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Published by: Institute of History, Research Centre for the Humanities, Hungarian Academy of Sciences

2019_1_Demeter

pdfVolume 8 Issue 1 CONTENTS

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)

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1 HGIS = Historical Geographical Information System (or GIStory, or GIS-aided historical research). For GIS-aided historical research the term HGIS is more common than GIStory. See Gregory, Ian N. A place in History: A short introduction to HGIS by the lead developers of GBHGIS. http://hds.essex.ac.uk/g2gp/gis/index.asp; or https://www.gislounge.com/find-gis-data-historical-country-boundaries/ and http://www.hgis-germany.de/, http://www.hgis.org.uk/resources.htm#top. GIStory is also accepted (see GIS and the City conference in Darmstadt, 2018: https://www.geschichte.tu-darmstadt.de/index.php?id=3633). Many thanks to János Mazsu for drawing our attention to the terminological problems.

2 Project OTKA 81 488. Principal investigator: János Mazsu. The reconstruction of social and spatial patterns of Debrecen, 1870–72 was considered the predecessor of this investigation. Recently, Réka Gyimesi initiated a similar project.

3 For the results, see http://www.gistory.hu/g/hu/gistory/gismaps and http://www.gistory.hu/g/en/gistory/otka.

4 Source: MNL–BAZML SFL XV. 83. box. 77–79. Now www.hungaricana.hu and www.mapire.eu (containing settlement level cadastral maps) offer new instruments to find maps with good resolution and information on identification numbers.

5 The data sheets from Zemplén, Ung, and Sáros Counties also survived almost intact in the county archives.

6 The term household and family are not synonyms: a word describing the situation more properly is the German “Wohnparthei”. In the following, we use the three terms as synonyms despite the minor differences.

7 Demeter and Bagdi, A társadalom.

8 This value is high compared to neighboring towns and towns with similar sizes and functions. In Mukačeve (Munkács) the same figure was only 45 percent. Dányi describes Sátoraljaújhely as a “para-center.” Dányi, “Regionális vándorlás,” 99–103. Despite its development, the town was still unable to attract its larger “Hinterland” in the nineteenth century (despite the high birth rate the population decreased in the northern part of Zemplén County and in the northern part of Sáros County by 20 percent between 1880 and 1910 due to massive emigration to America and not to local centers.

9 While Eger became peripheral as major railway routes bypassed it, Sátoraljaújhely became a traffic center, an intermediate station of population movements towards Budapest. The main source area was Upper Hungary: the proportion of migrants arriving to Sátoraljaújhely from this direction was higher than that of migrants arriving from Zakarpatiya and from the regions beyond the Tisza River. Demeter and Bagdi, “Sátoraljaújhely,” Table 3.

10 The country averages were as follows: Roman Catholic: 52 percent, Greek Catholic: 10 percent, Calvinist: 12,5 percent, Israelites: 4.5 percent, Lutheran: 6.5 percent. So Greek Catholics and Jews were overrepresented and Roman Catholics and Lutherans were underrepresented in the town compared to national average. Katus, A modern Magyarország, 483.

11 The average for Pest County in 1896 was 4.6. Őri, “Család és házasodás,” 75. For Istanbul, this figure was 4.1 people around 1900. In some of the immigrant-dominated quarters it fell below 3.8. Based on a sample of 2,500 people, the average Bulgarian and Muslim household size in towns in the 1860s was 4.4 and 4.7 people respectively, while in Muslim villages this reached 4.9. Todorova, “Situating the family,” 452.

12 In 1930, 70 percent of the houses in Slovenia had only one room. Malojčić, Selo i tuberkuloza.

13 Three rooms are considered as a minimum to consider a family “middle class” according to Gerő. Thus, in Sátoraljaújhely, approximately 13 percent of the households fit into this category. Gerő, Dualizmusok, 149.

14 Ibid., 148.

15 Malojčić, Selo i tuberkuloza.

16 Veliky, A változások kora.

17 Of course, migration was not the only factor. A family name might go extinct if there were no sons, and this limits the relevance of our investigations.

18 Barta, Ha Zemplin vármegyét, 298. 312–13.

19 The census does not mention family ties between the Wohnpartheys. This hinders reconstructions without the aid of parish registers. The same constraints are valid for the investigations of matrilocality or patrilocality.

20 http://www.gistory.hu/g/hu/gistory/gismaps. See maps: chapter 8, urban society.

21 The blocks inhabited by Jews cannot be considered fully homogeneous because of the Christian servants and maids. The sources provide no information regarding the separation of Orthodox and Neologue Jews: in Sátoraljaújhely each group had a synagogue.

22 Most of the Jews in Debrecen also lived in the city center (along Hatvan Str. and Piac Str. near the Great Church of the Calvinists): 40 percent of the Jewish households dwelled in six streets. See Mazsu, “Inside borders” and Mazsu, “Piac, kereskedelem, kapitalizálódás.” In Sátoraljaújhely the preference of north-south and east-west main roads was observable among Jews, and though the east-west axis was of secondary importance regarding migration routes, it was a non-negligible direction concerning the movements of goods (grain trade).

23 Gyimesi and Kehl, “Spatial analysis of the socio-economic structure.”

24 Pozsgai registered 5–7.5 percent in the two districts and cca. 40 settlements in the rural Torna County in 1870. Compared to this, Sátoraljaújhely was really functioning as a melting pot. See Pozsgai, “Görög és római katolikus nemzetiségek.”

25 Supplemented by craftsmen serving the high-elite with their specialized knowledge.

26 The demographic transition in Hungary began only after the last great cholera epidemics (1873).

27 The proportion of the indigenous population reached 50 percent only together with the children, among whom immigrants were rare.

28 Their representation in the urban and county elite was traditionally higher.

29 In the larger city of Debrecen (which at the time only had 2,000 Jewish inhabitants), only 30 percent of the Jews were local-born. Another 20 percent was indigenous in the county, and another 30 percent arrived from the northeast. The average size of the 340 Jewish households indicates larger family sizes (5.5) than the town average, as was also true in Sátoraljaújhely (4.5). See Mazsu, “Inside borders.”

30 The Jews in Sátoraljaújhely were divided among traditionalist, modernist, and “status quo ante” factions.

31 Without own home/Wohnparthey, cca 1000 persons.

32 Pharmacists, assistant teachers, waiters, and merchant-assistants were also grouped here.

33 Demeter, “A dualizmus kori Eger.”

34 In Eger, the elite was underrepresented within the immigrant society. In the middle class, artisans were overrepresented, while lower “national” officials (porters, policemen, postmen) were recruited from local-born people.

35 In the case of Eger, the use of sources of a different character, namely the parish registers, limited the reliability of the classification and the comparison. The statistics were based on 167 marriages from 1883, where the occupation and place of origin of the husband, the husbands’ father, and the wives’ father were mentioned too.

36 In Eger, the local elite was also stronger compared to the immigrant elite society (22 vs. 12 percent).

37 In Varannó, the officials, bureaucrats, and lower-ranking state officials were all immigrants. Lacking a secondary school, the townlet was unable to reproduce its elite. Merchants, artisans, and entrepreneurs were underrepresented among immigrant earners (constituting 57 percent of all earners in Varannó, but 67 percent in Sátoraljaújhely, Table 17). 60 percent of the locals were classified into the middle classes (among migrants, this figure was only 40 percent). 33 percent of the local-born society was poor. 42 percent of the migrant society was poor.

38 Widow(er)s (family heads) were treated separately, as we did not have information about their professions.

39 Discriminant analysis was applied as a control for clusterization.

40 The success rate of reclassification by discriminant analysis was low, under 50 percent.

41 They could be considered the “core elite,” followed by a “buffer-transition” group of an additional 100 families.

42 Canbakal and Filiztekin, “Wealth and Inequality.”

43 The richest 2 percent owned 25 percent of wealth in China. In New-Spain, the richest 10 percent owned 55 percent of the wealth in 1790. In Bihar (India), in 1804 the richest 20 percent owned 50 percent of the wealth, and in Naples in 1811 the richest 10 percent owned 33 percent of the wealth. Milanovic, Lindert and Williamson, “Measuring Ancient Inequality.”

44 In Belgrade 60 percent of the houses had not more than one room in 1907 (as in the case of Wohnpartheys in Sátoraljaújhely), but the density was 3.5 prs/house, while in the Hungarian town it was 9 prs (calculating with two households/house). Vuksanović-Anić, “Urbanistički razvitak Beograda,” 458–65.

45 The narrow elite (group 11–13) was characterized by a low number of children, but this was equalized by the auxiliary workforce (Table 19). The proportion of earners was higher than the city average. The average population density (prs/room) and number of rooms in the households of the elite (above two) were similar to the figures measured in groups 9 and 10.

46 In 1926, a merchant family or the family of an official in Belgrade had 2.5 rooms, artisans had 1.9, and workers had 1.5. The former values are similar to the values for Hungary, while the latter is higher. Calic, Sozialgeschichte Serbiens, 323–25.

47 Or, using a different approach, in cluster 1 each family had two or more than two rooms (90 percent had more than 3), while it was only 60 percent in cluster 2.

48 The number of rooms per family was high along the north-south axis of the town, while population density was great in the north and on the eastern outskirts and in Zsólyomka.

* 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.

51_old.jpg

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

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

fig%202%20religion.tif
legend%20fig%202.JPG
Demeter3a.jpg
Demeter3b.jpg

Figure 3. Religious differentiation (occupations)

fig%204%20person%20per%20room.tif
legend%20fig%204.JPG

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.

fig5.jpg
fig6.jpg
fig%207%20vagyon.tif

Figure 7. Spatial pattern of wealth based on the method using an equation composed of sociodemographic indicators, 1870

 

legend%20fig%207.JPG
fig%208%20sauh%20tarsadalom.tif

Figure 8. Spatial pattern of social groups in Sátoraljaújhely in 1870

For the detailed legend see Table 18a.

legend%20fig%208%20kimaradt.JPG
Demeter9.jpg

Figure 9. Connection between religion and economic potential based on the complex indicator (average, std. dev.)

 

Demeter11.jpg

Figure 11. Connection between average economic potential (complex indicator based on the equation) and the age of the family head

 

Demeter10.jpg

Figure 10. Differences in population density (inhabitants /room) based on religion (average and std. dev.) 1

1 Mean is dark. Std. Deviation is indicated by light grey.

Demeter12.jpg

Figure 12. Differences in population density (inhabitants /room) based on social groups defined by the prestige of occupation (Erdei-Weber method) (average and std. dev.)

 

Demeter13a.jpg
Demeter14.jpg
Demeter13b.jpg

Total

79987.png
79980.png

2019_1_Koloh

pdfVolume 8 Issue 1 CONTENTS

Rural Society at the Time of the Cholera Outbreak: Household and Social Structure, Taxation and the Cholera Outbreak in Endrőd (1834–1836)

Gábor Koloh
Hungarian Agricultural Museum
This email address is being protected from spambots. You need JavaScript enabled to view it.

Endrőd is a village in Békés County along the Körös River. A census taken by the local church administration presents the composition of 663 household from 1835. From the perspective of household structure studies, this source is unique in length, age, and complexity. Furthermore, cholera destroyed the settlement the year before and after the census was taken. The census and parish registers offer sources on which one can study the impact of the epidemic on households. The tax register from 1834/1835 allows for the classification of family heads into tax categories, so we can extend the test to the relationship between financial background and mortality rate. This multivariate analysis uses the sources and methods used in epidemic history, social history, and historical demography.

Keywords: cholera, historical demography, tax registers 1834/35, mortality and welfare, spatial patterns

While browsing the archives of the parish of Endrőd, I came across a parish family book (“register of souls”) dated 1835, the first page of which (after the cover decorated with floral patterns) bore the title Az Endrődi Hivek Összeirása 1835ik Esztendötöl Kezdve G[öndöcs] J[ózsef káplán] (“Register of the Believers of Endrőd as of 1835 A.D. [Chaplain] J[ózsef] G[öndöcs].”

Endrőd today forms part of the town of Gyomaendrőd in southeastern Hungary on the banks of the Körös River. According to András Vályi’s description, it is a “Hungarian village in Békés County, the lord of the manor is Baron Harucher, the inhabitants are Catholic, situated near Gyoma and Ötsöd, belonging to the estate of Gyula, its arable lands are mostly good, meadows similarly, pasture is suitable for cattle of several herds, though some parts of its arable lands are flooded and some parts are nitrous, few woods and reeds, mill is negligible, marketplace is second-class due to its distance.”1 It would require a separate analysis to determine what Vályi meant precisely by “Hungarian village.” In fact, Johann Georg Harruckern, council member of the Hofkammer (the Exchequer of the Habsburg Empire), who received the settlement as part of the estate of Gyula, settled Hungarians and Slavs here in the 1720s and 1730s, mainly from the north of the Kingdom of Hungary, but following the initial period, during the work of parish priest Sámuel Pálfy (1772–1780), celebration of the mass in Slavic languages stopped,2 and as Elek Fényes put it in the mid-nineteenth century, “Slovaks also came, but they have now become entirely monolingual Hungarian.”3 In Fényes’s description, the arable lands are not only “mostly good,” but “they have such fertile, black clay soil mixed with sand that its winter wheat produces 15 seeds and its spring wheat produces 20.”4 Almost all (according to Fényes, 98 percent) of the inhabitants were Roman Catholic. The lord of the manor in the period under examination was baron Flórián Drechsel’s wife, Countess Karolina Stockhammer of the naturalized Stockhammer family.5 Regarding its geographical location, the village is a blank spot for analyses from the perspective of household structure, historical demography, or a deeper social history; only local ethnographic research has produced some serious results.6

The scholarship on household structure is “confusingly rich,”7 so I can present here only a very brief overview. In his book Property, Production, and Family in Neckarhausen 1700–1870, which was published in 1990, David Warren Sabean outlined the following evolution of household structure research: he named Frédéric Le Play and Wilhelm Riehl as the prominent representatives of the first generation of researchers in the field.8 Although the closely related Hungarian literature considers Le Play a sociologist, Sabean emphasizes the ethnographic character (Volskunde) of the research and conclusions of the first generation, where Le Play and Riehl saw the original patriarchal structure of the family9 as a continuous and functional whole with a head and dependent members.10 Le Play defined the stem family (famille-souche, when a married child remains in the parents’ household) and Riehl the enclosed household estate11 (das ganze Haus) as transformations of this patriarchal structure. According to Le Play’s concept, the parent couple lived together with one of their children and his or her family, while the others left the household.12 Sabean regards Karl Bücher as a member of the second generation of researchers. According to Bücher, the basis of the functioning of a household is production and consumption, producing for its own needs, and the family members do not participate in the production of goods. Like Bücher, Alekxander Chayanov, in his analysis of Russian peasant society, also saw the key to the functioning of the household in the close interrelationship of production and consumption.13 The third approach was built on these concepts. It originated in the study of historical demography, mainly in the work of Peter Laslett, who by that time had serious doubts as to the reliability of the widely known concept formulated by Le Play.14

Laslett questioned the “statements regarding the average size and structure of pre-industrial families and households and the historical change they allegedly underwent.”15 He objected to the fact that, although it had not become an exclusively accepted concept (research by Marion Levy explicitly refuted this hypothesis), it still was a recurrent “stereotype to talk about structures consisting of 30–40 members and three to ten families. When, however, historians analyzed the totality of households of a settlement or estate on the basis of surviving census records, it turned out that in reality, most peasant households were significantly smaller than this.”16 Laslett et al. conducted research covering England and northwestern Europe in the seventeenth, eighteenth, and nineteenth centuries, which revealed a generally higher rate of nuclear families. Deviating results were found in analyses of family structures in the Balkans, where larger, more complex households occurred relatively often.17 The concept of patriarchal (married sons living in the same household with the parents) and stem family cohabitation was thus refuted, facilitating an understanding of the profound economic and social (including demographic) processes taking place in the nineteenth century. At the same time, Laslett’s typology of household structures and John Hajnal’s typology18 (its excessive complexities notwithstanding) also highlighted the relevance of cultural differences and the composition of the community, even if the acceptance of the role of the latter has now been overshadowed.19 The greatest difficulty faced in the research, hence, lies not in the various concepts, hypotheses, and further research prospects, but rather the lack of usable, reliable, and in particular dynamic sources. Although it is true that a dynamic analysis of the evolution of households would and could be more practical for the purpose of understanding the quality of cohabitation and also more meaningful than the mere exploration of regional samples, unfortunately these kinds of analyses can only be done in exceptional cases. Albeit Chaplain Göndöcs also started the parish family book with high hopes in 1835, by 1836 he mostly had recorded only the births up until that time and the information concerning those who had died of cholera (and not even everyone who belonged to this latter group!), and by 1837 only a small number of new or corrected entries had been added, and none were added in 1838. The national census of 1869 is the nearest in time to this period, but its record sheets have not survived from Endrőd (Mezőberény is the only settlement in the county for which the records survived).20

But this is just, so to speak, one of the basic problems regarding the analysis of households. The relevant literature has been discussing the problems of the term “household” for a long time. Gyula Benda used a succinct and witty definition, so it is worth quoting it in its entirety: “The household, i.e. basically a group people living under the same roof and of the same bread, is both an economic and social basic unit before industrialization. In the case of family estates, which were still dominant in the Early Modern period (whether agricultural or artisan in nature), the unit of production (and thus taxation) is also this cohabiting group. The family and the household are also the basal cell of accumulating and transferring wealth—their characteristics are closely related to the systems of inheritance. Finally, it is also a unit of consumption, everyday life is organized in its context.”21 Tamás Faragó, comparing the definition of the household with the definition of the family, wrote that the “household is different from the family both in its concept, content, organization, and system of activities, particularly in the pre-industrial era. Its members are bound by kinship (consanguinity, affinity, or fictive kinship) and by legal relationships (e.g. servants) and functional ties. Its core is usually but not necessarily a family.”22 Understanding and using the term becomes more difficult when it becomes apparent that households have various structures and different sizes even within individual settlements. In such cases, according to Benda, different models are developed which attempt either to present the different variations in their entirety or to present the shades of the various types through in-depth qualitative research, both on the international and domestic levels.23 The interpretation of the function of the households poses another set of problems. More than half a century ago, József Tamásy regarded them as mere economic communities, while Faragó emphasizes that the household group creates the necessary living conditions and ensures the socialization of new members, providing a material and mental “home space.”24 The more recent research of Péter Őri and Levente Pakot highlights the demographic and economic roles of the household, which are easier to grasp in quantitative terms.25

Tamásy highlighted the cohabitation of Croatian extended families in the eighteenth-century Kingdom of Hungary, where the average number of people in one household was over eight, while in Transylvania, Transdanubia, the Great Hungarian Plain, and the northern region of the country not many more than five people lived in the same household (with only minor differences in the different regions).26 Later domestic macroanalyses confirmed the proportion of households with an average of more than five people from the second half of the eighteenth century to the first decades of the nineteenth. Although the nuclear household could still be regarded as the dominant type, “the proportion of complex (extended and multiple-family) households was not insignificant, and at least in some areas, the majority of the population lived in such types of households type in one or another stage of their lives (...).”27 Furthermore, it is important to note that “households with a great number of people and a complex structure occurred primarily among serf peasantry, and only very rarely among landless layers of society.”28 Even though Faragó emphasizes the lack of sources, he did demonstrate the dynamic transformation of the household structure in the period of more than half a century in question. He concludes that in order to avoid the fragmentation of estates, becoming a landless serf (zsellér), or impoverishment, the proportion of complex households increased between 1787 and 1828, but at the same time, the household structures of different villages show various differences on a regional and ethno-cultural level.29 Micro research both confirmed the above conclusions and may have also refined them with restrictions to local circumstances. Such research includes the study conducted by Andorka and Sándor Balázs-Kovács in Sárpilis, where they repeated the above with respect to the size and composition of the households. Faragó broke down his data according to social strata in his examples from Pest county, but his micro findings verify the nationwide conclusions. The study by Magdolna Balázs and László Katus focusing on Central Transdanubia emphasizes the similarity with the Balkan and eastern household structure, while Gyula Benda’s analysis in Keszthely also establishes the dominance of the nuclear family and the more complex structures observed among farmers, serfs and merchants. Thanks to her sources, Ildikó Husz was able to perform an in-depth analysis of the households of Zsámbék in their dynamics, and she confirmed Faragó’s conclusion regarding the temporary increase in households of a more complex composition, similarly to Balázs Heilig’s analysis in Szőlősardó.30

My source, in the absence of any reference to a higher order, is a “church register of souls,” or a status animarum.31 In the source, the households are not distinguished from one another consistently, which also reinforces the foregoing. At the beginning of the family book, the relationships to the head of the household are accurately described, and later the indication of relations perceived as unambiguous (i.e. children) is omitted. In the second half of the book, even the status of alien persons (mainly servants) is often omitted. The heading of the family book is the following: Háznak a’ száma, Vezeték és Kereszt Nevek, Sorsa, Kora (Eszt., Holn., Nap), Egy Házi Család Száma, Idegen Vallásúak, Észrevételek, or House Number, Family and Given Names, Fate, Age (Year, Month, Day), Number of People in the Household, Foreign Faith, Comments. As regards people who belonged to a so-called foreign faith, József Göndöcs recorded their number but failed to provide more details. Taking house number 9 as an example, we can first see the name of Mihály Bentsik (Fate: Landowner farmer), followed by his wife and daughters, then a female servant. Without any separation, the records continue with György Vaszkó (Fate: tenant) with his wife, daughter, and siblings. This row is then closed by a horizontal line, the Number of People in the Household is 10, then István Bálint (Fate: in the great vineyard) with his wife and two children. The family number thus increases to 14 people. As far as I know, there were no close family relations between Mihály Bentsik and György Vaszkó, but still, the two family heads were not separated from each other in the family book. In another example, in the case of house number 96, tenant Imre Fülöpp starts the row, followed by his wife, then Mihály Denitska, furrier, tenant, and his wife and daughter. The row is separated by a line from István Farkas (Fate: in the great vineyard), his wife, and son. Then, another line separates them from György Batsa, homeowner farmer, and his family, who should have been in the first place according to the generally applied logic of the family book.

If one compares the values of the earlier censuses and our source, although the number of households would probably have approximated the previous values if I had calculated the number of households along the lines drawn by the chaplain, due to the inconsistencies indicated above, it seemed more practical to apply the considerations of Őri and Pakot. While processing and coding the data, I considered one household where even though several family nuclei lived together, it was clear that they were close relatives, and I distinguished them from those in which, though not separated by a line, the tenants, gardeners, servants, and other employees were not relatives, but had a family.32 According to this method, a total of 960 households could be unambiguously distinguished.

 

Table 2. Average size of households and the number of married men per household, Endrőd, 1835

De facto

population

Number of married men

Number of households

Average

size of households

Number of married men

per household

5,527

1,109

960

5.75

0.92

Source: Believers of Endrőd 1835. GySzIPL

The average size of households in 1835 does not indicate a cardinal deviation from the value of slightly more than five, which is treated as average in the literature. Therefore, the values of Endrőd correspond to the national average, so they (including the number of married men per household) can be considered representative values.

I used the Laslett–Hammel typology to classify the households in which (as seen from Table 3) 65 percent were nuclear households, which fits well in the series of literature refuting the theory of the dominance of stem families. According to the source, in addition to then 25-year-old homeowner farmer, Mátyás Juhász, who was in the lower category of taxpayers with his tax of 3 forints and 5 kreutzer, three widows lived alone: Mrs. István Palócz aged above seventy, Mrs. Mátyás Roncsek nearing her fortieth year of age (Widow Landowner), and Mrs. Mátyás Tímár (aged 22) spent their year of mourning in the period of the family book (October/November 1835).33 Those living in households with no family included János Lábos, the parish priest of Endrőd between 1825 and 1840, the Curator of the Church (caretaker) József Szölösy, and the unmarried manservants working at the slaughterhouse. Lábos’s household included the author of the source, the 28-year-old chaplain József Göndöcs, as well as chaplain János Piringer, the priest’s sister, and two servants. I considered “unclassifiable” the House of the Lord of the Manor, the House of the Village, and the Arany Patkó lodging house, which Göndöcs records as a separate house, even though he also notes that its tenants have been recorded under house no. 2. In the case of another two houses, albeit the Tenants themselves are known, the source only comments on the others that “at this house live a total of ununited Vlachs: 7.” In these cases, the relationships were impossible to explore.

The rate of 19.7 percent of households with multiple families is nearest to the 1808 value of Tiszacsege (18.4 percent), so corresponding to the classification of Faragó with the help of the Laslett–Hammel system, it constituted a temporary group.34 This is worth noting because for Faragó’s group, this temporary nature can be demonstrated in both Calvinist and Roman Catholic settlements, as well as in both Hungarian-speaking and Slovak-speaking settlements, and in this regard, Endrőd has all these attributes. It was predominantly Catholic but with a significant proportion of neighboring Calvinist settlements; it was Hungarian but part of the population was of Slavic origin. The average size of households (obviously) increased with the complexity of the households, and in comparison with the 1869 value, the values of Endrőd (apart from nuclear households) are on average higher by one person.35

 

Table 4. Breakdown of households according to the gender and age of the household head, Endrőd, 1835

 

Age groups

Total

<25

25–34

35–44

45–54

55–64

64<

no data

N

( percent)

Male

66

218

225

174

136

56

3

878

91.5

Female

4

15

13

18

20

7

2

79

8.2

No data

0

0

0

0

0

0

3

3

0.3

Total

70

233

238

192

156

63

8

960

100.0

Source: Believers of Endrőd 1835. GySzIPL

If we look at the distribution of household heads according to gender, the dominance of male household heads is apparent. Men aged between 25 and 44 constituted the main body. More than half of all the men belonged to this age group, while this ratio is only 6.4 percent in the case of men above 64. However, only rarely were older men living in the family not the head of the household as well: in all seven such cases, the man (whether he was the household head’s father or other) was 70 years old or older. In the case of women, a greater number in the older age group of 45–64 became household heads upon becoming widows. The age of non-head cohabiting elder women was 65 or higher.

 

Table 5. Households according to the household head’s gender and the main categories of household structure, Endrőd, 1835

 

Household type

Total

1

2

3

4

5

6

No data

N

percent

Male

0.1

0.7

66.1

13.1

19.9

0.1

0.0

878

91.5

Female

3.8

1.3

55.7

20.3

17.7

1.3

0.0

79

8.2

Total

0.5

0.9

65.3

14.1

20.2

0.8

0.0

957

99.7

Source: Believers of Endrőd 1835. GySzIPL

The correlations between the household types and the household head’s gender are shown by the percentages in table 4. These indicate that higher rates of men are heads of nuclear and multiple-family households, while women have greater proportions in the other variations. The situation of female household heads belonging to the first type has been discussed above. Households with no family show higher values for women only because of the proportions: this is actually one woman, 23-year-old Ágnes Goda, who lived in a household with her siblings. In the case of households with complex families, we can speak about households in which widows lived together with one or more of their married children and the widow did not transfer the household headship to one of her children. This was the case for Mrs. András Cz. Tóth, the widow of a landowner farmer, who paid taxes on nine acres of arable land, 5.5 acres of meadow and 1.5 acres of vineyard and lived together with her two sons, András (25) and István (20) and their wives and children. Accordingly, the conclusions deriving from the values in the table correspond to the findings of the MOSAIC project.36

 

Table 6. Household structure according to the age groups of male household heads, Endrőd, 1835

Household category

Age groups

N

<25

25–34

35–44

45–54

55–64

64<

 

1. Solitaries

0.0

0.5

0.0

0.0

0.0

0.0

0.1

2. No family

4.5

0.5

0.0

0.6

0.7

0.0

0.7

3. Nuclear

71.2

75.2

81.8

64.4

41.9

23.2

65.9

4. Extended

19.7

18.3

8.0

9.8

11.8

19.6

13.1

5. Multiple

4.5

5.5

10.2

25.3

45.6

55.4

20.0

6. Unclassifiable

0.0

0.0

0.0

0.0

0.0

1.8

0.1

Total ( percent)

100.0

100.0

100.0

100.0

100.0

100.0

100.0

N

66

218

225

174

136

56

875

Source: Believers of Endrőd 1835. GySzIPL

The conclusions suggested by the values contained in table 6 also correspond to national trends. Male household heads under 54 years of age dominate in the case of nuclear households, while men in higher age groups are heads of multiple-family households. Those who became household heads young either became heads upon getting married and leaving the parents’ home or inherited the household after their parents had died. They most often were the heads of nuclear households. Less often, if they were not yet married, they lived alone or maybe with other unmarried persons.”37 Aging men, however, lived together with their married child(ren) and their families in increasing proportions.

 

Table 7. Household structure according to the age groups of female household heads, Endrőd, 1835

Household category

Age groups

N

<25

25–34

35–44

45–54

55–64

64<

 

1. Solitaries

25.0

0.0

7.7

0.0

0.0

14.3

3.9

2. No family

25.0

0.0

0.0

0.0

0.0

0.0

1.3

3. Nuclear

50.0

100.0

69.2

44.4

45.0

14.3

57.1

4. Extended

0.0

0.0

15.4

22.2

40.0

14.3

19.5

5. Multiple

0.0

0.0

7.7

33.3

15.0

57.1

18.2

6. Unclassifiable

0.0

0.0

0.0

0.0

0.0

0.0

0.0

Total (percent)

100.0

100.0

100.0

100.0

100.0

100.0

100.0

N

4

15

13

18

20

7

77

Source: Believers of Endrőd 1835. GySzIPL

 

In accordance with the above, the ratio of female household heads was continuously shifting from the nuclear to the complex household structure over time. In the latter cases, typically the widowed mothers were the heads of the households, so they continued to manage the household after their husbands deaths. The dynamics of change according to age groups can be seen in the case of both the male and female household heads. In Endrőd, too, younger household heads typically managed the simple (nuclear) households, while elders managed the complex households. It was less typical but did occur occasionally that the aged household head passed the management of the household on to one of his or her children.

For the analysis of the distribution of household structures according to social (specifically, social, occupational, and ethnic) strata, I followed the category system derived from the source, with minor simplifications. This resulted in a total of nine social strata (groups). I analyzed the Roma separately, although they primarily belonged to the landless serf (zsellér) or farmhand (béres) categories.

 

Table 8. Household structures according to the social / occupational situation of the household heads, Endrőd, 1835

Household category

intellectuals

landowner farmers

artisans

small traders

homeowner landless serfs

landless serfs without own home

gardeners

farmhands

Roma

no data

Total

1. Solitaries

0.0

0.3

0.0

0.0

1.1

0.0

0.0

0.0

0.0

0.0

4

2. No family

42.9

0.3

1.7

0.0

0.0

0.5

0.0

11.1

0.0

0.0

7

3. Nuclear

28.6

38.1

88.3

100.0

62.8

93.0

84.2

88.9

63.6

33.3

624

4. Extended

28.6

17.2

6.7

0.0

19.2

5.6

7.9

0.0

18.2

22.2

131

5. Multiple

0.0

44.0

3.3

0.0

16.9

0.0

7.9

0.0

18.2

11.1

189

6. Unclassifiable

0.0

0.0

0.0

0.0

0.0

0.9

0.0

0.0

0.0

33.3

5

Total (percent)

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

N

7

302

60

5

266

215

76

9

11

9

960

Source: Believers of Endrőd 1835. GySzIPL

The results of Table 8 reflect the findings of previous microanalyses and macroanalyses. Typically farming serfs (or “farmers” to use the term used in the source) lived in this era in multiple-family households in larger proportions, although I should note that their majority in comparison with nuclear households is only relative. For intellectuals, living in households with no family (as seen from the examples above) was characteristic of the lifestyle arising from the nature of their occupations. Local intellectuals were not connected to the local society as regards their family relations. They formed a passing stratum, so to speak: the tenants of the parish house, including the parish priest and the chaplains, were replaced over time, and they typically did not integrate into the local society from the perspective of their family relations. While approximately 36 percent of homeowner landless serfs and Roma lived in more complex households, the ratio was much lower or zero for the others.

The distribution can be refined by performing the above classification also based on the data of the tax census of 1834–1835. Albeit there seemed to be several ways to classify tax censuses, all of them require a more comprehensive processing work encompassing multiple sources, which is currently not possible. Relying on the correlations of production volumes and the amount of taxes paid,38 I evaluated the first nine, then the subsequent one hundred, two hundred and the other taxpayers based on tax values.

By connecting the tax censuses and the household heads, I managed to achieve a two-thirds identification rate. There are some taxpayers in the censuses from Csejt-puszta: administratively, they belonged to Endrőd at this time, but Göndöcs did not record them in his parish family book. The identification was made quite difficult by the fact that in the case of some family names that are very common locally, it was impossible to identify the correct persons without a full analysis of the registers: Hornoks, Tímárs, and Uhrins lived in the settlement in great numbers, and even if the taxpayer was distinguished by an indication of the father’s given name, this was not always adequate to remove all the doubts.

 

Table 9. Household structures according to the taxation category of the household heads, Endrőd, 1835

Household category

Taxpayer’s serial number (tax amount)

 

1–9

(136–295)

10–99 (40–118)

100–199 (19–40)

200–299

(11–19)

300–399

(6–11)

400–499 (2–6)

500–539 (0.1–2)

Total

1. Solitaries

0.0

0.0

0.0

0.0

0.0

0.7

0.0

4

2. No family

0.0

0.0

1.2

0.0

1.1

0.0

0.0

7

3. Nuclear

25.0

31.6

42.7

56.0

63.6

70.5

76.8

624

4. Extended

0.0

12.3

9.8

15.5

15.9

13.0

8.6

131

5. Multiple

75.0

56.1

46.3

28.6

19.3

14.4

14.6

189

6. Unclassifiable

0.0

0.0

0.0

0.0

0.0

1.4

0.0

5

Total (percent)

100.0

100.0

100.0

100.0

100.0

100.0

100.0

960

N

8

57

82

84

88

146

151

616

Source: Believers of Endrőd 1835. GySzIPL; MNL BéML IV. A. 6. 1834–1835.

It should be noted for the interpretation of Table 9 that the taxpayers’ serial number was the same in the case of equal tax amounts, and that is why each group of hundreds could contain more than one hundred taxpayers. However, due to the two-thirds identification rate indicated above, I was not able to include everyone in my analysis. The value of the tax amount was determined by converting the kreutzer to forints and adding it to the forint value. The table indicates that the biggest taxpayers lived in multiple-family households in an outstandingly large proportion (75 percent), but the majority of household heads belonging to the first hundred taxpayer classes also lived and farmed in this form of cohabitation. István Hanyecz paid the most taxes in the tax year of 1834–1835: 237 Forints and 38 kreutzer. He is followed by military officer Imre Mészáros, then Mihály Gubucz. Hanyecz lived together with his wife, two sons, and daughters-in-law, as well as his grandchildren, his sibling, and their family, as well as a 16-year old servant boy. Imre Mészáros lived with his wife, children, and the family of one of his sons, as well as one manservant and one female servant. Mihály Gubucz lived and farmed together with his two sons and their families.

All taxpayers in the first tax class are landowners, while the second class also includes a gardener, Imre Vaszkó, and a homeowner landless serf (zsellér), Imre Farkas. Both lived in nuclear households. The number of landless taxpayers increases in the third class, there is a growing number of homeowner landless serfs and also artisans. So, in fact, the tax census indicates that a direct proportionality can be identified among those living from agriculture between the extent of their farming activity and living in households of complex families.

 

Table 10. The proportion of households employing external labor according to household structure categories, Endrőd, 1835

 

Household structure categories

1

2

3

4

5

6

Total

Households employing external

labor ( percent)

0.0

57.1

15.9

27.5

34.4

0.0

21.3

N (total households)

4

7

624

131

189

5

960

Source: Believers of Endrőd 1835. GySzIPL

Households often employed external laborers for a shorter or longer period of time. Upon examining the household categories with families, one sees that the proportion of the households employing external labor increased together with the complexity of the household. These laborers, in most of the cases, were male or female servants. Gáspár Czinger, the town clerk, had two Lutheran housekeepers (though he belonged to a household with no families, while being in the second tax category), while the nobleman and cantor Károly Balla, who lived with his wife, son, and mother, had not only a female servant but also a coachman. The average age of manservants was 17. That of the female servants was 15.

 

Table 11. Ratio of average household size and households employing external laborers according to the household head’s age, Endrőd, 1835

 

Household head’s age

<25

25–34

35–44

45–54

55–64

64<

External laborer

15.7

19.7

20.2

19.3

22.4

38.1

General size of household

1.0

4.3

4.8

6.2

9.0

1.2

Source: Believers of Endrőd 1835. GySzIPL

Table 11 indicates that as the household head’s age increased, external laborers became increasingly involved in the management of the household. The higher percentages appearing in the older age groups suggest that aging household heads tried to replace the younger members of the family having left the household this way.

Regarding the year 1836, Historia Domus of Endrőd recorded the conditions according to which Kornélia Stockhammer leased her estates in Endrőd to the village, as well as (and especially) the assets the church purchased. It also noted that Mátyás Habdza had a wooden cross erected on the outskirts of Endrőd, for which he established a foundation of 50 Forints.39 Homeowner landless serf Mátyás Habdza died in July 1836, aged 75 according to the registers and 80 according to chaplain Göndöcs. The cause of death was senectus, which could be translated today as old age.40 Whether it was he who had the cross erected as a form of thanksgiving for his long life (particular for the era) or his son Mátyás (if one accepts that middle-aged Mátyás Habdza was the son of the deceased, Göndöcs’s error would be quite a big deviation, almost 10 years!) is impossible to determine based on this information: the Historia Domus did not record the month and the day. Either way, according to contemporary popular belief, erecting a cross could be justified by the fact that cholera, which had ravaged in Endrőd in the summer of 1836, had spared Mátyás Habdza’s household.41

Göndöcs made, among others, the following entries at the end of the book: Approx. 200 died in the Year 1831 A.D. in Cholera, and a little below that: in (Year) 1836 A.D. Again approx. 100 died of Cholera. As I have mentioned, Göndöcs completed the family book with, among others, the names of those died in the 1836 cholera outbreak in the following year. Comparing this with the entries of the death register, 75 people died between July 6 and August 21, 1836. In the parish family book, Göndöcs added cholera as the cause of death subsequently for 60 people. Collating the people’s data found in the register and the family book, Göndöcs indicated that a person died of cholera in 10 cases where this is not indicated in the register, and the register mentions cholera as the cause of death in a further 26 cases where it is not added to the family book. This means that a total of 86 people are known to have died of cholera, of whom 71 could be connected to the household register.

 

Table 12. Ratio of households with a member who died of cholera according to household structure categories, Endrőd, 1835

 

Nuclear

Extended

Multiple

Total

Households with a member who died of cholera (percent)

4.8

6.9

10.1

6.1

N (total households)

624

131

189

944

Source: Believers of Endrőd 1835, Register of Deaths of Endrőd, 1835–1836. GySzIPL

 

Our sample makes it possible to compare the ratio of households with a member who died of cholera and the composition of the households. Cohabitation, which meant frequent contact among multiple people, constituted a higher risk factor for the spread of diseases, as reflected by the values of Table 12. Those who died of cholera in the families were in larger proportions women (54.7 percent) than men (46.3 percent). A significant group of the deceased included those aged 1–3 and 45–65.

In this paper, I conducted a closer examination of a geographical area hitherto unexplored in terms of household structure analyses, namely the settlement of Endrőd in Békés County. It was useful to process the previously dormant parish family book to get a better understanding not only of the geographical space, but also of the period after 1828. I was able to use a complete source which is rather rare from the 1830s, or even the immediately preceding or subsequent decades, which could be used well both in terms of the richness and (with some reservations) the quality of the data. In summary, the results correspond nicely to the findings of earlier macroanalyses and microanalyses, and therefore the main conclusions can be extended to this region. My findings confirm the dominance of nuclear households. However, I was able to point out that due to the relatively higher proportion of complex households, the village has an interim character, so we may have managed to record a state in the ongoing process of the simplification of households. We can regard as a characteristic specific to this settlement that older household heads employed an external person in significantly larger proportions than the younger generations, which can be explained by the departure of the younger members of the family and thus can also be interpreted as a manifestation of disintegration. Furthermore, the analysis according to tax classes refines the uniform belief that typically peasant families lived in multiple-family household structures. The ratio of this type is much higher where the household head paid more taxes. The health risk arising from the cohabitation of multiple people is also worth noting, the real threat of which is reflected by the relevant difference in the number of deaths from cholera in each household structure.

Archival Sources

Békés County Archives of the Hungarian National Archives (MNL BéML)

IV. A. 6.: Békés vármegye adószedőjének iratai [Documents of the Tax Collector of Békés County] 1834–1835. Accesed on September 25, 2018. https://www.familysearch.org/search/catalog/435710?availability=Family percent20History percent20Library

V. B. 326. d.: Mezőberény nagyközség iratai [Documents of the Village of Mezőberény]. Census doc. 1869.

Archives of the Szent Imre Parish of Gyomaendrőd (GySzIPL)

Endrőd, Register of Deaths. Accessed on September 25, 2018

https://drive.google.com/drive/folders/0ByjN02LbwH6sb2FNdlg0NWFmdm8? usp=sharing

Believers of Endrőd 1835: Az Endrődi Hivek Összeirása 1835ik Esztendötöl Kezdve G[öndöcs] J[ózsef káplán] (Register of the Believers of Endrőd as of 1835 A.D. [Chaplain] J[ózsef] G[öndöcs]).

Historia Domus: Historia Ecclesiae, et Parochiae Endrődinensis conscriptu Anno 1833.

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1 Vályi, Magyarországnak leírása I, 577.

2 Márkus, Békés vármegye, 282; Pesty, Békés megye Pesty Frigyes helynévgyűjtésében, 40; Karácsonyi, Békésvármegye története II. kötet, 97; Iványi, 200 éves az endrődi Szent Imre templom, 52.

3 Fényes, Magyarország geographiai szótára.

4 Ibid.

5 Historia Domus: Historia Ecclesiae, et Parochiae Endrődinensis conscriptu Anno 1833, GySzIPL, 41; Szilágyi, “Egy 19. század eleji birtokelidegenítés esete,” 771–94; Szilágyi, “Indigenák és helyi társadalom,” 140–47.

6 See the Endrődi füzetek [Endrőd Journals] series published between 1992 and 2014.

7 Őri, and Pakot, “Háztartásszerkezet,” 165.

8 Sabean, Property, production, and family, 89.

9 For more detail, see Andorka, “A család és háztartás nagysága,” 147.

10 Andorka, “A család és háztartás nagysága,” 147; Melegh, “A tizenkilencedik század eleji városi háztartások,” 135.

11 Translated by Gergely Krisztián Horváth; see Horváth, Bécs vonzásában, 35.

12 Sabean, Property, production, and family, 89; Andorka, “A család és háztartás nagysága,” 147. Melegh, “A tizenkilencedik század eleji városi háztartások,” 135–36.

13 Sabean, Property, production, and family, 95.

14 Sabean, Property, production, and family, 99; Bácskai, Család, háztartás, társadalom, 7; Andorka, “A család és háztartás nagysága,” 147–48.

15 Melegh, “A tizenkilencedik század eleji városi háztartások,” 135.

16 Andorka, and Faragó, “Az iparosodás előtti,” 402.

17 Ibid., 402–3.

18 Hajnal, “European Marriage Patterns,” 101–43; Hajnal, “Two Kinds of Preindustrial Household,” 449–94.

19 Fauve-Chamoux, “Strategies of Household Continuity,” 138; Bácskai, “Család, háztartás, társadalom,” 7; Derosas, and Saito, “Introduction,” 1; Oris, and Ochiai, “Family Crisis,” 23; Őri, and Pakot, “Háztartásszerkezet,” 166; Szołtysek, “Rethinking Eastern Europe,” 389–427; Szołtysek, “Spatial Construction,” 11–52.

20 MNL BéML V. B. 326. d.

21 Benda, “A háztartások nagysága,” 109.

22 Faragó, “Nemek, nemzedékek, rokonság, család,” 393–483. 455.

23 Benda, “A háztartások nagysága.”

24 Tamásy, “Az 1784–1787. évi első,” 527; Faragó, “Nemek, nemzedékek, rokonság, család,” 455. Faragó distinguishes these functions from the family by giving the following explanation: “albeit the terms of family and household can coincide, it is an undeniable fact that the two are not always the same. A family is not necessarily characterized by cohabitation, the socialization of new family members and the performance of household functions do not always occur within the family, and the ‘home space’ also often extends beyond the family.” Faragó, “Nemek, nemzedékek, rokonság, család,” 455–56.

25 “In past societies where reproduction of the population was connected primarily to the institution of marriage and where the households (groups of people actually living together and cooperating, whether they were relatives or not) represented the basic unit of work and consumption in addition to demographic reproduction, the marriage customs and the rules of forming a household had a direct impact on population development.” Őri, and Pakot, “Háztartásszerkezet,” 164.

26 Tamásy, “Az 1784–1787. évi első,” 530–31. Regarding the usability of extended family, see: Andorka, and Faragó, “Az iparosodás előtti,” 414.

27 Andorka, and Faragó, “Az iparosodás előtti,” 437.

28 Ibid., 437.

29 Andorka, and Faragó, “Az iparosodás előtti,” 437; Faragó, “Nemek, nemzedékek, rokonság, család,” 460–68; Faragó, “Különböző háztartás-keletkezési,” 36–37.

30 Andorka, and Balázs-Kovács, “A háztartások jellemzőinek,” 229–33; Andorka, and Faragó, “Az iparosodás előtti,” 417–21; Balázs, and Katus, “Közép-dunántúli paraszti,” 166; Benda, “A háztartások nagysága,” 134. Husz, Család és társadalmi reprodukció, 69–74; Heilig, “Paraszti háztartások”, 253–54.

31 Andorka, and Faragó, “Az iparosodás előtti,” 403.

32 Őri, and Pakot, “Háztartásszerkezet,” 171–72.

33 Mátyás Roncsek died in January 1835, István Palócz in February, and Mátyás Tímár in September.

34 Faragó, “Rokonsági viszonyok,” 256.

35 Őri, and Pakot, “Háztartásszerkezet,” 174.

36 “In higher age groups, there was a greater chance to live together with one or more married children, much as there was a higher chance of remaining alone after becoming widowed or living under the same roof with people other than relatives. The phenomenon of women becoming heads of the households was related to special stages of the life cycles of the households. Living alone could be typical both of younger and older household heads, recently widowed household heads with children tended to be younger women (nuclear households), while living together with married children as the heads of the household was more typical of older women (households with extended or multiple families). In conclusion, the household heads’ gender was an important factor of the composition of the household.” Őri, and Pakot, “Háztartásszerkezet,” 176.

37 Ibid.

38 Kövér, A tiszaeszlári dráma, 111–18.

39 Historia Domus: Historia Ecclesiae, et Parochiae Endrődinensis conscriptu Anno 1833. GySzIPL, 41., 59.

40 Endrőd, Register of Deaths, 10 July 1836. GySzIPL

41 On the implications of cholera in Hungary, see: Mádai, “Kolerajárványok,” 2–3. 330–51; Dávid, “Az 1831. évi kolera,” 293–312; Gecsei, Cholera morbus; Boa, “Kolerajárványok a 19. századi,” 193–205; Tamás Faragó conducted an in-depth qualitative analysis for Maramureş County, see: Faragó, “Humanitárius katasztrófák,” 19–78. For its implications regarding Békés County, see Magyary-Kossa, Magyar orvosi emlékek, 114; Dávid, “Az 1831. évi kolera,” 293–312; Mádai, “Hat nagy kolerajárvány,” 68.

 

Table 1. Number of houses and households in Endrőd (1787–1835)

 

1787

1817

1828

1828–1829

1830–1831

1835

 

Census

census

national census

census

taxation-related

census

parish family book

Houses

388

607

705

664

640

665

Households

504

862

780

821

688

960

General size of household

5.38

5.54

–

8.13

–

5.75

Total number of inhabitants

2,712

4,779

–

5,401

–

5,527

Source: Erdei, Békés megye, 113; Believers of Endrőd 1835. GySzIPL

 

Table 3. Household structure according to main household categories, average size of households, Endrőd, 1835

Types

Households

Population

Average

size of households

N

percent

N

percent

1. Solitaries

4

0.4

4

0.1

1.0

2. No family

7

0.7

30

0.5

4.3

3. Nuclear

624

65.0

2,979

53.9

4.8

4. Extended

131

13.6

810

14.7

6.2

5. Multiple

189

19.7

1,698

30.7

9.0

6. Unclassifiable

5

0.5

6

0.1

1.2

Total

960

100.0

5,527

100.0

5.6

Source: Believers of Endrőd 1835. GySzIPL

2019_1_Gyimesi–Kehl

pdfVolume 8 Issue 1 CONTENTS

A Spatial Analysis of the Socio-economic Structure of Bonyhád Based on the Census of 1869*

Réka Gyimesi and Dániel Kehl
University of Pécs, Faculty of Humanities
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In this study, we examine the social structure of Bonyhád, a multi-ethnical and multi-confessional Transdanubian town in Tolna County. We analyze the individual level data of the census of 1869 and offer a visual rendering of the results on a historical map of the town. The surviving material of this inventory covers the entire population of Bonyhád, providing a detailed picture about 6,036 inhabitants. Records include the names, sex, birth year and place, marital status, occupation and occupational status, literacy, residence, and whether the person in question was present or absent at the time the census was taken. As in Tolna County a cadastral survey was finished in 1866, a contemporary cadastral map is also available. Combined, these sources provide rich information about the spatial structure of the town, because the coordinates are also available using the mapire.eu website, which is overlaid on the OpenStreetMap and the HERE satellite base map. One can use the degrees of longitude and latitude of each household and study the census and the map together in R, a free software environment for statistical computation and graphics.

Bonyhád was the economic center of a small region and had a position of strategic importance in the control of local trade routes. After the end of the period of Ottoman occupation, German settlers arrived and lived alongside the original Hungarian and Serb population. Later, a significant Jewish community settled in the area in the eighteenth and nineteenth centuries. The denominational composition of the population, according to the census of 1869, was 41 percent Roman Catholics, 31 percent Lutherans, 5 percent Calvinist and 23 percent Jewish. The analysis of the census-based information and the visual rendering of the results on the cadastral map explain valuable details about the socio-economic structure of Bonyhád, including the question of segregation, which would be difficult to demonstrate on the basis of qualitative sources, as is typically the case with historical research.

Keywords: socio-economic structure, spatial pattern, R software, segregation, nineteenth c. censuses

Introduction

Bonyhád acquired central functions in the Völgység, which can be described as an agricultural region in Tolna County. The settlement started to develop dynamically in the eighteenth century due to its role as a “geographical gate.” A trade route led through it, and two bridges made it possible for travelers to cross the valley. According to the secondary literature on the local history of the area, this increasingly urbanized town evolved into an industrial-commercial center, which became a market town in 1782 with the right to hold four fairs per year. In the 1850s, Bonyhád turned into the administrative center of the executive unit, called Völgység (which essentially means valley region).1 In the work of Vera Bácskai and Lajos Nagy on the urban structure of Hungary, Bonyhád was introduced as a settlement with local significance. Its fairs were mainly visited by its own inhabitants, as they did not attract people from a larger range.2 This essay also emphasizes the role of local merchants in arranging trade through Tolna County.3

After the Ottoman Era, the town was inhabited by Calvinist Hungarians and Orthodox Serbs, but a few years later, the settlement was considered uninhabited territory. Large-scale German settlement started in the early eighteenth century. It enjoyed the support of the state and the secular and clerical landowners, who sought to repopulate their lands. As a result of this process, Bonyhád evolved into a town with a Roman Catholic German majority.4 While in 1715, records indicate only seven Hungarian and nine Serb families were counted, in 1728 42 Hungarian and 15 German families were paying taxes, and in 1748, these figures had shifted to 11 Hungarian and 29 German families.5 Until the middle of the following century, the number of inhabitants steadily increased. In 1785, there were 2,999 people living in Bonyhád. By 1828, this number had risen to 4,639, and the census in 1850 indicated 6,524 inhabitants and the one in 1857 indicated 6,371.6

The German settlers were not all Roman Catholic. A large number of Lutherans also arrived. German Calvinists from Hessen settled in Bonyhád as they did in other towns in Tolna County, but they mostly assimilated into the Lutheran majority.7 Hungarians were Roman Catholics and Calvinists. In the eighteenth century, the settlement of Jews in the town began, a process which peaked in the 1780s, when there were more than 400 Jewish inhabitants in Bonyhád.8 As a result of the abovementioned denominational mix, five denominations and four churches were found in Bonyhád in the period in question. By the beginning of the nineteenth century, alongside the Roman Catholics, Calvinists and Lutherans were also building churches in the town, and a synagogue was also constructed.9

Sources and Methods

The analysis was based on the individual sheets of census 1869,10 which contain data concerning people living together in the same households. The fact that this source is even extant is exceptional, as the original individual sheets survived only in the case of a few settlements of present-day Hungary.11 Bonyhád offered a good research opportunity given the survival of these sources, and the population was heterogeneous from the religious and socio-economic perspective.

In our analysis, we examined and combined housing statistics and individual level data of inhabitants from the census material and projected the results on the nineteenth-century map of Bonyhád. In the first place, we concentrate on the denominational and occupational distribution of the population and the connection between these two variables. Our aim in this study is empirically to test some of the well-known relations between religious belonging and occupations (for example Jews were mainly occupied as merchants)12 and to compare results of previous studies to the data regarding Bonyhád. While there was no religious pattern or concentration of inhabitants in Sátoraljaújhely13 besides that of the Jewish population, we can assume that the results will be different in the case of a resettled community.14

According to the census, of the altogether 6036 inhabitants of Bonyhád in 1869, 2,961 were males and 3,075 were females, which means a sex ratio of 1,038 females to 1,000 males. The census registered housing statistics on a separate sheet (location, number of rooms and outbuildings, whether the building served as a place to live only or also as a shop, etc.), as it did in the case of domestic animals. On the middle sheet, the name, sex, year and place of birth, religion, marital status, occupation and occupational status, residence, presence or absence, and literacy of the inhabitants were given. Comments involving factors like e.g. illness, military service, place of absence, etc. were written in the last column. In cases of multiple households sharing the same house, a vertical line separated the Wohnparteien.15

The numbering of the houses was continuous in the settlement, so the figures started from one and increased to the number of the last house of the town, independently of the streets. 1306 Wohnparteien lived in Bonyhád in 763 houses, which means 1.7 households per houses. This figure is higher than the average for Pest County (1.3–1.4).16 The average size of households was 4.6 persons, which correspond to the national average at the time.17

In the course of our investigation, we applied five broader categories of occupations18 in order to increase the efficiency of analysis. We employed the method introduced by Péter Őri and Levente Pakot, who created the following socio-professional groups based on HISCLASS:19 (1) Groups of higher status (non-manual), (2) Craftsmen (artisans and merchants), (3) Farmers (landowners), (4) Groups of lower status (unskilled) and (5) Other.20

We do not endeavor or claim to offer any detailed examination of demographic characteristics like marital customs or the number of children in a family without the use of parish registers. We cannot arrive at reliable conclusions concerning demographic phenomena exclusively on the basis of census data, because census data provide detailed information on the population on a particular date. We know how many people lived in the town on December 31, 1869, but we have no information concerning the total number of children who were born in the family or the number of those who left their homes. Likewise, we do not know how many children were born after this day in the same family. The census material makes possible the analysis of the spatial pattern of the distribution of the household-types using Laslett’s categories.21 Laslett’s method introduced categories based on the relationships among the household-members, not the number of the inhabitants, so the uncertainty caused by the lack of all the life events can be solved by drawing on his work. Using the census data, we also can analyze the spatial distribution of the age-groups, but neither the age-distribution nor the Laslett classification showed characteristic spatial arrangement, so we decided to exclude these aspects in what follows.

We also examined household members who were not blood-related to the family, like servants or apprentices, and we compared them to their employers from the perspective of their religion or place of birth. Although there is a column for residence in the census sheet, in our opinion it’s not suitable to distinguish so-called foreigners from the resident population, because this distinction only refers to the period during which these people lived in the same place, not their origin (place of birth). The numbers of these columns confirm our assumption. 87 percent of the population belonged to the resident category according to which division, but only 75 percent had been born in Bonyhád.22

In the second half of the nineteenth century cadastral surveys were carried out in the Crownlands of Hungary, beginning in 1856 in the western part of the country and heading eastward. The survey of Tolna county was completed in the mid-1860s.23 Thus, we have a cadastral map from Bonyhád which is contemporary with the census.24 A historical map includes valuable information about the geographical situation of the town, but on the homepage of Mapire digitalized maps are available with coordinates. The webpage combined the historical maps with OpenStreetMap and Google Maps.25

By analyzing the spatial structure based on census data, we aimed to use free and/or open source software solutions that are also capable of performing transformations of raw data and proper statistical analysis. This approach makes this research much more reproducible and could help researchers conduct similar studies in the future.26 Steps followed in creating the maps are to be found at the end of our study in the annex.

Spatial Distribution

Housing statistics

Data from the census enable us to investigate housing circumstances of the inhabitants of Bonyhád. The differences are best shown by the population density (mean number of residents in a room). This value is 2.64 people/room on average in Bonyhád according to the 1869 census. However, there are differences among the houses in this respect, as shown on figure 1.27

As the map shows, most of the houses had a population density around the mean of 2.64, but there are some houses where more than four inhabitants shared one room. In the southern and southeastern parts of the town, we see buildings with low population densities. These bigger houses were owned by the Perczels and other landowning nobles. According to the map, in several cases, there were parks or large gardens on these properties behind the house. Of the 763 houses of the settlement, only 20 were two-story houses. The largest number of rooms was 23 in one house, but there were 20 households sharing the edifice, so number of rooms alone does not mean that the inhabitants were wealthy. That is why we decided to put the population density on the map, and based on the result, we can conclude that Bonyhád was more an agricultural settlement than urban.

Spatial distribution of denomination

Based on census data, Bonyhád had 2,463 Roman Catholic (40.8 percent), 1,890 Lutheran (31.3 percent), 1,359 Jewish (22.5 percent), 317 Calvinist (5.3 percent), and seven Orthodox (0.1 percent) inhabitants in 1869. In the literature, we find statements about the spatial patterns which agree in part with these figures. One source indicates that Hungarians settled down in the southern part of the town, while Germans chose the northern part.28 Others call the eastern line of houses the “Hungarian Bonyhád,” while the western line of houses was referred to as “German Bonyhád.”29 These two approaches were synthesized by Wilhelm Knabel, according to whom the two landholders of Bonyhád (baron Schilson and Ferenc Kun) split the settlement in 1729. To south and west of the main square, the “German village” developed, with the tavern, butchery, and three mills which belonged to the baron. Ferenc Kun gained the northern and eastern part of the settlement, the so-called “Hungarian village,” with the wine shop and the brewery. The part of the town inhabited primarily by German speakers tended to prefer Roman Catholic settlers, while the Hungarian-speaking community preferred Calvinists. Several Lutherans moved into the Hungarian part of the town from the surrounding settlements.30

This statement is underpinned by the map showing the spatial distribution of denominations. Protestants are found in the northern part of the settlement, and Roman Catholics populated the south. Religion seems to have had a stronger effect on spatial patterns than nationality. Many sources also state that Lutheran and Calvinist settlers did not live in the German village, and both villages had inhabitants belonging to both nationalities.31 The contention that denominational belonging was the most important single factor in determining settlement patterns within the town is also supported by the placement of cemeteries and churches, which reflects the spatial distribution observable on our map. This confirms the sources cited and also shows that the religiously differentiated structure which evolved at the time of resettlement remained stable one century later.

Jews formed their own closed community in the city center between the German and Hungarian villages. Their activities turned Bonyhád into the trading center of the region in this era.32

Spatial distribution of occupation

The census registered the occupation and occupational status of the inhabitants, and on the basis of this, we categorized the inhabitants of the settlement into the abovementioned five groups. These columns are usually left blank in the cases of women and small children, but data were available concerning household heads, older children, and other residents. Thanks to this data, we know the sources of income for 2,155 inhabitants of Bonyhád. Broken down into occupational groups, 99 of these people belonged to a stratum which had a higher status (they performed non-manual labor), 462 were artisans and merchants, 228 were landowners, and 1,302 were members of lower strata (i.e. unskilled laborers). 64 people couldn’t be categorized into the abovementioned classes (e.g. almsmen).

As over half of the inhabitants belonged to the unskilled category, which is in line with the agricultural characteristics of Bonyhád, we decided to create two subcategories. One of them includes unskilled agricultural workers only (e.g. unskilled farm workers, farm servants), while the other consists of unskilled workers who worked together with artisans (e.g. apprentices, journeymen) and other servants and maids.

As can be observed on the map, most of the merchants and artisans lived in the center of the settlement, while the northern and southern parts of the settlement were populated by landowners, especially the Protestant parts. Using our subcategories, we acquire a more detailed picture of the spatial pattern of the unskilled stratum: the distribution of unskilled workers reflects that of the artisans and landowners (agricultural unskilled workers lived outside the center of town, while apprentices and journeymen in the center). The inhabitants who did non-manual labor and therefore belonged to a higher social stratum also tended to live in the middle of the settlement.

The spatial distribution of different occupations is quite different. In some cases, artisans with the same profession lived throughout the settlement (e.g. masons), while most of the merchants were concentrated in the center, as were tailors. Weavers were only found on the periphery. Innkeepers and tavern owners opened shops both in the center and next to commercial routes in the southeastern area.

Occupation and religion

The settlement pattern according to denominational belonging shows similarities to that of occupation shown on the previous maps. We further analyzed this relationship between occupational class and denomination (leaving out Orthodox inhabitants due to their small number, we had information concerning the denominational belonging of 2,150 residents of the town). The resulting cross table shows a significant association between the two variables (p<0.001).

Table 1 shows the distribution of occupational groups within each of the four big denominations. The higher status group comprised 4.5 percent of the total population. This figure is somewhat less among Lutherans and higher in the case of Calvinists and Roman Catholics. The proportion of artisans is clearly highest among inhabitants belonging to the Jewish community (around 40 percent). It is close to 20 percent in the case of Roman Catholics and remains below 15 percent in the case of Protestants. Farmers made up almost 20 percent of the Lutheran and 15 percent of the Calvinist communities, while the ratio is below nine percent for Roman Catholics and under one percent in case of Jewish inhabitants. As we have already seen, the largest group was comprised of unskilled workers. Their proportion of the population remained under 50 percent in the case of Jews, but for people belonging to Christian denominations, it is between 60 and 66 percent. The last (Other) group has a low percentage of unskilled laborers, with minor differences between denominations.

 

Table 1. Distribution of occupational groups within denominations,
Bonyhád, 1869

 

Lutheran (percent)

Calvinist (percent)

Roman Catholic (percent)

Jewish (percent)

Total (percent)

Higher

1.21

6.62

6.54

4.50

4.51

Artisan

14.42

11.03

19.61

40.05

21.49

Farmer

18.82

14.70

8.68

0.71

10.60

Unskilled laborer

64.34

66.18

61.74

49.53

60.42

Other

1.21

1.47

3.43

5.21

2.98

Total

100.00

100.00

100.00

100.00

100.00

Nearly 200 different occupations are mentioned in the census data. Most professions had only a few representatives in the settlement. However, there are quite different occupational patterns in the case of the four denominations. We conducted a correspondence analysis which revealed that the main difference was between members of the Jewish community and people who belonged to the three Christian denominations. Most of the professions were avoided by Jews, while some were dominated by them. In some cases, however, the denominational distribution reflects the proportions of the population. Examples of each case are presented in Table 2.

 

Table 2. Denominational patterns in selected occupations,
Bonyhád, 1869

 

Lutheran

Calvinist

Roman Catholic

Jewish

Total

Carpenter

23

4

31

0

58

Furrier

0

0

0

20

20

Mason

11

3

26

0

40

Merchant

0

0

17

60

77

Shoemaker

1

9

4

0

14

Tailor

26

3

41

16

84

Tanner

24

3

42

6

73

Weaver

20

4

15

0

39

The relationship between denominations and occupations in itself is not novel, but the spatial analysis in this case of a resettled eighteenth-century town raises several questions. Sources and the map of the spatial pattern of denominations both underpin that the eighteenth-century separation of religions still strongly affected structure of society in the nineteenth century.

The relationship between Jews living in the settlement center and the concentration of artisans here seems obvious. For a long time, Jews were not allowed to own land.33 It is easy to see why they settled in the dense central parts of the town, where they could be more successful. But is the relationship between Protestants (in this case mainly Lutherans) and the class of farmers also that univocal? One simple explanation might be that the main goal of recruiting German settlers was to find farmers to (re)cultivate abandoned lands. However, sources and contemporary laws show evidence that allowances were given not only for agricultural workers, but also to artisans.34 New settlers arriving to Bonyhád were not only agricultural workers but also artisans. It seems clear that the settlement patterns in the eighteenth century were based on denomination, but the question remains: did this also cause the occupational differences, or did inhabitants adapt to this spatial structure and chose their occupation accordingly? In other words, the direction of the possible causal relationship between denomination and occupation is still unclear and requires further investigation.

Non-relatives – cooperation and separation

According to the census data, the denominations of servants and maids corresponded to the denomination of their employers. We also analyzed the birthplace of this group of non-relatives living together with a family , which was the most mobile stratum of the population of Bonyhád. Regarding the presence/absence columns, 498 people were absent (five people only temporarily), who were listed mainly as children in the households. Their occupations were not given in every case, but otherwise they were servants, maids, apprentices, journeymen, or people serving in the military, which demonstrates the extent of the mobility of these groups.

Table 3 presents the denominational or religious belonging of servants and maids alongside the denominational or religious belonging of their employers.

 

Table 3. Religion or denomination of servants and their employers, Bonyhád, 1869

 

Denominational belongings of heads of households (number)

Lutheran (18)

Calvinist (7)

Roman Catholic (44)

Jewish (37)

Total

Servant’s denominational belonging

Lutheran

15

2

10

5

32

Calvinist

1

1

6

3

11

Roman Catholic

4

8

54

18

84

Jewish

0

0

0

13

13

Total

20

11

70

39

140

Born in Bonyhád

5

1

11

7

24

Of these 140 servants, 28 were males and 112 were females. Therefore, in all cases the number of females was always higher than the number of males. All of the servants employed by Jewish households were female, including 13 Jewish maids. Jewish servants only served in Jewish households. We can observe a more open pattern among Roman Catholics and Protestants, and not only in the case of servants and maids, but also in the case of the craftsman-apprentice relationship (Table 4). 35

 

Table 4. Religion of apprentices and their employers, Bonyhád, 1869

 

Denominational belonging of heads of households (number)

Lutheran (28)

Calvinist (1)

Roman Catholic (42)

Jewish (19)

Total

Apprentice’s denominational belonging

Lutheran

12

0

10

0

22

Calvinist

1

0

3

0

4

Roman Catholic

24

2

67

5

98

Jewish

0

0

0

18

18

Total

37

2

80

23

142

Born in Bonyhád

3

1

7

4

15

On average, Roman Catholic heads of household employed the most servants (77 servants for 44 households) and apprentices (80 apprentices for 42 craftsmen). The most frequent number of servants/apprentices was one, but there were some exceptions.36 In Tables 2 and 3, the number of servants and apprentices who were born in Bonyhád is also presented. In all cases, we can see that the proportion of local born employees is quite low. This suggests that mobility was relatively high among members of this group.37

Summary

Based on the census of 1869, we examined the socio-economic spatial structure of the agricultural settlement of Bonyhád using the cadastral map from the 1860s as a visualization tool. After a short introduction of housing data in general, the study focused on settlement patters according to denomination and occupation. We verified that resettlement still had a strong influence on the denominational structure of the community in the nineteenth century. We demonstrated a statistically significant relationship between religion and occupation. Further analysis was completed about the denomination of non-relatives and households living together. As a result, we offered statistic evidence in support of contentions found in qualitative secondary literature and earlier studies according to which Jewish society in the town was much more closed than the Christian denominations. They only worked in houses belonging to people of their own religion and they lived in a well-separable place in the town center. Spatial patterns were investigated for every profession and some of them were represented on maps. In some cases, a particular occupation seemed to predominate among the community which belonged to a particular denomination, while other occupations seemed to have been less connected to a given religion or denomination. The study also indicated the complexity of the society under study and concluded that resettlement was an important factor which influenced the socio-economic and denominational structure of the town even a century later.

Our results underpin the strong relationship between denomination and occupation and settlement patterns within the town. However, the direction of the causation needs further investigation, as an important question remains unanswered: did the settlement patterns influence occupation, and if so, to what extent, or did settlers find their homes based on their profession.

Annex

Four steps were taken to complete the maps presented later in our paper:

1. based on the historical map, polygons were defined which represent houses, and they were used to connect data concerning inhabitants and their houses to the map;

2. the file containing the historical map was read into R and GPS coordinates were added;

3. polygons were read into R;

4. statistical calculations were made and the final maps were created based on previous results.

The first step was done in Inkscape,38 a free open source vector graphics editor. Inkscape uses the open standard SVG (Scalable Vector Graphics), which enables us to create small but scalable graphics. All other steps were performed in R,39 which is a free and open source software environment for statistical computing and graphics.40 The open source status makes it possible for users to contribute their own code to a central repository (CRAN). These contributions are called packages, and the number of packages grows rapidly. There are over 13,000 packages at the moment, and we use some of them for data manipulation and to create maps.

The resolution of the base map is 3080 x 6925 pixels. We read the base map into Inkscape and then used the appropriate tool to draw linear polygons on another layer to represent houses. The so-called Draw Bezier curves and straight lines tool seemed to be the best choice, as it is able to snap nodes to polygons which have already been defined, which means we could easily draw polygons which are perfectly matching and which cover the entire map. One property of all objects in Inkscape is their ID, where we used the house numbers of the census to make it easier to connect polygons to census data later on. Filling the polygons which had already been drawn with a somewhat transparent color makes the manual process even simpler.

Figure 5. Manually creating polygons in Inkscape

 

As a result, we created a vector graphic map of nineteenth-century Bonyhád which is zoomable, small, and easy to read. There are several format options available to store polygon data. We chose the so-called absolute coordinates (instead of relative coordinates), which are easier to process in R as an XML file. Once we finished drawing all the polygons, we could remove the base map and save the final vector graphic map of the settlement.

In the R environment, there are several plotting packages. We used ggplot241 and its extension for maps called ggmap.42 This latter package is applied to create visual renderings of spatial data on top of static maps from various online sources (Google Maps, OpenStreetMap, Stamen Mapsor CloudMade). The package assumes that one is plotting on a map which comes from the abovementioned online sources. However, we can convert our png file to a ggmap object by adding the bounding box data (lower left und upper right corner GPS coordinates). As a result of several lines of code in R, we now have a high resolution ggmap object which contains a raster and its place in the GPS coordinate system. As this is the basis of all the maps, we saved this into the native R datafile (RDa).

The next step was to read and convert the polygon dataset in R. As already mentioned, the svg file we created in the first step is basically an xml file which contains all polygons in nodes called path. All paths have multiple attributes, but we only need the ones named “d,” which contain the coordinates (in pixels), and the ones named “id,” which contain the house numbers. Reading and converting polygons to the GPS coordinate system enables us to produce different types of maps. On one side, we can draw the polygons with different colors representing various characteristics of the given house (e.g. population density, meaning people/room). On the other side, in several cases we plotted characteristics of the inhabitants of a given house. For instance, we put (equal size) pie charts in the center of the polygon (this approach seemed appropriate as the religion or denominational belonging of the inhabitants of a given building was usually not the same). We drew this type of map using the scatterpie package.43

 

 

Archival Sources

Tolna Megyei Levéltár [Tolna County Archives] (TML)

V. 709./c Közigazgatási iratok 1850–1949 [Administrational Documents, 1850–1949]. Bonyhád nagyközség iratai, 1869. évi népesség és háziállatok összeírása [Documents of Bonyhád, Census of 1869 and the Enumeration of Domestic Animals]. 512–513. box

Bibliography

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A Magyar Korona Országaiban az 1870. év elején végrehajtott népszámlálás eredményei a hasznos házi állatok kimutatásával együtt [Census 1870 in the Crownlands of Hungary, together with the numbers of farm animals], edited by Országos Magyar Királyi Statistikai Hivatal. Pest: Athenaeum, 1871.

Magyarország történeti statisztikai helységnévtára, 10. Tolna megye [Historico-statistical Gazetteer of Hungary, 10. Tolna County], edited by Andrásné Jeney, and Árpád Tóth. Budapest: MTA – KSH, 1966.

Tolna megye (Georeferált vármegyei kataszteri térképek): 1859–1864 [Tolna County (Georeferenced Cadastral Maps of the Counties): 1859–1864], edited by Sándor Biszak, and Gábor Timár. Budapest: Arcanum, 2010.

 

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Bácskai, Vera, and Nagy, Lajos. Piackörzetek, piacközpontok és városok Magyarországon 1828-ban [Market regions, market centers, and towns in Hungary in 1828]. Budapest: Akadémiai Kiadó, 1984.

Balázs Kovács, Sándor et al. Tolna megye kézikönyve [Handbook of Tolna county]. Magyarország Régiói, Dél-Dunántúli Régió [Regions of Hungary, region of South-Transdanubia], edited by Dezső Bunovácz. Budapest: Ceba Kiadó, 2005.

Bonyhád. Vasárnapi Ujság [Sunday Paper], 12, no. 20, (1865): 238. Accessed on September 19, 2018. http://epa.oszk.hu/00000/00030/00584/pdf/00584.pdf

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

Faragó, Tamás. “Különböző háztartás-keletkezési rendszerek egy országon belül – változatok John Hajnal tézisére” [Different household formation systems in hungary at the end of the eighteenth century: Variations on John Hajnal’s thesis]. In KSH NKI Történeti Demográfiai Évkönyv, edited by Tamás Faragó, and Péter Őri, 19–63. Budapest: KSH NKI, 2001.

Fényes, Elek. Magyarország geographiai szótára, mellyben minden város, falu és puszta, betürendben körülményesen leiratik [Geographical dictionary of hungary. accurate description of every town and village in alphabetical order]. Pest, 1851. Accessed on September 19, 2018. https://www.arcanum.hu/hu/online-kiadvanyok/Lexikonok-magyarorszag-geografiai-szotara-fenyes-elek-BABC3/b-BAD42/bonyhad-BB0CC/

Gyimesi, Réka. “Mohácsi háztartás-rekonstrukció: Az 1869-es népszámlálás felvételi íveinek feldolgozása” [Household-reconstruction in Mohács based on the individual data of census 1869]. Demográfia 57, no. 2–3 (2014): 183–212.

Guangchuang, Yu. scatterpie: Scatter Pie Plot. R package version 0.1.2. Accessed on September 23, 2018. https://CRAN.R-project.org/package=scatterpie

Hajnal, John. “European Marriage Patterns in Perspective.” In Population in history: Essays in historical demography, edited by David V. Glass, and David E. C. Eversley 101–43. London, 1965.

Horváth, Gergely Krisztián. “Város a városban: főhercegi ingatlanok Magyaróváron az 1869. évi népszámlálás tükrében” [Town within a town: Archducal properties in Magyaróvár according to the census of 1869]. In A város és társadalma: Tanulmányok Bácskai Vera tiszteletére: a Hajnal István Kör Társadalomtörténeti Egyesület 2010. évi, Kőszegen megrendezett konferenciájának kötete [The town and its society: Studies in honor of Vera Bácskai], edited by István H. Németh, Erika Szívós, Árpád Tóth, 249–57. Budapest: Hajnal István Kör Társadalomtörténeti Egyesület, 2011.

Kahle, David, and Wickham, Hadley. “ggmap: Spatial Visualization with ggplot2.” The R Journal, no. 5 (2013): 144–61. Accessed on September 23, 2018. http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf

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.

Knabel, Wilhelm. Geschichte Bonyháds (Bonnhards) von der Urzeit bis 1945 [The history of Bonyhád from prehistorical times to 1945]. Munnich, 1972.

Kolta, László. “A közigazgatás változásai Bonyhádon” [Changes in the administration of Bonyhád]. In A Völgység huszadik százada: Struktúrák és konfliktusok: III. Völgységi Konferencia [The twentieth century of Völgység. Structures and conflicts: the 3rd conference of Völgység], edited by László Szita, and Zoltán Szőts, 15–20. Bonyhád: Völgységi Múzeum, 2001.

Laslett, Peter. “Introduction: The history of the family.” In Household and family in past time, edited by Peter Laslett, and Richard Wall, 1–89. Cambridge: Cambridge University Press, 1972.

Lippényi, Zoltán, Marco H. D. van Leeuwen, Ineke Maas, and Péter Őri. “Social status homogamy in a religiously diverse society: Modernization, religious diversity, and status homogamy in Hungary between 1870–1950.” The History of the Family, (2017): 1–23. Accessed October 3, 2018. DOI: 10.1080/1081602X.2017.1319399

Őri, Péter, and Levente Pakot. “Háztartásszerkezet a 19. századi Magyarországon: A Mosaic-adatbázis elemzésének első eredményei” [Household structure in nineteenth-century hungary: first results of the analysis of the Hungarian MOSAIC Sample]. Korall, no. 65 (2016): 164–92.

Őri, Péter, and Levente Pakot. “Residence patterns in nineteenth-century Hungary: Evidence from the Hungarian MOSAIC sample.” Working Papers on Population, Family and Welfare, no. 20. Budapest: Hungarian Demographic Research Institute, 2014.

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

Őri, Péter. “Kiskunhalas népessége 1869-ben” [Population of Kiskunhalas in 1869]. In Kiskunhalas története 3: Tanulmányok Kiskunhalasról a 19. század közepétől a 20. század közepéig, [History of Kiskunhalas 3: Studies about Kiskunhalas from the middle of the nineteenth century to the middle of the twentieth century], 269–92. Edited by József, Ö. Kovács and Aurél Szakál. Kiskunhalas, 2005.

Pakot, Levente. “Családok és háztartások két székelyföldi településen a 19. század második felében” [Families and households in nineteenth-century Transylvania]. Demográfia 55, no. 4 (2012): 268–91.

Pozsgai, Péter. “Családok és háztartások: Torna megye társadalma a 19. század közepén.” [Families and households: Society of Torna County in the middle of the nineteenth century]. PhD diss., Budapest, Eötvös Loránd Tudományegyetem Bölcsészettudományi Kar, 2006.

R Development Core Team. R. A language and environment for statistical computing. R Foundation for Statistical Computing. Accessed September 23, 2018. http://www.R-project.org.

Schmidt, János. Német telepesek bevándorlása Hesszenből Tolna-Baranya-Somogyba a XVIII. század első felében [Immigration of German settlers from Hessen to Tolna–Baranya–Somogy in the first half of the eighteenth century]. Győr: Baross Nyomda, 1939.

Solymár, Imre. “A történeti Völgység” [The historical Völgység]. In Tanulmányok Bonyhád történetéből [Studies on the History of Bonyhád], edited by Ernő Bábel, and Péter László, 9–38. Bonyhád: Bonyhád Város Tanácsa és a Tolna Megyei Lapkiadó Vállalat, 1987.

Solymár, Imre. “Bonyhád – hajdan Bonyha” [Bonyhád – in bygone times Bonyha]. In Tanulmányok Bonyhád történetéből [Studies on the history of Bonyhád], edited by Ernő Bábel, and Péter László, 39–63. Bonyhád: Bonyhád Város Tanácsa és a Tolna Megyei Lapkiadó Vállalat, 1987.

Szilágyi, Mihály. “Az újratelepülő Tolna megye 1710–1720” [Tolna County resettled: 1710–1720]. In Tanulmányok Tolna megye történetéből 10 [Studies on the history of Tolna County], edited by János K. Balog, 33–169. Szekszárd, 1983.

Szita, László. “A lutheránus németség bevándorlása és településtörténete Tolna megyében a 18. században” [The immigration and history of the Lutheran Germans in Tolna County in the eighteenth century]. In Tolna Megyei Levéltári Füzetek 5, edited by Gyula Dobos 5–163. Szekszárd: Tolna Megyei Önkormányzat Levéltára, 1996.

Szőts, Zoltán. A völgységi nemzetiségi-etnikai csoportok együttélése a második világháborútól napjainkig [The cohabitation of national-ethnical groups in Völgység from World War II to the present day]. Völgységi Múzeum, 2007.

Török, Enikő. “A kataszteri részletes felmérés előrehaladása Magyarországon 1856 és 1916 között” [The progression of the cadastral detailed survey in Hungary between 1856 and 1916]. Catastrum: Évnegyedes Katasztertörténeti Folyóirat 2, no. 1 (2015): 11–18.

Várady, Zoltán. “Bonyhád a törökkor végétől Mária Terézia koráig [Bonyhád from the end of the era of Turkish occupation to the rise of Maria Theresa]. In Előkészületek Bonyhád monográfiájához: előadások a IV. Völgységi Konferencián [Preparatory work for a monograph on Bonyhád: Presentations held at the Fifth Völgység Conference], edited by János László, and Zoltán Szőts, 83–92. Bonyhád, 2006.

Van Leeuwen, M. H. D. and Maas, I. HISCLASS: A Historical International Social Class Scheme. Leuven: Leuven University Press, 2010.

Wickham, Hadley. ggplot2: Elegant Graphics for Data Analysis. New York: Springer, 2016.

1 Szőts, A völgységi nemzetiségi-etnikai csoportok együttélése, 196.

2 Bácskai and Nagy, “Piackörzetek,” 49, 222.

3 Ibid., 252.

4 Solymár, “Bonyhád – hajdan Bonyha,” 42.

5 Várady, “Bonyhád a törökkor végétől,” 88.

6 Magyarország történeti statisztikai helységnévtára, 42.

7 Schmidt, Német telepesek bevándorlása, 81.

8 Várady, “Bonyhád a törökkor végétől,” 88.

9 Szita, A lutheránus németség bevándorlása, 7–8. Fényes, Magyarország geographiai szótára; Bonyhád, 238.

10 TML V. 709./c

11 Péter Őri and Levente Pakot introduced the preservation of Hungarian individual-level materials of the census of 1869, see Őri and Pakot, “Háztartásszerkezet.” The following studies analysed these sources on the micro-level: Torna County: Pozsgai, “Családok és háztartások;” Magyaróvár: Horváth, “Város a városban;” Kiskunhalas: Őri, “Kiskunhalas népessége;” Sátoraljaújhely: Demeter and Bagdi, A társadalom differenciáltsága; Mohács: Gyimesi, “Mohácsi háztartás-rekonstrukció.”

12 Katus, Modern Magyarország, 158, 175.

13 Where a similar investigation was carried out for the census sheets of 1869.

14 Demeter and Bagdi, A társadalom differenciáltsága, 17.

15 The problematics of Wohnparteien is inevitable for researchers who are working with census materials. The expression was transferred to the Hungarian vocabulary from the German instructions for the census in 1850. The differentiation of the notions of Wohnpartei and households led to difficulties and differences in interpretation because of varying practices used by the census takers. Detailed explanation of this topic: Őri and Pakot Residence patterns, 14–15; Őri and Pakot, “Háztartásszerkezet,” 169–71. In our analysis, we use the notion of Wohnparteien in the sense of households adjusting to the practices of census takers.

16 Őri, “Család és házasodás,” 75. The difference can be caused by the abovementioned diversity of the practices of census takers, but in all likelihood it shows real disparity.

17 The average size of households in Pest County (1869): 4.65 people (Őri, “Család és házasodás,” 75.). In Mohács (1869): 4.5–4.6 people (Gyimesi, “Mohácsi háztartás-rekonstrukció,” 12.). In Sátoraljaújhely (1869): 4.6 people (Demeter and Bagdi, A társadalom differenciáltsága, 13, 60.). Levente Pakot found higher values in the Székely Land: 5.4 people per households (Pakot, “Családok és háztartások,” 272).

18 Almost two hundred different occupations were identified in this column.

19 Van Leeuwen and Maas, HISCLASS.

20 Őri and Pakot, “Residence patterns,” 17.

21 Laslett, Introduction, 1–89.

22 “filling in the column of ‘citizenship’ notice, according to which everyone who has been settled in the community for a year now and has lived there permanently and has no residence in another village at the time of the census is a resident.” Népszámlálás 1869. 4.

23 Török, “A kataszteri részletes felmérés,” 11.

24 Biszak and Timar, Tolna megye.

25 https://mapire.eu/hu/

26 Demeter and Bagdi did a similar analysis of the spatial patterns of settlement in Sátoraljaújhely. Our study attempts to reflect their aims. Demeter and Bagdi, A társadalom deifferenciáltsága.

27 The same indicator in Sátoraljaújhely in 1869 is 2.9 people/room (1.5 room/family), which means that in Bonyhád less residents were living in one room on average. Demeter and Bagdi, A társadalom differenciáltsága, 19.

28 Kolta, “A közigazgatás változásai Bonyhádon,” 15.

29 Solymár, “A történeti Völgység,” 20.

30 Knabel, Geschichte Bonyháds, 13.

31 Ibid., 14. Settlement according to denomination: Solymár, “A történeti Völgység,” 21. Principle of one village – one religion: Schmidt, Német telepesek, 49.

32 Knabel, Geschichte Bonyháds, 19.

33 Jews only began to be permitted to settle freely, engage in a trade freely, and purchase land in the 1840s. Katus, Modern Magyarország, 107.

34 The laws of Charles III encouraging resettlement with “1723. évi CIII. törvénycikk az ország benépesítéséről” [law of peopling the country] (promising 6 years of tax exemption for every free person). In the same year, another law arranged for the “support for the arrival of various craftsmen to the country” (1723. évi CXVII. törvénycikk), promising 15 years of tax exemption for them. Landlords also wanted to find workers to work on their estates, so in the early eighteenth century, they began to offer three years of tax exempt-status for the arable lands and mills and six years for the vineyards. Szilágyi, “Újratelepülő Tolna,” 35.

35 The isolation among denominational and occupational groups is observed in the case of marital customs. Roman Catholics and people belonging to the Orthodox Church were more closed in this respect than Lutherans and Calvinists. Marriage between Catholics and Jews was not allowed until the end of the nineteenth century. Lippényi et al., “Social status,” 8.

36 The results of analysis of employees partly correspond to the conclusions in case of Sátoraljaújhely (e.g. Jewish servants/maids served in Jewish households), but some issue was different according to the data of Bonyhád. The phenomenon of Calvinists preferring employees from the same denomination was not confirmed by our data, but the reason behind this could be the small number of Calvinists in Bonyhád. Demeter and Bagdi, A társadalom differenciáltsága, 21.

37 The employees emerged from younger age-groups than the average (the mean age of the total population in Bonyhád was 26.75, while in the case of servants it was 25.84 years and in the case of apprentices it was 25.91 years. Most of them were single, which fits the lifecycle-servant part of Hajnal’s theory (Hajnal, “European marriage patterns”). Hajnal thought this was a West European phenomenon, but more research has shown that this statement should perhaps be reconsidered. This topic is discussed in Faragó, “Különböző háztartás-keletkezési rendszerek.”

38 https://inkscape.org

39 https://www.r-project.org

40 R Development Core Team, 2008.

41 Wickham, ggplot2.

42 Kahle and Wickham, ggmap.

43 Guangchuang, scatterpie.

* Supported by the ÚNKP-18-3-IV-PTE-323 New National Excellence Program of the Ministry of Human Capacities.

Figure 1. Population density (people/room), Bonyhád, 1869

3_lakosuruseg_atl.png

Figure 2. Religion of the population, Bonyhád, 1869

4%20religion_atl%20(1).jpg

Figure 3. Occupation of the population, Bonyhád, 1869

5_occ_code2_atl%20(1).jpg

Figure 4. Spatial distribution of certain artisans, Bonyhád, 1869

6_nehany_fogl_atl.png
Gyimesi-k%c3%a9p.jpg
Gyimesi-k%c3%a9p2.jpg

Figure 6. Main square of Bonyhád on the final vector graphic map

2019_1_Paukert

pdfVolume 8 Issue 1 CONTENTS

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

Arlene Peukert
Andrássy Universität Budapest
This email address is being protected from spambots. You need JavaScript enabled to view it.

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.

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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.

 

1 For an introduction to the history of the railway and its impact on space and time, see: Schivelbusch, Geschichte der Eisenbahnreise, 35–50.

2 The notion of a plurality of spaces emerged once space was no longer perceived as a container (or dead, passive stage, as Schlögel puts it). Spaces are historically constituted. They have a beginning and an end. They can disappear again. Consequently, we are not dealing with only one space, but a multitude of spaces which exist parallelly. See Schlögel, Im Raume lesen wir die Zeit, 68–69; and also: Marc Augé, Orte und Nicht-Orte: Vorüberlegungen zu einer Ethnologie der Einsamkeit (Frankfurt am Main: S. Fischer, 1994).

3 Harley, “Maps, Knowledge, Power,” 279.

4 In his seminal essay, Harley assumes that every map is a socially constructed form of knowledge. The specific codes embedded in the wider geographical discourse can tell us (cartographic communication/cartographic manipulation of perception) about power structures logged by the mapmakers. Harley lists several scenarios in which maps can be employed to convey a distinct message or function as a tool of communication: maps in the context of military and bureaucratic utilization, maps as a propaganda tool, maps as a surveillance tool, maps for legitimizing territorial claims. Harley puts maps in the large family of images, which is why he suggests an iconological approach, as derived from Erwin Panofsky (1892–1968), to decode symbols and imagery of maps. Furthermore, he writes about a cartographic language. Methods drawn from semiotics and literary criticism are suitable to identify the rhetorical and persuasive mechanisms in maps. Lastly, Harley points out the social constructiveness of maps. On the basis of Michel Foucault’s (1926–1984) and Anthony Giddens’ (1938) theories on historiography and social systems, Harley raises the argument that (manipulated) map knowledge is in itself a form of power that lies mainly in the hands of state authorities and transports political and ideological messages. Compare: Harley, “Maps, Knowledge, Power,” 277–312. The author, Rainer Vollmar, delivers a very on-point summary of Harley’s approach: Vollmar, “Die Vielschichtigkeit von Karten,” 381–95.

5 Harley, “Maps, Knowledge, Power,” 277.

6 Wawrik, “Historische und Kulturhistorische Informationen,” 193.

7 Harley, “Maps, Knowledge, Power,” 278.

8 See: footnote 4.

9 The first research on Austrian railway maps was conducted by Bettina Krenn and Johannes Dörflinger. In her diploma thesis from 1998, Krenn lists railway maps of Austrian provenance from the nineteenth and early twentieth centuries according to their type and field of use and delivers a description of the maps. See Krenn, “Eisenbahnkarten,” 1–221. Johannes Dörflinger also published on cartography and Austrian maps. An essay about Austrian railway maps from the beginning of the era until the outbreak of World War II is part of the small canon of scientific literature about railway maps of Austrian origin. See Dörflinger, “Österreichische Eisenbahnkarten,” 157–74.

10 Within the framework of this paper, it is impossible to give a solid introduction to the concept of space as understood in the humanities and cultural studies. The reader can consult the following introductory literature: Henri Lefebvre The Production of Space, translated by Donald Nicholson-Smith, 33rd ed. Oxford: Blackwell Publishing, 2013; Jörg Dünne, Stephan Günzel, eds., Raumtheorie: Grundlagentexte aus Philosophie und Kulturwissenschaften. Frankfurt am Main: Suhrkamp, 2006; David Harvey, “On the History and Present Condition of Geography: An Historical Materialist Manifesto.” In The Professional Geographer, 36 no. 1 (February 1984): 1–11; Stephen Kern. The Culture of Time and Space, 1880–1918. Cambridge/Mass.: Harvard University Press, 1983; Karl Schlögel. Im Raume lesen wir die Zeit: Über Zivilisationsgeschichte und Geopolitik Munich/Vienna: Carl Hanser Verlag, 2003; Edward W. Soja. Postmodern Geographies: The Reassertion of Space in Critical Social Theory, 8th ed. London: Verso, 1989; Martin Warnke. Politische Landschaft: Zur Kunstgeschichte der Natur. Munich/Vienna: C. Hanser, 1992; Martina Löw. Raumsoziologie. Frankfurt am Main: suhrkamp taschenbuch wissenschaft, 2001.

11 Schlögel, Im Raume lesen wir die Zeit, 49–51.

12 On space and identity, see Aleida Assmann. Erinnerungsräume: Formen und Wandlungen des kulturellen Gedächtnisses. Munich: Beck, 2006.

13 Schlögel, Im Raume lesen wir die Zeit, 50.

14 Ibid.

15 Ibid., 48–51.

16 Ibid., 51.

17 Schivelbusch, Geschichte der Eisenbahnreise, 35–39.

18 Ibid., 43–44.

19 Regarding the function of mental maps and mental mapping in spatial research in the social sciences and humanities see Sabine Damir-Geilsdorf, ed. Mental maps, Raum, Erinnerung: Kulturwissenschaftliche Zugänge zum Verhältnis von Raum und Erinnerung. Münster: LIT, 2005; Roger M. Downs, David Stea. Maps in minds: Reflections on cognitive mapping. New York et al.: Harper & Row, 1982; Frithjof Benjamin Schenk. “Mental Maps: Die kognitive Kartierung des Kontinents als Forschungsgegenstand der europäischen Geschichte.” Europäische Geschichte Online (EGO), Mainz: Leibniz-Institut für Europäische Geschichte, June 5, 2013, accessed on October 4, 2018. http://www.ieg-ego.eu/schenkf-2013-de

20 For a history of the Austrian railway see Karl Gutkas, ed. Verkehrswege und Eisenbahnen: Beiträge zur Verkehrsgeschichte Österreichs aus Anlaß des Jubiläums “150 Jahre Dampfeisenbahn in Österreich.” Vienna: Österr. Bundesverl. 1989; Harald Heppner. Der Weg führt über Österreich: Zur Geschichte des Verkehrs- und Nachrichtenwesens von und nach Südosteuropa. 18. Jahrhundert bis zur Gegenwart. Vienna et al.: Böhlau, 1996.

21 De jure the railway concession law from 1854 set an end to the phase of railway construction under state initiative. However, it took until 1858 to transfer railway lines to private owners/ enterprises.

22 Krenn, “Eisenbahnkarten,” 7 and Bachinger, “Das Verkehrswesen,” 278–322.

23 Waldmüller, “Quellenkundliche Forschungen,” 73 and Krenn, “Eisenbahnkarten,” 7.

24 Krenn, “Eisenbahnkarten,” 7.

25 Praschinger, “Die österreichischen Eisenbahnen als wirtschaftlicher Faktor,” 104.

26 Needless to say, the railway and the new form of travel inspired arts, culture, and literature in the nineteenth century. William Turner’s (1775–1851) painting Rain, Steam and Speed – The Great Western Railway from 1844 or Claude Monet’s (1840–1926) railway and rail station paintings from the 1870s are a celebration of the new power of industrialization and travel. In the nineteenth century novel, authors like Max Eyth (1836–1906) and Max Maria von Weber (1822–1881) sought to capture every facet of life, putting the focus on engineers and train drivers. In his novel “Eine Winternacht auf der Lokomotive” from 1865 Weber portraits the hardship of a train driver during a winter night trying to keep the engine running, while the passengers enjoy themselves in the heated compartments. For further information on the railway as a motif in German literature see Mahr, Eisenbahnen, 46–51.

27 Lindner, “Landesaufnahmen deutscher Territorien,” 411–41.

28 Krenn, “Verkehrsgeschichte im Kartenbild,” 28–31. For more information on the stage coach system see Monika Diketmüller, “Von der Postkutsche zur Eisenbahn in Niederösterreich im 19. Jahrhundert,” PhD diss., University of Vienna, 1992; Christine Kainz. Österreichs Post. Vom Botenposten zum Postboten. Vienna: Verlag Christian Brandstätter, 1995.

29 Krenn, “Eisenbahnkarten,” 8.

30 Ibid.

31 Ibid.

32 Krenn, “Eisenbahnkarten,” 8–9.

33 For the history of railway travel in Burgenland, see Chmelar, 150 Jahre Eisenbahn.

34 In November 1844, the line between Wiener Neustadt and Ödenburg was commissioned by Emperor Ferdinand I. Capital stock of the railway came to 1.5 million Gulden; one stock was 200 Kronen. The commission and contract signed between the railway company and the vicegerent of Ofen was seen as valid for 50 years. Count Széchenyi, count Heinrich Zichy, and Eduard Tschurl signed the contract as representatives of the railway company. Chmelar, 150 Jahre Eisenbahn, 28; Benedek, Mattersburger Viadukt, 10–13.

35 Extract from a short speech in German delivered by Count Széchenyi during the general assembly for the commissioned railway line in March 1845 in Ödenburg. (Translation into English by the author.)
Hans Chmelar, 150 Jahre Eisenbahn, 14, quoted from Paul Mechtler. Die erste Eisenbahn im Burgenland. Burgenländische Heimatblätter, März 1962, 83.

36 Mathias Schönerer was a railway engineer of the Habsburg Monarchy. He was involved in the construction of the horse-drawn railway Linz–Budweis–Gmund (1827–1836). In 1841 the first railway tunnel on Austrian territory (near Gumpoldskirchen) was built under his lead. Later, he was responsible for the railway projects Vienna–Gloggnitz and Mödling–Laxenburg. During the revolution of 1848/49 Schönerer organized the first military transports via railway. From 1856 he was member of the board of administration of the Empress Elisabeth Railway (Kaiserin Elisabeth-Bahn), and from 1867 member of the board of administration of the Emperor Franz Joseph Railway (Kaiser Franz Josephs-Bahn). For his merits for the railway in the Habsburg Monarchy, Schönerer received knighthood in 1860. Accessed on 3 October, 2008.

http://www.literature.at/viewer.alo?viewmode=overview&olfullscreen=true&objid=12540&page=168; Benedek, Mattersburger Viadukt, 16.

37 Chmelar, 150 Jahre Eisenbahn, 18–19.

38 The double-tracked version of the line was not built, however, because of political tensions between Austria and Hungary in the 1840s. The Southern Railway should not run over Hungarian territory. See Chmelar, 150 Jahre Eisenbahn, 22 and Benedek, Mattersburger Viadukt, 10.

39 Chmelar, 150 Jahre Eisenbahn, 24–27 and Benedek, Mattersburger Viadukt, 12.

40 During the Hungarian revolution of 1848/49, traffic on the track between Ödenburg and Wiener Neustadt came to a halt. On April 10, 1848, local peasants of Mattersdorf damaged parts of the track markings because they never received compensation for their land, which they gave to the railway company in 1845. In an attempt to stop them, 224 soldiers from Ödenburg were sent to Mattersdorf. However, only troops from Vienna could finally cause the enraged peasants to withdraw. In autumn 1848, the border between Austria and the Hungarian lands was closed. These developments resulted in financial losses to the railway company. Although, the traffic in goods was profitable, the line generally did not yield the profit stockholders had hoped to get. In 1854, the line between Ödenburg and Wiener Neustadt was sold to the state. Chmelar, 150 Jahre Eisenbahn, 28, 35–36.

41 Figure 1 and accompanying metadata in the online database Hungaricana: https://maps.hungaricana.hu/en/MOLTerkeptar/3664/[October 5, 2018].

42 Because the railway line is a cross-border connection, the question of ownership and responsibility was addressed early in the planning process. It was foreseen that the part of the railway line on Austrian territory was run by the Vienna-Gloggnitz railway company, the newly founded Ödenburg–Wiener Neustadt company would be responsible for the part of the track on Hungarian soil. Both railway companies belonged to the banking empire of Sina. Finally, in March 1846, it was decided that the Austrian railway company should take over the management for the entire track. The division of responsibility between the two railway companies is not apparent, however, from the map, which suggests that it was of minor importance to the mapmaker. Chmelar, 150 Jahre Eisenbahn, 18.

43 On a map from 1838 entitled “Übersichtskarte der projectierten Tracen der Wien–Raaber Eisenbahn sammt Nebenzweigen. In der Ausführung begriffen unter der Leitung des Civil Ingenieurs M. Schönerer,” the railway line to Ödenburg was planned to follow a different route north of Mattersdorf. See Chmelar, 150 Jahre Eisenbahn, 12–13.

44 Ibid.

45 Denecke, 168–71.

46 The construction of the viaduct in Mattersdorf was challenging. Never before had a project of this size been completed. 4,000 workers, mostly from Bohemia, lived and worked under dreadful conditions. Landslides, accidents, and infectious diseases threatened the lives of the workers. Still, the viaduct was finished within two years. Benedek, Mattersburger Viadukt, 11.

47 The name Josephinian Land Survey relates to Maria Theresia’s son, Joseph (1741–1790), who from 1765 was responsible for military affairs and thus also supervised field measurements of the crownlands. See also:
Lindner, “Landesaufnahmen deutscher Territorien,” 426–428.

48 Krenn, “Eisenbahnkarten,” 8.

49 For detailed maps of the second Land Survey see https://mapire.eu/de/map/cadastral/?layers= osm%2C3%2C4&bbox=1790567.3900514918%2C6062444.842889478%2C1838837.3734135807%2C6077 732 248546513 Accessed on October 7, 2018.

50 For detailed maps of the Josephinian Land Survey see https://mapire.eu/de/map/firstsurvey-­hungary/?layers=osm%2C147&bbox=1846312.5028221803%2C6021959.64898766%2C1942852.469546358%2C6052534.460301731 Accessed on October 7, 2018.

51 For further information, study the georeferenced cadastral maps of the second land survey (Franziszeische Landesaufnahme) at https://mapire.eu/de/map/cadastral/?layers=osm%2C3%2C4&bbox= 1829206.8178942474%2C6055223.618854407%2C1853341.8095752918%2C6062867.321682924 Accessed on October 7, 2018.

52 The elongated format is typical for route maps. The format was first applied in England in the second half of the seventeenth century for the presentation of the most important streets in England. Later, especially during the 1830s and 1840s, route maps were used to document the first railway lines. The two main functions of these map types in the Habsburg Monarchy identified by Krenn, Kretschmer, and Dörflinger was to inform travelers and/or investors about details of the railway line (either for travel purposes or to provide an overview of the railway project). See Krenn, “Eisenbahnkarten,” 9; Ingrid Kretschmer. “Gebrauchskarten für den Verkehr.” In Austria Picta: Österreich auf alten Karten und Ansichten, edited by Franz Wawrik, Elisabeth Zeilinger. Graz: Akademische Druck- u. Verlagsanst., 1989, 172, and Johannes Dörflinger, “Eisenbahnkarte.” In Lexikon zur Geschichte der Kartographie, edited by Ingrid Kretschmer, Johannes Dörflinger, Franz Wawrik, Vol. 1, Vienna: Deuticke, 1986, 187.

53 Schivelbusch, Geschichte der Eisenbahnreise, 35, 37.

54 The crossing of the two railway lines is also indicated on the map from 1845.

55 In the early age of rail travel, the number of passengers on the few existing lines was rather low compared to the number of passengers in the second half of the century. In 1848, approximately three million passengers were transported by railway. In 1873, this number grew to 43 million passengers per year. With the increase in the number of passengers, the importance of railway maps for travel grew. See: Waldmüller, “Quellenkundliche Forschungen,” 75.

56 Krenn, “Eisenbahnkarten,” 79.

57 Franz Baltzarek, “Die Finanzierung des Eisenbahnsystems,” 222.

58 Monika Gibas uses the term “myth of the middle” in her essay on German collective identity to denote certain topoi in which a group of people identifies with the middle as a place of power and superiority. Myths or narratives about the middle oftentimes serve to establish a sense of belonging and shared identity or to preserve inner territorial stability. See Gibas, “Auf der Suche nach dem deutschen Kernland,” 198.

 

59 Schivelbusch, Geschichte der Eisenbahnreise, 35–37.

1_Uibersichtskarte%20der%20zwischen%20Oedenburg%20Peukert.jpg

Figure 1. Uibersichtskarte der zwischen Oedenburg und Wiener-Neustadt im Jahre 1845 im Bau begriffenen LOCOMOTIV-EISENBAHN, (overview map for the locomotive railway line under construction in 1845 between Oedenburg and Wiener-Neustadt), colored drawing on paper, 106x85 cm, 1845, Inv.nr. E 96 1845, 9:14, National Archives of Hungary. Accessed on October 7, 2018. (https://maps.hungaricana.hu/en/MOLTerkeptar/3664/)

FranzLA_Preitenegg.jpg

Figure 2. Von Roesgen, lieutenant, Historic map, 2nd land survey, sheet sectio 02 Eastern colonne III Eastern Carinthia, Koralm area, area Preitenegg, Hebalm, sea level, Waldenstein, 1834/35, Militärgeographisches Institut der österreichisch-ungarischen Monarchie - Franziszeische Landesaufnahme, Preitenegg westlich der Hebalm. Accessed on October 7, 2018. https://upload.wikimedia.org/wikipedia/commons/0/0e/FranzLA_Preitenegg.jpg

3_Situations%20Plan%20der%20Neu%20PEUKERT.jpg
ac04078710_lehmann_oesterreich_1869.jpg

Figure 4. Neueste Eisenbahnkarte der ÖSTERREICHISCH-UNGARISCHEN MONARCHIE: mit Berücksichtigung der Montan und Industrie Bahnen, (Newest railway map of the Austro-Hungarian Monarchy: considering also the montane and industrial railway), publisher: Lehmann & Wentzel 50 × 69 cm, lithography, on paper, Vienna 1869
http://sammlung.woldan.oeaw.ac.at/layers/geonode:ac04078710_lehmann_oesterreich_1869 Accessed on October 7, 2018.

 

2019_1_Szilágyi

pdfVolume 8 Issue 1 CONTENTS

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 20
th 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

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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.

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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

 

 

1 Quoted by Kidd and Nicholls, Introduction, xxi.

2 Benda, Zsellérből polgár; Novák, “Az erőszak topográfiája;” Kövér, A tiszaeszlári dráma;” Majorossy, “A foglalkozás;” Szilágyi, Homokváros.

3 Soja, Postmodern Geographies; Warf and Arias, The Spacial Turn; Szilágyi, “A társadalmi tér;” Izsák, “A tértudás;” Izsák and Dúll, “Városi térfordulatok.”

4 Győri, “Területi fejlettségi.”

5 Győri, “A térszerkezet.”

6 Győri, “Területi fejlettségi,” 233.

7 Human Development Report 1990, 109.

8 Hicks and Sreeten.

9 According to Farhad Noorbakhsh, GNP, the specific indicator (of measuring standard of living), was commonly adopted following a recommendation included in a UN report in 1954. Noorbakhsh, “A Modified Human,” 517.

10 Human Development Report 1990, 109.

11 Ibid.; Nemes Nagy, Terek, helyek, 301–05. Tomka, Gazdasági növekedés, 187–94.

12 OTKA K 111766: Implementation of geoinformatical system to execute research on the history of Hungary and the Austro–Hungarian Monarchy (1869–1910).

13 Beluszky, “Egy félsiker.”

14 Beluszky and Győri, Magyar városhálózat, 85–86.

15 Beluszky, A Nagyalföld történeti földrajza; Szilágyi, “A fejlettség területi különbségei.”.

16 Timár, Vidéki városlakók, 21; Beluszky, “Kárpát-medence országrészeinek,” 348; Beluszky, A Nagyalföld történeti földrajza, 239; Beluszky and Győri, Magyar városhálózat, 85.

17 Beluszky and Győri, Magyar városhálózat, 87; Beluszky, “Kárpát-medence országrészeinek,” 354.

18 Pénzes, Periférikus térségek, 14–18.

19 Demeter, “Történeti kérdések földrajzi szemszögből,” 30.

20 Tomka, “Gazdasági rekonstrukció;” Pogány, “A nagy háború hosszú árnyéka.”

21 177.200/1924 BM (Ministry of Interior), MSK Ús vol. 93: 14*.

22 100/1927 PM (Ministry of Finances), 10,000/1927, 1929-23-1§; 200/1927 PM, 20,000/1927 1929-2§, 1929-29§, 1390/1933 ME 1§; 400/1927 PM, 40,000/1927, 2030/1932 ME 6–10§, 1390/1933 ME 2§, 2600/1933 ME 4–6§. AS 1934: 49, 75, 147.

23 VE=S/X×100, where variation is indicated via S, average is indicated via X. Csite and Németh, Az életminőség területi, 31–38.

24 Crafts, The Human Development Index.

25 Tomka, Gazdasági növekedés, 199.

26 Prados, “Improving the Human Development Index.”

* Supported by the ÚNKP-17-4-III-DE-187 New National Excellence Program of the Ministry of Human Capacities.

hhr-fig-01.jpg
hhr-fig-02.jpg
végleges-600dpi -- HDI 1910-30 c.jpg
végleges-600dpi -- HDI 1910-30 különbség.jpg
végleges - konverg-diverg - 1910-30.jpg
végleges - településsűrűség - 1933.jpg
végleges - településsűrűség - 1933.jpg
végleges - településsűrűség - 1933.jpg

Figure 8. Changes in HIHD in Hungary based on the comparison of international data,
1870–1950
Source: WHD 1870–2015, own editing.

2019_1_Peykovska

pdfVolume 8 Issue 1 CONTENTS

Migration and Urbanization in Industrializing Bulgaria 1910–1946

Penka Peykovska
Bulgarian Academy of Sciences, Institute for Historical Studies
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Urbanization is among the most important demographic phenomena of the modern age. Today, half of the world’s population lives in cities, and by 2050 this share is expected to reach 70 percent. Urbanization theorists see this as a consequence of three mutually impacting processes: natural growth (population growth as a result of birth rates exceeding mortality rates), migration (mainly from the villages to cities), and reclassification (the administrative mechanism for giving urban status to former villages or urban settlements) – whose relative contribution to the urbanization process varies depending on the environment.

The processes of urbanization and internal migration in Bulgaria in 1910–1946 have not often been made the subject of rigorous study, perhaps because the scale of urbanization at the time was small and the pace slow compared to the period after World War II. At the same time, however, the first half of this period was characterized by intensive waves of refugees and immigrants (Bulgarians, Russians, and Armenians). Having in mind the lack of attention which this question has been given in the secondary literature, in this paper I examine the urbanization processes in Bulgaria at the time and the role of migration to and within the country in these processes. In particular, I monitor the significance of gender, nationality/“nationalité ethnique” in urbanization in Bulgaria and the roles of smaller and larger cities and the capital, Sofia. I rely heavily on the five censuses carried out between 1910 and 1946, which drew a distinction between local-born and non-indigenous populations, including people who had been born abroad. In other words, the data contain information on native-born people (i.e. born in the locality where they were enumerated or, as one might say “locals”), people who were enumerated in a locality different from their birthplace within the country (i.e. internal migrants, in-migrants), and people who were foreign-born (i.e. external migrants, immigrants).

Concerning the role of migration to and within the country in the urbanization process in Bulgaria, my quantitative analysis shows that urbanization in Bulgaria was influenced by migration (mainly internal migration), partly by the waves of refugees and immigrants during the war and in the interwar period, which accelerated the growth of cities. At the same time, the urbanization of small towns was due primarily to immigration. The trend towards urbanization (albeit at a slow pace) in Bulgaria was a result of the migration of the predominantly ethnic Bulgarian population from villages to cities, but the contribution of Armenian and Russian refugees was also notable.

 

 

Keywords: internal and external migration, immigration, in-migration, Bulgaria, urbanization, towns, cities, ethnicity, sex, 1910–1946

Urbanization is among the most important demographic phenomena underway today, when half of the world’s population lives in cities1 and the rapid growth of urban agglomerations which are already huge is being blamed for a number of negative phenomena (high levels of unemployment, infrastructural tensions, and environmental degradation, for instance).2 The study of urbanization as a historical process is increasingly pressing, since this process has implications for the present day, given the need to find successful mechanisms with which to address its negative effects.

Urbanization theorists see urbanization as a consequence of migration together and in interaction with natural population growth (which occurs as a result of birth rates exceeding mortality rates) and a process of reclassification (the administrative mechanism for giving urban status to former villages or surrounding settlements), the relative contribution to urbanization of which depends on the economic and social background.3 Migration within the country from rural to urban areas directly contributes to urbanization by causing a decline in rural populations and growth in urban ones. Furthermore, some cities attract significant numbers of immigrants from abroad, which also leads to an increase in the urban population.4 A transition to urban lifestyles and settlement patterns is also a consequence of economic modernization, industrialization, and changes in the demographic makeup of the population.

In the period under examination here, Bulgaria experienced relatively rapid demographic growth in spite of the Balkan Wars, First World War, and the accompanying loss of life. This growth was due not simply to a common trend in postwar population growth, but also to the immense inflow of refugees and immigrants5 generated by armed conflicts beginning in the second decade of the twentieth century, namely the Balkan Wars and World War I, not to mention the 1917 revolution and civil war in Russia, the Aster Revolution in Hungary, the Greek-Turkish war of 1919–1922, and subsequent events. By 1925, some 200,000 people had come into Bulgaria as immigrants. Most were of Bulgarian ethnic origin, but there were also 20,000 Russians and 15,000 Armenians among them. The population increased also because of higher birth rates in Bulgaria following the first demographic transition.6 The country was rural, and four fifths of its population were peasants. The majority of landowners had relatively small holdings. Bulgaria had an agriculture-centered development strategy, which, however, did not exclude industrialization. Economic modernization happened in agriculture and livestock breeding, which accounted for half of the GDP. The country crossed the threshold of industrialization in the late 1930s.7 Between 1926 and 1934, there were 97 rural towns (most of which were small) with populations under 10,000 (Table 2). Sofia saw the highest growth rate. Other rapidly-developing cities included Plovdiv, Varna, Burgas, and Ruse. The proportion of the urban population rose by 5.6 percent between 1910 and 1946. So, concerning the interrelated processes of internal migration, urbanization, and industrialization, there was some development, but it was rather slow, which explains why this development has been seen by some researchers more as stagnation than as any kind of progress.

In this essay, I examine the role of migration in Bulgaria’s urbanization during the period preceding accelerated industrialization. At that time, the importance of internal migration and immigration in the numerical growth of urban populations in Bulgaria increased – although immigration including refugees was significantly smaller than in-migration, and it continued more intensively only until 1926 (Table 3). (Here we would like to give a terminological clarification: unlike in our era when the “refugee” and the “immigrant” are separate categories,8 in the examined period refugees were usually considered immigrants.) There was a total of 217,328 in-migrants within the country in 1910 and 354,187 in 1926 (figures which greatly exceeded the number of immigrants into the country). So, there were 59,706 immigrants in 1910 and 166,761 in 1926 (their relative share in towns/cities was larger than in the villages). More than one third of the in-migrants and about half of the immigrants were predominantly directed to the big towns and cities, i.e. settlements with populations over 10,000. According to the data, in 1910, 89 percent of the immigrants (53,067 people) and 77 percent of the in-migrants (167,437 people) were encouraged to go to urban settlements, and in 1926, their figures were 80 percent (129,214 people) of immigrants and 77.5 percent (282,079 people) of in-migrants. Until 1926, the general trend was towards increases in the number of immigrants and in-migrants targeting the towns/cities.

 

Table 1. Number of towns/cities in Bulgaria according to the classification used in the population censuses, 1910–1946

Towns/cities with population

1910

1920

1926

1934

1946

Up to 10,000 people

42

53

53

48

43

Above 10,000 people

28

26

28

33

40

Above 20,000 people

8

9

12

12

17

Above 50,000 people

1

3

3

3

4

Above 100,000 people

1

1

1

1

2

Total

80

92

97

97

106

Earlier Findings, Data Sources, and Methods

Scholars have shown little interest in urbanization in Bulgaria and its interaction with (internal and external) migration processes during the period under examination. This may be the case in part because, at this initial stage (which started with the founding of the Third Bulgarian State in 1878 and ended in the late 1940s), the relative share of the urban population was growing slowly and the urban way of life was spreading slowly.9 Faster-paced, dynamic urbanization took place in the second half of the twentieth century. It accelerated under centrally planned economic development, as a result of which urban populations grew sharply. At the end of the 1960s, urban settlements accounted for more than fifty percent of the population, which was increasingly concentrated in the administrative centers.10

Some researchers on migratory and urbanization processes in Bulgaria have claimed that after 1880 (up to 1934, for example) there was a “progressive urbanization trend.” They have tended to support their theses with indicators such as the steadily increasing number and the growing relative share of the urban population.11 Other authors have contended that migration growth (i.e. the difference between the in-migrants and out-migrants, calculated on the basis of population censuses, which are, however, rather “rough” measurements) should be understood as an indicator of urbanization processes in Bulgaria.12 They have found that migration growth is always to the benefit of towns and cities. It leads to rises in the urban population and drops in rural populations.13 In the case of Bulgaria, the phenomenon was reflected by the 1905 census, after the Ilinden-Preobrazhenie Uprising (1903) and, then, in the first half of the 1920s.

 

Table 2. Migration growth of urban population in Bulgaria, in ‰14

 

For the urban

For the rural

population

Totev

Stefanov

Stefanov

1901–1905

2.3

 

1906–1910

0.8

1911–1920

13

1921–1926

16.3

1927–1934

10.5

9.8

4.2

1935–1946

12.6

14.8

6.7

Some scholars have supposed that the urbanization process was “decreasing” in the interwar period, and they explain this with the impact of territorial changes resulting from the Balkan Wars and World War I on the settlement system and the urban-rural population ratio.15 According to the Treaty of Bucharest and the Treaty of Neuilly-sur-Seine, eight towns16 were separated from Bulgaria (from Southern Dobrudja and the Western Outskirts) and transferred to Romania and the Kingdom of Serbs, Croats and Slovenes and another 1717 were added to the country through the newly acquired lands. However, urbanization was declining, because among the latter mentioned settlements, most were less economically developed towns, and their minority Turkish and Muslim populations were prone to emigration.18

Since the development of urbanization in Bulgaria between 1910 and 1946 has only rarely been made the subject of study and at the same time this period (and especially its first half) was characterized by intensive refugee and immigrant inflows of Bulgarians, Russians, and Armenians and the emigration of the local Greeks and Turks (under the bilateral agreements with Greece and Turkey for population exchange), I have devoted this inquiry to the role of migration in the urbanization process. The quantitative analysis, on the basis of which I have examined the interaction between migration and urbanization phenomena and processes, is itself based on data concerning the urban (and rural) populations in the Bulgarian censuses done in 1910, 1920, 1926, 1934, and 1946. We have turned to this type of source because of the lack of other statistics for the period in question. At that time, only a few countries were collecting statistics which provide an adequate basis for a thorough assessment of urbanization. For this reason indirect methods have commonly been used to calculate the components of the increase in the pace of urbanization based on census data.19 Often such studies are based on data concerning birthplace, and they apply different research approaches.

In our particular case, we have used the statistical data for the urban (and rural) populations recorded in correlation with the birthplace of the native-born (born in Bulgaria) population (for those born in a settlement other than the place of enumeration, i.e. for the in-migrants) and the foreign-born population (i.e. immigrants). Data for in-migrants provide information about origins within the country (i.e. another district within a given county, another county, or another locality in the country), and data for immigrants reflect origins by countries. This means that the statistical information “covers” the number of in-migrants at a given time point, not counting mortality, and refers only to the first generation of in-migrants (as opposed to the US censuses, for instance, which also collected information concerning geographical family origins for subsequent generations of families). In the case of statistical information concerning people who had been born outside of the country, this information did not in any way address the ways in which immigrants to Bulgaria moved (migrated) within the country after having entered the country. Most immigrants to Bulgaria, however, were very mobile for a time after having entered the country and did not immediately settle down. When trying to establish the contribution of internal migration to urbanization, the most important direction of this migration is from village to town/city. However, from the point of view of the migration and concerning the de facto population, in principle the Bulgarian censuses of 1910, 1920 and 1926 contain information on migration to towns/cities without reference to the settlements of departure (i.e. whether the settlement from which a migrant to a town/city came was a village or another town/city). Thus, this kind of database includes data on inter-town/city migrations too. In this specific case, there were significant patterns of migration from small urban centers to big urban centers. In the Bulgarian censuses there is evidence of population movement from villages to towns/cities only concerning the economically active population and not the total population. Only the 1934 census provides statistical information on migration in the direction of village–town/city. In the 1946 census, a very different methodology was used, which is why this census is practically incomparable with the previous censuses, at least from the perspective of the data they contain concerning the directions of migration.

We have tracked some of the processes for different subperiods (and not for the entire period under examination). This is because we do not have the relevant data due to the different methodologies according to statistical information was aggregated in 1934 and 1946.

We have based our quantitative analysis on some of the more important theoretical frameworks in today’s understanding of urbanization. Our choices of specific indicators were determined by these theoretical frameworks. Nowadays, demographers define urbanization as the growth in the proportion of the population living in urban areas.20 It is worth noting that this is not only a question of proportional growth, because urbanization does not simply mean growth in urban populations. It also comprises growth in the relative share of the urban population. In other words, if urban and rural populations grow at the same pace, this should not be understood as urbanization. Urban population growth is considered to be entirely the result of urbanization if the total population does not change but the relative share of urban population is increasing; then, the degree of urbanization (the degree of population growth in urban areas) is equal to the growth rate of the urban population.21 However, in most urbanizing countries, including Bulgaria, during the period in question, the total population was growing, and it is possible to distinguish the proportion of urban population growth resulting from urbanization from the proportion resulting from overall population growth (the latter is roughly equal to the degree of urbanization plus the rate of total population growth).

Using these definitions, in measuring processes and phenomena, we have proceeded from the standpoint that urbanization is present when the urban population growth rate exceeds the rural population growth, and we have used this indicator as the main one, measured as the percentage of the total urban or rural population, for the population of the small and big towns/cities,22 for the capital, and for the separate ethnic groups in Bulgaria. Our intention was to determine the contribution of the small and big towns/cities and the capital to urbanization in Bulgaria and also to consider differences in the makeup of urbanizing populations from the perspectives of sex and ethnicity. The final part of the text is devoted to the interrelationships among migration, urbanization, and industrialization and to some of the changes in the urban space. In order better to corroborate the trends we have identified, we have also monitored other indicators, such as the volume of migration and the number of in-migrants and immigrants-refugees per 1,000 locals. Of course, we are aware of the general nature of quantitative parameters and the presence of certain micro-processes and background processes which cannot be numerically measured, because urbanization is indeed primarily a result of migration, and it is reasonable to treat it as such. However, urbanization is not just a consequence of migration from village to city, especially if this migration is perceived as long-term or permanent resettlement. Firstly, urbanization is the net result of complex migratory movements between rural and urban areas, including circular migration back and forth. Actually, migration from village to town/city may be a result of people delaying their return or not returning to rural areas as they decide to remain in the city in which they have settled. Secondly, urbanization involves both the net movement of people to and within urban areas, the progressive expansion of urban boundaries, and the creation of new urban centers. As already mentioned, in principle, urbanization can also be accelerated by higher natural population growth in urban areas and particularly high еmigration from rural areas, although these factors are not considered very substantial.

Before undertaking the quantitative analysis, we would like to note that during the period in question, there were no legislative restrictions on population crowding in the cities. Administrative measures to limit migration were first introduced for the capital city of Sofia in 1943.

The Contributions of Migration to Urbanization

We start examining the growth of Bulgaria’s urban population as a percentage compared to the growth of the rural population, which is influenced by migration (mechanical growth) and natural growth (and perhaps reclassification of settlements).23 In the period from 1910 to 1946, the population of the country grew from 4 million to 7 million. Both urban and rural populations grew, but the share of the urban population increased from 19.1 percent in 1910 to 24.7 percent in 1946. This was due both to natural growth and to mechanical movement. The change in the proportions of the urban and rural populations was not as sharp as it was in the second half of the twentieth century, but it was smooth. Over the course of 36 years, the urban population more than doubled (+111.4 percent), while the rural population increased only by about half (+58.6 percent), so although the rural population grew in absolute terms, its relative share declined from 80.9 percent in 1910 to 75.3 percent in 194624 (and this growth in the relative share of the urban population was much greater than that in the years preceding World War I25). The greatest increase in the urban population as a proportion of the total population took place in 1911–1926 (+36 percent), then in 1927–1934 it was +15 percent and in 1935–1946 it was +33 percent.

For the period between 1910 and 1926, statistics indicate a significant difference in population growth in small and big towns/cities, i.e. in the towns/cities with populations up to 10,000 inhabitants on the one hand and over 10,000 inhabitants on the other. Table 3 shows that population growth in the big towns/cities outstripped growth in the small ones, but the determining factor in this process was the enormous growth of the capital city. If Sofia is excluded, population growth in small towns surpassed (albeit not by much) population growth in big towns, and the proportional growth of the urban population in Bulgaria up to 1926 was mainly due to the increase in the population of the capital, which more than doubled.

 

Table 3. Growth of the population in absolute terms in small and big towns, Sofia, and villages, 1910–192626

 

Growth in

 

Growth in

Population in

1910

1926

figures

%

1934

figures

%

Small towns

251,849

321,239

+69.390

+27.5

331,582

+10,343

+3

Big towns/cities, including Sofia

577,678

808,892

+231.214

+40

970,969

+162,077

+20

Big towns/cities, without Sofia

474,866

595,890

+121.024

+25.5

683,874

+87,984

+15

Sofia

102,812

213,002

+110.190

+107

287,095

+74,093

+35

Villages

3,507,991

4,348,610

+840.619

+24

4,775,388

+426,778

+10

We seek in our inquiry to determine the extent to which urbanization was influenced by migration in general (meaning both within the country and across its borders) and, within this, the extent to which it was influenced by in-migration on the one hand and immigration and emigration on the other. We establish the relative share of the increase in the number of in-migrants and immigrants in the towns/cities in relation to the increase in the urban population (for the territory of the country in the respective census year) based on the abovementioned birthplace data. Here, in the context of what has already been said about the specifics of this kind of statistical information on migration to towns/cities, we would like to point out again that migration to urban areas includes not only migrants coming from villages but also migrants coming from other towns/cities.27 Inter-town/city migration, and in particular migration from small towns/cities to big towns/cities, was not terribly large and did not affect major trends. In 1911–1926, total urban increase as a share of migration was 81 percent, and in 1927–1934 it was 61 percent. Generally speaking, during the period in question, urbanization in Bulgaria was mainly due to migration, and mainly to internal migration, representing 56 percent of the total migration growth in 1911–1926, despite the intense refugee inflows of Bulgarians, Russians, and Armenians as a consequence of the wars, and almost entirely to internal migration in 1927–1934, when external migration was declining (Table 2).

The 1934 census data, which took into account migration from villages to towns/cities, confirms this conclusion. We have analyzed a variety of data concerning in-migrants who moved from villages to towns/cities and concerning immigrants and refugees who came from foreign countries and settled in towns/cities in Bulgaria, because the mobility of immigrants within Bulgaria is not quantitatively known. There were almost twice as many in-migrants who moved from villages to towns/cities as there were immigrants to Bulgaria who settled in towns/cities. They constituted 64 percent of the people who settled in towns/cities (Table 1).

The rise in the number of in-migrants to towns/cities and the rise in the number of refugees and immigrants to towns/cities (per 1000 local people28) correspond to the abovementioned trends. In 1911–1934, the number of in-migrants who moved from villages to towns/cities was steadily growing, more than doubling and reaching almost half a million. Their number per 1,000 locals was gradually increasing too, in the first half of the 1920s much more significantly (reaching 402 in-migrants per 1,000 locals in 1934). This proportional increase was particularly significant in the first half of the 1920s. By 1934, in-migrants constituted almost one-third of the local population in the towns/cities of Bulgaria.29 The number of refugees and immigrants was one third or one fourth that of in-migrants to urban communities. The number of immigrants was twice to three times smaller than that of the internal migrants, and it was growing to the mid-1920s as a result of refugee flows. These refugee flows stopped, however, and in 1934 the proportion of foreigners from the population became lower (233 foreign-born per 1,000 locals) (Table 5).

 

Table 4. Number of in-migrants and immigrants/refugees among urban and rural de facto population, 1910–193430

 

In-migrants

Immigrants/refugees

Local population*

Total

among

population of Bulgaria

urban

rural

urban

rural

urban

rural

urban

rural

population

 

 

 

 

1910

217,328

468,763

59,706

59,965

551,916

2,977,966

828,950

3,505,794

1920

271,358

489,945

118,185

104,393

576,422

3,284,497

965,965

3,878,835

1926

354,187

635,717

166,761

137,735

609,156

3,575,131

1,130,104

4,348,583

1934

459,296

743,280

159,391

127,186

683,770

3,904,863

1,302,457

4,775,329

* Population born in the locality where it was enumerated during the census.

 

Table 5. Intensity of in-migrants and immigrants/refugees to the locals* among urban and rural de facto population, in ‰, 1910–193431

 

In-migrants

Immigrants/refugees

among

urban

rural

urban

rural

population

1910

393.8

157.4

108.2

20.1

1920

470.8

129.8

205.0

31.8

1926

581.4

149.2

273.8

38.5

1934

671.7

190.3

233.1

32.6

* Population born in the locality where it was enumerated during the census.

The Contributions of Sexes

During the period in question, a common gender characteristic of migration to towns/cities was that the majority of migrants were men,32 as opposed to the period after World War II, when predominantly women set off for urban areas.33 However, if one examines the data concerning numerical growth of migrants to towns/cities in 1911–1934, it becomes evident that this phenomenon concerned both sexes, but it was higher for women: +91 percent for male in-migrants and +138 percent for female ones, and +131 percent for male immigrants and +228 percent for female ones, bearing in mind that at the same time the number of in-migrants was twice or three times the number of immigrants. In this case, the historical and cultural background played an important role in determining the extent to which women had opportunities to migrate independently of men. The Bulgarian model of economic development at the time, however, also influenced the sex composition of the in-migration flow. Preferring to employ men, the urban occupation structures seem to be the main factor in setting limits for female migration to towns/cities. As we shall see, later the large number of (unmarried) women migrating towards the towns/cities was linked to employment opportunities, especially in the sector of “domestic service.”

The final result was a numerical preponderance of men in the cities in the mid-1920s, where, unlike in the villages, there was the usual demographic phenomenon of women outnumbering men because of longer life expectancies. (Here, however, I would like to note that before the wars, compared to the other countries, Bulgaria was distinguished by predominantly male populations in both cities and villages, and by the mid-1930s, the two sexes had gradually come to constitute roughly half of the population each, Table 6). In order to identify the source of male preponderance in towns/cities, we have used as an indicator the number of females per 1,000 males in the variations of the native-born and foreign-born urban populations. Within the native-born populations, we see the usual situation: women outnumbered men. But in the case of migrants, we find precisely the opposite. At first glance, the related data show a preponderance of men, and men were particularly numerous among refugees and immigrants having in mind that among Bulgarians there was more balance, because they lived predominantly as families. This was also true for the third-largest but still a dozen times smaller refugee stream of Armenians. The Russians, second in number but also dozens of times fewer, (being soldiers) were distinguished as a male refugee and immigrant flow. But this contribution of external migrants to urbanization is only seeming, since they were in principle half as many as in-migrants. So, in this case, the men who predominated in the in-migration flow to the cities were the determinants (Table 6).

 

Table 6. Number of females per 1000 males among urban and rural population in Bulgaria, 1910–193434

 

Urban

Rural

Locals*

In-migrants

Refugees and immigrants

Total

From the refugees and immigrants

Total

Bulgarians

Russians

Armenians

1910

1,062

752

612

935

544

 

639

973

1926

1,057

880

844

966

890

341

924

1,005

1934

1,014

944

874

971

 

 

 

996

* Population born in the locality where it was counted during the census.

The Contributions of the Ethnicities

The migration towards towns/cities among the native-born population of Bulgarian ethnicity was decisive for the process of urbanization, although the relative share of the urban population within its variation was very low, because being numerically dominant, it had an ascending trend (Table 7). However, we were curious to consider the contributions to urbanization of other ethnic groups recorded in the statistics. In understanding the analysis that follows, it should be taken into consideration that behind the high rates of growth there was a small number of migrants.

By volume, the resettlements in towns/cities prevailed among the indigenous, comparatively small ethnic groups, such as Armenians and Jews, with a tendency to increase between 1910 and 1926. However, they had come into being and existed as urban diasporas. In 1910, 96 percent of local Jews and 88 percent of local Armenians lived in towns/cities. This phenomenon is related to their occupations. Over half (54 percent) of the economically active Armenians were employed in industry (mostly in clothing and footwear production), and over half (52 percent) of the economically active Jews were traders (dealing with sales of clothing and footwear, food and beverages, foreign exchange, commissions and exports). Another 36 percent of the latter worked in industry (in the production of either clothing and footwear or beverage). Among the Armenians and Jews, the main direction of in-migration was from small to big towns/cities. They were concentrated in the big towns and cities, where their resettlements (compared to the local Jewish and Armenian population) were distinguished by their high number per 1000 locals, and therefore this movement did not contribute to urbanization understood as the movement of in-migrants from villages to towns/cities. In 1911–1926, among Armenians, quantitatively small in-migration can be observed in the opposite, town/city-to-village direction. The very high number of resettled people per 1000 locals within the Armenian rural population shows that their rural diaspora was at that time a relatively new phenomenon. A similar process can also be observed among the Jews in 1926. Hence, although among the local Armenians and Jews the relative share of resettlements to the towns/cities increased (among the Jews +46 percent and among the Armenians +41 percent) compared to their migration to villages, not they, but the Armenian refugee wave from the first half of the 1920s constituted the most significant contribution to urbanization in Bulgaria with their urban resettlements’ impressive growth of +246 percent.

Table 8 shows that among the different ethnic groups it was the rural population that predominated within the set of native-born people, except for the Jews, Armenians and Greeks. According to the 1926 census data for the foreign-born (i.e. the new refugees and immigrants), the Armenians, Bulgarians, Jews, and Russians were mainly targeting towns/cities with an upward trend. The Greek diaspora showed an interesting demographic trend for the period 1911–1926. Among the native-born Greeks, the urban population increased by more than 20 percent, and among the foreign-born Greeks, it decreased by five percent (although it was predominant there) (Table 7); the reason for this was their nearly total exodus35 as a result of the Greek-Bulgarian Convention on Voluntary Population Exchange of 27 November 1919. In 1910, about 91 percent of the total urban Greek diaspora lived in the towns of Kavakli (Topolovgrad), Stanimaka (Asenovgrad), Varna, Sozopol, Burgas, Anhialo (Pomorie), Mesemvria (Nesebar), and Plovdiv. It is obvious that after the wars, the local Greek population was increasingly concentrated in the towns/cities, and the displacements themselves took place first among immigrants. In their place, Bulgarian refugees were resettled. The native-born ethnic Turks were distinguished by a small urban diaspora, whereas foreign-born Turks concentrated in cities; in both variations there was a downward trend in migration of ethnic Turks to towns/cities; the drop was perceptibly lower among immigrants. Displacements which intensified during the wars and continued afterwards contributed to this, but they were not the only factor. The Turkish population started leaving towns/cities and resettling in villages, as evidenced by the rise in their numbers as a percentage of the populations in villages (Table 7 and 8). In the case of the Romanians and Tartars, there was a decrease in the urban population (in terms of number and relative share) compared to 1910 for both the native-born and foreign-born, but this was largely due to the cessation of Southern Dobrudja to Romania. Among the minority diasporas in Bulgaria, only the Russians turned from a rural community into urban one. This took place because of the tendency among new Russian refugees and immigrants to settle almost exclusively in the towns/cities. This caused an extraordinary increase in their urban population of +2009 percent (Table 7). Hoping to return to their home country soon, they did not accept Bulgarian citizenship, and so by law they had no right to receive agricultural land (this explains their low share in rural areas), unlike refugees of Bulgarian ethnic origin.

 

Table 7. Relative share of the urban population in Bulgaria among the different ethnic groups in correlation with native- and foreign-born (i.e. for the old and the new diasporas), de facto population, 1910, 192636

“nationality/natoinalité ethnique”

Native-born

Foreign-born

1910

1926

1910

1926

Armenians

85.8

92.6

90.3

93.0

Bulgarians

17.2

18.5

43.4

50.4

Jews

95.9

97.1

97.5

98.1

Greeks

59.3

79.8

74.5

70.5

Romanians

7.7

0.8

35.1

26.0

Russians

10.8

59.9

42.6

63.3

Tatars

27.7

16.2

63.2

45.5

Turks

15.0

11.9

63.7

42.6

Gypsies

25.4

24.0

26.9

16.7

Table 8. Increase/decrease in the number of in-migrants and immigrants/refugees among the urban and rural population of different ethnic groups in Bulgaria 1910–1926, in %37

“nationality/natoinalité ethnique”

In-migrants

Immigrants

rural

urban

rural

urban

population

population

Armenians

–46

+41

+151

+246

Bulgarians

+37

+69

+165

+251

Greeks

–72

–55

–48

–136

Jews

–13.5

+46

+8

+41

Romanians

+45

–62

–33

–57

Russians

+451

+429

+1098

+2009

Tatars

–53

–68

– 90

–75

Turks

+46.5

+30

+41

+15

Gypsies

+26

+31

+377

+160

The Contribution of the Small and Big Towns/Cities

Before considering the question referred to in the subtitle, we will try to explain the changes in the data concerning the native-born population, which may seem obvious at first glance. These changes are important because they influenced the formation of the indicator of migrants’ number per 1,000 locals, and since the analysis of the origin of these changes is a sign of whether it is a source of out-migration or emigration, and because of the dynamics of the urbanization itself. In the period from 1910 to 1926, the number of native-born population in Bulgaria decreased sharply in both small and big towns/cities (excluding Sofia). In small towns/cities, it decreased almost twice as much as it did in big ones (it doubled only in Sofia). It is interesting to see how much this phenomenon was due to migrations. We have tracked it at the settlement level and we have found out that in 1926 in 18 of the 26 big towns and cities the native-born population grew, and in some cases it grew considerably (in Burgas it doubled and in Plovdiv it grew by one third). In the remaining 8 big towns,38 it decreased from several hundred to not more than 1,500. In the case of big towns/cities, three-quarters of the reduction was a result of the secession of the three major towns in Southern Dobrudja after the Balkan wars (Silistra, Tutrakan, and Dobrich). The remaining loss was mainly due to the displacement of the Greeks from Burgas, Varna, Plovdiv, and Stanimaka and to a very small extent, due to mortality and other displacements. In the case of small towns, the decline of the native-born population by half was due to the secession of the five cities with the Treaty of Neuilly (Balchik, Kavarna, Bosilegrad, Tsaribrod, and Strumitsa). It also partly diminished because of the expulsion of the Greeks.39 This loss was not compensated by the 17 towns in the newly acquired territories and the reclassification (i.e. new settlements which were declared towns), probably owing to the in-migration and out-migration from the small to big towns/cities.

The loss of local urban population as a result of the secession of cities (both small and large) and as a result of the territorial losses from the wars was not only simply compensated in the period between 1926 and 1934 by still high birth rates due to intense external and internal migration (the latter of which was significantly larger), but as early as 1934 the pre-war number of the native-born population had been exceeded. That is why we can conclude that the secession of the towns/cities as a result of the wars lost by Bulgaria really had a negative impact on the urbanization of the country, and if that had not happened, the urbanization process would have been much stronger. However, it can not be denied that it was intense and intensifying and quantitatively managed to overcome the loss of the native-born urban population in less than ten years. In this sense, we cannot speak about its stagnation or lagging behind. It simply evolved in the context of changed territorial conditions.

The census statistics make it possible to identify the urbanization centers in Bulgaria, which coincide with the destination points of migration flows. Towns/cities differ in their socio-economic characteristics, so they have different attractive opportunities. In order to estimate them, we consider the cities in the two groups according to the number of their inhabitants (small and big). We have separated the capital of Sofia, which was (and still is) the administrative and cultural center of the country, from the group of other towns/cities, as its growth was unprecedented and incomparable with that of other cities. The data on settlements by groups of towns/cities show that the big towns/cities (except the capital of Sofia) had the greatest influx of in-migrants, refugees and immigrants by absolute number and by the indicator showing total number of in-migrants and immigrants-refugees per 1000 locals. This value in 1910 was twice as high as in the case of the small towns. Despite that between 1910 and 1926 the small towns had a much larger growth of migratory influx (both in number and percentage) than the big ones (a tendency which reversed between 1926 and 1934), but they were far behind in terms of migratory flows to the capital. (Таble 9) The latter surpassed the influx to both small and big towns/cities not only in their absolute numbers but in their intensity as well: in 1910, in the big towns/cities (except Sofia) the total number of migrants and (in-migrants and immigrants) per 1000 locals was twice as high. Sofia marked the greatest growth. There, the number of migrants was almost twice as much as that of the locals. In 1926, the local population declined in both small and big towns on account of a sharp rise in the number of migrants (almost six times within the external ones and 1.5 times within the internal ones) (Таble 9). Small towns strengthened their position of attractiveness, and they caught up with their lagging behind and the number of migrants per 1000 local people almost reached the level of big towns, although the volume of migration to them was smaller. The capital was once again distinct in scale from the other major cities. Migrants in the direction of Sofia were twice as numerous as local residents.

To quantify the role of immigration and in-migration in the urbanization of small and big towns/cities and the capital, we use an indicator that expresses the relative share of the increase in the number of immigrants/refugees and in-migrants in small and big towns/cities and Sofia compared to population growth in them. For the small towns, +44.5% belong to immigrants and +32% to in-migrants; for the big towns/cities +33% and +50% respectively, and for Sofia +21% and +51%. Or, in general, until 1926 Sofia and the big towns were growing predominantly by in-migrants, while small towns were increasing in size because of immigrants (Table 3 and 10).

Now we are going to track the most significant role of migration in the urbanization of separate towns/cities. In 1910, among the cities in Bulgaria, the biggest attraction centers for migration (internal and external), apart from the capital of Sofia, was the administrative center of the Burgas County, to which Bulgarian refugees were directed. (At that time, it was the largest such center in the county, with a population density below the average, and there were quite large reserves of state and municipal land funds.) So, in these two cities (Sofia and Burgas), 63 percent of the population consisted of in-migrants and immigrants/refugees. This figure was followed by Varna with 49 percent, Ruse with 45 percent, Plovdiv with 42 percent, and Shumen 30 percent. In 1926 the main centers of attraction for migration were the same cities but in a different sequence, and after the large refugee waves of Bulgarians from Thrace, Macedonia, Dobrudja and the Western Outskirts as well as Russians and Armenians, the number and the relative share of the settlers grew. Sofia gave its first place to Burgas, where the majority of the population was migrant (refugees, immigrants, in-migrants from other parts of the country) 87 percent, and ranked second with 68 percent, followed by Plovdiv 56 percent, Varna 55 percent, Ruse 52 percent, Haskovo 47 percent, Sliven 28 percent, Shumen 26 percent. Subsequently, in the second half of the 1920s, the immigration flow decreased considerably, stopping the refugee waves; so, Burgas (65 percent) relinquished to Sofia (68.5 percent) the leading position in the attraction of migrants. The abovementioned towns/cities (not taking into consideration the capital) were traditional industrial and commercial centers, with Ruse, Varna, and Burgas having the greatest ports on the Danube River and the Black Sea, respectively, and Plovdiv enjoying investment of German, French, and Belgian capital and a prospering food industry, Sliven being a center for the textile industry, and Haskovo developing tobacco production and trade; yet a few of them lost population through the expulsion of local Greeks (Burgas, Varna, Plovdiv), which was compensated by in-migrants and immigrants/refugees of Bulgarian ethnicity.

If we distinguish the urban attractiveness centers in relation to the extent of their attraction for the internal and external migration flows, we find that Sofia attracted an increasing percentage of the in-migration flow to towns/cities and the whole immigration flow (1926: 29 percent and 10 percent, respectively, in 1934: 33 percent and 13 percent, respectively). The capital city was followed by Plovdiv, which similarly showed an increase in its relative share in the internal migration to cities (1926: 8 percent and 3 percent, respectively, in 1934: 10 percent and 2 percent, respectively). Then, by a relative share of five to ten per cent compared to the in-migration to towns/cities, come Varna and Ruse in 1910 and 1934 and Shumen and Varna in 1926. Another several towns/cities developed as centers of attraction for refugees and immigrats (based on the indicator of immigrants’ relative share in the given city compared to all immigrants in the towns/cities in Bulgaria), with values clearly distinguishable from those of other towns/cities; they were Sofia (1926: 25 percent, 1934: 27.5 percent), followed by Plovdiv (1926: 12 percent, 1934: 19 percent), Varna (1934: 11 percent); refugees accepted into Svilengrad (1926: 6 percent), Burgas (1926: 5.4 percent, 1934: 5 percent), Haskovo (1926: 4 percent); but in the following years, the number of immigrants there was decreasing significantly due to displacement within the country.

In fact, the data shows that the main attraction center for migration was the capital, and the other four major Bulgarian cities of Plovdiv, Varna, Ruse, and Burgas lagged behind it, and only very seldom did migratory flows stand out in the urbanization of small towns. This is understandable considering that the aforementioned cities best suited the standard of living in Bulgaria at the time. Sofia was the most developed city in Bulgaria. It had electricity and good supplies of water. In the 1920s, the Rila water main was built, the construction of sewerage was started, and after the wars, the capital transformed from a predominantly consumer center and a city of clerks and officers into a commercial and industrial center with a large working class. The lack of settlements with truly urban profiles and with high standards of living, including better incomes and living facilities, contributed to Sofia’s becoming the most dynamically developing city in Bulgaria. In the second half of the 1930s, the Batova-Varna water pipeline was built, which supplied water to the sea capital. The new ports of Varna and Burgas, put into operation in the very beginning of the twentieth century, contributed to their urban revival.

 

Table 9. Total number of migrants (in-migrants and immigrants/refuges) and locals* and the number of migrants per 1,000 locals in small and big towns/cities, and in Sofia, 1910–193440

 

Towns with up to 10.000 inhabitants

Towns/cities with and above 10.000 inhabitants,

without Sofia

Sofia

Migrants

Locals*

Intensity

Migrants

Locals*

Intensity

Migrants

Locals*

Intensity

1910

56,530

195,096

289.8

220,504

356,820

618.0

64,993

37,768

1720.9

1926

109,955

144,211

762.5

267,028

328,862

812.0

144,265

68,714

2099.5

+/– in numbers

+53,425

–50,885

 

+46,524

–27,958

 

+79,272

+30,946

 

+/– %

+94.5

–26

 

+21

–8

 

+122

+82

 

1934

115,456

215,932

534.7

306,406

377,468

811.7

196,825

90,370

2178.0

+/– in numbers

+5501

+71.721

 

+39.378

+48.606

 

+52,560

+21,656

 

+/– %

+5

+49.7

 

+14.7

+14.8

 

+36.4

+31.5

 

* Population born in the locality where it was enumerated in the census.

Table 10. Number of immigrants and in-migrants together in the small and big towns/cities, and in Sofia, de facto population, 1910–192641

 

Immigrants in

In-migrants

Towns with up to 10.000 inhabitants

Towns/cities with and above 10.000 inhabitants, without Sofia

Sofia

Towns with up to 10.000 inhabitants

Towns/cities with and above 10.000 inhabitants, without Sofia

Sofia

1910

6,639

34,608

18,459

49,891

120,903

46,534

1926

37,547

87,357

41,857

72,108

179,671

102,408

+ / – in figures

+30,908

+52,749

+23,398

+22,217

+58,768

+55,874

To What Extent Was Urbanization Through Migration Related to the Modernization of Towns/Cities and to Industrialization?

Unfortunately, the Bulgarian censuses do not contain information about the inter-professional in-migrants’ mobility to towns/cities. In order to answer this question, we have used the data that we have on the sectoral structure of the economically active population within in-migrants coming from villages to towns/cities, but only for the population of Bulgarian ethnic origin. This type of statistics on refugees and immigrants of Bulgarian (Table 11) and other ethnic origin (Tables 12, 13) was not published in correlation with villages and towns/cities, and that is why the data are incomparable. We have only used them as a guideline.

The coefficient of economic activity among the in-migrants of Bulgarian ethnic origin (who predetermine the whole structure) in the village-to-town/city direction was higher (1920: 61.7 percent, 1926: 60.2 percent) than the average for the country (54 percent), which indicates that most of them were labor migrants moving in search of a livelihood. The coefficient of economic activity among foreign-born refugees and immigrants was even higher (63.8 percent for 1926). In the professional structure of economically active women who had moved from village to town (Table 12) the sector of “domestic servants” dominated (over 40 percent). The urbanization process means not only village–to–town migration, but also perception of the urban way of life as well. Part of the urban lifestyle of the upper stratum in this period included the hiring of domestic servants. Even a regular servant exchange was organized in Sofia. Girls from all over the country, led by parents and dragomans, came to Sveti Kral Square (St. Kral), today’s St. Nedelja Square (St. Holy Sunday) every St. George’s Day and St. Dimitar’s Day in order to seek employment. It is noteworthy that former maidservants were preferred by bachelors as wives, especially among the peasantry, because they were literate and well-informed.42 The data in Table 11 show that women hardly left home and farm work, and they very slowly entered the professional work. Female laborers were more likely to be employed in professional work. 18 percent of them were occupied in industry, and only 4 percent in public services and the liberal professions. Those occupied in industry (38 percent) predominated among the male village-to-town in-migrants; again, among them in second place was the sector of “public services and the liberal professions” (31 percent).

However, based on the available data, it can be summarized that in the first half of the 1920s, among in-migrants (both men and women), the number and relative share of those occupied in the industrial sector was growing markedly; in addition, the number of workers in the industrial sector was growing much more rapidly than the number of workers in the agricultural sector. The male in-migrants of Bulgarian ethnicity went predominantly into industry, as did male refugees and immigrants of non-Bulgarian ethnicity, as indirectly can be assumed on the basis of Tables 12 and 13.

 

Table 12. Professional structure of the economically active village-to-town in-migrants of Bulgarian ethnicity, de facto population, by sex, in figures and %, 1920–192643

 

1920

1926

male

female

total

male

female

total

male

female

total

male

female

total

%

In figures

%

In figures

Agriculture and live stockbreeding, hunting and fishing

14.8

35.5

19.5

10,131

7,105

17,236

12.7

35.6

18.6

11,364

11,099

22,463

Industry incl. mining, crafts and communications

28.7

11.0

24.7

19,589

2,198

21,787

38.0

17.7

32.8

34,164

5,510

39,674

Trade

11.6

1.4

9.3

7905

288

8,193

13.3

1.6

10.3

11,935

484

12,419

Public services and liberal professions

42.8

5.0

34.2

29,213

1,003

30,216

31.0

4.3

24.1

27,861

1,340

29,201

Domestic servants

0.5

46.8

11.0

310

9,364

9,674

0.4

40.7

10.8

382

12,693

13,075

Undetermined

1.6

0.3

1.3

1114

46

1,160

4.6

0.1

4.7

4,116

34

4,150

Total

100.0

100.0

100.0

68,262

20,004

88,266

100.0

100.0

100.0

89,822

31,160

120,982

 

Table 13. Professional structure of the economically active urban immigrants and refugees of non-Bulgarian ethnicity, de facto population, by sex, in figures and %, 192644

 

male

female

total

male

female

total

%

In figures

Agriculture and live stockbreeding, hunting and fishing

15.7

50.7

21.8

5,434

3,718

9,152

Industry incl. mining, crafts and communications

49.8

27.7

45.9

17,203

2,032

19,235

Trade

15.5

4.9

13.6

5,349

359

5,708

Public services and liberal professions

8.8

11.4

9.3

3,042

838

3,880

Domestic servants

0.7

5.2

1.5

231

383

614

Undetermined

9.5

0.1

7.9

3,280

10

3,290

Total

100.0

100.0

100.0

34,539

7,340

41,879

 

Table 14. Professional structure of the economically active refugees and immigrants of Bulgarian ethnicity, de facto population, by sex, in figures and %, 192645

 

male

female

total

male

female

total

%

In figures

Agriculture and live stockbreeding, hunting and fishing

48.4

84.9

61.1

48,178

45,240

93,918

Industry incl. mining, crafts and communications

28.6

10.1

22.1

28,425

5,401

33,826

Trade

8.3

0.8

5.7

8,315

332

8,647

Public services and liberal professions

7.7

2.7

6.0

7,651

1,437

9,088

Domestic servants

0.2

1.5

0.7

178

820

998

Undetermined

6.8

0.0

4.4

6,764

23

6,787

Total

100.0

100.0

100.0

99,511

53,253

152,764

Urbanization is also reflected in the creation of new structures in the organization of urban space. In fact, its main sign was the change in the economic structures of the urban space. By the Mid-twentieth century, a general characteristic of the Bulgarian towns/cities, including the big ones and the capital, was their rural appearance, resulting from the presence of large sectors with a high agricultural character. In order to establish the changes, we have compared the occupational structure of the economically active population of Bulgarian ethnicity in the towns/cities (locals and inter-town/city migrants, according to the correlation of “born in towns/cities and counted as residents in the census” of Bulgarian ethnicity) with the occupational structure of the village-to-town/city in-migrants of Bulgarian ethnicity (Table 10) during the first half of the twentieth century. In the occupational structure of the economically active Bulgarian-born population which was counted as urban residents in 1920 and 1926, a slight decrease from 30.7 percent to 29.8 percent is visible in the relative share of those employed in agriculture as well as a rise from 35.4 percent to 36.2 percent among those employed in industry. Economically active in-migrants of Bulgarian ethnicity headed from the villages to the towns/cities to work mainly in the industry, where their share increased considerably (from 24.7 percent to 32.8 percent) in the first half of the 1920s. (Table 12) Among them, for this relatively short period, the relative share of the people occupied in agriculture and livestock breeding decreased from 19.5 percent to 18.6 percent. Thus, by comparing the changes in the professional structure of the two variations of the predominant economically active population of Bulgarian ethnicity, we have found that the decline in the importance of the agricultural sector was minimal and had the same values (–0.9 percent) for both variations. Within the structure of the village-to-town in-migrants, the share of industrial sector increased by 8 percent. This means that the locals and the new residents were giving up just as little of their agricultural occupations in order to engage in some kind of urban one. And the “strengthening” of industrial production in the urban economy was definitely due to in-migration and was the result of a shift among the new citizens to industrial activities.

Conclusion

We can summarize the results of the quantitative analysis of the birthplaces of Bulgaria’s population from the perspective of the role of internal and external migration (i.e. in-migration and immigration) in the processes of urbanization as follows:

Urbanization in Bulgaria in the period in question was mainly due to migration and in particular to in-migration, although it was undoubtedly closely related to the refugee wave and immigration during the war and in the interwar period, which strengthened the expansion of the towns and cities. The drying-up of the refugee inflow did not lead to a decline in the urbanization process. On the contrary, there was intensified internal migration towards the towns and cities and specifically in the direction from village to town/city. This was a characteristic phenomenon for other countries as well. Similar phenomena were observed in the United States in the first decades of the twentieth century, but in relation to the strengthening of restrictions on immigration.

In the first half of the 1920s, many people (predominantly men) left the villages and began to engage in non-agricultural activities in the towns and cities. But an initial process of feminization of in-migration towards the towns/cities as well as of the industrial labor force was evident too.

There was a relationship between emigration, on the one hand, and internal migration and immigration on the other, which is well illustrated by the replacement of the displaced Greek population with Bulgarian refugees and in-migrants.

The decisive role of in-migration in the urbanization process in Bulgaria was determined by in-migration to the big towns and cities (including Sofia). This was because the urbanization of big towns/cities (understood as urban population growth) quantitatively exceeded the urbanization of small ones, and it was largely determined by inter-urban migration from small to big towns.

At the same time, the urbanization of small Bulgarian towns was primarily driven by immigration.

The trend of ascending development (albeit at a slow pace) of the urbanization process in Bulgaria was mainly due to in-migration from village to town/city of the predominantly Bulgarian ethnic population, but the contribution of Armenian and Russian refugees was also quantitatively visible.

The main destinations for immigrants, with values clearly distinguishable from those of other towns/cities, was Sofia. It attracted an increasing percentage of the in-migrant flow towards the towns and of the whole set of internal migrants. Sofia was followed by the second largest city in Bulgaria, Plovdiv, but the numbers in the case of Plovdiv were much smaller.

The urbanization of the capital Sofia, which was growing to the size of a super city (certainly with regard to the living and working conditions in Bulgaria), stood out from the perspective of its scale, even against the background of the so-called big towns and cities.

 

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Общи резултати от преброяването на населението в Царство България на 31.12.1920 г. Кн. І. План и организация на преброяването. София, 1927.

Общи резултати от преброяването на населението в Царство България на 31.12.1926 г. Кн. І. План и организация на преброяването. София, 1931.

Общи резултати от преброяването на населението в Царство България на 31.12.1926 г. Кн. ІІІ. Статистика на професиите. София, 1932.

Преброяване на населението на 31 декември 1934 г. Кн. I. Общи резултати. Пол, месторождение, поданство, вероизповедание, говорим език, грамотност и образование. София, 1938.

 

Secondary literature

Bencivenga, Valerie R., Bruce D. Smith. “Unemployment, Migration and Growth.” Journal of Political Economy 105, no. 3 (1997): 582–608.

Bilsborrow, Richard E. “Migration, Population Change and the Rural Environment.” In ECSP Report 8. Woodrow Wilson International Center for Scholars, Environmental Change and Security Project, 2002, 69–94.

Ivanov, Martin. The Gross Domestic Product of Bulgaria, 1870–1945. Sofia, 2012.

Kavzoglu, Taşkin. “Determination of Environmental Degradation due to Urbanization and Industrialization in Gebze, Turkey.” Environmental Engineering Science 25, no. 3 (2008): 429–38.

Kopsidis, Michael, Martin Ivanov. “Was Gerschenkron right? Bulgarian Agricultural Growth during the Interwar Period in the Light of Modern Development Economics.” ESHES Working Paper, no. 82 (July 2015): 29. Accessed on March 3, 2018. http://www.ehes.org/EHES_82.pdf

Lampe, John R., Marvin R. Jackson. Balkan Economic History, 1550–1950: From Imperial Borderlands to Developing Nations. Bloomington: Indiana University Press, 1982.

Long, Katy. “When refugees stopped being migrants: Movement, labor and humanitarian protection” Migration Studies 1, no. 1 (2013): 4–26.

Poston, Dudley, and Leon Bouvier. Population and Society: An Introduction to Demography. Cambridge: Cambridge University Press, 2010.

Tacoli, C., G. McGranahan, D. Satterthwaite. World Migration Report 2015: Urbanization, Rural–urban Migration and Urban Poverty. Background paper. December 2014. Accessed on January 14, 2018. https://www.iom.int/sites/default/files/our_work/ICP/MPR/WMR-2015-Background-Paper-CTacoli-GMcGranahan-DSatterthwaite.pdf

Teichova, Alice. “Industry.” In The Economic History of Eastern Europe, 1919–1975. Vol. 1, Economic Structure and Performance between the Two Wars, edited by M. Carl Kaser, and E. A. Radice. Oxford: Clarendon Press, 1985.

White, Michael J., ed. International Handbook of Migration and Population Distribution. Vol. 6. Heidelberg–New York–London, 2016.

 

Василева, Бойка. Миграционни процеси в България след Втората световна война [Migration processes in Bulgaria after the Second World War]. София, 1991.

Везенков, Александър. “Урбанизацията в България до Втората световна война: темпове и характер” [Urbanization in Bulgaria until the Second World War: its pace and character]. Минало, no. 3 (1999): 56–69.

Георгиев, Георги. Освобождението и етнокултурното развитие на българския народ, 1877–1900 [The liberation and ethnocultural development of the Bulgarian nation]. София, 1979.

Груев, Михаил. “Демографски тенденции и процеси в България в годините след Втората световна война” [Demographic tendencies and processes in Bulgaria in the years after the Second World War]. In Знеполски, Ивайло, ред. История на Народна република България: Режимът и обществото [History of the People’s Republic of Bulgaria: its regime and society]. София, 2009.

Даскалов, Румен. Българското общество, 1877–1939 [Bulgarian society 1877–1939]. Т. 2. София, 2005.

Данаилов, Георги. Изследвания върху демографията на България [Research on the demography of Bulgaria]. София, 1930.

Марчева, Илияна. “Социални измерения на урбанизацията в България след Втората световна война” [Social dimensions of urbanization in Bulgaria after the Second World War]. Балканистичен форум, no. 2 (1997): 119–130.

Марчева, Илияна. Политиката за стопанска модернизация в България по време на Студената война [Politics of economic modernization in Bulgaria during the Cold War] . София, 2016.

Минков, Минко. Миграция на населението [Population migration]. София, 1972.

Младенов, Чавдар., Емил Димитров. “Урбанизацията в България от Освобождението до края на Втората световна война” [Urbanization in Bulgaria from the Liberation to the end of the Second World War]. География 21, no. 1 (2009): 13–17.

Найденова, П. “Миграционни процеси в България през отминалите столетия” [Migration Processes in Bulgaria in the past centuries]. Население, no. 2 (2000): 3–15.

Попов, Кирил. Стопанска България [Economics of Bulgaria]. София, 1916.

Стефанов, Иван. и др. Демография на България [Demography of Bulgaria]. София, 1974.

Тотев, Анастас. “Населението на България, 1880–1980: Демографско-исторически очерк” [The population of Bulgaria, 1880–1980: a historical demographic sketch]. Год. на СУ, ЮФ, no. 2 (1968): 26–32.

Цеков, Николай. “Селската селищна мрежа като фактор в развитието на човешкия потенциал на българското село” [Rural village network as factor for development of the human potential of the Bulgarian village]. Население, nos. 1–2 (2011): 77–92.

Щерионов, Щ. “Демографският преход по българските земи – специфики и начални граници” [The demographic transition in the Bulgarian lands – specifics and initial time limits]. In Българското възрожденско общество - проблеми, борби и постижения. Сборник с изследвания в чест на 75-годишнината на доц. д-р Огняна Маждракова-Чавдарова, 248–58. София, 2012.

1 According to data for 2011. See: UN, 2014b. Accessed on March 2, 2018. http://onlinelibrary.wiley.com/doi/10.1002/psp.2036/full

2 Bencivenga and Smith, “Unemployment, Migration and Growth,” 582–608; Bilsborrow, “Migration, Population Change and the Rural Environment,” 69–94; Kavzoglu, “Determination of Environmental Degradation,” 429–438.

3 White, International Handbook, 474–75.

4 Найденова, 3–15.

5 In this essay, I use the term “immigrant” to refer to people who came, as immigrants, to the country from abroad. Similarly, the term “emigrant” refers to people who left the country. I use the term “in-migrant” to refer to people who migrated from one settlement to another within the country.

6 Груев, Демографски тенденции, 369–70.

7 Kopsidis, “Was Gerschenkron wright?” 9, 17; Lampe and Jackson, Balkan Economic History, 576–77; Ivanov, The Gross Domestic Product of Bulgaria, 105, 107; Teichova, “Industry,” 239.

8 For details see: The 1951 United Nations Convention Relating to the Status of Refugees. See also: Long, “When refugees stopped being migrantsm”, 4–26.

9 Младенов и Димитров, “Урбанизацията в България,” 13; Минков, Миграция на населението, 85; Стефанов, Демография на България, 258–59.

10 Василева, Миграционни процеси в България, 94; Марчева, “Социални измерения на урбанизацията” 127; Марчева, Политиката за стопанска модернизация, 396–97.

11 In 1880 the urban population in the Bulgarian Principality constituted 16.7 percent of the total population of the newly created state; in 1920 – 19.9 percent, and in 1934 – 21.4 percent. See: Василева, Миграционни процеси в България, 110; Георгиев, Освобождението и етнокултурното, 24; Попов, Стопанска България, 13.

12 Тотев, “Населението на България”, 26–32; Стефанов, Демография на България, 218; Даскалов, Българското общество, 143.

13 Стефанов, Демография на България, 218.

14 Тотев, Населението на България, 26–32; Стефанов, Демография на България, 218.

15 Везенков, “Урбанизацията в България,” 56–69.

16 From South Dobrudja – Silistra, Tutrakan, Dobrich, Balchik, Kavarna, and from the Western Outskirts – Bosilegrad, Strumitsa, Tsaribrod (Dimitrovdrad).

17 Ahtopol, Bansko, Gorna Dzhumaja (Blagoevgrad), Nevrokop (Gotse Delchev), Dyovlen (Devin), Daradere (Zlatograd), Ortakyoi (Ivalovgrad), Koshukavak (Krumovgrad), Kardzhali, Malko Tarnovo, Melnik, Mastanli (Momchilgrad), Petrich, Razlog, Mustafa pasha (Svilengrad), Pashmakli (Smolyan) and Vasiliko (Tsarevo).

18 Везенков, “Урбанизацията в България,” 60; Данаилов, Изследвания върху, 164–68.

19 The Components of Urban Growth in Developing Countries. Population Division. Department of Economic and Social Affairs. United Nations Secretariat. ESA/P/WP.169. Sept. 21. United Nations, 2001, 58. Accessed on June 26, 2018. https://population.un.org/wup/Archive/Files/studies/United%20Nations%20(2001)%20-%20The%20Components%20of%20Urban%20Growth%20in%20Developing%20Countries.pdf

20 Poston and Bouvier, Population and Society, 307–11.

21 Tacoli, C. et al., World Migration Report 2015.

22 Until 1926, the censuses used 10,000 inhabitants as the threshold for the distinction between small towns/cities and big towns/cities.

23 This indicator was used by the ethnographer G. Georgiev, in his study of the internal migration and urbanization processes in the years after the formation of the Third Bulgarian State. See: Георгиев, Освобождението и етнокултурното, 23.

24 Тотев, “Населението на България”, 177–79; Цеков, “Селската селищна,” 78.

25 In 1880–1900 for instance (i.e. for a period of 20 years), the urban population in Bulgaria increased by 36.6 percent and the rural one by 31.6 percent. See: Георгиев, Освобождението и етнокултурното, 23.

26 Sources: Общи резултати 1923, 14–17; Общи резултати 1927, 16–23; Общи резултати 1931, 16–23.

27 Clearly, in-migration from one city to another does not affect the national rate of urbanization.

28 Population born in the locality where it was enumerated during the census.

29 At the same time, the proportion of in-migrants among the rural population remained unchangeable until 1920 and only increased afterwards.

30 Sources: Общи резултати 1923, 14–17; Общи резултати 1927, 6–23; Общи резултати 1931, 16–23; Преброяване на населението, 3.

31 Sources: Общи резултати 1923, 14–17; Общи резултати 1927, 16–23; Общи резултати 1931, 16–23; Преброяване на населението 3.

32 Women mainly headed for villages.

33 Василева, Миграционни процеси в България, 110.

34 Sources: Общи резултати 1923, 14–17; Общи резултати 1931, 16–19; Преброяване на населението, 3.

35 Forty thousand were displaced and only ten thousand remained in Bulgaria.

36 Sources: Общи резултати 1923, 14; Общи резултати 1931, 18.

37 Sources: Общи резултати 1923; Общи резултати 1931.

38 Vratsa, Stanimaka (Assenovgrad), Samokov, Kazanlak, Chirpan, Svishtov, Shumen and Turnovo.

39 Among the Greek population in Bulgaria, until the Balkan wars there was relatively low mortality. See Щерионов, “Демографският преход,” 256.

40 Sources: Общи резултати 1923, 14–17; Общи резултати 1927, 16–23; Общи резултати 1931, 16–23; Преброяване на населението 3.

41 Sources: Общи резултати 1923, 14–17; Общи резултати 1927, 16–23; Общи резултати 1931, 16–23.

42 Даскалов, Българското общество, 153–54.

43 Sources: Общи резултати 1926, 4–5; Общи резултати 1932, 4–7.

44 Ibid.

45 Source: Общи резултати 1932, 4–7.

 

 

2019_1_Bán

pdfVolume 8 Issue 1 CONTENTS

Inner Territory and What Lies Behind It: An Inquiry Into the Hungarian Urban Hierarchy in 1930

Gergely Károly Bán
University of Debrecen
This email address is being protected from spambots. You need JavaScript enabled to view it.

The study of the emergence of the Hungarian urban hierarchy raises a number of methodological questions concerning the complex settlement structure and the unique urban development of the Carpathian Basin. Research on the Hungarian urban hierarchy reveals a strong positive correlation between the position of the cities in the hierarchy and the complexity of their urban functions. The aim of my inquiry is to provide a complex picture of the Hungarian urban hierarchy of the 1930s, or, more precisely, the potential hierarchies. I approach this issue from various perspectives. As there are different definitions of cities in judicial (administrative), statistical, economic, sociological, and geographical contexts, the questions remain open: what do we consider a city, and what makes a settlement a city in the interwar period in Hungary? One of the cornerstones of my research is the issue of the outskirts. In administrative terms, we can speak about a unit, but due to the differing patterns of urban development in Hungary, the relationship between the core territory and its periphery is complex. Since the classic homestead theory has been challenged, hierarchical investigations have had to address the problems involved in dividing the data between urban cores and urban peripheries. Hierarchic rankings based on the incorporation of outskirts are quite different from rankings which omit the latter zones, which tend to be dominated by scattered farms not linked functionally to the urban core. The differences also show strong regional patterns. This study, based on statistical data, tries to highlight these differences in the urban hierarchy using this new approach. This way, it becomes possible to put the study of the Hungarian urban hierarchy in the interwar period on a new methodological footing which differs in several significant ways from the foundations of earlier research on the subject in Hungary.

Keywords: periphery issue, settlement structure, urban hierarchy, Hungarian urban network, historical geography.

 “If society is inevitably spatial and the concept of space is impossible to separate from its social content, it not only means that social processes are to be analysed as they spatially present, but also means that what we consider to be spatial features are to be analysed theoretically and within social concepts.”1

In today’s era of interdisciplinarity, when the breakup of formal boundaries between disciplines is a common phenomenon, it is not easy to find a common language, common sets of concepts, and shared methods for different disciplines to use in their common research fields.2 A good example of this is the research on urban history, especially the research on urban hierarchies. The complexity of this research topic is illustrated by the fact that it is a relevant field and perspective of inquiry in several disciplines, including geography, history, sociology, statistics, and economics. If we were to ask which discipline offers the most relevant, most fitting definition for the city as a form of settlement, then the answer is, simply, all of them.

Any discipline that has the city within its scope of interest has had to come up with a fitting definition, fitting, at least, from their respective points of view. Understandably, each discipline identifies different factors as decisive, thus leading to different notions of the city. “In the case of a complex, complicated entity such as the city in particular, we can consider these differences natural.”3 Each discipline paints a one-sided picture of the city’s essence as it looks at the city from different angles and uses different conceptual sets to approach what it considers the most relevant feature of the city. Even if these essential factors are listed in a complex definition, the weight and the importance of them would also turn out to be differentiated at different moments in time. So, as a researcher, I cannot decide which discipline is right and which is not, because as a whole, these factors are not comparable across disciplines. “Sociology is no exception: it cannot shed light on the complex reality of the city”, Tibor Mendöl wrote in 1939.4 Sociology uses only one possible approach, and it understands the concept within its own context when grasping at the definition of city, but other perspectives are present in other disciplines, and a definition is not exclusive to any point of view.5 However, I find that the geographic approach is currently dominant in the research in Hungary.6

My long-term goal is to present a complex picture of the city hierarchy in Hungary in the 1930s. More specifically, I offer a picture of potential city hierarchies. I plan to investigate a city hierarchy and to approach the issue from several perspectives. The explanation for this is that the different disciplines work with different definitions of the city, which are definitely represented in research papers on urban history in the recent years.7 Legal (administrative), statistical, economic, sociological, and geographic concepts of the city all create different understandings of it. Why should not we talk about the definition of the city in the context of these city concepts, that is, administrative, statistical, sociological, etc. urban hierarchies. This will give way for a number of new aspects for the analysis of the settlement structure and hierarchy.

The background to the methodology I use for my urban hierarchy study, which is based on the geographic city concept, has already been published in the Rural History Yearbook8. The present work is a preliminary study, and I examine only one important methodological question: the question of the periphery, which is methodologically prominent both in geographic, sociological, and statistical urban hierarchy studies. The subject has been discussed a great deal both in works on urban geography and settlement stock,9 but it is rarely the true focus, except in studies which were written in the interwar period. A researcher who examines the Horthy-era town-farm theme can easily feel as if time has come to a standstill and the “research” has taken no steps forward. One major reason for this is that nowadays there is very little interest in similar issues and studies among professionals and readers alike. There is no question, however, that very little is known about the subject in a contemporary setting. It is essential that we re-approach the question, as further study could result in a better understanding of the hierarchical network of cities between the two world wars.10

Based on the factors outlined above, I find it justified to incorporate new approaches and methods into the research of the town-city relationship system and the city hierarchy between the two world wars. This allows us to get closer to the actual state of things.

The questions remain open: where does the periphery belong? How did the periphery affect the hierarchical ranking of the settlements between the two world wars? My aim in this preliminary study is to answer these questions empirically.

Periphery or Boondocks

The centuries-old history of the evolution of the “scattered farm” of the plains, by the nature of its complexity, has yet to be clearly unraveled. In the interwar period, ethnographer István Györffy hypothesized that the appearance of these “scattered farms” could be connected with the nomadic lifestyle of Hungarian settlers during the so-called Conquest.11 On the basis of this hypothetical connection, he derived the distinctive type of Hungarian city known as the “Alföld country town.” His position was that these cities used to be “two internal plot” (“két beltelkes”) so-called hutch-garden (“ólas-kertes”) settlements, which he thought to be the predecessors of the later scattered farm cities. His perspective was widely accepted by historians, geographers, ethnographers, and sociologists, so this concept became widespread. The idea that Kecskemét might also have been “two internal plot” settlements once came up,12 although no evidence has emerged to this day in support of this theory. Furthermore, the earliest maps which allow for morphological comparison suggest that it is unpersuasive. Also, at the end of the eighteenth century, quite a few plains settlements had this two inlot system. One could hardly base the notion that this was a prevailing system solely on the other two of the three cities in question, Cegléd and Nagykőrös, which exhibit this form. In recent years, the formation of the farms has been seen in new light thanks to István Orosz’s research on the Modern period land use of these farms on the plains.13 It shows that at the start of the eighteenth century, at least 107 settlements were listed on the Great Hungarian Plains where “parlagoló”14 agriculture was present, and plough fields and grasslands alternate systematically. Typically, a third of the land was used in a “parlagoló” system because communities on plains which were used to support livestock found it easier to renew grasslands using this method. One precondition of this was to have extended borders (because without extended borders, the “migration” of plough fields and hayfields was impossible to execute) and also to keep the population low in relation to these borders. The latter was important, since a growing population caused the grasslands to shrink with the extension of plough fields. Therefore, with a growing population, “parlagoló” systems only remained feasible as long as the land could be extended beyond the borders by the inclusion of new fields (plains). As the population of the Great Plain grew steadily in the eighteenth century, there were two main options for the “parlagoló” settlements; either to rent or buy new plains like Kecskemét or, if this was not possible, to give up “parlagolás” (often due to outside pressure). Whichever option was chosen, due to the growing demand for grains, further fields had to be cultivated, facilitating and speeding up the spread of farms on the borders. Farms existed even before the eighteenth century, mostly as a consequence of the “parlagoló” system. The use of a “parlagoló” system meant that a farmer’s land remained a single unit (as opposed to pressure cultivation), and this was both an indispensable prerequisite of modern agriculture and also allowed for the development of scattered farm agriculture. It is hardly a coincidence, then, that the boundaries of nineteenth-century scattered farm agriculture coincided with the spread of the earlier “parlagoló” system on the plains.15

The economic function of agrarian gardens changed seasonally. From spring to late autumn, they were was used for plant production, but in winter they were used to keep animals, and the food accumulated during the year provided food for the animals in the cold months. The agrarian garden under cultivation is known as a hibernacle. Early in the spring, the animals were kept on the fresh lawn between the gardens until April, when farmers were obliged to take their livestock out to the common pastures (and they faced punishment if they failed to do so). It is therefore evident that these agrarian gardens were one part of the estate. They lay on the city’s borders, and they were privately owned. These properties were often called moneyed gardens in the common parlance, as they were freely given and sold. Most of them lay on the southern boundary, beyond the inner Pasture belt, on the urban land, but there were also agrarian gardens in the west, on the border of the village of Nyíri and in Talfáj, which is the northern area of the city of Kecskemét today. All of them used to be moneyed agrarian garden, or at least the sources indicate that buildings (agricultural) had been erected on them by the seventeenth century. The construction of these kinds of building on land used for this purpose, however, only became common practice at the beginning of the eighteenth century.16 Quite a few of these properties also had dug wells, which increased the value of the estates. The water from these wells was consumed by the workers on the scattered farm, but from November to April, the wells were used to provide water for the animals, though it may also have been used for irrigation in smaller quantities. By the eighteenth century, large livestock farms gave the city its main economic profile. The domestic animals (milking cows, work stock) were usually kept close to the city and placed on the inner pastures. The animals intended for sale for their meat were placed on distant and rented plains, and they were brought closer to the town just before sale. Large herds were needed to keep huge supplies of livestock. When a city rented out fields, the better-quality parts with softer soil were separated and were distributed between the cattle and horse owners. The so called “livestock owner” (marhásabb) farmers were given whole hibernacles, and the less wealthy were given smaller parts. These agrarian gardens on the plains were called “scattered farms donated by the town”.17 The enclosed parts were then cultivated, ploughed, sown, or mowed. Like the “moneyed agrarian gardens” (pénzes mezei kert) in the city borders, they were hibernacles and were considered prohibited lands. Since agrarian gardens built on rented plains were not the property of Kecskemét, in general no buildings were constructed on them, given the renting conditions.

Due to the different ownership situation, the two types of agrarian garden differed not only in appearance but also in function. Though both the “moneyed agrarian gardens” (pénzes mezei kert) and the “city’s donation gardens” could be embodied. (The latter only until the lease over the plains lasted.) The sources indicate that the agrarian gardens that were formed in the seventeenth and eighteenth centuries and had different agricultural buildings erected and wells dug on them began to be called scattered farms to differentiate them from the town’s gift agrarian gardens, which had much simpler functions. In fact, in these “moneyed agrarian gardens” (pénzes mezei kert) it is possible to recognize the later (nineteenth and twentieth century) scattered farms, which were based on plant production. The spectacular rise in the number of gardens accelerated the transformation of gardens by the fact that, due to bad weather conditions in the area, it was necessary to produce the necessary wheat locally. Within the given geographic and economic context, the only viable route for this was to break up lands that were previously had not been tilled or cultivated. However, given the lower quality of the less-bound sandy soils of these lands, their capacity for production was exhausted after a few years of field cultivation, and most of them were not suitable for grazing for a long time. With the transformation of the methods of land use, the surrounding sand became mobile and began to move, a process which was significantly accelerated by climate change. The eighteenth century bore witness to warmer and drier weather in the area, as a result of which Lake Fertő was already low in the 1720s and even dried up twice, first in 1740 and then in 1773.18 The limited extent of arable land, the narrowing of the pastures, the inability to rent new plains which could be used for planning and grazing, and the warming of the climate after 1745 all contributed to a shift in the second half of the eighteenth century, as scattered farms became increasingly numerous on the borders. This process was captured as a snapshot of maps by the first military survey. With the transformation of “moneyed agrarian gardens” (pénzes mezei kertek), a new kind of farm management emerged based not on animal husbandry but plant production. This process was promoted by planting forests and orchards, viticulture, and last but not least, peaking grain prices from the middle of the nineteenth century. Additional momentum was brought by the appearance of the railroad.19

If we move to a specific conceptual background, it can be seen that all disciplines have put the scattered farm in different contexts, and everyone has approached the concept from a different perspective, just like the concept of the city, as mentioned in the introduction. Offering a definition, however, is always a perilous gesture, as any definition assigns significane to some aspects while apparently excluding others. Scattered farms have been examined from the perspectives of public administration (law), geography, sociology, economics, and ethnography.20 In this case, I present two types of definitions: geographic and sociological.

Geography has basically a landscape-oriented approach. Settlements are examined from the perspective of the relationship between man and landscape. In addition, the landscape itself offers opportunities for people in the given space, and geographers also consider how these opportunities are utilized by the people living there. The first researcher who looked at Nyíregyháza’s “bush formation farms” (bokortanyák) from the perspective of geography and gave a definition of them was Gyula Simkó. He was followed by a number of geographers, including Tibor Mendöl. Of the geographic approaches I am going to mention, the definition of certain communities as “scattered settlement” (szórványtelepülés) is one. In most cases, these farms were permanently inhabited by colonies, though administratively these colonies belonged to a particular settlement but formed a separate landscape.21 This interpretation of the scattered farm as a settlement within a settlement constituted a new approach.

The sociological approach, represented by Ferenc Erdei, contrasts with the notion of some cohesion between the scattered farm and the settlement (town/village) and suggests instead a geographic concept: the accessory settlement. This settlement is commonly referred to as an agricultural area within the living space of a given settlement. According to Erdei, the scattered farm was only of economic importance, and the place of residence was only secondary, because the actual homes of these lands as temporary domiciles were within the inner city. In addition, the established road network itself constituted another important argument for the relevance of the sociological approach. There was little to no connection between the farms, as in most cases the roads only led to the given settlement/town.22

To sum up, the two disciplines approached the economic and social factors of the farm and the city itself from different perspectives. The main starting point for the scattered farm is the extent to which it could be said to constitute a long-term form of settlement: periodically or permanently. Given these differences in perspective, it was only a matter of time before the representatives of the two disciplines arrived at varying interpretations of the scattered farm.

Given the uniqueness of the scattered farms (as settlement types), there is little mention of it in the international secondary literature, but the question of the Hungarian scattered farm and the outside area has attracted the attention of some foreign researchers, most notably, that of Berlin historian Konrad Schünemann (1901–1940). Professor A. N. J. Den Hollander has also written an accomplished book and some articles about the Hungarian Great Plain.23 This book is a rarity in this series of historical, sociological, and ethnographic works. In Hungary there is very rich secondary literature on the scattered farm.24 A smaller library could be filled with the scholarly works in Hungarian on this subject. A 1786 book by Samuel Tessedik comes to mind,25 and the works by the aforementioned Ferenc Erdei and Tibor Mendöl are also worth mentioning. Erdei and Mendöl both dealt with domestic farm research, and in some cases they differed significantly in their views. 26 In this paper, I focus more on empirical research.

The Methodology of the Research

My inquiry focuses on one specific moment in the history of Hungary: 1930, when a census was taken. By then, the situation of the country had stabilized after a period of relative economic prosperity (1925–29). These four years had been characterized by rapid growth.27 The world economic crisis (1929–1930) only caused stagnation at first, but a significant decline began in 1931.

One of the cornerstones of my preliminary study is that I separate the data concerning the inlot downtown and the data concerning the total area (the administrative town), so I set up two separate hierarchical ranges. Thus, the two territorial units are empirically comparable. This perspective is provided by the diverse development of the settlements in the country. I am referring to the differences between the settlements in the Great Plain and the settlements in Transdanubia and western parts of the country, but in a larger context I would also mention the differences between Eastern European and Western European urban development.28 Another important methodological background for this model is that the analysis of the population size and employment structure of settlements which contain outskirts between the two world wars does not necessarily reflect the real characteristics of the city network. Rather, it reflects the ideas of less well-informed researchers who leave out of consideration the critical analysis of historical statistical data.29

The point of view of the research topic is not completely unprecedented. However, the previous works,30 in contrast with my study, only accomplished the separation of the external and internal territory in a representative settlement layer, namely cities with legal implications.

In the course of my research, I used the “inventory” method31 to set up two hierarchies. I collected the data from the various censuses at the settlement level. Consequently, two complex databases containing quantified data have been constructed. It was important to create artificial variables which are available in central statistical records both for the inlot and for the whole area of the settlements

However, I must emphasize that for the year in question (1930), we do not have the same quantity and quality of settlement-level data sets as provided by the census in the beginning of the century. Therefore, given the current state of research, more complex internal indicators cannot be included.

The works of József Nemes Nagy32 and Pál Beluszky33 provided additional data which helped add to the mathematical and statistical basis of my inquiry. Furthermore, concerning the statistical sources, I should mention the central documents that were prepared for public access and are the basis of any research concerning twentieth-century Hungarian town networks or city hierarchies. These documents include the publications of the Hungarian Royal Hungarian Central Statistical Office, the gazetteer for the given years, and the various national economic and demographic data series, which are in many cases available in digital form34 today.

First, I grouped data from Hungary’s gazetteer of 1930, which recorded data for settlements with more than 1,000 residents. According to Beluszky’s research,35 we can talk about urban settlements in functional terms (“functional towns”) above 10,000 inhabitants in the Great Hungarian Plain and over 4,000 inhabitants over the Transdanubia in the 1910s. First, I focused on settlements with populations over 2,000, but later I thought it would be worth expanding the survey with data concerning settlements with smaller populations, considering that the modeling of small towns and near-urban processes can be particularly important in the study of peripheries. Accordingly, I lowered the population threshold so that my research would include more settlements and thus become broadly representative. With this shift, 1,634 settlements were recorded in the database, which was found to be a sufficient number compared to the total of 3,422 settlements36 (48 percent). Thus, the first step consisted of recording the names of the settlements and their populations.

For the next step, I used the 86th edition of the New Series of Hungarian Statistical Publications, which provided a large amount of data for my research. I recorded the number of inhabitants and the employment structure of the inlot of each settlement using the data from this volume. I also used this volume to record the abovementioned indicators at the administrative level. As I had used the data concerning the main employment groups, it was possible to determine the proportion of non-agricultural earners mathematically. This was important, because along with tertiarization, the proportion of the secondary sector37 was also an important factor in the evolution of a more urban existence. In addition, the use of the significance of surplus services formula has made it possible to establish the “rural part” of services. This method is one of the decisive methodological elements of Beluszky’s “inventory process,” which is based on the fact that the city is a rural provider. Consequently, the central role is based on the “surplus” service provided to the countryside. The aforementioned Walter Christaller also used this method in his research in southern Germany. The popularity of the theory notwithstanding, it is worth mentioning that the method itself may lead to distortions in certain cases, so we have to use it with caution. On the basis of the formula38 of the theory, we can conclude that the population belonging to the settlement is part of the agglomeration, like the area outside the administrative boundaries. Consequently, we must use this method together with methods which consider the population of the settlement or area. If this value is negative for a given settlement (see table), this means that the settlement cannot provide for its own population in the services sector. However, if it is positive, it will supply potential users beyond its own population. There was a plan to use a financial indicator, but the construction of the variable failed due to methodological problems. Between the two world wars, during the “fiókosítási program,”39 deposit data concerning “smaller sub-offices” (alfiókok) in certain settlements appeared in the central account censuses. This makes it practically impossible to record the settlements’ deposits.

In summary, the two databases contained six variables, three in the inlot and three in the total area database. The average of the variables gave the complex value which determined the hierarchy. Accordingly, the following variables are included in the two databases:

• Inlot population

• Total area population

• Inlot proportion of non-agricultural earners

• Total area proportion of non-agricultural earners

• Inlot significance of surplus services

• Total area significance of surplus services

Since there are different types of variables (population, ratio, etc.), I have unified the variables using a mathematical method. The method used was the formula for normalization,40 which prevented the creation of negative numbers and allowed the variables to be unified.

For this time-horizon, according to the present state of the research, we do not have the quantity and quality of inlot data sets to increase the complexity of this study. I could mention the financial indicator as an example. It is also important to note that the so-called total area database is made only for a representative purpose in order to examine the hierarchy of the two areas based on the same methodology and variables. However, this database is not properly complex, as the number of the indicators shows. Nevertheless, in this case, this function is not primary.

Finally, after the creation of the two databases and the two hierarchies, the positions were compared. Thus, I have constructed a brand new hierarchy for the inlot area at settlement-level, for which there was no example in Hungary in former researches. The two hierarchies make it possible to compare the differences and similarities between the inlot and the administrative positions in the period between the two world wars.

I would like to emphasize that I have created only one possible context in which to study the inlot area’s hierarchy with this methodological model. Understandably, there are as many methodological approaches as there are results.41

Placing Results in Context

More and more research has been done on this subject, and it has been necessary to isolate the external areas in the urban hierarchy. I am thinking of the work of Lajos Timár42 and Zsolt Szilágyi.43 However, the research that was done was only partial, as it only concerned settlements which were cities in legal terms.

In this inquiry, I open a new perspective on the issue, because I have completed the separation of inlot and outskirts on nearly 1,600 municipalities at the settlement level.

According to my a priori assumption, the separation of the external area adversely affects the position of these country towns of the Great Hungarian Plain. The results will be explained on two levels: on the one hand per se, and the on the other, the overall ranking of the inlot results. During the investigation, I omitted Budapest, since studies of Budapest in the year in question (1930) have already been done.

As can clearly be seen from the ranking table (Table 2), the internal hierarchy study confirmed the leading position of Debrecen after Budapest between the two world wars. I had arrived at this conclusion in the course of my previous examination as well. One of the concerns about this result was the role/prestige of the inhabitants of the city and the function of the city. The importance of the city grew in 1920, when the city of Oradea was made part of Romania in accordance with the Treaty of Trianon. The regional centers of Miskolc, Győr, Szeged, and Pécs were also included in my comparison.

Territorially, as can be seen on the map (Map 1), the leading settlements cover up the regions of Hungary, so we can say that the contrived hierarchy study in the field is more evenly distributed. The relativity is manifested as long as there is a regional center (Győr) and two county centers (Szombathely and Sopron) in the northwestern part of the country, with a distance of nearly 100 km separating them. But the area between the Danube River and the Tisza has no regional centers. This may be due to the development of a dynamic agglomeration zone to the west and northwest in response to economic and political developments. This area lies towards Vienna, and it reaches the border of the state. In addition, the city of Sopron got into the top ten settlements in this region (in my urban hierarchy).44 Furthermore, the advance of Budapest’s agglomeration is observable. In this case, the first twenty settlements included Újpest, Rákospalota, and Budafok. The positions of these cities are also well reflected in the aura of the capital and its outstanding role within the domestic settlement network (Map 1).

I have highlighted ten former country towns from the inlot ranking.45 Taking into account the positions of these cities, we can conclude that four of them rank among the first 15. In the case of these five settlements (Debrecen, Szeged, Kecskemét, Szolnok, Nyíregyháza), it is not clear that the unplugging of the external area would have affected them drastically. Using the same methodology, I also made an administrative (“total area”) ranking. This makes it possible to reconstruct the differences between the inlot and the total area hierarchies. It is important to mention that a significant position change was observable in the field of the vanguard (top10). Only in the case of two settlements, Debrecen and Szeged, remained the rankings the same (Table 1). Regarding the differences in the two urban hierarchies, the position of the inlot in the ten investigated cities was proven to be stronger, with the exception of Debrecen and Szeged. The conclusion is that in these predominantly agricultural-minded cities, the importance of the external area is insignificant in this time horizon. Moreover, the periphery is significantly weakened by the hierarchy position of former country towns. However, it is also noticeable that the scale of these derogations is highly variable. There are certain country towns with appreciable or moderate position changes (compare it to Kecskemét with 15, Gyula with 51 and Orosháza with 81 position changes etc.). Further anomalies can be observed in the table of rankings (Table 1). In particular, if one compares the first twenty settlements in the two lists of rankings, one observes that the significant increase, can only be detected in the agglomeration of the capital. Comparing the two rankings, I have found that the settlements in the vicinity of Budapest can be described by the increase in their overall area rankings. Yet at the beginning of the twentieth century, Hungarian industry, which was focused in Budapest, was characterized by a high degree of territorial concentration. At that time, Budapest had emerged as the country’s largest economic center, and the growth of the agglomeration was fast paced (Map 1).46

Finally, to offer answers to the questions raised in the introduction, it can be stated that the (hierarchic) ranking of an urban settlement is greatly influenced by the data of the peripheral areas (outskirts, farms), and not only in the settlements of the Great Plain. In this study, we can conclude that the periphery is not an integral (functional) part of the settlement. It was found that in all cases when this is possible, data on inlots should be calculated and used in hierarchic investigations in order to avoid distortions caused by different patterns of urban development.

Outlook

Overall, we can conclude that the city hierarchy of Hungary between the two world wars is an extremely complex field of research which creates an interdisciplinary space between historical science and geography. This complexity determines the methodology, though the result of this kind of research is also significantly influenced by the use or exclusion of certain methods. Furthermore, the domestic aspect of the subject itself is diverse and reflects on a number of areas that point in new directions which have not yet been pursued in the secondary literature. I am thinking, for instance, of research into quality of life, for which the necessary data are available, or studies on development, for which the HDI47 has to be adjusted. However, in my opinion, it would be more important to involve this indicator at the lower hierarchy levels, as the introduction of this new variable would not be sufficiently desirable for the higher-ranking settlements. The abovementioned methodological problem is difficult to comprehend in a domestic context between the two world wars, but research done according to this method would help further our understanding of a number of economic and social processes in villages.

With regard to the whole database, there are three important aspects missing from the related research. One would be a financial / economic dimension, which would place local interest rates in the center of the study at settlement level. This way, there should be two relevant financial indicators ready for the database. Also, while doing my research, I had the idea of adding data concerning literacy rates to the database, as this kind of data is often used in modernization studies (HDI, for example). However, in this case, it would make more sense to use this indicator at the lower hierarchy levels in my opinion, as the introduction of this new variable would not result in sufficient dispersion-deviation within settlements of higher rank. There is no doubt, however, that it provides a partial solution to the aforementioned methodological problem, and it would facilitate drawing distinctions at lower hierarchy levels.

I believe this study on modernization would be relevant to our understanding of small town and near-small settlements. Additionally, the so-called dispersion (Std. Deviation) value could turn out to be an important tool in determining the “scoring” variables of institutions, lawyers, and doctors.48 This would allow us to assign institutions hierarchy levels.

 

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Annex

Table 1

Hierarchical rank differences between the two territorial units surveyed in 1930

Ranking (inlot)

Name

1930

Name

Ranking (based on total area)

1

Debrecen

 

Debrecen

1

2

Szeged

Szeged

2

3

Miskolc

Újpest

3

4

Pécs

Pesterzsébet

4

5

Győr

Kispest

5

6

Nyíregyháza

Miskolc

6

7

Szombathely

Győr

7

8

Kecskemét

Pécs

8

9

Sopron

Rákospalota

9

10

Újpest

Szombathely

10

11

Szolnok

Pestszentlőrinc

11

12

Székesfehérvár

Csepel

12

13

Kaposvár

Budafok

13

14

Nagykanizsa

Sopron

14

15

Rákospalota

Székesfehérvár

15

16

Sátoraljaújhely

Szolnok

16

17

Békéscsaba

Kaposvár

17

18

Veszprém

Pestújhely

18

19

Baja

Sashalom

19

20

Budafok

Nyíregyháza

20

 

Table 2

The inlot urban hierarchy of Hungary in 1930

Rank

Name of settlement

v1

v2

v3

IUHI

Internal population (1930)

The proportion of non-agricultural earners in the area (1930) %

Significance of surplus services (person)

Inlot urban hierarchy complex indicator (based on normalized values)

I. REGIONAL CENTRES

1

Debrecen

66,834

78.85

52127.49

0.587

2

Szeged

89,621

77.13

40851.87

0.526

3

Miskolc

60,032

80.93

35836.29

0.490

4

Pécs

50,019

74.24

28861.28

0.439

5

Győr

49,886

86.83

25664.00

0.432

II. COUNTY CENTRES

6

Nyíregyháza

31,237

81.51

23837.13

0.410

7

Szombathely

34,945

83.27

23141.97

0.409

8

Kecskemét

34,788

69.43

18681.42

0.368

9

Sopron

32,441

72.39

17908.18

0.366

10

Újpest

66,541

91.96

11769.44

0.360

11

Szolnok

34,050

78.54

15583.35

0.359

12

Székesfehérvár

33,291

73.09

16419.22

0.358

13

Kaposvár

29,845

76.43

14669.71

0.350

III. MIDDLE CITIES

14

Nagykanizsa

30,389

69.66

12352.06

0.329

15

Rákospalota

42,278

83.56

8734.62

0.325

16

Sátoraljaújhely

17,585

78.89

9652.38

0.318

17

Békéscsaba

37,647

65.53

9696.77

0.312

18

Veszprém

17,792

78.34

8587.06

0.311

19

Baja

25,370

74.99

8569.74

0.310

20

Budafok

19,543

90.58

5341.70

0.305

21

Komárom

6,911

87.72

5968.79

0.301

22

Zalaegerszeg

12,157

76.66

6878.64

0.298

23

Vác

19,361

78.71

6007.52

0.297

24

Pápa

19,774

77.58

5667.48

0.294

25

Balassagyarmat

11,120

74.47

6440.84

0.292

26

Eger

30,196

57.55

7959.48

0.291

27

Gyula

17,030

68.06

6129.54

0.286

28

Szob

3,394

82.24

4449.25

0.286

29

Kisvárda

13,304

73.69

4457.07

0.281

30

Kiskunfélegyháza

20,271

64.20

5366.48

0.279

31

Orosháza

14,291

62.01

4987.72

0.278

32

Cegléd

25,521

55.45

5396.34

0.278

IV. SMALL TOWNS

33

Szentendre

5,418

74.17

3342.72

0.272

34

Keszthely

9,841

70.31

3635.68

0.271

35

Esztergom

15,549

59.12

5141.56

0.271

36

Celldömölk

5,961

74.50

2994.22

0.270

37

Gyöngyös

18,232

54.14

5587.58

0.269

38

Kőszeg

8,075

73.60

2850.33

0.269

39

Salgótarján

15,254

72.39

2621.44

0.269

40

Hatvan

14,333

64.64

3959.48

0.269

41

Kalocsa

11,323

64.71

4050.69

0.268

42

Mátészalka

9,125

70.80

3064.34

0.268

43

Szentes

21,540

60.08

4161.60

0.268

44

Szentgotthárd

3,152

83.23

1123.93

0.267

45

Magyaróvár

7,351

77.45

1819.62

0.267

46

Újdombóvár

2,125

82.50

1163.19

0.266

47

Tóváros

5,012

76.45

1930.32

0.265

48

Nagytétény

4,006

83.44

716.38

0.265

49

Hajmáskér

2,040

74.77

2265.81

0.265

50

Hódmezővásárhely

36,783

53.57

3621.20

0.263

 

 

 

Hungarian urban hierarchy in 1930

(Internal variables to a total area)

Rank

 

Name of settlement

v1

v2

v3

UHI

Population (1930)

The proportion of non-agricultural earners in the area (1930) %

Significance of surplus services (person)

Urban hierarchy complex indicator (based on normalized values)

1

Debrecen

117,275

67.84

43891.61

1.640

2

Szeged

135,071

54.15

22044.90

1.591

3

Újpest

67,400

91.84

11495.09

1.497

4

Pesterzsébet

67,907

91.02

12595.49

1.493

5

Kispest

64,512

88.64

17849.12

1.448

6

Miskolc

61,559

80.39

35525.01

1.356

7

Győr

50,881

86.54

25355.36

1.332

8

Pécs

61,663

74.77

28089.62

1.284

9

Rákospalota

42,949

83.39

8560.27

1.217

10

Szombathely

35,758

83.07

23040.50

1.178

11

Pestszentlőrinc

30,611

87.61

8861.56

1.173

12

Csepel

22,901

93.98

-3526.41

1.171

13

Budafok

19,691

90.54

5300.55

1.120

14

Sopron

35,895

73.45

18334.76

1.066

15

Székesfehérvár

40,714

70.33

15931.96

1.064

16

Szolnok

38,764

71.54

14156.68

1.060

17

Kaposvár

32,715

74.36

14169.78

1.047

18

Pestújhely

11,340

89.26

3819.58

1.042

19

Sashalom

11,792

88.09

2573.99

1.031

20

Nyíregyháza

51,308

58.03

17377.52

1.006

21

Albertfalva

3,331

91.12

1327.42

1.000

22

Rákosszentmihály

14,083

83.18

4375.46

0.995

23

Kecskemét

79,467

38.54

2680.42

0.979

24

Nagykanizsa

30,869

69.09

12192.30

0.972

25

Vác

20,960

75.68

5572.05

0.964

26

Veszprém

17,792

77.43

8389.54

0.963

27

Sátoraljaújhely

18,431

76.58

9437.81

0.960

28

Pápa

21,356

75.07

5092.84

0.959

29

Békásmegyer

8,447

83.72

464.11

0.954

30

Baja

27,935

69.89

7677.48

0.953

31

Komárom

7,562

83.54

5818.30

0.952

32

Nagytétény

7,160

82.07

62.33

0.926

33

Soroksár

14,387

77.32

-848.84

0.925

34

Békéscsaba

49,374

53.05

4798.41

0.920

35

Felsőgöd

3,024

83.87

1080.51

0.916

36

Diósgyőr

20,854

71.95

-1822.17

0.912

37

Rákoshegy

4,198

82.30

1552.36

0.908

38

Salgótarján

16,980

73.45

2353.60

0.905

39

Szob

3,486

81.75

4428.18

0.900

40

Szentgotthárd

3,258

82.13

1098.86

0.899

41

Magyaróvár

8,584

76.70

1514.63

0.878

42

Zalaegerszeg

13,072

72.48

6541.74

0.870

43

Pesthidegkút

6,030

77.70

1237.74

0.870

44

Piszke

1,436

80.82

-101.36

0.869

45

Kámon

2,143

80.00

891.59

0.866

45

Budakeszi

6,099

77.33

480.21

0.865

47

Ózd

7,322

76.24

9.18

0.861

48

Balassagyarmat

11,551

72.56

6291.08

0.860

49

Kisvárda

14,133

70.33

4217.64

0.851

50

Rákoscsaba

8,189

73.77

1629.64

0.842

1 Massey, Spatial Division of Labour.

2 Beluszky and Győri, “A város a láz a nyugtalanság.”

3 Tóth, “Tér- és időbeli sajátosságok a magyar városodásban,” 55.

4 Mendöl, “Az alföldi városokról,” 218.

5 Ibid., 218–19.

6 Bácskai and Nagy, Piackörzetek, piacközpontok; Timár, Vidéki városlakók; Beluszky and Győri, Magyar városhálózat.

7 Bácskai, “Vas megye várostörténeti munkáinak,” 137–52.

Gyáni, A város mint zárt és nyitott tér, 205–20.

8 Bán, Város, hierarchia, pozíció.

9 Timár, “Az alföldi és dunántúli városok,” 42–55; Beluszky and Győri, Magyar városhálózat; Beluszky, “Az ‘Alföld szindróma’;” Erdei, Magyar Tanya; Mendöl, “Az alföldi városokról,” 217–32.

10 Szilágyi, “Város és tanya kapcsolata.”

11 Györffy, Magyar tanya, 72–76.

12 Szilágyi, Kecskemét várostörténeti atlasz, 10–11.

13 Orosz “Parlagoló földművelés az Alföldön,” 2014. – We are saying thank you to Professor István Orosz for his manuscript.

14 Hungarian soil shifter agricultural system in which one part remains unsown.

15 Orosz, Parlagoló földművelés, 14–15.

16 Czettler, A tanyakérdés, 443–446.

17 Szilágyi, Kecskemét várostörténeti atlasz.

18 Rácz, “Magyarország környezettörténete,” 200.

19 Szabó, “A kecskeméti szőlő- és gyümölcstermesztés,” 6.

20 Erdei, Magyar tanya.

21 Erdei, Magyar tanya, 22–24.

22 Erdei, Magyar tanya.

23 Den Hollander, Az Alföld települései és lakói; Den Hollander, The Great Hungarian Plain: a European Frontier Area (I-II).

24 Szabó, A debreceni falurendszer; Erdei, Magyar Tanya; Györffy, Magyar falu, magyar ház; Szabó, A kecskeméti szőlő- és gyümölcstermesztés.

25 Thessedik, A paraszt ember Magyar Országban.

26 See the discussion: Mendöl, “Néhány szó az alföldi városokról,” 217–32; Mendöl, Egy könyv a magyar faluról, 204–8; Mendöl, Megjegyzések Erdei Ferenc, 113–15; Erdei, Magyar tanya; Erdei, Tanyás települések földrajzi szemlélete, 103–13; Publications about the discussion: Timár, “Sociology and Geography,” 86–92; Timár, “Vidéki városlakók,” 49–51; Timár et al., “Vita a magyar városokról,” 617–28; Szilágyi, “Város és tanya kapcsolata.”

27 Tomka, Gazdasági növekedés, fogyasztás.

28 Timár, Az alföldi és dunántúli városok, 42–55; Erdei, Magyar tanya; Gyáni, A város mint nyitott és zárt tér, 205–20.

29 Timár, “Az alföldi és dunántúli városok,” 42–55.

30 Erdei, Magyar Tanya; Mendöl, Az alföldi városokról, 217–32; Mendöl, Megjegyzések Erdei Ferenc, 113–15; Timár, Szociológia és geográfia.

31 conf. Beluszky and Győri, Magyar városhálózat, conf. Gál Zoltán, “A magyarországi városhálózat vizsgálata,” 50–65; conf. Major Jenő, “A magyar településhálózatról,” 32–65.

32 Nemes Nagy, Terek, helyek, régiók, 51–57.

33 Beluszky and Győri, Magyar városhálózat, 93–102.

34 https://library.hungaricana.hu/hu/collection/kozponti_statisztikai_hivatal_nepszamlalasi_digitalis_adattar/ Accessed on August 8, 2018.

35 Beluszky and Győri, Magyar városhálózat.

36 On the capital city, see Hajdú 2005, 150. Cf. Latest 1992, 187.

37 The particular branches included in the Statistical Bulletin have been classified into the basic economic sectors accepted by the reviewed geography following the methodology below. The primary sector contains the primary producers, who were mining and metallurgical workers, while the secondary sector was composed of the industry workers and day-labourers. The tertiary sector was the most extensive, including the workers involved in commerce and credit; transport; civil service and liberal professions; armed forces; house seekers, and, finally, the fourth, the so-called other group, the retired; other and unknown employees. Szilágyi 2012, 111.

38 Significance of surplus services formula (K): K=Fv–Lv∙Fm/Lm; Fv: the commercial turnover of the studied settlement; Fm: commercial turnover of the studied area; Lv: number of population in the studied settlement; Lm: number of population in the studied area.

39 Several smaller sub-offices which belonged to the central sub-office in the interwar period.

40 Normalization formula: ni=(xi – xmin ) – (xmax – xi) ; ni: normalized variable; xi: variable of the dataset; xmax: maximum of datas; xmin: minimum of datas.

41 Bán, “Magyarország városhierarchia-vizsgálatának módszertani kérdései,” 9.

42 Timár, Az alföldi és dunántúli városok, 45.

43 Szilágyi, Város tanya kapcsolata, 10.

44 Győri, “Bécs kapujában,” 231–51; Tóth, Tér- és időbeli sajátosságok.

45 Debrecen, Szeged, Kecskemét, Szolnok, Békéscsaba, Gyula, Hódmezővásárhely, Kiskunfélegyháza, Nyíregyháza, Cegléd.

46 Győri and Mikle, A fejlettség területi különbségeinek változása, 151.

47 Human Development Index, created by the UN.

48 Beluszky, “Adalékok a magyar településhierarchia változásaihoz,” 331.

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