Sukjin Han, Eric H. Schulman, Kristen Grauman, Santhosh Ramakrishnan
arXiv 6 Jul 2021 · Econometrics · 25 citations (OpenAlex)
arXiv:2107.02739 · PDF · DOI · OpenAlex · Extracted main text
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 these types 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 effects 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.
appendix boundary found by appendix_titled_section at “Supplemental Findings for Merger Analysis\label{sec:Supplemental-Findings-for}” · 94% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Schroff, F., D. Kalenichenko, and J. Philbin (2015) FaceNet: A unified embedding for face recognition and clustering, in | 0.874 | 5 | 2 | 100% |
| 2 | Berry, S. T. and J. Waldfogel (2001) Do Mergers Increase Product Variety? Evidence from Radio Broadcasting | 0.811 | 4 | 2 | 100% |
| 3 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program | 0.737 | 3 | 2 | 100% |
| 4 | Abadie, A. and J. Gardeazabal (2003) The economic costs of conflict: A case study of the Basque Country | 0.644 | 2 | 2 | 100% |
| 5 | Sun, Y., X. Wang, and X. Tang (2015) Deeply learned face representations are sparse, selective, and robust, in | 0.644 | 2 | 2 | 100% |
| 6 | Taigman, Y., M. Yang, M. Ranzato, and L. Wolf (2014) Deepface: Closing the gap to human-level performance in face verification, in | 0.644 | 2 | 2 | 100% |
| 7 | Fan, Y (2013) Ownership Consolidation and Product Characteristics: A Study of the US Daily Newspaper Market | 0.511 | 2 | 1 | 100% |
| 8 | Gross, D. P (2016) Creativity under fire: The effects of competition on creative production | 0.511 | 2 | 1 | 100% |
| 9 | Nevo, A. and M. D. Whinston (2010) Taking the dogma out of econometrics: Structural modeling and credible inference | 0.511 | 2 | 1 | 100% |
| 10 | Sweeting, A (2013) Dynamic Product Positioning in Differentiated Product Markets: The Effect of Fees for Musical Performance Rights on the Commerci… | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Copyright and Competition: Estimating Supply and Demand with Unstructured Data | 0.737 | 3 | 2 |