Transcript Naming and subcultures in the Netherlands

Analyses of first names in The
Netherlands: full population studies
Gerrit Bloothooft
Institute of Linguistics OTS
Utrecht University
Dutch studies on first names
 Limited scientific work so far
– Dictionary (20.000 entries)
– Few socio-linguistic studies
• Limited scope, small samples
 Topic is extremely popular in the media
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Research dimensions in onomastics
– Name
– Form and spelling
– Origin
– Motives
– Time
– Place
require a lot of data
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Full population
 Gemeentelijke Basis Administratie
(GBA), Civil Administration
 Electronically from 1994
 Legal right to use data for scientific
research
 16+ million people
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Connected!
 UiL-OTS and Meertens Institute are
connected to the GBA on June 1, 2006
 The right to make a rich data extraction
for the full population (all persons with
Dutch nationality): planned July 1, 2006
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Research proposal NWO
 The first name revolution in the 20th
century in The Netherlands – the first
name as a measure of social and
linguistic change
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Mile stones
 Traditional naming (after relatives) decreased
enormously during the 20th century, especially
second half
 Full freedom for parents through name law of
1970
-> Naming of children became a very personal
linguistic and social expression during the last
50 years
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Major topics
 Changes in naming after relatives
 Relations between names and social
classes (sets and spelling)
 Regional spread of names, dialectal
influences
 Life cycles of names
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What do we get (per person)
 All first names
 Date -, place -, postal code -, land of birth,
gender, date of decease (after 1994)
 Parents: first names, date & place of birth
 Children: first names, date & place of birth
 Administrative number of all persons with own
record
this is unprecedented (also internationally)!
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Looking for mechanisms
 All research topics can be described as
the search for large scale mechanisms
and relations
 Away from the individual name, towards
much higher aggregation levels
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Towards name sets
 From 16+ million names with
 over 200.000 different first names
 to a much lower number of name sets
that have homogeneous properties
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A previous study (2000-2004)
 First names from the National Social
Security Bank (SVB)
 All children born since 1983
– first name (official, no nick name, but..)
– year of birth
– family code (separate table)
– postal code (four digits)
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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!
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Naming and social classes
Hypothesis:
 There are social classes with own
naming preferences
 These classes/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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Research aims:
 Identification of social classes (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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Method (a chain of names)
 Parents choose first names from a set that is
popular in their subculture (relatives, friends,
neighbours,..) (with higher probability)
[Social Group size is about 150]
 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)
 Children in on 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, which 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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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 refine!
– 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
 four-digit postal code area level [3584]
– Big differences between pc areas
• city quarters
• villages (religion)
– Enough children for characterisation
• On average 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
32
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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Religion
Income
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
Arab
traditional
Turkish Dutch
elite
>foreign
traditional
Latin
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short
foreign
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EU constitution votes
Education level
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Educational level
Education level
Highest
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Lowest
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Conclusions
 Successful data reduction
 Name groups & subcultures
– language, income, education, religion
 Geographic representation
– four-digit postal code area just right
 The factor time should be included
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The Wegener connection
 Direct marketing company
 Organises twice a year a national
consumer questionnaire
 200.000 families per year
– Wide range of information
• Income, education level
– Includes first names and year of birth of all
family members
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Correlation at family level
(instead of postal code level)
 Name set &
– Income of parents
– Educational level (of both parents)
– (newspapers, underwear, cars, insurance,
holidays,…..) preferences of parents
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Mathematical studies
 Life cycle of a name
 Zipf’s behavior
– A few names with high frequency, a lot of
names that are unique
information function of a name in
communication
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Research dimensions in onomastics
– Name
– Form and spelling
– Origin
– Motives
– Time
– Place
YES, we can do great research on this with
the full population data!
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Contact
 Book:
Over voornamen, Het spectrum (2004)
 E-mail:
[email protected]
 Homepage:
www.let.uu.nl/~Gerrit.Bloothooft/personal
 Mail:
Trans 10, 3512 JK Utrecht, The Netherlands
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