First names in the Netherlands from preferences of parents

to socio-geographic representations

Gerrit Bloothooft

Institute of Linguistics OTS

Utrecht University

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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 subculturesHypothesis:

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 subculturesIssue:

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 subculturesResearch 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 socio-cultural 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

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 names1091 Johannes Maria

790 Johannes Johanna

754 Jeroen Martijn

727 Johanna Maria

….

572 Mohamed Fatima

459 Lars 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 2.7

Mirjam 2.4

Ruben 1.2

David 1.1

Judith:

Esther 2.7

Mirjam 1.6

Ruben 1.0

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-modernEnglish & French

Names from the Old Testament

Names from nature

Names from history and culture

Short modern Common Western

Pre-modern Common WesternFrench Scandinavian

Pre-modern Dutch

Short modern Dutch

Traditional Dutch

Short traditional Dutch

Frisian

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Dimensions

Long Short

Traditional Pre-modern Modern

Foreign

Common Western

Dutch, Frisian

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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 WesternFrench Scandinavian NIELS

CHARLOTTE Pre-modern Dutch

JEROEN Short modern Dutch BART

Traditional DutchJOHANNES | JAN Short traditional

Dutch TEUNFrisianJELLE

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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 (24 %)– Traditional Dutch (12 %)– Pre-modern (11 %)– Short (11 %)– History & Culture ( 6 %)– Frisian ( 2 %)

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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 Western

Pre-modern WesternFrench Scandinavian

Pre-modern Dutch

Short modern Dutch

Traditional Dutch

Short traditional Dutch

ShortPre-Modern

Foreign

Traditional Frisian

History & Culture

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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]

History & Culture[cities & suburbs,higher education,higher income]

Short[Catholic, rural]

ForeignForeign[lower [lower education]education]

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: 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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Religion

CatholicProtestant

None

Religion

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Religion

Lowest

Highest

Income

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Education level

Educational level

Lowest

Highest