FIM Global Survey on Orchestras presentation and results

FIM Global Survey on Orchestras presentation and results Colin Marchika, EHESS Scientific direction: P. M. Menger

Introduction A global approch 1) Multiple Correspondence Analysis (MCA) discriminating factors graphic representation 2) Hierarchical Clustering creating classes 3) Discussing the meaning of the classes

Sample description (1) • From 231 orchestras to 105 usefull questionnaires Date of fundation Geographical origin 95 90, 5 % N. R. Germany 32 30, 5 % Pre 1900 30 29, 7 % UK 15 14, 3 % 1900 – 1939 23 22, 7 % Spain 13 12, 4 % 1940 – 1969 25 24, 8 % USA 8 7, 6 % 1970 and after 23 22, 7 % Canada 5 4, 8 % Australia 2 2, 0 % Europe : 4

Sample description (2) Size (in number of jobs FTE) N. R. 20 Less than 70 33 From 70 to 90 Over 90 Relation to an institution Independent 50 47, 6 % 38, 8 % Opera house 32 30, 5 % 31 36, 5 % Broadcasting body 10 9, 5 % 21 24, 7 % others 13 12, 4 %

MCA - active variables • Variables for managing human ressources : • Wages-related variables : • Number of wage categories • Differential in wages between soloists and tutti players • Seniority – wages increase • Audition-related variables : • Proportion of orchestra membres on recruitment audition panels • Proportion of union representatives on recruitment audition panels • Existence of re-audition • Organisational variables : • Working hours : • Maximum number of working hours per day • Maximum number of working hours per month • Extra-orchestra activities : • Autorisation for other occupationnal activities • Incentives for individual activities

MCA – axis description 3 main axes • AXIS 1 : size of orchestras • wage increase (low vs high) • Number of hours per day, per month • Re-auditionning + budget, box office vs funding • AXIS 2 : personnal commitment • wage increase (very high), differentiation of the soloist • Involvment of musicians on audition panels • AXIS 3 : commitment by salary vs commitment to the life of the orchestra

MCA – factor plan

MCA – nationalities in factor plan

Clustering : 5 classes or 3 classes Very small orchestras small Small orchestras « old » orchestras old Large orchestras (budget) large Large orchestras ( « hardworkers » )

Clustering : classes in factor plan

Clustering : describing the classes 1) with the help of actives MCA variables : managing human ressources & organisational variables 2)with the help of illustratives MCA variables : Size (budget, number of jobs, number of representation, institution, etc. 3) with all the others variables from the survey questionnaire : recordings, tours & travels, representation at work place, health and safety at work, etc.