The Notion of Space on Railway Maps of the Habsburg Monarchy / Austria–Hungary

Arlene Peukert
Andrássy Universität Budapest
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In this article, the notion of space on railway maps of the Habsburg Monarchy/Austria-Hungary is analyzed and interpreted. Two railway maps from the 1840s and one network map from the 1860s are examined from the perspectives of their visual language and inherent communication mechanisms. A reciprocal approach to maps is applied. The context in which maps are created (production and consumption) is taken into consideration, as is the context which is created by maps (spaces as cultural products). The desired outcome is a synopsis of the plurality of spaces envisioned in the mid-nineteenth century contrasted with the process of unification of space spurred on by the continuous expansion of railway networks. Topics addressed in this article are the rendering of nature and terrain on maps, the beginning development of a railway corridor into a network of lines, the depiction of networks, the hierarchization of territory in the visual language of maps, and the marking of space as a national territory.

Keywords: railway, maps, cartography, space, network, Habsburg Monarchy, Austria–Hungary

Introduction

In the first half of the nineteenth century, the railway started to transform the landscape and, with it, people’s perceptions of the world around them and the ways in which they moved through it.1 Novel notions of space found themselves translated into railway maps produced by engineers, planers, railway companies, publishing houses of maps, guide books, and atlases.
This paper focuses on three railway maps from the middle of the nineteenth century (1845, 1843, 1869), with the aim to show how the presentation of space in the Habsburg Monarchy/Austria-Hungary differed from map to map in the same railway project and also changed significantly over the course of only two to three decades. I outline factors accounting for these altered perceptions of space and their manifestations on maps.
My intention is to provide a synopsis of the diversity of spaces (physical, perceived, and conceived)2 on mid-century railway maps of Austrian/Austro-Hungarian provenance.

In his 1988 essay “Maps, knowledge, and power,” historian and geographer John Brian Harley (1932–1991) formulates the hypothesis that maps are cultural products which have different layers of meaning.3 Maps are never to be seen only as a presentation of geographical features, but rather must be read as a form of manipulated knowledge.4 Contextualizing maps is, according to Harley, an effective method of making maps speak about the “social worlds of the past.”5 Since at least the Middle Ages, when new structures of governance started to form, maps were used to document and legitimize claims of power in space. Images and symbols on the maps which dealt with historical, political, and mythological episodes underline these claims and are part of the communicative vocabulary of cartography.6 Although maps over time became more accurate due to improving measuring techniques and gained an aura of relative objectivity, they were nevertheless value-laden products of society.7

In order to decode the visual language of maps an interdisciplinary approach is advisable. Following Harley’s methodology, pictorial, textual, and sociological components of railway maps are going to be taken into consideration to reveal communicative patterns and mechanisms of power in maps8 and offer, on the basis of this, insights into the ways in which maps can be interpreted as expression of and tools with which to shape perceptions of space.

Railway maps are a relevant addition to the broad field of research related to the history of railway and railway transport in the Habsburg Monarchy/Austria-Hungary. Although in recent decades, especially since the proclamation of different “turns” in the humanities and social sciences, more attention has been paid to the cultural, social, economic, etc. aspects of the railway, plans and maps of railway lines and the inscribed notions of space continue to constitute a hitherto overlooked topic.9 Consequently, reflecting on historic topics from a spatial perspective can perhaps yield new insights which will prompt further research on railway history.

How the railway Transformed Space and Time – Manifestations of Spatial Perceptions on Railway Maps

Space10 and time are complex phenomena. They constitute the coordinate system of our terrestrial existence in which, knowingly or instinctively, we place every subject, object, and act. Orientation without the context of space and time is impossible.11 Without reference points, we would inevitably be lost, and we would lack any understanding of who we are and where we come from.12 Humanity has continuously endeavored to develop an understanding of time and space and arrive at systems with which to measure them. The invention of calendars and clocks turned time into a cultural product. The superimposed linearity and sequentialness of time, which are also reflected in the ways in which some human languages are composed, make it easy for us to locate events in a chronological order.13 Historical events become narratable: event A happened at a point in time before event B took place. Both events can be marked with a clear beginning and ending and stand in relation to each other.14

Space, however, eludes from our efforts to document and narrate it due to its multidirectional dimensions and the simultaneousness and coexistence of coordinates. Space is not linear.15 In order not to get lost, we apply similar methods to tracing space as we use to structure time. Movements in space are transformed into lines which can then be transferred to a two-dimensional surface: a map. In the form of lines and points on maps, space, which has no beginning and no ending and is consequently hard to narrate, becomes fixed and more controllable.16 Maps, thus, are always a reflection of how people see the environment.

The railway system (and maps thereof) can be understood as manifestation of a new spatial awareness and at the same time as tool(s) which shaped space and produced a new form of cultural space.

Historian Wolfgang Schivelbusch argues, that with the reduction of travel time, the railway helped shrink space and brought places closer together. At the same time, the increased speed of travel meant that people could reach faraway places in a much shorter time. For travelers, the space between stations lost importance, while beginning and end points of travel became increasingly significant.17

Last but not least, schedules oriented around departure and arrival times made the introduction of a standard time necessary, that by the 1890s replaced local times in Central Europe.

The railway touched and changed many parts of life in the nineteenth century and consequently also replaced an old space-time continuum with a new one.18 By tracing this novel perception of space on railway maps, we can enhance our understanding of the specific view map producers and map users had of a place or territory (mental maps19) and the ways in which this view changed over time. We can learn how authorities, stakeholders, constructors, landowners, and key political players positioned themselves and others in space, how they constructed their identities within a newly emerging understanding of space, and how this understanding of space itself was shaped and controlled.

The Development of the Railway and Railway Maps in the Habsburg Monarchy/Austria–Hungary

The history of the Austrian railway in the nineteenth and early twentieth centuries is commonly divided according to the phases of ownership and financing of railway projects.20 It should be mentioned however, that a clear timeline of railway periods cannot always be followed, as gaps between the order and final implementation of railway-related laws occurred.

Following a pioneering phase of private funding and planning of the first railway lines between 1824 and 1841, a phase of railway construction under state initiative took place from 1841 to 1854/5821. Having finally grasped the potential of this new means of transportation, the state wanted to bring the railway under its control in order to push the construction of new lines and connections independent of the financial aims of private investors. The expansion of the lines of the Emperor Ferdinand Northern Railway and the Southern Railway were among the most urgent infrastructural development plans.22 Furthermore, the Milan-Venice railway line (Venedig-Mailänder Bahn) was completed in 1846, and the challenging Semmering railway (Semmering Bahn), as part of the Southern Railway, and the Empress Elisabeth Railway (Kaiserin Elisabeth-Bahn) were built under state control. Financial restrictions put an end to the first state phase in 1854. A new railway law aimed at private investors obliged them to disclose the details of their planned railway projects for the state to check and approve.23 Between 1854/58 and 1873/80 the railway network of the monarchy grew significantly. However, private investors recoiled from financing railway projects that made sense only for the infrastructural development of the monarchy and promised less profit. Lines deemed important by the state, like the Arlberg Railway (Arlbergbahn) or a railway along the Dalmatian coast, could not be realized during that period. The financial crisis of 1873 forced the state to engage more actively in the railway program once more. The construction of the Arlberg tunnel in 1880 marked the beginning of a second phase of railway construction under state control.24 In addition to investing more money in private railway projects, the state also funded the construction of lines of pressing importance. In 1896, the k.k. Railway Ministry in Vienna was founded with the function of monitoring and controlling railway traffic and railway projects in the Austrian lands of the Dual Monarchy. In the last phase of railway politics, the New Alps Railways were built.25 Also, minor connections were created. The collapse of the Austro-Hungarian Monarchy in 1918 lead to the breakup of the vast railway network, as huge parts of it were then situated in the neighboring countries, two of which were newly created states.

Numerous diary entries, episodes from fictional literature, drawings, and paintings demonstrate how the novelty of rail travel was perceived in the nineteenth century.26 This new medium not only found novel forms of expression in art and literature, it also demanded improved techniques and approaches in the scientific documentation of railway tracks.

Before people engaged in travel on a grand scale, the military and the state were the primary users of most of the manuscript maps produced in the eighteenth and nineteenth centuries. These maps were to a large extent kept under strict control and treated as secrets, as in times of conflict and war detailed maps of the territory could provide the enemy with crucial information.27 The development of the street network in the Habsburg Monarchy and the emergence of the stage coach system in the seventeenth century resulted in the production of new road maps and stage coach maps which were made available to the public as well.28 At the beginning of this phase, roads and stage coach connections were often added to topographic maps, for instance from the Austrian land surveys.29 Later, with the rise of rail and steam boat travel, further traffic connections had to be integrated into the maps. In the interest of legibility, thematic travel maps were made in the nineteenth century.30 Slowly but surely, railway maps started to supersede the stage coach maps.31 Due to the growing density of the railway network from the middle of the century onwards, railway maps grew in scale and complexity; detailed traffic and railway atlases were published. Furthermore, thematic travel maps were adapted to the users’ needs.32

The railway line Wiener Neustadt–Ödenburg – A case study on Two Different Perceptions of Space In Early Railway Maps

One of the earliest railways of the monarchy, the line between Ödenburg (Sopron, Šopron) in the Hungarian lands of the empire and Wiener Neustadt was planned and built between 1840 and 1847.33 The plan for this line was a joint venture of the Hungarian aristocrats Pál Esterházy (1786–1866), count István Széchenyi (1791–1860), and the banker Georg Simon von Sina (1783–1856). The Hungarian nobles wanted the railway to come to Hungarian lands. Count Széchenyi greeted the project commissioned by the king Ferdinand I of Austria (1793–1875)34 with great enthusiasm:

With this [project] a bright star rose for the West of Hungary; its growing radiance will illuminate the tracks of its [Hungary’s] future rapid progress. (Ein heller Stern ist damit dem Westen Ungarns aufgegangen, dessen wachsender Strahlenglanz die Bahnen seines zukünftigen raschen Fortschrittes erleuchten wird.)35

In 1845, construction work under the oversight of Mathias Schönerer (1807–1881)36 began. The track between Ödenburg and Wiener Neustadt is 31.9 kilometers long and passes through slightly hilly terrain. Leaving Wiener Neustadt, the train crosses the river Leitha (Lajta) and, thus, the former Austrian-Hungarian border. On its way to Ödenburg, the train passes by the villages Katzelsdorf and Neudorf/Neudörfl (Lajtaszentmiklós/Najderflj). The track then runs alongside the Rosalien Mountains. Several embankments and cuttings were built to cover height differences of the terrain. Before reaching Mattersdorf (since 1924 Mattersburg/Nagymarton/Matrštof), the train has to ascend the steepest part of the track (with an incline of 10.5 percent). The station in Mattersdorf was the largest on the entire line. After crossing another hill, the train passes by the villages Marz (Martz/Márcfalva/Marca), Rohrbach (Fraknónádasd/Orbuh), Loipersdorf (Lépesfalva), Schattendorf (Somfalva/Šundrof), and Agendorf (Ágfalva/Agendrof). The station on the western periphery of Ödenburg was then to constitute the end of the line.37 From the beginning of the planning period, the railway line was laid out to be double-tracked, which shows that planners expected a high volume of traffic for the line, which potentially would be prolonged to the south.38

Two monumental viaducts were built by Schönerer for the track: the Mattersburger Viadukt and the Wiesenviadukt. Both viaducts show architectural features similar to the architecture later employed in the Semmering route. It is likely that ideas for the challenging Semmering project built as part of the Southern Railway between 1848 and 1854 by Carl von Ghega (1802–1860) were put to the test in this less demanding terrain.39 Shortly after the line opened in 1847, traffic volume on the route was high, bringing economic growth to the region of Mattersdorf and Ödenburg for a short time.40

A brief comparison of the two railway maps of the same line from Ödenburg to Wiener Neustadt produced in 1843 and 1845 (Figure 1 and Figure 3) shows that different ways of presenting one and the same railway line can lead to very different visual results. Consequently, the spaces captured and reimagined by the mapmakers and commissioners differ to some extent.
Different reasons might explain the different approaches to the representations chosen for the manuscript maps, with the date of map production, the function of the map, and the prospective audiences being the most obvious. However, we often have little information concerning these kinds of factors, in particular the functions of the maps and the prospective audiences, and thus we can do little more at this point than venture guesses. If we consider the possible visual strategies of which these two maps seem to be the product, we can, however, hazard some hypotheses concerning the aims of the mapmakers.

The Role of Nature In Early Railway Maps

Map number one, entitled Uibersichtskarte der zwischen Oedenburg und Wiener-Neustadt im Jahre 1845 im Bau begriffenen LOCOMOTIV-EISENBAHN (Figure 1), is a manuscript map on paper with relatively large measurements (106 × 85 cm). The terrain is not shown in its entirety and the image does not fill the sheet; rather, the user of the map is given a cut-out of the topographic landscape stretching diagonally between Wiener Neustadt and Ödenburg. Large blank spaces on the map’s edges are used for the heading (top center) and a scale bar (bottom left). Linear measures on the map are indicated in Wiener Klafter (Vienna fathom), 158 mm = 2400 Kl. or 1 : 29,000.41 Further inscriptions are featured either directly on the topographic drawing or next to it. Roads are featured as thin black lines. The border between Austria and Hungary is shown as a thicker, broken line. Still, the border is not over-accentuated or strikingly prominent on this map.42

The projected railway line is colored red, establishing also a visual connection between Ödenburg and Wiener Neustadt. Interestingly, shortly before it reaches Mattersdorf, the track forks, which shows that in 1845 (relatively late in the planning process of the line), another option for the track which had been discussed in 1838 had not been ruled out.43 Crossing the valley near Mattersdorf was a challenging task which Schönerer was only able to solve almost ten years after the first plans were made with better knowledge of railway construction, which he acquired in part during his travels to England and America.44 The two town plans of Ödenburg and Wiener Neustadt are executed in greater detail and colored red as well. This accentuation automatically establishes a visual hierarchy among the villages and towns on the map: Ödenburg and Wiener Neustadt are of greater significance.

The visually most striking feature of the map, however, is the depiction of the terrain. Although declared in the title of the map, the projected railway track is not its sole focus. The topography of the landscape is much more prominent to the eye. This begs the question: why did the mapmaker, whose name was not indicated, chose this mode of presentation? Can we perhaps identify visual traditions to which the mapmaker was harking back which would explain the accentuation of nature and the terrain?

The dense placement of hachures to model the form and height of hills and slopes and the use of primarily dark colors like browns and greens make it hard to read the cartographic symbols and labels and spot the course of the railway line at a single glance. Color patches and hachures form a solid visual entity. The visual dominance of landscape features and landscape rendering indicates that the concept of space inscribed into the map was still routed in an environment dominated by nature. Building traffic infrastructure still meant an adaption to landscape. Although humans remodeled the environment according to their needs, until the nineteenth century, hills, mountains, and rivers still presented barriers that could only be overcome only with difficulty and effort. The course of a street or the position of a dwelling, harbor, or bridge were strongly geo-determined.45 It is thus comprehensible that topographic features seem to be disproportionally presented in especially early railway maps: the supremacy of nature and the achievement of partly overcoming natural barriers (for instance by building viaducts46) are inscribed into the visual language of the map: the railway starts to subdue nature. Moreover, for constructors and financers, the exact course of the line, possible obstacles on the way, the position of stations and the feasibility of a project (which depended on these factors) were of central importance. Maps for this user group had to be detailed and precise.

The visual language of the map from 1845 is from many perspectives in line with stylistic traditions of eighteenth-century and early nineteenth-century topographic cartography. Extensive field measurements were being taken with increasing frequency and regularity in Western and Central Europe in the eighteenth century, as absolute rulers sought to document their entire sovereign space. These were the first attempts to measure every detail of the environment scientifically, resulting in accurate maps of the terrain. The first official field measurements of the Austrian crownlands were taken during the reign of Maria Theresia (1717–1780) by the military (Josephinian Land Survey/Josephinische Landesaufnahme/Erste Landaufnahme, 1764–1786).47 Between 1764 and 1786, more than 3,500 maps were drawn. A second Austrian land survey was conducted in the first half of the nineteenth century (Franziszeische Landesaufnahme, 1806–1869). Topographic maps produced during both land surveys show stylistic characteristics similar to the stylistic characteristics of the railway map from 1845. From the perspectives of image section, choice of colors, script, cartographic symbols, depiction of terrain, use of hachures, depiction of infrastructure, framing of the cartographic content, etc., especially maps from the second Austrian land survey show striking similarities to railway map.

As pointed out by Krenn, particularly during an early stage of the railway age, railway lines were additionally drawn into older topographic maps.48 Is the railway map from 1845 thus actually an updated version of an older topographic map from the region between Ödenburg and Wiener Neustadt? Without an in-depth analysis of topographic maps from the two land surveys, this assumption cannot be proven or ruled out.

New topographic maps of the area around Wiener Neustadt were made in 1820.49 For the Hungarian land, however, the latest maps were only from around 1782–1785.50 The area behind the Austrian-Hungarian border was not officially mapped again until 1856 (e.g. Pöttsching (Pecsenyéd/Pečva), Pöttelsdorf (Petőfalva), Mattersdorf, Sopron, Agendorf).51 The different dates of origin of the survey maps suggest that the mapmaker of the railway map from 1845, rather than adding information to an older map, used different topographic maps from the area to draw the railway map Uibersichtskarte der zwischen Oedenburg und Wiener-Neustadt im Jahre 1845 im Bau begriffenen LOCOMOTIV-EISENBAHN.

Because no information about the cartographer of the map, its customer, or place of presentation or publication is given on the map sheet, we can only speculate about the purpose of the map. Furthermore, we do not know whether copies of the manuscript map were made. A higher number of publicly available copies would also imply a larger circle of potential map users. Given the year in which the map was produced (in 1845, the railway line was still under construction), the prominent heading, and the way in which the railway connection is presented as a red line cutting across the hilly and challenging landscape, it is imaginable that the map addressed potential buyers of stocks for the railway line rather than travelers. In April 1845, during the general assembly in Ödenburg, it became obvious that the railway project would be much more expensive than estimated. Instead of 1.5 million Gulden, construction of the railway line would cost more than 2 million Gulden. The two monumental viaducts, changes in the track, and a restaurant near the station in Ödenburg led to an increase in costs. New stocks had to be sold in order to cover the expenses and advance construction work.

Space as a corridor: The narrow view of mapmakers concerning
the railway line from Ödenburg to Wiener Neustadt

A phenomenon present especially in early railway maps is a corridor-like view of the mapmakers concerning the railway lines and the landscapes along the track. The geo-determinacy of infrastructure apparent from the visual language of map one is also reflected in the mapmaker’s relatively narrow view of the terrain. Apart from the projected railway line, other factors relevant for the construction of the track are mapped, such as the terrain, nearby settlements, and infrastructure. As noticeable from map one, a favorable course of the route through uneven, hilly landscape required to some extent an adaption of the track to the terrain, resulting in a situation in which the railway line is more or less enclosed by natural barriers (hills, slopes, rivers, streams, etc.). One gets the impression of a natural corridor. The terrain outside the sphere of influence of the railway line is irrelevant to the project and user groups of the map, and consequently, this area is not featured (blank spaces on map).

In addition to creating a depiction of nature and natural barriers as a corridor, the engineers’ and mapmakers’ view of space also resulted in a narrow corridor perspective that ultimately was translated onto the map. The design of the railway track, the beginning and end points, and stops on the way compose the corridor. Especially in the first decade of steam-powered rail traffic, when a network of rails had not yet been established and connections existed primarily between cities or other points of economic interest, it was not yet seen as necessary to document other long-distance traffic connections. From the point where a railway line stopped or ended, travel was continued using means which had been in use before the age of the railway: by stage coach or on foot.

On the railway map from 1845 as well as on the map from 1843, roads are shown, but they mostly lead to nowhere. Still, we find indications of direction (e.g. Weg nach Froschdorf, Figure 1).

The railway map from 1843, Situations Plan der Neu anzulegenden Eisenbahn, von Oedenburg bis Wiener Neustadt (Figure 3), pushes the notion of space as corridor even further.

 

Figure 3. Situations Plan der Neu anzulegenden Eisenbahn, von Oedenburg bis Wiener Neustadt, (General site plan for the future railway from Oedenburg to Wiener Neustadt), Mihály Vágner, (222 × 48,5 cm), hand drawn, colored, on paper, 1843. Accessed on October 7, 2018.
https://maps.hungaricana.hu/en/MOLTerkeptar/7688/view/?bbox=-1170%2C-8926%2C27792%2C2862

 

Map three shows another, not realized trace design in which the railway track was planned to go through the villages Pöttelsdorf, Draßburg (Darufalva/Rasporak), and Baumgarten (Sopronkertes/Pajngrt). The mapmaker was Mihály Vágner from Ödenburg. The map is relatively large in size (222 × 48,5 cm), has an elongated format,52 and is hand-drawn on paper. The elongated manuscript map focuses almost exclusively on the planned railway line, and the surrounding area is left out. From the perspective of style, the map resembles traditional road plans and maps for waterways.

In comparison to the map from 1845, surroundings are rendered more schematic. Landscape characteristics are presented in a plain, nearly geometrical form, as was typical for cadastral maps and site plans of that time. The environment along the track is given little importance. Forests, fields, and streams are cut off at the edge of the corridor. The planned railway line is superimposed onto the existing network of villages and roads, establishing a linear connection between both cities and, thus, a new hierarchy within the region. Within the spatial corridor of the future railway line, the distance between the cities Ödenburg and Wiener Neustadt shrinks significantly. The space to the left and the right the track is considered irrelevant to the new form of travel. Or to use Schivelbusch’s phrasing, the space untouched by the railway gets eliminated.53

Another interesting component of the map from 1843 is that the area behind the Austro-Hungarian border (around Wiener Neustadt) is almost left blank. It is possible that Vágner, who was a Hungarian engineer, official of Sopron County, and land surveyor, had no detailed cadastral information about the Austrian land and that part of the railway line at hand. Also, in 1843, there were still two railway companies responsible for the construction of the line, which is why Vágner might have produced this manuscript map especially for the Hungarian planning team of the Ödenburg-Wiener Neustadt company. A signature on the map sheet with the note “Copirt” indicates that the map is a copy of the original Vágner Situations Plan. The map thus might have been copied several times and spread among a wider group of users. We do not yet know by whom (e.g. constructors, investors, the public) and to what purpose copies of the Vágner railway plan were used.

From corridor to network – The growing importance of traffic junctions on railway maps

Although, as discussed above, the Situations Plan from 1843 does not provide information about the landscape on the Austrian side of the planned railway line, the future traffic junction in Wiener Neustadt (marked as Stationsplatz) is already indicated on the map.54 Here, the line from Gloggnitz to Vienna was going to cross, forming a traffic connection between the Austrian and the Hungarian lands. Though frequent travel by train was not yet very common in the 1840s because a network of lines had not yet been established,55 both maps nonetheless seem to presage the importance of traffic junctions for movement and communication in the Habsburg Monarchy. Although in 1843 the line between Ödenburg and Wiener Neustadt had not yet been built and the southbound railway line was only completed between Vienna Südbahnhof and Gloggnitz, Vágner and/or the potential initiator of the map deemed this traffic junction and the growing network of lines significant for the region.

For the next roughly eighty to one hundred years to follow, until the emergence of automobiles and air traffic, railway lines and train stations remained the most powerful hubs and channels along which people, goods, ideas, images, innovation, and ideologies traveled. They hastened the pace of industrialization, migration, and urbanization, as well as the exploitation of nature.

 

The railway map published in 1869 by Lehmann & Wentzel in Vienna entitled Neueste Eisenbahnkarte der ÖSTERREICHISCH-UNGARISCHEN MONARCHIE: mit Berücksichtigung der Montan und Industrie Bahnen (Figure 4) shows many characteristics with which the modern-day user of traffic maps is accustomed: a stereographic projection of the area’s surface, a network indicating actual geographic position and schematized layout, station names arranged above one another for better legibility, and a color code for the single branches to simplify orientation. The user finds a coordinate system and a legend listing railway lines and associated color codes. Lines planned or under construction are marked with different graphic signatures (e.g. two thin black lines for a planned track and an alternating pattern of black and white stripes for a railway line under construction). Over the course of twenty to thirty years, a map language for railway lines used in travel developed in Europe and the Habsburg Monarchy/Austria-Hungary which in many ways is still valid today. Given the need to document the growing network and most of all to facilitate travel, the map language focuses on overview and orientation.

The title of the map, displayed in a rectangular cartouche, denotes the fast rate with which the railway network grew at the time. The user holds in his/her hands the newest railway map (die Neueste Eisenbahnkarte) which shows that map production tried to keep pace with the expansion of the network. In the second half of the century, updated maps had to be published frequently; also, the demand for maps was high. Network maps were among the most common in the second half of the century.56 Between 1857 and 1866 the railway network of the monarchy grew at a yearly rate of 327.5 kilometers. As of 1867, that rate rose to 1,352 kilometers of new railway tracks per year.57 Isolated corridors evolved into far-reaching networks with travel connections to many parts of the Dual Monarchy and beyond. The network stretches from the Austrian-German border in the northwest to the Adriatic coast in the south, from Innsbruck in the west to Karlsburg (Alba Iulia/Gyulafehérvár) in Transylvania. Particularly in the northwest, Austrian railway lines connect with the German network, making travel and trade truly international.

As a single glance at the map reveals, Vienna is in the center of the railway network. A majority of the lines built by the middle of the century radiate from the capital Vienna towards national traffic junctions, the most important of them being Pest/Buda, Brünn (Brno), and Prague. From here, the network further expands to regional traffic junctions. In the Austrian part of the empire, the railway network is much denser than in the eastern lands of the monarchy. Many of the lines towards Galicia and Transylvania were still under construction at the end of the 1860s, resulting in cities like Lemberg (Lwiw/Lwów), Czernowitz (Csernyivci, Czerniowce, Cernăuţi), Kronstadt (Braşov/Brassó), and Hermannstadt (Sibiu/Nagyszeben/Hermestatt) being at the far-flung periphery of the monarchy’s network and thus difficult to reach.

The map language, cartographic symbols, and layout and arrangement of content on the map sheet direct the user’s gaze and influence the way the map is read. The center-periphery dichotomy, for example, automatically results in a hierarchy in the virtual space created by the map, which also translates back into perceptions of the physical space. When they see a given site in a central position, map users consciously or unconsciously associate it with power and control.58 All the other points on the map are of subordinate importance compared to the center, in this case, Vienna. Spatial distance is one factor in the establishment or maintenance of a hierarchy. The duration, frequency, and possibility of travel to a place are others. Mapmakers inevitably create hierarchies in space in the sense that the map language always implies a syntactic ordering of its elements. The reader of a map cannot avoid comparing the sites designated on the map and constructing hierarchical relationship among them.

In contrast to the map from 1845, the display of terrain and landscape features is of minor significance on the network map from 1869. On map four, landscape characteristics were reduced to mere markers for orientation. Lakes, rivers, and coastlines help the user of the map get a rough sense of location. Compared to the visual language of the railway map from 1845, where the terrain was very prominent to the eye, there is nothing overwhelming anymore in nature or natural barriers on the 1869 map. The reasons for this are, on the one hand, the changed purpose and thus user group of network maps and, on the other, the modified significance of nature for the railway. The most important reason, however, was simply the growth in rail travel. In the era of industrialization and growing railways, more than ever before, men remodeled nature according to their needs. Tunnels, viaducts, bridges, and embankments are evidence of men’s desire to tame nature and foster mobility. If feasible, a railway track no longer adapts to the terrain. Rather, it cuts through nature in a straight, linear path. Seen from the window of a train, nature and natural barriers lose parts of their daunting quality. While nature is still of importance for engineers, constructors, and investors in railway lines, for passengers, as can be seen in the network map from 1869, the environment becomes a sign on a sheet of paper, helpful if one wants an overview.

In the same sense, as the significance of natural barriers fade, the importance of the display of the country’s frontier rises. Apart from the railway lines, the border is the only feature on the map rendered in color (light red), and this draws the attention of the map user to it. Furthermore, the width of the border is remarkable. In comparison to the border of Austria-Hungary, the inner frontiers are barely visibly, presented as fine, dotted lines which can easily be overlooked among the railway lines and rivers. The idea of space and territory envisioned by the commissioner and/or mapmaker is one of unification and openness. The map talks about one space: one space of traffic, even one space of language and nationality, communicated by the exclusive use of German. Not only are the title and the legend of the map in German (only), names of cities, towns, and lands are also given only in German (assuming they had German names). This gesture erases or denies differences in language and ethnicity, making space seem more national. The multi-ethnic nature of the Dual Monarchy is overlooked (or denied) on the map. The network of railway lines is what binds the space together.

Conclusion

One objective of this paper was to show, on the basis of three railway maps of Austrian /Austro-Hungarian provenience, how the railway shaped space and produced new forms of (cultural) space and how these forms of altered spatial awareness found expression in maps. Taking the methodological approach of Harley into consideration, I analyzed two railway maps of the same railway project, the line from Wiener Neustadt to Ödenburg, from the perspective of the presentation of certain visual components. I showed that the dominance of presentations of nature in early railway cartography was related to a stronger geo-determinacy of early railway lines. Nature was still seen and also depicted in maps as a barrier which confined travel and was only overcome progressively by the middle of the century. In addition, the purpose and user groups of early railway maps could account for the strong accentuation of the terrain and nature in maps. In particular, investors wanted to be informed about the exact course of the line, the terrain, stations and stops along the track, etc. Natural barriers and the (comparatively narrow) range of use of early maps resulted in a corridor perspective concerning the railway lines. Once a railway line was finished, route maps were also used by travelers. Findings drawn from the 1843 map align with the general notion that the railway helped shrink space and even make space disappear.59 As fast train connections between important cities and villages were established, the space between stations lost its relevance for travelers, merchants, etc. It started to disappear from maps and, consequently, also from people’s mental maps. The railway also accelerated the hierarchization of space, which gains increasingly importance with the network maps appearing in the second half of the nineteenth century. Network maps were aimed at a broader public wishing to travel through the monarchy. Nature in these maps has lost its restricting character and became, as shown in the map of 1869, a marker for orientation. At the same time, while space was being hierarchized (e.g. a hierarchy of centers versus peripheries), it was also bound together and unified by the network of railway lines, which went parallel with the political aspirations of the time in the Dual Monarchy. The visual language of the network map from 1869 also suggests the nationalization of space. The perspective chosen on the land, the use of German, the emphasis on governmental centers (and thus power), and the stressing of the outer border of the Dual Monarchy are indications of a progressing nationalization and delimitation of space towards neighboring countries. Further research on the notion of space in railway maps will help provide answers to some of the questions raised in this essay.

Bibliography

Bachinger, Karl. “Das Verkehrswesen.” In Die Habsburgermonarchie 1848–1918, Volume 1: Die wirtschaftliche Entwicklung, edited by Adam Wandruszka, Peter Urbanitsch, 278–322. Vienna: Verlag der Österreichischen Akademie der Wissenschaften, 1973.

Baltzarek, Franz. “Die Finanzierung des Eisenbahnsystems.” In Eisenbahnbau und Kapitalinteressen in den Beziehungen der österreichischen mit den südslawischen Ländern, edited by Richard G. Plaschka, Anna M. Drabek, Birgitta Zaar, 221–31. Vienna: Verlag der Österreichischen Akademie der Wissenschaften, 1993.

Benedek, Anna. Mattersburger Viadukt und seine Zeit: Wahrzeichen der Stadt und seine Bedeutung. Mattersburg, 2018. http://www.70haus.at/buch/viadukt/viadukt.pdf.

Chmelar, Hans, ed. 150 Jahre Eisenbahn im Burgenland: Feuer, Wasser, Kohle, catalog accompanying the special exhibition from 1997, edition 19. Eisenstadt, 1997.

Deák, Ernő. “Änderungen in der ethnischen Zusammensetzung der Bevölkerung im heutigen Burgenland.” In Ungarn-Jahrbuch, vol. 25 (2000/2001), 79–96. http://epa.oszk.hu/01500/01536/00025/pdf/UJ_2000_2001_079-096.pdf.

Denecke, Dietrich. Wege der historischen Geographie und Kulturlandschaftsforschung. Edited by Klaus Fehn. Stuttgart: Franz Steiner Verlag, 2005.

Dörflinger, Johannes. “Österreichische Eisenbahnkarten bis zum Ersten Weltkrieg.” In Kartographiehistorisches Colloquium Wien ’86, edited by Wolfgang Scharfe, Ingrid Kretschmer, Franz Wawrik, 157–74. Berlin: Dietrich Reimer Verlag, 1987.

Gibas, Monika. “Auf der Suche nach dem ‘deutschen Kernland’:” ‘Mitte’-Mythen im Deutschland der Zwischenkriegszeit (1919 bis 1939) und nach 1990.” In Kultur der Propaganda, edited by Rainer Gries, Wolfgang Schmale, 195–210. Bochum: Winkler, 2005.

Gutkas, Karl, ed. Verkehrswege und Eisenbahnen. Beiträge zur Verkehrsgeschichte Österreichs aus Anlaß des Jubiläums “150 Jahre Dampfeisenbahn in Österreich.” Vienna: Österr. Bundesverl., 1989.

Harley, John Brian. “Maps, knowledge, and power.” In The Iconography of Landscape: Essays on the Symbolic Representation, Design and Use of Past Environments, edited by Denis Cosgrove, Stephen Daniels, 277–312. Cambridge: Cambridge University Press, 1988.

Harvey, David. “On the History and Present Condition of Geography: An Historical Materialist Manifesto,” The Professional Geographer 36, no. 1 (February 1984): 1–11.

Kaschuba, Wolfgang. Die Überwindung der Distanz: Zeit und Raum in der europäischen Moderne. Frankfurt am Main: Fischer Taschenbuch Verlag, 2004.

Köster, Burkhard. Militär und Eisenbahn in der Habsburgermonarchie 1825–1859. Munich: Oldenbourg, 1999.

Krenn, Bettina. “Eisenbahnkarten der Österreichisch-Ungarischen Monarchie vom Beginn des Eisenbahnwesens bis zur Jahrhundertwende,” diploma thesis, University of Vienna, 1998.

Krenn, Bettina. “Verkehrsgeschichte im Kartenbild: Verkehr und Kartographie in Österreich von der Römerzeit bis zum Beginn des Eisenbahnzeitalters,” PhD diss., University of Vienna, 2003.

Lindner, Klaus. “Landesaufnahmen deutscher Territorien: Beispiele der Militär­kartographie und ihr historischer Quellenwert.” In Geschichtsdeutung auf alten Karten: Archäologie und Geschichte, edited by Dagmar Unverhau, 411–41. Wiesbaden: Harrassowitz Verlag, 2003.

Mahr, Johannes. Eisenbahnen in der deutschen Dichtung: Der Wandel eines literarischen Motivs im 19. und im beginnenden 20. Jahrhundert. Munich: Wilhelm Fink Verlag, 1982.

Praschinger, Harald. “Die österreichischen Eisenbahnen als wirtschaftlicher Faktor.” In Verkehrswege und Eisenbahnen: Beiträge zur Verkehrsgeschichte Österreichs aus Anlaß des Jubiläums “150 Jahre Dampfeisenbahn in Österreich,” edited by Karl Gutkas, Ernst Bruckmüller, 100–23. Vienna: Österr. Bundesverl., 1989.

Rumpler, Helmut, Martin Seger, Peter Urbanitsch, et al. Die Habsburgermonarchie 1848–1918, volume IX, part 2, “Soziale Strukturen: Die Gesellschaft der Habsburgermonarchie im Kartenbild. Verwaltungs-, Sozial- und Infrastrukturen. Nach dem Zensus von 1910.” Vienna: Verlag der Österreichischen Akademie der Wissenschaften, 2010.

Schivelbusch, Wolfgang. Geschichte der Eisenbahnreise: Zur Industrialisierung von Raum und Zeit im 19. Jahrhundert. Frankfurt am Main: Fischer Taschenbuch, 2015.

Schlögel, Karl. Im Raume lesen wir die Zeit: Über Zivilisationsgeschichte und Geopolitik. Munich/Vienna: Carl Hanser Verlag, 2003.

Vollmar, Rainer. “Die Vielschichtigkeit von Karten als kulturhistorische Produkte.” In Geschichtsdeutung auf alten Karten, Archäologie und Geschichte Wolfenbütteler Forschungen, edited by Dagmar Unverhau, 381–95. Wiesbaden: Harrassowitz Verlag, 2003.

Waldmüller, Hildegard. “Quellenkundliche Forschungen zur Sozialgeschichte des Eisenbahnbetriebs in Österreich 1824 bis 1865.” PhD diss., University of Vienna, 2016.

Wawrik, Franz. “Historische und Kulturhistorische Informationen in den Werken österreichischer Kartographen des 16. Jahrhunderts, mit besonderer Berücksichtigung des Wolfgang Lazius.“ In Geschichtsdeutung auf alten Karten, Archäologie und Geschichte Wolfenbütteler Forschungen, edited by Dagmar Unverhau, 193–212. Wiesbaden: Harrassowitz Verlag, 2003.

 

Social Differentiation and Spatial Patterns in a Multiethnic City in the Nineteenth Century: Potential Uses of GIS in the Study of Urban History*

Gábor Demeter and Róbert BagdiŰ
Research Centre for the Humanities, Hungarian Academy of Sciences
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This study is a GIS-aided quantitative statistical analysis which aims to explain the spatial patterns of sociodemographic phenomena in an urban community in the era of transition from preindustrial to industrial society. It is also a methodological attempt to use a unique source type and compare different methods used for social classification. Using the Hungarian census data from 1870, we tried to assess the wealth levels of different social groups indirectly and compare the internal inequalities within these groups with internal inequalities within social groups in other regions. The source also provided material on the basis of which we were able to reconstruct social networks, migration patterns, different strategies adopted by different religious communities, patterns involving occupation and age group, etc. We were able to compare the potential uses (and limits) of this source with the uses and limits of other sources. Our main goal was to put more emphasis on a spatial-regional approach, which is underrepresented in the Hungarian historiography, while geographers tend to refrain from putting their research into historical frames and contexts.

Keywords: HGIS (GIStory), urbanization, spatial patterns, social stratification, classification methods, quantitative analysis, wealth, 1870 census data

Aims

Although our study essentially aimed to (1) analyze and explain spatial patterns of sociodemographic phenomena in an urban community in the era of transition from preindustrial to industrial society by testing the potentials of a unique source (the census of 1870), other, primarily methodological aspects also arose which are worth further discussion and which put this article into a broader context. We have attempted (2) to outline three different methods which can help researchers identify different social layers in urban societies. We also attempted (3) to give an indirect estimate of the wealth levels of different social groups in the late nineteenth century by using the census data and to compare local internal inequalities with inequalities measured in other urban settlements and regions. We also considered (4) the potential applications and limitations of the source in question in attempts to reconstruct social networks and migration patterns, and we compared the uses of this source to other source types.

The applications of HGIS1 might be familiar to geographers and historians in the West, but the use of this method in Hungarian historical research is underrepresented at the moment (the only existing concise database, compiled for the city of Debrecen on the basis of census data from 1870, remains unevaluated).2 Geographers dealing with GIS-aided planning refrain from engaging in research focusing on the past, though the lack of knowledge of the histories of peripheral areas may lead to the adoption of mistargeted policies in development planning. Historians use a “vertical” (sociological) approach instead of spatial (regional) one, but recent studies have shown that the regional diversity in Hungary was not negligible. Thus, generalizations based on small datasets extrapolated to the whole country (and terms like “average”) can be misleading. Our fifth goal, therefore, was to test the applicability of GIS in the field of history. This study can be considered a draft project for the later, more broadly framed projects, such as GISta Hungarorum (2015–2017).3

Data

The source on which we based our inquiry was chosen because of its uniqueness. which enabled us to investigate and map certain phenomena into which other sources yielded no insights. The census of 1870 was the first modern census taken by Hungarian authorities, and (far more importantly) it was the only state inquiry that was based on household level (Figure 1) and not on individual data sheets (later censuses were based on individual data sheets). Furthermore, almost at the same time, a cadastral mapping was also done in 1865 indicating every house with its identification number, which was identical with that of the numbers used in the census sheets.4 This temporal proximity and the survival of the original unpublished sheets in some counties5 (data were published officially only at the district level in the census volumes) made it possible for us to illustrate sociodemographic phenomena on maps at the household level and even to assess wealth levels based on property at the beginning of the era of industrialization.

The original census sheets from 1870 contained the name, age, address, birthplace, occupation, and religion of the head of the family, and these data were repeated for the wife, children, coworkers/employees, servants, and housemaids living in the same “household.”6 The sheets also provided the number of rooms, kitchens, auxiliary buildings (storage areas, stables, cellars) for each household. As the census did not contain income data, some of the abovementioned variables were utilized as proxies for wealth in order to divide the population into social (i.e. income-related) layers. Beyond wealth, general sociodemographic phenomena with or without spatial patterns (such as the average number of children of different occupational groups, the average number of children of groups belonging to different religions, migration patterns, interreligious marriages, territorial aspects of marriage patterns, territorial distribution of religious groups, etc.) were also traced using the aforementioned variables.7 The data also made it possible to create new indicators beyond those given in the census, such as population density (room/person) and ratio of earners per family. These derived data were also used as proxy variables to approximate wealth.

Our household-level database contained 2,150 entities (families, Wohnparthey), cca. 1,000 houses with approximately 10,000 persons and a dozen indicators. Phenomena with spatial pattern were analyzed using GIS (ArcGIS 10.1), while within-group and intergroup differences (like religious composition of occupation groups, differences in wealth levels of religious groups and occupations, ageing, migration, differences in fertility rate, etc.) were evaluated using SPSS.

Figure 1. Pages from the census, Nagy Piac str., nr. 9.

Source: MNL-BAZML SFL XV. 83. box 77–79.

The Place

The selection of the town of Sátoraljaújhely (the county seat of Zemplén County) as a sample area was ideal from several perspectives. The original census sheets were available for 2,150 households, thus offering substantial material for quantitative statistical analysis, and even the timing of the census itself (1870) was fortunate from the perspective of our inquiry, which focuses on the identification of persisting and transforming urban structures. As a basic step towards industrialization, the railway was opened in 1870, while guilds were dissolved only in 1872, and this implied the parallel coexistence of both traditional and modern social patterns and social layers. In addition, the town had had an inherently positive geographical position for centuries, as it was located along the market line, where the goods produced in the plains and in the mountains were exchanged. The physical geographical conditions allowed a north-south pattern of migration from the peripheries of Zemplén County (the border of which was also a state border) to the county seat, while in the southern part of the county an east-west migration route developed from the Great Plains towards the capital, Budapest. Although in 1775, the county seat was so peripheral that it was unable to extend its attraction zone very far even within its own administrative district, between 1810 and 1870, its population tripled, and this population growth was among the largest in comparison with the neighboring towns (Table 1). The nearby city of Eger, which was similar in size and had similar functions (it was also a county seat), showed only a 40 percent increase. By 1900, 50 percent of the inhabitants of Sátoraljaújhely were registered as not indigenous (i.e. born in a different locality),8 a figure which confirms the great role of horizontal mobility and migration. As the average number of children per household was only 1.8 in Sátoraljaújhely (1870), without migration, the population would not have increased at all.9 The acceleration of urbanization processes became more evident during industrialization (the population increase was only 50 percent between 1784–1825 and 1825–1870, but then it doubled in the next 40 years, exceeding the country average), making a melting pot of the town. This was reflected in its religious diversity. In 1870, 35 percent of the population was of Jewish origin, Roman Catholics constituted 30 percent, Calvinist protestants 12–14 percent, Greek Catholics approximately 18–20 percent, and there were some Lutheran inhabitants too. 10

 

Table 1. Population increase referring to the rate of urbanization (1825–1900) in Sátoraljaújhely compared to the surrounding significant towns

Town	Population increase (1825–1900)	Population in 1,000 (1825)	Population in 1,000 (1900)
Eger	+40%	17.5	24.5
Kassa (Košice)	+180%	13	38
Miskolc	+80%	22	40
Sátoraljaújhely	+200%	4 (1784), 6.3 (1825)	10 (1870), 19.9 (1910)

Source: Beluszky, Magyarország településföldrajza.

General Features of the Urban Society

The evaluation of the urban society began by creating a correlation matrix containing the quantifiable variables of the database. The correlation between demographic indicators was weak in many cases (no connection was observable between number of children and family wealth or between the proportion of earners and wealth) (Table 2), thus many of the recorded indicators can be interpreted statistically as independent variables. However, some of the indicators still showed correlations with other variables. Therefore, in order to interpret these phenomena, diagrams illustrating the internal distributions were also created. Some of the variables were not quantifiable (like religion), thus correlations could not be calculated. The relationships between these variables and other indicators were also illustrated on diagrams. In order to illustrate the internal differentiation within the dataset, both mean and standard deviation values were calculated for the whole population and were used as reference points when comparing subsets (Tables 3–11).

 

Table 2. Correlation between the quantifiable variables (for each family). Strong correlations are indicated by grey background

Indicator	Age	Servants	Coworkers	Total inhabitants	Proportion of earners	Number of rooms	Proportion of children	Inhabitant per 1 room	Wealth 1	Wealth 2
Age	1.000	-0.011	-0.134**	-0.047*	-0.006	-0.141**	-0.099**	0.099**	-0.158**	-0.171**
Servants	-0.011	1.000	0.097**	0.427**	-0.276**	0.513**	-0.071**	-0.122**	0.369**	0.537**
Coworkers	-0.134**	0.097**	1.000	0.408**	0.240**	0.236**	0.074**	0.152**	0.113**	0.426**
Total inhabitants	-0.047*	0.427**	0.408**	1.000	-0.560**	0.424**	0.610**	0.501**	-0.197**	0.103**
Proportion of earners	-0.006	-0.276**	0.240**	-0.560**	1.000	-0.194**	-0.539**	-0.330**	0.234**	0.183**
Number of rooms	-0.141**	0.513**	0.236**	0.424**	-0.194**	1.000	0.063**	-0.530**	0.613**	0.710**
Proportion of children	-0.099**	-0.071**	0.074**	0.610**	-0.539**	0.063**	1.000	0.523**	-0.416**	-0.304**
Inhabitant per 1 room	0.099**	-0.122**	0.152**	0.501**	-0.330**	-0.530**	0.523**	1.000	-0.796**	-0.601**
Wealth 1	-0.158**	0.369**	0.113**	-0.197**	0.234**	0.613**	-0.416**	-0.796**	1.000	0.911**
Wealth 2	-0.171**	0.537**	0.426**	0.103**	0.183**	0.710**	-0.304**	-0.601**	0.911**	1.000

Explanation:

Coworker: inhabitant living together with the family-head but having his or her own earnings but not his or her own home (servants are not included in this group, but craftsmen-students are); employees of the family head, or grown up relatives of the family head employed elsewhere.

Wealth 1: indicator for the economic potential of the “Wohnparthey” calculated based on an equation containing the number of household servants, coworkers, economic buildings, number of rooms, and family size.

Wealth 2: indicator for the economic potential of the “Wohnparthey” containing the number of household servants, coworkers, economic buildings, and number of rooms but not family size.

** significant, p=0.05. Calculated-derived indicators are indicated by italicized letters.

Base data: MNL-BAZML SFL XV. Census data from 1870.

 

Table 3. The size of “Wohnparthey” in Sátoraljaújhely in 1870 (prs and %)

Family members	1	2	3	4	5	6	7	8	9+	Total
household number	123	381	415	345	305	198	162	84	134	2,147
%	5.73	17.75	19.33	16.07	14.21	9.22	7.55	3.91	6.24	9434

 

Table 4. Inhabitant/room values for the “Wohnparthey” in Sátoraljaújhely (prs and %)

0–1	1.1–1.5	1.6–2	2.1–2.5	2.6–3	3.1–4	4+	Altogether
214	125	375	120	352	391	529	2,147
9.97	5.82	17.47	5.59	16.39	18.21	24.64	100

The general sociodemographic features of the town can be summarized as follows. The town had cca. 1,000 houses, but 2,150 registered “families,” which means that on average one house was home to at least two Wohnpartheys. (For example, one kitchen was often used jointly by two or three families). The average family size was 4.4 people for one Wohnparthey in 1870 in Sátoraljaújhely. 25 percent of the households had six or more and 23 percent had two or less members.11 The average population density was three people per room, but there was significant variety. 25 percent of the households were characterized by density above four people per room. In 10 percent of the families, at least every second family member was an earner, while in 8 percent of the families the earnings of one person were enough to maintain a family of ten. The average number of rooms per family was 1.5 in the town, but here too there were considerable discrepancies, and the average value was hardly greater than the value measured in villages.12 50 percent of families had only one room, and 8 percent had less than one, while only 10 percent had three or more rooms. 13 In Hungary, the average was 3.8 people per room in 1869 (and 3.5 in 1910). In Sátoraljaújhely, it was three people per room.14 Servants were abundant in only 25 percent of the households. They constituted 7.3 percent of the society. The average number of servants was 0.33 per family for the whole town. Earners without their own Wohnparthey constituted 10 percent of the population (978 persons), but only in 10 percent of the Wohnpartheys do we find more than one coworker, and 75 percent of the families had none. 28 percent of the “families” had no children (the family head was too young or was older and the children had already left the family home). In Belgrade, this figure was only 17 percent in 1900.15 On the other hand, 30 percent of the Wohnpartheys had more than two children (in Belgrade this was 26 percent). The average number of children was 1.8 per family. Jewish families had 2.4 children of average, Greek Catholics had only 1.4, and Roman Catholics and Calvinists had 1.6. Only 11 percent of the family heads were younger than 30. 11 percent was older than 60 (the average was 39). Altogether, 39 percent of the total population was under 18 years of age (the figure was similar for the whole of Hungary).

 

Table 5. Proportion of earners in the “Wohnpartheys” of Sátoraljaújhely in 1870 (prs and %)

0	0.1	0.2	0.3	0.4	0.5	0.6–0.9	1.0	Total
70	173	676	467	116	401	104	140	2,147
3.26	8.06	31.49	21.75	5.40	18.68	4.84	6.52	100

Table 6. Average number of rooms / family (Wohnparthey) in 1870 in Sátoraljaújhely
(number of rooms and %)

Number of rooms	under 0.5	1	2	3	4	5+	Total
households	170	1,175	488	150	69	55	2,147
%	7.92	54.73	22.73	6.99	3.21	2.56	100

Table 7. The number of servants in family households in 1870 in Sátoraljaújhely (prs and %)

Servants (prs)	0	1	2	3	4+	Altogether
households	1,665	336	91	34	21	2,147
%	76%	15.65	4.24	1.58	0.98	730

Table 8. Number of coworkers and earners (not in family-head position) in Sátoraljaújhely in 1870 (prs and %)

Coworkers	0	1	2	3	4+	Altogether
households	1537	383	143	46	38	2,147
%	71.59	17.84	6.66	2.14	1.77	100

Table 9. Number of children in the Wohnpartheys/families in Sátoraljaújhely in 1870 (prs and %)

Number of children	0	1	2	3	4	5+	Altogether
households	619	462	424	303	165	174	2,147
%	28.83	21.52	19.75	14.11	7.69	8.10	100

In Belgrade these figures were 17, 34, 24, 11, 7, and 7% respectively around 1900.

 

Table 10. The distribution of family heads in Sátoraljaújhely based on their date of birth
(prs and %)

Year of birth	–1809	1810–1819	1820–1829	1830–1839	1840–1849	after 1850	Altogether
family heads	238	447	578	645	236	3	2,147
%	11.09	20.82	26.92	30.04	10.99	0.14	100

Table 11. Demographic indicators in Sátoraljaújhely in 1870 (prs and %)

Indicator	Lutheran	Greek Cath.	Jew	Calvinist	Roman Catholic	Altogether
Total number of children	71	519	1,655	483	1,153	3,881 (39%)
%	1.83	13.37	42.64	12.45	29.71	100
number of families	41	373	692	302	735	2,143
%	1.91	17.41	32.29	14.09	34.30	100
children/Wohnparthey	1.73	1.39	2.39	1.60	1.57	1.81

Source: MNL–BAZML SFL XV. Census of 1870.

Local Mobility – Local Networks

As the registry of 1870 offers only a “snapshot” of the social situation, and as its structure differs from the later censuses, the usefulness of this material (unlike the usefulness of parish registers, for example) to identify social networks and relationships or to trace patterns of change of residence among members of the younger generation is rather limited. But in certain cases, the registry still offers significant data on the basis of which one can venture hypotheses concerning trends or patterns in household composition. The marriage of the Calvinist noble landowner family Evva, which played a crucial role in the life of the county and had five rooms and an additional two rooms rented to Jewish grain merchants, and the influential and rich Catholic Farkas family (a lawyer dynasty with eight servants and coworkers, owning six rooms and renting two rooms to merchants) offers an example of the unification of two elite families with different social roots and belonging to different denominations. (Inter-denominational marriages were relatively rare, coming to only 15 percent of all marriages). The old family head András Evva (1805–1888) had already been mentioned prior to 1848 as the leader of the reformist political opposition in Zemplén.16 He managed to keep his position even after the repressions between 1849 and 1867, and he became the president of the county jurisdiction. His wife, Teréz Balásházy, also hailed from an old, local noble family, mentioned early in the eighteenth century as one of the “urban” noble families.

Another example of the decreasing role of religion within the noble elite is given by the Catholic Spek family. Irma (1847–), a relative of Antal Spek (1804–) who was a member of the local town council, married the Lutheran lawyer Ignác Boros and settled down in the main street of the town (Kazinczy Street) near the widow of Ferenc Spek (house nr. 651 and 655). Thus, they were able to look after each other. Furthermore, the elder daughter of the latter widow married a royal official, thus broadening the family network. We may point out that, while at this time the intermingling traditional landowner and administrative elite had already accepted the “honoratior” layer (highly educated non-nobles in important position) as equal partners, the traditional elite living in the town still refrained from entering into relationships with the new financial elite.

The tightness of the relations among relatives can often be measured through territorial concentration, as the above example showed. Social networks had spatial patterns too, but there were remarkable differences in the cases of different strata. For example, the innkeepers of the town also tended to enter into family relationships with one another, but they settled relatively distant from one another as their main aim was to distribute the market between the possible competitors in order to maximize income and minimize competition.

A comparison of other (earlier) registries with ours offers even greater potential as a method of identifying networks, social (vertical) mobility, migration processes (horizontal mobility), etc., but it also requires more work. The noble Kapy family, the richest at the end of the eighteenth century with 90 hectares of land, had almost disappeared by 1870. Apart from one young a child, only one person from this family was registered as an inhabitant in Sátoraljaújhely, the wife (1837–) of Calvinist county official József Bárczy.17 The Marchalko family was also a prominent noble family in the eighteenth century in the town, but by 1870 only one person, the Roman Catholic wife (1817–) of another Calvinist, István Somogyi, bore this name.18 This also indicates that the fusion of the elites of different origins and denominations was in an advanced phase by that time. Protestants traditionally held leading positions in the urban and county administration in Zemplén (this is a specific feature of the county), and they were overrepresented compared to their proportion in the whole urban population. Roman Catholics were mainly landlords, and their weight in the county council and the urban government was smaller in the first half of the nineteenth century. Intermarriage and the general decline in the number of Protestants enhanced their position first on the urban council and then on the county council.

Family and kinship networks which existed at the time the registry was drawn up can also be traced, but only within limits.19 The maiden name of the wife of tailor János Keller, who lived at Papsor nr. 474, was Sztropkovics. Her mother also lived in the same household, while in the same house, but in another ’Wohnparthey’ a Sztropkovics boy established a family. In this case, the relatives remained relatively close to one another because of their limited financial means. The house was divided between the two Sztropkovics descendants, and the husband moved into his mother-in-law’s house. Another example of relatives from different communities living relatively close to one another reveals family and business strategies. Eszter Hell, the widow of a Jewish textile merchant (haberdasher) named Svajger, and the textile merchant Salamon Hell (who was her close relative) also lived in neighboring households (nr. 475 and 477). Another relative of her sons (the Svajger-children), Samuel Svajger also lived in the neighborhood (nr. 490, Széchenyi Square). Samuel Svajger was also a textile merchant (haberdasher). Adolf Hell, another haberdasher and relative, lived at nr. 498. Kinship and family ties also influenced business behavior. The marriage between the Svajger and the Hell merchant families promoted accumulation of capital, while it decreased competition. At the same time, the relative closeness made it easier for members of the families to provide care for widows, orphans etc.

Spatial Patterns: Religion, Occupation, Population Density

Though the town was depicted as a melting pot, the Jewish community had not been granted full rights in all fields of life in the 1860s. This naturally raises a question. Was there was any segregation observable between religious communities despite the diversity? Based on the map illustrating the religious distribution of the population (Figure 2),20 Jewish households were concentrated in the center of the city (they did not own the houses, but rather rented them from the local protestant elite). These houses were predominantly located at some of the major crossroads (Óhíd Str., now Dózsa Str.; Újhíd Str., now Rákóczi Str.; and Malom Str., now Munkácsy Str.) which ran perpendicular to the main road, which led in a north-south direction. Despite the presence of some clusters of houses inhabited exclusively by Jews21 and the prohibition of interreligious marriages between Jews and Christians at the time, we cannot speak about the segregation of Jews for two main reasons. First, the area of the settlement in which Jews lived in high concentrations included the road where the local elite lived and the major scenes of urban life (community spaces, administrative buildings) took place. The presence of Jewish residents of the town was also traced in the secondary main road leading eastward through the Ronyva-bridge, which means that they were integral part of the town. The fact that Jews were able to pay the high prices for rental properties in the center of the town and that the families of the elite lived alongside Jewish families (see the example of the Evva family) means that (1) the Jewish society (or societies) was a differentiated one and (2) the elite tolerated their presence, because Jews served as significant source of income for the traditional local elite, which refrained from capital investment in industry. The second reason is that still there were intersections and blocks of a religiously mixed character. 22

Calvinists lived in houses along the main streets running north to south. Some of these streets bear the names of traditional handicrafts (Gubás Str., now Esze Tamás Str.). Thus, protestants living in homes on these streets represented the imprints of the traditional socioeconomic structure (and this also reflects their once higher proportion and prestige within the population). Their spatial pattern originally showed a continuous line along the main road, but this was broken up by 1870, and the rich Calvinists (based on population/room, total number of rooms, etc.) in the city center became separated from the Calvinists craftsmen who belonged to the lower middle-class.

Greek Catholics lived in the northern and southernmost outskirts of the town, near the vineyards (which lay to the north and northwest) and the arable lands (which lay to the south). This clearly indicates their sectoral distribution and social position. Most of them were agrarian wage laborers or craftsmen of less prestigious occupations. Roman Catholics were abundant in the city center (mixed with Protestants) and on the fringes, which indicates advanced social differentiation among them. Jews also had a lower-class layer located on the outskirts, which was separated from the richer layers.

To summarize, though there were relatively homogeneous blocks or street sections (the Jewish blocks in the center, the streets in the north and the southeast—Kis Pazsic, Baracz—which were dominated by Greek and Roman Catholics, and the quarter inhabited by Protestant craftsmen in the south), segregation was not as characteristic of Sátoraljaújhely as it was of Bonyhád, for example.23 The spatial differentiation among people who belonged to different religions or denominations and people who pursued different occupations was advanced by 1870 and this differentiation was more based on social position than on the denominational differences. Interreligious marriages constituted 15 percent of the total, 24 though half of these took place between Greek and Roman Catholics and 23 percent between Roman Catholics and Calvinists. Houses were often inhabited by families belonging to different denominations, and sometimes even the distribution of markets was observable: the Jewish butcher shared a house with a Greek Catholic bacon-maker. This strange phenomenon drew our attention to another one: among butchers, Jews were overrepresented. They met the demands of their co-religionist population, but also those of other denominations. This indicates practical trust and reception of Jews in our interpretation, who were also overrepresented among merchants (Figure 3). Another (rather symbolic) sign of their emancipation was the fact that Jews and Greek Catholics (the latter constituted the poorer half of society) were also found among the urban and county officials (represented by 1-1 scribe), who were primarily Calvinists (Figure 9).

As for the spatial pattern of occupations, our general observation is that industrialization was not yet advanced enough (two years before the abolishment of guilds) to ruin traditional old structures completely. Tanners still lived along the Ronyva River, as water was essential to their craft. Their downstream and upstream concentration was also not surprising. Because of the stench (a by-product of their work), they were pushed out from the surroundings of the bridge across the Ronyva, which functioned as the main supply route leading to the town’s railway station. Tanners who were living downstream along the Ronyva did not affect the urban neighborhood negatively with their activity. The craftsmen who made heavy mantles lived mainly in the street named after them in the south (“Gubás,” from “guba,” a term used to refer to a mantle made of wool or felt) and in the north (dominated by the poor), and they were mostly Greek Catholics (for their relative wealth, see Table 22). Bootmakers, who were primarily Calvinists, lived in the southern districts on a “hidden” road parallel to the north-south main road, but many of them also lived on the western fringes called Zsólyomka, which was also among the poorer districts. Joiners (middlemen, based on Table 22) lived scattered and evenly dispersed, while butchers were lived to the west of the main road (no butchers lived in the northern districts). Tailors lived around the town center (Figure 8).

Investigations (discussed later in detail) proved that the location of the residences of people who pursued different occupations (i.e. the distance from the functional center of the town) correlates with the people’s wealth or social prestige. Urban and county officials lived along the north-south axis (teachers, school inspectors, state attorneys, judges, crown counsels, prosecutors), surrounded by representatives of freelance professions25 (pharmacists, architects, vets, doctors, goldsmiths, private lawyers, house owners). The outer circle of the town center was dominated by assistant officials, clerks (urban, financial, insurance, postmen, policemen) and by financial experts (banking). This was followed by the zone which was inhabited by craftsmen and the outermost circle, which was inhabited by agrarian workers (Figure 8). (Servants and agrarian daily wage-laborers dominated in the northern districts, the southeastern parts of the settlement, and the west, in Zsólyomka.)

Inns, mansions, and restaurants were concentrated in the center or around the bridge over the Ronyva and in the western parts of the town near the vineyards and arable lands, from where daily-wage laborers returned tired and thirsty day after day. The first houses along the streets leading to the town also functioned as inns or restaurants to offer shelter to those who arrived on foot or by cart from the surrounding regions. (The persistence of these suburban inns indicates that railway had not yet modified the traffic patterns; Figure 8). Merchants were concentrated in the town center and the west-east road leading to the Ronyva bridge, while shopkeepers (including chandlers and grocers) targeting different layers frequently lived in the eastern and western outskirts along the main roads leading to the arable lands.

 

 

 

 

 

 

 

Figure 4. The spatial pattern of population density (person/room) in Sátoraljaújhely in 1870

The Social and Religious Composition of Migrants

In urban environments, the role of natural reproduction in population growth has usually been smaller than that of migration. Even in the introverted Eger, which had an increase in its population of only 40 percent between 1825 and 1900 (the population of Sátoraljaújhely tripled over the course of this period), more than 75 percent of the increase was the result of migration, as the natural growth rate until 1873 was critically low (demographic pattern was characterized by high mortality beside the and a high birth rate).26 In Sátoraljaújhely, the main source of population growth was also migration, which played a key role in the transformation of the city’s character.

The transformation of traditional structures can also be examined by measuring the frequency of migrant intermarriages (and the spatial pattern of migrant intermarriages) alongside the frequency of religious intermarriages or the spatial pattern of occupations. (The latter two can also indicate theses transformations: a dispersed spatial pattern usually indicates the dissolution of original structures). Altogether, 33 percent of family heads were indigenous to the settlement, while the proportion of local-born wives was somewhat higher, reaching 45 percent. This means that the male population was more mobile and also that local-local marriages could not have been more than 30 percent in the town.27 In contrast, in the more traditional southern districts (note the abundance of guildsmen occupying certain jobs niches based on religious differences), which comprised 33 percent of the households, marriages between local born males and females reached 50 percent (178 cases). This indicates a higher degree of introversion in this district of the town. On the other hand, immigrant-immigrant marriages were overrepresented in the north. The latter indicates the belated integration of certain layers. Immigrant-indigenous marriages had no spatial pattern.

The changes in religious proportions also refer to transformations. The proportion of Calvinists decreased from 18 percent in the 1840s below the country average by 1870,28 while that of the Jews increased from 17 percent to 35 percent (their share among children was even higher, 42 percent in 1870). It fell back to 29 percent by 1910. (The increasing presence of Jews usually indicated industrialization and the emergence and spread of capitalism in Hungary). The proportion of Greek Catholics gradually decreased from 23 percent to 15 percent, which, given their primary occupations (for the most part, they were agrarian wage laborers and low-prestige craftsmen and artisans), also indicates transformations in general (Table 12).

These changes were partly driven by the changes in migration patterns and social strategies and partly by the different birth rates of the different denominations. Our database offers possibilities to estimate the role both of migration and natural growth rate for religious communities, and to reconstruct the social strategies of classes and denominations.

 

Table 12. The change in proportion of religious denominations in Sátoraljaújhely between 1840 and 1910

Year, %	R. Cath.	Greek Cath.	Calvinist	Lutheran	Orthodox	Israelite	Altogether
1910, prs	7,936	2,943	2,878	381	34	5,730	19,902
1910, %	39.9	14.8	14.5	1.9	0.2	28.8	100
1870, prs*	3,335	1,676	1,195	155	12	3,215	9,946*
1870, %	34.5	17.0	12.5	1.6	0.1	33.5	100
cca. 1840, prs	2,401	1,464	1,174	120	26	1,125	6, 310
cca. 1840, %	38.1	23.2	18.6	1.9	0.4	17.8	100

* only 9587 known cases.

It is not surprising that the proportion of immigrants was higher among the cohort of 20-30 year old (over 65%), than among the inhabitants between 50 and 60 years (50%). More interesting conclusions can reached when investigating the subsets of the social classes, occupation groups, and denominations. The proportion of indigenous people exceeded the urban average only among the Jewish family heads (45 percent) and their wives, so the Jewish community must have been the most insular. This is surprising compared to old topoi and their behavior in other towns.29 The growth in numbers was the result of the high internal reproduction rate (an average of 2.4 children/Jewish Wohnparthey) and not of immigration (Table 11). The decrease in the proportion of Jews in the town after1870 (Table 12) despite the high number of children may indicate that Jews reached the “saturation point”: the town as a market did not have a demand for the professions typically practiced by Jews at that stage and pace of development, and this made it less appealing for potential Jewish immigrants and increased competition for the niches among the different factions.30

In contrast, Lutheran family heads were dominantly immigrants. Many of them were foreigners with special skills and occupations who came as experts to meet the demand generated by industrialization, which Hungarian schools were not yet able to cope with. The number of Lutherans in the town tripled between 1840 and 1910, a pace of growth which equaled the average growth rate of the whole town. The average number of children among them was only 1.8, which means that migration played a larger role than natural growth. (On the other hand, Lutheran family heads were somewhat younger than the average, as were Greek Catholic family heads, and this also explains the low birth rate within their households).

Among the Greek Catholic family heads, the proportion of newcomers was 75 percent, thus the gradual decrease in their share of the total population can be explained by their low birth rate (an average of 1.4/Wohnparthey in 1870) and by religious intermarriages. They were also relatively poorly off from the perspective of their social situation (the proportion of Wohnpartheys with only one room or less was the highest among them). The proportion of indigenous Roman Catholic family heads (compared to local Roman Catholic family heads) was also below the town average. The Calvinists tried to “balance” their bad demographic indicators (an ageing society with less than the average number of children) by relying on immigrants. Regarding the origins of wives and husbands, there was a great difference measured in the case of both Roman Catholics and Calvinists: mainly the men were newcomers, while most of the wives were local born inhabitants (Table 13).

Considering the group of coworkers and employees31 the share of Jews reaching 25 percent was well below their proportion measured among family heads and wives. This means, based on the general character of this social category comprising dominantly craftsmen,32 that among Jews, the significance of traditional guild-industry was of secondary importance. Though after 1848, Jews were allowed to work in guilds, they still tended to take other occupations. The proportion of Calvinists among employees (18 percent) was higher than their share of the total city population (12–13 percent), which implies a more traditional social structure and a strategy differing from that of the Jews. In the case of the Calvinists, employers showed a preference in their selection of employees/coworkers for other Calvinists. This preferential cooperation meant that a Calvinist guildsman was more likely to choose a Calvinist apprentice. This does not imply exclusiveness, however. Calvinists also hired Roman Catholic apprentices. This also meant that the children of lower middle-class Calvinists were more likely to turn to handicrafts than to pursue other occupations, and they were more likely to pursue these crafts than the children of Jews and Lutherans. These differences in strategies based on religion/denomination indicate the persistence of old structures.

Among the social group of servants, the proportion of Greek and Roman Catholics (26 and 41 percent respectively) exceeded their share of the total population, while Calvinists (9 percent) and Jews (15 percent) were underrepresented. This also reflects the different strategies they adopted in the pursuit of a livelihood. Jews, for example, tended to employ non-Jewish immigrants as servants, much as Calvinists tended to employ non-Calvinists.

Among employees and coworkers (without their own Wohnparthey), the proportion of local-born (except for the Jews with their 51 percent) remained under the city average (40 percent) (Table 13). The high share of local-born Jews among employees also indicates an insular society and a strategy differing from that of the Christians. In contrast with Jews, Calvinists preferred immigrants as coworkers and employees. The proportion of Roman Catholics among immigrant employees reached 40 percent (overrepresented compared to the proportion of Roman Catholic family heads and their wives). The share of Calvinists reached 22 percent (also overrepresented, much as Greek Catholics were too, with their 22 percent), while the proportion of Jews in the town remained around 20 percent. In contrast, in the whole set of coworkers and employees (including indigenous and immigrant), Roman and Greek Catholics were underrepresented compared to their share of the total population (24 percent vs. 33 percent of family heads and 11 percent vs. 17 percent of family heads, respectively). This means that the proportion of indigenous Greek Catholic employees was small and also that their proportion was high among servants. In the case of these two denominations, low-prestige fieldwork dominated among immigrant employees (as their geographic location within the town confirmed earlier).

Among the local-born servants and housemaids, Roman Catholics were overrepresented (while among employees they were underrepresented). 85 percent of the servants and housemaids were immigrants, which indicates that the strategy of local-born, lower-class/declassed people aimed to avoid these lines of work by becoming apprentices or coworkers. Among newcomer servants, Greek Catholics comprised 26 percent (a higher value than their share of the total urban population), while Jews reached only 15 percent (Table 14).

 

Table 13. The proportion of immigrants among occupational (family head-earners; employees-coworkers; servants and maids) and denominational groups

Family-heads*	Total persons	Local-born (%)	Local-born (%)	Wives	Total persons	Local-born (%)	Local-born (%)
Lutheran	41	12.2	0.7	Lutheran	33	27.3	1.1
Gr. Cath.	373	24.4	12.5	Gr. Cath.	309	33.0	12.6
Jew	692	44.5	42.5	Jew	619	47.3	36.2
Orthodox	3	33.3	0.1	Orthodox	5	60.0	0.4
Calvinist	302	35.8	14.9	Calvinist	193	60.6	14.4
R. Cath.	735	28.8	29.2	R. Cath.	552	51.6	35.2
Altogether	2147	33.8	100	Altogether	2147**	37.7	100

Coworkers, employees	Total persons	Local-born (%)	Local-born (%)	Servants, maids	Total persons	Local-born (%)	Local-born (%)
Lutheran	10	20.0	0.8	Lutheran	8	0.0	0.00
Gr. Cath.	109	24.0	10.8	Gr. Cath.	135	9.6	21.6
Jew	146	51.4	31.4	Jew	80	12.5	16.6
Calvinist	110	25.5	11.7	Calvinist	50	10.0	8.3
R. Cath.	212	27.0	23.8	R. Cath.	216	14.4	51.6
Altogether	600	40.0	100	Altogether	520	11.5	100

* Including widows (women) registered as family-heads.

** The difference between the number of Wohnparthey and the partial sums is due to the cca. 200 widows and widowers (10%) divorced and yet not remarried.

 

Table 14. The distribution of immigrants (%) based on religion and social groups

	All family heads as a %	Immigrant family heads as a %	Immigrant wives as a %	Immigrant employees as a %	All employees as a %	All servants as a %	Immigrant servants as a %
Lutheran	2.0	2.7	2.7	2.2	1.6	1.5	1.7
Gr. Cath.	17.0	19.8	22.2	21.0	18.1	26.0	26.5
Jew	33.2	27.0	36.3	19.6	24.3	15.4	15.2
Calvinist	12.5	13.6	8.4	21.7	18.3	9.6	9.8
R. Cath.	35.1	36.8	29.6	41.0	35.3	41.5	40.2

The theoretical aggregated value in columns is 100% – differences are due to lack of data and rounding errors.

Social stratification of immigrants

With regards to the social elite (the methods according to which we have defined this group and identified the people who belonged to it are discussed later), in the case of family heads, 25 percent were born in Sátoraljaújhely. In the case of wives, this figure was a bit higher, 33 percent. This indicates the generally smaller horizontal mobility of women at time. Compared to the figures in the city of Eger, this still indicates an open society.33 Among the lower-class and deprived (for instance agrarian wage laborers and washerwomen, sewers, bread-makers, etc.), the proportion of local-born people was also low, around 30 percent (in the case of their wives, it was 37 percent), while in the case of the middle class (for instance merchants, innkeepers, shopkeepers, and chandlers), the figures were 40 and 48 percent, respectively. In the case of landowners, the proportion of local-born urban dwellers was around 50 percent, and in the case of people earned their livelihoods doing handicrafts, it was similarly high (41–58 percent). Thus, the latter two occupational groups can be considered the basis of the indigenous middle-class (Table 15).

 

Table 15. The proportion of local-born husbands and wives in 1870 in Sátoraljaújhely

Group	Husband (persons)	Wife (persons)	Husband, (local) %	Wife (local), %
elite, official elite, freelance professions	59	81	25	33
merchants, chandlers	140	166	40	48
artisans, craftsmen	278	396	41	58
poor, lower-class (cartmen, footmen, sewers, rag-pickers, washerwomen, itinerant merchants, etc.)	156	208	30	36
smallholders and large estate owners	54	57	46	49

The abovementioned “openness” of Sátoraljaújhely (which is a feature of towns which were becoming increasingly industrialized) is indicated by another fact: among the immigrant earners, the share of those who belonged to the elite was higher than among the local-born society (Table 16), in contrast with the situation in Eger.34 In Sátoraljaújhely local-born earners were overrepresented within the middle class, while lower layers were dominated by newcomers. However, the proportion of immigrants working in the agrarian sector did not exceed the proportion of local-born working in the same sector. From the perspective of their numbers and their share of the total population, newcomers were overrepresented among the industrial and tertiary low-wage earners.

The comparison of earners in the comparatively secluded city of Eger (a nearby county seat), the small town of Varannó (Vranov; a district center in Zemplén County), and Sátoraljaújhely (the county seat of Zemplén) yielded interesting results (Table 16). The lower middle class was the largest in the traditional Eger (this was particularly true of the autochtonous population), and the lower classes and middle class were both thinner (partly because of the larger lower middle class, partly because of the lack of industrial workers). The elite was also the broadest in Eger (15–20 percent vs. 3.5 and 7 percent; with its Lyceum, the town was able to reproduce its intelligentsia),35 despite the smaller significance of the elite among immigrants.36 In Varannó, the lower class was thin among immigrants, while among the autochtonous population lower layers were underrepresented).37

 

Table 16. The social stratification of the earners’ society in Eger,
Varannó and Sátoraljaújhely towns

Layer	Varannó, total (%)	S.újhely, total (%)	Eger,* total (%)	Varannó, migrant (%)	S. újhely, migrant (%)	Eger,* migrant (%)	Varannó, local-born (%)	S. újhely, local-born (%)	Eger,* local-born (%)
Elite	7.1	3.4	20	8.1	3.8	12	5.8	2.5	22
Middle	48.3	41	33	40.8	36	49	58.2	50	25
Lower middle	6.1	3.5	24	8.6	3.3	12	2.9	5	28
Lower	38.5	52	22	42.5	58	25	33.1	39	20
Total (prs)	100% (720)	100% (2,656)	100% (800)*	100% (409)	100% (1,783)	*	100% (311)	100% (873)	*

Social stratification based on Ferenc Erdei’s theory of “staggered society” and the prestige of occupations according to Max Weber.

* Data for Eger are from 1883 based on marriages in parish registers (sample size cca. 250. The town was predominantly Roman Catholic)

Sources for Sátoraljaújhely and Varannó: MNL-BAZML SFL XV. Census of 1870;

Source for Eger: MNL-HML IV-416. Marriage registers from 1883.

 

Table 17. The representation of migrants in different social layers of Varannó and Sátoraljaújhely

Layer	Immigrants (%) of the layer, Sátoraljaújhely	Immigrants (%) of the layer, Varannó
Elite	74	65
Middle	60	48
Lower middle	62	80
Lower	75	63
Total	67 (1,783 immigrants)	57 (409 immigrants)

Measuring Wealth and Social Differentiation:
Methods, Spatial Patterns and Internal Differentiation Among Layers

In order to illustrate both spatial patterns and the distribution of wealth among social groups, wealth levels first had to be quantified. As income data were not available, we had to rely on the indirect census data referring to wealth. Because of this, the relevance of our investigation is limited. In order to reduce the subjective elements when classifying the single families into social groups, three different methods were tested.

The first method was based on Marxist sociologist and politician Ferenc Erdei’s concept of the so-called “staggered society.” Erdei contended that, in Hungary, each traditional class had a modern, capitalistic variant, and these variants existed in parallel and coalesced only gradually. We combined this theory with Max Weber’s classification based on the social prestige of given occupations. Though Erdei’s theory has been challenged and the classification based on Weber is considered too subjective, abandoning these old classifications and relying only on modern ones would render our investigations incomparable with old results. The results of this classification, including a sectoral distribution too, can be seen in Table 18 and 19.

 

Table 18. Social groups based on Erdei’s model of a “staggered” society and on the prestige of occupations (Weber) (method 1; prs and %)

e1	town and county elite	lawyers, chief clerks (state servants)	47	2.2%2
f	landowners	mainly middle estate owners	116	5.4%
p	freelance civil professions	teachers, doctors, railway engineers, photographers, clockmaker	91	4.2%
h	officials	state (lower class compared to ’e’) and private (in banking and finances)	108	5%
g	agrarian experts	not independent but highly skilled agrarian wage-earners	34	1.6%
n		policemen, pandurs, postmen, etc.	30	1.5%
kk	merchants	innkeepers, railway entrepreneurs, merchants	216	10.1%
k, ka		lower financial officials (clerks), poor merchants, chandlers, grocers	151	7.0%
m	craftsmen	guild members: tailors, potters, bootmakers, etc.	677	31.5%
q	lower tertiary	transportation: cartsmen, waiters	60	2.8%
s	poor	daily wage earners in agriculture, beggars, bakers (women), washerwomen, scrap-iron collectors	508	23.7%
ö	widows		101	4.7%

Layers wealthier than the city average are indicated by grey.

1 Abbreviations used in maps and in charts.

2 This table did not contain data on 1,100 coworkers and 700 servants, thus the percentage values refer to 2,150 people and not to 4,000.

 

Table 19. Hypothetic social stratification based on the prestige of occupation
(family heads; %)

Group	Agrarian	Industrial		Tertiary	Private tertiary	Altogether	%
Upper	f (116)			e (47)	p (91)	cca. 250	12%* (7%)
Middle	g (34)	m (677)	kk (30)	h (108)	kk (190), h	cca. 550	25% (25%)
Lower middle				n (30)	k (132)	cca. 500	23% (25%)
Lower	s (343)				s (160), q (60)	570 + some craftsmen = 800	38% (43%)
Total	cca. 500	cca. 700		cca. 200	cca. 600	cca. 2100	+101 widow households
%	25%	35%		10%	30%	100%

* Servants or coworkers not registered as family heads were omitted. See corrected % values including these layers in brackets.

These categories do not strictly refer to wealth or social status. Group “p” was traditionally considered as the part of the elite, although the wealth and economic power of the civil professions (including state teachers) was significantly weaker than that of groups “f” (landowners) and “e” (official-bureaucratic elite) based on number of rooms and the other two classification methods described later. Category “f” was also not homogeneous regarding wealth. Smallholders and large estate owners were also included here because of the lack of census data concerning estate size. Freelance civil professionals and state clerks were underrepresented in Sátoraljaújhely compared to other towns with similar functions, where their proportion exceeded 15 percent of the earners. Compared to this, the layer of merchants (kk, k) was quite strong (17 percent), possibly as the result of relatively high number of Jews in the town and its geographical location. The proportion of craftsmen (m) was high, but not remarkably. The same percent was measured in the larger city of Debrecen.38

The sectoral distribution of these groups is given in Table 18b. 35 percent of the family heads were involved in industry, but modern industrial branches were represented only by some 10 percent of the total family heads involved in industry. Guilds still dominated in this transitional period. The private tertiary reached 30 percent, reflecting the transformations (urbanization), while agriculture had already lost its dominant position (25 percent).

The second classification was based on quantifiable socioeconomic indicators derived from the census sheets (number of rooms, auxiliary buildings, number of servants, number of employed workers, household size). We used an equation to aggregate the values of the single indicators for all families, resulting in a dimensionless number, which refers to the per capita economic potential of the family. Based on the method of natural breaks, the 2,147 Wohnpartheys/families were divided into 13 groups of different sizes. The aggregated values in group 9–13 (comprising 30 percent of the households) exceeded the total town average (Table 20).

Table 20. The sociodemographic features of the 13 “social groups” (i.e. groups with different levels of wealth) defined by the method based on the equation using socioeconomic indicators (values above the average are indicated by bold letters: the average represents intergroup differences, standard deviation represents within-group differences)

Social group based on equation		Average number of children	Average number of servants	Household size	Proportion of earners	Average number of rooms	Average inhabitants per room
1 (127, 6%)	Mean	2.09	0.01	4.07	0.29	0.51	7.84
	St. Dev.	1.60	0.09	1.73	0.20	0.39	3.61
2 (140, 6.5%)	Mean	2.24	0.01	4.32	0.28	0.81	5.31
	St. Dev.	1.75	0.12	1.90	0.19	0.30	1.63
3 (233, 11%)	Mean	2.26	0.03	4.37	0.24	0.99	4.70
	St. Dev.	1.50	0.20	1.60	0.10	0.29	2.43
4 (258, 12%)	Mean	1.65	0.04	3.81	0.33	1.06	3.60
	St. Dev.	1.62	0.20	1.91	0.19	0.37	1.51
5 (158, 7.5%)	Mean	2.36	0.11	4.63	0.28	1.20	4.10
	St. Dev.	1.77	0.32	1.92	0.16	0.49	1.65
6 (203, 9.5%)	Mean	1.87	0.11	4.17	0.33	1.22	3.52
	St. Dev.	1.89	0.33	2.19	0.15	0.49	1.62
7 (264, 12%)	Mean	1.43	0.18	3.64	0.45	1.36	2.75
	St. Dev.	1.73	0.40	2.24	0.30	0.58	1.64
8 (104, 5%)	Mean	1.94	0.36	4.55	0.35	1.60	2.91
	St. Dev.	2.00	0.59	2.55	0.20	0.77	1.50
9 (164, 7.5%)	Mean	1.63	0.37	4.37	0.39	1.78	2.64
	St. Dev.	1.62	0.59	2.42	0.25	0.83	1.58
10 (151, 7%)	Mean	1.28	0.49	3.90	0.43	1.95	2.10
	St. Dev.	1.61	0.70	2.33	0.27	0.77	1.39
11 (83, 4%)	Mean	1.51	0.70	5.01	0.42	2.17	2.52
	St. Dev.	1.69	0.79	2.95	0.30	1.07	1.65
12 (99, 4.5%)	Mean	1.60	0.88	5.14	0.41	2.59	2.18
	St. Dev.	1.70	0.97	2.99	0.29	1.28	1.45
13 (162, 7.5%)	Mean	1.69	1.87	6.57	0.37	3.73	2.04
	St. Dev.	1.89	1.62	3.87	0.26	1.66	1.64
Total (2,149)	Mean	1.81	0.34	4.39	0.35	1.53	3.50
	St. Dev.	1.74	0.80	2.45	0.23	1.09	2.28

The third classification was also based on a quantitative approach using the same socioeconomic and demographic indicators, but this time automatic cluster analysis was used. (The subjective element here was the setting of cluster numbers. The reliability of this method was validated by discriminant analysis). As this classification did not contain family size as a variable, the results indicate the economic potential of the Wohnparthey as a whole.

Though automatic classifications usually lack any preconception (unlike method 1, based on the prestige of occupation), groups with well-definable social characteristics were generated when applying cluster analysis. Cluster 6, cluster 5, and cluster 1 were easily distinguishable from one another based on their socioeconomic characteristics (Table 21: the success rate of reclassification was above 90 percent here).39 The boundaries of other groups were unconsolidated, fuzzy (groups 2, 3, and 4).40 The fuzzy cluster 2 had one specific, conspicuous, distinctive feature: the proportion of Jews here was over 50 percent, which exceeded the town average (34 percent) and the proportion of Jews measured in other clusters. It seems that automatic clusterization confirmed the existence of the so-called “par excellence Jewish-middle class,” a layer that evolved parallel to the traditional middle class during the process of emancipation and the spread of capitalism, as supposed by Erdei. Its “fuzziness” indicates its transitional, unconsolidated character (as well as its wealth conditions), which also reflects its potential for assimilation to other groups.

 

Table 21. General sociodemographic characteristics of groups created by automatic clusterization of households

Cluster 6:	the poor: high children ratio, low proportion of earners, number of rooms under one
Cluster 5:	the poor: no servants, small household size (3 prs!), number of rooms around one
Cluster 1:	the rich: more than 2 servants, a low proportion of earners (0.2 – contrary to groups defined by the previous method, where it was over 0.4 – revealing that the two methods of defining the elite are not equivalent!), number of rooms around 4
Cluster 2:	the proportion of Jews within the group is over 50%: ’par excellence Jewish middle-class’

To test the correspondence/overlap of the three methods, a cross-tabulation matrix was created, which proved that, although there was a 70-70-70 percent overlap between the results of the 3 methods and the correlation coefficient was higher than 0.7, the three classifications are not equivalent (Figure 6). For example, the richest three groups (11–13) consisted of 341 families (15 percent) in the case of the second method (i.e. the equation referring to per capita economic power), while the richest two clusters comprised 332 family heads (the third method), but only 192 of the cases were common (60 percent).41 This means that the interpretation of the results is not independent from the selected method. Thus, in order to avoid preconceptions during generalization (i. e. the classification of earners into “social groups”), the economic potential was calculated for the different occupations as grouping variables, too (Table 22). Lawyers and doctors (33 persons), the thin layer of engineers and entrepreneurs, the 60 merchants, and the 60 innkeepers proved the wealthiest according to all three different calculations (see rankings in Table 22), though their household structure was quite different (for instance the number of children, proportion of earners, etc.).

Was social differentiation advanced at the time? According to Williamson, income inequalities (including both spatial and social differences) regularly grew in the first stage of capitalist transformations. Due to the lack of income data, we cannot test the relevance of this thesis. But based on “complex economic potential” calculated on the basis of the equation comprising socioeconomic indicators, some sort of social differentiation became measurable. The richest 15 percent of the Wohnpartheys comprised 20 percent of the cumulative wealth (for the sake of comparison, this figure could reach 40 percent in Ottoman towns in the eighteenth century).42 The second richest 15 percent was not significantly poorer than the first group. Altogether, one-third of the families (750) had higher per capita economic potential than the city average, and they accounted for 50 percent of the total wealth. The poorest 50 percent shared 25 percent of the total calculated wealth (see Figure 5 and compare it with the differences observed between the wealth levels and sizes of groups “e” and “s” in Table 18). In other words, the richer 50 percent of the population was three times richer than the poorer half. This inequality is not considered great compared to other regions in the world at the time.43

Figure 5. The distribution of economic potential (vertical axis) between groups of families (horizontal axis) as a %

The society was quite differentiated even based on single indicators, such as number of rooms, which indicated differing levels of wealth. Only 22 percent of the families had two rooms, and only 10 percent had three or more rooms (Table 6). On the other hand, the average 1.5 room/family is not greater than the value measured in Belgrade after 1900.44 While the average population density was 3.5 persons/room (and in 25 percent of households there were four or more inhabitants per room), in wealth groups 9–13 (representing 15 percent of Wohnpartheys), this improved to 1.5 person/room.45

The classification results also confirm, that our pre-defined categories (method 1: based on the prestige of occupation) “e,” “f,” “kk,” and “h” are considered the richest, followed by “p.” Thus, our preconception is not flawed (Table 23). The minor differences between the cluster-based and equation-based classification are due to the fact that the latter measures total wealth of a family regardless of family size. Group “f” is considered poorer if per capita wealth is calculated (instead of household wealth), because agriculture was (and remained) a labor intensive sector in Hungary, traditionally characterized by larger family size.

As for the internal differentiation among these groups, 90 percent of family heads had two or more than two rooms in group “e.” This figure was 60 percent in group “f,” 70 percent in groups “kk” and “Hungary,”46 and only 40 percent among households in category “p” (freelance professions).47 In the case of layers “s,” “q,” and “n,” 60 percent of the families were classified into the poorest four categories (1–4), while this was under 10 percent among inhabitants grouped into categories “kk,” “f,” “p,” “e,” and “h.” In these latter categories, the wealthiest four (9–13) constituted 40–70 percent of these groups (Figure 6). This figure reached 70 percent in group “e” (official-bureaucratic elite) and only 40 percent in group “p” (freelance professions).

These data also reflect the weakening of the traditional agrarian elite (or the fact that smallholders were also included in this group), but the merchant elite was not yet strong enough to take over the positions of the bureaucrats. The agrarian elite successfully transformed its economic power into political power, while the positions of people with freelance occupations were relatively weak compared to those of the state bureaucracy. As groups 9–13 represent a broad swath of more than 600 hundred families, it is not surprising that some artisans (20 percent) also appear in these aggregated groups.

 

Table 23. The rankings of the social layers pre-defined by prestige of occupation – using the two different statistical classification methods (cluster-based; equation-based)

	e (47)	h (108)	f (116)	kk (214)	p (91)	ö (101)	Total (2149)	k (132)	m (677)	g (34)	q (60)	n (30)	s (508)
average cluster membership	2.45	2.8	3.2	3.06	3.71	3.85	3.93	3.91	3.97	4.21	4.49	4.48	4.75
ranking	1	2	4	3	5	6	8	7	9	10	12	11	13
average equation-­based wealth	4.52	2.85	2.57	2.12	1.84	1.81	1.49	1.41	1.33	1.04	0.83	0.82	0.66
ranking	1	2	3	4	5	6	7	8	9	10	11	12	13

Compare with Table 22. The numbers in brackets represent the family heads classified into the group.

Figure 6. Internal differentiation among social groups based on the prestige of occupations

Groups 1–4 refer to poor, groups 9–13 are wealthier than the average.

Spatial Pattern of Wealth and Social Classes

We have already investigated the spatial pattern of religions and occupations, but the spatial pattern of wealth also shows interesting features. The town was generally characterized by a concentric center-periphery accommodation pattern. This is true both for social groups (first method) and wealth classes. The wealthiest families lived along the main street of the town, which formed a north-south axis (Figure 7). Perpendicular to this street another road led to the east across the Ronyva River, where the concentration of rich people was also higher compared to other parts of the town. Based on the complex indicator of wealth, the northernmost and southernmost districts were inhabited by the poor. The map showing the social classes (based on the modified Erdei-model, Figure 8) and the map illustrating the number of rooms per family (used as a proxy for wealth) also confirms this phenomenon. The picture becomes more complicated if population density is illustrated on the map (Figure 4), 48 because one can find both large and small families among both the rich and the poor. In other words, the correlation between the size of the Wohnparthey (or number of children) and wealth was insignificant. On the contrary, based on these maps, there seemed to be evident connection between wealth and certain religions (Figure 2 and 7; Figure 9) and between wealth and occupation (Figures 7, 8, and 15). These variables were previously omitted from the investigations as they were not quantifiable. In order to measure and compare the relative wealth levels of different religious communities and occupations, a statistical analysis was carried out (Table 23).

With regards to religious differences, the Protestants (both Calvinists and Lutherans) had the greatest economic potential, followed by Jews (Figure 9). Greek Catholics were poorer than the average. Differentiation within the religious groups also advanced by 1870. Standard deviation values were high (there were poor artisans among Protestants and beggars and scrap-metal collectors among Jews). Protestants were overrepresented within category “h,” while Jews were overrepresented among members of group “kk” (both constituting the part of the elite). Within group “e” and group “f,” no similar trends could be observed (Figure 13). The differences in population density (persons/room) regarding religions were also significant (Figure 10). Age also influenced wealth (Figure 11).

Figure 13. Differences in religious composition of different occupation groups
(based on the Erdei-Weber method)

 

Figure 14. Differences in religions regarding the number of rooms / Wohnparthey

 

Figure 15. Internal differentiation among occupations based on number of rooms

Summary

To summarize our results, the GIS-aided evaluation of the 1870 census sheets managed to bring a new approach (an examination of various social divisions from the perspective of settlement patterns) into Hungarian urban and social history. HGIS contributed to the reevaluation of debated questions (the existence of a Jewish middle class, the transformation of the elite, the shift of power from the old agrarian elite, spatial segregation of Jews, the extent of amalgamation of emerging capitalist social divisions and the traditional classes, etc.). Some phenomena formerly investigated through individual case studies were statistically verified. We managed to reconstruct the accommodation pattern of the town in the beginning of the period of industrialization, and we also succeeded in tracing persisting and transforming elements regarding the location of occupations (tanners lived near water, bootmakers were concentrated in one street in the southern quartier) and the marriage behavior of different communities. The role of migration in the transformation processes was examined in a comparative context (by analyzing the immigrant and host societies of three towns), and the participation of different occupational and religious groups in this was also traced, along with their strategies. At the same time, we tried to utilize the hidden pontentials of the 1870 census by creating new sociodemographic indicators (proportion of children/family; proportion of earners/family; population density measured by inhabitants/room, room/family, etc.) and to measure the wealth or economic potential of the households. We tested three different methods to classify the population into social groups, and the three methods yielded partly corresponding results. The spatial patterns of the investigated sociodemographic phenomena and indicators were also mapped.

The core of the elite can be described as the common set of the three different methods (190 households). Altogether a maximum of 15 percent of the households could have been said to have belonged to the upper class. We defined the local elite as households with three rooms or more and two servants/coworkers. Protestants were overrepresented among them, but their positions were declining, and they were bound to the traditional official-bureaucratic elite. The new capitalist elite, composed of Jewish merchants, entrepreneurs, and Lutheran engineers was still weak in 1870. Despite their physical closeness of these two groups (living in the same streets), they did not really begin to amalgamate.

 

Archival Sources

Magyar Nemzeti Levéltár Borsod-Abaúj Megyei Levéltárának Sátoraljaújhelyi Fióklevéltára [Hungarian National Archives, County Archives of Borsod-Abaúj-Zemplén, Archives at Sátoraljaújhely] (MNL-BAZML SFL) XV. and XXXIII.

Magyar Nemzeti Levéltár Heves Megyei Levéltára [Hungarian National Archives, County Archives of Heves] (MNL-HML IV-416)

Bibliography

Barta, János. “Ha Zemplin vármegyét az útas visgálja…” Adattár Zemplén megye 18. századvégi történetéhez [When Zemplén County is investigated by travelers… Material for studying the history of Zemplén County at the end of the eighteenth century]. Vol. 2. Speculum Historiae Debreceniense 20. Debrecen: Debreceni Egyetem Történeti Intézete, 2015.

Beluszky, Pál. Magyarország településföldrajza: Általános rész [Settlement geography in Hungary: General part]. Budapest – Pécs: Dialóg Campus, 1999.

Calic, Marie-Janine. Sozialgeschichte Serbiens 1815–1941: Der aufhaltsame Fortschritt während der Industrialisierung. München: Oldenbourg, 1994.

Canbakal, Hülya, and Filiztekin, Alpay. “Wealth and Inequality in Ottoman Lands in the Early Modern Period.” Working Paper, 2013. Accessed on December 30, 2017. http://aalims.org/uploads/Rice_v1.pdf

Dányi, Dezső. “Regionális vándorlás, urbanizáció a XIX. század végén” [Regional migration, urbanization at the end of the nineteenth c.]. In Migráció Tanulmány­gyűjtemény [Collected studies on migration] Vol 1. 87–114. Budapest: KSH Népesség­tudományi Kutató Intézet, 1998.

Demeter, Gábor. “A dualizmus kori Eger lassú népességnövekedésének demográfiai hátteréről” [About the demographic background of the population increase in Eger under Dualism]. Acta Geographica Debrecina 37 (2006): 181–97.

Demeter, Gábor, and Róbert Bagdi. “Sátoraljaújhely migráns és helyi társadalma az 1869. évi népszámlálás alapján“ [Indigenous and migrant societies in Sátoraljaújhely based on the census data of 1869]. Történelmi Szemle 57, no. 3 (2015): 381–410.

Demeter, Gábor, and Róbert Bagdi. A társadalom differenciáltságának és térbeli szerveződésének vizsgálata Sátoraljaújhelyen 1870-ben [The analysis of differentiation and spatial patterns of society in Sátoraljaújhely in 1870]. Debrecen – Budapest, 2016.

Erdei, Ferenc. “A magyar társadalom a két háború között: A magyar társadalomszerkezet” [Hungarian society in the Interwar period: The structure of society]. In Magyarország társadalomtörténete [The social history of Hungary]. Vol. I/1, edited by György Kövér. 30–40. Budapest: Nemzeti Tankönyvkiadó, 1995.

Gerő, András. Dualizmusok: A Monarchia Magyarországa [Hungary of the Monarchy]. Budapest: Új Mandátum Kiadó, 2010.

Gregory, Ian N. A place in History: A short introduction to HGIS by the lead developers of GBHGIS. Accessed on March 20, 2019 http://hds.essex.ac.uk/g2gp/gis/index.asp

Gyimesi, Réka, and Dániel Kehl. “A Spatial Analysis of the Socio-economic Structure of Bonyhád Based on the Census of 1869. ” Hungarian Historical Review 8, nr. 1. 2019: 28–51.

Katus, László. A modern Magyarország születése – Magyarország története 1711–1914 [The birth of modern Hungary – the history of Hungary, 1711–1914]. Pécs: Kronosz Kiadó, 2012.

Malojčić, Miron. Selo i tuberkuloza [Village and tuberculosis]. Zagreb: Škola Narodnog zdravlja, 1936.

Mazsu, János. “Piac, kereskedelem, kapitalizálódás és piactér Debrecenben a 19. században” [Markets, trade, the spread of capitalism, and marketplaces in Debrecen in the nineteenth century] I-II. Metszetek 2 nos. 3, 4. (2014)

Mazsu, János. “Inside borders: Jewish settlement in banned cities: Jewish immigration in Debrecen (Hungary) in the period between 1790–1870.” AHEA–American Hungarian Educators Association 37th Annual Conference, 26–29 April 2012, Long Island University, Brooklyn Campus, NY.

Mazsu, János. “‘Inside borders.’ Jewish settlement in banned cities: Jewish immigration in Debrecen (Hungary) in the period between 1790 –1870.” Metszetek 7, no. 2. (2018)

Milanovic, Branko, Peter Lindert, and Jeffrey G. Williamson. “Measuring Ancient Inequality.” NBER Working Papers, 13550. Cambridge, MA, 2007.

Őri, Péter. “Család és házasodás a 18–19. századi Magyarországon: Pest–Pilis–Solt–(Kiskun) megye, 1774–1900” [Family and marriage in the eighteenth and nineteenth centuries in Hungary: Pest–Pilis–Solt–(Kiskun) County, 1774–1900.] Korall 8, no. 30 (2007): 61–98.

Pozsgai, Péter. “Görög és római katolikus nemzetiségek házasságainak jellemzői Torna megyében a 19. század közepén” [Marriage patterns among Greek and Roman Catholic minorities in Torna County in the nineteenth c.]. Korall 8, no. 27 (2007): 45–93.

Todorova, M.: Situating the family of Ottoman Bulgaria within the European pattern. The History of the Family 1 no. 4 (1996): 443-459.

Veliky, János. A változások kora. Polgári szerepkörök és változáskoncepciók a reformkor második évtizedben. Budapest: Új Mandátum, 2009.

Vuksanović-Anić, D. “Urbanistički razvitak Beograda u periodu izmedu dva svetska rata (1919–1941)” [Urban development of Belgrade during the Interwar period, 1919–1941]. In Istorija XX. veka. Zbornik radova 9. 458–465. Beograd, 1968.

Sólem Áléchem. Tevje a tejesember, avagy hegedűs a háztetőn [Tevje the Milkman, or Fiddler on the Roof]. Budapest: Tericum, 2001.

* This study was realized with the support of the NKFIH FK 128 978 (Knowledge, Landscape, Nation and Empire: Practices of Knowing and Transforming Landscape in Hungary and the Balkans, 1850–1945) research project.

Figure 2. Spatial patterns of religious and denominational belonging
(family heads) in Sátoraljaújhely in 1870

Source: MNL-BAZML SFL XV. 83. box. 77–79.

Figure 3. Religious differentiation (occupations)

Table 22. The sociodemographic features of occupations
(values under the average are indicated by Italic letters)

Occupation	Average number of children per family	Proportion of earners	Average number of rooms	Inhabitant /room (avg.)	Average wealth (equation)	Average household size	Average number of servants	Average coworker number	Relative ranking based on wealth (equation)	Relative ranking based on cluster-membership	Relative ranking based on number of rooms
lawyer and doctor (33)	1.39	0.36	3.64	1.43	4.01	5.36	1.91	0.24	1	1	1
innkeeper, restaurant owner (60)	2.9	0.27	2.32	2.77	2.39	5.73	0.68	0.42	5	2	2
landowner (106)	2.03	0.35	2.3	2.92	2.66	4.85	0.82	0.39	2	3	3
wheat and flour merchant (21)	2.48	0.22	1.81	3.69	1.35	5.62	0.57	0.05	11	4	8
merchant (38)	0.83	0.46	1.89	1.85	2	3.28	0.83	0.06	3	5	5
engineer (18)	0.83	0.46	1.89	1.85	2	3.28	0.83	0.06	4	6	6
joiner (35)	1.69	0.39	1.84	3.52	2.24	5.57	0.23	1.63	6	7	7
entrepreneur (13)	2.23	0.23	2.08	2.67	1.35	4.85	0.31	0.31	7	8	4
butcher (27)	2.15	0.27	1.56	3.76	1.25	5.04	0.44	0.44	9	9	10
tanner (37)	1.86	0.36	1.27	3.58	1.21	4.22	0.19	0.41	12	10	16
craftsmen who made heavy mantles (46)	1.57	0.37	1.34	3.06	1.02	3.93		0.7	17	11	13
bootmaker (144)	2.19	0.37	1.33	4.01	1.03	4.78			14	12	14
Total sample	1.81	0.35	1.52	3.52	1.49	4.4	0.34	0.46	13	13	11
grocer, chandler (27)	2.63	0.25	1.19	4.39	0.81	5	0.41	0.11	18	14	18
teacher (15)	2.27	0.32	1.77	2.91	1.22	4.67	0.53	0.07	10	15	9
tailor (103)	1.81	0.37	1.33	3.67	1.16	4.52	0.17	0.64	15	16	15
shoemaker (47)1	1.55	0.33	1.36	3.67	1.16	4.87	0.19	0.79	16	17	12
bread-maker and sewer women (37)	1.51	0.61	1.2	2.58	1.46	2.78	0.03	0.54	8	18	17
cartmen (52)	1.75	0.35	1.03	4.05	0.87	4.12	0.17	0.19	20	19	19
personal servant (55)	1.36	0.48	1	3.93	0.79	3.27	0.11	0.29	19	20	20
agrarian wage laborer (343)	1.28	0.39	0.86	4.41	0.54	3.28	0.01	0.15	21	21	21

1 Shoemakers were not considered wealthy by contemporary writers. Among Jews, this was a despised (but frequent) occupation according to Sólem Áléchem.

Total

Regional Differences in Development and Quality of Life in Hungary During the First Third of the Twentieth Century*

Zsolt Szilágyi
University of Debrecen
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In this essay, I look for answers to the following three questions: to what extent did the borders of Hungary after the 1920 Treaty of Trianon overlap with borders of structural development in 1910 and in 1930; what does the term “development” mean when we are talking about the Carpathian Basin; and how did geographical differences in standards of living change in the territories under discussion over the course of these two decades. To some extent, the new political borders which were drawn in 1920 in the Carpathian Basin overlapped with the borders which reflected the different levels and patterns of development in the region. This is a consideration which has been given little attention in the secondary literature in Hungary. The developmental structure of the Carpathian Basin in 1910 can be mapped using the GISta Hungarorum Database. One discerns in this structure a major line of development. Within this line, one finds an area in which the level of development was higher than average and, in some places, considerably higher than average. Another distinctive feature of this area was that is had several centers, and this fact was of particular importance from the perspective of the Treaty of Trianon and its alleged consequences. In recent years, groundbreaking research on economic history has persuasively shown that Hungary managed to recover economically relatively quickly after 1920. Numerous factors played a role in this recovery. One of the more decisive, I argue in this study, was the geographical developmental structure of Trianon Hungary, which had several centers. Although the territory of Trianon Hungary was considerably more developed than other areas of the Carpathian Basin, it is quite clear that the economic fault lines which existed after Trianon had in fact existed before Trianon too, and the internal peripheral areas had already formed (and remained essentially unchanged throughout the interwar period). Thus, the Treaty of Trianon did not play any role in the emergence of formation of these areas. The treaty may well have had grave consequences for the country and region, but the developmental geographical structure of Hungary in the interwar period, which ultimately exerted a shaping influence on development in Hungary for the rest of the twentieth century, was not a result of Trianon.

Keywords: HDI change, regional differences in development, Interwar Hungary

Theoretical and Methodological Frameworks

During the last roughly three decades of the twentieth century, both in the fields of geography and history, research focusing on structural analyses was gradually pushed into the background as new analytical perspectives and frameworks gained ground and agent experience became a priority. Thus, quantitative sources and methods which rely on quantitative sources seemed to lose a lot of their significance by the turn of the century. A series of novel postmodern approaches gained ground. This prompted some scholars to raise scientific concerns. For instance, Geoffrey Crossick, professor at the University of London, highlighted that overemphasis on cultura l questions leads to the striking neglect of structural issues and a drop in the number of empirical studies. 1

Crossick was one of the first scholars to encourage the renewal of empirical studies, which was appreciably furthered by the digital revolution, which accelerated dramatically at the turn of the twentieth and twenty-first centuries. Due to the widespread use of personal computers, the sophisticated table management and data management programs, and the increasing use of the geospatial systems in the science of history, a new era of empirical studies dawned. The new quantitative historical studies were inspired in part by a need for a “new materialism” that came in the wake of postmodern history recordings and also by the overwhelmingly popular2 spatial turn.3

The pioneering 2006 study by Róbert Győri entitled “Bécs kapujában” (“At the Gates of Vienna”),4 which was published in the Hungarian periodical Korall, has played a crucial part in scholarship and research in Hungary. The study is an extended chapter from Győri’s doctoral dissertation, in which he lays a new historical geographic bases for measuring differences in the rates of local regional development.5 As far as the selection of variables was concerned, Győri chose indicators of literacy, economics, and infrastructure.6 He used the following six indicators (Table 1–2).

 

Table 1
Indicators of regional developmental studies conducted by Győri

Code	Specification	Source
m1	literacy rate among the population over 6 in 1910	MSK Ús. Vol. 42
m2	rate of patients undergoing medical treatment between1901 and 1910	MSK Ús. Vol. 46
m3	rate of high-quality residential buildings in 1910	MSK Ús. Vol. 42
m4	rate of migration balance between 1901–1910	MSK Ús. Vol. 46
m5	rate of non-agricultural workers in the labor force in 1910	MSK Ús. Vol. 48
m6	net cadastral income per agricultural employee in 1908/1910	MSK Ús. Vol. 39*
Source: Győri, “Bécs kapujában,” 233. Remark: *) rates of net cadastral income recorded by Győri followed by the corrections published in 1914, while during a later inspection of the Alföld region, the same process was conducted based on the data from 1935 (Szilágyi, “A fejlettség területi különbségei,” 49).

Table 2
CDI calculation method for component indicators

Indicators (m1–6), base variables (v1–13)			Number of records	Data missing	Mathematical formulas for indicator calculation
Code		Description
m1	v01	number of people under 6, 1910	12 542	0	m1=v03×100/(v02–v01)
	v02	total population in 1910	12 542	0
	v03	literacy rate, 1910	12 542	0
m2	v04	annual mortality rate, 1901–1910	12 535	7	m2=v05×100/v04
	v05	annual average rate of fatalities receiving medical treatment (from all deaths), 1901–10	12 536	6
m3	v06	number of stone or brick houses, 1910	12 542	0	m3=(v06+v07)×100/v08
	v07	number of adobe or mud houses with stone or brick foundation, 1910	12 542	0
	v08	total number of houses, 1910	12 542	0
m4	v09	total population in 1900	12 537	5	m4=(v02–v09–v10)×100/v09
	v02	total population specific to the date 1910	12 542	0
	v10	rate of natural population change, 1901–10	12 535	7
m5	v11	number of agricultural traders, 1910	12 542	0	m5=(v12–v11)*100/v12
	v12	total number of earners in 1910	12 542	0
m6	v13	cadastral net income from total land tenures in Hungarian Koronas, 1908	12 434	108	m6=v13/v11
	v11	number of agricultural earners, 1910	12 542	0
Totals (v1–v13)			162 913	133
Source: CBRDD, compared to the original sources, GHD <> MSK Ús. 39, 42, 46, 48, own editing. Note: variables in italics have been listed previously. Description of m1–6 indicators are included in Table 1.

The average derived from the normalized value of six developmental indicators (m1–6) makes the Complex Developmental Index (CDI). If this methodological procedure is taken as the basis on which to identify and compare regional differences, then we are given not an overall picture of the rate of modernization and development, but rather an incomplete sketch based on subsequently selected indicators. In practical terms, we can only see what the development indicators measure compared to prior circumstances, which allows for interpretation of the developmental overview of a simplified version.

As for the rate of development and the quality of life, further methods are available with which to measure them. In recent decades, the use of Human Development Index (HDI)7 has gained ground, especially in the social sciences. Today, primarily sociology, geography, and political science utilize HDI. This multivariable index is adapted mainly to classify the regions as “developed,” “less developed,” and “underdeveloped” and also to map the regional differences in the quality of life. In the 1970s and 1980s, there was a growing need among social researchers to develop a multivariable index8 which would replace the “one-dimensional” GDP9 already widely used to measure the rate of economic development. There was need of an index which would be reactive not just to economic factors, but also to other (individual) circumstances (skills and opportunities). Income is one factor on the basis of which “human welfare” can be gauged. But human welfare is perhaps better gauged via an assessment of choice options. In particular, the extension of choice options as a process gives meaning to the term “human development.”

The method of according to which the HDI is attainted was published in the first issue of the series Human Development.10 The calculation method on which HDI is based has been refined over the course of the last couple of years (e.g. in 1991, 1999), but the process itself has remained unchanged. The value of HDI takes the arithmetic average of three component indicators (lifespan, knowledge gained from education, and standard of living). The component indicators are defined as follows: lifespan via life expectancy at birth; knowledge via the average of literacy and numeracy added to the combined key indicators of the elementary, secondary, and higher education levels; standard of living via the volume index of per capita GDP measured by purchasing power parity (PPP).11 The Hungarian historical sources do not allow us to map differences in development within the area of the country via the UN method of HDI calculations. In order to arrive at an informative map, HDI must be modified in the Hungarian case. The rates used are as follows: rate of life expectancy at birth instead of raw death rates, literacy rate among those above six years of age instead of education component indicator; rate of land tax, real estate tax, corporation tax, and tantième tax out of the ordinary tax system instead of GDP (Table 3).

Table 3
Source of required variables for HDI component indicator

Code	Description	Source
k1	Average of deaths (1901–10)	MSK Ús. Vol. 46
	Population (1910)	MSK Ús. Vol. 42
	Average of deaths (1921–30), data broken down by year	KSH 1969.
	Population (1930)	MSK Ús. Vol. 83
k2	Literacy rate (1910)	MSK Ús. Vol. 42
	Population above 6 (1910)	MSK Ús. Vol. 42
	Population (1910)	MSK Ús. Vol. 42
	Literacy rate (1930)	MSK Ús. Vol. 83
	Population above 6 (1930)	MSK Ús. Vol. 83
	Population (1930)	MSK Ús. Vol. 83
k3	Municipal substitute taxation of which base relies on state taxation of 1908 (K)	MSK Ús. Vol. 39
	Land tax, house tax, income tax levied on urban residents, taxes and other direct taxes levied on guilds, companies liable to public accountability (1910, K)	MSK Ús. Vol. 58
	Population (1910)	MSK Ús. Vol. 42
	Total state taxes serving as the basis for municipal substitute taxation (1934, P)	MSK Ús. Vol. 93
	Tax estimates for towns (method of calculation is listed in the text):
	· Land tax paid by municipal cities (1933/34, P)	AS 1934: 51
	· House tax paid by municipal cities (1933/34, P)	AS 1934: 77
	· Company tax and tantième tax paid by municipal cities (1933/34, P)	AS 1934: 149
	· Land tax paid in county towns (corporate towns) (1933/34, P)	AS 1934: 51
	· Total of land tax paid within the country (1933/34, P)	AS 1934: 51
	· Cadastral income from lands agriculturally cultivated by towns (1935, AK)	MSK Ús. Vol. 99
	· Total of house tax paid in county towns (1933/34, P)	AS 1934: 77
	· Utility value of dwellings used by owners in county towns (1933/34, P)	AS 1934: 82
	· Raw income from leased dwellings in county towns (1933/34, P)	AS 1934: 83
	· Company and tantième tax paid by county towns (1933/34, P)	AS 1934: 149
	· Number of residents working in industry, trade, and travel (1930)	MSK Ús. Vol. 86
Sources: in addition to the above, the date 1910 is listed: GHD, own editing. Note: the dissolving of k1–3 is listed in the methodological description of HDI calculation.

I have obtained details from three databases for the calculations of territorial inequalities in regional development and quality of life: 1. GISta Hungarorum Database (GHD, 7.3 million data entries, Gábor Demeter),12 2. Kárpát-medencei Területi Fejlettségi Adatbázist /Carpathian Basin Regional Development Database/ (CBRDD, 0.4 million data entries, Zsolt Szilágyi), 3. Magyarországi Életminőség-alakulás Történeti Adatbázisa (Hungarian Quality of Life Historical Database (HQLHD, 0.5 million data entries, Zsolt Szilágyi).

The development the Spatial Structure of the Carpathian Basin
at the Beginning of the 20th Century (CDI)

The first complex, multivariable development studies of the Carpathian Basin were done relatively late, in 2000, when Pál Beluszky published his findings.13 Beluszky used twelve indicators in his study.14 He sought to select indicators (drawing on his years of scientific experience and his intuition) which would enable him to map both the economic and social changes effectively. The results profoundly rewrote all the concepts formed on the spatial structure of modernization in the Carpathian Basin at the turn of the nineteenth and twentieth centuries.15

On the basis of Beluszky’s findings, we can conclude that the majority of the country had reached a level of modernization at the beginning of the century. Beluszky introduced the Kisalföld and the Great Plain as the regions which had led the process of modernization,16 where the former market towns claimed the leading position in this process.17 No further advancement has been made until now. (With regards to national politics of regional development, János Pénzes has recently done studies from the perspective of geography.)18

Figure 1 was created using the unified development indicators (m1–6) after the standardization of the indicators based on the average values (CDI). It indicates regional differences. The two central regions, Vienna and Budapest, conspicuously stand out. The leap of development in Budapest, which was influenced from the east, is significantly harsher than it was in the case of Vienna. Apparently, the development of the region between the two capital cities was outstandingly high: probably the two metropolises enhanced each other’s influence. It is also obvious that spatial contact was stronger between the mine basin around Tatabánya (Dorog) and the capital than it was between any other regions. It is also clear that the Hungarian capital’s economic hinterland was made up not just of the abovementioned regions, but also of the areas to the south of Budapest along the Danube, which were rich in German horticultures, and areas to the southeast of Budapest, which were fruit and vegetable farmlands at the rim of the towns of Kecskemét, Nagykőrös, and Cegléd.

Figure 1. Development spatial structure in the Carpathian Basin based on CDI, 1910
Source: CBRDD, own calculations and compositions

 

Central regions which as peaks stood out with significantly lower rates of modernization were the surroundings of Resicabánya (today Reşiţa in Romania), Petrozsény (Petroşani, Romania), and Beszterce (Bistriţa, Romania). Regions which showed less significant development were around the cities of Rozsnyó (Rožňava, Slovakia) and, in the south, Zombor (Sombor, Serbia; Sombor lies in the region known as Eszék, which is not included in this study). At the beginning of the century, what at the time was known as Upper Hungary was a more or less coherent area with an above-average level of development. It included the cities of Zsolna (Žilina), Poprád (Poprad), Kassa (Košice), Rozsnyó, and Besztercebánya (Banská Bystrica), all of which are found in Slovakia today. The area around the cities of Nagykanizsa, Kaposvár, and Szekszárd was similarly developed, as were the triangle formed by Zombor, Szabadka (Subotica, Serbia), and Újvidék (Novi Sad, Serbia) and the market town belt over the Tisza River (the formed by the cities of Szeged and Debrecen). Towards the Székely Land, a region in the eastern stretch of Transylvania, two “development corridors” appeared: the gateway towards the north, which was bordered on either side by the cities of Szatmárnémeti (Satu Mare), Nagybánya (Baia Mare), and Beszterce (Bistriţa) to the northeast and Marosvásárhely (Târgu Mureş), Kolozsvár (Cluj), and Nagyvárad (Oradea) to the southwest, and the one lying towards the south, south of the Maros River, following the crest of the southern Carpathian Mountains across the so-called Saxon Lands (a region of Transylvania which had a strong Saxon present until the last decades of the twentieth century).

At the beginning of the century, the regions which had below-average development rates were the Zalai hills, the sand lands of Bugac, the plains of the Hortobágy, and the so-called Nyírség. These areas were either densely populated small villages with no regional centers or uninhabited areas where the biogeographic indicators (such as low total annual rainfall, etc.) impeded the emergence of settlements. Over the main structure line, in the north of the peripheral region, a narrow zone and in the east an expanded zone appeared, both with development rates which were well below average.

Based on the above descriptions of the different regions (which are confirmed by numerus sources in the Hungarian secondary literature), the so-called “development slope,” according to which the rate of development shows a gradual decrease following the direction from the western regions towards the eastern part within the territory of historical Hungary, proves incorrect. The new results allow us to deconstruct the “slope thesis.” We should not regard the surface forms of development as a slope, but rather should consider them a hilly land which slopes from the direction of west towards east and from south towards north and also shows rises in the form of coherent areas or islands. These “high areas” are divided by lowland valleys which prove to have high (metaphorical) altitudes in patches, but mostly have surprisingly low points. As a consequence, the rigid “slope image” should be rejected in favor of an image of a “development membrane” with varied and flexible forms.

The development membrane reveals the developmental spatial structure of the Carpathian Basin in the most visual way possible. The most apparent feature of Figure 2 is that the developmental terrain is the inverse of the geographical terrain. At places where tall mountains were found in reality these regions had low rates of development. In places where a basin was found, there can be found the most developed regions. Certainly, this statement is not well founded yet. However, it highlights the fact that though there had been raw material resources for possible industrial purposes in the mountainous area, and also energy resources were also easily available, the processing plants and the low energy-demand industries were set in the basin-related divisions. Literacy rates and access to basic health were better in the middle of the country (i.e. the flatlands), and immigration rates were higher. All these facts make is clear that the Carpathian Basin was at an above-average development level at the beginning of the twentieth century. This region was a dynamically developing part of the country at the beginning of the twentieth century, with a high level of economic innovation compared to its surroundings, and it offered higher standards of living. On the whole, this region was a basin which attracted people who hoped not simply to earn a livelihood, but also sought to invest.

 

Figure 2. Development terrain (membrane) of historical Hungary, 1910
Source: CBRDD, own calculations and compositions.

 

Based on this, we must reject the notion that, from the perspective of modernization, the two capital cities and the surrounding areas were the only parts of the Carpathian Basin at the beginning of the twentieth century which enjoyed promising rates of development. On the contrary, we can clearly construct a multi-centered developmental structure of the Carpathian Basin based on the subsequently selected indicators. Our study reveals that a developmental main structure line existed at the turn of the century in the Carpathian Basin, in other words a kind of “break line” (Figure 1). The areas over the main structure line can undoubtedly be regarded as peripheral in the narrative of the economic development rate of the area. Our study indicated the need for further research to determine whether this line overlaps with the eastern borders of Hungary established by the Treaty of Trianon and, if so, to what extent. Gábor Demeter has shown that “the new country borders, as internal break lines, existed before the Treaty of Trianon, and they did not simply constitute break lines defined merely by differences in language.”19 The extent to which some of the newly created national borders in the Carpathian Basin correlated with the developmental spatial structure of the greater area is unclear. This question merits further study.

Within the main line of development structure lay a region which was not homogeneous at all and showed above average (often very above average) rates of development (Figure 2). It was a multi-centered region, which gained specific meaning in the narrative of the Treaty of Trianon. The pioneering economic historic research of recent years have clearly proven that the country regained its stability relatively quickly after 1920.20 This economic success was due to many factors, but on the basis of our study, it is clear that one of the most important elements was the multi-centered developmental spatial structure of Hungary after the Trianon Peace Treaty.

Regional Differences in Quality of Life in Hungary in 1910–1930

Based on the calculations, the national average of HDI in 1910 was 0.451, which showed a slight rise of 2% to 0.461 as a result not just of the past economic and social changes but also as a consequence of distortion stemming from the adapted resources. Practically, in 1924, the community tax base components had seen modifications following an Administrative Circular specified by the Ministry of Home Affairs.21 Consequently, the calculations were based on four specific indicators: land tax, real estate tax, corporation tax, and tantième.22 Thus, income tax and mine tax were deleted from the base of substitute tax. Corporate tax and tantième were “theoretically” equal to the previous tax paid by public companies and associations also the tax on equity interest and the benefit tax. The conditions of taxability, however, had seen profound alterations in the meantime. Consequently, the substitute component indicators for GDP from 1910 and 1930 (which consist of the abovementioned taxes) can only be compared to a limited extend. With regard to these factors, the spatial structure of territorial inequalities related to quality of life had remarkable features: the major part of Transdanubia, the agglomeration of the capital city, and the rim of the towns in Tiszántúl were more developed according to this narrative than any other parts of the country. Societies in the northern regions which were industrially more developed were in a favorable position, as were town dwellers. An additional distinctive feature of the emerging spatial structure is that when taking into consideration the territory of the country as it was later defined by the Treaty of Trianon, the northeastern region of the Great Plain was acknowledged as a periphery even in 1910. Peripheral regions were clearly marked by the Nyírség, the region of Közép-Tisza and Jászság, and also parts in the Hills of Zala and the wider surroundings of Bugac. The results derived by two different methods of calculation (CDI, HDI) closely overlap (Figure 3).

Figure 3. Regional differences in the quality of life between 1910 and 1930
Source: GHA, MÉTA, own calculations and compositions.

The overall picture becomes more complex as we investigate the volume of changes in certain regions. It is clear that more than 40 percent of the territorial units were substantially “stable.” Between 1910 and 1930, there were no towns or districts in these regions that would have shown a “leap” forwards or backwards of more than 20 points in an imaginary ranking. This kind of regional attribute can be identified with most of Transdanubia, the Sárrétek district of Tiszántúl, the third of the western region between the Danube and the Tisza Rivers, the Zemplén, the Bükk, and the Cserhát Mountains. The northern area of the Great Plain was in a particularly disadvantageous situation, as were the districts of Kiskunhalas and Kiskunfélegyháza and the majority of the districts in the border areas east of the Danube River. This contributed to the emergence of a state in which the pre-Trianon internal peripheral regions faced further deterioration and their positions became more disadvantageous. In the districts that were transformed into border areas, the pace of development apparently became slower. By contrast, the towns, especially the capital city and its agglomeration and the towns of Northern Transdanubia (including Miskolc), kept their previous momentum. From the perspective of development, they made dramatic leaps in the national ranking. The Győri basin near Vienna was an interrelated unity which showed a different developmental trajectory, as were the extended environment of the Pre-Alps and the city of Szombathely. In the north, only Miskolc underwent this different process of development, and in the Great Plain, only the areas lying next to the railway between Budapest, Szolnok, and Debrecen and the southern parts of Békés County (Figure 4).

Figure 4. Changes in the quality of life between 1910 and 1930
Source: GHA, MÉTA, own calculations and compositions

A new consideration which is important if one seeks to place the data in a meaningful context lies with the calculation of the variation coefficient.23 A further question arises here as to whether the differences in development (quality of life) among the regions, towns, and villages showed decreasing or rising tendencies. If the value of the variation coefficient proves lower for the period under study then the rate of regional development discrepancies among the areas compared also shows a decrease, which indicates a favorable outcome. This case indicates convergence; otherwise, the opposite should indicate divergence. (Table 4, Figure 5).

 

Table 4
Variation coefficient changes within the area of Hungary after the Treaty of Trianon,
1910–1930

Description	Variance		Average		Variable coefficient		Difference
	1910	1930	1910	1930	1910	1930	points	%
	Covering the total area of the country after the Treaty of Trianon
Counrty area including Budapest	0.11	0.10	0.45	0.46	23.35	21.20	−2.15	−9.22
Country area excluding Budapest	0.10	0.09	0.45	0.46	22.28	20.51	−1.78	−7.97
Counties	0.07	0.07	0.44	0.46	16.75	15.32	−1.43	−8.55
Districts	0.09	0.08	0.42	0.44	20.24	17.91	−2.33	−11.51
Towns	0.12	0.12	0.52	0.52	22.51	22.10	−0.41	−1.84
Towns excluding Budapest	0.11	0.11	0.52	0.52	20.32	20.88	0.56	2.75
	Statistics by regions
Towns
Transdanubia	0.08	0.09	0.59	0.56	14.15	16.06	1.91	13.50
North Great Plain including	0.11	0.07	0.46	0.48	24.28	15.39	−8.89	−36.62
Budapest	0.12	0.13	0.51	0.51	24.42	25.07	0.65	2.65
Great Plain excluding Budapest	0.10	0.12	0.49	0.50	20.17	23.11	2.93	14.55
Districts
Transdanubia	0.05	0.05	0.49	0.49	10.30	9.69	−0.61	−5.97
North	0.05	0.04	0.40	0.41	13.67	10.36	−3.31	−24.23
Great Plain	0.09	0.09	0.37	0.40	23.04	22.23	−0.81	−3.50
Regions
Transdanubia	0.07	0.07	0.51	0.50	13.89	13.12	−0.77	−5.56
North	0.07	0.05	0.41	0.42	17.17	12.85	−4.32	−25.16
Great Plain	0.12	0.12	0.42	0.44	28.29	26.72	−1.57	−5.55
Source: GHD, HQLHD, own calculations.

Figure 5. Variation coefficient changes, 1910–1930
Source: HQLHD, own calculation and editing

Between 1910 and 1930, in the area of the country as it was defined by the Treaty of Trianon, there was a decrease not only in regional development disparities related to the rate between districts (−11.5%), but also related to the rate between towns (−1.8%), which suggests that, overall, the disparities among towns showed only minimal differences in comparison to the disparities among districts, where the rate of convergence was six times higher. If we examine the shifts in disparities among towns excluding Budapest, then a kind of divergence can be traced (+2.8%), which means that while the regional differences between the capital and the other towns decreases, in the case of the statuses among towns, a completely different tendency can be observed. An ongoing increase is traceable. The status is different if we inspect the differences based on regional sections. Convergence can only be seen among the towns of the northern region (−36.6%), while the gap between the towns on the Great Plain shows a more remarkable increase (+14.6%) than between Transdanubian towns (+13.5%). In contrast with these trends, the differences among the villages in the three macro-regions of the country showed further decreases, especially in Transdanubia, where the convergence of villages was five or six times more in volume than the villages in the other two regions. Therefore, the disparities among the villages in Transdanubia became less traceable at a remarkably higher space and rate than in any other region of the country.

As a consequence, we can also determine which region, given its own attributes, was more preferably influenced by the equalization process of regional differences. It is demonstrable that it was neither the Great Plain nor Transdanubia which marked the process, but surprisingly, the northern region proves to have taken the lead, where convergence reached rates five times higher than the rates found in other regions. This remarkably preferable status can be primarily attributed to the higher rate of disparity equalization between Northern towns (Figure 5).

Figure 6. Settlement density in Hungary, 1933
Source: HQLHD, own calculation and own editing.

Overall, the rate of gap decrease showed more considerable moderation for the villages in Transdanubia, while the rate of gap decrease showed more considerable moderation for towns in the northern regions. However, the data relevant to the Great Plain indicate that a process completely different from the formerly sketched ones took place. As was the case in Transdanubia, the disparities among the towns of the Great Plain continued to grow; the regional disparities among the villages continued to show no decrease, but only slight moderation, unlike in the other regions. So, in the Great Plain, travel processes between the villages and the towns slowed down after World War I, which was not typical neither to Transdanubia nor to the Northern Region. Furthermore, the regional differences in lifestyles showed faster growth than the “adjustment” itself, which indicates that the gap between the agricultural towns in the Great Plain and the villages saw further “depths.” This exceptional process can be correlated with the unique settlement structure of the land, and it also indicates that the population density of the Great Plain was much lower than the population density of other regions. (Figure 6).

The Development of Quality of Life in Hungary Based on International Comparisons

Using the data assembled by Nicholas Crafts,24 Béla Tomka has taken European data-based comparisons related to Hungary on the basis of HDI. Sine some of the data was unobtainable, Crafts could not determine the index related to Hungary at the beginning of the twentieth century, so the calculations for 1913 were made complete by Béla Tomka. This has enabled historians to analyze the status of Hungary in correlation with a Western European context. Based on the results, it is apparent that the quality of life in Hungary compared to Northern and Western Europe was clearly even more unpreferable than it had been in 1913. (Figure 7). Over the course of the following decades, the gap displayed significant shrinking: while at the beginning of the century the HDI index was only 78% of the Western European average, by the mid period of the century it took 93%.25

 

 

 

Figure 7. HDI rate in Hungary, compared to Western Europe, 1913
Source: Tomka, Gazdasági növekedés, 191. Own editing.

In recent years, Leandro Prados de la Escosura has collected the base data from different countries of the world. His work enables us to determine the three component indicators of HDI in 1870 when focusing on different time sections. The researcher had taken the point at the beginning of his studies that the HDI (UNHDI) calculated via UN methods can only be utilised at a confined birth rate in case of historical perspectives and in the global context, which induced him to make changes to the calculation methods (he has introduced the use of the geometric mean instead of the arithmetic mean) and also to give the index a new name: Historical Index of Human Development (HIHD).26

Based on the data available, Prados has published HIHD indexes about 164 countries. These indexes enable one to sketch a quantitative image generated via the most modern methods of the quality of life validatable for both countries and eras. Consequently, the time and space dynamics of the changes in the quality of life have become constructible. (Figure 8).

Based on the latest findings, the rate of “development” could have been more balanced than was suspected earlier. The region-based comparison also highlights the fact that, compared to other northern and western European countries, a significant improvement was traceable in Hungary between 1870 and 1925. It clear that the increase in the quality of life shows balance between 1870 and 1913, though perhaps a slight slowdown is observable at the turn of the century. Although, it is clear that in the regions of northern and western Europe there was a favorable improvement with higher rates and faster paces of modernization and improvements in quality of life (which theoretically was correlated to the prospering economy) at the beginning of the twentieth century until the outbreak of World War I. As for the quality of life, which was theoretically (also) in correlated to the prospering status of the economy, The trend commencing in 1913 went onwards, and this contributed to further apparent gap decreases in Hungary as compared to northern and Western European average. This trend stemmed from the increase in national HIHD between 1913 and 1925. Then, it correlated with the slowdown of transformation in the Northern and Western regions between 1925–1929. At the same time, it is also clear that in the Northern and Western European regions, the transformation of the quality of life was asserted with more unfavorable effects due to the recession (1929–1933) than in Hungary. The data also indicate that during the first half of the twentieth century, in 1938 the quality of life as a national average was the closest to the northern and Western European standard: while this average in 1870 was 54% of the former standard, and in 1913 was still just 64%, then in 1938, right before the outbreak of World War II, it took 81 percent. In addition, the “improvement” of national quality of life correlates or in other words relates with the central European processes, while as opposed to the southern- European status, it shows an acceleration in the speed of changes detouring the national quality of life into a favourable direction. Finally, from the perspective of Austria, it is essential to mention that in the decades right after the Austro-Hungarian Comprise, the quality of life shows more remarkable increases in Austria than in Hungary. Practically, Austria converged at an accelerating space towards the quality of life dictated by the northern and Western European countries, which it actually reached in 1929. From this point onwards, it advanced in complete correlation at the same level.

Conclusion

On the basis of our study, in the narrative of Carpathian Basin, the territory of post-Trianon Hungary was significantly more developed as compared to its surrounding regions. Even prior to the Treaty of Trianon, the break lines already existed, and the internal/peripheral regions had already emerged. As a result, the emergence of these gaps cannot be attributed to the consequences of Treaty of Trianon. The territorial inequalities related to quality of life owned remarkable features: the territory of the country was divided into a western region of the Danube and an eastern part of the Danube. It is essential to emphasize that although the Great Plain had a multi-centered development spatial structure as an agricultural region, it still ensured a sustainable basis for economic stability; and via the developed status of its center divisions it also ensured the balance of transition. These regions were the innovation centers that ensured the background to structure transition and to the temporary expansion of garden cultivation culture. The Treaty of Trianon has had serious consequences, but one must admit that it was not the Treaty of Trianon that resulted in the internal spatial structure which defined the developmental spatial potentials of Hungary for the rest of the twentieth century.

Bibliography

Printed sources

Adóstatisztika [Tax statistics]. 5. füzet. 1934. Budapest: Magyar Királyi Pénzügyminiszter, 1935.

MSK Ús. 39. kötet = Magyarország községeinek háztartása az 1908. évben [The household-level settlement statistics of Hungary in 1908]. Magyar Statisztikai Közlemények. Új sorozat 39. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1913.

MSK Ús. 42. kötet = A magyar szent korona országainak 1910. évi népszámlálása. Első rész. A népesség főbb adatai községek és népesebb puszták, telepek szerint [Official census of the Hungarian Kingdom in 1910. Part 1. Basic population and demographic data of settlements]. Magyar Statisztikai Közlemények. Új sorozat 42. Budapest: Magyar Királyi Központi Statisztikai Hivatal, Athenaeum Irodalmi és Nyomdai R.-Társulat, 1912.

MSK Ús. 46. kötet = A magyar szent korona országainak 1901–1910. évi népmozgalma községenkint [Official census of the Hungarian Kingdom in 1910. Population data and demographic movements of settlements in 1901–1910]. Magyar Statisztikai Közlemények. Új sorozat 46. Budapest: Magyar Királyi Központi Statisztikai Hivatal, Athenaeum Irodalmi és Nyomdai Részvénytársulat, 1913.

MSK Ús. 48. kötet = A magyar szent korona országainak 1910. évi népszámlálása. Második rész. A népesség foglalkozása és a nagyipari vállalatok községenként. [Official census of the Hungarian Kingdom in 1910. Part 2. The occupation of the population and the large industrial enterprises: settlement level statistics]. Magyar Statisztikai Közlemények. Új sorozat 48. Budapest: Magyar Királyi Központi Statisztikai Hivatal. Athenaeum Irodalmi és Nyomdai R.-Társulat, 1913.

MSK Ús. 58. kötet = Magyarország városainak háztartása az 1910. évben [The household structure of the Hungarian towns in 1910]. Magyar Statisztikai Közlemények. Új sorozat 58. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1916.

MSK Ús. 83. kötet = Népszámlálás. I. rész. Demográfiai adatok községek és külterületi lakotthelyek szerint [The official census of Hungary. Part 1. Settlement level demographic data]. Magyar Statisztikai Közlemények. Új sorozat 83. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1932.

MSK Ús. 86. kötet = Népszámlálás. II. rész. Foglalkozási adatok községek és külterületi lakotthelyek szerint, továbbá az ipari és kereskedelmi nagyvállalatok [The official census of Hungary. Part 2. Settlement level data on occupation and industrial and commercial large enterprises]. Magyar Statisztikai Közlemények. Új sorozat 86. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1934.

MSK Ús. 93. kötet = Magyarország községeinek háztartási viszonyai az 1934. évi községi költségelőirányzatok szerint [Household statistics according to the budget of settlements in 1934]. Magyar Statisztikai Közlemények. Új sorozat 93. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1935.

MSK Ús. 99. kötet = Magyarország földbirtokviszonyai az 1935. évben I. Törvényhatóságok és községek (városok) szerint [Landownership in 1935. Part 1. Settlement level data]. Magyar Statisztikai Közlemények. Új sorozat 99. Budapest: Magyar Királyi Központi Statisztikai Hivatal, 1936.

 

Secondary literature

Beluszky, Pál, and Róbert Győri. Magyar városhálózat a 20. század elején [The Hungarian settlement structure in the beginning of the 20th c.]. Budapest–Pécs: Dialóg Campus Kiadó, 2005.

Beluszky, Pál. “Egy félsiker hét stációja: avagy a modernizáció regionális különbségei a századelő Magyarországán” [Seven stages of a half-success: the regional differences of modernization in Hungary in the beginning of the 20th c.]. In Alföld és nagyvilág: Tanulmányok Tóth Józsefnek [The Great Plains and the wide world], edited by Zoltán Dövényi, 299–326. Budapest: MTA Földrajztudományi Kutatóintézet, 2000.

Beluszky, Pál 2001. A Nagyalföld történeti földrajza [A Historical geography of the Great Plains]. Budapest–Pécs: Dialóg Campus Kiadó, 2001.

Beluszky, Pál. “Magyarország ipara a századelőn” [The industry of Hungary in the beginning of the 20th c.]. In Vol. 1 of Magyarország történeti földrajza [A historical geography of Hungary], edited by Pál Beluszky, 396–443. Studia Geographica, Dialóg Campus Tankönyvek, Területi és Települései Kutatások 27. Budapest–Pécs: Dialóg Campus Kiadó, 2005.

Beluszky, Pál. “Kárpát-medence országrészeinek (régióinak?) rövid jellemzése” [Characteristics of the Regions of the Carpathian Basin]. In Vol. 2 of Magyarország történeti földrajza. [A historical geography of Hungary], edited by Pál Beluszky, 325–411. Studia Geographica, Dialóg Campus Tankönyvek, Területi és Települései Kutatások 27. Budapest–Pécs: Dialóg Campus Kiadó, 2008.

Benda, Gyula. Zsellérből polgár – társadalmi változás egy dunántúli kisvárosban: Keszthely társadalma, 1740–1849 [Burghers from peasants: Social changes in a small town of Transdanubia – the society of Keszthely, 1740–1849]. Budapest: L’Harmattan, 2008.

Crafts, Nicholas F. R. The Human Development Index: some Historical Comparisons. LSE Working Papers in Economic History no. 33/96. London: London School of Economics and Political Science, 1996.

Crafts, Nicholas F. R. “The Human Development Index, 1870–1999: Some Revised Estimates.” European Review of Economic History 6 (2002): 395–405.

Csite, András, and Nándor Németh. Az életminőség területi differenciái Magyarországon: a kistérségi szintű HDI becslési lehetőségei [Regional differences in the Standard of Living in Hungary: District Level Estimations on HDI]. Budapesti Munkagazdaságtani Füzetek 3. Budapest: MTA Közgazdaságtudományi Intézet, Budapesti Corvinus Egyetem Emberi Erőforrások Tanszék, 2007.

Demeter, Gábor 2017. “Történeti kérdések földrajzi szemszögből: mi az, amit másképp lát egy földrajzos? A Trianonhoz vezető út regionális aspektusai” [Historical questions from geographical aspects: What the geographer can see – the regional aspects of the road to Trianon]. Történeti Földrajzi Közlemények 5, nos. 3–4 (2017): 22–36.

Győri, Róbert. “A térszerkezet átalakulásának elemei a Kisalföld déli részén (a XVIII. század végétől a XX. század elejéig)” [The Transformation of the regional structure in the southern half of the Kisalföld]. PhD diss., Eötvös Lóránd Tudományegyetem, Természettudományi Kar, Földtudományi Doktori Iskola, 2005.

Győri, Róbert. “Bécs kapujában: Területi fejlettségi különbségek a Kisalföld déli részén a 20. század elején” [At the gates of Vienna: spatial differences in development in the southern half of the Kisalföld at the beginning of the 20th c.]. Korall 24–25, (2006): 231–50.

Human Development Report 1990. United Nations Development Programme. New York–Oxford: Oxford University Press, 1990.

Hicks, Norman, and Paul Streeten. “Indicators of Development: The Search for a Basic Needs Yardstick.” World Development 7, (1979): 567–80.

Izsák, Éva, and Andrea Dúll. “Városi ‘térfordulatok:’ a város interdiszciplináris megközelítése [Spatial turn in urban geography: an Interdisciplinary Approach to Towns]. In Tér-rétegek: tanulmányok a 21. század térfordulatairól [Layers of space: studies in the spatial turns in the 21st c.], edited by Andrea Dúll, and Éva Izsák, 69–76. Kívül-belül 3. Budapest: L’Harmattan, 2014.

Izsák, Éva. “A tértudás változásának fordulatai” [Turns in knowledge on space]. In Tér-rétegek: tanulmányok a 21. század térfordulatairól [Layers of space: studies in the spatial turns in the 21st c.], edited by Andrea Dúll, and Éva Izsák, 19–29. Kívül-belül 3. Budapest: L’Harmattan, 2014.

Kidd, Alan, and David Nicholls. “Introduction: The Making of the British Middle Class?” In The Making of the British Middle Class? Studies of Regional and Cultural Diversity since the Eighteenth Century, edited by Alan Kidd, and David Nicholls, xv–xl. Cornwall: Sutton Publishing, 1998.

Kövér, György. A tiszaeszlári dráma: Társadalomtörténeti látószögek [The drama of Tiszaeszlár: socio-historical perspectives]. Budapest: Osiris Kiadó, 2011.

Majorossy, Judit 2011. A foglalkozás topográfiája. A társadalmi tértől a személyes térig: a társadalmi mobilitás térbeli elemei a 15. századi Pozsonyban [The Topography of occupations. From social space to personal space: spatial elements of social mobility in th 15th c. Bratislava]. Korall 45 (2011): 102–35.

Nemes Nagy, József. Terek, helyek, régiók: A regionális tudomány alapjai [Spaces, places, regions: the fundaments of regional science]. Budapest: Akadémiai Kiadó, 2009.

Noorbakhsh, Farhad. “A Modified Human Development Index.” World Development 26, no. 3 (1998): 517–28.

Novák, Veronika. Az erőszak topográfiája: Hétköznapi térhasználat a 15–16. századi párizsi bűnesetekben [The topography of violence: everyday use of urban spaces in crimes committed in 15–16 century Paris]. Korall 45 (2011): 59–79.

Pénzes, János. Periférikus térségek lehatárolása: dilemmák és lehetőségek [Delimiting peripheral regions: dilemmas and possibilities]. Debrecen: Didakt Kft., 2014.

Pogány, Ágnes 2015. “A nagy háború hosszú árnyéka: az I. világháború gazdasági következményei” [The long shadow of the Great War: the economic consequences of the First World War]. In Az első világháború következményei Magyarországon [The consequences of the First World War in Hungary] edited by Béla Tomka, 257–81. Budapest: Országgyűlés Hivatala, 2015.

Prados de la Escosura, Leandro. Improving the Human Development Index: New Estimates for Europe and its Offshoots, 1850–1990. Manuscript. Accessed on April 25, 2019. http://www.aehe.es/wp-content/uploads/2005/10/a1_prados_de_la_escosura.pdf

Soja, Edward William. Postmodern Geographies: The Reassertion of Space in Critical Social Theory. London and New York: Verso Press, 1989.

Szilágyi, Zsolt. Homokváros: Kecskemét történeti földrajzi látószögek metszetében [The city of sand: Kecskemét in the section of different historical geographical approaches]. Kecskeméti Örökség Könyvek 2. Kecskemét: Kecskemét Írott Örökségéért Alapítvány, 2012.

Szilágyi, Zsolt. “A társadalmi tér használata a kecskeméti multifunkcionális elit gyakorlata szerint, 1920–1940” [The use of social spaces in the practice of the multifunctional elite of Kecskemét]. Tér és Társadalom 26, no. 3 (2012): 3–29.

Szilágyi, Zsolt. “A fejlettség területi különbségei az Alföldön a 20. század elején” [Regional differences in the development of the Great Plains in the beginning of the 20th c.]. In Föld, parasztság, agrárium: Tanulmányok a XX. századi földkérdésről a Kárpát-medencében [Land, peasantry, agriculture: studies in the agrarian question in the Carpathian Basin], edited by Zsuzsanna Varga, and László Pallai, 37–112. DE Történelmi Intézet, DE Történelmi és Néprajzi Doktori Iskola, Hajdúnánás Város Önkormányzata, ELTE Új- és Jelenkori Magyar Történelem Doktori Program, MTA Agrártörténeti és Faluszociológiai Osztályközi Állandó Bizottság, Hajdúnánás, 2015.

Timár, Lajos. Vidéki városlakók: Debrecen társadalma 1920–1944 [Provincial urban dwellers: the society of Debrecen, 1920–1944]. Mikrotörténelem 6. Budapest: Magvető Könyvkiadó, 1993.

Tomka, Béla. Gazdasági növekedés, fogyasztás és életminőség: Magyarország nemzetközi összehasonlításban az első világháborútól napjainkig [Economic growth, consumption and living standards: Hungary in international comparison from the end of the First World War until nowadays]. Budapest: Akadémiai Kiadó, 2011.

Tomka, Béla. “Gazdasági rekonstrukció Magyarországon az első világháború után: régi és új szempontok” [Economic reconstruction in Hungary after the First World War: old and new aspects]. In Gróf Bethlen István és kora [Count István Bethlen and His Age], 75–95. Budapest: Osiris Kiadó, 2014.

Warf, Barney, and Arias Santa, eds. The Spatial Turn: Interdisciplinary Perspectives. Routledge Studies in Human Geography 26. Routledge, London and New York, 2009.

 

APPENDIX

Changes in differences in the quality of life (HDI- Human Development Index) in Hungary after the Treaty of Trianon between 1910 and 1930

Remarks on the Table

Adapted details have been published based on the decreasing order of HDI records of 1930.

ID	Identification number. Consists of three parts (separated by periods). First part: processus/district (1) or town (2), second part: codes for a county in the Kingdom of Hungary (1–25), third part: the number for a processus/district or town within a county

The counties of Hungary in 1930: vm. = vármegye (county), keevm. = közigazgatásilag egyelőre egyesített vármegye (county administratively unified), MH 1933

01 =	Abaúj-Torna vm.	10 =	Győr, Moson és Pozsony keevm.	19 =	Szabolcs és Ung keevm.
02 =	Bács-Bodrog vm.	11 =	Hajdu vm.	20 =	Szatmár, Ugocsa és Bereg keevm.
03 =	Baranya vm.	12 =	Heves vm.	21 =	Tolna vm.
04 =	Békés vm.	13 =	Jász-Nagykun-Szolnok vm.	22 =	Vas vm.
05 =	Bihar vm.	14 =	Komárom és Esztergom keevm.	23 =	Veszprém vm.
06 =	Borsod, Gömör és Kishont keevm.	15 =	Nógrád és Hont keevm.	24 =	Zala vm.
07 =	Csanád, Arad és Torontál keevm.	16 =	Pest-Pilis-Solt-Kiskun vm.	25 =	Zemplén vm.
08 =	Csongrád vm.	17 =	Somogy vm.
09 =	Fejér vm.	18 =	Sopron vm.

 

A	j. = járás (district), rtv. = rendezett tanácsú város (corporate town), szfv. = székesfőváros (royal seat and capital), thjv. = törvényhatósági jogú város (municipal town).
B	1910–1930 HDI difference (100%=1910).
C	The country average of HDI in 1910 100%=0,451, while in 1930 100%=0,461
D	The difference between the relative positions of 1910 and 1930.
E	The direction of the change in position (+).

Sources for Tables

Databases: GHA, MÉTA

Statistical journals: AS 1934, KSH 1969, MH 1933, MSK Ús 39, 42, 46, 58, 83, 86, 93, 99 volume.

Own calculations and compositions.

*

 

HDI regional differences in the area of Hungary after the Treaty of Trianon broken down by processus and towns between 1910 and 1930

ID	Name of administrative unit	A	HDI			Compared to the average HDI (%)			Relative position (order)			E
			1910	1930	B (%)	1910	1930	C	1910	1930	D
2.16.13	Budapest	szfv.	0.933	0.844	−9.55	206.78	183.27	−23.52	1	1	0
2.16.09	Rákospalota	rtv.	0.635	0.731	15.17	140.70	158.78	18.07	11	2	9	+
2.16.06	Kispest	rtv.	0.673	0.729	8.33	149.19	158.36	9.17	4	3	1	+
2.16.08	Pestszenterzsébet	rtv.	0.638	0.729	14.29	141.27	158.20	16.93	10	4	6	+
2.16.01	Budafok	rtv.	0.575	0.728	26.66	127.42	158.14	30.72	23	5	18	+
2.16.11	Újpest	rtv.	0.662	0.691	4.49	146.60	150.10	3.50	6	6	0
2.18.01	Sopron	thjv.	0.718	0.686	−4.40	159.04	148.98	−10.06	2	7	−5
2.14.02	Komárom	rtv.	0.633	0.676	6.69	140.32	146.69	6.37	12	8	4	+
2.10.02	Győr	thjv.	0.687	0.670	−2.50	152.31	145.51	−6.81	3	9	−6
2.22.01	Kőszeg	rtv.	0.653	0.636	−2.59	144.66	138.06	−6.60	7	10	−3
1.16.11	Központi (PPSK)	j.	0.552	0.617	11.85	122.20	133.93	11.73	29	11	18	+
2.22.02	Szombathely	rtv.	0.671	0.612	−8.84	148.74	132.85	−15.89	5	12	−7
2.09.01	Székesfehérvár	thjv.	0.623	0.603	−3.09	137.93	130.97	−6.96	13	13	0
2.23.01	Pápa	rtv.	0.644	0.602	−6.43	142.65	130.78	−11.86	9	14	−5
2.16.10	Szentendre	rtv.	0.449	0.588	30.94	99.48	127.63	28.15	108	15	93	+
2.06.01	Miskolc	thjv.	0.609	0.585	−3.99	134.95	126.96	−7.99	14	16	−2
1.18.01	Csepregi	j.	0.597	0.581	−2.57	132.22	126.23	−5.99	15	17	−2
1.22.03	Sárvári	j.	0.582	0.576	−1.07	129.06	125.10	−3.96	18	18	0
2.03.02	Pécs	thjv.	0.581	0.572	−1.50	128.70	124.22	−4.49	19	19	0
2.10.01	Magyaróvár	rtv.	0.572	0.571	−0.23	126.77	123.92	−2.85	24	20	4	+
1.22.05	Szombathelyi	j.	0.578	0.570	−1.28	127.99	123.80	−4.19	20	21	−1
2.16.12	Vác	rtv.	0.540	0.567	4.93	119.71	123.08	3.37	33	22	11	+
2.08.04	Szeged	thjv.	0.544	0.564	3.57	120.56	122.35	1.78	32	23	9	+
1.18.04	Soproni	j.	0.545	0.562	3.00	120.85	121.97	1.11	31	24	7	+
2.04.01	Békéscsaba	rtv.	0.393	0.562	43.06	86.99	121.94	34.95	158	25	133	+
1.10.01	Magyaróvári	j.	0.577	0.561	−2.76	127.83	121.80	−6.03	22	26	−4
1.24.02	Balatonfüredi	j.	0.571	0.557	−2.44	126.49	120.91	−5.58	25	27	−2
2.11.05	Debrecen	thjv.	0.644	0.557	−13.56	142.74	120.89	−21.85	8	28	−20
1.16.06	Gödöllői	j.	0.480	0.549	14.38	106.34	119.18	12.84	74	29	45	+
2.15.02	Salgótarján	rtv.	0.515	0.548	6.46	114.01	118.93	4.92	46	30	16	+
1.23.02	Enyingi	j.	0.584	0.545	−6.77	129.44	118.25	−11.20	17	31	−14
1.18.02	Csornai	j.	0.519	0.542	4.41	114.92	117.57	2.65	43	32	11	+
1.22.01	Celldömölki	j.	0.529	0.541	2.22	117.30	117.48	0.18	37	33	4	+
2.24.02	Zalaegerszeg	rtv.	0.595	0.541	−9.04	131.78	117.44	−14.33	16	34	−18
1.16.17	Váci	j.	0.524	0.541	3.12	116.15	117.37	1.21	39	35	4	+
1.10.04	Tósziget-csilizközi	j.	0.491	0.538	9.65	108.71	116.79	8.08	70	36	34	+
1.18.03	Kapuvári	j.	0.491	0.538	9.39	108.88	116.70	7.83	69	37	32	+
1.10.03	Sokoróaljai	j.	0.516	0.537	3.99	114.40	116.57	2.17	44	38	6	+
1.21.06	Völgységi	j.	0.530	0.536	1.19	117.44	116.44	−1.00	36	39	−3
2.23.02	Veszprém	rtv.	0.551	0.536	−2.59	121.99	116.42	−5.56	30	40	−10
2.13.05	Szolnok	rtv.	0.465	0.534	14.94	103.03	116.03	13.00	89	41	48	+
1.22.06	Vasvári	j.	0.505	0.531	5.29	111.82	115.36	3.54	53	42	11	+
1.14.02	Gesztesi	j.	0.504	0.527	4.66	111.58	114.42	2.84	56	43	13	+
1.22.02	Körmend–németújvári	j.	0.523	0.524	0.23	115.93	113.85	−2.08	40	44	−4
2.13.03	Kisújszállás	rtv.	0.577	0.523	−9.46	127.86	113.43	−14.43	21	45	−24
1.21.04	Simontornyai	j.	0.526	0.522	−0.72	116.45	113.28	−3.17	38	46	−8
2.14.01	Esztergom	rtv.	0.537	0.520	−3.19	118.91	112.80	−6.11	35	47	−12
1.14.03	Tatai	j.	0.475	0.518	9.04	105.31	112.52	7.21	78	48	30	+
1.03.01	Baranyavári	j.	0.505	0.516	2.26	111.80	112.02	0.22	54	49	5	+
2.13.04	Mezőtúr	rtv.	0.492	0.515	4.78	108.91	111.82	2.91	68	50	18	+
1.09.04	Székesfehérvári	j.	0.560	0.515	−8.03	124.08	111.81	−12.27	28	51	−23
1.21.01	Dombóvári	j.	0.500	0.515	2.98	110.78	111.77	1.00	59	52	7	+
1.16.07	Gyömrői	j.	0.462	0.512	10.73	102.44	111.14	8.70	92	53	39	+
1.23.04	Veszprémi	j.	0.483	0.510	5.52	107.08	110.71	3.63	73	54	19	+
1.04.04	Orosházi	j.	0.449	0.509	13.29	99.58	110.54	10.96	106	55	51	+
2.08.03	Hódmezővásárhely	thjv.	0.567	0.506	−10.82	125.59	109.75	−15.84	27	56	−29
1.04.05	Szarvasi	j.	0.510	0.504	−1.14	112.95	109.42	−3.53	48	57	−9
2.16.02	Cegléd	rtv.	0.497	0.502	1.04	110.09	108.99	−1.10	64	58	6	+
2.17.01	Kaposvár	rtv.	0.498	0.501	0.70	110.31	108.84	−1.47	61	59	2	+
1.16.13	Monori	j.	0.446	0.500	12.05	98.87	108.55	9.68	111	60	51	+
1.15.06	Szobi	j.	0.476	0.499	4.69	105.50	108.22	2.72	77	61	16	+
1.10.02	Pusztai	j.	0.466	0.497	6.68	103.23	107.90	4.67	88	62	26	+
1.09.05	Váli	j.	0.500	0.494	−1.17	110.68	107.18	−3.50	60	63	−3
1.17.09	Tabi	j.	0.540	0.494	−8.55	119.57	107.14	−12.43	34	64	−30
1.23.01	Devecseri	j.	0.496	0.493	−0.68	109.87	106.92	−2.94	66	65	1	+
1.08.03	Mindszenti	j.	0.399	0.492	23.31	88.45	106.86	18.42	153	66	87	+
2.24.01	Nagykanizsa	rtv.	0.519	0.492	−5.18	114.95	106.80	−8.15	41	67	−26
1.04.01	Békési	j.	0.461	0.489	6.14	102.14	106.22	4.09	94	68	26	+
1.06.06	Putnoki	j.	0.450	0.489	8.69	99.64	106.12	6.48	103	69	34	+
1.23.03	Pápai	j.	0.508	0.488	−4.08	112.64	105.86	−6.78	49	70	−21
2.07.01	Makó	rtv.	0.504	0.485	−3.79	111.62	105.22	−6.40	55	71	−16
1.21.05	Tamási	j.	0.501	0.483	−3.51	111.00	104.94	−6.06	58	72	−14
2.12.01	Eger	rtv.	0.429	0.483	12.78	94.96	104.94	9.98	124	73	51	+
1.04.02	Gyomai	j.	0.447	0.482	7.68	99.10	104.56	5.46	110	74	36	+
1.03.05	Pécsváradi	j.	0.479	0.481	0.42	106.08	104.38	−1.70	75	75	0
1.16.04	Biai	j.	0.462	0.479	3.82	102.33	104.09	1.76	93	76	17	+
1.16.16	Ráckevei	j.	0.477	0.476	−0.23	105.62	103.25	−2.37	76	77	−1
1.24.03	Keszthelyi	j.	0.470	0.475	1.01	104.23	103.17	−1.07	83	78	5	+
2.08.02	Szentes	rtv.	0.503	0.474	−5.64	111.38	102.98	−8.40	57	79	−22
1.03.03	Mohácsi	j.	0.450	0.473	5.26	99.65	102.78	3.13	102	80	22	+
2.11.04	Hajduszoboszló	rtv.	0.460	0.473	2.87	101.94	102.74	0.81	96	81	15	+
1.24.11	Zalaszentgróti	j.	0.464	0.473	1.96	102.77	102.68	−0.10	90	82	8	+
1.06.04	Miskolci	j.	0.463	0.473	2.18	102.55	102.67	0.12	91	83	8	+
1.24.09	Tapolcai	j.	0.452	0.473	4.56	100.18	102.63	2.46	99	84	15	+
1.09.03	Sárbogárdi	j.	0.487	0.473	−2.98	107.92	102.59	−5.33	71	85	−14
1.16.15	Pomázi	j.	0.428	0.472	10.41	94.73	102.49	7.76	125	86	39	+
2.19.01	Nyíregyháza	rtv.	0.472	0.472	0.00	104.47	102.36	−2.11	81	87	−6
1.11.02	Püspökladányi	j.	0.467	0.471	0.96	103.40	102.29	−1.11	87	88	−1
1.17.05	Lengyeltóti	j.	0.449	0.471	4.73	99.54	102.15	2.61	107	89	18	+
1.16.05	Dunavecsei	j.	0.495	0.470	−5.03	109.73	102.11	−7.63	67	90	−23
2.16.07	Nagykőrös	rtv.	0.512	0.470	−8.23	113.41	101.98	−11.44	47	91	−44
1.24.08	Sümegi	j.	0.452	0.470	3.98	100.05	101.93	1.89	100	92	8	+
1.07.05	Torontáli	j.	0.356	0.469	31.79	78.84	101.81	22.97	169	93	76	+
1.21.03	Központi (Tolna)	j.	0.515	0.469	−9.07	114.16	101.72	−12.44	45	94	−49
1.09.02	Móri	j.	0.421	0.467	10.86	93.25	101.29	8.04	137	95	42	+
1.17.02	Csurgói	j.	0.506	0.464	−8.24	112.02	100.71	−11.31	52	96	−44
1.17.03	Igali	j.	0.485	0.464	−4.33	107.42	100.70	−6.72	72	97	−25
1.22.04	Szentgotthárd–muraszombati	j.	0.445	0.464	4.21	98.55	100.63	2.08	113	98	15	+
1.09.01	Adonyi	j.	0.450	0.462	2.50	99.80	100.24	0.44	101	99	2	+
1.24.07	Pacsai	j.	0.415	0.458	10.24	91.95	99.32	7.37	142	100	42	+
1.23.05	Zirci	j.	0.427	0.457	6.91	94.65	99.15	4.50	127	101	26	+
1.24.10	Zalaegerszegi	j.	0.443	0.454	2.51	98.08	98.51	0.43	115	102	13	+
2.13.02	Karcag	rtv.	0.568	0.452	−20.45	125.79	98.04	−27.75	26	103	−77
1.17.06	Marcali	j.	0.475	0.451	−4.91	105.20	98.01	−7.19	79	104	−25
1.24.05	Nagykanizsai	j.	0.443	0.451	1.76	98.25	97.96	−0.29	114	105	9	+
1.24.06	Novai	j.	0.394	0.450	14.37	87.24	97.77	10.53	157	106	51	+
1.05.01	Berettyóújfalusi	j.	0.427	0.450	5.39	94.62	97.71	3.09	128	107	21	+
2.16.03	Kalocsa	rtv.	0.471	0.450	−4.45	104.34	97.69	−6.65	82	108	−26
1.15.01	Balassagyarmati	j.	0.461	0.450	−2.39	102.12	97.66	−4.46	95	109	−14
1.07.03	Központi (CsAT)	j.	0.421	0.450	6.72	93.38	97.64	4.26	136	110	26	+
2.11.03	Hajdunánás	rtv.	0.496	0.449	−9.39	109.90	97.58	−12.33	65	111	−46
1.05.05	Sárréti	j.	0.412	0.449	8.94	91.27	97.42	6.15	146	112	34	+
1.14.01	Esztergomi	j.	0.425	0.447	5.26	94.13	97.08	2.95	131	113	18	+
1.01.04	Szikszói	j.	0.445	0.446	0.18	98.61	96.79	−1.81	112	114	−2
1.15.02	Nógrádi	j.	0.450	0.444	−1.16	99.62	96.47	−3.14	104	115	−11
1.01.05	Tornai	j.	0.450	0.444	−1.16	99.60	96.46	−3.14	105	116	−11
1.17.07	Nagyatádi	j.	0.473	0.444	−6.13	104.71	96.31	−8.40	80	117	−37
1.17.01	Barcsi	j.	0.470	0.443	−5.83	104.13	96.09	−8.04	84	118	−34
1.03.02	Hegyháti	j.	0.439	0.442	0.65	97.36	96.01	−1.35	120	119	1	+
1.03.07	Szentlőrinci	j.	0.497	0.441	−11.27	110.11	95.73	−14.37	63	120	−57
1.06.07	Sajószentpéteri	j.	0.441	0.440	−0.27	97.67	95.44	−2.23	117	121	−4
1.06.05	Ózdi	j.	0.417	0.437	5.02	92.29	94.97	2.68	139	122	17	+
1.21.02	Dunaföldvári	j.	0.427	0.434	1.54	94.70	94.22	−0.48	126	123	3	+
1.17.04	Kaposvári	j.	0.454	0.434	−4.39	100.51	94.16	−6.35	97	124	−27
2.02.01	Baja	thjv.	0.453	0.433	−4.36	100.26	93.95	−6.31	98	125	−27
1.15.05	Sziráki	j.	0.405	0.432	6.84	89.69	93.89	4.20	149	126	23	+
1.05.04	Derecskei	j.	0.469	0.432	−7.81	103.91	93.86	−10.04	86	127	−41
2.25.01	Sátoraljaújhely	rtv.	0.497	0.432	−13.08	110.16	93.82	−16.34	62	128	−66
1.04.06	Szeghalmi	j.	0.413	0.432	4.46	91.54	93.70	2.16	144	129	15	+
1.16.03	Aszódi	j.	0.398	0.431	8.18	88.22	93.51	5.29	155	130	25	+
2.03.01	Mohács	rtv.	0.422	0.430	1.87	93.52	93.35	−0.18	135	131	4	+
1.25.04	Tokaji	j.	0.415	0.428	2.92	92.05	92.82	0.78	141	132	9	+
1.01.01	Abaújszántói	j.	0.414	0.426	2.97	91.74	92.56	0.82	143	133	10	+
2.08.01	Csongrád	rtv.	0.334	0.426	27.60	74.00	92.52	18.52	176	134	42	+
1.16.08	Kalocsai	j.	0.448	0.425	−5.14	99.22	92.22	−7.00	109	135	−26
1.01.03	Gönci	j.	0.398	0.423	6.11	88.26	91.76	3.50	154	136	18	+
2.12.02	Gyöngyös	rtv.	0.274	0.421	53.76	60.64	91.37	30.72	194	137	57	+
1.24.01	Alsólendvai	j.	0.508	0.420	−17.20	112.48	91.26	−21.22	50	138	−88
1.16.09	Kiskőrösi	j.	0.387	0.420	8.57	85.69	91.15	5.46	159	139	20	+
1.16.12	Kunszentmiklósi	j.	0.416	0.418	0.62	92.15	90.85	−1.30	140	140	0
1.13.03	Központi (JNSz)	j.	0.372	0.418	12.55	82.37	90.84	8.46	164	141	23	+
1.03.04	Pécsi	j.	0.438	0.417	−4.77	97.09	90.59	−6.49	121	142	−21
1.17.08	Szigetvári	j.	0.423	0.417	−1.38	93.67	90.52	−3.16	133	143	−10
1.13.04	Tiszai alsó	j.	0.417	0.416	−0.21	92.46	90.40	−2.05	138	144	−6
1.05.02	Biharkeresztesi	j.	0.425	0.415	−2.48	94.24	90.05	−4.19	130	145	−15
1.06.02	Mezőcsáti	j.	0.409	0.415	1.37	90.61	90.00	−0.61	147	146	1	+
1.03.06	Siklósi	j.	0.519	0.414	−20.10	114.92	89.97	−24.95	42	147	−105
1.05.03	Cséffa-nagyszalontai	j.	0.413	0.413	0.03	91.52	89.70	−1.82	145	148	−3
2.16.14	Kecskemét	thjv.	0.423	0.412	−2.61	93.81	89.52	−4.29	132	149	−17
1.02.03	Jánoshalmi	j.	0.422	0.412	−2.36	93.58	89.52	−4.05	134	150	−16
1.07.01	Battonyai	j.	0.383	0.412	7.77	84.77	89.51	4.74	160	151	9	+
1.15.04	Szécsényi	j.	0.380	0.412	8.55	84.13	89.48	5.35	161	152	9	+
1.02.02	Bajai	j.	0.404	0.411	1.65	89.52	89.16	−0.36	150	153	−3
2.13.06	Túrkeve	rtv.	0.439	0.410	−6.64	97.37	89.07	−8.30	119	154	−35
1.25.03	Szerencsi	j.	0.432	0.409	−5.35	95.73	88.77	−6.95	123	155	−32
1.16.02	Alsódabasi	j.	0.371	0.407	9.67	82.28	88.41	6.13	165	156	9	+
2.15.01	Balassagyarmat	rtv.	0.442	0.404	−8.54	97.87	87.70	−10.17	116	157	−41
1.01.02	Encsi	j.	0.406	0.403	−0.88	90.00	87.41	−2.59	148	158	−10
1.07.04	Mezőkovácsházi	j.	0.301	0.398	32.25	66.68	86.41	19.73	191	159	32	+
1.04.03	Gyulai	j.	0.302	0.397	31.52	66.81	86.10	19.29	189	160	29	+
1.16.01	Abonyi	j.	0.341	0.396	15.96	75.65	85.96	10.31	175	161	14	+
1.12.03	Hatvani	j.	0.365	0.395	8.18	80.93	85.79	4.85	167	162	5	+
1.12.06	Tiszafüredi	j.	0.427	0.393	−7.81	94.52	85.38	−9.14	129	163	−34
1.06.03	Mezőkövesdi	j.	0.354	0.391	10.21	78.52	84.80	6.27	171	164	7	+
1.12.02	Gyöngyösi	j.	0.347	0.390	12.19	76.97	84.61	7.64	174	165	9	+
1.06.01	Edelényi	j.	0.404	0.388	−3.95	89.49	84.22	−5.27	151	166	−15
2.11.01	Hajduböszörmény	rtv.	0.507	0.388	−23.60	112.38	84.13	−28.25	51	167	−116
1.15.03	Salgótarjáni	j.	0.311	0.381	22.29	68.98	82.65	13.68	184	168	16	+
1.25.02	Sárospataki	j.	0.348	0.377	8.49	77.07	81.93	4.86	173	169	4	+
1.12.01	Egri	j.	0.326	0.377	15.80	72.13	81.84	9.71	179	170	9	+
1.02.01	Bácsalmási	j.	0.398	0.376	−5.42	88.12	81.66	−6.46	156	171	−15
1.13.06	Tiszai közép	j.	0.372	0.375	0.76	82.45	81.40	−1.05	163	172	−9
1.13.02	Jászsági felső	j.	0.314	0.370	17.87	69.49	80.25	10.76	183	173	10	+
1.13.05	Tiszai felső	j.	0.400	0.368	−8.10	88.66	79.84	−8.82	152	174	−22
1.08.01	Csongrádi	j.	0.239	0.368	53.61	53.04	79.83	26.79	201	175	26	+
1.13.01	Jászsági alsó	j.	0.355	0.367	3.24	78.69	79.60	0.91	170	176	−6
1.16.14	Nagykátai	j.	0.264	0.366	38.36	58.59	79.43	20.84	197	177	20	+
1.20.02	Fehérgyarmati	j.	0.373	0.363	−2.73	82.68	78.80	−3.88	162	178	−16
1.20.04	Vásárosnaményi	j.	0.320	0.361	12.93	70.90	78.45	7.55	181	179	2	+
1.24.04	Letenyei	j.	0.357	0.361	1.10	79.09	78.35	−0.74	168	180	−12
2.11.02	Hajduhadház	rtv.	0.440	0.360	−18.09	97.40	78.17	−19.23	118	181	−63
1.19.01	Dadai alsó	j.	0.366	0.358	−2.11	81.10	77.79	−3.31	166	182	−16
2.21.01	Szekszárd	rtv.	0.469	0.357	−23.99	103.99	77.45	−26.54	85	183	−98
2.04.02	Gyula	rtv.	0.318	0.355	11.56	70.46	77.01	6.56	182	184	−2
2.16.05	Kiskunhalas	rtv.	0.434	0.354	−18.56	96.20	76.77	−19.43	122	185	−63
1.07.02	Eleki	j.	0.311	0.349	12.18	68.85	75.69	6.83	185	186	−1
2.13.01	Jászberény	rtv.	0.333	0.348	4.77	73.68	75.64	1.96	177	187	−10
1.16.10	Kiskunfélegyházi	j.	0.267	0.345	29.03	59.20	74.84	15.64	196	188	8	+
2.16.04	Kiskunfélegyháza	rtv.	0.270	0.343	26.79	59.85	74.36	14.51	195	189	6	+
1.25.01	Bodrogközi	j.	0.320	0.338	5.39	70.98	73.30	2.32	180	190	−10
1.20.01	Csengeri	j.	0.301	0.337	11.88	66.66	73.07	6.41	192	191	1	+
1.05.06	Székelyhidi	j.	0.332	0.334	0.52	73.65	72.55	−1.11	178	192	−14
1.08.02	Kiskundorozsma	j.	0.301	0.333	10.39	66.78	72.23	5.45	190	193	−3
1.12.04	Hevesi	j.	0.354	0.332	−6.19	78.36	72.03	−6.34	172	194	−22
1.11.01	Központi (Hajdu)	j.	0.310	0.331	7.03	68.60	71.94	3.34	186	195	−9
1.19.02	Dadai felső	j.	0.302	0.331	9.48	66.93	71.80	4.86	188	196	−8
1.12.05	Pétervásári	j.	0.253	0.325	28.49	56.06	70.57	14.52	199	197	2	+
1.19.09	Tiszai	j.	0.306	0.294	−4.07	67.91	63.83	−4.08	187	198	−11
1.19.03	Kisvárdai	j.	0.290	0.275	−5.01	64.22	59.77	−4.45	193	199	−6
1.19.07	Nyírbátori	j.	0.225	0.255	13.64	49.81	55.46	5.65	203	200	3	+
1.20.03	Mátészalkai	j.	0.256	0.251	−1.80	56.72	54.58	−2.14	198	201	−3
1.19.08	Nyírbogdányi	j.	0.220	0.240	9.02	48.82	52.15	3.33	204	202	2	+
1.19.05	Nagykállói	j.	0.240	0.210	−12.39	53.17	45.64	−7.53	200	203	−3
1.19.06	Nyírbaktai	j.	0.226	0.209	−7.58	50.10	45.37	−4.73	202	204	−2
1.19.04	Ligetaljai	j.	0.148	0.149	0.86	32.71	32.32	−0.38	205	205	0