Appendix A Sample and Variables 270 Table A.1 Dictionary of variables based on figures of our 2005–07 comparative study Name of variable Definition Measurement method Average Range Observations value Artistic policy Alignment with classics % of performances of group 1 50 0–100 Group 1 works account for works over 50% of all performances Most performed periods % of performances of operas 78 23–100 composed in the 19th century and first half of the 20th century Modernity Programming of % of performances of operas 7 0–34 Lowest: the Châtelet, contemporary composed after 1950 Lausanne, Leipzig and operas Helsinki; highest: Vienna, Venice Fenice, Vancouver and Turin Regio Fame of conductors Average number of houses with 2.12 1.00–3.50 Total opera houses with guest conductors guest conductor for opera X, Fame of directors As for conductors 2.58 1.00–6.50 divided by the number of Fame of soloists As for conductors guest conductors. Same procedure for directors and singers. Vienna, Zurich, Barcelona and Munich head the list on these criteria Production policy Number of productions 14 4–48 Total number of 90 20–244 20 in Salt Lake City, 22 in San performances Diego, 244 at the Vienna 271 Staatsoper 272 Table A.1 (Continued) Name of variable Definition Measurement method Average Range Observations value Number of performances 25 0–109 of new productions Number of performances 58 0–204 of old productions Alternation of works Average number of works 2.1 1–4 4 in Vienna, Mannheim, performed in a week Hamburg Number of performances 5 0–64 64forOpéradeParis,52for of co-productions New York City Opera Number of performances 3 0–49 49 for Barcelona, 36 for Turin of rented productions Regio Number of touring 5 1–12 For houses that take venues productions on tour: 12 for Aarhus Environmental conditions Physical capacity Number of seats 1,820 481–3,995 Lowest: Heidelberg; in main highest: Metropolitan, New auditorium York Density of opera on offer Opera on offer Number of inhabitants per 14 0.6–110 0.6 in Santa Fe, between 2 and in the local ticket on sale 4 in the German-speaking urban area area, approximately 20 in North America, 110 in Tokyo Operatic tradition Length and % of operas composed in the – 1.5–47 47% in Italy, 32% in the degree of zone German-speaking area, 10% establishment in France, 1.5% in the USA (rooting) of the operatic tradition in the zone Average per capita gross Per capital gross national 36,460 ¤7,110–¤54,930/ Lowest: Poland; national income income (GNI) year highest: Switzerland Results Financial Autonomy Financial Box office income as 0.26 0.06–0.46 Lowest: Ostrava; resources related proportion of total budget highest: Zurich to activities Seat occupancy rate Average Tickets sold as proportion of 0.84 0.50–0.95 Lowest: Athens; occupancy rate tickets available highest: La Monnaie, for the main Brussels auditorium Average cost per ticket Total cost divided by number 288 98–806 Lowest: Montreal; of tickets sold highest: Athens Average ticket price Box office income divided by 60.50 13–173 Lowest: Warsaw; number of tickets sold highest: Zurich Average cost per Total cost divided by 377,000 77,000–828,000 Lowest: Tallinn, performance the number of opera Highest: La Scala, Milan performance 273 274 Table A.2 Opera house sample Aarhus Helsinki Prague Statni Amsterdam Houston Rome Antwerpen Köln Rostov Athens Lausanne Salt Lake City Barcelona Leeds Opera North Salzburg Bayreuth Leipzig San Diego Berlin Deutsche London ENO San Francisco Berlin Komische London Royal Opera Santa Fe Berlin Staatsoper Los Angeles Seattle Bregenz Lyon Stuttgart Bruxelles Monnaie Madrid Real Tallinn Cardiff WNO Mannheim Tokyo NNT Chicago Mainz Toronto Copenhagen Miami Turin Regio Dallas Milan Scala Vancouver Detroit Montréal Warsaw Wielki Dresden Munich Staatsoper Vienna Staatsoper Düsseldorf/Duisburg New York City Metropolitan Vienna Volksoper Frankfort New York City Opera Washington Geneva Nürnberg Zuid Graz Oslo Zurich Hamburg Ostrava Heidelberg Paris National Opera Appendix B The Statistical Analysis of Opera Achievements This appendix aims to explain the performances of opera houses with regard to two criteria: financial autonomy and the seat occupancy rate. Quantifications of artistic policies, production policies and key environmental factors will be proposed. It becomes clear that it is these factors – auditorium capacity, den- sity of opera on offer and operatic tradition – that essentially explain opera achievements. 1. Analysis method A model is constructed by aggregating available data in the form of factors char- acteristic of the principal policies of opera houses and their environments. These factors are then used to explain the achievements of opera houses. The sample analysed consists of 62 opera houses listed in Appendix A. It can- not be considered representative in the statistical sense of the term, since to the best of our knowledge, the characteristics of the population of opera houses are not reported anywhere. Nevertheless, it does correspond to the geographical dis- tribution of opera houses, strongly dominated by North America and Western Europe, Germany and the German-speaking zone in particular. Almost all the North American operas playing more than 20 times a year have been selected in the sample. Of the total, these 14 houses attract 4.3 million spectators per season. Then, a random sample of large German-speaking operas was selected on the basis of production volume and audience. It is composed of 19 opera houses ranging from 65 to 244 performances a season, and attracting the same amount of spectators as North American operas. For the rest of the world houses, mainly Western European ones, we used the available data from houses perform- ing 21–207 times a season before an equivalent audience. This sample correctly represents large houses attracting around 75 per cent of the total lyric art audi- ence. The results do not apply to small houses (less than 20 performances a year in North America, less than 60 in Europe). Festivals have been removed from the analysis. The data collected on 62 opera houses in the sample are used to construct variables characteristic of the concepts of policy and the environment. Artistic policy is represented by two factors characterizing the choice of works and the choice of guest artists: conductors, directors and soloists. For the programming, the following variables are aggregated using a factorial anal- ysis: alignment with the classics, periods most frequently performed and the modernity of works. The Cronbach alpha for this factor, named “programming conformity”, is 0.66. The factor named “fame of guest artists” is constructed by 275 276 Appendix B Table B.1 Conductors’ fame score House A House B Adam Fischer 5 Alexandro de Marchi 2 Alexander Joel 3 Andoli Levin 1 Bertrand de Billy 1 Baldo Podic 1 Piers Maxim 1 aggregating three scores reflecting the fame of conductors, directors and soloists. These scores are calculated using the index of conformity devised by Di Maggio and Stenberg (1985) to analyse theatre programming. They are computed by adding the number of opera houses in which each artist has performed or directed during the season, and calculating a “fame score” for each house and each cate- gory of artist. For the example shown in Table B.1, if opera houses A and B have invited the following conductors during the season, and in the course of that season those conductors have performed in the number of houses shown to the right of their name, house A’s score is (5 + 3 + 1 + 1)/4 or 2.25, while house B’s score is (2 + 1 + 1)/3 = 1. 33. The same process is applied for all opera houses and all three categories of artist. The Cronbach alpha for the “fame of guest artists” factor combining the above three scores is 0.65. This score may contain certain biases. Some opera houses (such as the New York Metropolitan) have resident conductors who direct many works locally and rarely perform elsewhere, and their score will be lower than the score for opera houses that practically always use guest conductors. This phenomenon is less marked for directors and soloists. Opera houses involved in co-productions tend to invite well-known artists, particularly directors, and have high fame scores. This is confirmed by the Pearson correlation coefficient between the number of joint productions and the fame of artists, which is significant at the 0.01 level. The production policy is represented by two factors: the volume of operatic activities and the volume of non-operatic activities. The first of these factors combines, for each season, the number of opera productions, the number of per- formances, the number of performances of productions revived from previous seasons and the average number of different works staged in a week. Its Cronbach alpha is 0.90. The second factor associates the number of ballets and concerts in each season, with an alpha score of 0.65. A third factor combining network activ- ities such as purchases and rental of productions, co-productions and touring productions was rejected due to its low alpha score. Environmental conditions are represented by a factor named “potential opera on offer” and a variable in the form of per capita gross national income. The potential opera on offer results from factor analysis applied to three variables: the physical or seating capacity of the principal auditorium, the density of opera availability as measured by the number of inhabitants in the urban area divided by the number of opera tickets available in the same geographic area, and the local operatic tradition, represented by the percentage of operatic works com- posed in the country or region.