arbiter
JournalsAccount

Demand Estimation with Text and Image Data

Giovanni Compiani et al.

RAND Journal of Economics2026https://doi.org/10.1111/1756-2171.70052article
AJG 4ABDC A*
Weight
0.50

Abstract

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.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1111/1756-2171.70052

Or copy a formatted citation

@article{giovanni2026,
  title        = {{Demand Estimation with Text and Image Data}},
  author       = {Giovanni Compiani et al.},
  journal      = {RAND Journal of Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/1756-2171.70052},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Demand Estimation with Text and Image Data

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.