Analyses of first names in The Netherlands: full population studies Gerrit Bloothooft Institute of...

Analyses of first names in The Netherlands: full population

studies

Gerrit Bloothooft

Institute of Linguistics OTS

Utrecht University

CTL colloqium June 2006 2

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


Research dimensions in onomastics

– Name– Form and spelling– Origin– Motives– Time– Place

require a lot of data


Full population

Gemeentelijke Basis Administratie (GBA), Civil Administration

Electronically from 1994 Legal right to use data for scientific

research 16+ million people


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


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


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


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


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)!


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


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


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)


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


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!


Naming and social classesHypothesis:

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,..)


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


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



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



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


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


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, …


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


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


Clustering

Start with strongly related name-pairs Add new name-pair to existing cluster or

start a new cluster Iterative procedure


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%)


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


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


Spanish & Italian

Long American & English

Short American & English

Pre-modernEnglish & 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 DutchLatin | Dutch Short traditional

DutchFrisian


Dimensions

Long Short

Modern

Pre-modern

Traditional

Foreign

Common Western

Dutch, Frisian


Spanish & Italian RICARDO

Long American & English MICHAEL


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


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


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

5

23


Spanish & Italian

Long 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

TraditionalLatin Dutch

Frisian

History & Culture


Traditional(Dutch)AaltjeBarendDirkjeEvertGeertjeHarmJantjeKlaasMargjeTeunis


Traditional(Latin form)AdrianaBernardusChristinaEduardElisabethFranciscusGeertruidaHubertusJohannaKrijnMaria


Frisian namesAafkeBaukeDouweFroukjeJoppeJitskeJelleMennoSietskeOnnoWietskeWiebe


Pre-modern names (Dutch, Western)AnniekAnitaCarlaFrankJochemJeroenLindaMarkMarloesPaulSuzanne


Foreign names(English)AmandaDennisDannyChantalHenryIsabellaKimKevinMelissaRicardoSamanthaStephen


Short names(modern, Dutch,Western, Skand)AnneBartEvaGijsLisaKajNielsSanneSofieTim


Religion

Short names - Religion

None

Protestant

Catholic


Old testamenthistory, culture,natureDaniëlEstherJudithNaomiWillemijnDiederikFrederiekeMauritsIrisFleurJasmijn


Religion

Income

Lowest

Highest


Arabic and Turkish names

FatimaMohamedNouraHamzaSaraYassinFatmaMustafaHaticeMehmet


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


10 profilesTraditional – Latin form

Traditional – Dutch

Transitional, Traditional Dutch to pre-modernTransitional, Traditional Latin form to foreign

Pre-modern

Foreign

Short

Elite

Arabic-Turkish

Frisian


Example profileTraditional – Latin form

Traditional – Latin formTraditional – DutchFrisian namesPre-modern namesForeign namesShort namesNames from OT, history, culture, natureArabic and Turkish namesother

%3718

18

12660

12


Naming map of the

Netherlands

short

foreign

Frisian

traditional Latin

elite

ArabTurkish

pre-modern

traditional Dutch

>foreign


Education level

EU constitution votes


Education level

Educational level

Highest

Lowest


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


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


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


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


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!


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

Analyses of first names in The Netherlands: full population studies Gerrit Bloothooft Institute of...

Documents

Transcript of Analyses of first names in The Netherlands: full population studies Gerrit Bloothooft Institute of...