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Performing arts attendance in Taiwan: who and how often?

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  • Wei-Jen Wen
  • Tsung-Chi Cheng

Abstract

This paper uses the logistic regression model and the count data model to identify the determinants of the likelihood and frequency of attendance at performing arts events in Taiwan. Using data collected from a telephone survey, we find that education and income, being determinants of the likelihood of attendance, are not significant in determining frequency of attendance. On the other hand, whereas members of performing arts groups attend more frequently than non-members, holding a membership or not does not seem to affect the likelihood of attendance. Personal relationships with the performing arts industry make one more likely to attend as well as to attend more frequently. Those who more often watch or listen to performing arts programs through the media or pay more attention to performing arts-related information are also likely to participate in live events and participate frequently. Lastly, age dose matter. We further find evidence of both life cycle and human capital effects in performing arts attendance. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Wei-Jen Wen & Tsung-Chi Cheng, 2013. "Performing arts attendance in Taiwan: who and how often?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(2), pages 309-325, May.
    • Handle: RePEc:kap:jculte:v:37:y:2013:i:2:p:309-325
    DOI: 10.1007/s10824-012-9180-8
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    References listed on IDEAS

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    Cited by:

    1. Karol J. Borowiecki & Catarina Marvao, 2015. "Dance Participation and Attendance in Denmark," ACEI Working Paper Series AWP-01-2015, Association for Cultural Economics International, revised Apr 2015.
    2. Yi, Jisu & Lee, Youseok & Suh, Jungmin & Kim, Sang-Hoon, 2022. "Psychological determinants of non-attendees’ resistance toward performing arts," Journal of Business Research, Elsevier, vol. 149(C), pages 690-699.
    3. Marta Zieba, 2017. "Cultural participation of tourists – Evidence from travel habits of Austrian residents," Tourism Economics, , vol. 23(2), pages 295-315, March.
    4. Pascal Courty & Fenghua Zhang, 2018. "Cultural participation in major Chinese cities," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 42(4), pages 543-592, November.
    5. Sara Suarez-Fernandez & Juan Prieto-Rodriguez & Maria Jose Perez-Villadoniga, 2020. "The changing role of education as we move from popular to highbrow culture," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(2), pages 189-212, June.
    6. Hahm, Joon Beom & Byon, Kevin K. & Hyun, Yeon Ah & Hahm, Jeongbeom, 2022. "The show must go on: The mediating role of self-assessment in the relationship between performers’ technology acceptance and satisfaction level with remote performances in Korea during the COVID-19 pa," Technology in Society, Elsevier, vol. 68(C).
    7. Junlong Wu & Keshen Jiang & Chaoqing Yuan, 2019. "Determinants of demand for traditional Chinese opera," Empirical Economics, Springer, vol. 57(6), pages 2129-2148, December.
    8. Chu-Chia Lin & Tsung-Chi Cheng & Shu-Chen Wang, 2014. "Measuring Subjective Well-Being in Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 116(1), pages 17-45, March.
    9. Kirstin Hallmann & Cristina Muñiz Artime & Christoph Breuer & Sören Dallmeyer & Magnus Metz, 2017. "Leisure participation: modelling the decision to engage in sports and culture," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(4), pages 467-487, November.
    10. Geng Chen & Pei Tang, 2021. "Similar but special: an econometric analysis of live performing arts attendance in mainland China," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(3), pages 459-490, September.
    11. Andrey David Ramos Ramírez & Nora Elena Espinal Monsalve, 2020. "Aplicación de modelos de elección discreta regularizados para el análisis de los determinantes del consumo cultural en Colombia: el caso de los bienes del patrimonio cultural [Aplicação de modelos reg," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 30(1), pages 37-68, January-A.
    12. Cristina Muñiz & Plácido Rodríguez & María José Suárez, 2017. "Participation in cultural activities: specification issues," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(1), pages 71-93, February.
    13. Marilena Vecco & Andrej Srakar & Michele Piazzai, 2016. "Visitor Attitudes to Deaccessioning in Italian Public Museums: An Econometric Analysis," ACEI Working Paper Series AWP-02-2016, Association for Cultural Economics International, revised Feb 2016.
    14. Michael Getzner, 2020. "Spatially Disaggregated Cultural Consumption: Empirical Evidence of Cultural Sustainability from Austria," Sustainability, MDPI, vol. 12(23), pages 1-19, December.
    15. Jesús Manuel De Sancha-Navarro & Juan Lara-Rubio & María Dolores Oliver-Alfonso & Luis Palma-Martos, 2021. "Cultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approach," Sustainability, MDPI, vol. 13(5), pages 1-15, March.

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    More about this item

    Keywords

    Count data model; Logistic regression model; Performing arts attendance; Recursive multivariate probit model; C35; D12; Z11;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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