Abstract:
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Many differentiated products have key attributes that are high-dimensional (e.g., design, text). Quantifying these attributes is important for economic analyses. This paper considers one of the simplest design products, fonts, and quantifies their shapes by constructing embeddings using a modern convolutional neural network. The embedding maps a font's shape onto a low-dimensional vector. Importantly, we verify the resulting embedding is economically meaningful by showing that the mutual information is large between the embedding and descriptions assigned to each font by font designers and consumers. This paper then conducts two economic analyses of the font market. We first illustrate the usefulness of the embeddings by a simple trend analysis of font style. We then study the causal effect of a merger on the merging firm's creative product differentiation decisions by using the embeddings in a synthetic control method. We find that the merger causes the merging firm temporarily to increase the visual variety of font design. Notably, such effects are not captured when using traditional measures for product offerings (e.g., number of products) constructed from structured data.
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