GIStorical
Studies
Nr. 1.
(2018)
Historical GIS series of the HAS RCH Institute of History
DEMETER, Gábor – BAGDI, Róbert
Tracing the Transforming Urban Elite and Methods
to Analyze Spatial Patterns, Social Composition and Wealth
Based on Census Data (Northeast-Hungary, 1870)
I. Aims
This contribution attempts to (1) outline methods that can help identify the elite in urban societies,
as well as (2) to analyze its spatial pattern and (3) social composition and welfare in the 19th c. Our
research was based on the census data of 1870.
II. Data
The census of 1870 was a specific one in the sense that the original household-level data sheets
(figure 1) survived in some of the towns and villages, making it possible to carry out a more detailed inquiry compared to the officially published material, which aggregated data at settlementlevel. Furthermore, the forthcoming censuses used personal datasheets instead of household-level
inquiries, making it more difficult to carry out similar investigations to those published in this
study. The original census sheets in 1870 contained the name, age, address, birth place, occupation
and religion of the head of family, repeating these data for the wife, children, co-workers, servants
and housemaids. It also provided the number of rooms, kitchens, economic buildings (stores, stables, cellars) for each household. As the census did not contain income data, the mentioned variables had to be used as proxies of wealth in order to classify population into social layers and to
identify the elite. Beyond wealth general socio-demographic phenomena – either with or without
spatial pattern (like the average children number of different occupation groups, average children
number of different religions, migration patterns, mixed marriages, territorial aspects of marriage
patterns, territorial distribution of religions etc.) – were also possible to trace using the mentioned
variables.1 The data also offered possibility to create new variables beyond those given in the census, like population density (room/person), which was also used up as a proxy variable to measure welfare.
Figure 1. Pages from the census, Nagy Piac str., nr. 9. Source:MNL-BAZML SFL XV. 83. box 77–79.
Demeter, G.-Bagdi, R.:A társadalom differenciáltságának és térbeli szerveződésének vizsgálata Sátoraljaújhelyen 1870-ben. (A GIS lehetőségei a történeti kutatásokban). Debrecen, 2016. (Investigating the Social Differentiation and Spatial Pattern of Society in Sátoraljaújhely, 1870. Applying GIS in Historical researches – in Hungarian).
The research was supported by the Hungarian Research Fund K 111 766.
1
2
III. The place
The selection of Sátoraljaújhely (the county seat of Zemplén County) as a sample area was ideal
from several aspects. Not only the origial census sheets were available for 2150 households (10 000
inhabitants) offering substantial material for quantitative statistical analysis, but the timing itself
was also fortunate.
The town was located along the market line, where the products of the plains and mountains were
exchanged. The physical geographical conditions allowed a N-S migration from the peripheries of
Zemplén County to the county seat, while in the southern part of the county an E-W migration route
developed towards the capital. Although in 1775 the county seat was unable to extend its attraction
zone even to its own administrativ district (figure 2), between 1810-1870 its population has tripled, and
this increase was among the greatest regarding their neighboring towns (table 1). By 1900 50% of the
inhabitants of the county seat were born in a different locality, confirming the great role of migration.
The acceleration of urbanization processes made a melting pot from the town reflected in its religious
diversity: 35% of the population was Jewish of origin, Roman catholics reached also 30%, Calvinist
protestants 12-14%, Greek Catholics approximately 20%. As a basic step of industrialization the railway was opened in 1870, while guilds were dissolved in 1872, thus the parallel coexistence of traditional and modern social patterns and layers are also observable owing to the date of conscription.
Table 1. Population increase referring to the rate of urbanization (1825–1900) in Sátoraljaújhely compared to the surrounding significant towns
Town
Population increase
Population in 1000 (1825)
Population in 1000 (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)
Beluszky P.: Magyarország településföldrajza. Általános rész. Budapest – Pécs, 1999.
EPERJES
MARKET CENTER
BÁRTFA
HOMONNA
NAGYMIHÁLY
SZTROPKÓ
VARANNÓ
GÁLSZÉCS
KASSA
TEREBES
SÁTORALJAÚJHELY
SÁROSPATAK
U
N
G
V
Á
R
TÁLLYA
ZEMPLÉN
J
KA
TO
DA
ÁR
SV
KI
Figure 2. Attraction zones based on market centers in Zemplén County in 1773.
3
Figure 3. The basemap, the database containing the digitized census data, and the households-entities organized in GIS:
Nagy Piac str. nr. 9. Evva-household (nobleman)
IV. Methods
In order to carry out the investigations, raw census data were organized into a database containing
the above mentioned variables, where each household represented an entity. Phenomena with
spatial pattern were analysed using GIS (ArcGIS 10.1),2 while phenomena with no spatial relevance (within- and intergroup differences, like religious distribution within/between occupation
groups, differences in welfare of religious groups and occupations, aging, differences in fertility
rate, etc.) were evaluated using SPSS. The application of GIS required a map containing the same
identifiers for households as the census data-sheets (topographical numbers), from the same era
(1865) (figure 3).3
In case of welfare both spatial approach and the analysis of within and intergroup differences
has relevance, thus both GIS and SPSS were utilized. The first step was to quantify welfare somehow. As social classifications are often not objective, we used 3 different methods to overcome social prejudices. Besides the traditional classification based on the the prestige of occupation multivariate statistics and SPSS were used for the other two classifications, both based on the quantified
raw data of the census.
(1) In order to analyze patterns of traditional and modern layers of society we used Ferenc Erdei’s model of ’accumulated society’, when classifying the cases into different social layers. This
means, that besides the traditional elite, middle and lower classes; paralelly capitalistic formations
evolved in each layer, and these were slowly fusioning by the 1910s.
(2) The second method to classify the inhabitants was based on an equation containing the room
numbers, number of economic buildings, number of co-workers, number of servants and housemaids, and the number of household members as input variables. Thus, this classification focused
on the per capita welfare, while the third one represented a classification, where the economic
power of the household as an entity was cumulated. The intervals were set according to natural
breaks, thus the groups did not have equal members.
A similar project led by Mazsu, János: OTKA 81488: The reconstruction of social and spatial pattern of Debrecen, 1870-72 was considered the predescessor of this, but remained mostly unevaluated.
3 Source: MNL-BAZML SFL XV. 83. box. 77–79. Now www.hungaricana.hu and www.mapire.eu (containing settlement level cadastral
maps) offers a new instrument to find detailed maps like this. The data sheets from Ung and Saros County are also left almost intact in
county archives, thus there are plenty of options to repeat such investihgations
2
4
(3) The third method was based an automatic cluster analysis executed in SPSS containing the same
variables except the number of family members. Classification results were checked by discriminance analysis. After numerous attempts the number of clusters was finally set to 6, as above this
value the proportion of successfully reclassified data began to drop, and the forming new groups
remained small. Neither this classification produced groups of similar size.
It is not surprising that the three classifications did not have identical results (although there
was strong correlation measured between the 3 classifications - over r=0.7 - and there was also
strong correlation between the wealth categories and room numbers; wealth and population density/room), which is confirmed by cross-tabulations, correspondance analysis (table 2).
Table 2. Correspondence between complex wealth categories based on the equation and automatic clusterization
Room numbers
Complex wealth categories based on the equation
Clusters
above
Total
6
1
2
3
4
5
6
7
8
9
10
11 12
13
0
1
2
3
4
1
0
1
6
6
8
11
13
8
16
22
14 11
56
0
59
57
19
20 10
7
172
2
0
0
0
0
0
1
2
4
17
21
18 31
66
0
1
9
74
40 19
17
160
3
1
0
20
28
41
46
63
23
46
40
30 37
28
10
222
131
32
6
2
0
403
4
0
1
15
34
44
61
92
47
70
56
16 19
11
3
146
289
25
3
0
0
466
5
24
86
153
180
55
79
90
18
13
11
4
0
0
60
656
0
0
0
0
0
716
6
74
49
39
8
10
4
2
1
2
1
0
0
0
97
93
0
0
0
0
0
190
Total
99
137
233
256
158
202
262
101
164
151
170 1177 486 150 69 31
24
2107
100%
28
90%
172
80%
181
70%
403
466
713
190
1
9-13,
9-13,
165
165
107
181
172
28
173
30%
5-8,
244
5-8, 740
1-4,
443
725
13
49
7
50
1
2
3
4
5
173
244
242
17
4
5
40
10%
2
3
723
170
40%
0%
total 172160
656
242
107
20%
161
3
17
60%
50%
82 98
5
6
1-4, 13
0 49 50
total
összes
0%
443
20%
40%
6
170
60%
80%
100%
Figure 4. Correspondence of the 2 statistical evaluation (6 clusters, 13 groups based on equation)
The richest three groups (9-11) comprised 341 families (15%) in case of the 2nd method (equation
referring to per capita economic power), while the richest 2 clusters comprised 332 family heads in
the 3rd classification method, but only 192 were common of these (60%) (confirmed by figure 4).
V. Tracing the elite(s)
(1) Groups based on the traditional classification
The traditional (manual) classification based on the model of Erdei, Ferenc and Weber, Max resulted in more than 10 categories with uneven size (table 3). Free civil professions and clerks were underrepresented here compared to other towns with similar functions. The layer of merchants was
quite strong possibly as the result of Jewish abundance and the geographical location. The proportion of craftsmen was high, but not remarkably – the same was measured in the larger Debrecen.
Widows were separated as we have no information about their profession and incomes.
5
The elite was composed of the ’f’, ’e’ and ’p’ categories, although as we will see, the wealth and
economic power of the latter (civil professions) group was significantly weaker (teachers), than the
other two according to data on room numbers, population density and the other two classification
methods. Nevertheless, the categories do not strictly refer to welfare or social status: in category ’f’
smallholders and large estate owners are also included. But since they tended to live in the centre
of the town, while agrarian wage-labourers (’s’) resided in the periphery, they were considered as
part of the traditional (declining) elite.
The sectoral distribution of these categories is given in table 4. 35% of the family heads were involved in industry, but modern industrial branches were represented only by some 10% of the
total family heads involved in industry – guilds still dominated referring to a transitional period.
The private tertiary reached 30% reflecting the transformations (urbanization), while agriculture
has already lost its dominant position (25%). Those, who did not own or rent separate flats (or
were not family heads), were omitted from the analysis (they ab ovo cannot be considered as members of elite), thus this table did not contain data on 1100 workers and 700 servants.4 In other
words, only half of the wage-earners was included, thus percentage data on the elite below should
be halved!
Table 3. Social groups according to the model of ’accumulated’ society (method 1; prs and %)
e
town and county elite
lawyers, chief clerks (state servants)
47
2.2%
f
p
landowners
mainly middle estate owners
116
5.4%
free civil professions
91
4.2
h
clerks
108
5%
g
n
kk
k, ka
m
q
agrarian experts
34
29
216
151
677
60
1.6%
1.5%
10.1%
7.0%
31.5%
2.8%
s
poor
teachers, doctors, railway engineers, photographers, clockmaker
state (lower class compared to ’e’) and private (in banking and
finances)
not independent, but skilled agrarian wage-earners
policemen
mason owners, railway entrepreneurs, merchants
lower officials: postmen, poor merchants
guild members: tailors, potters etc.
transportation: cartsmen, waiters
daily wage earners in agriculture, beggars, bakers (women),
washerwomen, peripatetic, scrap-iron collector
508
23.7%
ö
widows
101
4.7 %
merchants
craftsmen
lower tertiary
Table 4. Hypothetic-preconceptional social stratification based on the prestige of occupation (family heads; %)
Group
Agrarian
Upper
f (116)
Middle
Lower middle
Industrial
kk (30)
g (34)
Tertiary
Private tertiary
Altogether %
e (47)
p (91)
kb. 250
12%* (7%)
h (108)
kk (190), h
kb. 550
25% (25%)
n (30)
k (132)
kb. 500
23% (25%)
s (160),
q (60)
570 + some
craftsmen 38% (43%)
= 800
m (677)
Lower
s (343)
Total
cca. 500
cca. 700
cca. 200
cca. 600
cca. 2100
%
25%
35%
10%
30%
100%
+101 widow
households
*Servants or co-workers not conscripted as family heads were omitted. See corrected values including these layers in brackets.
(2)Groups based on the welfare-equation
The family heads were classified into 13 groups based on the natural breaks in welfare values
calculated by the equation. Groups 9-13 were definitely richer than the average. The richest 15% of
4
There were altogether more than 4000 persons conscripted with occupation, but only 2150 was family head.
6
families owned only 20% of the economic potential (it could reach 40% in Ottoman towns during
the 18th c.).5 The next 20% owned 25% (figure 5), thus there were not significant differences within
the elite. The poorest 20% owned only 13%. In other words the richer 50% of the population was 3
times richer, than the poorer half owning 25% of the economic potential. This inequality is not
great measured to other regions. 6
Based on figure 5 the aggregation of wealth categories 11-13 (comprising 15% of households)
may be reasonable. This is followed by group 9-10 including another 15% of the cases. (The reason
of the small differences between these two groups might be, that members of the real elite had
larger households, which decreased per capita economic potential. It is also true that within the
300 households there is a layer comprising 100 households with extremely high values).
30
25
20
15
10
5
0
7,50%
7,5-25%
25-45%
45-65%
65-85%
85-100%
Figure 5. The distribution of economic potencial (vertical axis) between groups of families (horizontal axis in %)
The classification results confirm, that categories (based on the prestige of occupation) ’e’, ’f’,
’kk’, ’h’ are considered to be the richest, followed by ’p’, thus our preconception was not flawed
(figure 6). The minor differences between the cluster-based and equation based classification (table
7) is due to the fact, that the latter measures total wealth of a family regardless of family size. Thus
group ’f’ is considered poorer is per capita wealth is calculated as agriculture was labour-force
intensive sector traditionally characterized by larger family size.
3
2,5
2
1,5
1
0,5
Figure 6. Differences in per capita economic potential between social groups based on prestige and occupation
H.– Filiztekin, A.: Wealth and Inequality in Ott oman Lands in the Early Modern Period.Working Paper.
The richest 2% owned 25% in China, in New-Spain in 1790, the richest 10% owned 55% of the wealth, in Bihar (India) in 1804 the
richest 20% owned 50%, and in Naples in 1811 it was 10 and 33% percent respectively. Milanovic, B–Lindert, P. H.–Williamson, J.
G.:Measuring Ancient Inequality. Working Paper 13550. NBER. Cambridge, MA, 2007. http://www.nber.org/papers/w13550.
5Canbakal,
6
7
Surpisingly the average room number/family was not higher than in rural areas (although the
average household size – 4.66 prs – was bit smaller), the 1.5 room/family is not greater value than
measured in Belgrade after 1900.7 Multistorey buildings were abundant only in the town centre, but
even houses only with ground floor were usually divided between families (often not relatives). Only
12% of the households (not houses!) had more than 2 rooms – this can be the real elite as there was a
strong correlation between room numbers and the calculated wealth based on the equation (table 5).
Further 22% had 2 rooms. Population density – calculated from room number and household size – is
also an index of welfare (table 6). In 25% of households there were 4 or more than 4 inhabitants per
room, while only 10% of families were characterized by density smaller than 1.5 person/room.
Table 5. Distribution of households based on room numbers in 1870 (prs and %)
No data
0.5 room and
under
1
2
3
4
5 rooms
and above
Total
40
170
1175
488
150
69
55
2147 (average: 1.5)
1.9
7.9
54.7
22.7
7.0
3.3
2.6
100 %
Table 6. Population density (prs/room) in Sátoraljaújhely in 1870 (prs and %)
under 1
1
1–1,5
1,6–2
2–2,5
2,5–3
3–4
above 4
Total
47
167
125
375
120
352
391
529
2147 (average: 2.85)
2.2
7.8
5.8
17.5
5.6
16.4
18.2
24.6
100 %
Table 7. The rankings of the pre-defined layers based on prestige of occupation using the two different statistical
classification (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
equationbased 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
Table 8. Socio-demographic characteristics of the layers defined by the equation (the average represents intergroup
differences, standard deviation represents within-group differences)
Social group based on
equation
1 (127, 6%)
2 (140, 6.5%)
3 (233, 11%)
4 (258, 12%)
Average
children
number
Average
number of
servants
Household Proportion
size
of earners
Average
room
number
Average
inhabitants
per room
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
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
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
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
(Continues on the next page)
In Belgrade in 1907 60% of the houses had only one room (as in the households of Sátoraljaújhely), but the density was 3.5 prs/house,
while in the Hungarian town it was 9 prs (2 households/house). Vuksanović-Anić, D.: Urbanistički razvitak Beograda u periodu izmedu
dva svetska rata (1919–1941). In: Istorija XX. veka. Zbornik radova IX. Beograd, 1968. 458–465. In 1926 an offcial or merchant family in
Belgrade had 2.5 rooms, artisans had 1.9, workers had 1.5. The former values are similar to the Hungarian, while the latter are higher.
Calic, M-J.: Sozialgeschichte Serbiens 1815–1941. Der aufhaltsame Fortschritt während der Industrialisierung. München, 1994. 323-325.
7
8
(Continues from the previous page)
Social group based on
equation
Average
number of
servants
Household Proportion
size
of earners
Average
room
number
Average
inhabitants
per room
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
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
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
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
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
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
5 (158, 7.5%)
6 (203, 9.5%)
7 (264, 12%)
8 (104, 5%)
9 (164, 7.5%)
10 (151, 7%)
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
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
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
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
11 (83, 4%)
12 (99, 4.5%)
13 (162, 7.5%)
Total (2149)
Average
children
number
The narrow elite (group 11-13) was characterized by low children number, but with large family
owing to the auxiliary workforce (table 8). The proportion of earners was higher than the city
average, room number was over 2, as in group 9-10. The average population density was similar to
that of group 9-10, because of the high number of servants (group 9-10 was characterized by
smaller household sizes). Since the room number of the real elite was also similar to that of the
middle class, it was the available auxiliary workforce that made real difference between them.
(3) Groups based on clusterization
Though the 3rd method to identify social strata was based on automathic classification, thus it
lacked any preconception, unlike method 1 (prestige of occupation), the classification resulted in
well-defined social characteristics (table 10), although the boundaries of some of the groups created
were unconsolidated (group 2-3) as the discriminance analysis proved (table 9). Better classification
results could not be achieved even when using less or more clusters.
Table 9. Discriminance analysis: successfully reclassed cases measured to total
Reclassed into
Original
cluster
1
2
3
4
5
6
Total case
number
1
123
8
0
40
1
0
172
2
21
10
30
53
34
12
160
3
0
11
270
17
94
11
403
4
1
1
16
353
95
1
467
5
0
0
0
0
716
0
716
6
0
0
2
6
71
111
190
9
Table 10. Socio-demographic characteristics of groups created by automatic clusterization
Cluster 6:
the poor, high children ratio, low proportion of earners, room number under 1
Cluster 5:
Cluster 1:
the poor, no servants, small household size (3 prs!), room number around 1
the rich, servant number over 2, 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!), room number around 4
average values, unconsolidated boundaries, fuzzy
Cluster 2–3–4:
the proportion of Jews within the group is over 50%: ’par excellence Jewish middleclass’
Cluster 2:
The classification based on cluster membership was also appropriate to define groups based
on their wealth as revealed by its combination with the classification based on the equation
(figure 7), although the relation is not one-to-one. The average cluster membership of cases
classified into equation-based groups 11-13 was under 3, while in the case of equation-based
groups 1-6 it was above 5.
7
6
5
4
3
2
1
Total
(2146)
13 (162)
12 (99)
11 (83)
10 (151)
9 (164)
8 (104)
7 (264)
6 (203)
5 (158)
4 (258)
3 (233)
2 (140)
1 (127)
0
Figure 7. The average cluster membership of classes based on the equation
V.1. Socio-demographic features of the elite
The 170 households of the elite was characterized by high children number (over 2) contrary to the
previous classification (with great standard deviation), but similarly to the high-middle class
(cluster 2, 160 households) (table 8 and 11). The average number of servants was also around 2,
while in cluster 2 it was under 1. The average household size exceeded 6.5 (with great standard
deviation), which was remarkably higher than in cluster 2 or cluster 3-5, but similar to in group 13.
The proportion of earners was lower than in any other clusters, except cluster 6 (0.2 with low standard deviation), and lower, than in group 13. Room number was 4, while it was only 2 in cluster 2
and 1 in cluster 5. Rich were averagely 5 years older (over 42 yrs), than measured in other clusters.
Population density was below 2 prs/room with low deviation (like in group 13), while it was over
3 in cluster 2 and over 7 in cluster 6. The average group membership in cluster 1 was above 11,
while households classified into cluster 2 had a value over 9. Clusters 3-4 had average wealth
based on the equation, while in the two categories comprising the poor the average group
membership was only 4 and 2.
10
Table 11. Socio-demographic features based on automatic clusterization (average represents inter-group differences,
standard deviation reveals within group differences)
Cluster (Wardmethod)
Elite
(172)
2 (160)
3 (403)
4 (467)
5 (716)
6 (190)
Total
(2108)
Number of
children
Number of
servants
Average
Household Proportion
room
size
of earners
number
Aging
Prs/room
Group
membership
based on the
equation
Mean
2.30
1.97
6.61
0.22
3.97
1828
1.79
11.62
St. Dev.
2.07
1.48
3.33
0.15
1.46
12.62
0.97
1.64
Mean
2.34
0.71
5.74
0.35
2.11
1832
3.29
9.54
St. Dev.
2.02
0.96
2.73
0.25
1.17
10.77
2.16
3.11
Mean
2.26
0.21
5.51
0.43
1.54
1828.5
4.03
8.02
St. Dev.
1.70
0.46
2.39
0.22
0.76
11.21
1.82
2.90
Mean
1.90
0.31
4.28
0.31
1.75
1829.6
2.71
7.58
St. Dev.
1.89
0.50
2.16
0.21
0.59
11.74
1.53
2.38
Mean
1.06
0.00
2.91
0.38
0.96
1833
3.07
4.44
St. Dev.
1.07
0.00
1.21
0.24
0.14
12.00
1.19
2.04
Mean
2.77
0.22
5.05
0.23
0.70
1833
7.84
2.34
St. Dev.
1.79
0.49
2.02
0.14
0.30
10.27
3.23
1.67
Mean
1.83
0.34
4.42
0.35
1.55
1831
3.51
6.61
St. Dev.
1.74
0.80
2.45
0.23
1.08
11.77
2.28
3.46
As for the categories based on traditional classification (the weberian prestige of occupation),
group ’e’ was characterized by more than 3 rooms, in case of group ’f’, ’h’ and ’kk’ it exceeded 2.
(Either owned or rented, room number represents economic power, figure 8).
3,5
3,0
2,5
2,0
1,5
1,0
0,5
0,0
Figure 8. Average room numbers per groups based on prestige of occupation
3,0
Mean
2,5
Std. Deviation
2,0
1,5
1,0
,5
,0
ev
gk
iz
ort
ref
rk
Total
Figure 9. The connection between economic potential (based on the equation) and religion
11
As for religious differences it was the Protestants (both Calvinists and Lutherans), whose
economic potential proved to be the greatest followed by Jews (figure 9). Greek Catholics were
poorer than the average. Although Calvinists constituted only 12% of the population they held
traditionally most of the leading offices. Differentiation within religious groups progressed by
1870: standard deviation was high (there were poor artisans among Protestants, and beggars,
scrap-metal collectors among Jews).
Internal differentiation
After analyzing the inter-group differences it is time to take a closer look on within-group
differences earlier depicted by standard deviation values. Cluster 1 comprising the elite was
dominated by group ’e’, ’kk’, ’h’ and ’f’ (’h’ and ’f’ constituted 20-20% of this group respectively
referring to the predominance of old bureaucratic-agrarian classes, the new layer of merchants also
reached 20% within cluster 1, but their proportion was still over 40% in the middle-class, cluster 2).
As these traditional categories based on the prestige of occupation can be found in other clusters as
well, this confirms, that the 2 methods are not equivalent (figure 10). Only 50% of members in
group ’e’ was classified into cluster 1, the chance to be the part of the elite was also 50% among ’h’
(officials), but only 15-15% in group ’kk’ and ’p’, while in group ’f’ 30% belonged to the elite. Almost 30% of group ’kk’ was classified into cluster 2 (representing merchant burgeoisie). Thus it
was category ’e’ and ’h’ that is considered dominantly the elite (these were officials referring to the
fact that capitalist transformation was one-sided as new layers were not yet members of the high
society here in 1870).
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
ö
e
f
h
p
1
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2
9
34
5
66
kk
2
3
k
4
m
5
g
n
q
56
494
278
39
87
134
666
220
42
68
63
224
40
214
33
10
4
1
2
s
Total
6
32
17
4
13
48
20
20
9
12
9
3
4
5
6
108
116
total
összes
s
q
n
g
m
k
kk
p
h
f
e
ö
Figure 10. The classification of traditional groups into clusters: within-group differences
12
Surprisingly category ’p’ cannot be unequivocally considered as the component of the elite (only
10% of the elite stemmed from this category, though it is still higher, than their proportion from the
total households, which is 5%; and only 15% of group ’p’ was classified as ’elite’ into cluster 1). This
also refers to defects in modernization process of towns compared to the western models.
The above mentioned are also true if we use the equation-determined categories instead of
automatic clusterization (figure 11). The proportion of cases classified into group 9-13 was over
70% in case of group ’e’ and ’h’, 50% in case of ’kk’ and ’f’, while 40% in group ’p’ (the agrarian
elite was weakening, but the merchant elite was not yet strong to take over the positions of the
bureaucrats – the agrarian elite transformed their power into political). As group 9-13 is a broader
interval composed of more than 600 hundred families, while cluster 1 represented only 7.5% of the
households (and cluster 2 gave further 7.5%), it is not surprising that artisans were also included
into these aggregated groups.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
total
1-4,
5-8,
9-13,
Total
9-13,
5-8,
1-4,
0%
20%
40%
60%
80%
100%
s
q
n
m
g
k
kk
f
p
e
h
ö
Figure 11. Internal differentiation (based on economic potential) within traditional occupation groups
As for the religious distribution within the categories of theelitewe may conclude, that
protestants were overrepresented within category ’h’ and Jews among members of group ’kk’
(both constituting the part of the elite), but within group ’e’ or group ’f’ no similar trends could be
observed (figure 12); the share of different religions in the latter categories was similar to that of the
total sample. The same phenomena can be observed if using the other two classifications.
Every family had 2 or more than 2 rooms (90% had more than 3) in cluster 1, while it was only
60% in cluster 2. 90% of family heads had 2 or more than 2 rooms in group ’e’, 60% in group ’f’, 70%
in group ’kk’ and ’h’, while it was only 40% among households classified into category ’p’ (figure 13).
13
Going down into a deeper level we also tried to identify the proper occupations of the elite,
within the pre-defined categories. Lawyers, doctors, engineers, landowners determined this group
either we used averege room number, the equation to determine wealth or cluster analyis (table
12).
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
ö
e
f
h
p
ev
összes
kk
k
m
g
gk iz ref rk
676
rk
iz
100
ö
e
f
h
p
kk
k
m
g
n
q
s
102
184
ev
18
20%
Total
40
155
0%
s
179
120
gk
q
507
283
ref
n
40%
60%
2
80%
100%
Figure 12. The frequency of different denominations among and within social groups based on prestige of occupation
100%
90%
80%
0,5 és alatta
70%
1
60%
50%
2
40%
3
30%
4
20%
4 over
felett4
10%
0%
Figure 13. The connection between room number and occupation
14
Table 12. Rankings of different occupations based on their economic potential
Lawyers and doctors (33)
Average
(category)
based on
equation (14)
11.24
mason owners (60)
skinner-weaver (10)
landowners (106)
wheat, flour merchants (21)
engineers (18)
8.70
9.40
9.08
6.76
8.83
5
2
3
14
4
2.69
3.10
3.17
3.33
3.35
2
3
4
5
6
2.32
2.9
2.3
1.81
3
2
4
9
other merchants (38)
7.61
8
3.46
7
1.87
7
grocers (11)
joiner (35)
entrepreneurs, railway
entrepreneurs (13)
butchers (27)
small merchants (10)
textile merchants (15)
6.82
7.66
12
7
3.55
3.65
8
9
1.55
1.84
12
8
7.31
9
3.69
10
2.08
5
6.89
6.10
8.40
11
18
6
3.74
3.78
3.87
11
12
13
1.56
1.00
2.0
11
26
6
tanner-skinner (37)
6.57
15
3.89
14
1.27
18
woolen coat maker (aba) (46)
bootmakers (144)
total sample (2144)
6.04
6.11
6.53
21
17
16
3.91
3.92
3.93
15
16
17
1.34
1.33
1.52
15
16
13
shopkeekers (27)
5.52
23
3.96
18
1.19
21
shoemakers (16)
teachers (15)
tailors (103)
washers, sewers, bakers (37)
6.00
6.80
6.10
7.27
22
13
19
108
4.00
4.07
4.23
4.24
19
20
21
23
1.13
1.77
1.33
1.20
22
10
17
20
cartsmen (52)
carpenter (33)
personal servants (55)
agrarian daily wage earners (343)
policemen (15)9
4.83
4.61
5.00
3.89
3.73
25
26
24
27
28
4.55
4.73
4.73
4.88
5.00
24
25
26
27
28
1.00
1.11
1.00
0.87
0.86
24
23
25
27
28
Occupation of the family head
Ranking
Cluster
(6)
Ranking
Average
room
number
Ranking
1
2.03
1
3.64
1
V.2. Reproducing the elite
It is evident, that in urban environment natural reproduction was subordinated compared to
migration as source of reproducing the elite. Even in the introverted Eger 75% of the measured
population increase was the result of migration processes, as the net reproduction rate until 1873
was low (high mortality besides the high birth rate). The demographic transition in Hungary
began only after the last cholera epidemics (1873).
External sources
As we mentioned, by 1900 55% of the urban dwellers in Sátoraljaújhely was born outside the
administrative area of the town, which functioned as a ’sink’. The social distribution of the
immigrant and indigenous society showed remarkable differences. High-class and upper classes
were overrepresented among migrants compared to indigenous or total population, while the
middle class was overrepresented in case of the indigenous population. Lower classes were also
overrepresented among newcomers, especially regarding industrial-tertiary occupations, which
clearly shows the changing workforce-demand of the transforming economy (table 13).
8 The differences in rankings using different classification method are due to the fact, that the equation calculates per capita potential
contrary to the other two indices, thus a poor, but small family shows better performance when the equation is used as classification
method.
9 Only heads of families included.
15
Table 13. Differences in social status of immigrant and indigenous society in Sátoraljaújhely (based on family heads)
Layers
Total
%
number
Indigenous %
Immigrant
%
Immigrants
from the
layer %
High class
50
2
13
1.5
2.1
74
High and upper
class
90
3.4
22
2.5
3.8
75
Middle class
1080
41
435
50
36
60
Lower middle class
96
3.5
36
4.1
3.3
62
Lower classes
1390
52.5 341
39
58
75
558
21
183
21
21
67
peasants, daily labourers
735
28
124
14
34
83
bricklayers, servants,
cartsmen, cooks
2656
100
873
100 100
Agrarian from
lower classes
Industrialtertiary from
lower classes
Altogether
Examples
priests, lawyers, doctors,
engineers
teachers, tax-collectors, railway
engineers, local and county highofficials
merchants, craftsmen, mason
owners
low-commission officers, grocers,
tailors, waiters
nurses, servants, scrap-metal
and textile collectors
67
A regional comparison can also be useful to identify common and specific patterns. We analyzed
the small Varannó oppid (Vranov, SK), a district center in the same county with only 2000
inhabitants, and the tradional, but introverted center, Eger (over 20 000), the county seat of Heves
County (table 14). Not surprisingly the elite was mainly recruited from newcomers even in the
small Varannó, as it had no school to educate its own elite. In the case of Eger, with its Lyceum, not
only the elite was stronger regarding its percentage value compared to the other two settlements
(20% vs. 3.5-7%) (although we used a different source type, the birth and marriage registers from
1883 containing only 870 persons and not the census data), but the local elite was also stronger
compared to the immigrant elite society (22 vs. 12%). In Eger the middle class was thinner.
Table 14. A comparison of social stratification of immigrant and indigenous societies in urban and semi-urban
environment
Layer
Eger,* Varannó S.újhely Eger,*
Varannó,
S.ujhely,
Varannó S.újhely,
total
migrant migrant migrant indigenous indigenous
total (%) total (%)
(%)
(%)
(%)
(%)
(%)
(%)
Elite
Eger,*
indigenous
(%)
7.1
3.4
20.0
8.1
3.8
12.0
5.8
2.5
22.0
Middle
48.3
41.0
33.0
40.8
36.0
49.0
58.2
50.0
25.0
Lower
middle
6.1
3.5
24.0
8.6
3.3
12.0
2.9
5.0
28.0
Lower
38.5
52.0
22.0
42.5
58.0
25.0
33.1
39.0
20.0
Total
409
1783
720
2656
x
311 (43%)
873 (33%)
prs
(57%)
(67%)
*Eger is based on birth registers from 1883, while the other two were based on census data from 1870.
Internal sources
In order to analyze the chances of passing down or inheriting social positions, we used a sample
of 100 persons (cca. 1% of the total population) between 1896 and 1906 collected from the birth
registers of Sátoraljaújhely (narrow time interval was needed to identify father-son relations). In
case of the elite both upward migration and the inheritance of social position was observable, but
the sample was statistically irrelevant (table 15). The sum of cases predicts, that upward
16
movements were a bit more characteristic for this urbanising community than declassation (12 vs.
5 cases in 1896 out of 43; and 8 vs. 5 cases out of 40 in 1906, however stability characterized more
than 50% of cases). As at least the sons were considered indigenous, thus the general improvement
in their social status reveals the role of towns in ameliorating livelihood, while it is also evident, that in
the society of (especially rural) immigrants lower classes dominated.
Table 15. Investigating social mobility in Sátoraljaújhely between 1896-1906 (father-son relations)
1896
cases
investigated
inherited
position
%
upward
movement
%
declassation
%
Upper classes
2
1
50%
1
50%
0
0%
Middle class
34
17
50%
11
33%
3
9%
5
71%
0%
2
28%
%
0
upward
movement
%
declassation
%
Lower classes
1906
7
cases
investigated
inherited
position
Upper classes
0
0
0%
0
0%
0
0%
Middle class
23
11
48%
8
34%
2
9%
Lower classes
17
9
53%
0
MNL-BAZML SFL, XXXIII-2. marriage registers from 1896, birth registers from 1906
0%
3
18%
V.3. Spatial pattern of the elite in different dimensions
After visualizing our data on maps, where each household was represented by an entity, we
managed to locate the elite, which was characterized by a center-periphery accommodation
pattern (Chart 1-5). Wealthy people were located along the main road in N-S direction (also
representing the general route of migration), then in the city center, and the road leading towards
the River Ronyva, perpendicular to the main road. The members of the elite often rented their
houses in the city center to Jewish merchants, who there claimed the majority by 1870. As we used
a multidimensional approach it is evident that the category of ’elite’ does not coincide in many cases
after overlying the maps (wealth based on equation, clusterization, traditional classification, room
numbers, density of dwellers).10
Contact with authors: Gábor Demeter (HAS, RCH, Institute of History),
[email protected]; Róbert Bagdi (Pallas Athena University),
[email protected]
10
17
Chart 1. Spatial pattern of population
density (prs/room within households)
18
Chart 2. Spatial pattern of households
(room / household)
19
e: városi és vármegyei elit / urban and county elite
f: földbirtokos /landowners
g: mezőgazdaságban önálló kereső /skilled agrarian workforce)
h: hivatalnok / officials
k: kiskereskedő, szatócs/ small-scale merchants, small shopkeepers;
kk: kereskedők, kocsmárosok, vállalkozók /merchants, entrepreneurs,
innkeepers
m: iparosok, mesteremberek /artizans, craftsmen
n: fegyveres altiszti (pl. hajdú) / lower authorities (portier, policemen)
p: polgári foglalkozások / free-lanced civil professions
q: fuvaros, kocsis, pincér / lower tertiary (transportation, waiters)
s: szegény (agrár és tercier) / poor wage labourers (agrarian, tertiary)
Chart 3. The location of the elite based on the
prestige of occupation
20
wealth
categories
Chart 4. The location of the elite based on the
equation referring to economic potential
21
Chart 5. The location of the elite based on
clusterization of raw census data
22
Chart 6. The spatial pattern of religious
distribution in Sátoraljaújhely
(Zemplén County, family heads), 1870
Lutheran (ev)
Greek Catholic (gk)
Jew (iz)
Orthodox (ort)
Calvinist (ref)
Roman Catholic (rk)
23
GISta Hungarorum
The Institute of History, Research Centre for the Humanities, Hungarian Academy of
Sciences together with the cooperation of the researchers at Pécs University, Debrecen
University and Eötvös Loránd University (Budapest) with the support of the Hungarian
Research Fund have elaborated a Geographical Information System (including numerous
databases and base maps as well) that enables researchers to carry out spatial analyses of
socio-demographic phenomena for Austria-Hungary between the 1850s and the 1910s.
The goal was to create a transparent, expandable and open system that can be operated
under QGIS to promote the work of geographers, historians, ethnographers and other
researchers from the neighboring countries in order to ask new questions or to find new
answers to old questions from different perspectives. This R&D was named GISta
Hungarorum (referring to the name of the Hungarian chronicles, Gesta Hungarorum) and
the datafiles and shape files of maps are available at: http://www.gistory.hu/g/en/gistory/otka
free of charge.
GIStorical Studies
Historical GIS series of the Institute of History, Research Centre
for the Humanities, Hungarian Academy of Sciences
In order to make GIS applications for historians more popular, as well as to advertise the
perspectives of historical geography we also launched a new peer-reviewed series named
GIStorical Studies in which we invite researchers who use GIS to conduct historical
geographical research (in a broad sense, including landuse, urban history, social history,
economic history, regional analysis, demography, migration, climate, cliometrics etc.) and
publish their studies both online and in print either based on the released database or on
other data sources from Hungary and Central-Europe. We welcome interdisciplinary and
multidisciplinary, as well as methodological researches focusing on the region using GIS
methods.
Editoral board: DEMETER, Gábor – SZULOVSZKY, János
Responsible for issuing: FODOR, Pál Director General • Hungarian Academy of Sciences
Research Centre for the Humanities Institute of History • H-1097 Budapest, 4 Tóth Kálmán Str.
Redaction: H-1097 Budapest, 4 Tóth Kálmán Street, Room 5.25. , Hungary
Postal address: GIStorical Studies, c/o MTA BTK TTI • H-1453 Budapest, p.o.b. 33. Hungary
E-mail:
[email protected]
ISSN 2560-2101
ISBN 978-963-416-097-7
Prepress preparation and printed in the HAS Research Centre for the Humanities, Institute
of History