Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts

Sukjin Han, Eric Schulman, Kristen Grauman and Santhosh Ramakrishnan

Bristol Economics Discussion Papers from School of Economics, University of Bristol, UK

Abstract: Many differentiated products have key attributes that are unstructured and thus high-dimensional (e.g., design, text). Instead of treating unstructured attributes as unobservables in economic models, quantifying them can be important to answer interesting economic questions. To propose an analytical framework for this type of products, this paper considers one of the simplest design products-fonts-and investigates merger and product differentiation using an original dataset from the world's largest online marketplace for fonts. We quantify font shapes by constructing embeddings from a deep convolutional neural network. Each embedding maps a font's shape onto a low-dimensional vector. In the resulting product space, designers are assumed to engage in Hotelling-type spatial competition. From the image embeddings, we construct two alternative measures that capture the degree of design differentiation. We then study the causal e ects of a merger on the merging firm's creative decisions using the constructed measures in a synthetic control method. We find that the merger causes the merging firm to increase the visual variety of font design. Notably, such effects are not captured when using traditional measures for product offerings (e.g., specifications and the number of products) constructed from structured data.

Date: 2021-07-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-com
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Working Paper: Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:bri:uobdis:21/750

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