Abstract
The purpose of this article was twofold. Firstly, to investigate the heterogeneity among artists as an occupational category and secondly, to define arts as a profession and thereby to make a distinction between professional artists and amateurs. Artists' income and working conditions have been the subject of several studies, and many different sampling criteria have been used. Scholars have not yet achieved consensus on who should be included in the profession. In this article, we make an innovative contribution to this conversation. By applying a finite mixture model, which combines latent profile and latent class analysis, we have been able to identify different segments of artists in terms of professionalism. Each of these mutually exclusive classes is characterized by a particular income and working situation. We also include a membership function, estimated through a logistic regression, which allows prediction of the probability that an individual will belong to each class, given his/her socioeconomic characteristics. The subject of our study is Danish visual artists. The dataset consists of a combination of register data from Statistics Denmark and data collected from a questionnaire survey with 892 respondents. Based on the artists’ civil registration numbers, the two sources have been merged into a unique dataset. Our finite mixture model shows the heterogeneity among artists. Combined with a theoretically definition of arts as a profession, our research propose a distinction between professional artists and amateurs that cuts across categories used in prior literature. The results can be beneficial to cultural policy.
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Notes
In Bakhshi et al. (2013), the model is further developed into what is called dynamic mapping.
Based on this view, the reputation of the artists among the general public cannot be seen as an indicator of high professional standards.
179 individuals have not reported how their income is divided between jobs with more or less connection to the arts and/or how their working time is divided between these jobs. Because of these missing values, we have excluded them from the analysis.
Statistics Denmark is a governmental organization collecting and maintaining statistical data on Danish society.
In Denmark, every resident has a Central Person Register number.
This variable comes from information from the tax authorities.
This includes Danish students’ Grants and Loans Scheme, sick leave benefits, unemployment benefits, and cash benefits. Pensions are excluded from this income category.
For membership of the Danish Artists’ Society (Kunstnersamfundet), a jury assesses the quality of a candidate's artistic production and qualifications. Membership therefore depends solely on the person's artistic skills and the quality of the artistic works.
Exchange rate: 1 EUR = 7.5 DKK (June 28, 2020).
Notice that, for each class, the sum of the percentage of income equals 100. For some classes, the sum of the percentage of working time does not equal 100: this is because Stata treats each individual working time indicator as independent of the other two in the set. Even if this seems to violate the conditional independence assumption, in our estimation it has been relaxed by allowing the error terms to be correlated. In Fig. 2, for visualization purposes, we have normalized the percentages such that all the vertical bars add up to 100.
Many contingency tables (not reported here) are built crossing the assigned class with the variables used, in order to analyze the main characteristics of each class. Concerning the continuous variables of income and age, we have converted these into categorical variables with 11 categories for the income variable (from less than 100.000 DKK to over one million DKK, with a range of 100.000 DKK for each level of income), and six categories for the age variable (from the category 18–29 to over 67, with a range of 9 years for each level of age).
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The project has been financially supported by Ny Carlsbergfondet, Bikubenfonden and the Danish Arts Council.
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Appendix: The representativeness of the research sample
Appendix: The representativeness of the research sample
Variable | Group | N | % of sample | % of population | Test |
|---|---|---|---|---|---|
Gender | Male | 386 | 43.27 | 46.90 | \(\chi^{2}\) = 4.72; df = 1; p value = 0.029 |
Female | 506 | 56.73 | 53.10 | ||
Received grants | Yes | 234 | 26.23 | 24.21 | \(\chi^{2}\) = 1.99; df = 1; p value = 0.158 |
No | 658 | 73.77 | 75.79 | ||
Arts education | Yes | 317 | 35.45 | 38.65 | \(\chi^{2}\) = 3.65; df = 1; p value = 0.056 |
No | 575 | 64.46 | 61.35 | ||
Members of Artists society | Yes | 190 | 21.30 | 20.31 | \(\chi^{2}\) = 0.54; df = 1; p value = 0.462 |
No | 702 | 78.70 | 79.69 | ||
Retired | Yes | 227 | 25.45 | 27.18 | \(\chi^{2}\) = 1.35; df = 1; p value = 0.245 |
No | 665 | 74.55 | 72.82 |
Variable | Sample | Population | Test |
|---|---|---|---|
Income | Mean = 244,514.2 SD = 307,100.6 | Mean = 243,780.4 SD = 449,872 | z = 0.0487 p value = 0.9612 |
Age | Mean = 52.05 SD = 14.86 | Mean = 50.88 SD = 16.19 | z = 2.1502 p value = 0.0315 |
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Baldin, A., Bille, T. Who is an artist? Heterogeneity and professionalism among visual artists. J Cult Econ 45, 527–556 (2021). https://doi.org/10.1007/s10824-020-09400-5
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DOI: https://doi.org/10.1007/s10824-020-09400-5