does medieval trade still matter? historical trade centers,...
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Does Medieval Trade Still Matter? Historical Trade Centers,Agglomeration and Contemporary Economic Development
Fabian Wahl∗
University of Hohenheim
August 2, 2013
Abstract
This study empirically establishes a link between medieval trade, agglomerationand contemporary regional development in ten European countries. It documentsa statistically and economically significant positive relationship between prominentinvolvement in medieval trade and regional economic development today. This find-ing is robust to inclusion of various historical, economical and geographical controlvariables and to controlling for endogeneity via IV estimations. A mediation analysisshows that, as theoretically postulated, the majority of this long-lasting effect trans-mits via the impact of medieval trade on contemporary agglomeration and industryconcentration. Thus, this research highlights the long-run importance of medievaltrade in shaping contemporary spatial patterns of economic activity throughoutEurope. The path-dependent regional development processes caused by medievaltrading activity can also provide an explanation for the observed persistence of re-gional differences in development across the considered European countries.
Keywords: Medieval Trade, Agglomeration, Regional Economic Development, Path-Dependency, New Economic GeographyJEL Classification: F14, N73, N93, O18, R12
∗Department of Economics, University of Hohenheim. Chair of Economic and Social History, Speise-meisterflugel, Stuttgart, Germany. [email protected]. The author especially wouldlike to thank Bas van Bavel, Sibylle Lehmann, Alexander Opitz, Alfonso Sousa-Poza, Oliver Volckartand Nicole Waidlein for the helpful comments and discussions. Additionally he is indebted to T.Matthew Ciolek for his helpful suggestions and for discussing his medieval European trade routemaps.
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1 Introduction
There is ample evidence that trade is an important determinant of both long- and short-run economic development. However, most of the existing literature focuses on the im-pact of 19th century trade on market integration or the “Great Divergence” (e.g. Galorand Mountford 2008 or O’Rourke and Williamson 2002) or on the impact of contem-porary, Post-World War II trade activities on recent economic growth and developmentperformance across countries (Dollar and Kraay 2003, Frankel and Romer 1999). Thereis only one study (Acemoglu et al. 2005) considering the effect of cross country tradein earlier periods. They investigate the impact of long-distance overseas trade on in-stitutional developments and the pre-industrial development process across Europeancountries.
Hence, until now there is no study exploring the possible long-lasting effects of tradein European cities throughout the High and Late Middle Ages. The importance ofmedieval trade for the development of cities and regions in the Middle Ages and thefollowing centuries is well-known and widely acknowledged. Apart from this, no re-search acknowledged the fact that medieval trade could have also long-term influenceson regional development persisting until today. This despite the fact that medievaltrade through its influence on agglomeration and spatial concentration of industry couldhave lead to path-dependent regional development processes resulting in developmentdifferences outlasting the centuries in between.
The aim of this study is to investigate whether medieval trade had caused differencesin regional development which are still visible today due to its its impact on agglomera-tion. If this is the case it could provide a new explanation for the uneven distribution ofeconomic activity and significant spatial concentration of industries throughout Europe(e.g. Chasco et al. 2012, Koh and Riedel 2012, Roos 2005). Furthermore, it can con-tribute to the understanding of persistent differences in regional economic development(Becker et al. 2010, Maseland 2012, Tabellini 2010 or Waidlein 2011).
To establish a link between medieval trade, agglomeration and contemporary perfor-mance we link typical characteristics of medieval trade and cities to the determinantsof agglomeration suggested by New Economic Geography (NEG) and agglomerationeconomics (e.g. Krugman 1991, Glaeser et al. 1992). In a second step, based on stud-ies combining NEG and endogenous growth models and the theory of path-dependence(David 2007) we propose a positive connection between agglomeration, industrial con-centration and contemporary development.
Afterwards, we test the causal chain from medieval trade through agglomeration to
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contemporary regional economic development using a rich data set (where we choose aNUTS-3 region as unit of observation) and a wide range of empirical methods. In general,the detailed empirical analysis shows indeed medieval trade is robustly associated withcontemporary regional economic performance. Moreover, we also find that the majorityof the effect of medieval trade on contemporary regional development can be explained byits influence on agglomeration patterns. Most importantly, we show that our hypothesesare robust to the inclusion of many geographical, political, economical and historicalcovariates of development and agglomeration and are not biased by endogeneity.
Finally, a mediation analysis shows that medieval trade activities are strong predictorsof today’s spatial distribution of economic activity and population and that around twothird of the influence of medieval trade on contemporary regional GDP per capita canbe attributed to this influence of medieval trade on agglomeration.
The remainder of the article proceeds as follows. First, we theoretically establish thelink between medieval trade, agglomeration and present-day’s economic development.Afterwards, we introduce and discuss the most important variables and data and explainthe empirical setting. Next, we conduct our empirical analysis and interpret and discussthe results in detail. At last, we conclude and summarize the main findings.
2 Theory and Hypotheses
It is a well established idea that trade was a decisive factor in the development of medievalcities and the revival of city growth during the period of the so called “Commercial Rev-olution” (e.g. Borner and Severgnini 2012, Epstein 2000, Habermann 1978, Holtfrerich1999, King 1985, Postan 1952, Pounds 2005 and van Werveke 1952). History providesmany examples of cities owing their importance primarily to their function as centersof trade, like the German cities of Nuremburg (Nicholas 1997), Frankfurt (Holtfrerich1999) or Cologne (King 1985) or the Polish city of Gdansk.1
Using concepts developed by NEG (Krugman 1991) and agglomeration economics, one
1 Obviously, there are exceptions from this story, i.e. cities and regions becoming large and importantagglomerations without being important in medieval trade. This is true for example for Stuttgart(the sixth largest German city today) and Munich two of the richest and economically prosperouscities and agglomeration areas in present day’s Germany. Stuttgart was not important until after theNapoleonic Wars it became the capital of the newly founded kingdom of Wurttemberg. The rise ofMunich (today the third largest city of Germany) followed a similar pattern, albeit the capital of akingdom and residence of a bishop (and later archbishop) Munich began to become a large city notbefore late 18th century. Again, it experienced large population growth in the nineteenth centuryafter the Napoleonic Wars until World War I. Even more, Bavaria and Munich as it’s center stayedrelatively poor till the 1950ies (when e.g. the Siemens corporation moved its headquarter from Berlinto Munich). Additionally, the largest agglomeration in Germany the Ruhr Area largely results from
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can explain why medieval trade was important for the rise of cities in medieval Europe.This can be done by linking the characteristics of medieval trade and trade cities tosecond nature causes of agglomeration (for an overview over these see e.g. Christ 2009,Glaeser et al. 1992, Henderson et al. 2001). In medieval times, the economy, especiallythe urban economy was characterized by a high degree of regional specialization (Am-mann 1955, King 1985, Lopez 1952, Nicholas 1997,Postan 1952, Pounds 2005 and vanWerveke 1963).2 The Southern German cities that became important trade centers inthe later medieval for instance were specialized in textile (Barchent etc.) and paper pro-duction, while other areas had specialized in mining (like e.g. the Saxon town of Freibergor Liege in today’s Belgium that had the this times most productive coal field), or infood and salt (where the cities at the French Atlantic coast were the main exporters).The different regions exported in what they were specialized in – or had an comparativeadvantage in e.g. due to natural endowments– and imported what they did not havethemselves.3 This specialization of trade cities on a particular industry or sector gaverise to the existence of technological (non-pecuniary) externalities like Marshall-Arrow-Romer (MAR) externalities (Marshall 1890, Romer 1986) or Porter externalities.4 Thosetype of externalities arise as knowledge spillovers between firms in the same industry andcontribute therefore to the growth of both industry and city (Glaeser et al. 1992).5. In-deed Epstein (1998) and more broadly Epstein and Prak(2008) in an anthology aboutthe Guilds and Innovation they edited show that the guild as the dominant economicinstitution of the later medieval city indeed could have fostered innovation and enableknowledge spillovers and diffusion within the urban economy (and through migrationalso between cities).6
A second important characteristic of medieval trade cities was the comparatively high
the rich endowments with coal and iron making it to one of the most important nucleus of Germanindustrialization.
2A comprehensive illustration of medieval trade activities is provided in Postan (1952) and Lopez(1952).
3A review of the general geographical patterns of trade and industry specialization in the middle agesis provided among others by King (1985).
4Nicholas (1997) additionally points to the fact that over the course of the Middle Ages the industrydominating in a city e.g. the textile industry did more and more diversify. This intra-industrydiversification could be an additional channel through which technological externalities could hadbeen arisen.
5Such knowledge spillovers between firms might appear because of imitations, movements of skilledworkers between the different firms in the industry etc.
6For evidence about the high mobility of skilled craftsmen in this period see Reith (2008) in thisanthology. Of course, among historians there is no consensus about the role of guild and whethertheir negative or positive effects for economic development are more dominant. However, at leastthe more recent contributions clearly brought forward evidence that guilds indeed could had largepositive impacts through their positive influence on innovativeness.
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variety of available goods. Those varieties of goods were available first at the localmarkets, then at the big trade fairs in the Champagne and other important trade cities(like Frankfurt, Cologne, Ulm etc.) and then, in the late medieval age in the branchesand kontors of the Hanseatic League and the trading companies (“super-companies”)like the Fugger in Augsburg78 Especially the latter two also provide supply with luxurygoods and exotic commodities from far east, as long-distance trade was reestablished atthe beginning of Late Middle Ages. This high variety can be considered as an importantdemand-side driven agglomeration force, because it makes it more attractive to settle ina city.9
Additionally, the large variety of goods and prospering industry gave rise to the self-reinforcing circular causation caused by backward and forward linkages and leading toagglomeration and core-periphery patterns in NEG models (Krugman 1991, Ottavianoand Thisse 2004). Because trade cities provided a higher variety of goods, employmentfor high-skilled specialized workers and –as consequence of the higher labor demand– alsohigher wages, they attracted additional workers. When more and more workers made useof the opportunity to work in the city as e.g. textile workers or craftsmen, employmentand the number of firms increased. This decreased the price index, raised real wages andtherefore resulted in the immigration of even more workers to the city. Consequently, thispecuniary externality (forward linkage) caused increased agglomeration and industryconcentration in the city. Supplementary, more workers lead to a higher demand forgoods produced and/or traded in the city. The higher demand once more lead to theexpansion of markets and industries, raising labor demand and real wages resulting againin additional immigration. This is the so called “home market effect” or the backwardlinkage. In short, this amounts to the logic that industry will tend to concentratewhere there is a large market, whereas the market is large at the area where industryis already located. Thus, forward and backward linkages constitute the virtuous circlethat generates agglomeration and uneven spatial distribution of population and economicactivity.10
7For a detailed description of the business activities of the Hanseatic League the reader is reffered toDollinger (1966). An illustration of the medieval early medieval markets and fairs is found in vanWerveke (1963).
8A comprehensive description of the medieval super-companies can be found in Hunt and Murray(1999). An transaction economic analysis of the super companies using the example of the Fugger isprovided by Borner (2002).
9This follows clearly from love of variety preferences commonly assumed in NEG models. Additionally,one can make a transaction cost argument, because e.g. when living in a city there are no costs oftransporting the sold commodities back to the village.
10Of course, the medieval city was a highly cartelized and regulated economy with dominant guilds andsignificant rent-seeking activities (e.g. Braudel 1986). However as Braudel (1986) concludes since the
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Furthermore, after the process of agglomeration lasted for some time other types oftechnological externalities occurred. Conditional on certain factors (i.e. geographicalposition or natural endowments) other industries located in the previously specializedcities, e.g. in the Southern German city of Ravensburg (an important trade center in the15th century) the traditional textiles industry was supplemented by paper production atthe beginning of the 15th century (Schelle 2000). In addition, there were also incentivesto locate in a trade city for firms using special commodities as inputs or that producedinputs used in the industry the city was specialized in.11 Therefore also Jacobs (1969)externalities occurred in the late medieval cities.12
A first test whether the story fits to stylized empirical facts about city population andcity growth in the Middle Ages delivers the regressions in Table1. There we regress
However, the main argument of this paper is that medieval trade has significant conse-quences on economic development today. Reassuringly, the self-reinforcing nature of thedescribed agglomeration and concentration processes implies a path-dependent processof city development. This path-dependent development process results in differences inconcentration of economic activity and population that are persistent until today. Citiesinvolved in medieval trade activities over a sufficient period of time got locked in on asuperior development path as compared to other cities which were not involved. This isa typical characteristic of processes caused by increasing returns or positive feedback andthat are characterized by multiple equilibria (David 2007). There are many examples ofhistorical events and phenomenons having long-run impacts on economic development,e.g. Colonization (e.g. Acemoglu et al. 2001, 2002), Slave Trade (Nunn 2008, 2011),the Neolithic revolution (e.g. Ashraf and Galor 2011, Olsson and Hibbs 2005 or Putter-man 2008), the capacity to adopt and develop new technologies (Comin et al. 2010) orthe timing of human settlement (Ahlerup and Olsson 2012).13 Additionally, Maseland(2012) shows, that regional development disparities in Germany are persistent and can
13th century something like market integration (to some extent) existed with prices varying in themarkets of cities every week according to supply and demand. Furthermore, the increasing spreadof the “Verlagssystem” sometimes might had limited the power of the guilds. Concerning the urbanrural wage differential evidence in general is limited for this period in time Braudel (1986) notes thatin general, also due to the power of guilds the wages in the city can be considered to be usuallyhigher than in rural areas. In line with this, Munro (2002) comparing the real wages in Englandand Flanders between 1300 and 1500 found that the real wages in the cities were higher than inrural areas and showed a higher downward rigidity. van Bavel and van Zanden (2004) in additionnotice that in pre industrial societies the relationship between city size and nominal wages usuallywas positive.
11 The idea that vertical linkages along the supply chain can lead to agglomeration is developed inKrugman and Venables (1995).
12Jacobs externalities are knowledge spillovers arising between firms of different industries.13A comprehensive review of such events caused path-dependent developments is Nunn (2009).
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largely be explained by strong and increasing differences between core areas and theperiphery. We argue that medieval trade can be added to the list of such events.
Finally, the positive connection between agglomeration, industry concentration andregional economic growth is reported by several theoretical studies (e.g. Baldwin andMartin 2004, Martin and Ottaviano 2001, Yamamoto 2003 or Bertinelli and Black 2004)linking growth e.g. through innovations and agglomeration by combining standard NEGand endogenous growth models. In addition, studies like Hohenberg and Lees (1995)or Fujita and Thisse (2002) also establish empirically the positive relationship betweenagglomeration and regional growth.
In conclusion, we postulate the following two hypotheses about the relationship be-tween medieval trade and contemporary regional development:
Hypothesis 1. There is a positive and significant relationship between involvement inmedieval trade activities and regional economic performance today, i.e. cities that werecenters of medieval trade show a higher GDP per capita today than cities not involvedin medieval trade.
Hypothesis 2. Medieval trade activities influence contemporary regional economic de-velopment through their positive effect on agglomeration and industry concentration, i.e.there is a positive and significant relationship between medieval trade centers, agglomer-ation and industry concentration measures and current regional economic development.
3 Data and Setting
3.1 Setting and Level of Analysis
Because medieval trade took place in cities and agglomeration is a regional phenomenon,our empirical analysis is based on regional level data. We stick to the NUTS (“Nomen-clature of Units for Territorial Statistic”) regional classification, the official regionalreference unit systematic used in the European Union (EU).14 Furthermore the officialregional statistics of Eurostat are available for those territorial units. Additionally, dif-ferent regions on the same NUTS level have the advantage of being relatively comparableto each other since they are defined according to a particular range of inhabitants.15 We14A detailed description and overview over the NUTS classification scheme and the regions can be found
in the data appendix and the references mentioned there.15Although the population thresholds are defined very widely, e.g. a NUTS-3 region can have 150.000
and 800.000 inhabitants. Again, sometimes there are exceptions so that some NUTS-3 regions showa larger population. From this it follows also, that more densely populated regions cover on averagea smaller area. To overcome potential biases resulting from the this, we will control for the area of
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choose to conduct our analysis on the most disaggregated level for which our essentialdata (e.g. GDP per capita) is available. Therefore we conduct our analysis with aNUTS-3 region as observational unit.
NUTS-3 regions are identical to existing administrative units in most of the countriesin our sample, which is an additional advantage of using them. In Germany for examplethey are mostly identical to districts or district-free cities, in France to Departmentsand in Italy to Provinces. A potential bias resulting from considering regions instead ofactual cities that were subject to medieval trade is limited as heterogeneity within NUTS-3 regions should not be of significant size. However, some control variables are availableonly at NUTS-2 or NUTS-1 level. In these cases we include the respective variables at thelevel where they are provided. Another advantage of sticking to the NUTS classificationis that it enables to use fixed effects for the different NUTS-levels (countries, federalstates etc.). This allows to appropriately handle all kinds of heterogeneity on countryand regional levels. Besides this, one can also account for cross-sectional and spatialdependence among the regions in the dataset. The latter being a important advantageof regional empirical analyses especially when compared to country level investigations.16
3.2 Dependent Variables and Agglomeration Measures
As dependent variable we use the natural logarithm (ln) of GDP per capita in a NUTS-3region, originating from the Eurostat regional statistics database. We take the latestavailable values from the year 2009. All other time-variant variables are also taken fromthe year 2009 to enable comparability.
As measure of spatial industry agglomeration we follow Roos (2005), Chasco et al.(2012) and others in using the ln of the relative GDP density as measure for the spa-tial distribution of economic activity. The measure is calculated by dividing a region’sshare of GDP per capita through its share of the country’s total area. This means itshows whether the concentration of economic activity in a region is below or above thecountry’s average.17 As such this is a more direct measure of economic agglomerationthan population density. Additionally, we present results using the ln of a regions pop-ulation density in 2009 as a more general measure of agglomeration, i.e. as a variableidentifying more densely populated places. These results are reported in Appendix C.
a region and introduce dummy variables for city districts, city states and districit-free cities (regionswith a high population density, i.e. a large population but a small area).
16Chasco et al. (2012) discuss further advantages of using NUTS-3 regions as observational units in thecontext of spatial economic analyses.
17The exact formula according to which the relative GDP Density is calculated is shown in the dataappendix.
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We think that the relative GDP Density is a more direct measure of industry agglomer-ation and concentration and is therefore should more suitable for our empirical analysis.However, since population density might capture additional aspects of agglomerationthat might be important for economic activities indirectly and therefore can provideadditional insights.
Table A.1 in the data appendix gives a descriptive overview over all variables usedin the following empirical analysis. The exact sources and further explanations of allvariables are provided in the data appendix.
3.3 Independent Variables
This study aims to investigate the impact of trade between cities during the medievalage.18 To be able to identify the theoretically assumed effect of medieval trade on ag-glomeration we focus on the most important trade cities, i.e. cities where trade probablyhad the most powerful and long-lasting impact. Since agglomeration is a long-lastingprocess unfolding its effects only after some time, it is important to ensure that tradetook place long enough in a city to influence agglomeration there in a sufficient way.Stated differently, trade had to take place long enough in a city to lock it in on a supe-rior development path. To account for this fact, we focus on important trade cities atthe end of the medieval period (i.e. around 1500 AD). This is due to the fact that citiesimportant at the end of the medieval period are most likely also having experiencednoticeable trade activities in the years before (i.e. over a longer time period).
Our main source of information about important medieval trade activities are mapsprinted in historical atlases or monographs. We focus on maps because they providea much more comprehensive source of trade cities and activities then the informationavailable historical monographs. In addition, their information usually can assigned toa certain period much clearer than that contained in books. In consequence, we collectinformation about cities prominently involved in trade from four historical maps provid-ing evidence about cities located on “major” or “important” trade routes around 150018It is important to note that between the breakdown of the Roman Empire and the early medieval (the
foundation of Francia) there were comparatively small trade activities. Trade began to increase notbefore the tenth century (Postan 1952, Braudel 1986). Furthermore, after the end of the medieval inthe course of the 16th century, overseas trade (e.g. with the colonies) and long-distance trade becameincreasingly important. Due to this, the character of trade (e.g. rising importance of slaves trade)as well as the leading trade centers changed (Spain and Portugal came to rise). Compatibly withthat, the leading actors of medieval trade like e.g. the Hanseatic League lost their importance in theperiod following the medieval. In consequence, it is possible to isolate the medieval trade activitiesin cities from trade activities before and after the medieval. This ensures that the effects we measureempirically can actually be attributed to medieval trade activities and not trade in general or tradein other periods.
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AD (i.e. the late medieval). Due to the fact that there is no consensus or quantitativeevidence about the exact importance of trade cities and trade routes during the me-dieval period we have to consult several different sources to become sufficiently reliabledata. The first is a map printed in Davies and Moorehouse (2002), the second is a mapprinted in King (1985). The third source is a map on Central European trade publishedin Magocsi’s (2002) Historical Atlas of Central Europe.19 At last, we consult severalmaps included in “Westermanns Atlas zur Weltgeschichte” (Stier et al. (1956). Moreinformation about the kind of information and the geographical and temporal scope ofthose maps is provided in the Data Appendix. There, we also list the primary sourceson the basis of which the maps are drawn – if we were able to identify them. We includea city if it is mentioned in one of these maps. We include only cities located in EUcountries, since only for those the Eurostat regional statistics database provides data.
Despite this, in some cases we included cities in the sample not mentioned by the mapsbut by other sources of information. For example, we include the eastern German cityof “Zwickau” because it is prominently recognized in Spufford (2002) standard accounton medieval commerce and is known for its importance in salt trade. In other cases, weincluded cities not mentioned in the maps but in other sources for robustness checks.Furthermore, we stick to other qualitative information in our judgment of the importanceof the included trade cities. For example, we look whether a city was an importantmember of the Hanseatic League or a capital of a quarter or a third (like e.g. Dortmund orCologne). Information about this is provided by Dollinger (1966). Additionally, we alsolook whether, especially for not so prominent trade cities (Paderborn, Soest, Harfleur,Tarent etc.) they lied on well-known trade routes like the “Hellweg” in German (as it isthe case e.g. for Soest). Moreover, we consult several historical standard sources aboutmedieval trade activities in different Central European regions (e.g. Dietze 1923, Huntand Murray 1999, Schulte 1966, Spufford 2002 etc.) and look whether they mention acity as being prominently involved in trade or having an over-regional importance asmarket, trading place or fair city. Finally we also draw on other historical atlas likethat by Kinder and Hilgemann (1970) and other e.g. regional trade route maps (e.g.Schulte 1966) as sources for validating the information in the primary maps.In the DataAppendix (Table A.3) we report and discuss all these source and provide informationabout which city is mentioned by which source.
Overall these sources have left us with 119 trade cities located in 10 European coun-
19As we are not interested in information about only regionally important trade cities an additionalreason for choosing this particular maps is that they provide cross-national information about tradeactivities.
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tries. Our dataset encompasses all 839 NUTS-3 regions in these countries.20 The DataAppendix offers a detailed description of how we construct our database of importantlate medieval trade cities.
Even with the information in these sources, the relative importance of cities is notalways clear. Additionally, there is also a different degree of uncertainty about theextent and location of trade activities and the course of main routes, i.e. the actualimportance of a certain trade route at a particular point in time is not always clear.However, there are cities that undoubtedly were important centers of trade like theNorthern Italian city states (Milan or Genoa etc.), some Southern German imperialcities (like Augsburg, Nuremburg or Ulm) and the leading centers of the HanseaticLeague (Hamburg, Bremen, Lubeck, Cologne etc.) . On the other hand, there are caseswere only some sources mention the city as important trade center or lying on a maintrade route, like in the case of Paderborn, Minden or some port cities in France e.g.Harfleur or some smaller cities in Italy (Brindisi,Mantoa or Udine). This uncertainty isa natural result of the qualitative — and therefore to some extent always subjective —nature of the collected information and the scares amount of overall information aboutthe medieval period and the trade activities back then. To account for this uncertainties,we will re-estimate the most important of our empirical results with alternative samplesof trade cities where we first remove cities mentioned only by one of our sources. Second,we exclude cities reported in some of the maps or sources but actually do not lay on awell-known important trade route, where no important member of the Hanseatic League(according to Dollinger and Stier et al. 1956) or are not mentioned by any of our otherhistorical sources as being of notable importance in later medieval trade (albeit therewas probably some extent of trade activity). Those cities include e.g. Amberg, Einbeck,Como, Paderborn, Parma or St. Melo. 21 What is more, we also conduct our empiricalanalysis with a sample of trade cities including additional cities (Dijon, Piacenza orAigues Mortes) that are mentioned by some of the sources, but for which we — afterconsulting several different information about the history of the respecitve places — arein doubt of their actual importance in medieval trade, at least over a longer period.
At last, we try to ensure that we do not include trade cities that only experiencedsignificant trade activities for a a short period and therefore not long enough for result-ing in a lock-in to a superior development path. To overcome this problem, that woulddownward bias our results, we construct a fourth alternative sample of trade centers20We exclude the islands of Elba, Corse and Sicily from our sample because they are not comparable
with regions on the continent with respect to trade flows. (This follows Chasco et al. 2012 who alsoexclude island regions).
21A full list of excluded cities is reported in the Data Appendix.
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only considering cities for which we found records of recognizable trade activities in ear-lier periods than the late 15th and early 16th century. The sources consulted here aree.g a volume about medieval trade in the Levant by Heyd (1879a,b) and the alreadymentioned volume about the history of German trade by Dietze (1923). Furthermore,also Dollinger (1966) presents some evidence about trade activities in the periods pre-ceding the late medieval in a map, where he e.g. depicts cities lying on the Hellweg and“other important trade routes” in the period between 1286 and 1336. Additional, thismap also reports the signers of the treaty of Smolensk in 1229 (i.e. the most importanttrade cities in this times Western Dvina trade) and additionally some information ofmaps digitized by the Old World Trade Routes Project (OWTRAD) website, primar-ily containing information about trade in Eastern Europe, especially Poland.22 Exactinformation about the construction of this alternative sample is provided in the DataAppendix. Such information about earlier trade activities could be collected for 70 ofthe originally 119 trade cities. As such, this last sample represents the most selective oneand probably contains only cities for which important medieval trade activities are mostsure. Overall, we consulted fifteen different sources to construct our different samplesof trade cities. However, even with this amount of sources one cannot be sure that thecoding of the trade city dummy variables is perfect. Regardless of this fact, there seemsto be no reason why the inclusion of cities that were probably not that important thanother cities or experienced trade activities for only a short period of time should morethan downward bias our estimates. The estimates obtained using this kind of dummyvariable should therefore considered to be a lower bound of the actual long-term effectof medieval trade.
We will use two different variables as measures of late medieval trade and its impacton contemporary regional development. First, we will use a dummy variable “TradeCenter” that is equal to one if a region includes at least one medieval trade city. Thelack of quantitative information and the limited availability of qualitative judgmentsleads us to use a simple dummy variable coding important trade cities. Of course, thisimplies that we treat all trade cities being the same with respect to the scale of tradeactivities and agglomeration forces working there. However, since we try to focus oncities located on “major” or “important” trade cross-national trade routes and also relyon qualitative judgments of importance —when available– we should be able to reducethe heterogeneity among the trade cities. Additionally, the construction of a dummyvariable allows also for the construction of a second variable “Distance to Trade Center”representing the distance (in degrees) between a region and the closest medieval trade22http://www.ciolek.com/owtrad.html
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city.footnoteThe variable is zero in regions that are coded as trade centers. This variableoffers a very useful direct test of our hypothesis that medieval trade contributed to theemergence of time persistent core-periphery patterns and therefore can act as a notableexplanation for contemporary regional income differences.
Table 1 provides a summary of our trade city data. For each country, the total numberof NUTS-3 regions, the number of regions with trade cities, the share of trade centerregions and the average distance of a region to the closest trade city is listed.
[Table 1 about here]
As reported in the table, the average distance to a medieval trade center is about1.5 degrees (e0.432) that is approximately 170 km. Overall around 14% of all regionsare considered as containing medieval trade centers. A list with the name, NUTS-3region and country of all trade cities is provided in Table A.2 in the data appendix.Furthermore Figure 1 shows a map that depicts all included NUTS-3 regions and theregions with medieval trade centers (reddish colored).23
[Figure 1 about here]
4 Empirical Analysis
4.1 Medieval Trade and Contemporary Development
4.1.1 Descriptive Evidence
Some first insights about the relationship between medieval trade centers, agglomerationand contemporary economic performance can be obtained from a descriptive look on therelevant variables.
At first, we consider simple bivariate correlations between the ln of GDP per capita,the trade center dummy, the ln of the distance to the next trade center and our twomeasures of agglomoration, ln population density and ln relative GDP density. Thesecorrelations are shown in Table 2.
[Table 2 about here]
In general, we see that there is a high and significant correlation between all the variables.Additionally, the sign of the correlation coefficients are as expected (e.g. there is a23The geographical distribution of medieval trade cities in the map is largely consistent with what King
(1985) wrote about the location of leading trade and economic centers in medieval Europe (King1985, p. 220)
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strong positive relationship between agglomeration measures and GDP per capita. Viceversa we found a negative association between distance to a trade center and bothagglomeration and GDP). The correlation between GDP per capita and the trade centerdummy is significant and positive, but comparatively low. On the one hand, this lowcorrelation could be the result of considerable heterogeneity of GDP per capita acrossregions and countries in the sample that is not accounted for in these simple pairwisecorrelations. On the other hand, the high correlation between the trade center dummyand the agglomeration measures on the on side and agglomeration measures and GDPper capita on the other side indicates that the effect of trade centers is largely runningthrough agglomeration. Therefore the observed correlations provide preliminary supportfor our theoretical reasoning.
Another way to illustrate the stylized relationship between medieval trade, agglomer-ation and present day’s regional economic development is to compare averages values ofGDP per capita and agglomeration measures for late medieval trade centers and non-trade centers. This is done in Table 3 both separately for each country as well as forthe whole sample of regions. From the last line of Table 3 we can infer that in total,i.e. pooled over all regions and countries in the sample, regions with late medieval tradecities have a significant “GDP Advantage”, that is, their average GDP per capita isaround 5000 Euro higher than that of regions without trade cities. Furthermore, theyalso exhibit significantly higher population and relative GDP densities.24 This resultdoes also hold within all countries apart from Lithuania where trade center regions showa higher GDP per capita but the differences is insignificant. For relative GDP Densitythe within country results are not that clear. In Belgium and the Netherlands the rel-ative GDP Density is lower, although the difference is not significant.25 However, inAustria, Germany, France and Poland the countries account for three quarters of thesample, there is a statistically and economically significant advantage of trade centerswith respect to both regional economic development and relative GDP Density.
[Table 3 about here]
Finally, we estimate the kernel densities of ln relative GDP for all regions, for regionswith medieval trade cities and for regions without them. The kernel density of ln relativeGDP density is shown in Figure 2. The density for regions with and without medieval24The significance of the Difference between trade regions and non trade regions is tested by a two-sample
t test.25In the smaller countries (like Lithuania, the Czech Republic or Belgium) the insignificance of the
differences is probably attributable to the insufficient total number of regions/ trade centers. Here,the numbers should be treated with caution.
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trade centers is depicted in Figure 3. A comparison of those kernel densities revealsthat the variable’s kernel density over all regions is clearly leftly skewed and shows anadditional notable local peak on the right.26 The latter indicates that there is a clusterof regions showing a relatively high spatial concentration of economic activity.
However, what is more interesting for our argumentation is the comparison of thedensity across groups of regions with and without medieval trade cities. One can inferfrom Figure 3, that as expected the kernel density across both groups differs consider-ably.27 Most importantly, the density function for regions with medieval trade centersclearly show a larger mass in the right tail supporting the idea that agglomeration andconcentration of economic activity are higher in regions with medieval trade centers. Wealso run similar estimations using population density as agglomeration measure. Theresult of this task are shown in Appendix C (Figure C.1).
In sum, the descriptive analysis of the data delivers strong preliminary evidence forour hypotheses.
[Figure 2 and 3 about here]
4.1.2 OLS Regressions
To test our main hypothesis, i.e. that regions with cities involved in medieval tradeexhibit higher levels of economic development today we estimate the following regressionusing Ordinary Least Squares (OLS):
ln(GDP )cijk = α+ βTCcijk + γ′1Xcijk + γ′2Xcij + δc + θi + λj + εcijk (1)
Where ln(GDP )cijk is the natural logarithm of GDP per capita in NUTS-3 region k
NUTS-2 Region j in NUTS-1 region i of country c. TCcijk is a dummy variable “TradeCenter” that is equal to one if a NUTS-3 region includes a medieval trade city and zerootherwise. Xcijk andXcij are vectors of NUTS-3 or NUTS-2 level covariates, respectively.δc, θi and λj are country, NUTS-1 and NUTS-2 region fixed effects. At last, εcijk is theerror term capturing all unobserved factors.28 Equation (1) is a straightforward way toestablish a significant direct link between late medieval trade activities and contemporaryeconomic performance. Our expectation is that β > 0 and significantly different fromzero.
26Accordingly, a Shapiro-Wilk test clearly rejects the null hypothesis of normality for the kernel density27Conversely, a Kolomogorov-Smirnov rejects the equality of both group’s densities.28 As mentioned before, all time-variant variables are measured in the year 2009 so we do not report an
index for the period of measurement.
15
But, even when medieval trade still matters today, does its impact transmit via ag-glomeration and concentration of economic activities in places where it took place? Asimple way to test this additional hypothesis is to look whether GDP per capita becomeslower when the distance to medieval trade centers increases. Expressed differently, if theeffect of trade works through agglomeration, then, a “classical” core-periphery patternshould emerge, with the medieval trade cities as core and the regions far away as pe-riphery. One can therefore modify equation 1 by substituting the trade center dummythrough a variable representing the distance between a region’s centroid and the closesttrade city. Equation 1 can be rewritten as:
ln(GDP )cijk = α+ ρln(Dist TC)cijk + γ′1Xcijk + γ′2Xcij + δc + θi + λj + εcijk (2)
Where Dist TCcijk is the natural logarithm of the distance from a region’s centroid tothe closest trade city measured in degrees. We expect ρ to be negative and significant.
4.1.3 Baseline Results
First, we estimate equations one and two using NUTS-1, NUTS-2 and country fixedeffects. They are included to account for shocks common to all observations at the re-spective geographical unit. Additionally, they are included to exploit the pure variationbetween NUTS-3 regions.29 We also add a set of basic geographical controls, includinglatitude, longitude and altitude of a NUTS-3 region. The latter set of variables shouldcapture the general geographical pattern of development in Central Europe. This means,that economic development roughly increases from South to North (i.e. with increasinglatitude) and decreases - in our sample- from West to East (i.e. with increasing longi-tude). Furthermore, it is a well known fact that regions with a higher latitude are moredifficult to reach - what seems relevant for trade- and show a less favorable climate sothat we expect a negative influence of altitude.
The results of these regressions are shown in Table 4. There, we report three differ-ent standard errors above each coefficient. At first, in parentheses there are reportedheteroskedasdicity robust standard errors. Below those, in brackets we present standarderrors obtained by multiway clustering on NUTS-1 and NUTS-2 region level accordingto the methodology of Cameron et al. (2011). The use of multiway clustering is justifiedbecause it is likely that the development in NUTS-3 regions is not independent of that29Overall, there are 49 NUTS-1 regions and 143 NUTS-2 regions in our dataset. In the regression some
of them are omitted, because of multi-collinearity. The multi-collinearity is most often caused by thefact, that sometimes, like in the case of the German city states Berlin, Hamburg or Bremen NUTS-1,NUTS-2 and NUTS-3 regions are identical.
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in NUTS-1 or NUTS-2 regions.30 Supplementary, because multiway clustering allowsfor arbitrary residual correlation across both included dimensions, it also accounts forpossible spatial correlation. Finally, the third standard errors (in curley brackets) areadjusted for two-dimensional spatial correlation using the method proposed by Conley(1999).31
[Table 4 about here]
A look at the estimation results confirms our expectations and the descriptive evi-dence brought forward before. Regions with medieval trade centers show a significantlyhigher GDP per capita than regions without such cities. The coefficient of the tradecenter dummy remains relatively stable and significant at 1 % level, regardless whichcombination of control variables and fixed effects is used. According to column (3) ofTable 4, where we include the full set of country and region dummy as well as the basicgeographic controls, regions with medieval trade centers on average have around 30 %higher GDP per capita than regions without. This means that the effect of medievaltrade is not only statistically but also economically of considerable significance.
This holds also true for the coefficients of the distance to trade center. They arealways highly significant and are quantitatively in the same range as that of the tradecenter dummy. Furthermore, they show the anticipated negative sign.
The clear positive relationship between contemporary GDP per capita and medievaltrade centers is also illustrated graphically in Figure 3, a partial regression plot of theTrade Center Dummy based on the full baseline specification in column (3). And inFigure 4 the same is done for the negative relationship between the distance to a medievaltrade center and present days GDP per capita.
Regarding the geographical controls latitude and longitude turn out to be insignificantthroughout all estimations. Altitude, to the contrast, is always significant and its coef-ficient shows the expected negative sign. Furthermore, the NUTS-2 dummies are oftennot significant and do – according to the adjusted R2 – add nothing to the explanatorypower of the model. For this reason, they would only introduce additional noise in theestimation and are therefore excluded from the remaining regressions.
30It might even be the case that the development of included variables regional variables is correlatedwithin countries. However, due to the fact that we only have ten countries in our sample and clusteredstandard errors are only consistent asymptotically, clustering at country level is no option.
31Conley’s (1999) standard errors are obtained using a cutoff point of 3 degrees (approx. 330 km) afterwhich the spatial correlation is assumed to be zero. We experimented with several different cutoffpoints and this cutoff produced the most conservative standard errors.
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The three different types of standard errors in general do not differ substantially. Ifany, the standard errors in brackets, adjusted fro multiway clustering are a little bitlarger than the other two. Because of that, we will use standard errors clustered onNUTS-1 and NUTS-2 level, for all remaining specifications if possible.
[Figures 3 and 4 about here]
4.1.4 Controlling for Determinants of Agglomeration and Development
To ensure that the significant positive relation between medieval trade and contempo-rary economic development is not driven by omitted variables bias we have to controlfor relevant determinants of both agglomeration and economic development. As a nextstep, we therefore add several sets of control variables to the baseline specification. Inagglomeration economics, the causes of agglomeration are categorized in first nature(physical and political geography, climate etc.) and second nature causes of agglom-eration (man-made factors, i.e. agglomeration resulting from spatial spillovers or scaleeffects) (e.g. Chasco et al. 2012, Christ 2009, Ellison and Glaeser 1999, Krugman 1993,Roos 2005). This literature assumes that there are direct effects of both types of causes,as well as an additional indirect effect of second nature through its interaction with firstnature. Because medieval trade is supposed to be a first nature cause of agglomeration,this indirect effect geography and other natural factors exert on first nature causes iswhat we especially have to control for.
Additionally to standard economic agglomeration and growth literature we also haveto account for potentially important historical causes of agglomeration and develop-ment. This clearly follows from our argument that medieval trade influenced regionaldevelopment processes through its impact on agglomeration and industry concentration.
In conclusion, we decide to group the control variables in four set of variables we addseparately to the baseline specification (without NUTS-2 dummies).
The first set of variables controls for the “geographic centrality” of regions. It includesvariables measuring the distance of a region to the closest important infrastructure facil-ities (airports, roads and railroads) and to important political and physical geographicfeatures (coasts and borders).32 Especially, the last two are found to be important firstnature determinants of agglomeration (e.g. Roos 2005, Ellison and Glaeser 1999). Ad-ditionally, the ln of the distance of each region to the geographically nearest major river
32Holl (2004) and Martin and Rogers (1995) establish empirical and theoretical evidence on the impor-tance infrastructure facilities for industry location. This justifies the inclusion of distance to road,airports and railroads as control variables.
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is included as control.33 Rivers are geographical features important for both medievaltrade, industry and city location (Borner and Severgnini 2012, Bosker and Buringh 2010,Ellison and Glaeser 1999, Roos 2005 and Wolf 2009). The idea behind this set of con-trols is to ensure that we do not simply capture the impact of many medieval trade citiesbeing located at geographically favorable places today or in the past.
A second set of variables controls for relevant contemporary characteristics of the in-cluded regions. It comprises out of dummy variables for district-free cities in Germany(which are by definition larger or more densely populated places than others), for theregions that include a country’s capital or the capital of an autonomous region.34 Addi-tionally, a categorical variable identifying the degree to which a region can be consideredas a“mountain regions” is included. Furthermore the set includes dummies for regionswith coal or ore mines (or mining firms), for regions located in the former GDR and forregions located in Easter European post-communistic transition countries. At last, itincludes the ln of a region’s area. In consequence, this set of controls accounts for manyimportant first nature causes of agglomeration (political geography and resource endow-ments) as well as for relevant historical facts that could have influence the contemporaryeconomic performance of a region (like communism).
The next set of controls captures historical characteristics of the regions that couldmatter for both present day’s agglomeration and economic performance. Here we con-sider dummy variables indicating regions with a university founded before 1500 AD andregions that adopted printing technology before 1500 AD. As unearthed by Cantoni andYuchtman (2012), Dittmar (2011) and Rubin (2011) both universities and printing tech-nology are important factors in explaining the late medieval commercial revolution andcity growth. To account for the positive influence Protestantism probably had on eco-nomic development (Woesmann and Becker 2009, Rubin 2011) we also include ln distanceto Wittenberg as variable in this set of controls. Furthermore, we also include dummiesfor regions containing at least one imperial city or at least one city that was memberof the Hanseatic League. Finally, we also control for the possible long-lasting influenceof roman heritage and low transport costs for trade and agglomeration in including adummy for cities located at an important imperial road (Postan 1952).35
The fourth set controls for the most important covariates of economic growth anddevelopment. Here we use the share of people aged between 25 and 64 with tertiary
33In Germany for example we consider Elbe, Danube, Rhine and the Oder as major rivers.34An autonomous region is considered to be a Belgian or Italian Region or a German or Austrian federal
state (“Bundesland”).35This variable considers the Via Regia, the Via Regia Lusatiae Superioris and the Via Imperii as the
probably important imperial roads more or less following the route of former Roman roads.
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education (on NUTS-2 level) as measure for regional human capital.36 As variable tomeasure the quality of regional economic and political institutions we use the quality ofgovernment index developed by the Quality of Government Institute at the university ofGothenburg which provides a measure for regional institutional quality design similar tothe World Governance Indicators (WGI) of the World Bank.37 As measure for regionalinequality we construct the ratio of average workers compensation to GDP per capita.As measure of innovative activity in a region we use the number of patents registered by aregion’s firms again at NUTS-2 level. Furthermore, we include a region’s unemploymentrate, ln of the average workers compensation and the ln of the average fixed capital of aregion’s firm.
Finally, the last set of controls include all robust covariates from the regressions before.The robust controls are obtained by including all variables in one regression that weresignificant both times when added with one of the other four sets of controls to the base-line specification. In the next step, we did remove the variables becoming insignificantin that regression. We repeat this procedure until only significant controls remain in thespecification.38 This procedure results in a set of 12 variables robustly associated withGDP per capita. These are altitude, the ln distances to airports, railroads and rivers,dummies for district free cities, capital cities, capital cities of autonomous regions, post-communistic transition countries, Eastern Germany, the ln of a region’s area, the shareof people with tertiary education, the inequality measure and the printing press before1500 AD dummy. Once more, this highlights the importance of human capital and polit-ical geography. Furthermore the robust influence of printing confirms Dittmar’s (2011)claim that printing technology fostered - similar to medieval trade- localized spilloversand forward- and backward linkages.
The results of the regressions are shown in Table 5. There we first add the first fourset of controls separately to the baseline specification and then we include as fifth setall robust covariates to the country and NUTS-1 region fixed effects. We see that thecoefficient of the trade center dummy and the distance variable remain significant inevery of the specifications although the sizes of the coefficients is considerably reducedcompared to the baseline estimates.
[Table 5 about here]
The coefficient is smallest (e.g. 0.07 in the case of the trade center dummy) in the36Again, we take the values for the year 2009.37This variable is for some countries available at NUTS-1 level and for others it is available at NUTS-2
level. For details consult the data appendix.38These regressions are not shown but are available from the author upon request.
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specification with all robust covariates added to the baseline model. This is not surpris-ing since in this specification we added only the variables with the highest explanatorypower to the regression. It suggests, that medieval trade center regions have today aGDP per capita around 7 % higher than other regions. Based on the average regionalGDP per capita in our sample this corresponds to a GDP per capita that is approxi-mately 1200 Euros higher. When looking at the different set of controls it is evident fromthe adjusted R2, that region characteristics and growth covariates add most additionalexplanatory power to the model. Apart from mountain and mining region dummies,each variable in the regional characteristics set is significant and especially the effectsof political geography (capital regions or regions with a capital of a autonomous re-gion) seem to be important. And regarding the growth covariates especially inequality(with an remarkable negative sign) and human capital exert a strong effect on GDP percapita.39 In general, the historical region characteristics are least important in explainingcontemporary regional economic development. But regions with universities and citiesadpoted printing technology before 1500 AD seem to have a significantly higher GDPper capita even today, once again highlighting the importance of human capital.40 How-ever, the university before 1500 AD dummy becomes insignificant when added jointlywith the measure of current regional human capital. This suggests universities lead toadvantages of regions concerning their human capital persisted until today. Finally, therobustly negative impact of the distance to river variable again shows the already widelyacknowledge role of first nature geography for regional economic development.
Overall, we see that the relationship between medieval trade and contemporary re-gional development is robust to the inclusion of a wide range of control variables andother important determinants of agglomeration and economic performance. The oneexception is the estimation in column (10) where distance to trade center becomes in-significant.
4.1.5 Accounting for Endogeneity
Even after controlling for many factors endogeneity of the medieval trade variables re-mains a serious issue. Endogeneity primarily could arise through unobserved factors,influencing both contemporary regional development and medieval trade. Geography
39This finding is for example in line with Simon (1998) and Gennaioli et al. (2013) who highlight theimportance of human capital for regional development and city growth.
40In the specification with the distance to trade center variable and historical region characteristics(column (7)) also the other historical region characteristics seem to be significant (at least at 10%level). This indicates that some of the effects captured in distance to trade centers are in fact e.g.are attributed to the course of important imperial roads like the Via Regia.
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might be a prominent factor for which this holds true. However, we can control forgeography in our regressions. But there are many other unobservable factors that mightaffect both our right- and left-hand side variables. A prominent example is institutionalquality in medieval cities an important factor in medieval trade and the commercialrevolution (e.g. Greif, 1992, 1993 and 1994).Other cases are cultural differences betweenregions and countries –apart from being protestant or not– or historical differences inpolitics between regions.
To solve the endogeneity issue, we therefore run IV Regressions using the LimitedInformation Maximum Likelihood (LIML) method.41
In order to be able to test the validity of the exclusion restriction we choose twoinstrument variables.
The first considered instrument variable is a categorial variable (taking the valueszero, one, two and three) indicating whether a region is classified as a mountain regionby the official EU regional statistics. The variable is zero if a region is not classifiedas a mountain region. It is equal to two or three if the region is a mountain regionaccording to two different set of criteria (for details about the exact definition consultthe Data Appendix).42 The idea behind this variable is intuitively plausible. In moun-tain regions, characterized by higher trade costs, less favorable climate and many otheradverse features trade activities were lower than in region located at large rivers, thecoast or in low altitude areas with fertile soils and less rugged terrain. Especially inthe medieval age, where no advanced transport technologies are available — especiallyfor over-land transport — mountains constituted a severe hindrance of trade (Spufford2002).43 Furthermore, as highlighted by Bosker and Buringh (2010) high elevation (aswell as differences in elevation between places) has a considerable negative effect on city
41This estimation method has better small sample properties and is most often more efficient than thestandard 2SLS method,especially in the presence of weak instruments. Its confidence intervals aremore reliable and it is unbiased in the median when the instruments are weak (Stock and Yogo 2005).
42Albeit this variable is of categorial nature we choose to include it as a single variable and not by usingthree different dummies as instruments. This is primarily motivated by guaranteeing a parsimoniousset of instruments since the IV estimates are biased towards the OLS estimates when the numberof instruments increases. Furthermore, the test of overidentifying restrictions wouldn’t be valid ifone include several instruments following the same reasoning or originating from the same measurephenomenon as excluded instruments in the first stage. However, the results are fully robust to usingthe three different categories of the mountain region variable as separate instruments. They are alsorobust to recoding the three categories to one and include the variable as binary dummy variable.Results not shown but available from the author.
43Evidently, the large amount of trade activities between the northern Italian city states and the southernGerman cities (Ulm, Ravensburg etc.) require that the traded goods are transported over the alps,e.g. through the Splugen Pass (Schulte 1966). However, the transport probably took place over onlya few important passes and none of the small villages and populated places along those mountainroutes could develop to an remarkable center of trade.
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growth and urban potential of a place. The exogeneity of this geographical characteristicof a region should not be a concern.
The second instrument variable we will use is a dummy variable for cities that wereresidential cities of bishops before 1000 AD. The church as political, spiritual and eco-nomical power had a significant impact on the development of cities in the medievalage (e.g. Baker and Holt 2004, Isenmann 1988, King 1985 and).44 Because of this it isprobable that ecclesiastical centers, like residential cities of bishops did grow larger andhad a higher probability of becoming a trade center. In line with this reasoning, Bornerand Svergnini (2012) could show that trading activity (in- and outflows of commodities)were higher in bishop residence cities. Additionally, Bosker and Buringh (2010) foundthat the presence of a bishop was a important factor in the foundation and developmentof cities during the Middle Ages. The exogeneity of this measure is not as sure as inthe case of distance to river. But nevertheless, since we can control for geography itis hard to find a variable that could potentially influence both the location of bishopresidences in 1000 AD and contemporary regional development. First, in 1000 AD mostof the political and economical institutions emerged in the late medieval did not exist.Even the central political power of our sample countries during the middle age, the HolyRoman Empire, was found in the second half of the 10th century and couldn’t thereforehave larger influences on bishops residences founded before 1000 AD. This is especiallytrue because many of the considered dioceses or archbishoprics are already establishedwhen the Empire was found in 962 AD. Second, we control for many other historical fac-tors like being located on an important imperial road or early adoption of printing thatmight had influenced both the location of trade cities, bishop residences and economicdevelopment today. Third as explained e.g. in Pounds (2005) the dioceses built in theearly medieval period were virtually identical to the territory of predated Roman cities.In consequence, their location was determined centuries before the early medieval periodwhich makes it even more unlikely that they are endogenous to contemporary economicdevelopment.
In other words, there are many reason to conclude that bishop residences before 1000AD are exogenous and can be used as instrument.
Additional to those instruments, we make use of Lewbel’s (2012) approach that ex-44King (1985) describes the importance of the church for commercial activities and trade, i.e. they
mentioned that in many cases the local fairs and markets are managed and organized by the church.Pounds (2005) and Nicholas (1997) additionally emphasize the importance of bishops for the de-velopment of cities in the early middle age, when traditional trade declined during the economicdepression in the eighth and ninth century. Finally, Hunt and Murray (1999) notice the significanceof the church for city development and commerce arising from fostering ecclesiastical tourism andpilgrim activities.
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ploits heteroskedastic first stage errors terms to generate artificial instruments not cor-related with the product (covariance) of the first stage’s heteroskedasdic errors.45 Thismethod can provide more reliable estimates if it is doubtful, that the instruments meetthe exclusion restriction or are weak. Since at least the exogeneity of the bishop seatscan be disputed in principle this method ensures that we do not produce invalid IVestimates. The strength of these generated instruments depend on the amount of scaleheteroskedasdicity in the error. The presence of heteroskedasdicity in our first stageregression is tested with a Pagan-Hall test. The test clearly rejects the presence of ahomoskedasdic disturbance (p-value<0.000). Therefore, the method can yield reliableestimates although first stages statistics are not available. 46
We run LIML IV regressions using the instruments outlined above and using Lewbel’s(2012) approach with generated instruments for the trade center dummy and the distancevariable. We include the set of robust covariates as well as NUTS-1 region and countryfixed effects as controls, i.e. we reestimate columns (5) and (10) of Table 5. The resultsof these estimations are shown in Table 6.
The first important result is that throughout all specifications the trade center dummyand the distance to trade center variable are significant and retain there signs. Evenmore, the size of the coefficients increased remarkably, at least in the case of the con-ventional IV regressions in columns (1) and (3). Moreover, the distance to trade centervariable that was insignificant before in column (10) of Table 5 regains significance at1 % level. This can be interpreted as endogeneity downward biased the OLS results,probably due to measurement error or a negative correlation between an unobservedfactor and our medieval trade measures. Concerning the validity of the instruments theoveridentification tests (Hansen J-statistic) informs us that the validity of the exclusionrestriction cannot be rejected in almost all case at the common levels of significance.The exception is the last specification where we cannot reject the null at all levels ofsignificance. Due to this, one should be cautious in interpreting the results from thelast columns here. Nevertheless, in line with our arguments above it seems the case thatthe being a mountain region and bishop residences before 1000 AD affect contemporarylevels of development solely through their impact on which cities became medieval tradecenters.47 Furthermore, at least in the case of the trade center dummy, Lewbel’s (2012)45The vector of instruments Zj is constructed by multiplying the first stage error terms with each of
the included exogenous, mean-centered regressors (all or a subset of the first stage regressors), i.e.Zj = (Xj −X)ε (Lewebel 2012).
46Lewbel 2012 mention several papers that already applied this method resulting in plausible estimatese.g Sabia (2007) or Kelly and Markovitz (2009).Thus, the method has proven to provide reliableestimates in different empirical settings.
47 In fact, it is very likely that geographic characteristics like being a region in the mountains also
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approach show, that our results hold even when we do not use external instruments butinstruments that are exogenous by construction. However, the coefficients obtained withLIML IV are much larger as that resulting from Lewbel’s (2012) approach that are inmuch closer to the original OLS estimates. Since Lewbel’s (2012) approach relies onsecond moment conditions and additionaly produces a comparatively large number ofinstruments it is likely that this results reflect the lower bound of the true estimates.
Turning to the first stage results, it emerges that both instruments are indeed signif-icant and strong predictors of medieval trade. The bishop dummy is highly significantin both specifications. This is also true for mountain region dummy, although it is onlymarginally significant when the trade center dummy is instrumented . The underidenti-fication test and the Angrist-Pischke F statistic of excluded instruments always indicatethat the instruments are strong and relevant.
Altogether, the IV estimations show that endogeneity does not affect the detectedsignificant relation between medieval trade and contemporary economic development. Ifanything, endogeneity downward biases the OLS estimations and therefore lead us tounderestimate the true effect.48
[Table 6 about here]
4.2 Further Results - Index of Medieval Commercial Importance
Although the evidence brought forward in the previous section provide robust empiricalsupport for a significant relationship between medieval trade and contemporary regionaldevelopment, the data on which the results are based has its limitations. First andforemost, the evidence so far is solely based on a dummy variable constructed accordingto whether a city was located at an important trade route and few other qualitativejudgments about their importance. In treating all trade cities as equal this approach isprobably not able to capture all the dimensions and factors that made a city an importantcenter of commercial activity throughout the medieval. In consequence, we possibly donot catch the true effect of medieval trade or commercial activities on contemporarydevelopment levels. However, based on the data set at hand and historical evidence aboutimportant determinants of trade, economic and commercial activities in the middle agesone can construct an “Index of Commercial Importance” for each region in our sample.Among the many potential determinants of medieval commercial activity, we choose eight
influenced which cities became residence cities of medieval bishops but since we include both variablesjointly in the first stage we take into account this correlation.
48A test of endogeneity of the instrumented variables rejects the null of actual exogeneity in at 1 % levelin every LIML IV regression.
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to construct the index. At first, we include out trade center dummy, representing citieslocated on important trade routes. Second, we consider the variable indicating citiesthat were residence of a bishop or archbishop before 1000 AD. As already outlined, thechurch was found to be one of the most important factors in the development of medievalcities and trade. Hence, the presence of a bishop should be a valid proxy variable forcities of notable commercial importance. Third, we include the ln distance to coast ofeach region’s centroid, representing the distance of each city to a potential sea harborand the significant trade cities located at the coast (like e.g. many of the Hanseaticcities). Fourth, we include the dummy variable identifying important members of theHanseatic League. Since the Hanseatic League was one of the leading actors in medievalcommerce, its important members cities very likely were subject to significant commercialactivity. Fifth, we adopt the dummy variables representing cities that had the statusof an imperial city or that were located at an important imperial road. As transportcost were a crucial factor in medieval trade, the presence of a paved and protected roadshould be an important economic advantage for the cities located at it (e.g. Spufford2002). On the other hand, most of the important trade cities in the Holy Roman Empirethat were not member of the Hanseatic League were free or imperial cities. Due to this,imperial cities, with their political and institutional microcosm can be seen as germ cellsof commercial activity in the medieval period(Cantoni and Yuchtman 2012). Sixth, weinclude a variable depicting regions in which medieval mining activties (copper and saltmining) took place. This accounts for the fact that salt and copper —as raw materialsin general— were some of the major commodities trade in medieval Europe (e.g. Postan(1952), King 1985, Spufford 2002). Finally, we follow the reasoning of a recent study byCantoni and Yuchtman (2012) showing that universities decisively fostered commercialactivities and market establishment in the area around them. Consequently, we includethe dummy variable reporting cities with universities founded before 1500 AD as lastvariable. The index is constructed by simply adding up this variables combining themin one index ranging from zero to eight.Thereafter, we substract the mean of the indexfrom all its values so that the average region would have a value of zero. Regions with anbelow average value therefore have a negative and regions with an above average valuehave a positive value. We also construct an alternative version where we include the lndistance to trade center variable instead of the trade center dummy.49
Clearly, there are other determinants of commercial activity in the middle age. Never-theless, we choose this set of variables because these variables are significant predictors
49We recode this variable so that it is positively associated with economic development and agglomerationas the other seven variables in the index.
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of the original trade center dummy when jointly included in probit model. Together,they produce a pseudo R2 of around 0.2.50 This result serves as a initial hint confirmingthe relevance of our variables for explaining commercial activity in the medieval age.
We now perform OLS and instrumental variable regressions (as before with the LIMLand Lewbel’s (2012) method) using both versions of the index of medieval commercialimportance as independent and the ln of GDP per capita in 2009 as dependent variable.We include the complete baseline specification (NUTS-1, NUTS-2 and country fixedeffects as well as the basic geographic controls) and the set of robust covariates employedin Tables 5 and 6 supplemented by NUTS-1 region and country fixed effects. This ensuresthat the results are comparable to that obtained before using the simple trade centerdummy and the distance variable. The results are shown in Table 7.
[Table 7 about here]
All in all, the index of commercial importance, in both the original and the alternativeversion, shows up significant with a positive sign in every regression. Reassuringly, theLIML IV regressions using the same instrumental variables as before and a version ofthe index without the bishop before 1000 AD dummy, yield a more significant andremarkably higher coefficient. This is similar to the IV regressions using the dummyvariable. The coefficient obtained with Lewbel’s (2012) generated instruments is muchcloser to the original OLS estimate but keeps its significance. Furthermore, the Lewbelestimate has to be treated with some care since the overidentification test does rejectthe null of a valid exclusion restriction at the marginal significance level.
To sum up, the index of commercial importance confirm the results of the regressionsusing a simple dummy variable. Therefore, it is fair to conclude that there is a statis-tically robust relationship between medieval trade and commerce and today’s regionaleconomic development.
4.3 Medieval Trade, Agglomeration and Contemporary EconomicDevelopment - Establishing Causality
Until now, we only indirectly show that medieval trade influences present-day’s regionaleconomic development through its impact on agglomeration. We did so by showingthat the distance of a region to the next trade city is robustly negatively associatedwith regional GDP per capita. In this section we will conduct a more direct test of the
50Regression not shown but available from the author.
27
proposed causal chain going from medieval trade activities to medieval city growth tocontemporary agglomeration patterns and from there to regional economic performance.
4.3.1 Trade and City Growth in the Medieval Age
The first building block of our argument is that there should be a positive associationbetween involvement in medieval trade activities and city growth during that period. Toillustrate that the theoretically proposed relationship between medieval trade and citygrowth does actually exist, we run a set of regressions were we explain ln city growthin the medieval period by the trade center dummy and other covariates of medieval citygrowth identified in the literature.The population data on which the city growth variableis based originates from Bairoch’s (1988) compilation of European city population datafrom 800 to 1850. We include every city for which there is population data in Bairoch(1988) in 1500 AD and that is located in one of our ten sample countries. This leaves uswith 361 cities from which 90 are coded as trade cities based on the same informationthan in the NUTS-3 region sample. A list of all included trade cities is provided in theData Appendix.
The estimated results are depicted in Table 8. There, in columns (1) to (3) we runcross-sectional OLS regressions with the natural logarithm (ln) of city growth between1500 AD (the end of the medieval period) and 1200 AD, 1300 AD and 1400 AD. Wechoose these three variables to demonstrate that the results are not dependent on thechosen period and furthermore are stronger when we consider a longer period of citygrowth. The latter would be an indication that the impact of trade on city growthworks trough agglomeration processes unfolding there effect only after a longer period oftime. In every of the regressions we include country fixed effects as well as a set of otherset of historical determinants of city development as controls. We control for first natureagglomeration forces by including the distance of a city to the next river or coast and alsoa city’s latitude and longitude and whether it is classified as a mountain region and wastherefore difficult to reach(e.g. Bosker and Buringh 2010, Spufford 2002). Furthermore,we consider several dummy variables indicating whether a city was residence of a bishopbefore 1000 AD, had the status of imperial city, was located at an important imperialroad or was a member of the Hanseatic League.51 At last, we always include the lnof the initial city population at the beginning of the considered growth period. This
51This variables were already used in the preceding empirical analysis on regional level data. However,the NUTS-3 level variables do not always fully coincide with the city level variables. This is dueto the fact that a NUTS-3 region could harbor an archbishop in 1000 AD but none of the cities weconsider in this sample and are located in this region.
28
accounts for the fact that city growth is concave in city size and in consequence thegrowth rate of a city strongly depend on there initial size.52 This is, we estimate thefollowing regression specification:
ln(CityGrowth)i, 1500t
= α+ βTCi + γPOPt + δ′Xi + θc + εi (3)
Where ln(CityGrowth)i, 1500t
is the ln the growth in population in a city between 1500 ADand period t with t=1200, 1300 or 1400 AD. TCi is the trade center dummy POPt is theln city population begin of the period and Xi is a set of time-invariant covariates and θc
are country fixed effects.We also estimated this equation using the Index of CommercialImportance insteas of the trade center dummies. These results, that do not generallynot differ from that reported here are available in Appendix C (Table C.2).
Turning to the interpretation of the results, we clearly find that the trade cities showsignificantly higher growth throughout the medieval than non trade cities. This is clearevidence in favor of our theoretical reasoning that medieval trade contributed to citygrowth and agglomeration. Furthermore, we also see a highly significant and negativeeffect of the initial population level showing that indeed already large cities did growslower.
What is more, in columns (4) and (5) we also run random effect (RE) estimations usinga panel data set comprising out of the same sample and variables as the cross section. Inthese estimations we first regress the ln of the city population in every of our consideredyears (1200, 1300, 1400 and 1500 AD) on the trade center dummy and the same set ofcontrols as previously in the cross sectional estimates and additionally we add year fixedeffects. Again, pooled over all years, the population of a city is significantly higher if thecity is a important medieval trade city. At last, we regress the change in ln populationbetween every of our base years on the trade center dummy and additionally include thelagged population in the regression (what is similar to the cross sectional estimations).Once more, we found a significantly positive association between being a trade centerand changes in population throughout the period from 1200 AD through 1500 AD.
In sum, this results suggest that medieval trade can indeed be regarded as an im-portant determinant of city growth and agglomeration during the middle ages. Havingestablished this, in the following we will focus on a detailed investigation of the rela-tionship between medieval trade activities, contemporary agglomeration patterns andregional economic growth.
52A descriptive overview over all variables used in the city level estimations is available in Table A.2 inthe Data Appendix
29
[Table 8 about here]
4.3.2 The Medieval Legacy of Contemporary Economic Agglomeration Patterns
The next step in our causal chain is to link medieval city growth and contemporaryeconomic agglomeration patterns, i.e. we have to establish that there is a significantamount of path-dependency in city development throughout the regions in our sample.To do so, we regress the ln of the relative GDP density of a region on the three medievalcity growth variables used in the previous subsection, the initial city population atthe beginning of the considered growth period and again NUTS-1 region and countryfixed effects and the robust covariates used already in the preceding estimations. Moreformally spoken following regression equation is estimated using OLS:
ln(RGDPD)cijk = α+βln(CityGrowth)cijk, 1500t
+γPOPcijk,t+δ′Xcijk+θc+i+εcijk (4)
Where ln(RGDPD)cijk is the ln of the relative GDP Density in a NUTS-3 region,ln(CityGrowth)cijk, 1500
tis the ln of a city’s population in 1500 AD divided by its pop-
ulation in t with t being either 1200, 1300 or 1400 AD. γPOPcijk,t represents the ln ofthe city’s population at the t, i.e. the beginning of the considered growth period. Xcijk
is the set of robust covariates used several times before. θc and i are NUTS-1 or countryfixed effects, respectively. εcijk finally is the error term.
The final step, is then to establish the relationship between medieval trade, contem-porary economic agglomeration (via path dependent agglomeration processes as shownabove) and regional economic development.
We will achieve this by conducting a causal mediation analysis (estimation of me-diation effects) following the method developed by Imai et al. (2010, 2011).53 Me-diation analysis enables to disentangle direct and indirect effects –via determiningagglomeration– of medieval trade on contemporary development. Since we cannot ruleout that there are direct effects or –what amounts to the same– indirect effects of me-dieval trade working via other channels this methodology seems to be appropriate forour setting. The estimation of mediation effects is based on a set of three different linearestimation equations (Imai et al. 2010):
53The method suggested by Imai et al. is a generalization of the traditional mediation analysis (MacK-innon 2008) that implement it as a variant of linear structural equation modeling (LSEM).
30
Ycijk = α1 + β1Tcijk + γ′11Xcijk + γ′12Xcij + δc + θi + λj + εcijk1 (5)
Mcijk = α2 + β2Tcijk + γ′21Xcijk + γ′22Xcij + δc + θi + λj + εcijk2 (6)
Ycijk = α3 + β3Tcijk + πMcijk + γ′31Xcijk + γ′32Xcij + δc + θi + λj + εcijk3 (7)
Where Ycijk represents ln GDP per capita in a NUTS-3 region, Tcijk represents ourvariables of interest (treatment variable), i.e. the trade center dummy, the ln distance totrade center measure and the index of medieval commercial importance. Mcijk representsthe mediating variable, that is ln relative GDP density as measure of the spatial distri-bution of economic activity. Xcijk is defined as before and stands for a set of NUTS-3level covariates. Accordingly, Xcij is a set of NUTS-2 level covariates. δc, θi and λj
are again country, NUTS-1 and NUTS-2 region fixed effects. The epsilons represent theerror terms. This means that equation (4) is identical to equation (2) or (3) respectively,while in equation (5) we regress the medieval trade variables on the agglomeration mea-sures and in equation (6) finally we include both the medieval trade variables and theagglomeration measures in one regression on ln GDP per capita.
The “average causal mediation effect” (ACME) is estimated by the product of thecoefficients β2 and π (β2π) and is obtained through a two-step procedure described indetail in Imai et al. (2011).54 The ACME represents the indirect effect of medievaltrade on contemporary GDP per capita, i.e. that part of the overall effect of medievaltrade running through agglomeration. Correspondingly, β1 measures the total (average)effect of medieval trade on regional GDP per capita and β3 represents the direct effectof medieval trade, i.e. that part of the effect not mediated by agglomeration (but maybeother factors). In consequence, this methodology of separating direct and indirect effectsenables to calculate which amount of the total effect of medieval trade works via increasedagglomeration. We expect β2 > 0 in the case of the trade center dummy and β2 < 0 inthe case of the distance to trade center variable. Even more, we also hypothesize that onaverage, the majority of the effect of medieval trade should run through agglomeration.This leads us expecting the ACME being significantly different from zero and greaterthan the direct effect (|β2π| > |β3|). Moreover, since it holds that β1 = β2π + β3
equation (4) is redundant given equations (5) and (6) and therefore we only estimate
54In the classical case, where the mediation analysis is conducted using LSEM the coefficients areobtained by separately estimating equations (5) and (6) using OLS.
31
those two equations.55 Last, we assume π > 0, i.e. a significant positive direct effect ofagglomeration on regional GDP per capita.
The results of both the regressions of medieval city growth on ln GDP density andthe mediation analysis are presented in Table 9. Supplementary to those result, weestimated Table 9 with ln population density as mediating agglomeration measure. Theresults are similar and available in Appendix C (Table C.1).
[Table 9 about here]
Columns(1) to (3) show the results for the estimation of equation (4). We clearlysee that there is a robust and positive relationship between medieval city growth indifferent time periods and the contemporary relative GDP density of the NUTS-3 regionsin which the cities are located. The smallest estimate, resulting from the estimationwith city growth between 1400 and 1500 AD as regressor, implies that on average, onepercentage of city growth in this period leads to a around 0.17 percent higher relativeGDP density. This shows that there is indeed a considerable amount of path-dependencyin the development of European cities, i.e. cities that grew larger in the medieval agedue to trade are the economic centers and agglomeration areas still today.
Turning to the results of the mediation analysis (columns (4) to (6)) we again findstrong empirical support for our theory. As expected based on the previous empiricalresults, all three measures of medieval trade (the dummy, the distance variable and theindex of commercial importance) are strong predictors of contemporary relative GDPdensity. The coefficients are both significant from a statistical and economical point ofview. The coefficient of the trade center dummy for instance implies that regions withan important medieval trade center shows on average a around 40 % higher relativeGDP density than non trade center regions. What is more, the results clearly show thata higher distance to a trade center largely corresponds to a higher distance to areaswhere the economic activity is concentrated.Thus, according to those estimates, thereis a significant and robust positive relation between present day’s spatial distributionof economic activity and medieval trade. Moreover, from the estimations of equation(7) we see that the significant effect of the medieval trade measures on the ln GDPper capita does completely disappear when we include the relative GDP density in theregression estimation. The relative GDP density in contrast, enters with a positive andsignificant sign in each of the three regressions. Thus, areas with a high concentration ofeconomic activity are also the regions with a higher GDP per capita. Most importantly,55Finally, this also implies that the share of the total effect of medieval trade running through agglom-
eration is (β2π)β1
.
32
this also implies that the vast majority of the observed strong effect of medieval trade onregional development levels works through its impact on the patterns of spatial industryagglomeration. In line with this, the ACME is always significant and on average above100 % indicating that the insignificant remaining effect of medieval trade is even negativein some cases.
Thus, it is fair to conclude that the effect of medieval trade indeed runs throughagglomeration as proposed in this paper.
4.4 Robustness of the Results
Our results have proven to be robust to the inclusion of many important covariatesand to endogeneity issues. However, there remain some additional concerns about therobustness of the obtained estimates. To account for these objections, we conduct variousrobustness checks. The results of these tasks are reported in appendix B (Tables B.1 toB.8).
At first, we account for the effect some additional variables might have on both thecurrent level of regional development and/or medieval trading activities. In order to doso, we add four different variables to the set of control variables used in Tables 5 and 6.56
We add a dummy variable indicating regions with copper mining sites in the medievalage to look whether such type of economic activities at least partly causes the significanteffects we attribute to medieval trade activities. This could be possible if e.g. miningactivities actually led to higher trade activities in the regions they took place. We addthis variable to the specifications three and eight in Table 6, i.e. we add the variable tothe set of control variables capturing historical region characteristics.
Additionally, we include an interaction term of latitude and longitude of a region’scentroid to the set of basic geographic controls and re-estimate specifications three andsix of Table 5 including this interaction effect. The justification for this is to look whetherdevelopment levels systematically differ when changing latitude and changing longitudeand vice versa. In this way we can for example identify effects of different climaticconditions varying along different latitudes for countries located at the same longitudes.
Furthermore, we add the share of Roman Catholic people in a country’s populationin 2009 to the set of growth covariates and the re-run the regression in Table 6 columns(4) and (9). This takes account of the fact that the impact of Protestantism (or religionin general) on economic outcomes might not be captured adequately by the Distance toWittenberg variable, at least not today 500 years after the Reformation.56A descriptive overview over these variables is provided in Table B.9. A detailed description of the
variables and their sources is available in appendix B.
33
At last, we add a dummy variable equal to one if a region includes an importantresidence city of a clerical or secular ruler. Residence cities of important rulers werethe centers of political and economic power in the territory of the ruler. Therefore, itis quite likely that they showed high growth rates of population and economic activityand maybe explain a significant part of medieval trade and its long-lasting effects onagglomeration and development (e.g. Ringrose 1998).
The results obtained when adding these supplementary variables to the mentionedregression specifications are shown in Table B.1. The dummy for medieval copper miningregions and the latitude longitude interactions are not significant (Columns one to fourin Table B.1). Apart from the fact, that some of the included covariates seem to besignificant (e.g. the catholic variable) the trade center dummy and the distance to tradecenter variable retain there significance and the size of the coefficients is comparable tothat obtained in the original estimates or larger.
A second robustness check is to look whether our results are sensitive to removinginfluential observations. To test this we re-estimate Table 6 but remove regions that showa high leverage, i.e. have a large impact on the coefficient estimate. This can be doneby computing the DFITS statistics, developed by Belsely et al. (1980). They suggestto consider an observation as influential if |DFITSj | > 2
√k \N (with k indicating
the number of regressors and N denoting the number of observations in the sample).Following their suggestion in each regression the regions having a DFITS statistic abovethis threshold are removed from the sample and then the estimations are based on thisreduced sample. The results of this task are shown in Table B.2. Once again, theexclusion of influential observations does only lead to minor quantitative changes in thecoefficient values (in both directions). Qualitatively, the results seem to be completelyunaffected by influential observations.
As already discussed in the data section, there is a considerable amount of uncer-tainty in the historical sources and information on which our identification of importantmedieval trade centers is based. In consequence, it is adequate to test, whether ourempirical results hold, when alternative sample of trade cities are used in the regres-sions. We therefore re-estimate the all important results that depend on the tradecenter dummy using the four different alternative samples of trade regions introducedin section 3.3 and further elaborated in Appendix B. For each of this four alternativetrade center dummies we re-run the regression specification in Table 5 column (5) wherewe employed all robust covariates from the previous regressions as controls. This spec-ification is used —as in most parts of the analysis above– because it yields the mostconservative estimates. We further repeat the LIML and Lewbel (2012) instrumental
34
variables regressions from Table 6 columns (1) and (2) as well as the estimation in Table8 column (1) where we regress the ln city growth between 1200 and 1500 AD with thetrade center dummy, the inital population level and appropriate historical controls. Atlast, we re-do the mediation analysis with ln relative GDP density as mediator variables(originally reported in Table 9 column (4)). The results of this re-estimations are shownin Appendix B, Tables B.3–B.7.
As one can infer from the results in these Tables the results most often do onlymarginally change with the alternative trade center variables. They coefficients eventend to be a little bit larger than with the original sample of trade cities. However,this does not hold for the estimations from Table 8. At least, with the last sampleof trade cities containing cities with reported trade activities in earlier periods. Thecoefficient of the trade center dummy becomes insignificant when using this alternativesample. However, in sum, none of our conclusions and general results is invalidated bythe alternative samples of trade cities. As such, the results are robust to considerablechanges in the sample due to uncertainty of historical information and underlying dataselection criteria.
5 Conclusion
This paper argues that medieval trade led to agglomeration and concentration of eco-nomic activities in the region it took place. It further postulates that the observed spatialdistribution of population and economic activity across Europe today is still shaped bythe self-reinforcing and long-lasting agglomeration processes originating from medievaltrade activities.
An empirical tests of these hypotheses brought forward that, as expected, there is astatistically and economically significant positive relationship between medieval tradeactivities and contemporary regional economic development. The analysis further un-earthed that this relationship is indeed caused by the influence medieval trade exerted onthe emerging patterns of agglomeration and spatial concentration of industrial activitiesthroughout European regions. Based on the result of this paper we are able to confirm acausal chain running from medieval trade activities through medieval city growth to con-temporary industry concentration and regional economic development. Medieval tradetherefore can considered to be an important determinant of modern economic develop-ment. Further quantitative analyses of medieval trade activities maybe based on moredetailed historical data can therefore help to significantly improve our understanding ofthe sources of long-lasting economic and social prosperity.
35
Tables and Figures
Figure 1: NUTS-3 Regions with Medieval Trade Cities
36
0.1
.2.3
.4D
ensi
ty
2 4 6 8 10ln(Population Density)
Figure 2: Kernel Density Estimate for ln(Relative GDP Density)
0.0
5.1
.15
.2.2
5D
ensi
ty
-5 0 5 10ln(Relative GDP Density)
Kernel Density Trade Center=1Kernel Density Trade Center=0
Figure 3: Kernel Density Estimates for Trade Centers and Non Trade Centers
37
-.5
0.5
1e(
ln(G
DP
per
cap
ita)
| X )
-1 -.5 0 .5 1e(Trade Center | X )
Figure 4: GDP p.c and Trade Centers - Partial Regression Plot
-.5
0.5
1e(
ln(G
DP
per
cap
ita)
| X )
-1 -.5 0 .5e( ln(Distance to Trade Center) | X )
Figure 5: GDP p.c. and Distance to Trade Centers - Partial Regression Plot
38
Table 1: The Data on Medieval Trade Centers
Country No. ofRegions
No. of TradeCenters
Share TradeCenters
Mean ln(Distanceto Trade Center)
Austria 35 7 20 0.36Belgium 44 3 6.8 0.41Czech Republic 14 4 28.6 0.43France 94 20 21.3 0.53Germany 429 37 8.6 0.39Hungary 20 2 10.0 0.69Italy 90 25 27.8 0.41Lithuania 7 2 28.6 0.56Netherlands 40 7 17.5 0.29Poland 66 12 18.18 0.55Total 839 119 14.8 0.425
39
Table 2: Bivariate Correlations of the Main Variables
Trade Center ln(Distance toTrade Center)
ln(PopulationDensity)
ln(GDP percapita)
ln(RelativeGDP Density)
Trade Center 1ln(Distance toTrade Center)
-0.529***(0.000) 1
ln(PopulationDensity)
0.228***(0.000) -0.36*** (0.000) 1
ln(GDP percapita)
0.12***(0.108)
-0.356***(0.000)
0.461***(0.000) 1
ln(RelativeGDP Density)
0.218***(0.000)
-0.303***(0.000)
0.921***(0.000)
0.434***(0.000) 1
Notes. Correlation coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level.Reported are pairwise correlation coefficients using the whole sample of NUTS-3 regions.
40
Tabl
e3:
Med
ieva
lTra
de,A
gglo
mer
atio
nan
dR
egio
nalD
evel
opm
ent
-Des
crip
tive
Ove
rvie
w
coun
try
Av.
GD
Pp.
c.tr
ade
cent
ers
GD
Pp.
c.no
ntr
ade
cent
ers
“GD
PA
dvan
tage
”tr
ade
cent
ers
Rel
.G
DP
Den
s.tr
ade
cent
ers
Rel
.G
DP
Den
s.no
ntr
ade
cent
ers
“Rel
.G
DP
Den
s.A
dvan
tage
”tr
ade
cent
ers
Aus
tria
3742
8.71
2688
5.71
1054
2.28
***
(256
9.8)
19.2
10.
453
18.7
6**
(8.5
)Be
lgiu
m35
566.
6625
014.
610
552.
03**
(466
9.6)
1.02
3.00
-1.9
8(8
.43)
Cze
chR
epub
lic15
950
1110
048
50*
(257
4.7)
31.9
40.
247
31.7
(18.
79)
Fran
ce29
680
2451
3.5
5166
.48*
*(2
267.
2)13
7.07
13.7
112
3.36
*(7
2.72
)G
erm
any
3438
1.08
2634
2.86
8038
.22*
**(1
692.
8)14
.02
5.91
8.1*
**(2
.5)
Hun
gary
1350
066
77.7
868
22.2
3***
(204
9)75
.51
.174
75.3
4***
(18.
73)
Ital
y27
576
2409
5.38
3480
.62*
**(1
220.
9)3.
042.
230.
818
(1.7
3)Li
thua
nia
8200
6439
.99
1760
(239
7.35
)1.
640.
710.
924
(0.4
71)
Net
herla
nds
3614
2.86
3043
0.3
5712
.56*
(288
3.3)
1.81
2.97
-1.1
5(2
.0)
Pola
nd10
475
6822
.22
3652
.78*
**(9
21.2
)42
.94.
1638
.74*
**(9
.00)
Tota
l28
652.
923
779.
248
73.7
7***
(105
0.28
)35
.99
5.48
30.5
1***
(9.7
)N
otes
.T
hest
atist
ical
signi
fican
ceof
diffe
renc
esin
GD
Ppe
rca
pita
,pop
ulat
ion
dens
ityan
dre
lativ
eG
DP
dens
itybe
twee
ntr
ade
cent
ers
and
non
trad
ece
nter
sis
test
edby
atw
o-sa
mpl
et
test
(ass
umin
geq
ualv
aria
nces
).D
iffer
ence
sbe
twee
ntr
ade
cent
ers
and
non
trad
ece
nter
sar
est
atist
ical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
Stan
dard
erro
rsof
the
tte
sts
are
repo
rted
inpa
rent
hese
s.
41
Table 4: Medieval Trade and Contemporary Economic Development - Baseline Esti-mates
Dep. Var. ln(GDP per capita)(1) (2) (3) (4) (5) (6)
Trade Center 0.244***0.272***0.264***(0.026) (0.028) (0.028)[0.03] [0.033] [0.031]{0.03} {0.029} {0.27}
ln(Distance toTrade Center)
-0.232***(0.039)
-0.31***(0.046)
-0.29***(0.046)
[0.047] [0.053] [0.055]{0.038} {0.045} {0.043}
Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesNUTS-2 Dummies No Yes Yes No Yes YesBasic GeographicControls
No No Yes No No Yes
Obs. 839 839 839 839 839 839Adj. R2 0.78 0.778 0.778 0.765 0.762 0.763Notes. Below each coefficient three standard errors are reported. First, heteroskedasdictyrobust standard errors are reported in parentheses. Second, standard errors adjusted fortwo-way clustering within NUTS-1 and NUTS-2 regions are reported in square brackets.Third, standard errors adjusted for two-dimensional spatialcorrelation according to Con-ley’s (1999) method are reported in curley brackets. The standard errors are constructedassuming a window with weights equal to one for observations less than 3 degrees apartand zero for observations further apart. Coefficient is statistically different from zero at the***1 %, **5 % and *10 % level. The basic geographic controls include a NUTS-3 region’slatitude, longitude and altitude. Each regression contains a constant not reported.
42
Tabl
e5:
Med
ieva
lTra
dean
dC
onte
mpo
rary
Econ
omic
Dev
elop
men
t-A
ddin
gFu
rthe
rC
ontr
ols
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
Trad
eC
ente
r0.
175*
**0.
105*
**0.
181*
**0.
0701
***0
.045
**(0
.025
)(0
.024
)(0
.024
)(0
.027
)(0
.021
)
ln(D
istan
ceto
Trad
eC
ente
r)-0
.105
**(0
.044
)-0
.085
7*(0
.044
)-0
.135
**(0
.053
)-0
.138
***
(0.0
41)
-0.0
529
(0.0
41)
Cou
ntry
Dum
mie
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sN
UT
S-1
Dum
mie
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sBa
sicG
eogr
aphi
cC
ontr
ols
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Geo
grap
hic
Cen
tral
ityC
ontr
ols
Yes
No
No
No
No
Yes
No
No
No
No
Reg
ion
Cha
ract
erist
ics
No
Yes
No
No
No
No
Yes
No
No
No
Hist
oric
alR
egio
nC
hara
cter
istic
sN
oN
oYe
sN
oN
oN
oN
oYe
sN
oN
oG
row
thC
ovar
iate
sN
oN
oN
oYe
sN
oN
oN
oN
oYe
sN
oA
llR
obus
tC
ontr
ols
No
No
No
No
Yes
No
No
No
No
Yes
Obs
.83
983
983
951
881
883
983
983
951
881
8A
dj.R
20.
809
0.87
30.
784
0.87
80.
878
0.79
80.
859
0.77
60.
872
0.87
7N
otes
.St
anda
rder
rors
adju
sted
for
two-
way
clus
terin
gw
ithin
NU
TS-
1an
dN
UT
S-2
regi
ons
are
repo
rted
inpa
rent
hese
s.C
oeffi
cien
tis
stat
istic
ally
diffe
rent
from
zero
atth
e**
*1%
,**5
%an
d*1
0%
leve
l.T
heun
itof
obse
rvat
ion
isa
NU
TS-
3re
gion
.T
heba
sicge
ogra
phic
cont
rols
incl
ude
are
gion
’sla
titud
e,lo
ngitu
dean
dal
titud
e.T
hege
ogra
phic
cent
ralit
yco
ntro
lsin
clud
eth
eln
dist
ance
sofa
regi
on’s
cent
roid
toth
ene
ares
tai
rpor
t,ra
ilroa
d,ro
ad,b
orde
ran
dco
ast
poin
t.R
egio
nch
arac
teris
ticco
ntro
lsin
clud
ea
dum
mie
sfo
rre
gion
sin
Ger
man
yth
atar
edi
stric
t-fr
eeci
ties,
for
regi
ons
incl
udin
ga
coun
try’
sca
pita
l,ar
ecl
assifi
edas
mou
ntai
nre
gion
s,w
ithor
eor
coal
min
es,l
ocat
edin
the
form
erG
DR
and
loca
ted
inan
East
ern
Euro
pean
post
-com
mun
istic
tran
sitio
nco
untr
y.Fu
rthe
rmor
eit
enco
mpa
sses
the
lnof
are
gion
sar
ea.
The
hist
oric
alre
gion
char
acte
ristic
sco
nsist
ofa
dum
my
varia
bles
indi
catin
gre
gion
sw
itha
univ
ersit
yfo
unde
dbe
fore
1500
AD
,tha
tad
opte
dpr
intin
gte
chno
logy
befo
re15
00A
D,c
onta
inci
ties
that
wer
em
embe
rsof
the
Han
seat
icLe
ague
,with
form
erim
peria
lciti
esan
dw
ere
loca
ted
onan
impe
rialr
oad.
Mor
eove
rit
incl
udes
the
lnof
the
dist
ance
ofa
regi
on’s
cent
roid
toW
itten
berg
.T
hegr
owth
cova
riate
sen
com
pass
are
gion
’sun
empl
oym
ent
rate
,num
ber
ofre
gist
ered
pate
nts,
aver
age
firm
lnfix
edca
pita
lsto
ck,a
vera
gew
orke
rco
mpe
nsat
ion.
Furt
herm
ore,
itin
clud
esth
esh
are
ofpe
ople
aged
betw
een
25-6
4w
ithte
rtia
ryed
ucat
ion
onN
UT
S-2
leve
l,th
equ
ality
ofgo
vern
men
tin
dex
onN
UT
S-1/
NU
TS-
2le
vela
ndth
era
tioof
anav
erag
ew
orke
rsco
mpe
nsat
ion
toa
regi
on’s
GD
Ppe
rca
pita
asin
equa
lity
mea
sure
.T
hese
tof
allr
obus
tco
varia
tes
enco
mpa
sses
altit
ude,
the
lndi
stan
ces
toai
rpor
ts,r
ailro
ads
and
river
s,du
mm
ies
for
dist
rict
free
citie
s,ca
pita
lci
ties,
capi
talc
ities
ofau
tono
mou
sre
gion
s,po
st-c
omm
unist
ictr
ansit
ion
coun
trie
s,Ea
ster
nG
erm
any,
the
lnof
are
gion
’sar
ea,t
hesh
are
ofpe
ople
with
tert
iary
educ
atio
n,th
ein
equa
lity
mea
sure
and
the
prin
ting
pres
sbe
fore
1500
AD
dum
my.
Each
regr
essio
nin
clud
esa
cons
tant
not
repo
rted
.
43
Table 6: Medieval Trade and Contemporary Economic Development - IV Regressions
(1) (2) (3) (4)Method LIML Lewbel (2012) LIML Lewbel (2012)
2. Stage ResultsDep. Var. ln(GDP per capita)
Trade Center 0.306*** 0.0787***(0.105) (0.0247)
ln(Distance to Trade Center) -0.519*** -0.155***(0.173) (0.0503)
R2 (centered) 0.563 0.632 0.508 0.880F-value 55.02 86.43 51.52 131.85Overidentification Test(Hansen J statistic)
0.307 66.64 0.0981 78.26
p-value 0.580 0.116 0.754 0.008
1. Stage ResultsDep. Var. Trade Center ln(Distance to Trade Center)
Mountain Region -0.0232* 0.0259***(0.013) (0.01)
Bishop before 1000 AD 0.2553*** -0.1342***(0.071) (0.039)
Country Dummies Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes
Obs. 818 818 818 818Angrist-Pischke F statistic ofexcluded IV’s (p-value)
8.39 44.51 9.32 13.47
R2(centered) 0.273 0.837 0.206 0.699Underidentification Test 14.06 194.6 16.25 158.2p-value 0.000 0.000 0.000 0.000Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different
from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. Theset of all robust covariates encompasses altitude, the ln distances to airports, railroads and rivers,dummies for district free cities, capital cities, capital cities of autonomous regions, post-communistictransition countries, Eastern Germany, the ln of a region’s area, the share of people with tertiaryeducation, the inequality measure and the printing press before 1500 AD dummy. Each regressionincludes a constant not reported. The Overidentification test reporst the Hansen J-statistic andthe Underidentification test reports the Kleibergen-Paap rk LM statistic (null hypothesis: equationis underidentified). Lewbel’s (2012) approach uses a vector of generated instruments that areuncorrelated with the product of the heteroskedasdic first stage’s errors as instruments. Theseinstruments are not included in the table due to space restrictions, but their coefficients andstandard errors are available from the author upon request.
44
Table 7: Medieval Commercial Importance and Contemporary Regional Development
Dep. Var ln(GDP per capita)(1) (2) (3) (4) (5) (6)
OLS LIML IVLewbel (2012)
Commercial Importance 0.0964***0.0211** 0.153*** 0.0232**(0.014) (0.009) (0.055) (0.01)
Commercial ImportanceAlternative
0.0972***0.0181*(0.016) (0.011)
Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesNUTS-2 Dummies Yes No Yes No No NoAll Robust Controls No Yes No Yes Yes Yes
Obs. 839 818 839 818 818 818Adj.R2 \R2 0.776 0.877 0.77 0.877 0.502 0.621Underidentification Test 16.45 224.5p-value 0.000 0.000Overidentificaton Test 0.129 69.41p-value 0.719 0.0772AP F-statistic of excludedIV’s
9.15 32.72
p-value 0.000 0.000Notes.Standard errors adjusted for two-way clustering within NUTS-1 and NUTS-2 regions arereported in parentheses. In column (5) and (6) heteroskedasdicity robust standard errors are re-ported. Coefficient is statistically different from zero at the ***1 %, **5 % and *10 % level. Theunit of observation is a NUTS-3 region. The index of commercial importance of a medieval city isconstructed by adding up the coast region dummy, the trade center, bishop in 1000 AD, imperialcity and road, hanseatic league, medieval mining region and university before 1500 AD dummyvariables. The alternative index of commercial importance includes the distance to trade centervariable instead of the dummy (recoded to be positively related to GDP). In the case of the LIMLIV regression a version of the index is used that does not include the bishop before 1000 AD dummysince this variable is used as excluded instrument in that estimation. The set of covariates encom-passes altitude, the ln distances to airports, railroads and rivers, dummies for district free cities,capital cities, capital cities of autonomous regions, post-communistic transition countries, EasternGermany, the ln of a region’s area, the share of people with tertiary education, the inequalitymeasure and the printing press before 1500 AD dummy. Each regression includes a constant notreported. The Overidentification test reporst the Hansen J-statistic and the Underidentificationtest reports the Kleibergen-Paap rk LM statistic (null hypothesis: equation is underidentified).Lewbel’s (2012) approach uses a vector of generated instruments that are uncorrelated with theproduct of the heteroskedasdic first stage’s errors as instruments. These instruments are not in-cluded in the table due to space restrictions, but their coefficients and standard errors are availablefrom the author upon request. The first stage regressions are also not reported but are availablefrom the author.
45
Tabl
e8:
Med
ieva
lTra
deA
ctiv
ityan
dC
ityG
row
th
Dep
.Va
r.ln
(Po
pu
lati
on
1500
Po
pu
lati
on
1200
)ln(
Po
pu
lati
on
1500
Po
pu
lati
on
1300
)ln(
Po
pu
lati
on
1500
Po
pu
lati
on
1400
)ln(
Popu
latio
n)ln
(∆Po
pula
tion)
(1)
(2)
(3)
(4)
(5)
Met
hod
OLS
RE
Trad
eC
ity0.
65**
*0.
49**
*0.
448*
**0.
777*
**0.
393*
**(0
.215
)(0
.121
)(0
.151
)(0
.094
)(0
.072
)ln
(Pop
ulat
ion
1200
AD
)-0
.66*
**(0
.148
)ln
(Pop
ulat
ion
1300
AD
)-0
.62*
**(0
.068
)ln
(Pop
ulat
ion
1400
AD
)-0
.427
***
(0.0
8)ln
(Pop
ulat
ion t
−1)
-0.4
33**
*(0
.049
)
Obs
.86
199
180
826
390
Adj
.R
2 \ov
eral
lR2
0.39
0.39
80.
222
0.28
80.
369
Num
ber
ofC
lust
ers
361
194
Not
es.
Rob
ust
stan
dard
erro
rsar
ere
port
edin
pare
nthe
ses
inco
lum
ns(1
)-
(3).
Stan
dard
erro
rscl
uste
red
atci
tyle
vela
rere
port
edin
pare
nthe
ses
inco
lum
ns(4
)an
d(5
).C
oeffi
cien
tis
stat
istic
ally
diffe
rent
from
zero
atth
e**
*1%
,**
5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aci
ty.
The
set
ofco
varia
tes
enco
mpa
sses
the
lndi
stan
ces
ofa
city
toth
ene
xtriv
eror
coas
t,du
mm
ies
indi
catin
gci
ties
that
wer
ere
siden
ceof
abi
shop
befo
re10
00A
D,h
adth
est
atus
ofan
impe
rialc
ity,w
ere
loca
ted
ata
mai
nim
peria
lroa
d,w
ere
mem
ber
ofth
eH
anse
atic
Leag
ueor
are
clas
sified
asa
mou
ntai
nre
gion
byth
eEU
regi
onal
stat
istic
s.Fu
rthe
rmor
e,w
eco
ntro
lfor
aci
ty’s
latit
ude
and
long
itude
and
incl
ude
coun
try
fixed
effec
ts.
Inco
lum
ns(4
)an
d(4
)w
ead
ditio
nally
incl
ude
year
fixed
effec
ts.
Each
regr
essio
nin
clud
esa
cons
tant
not
repo
rted
.
46
Table 9: Medieval Trade, Relative GDP Density and Regional Economic Development
(1) (2) (3) (4) (5) (6)
Method OLS Mediation AnalysisCity Growth from to 1200–15001300–15001400–1500 Equation (7)Dep. Var. ln(Relative GDP Density) ln(GDP per capita)
P opulation1500P opulationt
0.337*** 0.178*** 0.172***(0.105) (0.067) (0.062)
ln(Relative GDP Density) 0.202*** 0.203*** 0.205***(0.011) (0.011) (0.011)
Trade Center 0.0048(0.017)
ln(Distance to Trade Center) 0.0103(0.023)
Commercial Importance -0.0074(0.007)
R2 0.964 0.955 0.947 0.919 0.919 0.919ACME 0.0661***-0.0786***0.0317***Direct Effect 0.0054 0.0111 -0.0072Total Effect 0.0715*** -0.0675** 0.0246***% of total mediated 92.1*** 115.1** 128.1***
Equation (6)ln(Relative GDP Density)
Trade Center 0.3316***(0.063)
ln(Relative GDP Density) -0.3799***(0.103)
Commercial Importance 0.1565***(0.023)
Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes Yes Yes
Obs. 85 179 197 818 818 818R2 0.939 0.938 0.94
Notes. Robust standard errors are reported in parentheses. Coefficient is statistically dif-ferent from zero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3region. The set of all robust covariates encompasses altitude, the ln distances to airportsand railroads, dummies for district free cities, capital cities, capital cities of autonomousregions, post-communistic transition countries, Eastern Germany, the ln of a region’s area,the share of people with tertiary education, the inequality measure and the printing pressbefore 1500 AD dummy. Each regression includes a constant not reported. ACME is the“Average Causal Mediation Effect” and means how much of the effect of medieval trade ismediate, i.e. works indirectly through the relative GDP density.
47
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A. Data Appendix
The level of an observation is a NUTS-3 region ( For example, in Germany this cor-responds to the “Landkreise”, in France to the “Departments” and in Italy to the“Provinicas”). If the variables are defined on an other NUTS level, this is indicatedin the description of the respective variable. City level information is matched to theNUTS-3 regions by the use of Eurostat (2007). We use the NUTS-2006 classification,since the most data is available only for this version of the NUTS classification. Andescriptive overview over all variables used in the empirical analysis is given in TableA.1 below.
Main Variables
Trade Centers. Primarily, the data on historical trade cities is based on four differentmaps. The first is a map printed in Davies and Moorhouse (2002) and includes ”Maintrade routes in the Holy Roman Empire and nearby countries” for the period around 1500AD. It contains the trade routes and the cities located on them. Davies and Moorhouse(2002) is a book about the history of the Polish city of Wrcolaw written by a renownedexpert for Polish and Eastern European history Norman Davies and his student RogerMoorhouse. According to google scholar it is cited around 60 times (at 24th June 2013)e.g. in articles in the Journal of the Royal Statistical Association. Therefore it consideredto be a reliable source for information about medieval trade activities.
Because this map only covers the area of Austria, Belgium ,Czech Republic, EasternFrance, Germany, Hungary Lithuania, the Netherlands, Poland and North Italy we makeuse of a second map published in King (1985) including ”Chief trade routes in Europe,Levant and North Africa 1300-1500 CE”. The map covers a wide area including partsof North Africa and the Near East. From this map, we primarily take the informationabout French trade cities, but we also include cities from other countries that are notmentioned in the first map. The original map is printed in a chapter about the “Currentsof Trade. Industry, Merchants and Money” in the medieval age as part of a volumeabout the “Flowering of the Middle Ages” edited by the Oxford-based medieval arthistorian Joan Evans. In this chapter Donald King illustrates the most important goodsof the medieval economy, discusses how they were produced and traded. He lays specialemphasis on the patterns of commerce and trade. He describes the most importantcenters of commerce and trade activity (Fair and market cities etc.) and also discussesthe importance of institutions (like contract security) etc. played for trade activities.Again, this volume seems to be an often cited source with around 50 citations in google
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scholar (24th June 2013). According to the bibliography of the volume King (1985)heavily draws on standard sources about medieval trade like Heyd (1879a,b), Lopez andRaymond (1955) or Postan and Rich (eds.)(1952).
As third source we employ an overview map of late medieval trade printed in Magocsi(2002) a historical atlas of central Europe and an often cited source for historical infor-mation about economic and cultural and political features. He is cited 222 (at 24th June2013) at google scholar. Among the papers using information provided by the atlas arethe historical economic papers by Borner and Severgnini (2012) and Dittmar (2011) aswell as Becker et al. (2011). It contains information on ”economic patterns” in CentralEurope around the year 1450. From this map, we primarily took the information aboutSouthern Italian trade cities not included in the other maps. Again, we also include citiesmentioned there but not in the other two sources. From this map, a city is consideredif it is located on a ”major” or ”important” trade route. The map also contains alsoinformation about members of the Hanseatic League (and their importance) as well ascommercial offices and foreign depots of the Hanseatic League. Further, it also depictsthe goods traded over the particular routes and the areas where they are the commodi-ties are typically produced. The map drawn in Magocsi’s atlas relies on other regionaland general historical atlases like the that of Darby and Fuller (eds.)(1978) or Lendl andWagner (1963) for Austria. However, Magocsi also consulted books about the history ofcertain cities like Dubrovnik (Carter 1972) or Wroclaw (Ochmanski 1982).
At last, we consult several maps included in “Westermanns Atlas zur Weltgeschichte”(Stier et al. 1956). To be precise, we consider the information of a map depicting the“Hanseatic League and its Opponents in the 15th century after the piece of Utrecht”.The map reports the location of Hanseatic cities, contours of the Hanseatic League inother countries and the main trade routes of the time as well as the traded goods. Thegeographical scope of the map is limited to the part of Germany northern of Prague,the Netherlands, the most part of today’s Belgium and Poland. We include a city,if it is located at one of the trade routes but regardless of whether it was a memberof the Hanseatic League or not. Second, we draw on a map in this atlas that limns“Western European Trade” in the late medieval and reports the course of “importanttrade routes” and the cities located on them. The scope of the map is south-westEurope (Spain and France) but it also includes West Germany and the north-westernItaly. Here again, we include a city if it is located on a major trade route. At last, we usethe information contained in a map about “Levant Trade in the Late Medieval and theOttoman Invasion”. This map among other information, limns the course of “important”trade routes (both on land and sea) and the cities located at them. We recognize cities
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on trade routes in the southern part of Germany, Hungary, Italy and the most parts ofFrance as well as parts of Poland.
Although not the only sources of information about medieval trade activities, thesefour maps seem to contain the most complete cross-national information about importanttrade activities in the later medieval period.
To validate the information of these maps and obtaining additional evidence aboutmedieval trade we consult other sources like a list depicting members of the Hanseaticleague from Dollinger (1966) a standard source for the history of the Hanseatic League.We only recognize cities that according to Dollinger ”played an important role in theHanseatic League” or that were capitals of thirds and quarters. Furthermore we con-sulted a map containing information about “North-South Trade Routes in the Alps Areain the Medieval Period” from Schulte (1966), two very general maps printed in Kinderand Hilgemann (1970) focusing on Baltic Sea and Levant trading activities in 1400 AD, amap published in Ammann (1955) focusing on trade routes for Southern Germany textileproducts (Barchent) and the map “Business Centers and Maritime Trade Routes HighMiddle Ages” printed in Hunt and Murray (1999).1 Furthermore, we draw on qualitativeinformation about the importance of a trade cities from Spufford’s (2002) standard workabout medieval trade and commerce and the monograph about the history of Germantrade written by Dietze (1923).In Table A.2, all trade cities and the corresponding regions for which the dummy vari-able is equal to one and the source(s) mention the respective city as trade center areshown. However, due to space restrictions we do not report any of the sources we con-sulted for becoming information about the validity of our sample of important tradecenters. For example, there is a three volume anthology by Escher and Hirschmann(eds.) (2005) where a group of researches developed an index of urban centrality forcities in the “Rhine-Meuse area” in the period from 1000 to 1350 AD (i.e. south-westGermany, and western Switzerland, east France , large parts of Belgium and the Southof the Netherlands). As part of the index of urban centrality they collected data aboutthe existence and number of markets, fairs, trade hall and the presence and importanceof long-distance trade activities. They also have data about the presence of certain man-ufacturing activities also being a good indicator for the presence of trade. They developa categorical index of centrality from the qualitative information the collect. From thetrade cities in our sample Aachen, Antwerp, Cologne, Dordrecht, Dortmund, Frankfurt,Maastricht, Metz, Munster, Paderborn, Rotterdam, Soest and Straßburg are included in
1Geographical scope, time period and level of generality sometimes differ between these maps, so across-validation is always possible only with limitations.
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the volume. For every of those cities, one or more markets, a fair or differently impor-tant long-distance trade are mentioned. But here, the range goes from Cologne (having4 markets, and ”very important” fairs and long-distance trade activities) to e.g. Pader-born where it is stated that it have a fair and long-distance trade. Due to this, it is notan easy task to say, that the information provided by this source can be used to validatewhether a city was important enough to be included in the sample. Furthermore, theperiod for which the index is constructed ends in the middle of the 14th century andtherefore earlier than our period of observation. Nevertheless, the information providedin the anthology of Escher and Hirschmann (eds.) (2005) can be useful to select citiesthat were probably not that important because e.g. the markets, fairs or trade therewas comparably limited in scope (i.e. according to the number of markets, halls, fairsor there importance) or time. Additionally, it provides clear evidence for the outstand-ing importance of Cologne and e.g. the over-regional importance (“very important”long-distance trade or fair) of Dortmund, Frankfurt, Munster and Soest.
As already mentioned, the information in those sources primarily is used to validatethat the information printed in the maps. However, as indicated in the main text wesometimes also include cities mentioned in these sources but not in the maps when weare in doubt about the actual importance of a city in medieval trade.
Furthermore, we construct several trade center dummies using alternative samples oftrade cities (as discussed in the main text). At first, we exclude cities mentioned by onlyone of our sources. These cities are Amberg, Bruck, Fulda, Maastricht, Malbork, Mantoa,Minden, Orleans, Parma, Pecs, Piotrkow Trybunalski, Plock, ,Rotterdam, St. Melo,Udine, Utrecht and Zwickau. Second, we exclude cities for which we are not sure aboutthere importance, altough they are reported in more than one of our sources. Thosecities are Paderborn, Einbeck, Greifswald, Braniewo, Gorlitz, Metz, Palanga, Como andStargard. For example, we exclude Paderborn because despite the fact that it was amember of the Hanseatic League and layed on the Hellweg, no other source mentioned itand Dollinger (1966) did not consider it as being a Hanseatic city of special importance.Furthermore, the data collected by Escher and Hirschmann (eds.) (2005) group impliesthat the existing trade activity in Paderborn was of relatively lower importance comparedto e.g. Cologne, Munster, Dortmund or other leading trade cities. Third, we add somecities to the original sample of trade cities. These cities are cases were a first look at theavailable information lead to the decision not to include the trade city. Even though, thecity is mentioned somewhere in one of the sources as a place of certain relevance for trade.This is for example the case for Anklam, a member city of the Hanseatic League lyingon an important trade route according to a map in Stier et al. (1956). However, none of
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the other sources mention Anklam as important trade center and Dollinger (1966) didnot intend a special role for Anklam within the Hanseatic League.
Finally, we build a last alternative sample of trade cities that only includes cities forwhich historical sources indicate long-run trade activities (i.e. cities that are importanttrade cities around 1500 AD and that were important also in the period before). Anoverview over these cities the earliest period in which trade activities are reported andthe source mentioned the respective city are depicted in Table A.4. This re-coding isbased on information primarily derived from the 2 Wilhelm Heyds two volumes aboutmedieval Levant trade (Heyd 1879a and 1879b). He provides information about medievaltrade activities in the Levant and the most important involved parties in a chronologicalorder beginning with the end of migration period (“Barbarian Invasions”). We take theperiod mentioned in the chapter headings of the chapter where the trade activities of acity are firstly mentioned as the period with the earliest authenticated trade activities.If Heyd explicitly reports a date or a period we take this date. Heyd (1879a,b) providesinformation about trade activities of Austrian, Belgian, French, German and Italiancities. Additionally, the monograph about the Hanseatic League written by Dollinger(1966) includes a couple of maps depicting e.g. the main Hanseatic trade routes and tradecities before 1250, between 1250 and 1350 and 1350 and 1500 (always AD). Another mapreport important trade routes (e.g. the salt way) and the cities that signed the treatyof Smolensk in 1229 AD a trade agreement between German trade cities and the Dukeof Smolensk. According to Dollinger (1966), this map covers the period from 1286 toapproximately 1336. We stick to the dates given in these maps when assigning therespective cities the dates when they are mentioned first. All in all, this and the othermaps in Dollinger (1966) contain information about trade activities in France, Germany,Lithuania and Poland. Finally, for Germany, Italy and France the book of Dietze (1923)about the history of German trade reports significant trade activities and places sincethe ”pre-historical” period. We include a city in the sample if Dietze (1923) reports acity to be an important player in early and high medieval trade.
For Austria, the Czech Republic and Poland information is provided by three digi-tized maps from T. Matthew Ciolek’s OWTRAD website. The first is based on a mapprinted in Humnicki and Borawska (1969) and shows “Central European Trade Routes800 – 900 CE”.2 The second map originates from Wojtowicz (1956) and according to theOWTRAD website reports “Major trade roads in Poland and adjacent border regions
2The map can be found under the following URL: http://www.ciolek.com/OWTRAD/DATA/tmcCZm0800.html; accessed at June 11th, 2013.
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1340 – 1400 CE”.3 Form this map we include information about Polish trade cities. Thelast map from the OWTRAD project is based on Rutkowski (1980) and is about ‘Majortrade roads in Poland and adjacent border regions in 1370 CE”.4 From this map wesolely include the German city of Gorlitz since all the other relevant cities in the mapwere mentioned by another source depicting trade in an earlier period. Overall are ableto found information about 68 of our 115 medieval trade cities.ln(Distance to Trade Center). This variable is calculated using the ArcGIS Near Tool.It represents the natural logarithm (ln) of the distance between a region’s centroid andthe closest trade city in degrees. The variable takes the value 0 for regions that containmedieval trade cities (i.e. for which the trade center dummy is equal to one). Trade City.Variable used for the city-level regressions in Table 3. The collection of cities coded astrade cities stem from Bairoch’s (1988) data, as explained in the main text. The citiesare coded according to the procedure described in detail below in the explanation of thetrade center dummy on regional level. The cities coded as trade cities are: Amsterdam,Antwerp, Augsburg, Avignon, Bari, Berlin, Bordeaux, Braniewo, Brunswick, Bremen,Brno, Bruges, Budapest, Chalon-Sur-Saone, Como, Deventer, Dordrecht, Dortmund,Einbeck, Elblag, Erfurt, Florence, Frankfurt (Main), Frankfurt (Oder), Gdansk, Genoa,Ghent, Gorlitz, Graz, Hamburg, Hannover, Hildesheim, Imola, Innsbruck, Kampen,Cologne, Cracow, Leipzig, Linz, Lubeck, Lucca, Lyon, Maastricht, Magdeburg, Mantoa,Marseille, Metz, Milan, Minden, Montpellier, Munster, Naples, Narbonne, Nuremberg,Orleans, Osnabruck, Padoa, Paris, Parma, Perpignan, Plock, Poznan, Prague, Prato,Ravensburg, Regensburg, Reims, Rome, Rostock, Rotterdam, Salzburg, Soest, St. Malo,Stralsund, Straßbourg, Torun, Toulouse, Tours, Treviso, Troyes, Udine, Ulm, Utrecht,Venice, Verona, Warsaw, Vienna, Wismar and Wroclaw.ln (GDP per capita). The natural logarithm of GDP per capita on NUTS-3 level isfrom the Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e3gdp&lang=en; accessed at October 10th 2012). Itis in measured in current market prices. We took values from 2009 the latest year forwhich data is available.Commercial Importance. Variable that should measure the commercial importance of acity according to different, historically relevant characteristics. The exact constructionis explained in the main text. It is the sum of following five dummy variables: trade
3The original title of the map is (according to the OWTRAD website) “Trade roads at the timesof Casimir the Great”). The map is available at the OWTRAD website under this link http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370a.html; accessed at June 11th, 2013.
4The map can be accessed under the URL http://www.ciolek.com/OWTRAD/DATA/tmcPLm1370.html;accessed at June 11th, 2013.
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center, imperial city, hanseatic league, imperial road, medieval mining, coast region anduniversity before 1500 AD. This variable is constructed by the author.Commercial Importance Alternative. Identical to the variable commercial importancebut instead of the trade center dummy, it constains the distance to trade center vari-able, recoded in a way that it is positively associated with the GDP per capita (as theother variables). ln(Population Density). A region’s Population Density comes from theEurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_d3dens&lang=en; accessed at October 10th 2012). The val-ues are from 2009.ln(Relative GDP Density). This variable is calculated using the following formula (Roos2005):
rdi = Yi/∑
Yi
Ai/∑
Ai
Where rdi is the relative GDP Density of a region. Yi is a region’s GDP (calculated bymultiplying the GDP per capita with the population density) and Ai is a region’s area.Therefore, the relative GDP Density is the GDP density of a region (GDP per km2)relative to the average density of all other regions. Alternatively, it is the ratio of aregions share of GDP relative to its share of a country’s overall area. In consequence, ifthe relative GDP Density is larger than one this means that a region shows concentrationof economic activity higher than the average region in a country (Roos 2005). For theempirical estimations, we take the natural logarithm of the variable, so that it is greaterthan zero for above average levels of spatial economic concentration. GDP per capita,the population density and the area of a region are all from the sources listed in thisappendix.
Control Variables and Instruments
Altitude. The Altitude of a region is from the website gpsvisualizer.com (accessed atNovember 8th 2012) and based on the coordinates of its centroid.Bishop before 1000 AD. Dummy variable equal to one if a region includes a citythat was seat of a bishop (or in France and Italy of an archbishop) before theyear 1000 AD. The variable is coded according to information from the websitehttp://www.catholic-hierarchy.org (accessed at November 27th, 2012). For bish-oprics in the Holy Roman Empire additionally Oestreich and Holzer (1970b) is consulted.When there were doubts on whether the diocese or archbishopric was founded before 1000AD wikipedia and the catholic encyclopedia (http://www.newadvent.org/cathen/;
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accessed at November 27th, 2012) are consulted.Capital. A dummy variable equal to one if a region includes the capital of a sovereignstate. Coded by the author.Capital Autonomous Region. A Dummy Variable equal to one if a region includes thecapital of a partly autonomous administrative unit, i.e. a German or Austrian State(“Bundesland”) or an Italian or Belgian Region. Coded by the author.District-Free City. A dummy variable equal to one for German NUTS-3 regions beingdistrict-free cities (“Kreisfreie Stadte” or “Stadtkreis”). Coded by the author.Eastern German Region. Binary variable equal to one if a region in Germany is locatedin the former GDR. Coded by the author.Education. We measure human capital of a NUTS-2 region with the share (in percent)of persons aged 25-64 with tertiary education attainment. The variable is obtained fromthe Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=edat_lfse_11&lang=en; accessed at October 10th, 2012). Wetook the values from 2009.Hanseatic League. Binary variable equal to one if a region contains at least one citythat was a member of the Hanseatic League. Coded according to Dollinger (1966).Imperial City. A Dummy Variable equal to one if a region includes at least one citythat was an imperial city in the Holy Roman Empire. The variable is coded followingOestreich and Holzer (1970a).Imperial Road. Dummy variable equal to one if a region contains at least one citythat was located on an important imperial city, i.e. the Via Imperii, the Via Re-gia or the Via Regia Lusatiae Superioris. The variable is coded according to in-formation provided by Kuhn (2005), the entry “Hohe Landstraße” in the onlineversion of “Meyers Großes Konversations-Lexikon” a general german encyclopedia(http://www.zeno.org/Meyers-1905/A/Hohe%20Landstra%DFe; accessed at Decem-ber 18th 2012), a map from a website of the federal government of the GermanState Saxony on regional development (http://www.landesentwicklung.sachsen.de/download/Landesentwicklung/ED-C_III_Via_Regia_Verlauf.jpg; accessed atDecember 18th, 2012) and wikipedia entries.Inequality. We measure inequality as ratio of average workers compensation to the GDPper capita. The Sources of GDP per capita and average workers compensation are aslisted in this appendix.Latitude. The values of this variable represent the latitude in decimal degrees of aregion’s centroid and are obtained from a GIS map of NUTS territories provided by theEurostat GISCO Database.
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(http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_SH.zip; accessed at November 8th, 2012).ln(Area). The natural logarithm of a region’s area is taken from the Eurostat regionalstatistics database http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_d3area&lang=en; accessed at January 10th, 2013. As always, we use the valuesfrom 2009.ln(Distance to Airport). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest international airport in degrees. It is calcu-lated using the ArcGIS Near Tool. The coordinates of airports are from the GIS map“Airports and Ports” from ArcGIS Online Database (accessed at November 9th, 2012).ln(Distance to Border). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of the country’s border. It is calculatedusing the ArcGIS Near Tool. The coordinates of borderlines are taken from a GIS mapof EU countries provided by the Eurostat GISCO Database (http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/CNTR_2010_03M_SH.zip; accessed at January10th, 2013).ln(Distance to Coast). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s coastline. It is cal-culated using the ArcGIS Near Tool. The coordinates of a country’s coastlines aretaken from the GIS map “Corine land cover 2000 coastline” provided by EuropeanEnvironment Agency (EEA) (http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-coastline; accessed at November 8th, 2012).ln(Distance to Railroad). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s major railroad. It iscalculated using the ArcGIS Near Tool. The coordinates of the railroads are obtainedfrom the map “World Railroads” from ArcGIS Online Database (accessed at November9th 2013).ln(Distance to River). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s major waterway (e.g. inGermany these are Elbe, Danube, Rhine and Oder). It is calculated using the ArcGISNear Tool. The coordinates of the rivers are taken from the GIS map “WISE Largerivers and large lakes” provided by European Environment Agency (EEA) (http://www.eea.europa.eu/data-and-maps/data/wise-large-rivers-and-large-lakes;accessed at November 8th, 2012).ln(Distance to Road). The variable represents the natural logarithm of the distancebetween a region’s centroid and the closest point of a country’s roads. It is calculated
56
using the ArcGIS Near Tool. The coordinates of the roads are obtained from the GISMap “World Roads” from ArcGIS Online Database (accessed at November 9th, 2012).ln(Distance to Wittenberg). Variable containing the geodesic distances between eachregion’s centroid and the city of Wittenberg in the German State of Saxony-Anhalt.The coordinates of Wittenberg are taken from the website geonames.com (accessed atNovember 8th, 2012).ln(Employees Compensation). Natural logarithm of average of employees compensation(wages, salaries and employer’s social contributions) at NUTS-2 level measured atcurrent prices and from the year 2009. Data was obtained from the Eurostat regionalstatistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e2rem&lang=en; accessed at October 10th, 2012).ln(Fixed Capital). Gross fixed capital formation by NUTS-2 regions measured for2009. Data is obtained from the Eurostat regional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e2gfcfr2&lang=en;accessed at October 10th, 2012).Longitude. The values of this variable represent the longitude in decimal degrees of aregion’s centroid and are obtained from a GIS map of NUTS territories provided bythe Eurostat GISCO Database (http://epp.eurostat.ec.europa.eu/cache/GISCO/geodatafiles/NUTS_2010_03M_SH.zip; accessed at November 8th, 2012).Medieval Mining. Binary Variable depicting regions with medieval copper or salt miningsites. The variable is coded according to a map in Elbl (2007) as well as information inSpufford (2002).Mining Region. Dummy variable equal to one if in a region at least one ore or coalmine (or mining firm) is located. The information on which the coding is based origi-nate from the structural business statistics included in the Eurostat regional statisticsdatabase (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_r_nuts06_r2&lang=en accessed at January 28th, 2012).Mountain Region. Categorial variable equal to one if in a region more than 50% of theirpopulation living in mountain areas according to the ESPON (European ObservationNetwork for Territorial Development and Cohesion) regional typologies project. Thevariable is equal to one if more than 50% of a region’s population live in a mountain area.It is two if more than 50% of a region’s surface is covered by mountain areas. At last,it is three for regions with more than 50% of their surface covered by mountain areasand with more than 50% of their population living in mountain areas. It is zero whena region fulfills none of this criteria. The data and an explanation of the classificationscan be downloaded from http://www.espon.eu/export/sites/default/Documents/
57
ToolsandMaps/ESPONTypologies/Typologies_metadata_data_final.xls (accessedat November 8th, 2012).Patents. Total number (over all IPO section and classes) of patent applications to theEuropean Patent Office (EPO) in each region in 2009. Data available from the Eurostatregional statistics database (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=pat_ep_ripc&lang=en; accessed at October 10th, 2012).Post Communistic Country. A binary variable equal to one if a region lies in an EasternEuropean post communistic transition country, i.e. the Czech Republic, Hungary,Lithuania or Poland. Coded by the author.Printing Press before 1500 AD. Dummy variable equal to one if at least one city ina region had adopted printing technology before 1500 AD. The coding is based oninformation in Benzing (1982), Clair (1976) and the Incunabula Short Title Catalogue(ISTC) of the British library (http://www.bl.uk/catalogues/istc/index.html; ac-cessed at November 18th, 2012). A region is included if any of these sources mentioneda city in this region.Quality of Government. The European Regional Quality of Government Index (EQI)as developed by the Quality of Government Institute at the university of Gothenburgin Denmark. The index is constructed in a similar way than the World Governance(WGI) Indicators of the World Bank (further information on the index design and thedata can be found here: http://www.qog.pol.gu.se/digitalAssets/1362/1362471_eqi---correlates-codebook.pdf; accessed at January 28th 2013). The data on whichthe indix is based are collected in 2009. In Belgium, Germany, Netherlands and Hungarythe index report values at NUTS-1 level in the other countries in our dataset it reportsvalues at NUTS-2 level. The data can be downloaded from http://www.qog.pol.gu.se/digitalAssets/1362/1362473_eqi-and-correlates--qog-website-.xlsx (ac-cessed at January 28th, 2013).Unemployment. The average annual unemployment rate (in percent) in a region in 2009(including people above the age of 15). Data is from the Eurostat regional statisticsdatabase (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfst_r_lfu3rt&lang=en; accessed at October 10th, 2012).University before 1500. Dummy variable equal to one if at least one city in a region hasa university founded before 1500 AD. Coding according to Eulenburg (1994), Kinderand Hilgemann (1970) and Ruegg (1993). The a city is recognized if it is mentionedby any of these sources. If there were doubts on the founding date of a university (orcontradicting dates) Cantoni and Yuchtman (2012) or wikipedia are used as validation.
58
59
Table A.1: Descriptive Data Overview – Regional Level Variables
Variable Obs Mean Std. Dev. Min Max
Altitude 839 279.230 320.194 -6.200 2472.600Bishop before 1000 AD 839 .064 .246 0 1Capital 839 0.011 0.103 0 1Capital Autnomous Region 839 0.051 0.221 0 1Commercial Importance 839 0.67 0.955 0 5Commercial Importance Alt. 839 1.46 0.866 0 5.357District-Free City 839 0.147 0.354 0 1Eastern German Region 839 0.122 0.327 0 1Education 832 24.211 6.319 8.4 48.6Hanseatic League 839 0.108 0.311 0 1Imperial City 839 0.069 0.254 0 1Imperial Road 839 0.045 0.208 0 1Inequality 825 1.134 0.921 0.037 8.425Latitude 839 49.460 3.088 38.245 55.939ln(Area) 839 7.032 1.297 3.575 9.400ln(Distance to Airport) 839 -0.645 0.727 -4.142 0.792ln(Distance to Border) 839 -0.825 1.083 -5.532 1.16ln(Distance to Coast) 839 0.308 1.204 -5.566 1.882ln(Distance to Railroad) 839 -2.111 1.390 -7.365 0.429ln(Distance to River) 839 -.675 1.322 -7.185 1.944ln(Distance to Road) 839 -4.001 1.376 -10.868 -1.194ln(Distance to Trade Center) 839 0.432 0.272 0 1.665ln(Distance to Wittenberg) 839 6.027 0.804 -7.447 7.335ln(Employees Compensation) 825 9.867 0.924 7.086 12.331ln(Fixed Capital) 803 9.141 0.818 6.802 11.494ln(Population Density) 839 5.351 1.137 2.709 9.964ln(Relative GDP Density) 839 -.077 1.262 -2.461 6.194Longitude 839 10.228 5.012 -4.091 25.573Medieval Mining 839 0.027 0.16 0 1Mining Region 839 0.228 0.420 0 1Mountain Region 839 0.479 1.022 0 3Patents 803 83.094 89.654 0.286 764.717Post Communistic Country 839 0.111 0.314 0 1Printing Press before 1500 839 0.199 0.4 0 1Quality of Government 839 72.130 17.163 10.18 97.61Trade City 361 .249 .433 0 1Trade Center 839 0.137 0.344 0 1Unemployment 582 8.237 3.435 1.9 19.1University before 1500 839 0.052 0.223 0 1
60
Table A.2: Descriptive Data Overview – City Level Variables
Variable Obs Mean Std. Dev. Min Max
Bishop 10000 AD 361 0.127 0.334 0 1Imperial Road 361 0.078 0.268 0 1Imperial City 361 0.122 0.328 0 1Hanseatic League 361 0.155 0.363 01Latitude 361 48.453 3.633 40.11 54.473Longitude 361 8.727 5.048 -4.29 22Mountain Region 361 0.385 0.887 0 3ln(Distance to Coast) 361 -0.24 1.326 -5.566 1.762ln(Distance to River) 361 -0.541 1.504 -7.185 1.944ln(Population 1200 AD) 86 9.533 0.812 6.908 11.608ln(Population 1300 AD) 199 9.114 1.104 6.908 11.918ln(Population 1400 AD) 180 9.053 1.063 6.908 12.524ln(Population 1500 AD) 361 8.817 0.983 6.908 12.324Trade City 361 .249 .433 0 1
61
Tabl
eA
.3:O
verv
iew
over
the
incl
uded
Trad
eC
ities
and
Reg
ions
Trad
eC
ityN
UT
S-3
Reg
ion
coun
try
Map
Sour
ces
(Prim
ary)
Oth
erH
istor
ical
Rec
ords
Bruc
kO
stlic
heO
bers
teie
rmar
kA
ustr
iaM
agoc
si(2
002)
Inns
bruc
kIn
nsbr
uck
Aus
tria
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(1
985)
,Mag
ocsi
(200
2)an
dSt
iere
tal.
(195
6)
Schu
lte(1
966)
,Spu
fford
(200
2)
Gra
zG
raz
Aus
tria
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Linz
Linz
-Wel
sA
ustr
iaD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,St
ier
etal
.(1
956)
Vie
nna
Wie
nA
ustr
iaD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,St
ier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)
Vill
ach
Kla
genf
urt-
Vill
ach
Aus
tria
Mag
ocsi
(200
2)Sc
hulte
(196
6)Sa
lzbu
rgSa
lzbu
rgun
dU
mge
bung
Aus
tria
Dav
ies
and
Moo
rhou
se(2
002)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Schu
lte(1
966)
,Spu
fford
(200
2)
Ant
werp
Arr
.A
ntwe
rpen
Belg
ium
Dav
ies
and
Moo
rhou
se(2
002)
,Stie
ret
al.
(195
6)A
mm
ann
(195
5),H
unt
and
Mur
ray
(199
9),
Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)Br
uges
Arr
.Br
ugge
Belg
ium
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(1
985)
,Stie
ret
al.
(198
5)
Hun
tan
dM
urra
y(1
999)
,K
inde
ran
dH
ilgem
ann
(198
2),S
puffo
rd(2
002)
62
Tabl
eA
.3–
Con
tinue
dG
hent
Arr
.G
ent
Belg
ium
Stie
ret
al.
(195
6)H
unt
and
Mur
ray
(199
9),
Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Brno
Jiho
mor
avsk
ykr
ajC
zech
Rep
ublic
Dav
ies
and
Moo
rhou
se(2
002)
,Mag
ocsi
(200
2)K
utna
Hor
aSt
redo
cesk
ykr
ajC
zech
Rep
ublic
Mag
ocsi
(200
2)Sp
uffor
d(2
002)
Olm
ouc
Olo
mou
cky
kraj
Cze
chR
epub
licD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
Prag
ueH
lavn
ımes
toPr
aha
Cze
chR
epub
licD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,St
ier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)
Avig
non
Vauc
luse
Fran
ceK
ing
(198
5),S
tier
etal
.(1
956)
Hun
tan
dM
urra
y(1
999)
,Sp
uffor
d(2
002)
Bayo
nne
Pyre
nees
-Atla
ntiq
ueFr
ance
Stie
ret
al.
(195
6)Sp
uffor
d(2
002)
Bord
eaux
Giro
nde
Fran
ceSt
ier
etal
.(1
956)
Spuff
ord
(200
2)C
halo
n-su
r-Sa
one
Saon
e-et
-Loi
reFr
ance
Stie
ret
al.
(195
6)Sc
hulte
(196
6),
Spuff
ord
(200
2)H
arfle
urSe
ine-
Mar
itim
eFr
ance
Kin
g(1
985)
,Stie
ret
al.
(195
6)Li
mog
esH
aute
-Vie
nne
Fran
ceK
ing
(198
5),S
tier
etal
.(1
956)
Lyon
Rho
neFr
ance
Stie
ret
al.
(195
6)A
mm
ann
(195
5),H
unt
and
Mur
ray
(199
9),K
inde
ran
dH
ilgem
ann
(198
2),S
chul
te(1
966)
,Spu
fford
(200
2)M
arse
ille
Bouc
hes-
du-R
hone
Fran
ceK
ing
(198
5),S
tier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)M
etz
Mos
elle
Fran
ceD
avie
san
dM
oorh
ouse
(200
2)Sc
hulte
(196
6)M
ontp
ellie
rH
erau
ltFr
ance
Kin
g(1
985)
Spuff
ord
(200
2)
63
Tabl
eA
.3–
Con
tinue
dN
arbo
nne
Aud
eFr
ance
Kin
g(1
985)
,Stie
ret
al.
(195
6)O
rlean
sLo
iret
Fran
ceSt
ier
etal
.(1
956)
Paris
Paris
Fran
ceD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),S
tier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,H
unt
and
Mur
ray
(199
9),S
chul
te(1
966)
,Spu
fford
(200
2)Pe
rpig
nan
Pyre
nees
-Orie
ntal
esFr
ance
Kin
g(1
985)
Spuff
ord
(200
2)R
eim
sM
arne
Fran
ceSt
ier
etal
.(1
956)
Schu
lte(1
966)
,Spu
fford
(200
2)St
.M
elo
Ille-
et-V
ilain
eFr
ance
Stie
ret
al.
(195
6)St
rasb
ourg
Bas-
Rhi
nFr
ance
Dav
ies
and
Moo
rhou
se(2
002)
,St
ier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,Sc
hulte
(196
6),S
puffo
rd(2
002)
Toul
ouse
Hau
te-G
aron
neFr
ance
Stie
ret
al.
(195
6)Sp
uffor
d(2
002)
Tour
sIn
dre-
et-L
oire
Fran
ceSt
ier
etal
.(1
956)
Spuff
ord
(200
2)Tr
oyes
Aub
eFr
ance
Stie
ret
al.
(195
6)Sc
hulte
(196
6),S
puffo
rd(2
002)
Am
berg
Am
berg
,D
istric
t-Fr
eeC
ityG
erm
any
Mag
ocsi
(200
2)
Aug
sbur
gA
ugsb
urg,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Die
tze
(192
3),K
inde
ran
dH
ilgem
ann
(198
2),S
chul
te(1
966)
,Spu
fford
(200
2)Be
rlin
Berli
nG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,M
agoc
si(2
002)
,Stie
ret
al.
(195
6)Br
unsw
ickBr
auns
chwe
ig,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),
Kin
g(1
985)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
Brem
enBr
emen
,D
istric
t-Fr
eeC
ityG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,Stie
ret
al.
(195
6)D
ollin
ger
(196
6),K
inde
ran
dH
ilgem
ann
(198
2),S
puffo
rd(2
002)
Brem
erha
ven
Brem
erha
ven,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),S
tier
etal
.(1
956)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)
64
Tabl
eA
.3–
Con
tinue
dC
olog
neC
olog
ne,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),S
tier
etal
.(1
956)
Am
man
n(1
955)
,Dol
linge
r(1
966)
,H
unt
and
Mur
ray
(199
9),K
inde
ran
dH
ilgem
ann
(198
2),S
puffo
rd(2
002)
Con
stan
ceK
onst
anz
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),S
tier
etal
.(1
956)
Die
tze
(192
3),S
chul
te(1
966)
,Sp
uffor
d(2
002)
Dor
tmun
dD
ortm
und,
Dist
rict-
Free
City
Ger
man
ySt
ier
etal
.(1
956)
Dol
linge
r(1
966)
Einb
eck
Nor
thei
mG
erm
any
Stie
ret
al.
(195
6)Er
furt
Erfu
rt,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Die
tze
(192
3),K
inde
ran
dH
ilgem
ann
(198
2)Fr
ankf
urt
(Ode
r)Fr
ankf
urt
(Ode
r),
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)Fr
ankf
urt
(Mai
n)Fr
ankf
urt
amM
ain,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
,Sc
hulte
(196
6),S
puffo
rd(2
002)
Fuld
aFu
lda
Ger
man
ySt
ier
etal
.(1
956)
Gor
litz
Gor
litz,
Dist
rict-
Free
City
Ger
man
yM
agoc
si(2
002)
Spuff
ord
(200
2)
Gre
ifswa
ldG
reifs
wald
,D
istric
t-Fr
eeC
ityG
erm
any
Stie
ret
al.
(195
6)D
ollin
ger
(196
6)
Ham
burg
Ham
burg
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Am
man
n(1
955)
,Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)H
anno
ver
Reg
ion
Han
nove
rG
erm
any
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Hild
eshe
imH
ildes
heim
Ger
man
ySt
ier
etal
.(1
956)
Dol
linge
r(1
966)
Leip
zig
Leip
zig,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Am
man
n(1
955)
,Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)
65
Tabl
eA
.3–
Con
tinue
d
Lube
ckLu
beck
,D
istric
t-Fr
eeC
ityG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(1
985)
,Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Am
man
n(1
955)
,Die
tze
(192
3),
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
and
Hun
tan
dM
urra
y(1
999)
Lune
burg
Lune
burg
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)M
agde
burg
Mag
debu
rg,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
,St
ier
etal
.(1
956)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
Min
den
Min
den-
Lubb
ecke
Ger
man
ySt
ier
etal
.(1
956)
Mun
ster
Mun
ster
,D
istric
t-Fr
eeC
ityG
erm
any
Stie
ret
al.
(195
6)
Nur
embe
rgN
urem
berg
,D
istric
t-Fr
eeC
ityG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Am
man
n(1
955)
,Die
tze
(192
3),
Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Osn
abru
ckO
snab
ruck
,D
istric
t-Fr
eeC
ityG
erm
any
Stie
ret
al.
(195
6)D
ollin
ger
(196
6)
Pade
rbor
nPa
derb
orn
Ger
man
ySt
ier
etal
.(1
956)
Dol
linge
r(1
966)
Rav
ensb
urg
Rav
ensb
urg
Ger
man
ySt
ier
etal
.(1
956)
Die
tze
(192
3),S
puffo
rd(2
002)
Reg
ensb
urg
Reg
ensb
urg,
Dist
rict-
Free
City
Ger
man
yD
avie
san
dM
oorh
ouse
,Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Schu
lte(1
966)
,Sp
uffor
d(2
002)
Ros
tock
Ros
tock
,D
istric
t-Fr
eeC
ityG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,K
ing
(198
5),M
agoc
si(2
002)
,St
ier
etal
.(1
956)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
Soes
tSo
est
Ger
man
ySt
ier
etal
.(1
956)
Dol
linge
r(1
966)
66
Tabl
eA
.3–
Con
tinue
dSt
ralsu
ndSt
ralsu
nd,
Dist
rict-
Free
City
Ger
man
yM
agoc
si(2
002)
,Stie
ret
al.
(195
6)D
ollin
ger
(196
6)
Ulm
Ulm
,Urb
anD
istric
tG
erm
any
Dav
ies
and
Moo
rhou
se(2
002)
,St
ier
etal
.(1
956)
Die
tze
(192
3),K
inde
ran
dH
ilgem
ann
(198
2),S
chul
te(1
966)
,Spu
fford
(200
2)W
ismar
Wism
ar,
Dist
rict-
Free
City
Ger
man
ySt
ier
etal
.(1
956)
Dol
linge
r(1
966)
Buda
pest
Buda
pest
Hun
gary
Dav
ies
and
Moo
rhou
se(2
002)
,M
agoc
si(2
002)
,Stie
ret
al.
(195
6)Sp
uffor
d(2
002)
Pecs
Bara
nya
Hun
gary
Mag
ocsi
(200
2)A
ncon
aA
ncon
aIt
aly
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Spuff
ord
(200
2)Ba
riBa
riIt
aly
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Spuff
ord
(200
2)Bo
logn
aBo
logn
aIt
aly
Kin
g(1
985)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)Sc
hulte
(196
6)
Boze
nBo
lzan
o-Bo
zen
Ital
yM
agoc
si(2
002)
,Stie
ret
al.
(195
6)D
ietz
e(1
923)
,Kin
der
and
Hilg
eman
n(1
982)
,Sch
ulte
(196
6)C
omo
Com
oIt
aly
Stie
ret
al.
(195
6)Sc
hulte
(196
6)Fl
oren
ceFi
renz
eIt
aly
Mag
ocsi
(200
2),K
ing
(198
5),
Stie
ret
al.
(195
6)D
ietz
e(1
923)
,Kin
der
and
Hilg
eman
n(1
982)
,Hun
tan
dM
urra
y(1
999)
,Spu
fford
(200
2)G
enoa
Gen
ova
Ital
yD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),S
tier
atal
.(1
956)
Am
man
n(1
955)
,Die
tze
(192
3),
Hun
tan
dM
urra
y(1
999)
,K
inde
ran
dH
ilgem
ann
(198
2),
Schu
lte(1
966)
,Spu
fford
(200
2)Lu
cca
Lucc
aIt
aly
Stie
ret
al.
(195
6)D
ietz
e(1
923)
,Spu
fford
(200
2)M
anto
aM
anto
vaIt
aly
Mag
ocsi
(200
2)
67
Tabl
eA
.3–
Con
tinue
dM
ilan
Mila
noIt
aly
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(1
985)
,St
ier
etal
.(1
956)
Die
tze
(192
3),H
unt
and
Mur
ray
(199
9),K
inde
ran
dH
ilgem
ann
(198
2),S
chul
te(1
966)
,Spu
fford
(200
2)N
aple
sN
apol
iIt
aly
Kin
g(1
985)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)H
unt
and
Mur
ray
(199
9),
Kin
der
and
Hilg
eman
n(1
982)
,Sc
hulte
(196
6),S
puffo
rd(2
002)
Pado
aPa
dova
Ital
yM
agoc
si(2
002)
Schu
lte(1
966)
Parm
aPa
rma
Ital
yM
agoc
si(2
002)
Prat
oPr
ato
Ital
yK
ing
(198
5)Sp
uffor
d(2
002)
Rom
eR
oma
Ital
yK
ing
(198
5),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Hun
tan
dM
urra
y(1
999)
,Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Sien
aSi
ena
Ital
yK
ing
(198
5),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)Sp
uffor
d(2
002)
Tren
toTr
ento
Ital
yM
agoc
si(2
002)
Schu
lte(1
966)
Trev
isoTr
eviso
Ital
yM
agoc
si(2
002)
Schu
lte(1
966)
Udi
neU
dine
Ital
yM
agoc
si(2
002)
Veni
ceVe
nezi
aIt
aly
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(1
985)
,M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Die
tze
(192
3),H
unt
and
Mur
ray
(199
9),K
inde
ran
dH
ilgem
ann
(198
2),S
chul
te(1
966)
,Spu
fford
(200
2)Ve
rona
Vero
naIt
aly
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Schu
lte(1
966)
Kla
iped
aK
laip
edos
apsk
ritis
Lith
uani
aD
avie
san
dM
oorh
ouse
(200
2),M
agoc
si(2
002)
68
Tabl
eA
.3–
Con
tinue
dK
ovno
Kau
noap
skrit
isLi
thua
nia
Kin
g(1
985)
,Mag
ocsi
(200
2)K
inde
ran
dH
ilgem
ann
(198
2)Pa
lang
aK
laip
edos
apsk
ritis
Lith
uani
aSt
ier
etal
.(1
956)
Am
ster
dam
Gro
ot-A
mst
erda
mN
ethe
rland
sK
ing
(198
5),S
tier
etal
.(1
956)
Dol
linge
r(1
966)
,Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Dev
ente
rZu
idwe
st-O
verji
ssel
Net
herla
nds
Kin
g(1
985)
,Stie
ret
al.
(195
6)D
ollin
ger
(196
6)
Dor
drec
htZu
idoo
st-Z
uid-
Hol
land
Net
herla
nds
Kin
g(1
985)
,Stie
ret
al.
(195
6)D
ollin
ger
(196
6),
Spuff
ord
(200
2)K
ampe
nN
oord
-Ove
rjiss
elN
ethe
rland
sK
ing
(198
5)D
ollin
ger
(196
6),
Spuff
ord
(200
2)M
aast
richt
Zuid
-Lim
burg
Net
herla
nds
Stie
ret
al.
(195
6)R
otte
rdam
Gro
ot-R
ijnm
ond
Net
herla
nds
Stie
ret
al.
(195
6)U
trec
htU
trec
htN
ethe
rland
sSt
ier
etal
.(1
956)
Bran
iewo
Elbl
aski
Pola
ndSt
ier
etal
.(1
956)
Dol
linge
r(1
966)
Cra
cow
Mia
sto
Kra
kow
Pola
ndD
avie
san
dM
oorh
ouse
(200
2),
Kin
g(1
985)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Am
man
n(1
955)
,Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Elbl
agEl
blas
kiPo
land
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Kin
der
and
Hilg
eman
n(1
982)
Gda
nsk
Gda
nski
Pola
ndD
avie
san
dM
oorh
ouse
(200
2),K
ing
(198
5),M
agoc
si(2
002)
,Stie
ret
al.
(195
6)
Am
man
n(1
955)
,Die
tze
(192
3),D
ollin
ger
(196
6),
Kin
der
and
Hilg
eman
n(1
982)
,Spu
fford
(200
2)M
albo
rkSt
arog
ardz
kiPo
land
Kin
g(1
985)
Piot
rkow
Tryb
unal
ski
Piot
rkow
ski
Pola
ndD
avie
san
dM
oorh
ouse
(200
2)Pl
ock
Cie
chan
owsk
o-pl
ocki
Pola
ndM
agoc
si(2
002)
69
Tabl
eA
.3–
Con
tinue
dPo
znan
Pozn
ansk
iPo
land
Dav
ies
and
Moo
rhou
se(2
002)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Am
man
n(1
955)
Toru
nBy
dgos
ko-T
orun
ski
Pola
ndD
avie
san
dM
oorh
ouse
(200
2),
Kin
g(1
985)
,Mag
ocsi
(200
2),
Stie
ret
al.
(195
6)
Dol
linge
r(1
966)
,Sp
uffor
d(2
002)
War
saw
Mia
sto
War
szaw
aPo
land
Dav
ies
and
Moo
rhou
se(2
002)
,M
agoc
si(2
002)
,Stie
ret
al.
(195
6)A
mm
ann
(195
5)an
dK
inde
ran
dH
ilgem
ann
(198
2)W
rocl
awM
iast
oW
rocl
awPo
land
Dav
ies
and
Moo
rhou
se(2
002)
,Kin
g(19
85),
Mag
ocsi
(200
2),S
tier
etal
.(1
956)
Am
man
n(1
955)
,Die
tze
(192
3),
Kin
der
and
Hilg
eman
n(1
982)
,Sp
uffor
d(2
002)
Star
gard
Szcz
ecin
ski
Pola
ndSt
ier
etal
.(1
956)
Dol
linge
r(1
966)
70
Tabl
eA
.4:M
edie
valT
rade
Citi
esan
dR
egio
nsw
ithlo
ng-r
untr
ade
activ
ity
Trad
eC
ityN
UT
S-3
Reg
ion
coun
try
men
tione
dea
rlies
tby
earli
est
perio
dm
entio
ned
Linz
Linz
-Wel
sA
ustr
iaH
umni
ckia
ndBo
raw
ska
(eds
.)(1
969)
9th
cent
ury
Vie
nna
Wie
nA
ustr
iaD
ietz
e(1
923)
befo
re14
thce
ntur
yA
ntwe
rpA
rr.
Ant
werp
enBe
lgiu
mH
eyd
(189
7b)
14th
cent
ury
Brug
esA
rr.
Brug
geBe
lgiu
mH
eyd
(189
7b)
14th
cent
ury
Brno
Jiho
mor
avsk
ykr
ajC
zech
Rep
ublic
Hum
nick
iand
Bora
wsk
a(1
969)
9th
cent
ury
Olm
ouc
Olo
mou
cky
kraj
Cze
chR
epub
licH
umni
ckia
ndBo
raw
ska
(196
9)9t
hce
ntur
y
Prag
ueH
lavn
ımes
toPr
aha
Cze
chR
epub
licH
umni
ckia
ndBo
raw
ska
(196
9)9t
hce
ntur
y
Avig
non
Vauc
luse
Fran
ceH
eyd
(187
9b)
high
med
ieva
lBo
rdea
uxG
irond
eFr
ance
Dol
linge
r(1
966)
15th
cent
ury
Lim
oges
Hau
te-V
ienn
eFr
ance
Hey
d(1
879a
)be
fore
12th
cent
ury
Lyon
Rho
neFr
ance
Dol
linge
r(1
966)
15th
cent
ury
Mar
seill
eBo
uche
s-du
-Rho
neFr
ance
Hey
d(1
879a
)be
fore
10th
cent
ury
Met
zM
osel
leFr
ance
Hey
d(1
879b
)14
thce
ntur
yM
ontp
ellie
rH
erau
ltFr
ance
Hey
d(1
879a
)be
fore
12th
cent
ury
Nar
bonn
eA
ude
Fran
ceH
eyd
(187
9a)
befo
re12
thce
ntur
yPa
risPa
risFr
ance
Dol
linge
r(1
966)
15th
cent
ury
Stra
sbou
rgBa
s-R
hin
Fran
ceD
ollin
ger
(196
6)be
fore
1250
Troy
esA
ube
Fran
ceD
ietz
e(1
923)
befo
re9t
hce
ntur
yA
ugsb
urg
Aug
sbur
g,D
istric
t-Fr
eeC
ityG
erm
any
Die
tze
(192
3)be
fore
9th
cent
ury
Berli
nBe
rlin
Ger
man
yD
ollin
ger
(196
6)15
thce
ntur
y
71
Tabl
eA
.4–
Con
tinue
dBr
unsw
ickBr
auns
chwe
ig,D
istric
t-Fr
eeC
ityG
erm
any
Die
tze
(192
3)be
fore
9th
cent
ury
Brem
enBr
emen
,Dist
rict-
Free
City
Ger
man
yH
eyd
(187
9a)
befo
re12
thce
ntur
yBr
emer
have
nBr
emer
have
n,D
istric
t-Fr
eeC
ityG
erm
any
Hey
d(1
879a
)be
fore
12th
cent
ury
Col
ogne
Col
ogne
,Dist
rict-
Free
City
Ger
man
yD
ietz
e(1
923)
befo
re9t
hce
ntur
yC
onst
ance
Kon
stan
zG
erm
any
Die
tze
(192
3)be
fore
9th
cent
ury
Erfu
rtEr
furt
,Dist
rict-
Free
City
Ger
man
yH
eyd
(187
9a)
befo
re12
thce
ntur
yFr
ankf
urt
(Ode
r)Fr
ankf
urt
(Ode
r),D
istric
t-Fr
eeC
ityG
erm
any
Hey
d(1
879a
)be
fore
12th
cent
ury
Fran
kfur
t(M
ain)
Fran
kfur
tam
Mai
n,D
istric
t-Fr
eeC
ityG
erm
any
Die
tze
(192
3)be
fore
9th
cent
ury
Gor
litz
Gor
litz,
Dist
rict-
Free
City
Ger
man
yRu
tkow
ski(
1980
a)14
thce
ntur
y(1
370)
Gre
ifswa
ldG
reifs
wald
,Dist
rict-
Free
City
Ger
man
yD
ietz
e(19
23)
befo
re14
thce
ntur
yH
ambu
rgH
ambu
rgG
erm
any
Dol
linge
r(1
966)
befo
re12
50H
ildes
heim
Hild
eshe
imG
erm
any
Dol
linge
r(1
966)
13th
–14
thce
ntur
yLu
beck
Lube
ck,D
istric
t-Fr
eeC
ityG
erm
any
Hey
d(1
879a
)Tr
eaty
ofSm
olen
sk(1
229)
Lune
burg
Lune
burg
,Dist
rict
Ger
man
yD
ollin
ger
(196
6)13
th–
14th
cent
ury
Mag
debu
rgM
agde
burg
,Dist
rict-
Free
City
Ger
man
yH
eyd
(187
9a)
befo
re10
thce
ntur
yM
inde
nM
inde
n-Lu
bbec
keG
erm
any
Dol
linge
r(1
966)
13th
–14
thce
ntur
yM
unst
erM
unst
er,D
istric
t-Fr
eeC
ityG
erm
any
Dol
linge
r(1
966)
Trea
tyof
Smol
ensk
(122
9)N
urem
berg
Nur
embe
rg,D
istric
t-Fr
eeC
ityG
erm
any
Die
tze
(192
3)be
fore
9th
cent
ury
Osn
abru
ckO
snab
ruck
,Dist
rict-
Free
City
Ger
man
yD
ollin
ger
(196
6)13
th–
14th
cent
ury
Pade
rbor
nPa
derb
orn
Ger
man
yD
ollin
ger
(196
6)13
th–
14th
cent
ury
Reg
ensb
urg
Reg
ensb
urg,
Dist
rict-
Free
City
Ger
man
yD
ietz
e(1
923)
befo
re9t
hce
ntur
yR
osto
ckR
osto
ck,D
istric
t-Fr
eeC
ityG
erm
any
Dol
linge
r(1
966)
13th
–14
thce
ntur
ySo
est
Soes
tG
erm
any
Dol
linge
r(1
966)
13th
–14
thce
ntur
ySt
ralsu
ndSt
ralsu
nd,D
istric
t-Fr
eeC
ityG
erm
any
Die
tze
(192
3)be
fore
14th
cent
ury
Ulm
Ulm
,Urb
anD
istric
tG
erm
any
Die
tze(
1923
)be
fore
9th
cent
ury
Wism
arW
ismar
,Dist
rict-
Free
City
Ger
man
yD
ollin
ger
(196
6)13
th–
14th
cent
ury
Buda
pest
Buda
pest
Hun
gary
Woj
tow
icz
(195
6)14
thce
ntur
y
72
Tabl
eA
.4–
Con
tinue
dA
ncon
aA
ncon
aIt
aly
Hey
d(1
879a
)be
fore
12th
cent
ury
Bari
Bari
Ital
yH
eyd
(187
9a)
befo
re12
thce
ntur
yBo
logn
aBo
logn
aIt
aly
Hey
d(1
879b
)14
thce
ntur
yFl
oren
ceFi
renz
eIt
aly
Hey
d(1
879b
)14
thce
ntur
yG
enoa
Gen
ova
Ital
yH
eyd
(187
9a)
befo
re12
thce
ntur
yLu
cca
Lucc
aIt
aly
Hey
d(1
879a
)be
fore
13th
cent
ury
Mila
nM
ilano
Ital
yH
eyd
(187
9b)
14th
cent
ury
Nap
les
Nap
oli
Ital
yH
eyd
(187
9b)
befo
re12
thce
ntur
yPa
rma
Parm
aIt
aly
Hey
d(1
879b
)14
thce
ntur
yPi
saPi
saIt
aly
Die
tze
(192
3)be
fore
14th
cent
ury
Rom
eR
oma
Ital
yH
eyd
(187
9a)
befo
re12
thce
ntur
ySi
ena
Sien
aIt
aly
Hey
d(1
879b
)13
thce
ntur
y(1
209)
Veni
ceVe
nezi
aIt
aly
Hey
d(1
879a
)be
fore
12th
cent
ury
Kov
noK
auno
apsk
ritis
Lith
uani
aD
ollin
ger
(196
6)be
twee
n13
50an
d15
00C
raco
wM
iast
oK
rako
wPo
land
Hum
nick
iand
Bora
wsk
a(1
969)
9th
cent
ury
Gda
nsk
Gda
nski
Pola
ndD
ollin
ger
(196
6)13
th–
14th
cent
ury
Mal
bork
Star
ogar
dzki
Pola
ndW
ojto
wic
z(1
956)
14th
cent
ury
Piot
rkow
Tryb
unal
ski
Piot
rkow
ski
Pola
ndW
ojto
wic
z(1
956)
14th
cent
ury
Ploc
kC
iech
anow
sko-
ploc
kiPo
land
Woj
tow
icz
(195
6)14
thce
ntur
yPo
znan
Mia
sto
Pozn
anPo
land
Woj
tow
icz
(195
6)14
thce
ntur
ySz
czec
inM
iast
oSz
czec
inPo
land
Woj
tow
icz
(195
6)14
thce
ntur
yTo
run
Bydg
osko
-Tor
unsk
iPo
land
Dol
linge
r(1
966)
13th
–14
thce
ntur
yW
arsa
wM
iast
oW
arsz
awa
Pola
ndW
ojto
wic
z(1
956)
14th
cent
ury
Wro
claw
Mia
sto
Wro
claw
Pola
ndD
ollin
ger
(196
6)13
th–
14th
cent
ury
73
B. Robustness Checks
Robustness to Influential Observations and Additional ControlsIn this appendix we report the results of several robustness checks and additional results men-tioned in the main text of the study. To be precise, in Table B.1 we re-run some specificationsfrom Table 5 and 6 in the main text, including additional control variables (a dummy variable formedieval copper mining regions, an interaction term of latitude and longitude, the country-evelshare of Catholics and a dummy for regions containing important medieval residence cities).InTable B.2 we look whether the results are sensitive to the exclusion of influential observations,identified by the DFITS statistics (see main text for a detailed description).
74
Tabl
eB
.1:I
nclu
sion
ofA
dditi
onal
Con
trol
Varia
bles
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)M
odifi
edSp
ecifi
catio
nTa
ble
6co
lum
n(3
)Ta
ble
6co
lum
n(6
)Ta
ble
5co
lum
n(3
)Ta
ble
5co
lum
n(6
)Ta
ble
6co
lum
n(4
)Ta
ble
6co
lum
n(9
)Ta
ble
6co
lum
n(3
)Ta
ble
6co
lum
n(8
)
Mod
ifica
tion
Add
ing
Dum
my
for
med
ieva
lmin
ing
regi
ons
Add
ing
ain
tera
ctio
nva
riabl
eof
latit
ude
and
long
itude
Add
ing
shar
eof
Cat
holic
sin
aco
untr
yA
ddin
ga
dum
my
for
impo
rtan
tre
siden
ceci
ties
Add
ition
alVa
riabl
esig
nific
ant
No
No
Yes
No
Yes
Trad
eC
ente
r0.
181*
**0.
264*
**0.
13**
*0.
181*
**(0
.029
)(0
.031
)(0
.027
)(0
.03)
ln(D
istan
ceto
Trad
eC
ente
r)-0
.134
**-0
.291
***
-0.1
38**
*-0
.135
*(0
.053
)(0
.055
)(0
.041
)(0
.053
)
Obs
.83
983
983
983
951
851
883
983
9A
dj.
R2
0.78
40.
776
0.77
80.
762
0.87
80.
872
0.78
40.
776
Not
es.
Stan
dard
erro
rsad
just
edfo
rtw
o-w
aycl
uste
ring
with
inN
UT
S-1
and
NU
TS-
2re
gion
sar
ere
port
edin
pare
nthe
ses.
Coe
ffici
ent
isst
atis
tical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aN
UT
S-3
regi
on.
For
the
cont
rols
incl
uded
inea
chsp
ecifi
catio
nco
nsul
tth
em
ain
text
orth
eno
tes
toth
eor
igin
alta
bles
men
tione
din
the
thir
dro
w.
Eac
hre
gres
sion
incl
udes
aco
nsta
ntno
tre
port
ed.
75
Tabl
eB
.2:R
egre
ssio
nsof
Tabl
e5
With
out
Influ
entia
lObs
erva
tions
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)Tr
ade
Cen
ter
0.17
***
0.11
***
0.15
3***
0.11
7***
0.07
94**
(0.0
22)
(0.0
24)
(0.0
25)
(0.0
26)
(0.0
21)
ln(D
istan
ceto
Trad
eC
ente
r)-0
.108
***
-0.0
81**
-0.1
11**
*-0
.12*
**-0
.064
*(0
.038
)(0
.039
)(0
.046
)(0
.043
)(0
.038
)
Cou
ntry
Dum
mie
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sN
UT
S-1
Dum
mie
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sYe
sN
UT
S-2
Dum
mie
sYe
sYe
sYe
sYe
sN
oYe
sYe
sYe
sYe
sN
oBa
sicG
eogr
aphi
cC
ontr
ols
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Geo
grap
hic
Cen
tral
ityC
ontr
ols
Yes
No
No
No
No
Yes
No
No
No
No
Reg
ion
Cha
ract
erist
ics
No
Yes
No
No
No
No
Yes
No
No
No
Hist
oric
alR
egio
nC
hara
cter
istic
No
No
Yes
No
No
No
No
Yes
No
No
Dev
elop
men
tC
ovar
iate
sN
oN
oN
oYe
sN
oN
oN
oN
oYe
sN
oA
llR
obus
tC
ontr
ols
No
No
No
No
Yes
No
No
No
No
Yes
No.
ofre
mov
edre
gion
s40
4540
4147
4045
4143
44O
bs.
799
794
799
477
771
799
794
798
475
774
Adj
.R
20.
844
0.89
10.
829
0.91
10.
901
0.83
70.
887
0.81
60.
904
0.89
9N
otes
.St
anda
rder
rors
adju
sted
for
two-
way
clus
terin
gw
ithin
NU
TS-
1an
dN
UT
S-2
regi
ons
are
repo
rted
inpa
rent
hese
s.C
oeffi
cien
tis
stat
istic
ally
diffe
rent
from
zero
atth
e**
*1%
,**5
%an
d*1
0%
leve
l.T
heun
itof
obse
rvat
ion
isa
NU
TS-
3re
gion
.T
heba
sicge
ogra
phic
cont
rols
incl
ude
are
gion
’sla
titud
e,lo
ngitu
dean
dal
titud
e.T
hege
ogra
phic
cent
ralit
yco
ntro
lsin
clud
eth
eln
dist
ance
sof
are
gion
’sce
ntro
idto
the
near
est
airp
ort,
railr
oad,
road
,bor
der
and
coas
tpo
int.
Reg
ion
char
acte
ristic
cont
rols
incl
ude
adu
mm
ies
for
regi
ons
inG
erm
any
that
are
dist
rict-
free
citie
s,fo
rre
gion
sin
clud
ing
aco
untr
y’s
capi
tal,
are
clas
sified
asm
ount
ain
regi
ons,
with
ore
orco
alm
ines
,lo
cate
din
the
form
erG
DR
and
loca
ted
inan
East
ern
Euro
pean
post
-com
mun
istic
tran
sitio
nco
untr
y.Fu
rthe
rmor
eit
enco
mpa
sses
the
lnof
are
gion
sar
ea.
The
hist
oric
alre
gion
char
acte
ristic
sco
nsist
ofa
dum
my
varia
bles
indi
catin
gre
gion
sw
itha
univ
ersit
yfo
unde
dbe
fore
1500
AD
,tha
tad
opte
dpr
intin
gte
chno
logy
befo
re15
00A
D,c
onta
inci
tiest
hatw
ere
mem
bers
ofth
eH
anse
atic
Leag
ue,w
ithfo
rmer
impe
rialc
ities
and
wer
elo
cate
don
anim
peria
lroa
d.M
oreo
veri
tinc
lude
sthe
lnof
the
dist
ance
ofa
regi
on’s
cent
roid
toW
itten
berg
.T
hegr
owth
cova
riate
senc
ompa
ssa
regi
on’s
unem
ploy
men
tra
te,n
umbe
rof
regi
ster
edpa
tent
s,av
erag
efir
mln
fixed
capi
tals
tock
,ave
rage
wor
ker
com
pens
atio
n.Fu
rthe
rmor
e,it
incl
udes
the
shar
eof
peop
leag
edbe
twee
n25
-64
with
tert
iary
educ
atio
non
NU
TS-
2le
vel,
the
qual
ityof
gove
rnm
ent
inde
xon
NU
TS-
1/N
UT
S-2
leve
land
the
ratio
ofan
aver
age
wor
kers
com
pens
atio
nto
are
gion
’sG
DP
per
capi
taas
ineq
ualit
ym
easu
re.
The
set
ofal
lrob
ust
cova
riate
sen
com
pass
esal
titud
e,th
eln
dist
ance
sto
airp
orts
and
railr
oads
,dum
mie
sfo
rdi
stric
tfr
eeci
ties,
capi
talc
ities
,cap
italc
ities
ofau
tono
mou
sre
gion
s,po
st-c
omm
unist
ictr
ansit
ion
coun
trie
s,Ea
ster
nG
erm
any,
the
lnof
are
gion
’sar
ea,t
hesh
are
ofpe
ople
with
tert
iary
educ
atio
n,th
ein
equa
lity
mea
sure
and
the
prin
ting
pres
sbe
fore
1500
AD
dum
my.
Are
gion
isre
mov
edfr
omth
ees
timat
ion
ifits
DFI
TS
valu
eis
abov
eth
ecu
t-off
of|D
FIT
Sj|>
2√k
\N(w
ithk
indi
catin
gth
enu
mbe
rofr
egre
ssor
san
dN
deno
ting
the
num
bero
fobs
erva
tions
inth
esa
mpl
e).
Each
regr
essio
nin
clud
esa
cons
tant
not
repo
rted
.
76
Results for Alternatively Coded Medieval Trade VariablesIn Tables B.3 and B.6 we conduct the OLS, IV and mediation analysis estimations with alter-natively coded medieval trade variables, i.e. alternative samples of medieval trade cities. Here,Table B.3 show the estimation results with when we only consider trade cities mentioned in morethan one of the sources. In Table B.4 we redo this estimations this time excluding cities for whichthe actual importance in trade is in doubt. To continue, in Table B.5 we repeat this, using theoriginal sample and include additional cities for which we think they might be important, albeitthey are not mentioned by our main sources. At last, in Table B.6 we show the results for asample of trade cities that only includes cities for which historical sources indicate long-run tradeactivities (i.e. cities that are important trade cities around 1500 AD and that were important alsoin the period before). An overview over these cities the earliest period in which trade activitiesare reported and the source mentioned the respective city are depicted in Table A.4.
77
Tabl
eB
.3:R
esul
tsfo
rA
ltern
ativ
eTr
ade
Cen
ter
Dum
my
–W
ithou
tR
egio
nsM
entio
ned
byO
nly
One
Sour
ce
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)ln
(City
Gro
wth
)ln
(Rel
ativ
eG
DP
Den
sity)
ln(G
DP
per
capi
ta)
(1)
(2)
(3)
(4)
(5)
(6)
Met
hod
OLS
LIM
LIV
Lew
bel(
2012
)O
LSM
edia
tion
Ana
lysis
Estim
ated
Equa
tion
Equa
tion
(6)
Equa
tion
(7)
Estim
ated
Spec
ifica
tion
Tabl
e5
Col
umn
(5)
Tabl
e6
Col
umn
(1)
Tabl
e6
Col
umn
(2)
Tabl
e8
Col
umn
(1)
Tabl
e9
Col
umn
(4)
Tabl
e9
Col
umn
(4)
Trad
eC
ente
r0.
0543
**0.
363*
**0.
0613
**0.
479*
*0.
3267
***
-0.0
0912
(0.0
225)
(0.1
33)
(0.0
260)
(0.2
32)
(0.0
71)
(0.0
181)
ln(R
elat
ive
GD
PD
ensit
y)0.
203*
**(0
.010
9)
Obs
.81
881
881
886
818
818
Cen
tere
dR
2\R
20.
877
0.53
40.
629
0.34
40.
938
0.92
4A
CM
E0.
0654
Dire
ctEff
ect
-0.0
085
Tota
lEffe
ct0.
0569
%of
tota
lmed
iate
d11
2.7
Und
erid
entifi
catio
nTe
st14
.45
173.
3p-
valu
e0.
000
0.00
0O
verid
entifi
catio
nTe
st0.
000
61.0
9p-
valu
e1.
000
0.23
6A
PF-
stat
istic
ofex
clud
edIV
’s8.
2746
.53
p-va
lue
0.00
00.
000
Not
es.
Stan
dard
erro
rsad
just
edfo
rtw
o-w
aycl
uste
ring
with
inN
UT
S-1
and
NU
TS-
2re
gion
sar
ere
port
edin
pare
nthe
ses.
Coe
ffici
ent
isst
atis
tical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aN
UT
S-3
regi
on.
For
the
cont
rols
incl
uded
inea
chsp
ecifi
catio
nco
nsul
tth
em
ain
text
orth
eno
tes
toth
eor
igin
alta
bles
men
tione
din
the
thir
dro
w.
Inco
lum
ns(1
)an
d(2
)th
ead
just
edR
2is
repo
rted
.In
colu
mn
(3)
and
(4)
the
cent
ered
R2
issh
own
and
inco
lum
ns(5
)an
d(6
)th
eR
2.
Inco
lum
n(3
)th
ere
sults
ofth
efir
stst
age
are
omitt
edbu
tav
aila
ble
from
the
auth
or.
Eac
hre
gres
sion
incl
udes
aco
nsta
ntno
tre
port
ed.
78
Tabl
eB
.4:R
esul
tsfo
rA
ltern
ativ
eTr
ade
Cen
ter
Dum
my
–C
ities
with
Unc
erta
inIm
port
ance
Rem
oved
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)ln
(City
Gro
wth
)ln
(Rel
ativ
eG
DP
Den
sity)
ln(G
DP
per
capi
ta)
(1)
(2)
(3)
(4)
(5)
(6)
Met
hod
OLS
LIM
LIV
Lew
bel(
2012
)O
LSM
edia
tion
Ana
lysis
Estim
ated
Equa
tion
Equa
tion
(6)
Equa
tion
(7)
Estim
ated
Spec
ifica
tion
Tabl
e5
Col
umn
(5)
Tabl
e6
Col
umn
(1)
Tabl
e6
Col
umn
(2)
Tabl
e8
Col
umn
(1)
Tabl
e9
Col
umn
(4)
Tabl
e9
Col
umn
(4)
Trad
eC
ente
r0.
0670
***
0.37
5***
0.07
3***
0.46
8*0.
3621
***
-0.0
036
(0.0
23)
(0.1
4)(0
.027
)(0
.235
)(0
.074
)(0
.02)
ln(R
elat
ive
GD
PD
ensit
y)0.
203*
**(0
.011
)
Obs
.81
881
881
886
818
818
Cen
tere
dR
2\R
20.
877
0.54
40.
621
0.34
20.
939
0.91
9A
CM
E0.
0724
***
Dire
ctEff
ect
-0.0
029
Tota
lEffe
ct0.
0694
**%
ofto
talm
edia
ted
102.
8**
Und
erid
entifi
catio
nTe
st15
.18
160.
2p-
valu
e0.
000
0.00
0O
verid
entifi
catio
nTe
st0.
008
58.4
1p-
valu
e0.
930.
317
AP
F-st
atist
icof
excl
uded
IV’s
8.57
43.9
2
p-va
lue
0.00
00.
000
Not
es.
Stan
dard
erro
rsad
just
edfo
rtw
o-w
aycl
uste
ring
with
inN
UT
S-1
and
NU
TS-
2re
gion
sar
ere
port
edin
pare
nthe
ses.
Coe
ffici
ent
isst
atis
tical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aN
UT
S-3
regi
on.
For
the
cont
rols
incl
uded
inea
chsp
ecifi
catio
nco
nsul
tth
em
ain
text
orth
eno
tes
toth
eor
igin
alta
bles
men
tione
din
the
thir
dro
w.
Inco
lum
ns(1
)an
d(2
)th
ead
just
edR
2is
repo
rted
.In
colu
mn
(3)
and
(4)
the
cent
ered
R2
issh
own
and
inco
lum
ns(5
)an
d(6
)th
eR
2.
Inco
lum
n(3
)th
ere
sults
ofth
efir
stst
age
are
omitt
edbu
tav
aila
ble
from
the
auth
or.
Eac
hre
gres
sion
incl
udes
aco
nsta
ntno
tre
port
ed.
79
Tabl
eB
.5:R
esul
tsfo
rA
ltern
ativ
eTr
ade
Cen
ter
Dum
my
–C
ities
with
Unc
erta
inIm
port
ance
Add
ed
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)ln
(City
Gro
wth
)ln
(Rel
ativ
eG
DP
Den
sity)
ln(G
DP
per
capi
ta)
(1)
(2)
(3)
(4)
(5)
(6)
Met
hod
OLS
LIM
LIV
Lew
bel(
2012
)O
LSM
edia
tion
Ana
lysis
Estim
ated
Equa
tion
Equa
tion
(6)
Equa
tion
(7)
Estim
ated
Spec
ifica
tion
Tabl
e5
Col
umn
(5)
Tabl
e6
Col
umn
(1)
Tabl
e6
Col
umn
(2)
Tabl
e8
Col
umn
(1)
Tabl
e9
Col
umn
(4)
Tabl
e9
Col
umn
(4)
Trad
eC
ente
r0.
0686
***
0.32
4***
0.07
65**
*0.
544*
*0.
3268
***
0.00
38(0
.021
)(0
.114
)(0
.024
)(0
.225
)(0
.62)
(0.0
16)
ln(R
elat
ive
GD
PD
ensit
y)0.
202*
**(0
.011
)
Obs
.81
881
881
886
818
818
Cen
tere
dR
2\R
20.
878
0.55
20.
623
0.35
80.
939
0.91
9A
CM
E0.
0652
***
Dire
ctEff
ect
0.00
44To
talE
ffect
0.06
96**
*%
ofto
talm
edia
ted
93.3
***
Und
erid
entifi
catio
nTe
st13
.03
203.
9p-
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
with
inN
UT
S-1
and
NU
TS-
2re
gion
sar
ere
port
edin
pare
nthe
ses.
Coe
ffici
ent
isst
atis
tical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aN
UT
S-3
regi
on.
For
the
cont
rols
incl
uded
inea
chsp
ecifi
catio
nco
nsul
tth
em
ain
text
orth
eno
tes
toth
eor
igin
alta
bles
men
tione
din
the
thir
dro
w.
Inco
lum
ns(1
)an
d(2
)th
ead
just
edR
2is
repo
rted
.In
colu
mn
(3)
and
(4)
the
cent
ered
R2
issh
own
and
inco
lum
ns(5
)an
d(6
)th
eR
2.
Inco
lum
n(3
)th
ere
sults
ofth
efir
stst
age
are
omitt
edbu
tav
aila
ble
from
the
auth
or.
Eac
hre
gres
sion
incl
udes
aco
nsta
ntno
tre
port
ed.
80
Tabl
eB
.6:R
esul
tsfo
rA
ltern
ativ
eTr
ade
Cen
ter
Dum
my
–O
nly
Citi
esw
ithLo
ng-R
unTr
ade
Act
ivity
Dep
.Va
r.ln
(GD
Ppe
rca
pita
)ln
(City
Gro
wth
)ln
(Rel
ativ
eG
DP
Den
sity)
ln(G
DP
per
capi
ta)
(1)
(2)
(3)
(4)
(5)
(6)
Met
hod
OLS
LIM
LIV
Lew
bel(
2012
)O
LSM
edia
tion
Ana
lysis
Estim
ated
Equa
tion
Equa
tion
(6)
Equa
tion
(7)
Estim
ated
Spec
ifica
tion
Tabl
e5
Col
umn
(5)
Tabl
e6
Col
umn
(1)
Tabl
e6
Col
umn
(2)
Tabl
e8
Col
umn
(1)
Tabl
e9
Col
umn
(4)
Tabl
e9
Col
umn
(4)
Trad
eC
ente
r0.
0568
**0.
320*
**0.
0743
**0.
123
0.32
18**
*-0
.006
1(0
.027
)(0
.112
)(0
.032
)(0
.253
)(0
.088
)(0
.024
)ln
(Rel
ativ
eG
DP
Den
sity)
0.20
2***
(0.0
11)
Obs
.81
881
881
886
818
818
Cen
tere
dR
2\R
20.
877
0.57
40.
620.
305
0.93
80.
919
AC
ME
0.06
41**
*D
irect
Effec
t0.
0053
Tota
lEffe
ct0.
0588
**%
ofto
talm
edia
ted
105.
0**
Und
erid
entifi
catio
nTe
st14
.84
140.
80p-
valu
e0.
001
0.00
0O
verid
entifi
catio
nTe
st0.
406
65.7
p-va
lue
0.52
40.
132
AP
F-st
atist
icof
excl
uded
IV’s
9.16
78.5
4
p-va
lue
0.00
10.
000
Not
es.
Stan
dard
erro
rsad
just
edfo
rtw
o-w
aycl
uste
ring
with
inN
UT
S-1
and
NU
TS-
2re
gion
sar
ere
port
edin
pare
nthe
ses.
Coe
ffici
ent
isst
atis
tical
lydi
ffere
ntfr
omze
roat
the
***1
%,*
*5%
and
*10
%le
vel.
The
unit
ofob
serv
atio
nis
aN
UT
S-3
regi
on.
For
the
cont
rols
incl
uded
inea
chsp
ecifi
catio
nco
nsul
tth
em
ain
text
orth
eno
tes
toth
eor
igin
alta
bles
men
tione
din
the
thir
dro
w.
Inco
lum
ns(1
)an
d(2
)th
ead
just
edR
2is
repo
rted
.In
colu
mn
(3)
and
(4)
the
cent
ered
R2
issh
own
and
inco
lum
ns(5
)an
d(6
)th
eR
2.
Inco
lum
n(3
)th
ere
sults
ofth
efir
stst
age
are
omitt
edbu
tav
aila
ble
from
the
auth
or.
Eac
hre
gres
sion
incl
udes
aco
nsta
ntno
tre
port
ed.
81
Description and Sources of the Additional VariablesResidence city. Binary variable that represents important residence cities (of Dukes, Kings . . . )in the Holy Roman Empire or the German Reich (after 1871). The coding follows a wikipedialist at http://de.wikipedia.org/wiki/Residenzstadt (accessed February, 24th 2013) andKobler (1988). It also includes residences of electors (“Kurfursten”) and prince-bishoprics.Furthermore, it represents the capitals or residence cities of Italian duchies, kingdoms andrepublics (like Venice, Lombardy, Sardinia, Parma, Modena, Tuscany, Naples or the Kingdomof the two Sicilies). For all other countries it marked the capitals of pre-existing states orkingdoms, duchies etc. (e.g. in Poland it includes the residence of the kings of the Kingdom ofPoland, in Lithuania the residence of the grand duke of Lithuania. . . ). The coding here followsthe author’s information or different versions of Putzgers historical atlas (Bruckmuller (eds.)2011 and Baldamus et al. (eds.) 1914).Share of Catholics. The share of people with Roman Catholic denomination (in percentof total population) in a country is taken from “The World Religion Dataset, 1945 -2010” (Zeev and Henderson 2013) available from the “Correlates of War” project website(http://www.correlatesofwar.org/COW2%20Data/Religion/WRD_national.csv; accessed atMay, 8th 2013). As always, we took the values from 2009.
An overview over the additional variables used for the robustness checks is provided in TableB.6 above:
Table B.7: Descriptive Overview over the Additional Variables
Variable Obs Mean Std. Dev. Min MaxLatitude*Longitude 839 507.123 253.213 -197.378 1401.973Residence City 839 0.067 0.25 0 1Share of Catholics 839 49.623 22.29 26.85 89.15
82
C. Additional ResultsHere the result of the estimation of Table 9 using the ln population density of a NUTS-3 region asmediating agglomeration measure is shown. The results are almost identical to that obtained withthe relative GDP density. However, the probably biggest difference between both estimationsis that the average ACME using the population density is clearly lower. Neverthless, since it isalways significant and on average around three quarters of the effect of medieval trade on ln GDPper capita is mediated by the ln population density our main conclusion does hold. Furthermorewe report the results of estimating Table 8 using the Index of Commercial Importance insteadof the trade city dummy (Table C.2). We see that the result are a little bit weaker (especiallyconcerning the results for city growth between 1200 and 1500 AD). Nevertheless, the overallresults and therefore also the general implications of the results do stay the same.
83
Table C.1: Medieval Trade, Population Density and Regional Economic Development
(1) (2) (3) (4) (5) (6)
Method OLS Mediation AnalysisCity Growth from to 1200–1500 1300–1500 1400–1500 Equation (7)Dep. Var. ln(Population Density) ln(GDP per capita)
P opulation1500P opulationt
0.337*** 0.178*** 0.172***(0.105) (0.067) (0.062)
ln(Population Density) 0.135*** 0.139*** 0.137***(0.015) (0.015) (0.015)
Trade Center 0.0308(0.019)
ln(Distance to Trade Center) -0.007(0.027)
Commercial Importance 0.0067(0.008)
R2 0.964 0.955 0.947 0.889 0.888 0.888ACME 0.0405*** -0.0605*** 0.0178***Direct Effect 0.0314 -0.0062 0.0067Total Effect 0.0719*** -0.0667** 0.0247***% of total mediated 55.7*** 90.0** 70.8***
Equation (6)ln(Relative GDP Density)
Trade Center 0.3043***(0.053)
ln(Distance to Trade Center) -0.4313***(0.108)
Commercial Importance 0.1318***(0.019)
Country Dummies Yes Yes Yes Yes Yes YesNUTS-1 Dummies Yes Yes Yes Yes Yes YesAll Robust Controls Yes Yes Yes Yes Yes Yes
Obs. 85 179 197 818 818 818R2 0.867 0.87 0.87
Notes. Robust standard errors are reported in parentheses. Coefficient is statistically different fromzero at the ***1 %, **5 % and *10 % level. The unit of observation is a NUTS-3 region. The set ofall robust covariates encompasses altitude, the ln distances to airports and railroads, dummies fordistrict free cities, capital cities, capital cities of autonomous regions, post-communistic transitioncountries, Eastern Germany, the ln of a region’s area, the share of people with tertiary education,the inequality measure and the printing press before 1500 AD dummy. Each regression includes aconstant not reported. ACME is the “Average Causal Mediation Effect” and means how much of theeffect of medieval trade is mediate, i.e. works indirectly through the relative GDP density.
84
Tabl
eC
.2:M
edie
valT
rade
Act
ivity
and
City
Gro
wth
-Est
imat
ions
usin
gth
eIn
dex
ofC
omm
erci
alIm
port
ance
Dep
.Va
r.ln
(Po
pu
lati
on
1500
Po
pu
lati
on
1200
)ln
(Po
pu
lati
on
1500
Po
pu
lati
on
1300
)ln
(Po
pu
lati
on
1500
Po
pu
lati
on
1400
)ln
(Pop
ulat
ion)
ln(∆
Popu
latio
n)(1
)(2
)(3
)(4
)(5
)M
etho
dO
LSR
E
Com
mer
cial
Impo
rtan
ce0.
301*
0.26
6***
0.10
50.
394*
**0.
156*
**(0
.155
)(0
.084
)(0
.093
)(0
.065
)(0
.052
)ln
(Pop
ulat
ion
1200
AD
)-0
.605
***
(0.1
48)
ln(P
opul
atio
n13
00A
D)
-0.6
07**
*(0
.069
)ln
(Pop
ulat
ion
1400
AD
)-0
.362
***
(0.0
76)
ln(P
opul
atio
n t−
1)-0
.416
***
(0.0
5)
Obs
.86
199
180
826
390
Adj
.R
2 \ov
eral
lR2
0.34
60.
381
0.17
30.
344
0.26
Num
ber
ofC
lust
ers
361
194
Not
es.
Rob
ust
stan
dard
erro
rsar
ere
port
edin
pare
nthe
ses
inco
lum
ns(1
)-(
3).
Stan
dard
erro
rscl
uste
red
atci
tyle
vela
rere
port
edin
pare
nthe
ses
inco
lum
ns(4
)an
d(5
).C
oeffi
cien
tis
stat
istic
ally
diffe
rent
from
zero
atth
e**
*1%
,**5
%an
d*1
0%
leve
l.T
heun
itof
obse
rvat
ion
isa
city
.T
hese
tof
cova
riate
sen
com
pass
esth
eln
dist
ance
sof
aci
tyto
the
next
river
orco
ast,
dum
mie
sin
dica
ting
citie
sth
atw
ere
resid
ence
ofa
bish
opbe
fore
1000
AD
,had
the
stat
usof
anim
peria
lcity
,wer
elo
cate
dat
am
ain
impe
rialr
oad,
wer
em
embe
rof
the
Han
seat
icLe
ague
orar
ecl
assifi
edas
am
ount
ain
regi
onby
the
EUre
gion
alst
atist
ics.
Furt
herm
ore,
we
cont
rolf
ora
city
’sla
titud
ean
dlo
ngitu
dean
din
clud
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.
85
References
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