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European cultural statistics in a comparative perspective: index of economic and social condition of culture for the EU countries

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Abstract

In the article, we present the construction of an index of economic and social condition of culture using datasets of Eurostat’s Cultural Statistics Pocketbooks from 2007 and 2011 and Eurostat’s COFOG data. The datasets allow us a broad perspective over a set of more than 200 variables in 12 domains for the EU-27 member states. Using high-dimensionally adjusted factor analysis (Metropolis–Hastings Robbins–Monro algorithm), we construct an index and determine a set of its several dimensions (as seen from the cultural statistics viewpoint). Using cluster analysis, we determine the general similarities and differences among the analysed countries and show several broadly different groupings that roughly, but not exclusively follow the divide speculated in some previous studies. The analysis therefore brings a novel and statistically developed tool to empirically follow the changes in the economic and social condition of culture from the viewpoint of cultural statistics, while the clustering of models has important consequences for empirical cultural policy and has to be verified in future studies.

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Notes

  1. In the analysis, only the year 2009 is included among the years of the financial crisis. For this reason, our testing of the hypothesis can of course provide only a partial answer to the effects of financial crisis on the positions of individual countries—in the next years with more data, more information on this topic would hopefully be provided.

  2. Economic indicators on cultural sectors can be found using harmonised SBS (Structural Business Statistics) data collected by Eurostat (see Eurostat 2007, 2011). Among the included sectors for the cultural industries are: publishing (for both 2005 and 2009); motion picture, video and television programme production, sound recording and music publishing activities (for both 2005 and 2009); programming and broadcasting activities (for 2009).

  3. Data on cultural employment based on the EU-LFS were calculated using a matrix crossing cultural economic activities (‘sectors’) with cultural occupations. This method counts all jobs in cultural activities (classified by NACE) and all cultural occupations (classified by ISCO) found in other (non-cultural) sectors. This matrix is based on the NACE Rev.1.1 and ISCO-88 classifications (Eurostat 2011).

  4. For public financing in culture, we use level of public budget per capita. This usage is justified by some previous analyses on international level (e.g. Čopič et al. 2013). The data for the public funding of culture are taken from the COFOG Eurostat's database which has also two additional measures of government funding for cultural services: percentage of GDP and percentage of total government expenditure. As there is much less variation in these two variables among countries (cross-section dimension), we use only level of public budget per capita as a variable in our index. Most of the results have been tested also with the usage of two other measures and have been corroborated.

  5. The notation »reversely signed« means that the best countries in this dimension score worst on the index and vice versa. The index was therefore transformed by subtracting all the estimated values from 100.

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Acknowledgements

For the comments, we kindly thank Marilena Vecco, Marc Verboord, Tjaša Bartolj and the participants at conferences and symposiums of Eurasian Business and Economic Society (EBES) Istanbul 2014, EBES Barcelona 2014, Association for Cultural Economics International (ACEI) Montreal 2014, International Conference on Cultural Policy Research (ICCPR) Hildesheim 2014, European Workshop on Applied Cultural Economics (EWACE) Vienna 2015 and Economic and Business Review (EBR) Ljubljana 2015. All remaining errors are our own.

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  1. Faculty of Economics, University of Ljubljana and Institute for Economic Research, Ljubljana, Kardeljeva Ploščad 17, 1000, Ljubljana, Slovenia

    Andrej Srakar & Miroslav Verbič

  2. Faculty of Social Sciences, University of Ljubljana, Kardeljeva Ploščad 5, 1000, Ljubljana, Slovenia

    Vesna Čopič

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  1. Andrej Srakar
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Appendix

Appendix

See Tables 10, 11 and 12.

Table 10 Definitions of included variables
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Table 11 Factor analysis, MHRM algorithm, year 2005
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Table 12 Factor analysis, MHRM algorithm, year 2009.
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Srakar, A., Čopič, V. & Verbič, M. European cultural statistics in a comparative perspective: index of economic and social condition of culture for the EU countries. J Cult Econ 42, 163–199 (2018). https://doi.org/10.1007/s10824-017-9312-2

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