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Discussion Paper 82-2013

DOES MEDIEVAL TRADE STILL MATTER? HISTORICAL TRADE CENTERS,

AGGLOMERATION AND CONTEMPORARY ECONOMIC DEVELOPMENT

Fabian Wahl

Universität Hohenheim | Forschungszentrum Innovation und Dienstleistung

www.fzid.uni-hohenheim.de

Discussion Paper 82-2013

Does Medieval Trade Still Matter?

Historical Trade Centers,

Agglomeration and Contemporary

Economic Development

Fabian Wahl

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Does Medieval Trade Still Matter? Historical Trade Centers,Agglomeration and Contemporary Economic Development

Fabian Wahl∗

University of Hohenheim

November 3, 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 commercial activities and regional economic de-velopment today. Further empirical analyses show that medieval trade positivelyinfluenced city development both during the medieval period and in the long run;they also reveal a robust connection between medieval city growth and contemporaryregional agglomeration and industry concentration. A mediation analysis indicatesthat a long-lasting effect of medieval trade on contemporary regional development isindeed transmitted via its effect on agglomeration and industry concentration. Thisresearch thus highlights the long-run importance of medieval trade in shaping thedevelopment of cities as well as the contemporary spatial distribution of economicactivity throughout Europe. The path-dependent regional development processescaused by medieval commercial activities help explain the observed persistent re-gional development differences across the European countries considered.

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. [email protected]. The author would like to thankthe participants of the 8th EHES Summer School at the University Carlos III. Madrid and of theEHES Conference 2013 at the London School of Economics. Furthermore, he is indebted to Basvan Bavel,T. Matthew Ciolek, Sibylle Lehmann, Alexander Opitz, Ulrich Pfister, Nadine Riedel, Al-fonso Sousa-Poza, Oliver Volckart and Nicole Waidlein for their helpful comments, discussions andsuggestions. Additionally, he thanks Maarten Bosker for sharing his data.

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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, in which the authors investigate the impact of long-distance overseastrade for institutional developments and the pre-industrial development process acrossEuropean countries.

Hence, until now there is no study exploring the possible long-lasting effects of tradeand commerce in European cities during the High and Late Middle Ages. The impor-tance of medieval trade for the development of cities and regions in the Middle Agesand the following centuries is well-known and widely accepted. Apart from this, noresearch has acknowledged the fact that medieval trade might also have long-term influ-ences on regional development persisting until today; this despite the fact that medievaltrade, through its potential impact on agglomeration and spatial concentration of in-dustry, could have led to path-dependent regional development processes resulting indevelopmental differences surviving over the centuries.1

The aim of this study is to investigate whether medieval trade, as a result of its impacton agglomeration, has caused differences in regional development that remain visible to-day. If this is the case, it could provide a new explanation for the uneven distributionof economic activity and significant spatial concentration of industries throughout Eu-rope (e.g., Chasco et al. 2012, Koh and Riedel 2012, Roos 2005). Furthermore, it cancontribute to the understanding of the persistent differences in regional economic devel-opment (Becker et al. 2010, Maseland 2012, Tabellini 2010 or Waidlein 2011). Finally,this study contributes to a growing literature reporting on the persistence and path-dependent nature of spatial equilibria (e.g., in industry concentration) and city growthprocesses (Bosker et al. 2007, Bleakly and Lin 2012, Davis and Weinstein 2002, Davisand Weinstein 2008, Miguel and Roland 2011 and Redding et al. 2011). To establisha link between medieval trade, agglomeration and contemporary performance we link

1Van Zanden (2008) provides evidence for the medieval origins of the “Great Divergence”. Thus, hisstudy give rise to the conjecture that medieval developments, e.g. institutional differecnes betweenEurope and Southeast Asia had long lasting effects and persisted over centuries. In what follows, wewill make similar arguments for medieval trade activities.

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the typical characteristics of medieval trade and cities to the determinants of agglom-eration suggested by New Economic Geography (NEG) and agglomeration economics(e.g., Krugman 1991, Glaeser et al. 1992). In a second step, based on studies combin-ing NEG, endogenous growth models, and the theory of path-dependence (David 2007),we propose a positive connection between agglomeration, industrial concentration andcontemporary development.

Afterwards, we test the causal chain from medieval trade through agglomeration andon to contemporary regional economic development by using rich regional and city leveldata sets and a wide range of empirical methods. In this empirical investigation we usethree variables to capture medieval trade activities. First, based on several historicalsources and trade route maps, we construct a dummy variable identifying cities thatwere important centers of trade and commerce during the medieval period. Second, wecalculate a variable that shows the distance between each region or city and the closestof these trade cities. This variable enables us to test whether trade activities lead to theemergence of spatial core-periphery patterns as implied by our theoretical expectations.Third, we build an index of the medieval commercial importance of a city. Based onhistorically and empirically important predictors of medieval trade activities, this indexis therefore able to provide a more complete and differentiated account of regional andurban medieval economic activities.

The results of the empirical estimations provide strong evidence for a significant re-lationship between medieval trade and contemporary regional economic performance.Furthermore, a detailed empirical investigation on city level shows that medieval tradeand commercial activities are robustly positively associated with city development bothduring the medieval period and in the long run. Therefore, the observed path-dependentdevelopment process of European cities is partly rooted in the persistent effect of tradeand commercial activity in the Middle Ages. Moreover, we also find that the effect ofmedieval trade on contemporary regional development can be explained by its influenceon agglomeration patterns. This is shown by empirically establishing a link betweencity development in the medieval period and the regional industry concentration andagglomeration patterns of the present day. Finally, a mediation analysis reveals thatmedieval trade activities are strong direct predictors of today’s spatial distribution ofeconomic activity and population. It also enables us to demonstrate that the influenceof medieval trade on contemporary regional GDP per capita is wholly attributable tothe “agglomeration effect” of medieval trade.

Importantly, we show that our hypotheses are robust to the inclusion of many geo-graphical, political, economical and historical covariates of development and agglomer-

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ation, as well as different samples, data sets and medieval trade measures; we also showthey are not biased by endogeneity issues.

The remainder of the article proceeds as follows. First, we theoretically establish thelink between medieval trade, agglomeration and the 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. Finally, 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 me-dieval cities and the revival of city growth during the period of the so called “CommercialRevolution” (e.g., Borner and Severgnini 2012, Epstein 2000, Habermann 1978, Holt-frerich 1999, King 1985, Postan 1952, Pounds 2005 and van Werveke 1952). Historyprovides many examples of cities owing their importance primarily to their function ascenters of trade, such as the German cities of Nuremburg (Nicholas 1997), Frankfurt(Holtfrerich 1999) or Cologne (King 1985) or the Polish city of Gdansk.2

Using concepts developed by NEG (Krugman 1991) and agglomeration economics, onecan explain why medieval trade was important for the rise of cities in medieval Europe.This is achieved by linking the characteristics of medieval trade and trade cities to sec-ond 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).3 For instance, the Southern German cities that became important trade

2 Obviously, there are exceptions to this story, i.e. cities and regions becoming large and importantagglomerations without being important centers of medieval trade. For example, this is true forStuttgart (the sixth largest German city today) and Munich two of the richest and economicallymost prosperous cities and agglomeration areas in present day’s Germany. Stuttgart only becameimportant after the Napoleonic Wars when it became the capital of the newly founded kingdomof Wurttemberg. The rise of Munich (today the third largest city in Germany) followed a similarpattern, albeit as the capital of a kingdom and residence of a bishop for a longer period (and laterarchbishop) Munich only began to become a large city after the late 18th century. Again, it experi-enced significant population growth in the nineteenth century after the Napoleonic Wars until WorldWar I. Bavaria, and Munich as its center, remained relatively poor until the 1950s (when, e.g., theSiemens corporation moved its headquarter from Berlin to Munich). Additionally, the Ruhr Area, thelargest agglomeration in Germany, largely results from its rich endowments in coal and iron makingit one of the most important nucleus of German industrialization.

3A comprehensive illustration of medieval trade activities is provided in Postan (1952) and Lopez(1952).

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centers in the later medieval era specialized in textiles (Barchent etc.) and paper produc-tion. Other areas had specialized in mining (for example, the Saxon town of Freiberg,or Liege in today’s Belgium (which had the most productive coal fields), or in food andsalt (of which the cities on the French Atlantic coast were the main exporters). Thedifferent regions exported what they specialized in —or had a comparative advantagein, e.g., due to natural resources— and imported what they did not have themselves.4

This specialization of trade cities on a particular industry or sector gave rise to the exis-tence of technological (non-pecuniary) externalities like Marshall-Arrow-Romer (MAR)externalities (Marshall 1890, Romer 1986) or Porter externalities.Furthermore, this em-phasizes the close connection between industrial production and commercial activitiesthat was typical for the medieval urban economy. 5 Those types of externalities ariseas knowledge spillovers between firms in the same industry and therefore contribute tothe growth of both industry and city (Glaeser et al. 1992).6 Indeed Epstein (1998), andmore broadly Epstein and Prak (2008) show that the guild as the dominant economic in-stitution of the later medieval city could have fostered innovation and enabled knowledgespillovers and diffusion within the urban economy (and also through migration betweencities).7

A second important characteristic of medieval trade cities was the comparatively highvariety of goods that were available. Those assortments of goods were available firstat the local markets, then at the large trade fairs in the Champagne region and otherimportant trade cities (such as Frankfurt, Cologne, Ulm, etc.), and then, in the latemedieval age, in the branches and kontors of the Hanseatic League and trading companies(“super-companies”) like the Fugger in Augsburg.8 The latter two in particular alsosupplied luxury goods and exotic commodities from the Far East, as long-distance trade

4A review of the general geographical patterns of trade and industry specialization in the Middle Agesis provided, among others, by King (1985).

5Nicholas (1997) additionally points to the fact that over the course of the Middle Ages the industrydominating in a city, e.g. the textile industry, increasingly diversified. This intra-industry diversifi-cation could be an additional channel through which technological externalities could have arisen.

6Such knowledge spillovers between firms might appear because of imitations, or from the transfer ofskilled workers between different firms within the industry etc.

7For evidence about the high mobility of skilled craftsmen in this period, see Reith (2008). Of course,among historians there is little consensus about the role of the guilds and whether they had neg-ative or positive effects for economic development. However, the more recent contributions haveclearly offered evidence that guilds had significant positive impacts through their positive influenceon innovativeness.

8For a detailed description of the business activities of the Hanseatic League, see Dollinger (1966). Adiscussion of the early medieval markets and fairs is found in van Werveke (1963). A comprehensivedescription of the medieval super companies is provided in Hunt and Murray (1999). A transactioneconomic analysis of the super companies (using the example of the Fugger Company) is providedby Borner (2002).

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was reestablished at the beginning of the Late Middle Ages. We can consider this highvariety of goods as an important demand-side driven agglomeration force, as it makes acity more attractive to settle in.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—higher 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 wagesand therefore resulted in the migration of even more workers to the city. Consequently,this pecuniary externality (forward linkage) resulted in increased agglomeration andindustry concentration in the city. In addition, more workers led to a higher demandfor goods produced and/or traded in the city. The higher demand once more led 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

Furthermore, as the process of agglomeration lasted for some time, other kinds 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 the

9This follows clearly from the love of variety preferences commonly assumed in NEG models. Addi-tionally, one can make a transaction cost argument: as living in a city means 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, sincethe 13th century something akin to market integration (to some extent) existed with prices varyingin the markets of cities every week according to supply and demand. Furthermore, the increasingspread of the “Verlagssystem” might have limited the power of the guilds. Concerning the urbanrural wage differential, evidence in general is limited for this period. Braudel (1986) notes that, ingeneral, and due to the power of guilds, the wages in the city can usually be considered as higher thanthose in rural areas. Indeed Munro (2002), when comparing the real wages in England and Flandersbetween 1300 and 1500, found that the real wages in the cities were higher than in rural areas andshowed a higher downward rigidity. In addition, van Bavel and van Zanden (2004) notice that inpre-industrial societies the relationship between city size and nominal wages was usually positive.

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15th century) the traditional textiles industry was supplemented by paper production atthe beginning of the 15th century (Schelle 2000). In addition, there were also incentivesto be located in a trade city for firms using special commodities as inputs or firms thatproduced inputs used in the industry the city was specialized in.11 Therefore, Jacobsexternalities (Jacobs 1969) also occurred in the late medieval cities.12

However, the main argument of this paper is that medieval trade had significant conse-quences for 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 remain evident today. Citiesthat were involved in medieval trade activities over a sufficient period of time becamelocked onto a superior development path by comparison to other cities without that his-tory. This is a typical characteristic of processes caused by increasing returns or positivefeedback (David 2007). Several studies (e.g., Bosker et al. 2007 and Davis and Wein-stein 2002, 2008) show that city growth (and therefore also city size) is characterized bya long-run persistence that is immune even to such shocks as the Second World War.Thus, there is a fair amount of empirical evidence pointing towards the path-dependentcharacter of agglomeration processes and city development. In addition, there are nu-merous examples of historical events and phenomena with long-run impacts on economicdevelopment. For example: Colonization (e.g., Acemoglu et al. 2001, 2002); the SlaveTrade (Nunn 2008, 2011); gender roles (Alesina et al. 2013); the Neolithic revolution(e.g., Ashraf and Galor 2011, Olsson and Hibbs 2005 or Putterman 2008); the capacityto adopt and develop new technologies (Comin et al. 2010); or the timing of humansettlement (Ahlerup and Olsson 2012).13 We argue that medieval trade can be addedto the above list. Additionally, Maseland (2012) shows that regional development dis-parities in Germany are persistent and are largely explained by strong and increasingdifferences between core areas and the periphery.

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 such as those of Hohenberg and

11The 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 causing path-dependent developments is Nunn (2009).

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Lees (1995) or Fujita and Thisse (2002) also establish empirically the positive relationshipbetween agglomeration 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 that where notinvolved in 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,we base our empirical analysis on regional level data. We adhere to the NUTS (“Nomen-clature of Units for Territorial Statistic”) regional classification, the official regional ref-erence 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 Wechoose 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 example,they are mostly identical to districts or district-free cities, in France to Departments14A detailed description and overview of the NUTS classification scheme and the regions is given in the

Data Appendix and in 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, there are exceptions: some NUTS-3 regions show a larger population.From this it also follows that more densely populated regions cover on average a smaller area. Toovercome potential biases resulting from this, we will control for the area of a region as well asa country’s average region size and introduce dummy variables for city districts, city states anddistrict-free cities (regions with a high population density, i.e. a large population but a small area).

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and in Italy to Provinces. Potential bias, resulting from considering regions rather thanactual cities that were subject to medieval trade, is limited as heterogeneity withinNUTS-3 regions should not be of significant size. However, some control variables areavailable only at NUTS-2 or NUTS-1 level. In these cases, we include the respectivevariables at the level at which they are provided. Another advantage of adhering to theNUTS classification is that it facilitates the use of fixed effects for the different NUTS-levels (countries, federal states etc.). This allows us to appropriately handle all kindsof heterogeneity on country and regional levels. Furthermore, one can also account forcross-sectional and spatial dependence among the regions in the data set. The latterbeing an important advantage of regional empirical analyses, especially when comparedto 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 also come from theyear 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 Additionally, we present results using the ln of a regions populationdensity in 2009 as a more general measure of agglomeration, i.e. as a variable identifyingmore densely populated places. We hold that the relative GDP Density is a more directmeasure of industry agglomeration and concentration and is therefore should more suit-able for our empirical analysis. However, population density could capture additionalaspects of agglomeration that might be important for economic activities indirectly andtherefore can provide additional insights.

Table A.1 in the Data Appendix gives a descriptive overview of all variables used inthe following empirical analysis. The exact sources and further explanations of thesevariables are also provided.

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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3.3 Independent Variables

This study aims to investigate the impact of trade between cities during the medieval age.To be able to identify the theoretically assumed effect of medieval trade on agglomerationwe focus on the most important trade cities, i.e. the cities where trade was likely to havehad the most powerful and long-lasting impact. Since agglomeration is a long-lastingprocess, its effects unfolding only after some time, it is important to ensure that tradetook place long enough in a city to influence agglomeration in a sufficient way. Stateddifferently, trade had to take place for long enough that a city became locked on asuperior development path. To account for this fact, we focus on important trade citiesat the end of the medieval period (i.e., around 1500 AD). This is because the citiesthat were important at the end of the medieval period are those most likely to haveexperienced noticeable trade activities in the preceding years (i.e., over a longer timeperiod).

Our main sources of information on important medieval trade activities are mapsprinted in historical atlases or monographs. We focus on maps because they provide afar more comprehensive source of information on trade cities and trade activities thanhistorical monographs. In addition, the information they contain can often be assignedto a particular period —far more than that contained in books. In consequence, wecollect information about cities prominently involved in trade from four historical mapsproviding information about cities located on “major” or “important” trade routes inaround 1500 AD (i.e., the late medieval period). Because there is no consensus orquantitative evidence about the exact importance of trade cities and trade routes duringthe medieval period we consult several different sources to gather 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.18 Finally, 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; we also list the primary sources on thebasis of which the maps are drawn —where we were able to identify them. We include acity if it is mentioned in one of these maps. We include only those cities located in theEU since the Eurostat regional statistics database only provides data for EU countries.

18As 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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Nonetheless, in certain cases we included cities in the sample not mentioned by themaps but by other sources of information. For example, we include the eastern Germancity of Zwickau as it is prominently recognized in Spufford’s (2002) standard accountof medieval commerce, and it is known for its importance in the salt trade. In othercases, we included cities that are not mentioned by the maps but in other sources forrobustness checks. Furthermore, we use other qualitative information in our judgmentof the importance of the trade cities included. For example, we look at whether a citywas an important member of the Hanseatic League or the capital of a quarter or a third(like, e.g. Dortmund or Cologne). Information on this is provided by Dollinger (1966).Additionally, and especially for less prominent trade cities (Paderborn, Soest, Harfleur,Tarent etc.), we also look at whether they were situated along well-known trade routeslike the “Hellweg” in Germany (as is the case, e.g., for Soest). Moreover, we consultseveral standard historical sources on medieval trade activities in different Central Eu-ropean regions (e.g., Dietze 1923, Hunt and Murray 1999, Schulte 1966, Spufford 2002etc.) and look at whether they mention a city as being prominently involved in tradeor as having over-regional importance as a market, fair, or trading city. Finally, we alsodraw on other historical atlases —such as that of Kinder and Hilgemann (1970)— andother regional trade route maps (e.g., Schulte 1966) as sources for validating the infor-mation in the primary maps. The Data Appendix offers a detailed description of how weconstruct our database of important late medieval trade cities. What is more, in TableA.4 in this Appendix we report and discuss all these sources 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-tries. Our data set encompasses all 839 NUTS-3 regions in these countries.19

Even when armed with the information in these sources, the relative importance ofcities is not always clear. There is also a different degree of uncertainty about the extentand location of trade activities, and the course of main routes, i.e. the actual importanceof a particular trade route at a certain point in time is not always clear. However, thereare cities that were undoubtedly important centers of trade like the Northern Italiancity-states (Milan, Genoa etc.), some Southern German imperial cities (like Augsburg,Nuremburg or Ulm), and the leading centers of the Hanseatic League (Hamburg, Bremen,Lubeck, Cologne etc.). On the other hand, there are cases where only some sourcesmention the city as an important trade center or as sitting along a major trade route,

19We exclude the islands of Elba, Corsica and Sicily from our sample because they are not comparablewith regions on the continent with respect to trade flows. (This follows Chasco et al. 2012 who alsoexclude island regions).

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as in the case of Paderborn, Minden, and certain port cities in France (e.g., Harfleur) orsome smaller cities in Italy (Brindisi, Mantua or Udine). This uncertainty is a naturalresult of the qualitative —and therefore to some extent always subjective— nature of thecollected information and the scarce amount of general information about the medievalperiod and the trade activities during that time.

To deal with this uncertainties we re-estimate all the important results of the paperwith four different alternative trade city samples, i.e. we construct alternative versionsof the trade center dummy excluding cities that are mentioned only in one historicalsource or for which their actual importance is in doubt —given the history of the city.Conversely, we also include cities that are excluded from the original sample becausethey seem not to be as important as the other cities. Finally, we restrict the sample oftrade centers to such cities for which historical evidence about trade activities in earlierperiods is available. This ensures that the results are not biased by places that wereinvolved in medieval trade only for a short time period. A detailed description of theconstruction of this alternative samples and the cities included or excluded is given inAppendix A (e.g., Table A.5) and Appendix B.

Overall, we consulted seventeen different sources to construct our different samplesof trade cities. However, even with this number 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 likely to have been less importantthan others or that experienced trade activities only for a short time should more thandownward bias our estimates. The estimates obtained using this kind of dummy variableshould therefore be considered as a lower bound of the actual long-term effect of medievaltrade.

Predominantly, we use two different variables as measures of late medieval trade andits impact on contemporary regional development. First, we will use a dummy variable“Trade Center” that is equal to one if a region includes at least one medieval tradecity. The lack of quantitative information and the limited availability of qualitativejudgments led us to use a simple dummy variable coding important trade cities. Ofcourse, this implies that we treat all trade cities as the same with respect to the scale oftrade activities and the agglomeration forces at work. However, since we try to focus oncities located on “major” or “important” cross-national trade routes, as well as relyingon qualitative judgments of importance —when available— we should be able to reducethe heterogeneity among the trade cities. Additionally, the construction of a dummyvariable also allows for the construction of a second variable “Distance to Trade Center”representing the distance (in degrees) between a region and the closest medieval trade

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region20 This variable offers a very useful direct test of our hypothesis that medieval tradecontributed to the emergence of time persistent core-periphery patterns and thereforecan act as a notable explanation for contemporary regional income differences.

Table 1 provides a summary of our trade city data. For each country, the total 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 about 1.5degrees (e0.432) which is approximately 170 km. Overall, around 14% of all regions areconsidered as containing medieval trade centers. Table A.2 in the Data Appendix liststhe name, NUTS-3 region, and country of all trade cities. Furthermore, Figure 1 showsa map that depicts all included NUTS-3 regions and the regions with medieval tradecenters (reddish colored).21

[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 agglomeration, ln population density and ln relative GDP density. Thesecorrelations are shown in Table 2.


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 (i.e., there is a

20The variable is zero in regions coded as trade centers.21The geographical distribution of medieval trade cities in the map is largely consistent with that which

King (1985) stated about the location of leading trade and economic centers in medieval Europe.

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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 indicates that the effect of trade centers largely runs throughagglomeration. Therefore the observed correlations provide preliminary support for ourtheoretical reasoning.

Another way to illustrate the stylized relationship between medieval trade, agglom-eration and the present day’s regional economic development is to compare averagesvalues of GDP per capita and agglomeration measures for late medieval trade centersand non-trade centers. This is done in Table 3 both separately for each country as wellas for the whole sample of regions. From the last line of Table 3 we can infer that intotal, i.e. pooled over all regions and countries in the sample, regions with late medievaltrade cities have a significant “GDP Advantage”, that is, their average GDP per capitais around 5000 Euro higher than that of regions without trade cities. Furthermore, theyalso exhibit significantly higher population and relative GDP densities.22 This resultholds true for all countries except from Lithuania where trade center regions show ahigher GDP per capita but the differences is insignificant. For relative GDP Densitythe within country results are not so clear. In Belgium and the Netherlands the rel-ative GDP Density is lower, although the difference is not significant.23 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.


In sum, the descriptive analysis of the data delivers strong preliminary support forour hypotheses.24

22The significance of the Difference between trade regions and non trade regions is tested by a two-samplet test.

23In the smaller countries (such as Lithuania, the Czech Republic, or Belgium), the insignificance of thedifferences is probably attributable to the insufficient total number of regions/trade centers. Here,the numbers should be treated with caution.

24In the working paper version additional descriptive evidence supporting our hypotheses is presented.

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4.1.2 OLS Regressions

To test our main hypothesis, that regions with cities involved in medieval trade exhibithigher levels of economic development today, we estimate the following regression usingOrdinary 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.25 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.

Yet, even when medieval trade still matters today, does its impact transmit via agglom-eration and concentration of economic activities in the places it took place historically?A simple way to test this additional hypothesis is to look at whether GDP per capitalowers when the distance to medieval trade centers increases. Expressed differently, ifthe effect of trade works through agglomeration then a “classical” core-periphery patternshould emerge, with the medieval trade cities as core and the regions as periphery. Onecan therefore modify equation (1) by substituting the trade center dummy through avariable representing the distance between a region’s centroid and the closest trade city.Equation (1) can be rewritten as:

ln(GDP )cijk = α+ ρln(Dist 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-

25As mentioned before, all time-variant variables are measured in the year 2009 so we do not report anindex for the period of measurement.

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spective geographical unit. Additionally, they are included to exploit the pure variationbetween NUTS-3 regions.26 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 widely acknowledged that regions with higher altitude are moredifficult to reach —which seems especially relevant for trade— and have less favorableclimates, thus we expect a negative influence of altitude.

The results of these regressions are shown in Table 4. There, we report three dif-ferent standard errors above each coefficient. First, in parentheses, heteroskedasdicityrobust standard errors are reported. Below those, in brackets, we present standard er-rors obtained by multiway clustering on NUTS-1 and NUTS-2 region level accordingto the methodology of Cameron et al. (2011). Multiway clustering is justified as itseems very likely that development in NUTS-3 regions is not independent from that inNUTS-1 or NUTS-2 regions. Moreover, because multiway clustering allows for arbitraryresidual correlation across both included dimensions, it also accounts for possible spa-tial correlation. Finally, the third standard errors (in curly brackets) are adjusted fortwo-dimensional spatial correlation using the method proposed by Conley (1999).27


A glance at the estimation results confirms our expectations and the descriptive ev-idence brought forward before. Regions with medieval trade centers (cities) show asignificantly higher GDP per capita than regions without such cities. The coefficient ofthe trade center dummy remains relatively stable and significant at 1% level, regardlessof which combination of control variables and fixed effects is used. According to column(3) of Table 4, where we include the full set of country and region dummies as well as thebasic geographic controls, regions with medieval trade centers have a GDP per capitathat is on average around 30% higher than regions without medieval trade centers. Thismeans that the effect of medieval trade is not only statistically but also economically ofconsiderable significance.

This also holds true for the coefficients of the distance to trade center. They are

26Overall, there are 49 NUTS-1 regions and 143 NUTS-2 regions in our dataset.27Conley’s (1999) standard errors are obtained using a cutoff point of 3 degrees (approx. 330 km) after

which the spatial correlation is assumed to be zero. We experimented with several different cutoffpoints and this cutoff produced the most conservative standard errors.

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always highly significant and, quantitatively, are 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 2a, showing a partial regressionplot of the Trade Center Dummy based on the full baseline specification in column (3).In Figure 2b the same is done for the negative relationship between the distance to amedieval trade center and the present GDP per capita.

Regarding the geographical controls, latitude and longitude turn out to be insignifi-cant throughout all estimations. Altitude, on the contrary, is always significant and itscoefficient shows the expected negative sign. Furthermore, the NUTS-2 dummies areoften insignificant and —according to the adjusted R2— add nothing to the explanatorypower of the model. For this reason, they would only introduce additional noise to theestimation and are therefore excluded from the remaining regressions.

In general, the three different types of standard errors do not vary substantially. Ifanything, the standard errors in brackets adjusted from multiway clustering are some-what larger than the other two. In view of this, we will use standard errors clustered onNUTS-1 and NUTS-2 level, for all remaining specifications if possible.

[Figure 2 about here]

4.1.4 Controlling for Determinants of Agglomeration and Development

To ensure that the significant positive relationship between medieval trade and contem-porary economic development is not driven by omitted variables bias we need 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 as first nature(physical and political geography, climate etc.) or second nature causes (man-made fac-tors, i.e. agglomeration resulting from spatial spillovers or scale effects) (e.g., Chascoet al. 2012, Christ 2009, Ellison and Glaeser 1999, Krugman 1993, Roos 2005). Thisliterature assumes that there are direct effects of both types of cause, as well as an addi-tional indirect effect of second nature through its interaction with first nature. Becausemedieval trade is thought to be a second nature cause of agglomeration, an indirecteffect that geography and other natural factors may exert on first nature causes is whatwe need to be especially careful to control for.

In addition to standard economic agglomeration and growth literature, we must alsoaccount for potentially important historical causes of agglomeration and development.

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This clearly follows from our argument that medieval trade influenced regional develop-ment processes through its impact on agglomeration and industry concentration.

In conclusion, we decided to group the control variables into four sets of variables thatwe add separately 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 (coastlines and borders).28 In particular, the latter two are important first na-ture determinants of agglomeration (according to, e.g., Roos 2005, Ellison and Glaeser1999). Additionally, the ln of the distance of each region to the geographically nearestmajor river is included as control.29 Rivers are geographical features important for bothmedieval trade, industry and city location (Borner and Severgnini 2012, Bosker and Bur-ingh 2012, Ellison and Glaeser 1999, Roos 2005 and Wolf 2009). The idea behind thisset of controls is to ensure that we do not simply capture the impact of many medievaltrade cities being located at geographically favorable places, either today or in the past.

A second set of variables controls for relevant contemporary characteristics of theincluded regions. It comprises dummy variables for district-free cities in Germany (whichare, by definition, larger or more densely populated places than others), for the regionsthat include a country’s capital or the capital of an autonomous region.30 Additionally,a categorical variable identifying the degree to which a region may be considered a“mountain region” is included. Furthermore, the set includes dummies for regions withcoal or ore mines (or mining firms); for regions located in the former GDR; and for regionslocated in Eastern European post-communist transition countries. Finally, it includesthe ln of a region’s area. In consequence, this set of controls accounts for many importantfirst nature causes of agglomeration (political geography and resource endowments) aswell as for relevant historical facts that might influence the contemporary economicperformance of a region (such as a communist history).

The next set of controls captures the historical characteristics of regions that couldbe relevant for both present day’s agglomeration and economic performance. Here weconsider dummy variables indicating regions with a university founded before 1500 ADand regions that adopted printing technology before 1500 AD. As Cantoni and Yuchtman

28Holl (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.

29In Germany, for example, we consider the Elbe, Danube, Rhine and the Oder as major rivers.30An autonomous region is considered to be a Belgian or Italian Region or a German or Austrian federal

state (“Bundesland”).

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(2012), Dittmar (2011), and Rubin (2011) have demonstrated, both universities andprinting technology are important factors in explaining the late medieval commercialrevolution and city growth. To account for the positive impact that Protestantismprobably had on economic development (Woesmann and Becker 2009, Rubin 2011) wealso include ln distance to Wittenberg as a variable in this set of controls. Furthermore,we also include dummies for regions containing at least one imperial city or at least onecity that was member of the Hanseatic League. Finally, we also control for the possiblelong-lasting effect of a Roman Empire legacy and low transport costs for trade andagglomeration by including a dummy for cities located along or near to an importantimperial road (Postan 1952).31

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 tertiaryeducation (on NUTS-2 level) as a measure for regional human capital.32 As a variableto measure the quality of regional economic and political institutions we use the qualityof government index developed by the Quality of Government Institute at the Universityof Gothenburg, which provides a measure for regional institutional quality design similarto the World Governance Indicators (WGI) of the World Bank. To measure for regionalinequality we construct the ratio of average workers compensation to GDP per capita.As measure of innovative activity within a region we use the number of patents registeredby a region’s firms 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 includes all robust covariates from the previous re-gressions. The robust controls are obtained by including all variables in one regressionthat were significant when added with one of the four sets of controls to the baselinespecification. In the next step, we remove the variables that become insignificant inthat regression. We repeat this procedure until only significant controls remain in thespecification. 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. This highlights, once again, the importance of human capital and

31This variable considers the Via Regia, the Via Regia Lusatiae Superioris and the Via Imperii as theprobably important imperial roads —that more or less following the route of former Roman roads.

32Again, we take the values for the year 2009.

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political geography. Furthermore the robust influence of printing confirms Dittmar’s(2011) claim that printing technology fostered —similar to medieval trade— localizedspillovers and forward- and backward-linkages.

The results of the regressions are shown in Table 5. First, we add the four sets ofcontrols separately to the baseline specification and then we include as a fifth set allrobust 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 ineach of the specifications, although the sizes of the coefficients are reduced considerablywhen compared to the baseline estimates.


The coefficient is smallest (e.g., 0.045 in the case of the trade center dummy) in thespecification with all robust covariates added to the baseline model. This is unsurprisinggiven that in this specification we added to the regression only those variables withthe highest explanatory power. It suggests that medieval trade center regions todayhave a GDP per capita of some 4.5% higher than other regions. Based on the averageregional GDP per capita in our sample this corresponds to a GDP per capita that isapproximately 1100 Euros higher. When looking at the different set of controls it isevident from the adjusted R2, that region characteristics and growth covariates add themost additional explanatory power to the model. Apart from the mountain and miningregion dummies, each variable in the regional characteristics set is significant and theeffects of political geography in particular (capital regions or regions with a capital of aautonomous region) seem to be important. Regarding the growth covariates, especiallyinequality (with a remarkable negative sign) and human capital exerts a strong effecton GDP per capita.33 In general, the regions’ historical characteristics are of the leastimportance when explaining contemporary regional economic development, neverthelessregions with universities and cities that adopted printing technology before 1500 AD seemto have a significantly higher GDP per capita, even today, highlighting once again theimportance of human capital.34 However, the university before 1500 AD dummy becomesinsignificant when added to the measure of current regional human capital. This suggeststhat universities lead to advantages among regions concerning their human capital that33This finding is for example in line with Simon (1998) and Gennaioli et al. (2013) who highlight the

importance of human capital for regional development and city growth.34In the specification with the distance to trade center variable and historical region characteristics

(column (7)), the other historical region characteristics also seem to be significant (at least at the10% level). This indicates that some of the effects captured in distance to trade centers are in fact,e.g. attributed to the course of important imperial roads like the Via Regia.

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have persisted until today. Finally, the robustly negative impact of the distance to rivervariable again reveals the already widely acknowledged role of first nature geography forregional 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 could arise primarily through unobserved factors,influencing both contemporary regional development and medieval trade. Geographymight be a prominent factor for which this holds true. However, we can control for geog-raphy in our regressions; whereas 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 examples are cultural differencesbetween regions and countries or historical differences in politics between regions.

To resolve the endogeneity issue, we therefore run IV Regressions using the LimitedInformation Maximum Likelihood (LIML) method.35

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

The first instrument variable considered is a categorical variable (taking the valueszero, one, two, and three) indicating whether a region is classified as a mountain regionby the official EU regional statistics. If a region is not classified as a mountain region thevariable is zero. It is equal to two or three if the region is a mountain region accordingto two different sets of criteria (for details about the exact definition consult the DataAppendix).36 The idea behind this variable is fairly intuitive: in mountainous regions,

35This estimation method has better small sample properties and is often more efficient than the standard2SLS method,especially in the presence of weak instruments. Its confidence intervals are more reliableand it is unbiased in the median when the instruments are weak (Stock and Yogo 2005).

36Although this variable is of a categorical nature, we choose to include it as a single variable and notby using three different dummies as instruments. This is primarily motivated by guaranteeing aparsimonious set of instruments since the IV estimates are biased towards the OLS estimates whenthe number of instruments increases. Furthermore, the test of overidentifying restrictions wouldn’tbe valid if one included several instruments following the same reasoning or originating from the samephenomenon as excluded instruments in the first stage. However, the results are fully robust to using

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characterized by higher trade costs, less favorable climates, and many other adversefeatures, trade activities were lower than in regions located at large rivers, along the coastor in low altitude areas with fertile soils and less rugged terrain. During the medievalage in particular, where no advanced transport technologies were available —especiallyfor over-land transport— mountains constituted a severe hindrance to trade (Spufford2002). Furthermore, as highlighted by Bosker and Buringh (2012) high elevation (aswell as differences in elevation between places) has a considerable negative effect on citygrowth 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 werethe residential cities of bishops before 1000 AD. The church as a political, spiritual andeconomical power had a significant impact on the development of cities in the medievalage (e.g., Baker and Holt 2004, Isenmann 1988, King 1985). In light of this, it isprobable that ecclesiastical centers, like the residential cities of bishops, did grow largerand had a higher probability of becoming a trade center. In line with this reasoning,Borner and Severgnini (2012) have demonstrated that trading activity (in- and outflowsof commodities) were higher in bishop residence cities. Additionally, Bosker and Buringh(2010) found that the presence of a bishop was an important factor in the foundationand development of cities during the Middle Ages. The exogeneity of this measureis not as sure as in the case of distance to river. Nevertheless, since we can controlfor geography it is difficult to find a variable that could potentially influence both thelocation of bishop residences in 1000 AD and contemporary regional development. First,in 1000 AD most of the political and economical institutions that would emerge in thelate medieval era did not yet exist. Even the central political power of our samplecountries during the Middle Ages, the Holy Roman Empire, was only founded in thesecond half of the 10th century and could not therefore have had a significant influenceon bishops residences founded before 1000 AD. This is especially true because many ofthe considered dioceses or archbishoprics were already established when the Empire wasfound in 962 AD. Second, we control for many other historical factors such as beinglocated on an important imperial road or the early adoption of printing that might hadinfluenced both the location of trade cities, bishop residences, and economic developmenttoday. Third, as explained by, e.g., Pounds (2005) the dioceses built in the early medievalperiod were virtually identical to the territory of predated Roman cities. In consequence,

the 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 are available from the author upon request.

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their location was determined centuries before the early medieval period, making it evenmore unlikely that they are endogenous to contemporary economic development.

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

In addition to those instruments, we make use of Lewbel’s (2012) approach that ex-ploits heteroskedastic first stage errors terms to generate artificial instruments not cor-related with the product (covariance) of the first stage’s heteroskedasdic errors. Thismethod can provide more reliable estimates if it is doubtful that the instruments meetthe exclusion restriction or are weak. Since the exogeneity of the bishop seats can atleast be disputed in principle, this method ensures that we do not produce invalid IVestimates. The strength of these generated instruments depends 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 stage statistics are not available.

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 re-estimate 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 their signs. Evenmore significant, the size of the coefficients increased remarkably, at least in the caseof the conventional IV regressions in columns (1) and (3). Moreover, the distance totrade center variable that was insignificant before in column (10) of Table 5 regainssignificance at 1% level. This can be interpreted as endogeneity downward biased theOLS results, probably due to a measurement error or a negative correlation betweenan unobserved factor and our medieval trade measures. Concerning the validity of theinstruments, the over identification tests (Hansen J-statistic) inform us that the validityof the exclusion restriction cannot be rejected in almost all case at the common levels ofsignificance. The exception is the last specification where we cannot reject the null atall levels of significance. Due to this, one should be cautious of interpreting the resultsfrom the last columns here. Nevertheless, in line with our arguments above it seemsto be the case that being a mountainous region and a bishop’s residence before 1000AD affects contemporary levels of development solely through their impact on whichcities became medieval trade centers. Furthermore, at least in the case of the trade

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center dummy, Lewbel’s (2012) approach shows that our results hold even when wedo not use external instruments but instruments that are exogenous by construction.However, the coefficients obtained with LIML IV are much larger than those resultingfrom Lewbel’s (2012) approach that are much closer to the original OLS estimates. SinceLewbel’s (2012) approach relies on second moment conditions and additionally producesa comparatively large number of instruments it is likely that this results reflect the lowerbound 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 leads us tounderestimate the true effect.37


4.2 Further Results — Index of Medieval Commercial Importance

Although the evidence offered in the previous section provides robust empirical supportfor a significant relationship between medieval trade and contemporary regional develop-ment, the data upon which the results are based has its limitations. First and foremost,the evidence thus far is solely based on a dummy variable constructed according towhether a city was located at an important trade route and few other qualitative judg-ments about their importance. In treating all trade cities as equal, it is unlikely that thisapproach is able to capture all the dimensions and factors that made a city an impor-tant center of commercial activity throughout the medieval age. In consequence, we maynot capture the true effect of medieval trade or commercial activities on contemporarydevelopment levels.

However, based on the data set at hand and historical evidence about importantdeterminants of trade, economic and commercial activities in the Middle Ages one canconstruct an “Index of Commercial Importance” for each region in our sample. Among

37A 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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the many potential determinants of medieval commercial activity, we choose eight toconstruct the index. At first, we include out trade center dummy, representing citieslocated on important trade routes. Second, we consider the variable indicating cities thatwere the 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 the coastof each region’s centroid, representing the distance of each city to a potential seaportand 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 were very likely to be subject to significantcommercial activity. Fifth, we adopt the dummy variables representing cities that hadthe status of an imperial city or that were located at an important imperial road. Astransport cost were a crucial factor in medieval trade, the presence of a paved andprotected road should be an important economic advantage for the cities located alongit (e.g., Spufford 2002). On the other hand, most of the important trade cities in the HolyRoman Empire that were not members of the Hanseatic League were free or imperialcities. Due to this, imperial cities —with their political and institutional microcosms—may be seen as the early buds of commercial activity in the medieval period (Cantoniand Yuchtman 2012). Sixth, we include a variable depicting regions in which medievalmining activities (copper and salt mining) took place. This accounts for the fact that saltand copper —as raw materials in general— were some of the major trade commoditiesin medieval Europe (e.g., Postan 1952, King 1985, Spufford 2002). Finally, we follow thereasoning of a recent study by Cantoni and Yuchtman (2012) showing that universitiesdecisively fostered commercial activities and market establishment in the surroundingarea. Consequently, we include the dummy variable reporting cities with universitiesfounded before 1500 AD as last variable. The index is constructed by simply adding upthe variables, combining them in one index ranging from zero to eight. Thereafter, wesubtract the mean of the index from all its values so that the average region would have avalue of zero. Regions with a below average value therefore have a negative and regionswith an above average value have a positive value. We also construct an alternativeversion in which we include the ln distance to trade center variable instead of the tradecenter dummy.38

38We recode this variable so that it is positively associated with economic development and agglomera-tion, as are the other seven variables in the index.

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Clearly, there are other determinants of commercial activity in the Middle Age. Nev-ertheless, we choose this set of variables because these variables are significant predictorsof the original trade center dummy when jointly included in a probit model. Together,they produce a pseudo R2 of around 0.2.39 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 those obtained before using the simple trade centerdummy and the distance variable. The results are shown in Table 7.


Overall, the index of commercial importance, in both its original and alternativeversions, is significant with a positive sign in every regression. Reassuringly, the LIMLIV regressions using the same instrumental variables as before and a version of the indexwithout the bishop before 1000 AD dummy, yield a more significant and remarkablyhigher coefficient. This is similar to the IV regressions using the dummy variable. Thecoefficient obtained with Lewbel’s (2012) generated instruments is much closer to theoriginal OLS estimate but keeps its significance. Furthermore, the Lewbel estimate hasto be treated with some care since the overidentification test does reject the null of avalid exclusion restriction at the marginal significance level.

To sum up, the index of commercial importance confirms the results of the regressionsusing a simple dummy variable. Therefore, it is fair to conclude that exists a statisticallyrobust relationship between medieval trade and commerce and today’s regional economicdevelopment.

4.3 Medieval Trade, Agglomeration and Contemporary EconomicDevelopment — Establishing Causality

Until now, we have only indirectly shown that medieval trade influences today’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 associated39Regression not shown but available from the author.

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with regional GDP per capita. In this section we will conduct a more direct test of theproposed causal chain, going from medieval trade activities to medieval city growth, tocontemporary agglomeration patterns and from there to regional economic performance.

4.3.1 Medieval Trade and City Development

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 in which 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 city growth variableis based originates from the historical city population data compiled by the Centre forGlobal Economic History (CEGH) at Utrecht University. This database provides themost comprehensive and recent population figures for European cities between 1500 and2000 AD.40 For population estimates prior to 1500 AD (1200, 1300 and 1400 AD) werely on data from Bairoch et al. (1988).41 We include every city for which there ispopulation data in Bairoch et al. (1988) in 1500 AD and that is located in one of ourten sample countries. This leaves us with 372 cities from which 92 are coded as tradecities based on the same information than in the NUTS-3 region sample. A list of allincluded trade cities is provided in the Data Appendix.

We then run cross-sectional OLS regressions with the ln of city growth between 1500AD (the end of the medieval period) and 1200 AD, 1300 AD and 1400 AD. We choosethese three variables to demonstrate that the results are not dependent on the chosenperiod. In each of the regressions we include country fixed effects as well as a set ofhistorical determinants of city development as controls. We control for first nature ag-glomeration forces by including the distance of a city to the closest river or coast andalso a city’s latitude and longitude and whether it is classified as a mountain region andwas therefore difficult to reach (e.g., Bosker and Buringh 2010, Spufford 2002). Further-more, we consider several dummy variables indicating whether a city was residence ofa bishop before 1000 AD; had the status of imperial city; was located at an importantimperial road; or was a member of the Hanseatic League. All the control variables usedhere and throughout all estimations in this section are coded on city level and are oth-

40Details about the data base are available in the Data Appendix of the paper. The data base includesthe city population for every fifty years between 1500 and 2000 AD.

41We follow the recent update of the Bairoch et al. (1988) population figures by Bosker et al. (2013)and include the new, smaller figures for Bruges and Paris.

27

erwise similar to the variables used in the previous regional level analysis.42 At last, wealways include the ln of the initial city population at the beginning of the consideredgrowth period. This accounts for the fact that city growth is concave in city size and inconsequence the growth rate of a city strongly depends on the city’s initial size.43 Thisis, we estimate the following regression specification:

ln(POPic,1500POPic,t

) = α+ βTCic + γPOPic,t + δ′Xic + θc + εi (3)

Where ln(P OPic,1500P OPic,t

) is the ln the growth in population in a city between 1500 AD andperiod t with t=1200, 1300 or 1400 AD. TCic is the trade center dummy POPic,t is theln city population begin of the period and Xic is a set of time-invariant covariates and θc

are country fixed effects. We also estimate this equation using the Index of CommercialImportance instead of the trade center dummies. These results, which do not generallydiffer from that reported here are available in Appendix C (Table C.1).44

The estimation results are depicted in Table 8, columns (1)–(3). We clearly find thatthe trade cities show significantly higher growth throughout the medieval period thannon trade cities. This is clear evidence in favor of our theoretical reasoning that medievaltrade contributed to city growth and agglomeration. Furthermore, we also see a highlysignificant and negative effect of the initial population level showing, indeed, that alreadylarge cities did grow at a slower rate.

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.45

In these estimations we first regress the ln of the city population in each of our consideredyears (1200, 1300, 1400, and 1500 AD) on the trade center dummy. However, this timewe exploit the available historical information about the earliest occurrence of trade in arespective city (see Table A.5 in the Data Appendix) to take into account the dynamicsof trade activities throughout the medieval period. That is, we code the trade centerdummy as one beginning with the earliest year for which important trade activities in acity are reported by our sources (i.e., 1200, 1300, 1400, or 1500 AD) so that it becomesa time varying variable. We use the same set of controls as previously used in the crosssectional estimates and additionally we add year fixed effects. Furthermore, the imperial42That is, they originate from the same source and are coded according to the same criteria.43A descriptive overview over all variables used in the city level estimations is available in Table A.2 in

the Data Appendix44In the regressions using the index of commercial importance we do not include the controls used

to construct the index. This is the set of control variables is reduced. The exact set of includedcovariates is listed in the notes to Table C.1 and in Appendix C.

45Due to the time-invariant nature of important control variables we prefer the random effects method.

28

city dummy and Hanseatic League are also coded by taking into account the exact dateat which the city become an imperial city or member of the Hanseatic League, i.e. theyare now time varying variables during the medieval period.46 Thus, we estimate thefollowing equation:

ln(POP )ic,t = α+ βTCic,t + δ′1Xic + δ′2Xic,t + θc + πt + εi (4)

Where ln(POP )ic,t is the ln of a city’s population as described above and δ′2Xic,t

represents the time varying control variables (the imperial city and Hanseatic leaguedummy).πt are the year fixed effects. All other variables are the same as in the case ofequation (3). Again, pooled over all years, the population of a city is significantly higherif the city is an important medieval trade center. Finally, we regress the growth in lnpopulation between each of our base years on the trade center dummy and additionallyinclude the lagged population in the regression (which is similar to the cross sectionalestimations). Thus, equation (4) is modified to:

ln(POPic,t+1POPic,t

) = α+ βTCic,t + γln(POPic,t) + δ′1Xic + δ′2Xic,t + θc + πt + εi (5)

With ln(P OPic,t+1P OPic,t

) is ln of the city’s population growth between two base years andln(POPic,t) is the city population in the contemporary period. Once more, we found asignificantly positive association between being a trade center and changes in populationthroughout the period from 1200 AD to 1500 AD. All other variables are analogous toequation (4).

In sum, these 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.


In addition, the city level data allows to shed light on the impact medieval tradehad on city development (i.e., city size and growth) over the long-run. If our claim thatmedieval trade and economic activities cause a path-dependent city development process

46A descriptive overview of the panel data set is given in Table A.3 in the Data Appendix.

29

and therefore have long-run effects is correct, medieval trade should have a significantinfluence on city size and growth also in the periods after the medieval.

To test this empirically, we conduct three different types of regression. First, we re-estimate the panel RE regressions in columns (4) and (5) of Table 8, this time alsousing the city population data for the years after the medieval period. In particular, weuse the city population levels in 1200, 1300, 1400, 1500, 1550, 1600, 1650, 1700, 1750,1800, 1850, 1900, 1950 and 2000 AD as dependent variable. For those estimations, weused the same set of controls as in Table 8 but (since we now focus on later periods)supplemented with a time varying version of the university before 1500 AD dummy aswell as the printing press before 1500 AD dummy.47 Alternatively, we use the growth incity population between these years as measure for city development. Second, we returnto the cross-sectional framework and regress the ln of a city’s population in 1500 AD,1600, 1700, 1800, 1900 and 2000 AD on the trade center dummy and the city populationin 1300 AD.48 Again, we use the time varying version of our trade city dummy and thecontrol variables. This is, we estimate the following cross sectional equation using OLSfor each of the above mentioned years separately:

ln(POP )ic = α+ βTCic + γln(POPic,1300) + δ′1Xic + θc + εi (6)

In equation (6) ln(POP )ic represents the ln of a city’s population in the respectiveconsidered year and ln(POPic,1300) is the ln of the city’s population in 1300 AD. Theother variables are analogously to equation (5).

Third, we estimate equation (6) using the growth in city population between 1500 ADand 2000 AD, respectively. Thus, we look whether medieval trade and economic activityis positively related to long-run city growth. In those regressions, we include the initialcity population (i.e, in 1500 AD). In doing so, equation (6) becomes to:

ln(POPic,2000POPic,1500

) = α+ βTCic + γln(POPic,1500) + δ′1Xic + θc + εi (7)

With ln(P OPic,2000P OP1500

) being the growth in city population between 1500 AD and 2000 AD.Complementary, ln(POPic,1500) is the city population in 1500 AD. The estimations areshown in Table 9.

47However, the printing press before 1500 AD dummy does not take into account the exact date ofadoption (i.e., the earliest year for which a book printed in the city is known). The reason for this isthat the invention of the printing press was in 1450 and therefore these dates completely fall betweentwo of our observation periods.

48We choose 1300 AD as year for the initial population levels since for 1200 AD the data about citypopulations is far more limited.

30


The results show that indeed, trade cities show a significantly higher population orcity growth than non trade centers throughout the entire period from 1200 to 2000(as shown by the panel regressions) as well as for the considered base years. Thus, itis fair to conclude that medieval trade has a long lasting and persistent effect on thedevelopment of central European cities (although this is not equally significant in everyperiod). Moreover, in columns (3) to (8) we see that the initial city population in 1300AD is a significant predictor of city population until 1900 and in column (9) the initiallevel of population still significantly influences the long-run growth rate of cities.

The results are similar, when one uses a city level version of the index of commercialimportance instead of the trade city dummy. Though regressions using this alternativemeasure are not shown in the main text due to space restrictions they are reported inAppendix C (Table C.2).49

Finally, we estimate equations (6) and (7) using the data set of Bosker et al. (2013),which contains city population figures for the years 800—1800 AD and a large set of vari-ables representing important determinants of city development. Using this data offersthe possibility to additionally control for urban potential, i.e. the urban environment inwhich a city is integrated, as well as controlling for climatic conditions, political institu-tions and short-run shocks like the plunder of a city. Moreover, all the non-geographicalvariables are time varying throughout the whole observation period. Consequently, inusing this data one is able to capture the full dynamics of city development. We esti-mate equation (6) and (7) with this data set, all important controls and both the tradecity dummy and an alternative version of our index of commercial importance. Thisalternative version is coded using the variables in their data set. The estimations areshown in Appendix C, Table C.4. In Appendix C the estimations, variables, and resultsare also discussed in more detail. In general, the trade city dummy and the index ofcommercial importance remain robustly significant predictors of city population and citygrowth with this data set and control variables.

In conclusion, the development of the considered cities follows a path-dependent pro-cess that is significantly influenced by the long-run effects of medieval trade and economicactivity. Thus, the empirical evidence about medieval trade and city development fullyconfirms our theoretical reasoning.

49In the regressions using the index of commercial importance we do not include the controls used toconstruct the index. Thus, the set of control variables is reduced. The exact set of included covariatesis listed in the notes to Table C.2 and in Appendix C.

31

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. In other words, we need to establish that there isa significant amount of path-dependency in city development throughout the regionsin our sample. To do so, we regress the ln of the relative GDP density of a regionon the three medieval city growth variables used in the previous subsection, the initialcity population at the beginning of the considered growth period and again NUTS-1region and country fixed effects and the robust covariates already used in the precedingestimations. Expressed more formally, the following regression equation is estimatedusing OLS:

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

) + γPOPcijk,t + δ′Xcijk + θc + λi + εcijk (8)

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

P OPic,t) is the ln of a city’s population in 1500 AD divided by its population in

t with t being either 1200, 1300 or 1400 AD. γPOPcijk,t represents the ln of the city’spopulation at the t, i.e. the beginning of the considered growth period. Xcijk is the setof robust covariates used several times before. θc and λi are NUTS-1 or country fixedeffects, respectively. εcijk finally is the error term.

The final step is to establish the relationship between medieval trade, contemporaryeconomic agglomeration (via path dependent agglomeration processes as shown above)and regional economic development.

We will achieve this by conducting a causal mediation analysis (estimation of me-diation effects) following the method developed by Imai et al. (2010, 2011). Medi-ation analysis enables us to disentangle direct and indirect effects —via determiningagglomeration— of medieval trade on contemporary development. Since we cannot ruleout the possibility that there are direct effects or —amounting to the same— indirecteffects of medieval trade working via other channels, this methodology seems to be ap-propriate for our setting. The estimation of mediation effects is based on a set of threedifferent linear estimation equations (Imai et al. 2010):

Ycijk = α1 + β1Tcijk + γ′11Xcijk + γ′12Xcij + δc + θi + λj + εcijk1 (9)

Mcijk = α2 + β2Tcijk + γ′21Xcijk + γ′22Xcij + δc + θi + λj + εcijk2 (10)

Ycijk = α3 + β3Tcijk + πMcijk + γ′31Xcijk + γ′32Xcij + δc + θi + λj + εcijk3 (11)

32

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 a measure of the spatialdistribution of economic activity. Xcijk is defined as before and stands for a set ofNUTS-3 level covariates. Accordingly, Xcij is a set of NUTS-2 level covariates. δc,θi and λj are again country, NUTS-1 and NUTS-2 region fixed effects. The epsilonsrepresent the error terms. This means that equation (9) is identical to equation (1) or(2) respectively, while in equation (10) we regress the medieval trade variables on theagglomeration measures and in equation (11) finally we include both the medieval tradevariables and the agglomeration 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 describedin detail in Imai et al. (2011). 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 effect ofmedieval trade, i.e. that part of the effect not mediated by agglomeration (but perhaps byother factors). In consequence, this methodology of separating direct and indirect effectsenables us to calculate how far 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. Moreover, we also hypothesize that, onaverage, the majority of the effect of medieval trade should run through agglomeration.This leads us to expect the ACME to be significantly different from zero and greaterthan the direct effect (|β2π| > |β3|). Moreover, since it holds that β1 = β2π + β3

equation (9) is redundant given equations (10) and (11) and we therefore only estimatethose two equations.50 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 10. Supplementary to those results, weestimated Table 10 with ln population density as mediating agglomeration measure. Theresults are similar and available in Appendix C (Table C.3).


50Finally, this also implies that the share of the total effect of medieval trade running through agglom-eration is (β2π)

β1.

33

Columns (1) to (3) show the results for the estimation of equation (8). We see clearlythat there is a robust and positive relationship between medieval city growth in differ-ent time periods and the contemporary relative GDP density of the NUTS-3 regions inwhich the cities are located. The smallest estimate, resulting from the estimation withcity growth between 1400 and 1500 AD as regressor, implies that on average, one per-centage of city growth in this period leads to around a 0.17 percent higher relative GDPdensity. This shows that there is indeed a considerable amount of path-dependency inthe development of European cities, i.e. the cities that grew larger during the medievalage due to trade are the economic centers and agglomeration areas of today.

Turning to the results of the mediation analysis (columns (4) to (6)) again we findstrong empirical support for our theory. As expected, and based on the previous empir-ical results, all three measures of medieval trade (the dummy, the distance variable andthe index 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 around a 40% higher relative GDPdensity than non trade center regions. What is more, the results clearly show that ahigher distance to a trade center largely corresponds to a higher distance to areas wherethe economic activity is concentrated. Thus, according to those estimates, there is asignificant and robust positive relation between present day’s spatial distribution of eco-nomic activity and medieval trade. Moreover, from the estimations of equation (11) wesee that the significant effect of the medieval trade measures on the ln GDP per capitadoes completely disappear when we include the relative GDP density in the regressionestimation. The relative GDP density by contrast, enters with a positive and significantsign in each of the three regressions. Thus, areas with a high concentration of economicactivity are also the regions with a higher GDP per capita. Most importantly, this alsoimplies that most of the observed strong effect of medieval trade on regional developmentlevels works through its impact on the patterns of spatial industry agglomeration. Inline with this, the ACME is always significant and on average above 100% —indicatingthat the insignificant remaining effect of medieval trade is even negative in some cases.

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

34

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 issues, we conduct variousrobustness checks. Further discussions and explanations of these tasks are available inAppendix B, where also the results are reported in Tables B.1 to B.7

We conduct regressions including additional control variables (e.g., a dummy for resi-dence cities and medieval mining), excluding influential observations and accounting forthe considerable differences in the size of the NUTS-3 regions of the respective countries(Tables B.1 to B.3). Furthermore, we re-estimate the regressions using the alternativesamples of trade cities introduced in the data section (Tables B.4 to B.7).

Overall, we find that none of these robustness checks changed the results in a waythat would contradict the conclusions drawn from the main regressions. Therefore, ourtheoretical postulates can considered to be supported by robust empirical evidence.

5 Conclusion

This paper argues that medieval trade led to agglomeration and a concentration ofeconomic activities within the region it took place. It further postulates that the observedspatial distribution of population and economic activity across Europe today is stillshaped by the self-reinforcing and long-lasting agglomeration processes that have theirorigins in medieval trade activities.

Empirical tests of these hypotheses confirmed, as expected, that there is a statisticallyand economically significant positive relationship between medieval trade activities andcontemporary regional economic development. The analysis further revealed that thisrelationship is indeed caused by the influence that medieval trade exerted on the emergingpatterns of agglomeration and spatial concentration of industrial activities throughoutEuropean regions. Based on the result of this paper we are able to confirm a causal chainrunning from medieval trade activities through medieval city growth to contemporaryindustry concentration and regional economic development. Medieval trade can thereforebe considered as an important determinant of modern economic development and long-run city development. Further quantitative analyses of medieval trade activities basedon detailed historical data will clearly improve our understanding of the sources of long-lasting economic and social prosperity —a subject that is of interest to researchers in anumber of academic fields.

35

Tables and Figures

Figure 1: NUTS-3 Regions with Medieval Trade Cities

36

-.5

0.5

1e(

ln(G

DP

per

cap

ita)

| X )

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

(a) Trade Center

-.5

0.5

1e(

ln(G

DP

per

cap

ita)

| X )

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

(b) Distance to Trade Center

Figure 2: GDP p.c and Medieval Trade - Partial Regression Plots

37

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

38

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***(0.000)

-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.

39

Tabl

e3:

Med

ieva

lTra

de,A

gglo

mer

atio

nan

dR

egio

nalD

evel

opm

ent

—M

ean

Com

paris

ons

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.

40

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.

41

Tabl

e5:

Med

ieva

lTra

dean

dC

onte

mpo

rary

Econ

omic

Dev

elop

men

t—

Add

ing

Furt

her

Con

trol

s

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.

1791

***0

.070

1***

0.04

5**

(0.0

25)

(0.0

24)

(0.0

3)(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

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

,bo

rder

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

,loc

ated

inth

efo

rmer

GD

Ran

dlo

cate

din

anEa

ster

nEu

rope

anpo

st-c

omm

unist

ictr

ansit

ion

coun

try.

Furt

herm

ore

iten

com

pass

esth

eln

ofa

regi

ons

area

.T

hehi

stor

ical

regi

onch

arac

teris

tics

cons

istof

adu

mm

yva

riabl

esin

dica

ting

regi

ons

with

aun

iver

sity

foun

ded

befo

re15

00A

D,t

hat

adop

ted

prin

ting

tech

nolo

gybe

fore

1500

AD

,con

tain

citie

sth

atw

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

ver

itin

clud

esth

eln

ofth

edi

stan

ceof

are

gion

’sce

ntro

idto

Witt

enbe

rg.

The

grow

thco

varia

tes

enco

mpa

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

,rai

lroad

san

driv

ers,

dum

mie

sfo

rdi

stric

tfr

eeci

ties,

capi

talc

ities

,ca

pita

lciti

esof

auto

nom

ous

regi

ons,

post

-com

mun

istic

tran

sitio

nco

untr

ies,

East

ern

Ger

man

y,th

eln

ofa

regi

on’s

area

,the

shar

eof

peop

lew

ithte

rtia

ryed

ucat

ion,

the

ineq

ualit

ym

easu

rean

dth

epr

intin

gpr

ess

befo

re15

00A

Ddu

mm

y.Ea

chre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

42

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) (0.025)

ln(Distance to Trade Center) -0.519*** -0.155***(0.173) (0.05)

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

8.39 44.51 9.32 13.47

p-value 0.000 0.000 0.000 0.000R2(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 reports 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.

43

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 0.0963***0.0209** 0.149*** 0.0232**(0.014) (0.009) (0.053) (0.01)

Commercial ImportanceAlternative

0.0974***0.0179*(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 0.508 0.621Underidentification Test 17.02 224.5p-value 0.000 0.000Overidentificaton Test 0.109 69.41p-value 0.741 0.077AP F-statistic of excludedIV’s

9.53 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.

44

Tabl

e8:

Trad

eA

ctiv

ityan

dC

ityG

row

thin

the

Med

ieva

lPer

iod

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

pu

lati

on

t+

1P

op

ula

tio

nt

)(1

)(2

)(3

)(4

)(5

)M

etho

dO

LSR

E

Trad

eC

ity0.

503*

*0.

367*

**0.

293*

0.66

5***

0.28

***

(0.2

27)

(0.1

2)(0

.148

)(0

.071

)(0

.094

)ln

(Pop

ulat

ion

1200

)-0

.66*

**(0

.148

)ln

(Pop

ulat

ion

1300

)-0

.614

***

(0.0

64)

ln(P

opul

atio

n14

00)

-0.4

01**

*(0

.078

)ln

(Pop

ulat

ion t

)-0

.415

***

(0.0

55)

Obs

.86

207

183

879

451

Adj

.R

2 \ov

eral

lR2

0.35

30.

401

0.17

50.

354

0.26

3N

umbe

rof

Clu

ster

s37

220

9N

otes

.R

obus

tst

anda

rder

rors

are

repo

rted

inpa

rent

hese

sin

colu

mns

(1)

-(3

).St

anda

rder

rors

clus

tere

dat

city

leve

lare

repo

rted

inpa

rent

hese

sin

colu

mns

(4)

and

(5).

Coe

ffici

ent

isst

atist

ical

lydi

ffere

ntfr

omze

roat

the

***1

%,*

*5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aci

ty.

Inco

lum

ns(1

)an

d(2

)th

eov

eral

lR2

isre

port

ed,i

nal

loth

erco

lum

ns,t

heA

dj.

R2

issh

own.

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

bero

fthe

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

eco

untr

yfix

edeff

ects

.In

colu

mns

(4)

and

(4)

we

addi

tiona

llyin

clud

eye

arfix

edeff

ects

.Ea

chre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

45

Tabl

e9:

Med

ieva

lTra

dean

dLo

ng-r

unC

ityD

evel

opm

ent

Dep

.Va

r.ln

(Pop

ulat

ion)

ln(P

opulation

t+

1Population

t)l

n(Po

pula

tion

1500

)ln

(Pop

ulat

ion

1600

)ln

(Pop

ulat

ion

1700

)ln

(Pop

ulat

ion

1800

)ln

(Pop

ulat

ion

1900

)ln

(Pop

ulat

ion

2000

)ln

(Population

2000

Population

1500

)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Trad

eC

ity0.

622*

**0.

0991

***

0.33

***

0.39

8***

0.58

5***

0.45

4***

0.49

8***

0.63

4***

0.71

4***

(0.1

04)

(0.0

29)

(0.1

18)

(0.1

03)

(0.1

4)(0

.132

)(0

.165

)(0

.153

)(0

.146

)ln

(Pop

ulat

ion

1300

)0.

347*

**0.

321*

**0.

155*

0.20

9***

0.21

3**

0.09

47(0

.067

)(0

.06)

(0.0

84)

(0.0

79)

(0.1

03)

(0.0

9)

ln(P

opul

atio

n t)

-0.1

04**

*(0

.014

)ln

(Pop

ulat

ion

1500

)-0

.81*

**(0

.067

)

Obs

.3,

501

2,33

620

717

317

820

519

020

335

9ov

eral

lR2 \

Adj

.R

20.

613

0.29

40.

461

0.59

30.

450.

426

0.28

0.30

20.

408

Num

ber

ofC

lust

ers

372

369

Not

es.

Rob

ust

stan

dard

erro

rsar

ere

port

edin

pare

nthe

ses

inco

lum

ns(3

)-

(10)

.St

anda

rder

rors

clus

tere

dat

city

leve

lar

ere

port

edin

pare

nthe

ses

inco

lum

ns(1

)an

d(2

).C

oeffi

cien

tis

stat

isti

cally

diff

eren

tfr

omze

roat

the

***1

%,

**5

%an

d*1

0%

leve

l.T

heun

itof

obse

rvat

ion

isa

city

.In

colu

mns

(1)

and

(2)

the

over

allR

2is

repo

rted

,in

all

othe

rco

lum

ns,

the

Adj

.R

2is

show

n.T

hese

tof

cova

riat

esen

com

pass

esth

eln

dist

ance

sof

aci

tyto

the

next

rive

ror

coas

t,an

da

dum

my

whe

ther

aci

tylie

sin

are

gion

clas

sifie

das

am

ount

ain

regi

onby

the

EU

regi

onal

stat

isti

cs.

Inth

ees

tim

atio

nsof

colu

mn

(3)

onw

ard

the

dum

my

vari

able

indi

cati

ngw

heth

era

city

adop

ted

prin

ting

tech

nolo

gypr

ior

to15

00A

Dis

addi

tion

ally

incl

uded

.Fu

rthe

rmor

e,w

eco

ntro

lfor

aci

ty’s

lati

tude

and

long

itud

ean

din

clud

eco

untr

yfix

edeff

ects

.In

colu

mns

(4)

and

(4)

we

addi

tion

ally

incl

ude

year

fixed

effec

ts.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

46

Table 10: Medieval Trade, Relative GDP 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 (11)Dep. Var. ln(Relative GDP Density) ln(GDP per capita)

P opulation1500P opulationt

0.341*** 0.186*** 0.175***(0.101) (0.064) (0.059)

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 0.948 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 (10)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. 85 182 203 818 818 818R2 0.939 0.938 0.94

Notes. Robust standard errors are reported in parentheses. Coefficient is statistically differentfrom 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 airports andrailroads, 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 before 1500AD dummy. Each regression includes a constant not reported. ACME is the “Average CausalMediation Effect” and means how much of the effect of medieval trade is mediate, i.e. worksindirectly through the relative GDP density.

47

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Appendices forDoes Medieval Trade Still Matter? Historical Trade Centers,

Agglomeration and Contemporary Economic Development

Fabian Wahl∗

University of Hohenheim

November 3, 2013

A. Data Appendix

The level of an observation is a NUTS-3 region (for example, in Germany this correspondsto the “Landkreise”, in France to the “Departments”, and in Italy to the “Provincias”).If the variables are defined on another NUTS level, this is indicated in the description ofthe respective variable. City level information is matched to the NUTS-3 regions by theuse of Eurostat (2007). We use the NUTS-2006 classification, since most of the data isavailable only for this version of the NUTS classification. A descriptive overview of allvariables used in the empirical analysis is given in Table A.1 below.

A.1 Main Variables

Trade Centers. The data on historical trade cities is based primarily on four maps. Thefirst is printed in Davies and Moorhouse (2002) and includes the “Main trade routes inthe Holy Roman Empire and nearby countries” for the period around 1500 AD, showingthe trade routes and the cities located on them. Davies and Moorhouse (2002) is a bookabout the history of the Polish city of Wroclaw written by a renowned expert on Polishand Eastern European history, Norman Davies, and Roger Moorhouse. According toGoogle Scholar it has been cited around 60 times (as of 24th June 2013) e.g., in articlesin the Journal of the Royal Statistical Association. It is therefore considered to be areliable source of information about medieval trade activities.

∗Department of Economics, University of Hohenheim. Chair of Economic and Social History, Speise-meisterflugel, Stuttgart, Germany. [email protected].

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Because this map only covers the areas of Austria, Belgium, the Czech Republic, East-ern France, Germany, Hungary Lithuania, the Netherlands, Poland and North Italy wemake use of a second map published in King (1985) including “Chief trade routes inEurope, Levant and North Africa 1300-1500 CE”. This map covers a wide area includingparts of North Africa and the Near East. From this map we primarily take 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 entitled the “Cur-rents of Trade: Industry, Merchants and Money” in “The Flowering of the Middle Ages”edited by the Oxford-based medieval art historian Joan Evans. In this chapter, DonaldKing introduces the most important goods of the medieval economy, and discusses howthey were produced and traded. He places special emphasis on the patterns of commerceand trade, as well describing the most important centers of commerce and trade activ-ity (Fair and market cities etc.). He also discusses the importance of institutions (likecontract security) etc., and the role they played for trade activities. Again, this volumeappears to be a frequently cited source with around 50 citations in Google Scholar (asof 24th June 2013). According to the bibliography of the volume, King (1985) drawsheavily on standard sources for medieval trade including Heyd (1879a,b), Lopez andRaymond (1955) and Postan and Rich (eds.)(1952).

As a third source we use an overview map of late medieval trade printed in Magocsi(2002), an historical atlas of central Europe and an often cited source for historicalinformation about economic, cultural, and political features. He is cited 222 times byGoogle scholar (as of 24th June 2013). Among the papers using the information providedby the atlas are the historical economic papers by Borner and Severgnini (2012) andDittmar (2011), as well as Becker et al. (2011). The map contains information on the“economic patterns” in Central Europe around the year 1450 AD. We primarily tookfrom this map information about Southern Italian trade cities not included in the othermaps. Again, we also include cities mentioned there but not in the other two sources.From this map, a city is considered if it is located on a “major” or “important” traderoute. The map also contains information about members of the Hanseatic League(and their importance) as well as commercial offices and foreign depots of the HanseaticLeague. Further, it also depicts the goods traded over the particular routes and theareas in which the commodities are typically produced. The map drawn in Magocsi’satlas relies on other regional and general historical atlases, like those of Darby and Fuller(eds.) (1978) or Lendl and Wagner (1963) for Austria. However, Magocsi also consultedbooks about the history of particular cities like Dubrovnik (Carter 1972) or Wroclaw(Ochmanski 1982).

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Finally, we consult several maps included in Westermanns Atlas zur Weltgeschichte(Stier et al. 1956). To be precise, we used the information given by a map depicting the“Hanseatic League and its Opponents in the 15th century after the Peace of Utrecht”.The map reports the location of Hanseatic cities, the kontors of the Hanseatic League inother countries and the main trade routes of the time, as well as the goods traded. Thegeographical scope of the map is limited to the part of Germany north of Prague, theNetherlands, and the majority of today’s Belgium and Poland. If a city is located on oneof the trade routes, we include it regardless of whether it was a member of the HanseaticLeague. Second, we draw information from a map in the atlas that shows “WesternEuropean Trade” in the late medieval age and reports the course of the “importanttrade routes” and the cities located along them. The scope of the map is south-westEurope (Spain and France) but it also includes West Germany and the north-west ofItaly. Here again, we include a city if it is located on a major trade route. Finally, weuse the information contained in a map regarding “Levant Trade in the Late Medievaland the Ottoman Invasion”. This map, in addition to other information, delineates thecourse of “important” trade routes (both on land and sea) and the cities located onthem. We recognize cities on trade routes in the southern part of Germany, Hungary,Italy and most parts of France as well as parts of Poland.

Although far from being the only sources of information on medieval trade activities,these four maps seem to contain the most complete cross-national information aboutimportant trade activities in the later medieval period.

To validate the evidence offered by these maps and obtain additional information aboutmedieval trade we consult other sources including a list of the members of the Hanseaticleague from Dollinger (1966), a standard source for the history of the Hanseatic League.We only recognize those cities that, according to Dollinger, “played an important role inthe Hanseatic League”, or were capitals of thirds and quarters. Furthermore we consulteda map containing information about “North-South Trade Routes in the Alps Area in theMedieval Period” from Schulte (1966); two very general maps printed in Kinder andHilgemann (1970) focusing on Baltic Sea and Levant trading activities in 1400 AD; amap published in Ammann (1955) focusing on trade routes for Southern German 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 work

1Geographical scope, time period and level of generality sometimes differ between these maps, so across-validation is not always possible to a full extent.

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about 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 dummyvariable is equal to one and the source(s) mention the respective city as a trade centerare shown. However, due to space restrictions we do not report any of the sources weconsulted for getting information about the validity of our sample of important tradecenters. For example, there is a three-volume anthology by Escher and Hirschmann(eds.) (2005) in which a group of researchers developed an index of urban centralityfor cities in the “Rhine-Meuse area” for the period 1000 to 1350 AD (i.e., south-westGermany, western Switzerland, eastern France, large parts of Belgium and the South ofthe Netherlands). As part of the index of urban centrality, they collected data aboutthe existence and number of markets, fairs, trade halls and the presence and importanceof long-distance trade activities. They also have data about the existence of certainmanufacturing activities that are also good indicators of the presence of trade. Theydevelop a categorical index of centrality from the qualitative information they collect.The trade cities in our sample that are included in the volume are: Aachen, Antwerp,Cologne, Dordrecht, Dortmund, Frankfurt, Maastricht, Metz, Munster, Paderborn, Rot-terdam, Soest and Straßburg. For each of those cities, one or more markets, fairs, orimportant long-distance trade is mentioned. But, here, the range goes from Cologne(with 4 markets, and “very important” fairs and long-distance trade activities) to, e.g.,Paderborn ––where it is stated that it has a fair and long-distance trade. Due to this,it is not easy to say if the information provided by this source can be used to validatea city’s importance and thus if it should be included in the sample. Furthermore, theperiod for which the index is constructed ends in the middle of the 14th century, andthus earlier than our period of observation. Nevertheless, the information provided inthe anthology of Escher and Hirschmann (eds.) (2005) can be useful to select cities thatwere possibly less important because the markets, fairs or trade there were comparablylimited in scope (i.e., according to the number of markets, halls, fairs or their impor-tance) or time. Additionally, it provides clear evidence for the outstanding importanceof Cologne and, e.g. the over-regional importance (“very important” long-distance tradeor fair) of Dortmund, Frankfurt, Munster and Soest.

As already mentioned, the information in those sources is used primarily to validate theinformation printed in the maps. However, as indicated in the main text we sometimesinclude cities mentioned in these sources but not in the maps.

ln(Distance to Trade Center). This variable is calculated using the ArcGIS Near Tool.It represents the natural logarithm (ln) of the distance between a region’s centroid and

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the closest trade center region in degrees. The variable takes the value 0 for regions thatcontain medieval trade cities (i.e. for which the trade center dummy is equal to one).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 eight dummy variables: tradecenter, bishop before 1000 AD, imperial city, Hanseatic league, imperial road, medievalmining, coast region and university before 1500 AD. This variable is constructed by theauthor.Commercial Importance Alternative. Identical to the variable commercial importancebut instead of the trade center dummy, it contains the distance to trade center variable,recoded in a way that it is positively associated with the GDP per capita (as the othervariables). ln(Population Density). A region’s Population Density comes from the Euro-stat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_d3dens&lang=en; accessed on October 10th 2012). The values arefrom 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.

A.2 Control Variables and Instruments

Altitude. The Altitude of a region is taken from the website gpsvisualizer.com (ac-cessed at November 8th 2012) and based on the coordinates of its centroid.Bishop before 1000 AD. Dummy variable equal to one if a region includes a city

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that 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 on November 27th, 2012). Forbishoprics in the Holy Roman Empire Oestreich and Holzer (1970b) are addi-tionally consulted. When there were doubts as to whether a diocese or arch-bishopric was founded before 1000 AD, Wikipedia and the catholic encyclopedia(http://www.newadvent.org/cathen/; accessed on November 27th, 2012)were con-sulted.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 thatare district-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 the human capital of a NUTS-2 region with the share (inpercent) of persons aged 25-64 to have attained tertiary education. The variable isobtained from the Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=edat_lfse_11&lang=en; accessed on October 10th,2012). We took 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). They provide a list of cities mentioned as ”Reichsstadte”in the Reichsmatrikel of 1521 (sometimes called the “Wormser Matrikel”). However,there is a considerable amount of uncertainty about when, and after how long, a cityactually becomes an imperial city and what criteria have to be fulfilled. Indeed thereare cases where it is not clear if a city mentioned in the Reichsmatrikel was actuallyan imperial city or not. There are also some cities (e.g., Duren or Chemnitz) that losttheir status as an imperial city after a short period of time or at least prior to 1500 AD.Moreover, according to Wikipedia there seem to be some Dutch cities such as Kampen,Deventer, and Zwolle that became imperial cities in 1495 but are not mentioned inOestreich and Holzer (1970a) or the Reichsmatrikel. We are nevertheless aware that

6

the reliability of some sources, particularly for the Wikipedia article, cannot be verified.What is more, we are interested in the long-run effect that the status of an imperialcity can have on the development of a city. For this reason it might suffice to includethe cities mentioned in the Reichsmatrikel, and that had the status of an imperial citynot only over a short time period. This is also why we do not exclude Duren since itwas imperial city for more than 200 years (from around 1000 AD to 1241 AD) when itbecame property of the earl of Julich.Imperial Road. Dummy variable equal to one if a region contains at least one citythat was located on an important imperial road, i.e. the Via Imperii, the ViaRegia or the Via Regia Lusatiae Superioris. The variable is coded according toinformation 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 December18th, 2012), a map from a website of the federal government of the German State Saxonyon regional development (http://www.landesentwicklung.sachsen.de/download/Landesentwicklung/ED-C_III_Via_Regia_Verlauf.jpg; accessed December 18th,2012) and Wikipedia entries.Inequality. We measure inequality as the ratio of average workers compensation to theGDP per capita. The Sources of GDP per capita and average workers compensationare as listed 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.(http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_SH.zip; accessed on November 8th, 2012).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.(http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_SH.zip; accessed on November 8th, 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 by theEurostat GISCO Database.(http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_SH.zip; accessed on November 8th, 2012).ln(Area). The natural logarithm of a region’s area is taken from the Eurostat regional

7

statistics database http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_d3area&lang=en; accessed on 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 on 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 on 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 on 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 on 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 on 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 calculatedusing the ArcGIS Near Tool. The coordinates of the roads are obtained from the GISMap “World Roads” from ArcGIS Online Database (accessed on November 9th, 2012).ln(Distance to Wittenberg). Variable containing the geodesic distances between each

8

region’s centroid and the city of Wittenberg in the German State of Saxony-Anhalt.The coordinates of Wittenberg are taken from the website geonames.com (accessed onNovember 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 on 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 on 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 on 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 on January 28th, 2012).Mountain Region. Categorical variable equal to one if more than 50% of a populationin a region live in mountainous areas according to the ESPON (European ObservationNetwork for Territorial Development and Cohesion) regional typologies project. Thus,the variable is equal to one if more than 50% of a region’s population live in a mountainarea; it is two if more than 50% of a region’s surface is covered by mountain areas;and it is three if a region has more than 50% of its surface covered by mountainareas and more than 50% of the population live in mountain areas. It is zero when aregion fulfills none of these criteria. The data and an explanation of the classificationscan be downloaded from http://www.espon.eu/export/sites/default/Documents/ToolsandMaps/ESPONTypologies/Typologies_metadata_data_final.xls (accessedon November 8th, 2012).Patents. Total number (over all IPO section and classes) of patent applications submit-

9

ted to the European Patent Office (EPO) in each region in 2009. Data available fromthe Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=pat_ep_ripc&lang=en; accessed on October 10th, 2012).Post Communist Country. A binary variable equal to one if a region lies in an East-ern European post-communist 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 on November 18th, 2012). A region is included if any of these sources mention acity 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 to the World Governance(WGI) Indicators of the World Bank (further information on the index design anddata can be found at: http://www.qog.pol.gu.se/digitalAssets/1362/1362471_eqi---correlates-codebook.pdf; accessed on January 28th 2013). The data on whichthe index is based were collected in 2009. In Belgium, Germany, Netherlands and Hun-gary the index report values at NUTS-1 level in the other countries in our dataset it re-ports values at NUTS-2 level. The data can be downloaded from http://www.qog.pol.gu.se/digitalAssets/1362/1362473_eqi-and-correlates--qog-website-.xlsx(accessed on January 28th, 2013).Unemployment. We measure the average annual unemployment rate (in percent) ofa region in 2009 (including people above the age of 15). Data is from the Eurostatregional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfst_r_lfu3rt&lang=en; accessed on 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). A city is recognized if it is mentioned byany of these sources. If there were doubts about the founding date of a university (orcontradicting dates) Cantoni and Yuchtman (2012) or Wikipedia were used as valida-tion. Since we are only interested in the long-run effect of universities, cities with veryshort periods with universities like, e.g. Vicenza or Piacenza are not coded as having auniversity.

10

A.3 City Level Variables

Unless indicated, the city level variables originate from the same sources and are con-structed in the same way as the regional level variables.

Population. The data on city populations stems from the data set compiled by the Cen-tre of Global Economic History (CGEH) at Utrecht University. They present city popula-tion data for every fifty-year period between 1500 AD and 2000 AD. The data is availableat http://www.cgeh.nl/sites/default/files/def%20europe.xls (accessed Septem-ber 10th, 2013). The CGEH database is based on several different sources like, e.g.Bairoch et al. (1988) or the Encyclopaedia Britannica. For a detailed description ofthe data the reader is referred to http://www.cgeh.nl/sites/default/files/Data%20appendix%20of%20the%20Clio-infra%20database.doc (accessed September 10th,2013) The city population before 1500 AD (i.e. in 1400, 1300 and 1200 AD) is col-lected from Bairoch et al. (1988). The Bairoch et al. data is also used for cities withfewer than 5000 inhabitants between 800 and 1800 AD and are therefore not includedin the CGEH data set. We follow Bosker et al. (2013) and include corrected populationfigures for Bruges and Paris.City Latitude. The Latitude of a city is in decimal degrees adopted from the CEGH citypopulation dataset, or from Bairoch et al. (1988) if not available there.City Latitude. The Longitude of a city in decimal degrees is adopted from the CEGHcity population dataset, or from Bairoch et al. (1988) if not available there.Hanseatic League. Binary variable equal to one if a region contains at least one city thatwas a member of the Hanseatic League. Coded according to Dollinger (1966). For thepanel data regression this variable is time-variant, i.e., it takes into account when a citybecame member of the Hanseatic League. For most cities this information is provided byDollinger (1966). If there was no information in Dollinger (1966) we looked at reliableinformation about the city history (e.g., the city history at the official webpage of thecity or monographs about the history of the city). As an additional measure we consultthe Wikipedia entries of the respective cities. The variable remains time constant after1500 AD.Imperial City. A Dummy Variable equal to one if a region includes at least one city thatwas an imperial city in the Holy Roman Empire. The variable is coded following Oestre-ich and Holzer (1970a). They provide a commented list of cities mentioned as imperialcities (“Reichsstadte”) in the Reichsmatrikel of 1521 (sometimes called the “WormserMatrikel”). The panel version of this variable takes into account the available informa-tion about when a city took on the status of an imperial city (a so-called “Reichsunmit-

11

telbarkeit”). As mentioned, in the description of the regional level variable such infor-mation suffers from a marked amount of uncertainty. However, the German version ofWikipedia provides a list of imperial cities including information about the approximatedate the city become imperial city. This information is often based on corresponding de-crees of kings or emperors of the Holy Roman Empire available in the Regesta Imperii,an archive of all documentary and historiographic documents of the Roman-Germankings until Maximilian I (http://www.regesta-imperii.de/startseite.html). Thisarchive is maintained by the “Akademie der Wissenschaften und der Literatur Mainz”and is a reliable source of historical information.

Still, not all the dates in the list are validated by these sources. In these cases, werely on available information from city history or other sources to assign a date. Despitethese efforts, sometimes sources mention different dates or rely on other criteria. Wethen proceed by assuming the date seeming most plausible from what we infer fromcity history. In the case of the German city of Esslingen, for example, Wikipedia statesthat it was an imperial city from 1181 AD when Friedrich I. made it the administrativecenter of the surrounding hinterland. However, the official website of the city (http://www.tourist.esslingen.de/servlet/PB/menu/1297810_l1/index.html; accessedSeptember 18th, 2013) states that it was first officially mentioned as being an impe-rial city (i.e., “Reichsunmittelbar”) in 1298 AD. In another publication (Pfaff 1840)1209 AD is mentioned as the year that Esslingen became an imperial city. Here westick to the most conservative record and assume 1300 AD as the date. Where it wasimpossible to find satisfactory information, we code the city as an imperial city from1500 AD onward. We code cites as imperial from the time they gained that its statusuntil today. This is due to the fact that we are still primarily interested in the long-runeffect of being an imperial city. An additional source we use to validate the informationin Wikipedia is (apart from city history and official accounts of the city history on therespective city’s webpage) Hugo (1838).Trade City. The trade city dummy is coded according to the procedure described in detailbelow and follows that of the trade center dummy on regional level. The cities codedas trade cities are: Amsterdam, Antwerp, Augsburg, Avignon, Bari, Berlin, Bologna,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, Innsbruck, Kampen, Cologne, Cracow, Leipzig, Linz, Lubeck, Lucca, Lyon,Maastricht, Magdeburg, Mantua, Marseille, Metz, Milan, Minden, Montpellier, Munster,Naples, Narbonne, Nuremberg, Olomouc Orleans, Osnabruck, Padua, Paris, Parma, Per-

12

pignan, Pisa, Plock, Poznan, Prague, Prato, Ravensburg, Regensburg, Reims, Rome,Rostock, Rotterdam, Salzburg, Siena, Soest, St. Malo, Stralsund, Straßbourg, Torun,Toulouse, Tours, Treviso, Troyes, Udine, Ulm, Utrecht, Venice, Verona, Warsaw, Vi-enna, Wismar and Wroclaw. The panel version of the variable takes into considerationinformation about the period in which a city became an important center of trade. Thisinformation is depicted in Table A.5 in this Appendix. The consulted historical sourcesusually only provide basic information about when a city became important (e.g., “dur-ing the 14th century” or “before the 10th century”). As a result, we still have to choosethe exact period from which we code a city as being an important trade center. Here,we stick to the following rule: If the source states simply “9th century” we code thecity as a trade city from the year 900 AD onward. If the source reports, e.g., “beforethe 14th century” we code the city as a trade city from 1300 AD onwards. If it states“between the 13th and 14th century” we code the city as being a trade city from 1400AD onward. If a city signed the treaty of Smolensk in 1229 AD we code it as being atrade city since 1200 AD. In the case of Straßbourg, Dollinger (1966) reports the cityto be important “before 1250 AD”. In this case we coded Straßbourg as being a tradecity since 1200 AD. In general, if two different periods or years are available we alwayschoose the later year. However, the results are robust to the opposite coding, i.e. alwayschoosing the first year. Results are not shown but are available from the author.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). In the panel data version of the variable acity is recognized if it is mentioned by any of these sources. If there were doubts onthe founding date of a university (or contradicting dates) Cantoni and Yuchtman (2012)or Wikipedia are used as validation. Since we are only interested in the long-run effectof universities, cities with very short periods with universities like Vicenza or Piazencaare not coded as having a university. The information about the years in which theuniversities were founded originates from the same sources as indicated above (primarilyRuegg (1993)).

13

Table A.1: Descriptive Data Overview — Regional Level Variables

Variable Obs Mean Std. Dev. Min MaxAltitude 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

14

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

Variable Obs Mean Std. Dev. Min Max

Bishop 1000 AD 372 0.124 0.323 0 1City Latitude 372 48.644 3.615 40.18 56.57City Longitude 372 8.756 5.086 -4.48 22Commercial Importance 372 0.000 0.894 -0.887 3.113Imperial Road 372 0.075 0.264 0 1Imperial City 372 0.094 0.292 0 1Hanseatic League 372 0.145 0.353 0 1Mountain Region 372 0.39 0.888 0 3Printing Press before 1500 AD 372 0.317 0.466 0 1ln(Distance to Coast) 372 -0.454 1.818 -8.0465 1.787ln(Distance to River) 372 -0.637 1.675 -7.089 1.577ln(Population 1200 AD) 86 9.533 0.812 6.908 11.608ln(Population 1300 AD) 207 9.095 1.101 6.908 11.918ln(Population 1400 AD) 183 9.051 1.059 6.908 12.524ln(Population 1500 AD) 372 8.817 0.974 6.908 12.324ln(Population 1550 AD) 31 9.018 0.54 8.006 10.309ln(Population 1600 AD) 282 9.317 0.912 6.908 12.612ln(Population 1650 AD) 32 9.553 0.864 8.006 12.297ln(Population 1700 AD) 296 9.223 1.057 6.908 13.122ln(Population 1750 AD) 301 9.376 1.001 6.908 13.253ln(Population 1800 AD) 366 9.404 0.949 7.601 13.218ln(Population 1850 AD) 364 9.781 0.976 7.601 13.867ln(Population 1900 AD) 324 10.645 1.098 8.006 14.814ln(Population 1950 AD) 298 11.1 1.185 8.006 15.009ln(Population 2000 AD) 359 11.315 1.161 6.908 15.039Trade City 1200 AD 372 0.102 0.303 0 1Trade City 1300 AD 372 0.113 0.317 0 1Trade City 1400 AD 372 0.172 0.378 0 1Trade City 1500 AD 372 0.247 0.434 0 1University before 1500 AD 372 0.102 0.303 0 1

15

Table A.3: Descriptive Data Overview — City Level Panel Variables

Variable Obs Mean Std. Dev. Min Max

Bishop 1000 AD 5208 0.124 0.329 0 1City Latitude 5208 48.644 3.611 40.18 56.57City Longitude 5208 8.756 5.08 -4.48 22Commercial Importance 5208 0.000 0.914 -0.887 4.113Imperial Road 5208 0.075 0.264 0 1Imperial City 5208 0.087 0.282 0 1Hanseatic League 5208 0 .126 0 .332 0 1Mountain Region 5208 0.39 0.888 0 3Printing Press before 1500 AD 5208 0.317 0.465 0 1ln(Distance to Coast) 5208 -0.454 1.818 -8.047 1.787ln(Distance to River) 5208 -0.637 1.675 -7.089 1.577ln(Population) 3501 9.778 1.325 6.908 15.039Trade City 5208 0.222 0.416 0 1University before 1500 AD 5208 .089 .285 0 1

16

Tabl

eA

.4:I

nclu

ded

Trad

eC

ities

and

Reg

ions

—O

verv

iew

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)

17

Tabl

eA

.4–

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)

Olo

mou

cO

lom

ouck

ykr

ajC

zech

Rep

ublic

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2)Pr

ague

Hla

vnım

esto

Prah

aC

zech

Rep

ublic

Dav

ies

and

Moo

rhou

se(2

002)

,Mag

ocsi

(200

2),

Stie

ret

al.

(195

6)

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)

18

Tabl

eA

.4–

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)

19

Tabl

eA

.4–

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)

20

Tabl

eA

.4–

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)

21

Tabl

eA

.4–

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

antu

aM

anto

vaIt

aly

Mag

ocsi

(200

2)

22

Tabl

eA

.4–

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)

Padu

aPa

dova

Ital

yM

agoc

si(2

002)

Schu

lte(1

966)

Parm

aPa

rma

Ital

yM

agoc

si(2

002)

Pisa

Pisa

Ital

yK

ing

(198

5),S

tier

etal

.(1

956)

Spuff

ord

(200

2)Pr

ato

Prat

oIt

aly

Kin

g(1

985)

Spuff

ord

(200

2)R

ome

Rom

aIt

aly

Kin

g(1

985)

,Mag

ocsi

(200

2),S

tier

etal

.(1

956)

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)

23

Tabl

eA

.4–

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)

24

Tabl

eA

.4–

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)

25

Tabl

eA

.5:M

edie

valT

rade

Citi

esan

dR

egio

nsw

ithLo

ng-r

unTr

ade

Act

ivity

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

Ghe

ntA

rr.

Ghe

ntBe

lgiu

mD

ollin

ger

(196

6)12

thce

ntur

yBr

noJi

hom

orav

sky

kraj

Cze

chR

epub

licH

umni

ckia

ndBo

raw

ska

(196

9)9t

hce

ntur

y

Olo

mou

cO

lom

ouck

ykr

ajC

zech

Rep

ublic

Hum

nick

iand

Bora

wsk

a(1

969)

9th

cent

ury

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

26

Tabl

eA

.5–

Con

tinue

dBe

rlin

Berli

nG

erm

any

Dol

linge

r(1

966)

15th

cent

ury

Brun

swick

Brau

nsch

weig

,Dist

rict-

Free

City

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yBr

emen

Brem

en,D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)be

fore

12th

cent

ury

Brem

erha

ven

Brem

erha

ven,

Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

befo

re12

thce

ntur

yC

olog

neC

olog

ne,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

9th

cent

ury

Con

stan

ceK

onst

anz

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yD

ortm

und

Dor

tmun

d,D

istric

t-Fr

eeC

ityG

erm

any

Dol

linge

r(1

966)

Trea

tyof

Smol

ensk

(122

9)Er

furt

Erfu

rt,D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)be

fore

12th

cent

ury

Fran

kfur

t(O

der)

Fran

kfur

t(O

der)

,Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

befo

re12

thce

ntur

yFr

ankf

urt

(Mai

n)Fr

ankf

urt

amM

ain,

Dist

rict-

Free

City

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yG

orlit

zG

orlit

z,D

istric

t-Fr

eeC

ityG

erm

any

Rutk

owsk

i(19

80a)

14th

cent

ury

(137

0)G

reifs

wald

Gre

ifswa

ld,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze(

1923

)be

fore

14th

cent

ury

Ham

burg

Ham

burg

Ger

man

yD

ollin

ger

(196

6)be

fore

1250

Hild

eshe

imH

ildes

heim

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Lube

ckLu

beck

,Dist

rict-

Free

City

Ger

man

yH

eyd

(187

9a)

Trea

tyof

Smol

ensk

(122

9)Lu

nebu

rgLu

nebu

rg,D

istric

tG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yM

agde

burg

Mag

debu

rg,D

istric

t-Fr

eeC

ityG

erm

any

Hey

d(1

879a

)be

fore

10th

cent

ury

Min

den

Min

den-

Lubb

ecke

Ger

man

yD

ollin

ger

(196

6)13

th–

14th

cent

ury

Mun

ster

Mun

ster

,Dist

rict-

Free

City

Ger

man

yD

ollin

ger

(196

6)Tr

eaty

ofSm

olen

sk(1

229)

Nur

embe

rgN

urem

berg

,Dist

rict-

Free

City

Ger

man

yD

ietz

e(1

923)

befo

re9t

hce

ntur

yO

snab

ruck

Osn

abru

ck,D

istric

t-Fr

eeC

ityG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yPa

derb

orn

Pade

rbor

nG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yR

aven

sbur

gR

aven

sbur

gG

erm

any

Schu

lte(1

923)

and

Sche

lle(2

000)

late

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)

Trea

tyof

Smol

ensk

(122

9)St

ralsu

ndSt

ralsu

nd,D

istric

t-Fr

eeC

ityG

erm

any

Die

tze

(192

3)be

fore

14th

cent

ury

27

Tabl

eA

.5–

Con

tinue

dU

lmU

lm,U

rban

Dist

rict

Ger

man

yD

ietz

e(19

23)

befo

re9t

hce

ntur

yW

ismar

Wism

ar,D

istric

t-Fr

eeC

ityG

erm

any

Dol

linge

r(1

966)

13th

–14

thce

ntur

yBu

dape

stBu

dape

stH

unga

ryW

ojto

wic

z(1

956)

14th

cent

ury

Anc

ona

Anc

ona

Ital

yH

eyd

(187

9a)

befo

re12

thce

ntur

yBa

riBa

riIt

aly

Hey

d(1

879a

)be

fore

12th

cent

ury

Bolo

gna

Bolo

gna

Ital

yH

eyd

(187

9b)

14th

cent

ury

Flor

ence

Fire

nze

Ital

yH

eyd

(187

9b)

14th

cent

ury

Gen

oaG

enov

aIt

aly

Hey

d(1

879a

)be

fore

12th

cent

ury

Lucc

aLu

cca

Ital

yH

eyd

(187

9a)

befo

re13

thce

ntur

yM

ilan

Mila

noIt

aly

Hey

d(1

879b

)14

thce

ntur

yN

aple

sN

apol

iIt

aly

Hey

d(1

879b

)be

fore

12th

cent

ury

Parm

aPa

rma

Ital

yH

eyd

(187

9b)

14th

cent

ury

Pisa

Pisa

Ital

yD

ietz

e(1

923)

befo

re14

thce

ntur

yR

ome

Rom

aIt

aly

Hey

d(1

879a

)be

fore

12th

cent

ury

Sien

aSi

ena

Ital

yH

eyd

(187

9b)

13th

cent

ury

(120

9)Ve

nice

Vene

zia

Ital

yH

eyd

(187

9a)

befo

re12

thce

ntur

yK

ovno

Kau

noap

skrit

isLi

thua

nia

Dol

linge

r(1

966)

betw

een

1350

and

1500

Dev

ente

rZu

idwe

st-O

verji

ssel

Net

herla

nds

Dol

linge

r(1

966)

14th

cent

ury

Kam

pen

Noo

rd-O

verji

ssel

Net

herla

nds

Dol

linge

r(1

966)

14th

cent

ury

Cra

cow

Mia

sto

Kra

kow

Pola

ndH

umni

ckia

ndBo

raw

ska

(196

9)9t

hce

ntur

y

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

yTo

run

Bydg

osko

-Tor

unsk

iPo

land

Dol

linge

r(1

966)

13th

–14

thce

ntur

y

28

Tabl

eA

.5–

Con

tinue

dW

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

29

B. Robustness Checks

B.1 Robustness to Influential Observations, Additional Controls andDifferent Region Size

First, we account for the effect some additional variables might have on both the current levelof regional development and/or medieval trading activities. In order to do so, we add fourdifferent variables to the set of control variables used in Tables 5 and 6.2 We add the dummyvariable indicating regions with copper or salt mining sites in the medieval age to establish ifsuch economic activities at least partly cause the significant effects we attribute to medieval tradeactivities. This might be possible if, e.g. mining activities actually led to higher trade activitiesin the regions in which they took place. We add this variable to the specifications three andeight in Table 6, i.e. we add the variable to the set of control variables capturing historical regioncharacteristics.

Additionally, we include an interaction term of latitude and longitude of a region’s centroidto the set of basic geographic controls and re-estimate specifications three and six of Table 5including this interaction effect. The justification for this is to look at whether developmentlevels systematically differ when changing latitude and longitude and vice versa. In this waywe can identify, for example, the effects of different climatic conditions varying along differentlatitudes for countries located at the same longitudes.

Furthermore, we add the share of Roman Catholic people in a country’s population in 2009 tothe set of growth covariates and then re-run the regression in Table 6 columns (4) and (9). Thistakes account of the fact that the impact of Protestantism (or religion in general) on economicoutcomes might not be captured adequately by the Distance to Wittenberg variable —at leastnot today, 500 years after the Reformation.

Finally, we add a dummy variable equal to one if a region includes an important residence cityof a clerical or secular ruler. Residence cities of important rulers were the centers of politicaland economic power in the territory of the ruler. Therefore, it is quite likely that they showedhigh growth rates of population and economic activity and perhaps explain a significant partof medieval trade and its long-lasting effects on agglomeration and development (e.g., Ringrose1998).

The results obtained when adding these supplementary variables to the mentioned regressionspecifications are shown in Table B.1. The dummy for medieval mining regions and the latitudelongitude interactions are not significant (Columns one to four in Table B.1). Apart from thefact, that some of the included covariates seem to be significant (e.g., the catholic variable) thetrade center dummy and the distance to trade center variable retain their significance and thesize of the coefficients is comparable to that obtained in the original estimates or larger.

A second robustness check is to look at whether our results are sensitive to removing influentialobservations. To test this we re-estimate Table 6 but remove regions that show a high leverage,

2A descriptive overview over these variables is provided in Table B.8. Furthermore, a detailed descrip-tion of the variables and their sources is available in section B.3

30

i.e. have a large impact on the coefficient estimate. This can be done by computing the DFITSstatistics, developed by Belsely et al. (1980). They suggest considering an observation influentialif |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 witha DFITS statistic above this threshold are removed from the sample and the estimations arethen based on this reduced sample. The results of this task are shown in Table B.2. Onceagain, the exclusion of influential observations only leads to minor quantitative changes in thecoefficient values (in both directions). Qualitatively, the results seem to be completely unaffectedby influential observations.

A third robustness check accounts for the obvious considerable differences in the size of theNUTS-3 regions resulting from political decisions as well as differences in population densityacross the included countries. In the previous estimations, we already included the area ofa individual NUTS-3 region as a control variable. However, the size of the NUTS-3 regionsprobably varies more between than within countries. In consequence, we include a country’saverage NUTS-3 region area as a supplementary control and re-estimate the specifications incolumn (5) and (10) of Table 5 as well as the IV regressions in column (1) and (3) in Table6. The results are shown in Table B.3. Although average region area has a significant andsurprisingly positive sign in the OLS estimations, the coefficients of the two trade measuresare virtually untouched. Therefore, the different size of the NUTS-3 regions, both within andbetween the countries, do not affect our results.

31

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

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.053

)(0

.055

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.053

)

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dard

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with

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ere

port

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pare

nthe

ses.

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ent

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tical

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roat

the

***1

%,*

*5%

and

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vel.

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unit

ofob

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atio

nis

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regi

on.

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the

cont

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incl

uded

inea

chsp

ecifi

catio

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ain

text

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the

thir

dro

w.

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sion

incl

udes

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port

ed.

32

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

***

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-0.1

11**

*-0

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**-0

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*(0

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)(0

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.038

)

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ntry

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mie

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UT

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mie

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mie

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aphi

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ols

Yes

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Yes

No

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Yes

Yes

Yes

No

Geo

grap

hic

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tral

ityC

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ols

Yes

No

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Yes

No

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No

No

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ion

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ract

erist

ics

No

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No

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No

No

Yes

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No

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Hist

oric

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cter

istic

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No

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No

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No

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elop

men

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ovar

iate

sN

oN

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sN

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ontr

ols

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No

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No.

ofre

mov

edre

gion

s40

4540

4147

4045

4143

44O

bs.

799

794

799

477

771

799

794

798

475

774

Adj

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20.

844

0.89

10.

829

0.91

10.

901

0.83

70.

887

0.81

60.

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0.89

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anda

rder

rors

adju

sted

for

two-

way

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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

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ion

isa

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e.T

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eth

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ance

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est

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free

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ing

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ons,

with

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ines

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cate

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form

erG

DR

and

loca

ted

inan

East

ern

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pean

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-com

mun

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sitio

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rthe

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sses

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lnof

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gion

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ea.

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oric

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gion

char

acte

ristic

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nsist

ofa

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my

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indi

catin

gre

gion

sw

itha

univ

ersit

yfo

unde

dbe

fore

1500

AD

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tad

opte

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intin

gte

chno

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befo

re15

00A

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tiest

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ere

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atic

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ue,w

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ities

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elo

cate

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dist

ance

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on’s

cent

roid

toW

itten

berg

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hegr

owth

cova

riate

senc

ompa

ssa

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on’s

unem

ploy

men

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te,n

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ker

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pens

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n.Fu

rthe

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incl

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n25

-64

with

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iary

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atio

non

NU

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2le

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ityof

gove

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ent

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xon

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ofan

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age

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kers

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pens

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nto

are

gion

’sG

DP

per

capi

taas

ineq

ualit

ym

easu

re.

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set

ofal

lrob

ust

cova

riate

sen

com

pass

esal

titud

e,th

eln

dist

ance

sto

airp

orts

and

railr

oads

,dum

mie

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eeci

ties,

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ities

,cap

italc

ities

ofau

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mou

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gion

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st-c

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unist

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trie

s,Ea

ster

nG

erm

any,

the

lnof

are

gion

’sar

ea,t

hesh

are

ofpe

ople

with

tert

iary

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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

.

33

Tabl

eB

.3:M

edie

valT

rade

and

Con

tem

pora

ryEc

onom

icD

evel

opm

ent

—A

ccou

ntin

gFo

rD

iffer

ent

Reg

ion

Size

Dep

.Va

r.ln

(GD

Ppe

rca

pita

)(1

)(2

)(3

)(4

)M

etho

dO

LSIV

OLS

IV

Trad

eC

ente

r0.

0701

***

0.30

6***

(0.0

21)

(0.1

05)

ln(D

istan

ceto

Trad

eC

ente

r)-0

.052

9-0

.519

***

(0.0

41)

(0.1

73)

ln(A

rea)

-0.0

432*

**-0

.053

5***

-0.0

395*

*-0

.033

9**

(0.0

16)

(0.0

16)

(0.0

17)

(0.0

15)

Aver

age

Are

a0.

0023

**-0

.003

10.

0222

***

-0.0

008

(0.0

01)

(0.0

03)

(0.0

04)

(0.0

03)

Obs

.81

881

881

881

8C

ente

red

R2

\R2

0.87

80.

569

0.87

70.

515

Und

erid

entif

catio

nTe

st14

.06

16.2

5p-

valu

e0.

000

0.00

0O

verid

entifi

catio

nTe

st0.

307

0.09

81p-

valu

e0.

580

0.75

4N

otes

.St

anda

rder

rors

adju

sted

fort

wo-

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%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aN

UT

S-3

regi

on.

The

set

ofco

varia

tes

enco

mpa

sses

altit

ude,

the

lndi

stan

ces

toai

rpor

ts,

railr

oads

and

river

s,du

mm

ies

for

dist

rict

free

citie

s,ca

pita

lciti

es,c

apita

lciti

esof

auto

nom

ous

regi

ons,

post

-com

mun

istic

tran

sitio

nco

untr

ies,

East

ern

Ger

man

y,th

eln

ofa

regi

on’s

area

,th

esh

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.

Inea

chre

gres

sion

anad

ditio

nal

varia

ble

isin

clud

edre

pres

entin

gth

eav

erag

esiz

eof

aN

UT

S-3

regi

onin

each

coun

try

incl

uded

divi

ded

by10

0(A

vera

geA

rea)

.A

part

from

the

lnof

aco

untr

y’s

area

the

coeffi

cien

tsan

dst

anda

rder

rors

ofth

eco

ntro

lvar

iabl

ear

eno

tre

port

ed.

Inco

lum

ns(2

)and

(4)w

here

IVre

gres

sions

ares

how

n,th

eCen

tere

dR

2is

repo

rted

inst

ead

ofth

eAdj

uste

dR

2 .The

thefi

rsts

tage

resu

ltsof

theI

Ves

timat

ions

are

omitt

edbu

tav

aila

ble

from

the

auth

or.

The

Ove

riden

tifica

tion

test

repo

rts

the

Han

sen

J-st

atist

ican

dth

eU

nder

iden

tifica

tion

test

repo

rts

the

Kle

iber

gen-

Paap

rkLM

stat

istic

(nul

lhyp

othe

sis:e

quat

ion

isun

derid

entifi

ed).

Each

regr

essio

nin

clud

esa

cons

tant

not

repo

rted

.

34

B.2 Results for Alternatively Coded Medieval Trade VariablesTo account for the uncertainties about medieval trade activities, discussed in the main text, weconduct OLS, IV and mediation analysis estimations with alternatively coded medieval tradevariables, i.e. alternative samples of medieval trade cities. To be precise, for each of thesefour alternative trade center dummies we re-run the regression specification in Table 5 column(5) where we employed all robust covariates from the previous regressions as controls. Thisspecification is used —as with most features of the analysis above— because it yields the mostconservative estimates. We further repeat the LIML and Lewbel (2012) instrumental variablesregressions from Table 6 columns (1) and (2) as well as the estimation in Table 8 column (1)where we regress the ln city growth between 1200 and 1500 AD with the trade center dummy,the initial population level and appropriate historical controls. At last, we repeat the mediationanalysis with ln relative GDP density as mediator variables (originally reported in Table 9 column(4)). The results of these re-estimations are shown in Tables B.3–B.7.

First, for the regressions in Table B.4 we construct a sample of trade cities excluding citiesmentioned by only one of our sources. These cities are: Amberg, Bruck, Fulda, Maastricht,Malbork, Mantoa , Minden, Orleans, Parma, Pecs, Piotrkow Trybunalski, Plock, Rotterdam,Saint-Malo , Udine, Utrecht and Zwickau.

Second, we exclude cities for which we are not completely sure about their importance, al-though they are reported in more than one of our sources. Those cities are Paderborn, Einbeck,Greifswald, Braniewo, Gorlitz, Metz, Palanga, Como, and Stargard. For example, we excludePaderborn because despite the fact that it was a member of the Hanseatic League and lay on theHellweg no other source mentioned it, and Dollinger (1966) did not consider it as a Hanseaticcity of special importance. Furthermore, the data collected by Escher and Hirschmann (eds.)(2005) implies that the existing trade activity in Paderborn was of relatively low importancecompared to, e.g. Cologne, Munster, Dortmund or other leading trade cities. The results usingthis sample of trade cities are reported in Table B.5

Third, as shown in Table B.6, we also conduct our empirical analysis with a sample of tradecities including additional cities mentioned by some of the sources, but for which we —havingconsulted several different sources about the history of the respective places— are in doubt oftheir actual importance during the middle ages, at least over a longer period. This is the case,for example, for Anklam, a member city of the Hanseatic League situated on an important traderoute according to a map in Stier et al. (1956). However, none of the other sources mentionAnklam as an important trade center and Dollinger (1966) did not note a special role for Anklamwithin the Hanseatic League. The other three cities we add are Dijon, Piacenza and Aigues-Mortes.

Finally, we try to ensure that we do not include trade cities that only experienced significanttrade activities for a short period —and thus not long enough to result in a lock-in to a superiordevelopment path. Thus, we build an alternative sample of trade cities that includes only thosecities for which historical sources indicate long-run trade activities (i.e., cities that were importanttrade cities around 1500 AD and that were also important before that time). An overview of

35

these cities, for the earliest period in which trade activities are reported, and the sources thatmention the respective cities are given in Table A.5. This re-coding is based on informationprimarily derived from Wilhelm Heyd’s two volumes on medieval Levant trade (Heyd 1879aand 1879b). Heyd provides information about medieval trade activities in the Levant and themost important parties involved, in chronological order beginning with the end of migrationperiod (“Barbarian Invasions”). We take the period mentioned in the chapter headings of thechapter in which the trade activities of a city are firstly mentioned as the period with the earliestauthenticated trade activities. If Heyd explicitly reports a date or a time frame then we use thisdate/time. Heyd (1879a,b) provides information about the trade activities of Austrian, Belgian,French, German and Italian cities. Additionally, the monograph about the Hanseatic Leaguewritten by Dollinger (1966) includes a number of maps depicting, e.g. the main Hanseatic traderoutes and trade cities before 1250, between 1250 and 1350 and 1350 and 1500 (always AD).Another map reports important trade routes (e.g., the salt way) and the cities that signed thetreaty of Smolensk in 1229 AD. According to Dollinger (1966), this map covers the period 1286to approximately 1336. We stick to the dates given in these maps when assigning the respectivecities the dates when they are mentioned first. All in all, this, along with other maps in Dollinger(1966), contains information about trade activities in France, Germany, Lithuania and Poland.Finally, for Germany, Italy and France the work of Dietze (1923), on the history of German trade,reports significant trade activities and locations since the “pre-historical” period. We include acity in the sample if Dietze (1923) reports that city to be an important player in early and highmedieval trade.

For Austria, the Czech Republic and Poland information is provided by three digitized mapsfrom T. Matthew Ciolek’s OWTRAD website. The first is based on a map printed in Humnickiand Borawska (1969) and shows “Central European Trade Routes 800 – 900 CE”.3 The secondmap originates from Wojtowicz (1956) and according to the OWTRAD website reports “Majortrade roads in Poland and adjacent border regions 1340–1400 CE”.4 Form this map we includeinformation about Polish trade cities. The last map from the OWTRAD project is based onRutkowski (1980) and is about ‘Major trade roads in Poland and adjacent border regions in 1370CE”.5 From this map, we solely include the German city of Gorlitz since all the other relevantcities in the map were mentioned by another source depicting trade in an earlier period.

As such, this is likely to represent the most selective sample and probably contains only thosecities in which important medieval trade activities are relatively certain. Overall we were ableto find information for about 75 of our 115 medieval trade cities. Results based on this sampleof trade cities are depicted in Table B.7

3The map can be found under the following URL: http://www.ciolek.com/OWTRAD/DATA/tmcCZm0800.html; accessed on June 11th, 2013.

4The original title of the map is (according to the OWTRAD website) “Trade roads at the times ofCasimir the Great”). The map is available from the OWTRAD website at http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370a.html; accessed June 11th, 2013.

5The map can be accessed under the URL http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370.html;accessed June 11th, 2013.

36

As one can infer from the results in these Tables, the results typically change only marginallywith the alternative trade center variables. Their coefficients even tend to be a little larger thanwith the original sample of trade cities. However, this does not hold for the estimations of Table8. At least, with the last sample of trade cities containing cities with reported trade activities inearlier periods. The coefficient of the trade center dummy becomes insignificant when using thisalternative sample.

37

Tabl

eB

.4: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

(Po

pu

lati

on

1500

Po

pu

lati

on

1200

)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

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363*

**0.

0613

**0.

499*

*0.

3267

***

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0912

(0.0

23)

(0.1

33)

(0.0

26)

(0.2

39)

(0.0

71)

(0.0

18)

ln(R

elat

ive

GD

PD

ensit

y)0.

203*

**(0

.010

9)

Obs

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881

881

886

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818

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ente

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ted

112.

7U

nder

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tion

cons

ult

the

mai

nte

xtor

the

note

sto

the

orig

inal

tabl

esm

entio

ned

inth

eth

ird

row

.In

colu

mns

(1)

and

(4)

the

adju

sted

R2

isre

port

ed.

Inco

lum

n(2

)an

d(3

)th

ece

nter

edR

2is

show

nan

din

colu

mns

(5)

and

(6)

the

R2

.In

colu

mns

(2)

and

(3)

the

resu

ltsof

the

first

stag

ear

eom

itted

but

avai

labl

efr

omth

eau

thor

.E

ach

regr

essi

onin

clud

esa

cons

tant

not

repo

rted

.

38

Tabl

eB

.5: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

(Po

pu

lati

on

1500

Po

pu

lati

on

t)

ln(R

elat

ive

GD

PD

ensit

y)ln

(GD

Ppe

rca

pita

)(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***

0.51

3**

0.36

21**

*-0

.003

6(0

.023

)(0

.14)

(0.0

27)

(0.2

47)

(0.0

74)

(0.0

2)ln

(Rel

ativ

eG

DP

Den

sity)

0.20

3***

(0.0

11)

Obs

.81

881

881

886

818

818

Adj

.R

2\C

ente

red

R2

\R2

0.87

70.

544

0.62

10.

347

0.93

90.

919

AC

ME

0.07

24**

*D

irect

Effec

t-0

.002

9To

talE

ffect

0.06

94**

%of

tota

lmed

iate

d10

2.8*

*U

nder

iden

tifica

tion

Test

15.1

816

0.2

p-va

lue

0.00

00.

000

Ove

riden

tifica

tion

Test

0.00

858

.41

p-va

lue

0.93

0.31

7A

PF-

stat

istic

ofex

clud

edIV

’s8.

5743

.92

p-va

lue

0.00

00.

000

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

wit

hin

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

.Fo

rth

eco

ntro

lsin

clud

edin

each

spec

ifica

tion

cons

ult

the

mai

nte

xtor

the

note

sto

the

orig

inal

tabl

esm

entio

ned

inth

eth

ird

row

.In

colu

mns

(1)

and

(4)

the

adju

sted

R2

isre

port

ed.

Inco

lum

n(2

)an

d(3

)th

ece

nter

edR

2is

show

nan

din

colu

mns

(5)

and

(6)

the

R2

.In

colu

mns

(2)

and

(3)

the

resu

ltsof

the

first

stag

ear

eom

itted

but

avai

labl

efr

omth

eau

thor

.E

ach

regr

essi

onin

clud

esa

cons

tant

not

repo

rted

.

39

Tabl

eB

.6: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

(Po

pu

lati

on

1500

Po

pu

lati

on

t)

ln(R

elat

ive

GD

PD

ensit

y)ln

(GD

Ppe

rca

pita

)(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.

572*

*0.

3268

***

0.00

38(0

.021

)(0

.114

)(0

.024

)(0

.235

)(0

.62)

(0.0

16)

ln(R

elat

ive

GD

PD

ensit

y)0.

202*

**(0

.011

)

Obs

.81

881

881

886

818

818

Adj

.R

2\C

ente

red

R2

\R2

0.87

80.

552

0.62

30.

360.

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-

valu

e0.

001

0.00

0O

verid

entifi

catio

nTe

st0.

192

70.5

6p-

valu

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

wit

hin

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

.Fo

rth

eco

ntro

lsin

clud

edin

each

spec

ifica

tion

cons

ult

the

mai

nte

xtor

the

note

sto

the

orig

inal

tabl

esm

entio

ned

inth

eth

ird

row

.In

colu

mns

(1)

and

(4)

the

adju

sted

R2

isre

port

ed.

Inco

lum

n(2

)an

d(3

)th

ece

nter

edR

2is

show

nan

din

colu

mns

(5)

and

(6)

the

R2

.In

colu

mns

(2)

and

(3)

the

resu

ltsof

the

first

stag

ear

eom

itted

but

avai

labl

efr

omth

eau

thor

.E

ach

regr

essi

onin

clud

esa

cons

tant

not

repo

rted

.

40

Tabl

eB

.7: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

(Po

pu

lati

on

1500

Po

pu

lati

on

t)

ln(R

elat

ive

GD

PD

ensit

y)ln

(GD

Ppe

rca

pita

)(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.

0578

**0.

3276

***

0.07

8**

0.26

40.

3072

***

-0.0

014

(0.0

26)

(0.1

14)

(0.0

3)(0

.262

)(0

.083

)(0

.022

)ln

(Rel

ativ

eG

DP

Den

sity)

0.13

75**

*(0

.008

)

Obs

.81

881

881

886

818

818

Adj

.R

2\C

ente

red

R2

\R2

0.87

70.

566

0.62

0.31

10.

938

0.92

4A

CM

E0.

0611

***

Dire

ctEff

ect

-0.0

006

Tota

lEffe

ct0.

0605

**%

ofto

talm

edia

ted

98.6

2**

Und

erid

entifi

catio

nTe

st15

.02

149.

52p-

valu

e0.

000

0.00

0O

verid

entifi

catio

nTe

st0.

206

64.0

5p-

valu

e0.

650

0.16

4A

PF-

stat

istic

ofex

clud

edIV

’s9.

1278

.01

p-va

lue

0.00

00.

000

Not

es.

Stan

dard

erro

rsad

just

edfo

rtw

o-w

aycl

uste

ring

wit

hin

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

.Fo

rth

eco

ntro

lsin

clud

edin

each

spec

ifica

tion

cons

ult

the

mai

nte

xtor

the

note

sto

the

orig

inal

tabl

esm

entio

ned

inth

eth

ird

row

.In

colu

mns

(1)

and

(4)

the

adju

sted

R2

isre

port

ed.

Inco

lum

n(2

)an

d(3

)th

ece

nter

edR

2is

show

nan

din

colu

mns

(5)

and

(6)

the

R2

.In

colu

mns

(2)

and

(3)

the

resu

ltsof

the

first

stag

ear

eom

itted

but

avai

labl

efr

omth

eau

thor

.E

ach

regr

essi

onin

clud

esa

cons

tant

not

repo

rted

.

41

However, in sum, none of our conclusions and general results are invalidated by the alternativesamples of trade cities. As such, the results are robust to considerable changes in the sample dueto uncertainty of historical information and underlying data selection criteria.

B.3 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 (e.g., 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; accessedMay, 8th 2013). As always, we took the values from 2009.

An overview of the additional variables used for the robustness checks is provided in TableB.8 below:

Table B.8: Descriptive Overview of 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

42

C. Additional Results

C.1 Further Results Using the Index of Commercial Importance and anAlternative Agglomeration Measure

We report the results of estimating Table 8 and Table 9 using the index of commercial importanceinstead of the trade city dummy (Table C.1 and C.2, respectively). In the panel data REestimations the imperial city, Hanseatic League, university before 1500 AD, and the trade citydummies are used as time varying variables as in all the panel data regressions. They vary in theyears from 1200 to 1500 AD and remain constant afterwards. Hence, the index of commercialimportance is a time varying variable until 1500 AD and afterwards it becomes a constant.However, for the cross-sectional regressions (Table 8 columns (1)–(3) and Table 9 columns (3)–(10)) we use the average of this index of commercial importance as dependent variable. In thepanel regressions (Table 8 column (4) and (5) and Table 9 columns (1) and (2)) for the yearsafter 1500 AD, we replace the values of the index with its medieval average (i.e. the averagebetween 1200 and 1500 AD) and before use the time varying version. Since we cannot includethe variables used for the construction of the index as controls, the set of covariates is reducedto the ln distances of a city to the next river or coast and a dummy whether a city lies in aregion classified as a mountain region by the EU regional statistics. Furthermore, we control fora city’s latitude and longitude and include country fixed effects. However, in the estimations ofTable C.2 column (3) onward the printing press before 1500 AD dummy is additionally included(as in the estimations in the main text).

Concerning the results of Table C.1, we see that sometimes the results are somewhat weakerwith respect to the significance of the variables (especially concerning the results for city growthbetween 1400 and 1500 AD). Nevertheless, the overall results, and thus also the general impli-cations of the findings, do remain the same.

In Table C.2 the results are virtually identical to those obtained with the trade city dummyvariable with respect to sign and significance of the obtained OLS regression coefficients.

Finally, the result of the estimation of Table 10 using the ln population density of a NUTS-3region as mediating agglomeration measure is shown (Table C.3). The results are almost identicalto those obtained with the relative GDP density. However, probably the biggest differencebetween both estimations is that the average ACME using the population density is clearlylower. Nevertheless, since it is always significant and on average around three quarters of theeffect of medieval trade on ln GDP per capita is mediated by the ln population density, our mainconclusion does hold.

43

Tabl

eC

.1:M

edie

valT

rade

Act

ivity

and

City

Gro

wth

—Es

timat

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(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)

(1)

(2)

(3)

(4)

(5)

Met

hod

OLS

RE

Com

mer

cial

Impo

rtan

ce0.

593*

**0.

272*

**0.

136

0.40

9***

0.16

3***

(0.9

3)(0

.065

)(0

.085

)(0

.033

)(0

.043

)ln

(Pop

ulat

ion

1200

AD

)-0

.709

***

(0.1

7)ln

(Pop

ulat

ion

1300

AD

)-0

.62*

**(0

.068

)ln

(Pop

ulat

ion

1400

AD

)-0

.35*

**(0

.077

)ln

(Pop

ulat

ion t

)-0

.401

***

(0.0

53)

Obs

.86

207

183

879

451

Adj

.R

2 \ov

eral

lR2

0.37

70.

394

0.15

60.

253

0.35

Num

ber

ofC

lust

ers

372

209

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

and

adu

mm

yw

heth

era

city

lies

ina

regi

oncl

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

eco

untr

yfix

edeff

ects

.In

colu

mns

(4)

and

(4)

we

addi

tiona

llyin

clud

eye

arfix

edeff

ects

.Ea

chre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

44

Tabl

eC

.2:M

edie

valC

omm

erci

alIm

port

ance

and

Long

-run

City

Dev

elop

men

t

Dep

.Va

r.ln

(Pop

ulat

ion)

ln(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)l

n(Po

pula

tion

1500

)ln

(Pop

ulat

ion

1600

)ln

(Pop

ulat

ion

1700

)ln

(Pop

ulat

ion

1800

)ln

(Pop

ulat

ion

1900

)ln

(Pop

ulat

ion

2000

)ln

(Po

pu

lati

on

2000

Po

pu

lati

on

1500

)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Com

mer

cial

Impo

rtan

ce0.

319*

**0.

0390

***

0.25

9***

0.29

1***

0.35

9***

0.32

2***

0.34

4***

0.43

9***

0.33

***

(0.0

38)

(0.0

13)

(0.0

65)

(0.0

55)

(0.0

78)

(0.0

71)

(0.0

88)

(0.0

89)

(0.0

79)

ln(P

opul

atio

n13

00)

0.35

5***

0.31

5***

0.14

6*0.

197*

*0.

217*

*0.

11(0

.068

)(0

.065

)(0

.088

)(0

.08)

(0.1

)(0

.087

)ln

(Pop

ulat

ion t

)-0

.090

4***

(0.0

14)

ln(P

opul

atio

n15

00)

-0.7

33**

*(0

.068

)

Obs

.3,

501

2,33

620

717

317

820

519

020

320

335

9ov

eral

lR2 \

Adj

.R

20.

583

0.28

80.

454

0.57

80.

393

0.37

10.

246

0.27

30.

366

Num

ber

ofC

lust

ers

372

369

Not

es.

Rob

ust

stan

dard

erro

rsar

ere

port

edin

pare

nthe

ses

inco

lum

ns(3

)-

(10)

.St

anda

rder

rors

clus

tere

dat

city

leve

lar

ere

port

edin

pare

nthe

ses

inco

lum

ns(1

)an

d(2

).C

oeffi

cien

tis

stat

isti

cally

diff

eren

tfr

omze

roat

the

***1

%,

**5

%an

d*1

0%

leve

l.T

heun

itof

obse

rvat

ion

isa

city

.In

colu

mns

(1)

and

(2)

the

over

all

R2

isre

port

ed,

inal

lot

her

colu

mns

,th

eA

dj.

R2

issh

own.

The

set

ofco

vari

ates

enco

mpa

sses

the

lndi

stan

ces

ofa

city

toth

ene

xtri

ver

orco

ast,

and

adu

mm

yw

heth

era

city

lies

ina

regi

oncl

assi

fied

asa

mou

ntai

nre

gion

byth

eE

Ure

gion

alst

atis

tics

.In

the

esti

mat

ions

ofco

lum

n(3

)on

war

dth

edu

mm

yva

riab

lein

dica

ting

whe

ther

aci

tyad

opte

dpr

inti

ngte

chno

logy

prio

rto

1500

AD

isad

diti

onal

lyin

clud

ed.

Furt

herm

ore,

we

cont

rol

for

aci

ty’s

lati

tude

and

long

itud

ean

din

clud

eco

untr

yfix

edeff

ects

.In

colu

mns

(4)

and

(4)

we

addi

tion

ally

incl

ude

year

fixed

effec

ts.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

45

Table C.3: 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

0.3104*** 0.1691*** 0.1524***(0.077) (0.055) (0.049)

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 0.954 0.887 0.907 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(Population 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. 85 182 203 818 818 818R2 0.867 0.87 0.871

Notes. Robust standard errors are reported in parentheses. Coefficient is statistically differentfrom 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 airports andrailroads, 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 before 1500AD dummy. Each regression includes a constant not reported. ACME is the “Average CausalMediation Effect” and means how much of the effect of medieval trade is mediate, i.e. worksindirectly through the relative GDP density.

46

C.2 Results Using the Bosker et al. (2013) Data SetIn Table C.4 we present the results of estimating equations (6) and (7) in the main text usingthe data set employed in the study of Bosker et al. (2013). We run separate estimations for themedieval period (800–1500 AD) and for the entire observation period (800–1800 AD). Among thevarious variables available, we choose the following as control variables for our regressions: thesea and river dummies to account for 1st order geography. We include the dummy representingcities located at a hub of roman roads or at roman road (but not a hub) to capture the impact ofthe Roman legacy on European city development in later periods. Then, we consider the bishopand archbishop dummies to account (as in the main regression specification) for the importantimpact that the church had on city development, especially during the medieval and early modernperiod. Next, we include a dummy variable reporting whether a city was capital of a state inthe respective period or part of a “large state”. Further, a dummy representing the presence ofa local participative government (such as the occurrence of a commune, town councils etc.) isincorporated. We also include the extended version of De Long and Shleifer’s (1993) “free/ princevariable” to account for the nature of political institutions in the country where a city is located.What is more, we include the university dummy and a variable showing the number of timesa city was plundered during each century. A plunder can be considered as a severe negativeshort-time shock for city development. Moreover, we include the urban potential variable asvariable in our regressions. This variable represents the distance weighted sum of the size of allChristian cities around a particular city. Hence, the variable accounts for the urban environmentin which a city is embedded. Concerning trade this variable can have different effects. On theone hand, many highly populated areas near a city can indicate a potentially large market forthe commodities traded in a city. From this perspective, the market potential variable could bepositively connected with trade. On the other hand, large cities in the neighborhood also meanstrong competition between the cities and thus urban potential might be negatively related tothe commercial importance of a city.6 Finally, we control for the impact of climatic conditions oncity development by using a measure of the agricultural potential (i.e., land productivity classes)of each city.7. Additionally, we include year and country fixed effects in each regression.

We construct an alternative version of our index of medieval commercial importance based onthe variables in the data set. To construct the index, we follow the same procedure as in the maintext. That is, we choose the nine variables that remain significant predictors of the trade citydummy when included simultaneously in a probit estimation (not shown) and are meaningfuldeterminants of commercial importance from a theoretical point of view.8 These variables are the

6Evidence offered in Bosker and Buringh (2012) points towards the latter, although the authors studythe impact of urban potential on the probability of becoming a city, not the development of alreadyexisting cities.

7Detailed information about these variables is available in the Data Appendix to the Bosker etal. (2013) paper that can be found here:https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtYWFydGVuYm9za2VyfGd4Ojc1YmU4M2IzZDExMDYzMzI; accessed on September9th, 2013

8As in the original version of the index, those variables yield a Pseudo R2 of around 0.2

47

sea, river, bishop, archbishop, capital city, hub of roman road, university and commune dummysupplemented by the trade city dummy itself. Similarly, as in the main text, we simply add upthe variables and subtract the mean of the index from each of its values. Again, as in the mainregressions, we use the values of the yearly index values for the medieval period and afterwardsthe average during the medieval period. We do not include the variables used to construct theindex as control variables in the estimations where we have used the index as trade measure.

In Table C.4 the first four columns show the results for the medieval period and in the remain-ing columns those for the full observation period are reported. We see that in every regression,the trade city dummy enters with a significantly positive coefficient that has approximately thesame size as in the regressions in the main text. The coefficients obtained using the index ofcommercial importance are even a little bit larger than those in the original regressions

Turning to the control variables, we record some noteworthy results. First, the importance ofthe church and political factors is underlined. The archbishop and commune dummy are signifi-cant at least in explaining the level of city population and the capital city is always significant.Second, (Christian) urban potential has a positive influence on city population but —at least forall periods— a negative on city growth. The latter indicates that it is probable that both of theeffects discussed above do exist. However, the effect of trade seems to be virtually unaffected bythe inclusion of this variable. Complementary to that, the urban potential variable is not foundto be a robust predictor of the trade city dummy.9 This might imply that both effects offset eachother, although this will need to be studied in more detail in future. Third, the free or princedummy of DeLong and Shleifer is significant only when the periods after the medieval era aretaken into account. Finally, agricultural productivity seems to matter for long-run city devel-opment although the three dummies representing different zones of agricultural productivity arenot always significant.

All in all, our conclusions remain intact when using a different data set and controlling foradditional factors.

9For example, it is insignificant when additionally included to the probit estimation with the eightvariables used for the index construction.

48

Tabl

eC

.4:M

edie

valT

rade

and

Long

-Run

City

Dev

elop

men

t—

Usin

gth

eBo

sker

etal

.(2

013)

Dat

a

Dep

.Va

r.ln

(Popu

lati

on

t)l

n(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)l

n(P

opu

lati

on

t)l

n(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)l

n(P

opu

lati

on

t)l

n(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)l

n(P

opu

lati

on

t)l

n(P

op

ula

tio

nt

+1

Po

pu

lati

on

t)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Med

ieva

l(80

0–15

00A

D)

All

Peri

ods

Trad

eC

ity0.

257*

**0.

0945

***

0.32

0***

0.07

69**

*(0

.063

)(0

.033

)(0

.055

)(0

.028

)C

omm

erci

alIm

port

ance

0.23

8***

0.06

81**

*0.

271*

**0.

0640

***

(0.0

31)

(0.0

16)

(0.0

32)

-(0.

013)

ln(P

opul

atio

n)-0

.149

***

-0.1

17**

*-0

.165

***

-0.1

22**

*(0

.026

)(0

.026

)(0

.018

)(0

.018

)Se

a0.

121

0.06

780.

16**

*0.

068*

*(0

.08)

(0.0

47)

(0.0

56)

(0.0

32)

Riv

er-0

.000

50.

0163

0.00

030.

0238

(0.0

77)

(0.0

4)(0

.055

)(0

.031

)R

oman

Roa

dH

ub-0

.233

**-0

.070

6*-0

.122

**-0

.073

1**

(0.0

91)

(0.0

42)

(0.0

58)

(0.0

29)

Rom

anR

oad

No

Hub

0.18

5**

0.00

252

-0.0

748

-0.0

704*

0.06

14-0

.012

1-0

.150

**-0

.081

6***

(0.0

88)

(0.0

46)

(0.0

85)

(0.0

41)

(0.0

6)(0

.032

)(0

.071

)(0

.03)

Bis

hop

0.06

430.

0550

0.15

9***

0.04

33*

(0.0

7)(0

.039

)(0

.046

)(0

.026

)A

rchb

isho

p0.

315*

**0.

0562

0.39

7***

0.02

99(0

.091

)(0

.047

)(0

.074

)(0

.035

)C

apita

l0.

695*

**0.

194*

**0.

915*

**0.

240*

**(0

.144

)(0

.048

)(0

.119

)(0

.05)

Larg

eSt

ate

0.10

8**

0.09

37**

*0.

0751

0.08

54**

*0.

0802

*0.

0604

**0.

0521

0.04

91**

(0.0

54)

(0.0

32)

(0.0

6)(0

.032

)(0

.041

)(0

.024

)(0

.049

)(0

.024

)C

omm

une

0.22

8***

0.04

960.

163*

**0.

0418

(0.0

54)

(0.0

36)

(0.0

47)

(0.0

27)

Free

Pri

nce

0.07

00-0

.002

570.

0396

-0.0

090.

157*

**-0

.056

7**

0.14

3***

-0.0

619*

*(0

.11)

(0.0

53)

(0.0

99)

(0.0

54)

(0.0

50)

(0.0

28)

(0.0

48)

(0.0

28)

Uni

vers

ity0.

207*

*0.

0641

0.24

2***

0.05

18*

(0.0

98)

(0.0

4)(0

.07)

(0.0

28)

Plu

nder

ed-0

.050

5-0

.055

7*-0

.041

4-0

.049

9*-0

.067

2-0

.010

7-0

.016

40.

0032

(0.0

57)

(0.0

31)

(0.0

61)

(0.0

29)

(0.0

59)

(0.0

3)(0

.061

)(0

.031

)U

rban

Pote

ntia

l0.

0431

**-0

.005

60.

0503

**-0

.005

50.

008*

-0.0

161*

**0.

0053

-0.0

174*

**(0

.02)

(0.0

11)

(0.0

22)

(0.0

11)

(0.0

05)

(0.0

04)

(0.0

06)

(0.0

04)

Eco

zone

=3

-0.2

230.

0537

-0.1

300.

105

-0.2

05**

-0.1

13*

-0.1

80-0

.084

8*(0

.153

)(0

.130

)(0

.167

)(0

.115

)(0

.104

)(0

.062

)(0

.117

)(0

.052

)E

cozo

ne=

40.

0814

-0.0

785*

0.14

9-0

.063

1-0

.079

6-0

.089

***

-0.0

726

-0.0

878*

**(0

.116

)(0

.046

)(0

.120

)(0

.046

)(0

.079

)(0

.032

)(0

.098

)(0

.032

)E

cozo

ne=

6-0

.018

5-0

.072

9-0

.045

8-0

.078

5-0

.218

**-0

.064

6-0

.236

*-0

.065

5(0

.17)

(0.0

67)

(0.1

77)

(0.0

67)

(0.1

06)

(0.0

47)

(0.1

31)

(0.0

51)

Obs

.74

874

874

874

81,

682

1,29

51,

682

1,29

5O

vera

llR

20.

419

0.26

40.

275

0.24

40.

419

0.26

40.

275

0.24

4N

umbe

rof

Clu

ster

s23

523

523

523

540

432

540

432

5N

otes

.St

anda

rder

rors

clus

tere

dat

city

leve

lar

ere

port

edin

pare

nthe

ses.

Coe

ffici

ent

isst

atis

tica

llydi

ffer

ent

from

zero

atth

e**

*1%

,**

5%

and

*10

%le

vel.

The

unit

ofob

serv

atio

nis

aci

ty.

Eac

hre

gres

sion

incl

udes

aco

nsta

ntno

tre

port

ed.

Add

itio

nally

,to

the

show

nco

ntro

lva

riab

les

inea

chre

gres

sion

coun

try

and

year

fixed

effec

tsar

ein

clud

ed.

49

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GENERALIZED BARRIERS TO ENTRY AND ECONOMIC DEVELOPMENT

IK

04-2009 Uwe Focht, Andreas Richter, and Jörg Schiller

INTERMEDIATION AND MATCHING IN INSURANCE MARKETS HCM

05-2009 Julian P. Christ and André P. Slowak

WHY BLU-RAY VS. HD-DVD IS NOT VHS VS. BETAMAX: THE CO-EVOLUTION OF STANDARD-SETTING CONSORTIA

IK

06-2009 Gabriel Felbermayr, Mario Larch, and Wolfgang Lechthaler

UNEMPLOYMENT IN AN INTERDEPENDENT WORLD ECO

07-2009 Steffen Otterbach MISMATCHES BETWEEN ACTUAL AND PREFERRED WORK TIME: Empirical Evidence of Hours Constraints in 21 Countries

HCM

08-2009 Sven Wydra PRODUCTION AND EMPLOYMENT IMPACTS OF NEW TECHNOLOGIES – ANALYSIS FOR BIOTECHNOLOGY

IK

09-2009 Ralf Richter and Jochen Streb

CATCHING-UP AND FALLING BEHIND KNOWLEDGE SPILLOVER FROM AMERICAN TO GERMAN MACHINE TOOL MAKERS

IK


Nr. Autor Titel CC

10-2010

Rahel Aichele and Gabriel Felbermayr

KYOTO AND THE CARBON CONTENT OF TRADE

ECO

11-2010 David E. Bloom and Alfonso Sousa-Poza

ECONOMIC CONSEQUENCES OF LOW FERTILITY IN EUROPE

HCM

12-2010 Michael Ahlheim and Oliver Frör

DRINKING AND PROTECTING – A MARKET APPROACH TO THE PRESERVATION OF CORK OAK LANDSCAPES

ECO

13-2010 Michael Ahlheim, Oliver Frör, Antonia Heinke, Nguyen Minh Duc, and Pham Van Dinh

LABOUR AS A UTILITY MEASURE IN CONTINGENT VALUATION STUDIES – HOW GOOD IS IT REALLY?

ECO

14-2010 Julian P. Christ THE GEOGRAPHY AND CO-LOCATION OF EUROPEAN TECHNOLOGY-SPECIFIC CO-INVENTORSHIP NETWORKS

IK

15-2010 Harald Degner WINDOWS OF TECHNOLOGICAL OPPORTUNITY DO TECHNOLOGICAL BOOMS INFLUENCE THE RELATIONSHIP BETWEEN FIRM SIZE AND INNOVATIVENESS?

IK

16-2010 Tobias A. Jopp THE WELFARE STATE EVOLVES: GERMAN KNAPPSCHAFTEN, 1854-1923

HCM

17-2010 Stefan Kirn (Ed.) PROCESS OF CHANGE IN ORGANISATIONS THROUGH eHEALTH

ICT

18-2010 Jörg Schiller ÖKONOMISCHE ASPEKTE DER ENTLOHNUNG UND REGULIERUNG UNABHÄNGIGER VERSICHERUNGSVERMITTLER

HCM

19-2010 Frauke Lammers and Jörg Schiller

CONTRACT DESIGN AND INSURANCE FRAUD: AN EXPERIMENTAL INVESTIGATION

HCM

20-2010 Martyna Marczak and Thomas Beissinger

REAL WAGES AND THE BUSINESS CYCLE IN GERMANY

ECO

21-2010 Harald Degner and Jochen Streb

FOREIGN PATENTING IN GERMANY, 1877-1932

IK

22-2010 Heiko Stüber and Thomas Beissinger

DOES DOWNWARD NOMINAL WAGE RIGIDITY DAMPEN WAGE INCREASES?

ECO

23-2010 Mark Spoerer and Jochen Streb

GUNS AND BUTTER – BUT NO MARGARINE: THE IMPACT OF NAZI ECONOMIC POLICIES ON GERMAN FOOD CONSUMPTION, 1933-38

ECO

Nr. Autor Titel CC

24-2011

Dhammika Dharmapala and Nadine Riedel

EARNINGS SHOCKS AND TAX-MOTIVATED INCOME-SHIFTING: EVIDENCE FROM EUROPEAN MULTINATIONALS

ECO

25-2011 Michael Schuele and Stefan Kirn

QUALITATIVES, RÄUMLICHES SCHLIEßEN ZUR KOLLISIONSERKENNUNG UND KOLLISIONSVERMEIDUNG AUTONOMER BDI-AGENTEN

ICT

26-2011 Marcus Müller, Guillaume Stern, Ansger Jacob and Stefan Kirn

VERHALTENSMODELLE FÜR SOFTWAREAGENTEN IM PUBLIC GOODS GAME

ICT

27-2011 Monnet Benoit Patrick Gbakoua and Alfonso Sousa-Poza

ENGEL CURVES, SPATIAL VARIATION IN PRICES AND DEMAND FOR COMMODITIES IN CÔTE D’IVOIRE

ECO

28-2011 Nadine Riedel and Hannah Schildberg-Hörisch

ASYMMETRIC OBLIGATIONS

ECO

29-2011 Nicole Waidlein

CAUSES OF PERSISTENT PRODUCTIVITY DIFFERENCES IN THE WEST GERMAN STATES IN THE PERIOD FROM 1950 TO 1990

IK

30-2011 Dominik Hartmann and Atilio Arata

MEASURING SOCIAL CAPITAL AND INNOVATION IN POOR AGRICULTURAL COMMUNITIES. THE CASE OF CHÁPARRA - PERU

IK

31-2011 Peter Spahn DIE WÄHRUNGSKRISENUNION DIE EURO-VERSCHULDUNG DER NATIONALSTAATEN ALS SCHWACHSTELLE DER EWU

ECO

32-2011 Fabian Wahl

DIE ENTWICKLUNG DES LEBENSSTANDARDS IM DRITTEN REICH – EINE GLÜCKSÖKONOMISCHE PERSPEKTIVE

ECO

33-2011 Giorgio Triulzi, Ramon Scholz and Andreas Pyka

R&D AND KNOWLEDGE DYNAMICS IN UNIVERSITY-INDUSTRY RELATIONSHIPS IN BIOTECH AND PHARMACEUTICALS: AN AGENT-BASED MODEL

IK

34-2011 Claus D. Müller-Hengstenberg and Stefan Kirn

ANWENDUNG DES ÖFFENTLICHEN VERGABERECHTS AUF MODERNE IT SOFTWAREENTWICKLUNGSVERFAHREN

ICT

35-2011 Andreas Pyka AVOIDING EVOLUTIONARY INEFFICIENCIES IN INNOVATION NETWORKS

IK

36-2011 David Bell, Steffen Otterbach and Alfonso Sousa-Poza

WORK HOURS CONSTRAINTS AND HEALTH

HCM

37-2011 Lukas Scheffknecht and Felix Geiger

A BEHAVIORAL MACROECONOMIC MODEL WITH ENDOGENOUS BOOM-BUST CYCLES AND LEVERAGE DYNAMICS

ECO

38-2011 Yin Krogmann and Ulrich Schwalbe

INTER-FIRM R&D NETWORKS IN THE GLOBAL PHARMACEUTICAL BIOTECHNOLOGY INDUSTRY DURING 1985–1998: A CONCEPTUAL AND EMPIRICAL ANALYSIS

IK

Nr. Autor Titel CC

39-2011

Michael Ahlheim, Tobias Börger and Oliver Frör

RESPONDENT INCENTIVES IN CONTINGENT VALUATION: THE ROLE OF RECIPROCITY

ECO

40-2011 Tobias Börger

A DIRECT TEST OF SOCIALLY DESIRABLE RESPONDING IN CONTINGENT VALUATION INTERVIEWS

ECO

41-2011 Ralf Rukwid and Julian P. Christ

QUANTITATIVE CLUSTERIDENTIFIKATION AUF EBENE DER DEUTSCHEN STADT- UND LANDKREISE (1999-2008)

IK

Nr. Autor Titel CC

42-2012 Benjamin Schön and

Andreas Pyka

A TAXONOMY OF INNOVATION NETWORKS IK

43-2012 Dirk Foremny and Nadine Riedel

BUSINESS TAXES AND THE ELECTORAL CYCLE ECO

44-2012 Gisela Di Meglio, Andreas Pyka and Luis Rubalcaba

VARIETIES OF SERVICE ECONOMIES IN EUROPE IK

45-2012 Ralf Rukwid and Julian P. Christ

INNOVATIONSPOTENTIALE IN BADEN-WÜRTTEMBERG: PRODUKTIONSCLUSTER IM BEREICH „METALL, ELEKTRO, IKT“ UND REGIONALE VERFÜGBARKEIT AKADEMISCHER FACHKRÄFTE IN DEN MINT-FÄCHERN

IK

46-2012 Julian P. Christ and Ralf Rukwid

INNOVATIONSPOTENTIALE IN BADEN-WÜRTTEMBERG: BRANCHENSPEZIFISCHE FORSCHUNGS- UND ENTWICKLUNGSAKTIVITÄT, REGIONALES PATENTAUFKOMMEN UND BESCHÄFTIGUNGSSTRUKTUR

IK

47-2012 Oliver Sauter ASSESSING UNCERTAINTY IN EUROPE AND THE US - IS THERE A COMMON FACTOR?

ECO

48-2012 Dominik Hartmann SEN MEETS SCHUMPETER. INTRODUCING STRUCTURAL AND DYNAMIC ELEMENTS INTO THE HUMAN CAPABILITY APPROACH

IK

49-2012 Harold Paredes-Frigolett and Andreas Pyka

DISTAL EMBEDDING AS A TECHNOLOGY INNOVATION NETWORK FORMATION STRATEGY

IK

50-2012 Martyna Marczak and Víctor Gómez

CYCLICALITY OF REAL WAGES IN THE USA AND GERMANY: NEW INSIGHTS FROM WAVELET ANALYSIS

ECO

51-2012 André P. Slowak DIE DURCHSETZUNG VON SCHNITTSTELLEN IN DER STANDARDSETZUNG: FALLBEISPIEL LADESYSTEM ELEKTROMOBILITÄT

IK

52-2012

Fabian Wahl

WHY IT MATTERS WHAT PEOPLE THINK - BELIEFS, LEGAL ORIGINS AND THE DEEP ROOTS OF TRUST

ECO

53-2012

Dominik Hartmann und Micha Kaiser

STATISTISCHER ÜBERBLICK DER TÜRKISCHEN MIGRATION IN BADEN-WÜRTTEMBERG UND DEUTSCHLAND

IK

54-2012

Dominik Hartmann, Andreas Pyka, Seda Aydin, Lena Klauß, Fabian Stahl, Ali Santircioglu, Silvia Oberegelsbacher, Sheida Rashidi, Gaye Onan und Suna Erginkoç

IDENTIFIZIERUNG UND ANALYSE DEUTSCH-TÜRKISCHER INNOVATIONSNETZWERKE. ERSTE ERGEBNISSE DES TGIN-PROJEKTES

IK

55-2012

Michael Ahlheim, Tobias Börger and Oliver Frör

THE ECOLOGICAL PRICE OF GETTING RICH IN A GREEN DESERT: A CONTINGENT VALUATION STUDY IN RURAL SOUTHWEST CHINA

ECO

Nr. Autor Titel CC

56-2012

Matthias Strifler Thomas Beissinger

FAIRNESS CONSIDERATIONS IN LABOR UNION WAGE SETTING – A THEORETICAL ANALYSIS

ECO

57-2012

Peter Spahn

INTEGRATION DURCH WÄHRUNGSUNION? DER FALL DER EURO-ZONE

ECO

58-2012

Sibylle H. Lehmann

TAKING FIRMS TO THE STOCK MARKET: IPOS AND THE IMPORTANCE OF LARGE BANKS IN IMPERIAL GERMANY 1896-1913

ECO

59-2012 Sibylle H. Lehmann,

Philipp Hauber, Alexander Opitz

POLITICAL RIGHTS, TAXATION, AND FIRM VALUATION – EVIDENCE FROM SAXONY AROUND 1900

ECO


SPECTRAN, A SET OF MATLAB PROGRAMS FOR SPECTRAL ANALYSIS

ECO

61-2012 Theresa Lohse and Nadine Riedel

THE IMPACT OF TRANSFER PRICING REGULATIONS ON PROFIT SHIFTING WITHIN EUROPEAN MULTINATIONALS

ECO

Nr. Autor Titel CC

62-2013 Heiko Stüber REAL WAGE CYCLICALITY OF NEWLY HIRED WORKERS ECO

63-2013 David E. Bloom and Alfonso Sousa-Poza

AGEING AND PRODUCTIVITY HCM


MONTHLY US BUSINESS CYCLE INDICATORS: A NEW MULTIVARIATE APPROACH BASED ON A BAND-PASS FILTER

ECO

65-2013 Dominik Hartmann and Andreas Pyka

INNOVATION, ECONOMIC DIVERSIFICATION AND HUMAN DEVELOPMENT

IK

66-2013 Christof Ernst, Katharina Richter and Nadine Riedel

CORPORATE TAXATION AND THE QUALITY OF RESEARCH AND DEVELOPMENT

ECO

67-2013 Michael Ahlheim,

Oliver Frör, Jiang Tong, Luo Jing and Sonna Pelz

NONUSE VALUES OF CLIMATE POLICY - AN EMPIRICAL STUDY IN XINJIANG AND BEIJING

ECO

68-2013 Michael Ahlheim and Friedrich Schneider

CONSIDERING HOUSEHOLD SIZE IN CONTINGENT VALUATION STUDIES

ECO

69-2013 Fabio Bertoni and Tereza Tykvová

WHICH FORM OF VENTURE CAPITAL IS MOST SUPPORTIVE OF INNOVATION? EVIDENCE FROM EUROPEAN BIOTECHNOLOGY COMPANIES

CFRM

70-2013 Tobias Buchmann and Andreas Pyka

THE EVOLUTION OF INNOVATION NETWORKS: THE CASE OF A GERMAN AUTOMOTIVE NETWORK

IK

71-2013 B. Vermeulen, A. Pyka, J. A. La Poutré, A. G. de Kok

CAPABILITY-BASED GOVERNANCE PATTERNS OVER THE PRODUCT LIFE-CYCLE

IK

72-2013

Beatriz Fabiola López

Ulloa, Valerie Møller, Alfonso Sousa-Poza

HOW DOES SUBJECTIVE WELL-BEING EVOLVE WITH AGE? A LITERATURE REVIEW

HCM

73-2013

Wencke Gwozdz, Alfonso Sousa-Poza, Lucia A. Reisch, Wolfgang Ahrens, Stefaan De Henauw, Gabriele Eiben, Juan M. Fernández-Alvira, Charalampos Hadjigeorgiou, Eva Kovács, Fabio Lauria, Toomas Veidebaum, Garrath Williams, Karin Bammann

MATERNAL EMPLOYMENT AND CHILDHOOD OBESITY – A EUROPEAN PERSPECTIVE

HCM

74-2013

Andreas Haas, Annette Hofmann

RISIKEN AUS CLOUD-COMPUTING-SERVICES: FRAGEN DES RISIKOMANAGEMENTS UND ASPEKTE DER VERSICHERBARKEIT

HCM

75-2013

Yin Krogmann, Nadine Riedel and Ulrich Schwalbe

INTER-FIRM R&D NETWORKS IN PHARMACEUTICAL BIOTECHNOLOGY: WHAT DETERMINES FIRM’S CENTRALITY-BASED PARTNERING CAPABILITY?

ECO, IK

76-2013

Peter Spahn

MACROECONOMIC STABILISATION AND BANK LENDING: A SIMPLE WORKHORSE MODEL

ECO

77-2013

Sheida Rashidi, Andreas Pyka

MIGRATION AND INNOVATION – A SURVEY

IK

78-2013

Benjamin Schön, Andreas Pyka

THE SUCCESS FACTORS OF TECHNOLOGY-SOURCING THROUGH MERGERS & ACQUISITIONS – AN INTUITIVE META-ANALYSIS

IK

79-2013

Irene Prostolupow, Andreas Pyka and Barbara Heller-Schuh

TURKISH-GERMAN INNOVATION NETWORKS IN THE EUROPEAN RESEARCH LANDSCAPE

IK

80-2013

Eva Schlenker, Kai D. Schmid

CAPITAL INCOME SHARES AND INCOME INEQUALITY IN THE EUROPEAN UNION

ECO

81-2013 Michael Ahlheim, Tobias Börger and Oliver Frör

THE INFLUENCE OF ETHNICITY AND CULTURE ON THE VALUATION OF ENVIRONMENTAL IMPROVEMENTS – RESULTS FROM A CVM STUDY IN SOUTHWEST CHINA –

ECO

82-2013 Fabian Wahl DOES MEDIEVAL TRADE STILL MATTER? HISTORICAL TRADE CENTERS, AGGLOMERATION AND CONTEMPORARY ECONOMIC DEVELOPMENT

ECO

U n i v e r s i t ä t H o h e n h e i m

F o r s c h u n g s z e n t r u m

Innovation und Dienstleistung

Fruwirthstr. 12

D-70593 Stuttgart

Phone +49 (0)711 / 459-22476

Fax +49 (0)711 / 459-23360

Internet www.fzid.uni-hohenheim.de