First names in the Netherlands
from preferences of parents
to socio-geographic representations
Gerrit Bloothooft
Institute of Linguistics OTS
Utrecht University
Dutch studies on first names
 Limited scientific work
– Dictionary (20.000 entries)
– Few socio-linguistic studies
• Limited scope, small samples
 Topic is extremely popular in the media
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First names data
 Hard to get from civil registration
– privacy issues
 New horizons because of digitization of
the population administration (and
archives)
– but distributed storage
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A full population study
 First names from the National Social
Security Bank (SVB)
 All children born since 1983
– first name (official, no call name, but..)
– year of birth
– family code (separate table)
unique!
– postal code
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A very rich source
 4.2 million children (1983-2002)
– 200.000 per year
 1.9 million families
 176.800 different first names
– 108.500 unique names
– 3.120 names with frequency > 100
represent 85% of the children
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Datareduction needed
 Far too many names to describe
one by one
 Names with common properties
– Not from etymological point of view
– Not from linguistic point of view
– Based on choices of parents
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name use!
6
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
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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Once again
 Analysis (grouping) of first names on
the basis of the choices of the parents,
i.e. name use
 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
Regional name profiles
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) in a family
 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
 Define measure that reflects
relationship between two names
 Combine names that mutually have a
strong relationship into a set
– Johannes, Maria, Johanna, …
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Name relationship measure
 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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Alternative measure
 In terms of probablities
Prob(name_pair) / indepentProb(name_pair)
indepentProb = if there is no specific preference
 Series of problems
– High-frequent name pairs should get a stronger
weight (estimation inaccuracies for low-frequent
pairs)
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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
 Start with strongly related name-pairs
 Add new name-pair to existing cluster or
start a new cluster
 Iterative procedure
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Clustering results
 4.013 first names
– Frequency of a pair > 4
 result: 340 name sets
– Limited number of large sets
– High number of small sets
 top-25 of sets 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
Long names from the
Old Testament
Names from nature
Long names from history
and culture
Short modern
Common Western
Pre-modern
Common
Western
Long French
Scandinavian
Pre-modern Dutch
Short modern
Dutch
Traditional Dutch
Latin | Dutch
Short traditional
Dutch
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Frisian
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Dimensions
Foreign
Long
Common Western
Short
Modern
Pre-modern
Traditional
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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Geographical distribution
 Postal code area level [3584]
– Big differences between pc areas
• city neighborhoods
• villages (religion)
– Enough children for characterisation
• ~1200 births per pc in 20 years
• Some further name grouping needed
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Further grouping
Traditional names (Latin form)
Traditional names (Dutch)
Frisian names
Pre-modern names (Dutch, Western)
Foreign names (English)
Short modern names (Dutch, Western, Skand)
Names from OT, history, culture, nature
Arabic & Turkish names [unrelated group]
Other [low frequent]
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%
8
5
3
12
24
13
7
5
23
27
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
French
Pre-modern
Western
Scandinavian
Pre-Modern
Pre-modern Dutch
Traditional
Traditional
Latin Dutch
Dutch
Short modern
Western
Short
Short modern
Dutch
Short traditional
Dutch
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Frisian
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Traditional
(Dutch)
Aaltje
Barend
Dirkje
Evert
Geertje
Harm
Jantje
Klaas
Margje
Teunis
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Traditional
(Latin form)
Adriana
Bernardus
Christina
Eduard
Elisabeth
Franciscus
Geertruida
Hubertus
Johanna
Krijn
Maria
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Frisian
names
Aafke
Bauke
Douwe
Froukje
Joppe
Jitske
Jelle
Menno
Sietske
Onno
Wietske
Wiebe
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Pre-modern
names (Dutch,
Western)
Anniek
Anita
Carla
Frank
Jochem
Jeroen
Linda
Mark
Marloes
Paul
Suzanne
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Foreign names
(English)
Amanda
Dennis
Danny
Chantal
Henry
Isabella
Kim
Kevin
Melissa
Ricardo
Samantha
Stephen
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Short names
(modern, Dutch,
Western, Skand)
Anne
Bart
Eva
Gijs
Lisa
Kaj
Niels
Sanne
Sofie
Tim
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Religion
Short names - Religion
None
Catholic
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Old testament
history, culture,
nature
Daniël
Esther
Judith
Naomi
Willemijn
Diederik
Frederieke
Maurits
Iris
Fleur
Jasmijn
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Income
Religion
Lowest
Highest
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Arabic and
Turkish names
Fatima
Mohamed
Noura
Hamza
Sara
Yassin
Fatma
Mustafa
Hatice
Mehmet
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Further geographical analysis
 Per pc area: percentage of children per
name group (8 values)
 These percentages reflect social
composition of the pc area
 Factor analysis on data from 3584 pc areas
 10 typical profiles
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10 profiles
Traditional – Latin form
Traditional – Dutch
Transitional, Traditional Dutch to pre-modern
Transitional, Traditional Latin form to foreign
Pre-modern
Foreign
Short
Elite
Arabic-Turkish
Frisian
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Example profile
Traditional – Latin form
Traditional – Latin form
Traditional – Dutch
Frisian names
Pre-modern names
Foreign names
Short names
Names from OT, history, culture, nature
Arabic and Turkish names
other
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%
37
18
1
8
12
6
6
0
12
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Naming map
of the
Netherlands
Frisian
premodern
elite
foreign
Arab
Turkish
trad.
Dutch
trad.
Latin
short
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foreign
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EU constitution votes
Education level
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Educational
Education level
level
Highest
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Lowest
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Naming
map of
Groningen
province
trad. Dutch >
pre-modern
foreign
elite
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premodern
45
Naming map of Groningen city
(typical city pattern)
% households with income
in highest 20% class
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Typical Groningen names
 Oldambt: Boelo, Doeko, Adzo, Elzo,
Popko, Rienko, Wubbo | Grieto, Trienko
 Frisian: Alke, Bouktje, Rikste, Eisse,
Wiert
 Peat-colonies: Hinderika, Harmannes,
Geessien, Hillechien
 regional names are becoming rare
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Conclusions
 Successful representation of
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Further studies
 Changes in naming
– Missing data 1940-1982; towards full population data
– Current study 1983-2002; towards 5 year period analysis
• Who starts name renewal, how does it spread
 Names
– Call names & official names
(using consumer questionnaires)
– Spelling choices
 Social factors in naming
– Role of naming after relatives (in first, second, third name)
– Gender dependencies
– Income, education, religion
 Mathematics of naming (chaos theory)
 Name pronunciation (for speech synthesis)
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Contact
 E-mail:
Gerrit.Bloothooft@let.uu.nl
 Homepage:
www.let.uu.nl/~Gerrit.Bloothooft/personal
 Mail:
Trans 10, 3512 JK Utrecht, The Netherlands
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