Artists' working and living conditions have been the subject of several studies (e.g. Alper and Wassall, 2006, Menger, 2006). This paper will build on this literature but take a new approach in explaining artists’ behavior and living conditions. A latent class analysis has been conducted, identifying different segments of artists, each of which are characterized by a different pattern of answers that reveals a particular working and living condition. The development of the latent class analysis includes a membership function, which is estimated through a logistic regression, which allows to predict the probability for an individual to belong to each latent class, given his/her socio-economic characteristics. The dataset consists of a combination of register data from Statistics Denmark and data collected in a survey to 3,028 visual artists in Denmark. Based on the personal identification number the two datasets have been merged. The results show that neither an artistic education, nor the gender differ significantly among latent classes. The visual artists can be segmented into 6 classes: Aspiring artists (10%), poor professional artists (19%), workers related to arts (13%), subsidized artists (26%), arts as a hobby or secondary activity (18%) and devoted to arts (14%). In this way, a latent class analysis can give a more nuanced picture of different groups of artists and their working and living conditions.
|Status||Udgivet - 2018|
|Begivenhed||The 20th International Conference on Cultural Economics. ACEI 2018 - RMIT University, Melbourne, Australien|
Varighed: 26 jun. 2018 → 29 jun. 2018
Konferencens nummer: 20
|Konference||The 20th International Conference on Cultural Economics. ACEI 2018|
|Periode||26/06/2018 → 29/06/2018|