Analysing the Symbolic Economy with Pierre Bourdieu: The World of Crafts

Abstract

The world of crafts belongs to the ‘economy of symbolic goods’ identified by Pierre Bourdieu. The theoretical aim of this article is to show that Bourdieu’s analytical framework and methods are particularly useful to comprehend the structure of the diverse French craft economy and to understand the different ways in which symbolic and economic dimensions combine. First, the use of quantitative and qualitative methods (especially geometric data analysis) enables to highlight the structure of the world of crafts which remains divided between a traditional pole and a contemporary pole. Then, the apparent opposition between economic and symbolic aspects of crafts, represented by the two poles, is qualified by pointing out the different forms of ‘denial of the economy’ adopted by artist-craftsmen according to their economic and social characteristics. Finally, the article shows how economic and symbolic dimensions combine through the symbolic construction of the economic value of craft products.

Keywords:

• Bourdieu
• crafts
• geometric data analysis
• symbolic goods
• value

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 The diversity of the ‘craft’ economy can be explained by the history of French métiers d’art: thanks to a study of institutional archives, we previously showed (Jourdain, 2014) that the French crafts emerged at two different periods. They were first born during the Industrial Revolution, partly influenced by the British arts and crafts movement but also especially by reformers worried about the growing industrial standardisation, to designate the know-how of the workers employed in art industries or small craft businesses. They reappeared after the Second World War with the ‘return to nature’ social phenomenon during which highly educated people massively became independent potters or weavers, sometimes with artistic pretentions.

2 We only introduced variables with approximately the same number of modalities as ‘active’ variables in the analysis so as to avoid any imbalance between the weight of the variables in the MCA. With its two modalities, sex is for instance included as a ‘supplementary’ variable. Moreover, we did not include variables which displayed too high a dependence or correlation in the analysis. For example, the variables ‘turnover’ and ‘sale prices’ were not integrated as ‘active’ variables (but only as ‘supplementary’ variables) because they are highly correlated with the variable ‘age of business’. For details about our methodology and statistical results, see Jourdain 2012.

3 Modified inertia rates were defined by statistician Jean-Paul Benzécri in order to determine the number of factorial axes which should be kept in the analysis, especially because inertia rates are usually low in MCA. A modified inertia rate represents the percentage of inertia explained by one axis among the axes whose associated eigenvalue is superior to the average eigenvalue.

4 These percentages may seem relatively low, but this result is due to the fact that we did not want to include variables which displayed too high a dependence or correlation in the analysis.

5 The AHC is an agglomerative classification, as opposed to the descending or divisive classification: ‘in an agglomerative (or ascending) classification, one starts with the objects regarded as a set of one-element classes, from which one proceeds to successive aggregations, until all objects are grouped within a single class’ (Le Roux & Rouanet, 2004, p. 108).

6 In order to preserve the interviewees’ anonymity, Henry, Isabelle and Roland are not their real first names.