Michelle vonAhn, Ruth Lupton and Dick Wiggins

1
Population, language, ethnicity and
socio-economic aspects of education

• Michelle vonAhn, Ruth Lupton and Dick Wiggins

2
Aims of the fellowship

• Analyse and map distribution of language across
  London
• What issues does this raise?
• Conduct some preliminary analysis between
  language and attainment
• Analyse the relationship between language,
  ethnicity and socio-economic indicators
• Provide guidance and training on the ways
  language data may be used with other data to
  answer social and educational research questions

3
A big issue in London
4
Updating Multilingual Capital
Published in 2000, using pupil data from 1999 to
identify and map languages in London
5
Pupil data
But data collection variability makes comparison
difficult
6
Language data ambiguity
7
Ambiguous language
8
Data inconsistency

• Some languages have variants, which are not
  consistently used within a local authority or
  across London, e.g.

9
Language classification
10
Geography

• Percentage comparisons are problematic due to
  data capture variability
• Comparative counts of boroughs not suitable due
  to differences in size
• Wards and postcodes also differ in population
  size
• New statistical geographies - Super Output Areas

11
LSOA map
12
MSOA map
13
English and Believed to be English
14
English and Believed to be English
15
Choosing a scale
16
Equal counts

• Aims for equal numbers of MSOAs in each category
• Hides extreme values

17
Equal ranges

• Aims to divide the whole range into equal
  segments
• Extreme values dominate

18
Natural break

• Elegantly captures both intensity and
  distribution
• Complex mathematics not made explicit by MapInfo,
  and therefore difficult to explain to non-expert
  viewers

19
Quantiles (or in this case, Quintiles!)

• Takes total count of pupils and creates target
  totals for each category so each category has
  about 20 of all pupils
• A compromise that captures intensity and
  distribution, relatively easy to explain

20
Patterns of clustering and dispersal
21
South Asian languages
22
Bengali/Sylheti, 1999
23
Bengali
London 46,681
24
Hindi/Urdu, 1999
25
Urdu
London 29,354
26
Panjabi
London 20,998
London 20,998
27
Gujarati
London 19,572
28
Tamil
London 16,386
29
Persian/Farsi
London 6,959
30
Chinese
London 5,905
31
Migration patterns over time

• Annual data could show change (if data is
  collected in a robust way)
• Established or magnet communities
• Recent arrivals

32
Turkish, 1999
33
Turkish
London 16,778
34
Greek
London 3,336
35
Polish
London 11,035
36
Lithuanian
London 2,974
37
Somali
London 27,126
38
Somali numbers have increased, but their
distribution has also become more dispersed
39
Language is not always enough

• French speakers
• 17 White
• 57 Black
• 26 Other
• Arabic speakers
• 57 Other
• 15 Black
• 10 Mixed
• 9 White
• 8 Asian

• Spanish speakers
• 35 White
• 4 Black
• 61 Other
• Portuguese speakers
• 54 White
• 19 Black
• 27 Other

40
French by ethnic group
London 13,020
41
French has an east-west distribution by ethnic
group
smaller numbers
42
Spanish by ethnic group
London 8,647
43
White Spanish speakers are more likely to be from
Europe, while Other Spanish are probably from
Central and Latin America
44
Language, ethnicity and attainment

• How are ethnicity and language related? Can we
  create useful ethnicity/language categories?
• How is language related to attainment? Does
  ethnicity/ language tell us more than ethnicity
  on its own?

45
Average points at Key Stage 2 by Ethnic Group
(London 2008)
46
Linguistic Breakdown for Selected Lower Attaining
Groups
Bangladeshi
Black other
47
Linguistic Breakdown for Selected Lower Attaining
Groups
Black African
White other
48
Diversity in the Black African group

• Lower attaining

• Higher attaining

49
Yoruba
London 13,961
50
Igbo
London 2,837
51
Akan/Twi/Fante
London 8,117
52
Diversity in the white other group

• Higher attaining

• Lower attaining

53
Next stages

• How are ethnicity/language categories related to
  socio-economic status?
• Explore FSM, IDACI, using London ASC
• Matching to local authority data (e.g. housing
  benefits, Council tax band), for a case study
  borough (Newham)
• How is the attainment of ethno-linguistic groups
  related to indicators of socio-economic status?

54
Data matching
Attainment and language data
Council Tax
GP register of patients
GP register of patients
LLPG addresses
Housing benefit
Electoral Register
PLASC (FSM)
55
Consultation

• Local authority views of the practical, legal,
  technical and ethical issues for data matching
  within and across authorities
• Identifying practical uses of matched data
• Goal to prepare guidance for other data users

56

• Michelle vonAhn
• Email michelle.von.ahn_at_newham.gov.uk
• Tel [phone]
• Ruth Lupton
• Email r.lupton_at_lse.ac.uk
• Tel [phone]