Demand Estimation with Text and Image Data

• 成果类型: Article; Early Access
• 署名作者: Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan
• 署名单位: University of Chicago; Northwestern University; Imperial College London; Centre for Economic Policy Research - UK
• 刊物名称: RAND JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0741-6261
• DOI: 10.1111/1756-2171.70052
• 发表日期: 2026
• 关键词: products

摘要

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes or when consumers value hard-to-quantify attributes such as visual design. Using a choice experiment, we show this approach substantially outperforms standard attribute-based models at counterfactual predictions of second choices. We also apply it to 40 product categories offered on Amazon.com and consistently find that unstructured data are informative about substitution patterns.