1. FP7 – EuroBroadMap Analysis of Question B
EUROBROADMAP PROJECT - FP7-SHS
Visions of Europe in the World
Claude GRASLAND (CNRS)
Timothée GIRAUD (CIST)
Laurent BEAUGUITTE (CNRS)

2. A Worldwide survey 18 Countries 43 Cities 9341 Students Arts Business
Politics
Engineer
Health
Social Science

3. The concepts of knowledge and asymetry
A. GENERAL FRAMEWORK
EUROBROADMAP PROJECT - FP7-SHS
Visions of Europe in the World
The concepts of knowledge and asymetry

5. General framework for analysis
DO NOT MENTION …
WOULD LIKE TO LIVE IN …
WOULD NOT LIKE TO LIVE IN …
LIVE OR HAS LIVED IN ..

6. (1) Analysis of countries quoted
DO NOT MENTION …
WOULD LIKE TO LIVE IN …
WOULD NOT LIKE TO LIVE IN …
LIVE OR HAS LIVED IN ..

7. (2) Analysis of countries knowledge or ignorance
DO NOT MENTION …
WOULD LIKE TO LIVE IN …
WOULD NOT LIKE TO LIVE IN …
LIVE OR HAS LIVED IN ..

8. (3) Analysis of countries appreciation by origin
DO NOT MENTION …
WOULD LIKE TO LIVE IN …
WOULD NOT LIKE TO LIVE IN …
LIVE OR HAS LIVED IN ..

9. Knowledge: Example of Tunisia

10. Knowledge: Comments on Tunisia
France is quoted in 1st rank by 79% of Tunisian, which is more than usual (51%)
USA are quoted in 2nd rank by 53% of Tunisian, which is a bit less than usual (60%)
Neighbours countries are over-mentionned ( Algeria, Lybia, Egypt, Italy …)
Iraq and Iran are less mentionned than usual (which means less negatively …)
Etc…

11. Knowledge: general results

12. Knowledge: General results

13. Asymmetry: Example of Tunisia

14. Asymmetry: Comments on Tunisia
Some countries are more positive for Tunisian : ex. France (+0.95 instead of +0.67)
Some countries are less positive for Tunisian : ex. UK (+0.31 instead of +0.77)
Some countries are more negative for Tunisian : ex. Morocco (-0.36 instead of -0.05)
Some countries are less negative for Tunisian : ex. Iran (-0.56 instead of -0.95)
Some countries reverse from negative to positive : ex. Turkey (+0.83 instead of -0.37) or Syria (-0.95 to +0.57)

15. Asymmetry: General results

16. Asymmetry: General results

17. K & A : Example of Tunisia

18. K & A: Example of Tunisia

19. Legend of the maps
In which countries would you like to live in the near future ?
The SIZE of circles should be proportional to the degree of KNOWLEDGE by students.
The COLOUR of circles should be related to ASYMMETRY. With opposite colours for LIKE and DISLIKE.

20. WORLD MAPS (Tunisia)

21. ZOOM MAPS (Tunisia)

22. ANAMORPHOSIS
From …
The SIZE of countries should be proportional to the degree of KNOWLEDGE by students.
The COLOUR of countries should be related to ASYMMETRY. With opposite colours for LIKE and DISLIKE.
… to

23. ANAMORPHOSIS (Tunisia)

24. B. COMPARISON OF MENTAL MAPS OF STUDENTS
EUROBROADMAP PROJECT - FP7-SHS
Visions of Europe in the World
Empirical analysis of states where students would like or not like to live

25. OLD MEMBER STATES

26. France

27. Belgium

28. Sweden

29. Portugal

30. NEW MEMBER STATES

31. Malta

32. Hungary

33. Romania

34. EASTERN NEIGHBORHOOD

35. Moldova

36. Turkey

37. Azerbaïdjan

38. Russia

39. SOUTHERN NEIGHBORHOOD

40. Egypt

41. Tunisia

42. Senegal

43. Cameroon

44. BIG EMERGING COUNTRIES

45. Brazil

46. India

47. China

48. SYNTHESIS
???

49. Typology of world states

50. Correlation between positive visions (LIKE)
Cameroon
&
Senegal
Egypt &
Tunisia
CHINA
INDIA
EUROPEAN UNION
RUSSIA
BRAZIL

51. Correlation between negative visions (NOT LIKE)
Cameroon
&
Senegal
CHINA
BRAZIL
Turkey
WESTERN
EUROPE
Egypt &
Tunisia
EASTERN
EUROPE
&
RUSSIA
INDIA

52. C. MODELIZATION OF STUDENTS PREFERENCES
EUROBROADMAP PROJECT - FP7-SHS
Visions of Europe in the World
Which factors can explain the countries where students would LIKE to live ?

53. Qualitative assumptions
Because they have heard of the country
Because it is rich
Because it is open to migration
Because it is not far
Because it has historical linkage
Because it has a common language …

54. Quantitative formalisation

55. (1) Size effect
Hypothesis: BIG countries are more attractive than SMALL countries. 83 2

56. (1) Size effect
H1 is true for all countries with very high level of significance

57. (2) Economic effect
Hypothesis: RICH countries are more attractive than POOR countries. 59 20

58. (2) Economic effect
H2 is true for all countries with very high level of significance

59. (3) Density effect (migratory policy)
Hypothesis: EMPTY countries (open to migration) are more attractive than FULL countries (closed to migration) 48 16

60. (3) Density effect
H3 is true for all countries but significant only for 6 out of 18

61. (4) Distance effect
Hypothesis: NEAR countries are more attractive than REMOTE countries
188 47

62. (4) Distance effect
H5 is true for all countries but not very significant for France and not at all for China

63. (5) Neighbourhood effect
Hypothesis: CONTIGUOUS countries are more attractive than NON CONTIGUOUS 15 2

64. (5) Neighbourhood effect
H5 is true only for France and Azerbaïdjan. The reverse hypothesis is true for Tunisia, Egypt, Cameroon and Russia.

65. (6) Language effect
Hypothesis: countries with COMMON LANGUAGE are more attractive than others 11 3

66. (6) Language effect
H6 is true and significant for the majority of countries. But it is sometime not significant and is reversed in the case of Azerbaijan

67. (7) Historical effect
Hypothesis: countries with former COLONIAL RELATIONS are more attractive than others 59
188

68. (7) Historical effect
H6 is true for some countries (India, Malta, Tunisia …) but reversed for others (Azerbaïdjan, Cameroon, Senegal).

69. D. FURTHER STEPS … EUROBROADMAP PROJECT - FP7-SHS
Visions of Europe in the World

70. 1. Benchmarking States / Cities

71. 1. Benchmarking States / Cities

72. 2. Countries focus (ex. USA)

73. 2. Countries focus (ex.USA)