Pierre Bourdieu, Geometric Data Analysis and the Analysis of Economic Spaces and Fields

This paper examines the relevance of Geometric Data Analysis (GDA) as a methodological tool for social economists. While mainstream economics frequently relies on regression analysis to test isolated variables, sociologists in the tradition of Pierre Bourdieu advocate for GDA to understand economic institutions as complex, multidimensional spaces. The methodology moves away from the 'all things being equal' approach, instead visualizing the interconnected relationships between agents, assets, and social positions.

GDA, which includes methods like Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA), is grounded in three principles: geometric modeling, a formal approach via linear algebra, and a commitment to description before inference. By constructing 'clouds of points' that represent individuals and variables, researchers can map the structure of a field—such as a market or a professional group—to reveal hidden power dynamics and symbolic assets that traditional deductive modeling often misses. For Bourdieu, this was not merely a statistical exercise but a way to formalize his theory of fields, where an individual's position in the social space dictates their strategic behavior.

Lebaron highlights how Bourdieu and his colleagues applied GDA to several key economic domains. In his seminal work, Distinction, Bourdieu used GDA to link consumer tastes and lifestyles to class positions, effectively integrating the demand side of the economy into a broader social framework. Later studies, including those on company leaders, the housing market, and the publishing industry, utilized GDA to map supply-side structures. By identifying clusters of actors based on their capital composition, these studies demonstrated how competitive strategies within firms are shaped by an agent's location within the field and their proximity to the state apparatus.