Adventures in Demand Analysis Using AI
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914309403049984 |
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| author | Bach, Philipp Chernozhukov, Victor Klaassen, Sven Spindler, Martin Teichert-Kluge, Jan Vijaykumar, Suhas |
| author_facet | Bach, Philipp Chernozhukov, Victor Klaassen, Sven Spindler, Martin Teichert-Kluge, Jan Vijaykumar, Suhas |
| contents | This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adventures in Demand Analysis Using AI Bach, Philipp Chernozhukov, Victor Klaassen, Sven Spindler, Martin Teichert-Kluge, Jan Vijaykumar, Suhas General Economics Economics Artificial Intelligence Applications Machine Learning This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced attributes, such as quality, branding, and visual characteristics, that traditional methods often struggle to summarize. Moreover, we fine-tune these embeddings for causal inference tasks. We show that the resulting embeddings substantially improve the predictive accuracy of sales ranks and prices and that they lead to more credible causal estimates of price elasticity. Notably, we uncover strong heterogeneity in price elasticity driven by these product-specific features. Our findings illustrate that AI-driven representations can enrich and modernize empirical demand analysis. The insights generated may also prove valuable for applied causal inference more broadly. |
| title | Adventures in Demand Analysis Using AI |
| topic | General Economics Economics Artificial Intelligence Applications Machine Learning |
| url | https://arxiv.org/abs/2501.00382 |