Adventures in Demand Analysis Using AI
from Best AI papers explained Hosted by Enoch H. Kang
This research explores how artificial intelligence (AI) can improve demand analysis by creating rich multimodal representations of products. Using a dataset of toy cars from Amazon, the study combines text descriptions, images, and tabular data to generate transformer-based embeddings. These embeddings capture subtle product attributes, such as quality and branding, which significantly enhance the predictive accuracy of sales ranks and prices. Furthermore, by fine-tuning these embeddings for causal inference, the researchers obtain more credible and heterogeneous estimates of price elasticity, demonstrating that AI-driven representations can modernize empirical economic analysis. The findings highlight that these AI features act primarily as modifiers of price elasticity, rather than confounders, revealing diverse consumer responses to price changes across different products.
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