Transcript Naming and subcultures in the Netherlands

First names in the Netherlands
from preferences of parents
to socio-geographic representations
Gerrit Bloothooft
Institute of Linguistics OTS
Utrecht University
The Netherlands
 Population of
16 million people
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A full population study
 (almost) all children born since 1983
– first name
– year of birth
– family code
– postal code
 from the National Social Security Bank
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A very rich source
 3.5 million children (1983-1999)
 1.9 million families
 152.274 different first names
– 100.868 unique names
– 3.120 names with frequency > 100
represent 3 million children
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Naming and subcultures
Hypothesis:
 There are subcultures with own naming
preferences
 These subcultures may relate to
– culture/language (Frisian, Arabic, Turkish,
Surinam, Antillean,..)
– religion (Catholic, Protestant, Islam,..)
– sociological status (education, income,..)
– geography (urban, rural, regional,..)
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Naming and subcultures
Issue:
 We don’t exactly know the subcultures
nor their membership
 Reversily: Can we identify subcultures
on the basis of the first names given to
children?
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Naming and subcultures
Research aims:
 Identification of subcultures (and their
naming preferences) on the basis of the
first names of children per family
 Study of the relation between these
subcultures (first names) and sociocultural and geographic factors
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Note
 Analysis (grouping) of first names on
the basis of the choices of the parents
NOT on any other scientific assumption
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Contents
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Method
Sets of first names
A map of name sets
Geographic distribution of name sets
Socio-cultural factors of name sets
Conclusions
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Method (a chain of names)
 Parents choose first names from a set that is
popular in their subculture (relatives, friends,
neighbors,..) (with higher probability)
 This is informative only if there is more than
one child (more than one name)
 Pairs of first names (from a family) as unit for
analysis
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Method (a chain of names)
 Family: Mark, Peter, Linda
If Mark is popular in a subculture, then Peter
and Linda may be popular as well
Name pairs:
Mark - Peter, Peter - Mark,
Mark - Linda, Linda - Mark,
Peter - Linda, Linda - Peter
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Method (a chain of names)
 Select all families with two or more children
(1.17 million families, 2.81 million children)
 Derive all pairs of first names (from a single
family) (in all, 2.12 million different pairs)
 Compute the frequency of each pair
 The higher the frequency of a pair, the more
likely the first names in the pair belong to the
same set
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Most frequent name pairs
Frequency
Pair of first names
1091
790
Johannes
Johannes
Maria
Johanna
754
727
….
572
459
Jeroen
Johanna
Martijn
Maria
Mohamed
Lars
Fatima
Niels
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Clustering of first names
Example:
 Esther
– 7.967 girls
– 12.973 brothers and sisters
– 276 times sister Judith (= 2.1 %)
 Judith
– 4.828 girls
– 8.033 brothers and sisters
– 276 times sister Esther (= 3.4 %)
 Geometric average (2.7 %)
– A symmetric measure of relationship between the two names
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Clustering of first names
 Name pairs from a (subculture-related)
set have the highest relation measure
Esther:
Judith:
Judith
2.7
Esther
2.7
Mirjam
2.4
Mirjam
1.6
Ruben
1.2
Ruben
1.0
David
1.1
Miriam
0.8
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Clustering of first names
 Iterative procedure to find sets of first
names
 4.013 first names
– frequency of a pair > 4
 340 name sets
 top-25 is most illustrative
– 2.887 first names
– 2.64 million children (75%)
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Features of name sets
 Period of maximum popularity
– Traditional, Pre-modern (1950-1980), Modern
 Language
– Dutch, Frisian, English, American, French,
Spanish, Italian, [Arabic, Turkish]
– Common Western
 Topic area
– Nature, History & Culture, Old Testament
 Length
– Short (one syllable), long
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A map of name sets
 Presentation of a map of name sets
– Based on mutual relations between name sets
 The closer two name sets on the map,
the more related the sets
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Spanish & Italian
Long American & English
Short American & English
Pre-modern
English & French
Names from the
Old Testament
Names from nature
Names from history
and culture
Short modern
Common Western
Pre-modern
Common Western
French
Scandinavian
Pre-modern Dutch
Short modern
Dutch
Traditional Dutch
Short traditional
Dutch
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Frisian
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Dimensions
Foreign
Long
Short
Common Western
Traditional
Pre-modern
Modern
Dutch, Frisian
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Spanish & Italian RICARDO
Long American & English MICHAEL
Short American & English
Pre-modern
English & French DENNIS
Names from the
Old Testament DANIËL
KIM
Names from nature
IRIS
Names from history
and culture LAURENS
Short modern TIM
Common Western
Pre-modern MARK
Common Western
French
Scandinavian NIELS
CHARLOTTE Pre-modern Dutch
JEROEN
Traditional Dutch
JOHANNES | JAN
Short modern Dutch
BART
Short traditional
Dutch TEUN
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Frisian
JELLE
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Intermediate conclusion
 Name sets can be identified
but
 What do parents have in common, who
choose first names from the same set?
– Geography
– Religion
– Income
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Geographical analysis
 Based on postal code (3.584 units)
 Further grouping of name sets into
– Foreign
– Traditional Dutch
– Pre-modern
– Short
– History & Culture
– Frisian
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(24 %)
(12 %)
(11 %)
(11 %)
( 6 %)
( 2 %)
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Spanish & Italian
Long American & English
Short American & English
Foreign
Pre-modern
English & French
Names from the
Names from nature
Old Testament History & Culture
Names from history
and culture
Pre-modern
Western
French
Short modern
Western
Scandinavian
Pre-Modern
Pre-modern Dutch
Short
Short modern
Dutch
Traditional Dutch
Traditional
Short traditional
Dutch
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Frisian
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Geographical analysis
Characterize each postal code area:
 Compute deviation from the grand
average percentage (NL) for each name
group
 Most deviating name group gets that
area
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Frisian
Pre-modern
[rural]
Foreign
[lower
education]
History & Culture
[cities & suburbs,
higher education,
higher income]
Short
[Catholic, rural]
Traditional Dutch
[Protestant, rural]
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Conclusions
 Full scale population studies are very
promising (and the only way to reliably study
naming patterns)
 The existence of subcultures can be derived
from naming within families
 Many more details were found but could not
be presented here
 Comparable studies of neighboring foreign
regions would add an interesting dimension
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Contact
 E-mail:
[email protected]
 Homepage:
www.let.uu.nl/~Gerrit.Bloothooft/personal
 Mail:
Trans 10, 3512 JK Utrecht, The Netherlands
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Religion
Religion
None
Catholic
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Income
Religion
Lowest
Highest
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Educational
level
Education level
Highest
Lowest
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