

(4)

and

(4)

we

addi

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llyin

clud

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arfix

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ects

.Ea

chre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

85

References

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Humnicki, J, Borawska, D. (1969): “Slowianszczyzna Zachodnia w IX w”. In: Lowmi-anski, H. (ed.). Historia Polski: Tom 1 do roku 1764, czesc III - Chronologia, Bib-liografia, Indexy, Tablice Genealogiczne, Mapy. Panstwowe Wydawnictwo Naukowe.

Hunt, E. S., Murray, J. M. (1999): A History of Business in Medieval Europe 1200 -1500. Cambridge University Press, Cambridge UK.

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