1. Analyses of first names in The Netherlands: full population studies
Gerrit Bloothooft Institute of Linguistics OTS
Utrecht University
Bloothooft, Gerrit (The Netherlands) Naming and subcultures in the Netherlands (2 a) Tuesday 14.00
Ladies and gentlemen, in The Netherlands the naming mechanisms have considerably changed during the last century.
Until 1950 the naming of relatives was dominant and far over 90 % of the children got the first name of their grandparents by tradition.
This has radically changed since then, leaving freedom for parents to follow their own preferences. I assume that these preferences are related to socio-cultural factors that originate in subcultures in society.
In this presentation I will demonstrate that this is indeed the case. This implies the identification of relevant subcultures and their naming preferences.

2. Dutch studies on first names
Limited scientific work so far
Dictionary ( entries)
Few socio-linguistic studies
Limited scope, small samples
Topic is extremely popular in the media
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3. Research dimensions in onomastics
Name
Form and spelling
Origin
Motives
Time
Place
require a lot of data
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4. 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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5. 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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6. 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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7. 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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8. 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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9. 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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10. 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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11. Towards name sets From 16+ million names with
over different first names
to a much lower number of name sets that have homogeneous properties
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12. A previous study ( )
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)
I was in the lucky circumstance that we recently acquired a database with the first name of all children born since 1983.
This database included year of birth, a family code – through which we knew what children belong to the same family - , and a postal code – allowing geographical studies.
The data came from the National Social Security Bank who is responsible for all social payments, among which those for children. Almost all parents are entitled to receive this payment for their children.
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13. A very rich source 4.2 million children (1983-2002)
per year
1.9 million families
different first names
unique names
3.120 names with frequency > 100 represent 85% of the children
The database is very large.
It contains the first name from over 3.5 million children from the period we studied: These children came from 1.9 million families.
These children had over 150 thousand different names, of which two-third occurred only once. A little more than 3000 names had a frequency over 100 and represent 85 % of all children.
Many studies can be envisioned using such a rich and complete source. I present only one investigation, specially targetted to the rare information on names of children per family.
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14. 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 name use!
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15. 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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16. 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 socio-cultural and geographic factors
I think so, and therefore the research aims are to try to identify subcultures and naming preferences on the basis of first names of children per family. If we succeed in this effort, we want to analyze these subcultures with respect to geographic and socio-cultural factors.
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17. 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
If parents choose names for their children from a certain set, this is only informative for us if there is more than one child. With two or more first names from a family we have the information that if one of the names belongs to a set, there is a likelihood that the other names belong to the set as well. We use pairs of first names (from the same family) as a unit for further analysis.
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18. 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
Here is an example. A family has the children Mark, Peter and Linda. If we have found that Mark is popular in a subculture, then this family suggests that Peter and Linda may be popular as well in that subculture.
The name pairs that underlie further analysis are in this case Mark - Peter, Peter - Mark, Mark - Linda, Linda - Mark, Peter - Linda, Linda – Peter. On the basis of such name pairs we can get information of the names of all brothers and sisters of Mark for instance, and their frequency.
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19. 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
In our database we had 1.17 million families with more than one child. These families had 2.81 million children.
In all 2.12 million DIFFERENT pairs of names were found (many of these with low frequency).
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20. Most frequent name pairs
Frequency
Pair of first names
1091
Johannes
Maria
790
Johanna
754
Jeroen
Martijn
727 ….
572
Mohamed
Fatima
459
Lars
Niels
Here are the most frequent pairs of first names.
We see Johannes and Maria on top followed by Johannes and Johanna. Interestingly the combination Johanna and Maria is already on the fourth place, indicating strong links between the three names Johannes, Maria and Johanna.
Other examples of closely related names are the Dutch names Jeroen and Martijn, the Arabic names Mohamed and Fatima, and the Scandinavian names Lars and Niels.
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21. 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, …
In some more detail, let us look at the names Esther and Judith.
There are almost 8000 girls with the name Esther, and these have about brothers and sisters. Of these, 276 were named Judith, which is 2.1 % of all brothers and sisters.
There were less girls named Judith but of course these had 276 sisters Esther as well, which is 3.4 % of all brothers and sisters of Judith.
We used the geometric mean of both percentages (which is 2.7 % in this case) as a measure for the relationship between both first names. If this percentage is 100, this would imply that all sisters of Esther would be named Judith, and reversily.
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22. Name relationship measure
Esther
7.967 girls
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
In some more detail, let us look at the names Esther and Judith.
There are almost 8000 girls with the name Esther, and these have about brothers and sisters. Of these, 276 were named Judith, which is 2.1 % of all brothers and sisters.
There were less girls named Judith but of course these had 276 sisters Esther as well, which is 3.4 % of all brothers and sisters of Judith.
We used the geometric mean of both percentages (which is 2.7 % in this case) as a measure for the relationship between both first names. If this percentage is 100, this would imply that all sisters of Esther would be named Judith, and reversily.
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23. Clustering of first names
Name pairs from a (subculture-related) set have the highest relation measure
Esther:
Judith
2.7
Mirjam
2.4
Ruben
1.2
David
1.1
Judith:
Esther
2.7
Mirjam
1.6
Ruben
1.0
Miriam
0.8
If we look at the most related names of brothers and sisters of both Esther and Judith, in the top we immediately see a preference for names from the Old Testament. These names are likely member of the same set.
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24. 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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25. Clustering results 4.013 first names result: 340 name sets
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%)
An iterative clustering procedure was used to establish the membership of a first name to a certain set. We limited the available material to first names from pairs that had a frequency higher than four. That were about four thousand first names.
The procedure resulted in 340 different name sets. Some included a large number of names, others only a few. The top-25 of these sets already proved to be very illustrative, they contained almost 2900 first names and had a coverage of 75 % of all 3.5 million children.
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26. Features of name sets Period of maximum popularity refine! Language
Traditional, Pre-modern ( ), 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
In general terms the name sets could be described using the following features:
The period of maximum popularity of the first names:
These are traditional names, predominantly used until 1950, names that gained popularity in the fifties and sixties which we called pre-modern names, and names that became popular since then, the modern names.
Furthermore, the language is an important factor. We have Dutch, Frisian, English, American, French, Spanish, Italian, Arabic and Turkish names. In this presentation I exclude the Arabic and Turkish first names because they form separate and closed classes. Then there are sets that include names that are quite commonly used in Western Societies, like Mark.
There are also sets related to topic areas, such as nature, history & culture and the Old Testament.
Finally the length of the name proved to be an important factor. A distinction showed between short names (of one syllable) and longer names.
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27. 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
Since it is impossible to discuss the resulting name sets one by one, I attempted a presentation of name sets in the form of a map. In such a map, the closer the name sets, the more related they are.
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28. Short American & English
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
Frisian
Here is the result. We see …
Bold type name sets are the relatively larger ones, while color indicates declining name sets.
Colors indicated declining name sets. This is most dramatically for the Traditional Dutch set, which dropped from over 90 to about 10 % of all names in a few generations, the pre-modern sets, but also the American & English sets already had their maximum popularity a few years ago.
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29. Dimensions Foreign Long Short Modern Pre-modern Common Western
Dutch, Frisian
Long Short Modern Pre-modern
Traditional
The previous map can be schematized with a couple of major dimensions:
The language factor: Dutch & Frisian -> Common Western -> Foreign
And independent from this one:
The time factor: Traditional -> Pre-modern -> Modern
The Length factor: short -> long names
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30. Spanish & Italian RICARDO
Long American & English MICHAEL
Short American & English
Pre-modern English & French DENNIS
KIM
Names from the Old Testament DANIËL
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
Short modern Dutch BART
Traditional Dutch JOHANNES | JAN
Short traditional Dutch TEUN
Frisian JELLE
For those of you who want to get a flavor of the first names themselves, here are the name sets again but each with the most frequent first name.
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31. 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
We now wanted to characterize each postal code area. For this, we computed for each name group the deviation from the grand average for The Netherlands. The most deviating name group (in a positive way) won.
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32. 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] % 8 5 3 12 24 13 7 23
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33. Traditional Latin Dutch
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 Western
Pre-modern Western
French
Scandinavian
Pre-modern Dutch
Short modern Dutch
Traditional Dutch
Short traditional Dutch
Foreign
History & Culture
Pre-Modern
Short
In terms of our previous map this grouping shows as follows
Traditional Latin Dutch
Frisian
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34. Traditional (Dutch)
Aaltje Barend Dirkje Evert Geertje Harm Jantje Klaas Margje Teunis
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35. Traditional (Latin form)
Adriana Bernardus Christina Eduard Elisabeth Franciscus Geertruida Hubertus Johanna Krijn Maria
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36. Frisian names
Aafke Bauke Douwe Froukje Joppe Jitske Jelle Menno Sietske Onno Wietske Wiebe
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37. Pre-modern names (Dutch, Western)
Anniek Anita Carla Frank Jochem Jeroen Linda Mark Marloes Paul Suzanne
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38. Foreign names (English)
Amanda Dennis Danny Chantal Henry Isabella Kim Kevin Melissa Ricardo Samantha Stephen
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39. Short names (modern, Dutch, Western, Skand)
Anne Bart Eva Gijs Lisa Kaj Niels Sanne Sofie Tim
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40. Short names - Religion None Protestant Catholic Religion
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41. Old testament history, culture, nature
Daniël Esther Judith Naomi Willemijn Diederik Frederieke Maurits Iris Fleur Jasmijn
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42. Income
Religion
Lowest
Highest
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43. Arabic and Turkish names
Fatima Mohamed Noura Hamza Sara Yassin Fatma Mustafa Hatice Mehmet
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44. 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
We now wanted to characterize each postal code area. For this, we computed for each name group the deviation from the grand average for The Netherlands. The most deviating name group (in a positive way) won.
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45. 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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46. 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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47. Naming map of the Netherlands
Frisian
pre-modern
Arab Turkish
traditional Dutch
elite
>foreign
traditional Latin
short
foreign
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48. EU constitution votes
Education level
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49. Educational level Highest Lowest Education level
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50. 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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51. The Wegener connection
Direct marketing company
Organises twice a year a national consumer questionnaire
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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52. 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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53. 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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54. CTL colloqium June 2006

55. 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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56. Contact Book: Over voornamen, Het spectrum (2004)
Homepage:
Mail: Trans 10, 3512 JK Utrecht, The Netherlands
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