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Activity Number: 389 - Novel Data Collection Strategies in Business and Economics
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #322245
Title: Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts
Author(s): Eric H Schulman* and Sukjin Han and Kristen Grauman and Santhosh Ramakrishnan
Companies: The University of Texas at Austin and University of Bristol and The University of Texas at Austin and The University of Texas at Austin
Keywords: Convolutional neural network ; embedding; high-dimensional product attributes; visual data; product differentiation; merger
Abstract:

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.


Authors who are presenting talks have a * after their name.

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